A growing literature characterizes climate change damages by relating temperature shocks to GDP. But theory does not clearly prescribe estimable forms of this relationship, yielding discretion to researchers and generating potentially considerable model uncertainty. We therefore employ model cross validation to assess the out-of-sample predictive accuracy of 400 variants of prominent models, identify the set of superior models, and characterize both model and sampling uncertainty. Estimates of GDP impacts vary substantially across models, especially those assuming temperature effects on GDP growth, rather than levels. The best-performing models have non-linear temperature effects on GDP levels, and imply global GDP losses of 1-2% by 2100.
The GDP Temperature Relationship: Implications for Climate Change Damages
In the absence of clear theoretical guidance on specific estimable forms for the aggregate GDP-temperature relationship, we consider the implications of model uncertainty for market damages of climate change.Download
On the Issues: Climate in the Democratic Debate, China’s Carbon Trading Breakthrough, and More
Connecting this week's environmental and energy news to RFF's economic research.
Explainer — Jan 16, 2020
How does discounting help decisionmakers understand the costs and benefits of choices and policies—and how does it apply to climate change?
Press Release — Jan 15, 2020
First-of-its-Kind Study Evaluates China’s New Nationwide CO2 Emissions Pricing System
Assessing the cost-effectiveness and distributional consequences of China’s new nationwide CO2 emissions pricing system