Impact of COVID-19 type events on the economy and climate under the stochastic DICE model
Pavel V. Shevchenko, Daisuke Murakami, Tomoko Matsui, Tor A. Myrvoll

TL;DR
This paper extends the classical DICE climate-economy model by incorporating stochastic shocks representing events like COVID-19, analyzing their impact on climate and economic outcomes under various recovery scenarios.
Contribution
It introduces a stochastic extension to the DICE model with jump processes for shocks and solves it as an optimal control problem, providing new insights into pandemic-like events on climate-economy dynamics.
Findings
COVID-19-like shocks have minimal long-term climate impact if full economic recovery occurs.
Persistent economic shocks can lead to a small but noticeable temperature reduction.
Applying deterministic policies under stochastic shocks results in larger temperature drops, enabling lower-cost mitigation.
Abstract
The classical DICE model is a widely accepted integrated assessment model for the joint modeling of economic and climate systems, where all model state variables evolve over time deterministically. We reformulate and solve the DICE model as an optimal control dynamic programming problem with six state variables (related to the carbon concentration, temperature, and economic capital) evolving over time deterministically and affected by two controls (carbon emission mitigation rate and consumption). We then extend the model by adding a discrete stochastic shock variable to model the economy in the stressed and normal regimes as a jump process caused by events such as the COVID-19 pandemic. These shocks reduce the world gross output leading to a reduction in both the world net output and carbon emission. The extended model is solved under several scenarios as an optimal stochastic control…
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