Few and Not So Far Between: A Meta-analysis of Climate Damage Estimates

Peter H Howard,Thomas Sterner

doi:10.1007/s10640-017-0166-z

Peter H Howard, Thomas Sterner

Open Access

https://doi.org/10.1007/s10640-017-0166-z

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Abstract

Given the vast uncertainty surrounding climate impacts, meta-analyses of global climate damage estimates are a key tool for determining the relationship between temperature and climate damages. Due to limited data availability, previous meta-analyses of global climate damages potentially suffered from multiple sources of coefficient and standard error bias: duplicate estimates, omitted variables, measurement error, overreliance on published estimates, dependent errors, and heteroskedasticity. To address and test for these biases, we expand on previous datasets to obtain sufficient degrees of freedom to make the necessary model adjustments, including dropping duplicate estimates and including methodological variables. Estimating the relationship between temperature and climate damages using weighted least squares with cluster-robust standard errors, we find strong evidence that duplicate and omitted variable biases flatten the relationship. However, the magnitude of the bias greatly depends on the treatment of speculative high-temperature (>4 ^{circ }C) damage estimates. Replacing the DICE-2013R damage function with our preferred estimate of the temperature–damage relationship, we find a three- to four-fold increase in the 2015 SCC relative to DICE, depending on the treatment of productivity. When catastrophic impacts are also factored in, the SCC increases by four- to five-fold.

Highlights

Climate change is one of the preeminent policy issues of our day, and the Paris agreement signals some international agreement that action is urgently needed
We present results using WLS regardless of whether we exclude high-temperature estimates; these results only slightly differ from the ordinary least squares (OLS) results
Given that our results demonstrate that duplication bias can significantly affect the temperature–damage relationship, it is important to determine the relative importance of duplication bias in driving the difference between our results and those of Nordhaus (2013) relative to the other forms of bias discussed earlier—omitted variable bias and measurement error—and relative to obtaining a larger dataset and improving the estimation technique

Summary

Introduction

Climate change is one of the preeminent policy issues of our day, and the Paris agreement signals some international agreement that action is urgently needed. Given the considerable uncertainty in climate impacts, meta-analyses of climate damage estimates are a key tool for depicting the relationship between temperature and climate damages, so as to communicate the current state of knowledge to model developers (Van den Bergh and Button 1999). By clarifying this relationship and the uncertainty underlying it, meta-analyses explain to policymakers arguably the best assessment of the risks that climate change poses to the global economic system and to human well-being.

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