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{"id":71299,"date":"2024-04-25T14:15:52","date_gmt":"2024-04-25T13:15:52","guid":{"rendered":"https:\/\/www.lse.ac.uk\/granthaminstitute\/?post_type=publication&p=71299"},"modified":"2024-08-19T14:15:06","modified_gmt":"2024-08-19T13:15:06","slug":"ai-and-climate-resilience-governance","status":"publish","type":"publication","link":"https:\/\/www.lse.ac.uk\/granthaminstitute\/publication\/ai-and-climate-resilience-governance\/","title":{"rendered":"AI and climate resilience governance"},"content":{"rendered":"\n

While artificial intelligence (AI) offers promising solutions to address climate change impacts, it also raises many application limitations and challenges. A risk governance perspective is used to analyze the role of AI in supporting decision-making for climate adaptation, spanning risk assessment, policy analysis, and implementation. This comprehensive review combines expert insights and systematic literature review. The study’s findings indicate a large emphasis on applying AI to climate “risk assessments,” particularly regarding hazard and exposure assessment, but a lack of innovative approaches and tools to evaluate resilience<\/em> and vulnerability<\/em> as well as prioritization<\/em> and implementation<\/em> process, all of which involve subjective, qualitative, and context-specific elements. Additionally, the study points out challenges such as difficulty of simulating complex long-term changes, and evolving policies and human behavior, reliance on data quality and computational resources, and the need for improved interpretability of results as areas requiring further development.<\/p>\n\n\n\n

Mehryar, Sara et al. iScience, Volume 27, Issue 6, 109812<\/strong><\/p>\n\n","protected":false},"excerpt":{"rendered":"\n

The authors of this paper adopt a risk governance perspective to analyze the role of AI in supporting decision-making for climate adaptation, spanning risk assessment, policy analysis, and implementation.<\/p>\n\n","protected":false},"author":12,"featured_media":0,"template":"","tags":[1920,4427,1013,1674],"publication-type":[436],"topic_area":[4692],"class_list":["post-71299","publication","type-publication","status-publish","hentry","tag-ai","tag-climate-resilience","tag-decision-making","tag-risk-assessment","publication-type-research-articles","topic_area-adaptation-and-resilience"],"acf":{"exclude_from_sync":{"ref_value":"field_560538b0e7350","value":"0","type":false,"post_type":""},"show_translations_widget":{"ref_value":"field_6203d588c41e8","value":"0","type":false,"post_type":""},"downloads":{"ref_value":"field_52f16cc1a80f2","value":false,"type":"repeater","post_type":""},"rss_newsletter":{"ref_value":"field_54f5c2c1544d8","value":"","type":false,"post_type":""},"profile_link":{"ref_value":"field_52f164b5189e9","value":["sara-mehryar"],"type":"relationship","post_type":"profile"},"article_link":{"ref_value":"field_52eee3c7f0586","value":"https:\/\/www.cell.com\/iscience\/fulltext\/S2589-0042(24)01034-4","type":false,"post_type":""},"article_link_title":{"ref_value":"field_5322399d89d42","value":"External link to paper","type":false,"post_type":""},"spotlight":{"ref_value":"field_52f80896506d3","value":"","type":false,"post_type":""}},"yoast_head":"\nAI and climate resilience governance - 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