[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2416":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":54},2416,"Artificial Intelligence for Socio-Ecological Resilience andSustainable Resource Governance","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.256","Climate change, biodiversity loss, resource depletion, and increasingly volatile environmental conditions require governance systems that can move beyond retrospective monitoring toward anticipatory and adaptive action. Artificial intelligence (AI) offers relevant capabilities, but current environmental applications remain fragmented across sensing, prediction, optimization, and decision support. This study develops the AI Enabled SocioEcological Resilience Framework (AI SERF) through a systematic literature review and qualitative conceptual synthesis of peer reviewed studies published between January 2022 and June 2026. The reported review process screened 412 records and retained 73 studies for thematic synthesis. The revised framework links four functional pillars, namely Autonomous Eco Monitoring, Predictive Resource Optimization, Adaptive Algorithmic Governance, and Eco Resilient Feedback Loops, to absorptive, adaptive, and transformative resilience capacities. Its novelty lies not in proposing another isolated AI architecture, but in connecting data acquisition, predictive intelligence, human supervised governance, ecological intervention, and learning within a single resilienceoriented cycle. The framework is operationalized through candidate data sources, AI models, governance actors, performance indicators, and responsible AI safeguards. Particular attention is given to explainability, energy and carbon efficiency, algorithmic bias, cyberphysical security, institutional capacity, and data limitations in tropical and archipelagic settings. The study provides a theoretically grounded and implementation oriented basis for future empirical validation of AI enabled environmental governance and clarifies its contribution to SDGs 9, 11, 13, 14, and 15","气候变化、生物多样性丧失、资源枯竭以及日益不稳定的环境条件，要求治理体系能够超越回溯性监测，转向前瞻性和适应性行动。人工智能（AI）具备相关能力，但当前的环境应用在感知、预测、优化和决策支持方面仍然相互割裂。本研究通过系统文献综述和对2022年1月至2026年6月间发表的同行评审研究的定性概念综合，构建了“人工智能赋能社会生态韧性框架”（AI-SERF）。所述综述过程筛选了412条记录，并保留73项研究进行主题综合。修订后的框架将四个功能支柱，即自主生态监测、预测性资源优化、适应性算法治理和生态韧性反馈回路，与吸收性、适应性和转型性韧性能力相连接。其新颖性不在于提出另一种孤立的AI架构，而在于将数据采集、预测智能、人类监督治理、生态干预和学习连接在一个以韧性为导向的单一循环中。该框架通过候选数据源、AI模型、治理主体、绩效指标和负责任AI保障措施加以操作化。研究特别关注热带和群岛环境中的可解释性、能源与碳效率、算法偏见、网络物理安全、制度能力和数据局限性。本研究为未来对AI赋能环境治理的实证验证提供了具有理论根基且面向实施的基础，并阐明了其对可持续发展目标9、11、13、14和15的贡献。",null,"AI Innovation and Resilience for the Environment (AIR)","2026-09-11T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,12,8,1,"系统综述构建AI赋能社会生态韧性框架，方法规范、数据规模明确，对农业环境治理与智慧农业有参考价值，但偏理论框架、尚待实证验证。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","遥感监测","生态韧性","资源治理",0,"10.68012\u002Fair.v1i2.256",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":45,"card":46,"direction":52,"ingested_from":53},"W7212282045",[36,38,40,43],{"name":37,"orcid":9},"Ayu Rimanda",{"name":39,"orcid":9},"Prima Wira Nanda",{"name":41,"orcid":42},"Zakia Hary Nisa","https:\u002F\u002Forcid.org\u002F0009-0003-2625-0518",{"name":44,"orcid":9},"Lilik Susilowati","https:\u002F\u002Fjournal.sundarapublishing.com\u002Findex.php\u002Fair\u002Farticle\u002Fdownload\u002F256\u002F135",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"通过系统综述构建AI赋能社会生态韧性框架，连接监测、预测、治理与反馈循环。","系统文献综述与定性概念综合，筛选412篇文献保留73篇。","提出AI-SERF框架，整合四大支柱与三类韧性能力，强调负责任AI保障。","农业绿色发展与碳","可在热带与群岛地区实证验证该框架，并探索AI碳效率与算法公平性。","智慧农业 \u002F 农业物联网","openalex","2026-09-14T23:30:11.299701Z"]