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Data anomalies and the economic commitment of climate change

Nature volume 644, pages E7–E11 (2025) Cite this article

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The Original Article was published on 17 April 2024

arising from: M. Kotz et al. Nature https://doi.org/10.1038/s41586-024-07219-0 (2024).

Kotz, Levermann and Wenz1 (henceforth, KLW) analysed how subnational gross domestic product (GDP) growth responds to year-to-year changes in temperature and precipitation. They reported that if historical relationships continue to hold, global GDP would be lowered by roughly 62% (central estimate) in 2100 under the Representative Concentration Pathway 8.5 ‘high emissions’ scenario, an impact roughly 3 times larger than similar previous estimates2,3. Here we show that (1) data anomalies arising from one country in KLW’s underlying GDP dataset, Uzbekistan, substantially bias their predicted impacts of climate change, (2) KLW underestimate statistical uncertainty in their future projections of climate impacts, and (3) additional data-quality concerns in KLW’s subnational GDP data warrant further investigation. When Uzbekistan’s data are removed and statistical uncertainty is corrected to account for spatial correlations, KLW’s central estimate aligns closely with previous literature and their results are no longer statistically distinguishable from mitigation costs at any time this century.

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Fig. 1: Anomalous Uzbekistan data in the paper by KLW and its impact on the projected effect of climate change on global economic growth.
The alternative text for this image may have been generated using AI.

Data availability

The analysis relies on replication data13 provided by Kotz et al.1, which include version 2 of the DOSE subnational GDP dataset and climate data from ERA5 (https://doi.org/10.5281/zenodo.11064757). We also use data from version 1 of the DOSE dataset14 (https://doi.org/10.5281/zenodo.4681306) and national income data for Uzbekistan from the World Bank (https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD?locations=UZ).

Code availability

The code required to replicate Fig. 1 is available on GitHub at https://github.com/Global-Policy-Lab/ma_klw. See the Github readme.md for details on where to download the raw data used in this note. To generate Fig. 1c, we use KLW’s replication code, but include additional lines dropping the Uzbekistan data before estimating the regressions that are used to make projections. This change is implemented on lines 41–43 of feols_bootstrap_regressions_modified.R.

References

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Author information

Authors and Affiliations

  1. Princeton University, School of Public and International Affairs, Princeton, NJ, USA

    Tom Bearpark

  2. Columbia University, School of International and Public Affairs, New York, NY, USA

    Dylan Hogan

  3. Global Policy Laboratory, Stanford Doerr School of Sustainability, Stanford University and National Bureau of Economic Research, Stanford, CA, USA

    Solomon Hsiang

Authors
  1. Tom Bearpark
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  2. Dylan Hogan
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  3. Solomon Hsiang
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Contributions

T.B., D.H. and S.H. equally contributed to designing the study, collecting the data, performing the analysis and writing the paper.

Corresponding author

Correspondence to Solomon Hsiang.

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Competing interests

The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Quality control procedures implemented in Wenz et al.10 were not sufficient to detect anomalous data used in Kotz et al.1.

Figure replicates Wenz et al.10 Figure 7, but without limiting the displayed axes. The red box indicates the bounds of the image published in Wenz et al. as their Technical Validation. X-axes are GDP per capita measures for subnational regions in DOSE v.2, Y-axes are corresponding subnational data from an alternative benchmark study by Gennaioli et al.10 GDP per capita estimates for Uzbekistan in DOSE v.2 differ from those reported by Gennaiolli et al. by up to 140%, although this is not discernible from visual inspection of these plots. Points that visibly disagree between data sets are for regions in other countries (i.e. not Uzbekistan). Figure adapted from ref. 10, Springer Nature Ltd.

Supplementary information

Supplementary Information (download PDF )

This Supplementary Information file contains 3 sections and additional references. Section 1: Background on data used in KLW. Section 2: Underestimated uncertainty. Section 3: Data-quality control.

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Bearpark, T., Hogan, D. & Hsiang, S. Data anomalies and the economic commitment of climate change. Nature 644, E7–E11 (2025). https://doi.org/10.1038/s41586-025-09320-4

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