Documentation of Environment - Societal Responses Model

mitigation technology implemented (dmnl)
= DELAY3I( Mitigation technology, time to implement mitigation technology, Mitigation technology)
Description: We assumed that the implementation of the developed technological capacity is not instantaneous but takes time. We modeled this delay using a third-order delay function, which is considered appropriate for capturing technological implementation delays (Sterman, 2000).
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#78
DE,A

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technology effect Technological improvements in mitigation reduce the flow of generated impacts (as seen in the IPAT equation). This variable represents this effect, where higher stock values of ‘Mitigation technology’ indicate greater system efficiency and lower impacts from affluence and population. Since the model is initialized at 1950 levels ('reference technology'), increasing 'mitigation technology implemented' reduces this variable proportionally. For instance, if the implemented mitigation technology is 2 (double the efficiency compared to 1950), the 'technology effect' will be 0.5, halving the 'impacts generation' flow.Note that technological mitigation not only includes technological improvement decreasing the impact generation per unit of consumption, but also enhancements in the sinks absorbing the impact generated (e.g., carbon capture and storage). However, confidence in the feasibility and desirability of these efforts remains low (Lane et al., 2021; Mackey et al., 2013; Rosa et al., 2020). Therefore, we primarily consider mitigation as technological improvements that reduce the generation of negative impacts without explicitly addressing the sinking component. Nevertheless, the insights gained in this work also apply in cases of increased 'sinks' capacity.

Feedback Loops: 2 (1.9%) (+) 1 [10,10] (-) 1 [11,11]

Top	(Type) Level Initial (8 Variables)
Group	Type	*Variable Name And Description*
Environment - Societal Responses Model	#11 LI,F,A	adaptation capacity increase rate (Impact units/Year) = adaptation capacity built per effort* adaptation effort per year Description: This flow computes the development of adaptation capacity over time. Present In 1 View: View 1 Used By Adaptation capacity The adaptation efforts accumulate into a stock of Adaptation Capacity, which represents infrastructure and other types of investments around the world that serve to relieve the immediate pressures of climate change. Adaptation capacity is best depicted as a stock because “adaptation can be classified as incremental or developmental. In incremental adaptation, when original facilities and inputs are insufficient to resist a natural disaster, considering the emerging climatic risks, investments are added onto existing communal facilities, and the action is specific for the new additional climatic risk.” (Engle, 2011; Zhao et al., 2018, p. 86). For example, investments to build levees and dams to reduce floods caused by extreme weather events or rising sea levels help alleviate the immediate pressures and threats of floods caused by climate change and can be further raised if needed. Other examples showing the breadth and cumulative nature of adaptation are using more and more nets to protect trees fruit crops against the worsening of extreme hail events (Manja & Aoun, 2019),protecting capital through more and more extensive insurance against climate change (Jørgensen et al., 2020; McLeman & Smit, 2006; Suarez & Linnerooth-Bayer, 2010; Thomas & Leichenko, 2011). Feedback Loops: 3 (2.8%) (+) 0 [0,0] (-) 3 [4,7]
Environment - Societal Responses Model	#51 LI,F,A	impacts absorption (Impact units/Year) = MAX(0,( Cumulative impacts- cumulative impacts target level)/ impacts absorption time) Description: The planet also absorbs impacts over time through its natural sinks ('exceeding impacts absorption'). This absorption process is assumed to exhibit goal-seeking behavior driven by a balancing loop, consistent with similar conceptualisations of CO2 and pollution stocks (Forrester, 1971; Meadows et al., 1972). Specifically, the system aims to reach the 'cumulative impacts balance' level, representing the level of impacts that the system operates under normal conditions. For instance, the CO2 parts per million (ppm) in the air is not zero under normal conditions (excluding human activity), but has been approximately 280 ppm over the eras. This outflow represents the system's tendency to reach and maintain that level. The 'absorption time' indicates the average duration the impacts stay in the system (the stock of ‘Cumulative impacts’) before being absorbed. The 'max' function ensures that the flow never becomes negative (i.e., the stock is smaller than the target) and it increases the stock, as it would be unrealistic. Present In 1 View: View 1 Used By CO2 absorption The resulting increasing trend in CO₂ absorption is consistent with descriptions in the literature, which similarly report rising absorption over time (Friedlingstein et al., 2025). The magnitude of the values is also comparable to those reported in that study. While we express absorption in gigatonnes of CO₂ (GtCO₂), Friedlingstein et al. (2025) report values in gigatonnes of carbon (GtC). Since 1 GtC corresponds to approximately 3.67 GtCO₂, converting their estimates into CO₂ units yields values of the same order of magnitude as those generated by our model.https:/essd.copernicus.org/articles/17/965/2025/ Cumulative impacts The flow of 'Impacts Generation' accumulates in the stock of 'Cumulative Impacts'. This formulation, where negative environmental externalities accumulate as stocks over time, is typical in the literature (Forrester, 1971; Meadows et al., 1972; Sterman, 2008). It captures the fact that impacts are not instantaneous occurrences that disappear immediately but rather accumulate over time. Feedback Loops: 2 (1.9%) (+) 0 [0,0] (-) 2 [2,4]
Environment - Societal Responses Model	#53 LI,F,A	impacts generation (Impact units/Year) = (( Population with high-affluence lifestyle* impact population high affuence lifestyle* technology effect)+( Population with low-affluence lifestyle* impact population low affluence lifestyle* technology effect)) Description: The current affluence-growth lifestyle is responsible for negative social and environmental impacts (Wiedmann et al., 2020). This flow captures the environmental impacts (e.g., GHG emissions, pollution) generated by the current enactment of lifestyles. It is computed by multiplying the number of people living a specific lifestyle (the stocks of ‘Population with high affluence lifestyle’ and ‘Population with low affluence lifestyle’) by the impact each lifestyle has on the global environment (captured in the variables ‘impact high affuence lifestyle’ and ‘impact low affluence lifestyle’) and by the current technological level (technology effect) that can potentially reduce the impacts if enhanced. This formulation is based on the established IPAT equation (Chertow, 2000; Ehrlich & Holdren, 1971; York et al., 2003): I=PATWhere I is the impact of society on the environment, P is the population size, A is the level of affluence or level of consumption per person, and T is the available technology or the environmental impact for each unit of affluence. Present In 1 View: View 1 Used By CO2 emissions The impacts ('impacts generation') have been converted into CO2 gigatonnes (Gt) ('CO2 Gt converter') to calibrate the model. The do-nothing scenario leads to approximately 90 CO2 Gt emissions per year, aligning with the extreme scenarios of the IPCC report (2023 - Synthesis Report, longer report, p.31), specifically scenarios SSP5-8.5 and SSP5-7.0. The base case scenario results in approximately 45 CO2 Gt per year, corresponding to the intermediate SSP2-4.5 scenario (IPCC, 2023 - Synthesis Report, longer report, p.31). In scenarios where fundamental mitigation policies are implemented, impacts generation approaches zero. This outcome is within the range of plausible scenarios highlighted by the IPCC (2023) and is close to some of the most optimistic scenarios (e.g., SSP1-2.6).Thus, we used the CO2 Gt emissions per year to calibrate the model outputs, ensuring they reproduce a range of scenarios consistent with the latest IPCC report (2023).Similar values can be found also in IPCC, 2023 - Synthesis Report, SPM, p.23.This can increase confidence in the robustness of model output. Cumulative impacts The flow of 'Impacts Generation' accumulates in the stock of 'Cumulative Impacts'. This formulation, where negative environmental externalities accumulate as stocks over time, is typical in the literature (Forrester, 1971; Meadows et al., 1972; Sterman, 2008). It captures the fact that impacts are not instantaneous occurrences that disappear immediately but rather accumulate over time. Feedback Loops: 65 (61.3%) (+) 32 [9,15] (-) 33 [9,15]
Environment - Societal Responses Model	#55 LI,C	initial Population with high-affluence lifestyle (dmnl) = 100 Description: Assumed value for the population embracing a high affluence and impact lifestyle at the beginning of the simulation. Given that the simulation starts in 1950 and considering the conceptual nature of the model, we assumed that a high-affluence lifestyle was embraced by the whole population at the start. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#56 LI,C	initial Population with low-affluence lifestyle (dmnl) = 0 Description: Assumed value for the population embracing a low affluence and low impact lifestyle at the beginning of the simulation. Given that the simulation starts in 1950 and considering the conceptual nature of the model, we assumed that a low-affluence lifestyle was not voluntarily embraced by anyone at the start. Present In 1 View: View 1 Used By Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#77 LI,F,A	mitigation technology development rate (dmnl/Year) = technological mitigation effort per year* mitigation technlogical development per effort Description: This flow computes the development of technological mitigation over time. Present In 1 View: View 1 Used By Mitigation technology This stock represents the level of mitigation technology developed within the system. It starts at 1, reflecting the technological efficiency level of 1950, and accumulates over time as investments are made to improve mitigation technology. Assuming an evolutionary perspective on technological development, this stock increases only, due to variations in the inflow. Higher values indicate scenarios with greater efficiency. For example,a value of 2 in Mitigation technology equals to have a techological mitigation efficiency (broadly intended) twice of what is was in the 1950s. Feedback Loops: 3 (2.8%) (+) 2 [4,10] (-) 1 [11,11]
Environment - Societal Responses Model	#138 LI,F,A	transition back to high-affluence lifestyle (dmnl/Year) = ( transition back innovators fraction* Population with low-affluence lifestyle+ imitation coefficient transition back* Population with low-affluence lifestyle* Population with high-affluence lifestyle/ total population)* relative attractiveness of high-afflluence lifestyle Description: The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the old high-affluence state is composed of innovators (transition back innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition back), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of Old Lifestyle) and on the potential population remaining to transition back.Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 85 (80.2%) (+) 41 [2,15] (-) 44 [2,15]
Environment - Societal Responses Model	#140 LI,F,A	transition to low-affluence lifestyle (dmnl/Year) = ( transition innovators fraction* Population with high-affluence lifestyle+ imitation coefficient transition* Population with low-affluence lifestyle* Population with high-affluence lifestyle/ total population)* relative attractiveness of low-affluence lifestyle Description: The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the new low-affluence state is composed of innovators (transition innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of New Lifestyle) and on the potential population remaining to transition.Diffusion theory has been at the foundation of the study of sustainability transitions (Geels & Johnson, 2018; de Haan & Rotmans, 2011; Lenton et al., 2022), and the same formal models to depict transition patterns (Keith et al., 2020; Struben & Sterman, 2008).Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 79 (74.5%) (+) 38 [2,15] (-) 41 [2,15]

Top	(Type) Initial (0 Variables)
Group	Type	*Variable Name And Description*

Top	(Type) Constant (90 Variables)
Group	Type	*Variable Name And Description*
Environment - Societal Responses Model	#0 C	A - diminishing returns in adaptation capacity built per effort multiplier (dmnl) = 0 Description: Parameter A in the logistic function computed for the base scenario in ‘diminishing returns in adaptation capacity built per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). This value expresses the assumption that adaptation capacity developed per unit of investment will ultimately decline to zero once the diminishing-returns threshold is crossed. Consequently, all uncertainty is concentrated in the M parameter, which governs both the rate of diminishing returns and the point in time at which marginal returns effectively reach zero (i.e., the function’s slope). Present In 1 View: View 1 Used By diminishing returns in adaptation capacity built per effort multiplier This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#1 C	A - dimishing returns in mitigation technological development per effort multiplier (dmnl) = 0 Description: Parameter A in the logistic function computed in ‘dimishing returns in mitigation technological development per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). This value implies that, due to diminishing returns, progress per unit of investment will eventually approach zero as the system nears its limit. The time at which this occurs depends on other model parameters, particularly the slope parameter M. In this way, M captures most of the uncertainty surrounding the shape of the diminishing returns curve, determining the slope of the function and when investment returns become negligible. Present In 1 View: View 1 Used By dimishing returns in mitigation technological development per effort multiplier This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#2 C	A - effect of pressure perception on adaptation priority (dmnl) = 0.04 Description: Parameter A in the logistic function computed for the base scenario in ‘effect of pressure perception on adaptation priority’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#3 C	A - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl) = 0.05 Description: Parameter A in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022).It is set to 0.05 because it captures the fact that even in the context of strong behavioural response there will still be a portion of the population to prefer the high-affluence lifestyle. Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#4 C	A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl) = 0.05 Description: Parameter A in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022).This value indicates when the logistic function aims. It is set to 0.05 because it captures the fact that even in the context of strong behavioural response there will still be a portion of the population to prefer the high-affluence lifestyle. Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#5 C	A - effect of pressures perception on effort - alternative scenario (dmnl) = 0 Description: Parameter A in the logistic function computed for the base scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022) Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#6 C	A - effect of pressures perception on effort - base scenario (dmnl) = 0 Description: Parameter A in the logistic function computed for the base scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022) Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#7 C	A - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl) = 0.05 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022).It is set to 0.05 because it captures the fact that even in the context of involuntary transition there will still be a portion of the population able to practice the high-affluence lifestyle. Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#15 C	affluence and population growth multiplier (dmnl/Year) = 0.1 Description: Data indicates that CO2 emissions in gigatons were approximately 5.5 in 1950 and 11 in 1960 (Friedlingstein et al., 2023), showing a 10% growth rate during that period. Based on this trend, we assumed a 10% annual growth rate as the reference impacts throughout the entire simulated period in the absence of corrective actions. Because impacts in the model are driven by population and affluence, we assign this 10% annual growth rate to their combined effect. In other words, since impacts in the model depend on population and affluence, we assume that their combined effect grows at this rate in the absence of corrective action.This assumption was made considering that the period from 1950 to 1960 represents an era when there were no significant concerns about affluence growth, making it an ideal untouched period where policies did not affect the growth trends in impacts - capturing what would have been if humanity did not care about the impact issue.This reflects a counterfactual baseline in which no policy or behavioral responses constrain growth.https:/ourworldindata.org/co2-emissionshttps:/www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide Present In 1 View: View 1 Used By affluence and population growth Affluence and population are assumed to grow over time in the model. This reflects empirical trends: GDP-commonly used as a proxy for affluence (Dietz & Rosa, 1994)-has historically increased, as has population, including in the Global North (UN data). These trends are also consistent with the observed increase in global CO₂ emissions (i.e., impacts) over time (Friedlingstein et al., 2023). This growth is computed by multiplying the time passing in the simulation (represented by the 'time effect' ranging from 0 to 150 as the simulation progresses from 1950 to 2100) by a 10% growth rate ('affluence growth multiplier') and adding this resulting value to 1. The outcome is a multiplier always greater than 1, which is then multiplied by the 'initial impact high affluence lifestyle' in the 'impact high affluence lifestyle' variable. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#16 C	alternative allocation to adaptation fraction (dmnl ) = 1 Description: This decision rule (ranging from 0 [none] to 1 [all]) determines how much of the resources are allocated to adaptation. The remainder is invested in technological mitigation. This rule is activated and used in prototypical scenarios to explore system behavior under conditions where either adaptation or technological mitigation is dominant. Change to 1 for 100% allocation to adaptation and change to 0 for 100% allocation to tech mitigation Present In 2 Views: View 1 View 2 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#19 C	behavioural mitigation threshold (dmnl ) = 1.1 Description: Although threat perception and appraisal (‘perceived pressures’) are crucial drivers for triggering, it does not automatically yield the desired long-term behavioural changes, as many additional barriers can hinder it (Beckage et al., 2018; García de Jalón et al., 2015; Lorenzoni et al., 2007), like knowledge, perceived efficacy, or memory, making the behavioural change from a social perspective highly inertial. For example, correct causal attributions may not be straightforward in complex socio-technical systems (Cheng et al., 2017), or people may have difficulty attributing responsibility to a specific behaviour when multiple people interact in a system (Cheng et al., 2017), and actions often do not involve direct consequences but delayed and (often indirect) harm (van de Poel & Nihlén Fahlquist, 2013). Or people may not understand that their constant pursuit of higher affluence is responsible for environmental disruption or are misled by some specific vested interests in not believing so (Grasso, 2020; Lamb et al., 2020; Painter et al., 2023). This mechanism is similar to ‘resources allocation threshold’: it is not automatic to take action once pressures are perceived.For this reason, the 'behavioural change threshold' provides an additional threshold and is set an higher value than the 'pressure tolerance threshold'.Multiple by 1000 if we want to turn this loop off for Rapid Beh Response scenario Present In 1 View: View 1 Used By action trigger for behavioural mitigation An increase in ‘perceived pressures’ is expected to lower the attractiveness of the old lifestyle, since the old lifestyle is responsible for the undesired environmental impacts. Once the global population perceives the ‘Cumulative impacts’ consequences, we assume that high-affluence behaviour will be deemed problematic and become less attractive. In fact, if the global population identifies the affluent lifestyle and behaviour as the cause of the pressure, then the attractiveness of the lifestyle itself will decrease. Consistent with protection motivation theory, the perception of risks and threats can be a powerful driver to promote societal behavioural change (Beckage et al., 2018; Eker et al., 2019). As long as a person or community perceives that their behaviour is responsible for some risks, they are more motivated to do something. There is substantial for this response mechanism related to climate change (Bockarjova & Steg, 2014; Hunter & Röös, 2016; Lujala et al., 2015; Venghaus et al., 2022; Wells et al., 2011). However, this attribution is not straightforward, as an additional threshold (‘behavioural change threshold’) has to be overcome before behavioural change is triggered. This additional threshold comprises all the additional barriers hindering behavioural change, and captures that changing behaviour from high-affluence to low-affluence consists of an additional step than just perceiving the pressures but also to acknowledge that the high-affluence behaviour is responsible for climate change. Once this threshold is exceeded, people in the model are pushed to attribute the responsibility for the generation of pressures to their lifestyle behaviour, which leads to a decrease in the attractiveness of the affluence-based lifestyle. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#20 C	behavioural mitigation threshold rapid response (dmnl ) = 1.05 Description: Value at which the rapid behavioural mitigation response is activated (if the 'SWT to rapid response after perception' activated). This parameter is calibrated to match the 'resource allocation threshold' variable, thereby replicating the threshold at which perceived pressures first led to resource mobilisation in the late 1970s and early 1980s, consistent with the First World Climate Conference (1979). In other words, the behavioural rapid-response regime is triggered when perceived pressures exceed the level required in the late 1970s to initiate the first large-scale allocation of climate-related resources.Gupta, J. A history of international climate change policy. Wiley Interdiscip. Rev. Clim. Chang. 1, 636-653 (2010). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#21 C	C - diminishing returns in adaptation capacity built per effort multiplier (dmnl) = 1 Description: Parameter C in the logistic function computed for the base scenario in ‘diminishing returns in adaptation capacity built per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By diminishing returns in adaptation capacity built per effort multiplier This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#22 C	C - dimishing returns in mitigation technological development per effort multiplier (dmnl) = 1 Description: Parameter C in the logistic function computed in ‘dimishing returns in mitigation technological development per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By dimishing returns in mitigation technological development per effort multiplier This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#23 C	C - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl) = 1 Description: Parameter C in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of old lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#24 C	C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl) = 1 Description: Parameter C in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of old lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#25 C	C - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl) = 1 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#28 C	CO2 Gt converter (CO2 Gt/Impact units) = 1100 Description: Variable to convert the impacts into CO2 gigatonnes (Gt). Thus, we used the CO2 Gt emissions per year to calibrate the model outputs, ensuring they reproduce a range of scenarios consistent with the latest IPCC report (2023). This value was selected to ensure the CO2 emission at the start of the simulation matched the 1950 real data (approximately 5.5 Gt of CO2). Present In 1 View: View 1 Used By CO2 absorption The resulting increasing trend in CO₂ absorption is consistent with descriptions in the literature, which similarly report rising absorption over time (Friedlingstein et al., 2025). The magnitude of the values is also comparable to those reported in that study. While we express absorption in gigatonnes of CO₂ (GtCO₂), Friedlingstein et al. (2025) report values in gigatonnes of carbon (GtC). Since 1 GtC corresponds to approximately 3.67 GtCO₂, converting their estimates into CO₂ units yields values of the same order of magnitude as those generated by our model.https:/essd.copernicus.org/articles/17/965/2025/ CO2 emissions The impacts ('impacts generation') have been converted into CO2 gigatonnes (Gt) ('CO2 Gt converter') to calibrate the model. The do-nothing scenario leads to approximately 90 CO2 Gt emissions per year, aligning with the extreme scenarios of the IPCC report (2023 - Synthesis Report, longer report, p.31), specifically scenarios SSP5-8.5 and SSP5-7.0. The base case scenario results in approximately 45 CO2 Gt per year, corresponding to the intermediate SSP2-4.5 scenario (IPCC, 2023 - Synthesis Report, longer report, p.31). In scenarios where fundamental mitigation policies are implemented, impacts generation approaches zero. This outcome is within the range of plausible scenarios highlighted by the IPCC (2023) and is close to some of the most optimistic scenarios (e.g., SSP1-2.6).Thus, we used the CO2 Gt emissions per year to calibrate the model outputs, ensuring they reproduce a range of scenarios consistent with the latest IPCC report (2023).Similar values can be found also in IPCC, 2023 - Synthesis Report, SPM, p.23.This can increase confidence in the robustness of model output. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#30 C	constant returns in adaptation capacity built per effort (Impact units/$ ) = 0.025 Description: This variable represents reference amount of adaptation capacity developed per unit of 'adaptation effort per year'. Present In 1 View: View 1 Used By adaptation capacity built per effort This variable represents amount of adaptation capacity developed per unit of 'adaptation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#31 C	constant returns in mitigation technological development built per effort (dmnl/$ ) = 0.09 Description: This variable represents reference amount of technological mitigation developed per unit of 'technological effort per year'. Present In 1 View: View 1 Used By mitigation technlogical development per effort This variable represents amount of technological mitigation developed per unit of 'technological mitigation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#33 C	cumulative impacts target level (Impact units) = 0.9 Description: This value represents the level of 'Cumulative Impacts' that the system naturally tends toward. Given that the 'Cumulative Impacts' stock is initialized at 1, representing 300 ppm CO2 in the atmosphere in 1950, and considering that historically, CO2 levels on the planet have averaged between 250-280 ppm (Friedlingstein et al., 2023), we assumed that the target balance level for CO2 in the atmosphere is approximately 270 ppm. This translates to a normalized value of 0.9 (since 270/300 = 0.9).https:/www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide Present In 1 View: View 1 Used By impacts absorption The planet also absorbs impacts over time through its natural sinks ('exceeding impacts absorption'). This absorption process is assumed to exhibit goal-seeking behavior driven by a balancing loop, consistent with similar conceptualisations of CO2 and pollution stocks (Forrester, 1971; Meadows et al., 1972). Specifically, the system aims to reach the 'cumulative impacts balance' level, representing the level of impacts that the system operates under normal conditions. For instance, the CO2 parts per million (ppm) in the air is not zero under normal conditions (excluding human activity), but has been approximately 280 ppm over the eras. This outflow represents the system's tendency to reach and maintain that level. The 'absorption time' indicates the average duration the impacts stay in the system (the stock of ‘Cumulative impacts’) before being absorbed. The 'max' function ensures that the flow never becomes negative (i.e., the stock is smaller than the target) and it increases the stock, as it would be unrealistic. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#34 C	cumulative impacts to CO2ppm equivalent (CO2 ppm/Impact units) = 300 Description: This variable converts the 'Cumulative Impacts' stock into CO2 ppm. We used the CO2 ppm levels in the atmosphere to calibrate the model outputs, ensuring they reproduce a range of scenarios consistent with the latest IPCC report (2023). The initial value was selected to match the 1950 real data, which was approximately 300 ppm (Friedlingstein et al., 2023; IPCC, 2023). Given that the 'Cumulative Impacts' stock starts at 1 in 1950, this converter is set to 300.https:/www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide Present In 1 View: View 1 Used By CO2 ppm The impacts (‘Cumulative impacts’) have been converted into CO2 ppm (‘cumulative impacts to CO2ppm equivalent’) to calibrate the model. The base results align with actual trends, with the model showing CO2 ppm starting at 300 in 1950 and reaching approximately 430 in 2020, compared to the real value of 420 (Friedlingstein et al., 2023; IPCC, 2023). The base scenario projects CO2 levels exceed 560 ppm by 2100, which seems plausible and aligns with intermediary IPCC scenarios and other research estimates, such as Szulejko et al. (2017), who estimated slightly above 620 ppm by 2100 based on extrapolated growth trends up to 2014 (a discrepancy that seems possible as some mitigation policies have been implemented meanwhile ).In the extreme scenario where no fundamental policies are implemented, the model projects an upper value of 970 ppm, implying that if humanity maintained the impact growth rate from the 1950s without any mitigation efforts, CO2 levels would reach such high values. This figure is plausible as it falls within the IPCC's extreme scenarios range (SSP5-8.5) and aligns with other extreme estimates in the literature, such as Hu et al. (2019), who assumed an upper-high CO2 level of 936 ppm.These results provide confidence in the robustness of the model output.https:/www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#45 C	fractional consumption from high- to low-affluence lifestyle (dmnl) = 0.3 Description: We assume a 70% reduction relative to the 2020 high-affluence impact (i.e., a 0.3 multiplier). This value represents the midpoint between the 90% potential reduction suggested by Wiedmann et al. (2020) and the 50% reduction mentioned by Seto et al. (2016). Present In 1 View: View 1 Used By impact population low affluence lifestyle In the model, the ‘impact low affluence lifestyle’ is assumed to be 70% lower than the high affluence one, in line with recent research showing that decent living standards can also be achieved with such reduction in per-capita energy use than currently utilised in affluent countries (Lockyer, 2017; Rao et al., 2019; Trainer, 2021; Wiedmann et al., 2020; Sato et al. 2016). To estimate this value, we simulated the do-nothing scenario, where no fundamental mitigation policies are implemented, and used the 2020 value of 'impact high affluence lifestyle' (as it aligns with the period of the referenced studies), computing 30% of that value. The minimum function ensures that if the model starts with an extremely low 'impact high affluence lifestyle', the 'impact low affluence lifestyle' is not greater. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#46 C	imitation coefficient transition (dmnl/Year) = 0.38 Description: The empirical average value of the imitation coefficient (also known in the literature as q/coefficient of imitation/internal influence/word-of-mouth effect) has been found to be 0.38, with a typical range between 0.3 and 0.5. (Mahajan et al., 1995) Present In 1 View: View 1 Used By transition to low-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the new low-affluence state is composed of innovators (transition innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of New Lifestyle) and on the potential population remaining to transition.Diffusion theory has been at the foundation of the study of sustainability transitions (Geels & Johnson, 2018; de Haan & Rotmans, 2011; Lenton et al., 2022), and the same formal models to depict transition patterns (Keith et al., 2020; Struben & Sterman, 2008).Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#47 C	imitation coefficient transition back (dmnl/Year) = 0.38 Description: The empirical average value of the imitation coefficient (also known in the literature as q/coefficient of imitation/internal influence/word-of-mouth effect) has been found to be 0.38, with a typical range between 0.3 and 0.5. (Mahajan et al., 1995) Present In 1 View: View 1 Used By transition back to high-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the old high-affluence state is composed of innovators (transition back innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition back), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of Old Lifestyle) and on the potential population remaining to transition back.Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#48 C	impact population high affluence lifestyle in 2020 (Impact units/Year) = 0.0004 Description: Because Wiedmann et al. (2020) derive their estimates of low-affluence lifestyle impacts using 2020 emission levels, we anchor our calibration to the model’s impact value in 2020 (which depends on affluence). This 2020 reference level is then used to compute the impact associated with a low-affluence lifestyle. Present In 1 View: View 1 Used By impact population low affluence lifestyle In the model, the ‘impact low affluence lifestyle’ is assumed to be 70% lower than the high affluence one, in line with recent research showing that decent living standards can also be achieved with such reduction in per-capita energy use than currently utilised in affluent countries (Lockyer, 2017; Rao et al., 2019; Trainer, 2021; Wiedmann et al., 2020; Sato et al. 2016). To estimate this value, we simulated the do-nothing scenario, where no fundamental mitigation policies are implemented, and used the 2020 value of 'impact high affluence lifestyle' (as it aligns with the period of the referenced studies), computing 30% of that value. The minimum function ensures that if the model starts with an extremely low 'impact high affluence lifestyle', the 'impact low affluence lifestyle' is not greater. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#54 C	initial impact high affluence lifestyle per person (Impact units/Year/People) = 5.56256e-14 Description: The initial value of 'impact of high-affluence lifestyle' is estimated using the CO2 Gt emissions in 1950 as a reference point, aligning the impacts with the values observed in 1950. Data shows that CO2 Gigatons emissions in 1950 were approx. 5.5. Given this value and the corresponding population in 1950, the per-capita impact of a high-affluence lifestyle is calculated accordingly (dividing 5.5 by the population value). This calibration ensures that the model outputs are consistent with the scenarios outlined in the latest IPCC report (2023).(Friedlingstein et al., 2023) https:/ourworldindata.org/co2-emissionshttps:/www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide Present In 1 View: View 1 Used By impact population high affuence lifestyle These are the impacts generated per person with the high-affluence lifestyle per year. They are computed by multiplying the 'initial impact high affluence lifestyle' by the estimated 'affluence growth' trends over time. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#55 LI,C	initial Population with high-affluence lifestyle (dmnl) = 100 Description: Assumed value for the population embracing a high affluence and impact lifestyle at the beginning of the simulation. Given that the simulation starts in 1950 and considering the conceptual nature of the model, we assumed that a high-affluence lifestyle was embraced by the whole population at the start. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#56 LI,C	initial Population with low-affluence lifestyle (dmnl) = 0 Description: Assumed value for the population embracing a low affluence and low impact lifestyle at the beginning of the simulation. Given that the simulation starts in 1950 and considering the conceptual nature of the model, we assumed that a low-affluence lifestyle was not voluntarily embraced by anyone at the start. Present In 1 View: View 1 Used By Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#57 C	K - diminishing returns in adaptation capacity built per effort multiplier (dmnl) = 1 Description: Parameter K in the logistic function computed for the base scenario in ‘diminishing returns in adaptation capacity built per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By diminishing returns in adaptation capacity built per effort multiplier This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#58 C	K - dimishing returns in mitigation technological development per effort multiplier (dmnl) = 1 Description: Parameter K in the logistic function computed in ‘dimishing returns in mitigation technological development per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By dimishing returns in mitigation technological development per effort multiplier This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#59 C	K - effect of pressure perception on adaptation priority (dmnl) = 0.95 Description: Parameter K in the logistic function computed for the base scenario in ‘effect of pressure perception on adaptation priority’. For more details see Ríos‐Ocampo & Gary (2022). We are assuming that even with very extreme perceived pressures 5% of the resources will be allocated to mitigation. Present In 1 View: View 1 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#60 C	K - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl) = 1 Description: Parameter K in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#61 C	K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl) = 1 Description: Parameter K in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#62 C	K - effect of pressures perception on effort - alternative scenario (dmnl) = 1 Description: Parameter K in the logistic function computed for the base scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022) Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#63 C	K - effect of pressures perception on effort - base scenario (dmnl) = 1 Description: Parameter K in the logistic function computed for the base scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022) Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#64 C	K - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl) = 1 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#65 C	lifestyle socio-technical regime effect (Attractiveness units/dmnl ) = 0.01 Description: This variable corresponds to the rr constant in Arthur's lock-in model (Arthur, 1989; Safarzyńska et al., 2012 – thoroughly explained in the "attractiveness of low affluence lifestyle" variable) that computes the network effect on preferences. In this context, the network effect consists of sociological forces (i.e., the more a lifestyle is adopted, the more socially acceptable and institutionalized it becomes) and technical forces (i.e., the more widespread a lifestyle is, the more the technical landscape adapts to suit its needs). Its value has been set to 0.015 based on an educated guess. It must be greater than 0, as we know that such an effect exists. We assumed it to be 0.015 so that if 100% of the population embraces a lifestyle, its attractiveness increases by 1.5, which is within a reasonable range considering that the intrinsic attractiveness of the current high-affluence lifestyle starts at a base value of 1. Present In 1 View: View 1 Used By attractiveness of high-affluence lifestyle The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness high affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The multiplier "effect of pressures perception on the attractiveness of the old lifestyle" reflects that an increase in the perception of socio-environmental pressures caused by climate change can lead to a decrease in the attractiveness of the high-affluence lifestyle, if people recognize it as a source of the problem.The multiplier ‘effect of pressures perception on attractiveness of high affluence lifestyle - rapid response’ reflects the decrease in attractiveness of the high affluence lifestyle if behavioural mitigation responses are implemented. The multiplier ‘involuntary effect of pressure perception attractiveness of high affluence lifestyle’ reflects the decrease in the attractiveness of the high affluence lifestyle if the surrounding environment is highly disrupted. attractiveness of low-affluence lifestyle The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness low affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The switch function captures the same function, with the addition of policies or actions designed to enhance the attractiveness of the low-impact lifestyle. In fact, external factors, like social and environmental pressures, taxes, or regulations, information or education, can alter the attractiveness of a way of living (Bergquist et al., 2023; Brown & Vergragt, 2016). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#66 C	M - diminishing returns in adaptation capacity built per effort multiplier (Impact units ) = 1.2 Description: Parameter M in the logistic function computed for the base scenario in ‘diminishing returns in adaptation capacity built per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Although there is uncertainty as to whether absolute limits to adaptation exist, current research suggests that such limits exists and may be closer than expected (Berkhout & Dow, 2023; Dow et al., 2013; more on this in the main manuscript). Assuming this to be the case, there is nevertheless very limited knowledge regarding the time required to reach these limits. As a baseline assumption, we propose that once diminishing returns set in, and provided that high levels of investment in adaptation continue, these limits would be reached after 50 years (around 15 years to halve capacity, followed by a more gradual decline towards marginal, near-zero gains). The lower bound of the parameter space is set at 1.17 based on the current model specification and calibration. At this value, the model yields convergence to near-zero gains within approximately 10 years.All calibrations make sure that the diminishing returns occurs after 2025 as of today we don't see evidence of such limitations. Present In 1 View: View 1 Used By diminishing returns in adaptation capacity built per effort multiplier This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#67 C	M - dimishing returns in mitigation technological development per effort multiplier (dmnl) = 2.75 Description: Parameter M in the logistic function computed in ‘dimishing returns in mitigation technological development per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). It remains uncertain whether absolute limits to technological mitigation exist. Consequently, even if such limits do exist, the rate of diminishing returns per unit of investment is also unknown. In this model, we assume that under sustained investment it would take approximately 75 years to reach an overall reduction of around 80%. This rate is assumed to be slightly slower than the adaptation limit, as adaptation is constrained not only by intellectual and technological factors but also by the physiological limits of the human body in coping with extreme conditions, as discussed in the main manuscript. All calibrations make sure that the diminishing returns occurs after 2025 as of today we don't see evidence of such limitations.Sensitivity analyses, reported in the supplementary materials, indicate that variations in this parameter do not alter the fundamental behavioural modes of the model.Lower value = 1.3, then = 2.75 Present In 1 View: View 1 Used By dimishing returns in mitigation technological development per effort multiplier This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#69 C	M - effect of pressure perception on adaptation priority for sensitivity analysis (dmnl) = 1.4 Description: This value should be linked to the 'M - effect of pressure perception on adaptation priority' parameter and used to replace both values in the IF THEN ELSE function, so that sensitivity analyses can be conducted Present In 1 View: View 1 Used By M - effect of pressure perception on adaptation priority Parameter M in the logistic function computed for the base scenario in ‘effect of pressure perception on adaptation priority’. For more details see Ríos‐Ocampo & Gary (2022). Higher values lead to higher allocations to technological mitigation. Although empirical data on the allocation of effort between mitigation and adaptation remain limited, the M parameter of this function has been calibrated under the base scenario (current pathway) so that the variables 'adaptation effort per year' and 'technological mitigation effort per year' are consistent with the available empirical estimates. Further details on this calibration are provided in the relevant model function descriptions.Base case = 1.4; Alternbative value (more Tech Mitigation) = 1.7 Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#70 C	M - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl ) = 1.4 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). This value is set to 1.4 so that the lifestyle transition under conditions of sustained and mounting pressure unfolds over approximately 40-60 years, consistent with Schot and Kanger’s (2018) review, which shows that deep socio-technical transitions historically unfold over several decades in the absence of strong external shocks or exceptional policy intervention. Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#71 C	M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl ) = 1.25 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022).This parameter produces a steeper response function, representing accelerated societal behaviour under high pressure. By definition, it is lower than the M parameter governing normal behavioural responses. We set this value to 1.25, reflecting a scenario in which sustained pressure triggers substantial lifestyle changes within a few decades, consistent with Sovacool (2016), who shows that socio-technical transitions can occur within one to two decades under favourable conditions. Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#72 C	M - effect of pressures perception on effort - alternative scenario (dmnl ) = 1.01 Description: Parameter M in the logistic function computed for the alternative scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022). This value delivers a rather steep function as it aims to capture the rapid societla response. Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#73 C	M - effect of pressures perception on effort - base scenario (dmnl ) = 1.5 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022) Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#74 C	M - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl) = 1.1 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#79 C	natural sinks degradation curve slope (dmnl/Impact units) = 0.6 Description: This value is used to assess the impact and calibrate the steepness of the 'Natural Sinks Degradation due to Cumulative Impacts Multiplier' function. Present In 1 View: View 1 Used By natural sinks degradation due to cumulative impacts multiplier Natural sinks can deteriorate with the increase of the cumulative impacts in the environment, decreasing the absorption rate (creating a reinforcing loop) (Canadell et al., 2007; Forrester, 1971; Le Quéré et al., 2009; Lenton et al., 2019; Meadows et al., 1972). This effect is captured in the model as follows: if 'Cumulative Impacts' exceed the 'Natural Sink Degradation Threshold', natural sinks start to deteriorate. If this threshold is not exceeded, the function value is 1 (due to the MAX function defining the minimum value). If the threshold is exceeded, the exponential function value becomes greater than 1, as the exponent is positive. The exponential function captures the nonlinear and exponential effects that surpassing the natural sink tipping point has on the absorption time. The output of this variable is a multiplier that affects the 'Reference Absorption Time' in the 'Absorption Time' variable. Finally, the 'Natural Sinks Degradation Curve Slope' is a variable used to regulate the steepness of the exponential function and to calibrate the model. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#81 C	natural sinks degradation due to cumulative impacts threshold (Impact units) = 1.4 Description: The threshold for triggering natural sinks degradation is set to 1.4 for the following reasons. The 'Cumulative Impacts' stock starts at a value of 1, which, according to the calibration, represents approximately 300 ppm CO2 in 1950. By 2020, early signs of potential natural sink deterioration and tipping points have been observed (Lenton et al. 2019). Given that the current CO2 ppm is approximately 420, we used this data to estimate the threshold for sink degradation: 420 ppm/300 ppm=1.4. Present In 1 View: View 1 Used By natural sinks degradation due to cumulative impacts multiplier Natural sinks can deteriorate with the increase of the cumulative impacts in the environment, decreasing the absorption rate (creating a reinforcing loop) (Canadell et al., 2007; Forrester, 1971; Le Quéré et al., 2009; Lenton et al., 2019; Meadows et al., 1972). This effect is captured in the model as follows: if 'Cumulative Impacts' exceed the 'Natural Sink Degradation Threshold', natural sinks start to deteriorate. If this threshold is not exceeded, the function value is 1 (due to the MAX function defining the minimum value). If the threshold is exceeded, the exponential function value becomes greater than 1, as the exponent is positive. The exponential function captures the nonlinear and exponential effects that surpassing the natural sink tipping point has on the absorption time. The output of this variable is a multiplier that affects the 'Reference Absorption Time' in the 'Absorption Time' variable. Finally, the 'Natural Sinks Degradation Curve Slope' is a variable used to regulate the steepness of the exponential function and to calibrate the model. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#84 C	perception delay (Year) = 20 Description: It is assumed that it takes 20 years for 'Cumulative Impacts' to generate tangible consequences for the human population. Present In 1 View: View 1 Used By socio-environmental consequences After a ‘perception delay’, the global population will perceive the effects of the ‘Cumulative impacts’ on the environment (e.g., extreme weather events and social turmoil) as ‘perceived cumulative impacts’.Note that, in reality, the global population is not constrained to wait to perceive the consequences of 'Cumulative Impacts' before taking action. Scientists have long warned about the consequences of cumulative impacts and proposed proactive measures to address them, yet these actions have not been taken on a large scale (Beck & Mahony, 2017; see also climate delay discourses in Lamb et al., 2020; Painter et al., 2023). Consequently, it is now too late to take action to maintain temperature rises below 1.5°C (Hulme, 2020; IPCC, 2023; Moser, 2020). For this reason, we assume that perception drives action, which aligns with other modeling work (Beckage et al., 2018; Eker et al., 2019). Given these dynamics, climate change has been termed the 'predictable surprise' (Bazerman, 2006). In our model, we assume that people act only when pressures are perceived, but anticipatory scenarios can also be explored by adjusting the delay structure.To translate perceived impacts into something more tangible, consider the following approach. In the most extreme scenarios, the increase in 'perceived cumulative impacts' ranges between 1 and about 2.65, representing a range of 1.65. By capturing the extreme scenarios in terms of CO2 behavior, we can relate them with the corresponding extreme consequences reported by the IPCC (2023), which suggests an upper limit of 5°C temperature variation.Therefore, we can divide the range of 1.65 by 5°C to assess how much a variation in 'perceived cumulative impacts’ corresponds to a temperature variation. This calculation yields 1.65/5 = 0.33. Hence, an increase of approximately 0.3 in 'perceived cumulative impacts' can roughly correspond to a temperature increase of 1°C.For interpreting the risks associated with each temperature increase, refer to the IPCC (2023 - Synthesis report- longer report - p.31), specifically the "Risks as Burning Embers" figure, which illustrates risks perceived associated per temperature variation. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#85 C	population 1950 (People) = 8.98867e+08 Description: Global North population in 1950. To calculate the Global North population, considering the countries listed here https:/worldpopulationreview.com/country-rankings/global-north-countries. The national population is taken from the United Nations https:/population.un.org/wpp/ (accessed 16/02/2026) (Total Population, as of 1 January) Present In 1 View: View 1 Used By impact population high affuence lifestyle These are the impacts generated per person with the high-affluence lifestyle per year. They are computed by multiplying the 'initial impact high affluence lifestyle' by the estimated 'affluence growth' trends over time. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#89 C	pressures to impact units converter (Impact units) = 1 Description: 'perceived pressures' are dimensionless (dmnl). However, their relationship to impact units is scaled to be 1:1. This aids in translating the variable's meaning and anchoring it to tangible values and realities. Present In 1 View: View 1 Used By perceived pressures - Cumulative impacts gap Variable measuring the gap between the state of the environment ('Cumulative impacts') and its actual perception ('pressure to respond'). The units converter is necessary to ensure accurate dimensional comparisons perceived pressures - socio-environmental consequences gap Variable measuring the gap between the state of the environment ('socio-environmental consequences') and its actual perception ('pressure to respond'). The units converter is necessary to ensure accurate dimensional comparisons Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#90 C	pressures tolerance threshold (dmnl) = 1 Description: The ‘pressures tolerance threshold’ represents the minimum level of discomfort (in impact units) that the ‘perceived cumulative impacts’ need to cause before people start paying attention to them. If ‘perceived cumulative impacts’ are low (e.g., minor increases in average temperature, slight decreases in average rainfall per season, or small increases in the number of extreme weather events) and do not exceed the tolerance threshold, people are unlikely even to recognise (and so respond) to them. The higher the ‘pressures tolerance threshold’, the more delayed any response will be to reduce the pressure.The value is set to 1. This is because the normal geological level of CO2 is at 0.9 impact units (270 ppm CO2) in our model. Therefore, the first perception of environmental change occurs when people perceive the consequences of CO2 levels reaching 300 ppm.Additionally, we assume that the perception threshold is constant over time. While this assumption seems plausible, the recent Covid-19 pandemic showed that societal risk thresholds can change over time as fatigue with precautions increases, making people more willing to take risks (Rahmandad & Sterman, 2022). This indicates room for further exploration, as the population could raise their tolerance threshold if subjected to prolonged pressures and called to follow strict and unpopular rules. Present In 1 View: View 1 Used By pressure to respond (perceived pressures) The global population begins to feel the 'perceived pressures' once the 'perceived cumulative impacts' exceed the adaptation capacity implemented ('adaptation implemented') and the non-offset by adaptation impacts also exceed the tolerance threshold ('pressures tolerance threshold').In fact, the scope and effect of adaptation is to reduce the perception or the pressures (Wheeler et al, 2021). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#91 C	Q - diminishing returns in adaptation capacity built per effort multiplier (dmnl) = 1 Description: Parameter Q in the logistic function computed for the base scenario in ‘diminishing returns in adaptation capacity built per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By diminishing returns in adaptation capacity built per effort multiplier This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#92 C	Q - dimishing returns in mitigation technological development per effort multiplier (dmnl) = 1 Description: Parameter Q in the logistic function computed in ‘dimishing returns in mitigation technological development per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By dimishing returns in mitigation technological development per effort multiplier This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#93 C	Q - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl) = 1 Description: Parameter Q in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#94 C	Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl) = 1 Description: Parameter Q in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#95 C	Q - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl) = 1 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#96 C	reference attractiveness low-affluence lifestyle (Attractiveness units ) = 0.25 Description: This variable represents the intrinsic attractiveness and utility of the new low-affluence lifestyle, capturing how inherently desirable it is to people, aside from any additional socio-technical benefits effect. It is set to 0.25 as the baseline starting value to capture that the low-affluence lifestyle is significantly less appealing at the moment than the current high-impact one. Present In 1 View: View 1 Used By attractiveness of low-affluence lifestyle The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness low affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The switch function captures the same function, with the addition of policies or actions designed to enhance the attractiveness of the low-impact lifestyle. In fact, external factors, like social and environmental pressures, taxes, or regulations, information or education, can alter the attractiveness of a way of living (Bergquist et al., 2023; Brown & Vergragt, 2016). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#97 C	reference attractivness high-affluence lifestyle (Attractiveness units ) = 1 Description: This variable represents the intrinsic attractiveness and utility of the old high-affluence lifestyle, capturing how inherently desirable it is to people, aside from any additional socio-technical benefits effect. It is set to 1 as the baseline starting value to serve as a reference point, representing the attractiveness of the current lifestyle. Present In 1 View: View 1 Used By attractiveness of high-affluence lifestyle The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness high affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The multiplier "effect of pressures perception on the attractiveness of the old lifestyle" reflects that an increase in the perception of socio-environmental pressures caused by climate change can lead to a decrease in the attractiveness of the high-affluence lifestyle, if people recognize it as a source of the problem.The multiplier ‘effect of pressures perception on attractiveness of high affluence lifestyle - rapid response’ reflects the decrease in attractiveness of the high affluence lifestyle if behavioural mitigation responses are implemented. The multiplier ‘involuntary effect of pressure perception attractiveness of high affluence lifestyle’ reflects the decrease in the attractiveness of the high affluence lifestyle if the surrounding environment is highly disrupted. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#98 C	reference impacts absorption time (Year) = 20 Description: The average time that additional cumulative impacts (exceeding the 'cumulative impacts balance') stay in the 'Cumulative Impact' stock is assumed to be 20 years. This value is an educated guess based on the varying absorption times of different pollutants and greenhouse gases (e.g., Methane 11.8 years, Nitrous Oxide 109 years, fluorinated gases ranging from a few weeks to thousands of years). For example, "carbon dioxide’s lifetime cannot be represented with a single value because the gas is not destroyed over time, but instead moves among different parts of the ocean/atmosphere/land system. Some of the excess carbon dioxide is absorbed quickly (for example, by the ocean surface), but some will remain in the atmosphere for thousands of years, due in part to the very slow process by which carbon is transferred to ocean sediments." Considering this range of absorption times, we made the educated guess that 20 years is a reasonable value that captures the diversity of absorption rates and aligns well with the conceptual needs of the model.https:/www.epa.gov/climate-indicators/greenhouse-gases Present In 1 View: View 1 Used By impacts absorption time This variable represents the average time it takes to absorb the excess 'Cumulative Impacts'. It is calculated by multiplying the 'reference impacts absorption time' by the 'natural sinks degradation due to cumulative impacts multiplier'. This multiplier exceeds one when 'Cumulative Impacts' increase to the point of deteriorating natural sinks. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#99 C	reference technology (dmnl) = 1 Description: This variable represents the mitigation technology starting point. As the stock of 'Mitigation technology' is initialised at 1, this variable assumes the value of 1. Present In 1 View: View 1 Used By technology effect Technological improvements in mitigation reduce the flow of generated impacts (as seen in the IPAT equation). This variable represents this effect, where higher stock values of ‘Mitigation technology’ indicate greater system efficiency and lower impacts from affluence and population. Since the model is initialized at 1950 levels ('reference technology'), increasing 'mitigation technology implemented' reduces this variable proportionally. For instance, if the implemented mitigation technology is 2 (double the efficiency compared to 1950), the 'technology effect' will be 0.5, halving the 'impacts generation' flow.Note that technological mitigation not only includes technological improvement decreasing the impact generation per unit of consumption, but also enhancements in the sinks absorbing the impact generated (e.g., carbon capture and storage). However, confidence in the feasibility and desirability of these efforts remains low (Lane et al., 2021; Mackey et al., 2013; Rosa et al., 2020). Therefore, we primarily consider mitigation as technological improvements that reduce the generation of negative impacts without explicitly addressing the sinking component. Nevertheless, the insights gained in this work also apply in cases of increased 'sinks' capacity. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#102 C	resources allocation threshold (dmnl ) = 1.05 Description: The ‘resources allocation threshold’ represents the minimum level perceived pressures (and so ‘socio-environmental consequences’) need to be before people start mobilising resources. This variable captures the fact that is not automatic to take action even if we perceive a problem. The higher the ‘resources allocation threshold’, the more delayed any response will be to reduce the pressure.The value is set to 1.05, indicating a 5% tolerance in the variation of ‘perceived pressures’ (and so of ‘perceived cumulative impacts’) before resources are mobilised. To translate this If 1 equals 300 ppm CO2, then this means that humanity does act until it perceives the consequences of CO2 levels up to 315 ppm. Present In 2 Views: View 1 View 2 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#103 C	rx - diminishing returns in adaptation capacity built per effort multiplier (Impact units ) = 1.15921 Description: Reference point rx in the logistic function computed for the base scenario in ‘diminishing returns in adaptation capacity built per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By diminishing returns in adaptation capacity built per effort multiplier This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#104 C	rx - dimishing returns in mitigation technological development per effort multiplier (dmnl) = 1 Description: Reference point rx in the logistic function computed in ‘dimishing returns in mitigation technological development per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By dimishing returns in mitigation technological development per effort multiplier This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#105 C	rx - effect of pressure perception on adaptation priority (dmnl) = 1 Description: Parameter rx in the logistic function computed for the base scenario in ‘effect of pressure perception on adaptation priority’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#106 C	rx - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl ) = 1 Description: Reference point rx in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#107 C	rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl) = 1 Description: Reference point rx in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#108 C	rx - effect of pressures perception on effort - alternative scenario (dmnl) = 1 Description: Reference point rx in the logistic function computed for the alternative scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#109 C	rx - effect of pressures perception on effort - base scenario (dmnl) = 1 Description: Reference point rx in the logistic function computed for the base scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#110 C	rx - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl) = 1 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#111 C	ry - diminishing returns in adaptation capacity built per effort multiplier (dmnl) = 0.99 Description: Reference point ry in the logistic function computed for the base scenario in ‘diminishing returns in adaptation capacity built per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By diminishing returns in adaptation capacity built per effort multiplier This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#112 C	ry - dimishing returns in mitigation technological development per effort multiplier (dmnl) = 0.99 Description: Reference point ry in the logistic function computed in ‘dimishing returns in mitigation technological development per effort multiplier’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By dimishing returns in mitigation technological development per effort multiplier This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#113 C	ry - effect of pressure perception on adaptation priority (dmnl) = 0.05 Description: Reference point ry in the logistic function computed for the base scenario in ‘effect of pressure perception on adaptation priority’. For more details see Ríos‐Ocampo & Gary (2022).We are assuming that even with low perceived pressures 5% of the resources will be allocated to adaptation. Present In 1 View: View 1 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#114 C	ry - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl ) = 0.95 Description: Reference point ry in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#115 C	ry - effect of pressures perception on effort - alternative scenario (dmnl) = 0.01 Description: Reference point ry in the logistic function computed for the alternative scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#116 C	ry - effect of pressures perception on effort - base scenario (dmnl) = 0.01 Description: Reference point ry in the logistic function computed for the base scenario in ‘effect of pressures perception on effort’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#117 C	ry - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl) = 0.95 Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#118 C	ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl) = 0.99 Description: Reference point ry in the logistic function computed for the base scenario in ‘effect of pressures perception on attractivenss of high affluence lifestyle’. For more details see Ríos‐Ocampo & Gary (2022). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#119 C	simulation start time (Year) = 1950 Description: Simulation starting time. Present In 1 View: View 1 Used By time effect This variable is calculated to represent the passage of time in the simulation, as affluence growth is dependent on time. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#122 C	SWT diminishing returns in adaptation capacity built per effort (dmnl ) = 1 Description: This switch activates the diminishing returns to adaptation mechanism, allowing the exploration of the limits to adaptation scenarios. Present In 2 Views: View 1 View 2 Used By adaptation capacity built per effort This variable represents amount of adaptation capacity developed per unit of 'adaptation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#123 C	SWT dimishing returns in mitigation technological development per effort (dmnl ) = 1 Description: This switch activates the diminishing returns to technological mitigation mechanism, allowing the exploration of the limits to technological development scenarios. Present In 2 Views: View 1 View 2 Used By mitigation technlogical development per effort This variable represents amount of technological mitigation developed per unit of 'technological mitigation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#124 C	SWT forced behavioural change loop (dmnl) = 1000 Description: Switch to activate the forced behavioural change loop. Set it to 1 to activate it. Set it to 1000 to deactivate it. Present In 1 View: View 1 Used By forced behavioural change threshold This value captures the threshold at which the perceived environmental disruption becomes so extreme that the high-affluence lifestyle becomes unsustainable. It is set to 1.6. Given that increases of approximately 0.3 impact units correspond to a 1°C variation in the model, this implies that if the population perceives the consequences of a 2°C variation compared to what they are adapted to, the high-affluence lifestyle becomes less attractive. The 2°C threshold is based on the IPCC report (2023, longer report, p. 31; Risk as burning embers figure), where at this level, human risk is considered very high. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#131 C	time to implement adaptation capacity (Year ) = 1 Description: The implementation of the developed adapatation capacity is not instantaneous and takes some time. However, this period is relatively short, especially when compared to the 'time to implement mitigation technology' (Zhao et al. 2018). Present In 1 View: View 1 Used By adaptation implemented We assumed that the implementation of the developed adaptation capacity is not instantaneous but takes time. We modeled this delay using a third-order delay function, which is considered appropriate for capturing technological implementation delays (Sterman, 2000). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#132 C	time to implement mitigation technology (Year) = 15 Description: The implementation of developed technological mitigation is not instantaneous and takes time. This period is relatively long, especially when compared to the 'time to implement adaptation technology,' because it takes a long time to broadly implement developed mitigation technologies (Schot et al., 2016; Sovacool, 2016). For this model, we assumed a value of 15 years. This value was chosen based on the famous Limits to Growth model (Meadows et al., 1972), where the time to implement technology was set at 20 years. We chose a slightly shorter period, believing that implementation delays have decreased a bit over time. Present In 1 View: View 1 Used By mitigation technology implemented We assumed that the implementation of the developed technological capacity is not instantaneous but takes time. We modeled this delay using a third-order delay function, which is considered appropriate for capturing technological implementation delays (Sterman, 2000). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#136 C	total potential effort per year ($/Year) = 1 Description: This variable captures the hypothetical total potential effort and resources that humanity can mobilise for adaptation and technological mitigation strategies to tackle climate change. For instance, annual GDP can be used as a proxy for the total potential effort available to the system per year. Present In 1 View: View 1 Used By effort taken against impact per year This variable calculates the actual effort mobilised by multiplying the 'total potential effort' by the effort humanity decides to exert ('effect of pressures perception on effort') based on the 'perceived pressures.' Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#137 C	transition back innovators fraction (dmnl/Year ) = 0.03 Description: The empirical average value of the innovators fraction (also known in the literature as p/coefficient of innovation/external influence/ advertising effect) has been found to be 0.03, with a typical range between 0.01 and 0.03 (Mahajan et al., 1995) Present In 1 View: View 1 Used By transition back to high-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the old high-affluence state is composed of innovators (transition back innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition back), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of Old Lifestyle) and on the potential population remaining to transition back.Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#139 C	transition innovators fraction (dmnl/Year ) = 0.03 Description: The empirical average value of the innovators fraction (also known in the literature as p/coefficient of innovation/external influence/ advertising effect) has been found to be 0.03, with a typical range between 0.01 and 0.03 (Mahajan et al., 1995) Present In 1 View: View 1 Used By transition to low-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the new low-affluence state is composed of innovators (transition innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of New Lifestyle) and on the potential population remaining to transition.Diffusion theory has been at the foundation of the study of sustainability transitions (Geels & Johnson, 2018; de Haan & Rotmans, 2011; Lenton et al., 2022), and the same formal models to depict transition patterns (Keith et al., 2020; Struben & Sterman, 2008).Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
.Control	#141 C	FINAL TIME (Year) = 2100 Description: The final time for the simulation. Present In 0 Views: Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
.Control	#142 C	INITIAL TIME (Year) = 1950 Description: The initial time for the simulation. Present In 0 Views: Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
.Control	#146 C	TIME STEP (Year ) = 0.25 Description: The time step for the simulation. Present In 0 Views: Used By SAVEPER The frequency with which output is stored. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]

Top	(Type) Flow (6 Variables)
Group	Type	*Variable Name And Description*
Environment - Societal Responses Model	#11 LI,F,A	adaptation capacity increase rate (Impact units/Year) = adaptation capacity built per effort* adaptation effort per year Description: This flow computes the development of adaptation capacity over time. Present In 1 View: View 1 Used By Adaptation capacity The adaptation efforts accumulate into a stock of Adaptation Capacity, which represents infrastructure and other types of investments around the world that serve to relieve the immediate pressures of climate change. Adaptation capacity is best depicted as a stock because “adaptation can be classified as incremental or developmental. In incremental adaptation, when original facilities and inputs are insufficient to resist a natural disaster, considering the emerging climatic risks, investments are added onto existing communal facilities, and the action is specific for the new additional climatic risk.” (Engle, 2011; Zhao et al., 2018, p. 86). For example, investments to build levees and dams to reduce floods caused by extreme weather events or rising sea levels help alleviate the immediate pressures and threats of floods caused by climate change and can be further raised if needed. Other examples showing the breadth and cumulative nature of adaptation are using more and more nets to protect trees fruit crops against the worsening of extreme hail events (Manja & Aoun, 2019),protecting capital through more and more extensive insurance against climate change (Jørgensen et al., 2020; McLeman & Smit, 2006; Suarez & Linnerooth-Bayer, 2010; Thomas & Leichenko, 2011). Feedback Loops: 3 (2.8%) (+) 0 [0,0] (-) 3 [4,7]
Environment - Societal Responses Model	#51 LI,F,A	impacts absorption (Impact units/Year) = MAX(0,( Cumulative impacts- cumulative impacts target level)/ impacts absorption time) Description: The planet also absorbs impacts over time through its natural sinks ('exceeding impacts absorption'). This absorption process is assumed to exhibit goal-seeking behavior driven by a balancing loop, consistent with similar conceptualisations of CO2 and pollution stocks (Forrester, 1971; Meadows et al., 1972). Specifically, the system aims to reach the 'cumulative impacts balance' level, representing the level of impacts that the system operates under normal conditions. For instance, the CO2 parts per million (ppm) in the air is not zero under normal conditions (excluding human activity), but has been approximately 280 ppm over the eras. This outflow represents the system's tendency to reach and maintain that level. The 'absorption time' indicates the average duration the impacts stay in the system (the stock of ‘Cumulative impacts’) before being absorbed. The 'max' function ensures that the flow never becomes negative (i.e., the stock is smaller than the target) and it increases the stock, as it would be unrealistic. Present In 1 View: View 1 Used By CO2 absorption The resulting increasing trend in CO₂ absorption is consistent with descriptions in the literature, which similarly report rising absorption over time (Friedlingstein et al., 2025). The magnitude of the values is also comparable to those reported in that study. While we express absorption in gigatonnes of CO₂ (GtCO₂), Friedlingstein et al. (2025) report values in gigatonnes of carbon (GtC). Since 1 GtC corresponds to approximately 3.67 GtCO₂, converting their estimates into CO₂ units yields values of the same order of magnitude as those generated by our model.https:/essd.copernicus.org/articles/17/965/2025/ Cumulative impacts The flow of 'Impacts Generation' accumulates in the stock of 'Cumulative Impacts'. This formulation, where negative environmental externalities accumulate as stocks over time, is typical in the literature (Forrester, 1971; Meadows et al., 1972; Sterman, 2008). It captures the fact that impacts are not instantaneous occurrences that disappear immediately but rather accumulate over time. Feedback Loops: 2 (1.9%) (+) 0 [0,0] (-) 2 [2,4]
Environment - Societal Responses Model	#53 LI,F,A	impacts generation (Impact units/Year) = (( Population with high-affluence lifestyle* impact population high affuence lifestyle* technology effect)+( Population with low-affluence lifestyle* impact population low affluence lifestyle* technology effect)) Description: The current affluence-growth lifestyle is responsible for negative social and environmental impacts (Wiedmann et al., 2020). This flow captures the environmental impacts (e.g., GHG emissions, pollution) generated by the current enactment of lifestyles. It is computed by multiplying the number of people living a specific lifestyle (the stocks of ‘Population with high affluence lifestyle’ and ‘Population with low affluence lifestyle’) by the impact each lifestyle has on the global environment (captured in the variables ‘impact high affuence lifestyle’ and ‘impact low affluence lifestyle’) and by the current technological level (technology effect) that can potentially reduce the impacts if enhanced. This formulation is based on the established IPAT equation (Chertow, 2000; Ehrlich & Holdren, 1971; York et al., 2003): I=PATWhere I is the impact of society on the environment, P is the population size, A is the level of affluence or level of consumption per person, and T is the available technology or the environmental impact for each unit of affluence. Present In 1 View: View 1 Used By CO2 emissions The impacts ('impacts generation') have been converted into CO2 gigatonnes (Gt) ('CO2 Gt converter') to calibrate the model. The do-nothing scenario leads to approximately 90 CO2 Gt emissions per year, aligning with the extreme scenarios of the IPCC report (2023 - Synthesis Report, longer report, p.31), specifically scenarios SSP5-8.5 and SSP5-7.0. The base case scenario results in approximately 45 CO2 Gt per year, corresponding to the intermediate SSP2-4.5 scenario (IPCC, 2023 - Synthesis Report, longer report, p.31). In scenarios where fundamental mitigation policies are implemented, impacts generation approaches zero. This outcome is within the range of plausible scenarios highlighted by the IPCC (2023) and is close to some of the most optimistic scenarios (e.g., SSP1-2.6).Thus, we used the CO2 Gt emissions per year to calibrate the model outputs, ensuring they reproduce a range of scenarios consistent with the latest IPCC report (2023).Similar values can be found also in IPCC, 2023 - Synthesis Report, SPM, p.23.This can increase confidence in the robustness of model output. Cumulative impacts The flow of 'Impacts Generation' accumulates in the stock of 'Cumulative Impacts'. This formulation, where negative environmental externalities accumulate as stocks over time, is typical in the literature (Forrester, 1971; Meadows et al., 1972; Sterman, 2008). It captures the fact that impacts are not instantaneous occurrences that disappear immediately but rather accumulate over time. Feedback Loops: 65 (61.3%) (+) 32 [9,15] (-) 33 [9,15]
Environment - Societal Responses Model	#77 LI,F,A	mitigation technology development rate (dmnl/Year) = technological mitigation effort per year* mitigation technlogical development per effort Description: This flow computes the development of technological mitigation over time. Present In 1 View: View 1 Used By Mitigation technology This stock represents the level of mitigation technology developed within the system. It starts at 1, reflecting the technological efficiency level of 1950, and accumulates over time as investments are made to improve mitigation technology. Assuming an evolutionary perspective on technological development, this stock increases only, due to variations in the inflow. Higher values indicate scenarios with greater efficiency. For example,a value of 2 in Mitigation technology equals to have a techological mitigation efficiency (broadly intended) twice of what is was in the 1950s. Feedback Loops: 3 (2.8%) (+) 2 [4,10] (-) 1 [11,11]
Environment - Societal Responses Model	#138 LI,F,A	transition back to high-affluence lifestyle (dmnl/Year) = ( transition back innovators fraction* Population with low-affluence lifestyle+ imitation coefficient transition back* Population with low-affluence lifestyle* Population with high-affluence lifestyle/ total population)* relative attractiveness of high-afflluence lifestyle Description: The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the old high-affluence state is composed of innovators (transition back innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition back), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of Old Lifestyle) and on the potential population remaining to transition back.Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 85 (80.2%) (+) 41 [2,15] (-) 44 [2,15]
Environment - Societal Responses Model	#140 LI,F,A	transition to low-affluence lifestyle (dmnl/Year) = ( transition innovators fraction* Population with high-affluence lifestyle+ imitation coefficient transition* Population with low-affluence lifestyle* Population with high-affluence lifestyle/ total population)* relative attractiveness of low-affluence lifestyle Description: The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the new low-affluence state is composed of innovators (transition innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of New Lifestyle) and on the potential population remaining to transition.Diffusion theory has been at the foundation of the study of sustainability transitions (Geels & Johnson, 2018; de Haan & Rotmans, 2011; Lenton et al., 2022), and the same formal models to depict transition patterns (Keith et al., 2020; Struben & Sterman, 2008).Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 79 (74.5%) (+) 38 [2,15] (-) 41 [2,15]

Top	(Type) Auxiliary (50 Variables)
Group	Type	*Variable Name And Description*
Environment - Societal Responses Model	#8 A	action trigger for behavioural mitigation (dmnl) = pressure to respond (perceived pressures)/( behavioural mitigation threshold* SWT behavioural mitigation loop) Description: An increase in ‘perceived pressures’ is expected to lower the attractiveness of the old lifestyle, since the old lifestyle is responsible for the undesired environmental impacts. Once the global population perceives the ‘Cumulative impacts’ consequences, we assume that high-affluence behaviour will be deemed problematic and become less attractive. In fact, if the global population identifies the affluent lifestyle and behaviour as the cause of the pressure, then the attractiveness of the lifestyle itself will decrease. Consistent with protection motivation theory, the perception of risks and threats can be a powerful driver to promote societal behavioural change (Beckage et al., 2018; Eker et al., 2019). As long as a person or community perceives that their behaviour is responsible for some risks, they are more motivated to do something. There is substantial for this response mechanism related to climate change (Bockarjova & Steg, 2014; Hunter & Röös, 2016; Lujala et al., 2015; Venghaus et al., 2022; Wells et al., 2011). However, this attribution is not straightforward, as an additional threshold (‘behavioural change threshold’) has to be overcome before behavioural change is triggered. This additional threshold comprises all the additional barriers hindering behavioural change, and captures that changing behaviour from high-affluence to low-affluence consists of an additional step than just perceiving the pressures but also to acknowledge that the high-affluence behaviour is responsible for climate change. Once this threshold is exceeded, people in the model are pushed to attribute the responsibility for the generation of pressures to their lifestyle behaviour, which leads to a decrease in the attractiveness of the affluence-based lifestyle. Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Feedback Loops: 21 (19.8%) (+) 11 [10,15] (-) 10 [10,14]
Environment - Societal Responses Model	#10 A	adaptation capacity built per effort (Impact units/$) = IF THEN ELSE( SWT diminishing returns in adaptation capacity built per effort=1, diminishing returns in adaptation capacity built per effort multiplier* constant returns in adaptation capacity built per effort, constant returns in adaptation capacity built per effort) Description: This variable represents amount of adaptation capacity developed per unit of 'adaptation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Present In 1 View: View 1 Used By adaptation capacity increase rate This flow computes the development of adaptation capacity over time. Feedback Loops: 1 (0.9%) (+) 0 [0,0] (-) 1 [4,4]
Environment - Societal Responses Model	#11 LI,F,A	adaptation capacity increase rate (Impact units/Year) = adaptation capacity built per effort* adaptation effort per year Description: This flow computes the development of adaptation capacity over time. Present In 1 View: View 1 Used By Adaptation capacity The adaptation efforts accumulate into a stock of Adaptation Capacity, which represents infrastructure and other types of investments around the world that serve to relieve the immediate pressures of climate change. Adaptation capacity is best depicted as a stock because “adaptation can be classified as incremental or developmental. In incremental adaptation, when original facilities and inputs are insufficient to resist a natural disaster, considering the emerging climatic risks, investments are added onto existing communal facilities, and the action is specific for the new additional climatic risk.” (Engle, 2011; Zhao et al., 2018, p. 86). For example, investments to build levees and dams to reduce floods caused by extreme weather events or rising sea levels help alleviate the immediate pressures and threats of floods caused by climate change and can be further raised if needed. Other examples showing the breadth and cumulative nature of adaptation are using more and more nets to protect trees fruit crops against the worsening of extreme hail events (Manja & Aoun, 2019),protecting capital through more and more extensive insurance against climate change (Jørgensen et al., 2020; McLeman & Smit, 2006; Suarez & Linnerooth-Bayer, 2010; Thomas & Leichenko, 2011). Feedback Loops: 3 (2.8%) (+) 0 [0,0] (-) 3 [4,7]
Environment - Societal Responses Model	#12 A	adaptation effort per year ($/Year) = effort taken against impact per year* effect of pressure to respond on adaptation priority Description: This variable calculates the annual effort allocated to technological mitigation by multiplying the actual resources humanity mobilises ('effort taken against impact per year') by the fraction of effort allocated to adaptation. Although historical data on adaptation and mitigation investment remains limited, recent research provides useful anchor points. For instance, Cortés Arbués et al. (2025) show that across European countries, private investment in adaptation increased exponentially between 2018 and 2023, reaching an average of approximately 0.20-0.25% of GDP in 2023 (see Figure 1 in their study). We use this estimate as an empirical anchor point for model calibration.https:/www.nature.com/articles/s43247-025-02454-3/figures/1Cortés Arbués, I., Chatzivasileiadis, T., Storm, S. et al. Private investments in climate change adaptation are increasing in Europe, although sectoral differences remain. Commun Earth Environ 6, 470 (2025). https:/doi.org/10.1038/s43247-025-02454-3 Present In 1 View: View 1 Used By adaptation capacity increase rate This flow computes the development of adaptation capacity over time. total actual effort Variable computing the total effort mobilised (adaptation + technological mitigation) in the simulation. Feedback Loops: 2 (1.9%) (+) 0 [0,0] (-) 2 [6,7]
Environment - Societal Responses Model	#13 SM,A	adaptation implemented (Impact units) = SMOOTH3I( Adaptation capacity, time to implement adaptation capacity, Adaptation capacity) Description: We assumed that the implementation of the developed adaptation capacity is not instantaneous but takes time. We modeled this delay using a third-order delay function, which is considered appropriate for capturing technological implementation delays (Sterman, 2000). Present In 1 View: View 1 Used By pressure to respond (perceived pressures) The global population begins to feel the 'perceived pressures' once the 'perceived cumulative impacts' exceed the adaptation capacity implemented ('adaptation implemented') and the non-offset by adaptation impacts also exceed the tolerance threshold ('pressures tolerance threshold').In fact, the scope and effect of adaptation is to reduce the perception or the pressures (Wheeler et al, 2021). Feedback Loops: 2 (1.9%) (+) 0 [0,0] (-) 2 [6,7]
Environment - Societal Responses Model	#14 A	affluence and population growth (dmnl) = 1+( time effect* affluence and population growth multiplier) Description: Affluence and population are assumed to grow over time in the model. This reflects empirical trends: GDP-commonly used as a proxy for affluence (Dietz & Rosa, 1994)-has historically increased, as has population, including in the Global North (UN data). These trends are also consistent with the observed increase in global CO₂ emissions (i.e., impacts) over time (Friedlingstein et al., 2023). This growth is computed by multiplying the time passing in the simulation (represented by the 'time effect' ranging from 0 to 150 as the simulation progresses from 1950 to 2100) by a 10% growth rate ('affluence growth multiplier') and adding this resulting value to 1. The outcome is a multiplier always greater than 1, which is then multiplied by the 'initial impact high affluence lifestyle' in the 'impact high affluence lifestyle' variable. Present In 1 View: View 1 Used By impact population high affuence lifestyle These are the impacts generated per person with the high-affluence lifestyle per year. They are computed by multiplying the 'initial impact high affluence lifestyle' by the estimated 'affluence growth' trends over time. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#17 A	attractiveness of high-affluence lifestyle (Attractiveness units) = ( reference attractivness high-affluence lifestyle+( Population with high-affluence lifestyle* lifestyle socio-technical regime effect))* effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation* effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response* effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change Description: The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness high affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The multiplier "effect of pressures perception on the attractiveness of the old lifestyle" reflects that an increase in the perception of socio-environmental pressures caused by climate change can lead to a decrease in the attractiveness of the high-affluence lifestyle, if people recognize it as a source of the problem.The multiplier ‘effect of pressures perception on attractiveness of high affluence lifestyle - rapid response’ reflects the decrease in attractiveness of the high affluence lifestyle if behavioural mitigation responses are implemented. The multiplier ‘involuntary effect of pressure perception attractiveness of high affluence lifestyle’ reflects the decrease in the attractiveness of the high affluence lifestyle if the surrounding environment is highly disrupted. Present In 1 View: View 1 Used By relative attractiveness of high-afflluence lifestyle A specular variable to the 'relative attractiveness of low affluence lifestyle' (with oppositive and complementary values) represents the fractional attractiveness of the old high-affluence lifestyle compared to the new low-impact one. This value regulates the transition backflow. total attractiveness of all lifestyle Variable calculating the toal attractivenss of all lifestyles in the system. Feedback Loops: 75 (70.8%) (+) 37 [4,15] (-) 38 [5,15]
Environment - Societal Responses Model	#18 A	attractiveness of low-affluence lifestyle (Attractiveness units) = ( reference attractiveness low-affluence lifestyle+( lifestyle socio-technical regime effect* Population with low-affluence lifestyle)) Description: The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness low affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The switch function captures the same function, with the addition of policies or actions designed to enhance the attractiveness of the low-impact lifestyle. In fact, external factors, like social and environmental pressures, taxes, or regulations, information or education, can alter the attractiveness of a way of living (Bergquist et al., 2023; Brown & Vergragt, 2016). Present In 1 View: View 1 Used By relative attractiveness of low-affluence lifestyle Here, the 'attractiveness of low affluence lifestyle' is divided by the 'total attractiveness of all lifestyles,' yielding a fractional value that compares the attractiveness of the new low-affluence lifestyle with that of the old high-affluence lifestyle. This captures that when the new alternative lifestyle becomes more attractive, people are more inclined to transition from the old lifestyle and adopt the new one. Conversely the transition does not occur (or can be reversed) as long as the old lifestyle remains more attractive. Theory shows how people move from one regime to another, adopting new technologies or behaviours for reasons such as convenience, preference, desire, perceived benefits, or fitness with the environment (Arthur, 1989; Geels, 2020; Rogers, 1962) total attractiveness of all lifestyle Variable calculating the toal attractivenss of all lifestyles in the system. Feedback Loops: 21 (19.8%) (+) 10 [4,15] (-) 11 [5,15]
Environment - Societal Responses Model	#26 A	CO2 absorption (CO2 Gt/Year) = impacts absorption* CO2 Gt converter Description: The resulting increasing trend in CO₂ absorption is consistent with descriptions in the literature, which similarly report rising absorption over time (Friedlingstein et al., 2025). The magnitude of the values is also comparable to those reported in that study. While we express absorption in gigatonnes of CO₂ (GtCO₂), Friedlingstein et al. (2025) report values in gigatonnes of carbon (GtC). Since 1 GtC corresponds to approximately 3.67 GtCO₂, converting their estimates into CO₂ units yields values of the same order of magnitude as those generated by our model.https:/essd.copernicus.org/articles/17/965/2025/ Present In 1 View: View 1 Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#27 A	CO2 emissions (CO2 Gt/Year) = impacts generation* CO2 Gt converter Description: The impacts ('impacts generation') have been converted into CO2 gigatonnes (Gt) ('CO2 Gt converter') to calibrate the model. The do-nothing scenario leads to approximately 90 CO2 Gt emissions per year, aligning with the extreme scenarios of the IPCC report (2023 - Synthesis Report, longer report, p.31), specifically scenarios SSP5-8.5 and SSP5-7.0. The base case scenario results in approximately 45 CO2 Gt per year, corresponding to the intermediate SSP2-4.5 scenario (IPCC, 2023 - Synthesis Report, longer report, p.31). In scenarios where fundamental mitigation policies are implemented, impacts generation approaches zero. This outcome is within the range of plausible scenarios highlighted by the IPCC (2023) and is close to some of the most optimistic scenarios (e.g., SSP1-2.6).Thus, we used the CO2 Gt emissions per year to calibrate the model outputs, ensuring they reproduce a range of scenarios consistent with the latest IPCC report (2023).Similar values can be found also in IPCC, 2023 - Synthesis Report, SPM, p.23.This can increase confidence in the robustness of model output. Present In 1 View: View 1 Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#29 A	CO2 ppm (CO2 ppm) = Cumulative impacts* cumulative impacts to CO2ppm equivalent Description: The impacts (‘Cumulative impacts’) have been converted into CO2 ppm (‘cumulative impacts to CO2ppm equivalent’) to calibrate the model. The base results align with actual trends, with the model showing CO2 ppm starting at 300 in 1950 and reaching approximately 430 in 2020, compared to the real value of 420 (Friedlingstein et al., 2023; IPCC, 2023). The base scenario projects CO2 levels exceed 560 ppm by 2100, which seems plausible and aligns with intermediary IPCC scenarios and other research estimates, such as Szulejko et al. (2017), who estimated slightly above 620 ppm by 2100 based on extrapolated growth trends up to 2014 (a discrepancy that seems possible as some mitigation policies have been implemented meanwhile ).In the extreme scenario where no fundamental policies are implemented, the model projects an upper value of 970 ppm, implying that if humanity maintained the impact growth rate from the 1950s without any mitigation efforts, CO2 levels would reach such high values. This figure is plausible as it falls within the IPCC's extreme scenarios range (SSP5-8.5) and aligns with other extreme estimates in the literature, such as Hu et al. (2019), who assumed an upper-high CO2 level of 936 ppm.These results provide confidence in the robustness of the model output.https:/www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide Present In 1 View: View 1 Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#35 A	diminishing returns in adaptation capacity built per effort multiplier (dmnl) = ( A - diminishing returns in adaptation capacity built per effort multiplier+( K - diminishing returns in adaptation capacity built per effort multiplier- A - diminishing returns in adaptation capacity built per effort multiplier)/( C - diminishing returns in adaptation capacity built per effort multiplier+ Q - diminishing returns in adaptation capacity built per effort multiplier(( A - diminishing returns in adaptation capacity built per effort multiplier( C - diminishing returns in adaptation capacity built per effort multiplier-1)+ K - diminishing returns in adaptation capacity built per effort multiplier- ry - diminishing returns in adaptation capacity built per effort multiplier* C - diminishing returns in adaptation capacity built per effort multiplier)/( Q - diminishing returns in adaptation capacity built per effort multiplier( ry - diminishing returns in adaptation capacity built per effort multiplier- A - diminishing returns in adaptation capacity built per effort multiplier)))^(( Adaptation capacity- M - diminishing returns in adaptation capacity built per effort multiplier)/( rx - diminishing returns in adaptation capacity built per effort multiplier- M - diminishing returns in adaptation capacity built per effort multiplier)))) Description:* This function captures the diminishing return in adaptation capacity implementation. After a certain threshold, the more effort is spent on adaptation capacity the less adaptive capacity is generated as we approach a technological development ceiling – ‘limits to adaptation’ debate (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function calculated to capture such an effect is an inverse (decreasing) s-shaped curve. It was selected as it believed that it approximates well the non-linearity of the approaching of the technological ceiling (slow initial reduction in output per effort that increases more and more over time until it reaches zero). It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Adapation capacity’ as input to the function. Present In 1 View: View 1 Used By adaptation capacity built per effort This variable represents amount of adaptation capacity developed per unit of 'adaptation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Feedback Loops: 1 (0.9%) (+) 0 [0,0] (-) 1 [4,4]
Environment - Societal Responses Model	#36 A	dimishing returns in mitigation technological development per effort multiplier (dmnl) = ( A - dimishing returns in mitigation technological development per effort multiplier+( K - dimishing returns in mitigation technological development per effort multiplier- A - dimishing returns in mitigation technological development per effort multiplier)/( C - dimishing returns in mitigation technological development per effort multiplier+ Q - dimishing returns in mitigation technological development per effort multiplier(( A - dimishing returns in mitigation technological development per effort multiplier( C - dimishing returns in mitigation technological development per effort multiplier-1)+ K - dimishing returns in mitigation technological development per effort multiplier- ry - dimishing returns in mitigation technological development per effort multiplier* C - dimishing returns in mitigation technological development per effort multiplier)/( Q - dimishing returns in mitigation technological development per effort multiplier( ry - dimishing returns in mitigation technological development per effort multiplier- A - dimishing returns in mitigation technological development per effort multiplier)))^(( Mitigation technology- M - dimishing returns in mitigation technological development per effort multiplier)/( rx - dimishing returns in mitigation technological development per effort multiplier- M - dimishing returns in mitigation technological development per effort multiplier)))) Description:* This function captures the phenomenon of diminishing returns in mitigation technology. Beyond a certain threshold, increasing effort in mitigation technology results in diminishing outputs as we approach a technological development ceiling (Alexander & Rutherford, 2019; Dow et al., 2013; Keary, 2016). The function used to represent this effect is an inverse (decreasing) S-shaped curve. This choice was made because it is believed to closely approximate the non-linearity of technological progress towards the ceiling (initially slow reduction in output per effort, accelerating over time until it reaches zero). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For a more detailed explanation of each parameter's meaning, please refer to their work.-Version of Generalized logistic equation computed with six parameters and reference points: A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘Mitigation technology’ as input to the function. Present In 1 View: View 1 Used By mitigation technlogical development per effort This variable represents amount of technological mitigation developed per unit of 'technological mitigation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Feedback Loops: 1 (0.9%) (+) 1 [4,4] (-) 0 [0,0]
Environment - Societal Responses Model	#37 A	effect of pressure to respond on adaptation priority (dmnl) = ( A - effect of pressure perception on adaptation priority+( K - effect of pressure perception on adaptation priority- A - effect of pressure perception on adaptation priority)/(1+(( K - effect of pressure perception on adaptation priority- ry - effect of pressure perception on adaptation priority)/( ry - effect of pressure perception on adaptation priority- A - effect of pressure perception on adaptation priority))^((( pressure to respond (perceived pressures)/ resources allocation threshold)- M - effect of pressure perception on adaptation priority)/( rx - effect of pressure perception on adaptation priority- M - effect of pressure perception on adaptation priority))))(1- SWT to static allocation rule)+ alternative allocation to adaptation fraction SWT to static allocation rule Description: In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Present In 1 View: View 1 Used By adaptation effort per year This variable calculates the annual effort allocated to technological mitigation by multiplying the actual resources humanity mobilises ('effort taken against impact per year') by the fraction of effort allocated to adaptation. Although historical data on adaptation and mitigation investment remains limited, recent research provides useful anchor points. For instance, Cortés Arbués et al. (2025) show that across European countries, private investment in adaptation increased exponentially between 2018 and 2023, reaching an average of approximately 0.20-0.25% of GDP in 2023 (see Figure 1 in their study). We use this estimate as an empirical anchor point for model calibration.https:/www.nature.com/articles/s43247-025-02454-3/figures/1Cortés Arbués, I., Chatzivasileiadis, T., Storm, S. et al. Private investments in climate change adaptation are increasing in Europe, although sectoral differences remain. Commun Earth Environ 6, 470 (2025). https:/doi.org/10.1038/s43247-025-02454-3 technological mitigation effort per year This variable calculates the annual effort allocated to technological mitigation by multiplying the actual resources humanity mobilises ('effort taken against impact per year') by the fraction of effort not allocated to adaptation. Although there is limited historical data on mitigation investment, useful proxies are available. For instance, Eurostat (2024) reports that private investment in mitigation in the EU amounts to approximately 0.55% of EU GDP. This suggests that total mitigation investment in 2020 is likely to have been of a similar order of magnitude, and potentially higher when including public investments. We use this estimate as an indicative reference point for model calibration.https:/ec.europa.eu/eurostat/statistics-explained/index.php?title=Investments_in_climate_change_mitigation(the trends overtime has similar modes of behaviour to the simulated output) Feedback Loops: 2 (1.9%) (+) 1 [10,10] (-) 1 [6,6]
Environment - Societal Responses Model	#38 A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl) = ( A - effect of pressures perception on attractivenss of high affluence lifestyle+( K - effect of pressures perception on attractivenss of high affluence lifestyle- A - effect of pressures perception on attractivenss of high affluence lifestyle)/( C - effect of pressures perception on attractivenss of high affluence lifestyle+ Q - effect of pressures perception on attractivenss of high affluence lifestyle(( A - effect of pressures perception on attractivenss of high affluence lifestyle( C - effect of pressures perception on attractivenss of high affluence lifestyle-1)+ K - effect of pressures perception on attractivenss of high affluence lifestyle- ry - effect of pressures perception on attractivenss of high affluence lifestyle* C - effect of pressures perception on attractivenss of high affluence lifestyle)/( Q - effect of pressures perception on attractivenss of high affluence lifestyle( ry - effect of pressures perception on attractivenss of high affluence lifestyle- A - effect of pressures perception on attractivenss of high affluence lifestyle)))^(( action trigger for behavioural mitigation- M - effect of pressures perception on attractivenss of high affluence lifestyle)/( rx - effect of pressures perception on attractivenss of high affluence lifestyle- M - effect of pressures perception on attractivenss of high affluence lifestyle)))) Description:* This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘action trigger for behavioural change’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high-affluence lifestyle and high 'perceived pressures'. This curve was selected because it approximates the gradual reduction in attractiveness over time, starting with a slow initial decline and accelerating as perceived pressures intensify. Notably, the function is not very steep, reflecting research findings that individuals may not automatically change behaviour despite being aware of the environmental problems associated with their lifestyle. For example, pro-environmental voting intentions do not always correspond to radical behavioural changes and lower carbon footprints (Malik et al., 2022). The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘action trigger for behavioural change’ as input to the function. Present In 1 View: View 1 Used By attractiveness of high-affluence lifestyle The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness high affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The multiplier "effect of pressures perception on the attractiveness of the old lifestyle" reflects that an increase in the perception of socio-environmental pressures caused by climate change can lead to a decrease in the attractiveness of the high-affluence lifestyle, if people recognize it as a source of the problem.The multiplier ‘effect of pressures perception on attractiveness of high affluence lifestyle - rapid response’ reflects the decrease in attractiveness of the high affluence lifestyle if behavioural mitigation responses are implemented. The multiplier ‘involuntary effect of pressure perception attractiveness of high affluence lifestyle’ reflects the decrease in the attractiveness of the high affluence lifestyle if the surrounding environment is highly disrupted. Feedback Loops: 21 (19.8%) (+) 11 [10,15] (-) 10 [10,14]
Environment - Societal Responses Model	#39 A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl) = SAMPLE IF TRUE(( SWT rapid behavioural response* pressure to respond (perceived pressures))/ behavioural mitigation threshold rapid response>1:AND:( A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response+( K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)/( C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response+ Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response(( A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response( C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response-1)+ K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response* C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)/( Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response( ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)))^((( pressure to respond (perceived pressures)/ behavioural mitigation threshold rapid response)- M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)/( rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response))))< effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response,( A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response+( K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)/( C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response+ Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response(( A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response( C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response-1)+ K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)/( Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response( ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)))^((( pressure to respond (perceived pressures)/ behavioural mitigation threshold rapid response)- M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)/( rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response- M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response)))),1) Description:* This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Present In 1 View:* View 1 Used By attractiveness of high-affluence lifestyle The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness high affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The multiplier "effect of pressures perception on the attractiveness of the old lifestyle" reflects that an increase in the perception of socio-environmental pressures caused by climate change can lead to a decrease in the attractiveness of the high-affluence lifestyle, if people recognize it as a source of the problem.The multiplier ‘effect of pressures perception on attractiveness of high affluence lifestyle - rapid response’ reflects the decrease in attractiveness of the high affluence lifestyle if behavioural mitigation responses are implemented. The multiplier ‘involuntary effect of pressure perception attractiveness of high affluence lifestyle’ reflects the decrease in the attractiveness of the high affluence lifestyle if the surrounding environment is highly disrupted. Feedback Loops: 21 (19.8%) (+) 10 [9,13] (-) 11 [9,14]
Environment - Societal Responses Model	#40 A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl) = ( A - forced effect of pressure perception attractiveness of high affluence lifestyle+( K - forced effect of pressure perception attractiveness of high affluence lifestyle- A - forced effect of pressure perception attractiveness of high affluence lifestyle)/( C - forced effect of pressure perception attractiveness of high affluence lifestyle+ Q - forced effect of pressure perception attractiveness of high affluence lifestyle(( A - forced effect of pressure perception attractiveness of high affluence lifestyle( C - forced effect of pressure perception attractiveness of high affluence lifestyle-1)+ K - forced effect of pressure perception attractiveness of high affluence lifestyle- ry - forced effect of pressure perception attractiveness of high affluence lifestyle* C - forced effect of pressure perception attractiveness of high affluence lifestyle)/( Q - forced effect of pressure perception attractiveness of high affluence lifestyle( ry - forced effect of pressure perception attractiveness of high affluence lifestyle- A - forced effect of pressure perception attractiveness of high affluence lifestyle)))^((( forced behavioural change trigger)- M - forced effect of pressure perception attractiveness of high affluence lifestyle)/( rx - forced effect of pressure perception attractiveness of high affluence lifestyle- M - forced effect of pressure perception attractiveness of high affluence lifestyle)))) Description:* This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Present In 1 View: View 1 Used By attractiveness of high-affluence lifestyle The attractiveness of a lifestyle depends on its intrinsic appeal (captured by the corresponding reference attractiveness) and is enhanced by the lifestyle's socio-technical regime. This mechanism reflects that a behavior becomes more socially appealing as more people adopt it and that as a lifestyle becomes more widespread, the technical infrastructure evolves to meet its needs. The attractiveness of the two lifestyles is then calculated following and adapting Arthur’s lock-in analytical formulations (Arthur, 1989; Safarzyńska et al., 2012; Sterman, 2000), so that the attractiveness of a lifestyle depends on the intrinsic utility and wellbeing a lifestyle provides (reference attractiveness high affluence lifestyle) plus the number of people in that state multiplied by a coefficient (Sociotechnical Regime Effect).According to this formulation, an agent will not choose between two goods or behaviours only based upon the intrinsic attractiveness a and b, but takes into account also the intensity of the network associated with the two goods, which depends on the number of people already adopting that good (Na and Nb) and on a network effect (r). Ultimately, even if b>a, the agent may still stick with option a because arNa<brNb (Arthur, 1989; Safarzyńska et al., 2012, p. 1017).The multiplier "effect of pressures perception on the attractiveness of the old lifestyle" reflects that an increase in the perception of socio-environmental pressures caused by climate change can lead to a decrease in the attractiveness of the high-affluence lifestyle, if people recognize it as a source of the problem.The multiplier ‘effect of pressures perception on attractiveness of high affluence lifestyle - rapid response’ reflects the decrease in attractiveness of the high affluence lifestyle if behavioural mitigation responses are implemented. The multiplier ‘involuntary effect of pressure perception attractiveness of high affluence lifestyle’ reflects the decrease in the attractiveness of the high affluence lifestyle if the surrounding environment is highly disrupted. Feedback Loops: 21 (19.8%) (+) 10 [10,14] (-) 11 [10,15]
Environment - Societal Responses Model	#41 A	effect of pressure to respond on effort (dmnl) = ( A - effect of pressures perception on effort - base scenario+( K - effect of pressures perception on effort - base scenario- A - effect of pressures perception on effort - base scenario)/(1+(( K - effect of pressures perception on effort - base scenario- ry - effect of pressures perception on effort - base scenario)/( ry - effect of pressures perception on effort - base scenario- A - effect of pressures perception on effort - base scenario))^((( pressure to respond (perceived pressures)/ resources allocation threshold)- M - effect of pressures perception on effort - base scenario)/( rx - effect of pressures perception on effort - base scenario- M - effect of pressures perception on effort - base scenario))))(1- SWT to rapid response after perception)+( A - effect of pressures perception on effort - alternative scenario+( K - effect of pressures perception on effort - alternative scenario- A - effect of pressures perception on effort - alternative scenario)/(1+(( K - effect of pressures perception on effort - alternative scenario- ry - effect of pressures perception on effort - alternative scenario)/( ry - effect of pressures perception on effort - alternative scenario- A - effect of pressures perception on effort - alternative scenario))^((( pressure to respond (perceived pressures)/ resources allocation threshold)- M - effect of pressures perception on effort - alternative scenario)/( rx - effect of pressures perception on effort - alternative scenario- M - effect of pressures perception on effort - alternative scenario)))) SWT to rapid response after perception Description: In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Present In 1 View: View 1 Used By effort taken against impact per year This variable calculates the actual effort mobilised by multiplying the 'total potential effort' by the effort humanity decides to exert ('effect of pressures perception on effort') based on the 'perceived pressures.' Feedback Loops: 2 (1.9%) (+) 0 [0,0] (-) 2 [7,11]
Environment - Societal Responses Model	#42 A	effort taken against impact per year ($/Year) = total potential effort per year* effect of pressure to respond on effort Description: This variable calculates the actual effort mobilised by multiplying the 'total potential effort' by the effort humanity decides to exert ('effect of pressures perception on effort') based on the 'perceived pressures.' Present In 1 View: View 1 Used By adaptation effort per year This variable calculates the annual effort allocated to technological mitigation by multiplying the actual resources humanity mobilises ('effort taken against impact per year') by the fraction of effort allocated to adaptation. Although historical data on adaptation and mitigation investment remains limited, recent research provides useful anchor points. For instance, Cortés Arbués et al. (2025) show that across European countries, private investment in adaptation increased exponentially between 2018 and 2023, reaching an average of approximately 0.20-0.25% of GDP in 2023 (see Figure 1 in their study). We use this estimate as an empirical anchor point for model calibration.https:/www.nature.com/articles/s43247-025-02454-3/figures/1Cortés Arbués, I., Chatzivasileiadis, T., Storm, S. et al. Private investments in climate change adaptation are increasing in Europe, although sectoral differences remain. Commun Earth Environ 6, 470 (2025). https:/doi.org/10.1038/s43247-025-02454-3 technological mitigation effort per year This variable calculates the annual effort allocated to technological mitigation by multiplying the actual resources humanity mobilises ('effort taken against impact per year') by the fraction of effort not allocated to adaptation. Although there is limited historical data on mitigation investment, useful proxies are available. For instance, Eurostat (2024) reports that private investment in mitigation in the EU amounts to approximately 0.55% of EU GDP. This suggests that total mitigation investment in 2020 is likely to have been of a similar order of magnitude, and potentially higher when including public investments. We use this estimate as an indicative reference point for model calibration.https:/ec.europa.eu/eurostat/statistics-explained/index.php?title=Investments_in_climate_change_mitigation(the trends overtime has similar modes of behaviour to the simulated output) Feedback Loops: 2 (1.9%) (+) 0 [0,0] (-) 2 [7,11]
Environment - Societal Responses Model	#43 A	forced behavioural change threshold (dmnl) = 1.6* SWT forced behavioural change loop Description: This value captures the threshold at which the perceived environmental disruption becomes so extreme that the high-affluence lifestyle becomes unsustainable. It is set to 1.6. Given that increases of approximately 0.3 impact units correspond to a 1°C variation in the model, this implies that if the population perceives the consequences of a 2°C variation compared to what they are adapted to, the high-affluence lifestyle becomes less attractive. The 2°C threshold is based on the IPCC report (2023, longer report, p. 31; Risk as burning embers figure), where at this level, human risk is considered very high. Present In 2 Views: View 1 View 2 Used By forced behavioural change trigger If the perceived pressures exceed the 'involuntary behavioral change threshold' (indicating when the perceived pressures become unbearable), the involuntary mechanisms that make the high-affluence lifestyle unfeasible are activated Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#44 A	forced behavioural change trigger (dmnl) = pressure to respond (perceived pressures)/ forced behavioural change threshold Description: If the perceived pressures exceed the 'involuntary behavioral change threshold' (indicating when the perceived pressures become unbearable), the involuntary mechanisms that make the high-affluence lifestyle unfeasible are activated Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ with the increasing ‘involuntary behavioural change trigger’. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing feasibility of the high-affluence lifestyle (captured by the attractiveness variable) with increasing 'perceived pressures'. Notably, the function is steep, reflecting that once natural critical thresholds are surpassed, the current lifestyle becomes rapidly impractical. For example, once an area starts experiencing more severe and frequent droughts, people will no longer be able to maintain their current activities, such as cultivating food, watering plants and animals, filling pools, and welcoming tourists. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘involuntary behavioural change trigger’ as input to the function.Note that the function is not irreversible. If the environment is severely disrupted but humanity implements fundamental solutions to improve it, this effect can be deactivated in the long run as the state of the environment improves. Feedback Loops: 21 (19.8%) (+) 10 [10,14] (-) 11 [10,15]
Environment - Societal Responses Model	#49 A	impact population high affuence lifestyle (Impact units/Year) = affluence and population growth* initial impact high affluence lifestyle per person* population 1950 Description: These are the impacts generated per person with the high-affluence lifestyle per year. They are computed by multiplying the 'initial impact high affluence lifestyle' by the estimated 'affluence growth' trends over time. Present In 1 View: View 1 Used By impact population low affluence lifestyle In the model, the ‘impact low affluence lifestyle’ is assumed to be 70% lower than the high affluence one, in line with recent research showing that decent living standards can also be achieved with such reduction in per-capita energy use than currently utilised in affluent countries (Lockyer, 2017; Rao et al., 2019; Trainer, 2021; Wiedmann et al., 2020; Sato et al. 2016). To estimate this value, we simulated the do-nothing scenario, where no fundamental mitigation policies are implemented, and used the 2020 value of 'impact high affluence lifestyle' (as it aligns with the period of the referenced studies), computing 30% of that value. The minimum function ensures that if the model starts with an extremely low 'impact high affluence lifestyle', the 'impact low affluence lifestyle' is not greater. impacts generation The current affluence-growth lifestyle is responsible for negative social and environmental impacts (Wiedmann et al., 2020). This flow captures the environmental impacts (e.g., GHG emissions, pollution) generated by the current enactment of lifestyles. It is computed by multiplying the number of people living a specific lifestyle (the stocks of ‘Population with high affluence lifestyle’ and ‘Population with low affluence lifestyle’) by the impact each lifestyle has on the global environment (captured in the variables ‘impact high affuence lifestyle’ and ‘impact low affluence lifestyle’) and by the current technological level (technology effect) that can potentially reduce the impacts if enhanced. This formulation is based on the established IPAT equation (Chertow, 2000; Ehrlich & Holdren, 1971; York et al., 2003): I=PATWhere I is the impact of society on the environment, P is the population size, A is the level of affluence or level of consumption per person, and T is the available technology or the environmental impact for each unit of affluence. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#50 A	impact population low affluence lifestyle (Impact units/Year) = MIN( impact population high affuence lifestyle,( impact population high affluence lifestyle in 2020* fractional consumption from high- to low-affluence lifestyle)) Description: In the model, the ‘impact low affluence lifestyle’ is assumed to be 70% lower than the high affluence one, in line with recent research showing that decent living standards can also be achieved with such reduction in per-capita energy use than currently utilised in affluent countries (Lockyer, 2017; Rao et al., 2019; Trainer, 2021; Wiedmann et al., 2020; Sato et al. 2016). To estimate this value, we simulated the do-nothing scenario, where no fundamental mitigation policies are implemented, and used the 2020 value of 'impact high affluence lifestyle' (as it aligns with the period of the referenced studies), computing 30% of that value. The minimum function ensures that if the model starts with an extremely low 'impact high affluence lifestyle', the 'impact low affluence lifestyle' is not greater. Present In 1 View: View 1 Used By impacts generation The current affluence-growth lifestyle is responsible for negative social and environmental impacts (Wiedmann et al., 2020). This flow captures the environmental impacts (e.g., GHG emissions, pollution) generated by the current enactment of lifestyles. It is computed by multiplying the number of people living a specific lifestyle (the stocks of ‘Population with high affluence lifestyle’ and ‘Population with low affluence lifestyle’) by the impact each lifestyle has on the global environment (captured in the variables ‘impact high affuence lifestyle’ and ‘impact low affluence lifestyle’) and by the current technological level (technology effect) that can potentially reduce the impacts if enhanced. This formulation is based on the established IPAT equation (Chertow, 2000; Ehrlich & Holdren, 1971; York et al., 2003): I=PATWhere I is the impact of society on the environment, P is the population size, A is the level of affluence or level of consumption per person, and T is the available technology or the environmental impact for each unit of affluence. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#51 LI,F,A	impacts absorption (Impact units/Year) = MAX(0,( Cumulative impacts- cumulative impacts target level)/ impacts absorption time) Description: The planet also absorbs impacts over time through its natural sinks ('exceeding impacts absorption'). This absorption process is assumed to exhibit goal-seeking behavior driven by a balancing loop, consistent with similar conceptualisations of CO2 and pollution stocks (Forrester, 1971; Meadows et al., 1972). Specifically, the system aims to reach the 'cumulative impacts balance' level, representing the level of impacts that the system operates under normal conditions. For instance, the CO2 parts per million (ppm) in the air is not zero under normal conditions (excluding human activity), but has been approximately 280 ppm over the eras. This outflow represents the system's tendency to reach and maintain that level. The 'absorption time' indicates the average duration the impacts stay in the system (the stock of ‘Cumulative impacts’) before being absorbed. The 'max' function ensures that the flow never becomes negative (i.e., the stock is smaller than the target) and it increases the stock, as it would be unrealistic. Present In 1 View: View 1 Used By CO2 absorption The resulting increasing trend in CO₂ absorption is consistent with descriptions in the literature, which similarly report rising absorption over time (Friedlingstein et al., 2025). The magnitude of the values is also comparable to those reported in that study. While we express absorption in gigatonnes of CO₂ (GtCO₂), Friedlingstein et al. (2025) report values in gigatonnes of carbon (GtC). Since 1 GtC corresponds to approximately 3.67 GtCO₂, converting their estimates into CO₂ units yields values of the same order of magnitude as those generated by our model.https:/essd.copernicus.org/articles/17/965/2025/ Cumulative impacts The flow of 'Impacts Generation' accumulates in the stock of 'Cumulative Impacts'. This formulation, where negative environmental externalities accumulate as stocks over time, is typical in the literature (Forrester, 1971; Meadows et al., 1972; Sterman, 2008). It captures the fact that impacts are not instantaneous occurrences that disappear immediately but rather accumulate over time. Feedback Loops: 2 (1.9%) (+) 0 [0,0] (-) 2 [2,4]
Environment - Societal Responses Model	#52 A	impacts absorption time (Year) = reference impacts absorption time* natural sinks degradation due to cumulative impacts multiplier Description: This variable represents the average time it takes to absorb the excess 'Cumulative Impacts'. It is calculated by multiplying the 'reference impacts absorption time' by the 'natural sinks degradation due to cumulative impacts multiplier'. This multiplier exceeds one when 'Cumulative Impacts' increase to the point of deteriorating natural sinks. Present In 1 View: View 1 Used By impacts absorption The planet also absorbs impacts over time through its natural sinks ('exceeding impacts absorption'). This absorption process is assumed to exhibit goal-seeking behavior driven by a balancing loop, consistent with similar conceptualisations of CO2 and pollution stocks (Forrester, 1971; Meadows et al., 1972). Specifically, the system aims to reach the 'cumulative impacts balance' level, representing the level of impacts that the system operates under normal conditions. For instance, the CO2 parts per million (ppm) in the air is not zero under normal conditions (excluding human activity), but has been approximately 280 ppm over the eras. This outflow represents the system's tendency to reach and maintain that level. The 'absorption time' indicates the average duration the impacts stay in the system (the stock of ‘Cumulative impacts’) before being absorbed. The 'max' function ensures that the flow never becomes negative (i.e., the stock is smaller than the target) and it increases the stock, as it would be unrealistic. Feedback Loops: 1 (0.9%) (+) 0 [0,0] (-) 1 [4,4]
Environment - Societal Responses Model	#53 LI,F,A	impacts generation (Impact units/Year) = (( Population with high-affluence lifestyle* impact population high affuence lifestyle* technology effect)+( Population with low-affluence lifestyle* impact population low affluence lifestyle* technology effect)) Description: The current affluence-growth lifestyle is responsible for negative social and environmental impacts (Wiedmann et al., 2020). This flow captures the environmental impacts (e.g., GHG emissions, pollution) generated by the current enactment of lifestyles. It is computed by multiplying the number of people living a specific lifestyle (the stocks of ‘Population with high affluence lifestyle’ and ‘Population with low affluence lifestyle’) by the impact each lifestyle has on the global environment (captured in the variables ‘impact high affuence lifestyle’ and ‘impact low affluence lifestyle’) and by the current technological level (technology effect) that can potentially reduce the impacts if enhanced. This formulation is based on the established IPAT equation (Chertow, 2000; Ehrlich & Holdren, 1971; York et al., 2003): I=PATWhere I is the impact of society on the environment, P is the population size, A is the level of affluence or level of consumption per person, and T is the available technology or the environmental impact for each unit of affluence. Present In 1 View: View 1 Used By CO2 emissions The impacts ('impacts generation') have been converted into CO2 gigatonnes (Gt) ('CO2 Gt converter') to calibrate the model. The do-nothing scenario leads to approximately 90 CO2 Gt emissions per year, aligning with the extreme scenarios of the IPCC report (2023 - Synthesis Report, longer report, p.31), specifically scenarios SSP5-8.5 and SSP5-7.0. The base case scenario results in approximately 45 CO2 Gt per year, corresponding to the intermediate SSP2-4.5 scenario (IPCC, 2023 - Synthesis Report, longer report, p.31). In scenarios where fundamental mitigation policies are implemented, impacts generation approaches zero. This outcome is within the range of plausible scenarios highlighted by the IPCC (2023) and is close to some of the most optimistic scenarios (e.g., SSP1-2.6).Thus, we used the CO2 Gt emissions per year to calibrate the model outputs, ensuring they reproduce a range of scenarios consistent with the latest IPCC report (2023).Similar values can be found also in IPCC, 2023 - Synthesis Report, SPM, p.23.This can increase confidence in the robustness of model output. Cumulative impacts The flow of 'Impacts Generation' accumulates in the stock of 'Cumulative Impacts'. This formulation, where negative environmental externalities accumulate as stocks over time, is typical in the literature (Forrester, 1971; Meadows et al., 1972; Sterman, 2008). It captures the fact that impacts are not instantaneous occurrences that disappear immediately but rather accumulate over time. Feedback Loops: 65 (61.3%) (+) 32 [9,15] (-) 33 [9,15]
Environment - Societal Responses Model	#68 A	M - effect of pressure perception on adaptation priority (dmnl ) = IF THEN ELSE( Time>=2026, M - effect of pressure perception on adaptation priority for sensitivity analysis, M - effect of pressure perception on adaptation priority for sensitivity analysis) Description: Parameter M in the logistic function computed for the base scenario in ‘effect of pressure perception on adaptation priority’. For more details see Ríos‐Ocampo & Gary (2022). Higher values lead to higher allocations to technological mitigation. Although empirical data on the allocation of effort between mitigation and adaptation remain limited, the M parameter of this function has been calibrated under the base scenario (current pathway) so that the variables 'adaptation effort per year' and 'technological mitigation effort per year' are consistent with the available empirical estimates. Further details on this calibration are provided in the relevant model function descriptions.Base case = 1.4; Alternbative value (more Tech Mitigation) = 1.7 Present In 1 View: View 1 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#75 A	mitigation technlogical development per effort (dmnl/$) = IF THEN ELSE( SWT dimishing returns in mitigation technological development per effort=1, dimishing returns in mitigation technological development per effort multiplier* constant returns in mitigation technological development built per effort, constant returns in mitigation technological development built per effort) Description: This variable represents amount of technological mitigation developed per unit of 'technological mitigation effort per year'. The switch allows for exploration of constant return and diminishing return scenarios. Present In 1 View: View 1 Used By mitigation technology development rate This flow computes the development of technological mitigation over time. Feedback Loops: 1 (0.9%) (+) 1 [4,4] (-) 0 [0,0]
Environment - Societal Responses Model	#77 LI,F,A	mitigation technology development rate (dmnl/Year) = technological mitigation effort per year* mitigation technlogical development per effort Description: This flow computes the development of technological mitigation over time. Present In 1 View: View 1 Used By Mitigation technology This stock represents the level of mitigation technology developed within the system. It starts at 1, reflecting the technological efficiency level of 1950, and accumulates over time as investments are made to improve mitigation technology. Assuming an evolutionary perspective on technological development, this stock increases only, due to variations in the inflow. Higher values indicate scenarios with greater efficiency. For example,a value of 2 in Mitigation technology equals to have a techological mitigation efficiency (broadly intended) twice of what is was in the 1950s. Feedback Loops: 3 (2.8%) (+) 2 [4,10] (-) 1 [11,11]
Environment - Societal Responses Model	#78 DE,A	mitigation technology implemented (dmnl) = DELAY3I( Mitigation technology, time to implement mitigation technology, Mitigation technology) Description: We assumed that the implementation of the developed technological capacity is not instantaneous but takes time. We modeled this delay using a third-order delay function, which is considered appropriate for capturing technological implementation delays (Sterman, 2000). Present In 1 View: View 1 Used By technology effect Technological improvements in mitigation reduce the flow of generated impacts (as seen in the IPAT equation). This variable represents this effect, where higher stock values of ‘Mitigation technology’ indicate greater system efficiency and lower impacts from affluence and population. Since the model is initialized at 1950 levels ('reference technology'), increasing 'mitigation technology implemented' reduces this variable proportionally. For instance, if the implemented mitigation technology is 2 (double the efficiency compared to 1950), the 'technology effect' will be 0.5, halving the 'impacts generation' flow.Note that technological mitigation not only includes technological improvement decreasing the impact generation per unit of consumption, but also enhancements in the sinks absorbing the impact generated (e.g., carbon capture and storage). However, confidence in the feasibility and desirability of these efforts remains low (Lane et al., 2021; Mackey et al., 2013; Rosa et al., 2020). Therefore, we primarily consider mitigation as technological improvements that reduce the generation of negative impacts without explicitly addressing the sinking component. Nevertheless, the insights gained in this work also apply in cases of increased 'sinks' capacity. Feedback Loops: 2 (1.9%) (+) 1 [10,10] (-) 1 [11,11]
Environment - Societal Responses Model	#80 A	natural sinks degradation due to cumulative impacts multiplier (dmnl) = MAX(1,EXP(( Cumulative impacts- natural sinks degradation due to cumulative impacts threshold)* natural sinks degradation curve slope)) Description: Natural sinks can deteriorate with the increase of the cumulative impacts in the environment, decreasing the absorption rate (creating a reinforcing loop) (Canadell et al., 2007; Forrester, 1971; Le Quéré et al., 2009; Lenton et al., 2019; Meadows et al., 1972). This effect is captured in the model as follows: if 'Cumulative Impacts' exceed the 'Natural Sink Degradation Threshold', natural sinks start to deteriorate. If this threshold is not exceeded, the function value is 1 (due to the MAX function defining the minimum value). If the threshold is exceeded, the exponential function value becomes greater than 1, as the exponent is positive. The exponential function captures the nonlinear and exponential effects that surpassing the natural sink tipping point has on the absorption time. The output of this variable is a multiplier that affects the 'Reference Absorption Time' in the 'Absorption Time' variable. Finally, the 'Natural Sinks Degradation Curve Slope' is a variable used to regulate the steepness of the exponential function and to calibrate the model. Present In 1 View: View 1 Used By impacts absorption time This variable represents the average time it takes to absorb the excess 'Cumulative Impacts'. It is calculated by multiplying the 'reference impacts absorption time' by the 'natural sinks degradation due to cumulative impacts multiplier'. This multiplier exceeds one when 'Cumulative Impacts' increase to the point of deteriorating natural sinks. Feedback Loops: 1 (0.9%) (+) 0 [0,0] (-) 1 [4,4]
Environment - Societal Responses Model	#82 A	perceived pressures - Cumulative impacts gap (Impact units) = Cumulative impacts-( pressure to respond (perceived pressures)* pressures to impact units converter) Description: Variable measuring the gap between the state of the environment ('Cumulative impacts') and its actual perception ('pressure to respond'). The units converter is necessary to ensure accurate dimensional comparisons Present In 1 View: View 1 Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#83 A	perceived pressures - socio-environmental consequences gap (Impact units) = socio-environmental consequences-( pressure to respond (perceived pressures)* pressures to impact units converter) Description: Variable measuring the gap between the state of the environment ('socio-environmental consequences') and its actual perception ('pressure to respond'). The units converter is necessary to ensure accurate dimensional comparisons Present In 1 View: View 1 Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#88 A	pressure to respond (perceived pressures) (dmnl) = ( socio-environmental consequences/ adaptation implemented)/ pressures tolerance threshold Description: The global population begins to feel the 'perceived pressures' once the 'perceived cumulative impacts' exceed the adaptation capacity implemented ('adaptation implemented') and the non-offset by adaptation impacts also exceed the tolerance threshold ('pressures tolerance threshold').In fact, the scope and effect of adaptation is to reduce the perception or the pressures (Wheeler et al, 2021). Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 perceived pressures - Cumulative impacts gap Variable measuring the gap between the state of the environment ('Cumulative impacts') and its actual perception ('pressure to respond'). The units converter is necessary to ensure accurate dimensional comparisons perceived pressures - socio-environmental consequences gap Variable measuring the gap between the state of the environment ('socio-environmental consequences') and its actual perception ('pressure to respond'). The units converter is necessary to ensure accurate dimensional comparisons action trigger for behavioural mitigation An increase in ‘perceived pressures’ is expected to lower the attractiveness of the old lifestyle, since the old lifestyle is responsible for the undesired environmental impacts. Once the global population perceives the ‘Cumulative impacts’ consequences, we assume that high-affluence behaviour will be deemed problematic and become less attractive. In fact, if the global population identifies the affluent lifestyle and behaviour as the cause of the pressure, then the attractiveness of the lifestyle itself will decrease. Consistent with protection motivation theory, the perception of risks and threats can be a powerful driver to promote societal behavioural change (Beckage et al., 2018; Eker et al., 2019). As long as a person or community perceives that their behaviour is responsible for some risks, they are more motivated to do something. There is substantial for this response mechanism related to climate change (Bockarjova & Steg, 2014; Hunter & Röös, 2016; Lujala et al., 2015; Venghaus et al., 2022; Wells et al., 2011). However, this attribution is not straightforward, as an additional threshold (‘behavioural change threshold’) has to be overcome before behavioural change is triggered. This additional threshold comprises all the additional barriers hindering behavioural change, and captures that changing behaviour from high-affluence to low-affluence consists of an additional step than just perceiving the pressures but also to acknowledge that the high-affluence behaviour is responsible for climate change. Once this threshold is exceeded, people in the model are pushed to attribute the responsibility for the generation of pressures to their lifestyle behaviour, which leads to a decrease in the attractiveness of the affluence-based lifestyle. effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. forced behavioural change trigger If the perceived pressures exceed the 'involuntary behavioral change threshold' (indicating when the perceived pressures become unbearable), the involuntary mechanisms that make the high-affluence lifestyle unfeasible are activated Feedback Loops:* 67 (63.2%) (+) 32 [9,15] (-) 35 [6,15]
Environment - Societal Responses Model	#100 A	relative attractiveness of high-afflluence lifestyle (1) = attractiveness of high-affluence lifestyle/ total attractiveness of all lifestyle Description: A specular variable to the 'relative attractiveness of low affluence lifestyle' (with oppositive and complementary values) represents the fractional attractiveness of the old high-affluence lifestyle compared to the new low-impact one. This value regulates the transition backflow. Present In 1 View: View 1 Used By transition back to high-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the old high-affluence state is composed of innovators (transition back innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition back), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of Old Lifestyle) and on the potential population remaining to transition back.Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Feedback Loops: 57 (53.8%) (+) 28 [4,15] (-) 29 [5,15]
Environment - Societal Responses Model	#101 A	relative attractiveness of low-affluence lifestyle (1) = attractiveness of low-affluence lifestyle/ total attractiveness of all lifestyle Description: Here, the 'attractiveness of low affluence lifestyle' is divided by the 'total attractiveness of all lifestyles,' yielding a fractional value that compares the attractiveness of the new low-affluence lifestyle with that of the old high-affluence lifestyle. This captures that when the new alternative lifestyle becomes more attractive, people are more inclined to transition from the old lifestyle and adopt the new one. Conversely the transition does not occur (or can be reversed) as long as the old lifestyle remains more attractive. Theory shows how people move from one regime to another, adopting new technologies or behaviours for reasons such as convenience, preference, desire, perceived benefits, or fitness with the environment (Arthur, 1989; Geels, 2020; Rogers, 1962) Present In 1 View: View 1 Used By transition to low-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the new low-affluence state is composed of innovators (transition innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of New Lifestyle) and on the potential population remaining to transition.Diffusion theory has been at the foundation of the study of sustainability transitions (Geels & Johnson, 2018; de Haan & Rotmans, 2011; Lenton et al., 2022), and the same formal models to depict transition patterns (Keith et al., 2020; Struben & Sterman, 2008).Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Feedback Loops: 39 (36.8%) (+) 19 [4,15] (-) 20 [5,15]
Environment - Societal Responses Model	#120 SM,A	socio-environmental consequences (Impact units) = SMOOTH( Cumulative impacts, perception delay) Description: After a ‘perception delay’, the global population will perceive the effects of the ‘Cumulative impacts’ on the environment (e.g., extreme weather events and social turmoil) as ‘perceived cumulative impacts’.Note that, in reality, the global population is not constrained to wait to perceive the consequences of 'Cumulative Impacts' before taking action. Scientists have long warned about the consequences of cumulative impacts and proposed proactive measures to address them, yet these actions have not been taken on a large scale (Beck & Mahony, 2017; see also climate delay discourses in Lamb et al., 2020; Painter et al., 2023). Consequently, it is now too late to take action to maintain temperature rises below 1.5°C (Hulme, 2020; IPCC, 2023; Moser, 2020). For this reason, we assume that perception drives action, which aligns with other modeling work (Beckage et al., 2018; Eker et al., 2019). Given these dynamics, climate change has been termed the 'predictable surprise' (Bazerman, 2006). In our model, we assume that people act only when pressures are perceived, but anticipatory scenarios can also be explored by adjusting the delay structure.To translate perceived impacts into something more tangible, consider the following approach. In the most extreme scenarios, the increase in 'perceived cumulative impacts' ranges between 1 and about 2.65, representing a range of 1.65. By capturing the extreme scenarios in terms of CO2 behavior, we can relate them with the corresponding extreme consequences reported by the IPCC (2023), which suggests an upper limit of 5°C temperature variation.Therefore, we can divide the range of 1.65 by 5°C to assess how much a variation in 'perceived cumulative impacts’ corresponds to a temperature variation. This calculation yields 1.65/5 = 0.33. Hence, an increase of approximately 0.3 in 'perceived cumulative impacts' can roughly correspond to a temperature increase of 1°C.For interpreting the risks associated with each temperature increase, refer to the IPCC (2023 - Synthesis report- longer report - p.31), specifically the "Risks as Burning Embers" figure, which illustrates risks perceived associated per temperature variation. Present In 1 View: View 1 Used By perceived pressures - socio-environmental consequences gap Variable measuring the gap between the state of the environment ('socio-environmental consequences') and its actual perception ('pressure to respond'). The units converter is necessary to ensure accurate dimensional comparisons pressure to respond (perceived pressures) The global population begins to feel the 'perceived pressures' once the 'perceived cumulative impacts' exceed the adaptation capacity implemented ('adaptation implemented') and the non-offset by adaptation impacts also exceed the tolerance threshold ('pressures tolerance threshold').In fact, the scope and effect of adaptation is to reduce the perception or the pressures (Wheeler et al, 2021). Feedback Loops: 65 (61.3%) (+) 32 [9,15] (-) 33 [9,15]
Environment - Societal Responses Model	#121 A	SWT behavioural mitigation loop (dmnl) = IF THEN ELSE( Time>=2026,1,1)1+IF THEN ELSE( Time>=2026,1000,1)0 Description: IF THEN ELSE(Time>=2026, 1000 , 1 ) If you want to turn off this feedback loop, you need to set the threshold parameter to a very high value. Present In 1 View: View 1 Used By action trigger for behavioural mitigation An increase in ‘perceived pressures’ is expected to lower the attractiveness of the old lifestyle, since the old lifestyle is responsible for the undesired environmental impacts. Once the global population perceives the ‘Cumulative impacts’ consequences, we assume that high-affluence behaviour will be deemed problematic and become less attractive. In fact, if the global population identifies the affluent lifestyle and behaviour as the cause of the pressure, then the attractiveness of the lifestyle itself will decrease. Consistent with protection motivation theory, the perception of risks and threats can be a powerful driver to promote societal behavioural change (Beckage et al., 2018; Eker et al., 2019). As long as a person or community perceives that their behaviour is responsible for some risks, they are more motivated to do something. There is substantial for this response mechanism related to climate change (Bockarjova & Steg, 2014; Hunter & Röös, 2016; Lujala et al., 2015; Venghaus et al., 2022; Wells et al., 2011). However, this attribution is not straightforward, as an additional threshold (‘behavioural change threshold’) has to be overcome before behavioural change is triggered. This additional threshold comprises all the additional barriers hindering behavioural change, and captures that changing behaviour from high-affluence to low-affluence consists of an additional step than just perceiving the pressures but also to acknowledge that the high-affluence behaviour is responsible for climate change. Once this threshold is exceeded, people in the model are pushed to attribute the responsibility for the generation of pressures to their lifestyle behaviour, which leads to a decrease in the attractiveness of the affluence-based lifestyle. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#125 A	SWT rapid behavioural response (dmnl) = IF THEN ELSE( Time>=2026,0,0) Description: Switch to trigger rapid behavioural response in 2026 if activated Present In 1 View: View 1 Used By effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response This function captures the decreasing attractiveness of ‘attractiveness of high affluence lifestyle’ when the rapid behavioural change policy is implemented. The function is designed as an inverse (decreasing) S-shaped curve to reflect the non-linear relationship between decreasing attractiveness of the high affluence lifestyle due to policy implementation as result of the increasing perceived pressures exceeding the policy activation threshold (‘behavioural mitigation threshold rapid response’). Notably, the function is steep, reflecting the rapid response nature. The function is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalised logistic equation computed with six parameters and reference points:A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))A minimumK maximumC parameterQ parameterM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input= (‘pressure to respond’/‘behavioural mitigation threshold rapid response’) as input to the function.Note that the function is of the type Sample if True, to make it the policy changes irreversible. This function should be read: IF inputSwitch function>1 AND the current value of the function<value of the function in the previous time step, THEN assume the current value of the function, ELSE 1 Feedback Loops:* 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#126 A	SWT to rapid response after perception (dmnl ) = IF THEN ELSE( Time>=2026,0,0) Description: Switch to activate the alternative prototypical scenario in which resource allocation is much much more rapid once perceived pressures exceed a certain threshold. Present In 2 Views: View 1 View 2 Used By effect of pressure to respond on effort In the base scenario form, this function captures the fact that, once the perceived pressures exceed the resource allocation threshold, the higher the perceived pressure the more resources will be allocated to contrast the problem, in line with theory (Beckage et al., 2018; Eker et al., 2019) and empirical findings (Demski et al., 2017), as there will be more risks associated with higher perceived pressures (IPCC, 2023 - Synthesis Report- Longer Report - p.31 - the figure "Risks as burning embers" reports risk perception and experience). In other words, Rising pressure increases the total amount of Effort Taken Against Impact and the allocation of that effort to either adaption or technological mitigation. The function used to model this relationship is an increasing S-shaped curve, reflecting slow initial allocation that accelerates and then decelerates as it approaches saturation.. It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one, still represented by a logistic S-shaped curve. In this alternative scenario, resource allocation increases much more steeply once perceived pressures exceed a certain threshold. This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by allocating all available resources. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#127 A	SWT to static allocation rule (dmnl ) = IF THEN ELSE( Time>=2026,0,0) Description: Switch to activate the alternative prototypical scenario in which resource allocation is static. Present In 2 Views: View 1 View 2 Used By effect of pressure to respond on adaptation priority In a world with limited resources, a decision must be made on how to allocate the total resources between these two policies. At a highly aggregated scale, like the global perspective adopted here, mitigation and adaptation compete for resources (De Bruin et al., 2009; Klein et al., 2007; Tol, 2005). So when pressures are high, all effort is directed to adaptation, because the priority is to decrease the current pressures as rapidly as possible – “adaptation has immediate effects, and on a regional scale, adaptation actions have higher incentive and urgency than mitigation.” (Zhao et al., 2018, p. 86). When the pressures are lower and adaptation short-term solutions are not quite as urgent, more of this effort can be allocated to mitigation. In the base scenario, this function captures the relationship between perceived pressures and resource allocation for adaptation. Once perceived pressures exceed the resource allocation threshold, higher perceived pressures lead to greater resource allocation to adaptation as it addresses immediate needs and reduces these pressures. When perceived pressures are high, it is more likely that resources will be allocated to adaptation policies that provide immediate benefits. The function used to model this relationship is an increasing S-shaped curve, reflecting a slow initial allocation to adaptation that accelerates as perceived pressures increase.It is computed based on the procedure developed by Ríos‐Ocampo & Gary (2022). For more details on the meaning of each parameter, please refer to their work. Version of Generalized logistic equation computed with four parameters and reference points:A + ((K-A)/(1+((K-ry)/(ry-A))^((input-M)/(rx-M))))A minimumK maximumM inflection point input independent variablerx reference point x-axisry reference point y-axisWith input ‘perceived pressures’/’resources allocation threshold’ as input to the function.-The switch changes the base/real scenario function to a prototypical alternative one. In this alternative scenario, resource allocation remains stable over time (e.g., a consistent 50:50 allocation throughout the simulation). This switch is used to simulate scenarios in which humanity promptly reacts to perceived pressures by consistently allocating all available resources to one of the two policies (adaptation or technological mitigation). Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#128 A	technological mitigation effort per year ($/Year) = effort taken against impact per year(1- effect of pressure to respond on adaptation priority) Description:* This variable calculates the annual effort allocated to technological mitigation by multiplying the actual resources humanity mobilises ('effort taken against impact per year') by the fraction of effort not allocated to adaptation. Although there is limited historical data on mitigation investment, useful proxies are available. For instance, Eurostat (2024) reports that private investment in mitigation in the EU amounts to approximately 0.55% of EU GDP. This suggests that total mitigation investment in 2020 is likely to have been of a similar order of magnitude, and potentially higher when including public investments. We use this estimate as an indicative reference point for model calibration.https:/ec.europa.eu/eurostat/statistics-explained/index.php?title=Investments_in_climate_change_mitigation(the trends overtime has similar modes of behaviour to the simulated output) Present In 1 View: View 1 Used By mitigation technology development rate This flow computes the development of technological mitigation over time. total actual effort Variable computing the total effort mobilised (adaptation + technological mitigation) in the simulation. Feedback Loops: 2 (1.9%) (+) 1 [10,10] (-) 1 [11,11]
Environment - Societal Responses Model	#129 A	technology effect (dmnl) = reference technology/ mitigation technology implemented Description: Technological improvements in mitigation reduce the flow of generated impacts (as seen in the IPAT equation). This variable represents this effect, where higher stock values of ‘Mitigation technology’ indicate greater system efficiency and lower impacts from affluence and population. Since the model is initialized at 1950 levels ('reference technology'), increasing 'mitigation technology implemented' reduces this variable proportionally. For instance, if the implemented mitigation technology is 2 (double the efficiency compared to 1950), the 'technology effect' will be 0.5, halving the 'impacts generation' flow.Note that technological mitigation not only includes technological improvement decreasing the impact generation per unit of consumption, but also enhancements in the sinks absorbing the impact generated (e.g., carbon capture and storage). However, confidence in the feasibility and desirability of these efforts remains low (Lane et al., 2021; Mackey et al., 2013; Rosa et al., 2020). Therefore, we primarily consider mitigation as technological improvements that reduce the generation of negative impacts without explicitly addressing the sinking component. Nevertheless, the insights gained in this work also apply in cases of increased 'sinks' capacity. Present In 1 View: View 1 Used By impacts generation The current affluence-growth lifestyle is responsible for negative social and environmental impacts (Wiedmann et al., 2020). This flow captures the environmental impacts (e.g., GHG emissions, pollution) generated by the current enactment of lifestyles. It is computed by multiplying the number of people living a specific lifestyle (the stocks of ‘Population with high affluence lifestyle’ and ‘Population with low affluence lifestyle’) by the impact each lifestyle has on the global environment (captured in the variables ‘impact high affuence lifestyle’ and ‘impact low affluence lifestyle’) and by the current technological level (technology effect) that can potentially reduce the impacts if enhanced. This formulation is based on the established IPAT equation (Chertow, 2000; Ehrlich & Holdren, 1971; York et al., 2003): I=PATWhere I is the impact of society on the environment, P is the population size, A is the level of affluence or level of consumption per person, and T is the available technology or the environmental impact for each unit of affluence. Feedback Loops: 2 (1.9%) (+) 1 [10,10] (-) 1 [11,11]
Environment - Societal Responses Model	#130 A	time effect (Year) = ( Time- simulation start time) Description: This variable is calculated to represent the passage of time in the simulation, as affluence growth is dependent on time. Present In 1 View: View 1 Used By affluence and population growth Affluence and population are assumed to grow over time in the model. This reflects empirical trends: GDP-commonly used as a proxy for affluence (Dietz & Rosa, 1994)-has historically increased, as has population, including in the Global North (UN data). These trends are also consistent with the observed increase in global CO₂ emissions (i.e., impacts) over time (Friedlingstein et al., 2023). This growth is computed by multiplying the time passing in the simulation (represented by the 'time effect' ranging from 0 to 150 as the simulation progresses from 1950 to 2100) by a 10% growth rate ('affluence growth multiplier') and adding this resulting value to 1. The outcome is a multiplier always greater than 1, which is then multiplied by the 'initial impact high affluence lifestyle' in the 'impact high affluence lifestyle' variable. Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#133 A	total actual effort ($/Year) = adaptation effort per year+ technological mitigation effort per year Description: Variable computing the total effort mobilised (adaptation + technological mitigation) in the simulation. Present In 1 View: View 1 Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]
Environment - Societal Responses Model	#134 A	total attractiveness of all lifestyle (Attractiveness units) = attractiveness of low-affluence lifestyle+ attractiveness of high-affluence lifestyle Description: Variable calculating the toal attractivenss of all lifestyles in the system. Present In 1 View: View 1 Used By relative attractiveness of high-afflluence lifestyle A specular variable to the 'relative attractiveness of low affluence lifestyle' (with oppositive and complementary values) represents the fractional attractiveness of the old high-affluence lifestyle compared to the new low-impact one. This value regulates the transition backflow. relative attractiveness of low-affluence lifestyle Here, the 'attractiveness of low affluence lifestyle' is divided by the 'total attractiveness of all lifestyles,' yielding a fractional value that compares the attractiveness of the new low-affluence lifestyle with that of the old high-affluence lifestyle. This captures that when the new alternative lifestyle becomes more attractive, people are more inclined to transition from the old lifestyle and adopt the new one. Conversely the transition does not occur (or can be reversed) as long as the old lifestyle remains more attractive. Theory shows how people move from one regime to another, adopting new technologies or behaviours for reasons such as convenience, preference, desire, perceived benefits, or fitness with the environment (Arthur, 1989; Geels, 2020; Rogers, 1962) Feedback Loops: 56 (52.8%) (+) 26 [5,15] (-) 30 [5,15]
Environment - Societal Responses Model	#135 A	total population (dmnl) = Population with high-affluence lifestyle+ Population with low-affluence lifestyle Description: The total population is normalized to 100, representing the full population in percentage terms. It is defined as the sum of the two lifestyle stocks, which together always equal 100. As no external demographic processes affect population size in the model, total population remains constant. Thus, the model captures redistribution between lifestyle groups while the overall population is fixed. Present In 1 View: View 1 Used By transition back to high-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the old high-affluence state is composed of innovators (transition back innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition back), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of Old Lifestyle) and on the potential population remaining to transition back.Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. transition to low-affluence lifestyle The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the new low-affluence state is composed of innovators (transition innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of New Lifestyle) and on the potential population remaining to transition.Diffusion theory has been at the foundation of the study of sustainability transitions (Geels & Johnson, 2018; de Haan & Rotmans, 2011; Lenton et al., 2022), and the same formal models to depict transition patterns (Keith et al., 2020; Struben & Sterman, 2008).Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Feedback Loops: 32 (30.2%) (+) 16 [3,14] (-) 16 [3,14]
Environment - Societal Responses Model	#138 LI,F,A	transition back to high-affluence lifestyle (dmnl/Year) = ( transition back innovators fraction* Population with low-affluence lifestyle+ imitation coefficient transition back* Population with low-affluence lifestyle* Population with high-affluence lifestyle/ total population)* relative attractiveness of high-afflluence lifestyle Description: The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the old high-affluence state is composed of innovators (transition back innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition back), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of Old Lifestyle) and on the potential population remaining to transition back.Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 85 (80.2%) (+) 41 [2,15] (-) 44 [2,15]
Environment - Societal Responses Model	#140 LI,F,A	transition to low-affluence lifestyle (dmnl/Year) = ( transition innovators fraction* Population with high-affluence lifestyle+ imitation coefficient transition* Population with low-affluence lifestyle* Population with high-affluence lifestyle/ total population)* relative attractiveness of low-affluence lifestyle Description: The transition flow rates between the two stocks captures the adoption of the different lifestyle behaviours. Specifically, the transition rate is built on the generalised Bass diffusion model (Bass et al., 1994), a formalisation of the theory of diffusion of innovations and novelties (Rogers, 1962). The generalised Bass diffusion model (Bass et al., 1994) expands the original Bass model (Bass, 1969) by including a preference function. In this case, the number of people adopting the new low-affluence state is composed of innovators (transition innovators fraction), i.e., people exploring novelties independently of others, and imitators (imitation coefficient transition), i.e., people who embrace novelties because of others, and depends on the preference for the new state compared to the old one (Relative Attractiveness of New Lifestyle) and on the potential population remaining to transition.Diffusion theory has been at the foundation of the study of sustainability transitions (Geels & Johnson, 2018; de Haan & Rotmans, 2011; Lenton et al., 2022), and the same formal models to depict transition patterns (Keith et al., 2020; Struben & Sterman, 2008).Note that we retained the original generalised diffusion model formulation by Bass. However, the model becomes undefined when the total population is 0 due to a division by 0. As expected, testing extreme parameters where the population is assumed to be 0 results in runtime errors. This issue could be resolved by using the ZIDZ (Zero-If-Divide-by-Zero) function in Vensim, but we chose to stick with the original model formulation for clarity and ease of interpretation by a broader audience. Instead, we tested the model using extreme values where the population is set to 1, avoiding the zero population case.Additionally, the flow remains robust even when imitation and innovation factors are increased unrealistically by a factor of 100. For even much larger values, runtime errors may occur, which could be addressed by using MIN and MAX functions (for implementation details and discussions on bass model robustness, see Sterman, 2000, Instructor Manual, p. 139-141). Nevertheless, we opted to stay as close as possible to the original model and found that the extreme values tested were sufficient to confirm the model’s robustness. Present In 1 View: View 1 Used By Population with high-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a high-affluence and impact lifestyle. Population with low-affluence lifestyle Given the conceptual nature of the model, we divided the total population into two categories: people with high-affluence and low-affluence lifestyles. This stock represents the % of population with a low-affluence and impact lifestyle. Feedback Loops: 79 (74.5%) (+) 38 [2,15] (-) 41 [2,15]
.Control	#144 A	SAVEPER (Year ) = TIME STEP Description: The frequency with which output is stored. Present In 0 Views: Used By Feedback Loops: 0 (0.0%) (+) 0 [0,0] (-) 0 [0,0]

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All Variables (141 Variables + 4 Control Variables)

Group	Type	Variable
Environment - Societal Responses Model	C	A - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	A - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	A - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	A - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	action trigger for behavioural mitigation (dmnl)
Environment - Societal Responses Model	L	Adaptation capacity (Impact units)
Environment - Societal Responses Model	A	adaptation capacity built per effort (Impact units/$)
Environment - Societal Responses Model	LI,F,A	adaptation capacity increase rate (Impact units/Year)
Environment - Societal Responses Model	A	adaptation effort per year ($/Year)
Environment - Societal Responses Model	SM,A	adaptation implemented (Impact units)
Environment - Societal Responses Model	A	affluence and population growth (dmnl)
Environment - Societal Responses Model	C	affluence and population growth multiplier (dmnl/Year)
Environment - Societal Responses Model	C	alternative allocation to adaptation fraction (dmnl )
Environment - Societal Responses Model	A	attractiveness of high-affluence lifestyle (Attractiveness units)
Environment - Societal Responses Model	A	attractiveness of low-affluence lifestyle (Attractiveness units)
Environment - Societal Responses Model	C	behavioural mitigation threshold (dmnl )
Environment - Societal Responses Model	C	behavioural mitigation threshold rapid response (dmnl )
Environment - Societal Responses Model	C	C - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	C - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	C - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	C - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	CO2 absorption (CO2 Gt/Year)
Environment - Societal Responses Model	A	CO2 emissions (CO2 Gt/Year)
Environment - Societal Responses Model	C	CO2 Gt converter (CO2 Gt/Impact units)
Environment - Societal Responses Model	A	CO2 ppm (CO2 ppm)
Environment - Societal Responses Model	C	constant returns in adaptation capacity built per effort (Impact units/$ )
Environment - Societal Responses Model	C	constant returns in mitigation technological development built per effort (dmnl/$ )
Environment - Societal Responses Model	L	Cumulative impacts (Impact units)
Environment - Societal Responses Model	C	cumulative impacts target level (Impact units)
Environment - Societal Responses Model	C	cumulative impacts to CO2ppm equivalent (CO2 ppm/Impact units)
Environment - Societal Responses Model	A	diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	A	dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on adaptation priority (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on effort (dmnl)
Environment - Societal Responses Model	A	effort taken against impact per year ($/Year)
Environment - Societal Responses Model	A	forced behavioural change threshold (dmnl)
Environment - Societal Responses Model	A	forced behavioural change trigger (dmnl)
Environment - Societal Responses Model	C	fractional consumption from high- to low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	imitation coefficient transition (dmnl/Year)
Environment - Societal Responses Model	C	imitation coefficient transition back (dmnl/Year)
Environment - Societal Responses Model	C	impact population high affluence lifestyle in 2020 (Impact units/Year)
Environment - Societal Responses Model	A	impact population high affuence lifestyle (Impact units/Year)
Environment - Societal Responses Model	A	impact population low affluence lifestyle (Impact units/Year)
Environment - Societal Responses Model	LI,F,A	impacts absorption (Impact units/Year)
Environment - Societal Responses Model	A	impacts absorption time (Year)
Environment - Societal Responses Model	LI,F,A	impacts generation (Impact units/Year)
Environment - Societal Responses Model	C	initial impact high affluence lifestyle per person (Impact units/Year/People)
Environment - Societal Responses Model	LI,C	initial Population with high-affluence lifestyle (dmnl)
Environment - Societal Responses Model	LI,C	initial Population with low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	K - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	K - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	K - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	K - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	lifestyle socio-technical regime effect (Attractiveness units/dmnl )
Environment - Societal Responses Model	C	M - diminishing returns in adaptation capacity built per effort multiplier (Impact units )
Environment - Societal Responses Model	C	M - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	A	M - effect of pressure perception on adaptation priority (dmnl )
Environment - Societal Responses Model	C	M - effect of pressure perception on adaptation priority for sensitivity analysis (dmnl)
Environment - Societal Responses Model	C	M - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on effort - alternative scenario (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on effort - base scenario (dmnl )
Environment - Societal Responses Model	C	M - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	mitigation technlogical development per effort (dmnl/$)
Environment - Societal Responses Model	L	Mitigation technology (dmnl)
Environment - Societal Responses Model	LI,F,A	mitigation technology development rate (dmnl/Year)
Environment - Societal Responses Model	DE,A	mitigation technology implemented (dmnl)
Environment - Societal Responses Model	C	natural sinks degradation curve slope (dmnl/Impact units)
Environment - Societal Responses Model	A	natural sinks degradation due to cumulative impacts multiplier (dmnl)
Environment - Societal Responses Model	C	natural sinks degradation due to cumulative impacts threshold (Impact units)
Environment - Societal Responses Model	A	perceived pressures - Cumulative impacts gap (Impact units)
Environment - Societal Responses Model	A	perceived pressures - socio-environmental consequences gap (Impact units)
Environment - Societal Responses Model	C	perception delay (Year)
Environment - Societal Responses Model	C	population 1950 (People)
Environment - Societal Responses Model	L	Population with high-affluence lifestyle (dmnl)
Environment - Societal Responses Model	L	Population with low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	pressure to respond (perceived pressures) (dmnl)
Environment - Societal Responses Model	C	pressures to impact units converter (Impact units)
Environment - Societal Responses Model	C	pressures tolerance threshold (dmnl)
Environment - Societal Responses Model	C	Q - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	Q - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	Q - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	Q - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	reference attractiveness low-affluence lifestyle (Attractiveness units )
Environment - Societal Responses Model	C	reference attractivness high-affluence lifestyle (Attractiveness units )
Environment - Societal Responses Model	C	reference impacts absorption time (Year)
Environment - Societal Responses Model	C	reference technology (dmnl)
Environment - Societal Responses Model	A	relative attractiveness of high-afflluence lifestyle (1)
Environment - Societal Responses Model	A	relative attractiveness of low-affluence lifestyle (1)
Environment - Societal Responses Model	C	resources allocation threshold (dmnl )
Environment - Societal Responses Model	C	rx - diminishing returns in adaptation capacity built per effort multiplier (Impact units )
Environment - Societal Responses Model	C	rx - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	rx - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	ry - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	ry - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	ry - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	ry - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	simulation start time (Year)
Environment - Societal Responses Model	SM,A	socio-environmental consequences (Impact units)
Environment - Societal Responses Model	A	SWT behavioural mitigation loop (dmnl)
Environment - Societal Responses Model	C	SWT diminishing returns in adaptation capacity built per effort (dmnl )
Environment - Societal Responses Model	C	SWT dimishing returns in mitigation technological development per effort (dmnl )
Environment - Societal Responses Model	C	SWT forced behavioural change loop (dmnl)
Environment - Societal Responses Model	A	SWT rapid behavioural response (dmnl)
Environment - Societal Responses Model	A	SWT to rapid response after perception (dmnl )
Environment - Societal Responses Model	A	SWT to static allocation rule (dmnl )
Environment - Societal Responses Model	A	technological mitigation effort per year ($/Year)
Environment - Societal Responses Model	A	technology effect (dmnl)
Environment - Societal Responses Model	A	time effect (Year)
Environment - Societal Responses Model	C	time to implement adaptation capacity (Year )
Environment - Societal Responses Model	C	time to implement mitigation technology (Year)
Environment - Societal Responses Model	A	total actual effort ($/Year)
Environment - Societal Responses Model	A	total attractiveness of all lifestyle (Attractiveness units)
Environment - Societal Responses Model	A	total population (dmnl)
Environment - Societal Responses Model	C	total potential effort per year ($/Year)
Environment - Societal Responses Model	C	transition back innovators fraction (dmnl/Year )
Environment - Societal Responses Model	LI,F,A	transition back to high-affluence lifestyle (dmnl/Year)
Environment - Societal Responses Model	C	transition innovators fraction (dmnl/Year )
Environment - Societal Responses Model	LI,F,A	transition to low-affluence lifestyle (dmnl/Year)
.Control	C	FINAL TIME (Year)
.Control	C	INITIAL TIME (Year)
.Control	A	SAVEPER (Year )
.Control	C	TIME STEP (Year )

Variable Link Detail (141 Variables + 4 Control Variables)

Group	Type	Variable	In/Out Counts	In/Out Ratio	In Links By Polarity	Out Links By Polarity
Environment - Societal Responses Model	InOutLinks	effect of pressure to respond on effort (dmnl)	13 \| 1	13.00	13\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl)	10 \| 1	10.00	0\| 0\|10	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	effect of pressure to respond on adaptation priority (dmnl)	9 \| 2	4.50	9\| 0\| 0	1\| 1\| 0
Environment - Societal Responses Model	InOutLinks	pressure to respond (perceived pressures) (dmnl)	3 \| 7	0.43	1\| 2\| 0	4\| 2\| 1
Environment - Societal Responses Model	InOutLinks	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl)	8 \| 1	8.00	5\| 1\| 2	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl)	8 \| 1	8.00	4\| 2\| 2	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	dimishing returns in mitigation technological development per effort multiplier (dmnl)	8 \| 1	8.00	4\| 2\| 2	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	diminishing returns in adaptation capacity built per effort multiplier (dmnl)	8 \| 1	8.00	3\| 2\| 3	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	transition to low-affluence lifestyle (dmnl/Year)	6 \| 2	3.00	5\| 1\| 0	1\| 1\| 0
Environment - Societal Responses Model	InOutLinks	transition back to high-affluence lifestyle (dmnl/Year)	6 \| 2	3.00	5\| 1\| 0	1\| 1\| 0
Environment - Societal Responses Model	InOutLinks	Population with low-affluence lifestyle (dmnl)	3 \| 5	0.60	2\| 1\| 0	5\| 0\| 0
Environment - Societal Responses Model	InOutLinks	Population with high-affluence lifestyle (dmnl)	3 \| 5	0.60	2\| 1\| 0	5\| 0\| 0
Environment - Societal Responses Model	InOutLinks	attractiveness of high-affluence lifestyle (Attractiveness units)	6 \| 2	3.00	6\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	Cumulative impacts (Impact units)	2 \| 5	0.40	1\| 1\| 0	5\| 0\| 0
Environment - Societal Responses Model	InOutLinks	impacts generation (Impact units/Year)	5 \| 2	2.50	5\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	impacts absorption (Impact units/Year)	3 \| 2	1.50	2\| 1\| 0	1\| 1\| 0
Environment - Societal Responses Model	InOutLinks	impact population high affuence lifestyle (Impact units/Year)	3 \| 2	1.50	3\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	attractiveness of low-affluence lifestyle (Attractiveness units)	3 \| 2	1.50	3\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	total population (dmnl)	2 \| 2	1.00	2\| 0\| 0	0\| 2\| 0
Environment - Societal Responses Model	InOutLinks	total attractiveness of all lifestyle (Attractiveness units)	2 \| 2	1.00	2\| 0\| 0	0\| 2\| 0
Environment - Societal Responses Model	InOutLinks	technological mitigation effort per year ($/Year)	2 \| 2	1.00	1\| 1\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	socio-environmental consequences (Impact units)	2 \| 2	1.00	2\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	natural sinks degradation due to cumulative impacts multiplier (dmnl)	3 \| 1	3.00	1\| 2\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	mitigation technlogical development per effort (dmnl/$)	3 \| 1	3.00	2\| 0\| 1	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	impact population low affluence lifestyle (Impact units/Year)	3 \| 1	3.00	3\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	effort taken against impact per year ($/Year)	2 \| 2	1.00	2\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	adaptation effort per year ($/Year)	2 \| 2	1.00	2\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	adaptation capacity built per effort (Impact units/$)	3 \| 1	3.00	2\| 0\| 1	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	action trigger for behavioural mitigation (dmnl)	3 \| 1	3.00	1\| 2\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	Mitigation technology (dmnl)	1 \| 2	0.50	1\| 0\| 0	1\| 0\| 1
Environment - Societal Responses Model	InOutLinks	Adaptation capacity (Impact units)	1 \| 2	0.50	1\| 0\| 0	1\| 1\| 0
Environment - Societal Responses Model	InOutLinks	time effect (Year)	2 \| 1	2.00	1\| 1\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	technology effect (dmnl)	2 \| 1	2.00	1\| 1\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	relative attractiveness of low-affluence lifestyle (1)	2 \| 1	2.00	1\| 1\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	relative attractiveness of high-afflluence lifestyle (1)	2 \| 1	2.00	1\| 1\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	perceived pressures - socio-environmental consequences gap (Impact units)	3 \| 0	Infinite	1\| 2\| 0	0\| 0\| 0
Environment - Societal Responses Model	InOutLinks	perceived pressures - Cumulative impacts gap (Impact units)	3 \| 0	Infinite	1\| 2\| 0	0\| 0\| 0
Environment - Societal Responses Model	InOutLinks	mitigation technology implemented (dmnl)	2 \| 1	2.00	0\| 0\| 2	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	mitigation technology development rate (dmnl/Year)	2 \| 1	2.00	2\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	M - effect of pressure perception on adaptation priority (dmnl )	2 \| 1	2.00	1\| 0\| 1	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	impacts absorption time (Year)	2 \| 1	2.00	2\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	forced behavioural change trigger (dmnl)	2 \| 1	2.00	1\| 1\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	affluence and population growth (dmnl)	2 \| 1	2.00	2\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	adaptation implemented (Impact units)	2 \| 1	2.00	2\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	adaptation capacity increase rate (Impact units/Year)	2 \| 1	2.00	2\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	total actual effort ($/Year)	2 \| 0	Infinite	2\| 0\| 0	0\| 0\| 0
Environment - Societal Responses Model	InOutLinks	SWT to static allocation rule (dmnl )	1 \| 1	1.00	0\| 0\| 1	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	SWT to rapid response after perception (dmnl )	1 \| 1	1.00	0\| 0\| 1	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	SWT rapid behavioural response (dmnl)	1 \| 1	1.00	0\| 0\| 1	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	SWT behavioural mitigation loop (dmnl)	1 \| 1	1.00	0\| 0\| 1	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	resources allocation threshold (dmnl )	0 \| 2	0.00	0\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	pressures to impact units converter (Impact units)	0 \| 2	0.00	0\| 0\| 0	0\| 2\| 0
Environment - Societal Responses Model	InOutLinks	lifestyle socio-technical regime effect (Attractiveness units/dmnl )	0 \| 2	0.00	0\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	forced behavioural change threshold (dmnl)	1 \| 1	1.00	1\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	CO2 ppm (CO2 ppm)	2 \| 0	Infinite	2\| 0\| 0	0\| 0\| 0
Environment - Societal Responses Model	InOutLinks	CO2 Gt converter (CO2 Gt/Impact units)	0 \| 2	0.00	0\| 0\| 0	2\| 0\| 0
Environment - Societal Responses Model	InOutLinks	CO2 emissions (CO2 Gt/Year)	2 \| 0	Infinite	2\| 0\| 0	0\| 0\| 0
Environment - Societal Responses Model	InOutLinks	CO2 absorption (CO2 Gt/Year)	2 \| 0	Infinite	2\| 0\| 0	0\| 0\| 0
Environment - Societal Responses Model	InOutLinks	transition innovators fraction (dmnl/Year )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	transition back innovators fraction (dmnl/Year )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	total potential effort per year ($/Year)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	time to implement mitigation technology (Year)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	time to implement adaptation capacity (Year )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	SWT forced behavioural change loop (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	SWT dimishing returns in mitigation technological development per effort (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	SWT diminishing returns in adaptation capacity built per effort (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	simulation start time (Year)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	ry - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	ry - effect of pressures perception on effort - base scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	ry - effect of pressures perception on effort - alternative scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	ry - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	ry - effect of pressure perception on adaptation priority (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	ry - dimishing returns in mitigation technological development per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	ry - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	rx - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	rx - effect of pressures perception on effort - base scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	rx - effect of pressures perception on effort - alternative scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	rx - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	rx - effect of pressure perception on adaptation priority (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	rx - dimishing returns in mitigation technological development per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	rx - diminishing returns in adaptation capacity built per effort multiplier (Impact units )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	reference technology (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	reference impacts absorption time (Year)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	reference attractivness high-affluence lifestyle (Attractiveness units )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	reference attractiveness low-affluence lifestyle (Attractiveness units )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	Q - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	Q - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	Q - dimishing returns in mitigation technological development per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	Q - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	pressures tolerance threshold (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	population 1950 (People)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	perception delay (Year)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	natural sinks degradation due to cumulative impacts threshold (Impact units)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	natural sinks degradation curve slope (dmnl/Impact units)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	M - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	M - effect of pressures perception on effort - base scenario (dmnl )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	M - effect of pressures perception on effort - alternative scenario (dmnl )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	M - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	M - effect of pressure perception on adaptation priority for sensitivity analysis (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	M - dimishing returns in mitigation technological development per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	M - diminishing returns in adaptation capacity built per effort multiplier (Impact units )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	K - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	K - effect of pressures perception on effort - base scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	K - effect of pressures perception on effort - alternative scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	K - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	K - effect of pressure perception on adaptation priority (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	K - dimishing returns in mitigation technological development per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	K - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	initial Population with low-affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	initial Population with high-affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	initial impact high affluence lifestyle per person (Impact units/Year/People)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	impact population high affluence lifestyle in 2020 (Impact units/Year)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	imitation coefficient transition (dmnl/Year)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	imitation coefficient transition back (dmnl/Year)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	fractional consumption from high- to low-affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	cumulative impacts to CO2ppm equivalent (CO2 ppm/Impact units)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	cumulative impacts target level (Impact units)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	constant returns in mitigation technological development built per effort (dmnl/$ )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	constant returns in adaptation capacity built per effort (Impact units/$ )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	C - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	C - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	C - dimishing returns in mitigation technological development per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	C - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	behavioural mitigation threshold (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 1\| 0
Environment - Societal Responses Model	InOutLinks	behavioural mitigation threshold rapid response (dmnl )	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	alternative allocation to adaptation fraction (dmnl )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	affluence and population growth multiplier (dmnl/Year)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	A - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	A - effect of pressures perception on effort - base scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	A - effect of pressures perception on effort - alternative scenario (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	A - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	A - effect of pressure perception on adaptation priority (dmnl)	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
Environment - Societal Responses Model	InOutLinks	A - dimishing returns in mitigation technological development per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
Environment - Societal Responses Model	InOutLinks	A - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	0 \| 1	0.00	0\| 0\| 0	0\| 0\| 1
.Control	InOutLinks	TIME STEP (Year )	0 \| 1	0.00	0\| 0\| 0	1\| 0\| 0
.Control	InOutLinks	SAVEPER (Year )	1 \| 0	Infinite	1\| 0\| 0	0\| 0\| 0
.Control	InOutLinks	INITIAL TIME (Year)	( 0\| 0)	Infinite	0\| 0\| 0	0\| 0\| 0
.Control	InOutLinks	FINAL TIME (Year)	( 0\| 0)	Infinite	0\| 0\| 0	0\| 0\| 0

Undocumented Variables (0 Variables + 0 Control Variables)

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Supplementary Variables (0 Variables + 0 Control Variables)

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Variable

Supplementary Variables Being Used (0 Variables + 0 Control Variables)

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Unused Variables (6 Variables + 0 Control Variables)

Group	Type	Variable
Environment - Societal Responses Model	A	CO2 absorption (CO2 Gt/Year)
Environment - Societal Responses Model	A	CO2 emissions (CO2 Gt/Year)
Environment - Societal Responses Model	A	CO2 ppm (CO2 ppm)
Environment - Societal Responses Model	A	perceived pressures - Cumulative impacts gap (Impact units)
Environment - Societal Responses Model	A	perceived pressures - socio-environmental consequences gap (Impact units)
Environment - Societal Responses Model	A	total actual effort ($/Year)

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Equations With Embedded Data (6 Variables + 0 Control Variables)

Group	Type	Variable
Environment - Societal Responses Model	A	forced behavioural change threshold (dmnl)
Environment - Societal Responses Model	A	M - effect of pressure perception on adaptation priority (dmnl )
Environment - Societal Responses Model	A	SWT behavioural mitigation loop (dmnl)
Environment - Societal Responses Model	A	SWT rapid behavioural response (dmnl)
Environment - Societal Responses Model	A	SWT to rapid response after perception (dmnl )
Environment - Societal Responses Model	A	SWT to static allocation rule (dmnl )

Nonmonotonic Lookup Functions (0 Variables + 0 Control Variables)

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Type

Variable

Non-Zero End Sloped Lookup Functions (0 Variables + 0 Control Variables)

Group

Type

Variable

Non-Zero

Cascading Lookup Functions (0 Variables + 0 Control Variables)

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Type

Variable

Equations With Step Pulse Or Related Functions (0 Variables + 0 Control Variables)

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Type

Variable

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Equations With If Then Else Functions (7 Variables + 0 Control Variables)

Group	Type	Variable
Environment - Societal Responses Model	A	adaptation capacity built per effort (Impact units/$)
Environment - Societal Responses Model	A	M - effect of pressure perception on adaptation priority (dmnl )
Environment - Societal Responses Model	A	mitigation technlogical development per effort (dmnl/$)
Environment - Societal Responses Model	A	SWT behavioural mitigation loop (dmnl)
Environment - Societal Responses Model	A	SWT rapid behavioural response (dmnl)
Environment - Societal Responses Model	A	SWT to rapid response after perception (dmnl )
Environment - Societal Responses Model	A	SWT to static allocation rule (dmnl )

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Equations With Min Or Max Functions (3 Variables + 0 Control Variables)

Group	Type	Variable
Environment - Societal Responses Model	A	impact population low affluence lifestyle (Impact units/Year)
Environment - Societal Responses Model	LI,F,A	impacts absorption (Impact units/Year)
Environment - Societal Responses Model	A	natural sinks degradation due to cumulative impacts multiplier (dmnl)

Complex Variable (Richardson's Rule Threshold = 3) (11 Variables + 0 Control Variables)

Group	Type	Variable	Complexity
Environment - Societal Responses Model	Complexity	impacts generation (Impact units/Year)	5
Environment - Societal Responses Model	Complexity	attractiveness of high-affluence lifestyle (Attractiveness units)	6
Environment - Societal Responses Model	Complexity	transition back to high-affluence lifestyle (dmnl/Year)	6
Environment - Societal Responses Model	Complexity	transition to low-affluence lifestyle (dmnl/Year)	6
Environment - Societal Responses Model	Complexity	diminishing returns in adaptation capacity built per effort multiplier (dmnl)	8
Environment - Societal Responses Model	Complexity	dimishing returns in mitigation technological development per effort multiplier (dmnl)	8
Environment - Societal Responses Model	Complexity	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl)	8
Environment - Societal Responses Model	Complexity	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl)	8
Environment - Societal Responses Model	Complexity	effect of pressure to respond on adaptation priority (dmnl)	9
Environment - Societal Responses Model	Complexity	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl)	11
Environment - Societal Responses Model	Complexity	effect of pressure to respond on effort (dmnl)	13

Complex Stock (0 Variables + 0 Control Variables)

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Type

Variable

Variables With Source Information (0 Variables + 0 Control Variables)

Group

Type

Variable

Sources

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Variables With Dimensionless Units (86 Variables + 0 Control Variables)

Group	Type	Variable
Environment - Societal Responses Model	C	A - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	A - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	A - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	A - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	action trigger for behavioural mitigation (dmnl)
Environment - Societal Responses Model	A	affluence and population growth (dmnl)
Environment - Societal Responses Model	C	alternative allocation to adaptation fraction (dmnl )
Environment - Societal Responses Model	C	behavioural mitigation threshold (dmnl )
Environment - Societal Responses Model	C	behavioural mitigation threshold rapid response (dmnl )
Environment - Societal Responses Model	C	C - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	C - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	C - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	C - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	A	dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on adaptation priority (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on effort (dmnl)
Environment - Societal Responses Model	A	forced behavioural change threshold (dmnl)
Environment - Societal Responses Model	A	forced behavioural change trigger (dmnl)
Environment - Societal Responses Model	C	fractional consumption from high- to low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	LI,C	initial Population with high-affluence lifestyle (dmnl)
Environment - Societal Responses Model	LI,C	initial Population with low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	K - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	K - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	K - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	K - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	M - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	A	M - effect of pressure perception on adaptation priority (dmnl )
Environment - Societal Responses Model	C	M - effect of pressure perception on adaptation priority for sensitivity analysis (dmnl)
Environment - Societal Responses Model	C	M - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on effort - alternative scenario (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on effort - base scenario (dmnl )
Environment - Societal Responses Model	C	M - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	L	Mitigation technology (dmnl)
Environment - Societal Responses Model	DE,A	mitigation technology implemented (dmnl)
Environment - Societal Responses Model	A	natural sinks degradation due to cumulative impacts multiplier (dmnl)
Environment - Societal Responses Model	L	Population with high-affluence lifestyle (dmnl)
Environment - Societal Responses Model	L	Population with low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	pressure to respond (perceived pressures) (dmnl)
Environment - Societal Responses Model	C	pressures tolerance threshold (dmnl)
Environment - Societal Responses Model	C	Q - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	Q - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	Q - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	Q - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	reference technology (dmnl)
Environment - Societal Responses Model	A	relative attractiveness of high-afflluence lifestyle (1)
Environment - Societal Responses Model	A	relative attractiveness of low-affluence lifestyle (1)
Environment - Societal Responses Model	C	resources allocation threshold (dmnl )
Environment - Societal Responses Model	C	rx - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	rx - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	ry - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	ry - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	ry - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	ry - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	A	SWT behavioural mitigation loop (dmnl)
Environment - Societal Responses Model	C	SWT diminishing returns in adaptation capacity built per effort (dmnl )
Environment - Societal Responses Model	C	SWT dimishing returns in mitigation technological development per effort (dmnl )
Environment - Societal Responses Model	C	SWT forced behavioural change loop (dmnl)
Environment - Societal Responses Model	A	SWT rapid behavioural response (dmnl)
Environment - Societal Responses Model	A	SWT to rapid response after perception (dmnl )
Environment - Societal Responses Model	A	SWT to static allocation rule (dmnl )
Environment - Societal Responses Model	A	technology effect (dmnl)
Environment - Societal Responses Model	A	total population (dmnl)

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Variables without Predefined Min or Max Values (141 Variables + 4 Control Variables)

Group	Type	Variable
Environment - Societal Responses Model	C	A - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	A - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	A - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	A - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	A - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	action trigger for behavioural mitigation (dmnl)
Environment - Societal Responses Model	L	Adaptation capacity (Impact units)
Environment - Societal Responses Model	A	adaptation capacity built per effort (Impact units/$)
Environment - Societal Responses Model	LI,F,A	adaptation capacity increase rate (Impact units/Year)
Environment - Societal Responses Model	A	adaptation effort per year ($/Year)
Environment - Societal Responses Model	SM,A	adaptation implemented (Impact units)
Environment - Societal Responses Model	A	affluence and population growth (dmnl)
Environment - Societal Responses Model	C	affluence and population growth multiplier (dmnl/Year)
Environment - Societal Responses Model	C	alternative allocation to adaptation fraction (dmnl )
Environment - Societal Responses Model	A	attractiveness of high-affluence lifestyle (Attractiveness units)
Environment - Societal Responses Model	A	attractiveness of low-affluence lifestyle (Attractiveness units)
Environment - Societal Responses Model	C	behavioural mitigation threshold (dmnl )
Environment - Societal Responses Model	C	behavioural mitigation threshold rapid response (dmnl )
Environment - Societal Responses Model	C	C - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	C - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	C - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	C - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	CO2 absorption (CO2 Gt/Year)
Environment - Societal Responses Model	A	CO2 emissions (CO2 Gt/Year)
Environment - Societal Responses Model	C	CO2 Gt converter (CO2 Gt/Impact units)
Environment - Societal Responses Model	A	CO2 ppm (CO2 ppm)
Environment - Societal Responses Model	C	constant returns in adaptation capacity built per effort (Impact units/$ )
Environment - Societal Responses Model	C	constant returns in mitigation technological development built per effort (dmnl/$ )
Environment - Societal Responses Model	L	Cumulative impacts (Impact units)
Environment - Societal Responses Model	C	cumulative impacts target level (Impact units)
Environment - Societal Responses Model	C	cumulative impacts to CO2ppm equivalent (CO2 ppm/Impact units)
Environment - Societal Responses Model	A	diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	A	dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on adaptation priority (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl)
Environment - Societal Responses Model	A	effect of pressure to respond on effort (dmnl)
Environment - Societal Responses Model	A	effort taken against impact per year ($/Year)
Environment - Societal Responses Model	A	forced behavioural change threshold (dmnl)
Environment - Societal Responses Model	A	forced behavioural change trigger (dmnl)
Environment - Societal Responses Model	C	fractional consumption from high- to low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	imitation coefficient transition (dmnl/Year)
Environment - Societal Responses Model	C	imitation coefficient transition back (dmnl/Year)
Environment - Societal Responses Model	C	impact population high affluence lifestyle in 2020 (Impact units/Year)
Environment - Societal Responses Model	A	impact population high affuence lifestyle (Impact units/Year)
Environment - Societal Responses Model	A	impact population low affluence lifestyle (Impact units/Year)
Environment - Societal Responses Model	LI,F,A	impacts absorption (Impact units/Year)
Environment - Societal Responses Model	A	impacts absorption time (Year)
Environment - Societal Responses Model	LI,F,A	impacts generation (Impact units/Year)
Environment - Societal Responses Model	C	initial impact high affluence lifestyle per person (Impact units/Year/People)
Environment - Societal Responses Model	LI,C	initial Population with high-affluence lifestyle (dmnl)
Environment - Societal Responses Model	LI,C	initial Population with low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	K - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	K - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	K - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	K - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	K - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	lifestyle socio-technical regime effect (Attractiveness units/dmnl )
Environment - Societal Responses Model	C	M - diminishing returns in adaptation capacity built per effort multiplier (Impact units )
Environment - Societal Responses Model	C	M - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	A	M - effect of pressure perception on adaptation priority (dmnl )
Environment - Societal Responses Model	C	M - effect of pressure perception on adaptation priority for sensitivity analysis (dmnl)
Environment - Societal Responses Model	C	M - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on effort - alternative scenario (dmnl )
Environment - Societal Responses Model	C	M - effect of pressures perception on effort - base scenario (dmnl )
Environment - Societal Responses Model	C	M - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	mitigation technlogical development per effort (dmnl/$)
Environment - Societal Responses Model	L	Mitigation technology (dmnl)
Environment - Societal Responses Model	LI,F,A	mitigation technology development rate (dmnl/Year)
Environment - Societal Responses Model	DE,A	mitigation technology implemented (dmnl)
Environment - Societal Responses Model	C	natural sinks degradation curve slope (dmnl/Impact units)
Environment - Societal Responses Model	A	natural sinks degradation due to cumulative impacts multiplier (dmnl)
Environment - Societal Responses Model	C	natural sinks degradation due to cumulative impacts threshold (Impact units)
Environment - Societal Responses Model	A	perceived pressures - Cumulative impacts gap (Impact units)
Environment - Societal Responses Model	A	perceived pressures - socio-environmental consequences gap (Impact units)
Environment - Societal Responses Model	C	perception delay (Year)
Environment - Societal Responses Model	C	population 1950 (People)
Environment - Societal Responses Model	L	Population with high-affluence lifestyle (dmnl)
Environment - Societal Responses Model	L	Population with low-affluence lifestyle (dmnl)
Environment - Societal Responses Model	A	pressure to respond (perceived pressures) (dmnl)
Environment - Societal Responses Model	C	pressures to impact units converter (Impact units)
Environment - Societal Responses Model	C	pressures tolerance threshold (dmnl)
Environment - Societal Responses Model	C	Q - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	Q - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	Q - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	Q - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	reference attractiveness low-affluence lifestyle (Attractiveness units )
Environment - Societal Responses Model	C	reference attractivness high-affluence lifestyle (Attractiveness units )
Environment - Societal Responses Model	C	reference impacts absorption time (Year)
Environment - Societal Responses Model	C	reference technology (dmnl)
Environment - Societal Responses Model	A	relative attractiveness of high-afflluence lifestyle (1)
Environment - Societal Responses Model	A	relative attractiveness of low-affluence lifestyle (1)
Environment - Societal Responses Model	C	resources allocation threshold (dmnl )
Environment - Societal Responses Model	C	rx - diminishing returns in adaptation capacity built per effort multiplier (Impact units )
Environment - Societal Responses Model	C	rx - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	rx - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	rx - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	ry - diminishing returns in adaptation capacity built per effort multiplier (dmnl)
Environment - Societal Responses Model	C	ry - dimishing returns in mitigation technological development per effort multiplier (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressure perception on adaptation priority (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )
Environment - Societal Responses Model	C	ry - effect of pressures perception on effort - alternative scenario (dmnl)
Environment - Societal Responses Model	C	ry - effect of pressures perception on effort - base scenario (dmnl)
Environment - Societal Responses Model	C	ry - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)
Environment - Societal Responses Model	C	ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)
Environment - Societal Responses Model	C	simulation start time (Year)
Environment - Societal Responses Model	SM,A	socio-environmental consequences (Impact units)
Environment - Societal Responses Model	A	SWT behavioural mitigation loop (dmnl)
Environment - Societal Responses Model	C	SWT diminishing returns in adaptation capacity built per effort (dmnl )
Environment - Societal Responses Model	C	SWT dimishing returns in mitigation technological development per effort (dmnl )
Environment - Societal Responses Model	C	SWT forced behavioural change loop (dmnl)
Environment - Societal Responses Model	A	SWT rapid behavioural response (dmnl)
Environment - Societal Responses Model	A	SWT to rapid response after perception (dmnl )
Environment - Societal Responses Model	A	SWT to static allocation rule (dmnl )
Environment - Societal Responses Model	A	technological mitigation effort per year ($/Year)
Environment - Societal Responses Model	A	technology effect (dmnl)
Environment - Societal Responses Model	A	time effect (Year)
Environment - Societal Responses Model	C	time to implement adaptation capacity (Year )
Environment - Societal Responses Model	C	time to implement mitigation technology (Year)
Environment - Societal Responses Model	A	total actual effort ($/Year)
Environment - Societal Responses Model	A	total attractiveness of all lifestyle (Attractiveness units)
Environment - Societal Responses Model	A	total population (dmnl)
Environment - Societal Responses Model	C	total potential effort per year ($/Year)
Environment - Societal Responses Model	C	transition back innovators fraction (dmnl/Year )
Environment - Societal Responses Model	LI,F,A	transition back to high-affluence lifestyle (dmnl/Year)
Environment - Societal Responses Model	C	transition innovators fraction (dmnl/Year )
Environment - Societal Responses Model	LI,F,A	transition to low-affluence lifestyle (dmnl/Year)
.Control	C	FINAL TIME (Year)
.Control	C	INITIAL TIME (Year)
.Control	A	SAVEPER (Year )
.Control	C	TIME STEP (Year )

Function Sensitivity Parameters (0 Variables + 0 Control Variables)

Group

Type

Variable

Data Lookup Tables (0 Variables + 0 Control Variables)

Group

Type

Variable

Variables Using Macros (0 Variables + 0 Control Variables)

Group

Type

Variable

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Quick Links:

Variables Not In Any View (0 Variables + 4 Control Variables)

Group	Type	Variable
.Control	C	FINAL TIME (Year)
.Control	C	INITIAL TIME (Year)
.Control	A	SAVEPER (Year )
.Control	C	TIME STEP (Year )

Equations With Unit Errors Or Warnings (0 Variables + 0 Control Variables)

Group

Type

Variable

Units

constant returns in mitigation technological development built per effort, mitigation technlogical development per effort

Equations Using Time As A Variable (6 Variables + 0 Control Variables)

Group	Type	Variable	Equation
Environment - Societal Responses Model	Equation	M - effect of pressure perception on adaptation priority (dmnl )	"M - effect of pressure perception on adaptation priority"=IF THEN ELSE(Time>=2026, "M - effect of pressure perception on adaptation priority for sensitivity analysis" , "M - effect of pressure perception on adaptation priority for sensitivity analysis" )
Environment - Societal Responses Model	Equation	SWT behavioural mitigation loop (dmnl)	SWT behavioural mitigation loop=IF THEN ELSE(Time>=2026, 1 , 1 )1+IF THEN ELSE(Time>=2026, 1000 , 1 )0
Environment - Societal Responses Model	Equation	SWT rapid behavioural response (dmnl)	SWT rapid behavioural response=IF THEN ELSE(Time>=2026, 0 , 0 )
Environment - Societal Responses Model	Equation	SWT to rapid response after perception (dmnl )	SWT to rapid response after perception=IF THEN ELSE(Time>=2026, 0 , 0 )
Environment - Societal Responses Model	Equation	SWT to static allocation rule (dmnl )	SWT to static allocation rule=IF THEN ELSE(Time>=2026, 0 , 0 )
Environment - Societal Responses Model	Equation	time effect (Year)	time effect=(Time-simulation start time)

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Units (9 Basic/7 Combined)

Units	Type	Alternates
1/$	Basic	[dmnl/$]
1/Impact units	Basic	[dmnl/Impact units]
1/Years	Basic	[dmnl/Year]
Attractiveness units	Basic	[Attractiveness units/dmnl]
CO2 ppm	Basic
Dmnl	Basic	[1, dmnl]
Impact units	Basic
People	Basic
Years	Basic	[Year]
$/Years	Combined	[$/Year]
CO2 Gt/Impact units	Combined
CO2 Gt/Years	Combined	[CO2 Gt/Year]
CO2 ppm/Impact units	Combined
Impact units/$	Combined
Impact units/Years	Combined	[Impact units/Year]
Impact units/Years*People	Combined	[Impact units/Year/People]

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Units Variables (16 Units)

Units

Variables

$/Years

adaptation effort per year, effort taken against impact per year, technological mitigation effort per year, total actual effort, total potential effort per year

1/$

1/Impact units

natural sinks degradation curve slope

1/Years

affluence and population growth multiplier, imitation coefficient transition, imitation coefficient transition back, mitigation technology development rate, transition back innovators fraction, transition back to high-affluence lifestyle,

transition innovators fraction, transition to low-affluence lifestyle

Attractiveness units

attractiveness of high-affluence lifestyle, attractiveness of low-affluence lifestyle, lifestyle socio-technical regime effect, reference attractiveness low-affluence lifestyle, reference attractivness high-affluence lifestyle, total attractiveness of all lifestyle

CO2 Gt/Impact units

CO2 Gt converter

CO2 Gt/Years

CO2 absorption, CO2 emissions

CO2 ppm

CO2 ppm/Impact units

cumulative impacts to CO2ppm equivalent

Dmnl

A - diminishing returns in adaptation capacity built per effort multiplier, A - dimishing returns in mitigation technological development per effort multiplier, A - effect of pressure perception on adaptation priority, A - effect of pressures perception on attractivenss of high affluence lifestyle, A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response, A - effect of pressures perception on effort - alternative scenario,

A - effect of pressures perception on effort - base scenario, A - forced effect of pressure perception attractiveness of high affluence lifestyle, action trigger for behavioural mitigation, affluence and population growth, alternative allocation to adaptation fraction, behavioural mitigation threshold,

behavioural mitigation threshold rapid response, C - diminishing returns in adaptation capacity built per effort multiplier, C - dimishing returns in mitigation technological development per effort multiplier, C - effect of pressures perception on attractivenss of high affluence lifestyle, C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response, C - forced effect of pressure perception attractiveness of high affluence lifestyle,

diminishing returns in adaptation capacity built per effort multiplier, dimishing returns in mitigation technological development per effort multiplier, effect of pressure to respond on adaptation priority, effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation, effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response, effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change,

effect of pressure to respond on effort, forced behavioural change threshold, forced behavioural change trigger, fractional consumption from high- to low-affluence lifestyle, initial Population with high-affluence lifestyle, initial Population with low-affluence lifestyle,

K - diminishing returns in adaptation capacity built per effort multiplier, K - dimishing returns in mitigation technological development per effort multiplier, K - effect of pressure perception on adaptation priority, K - effect of pressures perception on attractivenss of high affluence lifestyle, K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response, K - effect of pressures perception on effort - alternative scenario,

K - effect of pressures perception on effort - base scenario, K - forced effect of pressure perception attractiveness of high affluence lifestyle, M - dimishing returns in mitigation technological development per effort multiplier, M - effect of pressure perception on adaptation priority, M - effect of pressure perception on adaptation priority for sensitivity analysis, M - effect of pressures perception on attractivenss of high affluence lifestyle,

M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response, M - effect of pressures perception on effort - alternative scenario, M - effect of pressures perception on effort - base scenario, M - forced effect of pressure perception attractiveness of high affluence lifestyle, Mitigation technology, mitigation technology implemented,

natural sinks degradation due to cumulative impacts multiplier, Population with high-affluence lifestyle, Population with low-affluence lifestyle, pressure to respond (perceived pressures), pressures tolerance threshold, Q - diminishing returns in adaptation capacity built per effort multiplier,

Q - dimishing returns in mitigation technological development per effort multiplier, Q - effect of pressures perception on attractivenss of high affluence lifestyle, Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response, Q - forced effect of pressure perception attractiveness of high affluence lifestyle, reference technology, relative attractiveness of high-afflluence lifestyle,

relative attractiveness of low-affluence lifestyle, resources allocation threshold, rx - dimishing returns in mitigation technological development per effort multiplier, rx - effect of pressure perception on adaptation priority, rx - effect of pressures perception on attractivenss of high affluence lifestyle, rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response,

rx - effect of pressures perception on effort - alternative scenario, rx - effect of pressures perception on effort - base scenario, rx - forced effect of pressure perception attractiveness of high affluence lifestyle, ry - diminishing returns in adaptation capacity built per effort multiplier, ry - dimishing returns in mitigation technological development per effort multiplier, ry - effect of pressure perception on adaptation priority,

ry - effect of pressures perception on attractivenss of high affluence lifestyle, ry - effect of pressures perception on effort - alternative scenario, ry - effect of pressures perception on effort - base scenario, ry - forced effect of pressure perception attractiveness of high affluence lifestyle, ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response, SWT behavioural mitigation loop,

SWT diminishing returns in adaptation capacity built per effort, SWT dimishing returns in mitigation technological development per effort, SWT forced behavioural change loop, SWT rapid behavioural response, SWT to rapid response after perception, SWT to static allocation rule,

technology effect, total population

Impact units

Adaptation capacity, adaptation implemented, Cumulative impacts, cumulative impacts target level, M - diminishing returns in adaptation capacity built per effort multiplier, natural sinks degradation due to cumulative impacts threshold,

perceived pressures - Cumulative impacts gap, perceived pressures - socio-environmental consequences gap, pressures to impact units converter, rx - diminishing returns in adaptation capacity built per effort multiplier, socio-environmental consequences

Impact units/$

adaptation capacity built per effort, constant returns in adaptation capacity built per effort

Impact units/Years

adaptation capacity increase rate, impact population high affluence lifestyle in 2020, impact population high affuence lifestyle, impact population low affluence lifestyle, impacts absorption, impacts generation

Impact units/Years*People

initial impact high affluence lifestyle per person

People

population 1950

Years

FINAL TIME, impacts absorption time, INITIAL TIME, perception delay, reference impacts absorption time, SAVEPER,

simulation start time, Time, time effect, TIME STEP, time to implement adaptation capacity, time to implement mitigation technology

Feedback Loops (106|0 Loops ) Maximum Loop Length: 45 [2,15] | [0,0]

Group

Type

Variable

Loops

+/- Ratio

Loops (IVV)

+/- Ratio

Feedback...

transition back to high-affluence lifestyle (dmnl/Year)

85 (80.2%)

41 [ 2, 15]

44 [ 2, 15]

0.93

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

Population with high-affluence lifestyle (dmnl)

82 (77.4%)

40 [ 2, 15]

42 [ 2, 15]

0.95

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

Population with low-affluence lifestyle (dmnl)

82 (77.4%)

39 [ 2, 15]

43 [ 2, 15]

0.91

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

transition to low-affluence lifestyle (dmnl/Year)

79 (74.5%)

38 [ 2, 15]

41 [ 2, 15]

0.93

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

attractiveness of high-affluence lifestyle (Attractiveness units)

75 (70.8%)

37 [ 4, 15]

38 [ 5, 15]

0.97

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

pressure to respond (perceived pressures) (dmnl)

67 (63.2%)

32 [ 9, 15]

35 [ 6, 15]

0.91

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

Cumulative impacts (Impact units)

67 (63.2%)

32 [ 9, 15]

35 [ 2, 15]

0.91

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

impacts generation (Impact units/Year)

65 (61.3%)

32 [ 9, 15]

33 [ 9, 15]

0.97

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

socio-environmental consequences (Impact units)

65 (61.3%)

32 [ 9, 15]

33 [ 9, 15]

0.97

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

relative attractiveness of high-afflluence lifestyle (1)

Feedback...

57 (53.8%)

28 [ 4, 15]

29 [ 5, 15]

0.97

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

total attractiveness of all lifestyle (Attractiveness units)

56 (52.8%)

26 [ 5, 15]

30 [ 5, 15]

0.87

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

relative attractiveness of low-affluence lifestyle (1)

Feedback...

39 (36.8%)

19 [ 4, 15]

20 [ 5, 15]

0.95

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

total population (dmnl)

32 (30.2%)

16 [ 3, 14]

1.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

action trigger for behavioural mitigation (dmnl)

21 (19.8%)

11 [ 10, 15]

10 [ 10, 14]

1.10

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

attractiveness of low-affluence lifestyle (Attractiveness units)

21 (19.8%)

10 [ 4, 15]

11 [ 5, 15]

0.91

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl)

Feedback...

21 (19.8%)

10 [ 9, 13]

11 [ 9, 14]

0.91

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl)

Feedback...

21 (19.8%)

11 [ 10, 15]

10 [ 10, 14]

1.10

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl)

Feedback...

21 (19.8%)

10 [ 10, 14]

11 [ 10, 15]

0.91

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

forced behavioural change trigger (dmnl)

21 (19.8%)

10 [ 10, 14]

11 [ 10, 15]

0.91

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

adaptation capacity increase rate (Impact units/Year)

3 (2.8%)

0 [ 0, 0]

3 [ 4, 7]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

mitigation technology development rate (dmnl/Year)

3 (2.8%)

2 [ 4, 10]

1 [ 11, 11]

2.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

Adaptation capacity (Impact units)

3 (2.8%)

0 [ 0, 0]

3 [ 4, 7]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

Mitigation technology (dmnl)

3 (2.8%)

2 [ 4, 10]

1 [ 11, 11]

2.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

adaptation effort per year ($/Year)

2 (1.9%)

0 [ 0, 0]

2 [ 6, 7]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

adaptation implemented (Impact units)

2 (1.9%)

0 [ 0, 0]

2 [ 6, 7]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

effect of pressure to respond on adaptation priority (dmnl)

2 (1.9%)

1 [ 10, 10]

1 [ 6, 6]

1.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

effect of pressure to respond on effort (dmnl)

2 (1.9%)

0 [ 0, 0]

2 [ 7, 11]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

effort taken against impact per year ($/Year)

2 (1.9%)

0 [ 0, 0]

2 [ 7, 11]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

impacts absorption (Impact units/Year)

2 (1.9%)

0 [ 0, 0]

2 [ 2, 4]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

mitigation technology implemented (dmnl)

2 (1.9%)

1 [ 10, 10]

1 [ 11, 11]

1.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

technological mitigation effort per year ($/Year)

2 (1.9%)

1 [ 10, 10]

1 [ 11, 11]

1.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

technology effect (dmnl)

2 (1.9%)

1 [ 10, 10]

1 [ 11, 11]

1.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

adaptation capacity built per effort (Impact units/$)

1 (0.9%)

0 [ 0, 0]

1 [ 4, 4]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

diminishing returns in adaptation capacity built per effort multiplier (dmnl)

1 (0.9%)

0 [ 0, 0]

1 [ 4, 4]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

dimishing returns in mitigation technological development per effort multiplier (dmnl)

Feedback...

1 (0.9%)

1 [ 4, 4]

0 [ 0, 0]

Infinite

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

impacts absorption time (Year)

1 (0.9%)

0 [ 0, 0]

1 [ 4, 4]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

mitigation technlogical development per effort (dmnl/$)

1 (0.9%)

1 [ 4, 4]

0 [ 0, 0]

Infinite

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

natural sinks degradation due to cumulative impacts multiplier (dmnl)

1 (0.9%)

0 [ 0, 0]

1 [ 4, 4]

0.00

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

A - diminishing returns in adaptation capacity built per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

A - dimishing returns in mitigation technological development per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

A - effect of pressure perception on adaptation priority (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

A - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

A - effect of pressures perception on effort - alternative scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

A - effect of pressures perception on effort - base scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

A - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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affluence and population growth multiplier (dmnl/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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affluence and population growth (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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alternative allocation to adaptation fraction (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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behavioural mitigation threshold rapid response (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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behavioural mitigation threshold (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

C - diminishing returns in adaptation capacity built per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

C - dimishing returns in mitigation technological development per effort multiplier (dmnl)

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0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

C - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

C - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)

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0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

CO2 absorption (CO2 Gt/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

CO2 emissions (CO2 Gt/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

CO2 Gt converter (CO2 Gt/Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

CO2 ppm (CO2 ppm)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

constant returns in adaptation capacity built per effort (Impact units/$ )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

constant returns in mitigation technological development built per effort (dmnl/$ )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

cumulative impacts target level (Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

cumulative impacts to CO2ppm equivalent (CO2 ppm/Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

forced behavioural change threshold (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

fractional consumption from high- to low-affluence lifestyle (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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imitation coefficient transition back (dmnl/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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imitation coefficient transition (dmnl/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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impact population high affluence lifestyle in 2020 (Impact units/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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impact population high affuence lifestyle (Impact units/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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impact population low affluence lifestyle (Impact units/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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initial impact high affluence lifestyle per person (Impact units/Year/People)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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initial Population with high-affluence lifestyle (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

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initial Population with low-affluence lifestyle (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

K - diminishing returns in adaptation capacity built per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

K - dimishing returns in mitigation technological development per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

K - effect of pressure perception on adaptation priority (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

K - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

K - effect of pressures perception on effort - alternative scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

K - effect of pressures perception on effort - base scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

K - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

lifestyle socio-technical regime effect (Attractiveness units/dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

M - diminishing returns in adaptation capacity built per effort multiplier (Impact units )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

M - dimishing returns in mitigation technological development per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

M - effect of pressure perception on adaptation priority for sensitivity analysis (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

M - effect of pressure perception on adaptation priority (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl )

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

M - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

M - effect of pressures perception on effort - alternative scenario (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

M - effect of pressures perception on effort - base scenario (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

M - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

natural sinks degradation curve slope (dmnl/Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

natural sinks degradation due to cumulative impacts threshold (Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

perceived pressures - Cumulative impacts gap (Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

perceived pressures - socio-environmental consequences gap (Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

perception delay (Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

population 1950 (People)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

pressures to impact units converter (Impact units)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

pressures tolerance threshold (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Q - diminishing returns in adaptation capacity built per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Q - dimishing returns in mitigation technological development per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Q - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Q - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

reference attractiveness low-affluence lifestyle (Attractiveness units )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

reference attractivness high-affluence lifestyle (Attractiveness units )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

reference impacts absorption time (Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

reference technology (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

resources allocation threshold (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

rx - diminishing returns in adaptation capacity built per effort multiplier (Impact units )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

rx - dimishing returns in mitigation technological development per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

rx - effect of pressure perception on adaptation priority (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

rx - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

rx - effect of pressures perception on effort - alternative scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

rx - effect of pressures perception on effort - base scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

rx - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

ry - diminishing returns in adaptation capacity built per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

ry - dimishing returns in mitigation technological development per effort multiplier (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

ry - effect of pressure perception on adaptation priority (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

ry - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

ry - effect of pressures perception on effort - alternative scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

ry - effect of pressures perception on effort - base scenario (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

ry - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)

Feedback...

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

simulation start time (Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SWT behavioural mitigation loop (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SWT diminishing returns in adaptation capacity built per effort (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SWT dimishing returns in mitigation technological development per effort (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SWT forced behavioural change loop (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SWT rapid behavioural response (dmnl)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SWT to rapid response after perception (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SWT to static allocation rule (dmnl )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

time effect (Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

time to implement adaptation capacity (Year )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

time to implement mitigation technology (Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

total actual effort ($/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

total potential effort per year ($/Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

transition back innovators fraction (dmnl/Year )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

transition innovators fraction (dmnl/Year )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

FINAL TIME (Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

INITIAL TIME (Year)

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

SAVEPER (Year )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Feedback...

TIME STEP (Year )

0 (0.0%)

0 [ 0, 0]

0 (0.0%)

0 [ 0, 0]

Exogenous Variables Analysis (87 Variables + 4 Control Variables)

Group	Type	Variable	Variable Number	Data Source
Environment - Societal Responses Model	Exogenous	A - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	1	Hardcoded
Environment - Societal Responses Model	Exogenous	A - dimishing returns in mitigation technological development per effort multiplier (dmnl)	2	Hardcoded
Environment - Societal Responses Model	Exogenous	A - effect of pressure perception on adaptation priority (dmnl)	3	Hardcoded
Environment - Societal Responses Model	Exogenous	A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	4	Hardcoded
Environment - Societal Responses Model	Exogenous	A - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	5	Hardcoded
Environment - Societal Responses Model	Exogenous	A - effect of pressures perception on effort - alternative scenario (dmnl)	6	Hardcoded
Environment - Societal Responses Model	Exogenous	A - effect of pressures perception on effort - base scenario (dmnl)	7	Hardcoded
Environment - Societal Responses Model	Exogenous	A - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	8	Hardcoded
Environment - Societal Responses Model	Exogenous	affluence and population growth multiplier (dmnl/Year)	9	Hardcoded
Environment - Societal Responses Model	Exogenous	alternative allocation to adaptation fraction (dmnl )	10	Hardcoded
Environment - Societal Responses Model	Exogenous	behavioural mitigation threshold rapid response (dmnl )	11	Hardcoded
Environment - Societal Responses Model	Exogenous	behavioural mitigation threshold (dmnl )	12	Hardcoded
Environment - Societal Responses Model	Exogenous	C - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	13	Hardcoded
Environment - Societal Responses Model	Exogenous	C - dimishing returns in mitigation technological development per effort multiplier (dmnl)	14	Hardcoded
Environment - Societal Responses Model	Exogenous	C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	15	Hardcoded
Environment - Societal Responses Model	Exogenous	C - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	16	Hardcoded
Environment - Societal Responses Model	Exogenous	C - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	17	Hardcoded
Environment - Societal Responses Model	Exogenous	CO2 Gt converter (CO2 Gt/Impact units)	18	Hardcoded
Environment - Societal Responses Model	Exogenous	constant returns in adaptation capacity built per effort (Impact units/$ )	19	Hardcoded
Environment - Societal Responses Model	Exogenous	constant returns in mitigation technological development built per effort (dmnl/$ )	20	Hardcoded
Environment - Societal Responses Model	Exogenous	cumulative impacts target level (Impact units)	21	Hardcoded
Environment - Societal Responses Model	Exogenous	cumulative impacts to CO2ppm equivalent (CO2 ppm/Impact units)	22	Hardcoded
Environment - Societal Responses Model	Exogenous	fractional consumption from high- to low-affluence lifestyle (dmnl)	23	Hardcoded
Environment - Societal Responses Model	Exogenous	imitation coefficient transition back (dmnl/Year)	24	Hardcoded
Environment - Societal Responses Model	Exogenous	imitation coefficient transition (dmnl/Year)	25	Hardcoded
Environment - Societal Responses Model	Exogenous	impact population high affluence lifestyle in 2020 (Impact units/Year)	26	Hardcoded
Environment - Societal Responses Model	Exogenous	initial impact high affluence lifestyle per person (Impact units/Year/People)	27	Hardcoded
Environment - Societal Responses Model	Exogenous	initial Population with high-affluence lifestyle (dmnl)	28	Hardcoded
Environment - Societal Responses Model	Exogenous	initial Population with low-affluence lifestyle (dmnl)	29	Hardcoded
Environment - Societal Responses Model	Exogenous	K - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	30	Hardcoded
Environment - Societal Responses Model	Exogenous	K - dimishing returns in mitigation technological development per effort multiplier (dmnl)	31	Hardcoded
Environment - Societal Responses Model	Exogenous	K - effect of pressure perception on adaptation priority (dmnl)	32	Hardcoded
Environment - Societal Responses Model	Exogenous	K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	33	Hardcoded
Environment - Societal Responses Model	Exogenous	K - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	34	Hardcoded
Environment - Societal Responses Model	Exogenous	K - effect of pressures perception on effort - alternative scenario (dmnl)	35	Hardcoded
Environment - Societal Responses Model	Exogenous	K - effect of pressures perception on effort - base scenario (dmnl)	36	Hardcoded
Environment - Societal Responses Model	Exogenous	K - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	37	Hardcoded
Environment - Societal Responses Model	Exogenous	lifestyle socio-technical regime effect (Attractiveness units/dmnl )	38	Hardcoded
Environment - Societal Responses Model	Exogenous	M - diminishing returns in adaptation capacity built per effort multiplier (Impact units )	39	Hardcoded
Environment - Societal Responses Model	Exogenous	M - dimishing returns in mitigation technological development per effort multiplier (dmnl)	40	Hardcoded
Environment - Societal Responses Model	Exogenous	M - effect of pressure perception on adaptation priority for sensitivity analysis (dmnl)	41	Hardcoded
Environment - Societal Responses Model	Exogenous	M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl )	42	Hardcoded
Environment - Societal Responses Model	Exogenous	M - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )	43	Hardcoded
Environment - Societal Responses Model	Exogenous	M - effect of pressures perception on effort - alternative scenario (dmnl )	44	Hardcoded
Environment - Societal Responses Model	Exogenous	M - effect of pressures perception on effort - base scenario (dmnl )	45	Hardcoded
Environment - Societal Responses Model	Exogenous	M - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	46	Hardcoded
Environment - Societal Responses Model	Exogenous	natural sinks degradation curve slope (dmnl/Impact units)	47	Hardcoded
Environment - Societal Responses Model	Exogenous	natural sinks degradation due to cumulative impacts threshold (Impact units)	48	Hardcoded
Environment - Societal Responses Model	Exogenous	perception delay (Year)	49	Hardcoded
Environment - Societal Responses Model	Exogenous	population 1950 (People)	50	Hardcoded
Environment - Societal Responses Model	Exogenous	pressures to impact units converter (Impact units)	51	Hardcoded
Environment - Societal Responses Model	Exogenous	pressures tolerance threshold (dmnl)	52	Hardcoded
Environment - Societal Responses Model	Exogenous	Q - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	53	Hardcoded
Environment - Societal Responses Model	Exogenous	Q - dimishing returns in mitigation technological development per effort multiplier (dmnl)	54	Hardcoded
Environment - Societal Responses Model	Exogenous	Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	55	Hardcoded
Environment - Societal Responses Model	Exogenous	Q - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl)	56	Hardcoded
Environment - Societal Responses Model	Exogenous	Q - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	57	Hardcoded
Environment - Societal Responses Model	Exogenous	reference attractiveness low-affluence lifestyle (Attractiveness units )	58	Hardcoded
Environment - Societal Responses Model	Exogenous	reference attractivness high-affluence lifestyle (Attractiveness units )	59	Hardcoded
Environment - Societal Responses Model	Exogenous	reference impacts absorption time (Year)	60	Hardcoded
Environment - Societal Responses Model	Exogenous	reference technology (dmnl)	61	Hardcoded
Environment - Societal Responses Model	Exogenous	resources allocation threshold (dmnl )	62	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - diminishing returns in adaptation capacity built per effort multiplier (Impact units )	63	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - dimishing returns in mitigation technological development per effort multiplier (dmnl)	64	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - effect of pressure perception on adaptation priority (dmnl)	65	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	66	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )	67	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - effect of pressures perception on effort - alternative scenario (dmnl)	68	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - effect of pressures perception on effort - base scenario (dmnl)	69	Hardcoded
Environment - Societal Responses Model	Exogenous	rx - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	70	Hardcoded
Environment - Societal Responses Model	Exogenous	ry - diminishing returns in adaptation capacity built per effort multiplier (dmnl)	71	Hardcoded
Environment - Societal Responses Model	Exogenous	ry - dimishing returns in mitigation technological development per effort multiplier (dmnl)	72	Hardcoded
Environment - Societal Responses Model	Exogenous	ry - effect of pressure perception on adaptation priority (dmnl)	73	Hardcoded
Environment - Societal Responses Model	Exogenous	ry - effect of pressures perception on attractivenss of high affluence lifestyle (dmnl )	74	Hardcoded
Environment - Societal Responses Model	Exogenous	ry - effect of pressures perception on effort - alternative scenario (dmnl)	75	Hardcoded
Environment - Societal Responses Model	Exogenous	ry - effect of pressures perception on effort - base scenario (dmnl)	76	Hardcoded
Environment - Societal Responses Model	Exogenous	ry - forced effect of pressure perception attractiveness of high affluence lifestyle (dmnl)	77	Hardcoded
Environment - Societal Responses Model	Exogenous	ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response (dmnl)	78	Hardcoded
Environment - Societal Responses Model	Exogenous	simulation start time (Year)	79	Hardcoded
Environment - Societal Responses Model	Exogenous	SWT diminishing returns in adaptation capacity built per effort (dmnl )	80	Hardcoded
Environment - Societal Responses Model	Exogenous	SWT dimishing returns in mitigation technological development per effort (dmnl )	81	Hardcoded
Environment - Societal Responses Model	Exogenous	SWT forced behavioural change loop (dmnl)	82	Hardcoded
Environment - Societal Responses Model	Exogenous	time to implement adaptation capacity (Year )	83	Hardcoded
Environment - Societal Responses Model	Exogenous	time to implement mitigation technology (Year)	84	Hardcoded
Environment - Societal Responses Model	Exogenous	total potential effort per year ($/Year)	85	Hardcoded
Environment - Societal Responses Model	Exogenous	transition back innovators fraction (dmnl/Year )	86	Hardcoded
Environment - Societal Responses Model	Exogenous	transition innovators fraction (dmnl/Year )	87	Hardcoded
.Control	Exogenous	FINAL TIME (Year)	88	Hardcoded
.Control	Exogenous	INITIAL TIME (Year)	89	Hardcoded
.Control	Exogenous	TIME STEP (Year )	90	Hardcoded
Default	Exogenous	Time (Year)	91	Hardcoded

Endogenous Variables Analysis (54 Variables + 1 Control Variables) (Maximum Endogenous Path Length: 45)

Group	Type	Variable	Variable Number	Input Links	Direct Exogenous	Percent Exogenous	Indirect Exogenous	Indirect Minimum	Indirect Mean	Indirect Median	Indirect Maximum	Exogenous Unconnected	Exogenous Connected
Environment - Societal Responses Model	Endogenous	forced behavioural change threshold (dmnl)	1	1	1	100.0	0	NA	NA	NA	NA	90	1
Environment - Societal Responses Model	Endogenous	M - effect of pressure perception on adaptation priority (dmnl )	2	2	2	100.0	0	NA	NA	NA	NA	89	2
Environment - Societal Responses Model	Endogenous	SWT behavioural mitigation loop (dmnl)	3	1	1	100.0	0	NA	NA	NA	NA	90	1
Environment - Societal Responses Model	Endogenous	SWT rapid behavioural response (dmnl)	4	1	1	100.0	0	NA	NA	NA	NA	90	1
Environment - Societal Responses Model	Endogenous	SWT to rapid response after perception (dmnl )	5	1	1	100.0	0	NA	NA	NA	NA	90	1
Environment - Societal Responses Model	Endogenous	SWT to static allocation rule (dmnl )	6	1	1	100.0	0	NA	NA	NA	NA	90	1
Environment - Societal Responses Model	Endogenous	time effect (Year)	7	2	2	100.0	0	NA	NA	NA	NA	89	2
Environment - Societal Responses Model	Endogenous	diminishing returns in adaptation capacity built per effort multiplier (dmnl)	8	8	7	87.5	78	4	10.71	11.00	16	6	85
Environment - Societal Responses Model	Endogenous	dimishing returns in mitigation technological development per effort multiplier (dmnl)	9	8	7	87.5	78	4	10.24	11.00	16	6	85
Environment - Societal Responses Model	Endogenous	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation (dmnl)	10	8	7	87.5	78	2	8.85	9.00	13	6	85
Environment - Societal Responses Model	Endogenous	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change (dmnl)	11	8	7	87.5	78	3	8.92	9.00	12	6	85
Environment - Societal Responses Model	Endogenous	effect of pressure to respond on effort (dmnl)	12	13	11	84.6	74	2	8.18	8.00	12	6	85
Environment - Societal Responses Model	Endogenous	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response (dmnl)	13	10	8	80.0	77	2	7.95	8.00	12	6	85
Environment - Societal Responses Model	Endogenous	adaptation capacity built per effort (Impact units/$)	14	3	2	66.7	83	2	11.05	12.00	17	6	85
Environment - Societal Responses Model	Endogenous	attractiveness of low-affluence lifestyle (Attractiveness units)	15	3	2	66.7	83	2	9.78	11.00	16	6	85
Environment - Societal Responses Model	Endogenous	effect of pressure to respond on adaptation priority (dmnl)	16	9	6	66.7	79	2	8.15	8.00	12	6	85
Environment - Societal Responses Model	Endogenous	impact population high affuence lifestyle (Impact units/Year)	17	3	2	66.7	3	2	2.67	3.00	3	86	5
Environment - Societal Responses Model	Endogenous	impact population low affluence lifestyle (Impact units/Year)	18	3	2	66.7	5	2	3.00	3.00	4	84	7
Environment - Societal Responses Model	Endogenous	mitigation technlogical development per effort (dmnl/$)	19	3	2	66.7	83	2	10.61	12.00	17	6	85
Environment - Societal Responses Model	Endogenous	natural sinks degradation due to cumulative impacts multiplier (dmnl)	20	3	2	66.7	83	3	8.41	8.00	14	6	85
Environment - Societal Responses Model	Endogenous	adaptation implemented (Impact units)	21	2	1	50.0	84	4	10.27	11.00	16	6	85
Environment - Societal Responses Model	Endogenous	affluence and population growth (dmnl)	22	2	1	50.0	2	2	2.00	2.00	2	88	3
Environment - Societal Responses Model	Endogenous	CO2 absorption (CO2 Gt/Year)	23	2	1	50.0	85	2	9.24	9.00	15	5	86
Environment - Societal Responses Model	Endogenous	CO2 emissions (CO2 Gt/Year)	24	2	1	50.0	85	3	7.75	8.00	13	5	86
Environment - Societal Responses Model	Endogenous	CO2 ppm (CO2 ppm)	25	2	1	50.0	85	3	8.33	8.00	14	5	86
Environment - Societal Responses Model	Endogenous	effort taken against impact per year ($/Year)	26	2	1	50.0	84	2	8.25	8.00	13	6	85
Environment - Societal Responses Model	Endogenous	impacts absorption time (Year)	27	2	1	50.0	84	2	9.29	9.00	15	6	85
Environment - Societal Responses Model	Endogenous	mitigation technology implemented (dmnl)	28	2	1	50.0	84	4	9.80	10.50	16	6	85
Environment - Societal Responses Model	Endogenous	socio-environmental consequences (Impact units)	29	2	1	50.0	84	3	8.31	8.00	14	6	85
Environment - Societal Responses Model	Endogenous	technology effect (dmnl)	30	2	1	50.0	84	2	10.69	11.50	17	6	85
Environment - Societal Responses Model	Endogenous	action trigger for behavioural mitigation (dmnl)	31	3	1	33.3	84	2	8.11	8.00	12	6	85
Environment - Societal Responses Model	Endogenous	attractiveness of high-affluence lifestyle (Attractiveness units)	32	6	2	33.3	83	2	6.12	7.00	12	6	85
Environment - Societal Responses Model	Endogenous	impacts absorption (Impact units/Year)	33	3	1	33.3	84	2	8.32	8.00	14	6	85
Environment - Societal Responses Model	Endogenous	perceived pressures - Cumulative impacts gap (Impact units)	34	3	1	33.3	85	2	6.92	8.00	10	5	86
Environment - Societal Responses Model	Endogenous	perceived pressures - socio-environmental consequences gap (Impact units)	35	3	1	33.3	85	2	7.55	8.00	11	5	86
Environment - Societal Responses Model	Endogenous	Population with high-affluence lifestyle (dmnl)	36	3	1	33.3	84	2	8.76	10.00	15	6	85
Environment - Societal Responses Model	Endogenous	Population with low-affluence lifestyle (dmnl)	37	3	1	33.3	84	2	8.76	10.00	15	6	85
Environment - Societal Responses Model	Endogenous	pressure to respond (perceived pressures) (dmnl)	38	3	1	33.3	84	2	7.29	7.00	11	6	85
Environment - Societal Responses Model	Endogenous	transition back to high-affluence lifestyle (dmnl/Year)	39	6	2	33.3	83	2	7.92	9.00	14	6	85
Environment - Societal Responses Model	Endogenous	transition to low-affluence lifestyle (dmnl/Year)	40	6	2	33.3	83	2	8.83	10.00	15	6	85
Environment - Societal Responses Model	Endogenous	adaptation capacity increase rate (Impact units/Year)	41	2	0	0.0	85	2	8.24	9.00	14	6	85
Environment - Societal Responses Model	Endogenous	adaptation effort per year ($/Year)	42	2	0	0.0	85	2	7.87	8.00	13	6	85
Environment - Societal Responses Model	Endogenous	forced behavioural change trigger (dmnl)	43	2	0	0.0	85	2	8.09	8.00	11	6	85
Environment - Societal Responses Model	Endogenous	impacts generation (Impact units/Year)	44	5	0	0.0	85	2	6.75	7.00	12	6	85
Environment - Societal Responses Model	Endogenous	mitigation technology development rate (dmnl/Year)	45	2	0	0.0	85	2	7.81	9.00	14	6	85
Environment - Societal Responses Model	Endogenous	relative attractiveness of high-afflluence lifestyle (1)	46	2	0	0.0	85	2	6.95	8.00	13	6	85
Environment - Societal Responses Model	Endogenous	relative attractiveness of low-affluence lifestyle (1)	47	2	0	0.0	85	2	7.85	9.00	14	6	85
Environment - Societal Responses Model	Endogenous	technological mitigation effort per year ($/Year)	48	2	0	0.0	85	2	7.87	8.00	13	6	85
Environment - Societal Responses Model	Endogenous	total actual effort ($/Year)	49	2	0	0.0	85	3	8.87	9.00	14	6	85
Environment - Societal Responses Model	Endogenous	total attractiveness of all lifestyle (Attractiveness units)	50	2	0	0.0	85	2	6.93	8.00	13	6	85
Environment - Societal Responses Model	Endogenous	total population (dmnl)	51	2	0	0.0	85	2	9.65	11.00	16	6	85
Environment - Societal Responses Model	Endogenous	Adaptation capacity (Impact units)	52	1	0	0.0	85	3	9.24	10.00	15	6	85
Environment - Societal Responses Model	Endogenous	Cumulative impacts (Impact units)	53	2	0	0.0	85	2	7.33	7.00	13	6	85
Environment - Societal Responses Model	Endogenous	Mitigation technology (dmnl)	54	1	0	0.0	85	3	8.81	10.00	15	6	85
.Control	Endogenous	SAVEPER (Year )	55	1	1	100.0	0	NA	NA	NA	NA	90	1

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Macros (2 Variables)

Name	Macro Definition	Expanded Macro Definition
GenLogisticEquation	A + ((K-A)/(C+QEXP(beta(input-M))))	_$arg1$_+((_$arg2$_-_$arg1$_)/(_$arg3$_+_$arg4$_EXP(_$arg5$_(_$arg0$_-_$arg6$_))))
GenLogisticEquationRP	A + ((K-A)/(C+Q((A(C-1)+K-ryC)/(Q(ry-A)))^((input-M)/(rx-M))))	_$arg3$_+((_$arg4$_-_$arg3$_)/(_$arg5$_+_$arg6$_((_$arg3$_(_$arg5$_-1)+_$arg4$_-_$arg2$__$arg5$_)/(_$arg6$_(_$arg2$_-_$arg3$_)))^((_$arg0$_-_$arg7$_)/(_$arg1$_-_$arg7$_))))

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Positive Polarity Causal Links (123 Variables)

Cause	Effect	Polarity
A - effect of pressure perception on adaptation priority	effect of pressure to respond on adaptation priority	+
A - effect of pressures perception on attractivenss of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	+
A - effect of pressures perception on effort - alternative scenario	effect of pressure to respond on effort	+
A - effect of pressures perception on effort - base scenario	effect of pressure to respond on effort	+
A - forced effect of pressure perception attractiveness of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	+
Adaptation capacity	adaptation implemented	+
adaptation capacity built per effort	adaptation capacity increase rate	+
adaptation capacity increase rate	Adaptation capacity	+
adaptation effort per year	adaptation capacity increase rate	+
adaptation effort per year	total actual effort	+
affluence and population growth	impact population high affuence lifestyle	+
affluence and population growth multiplier	affluence and population growth	+
alternative allocation to adaptation fraction	effect of pressure to respond on adaptation priority	+
attractiveness of high-affluence lifestyle	relative attractiveness of high-afflluence lifestyle	+
attractiveness of high-affluence lifestyle	total attractiveness of all lifestyle	+
attractiveness of low-affluence lifestyle	relative attractiveness of low-affluence lifestyle	+
attractiveness of low-affluence lifestyle	total attractiveness of all lifestyle	+
C - diminishing returns in adaptation capacity built per effort multiplier	diminishing returns in adaptation capacity built per effort multiplier	+
C - effect of pressures perception on attractivenss of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	+
C - forced effect of pressure perception attractiveness of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	+
CO2 Gt converter	CO2 absorption	+
CO2 Gt converter	CO2 emissions	+
constant returns in adaptation capacity built per effort	adaptation capacity built per effort	+
constant returns in mitigation technological development built per effort	mitigation technlogical development per effort	+
Cumulative impacts	CO2 ppm	+
Cumulative impacts	impacts absorption	+
Cumulative impacts	natural sinks degradation due to cumulative impacts multiplier	+
Cumulative impacts	perceived pressures - Cumulative impacts gap	+
Cumulative impacts	socio-environmental consequences	+
cumulative impacts to CO2ppm equivalent	CO2 ppm	+
diminishing returns in adaptation capacity built per effort multiplier	adaptation capacity built per effort	+
dimishing returns in mitigation technological development per effort multiplier	mitigation technlogical development per effort	+
effect of pressure to respond on adaptation priority	adaptation effort per year	+
effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	attractiveness of high-affluence lifestyle	+
effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	attractiveness of high-affluence lifestyle	+
effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	attractiveness of high-affluence lifestyle	+
effect of pressure to respond on effort	effort taken against impact per year	+
effort taken against impact per year	adaptation effort per year	+
effort taken against impact per year	technological mitigation effort per year	+
forced behavioural change trigger	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	+
fractional consumption from high- to low-affluence lifestyle	impact population low affluence lifestyle	+
imitation coefficient transition	transition to low-affluence lifestyle	+
imitation coefficient transition back	transition back to high-affluence lifestyle	+
impact population high affluence lifestyle in 2020	impact population low affluence lifestyle	+
impact population high affuence lifestyle	impact population low affluence lifestyle	+
impact population high affuence lifestyle	impacts generation	+
impact population low affluence lifestyle	impacts generation	+
impacts absorption	CO2 absorption	+
impacts absorption time	impacts absorption	+
impacts generation	CO2 emissions	+
impacts generation	Cumulative impacts	+
initial impact high affluence lifestyle per person	impact population high affuence lifestyle	+
initial Population with high-affluence lifestyle	Population with high-affluence lifestyle	+
initial Population with low-affluence lifestyle	Population with low-affluence lifestyle	+
K - dimishing returns in mitigation technological development per effort multiplier	dimishing returns in mitigation technological development per effort multiplier	+
K - effect of pressure perception on adaptation priority	effect of pressure to respond on adaptation priority	+
K - effect of pressures perception on attractivenss of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	+
K - effect of pressures perception on effort - alternative scenario	effect of pressure to respond on effort	+
K - effect of pressures perception on effort - base scenario	effect of pressure to respond on effort	+
K - forced effect of pressure perception attractiveness of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	+
lifestyle socio-technical regime effect	attractiveness of high-affluence lifestyle	+
lifestyle socio-technical regime effect	attractiveness of low-affluence lifestyle	+
M - dimishing returns in mitigation technological development per effort multiplier	dimishing returns in mitigation technological development per effort multiplier	+
M - effect of pressure perception on adaptation priority	effect of pressure to respond on adaptation priority	+
M - effect of pressure perception on adaptation priority for sensitivity analysis	M - effect of pressure perception on adaptation priority	+
M - effect of pressures perception on effort - alternative scenario	effect of pressure to respond on effort	+
M - effect of pressures perception on effort - base scenario	effect of pressure to respond on effort	+
mitigation technlogical development per effort	mitigation technology development rate	+
Mitigation technology	dimishing returns in mitigation technological development per effort multiplier	+
mitigation technology development rate	Mitigation technology	+
natural sinks degradation due to cumulative impacts multiplier	impacts absorption time	+
perception delay	socio-environmental consequences	+
population 1950	impact population high affuence lifestyle	+
Population with high-affluence lifestyle	attractiveness of high-affluence lifestyle	+
Population with high-affluence lifestyle	impacts generation	+
Population with high-affluence lifestyle	total population	+
Population with high-affluence lifestyle	transition back to high-affluence lifestyle	+
Population with high-affluence lifestyle	transition to low-affluence lifestyle	+
Population with low-affluence lifestyle	attractiveness of low-affluence lifestyle	+
Population with low-affluence lifestyle	impacts generation	+
Population with low-affluence lifestyle	total population	+
Population with low-affluence lifestyle	transition back to high-affluence lifestyle	+
Population with low-affluence lifestyle	transition to low-affluence lifestyle	+
pressure to respond (perceived pressures)	action trigger for behavioural mitigation	+
pressure to respond (perceived pressures)	effect of pressure to respond on adaptation priority	+
pressure to respond (perceived pressures)	effect of pressure to respond on effort	+
pressure to respond (perceived pressures)	forced behavioural change trigger	+
Q - diminishing returns in adaptation capacity built per effort multiplier	diminishing returns in adaptation capacity built per effort multiplier	+
Q - effect of pressures perception on attractivenss of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	+
Q - forced effect of pressure perception attractiveness of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	+
reference attractiveness low-affluence lifestyle	attractiveness of low-affluence lifestyle	+
reference attractivness high-affluence lifestyle	attractiveness of high-affluence lifestyle	+
reference impacts absorption time	impacts absorption time	+
reference technology	technology effect	+
relative attractiveness of high-afflluence lifestyle	transition back to high-affluence lifestyle	+
relative attractiveness of low-affluence lifestyle	transition to low-affluence lifestyle	+
resources allocation threshold	effect of pressure to respond on adaptation priority	+
resources allocation threshold	effect of pressure to respond on effort	+
rx - effect of pressure perception on adaptation priority	effect of pressure to respond on adaptation priority	+
rx - effect of pressures perception on effort - alternative scenario	effect of pressure to respond on effort	+
rx - effect of pressures perception on effort - base scenario	effect of pressure to respond on effort	+
ry - diminishing returns in adaptation capacity built per effort multiplier	diminishing returns in adaptation capacity built per effort multiplier	+
ry - dimishing returns in mitigation technological development per effort multiplier	dimishing returns in mitigation technological development per effort multiplier	+
ry - effect of pressure perception on adaptation priority	effect of pressure to respond on adaptation priority	+
ry - effect of pressures perception on effort - alternative scenario	effect of pressure to respond on effort	+
ry - effect of pressures perception on effort - base scenario	effect of pressure to respond on effort	+
socio-environmental consequences	perceived pressures - socio-environmental consequences gap	+
socio-environmental consequences	pressure to respond (perceived pressures)	+
SWT forced behavioural change loop	forced behavioural change threshold	+
SWT to rapid response after perception	effect of pressure to respond on effort	+
SWT to static allocation rule	effect of pressure to respond on adaptation priority	+
technological mitigation effort per year	mitigation technology development rate	+
technological mitigation effort per year	total actual effort	+
technology effect	impacts generation	+
Time	time effect	+
time effect	affluence and population growth	+
TIME STEP	SAVEPER	+
time to implement adaptation capacity	adaptation implemented	+
total potential effort per year	effort taken against impact per year	+
transition back innovators fraction	transition back to high-affluence lifestyle	+
transition back to high-affluence lifestyle	Population with high-affluence lifestyle	+
transition innovators fraction	transition to low-affluence lifestyle	+
transition to low-affluence lifestyle	Population with low-affluence lifestyle	+

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Negative Polarity Causal Links (29 Variables)

Cause	Effect	Polarity
action trigger for behavioural mitigation	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	-
Adaptation capacity	diminishing returns in adaptation capacity built per effort multiplier	-
adaptation implemented	pressure to respond (perceived pressures)	-
behavioural mitigation threshold	action trigger for behavioural mitigation	-
C - dimishing returns in mitigation technological development per effort multiplier	dimishing returns in mitigation technological development per effort multiplier	-
cumulative impacts target level	impacts absorption	-
effect of pressure to respond on adaptation priority	technological mitigation effort per year	-
forced behavioural change threshold	forced behavioural change trigger	-
impacts absorption	Cumulative impacts	-
K - diminishing returns in adaptation capacity built per effort multiplier	diminishing returns in adaptation capacity built per effort multiplier	-
M - forced effect of pressure perception attractiveness of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	-
mitigation technology implemented	technology effect	-
natural sinks degradation curve slope	natural sinks degradation due to cumulative impacts multiplier	-
natural sinks degradation due to cumulative impacts threshold	natural sinks degradation due to cumulative impacts multiplier	-
pressure to respond (perceived pressures)	perceived pressures - Cumulative impacts gap	-
pressure to respond (perceived pressures)	perceived pressures - socio-environmental consequences gap	-
pressures to impact units converter	perceived pressures - Cumulative impacts gap	-
pressures to impact units converter	perceived pressures - socio-environmental consequences gap	-
pressures tolerance threshold	pressure to respond (perceived pressures)	-
Q - dimishing returns in mitigation technological development per effort multiplier	dimishing returns in mitigation technological development per effort multiplier	-
ry - effect of pressures perception on attractivenss of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	-
simulation start time	time effect	-
SWT behavioural mitigation loop	action trigger for behavioural mitigation	-
total attractiveness of all lifestyle	relative attractiveness of high-afflluence lifestyle	-
total attractiveness of all lifestyle	relative attractiveness of low-affluence lifestyle	-
total population	transition back to high-affluence lifestyle	-
total population	transition to low-affluence lifestyle	-
transition back to high-affluence lifestyle	Population with low-affluence lifestyle	-
transition to low-affluence lifestyle	Population with high-affluence lifestyle	-

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Function-based Polarity Causal Links (28 Variables)

Cause	Effect	Polarity
A - diminishing returns in adaptation capacity built per effort multiplier	diminishing returns in adaptation capacity built per effort multiplier	Inconsistent
A - dimishing returns in mitigation technological development per effort multiplier	dimishing returns in mitigation technological development per effort multiplier	Inconsistent
A - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
behavioural mitigation threshold rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
C - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
K - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
M - diminishing returns in adaptation capacity built per effort multiplier	diminishing returns in adaptation capacity built per effort multiplier	Inconsistent
M - effect of pressures perception on attractivenss of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	Inconsistent
M - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
Mitigation technology	mitigation technology implemented	Function[DELAY3I]
pressure to respond (perceived pressures)	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
Q - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
rx - diminishing returns in adaptation capacity built per effort multiplier	diminishing returns in adaptation capacity built per effort multiplier	Inconsistent
rx - dimishing returns in mitigation technological development per effort multiplier	dimishing returns in mitigation technological development per effort multiplier	Inconsistent
rx - effect of pressures perception on attractivenss of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation	Inconsistent
rx - effect of pressures perception on attractivenss of high affluence lifestyle - rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
rx - forced effect of pressure perception attractiveness of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	Inconsistent
ry - forced effect of pressure perception attractiveness of high affluence lifestyle	effect of pressure to respond on attractiveness of high-affluence lifestyle due to forced behavioural change	Inconsistent
ry -effect of pressures perception on attractivenss of high affluence lifestyle - rapid response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
SWT diminishing returns in adaptation capacity built per effort	adaptation capacity built per effort	If Then Else Switch
SWT dimishing returns in mitigation technological development per effort	mitigation technlogical development per effort	If Then Else Switch
SWT rapid behavioural response	effect of pressure to respond on attractiveness of high-affluence lifestyle due to behavioural mitigation - rapid response	Function[SAMPLEIFTRUE]
Time	M - effect of pressure perception on adaptation priority	If Then Else Switch
Time	SWT behavioural mitigation loop	If Then Else Switch
Time	SWT rapid behavioural response	If Then Else Switch
Time	SWT to rapid response after perception	If Then Else Switch
Time	SWT to static allocation rule	If Then Else Switch
time to implement mitigation technology	mitigation technology implemented	Function[DELAY3I]