Disaggregating climate change risk across regions: mitigation, impacts, and the hedging of risk

• Applicant: Professorin Dr. Karen Pittel
• Subject Area: Economic Policy, Applied Economics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 550373790

The World Economic Forum’s Global Risks Report ranks climate risks, including extreme weather events and the failure of climate-change mitigation and adaptation, at the top of their list. Global climate change risks and their implications for decision-making have been extensively studied in the literature. The risks include immediate shocks as well as long-term uncertainties about the future evolution of climate and the economy. However, most of these papers study aggregate risk. Yet, climate change impacts and the impacted regions themselves are highly heterogeneous and we lack a good understanding of how the underlying regional risks actually aggregate into global risks. The project studies how regional risks spread, how they can be hedged through trade, insurance, and capital flows, and how they aggregate across regions. Understanding how risks spread and aggregate is crucial to understanding the climate change challenge and designing mitigation and adaptation policies. It is also crucial to evaluate the patchwork of regional climate policies and adaptation projects in their international context. To fill this gap, the project analyzes shocks, risks, and uncertainties in a regional integrated assessment model of climate change. The model will be endowed with trade and technological interaction across regions to account for the shock spreading and hedging channels. Particular attention will be paid to the distinct conditions and challenges for developing versus fully developed economies. In this context, the diffusion of technologies and their interactions with climate mitigation and adaptation are analyzed. The project has two main contributions. First, it analyzes how technological evolution and diffusion, trade, and climate and insurance policies hedge or exacerbate climate risks across regions in a stochastic regional integrated assessment model. Understanding the dynamics of the risk makes it possible to explore how these regional risks affect the regions' mitigation and adaptation policies and the distributional consequences of aggregate risk. The second contribution is methodological. Cutting-edge numeric and analytic approaches are combined to solve the regional stochastic integrated assessment model. Uncertainty and shocks can be embedded into the regional assessment model based on recent progress in machine learning and approximate dynamic programming. The flexibility and power of machine learning allows to solve the required model with significant scale and complexity in the stochastic integrated assessment analysis as well as general macroeconomic modeling. This approach will be complemented with analytic solutions. In doing so, we gain tractability and economic intuition that is oftentimes missing in purely numeric approaches.

• DFG Programme: Research Grants