Research

As climate changes and natural disasters intensify, the threat of human displacement increases. This paper studies carbon taxation in the presence of international climate displacement. After providing evidence on the migration response to disasters, forced climate migration is introduced into a quantitative climate-macroeconomic model to theoretically characterize the global and local social cost of carbons—SCCs, equivalently, optimal carbon taxes. These change substantially when this type of migration is considered. A North-South calibration reveals that, while migration increases the local SCC in host regions—more so if political conflict is considered—the global and origin region’s SCCs remain largely unaffected.

Air pollution is a major concern that constitutes an important part of the climate change thread. Furthermore, since the outbreak of the pandemic, there is an increased interest in understanding the role of environmental conditions in determining the severity of infectious diseases. We contribute to this debate by quantifying the effect of short-term exposure to air pollution on COVID-19 mortality. We construct a novel county-level dataset of daily COVID-19 mortality and, in order to overcome the potential endogeneity problems caused by factors related to both the virus and mortality rates (such as county-level economic conditions), we use wind speed as an instrument for air pollution. Our results show that being exposed to one additional unit (μg/m3) of nitrogen dioxide, particulate matter and nitrogen oxides, increases COVID-19 mortality by a factor of between 1.1-1.3. By highlighting the positive effects that air pollution reduction policies can generate on population health, our results are relevant for the design of policy tools to be used during the next natural disaster events.

This paper investigates how patent policy can induce the transition to clean technology. It is well established that environmental policy should not only price emissions, but also induce innovation in emission-free technology. Although the combination of a price on emissions and a government subsidy to clean research has been shown to be first-best, we argue that this policy is unattainable. First, the magnitude of the necessary carbon tax seems unfeasible, and second, there can be large efficiency losses associated with public research funding. Using an endogenous growth model with directed technical change, we show how reducing patent protection on dirty technology can improve second-best outcomes. In numerical simulations, we find that combining environmental policy with patent policy can recover a substantial amount of the welfare loss in second-best, and at a lower carbon tax and clean innovation subsidy than in first-best. 

It is commonly understood that technological change is the ultimate tool to meet climate change targets and avoid catastrophic events. However, uncertainty about the impact of global warming challenges the design of policies. This study examines the consequences of imperfect information about climate damages on the optimal direction of innovation efforts. I assume that R&D can be directed either to improve energy efficiency or non-energy technology. I first abstract from uncertainty and show the optimal allocation paths under different targets and expected climate damages. I then incorporate uncertainty about climate damages into the analysis as well as dynamic learning in a Bayesian fashion.