Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors
Abstract While numerous studies have examined factors associated with climate-friendly beliefs and behaviors, a systematic, cross-national ranking of their key correlates is lacking. We use interpretable machine learning to quantify the extent to which different climate-relevant outcomes (climate ch...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Climate Action |
| Online Access: | https://doi.org/10.1038/s44168-025-00251-4 |
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| author | Boryana Todorova David Steyrl Matthew J. Hornsey Samuel Pearson Cameron Brick Florian Lange Jay J. Van Bavel Madalina Vlasceanu Claus Lamm Kimberly C. Doell |
| author_facet | Boryana Todorova David Steyrl Matthew J. Hornsey Samuel Pearson Cameron Brick Florian Lange Jay J. Van Bavel Madalina Vlasceanu Claus Lamm Kimberly C. Doell |
| author_sort | Boryana Todorova |
| collection | DOAJ |
| description | Abstract While numerous studies have examined factors associated with climate-friendly beliefs and behaviors, a systematic, cross-national ranking of their key correlates is lacking. We use interpretable machine learning to quantify the extent to which different climate-relevant outcomes (climate change belief, policy support, willingness to share information on social media, and a pro-environmental behavioral task) are predictable and to rank 19 individual- and nation-level predictors in terms of their importance across 55 countries (N = 4635). We find notable differences in explained variance for the outcomes (e.g., 57% for climate change belief vs. 10% for pro-environmental behavior). Four predictors had consistent effects across all outcomes: environmentalist identity, trust in climate science, internal environmental motivation, and the Human Development Index. However, most of the predictors show divergent patterns, predicting some but not all outcomes or even having opposite effects. To better capture this complexity, future models should include multi-level factors and consider the different contexts (e.g., public vs private) in which climate-related cognition and action emerge. |
| format | Article |
| id | doaj-art-aeb097ec58094724bb406053c0bd9693 |
| institution | DOAJ |
| issn | 2731-9814 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate Action |
| spelling | doaj-art-aeb097ec58094724bb406053c0bd96932025-08-20T03:09:21ZengNature Portfolionpj Climate Action2731-98142025-05-014111210.1038/s44168-025-00251-4Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviorsBoryana Todorova0David Steyrl1Matthew J. Hornsey2Samuel Pearson3Cameron Brick4Florian Lange5Jay J. Van Bavel6Madalina Vlasceanu7Claus Lamm8Kimberly C. Doell9Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaBusiness School, University of QueenslandBusiness School, University of QueenslandDepartment of Psychology, University of AmsterdamBehavioral Economics and Engineering Group, KU LeuvenDepartment of Psychology, New York University; New York UniversityDepartment of Environmental Social Sciences, Stanford Doerr School of SustainabilityDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of ViennaAbstract While numerous studies have examined factors associated with climate-friendly beliefs and behaviors, a systematic, cross-national ranking of their key correlates is lacking. We use interpretable machine learning to quantify the extent to which different climate-relevant outcomes (climate change belief, policy support, willingness to share information on social media, and a pro-environmental behavioral task) are predictable and to rank 19 individual- and nation-level predictors in terms of their importance across 55 countries (N = 4635). We find notable differences in explained variance for the outcomes (e.g., 57% for climate change belief vs. 10% for pro-environmental behavior). Four predictors had consistent effects across all outcomes: environmentalist identity, trust in climate science, internal environmental motivation, and the Human Development Index. However, most of the predictors show divergent patterns, predicting some but not all outcomes or even having opposite effects. To better capture this complexity, future models should include multi-level factors and consider the different contexts (e.g., public vs private) in which climate-related cognition and action emerge.https://doi.org/10.1038/s44168-025-00251-4 |
| spellingShingle | Boryana Todorova David Steyrl Matthew J. Hornsey Samuel Pearson Cameron Brick Florian Lange Jay J. Van Bavel Madalina Vlasceanu Claus Lamm Kimberly C. Doell Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors npj Climate Action |
| title | Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors |
| title_full | Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors |
| title_fullStr | Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors |
| title_full_unstemmed | Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors |
| title_short | Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors |
| title_sort | machine learning identifies key individual and nation level factors predicting climate relevant beliefs and behaviors |
| url | https://doi.org/10.1038/s44168-025-00251-4 |
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