Cited by 927Climate Change Policy and EconomicsEnergy, Environment, Economic GrowthEconomic Growth and Productivity

Global warming has increased global economic inequality

Open access
Diffenbaugh, Noah S., Burke, Marshall·2019·Proceedings of the National Academy of Sciences

Abstract

Significance: We find that global warming has very likely exacerbated global economic inequality, including ∼25% increase in population-weighted between-country inequality over the past half century. This increase results from the impact of warming on annual economic growth, which over the course of decades has accumulated robust and substantial declines in economic output in hotter, poorer countries—and increases in many cooler, wealthier countries—relative to a world without anthropogenic warming. Thus, the global warming caused by fossil fuel use has likely exacerbated the economic inequality associated with historical disparities in energy consumption. Our results suggest that low-carbon energy sources have the potential to provide a substantial secondary development benefit, in addition to the primary benefits of increased energy access.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.1816020116

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • References [101,187] use relatively simple local and polynomial regression techniques applied to extensive empirical data to estimate the past and future effects of temperature change on economic production.

    “References [101,187] use relatively simple local and polynomial regression techniques but applied to extensive empirical data to estimate the past and future effects of temperature change on economic production.”

    From Tackling climate change with machine learning (2023) · cites this paper as [187]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Diffenbaugh, N. S., & Burke, M.
Affiliations
  • Stanford University (US)
  • National Bureau of Economic Research (US)
Corresponding author
Diffenbaugh, Noah S.
Venue
Proceedings of the National Academy of Sciences
Published
2019-04-22
Volume
116
Issue
20
Pages
9808-9813
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2939349760
Cited by
927
References
34
Citations by year
2026: 125 · 2025: 155 · 2024: 150 · 2023: 167 · 2022: 117 · 2021: 132 · 2020: 56 · 2019: 24 · 2014: 1
FWCI
102.68
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Stanford University (NONE)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Energy, Environment, Economic Growth
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Productivity
Keywords
global warming, economic inequality, anthropogenic forcing, climate-growth relationships, GDP per capita, between-country inequality, fossil fuel consumption, global climate models

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review