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Understanding the Spatial Distribution of Welfare Impacts of Global Warming on Agriculture and its Drivers

Uris L. C. Baldos, Thomas W. Hertel and Frances C. Moore

American Journal of Agricultural Economics, 2019, vol. 101, issue 5, 1455-1472

Abstract: This paper explores the interplay between the biophysical and economic geographies of climate change impacts on agriculture. It does so by bridging the extensive literature on climate impacts on yields and physical productivity in global crop production, with the literature on the role of adaptation through international trade in determining the consequences of climate change impacts. Unlike previous work in this area, instead of using a specific crop model or a set of models, we employ a statistical meta‐analysis that encompasses all studies available to the IPCC‐AR5 report. This permits us to isolate specific elements of the spatially heterogeneous biophysical geography of climate impacts, including the role of initial temperature, differential patterns of warming, and varying crop responses to warming across the globe. We combine these climate impact estimates with the Global Trade Analysis Project model of global trade in order to estimate the national welfare changes that are decomposed into three components: the direct (biophysical impact) contribution to welfare, the terms of trade effect, and the allocative efficiency effect. We find that when we remove the spatial variation in climate impacts, the terms of trade impacts are cut in half. Given the inherent heterogeneity of climate impacts in agriculture, this points to the important role of trade in distributing the associated welfare impacts. When we allow the biophysical impacts to vary across the empirically estimated uncertainty range taken from the meta‐analysis, we find that the welfare consequences are highly asymmetric, with much larger losses at the low end of the yield distribution. This interaction between the magnitude and heterogeneity of biophysical climate shocks and their welfare effects highlight the need for detailed representation of both in projecting climate change impacts.

Date: 2019
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