Enhancing system resilience to climate change through artificial intelligence: a systematic literature review
The growing urgency of climate change necessitates innovative strategies to enhance system resilience across many sectors. Artificial Intelligence (AI) emerges as a transformative tool in this regard, yet existing research remains fragmented across sectors and regions. We conducted a systematic lite...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Climate |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fclim.2025.1585331/full |
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| author | Rym Ayadi Rym Ayadi Yeganeh Forouheshfar Omid Moghadas |
| author_facet | Rym Ayadi Rym Ayadi Yeganeh Forouheshfar Omid Moghadas |
| author_sort | Rym Ayadi |
| collection | DOAJ |
| description | The growing urgency of climate change necessitates innovative strategies to enhance system resilience across many sectors. Artificial Intelligence (AI) emerges as a transformative tool in this regard, yet existing research remains fragmented across sectors and regions. We conducted a systematic literature review of 385 peer-reviewed articles published between 2000 and early 2025, following the PRISMA protocol. The analysis classifies AI applications across nine key sectors and evaluates their relevance to adaptation, mitigation, or both. AI methodologies and regional distribution were also assessed. The findings show a dominant focus on adaptation (64.4%), with only 16% of studies addressing mitigation, and 19.4% engaging both. Classical Machine Learning techniques are the most used (51.4%), followed by deep learning models (22.3%). Regional disparities are evident: Asia and global-scale studies account for two-thirds of the literature, while Africa and South America are underrepresented. Sectorally, agriculture and urban infrastructure receive the most attention. Despite the promise of AI, major challenges persist in data access, model transparency, and equitable deployment, particularly in vulnerable regions. This review distinguishes itself by offering a comprehensive, cross-sectoral synthesis and emphasizing system-level resilience. It highlights the need for regionally tailored AI solutions, interdisciplinary collaboration, and ethical frameworks to ensure AI contributes meaningfully to global climate resilience efforts. |
| format | Article |
| id | doaj-art-94bb36eefaa44f0fbdc87700f8506d3e |
| institution | DOAJ |
| issn | 2624-9553 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Climate |
| spelling | doaj-art-94bb36eefaa44f0fbdc87700f8506d3e2025-08-20T03:07:06ZengFrontiers Media S.A.Frontiers in Climate2624-95532025-08-01710.3389/fclim.2025.15853311585331Enhancing system resilience to climate change through artificial intelligence: a systematic literature reviewRym Ayadi0Rym Ayadi1Yeganeh Forouheshfar2Omid Moghadas3Bayes Business School, City University London, London, United KingdomEuro-Mediterranean Economists Association (EMEA), Barcelona, SpainEuro-Mediterranean Economists Association (EMEA), Barcelona, SpainUniversité de Reims Champagne-Ardenne, CRIEG, REGARDS, Reims, FranceThe growing urgency of climate change necessitates innovative strategies to enhance system resilience across many sectors. Artificial Intelligence (AI) emerges as a transformative tool in this regard, yet existing research remains fragmented across sectors and regions. We conducted a systematic literature review of 385 peer-reviewed articles published between 2000 and early 2025, following the PRISMA protocol. The analysis classifies AI applications across nine key sectors and evaluates their relevance to adaptation, mitigation, or both. AI methodologies and regional distribution were also assessed. The findings show a dominant focus on adaptation (64.4%), with only 16% of studies addressing mitigation, and 19.4% engaging both. Classical Machine Learning techniques are the most used (51.4%), followed by deep learning models (22.3%). Regional disparities are evident: Asia and global-scale studies account for two-thirds of the literature, while Africa and South America are underrepresented. Sectorally, agriculture and urban infrastructure receive the most attention. Despite the promise of AI, major challenges persist in data access, model transparency, and equitable deployment, particularly in vulnerable regions. This review distinguishes itself by offering a comprehensive, cross-sectoral synthesis and emphasizing system-level resilience. It highlights the need for regionally tailored AI solutions, interdisciplinary collaboration, and ethical frameworks to ensure AI contributes meaningfully to global climate resilience efforts.https://www.frontiersin.org/articles/10.3389/fclim.2025.1585331/fullartificial intelligence (AI)system resilienceclimate changegreen transitionsustainable developmentclimate adaptation |
| spellingShingle | Rym Ayadi Rym Ayadi Yeganeh Forouheshfar Omid Moghadas Enhancing system resilience to climate change through artificial intelligence: a systematic literature review Frontiers in Climate artificial intelligence (AI) system resilience climate change green transition sustainable development climate adaptation |
| title | Enhancing system resilience to climate change through artificial intelligence: a systematic literature review |
| title_full | Enhancing system resilience to climate change through artificial intelligence: a systematic literature review |
| title_fullStr | Enhancing system resilience to climate change through artificial intelligence: a systematic literature review |
| title_full_unstemmed | Enhancing system resilience to climate change through artificial intelligence: a systematic literature review |
| title_short | Enhancing system resilience to climate change through artificial intelligence: a systematic literature review |
| title_sort | enhancing system resilience to climate change through artificial intelligence a systematic literature review |
| topic | artificial intelligence (AI) system resilience climate change green transition sustainable development climate adaptation |
| url | https://www.frontiersin.org/articles/10.3389/fclim.2025.1585331/full |
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