In response to the COVID-19 health crisis, the French government has imposed drastic lockdown measures for a period of 55 days. This paper provides a quantitative assessment of the economic and environmental impacts of these measures in the short and long term. We use a Computable General Equilibrium model designed to assess environmental and energy policies impacts at the macroeconomic and sectoral levels. We find that the lockdown has led to a significant decrease in economic output of 5% of GDP, but a positive environmental impact with a 6.6% reduction in CO 2 emissions in 2020. Both decreases are temporary: economic and environmental indicators return to their baseline trajectory after a few years. CO 2 emissions even end up significantly higher after the COVID-19 crisis when we account for persistently low oil prices. We then investigate whether implementing carbon pricing can still yield positive macroeconomic dividends in the post-COVID recovery. We find that implementing ambitious carbon pricing speeds up economic recovery while significantly reducing CO 2 emissions. By maintaining high fossil fuel prices, carbon taxation reduces the imports of fossil energy and stimulates energy efficiency investments while the full redistribution of tax proceeds does not hamper the recovery.
The effect of learning on climate policy is not straightforward when climate policy is concerned. It depends not only on the ways that climate feedbacks, preferences, and economic impacts are considered, but also on the ways that uncertainty and learning are introduced. Deep (or fat-tailed) uncertainty does matter for the optimal climate policy in that it requires more stringent efforts to reduce carbon emissions. However, learning may reveal thin-tailed uncertainty, weakening the case for emission abatement: learning reduces the stringency of the optimal abatement efforts relative to the no learning case even when we account for deep uncertainty. In order to investigate this hypothesis, we construct an endogenous (Bayesian) learning model with fat-tailed uncertainty on climate change and solve the model with stochastic dynamic programming. In our model a decision maker updates her belief on the total feedback factors through temperature observations each period and takes a course of action (carbon reductions) based on her belief. With various scenarios, we find that the uncertainty is partially resolved over time, although the rate of learning is relatively slow, and this materially affects the optimal decision: the decision maker with a possibility of learning lowers the effort to reduce carbon emissions relative to the no learning case. This is because the decision maker fully utilizes the information revealed to reduce uncertainty, and thus she can make a decision contingent on the updated information. In addition, with incorrect belief scenarios, we find JEL Classification: Q54
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