Matters Arising: Spatial correlation in economic analysis of climate change

TL;DR

This paper critiques a recent climate-economic analysis, highlighting that unaccounted spatial correlations significantly affect the statistical significance of projected damages, thus questioning the robustness of its policy implications.

Contribution

It reveals the importance of considering spatial correlations in economic climate impact studies, demonstrating that neglecting them can lead to overstated confidence in results.

Findings

Unaccounted spatial correlations increase uncertainty in economic climate damage estimates.

Proper correction for spatial correlations renders previous results statistically insignificant.

The study emphasizes the need for spatially-aware analysis in climate-economic research.

Abstract

Climate change poses substantial risks to the global economy. Kotz, Levermann and Wenz (Nature, 2024) statistically analyzed economic and climate data, finding significant projected damages until mid-century and a divergence in outcomes between high- and low-emission scenarios thereafter. We find that their analysis underestimates uncertainty owing to large, unaccounted-for spatial correlations on the subnational level, rendering their results statistically insignificant when properly corrected. Thus, their study does not provide the robust empirical evidence needed to inform climate policy.