Research

As climate changes and natural disasters intensify, the threat of human displacement increases. This paper explores how carbon should be taxed in the presence of international displacement caused by climate change. It first provides empirical evidence on the migration response to natural disasters from developing to developed countries. Second, it develops a climate-economy growth model with climate refugees and theoretically characterizes global and local social cost of carbon (SCC, equivalently, the optimal carbon tax) taking into account the economic and social impact of climate refugees. Third, it quantifies the SCCs in a North-South calibration. The main finding is that refugees enhance host regions' incentives to unilaterally fight climate change, leading to 26% increase in the local SCC, more so if political conflict is accounted for. This stands in contrast to the global SCC, and the origin regions SCC, which barely change in magnitude after accounting for refugees.

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. 

There is strong scientific evidence on the relationship between certain pollutants and respiratory diseases, as well as cardiovascular and inflammatory events. Given the predisposition of COVID-19 infected patients to develop respiratory problems, disentangling the effect of air pollution on the illness severity is of crucial relevance. While existing studies on  COVID-19 commonly analyze the effects of long term exposure on the fatality of the disease, the goal of this project is to study the short term exposure to air pollution using data on Catalan counties and municipalities. In order to overcome the potential endogeneity problems arising from the containment measures implemented in different phases of the pandemics, we use daily wind speed data to instrument for air pollution. We find compelling evidence of the effect of certain pollutants (NO2, NOX and O3) on COVID-19  fatality, while we do not find any effect resulting from other pollutants such as PM10.

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.