Estimating the Economic Impacts of Climate Change Using Weather Observations

Abstract

This article reviews methods that use historical data on weather, climate, economic activity, and other variables to statistically measure the effect of climate on economic outcomes. This has been an active area of research for several decades, with many recent developments and discussions in the literature concerning the best way to estimate climate damages. The article first presents a conceptual framework for estimating the costs of climate change impacts. It then examines several approaches proposed in the literature that use historical weather data to econometrically estimate climate change impacts. These include cross section, linear and nonlinear panel methods, long differences, and partitioning variation. For each approach we describe the type of impacts (short-run versus long-run) estimated, the type of weather or climate variation used (e.g., cross-sectional, time-series, or a combination of the two using panel data), and the advantages and disadvantages of the approach. We conclude with a summary of our findings and priorities for future research.

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