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. 2025;15(7):725-735.
doi: 10.1038/s41558-025-02372-4. Epub 2025 Jul 1.

Extreme weather event attribution predicts climate policy support across the world

Collaborators, Affiliations

Extreme weather event attribution predicts climate policy support across the world

Viktoria Cologna et al. Nat Clim Chang. 2025.

Abstract

Extreme weather events are becoming more frequent and intense due to climate change. Yet, little is known about the relationship between exposure to extreme events, subjective attribution of these events to climate change, and climate policy support, especially in the Global South. Combining large-scale natural and social science data from 68 countries (N = 71,922), we develop a measure of exposed population to extreme weather events and investigate whether exposure to extreme weather and subjective attribution of extreme weather to climate change predict climate policy support. We find that most people support climate policies and link extreme weather events to climate change. Subjective attribution of extreme weather was positively associated with policy support for five widely discussed climate policies. However, exposure to most types of extreme weather event did not predict policy support. Overall, these results suggest that subjective attribution could facilitate climate policy support.

Keywords: Attribution; Climate-change impacts; Climate-change policy; Psychology.

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Conflict of interest statement

Competing interestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Global evidence of the support for climate policies.
a, Weighted response probabilities for single items measuring support for climate policies. b, Mean support for climate policies in 66 countries (climate policy support was not measured in Argentina and Malaysia). Participants were asked: “Please indicate your level of support for the following policies.” Response option ‘not applicable’ is not shown. No data were available for countries shaded in light grey.
Fig. 2
Fig. 2. Subjective attribution of extreme weather events to climate change (mean index) over the past decades.
Data from 67 countries. Subjective attribution was not assessed in Albania. No data were available for countries shaded in light grey.
Fig. 3
Fig. 3. Exposed population across countries over the past few decades.
Exposed population refers to the average annual proportion of a country’s total population exposed to a specific weather-related hazard and averaged over the past few decades. The exact time frame varies slightly across events. Exposed population is modelled for the 68 countries included in the survey. a, Exposed population to droughts. b, Exposed population to European winter storms. c, Exposed population to heatwaves. d, Exposed population to heavy precipitation. e, Exposed population to river floods. f, Exposed population to tropical cyclones. g, Exposed population to wildfires. No data were available for countries shaded in light grey.
Fig. 4
Fig. 4. Weighted blockwise multilevel models predicting climate policy support.
Summary of seven multilevel models, one for each type of extreme weather event, with random intercepts across countries predicting climate policy support and controlling for socio-demographic variables and two additional interaction terms. Models include data from 65 countries. Error bars denote 95% confidence intervals. Circles denote standardized estimates. Filled circles denote significant effects at P < 0.05. Exact P values for non-significant effects of exposed population: droughts: P = 0.275; European winter storms: P = 0.466; heatwaves: P = 0.369; river floods: P = 0.278; tropical cyclones: P = 0.409. Full models for each event type can be found in Supplementary Tables 1–7.
Fig. 5
Fig. 5. Interactions between subjective attribution and exposed population to extreme weather events on climate policy support.
The lines represent varying levels of subjective attribution at −1s.d, the mean and +1 s.d., with shaded regions indicating 95% confidence intervals. The x axis shows the standardized exposed population size.

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