[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1225":3,"related-1225":70},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":43,"slug":46,"view_count":20,"doi":47,"paper":48,"created_at":69},1225,"Fishery facilities construction and fishers' income: evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1910473","Introduction Increasing fishers' income is central to achieving sustainable fisheries development, while fishery facilities construction, as a form of social overhead capital, ensures the efficient operation of the entire fisheries industry chain. This study aims to examine the relationship between fishery facilities construction and fishers' income. Methods Based on panel data from 30 Chinese provinces over the period 2000–2024, this study first applies the entropy-weighted TOPSIS method to comprehensively assess the level of fishery facilities construction. Fixed-effects and mediation models are then employed to empirically examine the relationship between fishery facilities construction and fishers' income. Results The results show a positive relationship between fishery facilities construction and fishers' income, and this finding remains robust after a series of robustness and endogeneity tests. Regarding potential mediating channels, fishery scientific and technological innovation, aquatic product output, and fishery industrial agglomeration each exhibit a positive mediating relationship between fishery facilities construction and fishers' income. These three mediators also show sequential and interaction-based mediating relationships. In addition, fishery productivity and fishery disaster intensity provide evidence of alternative mediating mechanisms. Furthermore, the positive relationship between fishery facilities construction and fishers' income is more pronounced in economically developed regions, coastal regions, and regions where fishers have stronger technical capabilities. Discussion The findings of this study not only enrich the theoretical literature on fishery facilities and fishers' income but also provide important insights for the strategic decision-making of governments and fishers. Nevertheless, this study has several limitations, including the lack of sufficiently disaggregated micro-level data and the difficulty of completely eliminating potential endogeneity concerns.",null,"Frontiers in Sustainable Food Systems","2026-08-28T00:00:00Z","论文",10,false,73,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,12,3,1,"基于省级面板数据的实证研究，揭示渔业设施建设对渔民收入的积极影响及中介机制，为政策制定提供依据。",[23],{"name":9,"url":6},[25,26,27,28,29],"可持续发展","渔业设施","渔民增收","科技创新","产业集聚",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":42},[37],{"code":38,"score":20,"supported":13,"title":39,"abstract":40,"tags":41},"G4",[],[],[29],"农业相关性需要复核",[44,45],"可持续发展 产业集聚 渔业设施 渔民增收","可持续发展 产业集聚","可持续发展产业集聚渔业设施渔民增收-1225","10.3389\u002Ffsufs.2026.1910473",{"doi":47,"openalex_id":49,"authors":50,"venue":9,"cited_by_count":60,"oa_url":61,"card":62,"direction":66,"ingested_from":68},"W7204545065",[51,54,56,58],{"name":52,"orcid":53},"Haohui Li","https:\u002F\u002Forcid.org\u002F0009-0002-4047-5919",{"name":55,"orcid":8},"Sihang Li",{"name":57,"orcid":8},"Yubo Tu",{"name":59,"orcid":8},"Changbiao Zhong",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1910473\u002Fpdf",{"tldr":63,"method":64,"finding":65,"direction":66,"opportunity":67},"基于中国省级面板数据，实证检验渔业设施建设对渔民收入的正向影响及作用机制。","熵权TOPSIS评估设施水平，固定效应与中介效应模型分析。","渔业设施建设显著提升渔民收入，科技创新、产量和产业集聚为中介。","数字乡村与农业信息化","可深入探究渔业设施建设对渔民收入的空间溢出效应，或利用微观数据验证异质性影响。","openalex","2026-09-01T04:03:08.540994Z",{"total":71,"page":20,"page_size":71,"items":72},6,[73,166,252,313,365,426],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":78,"content":8,"source_name":79,"source_url":76,"published_at":80,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":81,"score_detail":82,"sources":87,"tags":89,"primary_topic":94,"secondary_topics":100,"classification":101,"search_phrases":147,"slug":150,"view_count":60,"doi":151,"paper":152,"created_at":165},5476,"Digital Transformation of Haryana Agriculture: Strengthening Climate Resilience and Advancing the Sustainable Development Goals","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23491288","Climate change has surfaced as one of the most pivotal challenges confronting the agricultural sector, especially in agrarian regions vulnerable to resource degradation, water scarcity, and climate uncertainties. Haryana, a leading agricultural state in India, has faced significant environmental pressures in recent years, including declining groundwater levels, erratic rainfall patterns, rising temperatures, and soil degradation, necessitating the adoption of climate-resilient agricultural strategies. Against this backdrop, Information and Communication Technology has gained considerable importance as a transformative mechanism for strengthening agricultural sustainability, adaptive capacity, and informed decision-making among farmers. ICT-enabled interventions such as mobile-based advisory services, precision farming technologies, digital marketplaces, weather forecasting systems, remote sensing, and various e-governance platforms have increasingly contributed to improving agricultural efficiency and resilience. The present study aims to evaluate the role of ICT-enabled agricultural practices in Haryana and their contribution to climate resilience and to achieve the Sustainable Development Goals. This study applies a descriptive and analytical approach, drawing on secondary data and narrative review methodology. The study utilizes secondary data from government reports, policy documents, research articles, institutional publications, and international development reports on digital agriculture, climate change, and sustainable development, published between 2016 and 2026. The findings indicate that ICT-based agricultural innovations have substantially enhanced access to climate information, improved resource-use efficiency, increased market connectivity, and enabled adaptive agricultural management practices. Furthermore, ICT interventions have significant potential to support SDG 2: Zero Hunger, SDG 9: Industry, Innovation and Infrastructure, and SDG 13: Climate Action. However, challenges such as digital illiteracy, infrastructural disparities, limited technological accessibility among smallholders, and inadequate institutional support continue to hinder widespread adoption. The study emphasizes the need for inclusive digital policies, strengthened rural ICT infrastructure, farmer-centric skill development, and integrated climate-smart agricultural frameworks to facilitate sustainable, resilient agricultural development in Haryana.","气候变化已成为农业部门面临的最关键挑战之一，在易受资源退化、水资源短缺和气候不确定性影响的农业区域尤为如此。哈里亚纳邦是印度领先的农业邦，近年来面临显著的环境压力，包括地下水位下降、降雨模式不稳定、气温上升和土壤退化，因而亟需采取气候韧性农业策略。在此背景下，信息与通信技术（ICT）作为增强农业可持续性、适应能力以及农民知情决策的变革性机制，其重要性日益凸显。基于ICT的干预措施，如移动端咨询服务、精准农业技术、数字市场、天气预报系统、遥感以及各类电子治理平台，已日益为提升农业效率和韧性作出贡献。本研究旨在评估哈里亚纳邦基于ICT的农业实践所发挥的作用及其对气候韧性和实现可持续发展目标的贡献。本研究采用描述性与分析性方法，依托二手数据和叙述性综述方法。研究使用的二手数据来源于2016年至2026年间发布的政府报告、政策文件、研究论文、机构出版物以及关于数字农业、气候变化和可持续发展的国际发展报告。研究结果表明，基于ICT的农业创新大幅提升了气候信息的获取能力，改善了资源利用效率，增强了市场连通性，并促成了适应性农业管理实践。此外，ICT干预措施在支持可持续发展目标2：零饥饿、目标9：产业、创新和基础设施以及目标13：气候行动方面具有显著潜力。然而，数字文盲、基础设施差距、小农户技术可及性有限以及机构支持不足等挑战仍阻碍其广泛采用。研究强调，需要制定包容性数字政策、加强农村ICT基础设施、开展以农民为中心的技能培养以及构建综合气候智慧型农业框架，以促进哈里亚纳邦可持续且具有韧性的农业发展。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z",70,{"impact":83,"substance":16,"depth":84,"authority":18,"freshness":85,"relevant":20,"comment":86},16,15,9,"基于二手数据的综述性研究，系统梳理印度哈里亚纳邦ICT农业与气候韧性、SDGs的关系，结论可靠但方法新颖性一般，对国内智慧农业与气候适应有参考价值。",[88],{"name":79,"url":76},[90,91,25,92,93],"数字农业","智慧农业","气候韧性","农业ICT",{"code":95,"slug":96,"name":97,"path":98,"parent_code":99},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":102,"locked":13,"version":33,"method":34,"evidence":103},"classified",{"candidates":104,"agricultural_evidence":106},[105,113,121,128,135,141],{"code":95,"score":16,"supported":106,"title":107,"abstract":109,"tags":112},true,[108],"climate resilience",[110,111,108],"气候变化","climate change",[92],{"code":114,"score":115,"supported":106,"title":116,"abstract":117,"tags":120},"I1",11,[],[118,90,119],"精准农业","digital agriculture",[90,91],{"code":122,"score":71,"supported":106,"title":123,"abstract":124,"tags":127},"H2",[],[125,126],"土壤","soil",[],{"code":129,"score":71,"supported":106,"title":130,"abstract":131,"tags":134},"I3",[],[132,133],"遥感","remote sensing",[],{"code":136,"score":19,"supported":106,"title":137,"abstract":138,"tags":140},"G2",[],[139],"小农户",[],{"code":142,"score":19,"supported":106,"title":143,"abstract":144,"tags":146},"H3",[],[145],"水资源",[],[148,149],"Haryana 数字化转型","ICT 气候韧性","Haryana数字化转型-5476","10.5281\u002Fzenodo.23491288",{"doi":151,"openalex_id":153,"authors":154,"venue":79,"cited_by_count":60,"oa_url":76,"card":159,"direction":164,"ingested_from":68},"W7223011965",[155,157],{"name":156,"orcid":8},"Himani Rathi",{"name":158,"orcid":8},"Pardeep Kumar Duhan",{"tldr":160,"method":161,"finding":162,"direction":66,"opportunity":163},"评估印度哈里亚纳邦ICT农业实践对气候韧性与可持续发展目标的贡献。","描述性与分析性方法，基于2016-2026年政府报告等二手数据与叙事综述。","ICT显著提升气候信息获取、资源效率与市场连接，助力SDG 2\u002F9\u002F13，但数字素养与基础设施不足制","可研究小农户数字素养与ICT采纳门槛的实证机制，及包容性数字政策在气候智慧农业中的落地路径。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:08.814085Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":8,"source_name":79,"source_url":169,"published_at":172,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":81,"score_detail":173,"sources":175,"tags":177,"primary_topic":180,"secondary_topics":186,"classification":188,"search_phrases":235,"slug":238,"view_count":20,"doi":239,"paper":240,"created_at":251},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","2026-10-15T00:00:00Z",{"impact":16,"substance":83,"depth":84,"authority":18,"freshness":85,"relevant":20,"comment":174},"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[176],{"name":79,"url":169},[91,178,25,110,179],"农业人工智能","遥感监测",{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[187],{"code":95,"slug":96,"name":97,"path":98,"parent_code":99},{"status":102,"locked":13,"version":33,"method":34,"evidence":189},{"candidates":190,"agricultural_evidence":106},[191,200,204,210,214,221,228],{"code":181,"score":16,"supported":106,"title":192,"abstract":194,"tags":199},[193],"artificial intelligence",[195,196,197,198,193],"深度学习","机器学习","machine learning","deep learning",[178],{"code":95,"score":84,"supported":106,"title":201,"abstract":202,"tags":203},[111],[110,111],[110],{"code":114,"score":205,"supported":106,"title":206,"abstract":207,"tags":209},7,[],[118,208],"precision agriculture",[91],{"code":129,"score":205,"supported":106,"title":211,"abstract":212,"tags":213},[],[132,133],[179],{"code":215,"score":71,"supported":106,"title":216,"abstract":217,"tags":220},"H4",[],[218,219],"碳排放","carbon emission",[],{"code":222,"score":71,"supported":106,"title":223,"abstract":224,"tags":227},"H6",[],[225,226],"生物多样性","biodiversity",[],{"code":229,"score":71,"supported":106,"title":230,"abstract":231,"tags":234},"I5",[],[232,233],"物联网","internet of things",[],[236,237],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":239,"openalex_id":241,"authors":242,"venue":79,"cited_by_count":60,"oa_url":169,"card":245,"direction":164,"ingested_from":68},"W7222092855",[243],{"name":244,"orcid":8},"Deepmala Suryavanshi",{"tldr":246,"method":247,"finding":248,"direction":249,"opportunity":250},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":8,"content":8,"source_name":257,"source_url":8,"published_at":172,"category":11,"cover_url":8,"hotness":258,"is_selected":13,"score":60,"score_detail":259,"sources":261,"tags":265,"primary_topic":268,"secondary_topics":269,"classification":270,"search_phrases":292,"slug":295,"view_count":20,"doi":296,"paper":297,"created_at":312},5065,"Artificial intelligence policy, digital industry agglomeration, and industrial land intensive use: evidence from a quasi-natural experiment in China","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1858439\u002Ffull","Ning Wang、Yunpeng Fu、Zhihong Liu(辽宁大学经济学院)在《Frontiers in Sustainable Food Systems》2026-10-15发表的论文,利用2010-2023年271个中国地级及以上城市面板数据,以国家新一代人工智能创新发展试验区设立为准自然实验,考察人工智能政策、数字产业集聚与工业用地集约利用之间的因果关系。研究将数字产业集聚(集聚指数、产业链关联)作为中介或调节变量,采用双重差分等方法识别政策效应;研究背景聚焦城市工业用地的集约利用,属'AI+产业用地'方向,与农业信息化主题相关性较弱,但其方法与'数字乡村政策→农业产业链\u002F创新链'评估路径具有可比性。","Frontiers in Sustainable Food Systems 10 2026-10-15",25,{"impact":60,"substance":60,"depth":60,"authority":60,"freshness":60,"relevant":60,"comment":260},"研究主题为AI政策与工业用地集约利用，与三农及农业信息化相关性弱，不建议进入每日精选。",[262,263],{"name":257,"url":255},{"name":9,"url":264},"https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1858439",[266,178,29,267],"数字乡村","准自然实验",{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},[],{"status":102,"locked":13,"version":33,"method":34,"evidence":271},{"candidates":272,"agricultural_evidence":106},[273,277,283,288],{"code":181,"score":85,"supported":106,"title":274,"abstract":275,"tags":276},[193],[],[178],{"code":278,"score":279,"supported":106,"title":280,"abstract":281,"tags":282},"I6",4,[],[266],[266],{"code":114,"score":19,"supported":106,"title":284,"abstract":285,"tags":287},[],[286],"农业信息化",[],{"code":38,"score":20,"supported":13,"title":289,"abstract":290,"tags":291},[],[],[29],[293,294],"人工智能 试验区 工业用地","数字产业集聚 城市面板数据","人工智能试验区工业用地-5065","10.3389\u002Ffsufs.2026.1858439\u002Ffull",{"doi":296,"openalex_id":298,"authors":299,"venue":9,"cited_by_count":60,"oa_url":306,"card":307,"direction":66,"ingested_from":68},"W7222362134",[300,302,304],{"name":301,"orcid":8},"Ning Wang",{"name":303,"orcid":8},"Yunpeng Fu",{"name":305,"orcid":8},"Zhihong Liu","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1858439\u002Fpdf",{"tldr":308,"method":309,"finding":310,"direction":66,"opportunity":311},"以AI创新试验区为准自然实验，检验AI政策通过数字产业集聚促进工业用地集约利用。","2010-2023年271个城市面板数据，双重差分法，数字产业集聚作中介。","AI政策显著提升工业用地集约利用，数字产业集聚是关键传导机制。","可将该准自然实验框架迁移至数字乡村试点，评估政策对农业产业链与用地效率的影响。","2026-10-16T00:14:01.435697Z",{"id":314,"title":315,"url":316,"summary":317,"summary_zh":8,"content":318,"source_name":319,"source_url":8,"published_at":320,"category":321,"cover_url":8,"hotness":12,"is_selected":13,"score":322,"score_detail":323,"sources":328,"tags":330,"primary_topic":335,"secondary_topics":341,"classification":342,"search_phrases":360,"slug":363,"view_count":19,"doi":8,"paper":8,"created_at":364},5053,"四川农科院蒲宗君副院长出席2026全国稻米精深加工产业技术创新发展大会 \"川优1570\"荣获产品创新奖","https:\u002F\u002Fwww.chinawestagr.com\u002Ftszl\u002Fshowts.asp?id=67443","10月11日,2026全国稻米精深加工产业技术创新发展大会在成都龙泉驿区举行,中国科学院谢华安、中国工程院孙宝国、罗锡文、陈温福、胡培松、金征宇等院士出席。蒲宗君指出,川米产业\"大而不强\"的短板在加工,要贯通稻种到米制品全产业链。院作物所选育的早熟优质米粉专用水稻品种\"川优1570\"荣获产品创新奖,体现四川水稻加工产业链协同升级。","λԺ                ʱ䣺2026\u002F10\u002F12405\n\nΪƶҹ׾ӹҵת20261011գɹҵ׾ӹҵս2026ȫ׾ӹҵ·չڳɶȪСйѧԺԺʿлйԺԺʿﱦġ¸ɡȳϯ顣ԺίίԱԺھϯ鲢\n\nھָײҵ“ǿ”Ķ̰ڼӹҪڵ׼ӹƫdzͷҵȱƷƫ͵ȷ棬ƾֹؼרõƷֵӦãҪϵͳ˼άֵͨƷȫҵ‘ͷʳβ’‘ũͷβ’¡\n\n“ȵǿ Ƽ ȫ”Ϊ⡣йԺ¸ԺʿΪˮĹȥδı棬йԺԺʿΪ̾ӽı棬ҵ³ΰΪˮʵþ׼ЧּзӦõı档\n\nϣ䷢˵׾ӹļ½Ʒ½ĽԺѡ׷רˮƷ“1570”ٻƷ½","四川省农业科学院 合作处 2026年10月11日","2026-10-11T00:00:00Z","报道",60,{"impact":16,"substance":324,"depth":12,"authority":325,"freshness":326,"relevant":20,"comment":327},14,13,5,"全国性稻米精深加工行业会议，川优1570获产品创新奖，属细分行业进展，有实质信息但正文残缺、时效偏旧。",[329],{"name":319,"url":316},[331,332,28,333,334],"种业振兴","农产品加工","川优1570","稻米加工",{"code":336,"slug":337,"name":338,"path":339,"parent_code":340},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":102,"locked":13,"version":33,"method":34,"evidence":343},{"candidates":344,"agricultural_evidence":106},[345,350,355],{"code":336,"score":324,"supported":106,"title":346,"abstract":348,"tags":349},[347],"稻米",[338,347],[],{"code":351,"score":20,"supported":13,"title":352,"abstract":353,"tags":354},"E1",[],[],[332],{"code":356,"score":20,"supported":13,"title":357,"abstract":358,"tags":359},"F1",[],[],[331],[361,362],"四川省农科院 川优1570","稻米精深加工 产业技术创新","四川省农科院川优1570-5053","2026-10-16T00:13:12.190835Z",{"id":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":8,"source_name":371,"source_url":368,"published_at":372,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":373,"score_detail":374,"sources":379,"tags":381,"primary_topic":385,"secondary_topics":388,"classification":389,"search_phrases":400,"slug":403,"view_count":20,"doi":404,"paper":405,"created_at":425},4430,"Spatio temporal land use and land cover changes in a peri urban protected area of Kamrup Metropolitan District, Assam, India using geospatial techniques","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00134-4","Protection from human- induced pressures is vital to the survival of peri-urban protected areas, particularly in fast-growing urban areas. This research focuses on land use and land cover change in the Amchang Wildlife Sanctuary located in peri-urban region of Guwahati city in Kamrup Metropolitan district, Assam, India over 30 years period (1995–2025). Multi-temporal Landsat imagery was classified using Random Forest analysis with the help of NDVI, NDWI, NDBI, and delineated seven LULC categories dense forest, moderately dense forest, open forest, agriculture, built-up, barren land, and water bodies, with an overall Accuracy 81.57% and Kappa coefficient of 0.78. Landscape metrics such as Shannon’s Diversity Index, Patch Count, Edge Length were calculated and integrated with the Thiel-Sen’s slope calculation and the Mann–Kendall to determine fragmentation and longer-term trend analysis. The results showed a Net change in Dense Forest decreased by 51.7% (− 4.28 sq km), and Water Bodies by 73.5% (− 0.19 sq km), area increased in Built Up by 37.6% (+ 2.95 sq km), Agriculture by + 6.8% (+ 0.50 sq km) and Barren land by 7.3% (+ 0.15 sq km), as well as a type of secondary succession within Moderately Dense Forest by 35.5% (+ 6.11 sq km). This study is distinctive by the integration of Random forest classification, landscape fragmentation metrics and non-parametric trend analysis to weigh long term ecosystem vulnerability within peri-urban protected areas. Additionally, by connecting ecological monitoring and Sustainable Development Goals (i.e., 11, 13, 15) it will provide an established methodology for conservation planning\u002Frestoration strategies and sustainable expansion of urban areas in biodiversity hotspots.","保护免受人为压力对于城郊保护区的存续至关重要，在快速扩张的城市区域尤为如此。本研究聚焦印度阿萨姆邦卡姆鲁普大都会区古瓦哈提市城郊地区阿姆昌野生动物保护区30年期间（1995—2025年）的土地利用与土地覆盖变化。研究利用多时相Landsat影像，借助NDVI、NDWI、NDBI进行随机森林（Random Forest）分类，划分出7类土地利用与土地覆盖类型：密林、中度密林、疏林、农业用地、建设用地、裸地和水体，总体精度为81.57%，Kappa系数为0.78。研究计算了香农多样性指数、斑块数量、边缘长度等景观指标，并将其与Thiel-Sen斜率估计和Mann-Kendall检验相结合，以确定破碎化程度并开展长期趋势分析。结果表明，密林净减少51.7%（−4.28平方公里），水体净减少73.5%（−0.19平方公里）；建设用地增加37.6%（+2.95平方公里），农业用地增加6.8%（+0.50平方公里），裸地增加7.3%（+0.15平方公里）；中度密林内部出现一种次生演替，增加35.5%（+6.11平方公里）。本研究的独特性在于整合随机森林分类、景观破碎化指标和非参数趋势分析，以评估城郊保护区的长期生态系统脆弱性。此外，通过将生态监测与可持续发展目标（即目标11、13、15）相联系，本研究将为生物多样性热点地区的保护规划\u002F恢复策略及城市可持续扩张提供一套成熟的方法论。","Discover Forests","2026-10-06T00:00:00Z",65,{"impact":375,"substance":376,"depth":377,"authority":18,"freshness":375,"relevant":20,"comment":378},8,20,17,"基于Landsat与随机森林的30年城郊保护区土地利用变化研究，方法整合与数据规模扎实，但属区域性案例，公共影响有限。",[380],{"name":371,"url":368},[25,179,382,383,384],"土地利用","野生动物保护","生态脆弱性",{"code":222,"slug":226,"name":386,"path":387,"parent_code":99},"生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",[],{"status":102,"locked":13,"version":33,"method":34,"evidence":390},{"candidates":391,"agricultural_evidence":106},[392,396],{"code":222,"score":375,"supported":106,"title":393,"abstract":394,"tags":395},[],[225,226],[382,383],{"code":129,"score":20,"supported":13,"title":397,"abstract":398,"tags":399},[],[],[179],[401,402],"Amchang Wildlife Sanctuary 土地利用","Assam 保护区 遥感","AmchangWildlifeSanctuary土地利用-4430","10.1007\u002Fs44415-026-00134-4",{"doi":404,"openalex_id":406,"authors":407,"venue":371,"cited_by_count":60,"oa_url":418,"card":419,"direction":423,"ingested_from":68},"W7220738321",[408,410,413,416],{"name":409,"orcid":8},"Trishna Changkakoti",{"name":411,"orcid":412},"Parimita Saikia","https:\u002F\u002Forcid.org\u002F0000-0001-9464-3450",{"name":414,"orcid":415},"Palme Borthakur","https:\u002F\u002Forcid.org\u002F0000-0002-0679-2040",{"name":417,"orcid":8},"Monshree Roy","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44415-026-00134-4.pdf",{"tldr":420,"method":421,"finding":422,"direction":423,"opportunity":424},"用遥感与景观指标分析印度阿萨姆城郊保护区30年土地利用变化与破碎化。","Landsat多时相影像、随机森林分类、NDVI\u002FNDWI\u002FNDBI、景观指标与","密林减少51.7%、水体减少73.5%，建设用地增37.6%，保护区破碎化加剧。","农业遥感与作物表型","可将该遥感-景观-趋势集成框架迁移到中国城郊保护区的耕地撂荒与生态脆弱性监测。","2026-10-08T23:30:26.862434Z",{"id":427,"title":428,"url":429,"summary":430,"summary_zh":431,"content":8,"source_name":432,"source_url":429,"published_at":433,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":434,"score_detail":435,"sources":437,"tags":439,"primary_topic":440,"secondary_topics":441,"classification":442,"search_phrases":474,"slug":477,"view_count":478,"doi":479,"paper":480,"created_at":501},4211,"An integrated scientometric and BERTopic-based analysis on examining the role of artificial intelligence in achieving Sustainable Development Goals (SDGs)","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04841-y","This study investigates the role of Artificial Intelligence (AI) in achieving the United Nations Sustainable Development Goals (SDGs) through a comprehensive scientometric analysis. It explores the evolution of scholarly research, identifies leading contributors, maps thematic trends, examines how AI technologies contribute to advancing sustainability across multiple domains, and identifies latent semantic themes. A total of 3262 unique scholarly records published between 1989 and 2025, after deduplication and screening, were finalized from the Scopus and Web of Science databases. The dataset was analysed using Biblioshiny (R-based Bibliometrix) and VOSviewer to examine publication trends, citation impact, collaborative networks, thematic evolution, and conceptual structures. Indicators such as Relative Growth Rate (RGR), Doubling Time (DT), h-index, g-index, and Lotka’s Law were applied to evaluate research productivity and scholarly influence. In addition, BERTopic-based abstract-level topic modelling was applied to identify latent semantic themes, SDG-linked thematic domains, temporal evolution and potential research gaps. Results reveal a significant growth in AI-SDG research, particularly after 2015, with an accelerated increase post-2020. India, USA, and China emerged as the most productive contributors, while countries like USA, India, United Kingdom and Australia lead in research impact and citation quality. Thematic evolution analysis reveals a transition from foundational themes such as AI, machine learning, climate change, and decision support systems toward more integrated and sustainability-oriented research themes, including sustainable development and SDGs. Thematic linkages further demonstrate the persistent foundational role of AI and machine learning, alongside the growing centrality of climate change and sustainable development, reflecting the increasing convergence of AI research with global sustainability priorities. BERTopic analysis further revealed eleven interpretable abstract-level thematic domains after excluding one heterogeneous outlier category. The dominant themes included digital transformation and green innovation, AI-enabled education, digital health, smart agriculture, renewable energy systems, clean water and sanitation, remote sensing, NLP-based decision support, sustainable construction, tourism, and sustainable chemistry\u002Fpharmaceutical applications. This study maps the AI–SDG research landscape by integrating bibliometric indicators with thematic evolution, conceptual analysis, and abstract-level semantic topic modelling. The findings provide insights into the development and thematic orientation of AI-related research in the context of sustainable development. The use of BERTopic complements conventional bibliometric approaches by examining semantic patterns within publication abstracts alongside keyword and citation-based analyses.","本研究通过一项全面的科学计量分析，探讨了人工智能（Artificial Intelligence, AI）在实现联合国可持续发展目标（Sustainable Development Goals, SDGs）中的作用。研究考察了学术研究的演进历程，识别了主要贡献者，绘制了主题趋势图谱，分析了AI技术如何在多个领域推动可持续发展，并识别了潜在语义主题。经去重和筛选后，最终从Scopus和Web of Science数据库中确定了1989年至2025年间发表的3262条独特学术记录。使用Biblioshiny（基于R的Bibliometrix）和VOSviewer对数据集进行分析，以考察出版趋势、引用影响、合作网络、主题演进和概念结构。采用相对增长率（Relative Growth Rate, RGR）、倍增时间（Doubling Time, DT）、h指数、g指数和洛特卡定律（Lotka's Law）等指标评估研究生产力和学术影响力。此外，应用基于BERTopic的摘要级主题建模，以识别潜在语义主题、与SDG相关的主题领域、时间演变和潜在研究空白。结果显示，AI-SDG研究显著增长，尤其在2015年之后，2020年后增长加速。印度、美国和中国是最多产的贡献者，而美国、印度、英国和澳大利亚等国家在研究影响力和引用质量方面领先。主题演进分析揭示了从AI、机器学习、气候变化和决策支持系统等基础主题向更综合、面向可持续发展的研究主题（包括可持续发展和SDGs）的转变。主题关联进一步表明AI和机器学习的持续基础性作用，同时气候变化和可持续发展的中心性日益增强，反映了AI研究与全球可持续发展优先事项日益融合。BERTopic分析在排除一个异质性离群类别后，进一步揭示了十一个可解释的摘要级主题领域。主导主题包括数字化转型与绿色创新、AI赋能教育、数字健康、智慧农业、可再生能源系统、清洁水和卫生设施、遥感、基于自然语言处理的决策支持、可持续建筑、旅游业以及可持续化学\u002F制药应用。本研究通过整合文献计量","Discover Sustainability","2026-10-04T00:00:00Z",80,{"impact":16,"substance":17,"depth":16,"authority":325,"freshness":85,"relevant":20,"comment":436},"该研究以3262篇文献的计量与BERTopic主题建模系统梳理AI支撑SDGs的研究格局，其中智慧农业、遥感、数字健康等主题与农业信息化高度相关，方法新颖、数据规模大，值得进入每日精选。",[438],{"name":432,"url":429},[266,91,178,25,132],{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},[],{"status":102,"locked":13,"version":33,"method":34,"evidence":443},{"candidates":444,"agricultural_evidence":106},[445,450,455,459,463,470],{"code":181,"score":16,"supported":106,"title":446,"abstract":447,"tags":449},[193],[196,197,193,448],"decision support",[178],{"code":114,"score":205,"supported":106,"title":451,"abstract":452,"tags":454},[],[91,453],"smart agriculture",[91],{"code":129,"score":205,"supported":106,"title":456,"abstract":457,"tags":458},[],[132,133],[132],{"code":95,"score":71,"supported":106,"title":460,"abstract":461,"tags":462},[],[110,111],[],{"code":464,"score":71,"supported":106,"title":465,"abstract":466,"tags":469},"I7",[],[467,468],"文献计量","bibliometric",[],{"code":278,"score":20,"supported":13,"title":471,"abstract":472,"tags":473},[],[],[266],[475,476],"AI SDG 文献计量","BERTopic 可持续农业","AISDG文献计量-4211",2,"10.1007\u002Fs43621-026-04841-y",{"doi":479,"openalex_id":481,"authors":482,"venue":432,"cited_by_count":60,"oa_url":429,"card":495,"direction":164,"ingested_from":68},"W7219683711",[483,486,489,492],{"name":484,"orcid":485},"Reetu Verma","https:\u002F\u002Forcid.org\u002F0000-0002-0567-7219",{"name":487,"orcid":488},"Ashok Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-3279-2895",{"name":490,"orcid":491},"Kavita Pabreja","https:\u002F\u002Forcid.org\u002F0000-0001-9856-0900",{"name":493,"orcid":494},"Saurabh Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-5924-6425",{"tldr":496,"method":497,"finding":498,"direction":499,"opportunity":500},"用科学计量与BERTopic分析3262篇文献，揭示AI助力可持续发展目标的研究格局与主题演化。","Scopus\u002FWoS文献，Biblioshiny、VOSviewer计量分析加B","AI-SDG研究2015年后激增，主题从基础AI转向可持续发展，识别出智慧农业等11个主题域。","农业人工智能与决策模型","可聚焦智慧农业主题，探究AI在农业SDG中的具体贡献路径与区域差异，填补细粒度实证空白。","2026-10-05T23:30:09.006483Z"]