with Maria Alsina-Pujols
Abstract: 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.
Winner of the IAERE Young Environmental Economist Award 2020
Abstract: While economists have focused on the effect of mean temperatures on mortality, climate scientists have emphasized that global warming might not only lead to an increase in mean temperatures, but can potentially also affect temperature variability. This is the first paper to estimate the causal effect of temperature variability on mortality. Using monthly state level data for the US in the period 1969-2004, I offer three main results: (1) Increased monthly temperature variability causes increased mortality, (2) omitting the effect of temperature variability on mortality can severely bias our predictions on the number of temperature-induced fatalities caused by global warming, and (3) adaptation to increased temperature variability is more difficult than adaptation to increased mean temperatures.
Abstract: One of the most important research questions in climate economics is the relationship between temperatures and human mortality. This paper develops a procedure that enables the use of machine learning for estimating the causal temperature-mortality relationship. The machine-learning model is compared to a traditional OLS model, and although both models are capturing the causal temperature-mortality relationship, they deliver very different predictions of the effect of climate change on mortality. These differences are mainly caused by different abilities regarding extrapolation and estimation of marginal effects. The procedure developed in this paper can find applications in other fields far beyond climate economics.