Improved methods for the assessment of risk reduction achieved by NBS for insurance and for assessing co-benefits to society

The increase in frequency and severity of climate risk events highlights the need for investing in climate change mitigation and adaptation. To this end, nature-based solutions (NbS) could become an economically viable and effective alternative to traditional engineering solutions, as they provide co-benefits for society even in the absence of an adverse climate event. However, there are still several gaps and challenges when assessing the benefits of NbS, which can ultimately be a barrier to adoption. In this deliverable, we build upon a meta-analysis and systematic review, where several challenges in quantifying the risk-reduction benefits and co-benefits of NbS were identified.

There are still several challenges that can negatively impact the upscaling of investment in NbS, both for the public and the private sector. In this deliverable we explore these gaps both from the risk-reduction and the co-benefits (physical and economic) perspectives. Several limitations in deliverable 4.1. were identified regarding benefits assessment such as the limited amount of data and how some NbS are relatively recent. In this deliverable, we co-designed improved metrics and methods for assessing the risk-reduction and co-benefits of NbS with NATURANCE stakeholders in an Innovation Lab. The outcome of this co-design process was to focus this improved metrics and methods on NbS that reduce flood risk. Moreover, since NbS can take a longer time to fully realize their benefits, it may still be too early to draw definitive conclusions. From a risk reduction perspective, many existing assessments are conducted at a local scale, instead of catchment-wide evaluations, which are essential for understanding the full hydrological impact of NbS. Moreover, previous studies used a limited set of metrics to evaluate the risk reduction of NbS in physical units (e.g. changed hydrological conditions). For insurance companies and investors, it would be more useful to estimate monetary metrics, such as the reduction in annual expected losses from NbS. This is challenging because catastrophe models of natural disaster risk for insurance have historically not accounted for NbS, resulting in an underestimation of their risk-reduction potential.

The lack of co-benefits assessment would underestimate the total societal value of NbS when using cost-benefit analysis to compare with benefits of traditional engineered solutions. But co-benefit assessment also faces its own limitations and challenges. NbS co-benefits assessment has mostly focused on the positive aspects of NbS. However, NbS also provide certain “disbenefits”, such as vector-borne diseases, that should be included in the analysis and, in the case of our co-benefit assessment using a newly designed choice experiment, are included in the analysis for one of our attributes. This was also highlighted during the co-creation process of our new method by local stakeholders, leading to the inclusion of a land use change disbenefit category that would capture how much agricultural land would be converted into NbS. The social dimension of co-benefits also means that the design of assessment methods relies on knowing the challenges faced by local actors, increasing the importance of a co-creation approach. Lastly, the main challenge of co-benefit assessment is mostly about how to make these monetary results relevant for private sector stakeholders to increase funding in NbS projects that not only comes from the public sector.

Upon collaboration with NATURANCE stakeholders we decided to illustrate the new developments in methods for assessing the benefits of NbS using the European floods of the Summer of 2021 as our case study. This event showed that there is room for improvement when it comes to flood-risk management. The total economic damage, only in the Netherlands, amounted to 600 million Euros. Having the case study in mind, and to overcome the lack of involvement of stakeholders, we employed a co-creation approach that had two main components. The first was the “Innovation Labs”, which consisted of three different focus groups to discuss the new methods that will later be applied in our case study. In the second session, we discussed current limitations of flood-risk modelling and the lack of evidence of the impact of NbS in flood-risk reduction with several stakeholders from the insurance industry. The last session with policymakers and local stakeholders from Limburg, the main impacted area in the Netherlands by the 2021 floods, was crucial for the design of the methods since policymakers described which NbS are being considered, ensuring that our modelling and choice experiment results will be relevant for flood-risk management in the region.

We developed a new object level catastrophe model with a coupled hydrological and NbS modelling approach at a detailed geographical scale to estimate the flood risk reduction of NbS. Our risk-reduction results show the impact of reforestation and water storage ponds in reducing damage from flooding since these were potential measures considered by the local authorities to limit flood risk. The scenario for reforestation assumes a 10% increase in forest area, whereas for retention ponds it consists of an enlargement of current ponds and creation of new ones upstream. Results show that NbS can indeed reduce flood risk for all the metrics used in the catastrophe model. However, reforestation seems to be much more cost-efficient than retention ponds with a benefit-cost ratio of 1.06. Reforestation can reduce damage with approximately 16 million Euros in a 1-in-25-year flood event. This positive impact also maintains for larger and less frequent floods. On the other hand, retention ponds show a 0.24 benefit-cost-ratio. Including co-benefits in the analysis can make a compelling argument for investing in this NbS.

To complement the risk-reduction results, we developed a new choice experiment and survey method that was conducted with over 2,000 respondents across the Netherlands. The objective was to understand preferences for different co-benefits of NbS and to deliver new metrics of the monetized co-benefits of NbS. In particular, the willingness-to-pay, through an increase in the waterboard tax, was estimated using the model results. These NbS would be developed in current agricultural land. One of the key findings of this stage was to see the responsibility allocation of flood adaptation, as a high percentage of respondents highlighted that the national government should be responsible for these policies instead of individual adaptation or private sector entities (e.g. insurers). They also selected “low-income households” as the group that would benefit the most from these policies. However, if we guide investments by the risk-reduction results, this may lead to NbS being in wealthier regions, where the monetary damage would be higher, leading to a lack of protection for these low-income households who may not have the means to adapt. Although our analysis is based on perceptions and subjective preferences, we highlight the importance of addressing this social dimension of risk in NbS investments.

The choice model results highlight the reluctancy of our respondents to give up agricultural land for NbS. The coefficient for conversion of agricultural land to NbS was negative and statistically significant. This is certainly valuable as a proxy for policy support. For example, reforestation has been shown to have a higher benefit-cost ratio, but it also requires more hectares to be converted, which may clash with the preferences of the local community. Other results of the mixed-logit model show a higher willingness to pay for reforestation and storage ponds over other NbS, such as wetlands and grassland.

These results in this deliverable will contribute to the last deliverable D4.3 of Work Package 4, which focuses on designing NbS investment schemes for insurance, public and private sector. The risk-reduction benefits and the co-benefits will be combined in a social cost-benefit analysis, in order to make a comprehensive assessment of what the total economic value of these NbS would be. Moreover, D4.3 will estimate how much each actor, including the insurance sector, will benefit from implementing NbS. This is a key element for developing NbS financing strategies.