Increasingly complex linkages of the global economy can be a channel for the propagation of negative climate-related shocks with the potential to deteriorate resilience and sustainability in societies. Given projected increases in disaster risks due to climate change, developing models that more accurately simulate the propagation of shocks through economic networks becomes crucial for designing effective prevention and recovery strategies. Except for some studies that use macroeconomic models to assess indirect risk at the sectoral level, indirect risk at the firm level is rarely well quantified. In this work, I propose an indirect risk assessment approach that bridges macroeconomics and microenterprises. This approach divides the indirect risk of enterprises into two parts: the average risk of the industry determined by the characteristics of the industry to which the enterprise belongs, and the risk coefficient determined by the characteristics of the enterprise itself. We use an agent-based model equipped with general equilibrium theory to evaluate the former, and machine learning techniques to evaluate the latter. On top of a more comprehensive risk assessment, the proposed approach provides a modeling framework for analyzing the indirect risk face by specific firms.
Daoping Wang is a Hoffmann Research Fellow at the University of Cambridge. He worked with academics from the Department of Computer Science and Technology and the Judge Business School at the University of Cambridge, to explore the application of AI technologies in climate risk assessment and governance, especially the indirect risk propagated on global supply-chain networks. He is also in collaboration with the Centre for Climate Engagement in Hughes Hall and the World Economic Forum to bring knowledge on global climate governance to policymakers, stakeholders, and the general public.
