[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3778":3,"related-3778":82},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":53,"slug":56,"view_count":57,"doi":58,"paper":59,"created_at":81},3778,"Assessing the Sustainability Performance of Fishing Harbors Under Uncertainty: A Gaussian‐Neutrosophic Framework","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsd.71713","ABSTRACT Fishing harbors are increasingly required to meet sustainability expectations associated with the environmental, social, and economic development goals. Accordingly, assessing their sustainability performance has become an important concern for both policymakers, harbor operating organizations, and harbor managers. This study develops a decision support system (DSS) within a neutrosophic environment to evaluate the sustainability performance of small‐scale fishing harbors. For this purpose, a novel SVN‐Gaussian‐Entropy‐MABAC hybrid method is developed. Expert contribution levels are determined using SVNSs, sustainability performance criteria are weighted through a neutrosophic entropy method, and sustainability performance rankings are obtained using a neutrosophic MABAC approach. In addition, two new aggregation operators, namely Single‐Valued Neutrosophic Gaussian Weighted Averaging (SVNGWA) and Single‐Valued Neutrosophic Gaussian Weighted Geometric (SVNGWG) operators, are introduced. The proposed framework is demonstrated through a real‐world case study conducted in Bursa Province, Türkiye. The results indicate that waste management and recycling capacity is the most important sustainability criterion, while Arnavutköy Fishing Harbor achieved the highest sustainability performance score. Comparative robustness analyses using conventional aggregation operators and alternative neutrosophic ranking methods supported the stability of the proposed framework. Finally, the study provides methodological, theoretical, and practical implications for sustainability assessment, neutrosophic decision‐making, and fisheries infrastructure management.","摘要 渔港日益需要满足与环境、社会和经济发展目标相关的可持续性期望。因此，评估其可持续性表现已成为政策制定者、港口运营组织和港口管理者共同关注的重要问题。本研究在中智性（neutrosophic）环境下开发了一个决策支持系统（DSS），用于评估小型渔港的可持续性表现。为此，提出了一种新颖的SVN-高斯-熵-MABAC混合方法。专家贡献水平通过单值中智集（SVNSs）确定，可持续性表现标准通过中智熵方法赋权，可持续性表现排名通过中智MABAC方法获得。此外，引入了两种新的聚合算子，即单值中智高斯加权平均（SVNGWA）算子和单值中智高斯加权几何（SVNGWG）算子。所提出的框架通过一项在土耳其布尔萨省开展的真实案例研究得到了验证。结果表明，废物管理和回收能力是最重要的可持续性标准，而Arnavutköy渔港获得了最高的可持续性表现得分。使用传统聚合算子和替代中智排名方法进行的比较稳健性分析支持了所提出框架的稳定性。最后，本研究为可持续性评估、中智决策和渔业基础设施管理提供了方法学、理论和实践启示。",null,"Sustainable Development","2026-09-27T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,13,9,1,"提出中智模糊决策框架评估小型渔港可持续性，方法新颖且有土耳其实证案例，但属细分领域方法学研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30],"渔业港口","可持续渔业","模糊决策","渔业基础设施",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":52},[38,46],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"D",3,true,[],[44],"渔业",[],{"code":47,"score":40,"supported":41,"title":48,"abstract":49,"tags":51},"I2",[],[50],"decision support",[],"农业相关性需要复核",[54,55],"Bursa 渔港 可持续性","SVN-Gaussian-Entropy-MABAC 渔港","Bursa渔港可持续性-3778",2,"10.1002\u002Fsd.71713",{"doi":58,"openalex_id":60,"authors":61,"venue":10,"cited_by_count":71,"oa_url":72,"card":73,"direction":79,"ingested_from":80},"W7214549291",[62,65,68],{"name":63,"orcid":64},"Yakup As","https:\u002F\u002Forcid.org\u002F0009-0006-4844-0357",{"name":66,"orcid":67},"Karahan Kara","https:\u002F\u002Forcid.org\u002F0000-0002-1359-0244",{"name":69,"orcid":70},"Sait Gül","https:\u002F\u002Forcid.org\u002F0000-0002-6011-0848",0,"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsd.71713",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"提出SVN-Gaussian-Entropy-MABAC混合决策系统，评估小型渔港可持续性表现。","单值中智集、高斯熵权法、MABAC排序及两种新聚合算子，土耳其布尔萨案例。","废物管理与回收能力是最重要准则，Arnavutköy渔港可持续性得分最高。","其他","可将该不确定性决策框架迁移至农业基础设施（如灌区、粮库）的可持续性评估与选址优化。","智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:12.029053Z",{"total":71,"page":22,"page_size":22,"items":83},[]]