Roshen Fernando
Abstract: Climate change continues to be an existential threat to humanity. With intrinsic linkages to the natural environment, food and energy supply chains are two fundamental channels via which climate risks could spill over into the economy. This paper explores the global economic consequences of the physical climate impacts on agriculture and energy. Firstly, we construct a range of chronic and extreme climate risk indicators. Secondly, we incorporate those climate risk indicators, alongside the historical data on global agriculture and energy, in machine learning algorithms to estimate the historical responsiveness of agriculture and energy to climate risks. Thirdly, we project agriculture and energy production changes under three Shared Socioeconomic Pathways (SSPs). Finally, the derived shocks are introduced as economic shocks to the G-Cubed model, which is a global multisectoral intertemporal general equilibrium model. We evaluate the G-Cubed model simulation results for various economic variables, including real GDP, consumption, investment, exports and imports, real interest rates, and sectoral production. We observe substantial losses to all economies and adjustments to consumption and investment under the SSPs. The losses worsen with warming. Developing countries are disproportionately affected. However, we observe the potential for double dividends from transitioning to sustainable livestock production and renewable energy sources, preventing further warming and physical damages, and enhancing the resilience of food and energy supply chains to climate risks.
Keywords: Climate Change, Extreme Events, Physical Climate Risks, Macroeconomics, CGE, DSGE, Machine Learning
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