[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4226":3,"related-4226":74},{"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,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":46,"slug":49,"view_count":50,"doi":51,"paper":52,"created_at":73},4226,"A Prescriptive Analytics Framework for Optimizing the Indonesian Salt Supply Chain Using System Dynamics and Data-Driven Decision Support","https:\u002F\u002Fdoi.org\u002F10.48084\u002Fetasr.20173","The Indonesian salt supply chain is characterized by persistent price volatility, supply-demand imbalances, and limited decision-support tools for policy development. Current models are mostly descriptive or predictive and provide forecasts with little actionable advice. This study presents a prescriptive analytics framework that couples a calibrated system dynamics model with a mixed-integer linear programming optimization model to assist in monthly decision-making regarding buffer stock release, import quota allocation, and distribution routing. The system dynamics model was calibrated using historical data (2020–2025), and the key stocks, flows, and feedback loops in the Indonesian salt supply chain were captured. The optimization model minimizes the weighted objective function of price volatility, service level, and logistics cost under inventory balance, capacity, and non-negativity constraints. An evaluation with 100 stochastic scenarios shows that the proposed framework reduces the average monthly price volatility by 23.4% and improves the service level from 81.6% to 94.1% relative to the baseline heuristic policies, with a 6.3% increase in logistics cost. A sensitivity analysis indicates that a buffer stock capacity of at least 15% of the annual consumption is required to maintain price stability. The proposed framework offers a data-driven decision-support approach for translating supply chain forecasts into actionable policy recommendations.","印度尼西亚食盐供应链长期存在价格波动、供需失衡以及政策制定缺乏决策支持工具等问题。现有模型多为描述性或预测性模型，仅提供预测结果而缺乏可操作的建议。本研究提出一个规范性分析框架，将校准后的系统动力学模型与混合整数线性规划优化模型相耦合，以辅助月度决策，涉及缓冲库存投放、进口配额分配和配送路径规划。系统动力学模型利用2020—2025年历史数据进行校准，捕捉了印度尼西亚食盐供应链中的关键存量、流量和反馈回路。优化模型在库存平衡、产能和非负性约束下，最小化价格波动、服务水平和物流成本的加权目标函数。基于100个随机情景的评估表明，相较于基准启发式策略，所提框架将月均价格波动降低23.4%，服务水平从81.6%提升至94.1%，物流成本增加6.3%。敏感性分析表明，缓冲库存容量至少需达到年消费量的15%才能维持价格稳定。所提框架为将供应链预测转化为可操作的政策建议提供了一种数据驱动的决策支持方法。",null,"Engineering Technology & Applied Science Research","2026-10-02T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"将系统动力学与混合整数规划结合用于印尼盐供应链的处方性分析，方法新颖、数据与情景验证扎实，对农产品供应链数字化决策有参考价值，但属境外细分行业研究，公共影响层级有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"系统动力学","供应链优化","数据驱动决策","盐业","政策模拟",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":45},[38],{"code":39,"score":20,"supported":40,"title":41,"abstract":43,"tags":44},"I2",true,[42],"decision support",[],[26],"农业相关性需要复核",[47,48],"印尼 盐业 供应链","系统动力学 供应链 优化","印尼盐业供应链-4226",0,"10.48084\u002Fetasr.20173",{"doi":51,"openalex_id":53,"authors":54,"venue":10,"cited_by_count":50,"oa_url":64,"card":65,"direction":71,"ingested_from":72},"W7218850393",[55,58,61],{"name":56,"orcid":57},"Titus Kristanto","https:\u002F\u002Forcid.org\u002F0000-0003-2202-441X",{"name":59,"orcid":60},"Ahmad Saikhu","https:\u002F\u002Forcid.org\u002F0000-0001-8753-263X",{"name":62,"orcid":63},"Bilqis Amaliah","https:\u002F\u002Forcid.org\u002F0000-0003-3154-8653","https:\u002F\u002Fwww.etasr.com\u002Findex.php\u002FETASR\u002Farticle\u002Fdownload\u002F20173\u002F7147",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"构建系统动力学与混合整数规划耦合的规范性分析框架，优化印尼盐供应链决策。","系统动力学模型校准2020-2025数据，结合MILP优化与100次随机情景评估","框架使月均价格波动降23.4%，服务水平从81.6%升至94.1%，物流成本增6.3%。","农业人工智能与决策模型","可将该规范性分析框架迁移至其他农产品供应链，并探索实时数据驱动的动态决策优化。","智慧农业 \u002F 农业物联网","openalex","2026-10-05T23:30:15.582890Z",{"total":75,"page":21,"page_size":75,"items":76},2,[77,150],{"id":78,"title":79,"url":80,"summary":81,"summary_zh":82,"content":9,"source_name":83,"source_url":80,"published_at":84,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":85,"score_detail":86,"sources":89,"tags":91,"primary_topic":96,"secondary_topics":102,"classification":103,"search_phrases":123,"slug":126,"view_count":50,"doi":127,"paper":128,"created_at":149},2863,"Smart energy transition in agricultural industrial parks for sustainable food systems: policy coordination pathways and dynamic evolution simulation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1885233","Introduction Based on the profound evolution of global “dual carbon” goals and sustainable food systems, agri-food parks are key nodes with high energy consumption and high carbon emissions in the food supply chain. Their smart energy systems provide a feasible path for low-carbon transition, but face governance obstacles such as policy fragmentation, departmental silos, and instrument conflicts, which constrain sustainable food production. Methods This study constructs a system dynamics model encompassing four subsystems: energy technology, digital infrastructure, policy market, and environmental goals. It identifies six core feedback loops and, through simulation for the period 2025 to 2040, reveals the mechanisms of policy coordination, and use sensitivity analysis to ensure robustness. Meanwhile, the study adopts the micro-perspective of the agri-food park, incorporating the entire process of policy coordination into a systematic endogenous feedback structure. Results The results show that the smart energy system of agri-food park possesses endogenous growth potential. The cumulative installed capacity of smart energy increased from 50 MW to 885.269 MW, representing a nearly 16.7 fold growth over the 15 year period, with an average annual compound growth rate of approximately 20.6%.Compared with the no policy intervention scenario, the multi policy coordination scenario boosted installed capacity by about 229.8%, indicating that strong policy support is essential to overcome initial development thresholds; strengthening goal coordination, tool optimization, and departmental integration can reduce the technology lock in effect from 0.683 to 0.300, a decline of 56.1%, and lower carbon emission intensity from 2.498 tons\u002F10,000 yuan to 1.737 tons\u002F10,000 yuan, thereby effectively breaking through existing technological lock-in and institutional barriers; and an increase in the carbon emission factor of smart energy alters the trajectory of new investments in carbon-based energy, significantly curbing the expansion of high-carbon energy. Discussion The study confirms that policy coordination is a core lever for activating the system’s endogenous dynamics and driving the green transformation and upgrading of agri-food parks, thereby providing systematic scientific decision-making support for building a more resilient sustainable food supply chain and advancing the “dual carbon” goals.","引言 基于全球“双碳”目标与可持续食物系统的深刻变革，农业食品园区是食物供应链中高能耗、高碳排放的关键节点，其智慧能源系统为低碳转型提供了可行路径，但面临政策碎片化、部门壁垒与工具冲突等治理障碍，制约了可持续食物生产。方法 本研究构建涵盖能源技术、数字基础设施、政策市场与环境目标四个子系统的系统动力学模型，识别六条核心反馈回路，并通过2025—2040年的仿真模拟揭示政策协同的作用机制，同时采用敏感性分析保证稳健性。同时，本研究采用农业食品园区的微观视角，将政策协同的全过程纳入系统内生反馈结构。结果 结果表明，农业食品园区智慧能源系统具有内生增长潜力。智慧能源累计装机容量从50 MW增至885.269 MW，15年间增长近16.7倍，年均复合增长率约为20.6%。与无政策干预情景相比，多政策协同情景使装机容量提升约229.8%，表明强有力的政策支持对于跨越初期发展门槛至关重要；加强目标协同、工具优化与部门整合可将技术锁定效应从0.683降至0.300，下降56.1%，并将碳排放强度从2.498吨\u002F万元降至1.737吨\u002F万元，从而有效突破现有技术锁定与制度壁垒；智慧能源碳排放因子上升会改变碳基能源新增投资轨迹，显著抑制高碳能源扩张。讨论 研究证实，政策协同是激活系统内生动力、推动农业食品园区绿色转型升级的核心杠杆，从而为构建更具韧性的可持续食物供应链和推进“双碳”目标提供系统性科学决策支持。","Frontiers in Sustainable Food Systems","2026-09-17T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":87,"relevant":21,"comment":88},8,"系统动力学模拟农业园区智慧能源政策协同，数据详实、结论有决策参考价值，但属学术论文、时效性一般，可入精选。",[90],{"name":83,"url":80},[92,93,26,94,95],"智慧农业","低碳转型","政策协同","农业园区",{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":104,"locked":14,"version":34,"method":35,"evidence":105},"classified",{"candidates":106,"agricultural_evidence":40},[107,114,119],{"code":97,"score":108,"supported":40,"title":109,"abstract":110,"tags":113},7,[],[111,112],"碳排放","carbon emission",[93],{"code":115,"score":21,"supported":14,"title":116,"abstract":117,"tags":118},"I1",[],[],[92],{"code":39,"score":21,"supported":14,"title":120,"abstract":121,"tags":122},[],[],[26],[124,125],"农业产业园 智慧能源 低碳转型","农食园区 政策协同 双碳","农业产业园智慧能源低碳转型-2863","10.3389\u002Ffsufs.2026.1885233",{"doi":127,"openalex_id":129,"authors":130,"venue":83,"cited_by_count":50,"oa_url":80,"card":143,"direction":147,"ingested_from":72},"W7213496319",[131,134,137,139,141],{"name":132,"orcid":133},"Zhong Li","https:\u002F\u002Forcid.org\u002F0000-0001-9869-006X",{"name":135,"orcid":136},"Yixuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-4879-7165",{"name":138,"orcid":9},"Simiao Tong",{"name":140,"orcid":9},"Huabin Wu",{"name":142,"orcid":9},"Caijuan Hu",{"tldr":144,"method":145,"finding":146,"direction":147,"opportunity":148},"构建系统动力学模型，模拟农业产业园智慧能源转型的政策协同路径与动态演化。","系统动力学建模，含四个子系统与六个反馈回路，2025-2040年仿真及敏感性分析","多政策协同使智慧能源装机增229.8%，技术锁定降56.1%，碳排放强度降30.5%。","农业绿色发展与碳","可将园区级政策协同模型扩展到区域农业供应链，量化部门壁垒对减碳的阻滞效应。","2026-09-18T23:30:07.001188Z",{"id":151,"title":152,"url":153,"summary":154,"summary_zh":9,"content":155,"source_name":156,"source_url":9,"published_at":157,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":158,"score_detail":159,"sources":164,"tags":166,"primary_topic":9,"secondary_topics":172,"classification":173,"search_phrases":191,"slug":194,"view_count":21,"doi":9,"paper":9,"created_at":195},1003,"System Dynamics Simulation of the Resilience of Sustainable Food Systems in Urban–Rural Transition Zones Empowered by Digitalization","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546","原文标题: System Dynamics Simulation of the Resilience of Sustainable Food Systems in Urban–Rural Transition Zones Empowered by Digitalization。作者: Tianshu Shao、Simiao Tong、Huabin Wu、Yanshu Ji（浙江财经大学东方学院、同济大学马克思主义学院、南京大学商学院）。DOI: 10.3390\u002Fland15091546。本文基于生态智慧理论，构建『数字技术-生态感知-生态智慧资本』三维联动的系统动力学模型，模拟城市-乡村过渡区可持续食物系统在抵御生境破碎化风险与实现协同稳态演化的动态过程。结果表明：①可持续食物系统的合成稳态跃迁是一种自组织相变过程；②数字技术赋能对系统韧性提升存在显著时滞效应；③适度的数字生态监测投资能够加速系统自组织相变；④核心食物生产空间的生态承载能力具有自适应周期波动机制。","## 1. Introduction\n\nThe world is entering a new era centered on the collaborative governance of development and the environment, with the pursuit of sustainable development becoming a shared vision of human societies [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B1-land-15-01546),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B2-land-15-01546)]. As cities serve as key engines for driving the implementation of sustainable development goals, increasing attention is being given to the sustainability of their development pathways [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B1-land-15-01546),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B3-land-15-01546)], and the pace of urbanization continues to accelerate. According to UN-Habitat statistics, more than half of the global population currently resides in urban areas, and this proportion is expected to increase to 68% by 2050 [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B4-land-15-01546)], making the trend of urbanization irreversible. However, while rapid urbanization promotes socioeconomic progress, it also triggers a series of severe challenges, such as ecosystem degradation [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B5-land-15-01546),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B6-land-15-01546)]. The ecological evolution of urban–rural systems and their derived social consequences impose constraints on the overall achievement of sustainable development goals that cannot be ignored [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B7-land-15-01546),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B8-land-15-01546),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B9-land-15-01546),[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B10-land-15-01546)]. In urban–rural fringe areas, urban expansion presents a pattern of “cities surrounding villages, buildings cutting through nature,” leading to habitat fragmentation and the degradation of ecosystem services, which directly threaten the already fragile food systems in these regions. Peri-urban agricultural food systems are not only important sources of fresh agricultural products for cities but also the core carriers of farmland protection and biomass resource cycling. Their multifunctional transformation, ecological degradation, and conflict with the food supply have been identified as high-priority sustainability challenges [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B11-land-15-01546)]. Habitat fragmentation compresses space for food production and disrupts key ecosystem service flows, such as pollination, soil conservation, and water retention, thereby posing systemic risks to the stability and security of the urban food supply by undermining the cycling pathways of resource metabolism. Thus, correct valuation of ecosystem services of peri-urban agricultural spaces and understanding their deep interconnections with the resilience of food systems are prerequisites in order to reduce the disjointed logic among policies and stakeholder perceptions and coordinate regional ecological protection and agricultural development [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B12-land-15-01546)].\n\nIn the context of this challenge, nature-based solutions (NbS) offer a crucial approach to habitat restoration and food system resilience in urban–rural fringe zones. Previous research has indicated that NbS can leverage natural processes to address complex environmental and social challenges and restore degraded habitats and enhance landscape connectivity and ecosystem service supply capacity, thereby reversing the trend of habitat fragmentation [[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B13-land-15-01546),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B14-land-15-01546)]. Empirical studies in six European countries and urban and peri-urban areas in China have also confirmed that NbS is significantly effective at promoting ecological restoration, enhancing ecosystem services, and advancing sustainable urbanization [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B15-land-15-01546)]. Notably, the contribution of NbS to food system resilience extends beyond ecological restoration; by restoring pollination networks, improving soil fertility, and regulating microclimates, NbS directly strengthens the ecological foundation of food production. However, urban–rural fringe zones are complex social–ecological systems that are heavily disturbed by anthropogenic activities and are characterized by highly nonlinear and uncertain dynamics. Relying solely on traditional NbS often makes it difficult to achieve rapid responses and precise adaptation to critical changes. In recent years, the rapid development of digital technologies, artificial intelligence, and ecological information sensing has opened new possibilities for overcoming bottlenecks. The ability to capture both high-spatial and high-temporal resolution information on the status of habitats and the flow of ecosystem services can turn information into a useful resource. This, in turn, enables the precise siting, effect monitoring, and adaptive regulation of NbS, turning static ecological restoration into active resilience governance. Scholars noted that the use of digital technologies and artificial intelligence can help increase efficiency in agricultural production, minimize food waste, and make food systems more sustainable [[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B16-land-15-01546)], and such technologies have a major data-supportive role in the promotion of sustainable agriculture and the bioeconomy [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B17-land-15-01546)]. Nevertheless, current research on the resilience of food systems in urban–rural fringe zones does not elaborate on how digitalization could be combined with NbS to systematically mitigate food security risks through enhanced ecological intelligence caused by the fragmentation of habitats.\n\nBefore further analysis of the aforementioned research gaps, it is necessary to review the foundational research progress on habitat restoration and spatial governance within urban–rural fringe zones. Habitat fragmentation leads to biodiversity loss, substantial damage to core ecosystem functions, and exacerbation of the degradation of ecosystem services, making restoration measures that increase landscape connectivity extremely urgent [[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B18-land-15-01546)]. Fahrig systematically distinguished the independent effects of habitat area loss and spatial configuration fragmentation, further emphasizing the critical importance of addressing habitat fragmentation for biodiversity conservation [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B19-land-15-01546)]. Maintaining landscape connectivity positively contributes to ensuring ecosystem service supply and biodiversity maintenance, whereas habitat fragmentation disrupts ecological processes and weakens ecosystem regulation and service efficiency [[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B20-land-15-01546),[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B21-land-15-01546)]. In terms of spatial governance, Rahmoun and Zhao developed a spatial structure model to support sustainable urban–rural development. The model uses strategic clusters and spatial networks [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B22-land-15-01546)]. Aflaki Samani et al. proposed a step-by-step planning approach for informal settlements. This approach aims to reduce spatial inequality and improve land management efficiency [[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B23-land-15-01546)]. Overall, the existing research has four significant limitations in terms of addressing the resilience of peri-urban food systems. First, most studies fail to systematically link digital technologies, ecological wisdom, and the food-ecological coupling resilience of urban–rural fringe zones; in particular, dynamic simulation analyses of resource metabolism processes and security assurance capacities within food systems are lacking. Although the value of digital technologies in empowering agriculture and food systems has been recognized by the academic community, research based on system dynamics modelling in urban–rural fringe zones to reveal the synergistic effects of digital technologies and NbS is scarce. Second, research perspectives often adopt static empirical analyses, making it difficult to elucidate the multiloop, nonlinear dynamic interaction mechanisms between digital empowerment and ecological restoration in urban–rural fringe zones. Third, existing studies often examine the effects of digital technologies, the ecological impacts of urbanization, or individual NbS cases separately. Few studies combine digital technologies, ecological knowledge, and the resilience of peri-urban social–ecological systems into one framework for integrated analysis. Moreover, the link between resilience building and the coupling mechanisms of sustainable food systems remains unclear. Fourth, current research also shows a practical bias. Ecological protection practices mainly focus on the conservation and management of natural reserves. Less attention has been paid to urban–rural fringe areas, which have complex feedback processes and provide both urban food supply and ecological security functions.\n\nOn the basis of the aforementioned gaps, this study attempts to construct a system dynamics model with a three-dimensional linkage of “digital technology empowerment–ecological wisdom accumulation–system resilience building–food system sustainability,” focusing on whether and how digitalization can enhance the resilience of peri-urban food systems against habitat fragmentation by strengthening the implementation effectiveness of NbS. The model measures key variables, including the application level of digital technologies, ecological wisdom capital, ecological perception and accounting capabilities, system resilience, and resource metabolic efficiency. It examines how food systems in urban–rural fringe zones evolve from fragmented states toward synergistic and sustainable states, with a focus on evolutionary pathways and threshold effects.\n\nThis study follows the logic of “digital technology as the driving force → ecological wisdom capital as the regulatory factor → system resilience as the adaptive response → food system sustainability as the co-evolutionary outcome.” It aims to uncover the mechanisms behind resilience development and transition in urban–rural fringe food systems. Specifically, this study addresses three key questions: First, can digital technologies promote the accumulation of ecological wisdom capital and improve system synergy by enhancing ecological perception and accounting capabilities? Can they also strengthen the role of NbS in habitat restoration and food security? Second, what feedback loops and coupling mechanisms link resource metabolic efficiency with ecological resilience in urban–rural fringe food systems? Third, which key leverage variables help urban–rural fringe food systems cross critical thresholds and shift from fragmented states to synergistic and sustainable states?\n\nTo address these questions, this study starts from the two dimensions of technology and ecological wisdom and uses system dynamics methods to describe and quantify the roles of technological and ecological wisdom factors in the influencing process. It simulates the dynamic process through which digital technology drives urban–rural fringe food systems to resist habitat fragmentation and achieve synergistic and sustainable development and identifies the key mechanisms affecting system transition. This approach has academic value in several ways. First, system dynamics provide an effective tool for describing the multiple feedback loops and nonlinear relationships in the co-evolution between digital technologies and urban–rural fringe food systems. It helps fill the theoretical gap in understanding how resilience develops in peri-urban food systems from a dynamic perspective and provides a methodological reference for future studies. However, through simulation studies, different policy intervention scenarios can be modelled to obtain predictive data, providing more forward-looking scientific evidence for the governance of urban–rural fringe food systems. Additionally, this study introduces abstract concepts, such as “ecological wisdom capital” and “ecological perception and accounting capabilities,” into system dynamics analysis. It explores the key factors that help urban–rural fringe food systems cross synergistic thresholds. This approach addresses the limitation of traditional urban–rural governance studies, which often focus more on empirical description than on identifying underlying mechanisms. This study also provides a new application of advanced system dynamics models in urban–rural sustainable governance research. It strengthens the theoretical understanding of the co-evolution of urban–rural fringe food systems and expands the use of dynamic system theory in studies of urban–rural sustainable development.\n\n## 2. Literature Review and Research Methods\n\n### 2.1. The Connotation and Conceptual Definition of Key Variables\n\nThe core concepts addressed in this study follow a logical hierarchy of “Objective-State-Driving Force.” The sustainable food system is the main focus of this study. The resilience of the food–ecology coupled system and the synergy between food systems and landscape resource metabolism are the key variables used to describe the system’s operating state. Digital technologies and ecological wisdom capital are the main driving forces behind system evolution. These concepts are defined below.\n\n(1)\nSustainable Food Systems: Components of the Objective\n\nThe sustainable food system is the ultimate dependent variable in this study. Drawing on the definition framework of the Food and Agriculture Organization of the United Nations [[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B24-land-15-01546)], this study defines it as a system capable of continuously providing food security and nutrition across the three dimensions of the economy, society, and environment, without compromising the ecological foundation for future generations to meet their food needs. Specifically, its components include three pillars: economic sustainability (the economic viability and efficiency of food production activities), social sustainability (the fairness and accessibility of food distribution), and environmental sustainability (the minimal dependence on and depletion of ecosystem services by food production). In the context of the urban–rural fringe, which is the focus of this study, the sustainable food system is further operationalized as a system that, under the pressure of habitat fragmentation, can maintain the ecological foundation for food production, ensure the stability of the food supply for both urban and rural areas, and achieve closed-loop resource metabolism. This operational definition prioritizes environmental sustainability, emphasizing that economic and social sustainability can be realized only by restoring and maintaining healthy ecosystems. In the subsequent system dynamics model, the degree of realization of a sustainable food system is represented by the coupled evolution of the system resilience index and the resource metabolism synergy index.\n\n(2)\nResilience of the Food–Ecology-Coupled System: System Capacity to Cope with Shocks\n\nThe concept of resilience originates from the Latin word “resilio” or “reilire,” meaning “to bounce back or rebound” [[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B25-land-15-01546)], and was later introduced into the fields of ecology and social-ecological systems research. References to urban resilience are increasing [[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B26-land-15-01546)]. The Intergovernmental Panel on Climate Change [[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B27-land-15-01546)] defines resilience as the capacity of a system to cope with hazardous events, trends, or disturbances while maintaining its core functions and structural integrity during change. It is considered a positive attribute when it encompasses the ability to adapt, learn, or transform. The United Nations Office for Disaster Risk Reduction [[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B28-land-15-01546)] views it as the ability of a system or community exposed to hazards to possess six capacities: to resist, absorb, adapt, adjust, transform, and recover, emphasizing the timeliness and effectiveness of the response.\n\nAt a more specific ecological level, resilience refers to the ability of an ecosystem to maintain its stable state when subjected to shocks [[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B29-land-15-01546)]. The resilience of urban and rural systems can be enhanced through interdependent relationships and partnerships [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B15-land-15-01546)].\n\nThe resilience of the food–ecology coupled system proposed in this study is a contextualized definition based on the aforementioned resilience theories. It views urban and rural areas as one integrated system and focuses on the social–ecological system where food production, ecological security, and socio-economic factors interact across the urban–rural continuum. It examines three types of capacity. First, resistance capacity refers to the ability to withstand disturbances, such as habitat fragmentation, resource pressure, and climate variability, while maintaining basic food production functions. Second, recovery capacity refers to the ability to return to the original functional state after disturbances. Third, transformation capacity refers to the ability to shift toward a more sustainable state under long-term pressure by changing system structures, such as adopting NbS and applying digital technologies. In contrast to the general concept of ecological resilience, this concept emphasizes the coupling of food production and ecological functions; enhancing resilience must simultaneously serve the dual goals of food security and ecosystem health. This resilience inherently includes the capacity to restore habitats, improve landscape connectivity, and enhance ecosystem service supply through nature-based solutions, thereby consolidating the ecological foundation for food production. At the model quantification level, the system resilience index is represented on a standardized scale of 0–1, with an initial value set at 0.35, reflecting the system’s typical transitional state between vulnerability and resilience. Its dynamic evolution is driven by the evolution of resilience and decay rates.\n\n(3)\nFood system–landscape resource metabolism synergy: Degree of evolution from disorder to order\n\nSynergistic phenomena exist in all domains of nature. Synergetics originated from general systems theory and was formally established by the German Professor Hermann Haken in the 1970s. In any complex open system, the interactions and mutual influences among its internal subsystems generate comprehensive and collective effects, namely, synergistic effects, that drive the system from disorder to order, resulting in the formation of stable structures [[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1546#B30-land-15-01546)].\n\nIn this study, the degree of synergy between food system and landscape resource metabolism specifically refers to the degree of synergy between the food system and surrounding landscapes in urban–rural fringe areas, particularly in terms of the flow and transformation of key elements such as water resources, energy, and biomass required for food production. This concept characterizes the evolutionary progression of the food system from disordered fragmentation to orderly synergy, and its essence can be understood as the reduction in the mismatch coefficient of food production factors between urban and rural areas. When habitat fragmentation obstructs resource metabolism pathways,","MDPI Land 2026, 15(9), 1546 · 2026-08-24","2026-08-24T00:00:00Z",62,{"impact":160,"substance":17,"depth":161,"authority":160,"freshness":162,"relevant":21,"comment":163},12,16,4,"论文探讨数字化赋能城乡过渡区食物系统韧性，方法新颖，但影响范围有限，时效性一般。",[165],{"name":156,"url":153},[167,168,169,170,26,171],"模拟","数字赋能","城乡过渡区","可持续食物系统","MDPI Land 15 9",[],{"status":174,"locked":14,"version":34,"method":35,"evidence":175},"unclassified",{"candidates":176,"agricultural_evidence":40,"reason":190},[177,182,186],{"code":178,"score":21,"supported":14,"title":179,"abstract":180,"tags":181},"E2",[],[],[170],{"code":115,"score":21,"supported":14,"title":183,"abstract":184,"tags":185},[],[],[168],{"code":39,"score":21,"supported":14,"title":187,"abstract":188,"tags":189},[],[],[26],"缺少足够的主题证据",[192,193],"可持续食物系统 城乡过渡区 系统动力学 数字赋能","可持续食物系统 城乡过渡区","可持续食物系统城乡过渡区系统动力学数字赋能-1003","2026-08-27T00:05:20.371076Z"]