7
Crosscutting Themes

Workshop attendees participated in breakout groups broadly themed around the workshop’s sessions: macroeconomic modeling, economic and financial impacts of the energy transition, and public and financial sector risks and responses. The groups were separated by virtual and in-person participation and each group was tasked with discussing examples of emerging priorities, gaps, and opportunities. There were several overlapping themes across all groups.

EXAMPLES OF MODELING PRIORITIES AND OPPORTUNITIES

Four breakout groups discussed the complex questions and challenges in macroeconomic modeling in the context of climate change. One group highlighted the challenge of limited historical data, especially for extreme events, and the desire for further research on modeling their frequency, intensity, and concurrence to better understand how they cluster in time and space. Some participants noted that there is a potential for more useful climate data, including the significance of correlated events, extremes, and estimates of the duration of exposure. Although these aspects are challenging to model, they have a substantial impact on the economy. However, another group of participants raised the challenge of integrating climate data into econometric models and sought guidance on appropriate methods for such integration.

Another group’s discussion also touched on missing elements in macroeconomic modeling, such as supply-side factors, especially migration. They acknowledged that this omission is often due to political reasons but is crucial for understanding economic dynamics. There was also discussion around the limited research on the effects of climate change on human capital accumulation, which is important for economic growth, and the group suggested this could be an area for more research.

Other breakout groups emphasized the importance of regional, sectoral, and distributional perspectives in addressing climate change. Several participants noted that policymakers are increasingly interested in understanding the distributional consequences of climate change, including physical and transition risks, at spatial and sectoral levels. However, they noted that existing macroeconomic models may not adequately capture these aspects, suggesting the benefit of specialized models or approaches to address distributional concerns. Current macroeconomic models often lack the necessary regional and sectoral resolution to address these concerns. Furthermore, participants discussed the possibility of smaller regional damages interacting to lead to significant aggregate damages.

The discussion also highlighted the importance of robust uncertainty characterization and leveraging advancements in computing. Participants further emphasized considering higher-order moments in uncertainty analysis, not just mean and variance. The discussion also touched on the significance of addressing ambiguity and misspecification in addition to quantifiable uncertainty. Effective communication of uncertainty to policymakers was also noted.

A question emerged regarding whether traditional macroeconomic models, designed without climate considerations, can effectively project the effects of a low-carbon, climate-disrupted future. The discussion considered whether retrofitting existing models with climate factors is adequate or if entirely new classes of models are needed. Additionally, another group recognized that existing models often lack important feedback loops, hindering their ability to model human responses to climate change and disasters. They acknowledged an interest in more flexible models that can connect and provide a comprehensive understanding of climate impacts, including emerging approaches such as embedded and agent-based models.

Another group suggested a comprehensive multimodel approach instead of relying on a single macroeconomic model. For example, one model for net forecasts and another for sectoral or spatial details. The group suggested that this approach might be more politically viable and effective in addressing distributional concerns. Many participants recognized the importance of collaborative research in this area. One group brought up the idea of model intercomparison projects, such as those in climate science and agriculture, to assess and improve macroeconomic models. They explained that these projects involve convening modeling teams to examine scenarios, identify strengths and weaknesses, and foster collaborative learning among modelers. Other participants noted the lack of integration among sector-specific models and suggested bringing modelers together to compare models and learn from each other. Additionally, some participants suggested exploring synergies between models with granular spatial detail, such as regional or watershed-level models.

ENERGY TRANSITION IMPACTS

The breakout group that discussed the energy transition focused on modeling and managing the transition to renewable energy sources, recognizing the complexity and inherent risks. They discussed transitioning from 60 percent to 20 percent nonrenewable electricity generation and modeling how decreased renewable energy prices impact investment and productivity. Additionally, participants highlighted the importance of analyzing the labor share across sectors, particularly in the energy sector, and examining its reallocation over time and impacts to the overall labor share. Trade effects, such as the impact of reduced costs of imported final goods or inputs (e.g., semiconductor chips), served as examples from the past to inform future modeling.

The group discussed transition risks, which encompass both intended and unintended consequences of the energy transition. Intended consequences include a decrease in the value of fossil fuel assets, while unintended consequences involve workforce reallocation