[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4103":3,"related-4103":92},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":91},4103,"Blue-Green-Grey Infrastructure for Urban Carbon Reduction and Heat Resilience: A Review of Mechanisms, Methods, and Assessment Challenges","https:\u002F\u002Fdoi.org\u002F10.66033\u002Fjapr2026-305","Urban blue-green-grey infrastructure (BGGI) is increasingly discussed as a strategy for reducing carbon emissions and improving heat resilience, but evidence on its mechanisms and assessment methods remains fragmented. This review synthesizes BGGI studies retrieved from Web of Science and ScienceDirect, focusing on carbon reduction pathways, analytical approaches and unresolved assessment challenges. The review distinguishes direct carbon storage and sequestration in blue-green components from operational emission reduction linked to microclimate regulation and changes in building energy demand. It also considers the life-cycle carbon effects of grey infrastructure design and retrofitting. Existing studies use spatial analysis and remote sensing for observation, model simulation for scenario assessment, whereas machine learning supports prediction and model-based interpretation. Life-cycle assessment (LCA), life-cycle costing (LCC), and cost-benefit analysis (CBA) are used for environmental and economic evaluation. However, most assessments remain limited by fragmented treatment of infrastructure elements, insufficient integration of multiple ecosystem services, restricted spatial and temporal coverage, and incomplete consideration of embodied carbon and service-life differences. Comparative evidence across climate zones and seasons is also limited, making it difficult to evaluate trade-offs among cooling benefits, carbon sequestration and energy demand. The review identifies the need for more integrated assessment frameworks and more consistent carbon accounting. These advances could strengthen the theoretical and methodological support for low-carbon spatial planning and more precise urban decision making.","城市蓝绿灰基础设施（BGGI）作为减少碳排放和提升热韧性的策略日益受到关注，但关于其作用机制和评估方法的证据仍较为零散。本综述综合了从Web of Science和ScienceDirect检索到的BGGI研究，聚焦于碳减排路径、分析方法及尚未解决的评估挑战。综述区分了蓝绿组分的直接碳储存与碳汇，以及与小气候调节和建筑能源需求变化相关的运行减排。同时考虑了灰色基础设施设计与改造的生命周期碳效应。现有研究利用空间分析和遥感进行观测，利用模型模拟进行情景评估，而机器学习则支持预测和基于模型的解释。生命周期评价（LCA）、生命周期成本（LCC）和成本效益分析（CBA）被用于环境和经济评估。然而，大多数评估仍受限于基础设施要素的碎片化处理、多种生态系统服务整合不足、时空覆盖范围有限，以及对隐含碳和服役寿命差异的考虑不完整。跨气候带和季节的比较证据也较为有限，使得难以评估降温效益、碳汇与能源需求之间的权衡。本综述指出需要更综合的评估框架和更一致的碳核算。这些进展可为低碳空间规划和更精准的城市决策提供更强的理论与方法支撑。",null,"Journal of Architectural and Planning Research","2026-09-30T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文聚焦城市蓝绿灰基础设施的碳减排与热韧性评估，属城市规划与建筑领域，与三农、农业信息化、智慧农业、数字乡村等主题无直接关联，故不进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"生命周期评价","蓝绿灰基础设施","城市碳减排","热韧性",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":62},[32,41,48,55],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":40},"I2",6,true,[],[38,39],"机器学习","machine learning",[],{"code":42,"score":34,"supported":35,"title":43,"abstract":44,"tags":47},"I3",[],[45,46],"遥感","remote sensing",[],{"code":49,"score":34,"supported":35,"title":50,"abstract":51,"tags":54},"I4",[],[52,53],"空间分析","spatial analysis",[],{"code":56,"score":57,"supported":35,"title":58,"abstract":59,"tags":61},"H6",3,[],[60],"生态系统服务",[],"农业相关性需要复核",[64,65],"蓝绿灰基础设施 生命周期评价 城市碳减排 热韧性","蓝绿灰基础设施 生命周期评价","蓝绿灰基础设施生命周期评价城市碳减排热韧性-4103",1,"10.66033\u002Fjapr2026-305",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":15,"oa_url":82,"card":83,"direction":89,"ingested_from":90},"W7214921223",[72,75,78,80],{"name":73,"orcid":74},"Tian Wang","https:\u002F\u002Forcid.org\u002F0000-0002-2147-2459",{"name":76,"orcid":77},"Qinfeng Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-6107-0630",{"name":79,"orcid":9},"Xiaoqing Zhu",{"name":81,"orcid":9},"Weijun Gao","https:\u002F\u002Flockescience.org\u002Farticles\u002Fjapr\u002F2026\u002Fissue%203\u002Fpaper%205.pdf",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"综述蓝绿灰基础设施减碳与热韧性机制、评估方法及挑战。","Web of Science与ScienceDirect文献综述，归纳遥感、模型","评估受限于要素割裂、多服务整合不足、时空覆盖有限及隐含碳考虑不全。","农业绿色发展与碳","可构建整合多生态系统服务与全生命周期碳核算的跨气候区评估框架，支撑低碳空间规划。","农业遥感与作物表型","openalex","2026-10-03T23:30:38.026597Z",{"total":34,"page":67,"page_size":34,"items":93},[94,185,248,337,417,557],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":109,"tags":111,"primary_topic":116,"secondary_topics":122,"classification":128,"search_phrases":156,"slug":159,"view_count":67,"doi":160,"paper":161,"created_at":184},5193,"Explainable machine learning with a graphical user interface for estimating compressive strength and life cycle assessment based embodied carbon of rice husk ash concrete","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09656-0","Meeting the growing demand for concrete while reducing its environmental impact remains a crucial challenge for the construction industry. Although rice husk ash (RHA) offers substantial potential as a supplementary cementitious material to lower cement-related emissions, its widespread adoption is constrained by uncertainty in achieving a performance-environmental efficiency trade-off between mechanical performance and embodied carbon reduction. This paper proposes an explainable machine learning (ML) framework for the evaluation of compressive strength of RHA-based concrete across various curing stages, complemented by life cycle assessment (LCA) based embodied carbon evaluation. A refined database compiled from existing studies was adopted to train and optimise eight different ML models. The models were optimised using Optuna and assessed using multiple performance metrics, while SHapley Additive exPlanation (SHAP) was adopted to quantify global and local feature contributions. To support practical deployment, the best performing models were incorporated within an interactive decision support interface facilitating real time, coding free compressive strength prediction together with LCA-based embodied carbon calculation; thereby supporting performance-based mix design and accelerating the sustainable use of agricultural waste derived cementitious materials.","在满足日益增长的混凝土需求的同时降低其环境影响，仍是建筑行业面临的关键挑战。尽管稻壳灰（rice husk ash, RHA）作为辅助胶凝材料在降低水泥相关排放方面具有巨大潜力，但其广泛应用受到力学性能与隐含碳减排之间性能-环境效率权衡不确定性的制约。本文提出了一种可解释机器学习（machine learning, ML）框架，用于评估稻壳灰基混凝土在不同养护阶段下的抗压强度，并辅以基于生命周期评价（life cycle assessment, LCA）的隐含碳评估。采用从现有研究中汇编的精细化数据库训练并优化了八种不同的机器学习模型。模型通过Optuna进行优化，并采用多项性能指标进行评估，同时采用SHapley加性解释（SHapley Additive exPlanation, SHAP）量化全局与局部特征贡献。为支持实际应用，将性能最优的模型集成于交互式决策支持界面中，实现实时、免编程的抗压强度预测及基于生命周期评价的隐含碳计算，从而支持基于性能的配合比设计，并加速农业废弃物衍生胶凝材料的可持续利用。","Discover Applied Sciences","2026-10-15T00:00:00Z",69,{"impact":104,"substance":105,"depth":106,"authority":104,"freshness":107,"relevant":67,"comment":108},12,20,17,8,"将稻壳灰混凝土的抗压强度预测与隐含碳评估整合为可解释机器学习框架，并配有图形界面，方法新颖、数据扎实，对农业废弃物高值化利用有参考价值，但属专业论文，公共影响范围有限。",[110],{"name":100,"url":97},[112,21,113,114,115],"可解释机器学习","农业废弃物利用","稻壳灰混凝土","低碳建材",{"code":117,"slug":118,"name":119,"path":120,"parent_code":121},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[123],{"code":33,"slug":124,"name":125,"path":126,"parent_code":127},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":129,"locked":14,"version":28,"method":29,"evidence":130},"classified",{"candidates":131,"agricultural_evidence":35},[132,138,143,151],{"code":33,"score":133,"supported":35,"title":134,"abstract":135,"tags":137},18,[39],[38,39,136],"decision support",[112],{"code":117,"score":139,"supported":35,"title":140,"abstract":141,"tags":142},11,[118],[118],[],{"code":144,"score":145,"supported":35,"title":146,"abstract":147,"tags":150},"H5",7,[],[148,149],"农业废弃物","agricultural waste",[113],{"code":152,"score":67,"supported":14,"title":153,"abstract":154,"tags":155},"H4",[],[],[21],[157,158],"稻壳灰混凝土 抗压强度","稻壳灰 生命周期评价","稻壳灰混凝土抗压强度-5193","10.1007\u002Fs42452-026-09656-0",{"doi":160,"openalex_id":162,"authors":163,"venue":100,"cited_by_count":15,"oa_url":97,"card":178,"direction":183,"ingested_from":90},"W7222284745",[164,167,170,173,175],{"name":165,"orcid":166},"Stephen Adeyemi Alabi","https:\u002F\u002Forcid.org\u002F0000-0001-8731-6961",{"name":168,"orcid":169},"Ibrahim A. Tijani","https:\u002F\u002Forcid.org\u002F0000-0002-9423-1074",{"name":171,"orcid":172},"Alireza Moghayedi","https:\u002F\u002Forcid.org\u002F0000-0002-4165-0777",{"name":174,"orcid":9},"Jeffrey Mahachi",{"name":176,"orcid":177},"Samson Olalekan Odeyemi","https:\u002F\u002Forcid.org\u002F0000-0001-5217-3403",{"tldr":179,"method":180,"finding":181,"direction":87,"opportunity":182},"用可解释机器学习加图形界面预测稻壳灰混凝土抗压强度并评估隐含碳。","八种机器学习模型经Optuna优化，SHAP解释，结合生命周期评价与交互界面。","最优模型可实时无代码预测强度并算隐含碳，支撑性能导向配合比设计。","可将稻壳灰混凝土的强度-碳权衡模型扩展到其他农业废弃物建材并接入乡村低碳建造决策。","农业人工智能与决策模型","2026-10-17T23:30:53.298664Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":198,"tags":200,"primary_topic":9,"secondary_topics":205,"classification":206,"search_phrases":223,"slug":226,"view_count":15,"doi":227,"paper":228,"created_at":247},5140,"Edible films and coatings for sustainable food packaging: a critical review of materials, functional performance, sustainability, and industrial translation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1954572","Edible films and coatings (EFCs) are promising alternatives to conventional petrochemical food packaging due to their biodegradability and potential to extend food shelf life (SL). However, successful commercialization requires balancing mechanical strength, barrier performance, food safety, sensory acceptability, cost, and environmental sustainability. This comparative literature review analyzes polysaccharide, protein, lipid, composite, and multilayer EFCs, emphasizing structure–property–performance relationships, formulation strategies, processing technologies, and multifunctional additives. This study was conducted using Scopus, Web of Science, PubMed, and ScienceDirect, covering representative publications between 2021 and June 2026. The review examines fabrication methods; active and IP; nano-enhanced edible films; encapsulation and controlled-release systems; and the influence of formulation, plasticizer content, relative humidity (RH), temperature, film thickness, and food matrix composition on performance. It also compares laboratory-scale findings with practical food applications and distinguishes edible, bio-based, biodegradable, compostable, and sustainable packaging within an LCA framework. Furthermore, migration, toxicological, and allergenicity concerns, regulatory requirements, and commercial manufacturing challenges are critically discussed. The review concludes that composite and multilayer EFCs offer superior multifunctional performance, but their industrial success depends on optimized formulations, standardized characterization, regulatory compliance, economic feasibility, and comprehensive life-cycle assessment.","可食用膜与涂层（edible films and coatings, EFCs）因其可生物降解性及延长食品货架期（shelf life, SL）的潜力，成为传统石油基食品包装的有前景替代方案。然而，成功实现商业化需要在机械强度、阻隔性能、食品安全、感官可接受性、成本与环境可持续性之间取得平衡。本比较性文献综述分析了多糖类、蛋白质类、脂质类、复合型及多层EFCs，重点探讨结构—性能—表现关系、配方策略、加工技术及多功能添加剂。本研究通过Scopus、Web of Science、PubMed和ScienceDirect数据库检索，涵盖2021年至2026年6月间的代表性出版物。综述考察了制备方法；活性包装与智能包装（active and intelligent packaging, IP）；纳米增强可食用膜；包封与控释系统；以及配方、增塑剂含量、相对湿度（relative humidity, RH）、温度、膜厚度和食品基质组成对性能的影响。同时，将实验室规模研究结果与实际食品应用进行比较，并在生命周期评价（life cycle assessment, LCA）框架内区分可食用、生物基、可生物降解、可堆肥及可持续包装。此外，批判性讨论了迁移、毒理学及致敏性关切、法规要求与商业化生产挑战。综述结论认为，复合型与多层EFCs具有更优越的多功能性能，但其工业成功取决于优化配方、标准化表征、法规合规性、经济可行性及全面的生命周期评价。","Frontiers in Sustainable Food Systems","2026-10-16T00:00:00Z",79,{"impact":133,"substance":105,"depth":133,"authority":195,"freshness":196,"relevant":67,"comment":197},14,9,"系统综述可食膜与涂层在材料、性能、可持续性与产业化方面的进展，对农产品保鲜与绿色包装有参考价值，但属综述类论文，产业影响有限。",[199],{"name":191,"url":188},[201,202,203,21,204],"食品保鲜","食品包装","生物降解","可食膜",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":207},{"candidates":208,"agricultural_evidence":14,"reason":62},[209,216],{"code":210,"score":211,"supported":35,"title":212,"abstract":214,"tags":215},"E3",16,[213],"food packaging",[202,213],[201,202],{"code":217,"score":34,"supported":35,"title":218,"abstract":219,"tags":222},"E4",[],[220,221],"食品安全","food safety",[],[224,225],"可食膜 可食涂层 食品包装","生命周期评价 生物降解 食品保鲜 食品包装","可食膜可食涂层食品包装-5140","10.3389\u002Ffsufs.2026.1954572",{"doi":227,"openalex_id":229,"authors":230,"venue":191,"cited_by_count":15,"oa_url":188,"card":242,"direction":87,"ingested_from":90},"W7222504899",[231,233,235,237,239],{"name":232,"orcid":9},"Abhivyakti Mishra",{"name":234,"orcid":9},"Manshuk Iskapalli",{"name":236,"orcid":9},"Divya Rai",{"name":238,"orcid":9},"Sapnita Shinde",{"name":240,"orcid":241},"Prasanna Prakash Bhalerao","https:\u002F\u002Forcid.org\u002F0000-0003-4287-6169",{"tldr":243,"method":244,"finding":245,"direction":87,"opportunity":246},"综述可食膜与涂层的材料、性能、可持续性及产业化瓶颈。","文献综述，检索Scopus等数据库2021-2026年代表性研究。","复合与多层可食膜多功能性能最优，产业化依赖配方优化与标准化评估。","可食膜在生鲜农产品保鲜中的迁移安全与全生命周期碳足迹评估尚缺系统研究。","2026-10-17T23:30:06.650462Z",{"id":249,"title":250,"url":251,"summary":252,"summary_zh":253,"content":9,"source_name":254,"source_url":251,"published_at":255,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":256,"score_detail":257,"sources":260,"tags":262,"primary_topic":267,"secondary_topics":272,"classification":273,"search_phrases":303,"slug":306,"view_count":67,"doi":307,"paper":308,"created_at":336},4992,"Evaluating tools and methods for agroecosystem sustainability and performance: a critical review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01121-4","The sustainability and performance of agroecosystems are critical for confronting global challenges, from food security to biodiversity loss. This critical narrative review synthesizes 137 peer-reviewed sources and grey literature documents (2000-2024) to interrogate the epistemological assumptions, power dynamics, and contextual appropriateness of sustainability assessment frameworks. This comprehensive review systematically analyzes the diverse tools and methodologies; including Life Cycle Assessment (LCA), Response-Inducing Sustainability Evaluation (RISE), Framework for the Evaluation of Natural Resource Management Systems Incorporating Sustainability Indicators (MESMIS), and Tool for Agroecology Performance Evaluation (TAPE); used to evaluate this sustainability. This analysis moves beyond a simple comparison to offer a critical interrogation of these frameworks, exposing a fundamental tension between reductionist tools that offer precise, quantifiable data but often miss systemic interactions, and holistic, participatory approaches that capture socio-ecological complexity but struggle with standardization and scalability. This review underscores that sustainability assessment is not a neutral technical exercise but a value-laden process fraught with trade-offs, power dynamics, and epistemological conflicts, particularly between scientific and indigenous knowledge systems. The transformative potential of emerging technologies like Artificial Intelligence (AI) and remote sensing is examined, while cautioning against technological optimism that risks exacerbating inequities. Ultimately, this review argues for a shift towards reflexive, pluralistic assessment processes that prioritize equity, adaptive learning, and critical independence over standardized scoring, providing practical heuristics for researchers and practitioners navigating methodological trade-offs in diverse contexts.","农业生态系统的可持续性与表现对于应对从粮食安全到生物多样性丧失等全球性挑战至关重要。本批判性叙事综述综合了137篇同行评审文献和灰色文献（2000—2024年），旨在审视可持续性评估框架的认识论假设、权力动态及情境适宜性。本综述系统分析了用于评估该可持续性的多种工具和方法，包括生命周期评价（LCA）、诱导响应可持续性评估（RISE）、纳入可持续性指标的自然资源管理系统评价框架（MESMIS）以及农业生态表现评估工具（TAPE）。本分析超越了简单的比较，对这些框架进行了批判性审视，揭示了还原论工具与整体性参与式方法之间的根本张力——前者提供精确、可量化的数据，但往往忽视系统性交互；后者捕捉社会生态复杂性，却在标准化和可扩展性方面面临困难。本综述强调，可持续性评估并非中性的技术操作，而是一个充满价值负载的过程，充斥着权衡取舍、权力动态和认识论冲突，尤其是科学知识体系与本土知识体系之间的冲突。本文审视了人工智能（AI）和遥感等新兴技术的变革潜力，同时警示技术乐观主义可能加剧不平等。最终，本综述主张转向反思性、多元化的评估过程，将公平、适应性学习和批判性独立置于标准化评分之上，并为研究者和实践者在不同情境中应对方法论权衡提供实用启发式指南。","Discover Environment","2026-10-13T00:00:00Z",71,{"impact":104,"substance":105,"depth":133,"authority":258,"freshness":107,"relevant":67,"comment":259},13,"系统梳理LCA、RISE、MESMIS、TAPE等农业生态可持续性评估工具，指出还原论与整体论方法的张力，并讨论AI与遥感的赋能潜力，对农业信息化与智慧农业评估体系有参考价值。",[261],{"name":254,"url":251},[263,264,265,21,266],"农业人工智能","农业遥感","农业生态","可持续评价",{"code":56,"slug":268,"name":269,"path":270,"parent_code":271},"biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",[],{"status":129,"locked":14,"version":28,"method":29,"evidence":274},{"candidates":275,"agricultural_evidence":35},[276,282,286,293,299],{"code":56,"score":13,"supported":35,"title":277,"abstract":278,"tags":281},[],[279,265,268,280],"生物多样性","agroecology",[265],{"code":42,"score":145,"supported":35,"title":283,"abstract":284,"tags":285},[],[45,46],[264],{"code":287,"score":34,"supported":35,"title":288,"abstract":289,"tags":292},"A8",[],[290,291],"粮食安全","food security",[],{"code":33,"score":294,"supported":35,"title":295,"abstract":296,"tags":298},4,[],[297],"artificial intelligence",[263],{"code":152,"score":67,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[21],[304,305],"TAPE 农业生态 评价","RISE MESMIS 可持续评估","TAPE农业生态评价-4992","10.1007\u002Fs44274-026-01121-4",{"doi":307,"openalex_id":309,"authors":310,"venue":254,"cited_by_count":15,"oa_url":329,"card":330,"direction":335,"ingested_from":90},"W7221633095",[311,314,317,320,322,324,326],{"name":312,"orcid":313},"Pierre Marie Chimi","https:\u002F\u002Forcid.org\u002F0000-0003-4520-4165",{"name":315,"orcid":316},"Karimou Ngamsou Abdel","https:\u002F\u002Forcid.org\u002F0000-0002-8126-6235",{"name":318,"orcid":319},"Baruch Batamack Nkoué","https:\u002F\u002Forcid.org\u002F0000-0001-5601-3327",{"name":321,"orcid":9},"Eusebe Ydelphonse Nyonce Pokam",{"name":323,"orcid":9},"Angele Marie Ngon Dikoume",{"name":325,"orcid":9},"Benjamin Loïc Ndong Ndzana",{"name":327,"orcid":328},"William Armand Mala","https:\u002F\u002Forcid.org\u002F0000-0002-2240-3746","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01121-4.pdf",{"tldr":331,"method":332,"finding":333,"direction":87,"opportunity":334},"综述137篇文献，批判性评估农业生态系统可持续性评价工具的方法论假设与权衡。","批判性叙事综述，分析LCA、RISE、MESMIS、TAPE等框架及AI与遥感。","评价工具在简化精确与整体参与间存在根本张力，评价是价值负载过程。","可开发兼顾标准化与参与式、融合本土知识并反思技术乐观的混合评价框架。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:10.405237Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":9,"source_name":191,"source_url":340,"published_at":255,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":343,"score_detail":344,"sources":347,"tags":349,"primary_topic":354,"secondary_topics":355,"classification":356,"search_phrases":389,"slug":392,"view_count":15,"doi":393,"paper":394,"created_at":416},4869,"From life cycle assessment to life cycle thinking in agri-food systems: lessons from the avocado supply chain","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1875191","Agri-food systems face growing pressure to demonstrate environmental performance and support sustainability transitions. In this context, Life Cycle Assessment (LCA) generates evidence on environmental impacts, while Life Cycle Thinking (LCT) helps integrate that information into decision-making across supply chains. This review evaluates LCA and LCT for the avocado sector by synthesising current LCA evidence, analysing the transferability of results to LCA‑based ecolabels and certifications, examining how LCT related food‑policy instruments create or limit incentives for supply‑chain actors and exploring pathways for translating life-cycle information into coordinated sustainability action. The assessment combines a systematic appraisal of English‑language avocado LCAs with regionally relevant studies, in Spanish and German, and field insights from sustained engagement with Ecuadorian producers. Avocado LCAs have expanded in number, supply chain and geographic coverage, consistently identifying production (e.g., fertilisation, fuel, energy use) and distribution (e.g., transport distances) as key environmental hotspots. Despite mounting pressure to reduce impacts, producers and distributors face limited incentives to move beyond compliance with non‑LCA certifications (e.g., GLOBALG.A.P., Rainforest Alliance). Retailers increasingly promote LCA‑based approaches, yet their efforts are hindered by data fragmentation, methodological inconsistency, and traceability challenges. Consumers express strong interest in environmental information, particularly when presented through simplified, multi‑criteria ecolabels, though such formats risk oversimplification or misrepresentation. Methodological gaps persist, including inconsistent treatment of carbon sequestration, biodiversity, and nursery stages, while opportunities in circularity and by‑product valorisation remain underexplored. Overall, LCT and LCA offer essential guidance for improving the sustainability of avocado supply chains, but their effectiveness depends on methodological standardisation, ecolabel harmonisation, clear policy direction, credible differentiation schemes, and robust data systems. By connecting environmental assessment with implementation, governance, and decision-making processes, this review outlines a pathway for embedding LCT within policy and practice, enabling more coherent, transparent, and actionable sustainability governance for agri-food supply chains.","农食系统正面临日益增大的压力，需展示其环境绩效并支持可持续性转型。在此背景下，生命周期评价（Life Cycle Assessment, LCA）为环境影响提供证据，而生命周期思维（Life Cycle Thinking, LCT）则有助于将这些信息整合到供应链各环节的决策中。本综述通过综合现有LCA证据、分析结果向基于LCA的生态标签和认证的可迁移性、考察与LCT相关的食品政策工具如何为供应链参与者创造或限制激励，以及探索将生命周期信息转化为协调可持续行动的路径，对牛油果行业的LCA和LCT进行了评估。该评估结合了对英语牛油果LCA的系统性评价、西班牙语和德语的相关区域研究，以及与厄瓜多尔生产者持续合作获得的实地见解。牛油果LCA在研究数量、供应链覆盖和地理范围上均有扩展，一致识别出生产环节（如施肥、燃料、能源使用）和分销环节（如运输距离）为关键环境热点。尽管减少环境影响的压力不断增大，生产者和分销商在超越非LCA认证（如GLOBALG.A.P.、雨林联盟）合规要求方面面临的激励仍然有限。零售商日益推广基于LCA的方法，但其努力受到数据碎片化、方法学不一致和可追溯性挑战的制约。消费者对环境信息表现出浓厚兴趣，尤其是当信息以简化的多标准生态标签形式呈现时，但此类形式存在过度简化或误导的风险。方法学空白依然存在，包括碳固存、生物多样性和苗圃阶段处理方式的不一致，而循环性和副产品增值方面的机遇仍未得到充分探索。总体而言，LCT和LCA为改善牛油果供应链的可持续性提供了重要指导，但其有效性取决于方法学标准化、生态标签协调、明确的政策方向、可信的差异化方案以及稳健的数据系统。通过将环境评估与实施、治理和决策过程相连接，本综述勾勒出一条将LCT嵌入政策与实践的路径，从而为农食供应链实现更协调、透明和可操作的可持续性治理。",82,{"impact":133,"substance":345,"depth":133,"authority":195,"freshness":13,"relevant":67,"comment":346},22,"系统综述牛油果供应链LCA与生命周期思维，指出数据碎片化、方法不统一及生态标签激励不足，对农产品可持续治理与信息化追溯有参考价值。",[348],{"name":191,"url":340},[350,351,21,352,353],"可持续农业","牛油果","农产品供应链","生态标签",{"code":56,"slug":268,"name":269,"path":270,"parent_code":271},[],{"status":129,"locked":14,"version":28,"method":29,"evidence":357},{"candidates":358,"agricultural_evidence":35},[359,363,368,374,379,384],{"code":56,"score":34,"supported":35,"title":360,"abstract":361,"tags":362},[],[279,268],[],{"code":152,"score":294,"supported":35,"title":364,"abstract":365,"tags":367},[],[366],"carbon sequestration",[21],{"code":369,"score":57,"supported":35,"title":370,"abstract":371,"tags":373},"F3",[],[372],"施肥",[],{"code":375,"score":67,"supported":14,"title":376,"abstract":377,"tags":378},"B2",[],[],[351],{"code":380,"score":67,"supported":14,"title":381,"abstract":382,"tags":383},"E",[],[],[352],{"code":385,"score":67,"supported":14,"title":386,"abstract":387,"tags":388},"H8",[],[],[350],[390,391],"牛油果 供应链 生命周期评价","农产品供应链 生命周期评价 可持续农业 生态标签","牛油果供应链生命周期评价-4869","10.3389\u002Ffsufs.2026.1875191",{"doi":393,"openalex_id":395,"authors":396,"venue":191,"cited_by_count":15,"oa_url":340,"card":411,"direction":87,"ingested_from":90},"W7221595293",[397,399,402,404,406,408],{"name":398,"orcid":9},"Margarita Baquero",{"name":400,"orcid":401},"Villi Ieremia","https:\u002F\u002Forcid.org\u002F0000-0002-7106-0583",{"name":403,"orcid":9},"Anton Van Oyen",{"name":405,"orcid":9},"Beatriz Rivela",{"name":407,"orcid":9},"Annemie Geeraerd Ameryckx",{"name":409,"orcid":410},"Angel Diego Ramirez","https:\u002F\u002Forcid.org\u002F0000-0003-3846-3396",{"tldr":412,"method":413,"finding":414,"direction":87,"opportunity":415},"综述牛油果供应链LCA与生命周期思维，探讨如何将环境评估转化为可持续决策。","系统综述英文及西德文牛油果LCA研究，结合厄瓜多尔生产者实地调研。","生产与分销是环境热点，但数据碎片化、方法不一致和认证激励不足阻碍LCA落地。","可研究LCA标准化与生态标签协调机制，以及碳封存、生物多样性和副产物增值的方法学空白。","2026-10-14T23:30:06.874747Z",{"id":418,"title":419,"url":420,"summary":421,"summary_zh":422,"content":9,"source_name":423,"source_url":420,"published_at":424,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":425,"score_detail":426,"sources":429,"tags":431,"primary_topic":9,"secondary_topics":436,"classification":437,"search_phrases":484,"slug":487,"view_count":479,"doi":488,"paper":489,"created_at":556},4803,"Reducing Uncertainty in Cradle‐to‐Farmgate Carbon Intensity for Feedstock Using Model‐Data Fusion","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fgcbb.70179","ABSTRACT Biofuel carbon intensity (CI) quantifies greenhouse gas (GHG) emissions across the biofuel supply chain, providing a transparent metric guiding renewable fuel policies and enabling market‐based mechanisms to reduce GHG emissions. In this study, we focus on the cradle‐to‐farmgate feedstock segment of biofuel CI, the portion attributable to feedstock production from cradle to farm gate and directly influenced by agricultural management decisions. We quantify field‐level cradle‐to‐farmgate CI of corn and soybean feedstocks by integrating a process‐based model ( ecosys ) with a life cycle assessment (LCA) tool (GREET R&D FD‐CIC). We first identified CI uncertainty sources at a “supersite,” a representative farm in Illinois with comprehensive GHG measurements, using a Monte Carlo sampling framework that varied soil properties, management practices, and plant trait parameters across plausible ranges to quantify each source's contribution to CI uncertainty. Unknown plant trait parameters were the largest CI uncertainty source for both crops, with fertilizer information also important for corn because these inputs affect yield, N 2 O emissions, and soil organic carbon (SOC) stocks. We revealed widespread field‐level variability in cradle‐to‐farmgate CI within the same county, demonstrating the need for field‐level assessment. This variability was most strongly associated with SOC change for soybean (total‐effect index, 38%) and N 2 O emissions for corn (43%). While inherent uncertainty is inevitable, we demonstrated that a Model‐Data Fusion (MDF) approach, which integrates observational data including in situ flux measurements and satellite‐based remote sensing to constrain model parameters with known processes, can substantially reduce cradle‐to‐farmgate CI uncertainty. Specifically, we used crop photosynthesis and yield data for plant trait parameter calibration, and incorporated tillage and cover crop information as model inputs, achieving a 41%–52% CI uncertainty reduction. This work establishes a CI quantification framework composed of process‐based models, an LCA tool, MDF, and satellite data, enabling scalable, cost‐effective, field‐level cradle‐to‐farmgate CI quantification across regions.","摘要 生物燃料碳强度（CI）量化了生物燃料供应链中的温室气体（GHG）排放，为可再生燃料政策提供了透明的衡量指标，并使基于市场的机制能够减少温室气体排放。本研究聚焦于生物燃料碳强度中从摇篮到农场门的原料生产环节，即从摇篮到农场门阶段归属于原料生产且直接受农业管理决策影响的部分。我们通过将过程模型（ecosys）与生命周期评价（LCA）工具（GREET R&D FD-CIC）相结合，量化了玉米和大豆原料的田块尺度从摇篮到农场门碳强度。我们首先在一个“超级站点”——位于伊利诺伊州的一个具有全面温室气体测量数据的代表性农场——采用蒙特卡洛采样框架，在合理范围内变化土壤属性、管理措施和植物性状参数，以量化各来源对碳强度不确定性的贡献。未知植物性状参数是两种作物碳强度不确定性的最大来源，肥料信息对玉米同样重要，因为这些投入会影响产量、N₂O排放和土壤有机碳（SOC）储量。我们揭示了同一县内从摇篮到农场门碳强度存在广泛的田块尺度变异性，表明有必要进行田块尺度评估。这种变异性与大豆的土壤有机碳变化关联最强（总效应指数为38%），与玉米的N₂O排放关联最强（43%）。尽管固有不确定性不可避免，我们证明了一种模型-数据融合（MDF）方法可以大幅降低从摇篮到农场门碳强度不确定性，该方法整合了包括原位通量测量和卫星遥感在内的观测数据，以已知过程约束模型参数。具体而言，我们利用作物光合作用和产量数据校准植物性状参数，并将耕作和覆盖作物信息作为模型输入，实现了41%–52%的碳强度不确定性降低。本研究建立了一个由过程模型、生命周期评价工具、模型-数据融合和卫星数据组成的碳强度量化框架，能够实现跨区域可扩展、成本效益高的田块尺度从摇篮到农场门碳强度量化。","GCB Bioenergy","2026-10-12T00:00:00Z",81,{"impact":133,"substance":427,"depth":133,"authority":195,"freshness":107,"relevant":67,"comment":428},23,"将过程模型、LCA与遥感数据融合，实现田块级原料碳强度量化并降低不确定性，方法新颖、结论可靠，对农业碳核算与低碳政策有参考价值。",[430],{"name":423,"url":420},[432,433,434,21,435],"农业碳减排","遥感监测","生物燃料","玉米大豆",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":438},{"candidates":439,"agricultural_evidence":35,"reason":483},[440,447,451,458,465,471,478],{"code":441,"score":145,"supported":35,"title":442,"abstract":443,"tags":446},"A3",[],[444,445],"玉米","corn",[435],{"code":42,"score":145,"supported":35,"title":448,"abstract":449,"tags":450},[],[45,46],[433],{"code":452,"score":34,"supported":35,"title":453,"abstract":454,"tags":457},"A4",[],[455,456],"大豆","soybean",[],{"code":459,"score":34,"supported":35,"title":460,"abstract":461,"tags":464},"B7",[],[462,463],"温室","greenhouse",[],{"code":369,"score":34,"supported":35,"title":466,"abstract":467,"tags":470},[],[468,469],"肥料","fertilizer",[],{"code":472,"score":34,"supported":35,"title":473,"abstract":474,"tags":477},"H2",[],[475,476],"土壤","soil",[],{"code":152,"score":479,"supported":14,"title":480,"abstract":481,"tags":482},2,[],[],[432,21],"多个产业证据相当，需要确认主归属",[485,486],"ecosys GREET 生物燃料碳强度","玉米 大豆 农田碳排放 模型数据融合","ecosysGREET生物燃料碳强度-4803","10.1111\u002Fgcbb.70179",{"doi":488,"openalex_id":490,"authors":491,"venue":423,"cited_by_count":15,"oa_url":550,"card":551,"direction":335,"ingested_from":90},"W7221531583",[492,495,498,501,504,507,509,512,515,518,521,524,527,530,533,535,538,541,544,547],{"name":493,"orcid":494},"Ziqi Qin","https:\u002F\u002Forcid.org\u002F0000-0001-5854-0323",{"name":496,"orcid":497},"Kaiyu Guan","https:\u002F\u002Forcid.org\u002F0000-0002-3499-6382",{"name":499,"orcid":500},"Yalin Li","https:\u002F\u002Forcid.org\u002F0000-0002-8863-4758",{"name":502,"orcid":503},"Wang Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-8432-9828",{"name":505,"orcid":506},"Xinyu Liu","https:\u002F\u002Forcid.org\u002F0000-0003-4683-0272",{"name":508,"orcid":9},"Hao Cai",{"name":510,"orcid":511},"Bin Peng","https:\u002F\u002Forcid.org\u002F0000-0002-7284-3010",{"name":513,"orcid":514},"Tongxi Hu","https:\u002F\u002Forcid.org\u002F0000-0003-4773-1022",{"name":516,"orcid":517},"R. F. Grant","https:\u002F\u002Forcid.org\u002F0000-0002-8890-6231",{"name":519,"orcid":520},"Jinyun Tang","https:\u002F\u002Forcid.org\u002F0000-0002-4792-1259",{"name":522,"orcid":523},"Zhenong Jin","https:\u002F\u002Forcid.org\u002F0000-0002-1252-2514",{"name":525,"orcid":526},"Jeremy S. Guest","https:\u002F\u002Forcid.org\u002F0000-0003-2489-2579",{"name":528,"orcid":529},"Lexuan Ye","https:\u002F\u002Forcid.org\u002F0009-0005-1841-7175",{"name":531,"orcid":532},"Kuang‐Yu Chang","https:\u002F\u002Forcid.org\u002F0000-0002-7859-5871",{"name":534,"orcid":9},"Zhangliang Chen",{"name":536,"orcid":537},"DoKyoung Lee","https:\u002F\u002Forcid.org\u002F0000-0003-1401-9661",{"name":539,"orcid":540},"Andrew J. Margenot","https:\u002F\u002Forcid.org\u002F0000-0003-0185-8650",{"name":542,"orcid":543},"Carl J. Bernacchi","https:\u002F\u002Forcid.org\u002F0000-0002-2397-425X",{"name":545,"orcid":546},"Elizabeth A. Ainsworth","https:\u002F\u002Forcid.org\u002F0000-0002-3199-8999",{"name":548,"orcid":549},"William Eddy","https:\u002F\u002Forcid.org\u002F0000-0003-2215-9680","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fgcbb.70179",{"tldr":552,"method":553,"finding":554,"direction":87,"opportunity":555},"用模型-数据融合降低玉米大豆原料从摇篮到农场门的碳强度不确定性。","ecosys过程模型与GREET LCA耦合，蒙特卡洛分析，卫星与通量数据融合。","植物性状参数是最大不确定性源，模型-数据融合使碳强度不确定性降低41%–52%。","可将该碳强度量化框架扩展至更多作物与区域，结合遥感实现大范围田块级碳核算。","2026-10-13T23:30:08.889814Z",{"id":558,"title":559,"url":560,"summary":561,"summary_zh":9,"content":9,"source_name":562,"source_url":560,"published_at":563,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":256,"score_detail":564,"sources":566,"tags":568,"primary_topic":572,"secondary_topics":578,"classification":579,"search_phrases":591,"slug":594,"view_count":67,"doi":595,"paper":596,"created_at":616},4201,"How to address challenges in the life cycle assessment of innovative cropping systems: Insights from a comparative study of a pesticide-free and a conventional system","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105002","How to address challenges in the life cycle assessment of innovative cropping systems: Insights from a comparative study of a pesticide-free and a conventional system。Agricultural Systems","Agricultural Systems","2026-10-04T00:00:00Z",{"impact":104,"substance":105,"depth":106,"authority":195,"freshness":107,"relevant":67,"comment":565},"核心期刊论文，提出创新种植制度生命周期评价的方法学思路，专业价值高但属细分领域研究，公共影响有限。",[567],{"name":562,"url":560},[569,21,570,571],"农业绿色转型","无农药种植","种植制度",{"code":573,"slug":574,"name":575,"path":576,"parent_code":577},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":129,"locked":14,"version":28,"method":29,"evidence":580},{"candidates":581,"agricultural_evidence":35},[582,587],{"code":573,"score":139,"supported":35,"title":583,"abstract":585,"tags":586},[584],"pesticide",[584],[],{"code":152,"score":479,"supported":14,"title":588,"abstract":589,"tags":590},[],[],[569,21],[592,593],"无农药种植 生命周期评价","创新种植制度 对比研究","无农药种植生命周期评价-4201","10.1016\u002Fj.agsy.2026.105002",{"doi":595,"openalex_id":597,"authors":598,"venue":562,"cited_by_count":15,"oa_url":560,"card":9,"direction":9,"ingested_from":90},"W7219812990",[599,602,605,607,610,613],{"name":600,"orcid":601},"Jan Weik","https:\u002F\u002Forcid.org\u002F0000-0002-7033-0297",{"name":603,"orcid":604},"Beate Zimmermann","https:\u002F\u002Forcid.org\u002F0000-0002-0253-8109",{"name":606,"orcid":9},"Ingrid Claß‐Mahler",{"name":608,"orcid":609},"Pietro Goglio","https:\u002F\u002Forcid.org\u002F0000-0001-5709-5723",{"name":611,"orcid":612},"Enno Bahrs","https:\u002F\u002Forcid.org\u002F0000-0002-5687-0772",{"name":614,"orcid":615},"Iris M. Lewandowski","https:\u002F\u002Forcid.org\u002F0000-0002-0388-4521","2026-10-05T23:30:04.395646Z"]