Projecting Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA

Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).

GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).

GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: https://jgcri.github.io/gcam-doc/toc.html

The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP per capita) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.

Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:

• income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)
• residential energy consumption per capita by service and fuel, by state and income group, 2015-2045
• residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, and technology, by state and income group, 2015-2045
• estimated energy burden (Eq.1), by state and income group, 2015-2045
• residential heating service inequality (Eq.2), by state, 2015-2045

Table 1

Eq. 1

\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)

for income group i and service j

Eq. 2

\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)

where S is the residential heating service output per capita of the highest income group (d10) divided by the sum of that of the lowest four income groups (d1, d2, d3, and d4), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.

Reference

Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7227128

IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.