[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3266":3,"related-3266":54},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":53},3266,"Research on the impact effects of RCEP on China’s aquatic product trade: an analysis based on the GTAP model","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1904058","The implementation of the Regional Comprehensive Economic Partnership (RCEP) will reshape the regional industrial landscape and exert a significant influence on aquatic product trade. Drawing upon the officially signed agreement text, this study constructs a Global Trade Analysis Project (GTAP) model to examine the combined effects of RCEP-mandated tariff reductions and trade facilitation measures on China’s aquatic product trade. The findings reveal that RCEP effectively reduces regional trade costs, with the average tariff rate on aquatic products among member countries declining from 6.60 to 0.72%, representing a reduction of 89.10%. Concurrently, average import clearance time improves by 37.84%, while average export clearance time improves by 19.42%. The implementation of RCEP shifts China’s aquatic product sector from a domestically oriented pattern toward an export-oriented one. The agreement expands the overall scale of aquatic product trade, with both import and export values projected to increase; however, the growth rate of imports substantially outpaces that of exports. In terms of trading partners, intra-RCEP trade growth far exceeds the growth of trade with non-RCEP economies. Furthermore, following the conclusion of RCEP, the improvement in the terms of trade for aquatic products generates a positive welfare effect. The overall impact of RCEP on China’s aquatic product trade represents the combined outcome of trade creation, trade diversion, and trade displacement effects. In this context, promoting the sustainable development of aquatic product trade under the RCEP framework is significant for developing the regional economy and enhancing the long-term resilience of trade.","《区域全面经济伙伴关系协定》（RCEP）的实施将重塑区域产业格局，并对水产品贸易产生重要影响。本研究依据正式签署的协定文本，构建全球贸易分析模型（GTAP），考察RCEP框架下关税削减与贸易便利化措施对中国水产品贸易的叠加效应。研究发现，RCEP有效降低了区域贸易成本，成员国间水产品平均关税税率从6.60%降至0.72%，降幅达89.10%；同时，平均进口通关时间改善37.84%，平均出口通关时间改善19.42%。RCEP的实施推动中国水产品部门由以内需为主转向出口导向。该协定扩大了水产品贸易总体规模，进出口额均预计增长，但进口增速显著快于出口。从贸易伙伴来看，RCEP区域内贸易增长远超与区域外经济体的贸易增长。此外，RCEP生效后，水产品贸易条件改善产生了正向福利效应。RCEP对中国水产品贸易的总体影响是贸易创造、贸易转移与贸易替代效应共同作用的结果。在此背景下，推动RCEP框架下水产品贸易可持续发展，对发展区域经济和增强贸易长期韧性具有重要意义。",null,"Frontiers in Sustainable Food Systems","2026-09-22T00:00:00Z","论文",10,false,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,13,9,1,"基于GTAP模型的RCEP对中国水产品贸易影响研究，数据详实、结论明确，对农业贸易政策有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"RCEP","农产品贸易","水产贸易","GTAP模型","贸易便利化",[33,34],"RCEP 中国 水产品贸易","GTAP 水产品 关税","RCEP中国水产品贸易-3266",0,"10.3389\u002Ffsufs.2026.1904058",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":46,"direction":50,"ingested_from":52},"W7214022970",[41,44],{"name":42,"orcid":43},"Yu Sun","https:\u002F\u002Forcid.org\u002F0000-0002-7059-177X",{"name":45,"orcid":9},"Yani Zhu",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"用GTAP模型评估RCEP关税削减与贸易便利化对中国水产品贸易的影响。","基于RCEP协定文本构建GTAP模型，模拟关税削减与通关时间变化。","RCEP使中国水产品由内需导向转为出口导向，进口增速远超出口，区内贸易增长显著。","其他","可延伸研究RCEP下中国水产品贸易的可持续性与供应链韧性，结合数字贸易与智慧渔业。","openalex","2026-09-23T23:30:07.413243Z",{"total":55,"page":22,"page_size":55,"items":56},3,[57,102,129],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":10,"source_url":60,"published_at":63,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":65,"sources":67,"tags":69,"search_phrases":74,"slug":77,"view_count":36,"doi":78,"paper":79,"created_at":101},2762,"Food-supply resilience under policy, market, and biosecurity shocks: a diagnostic of import vulnerability, supplier substitution, and short-run callable capacity in RCEP agri-food networks","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1924874","Introduction Policy, market, and biosecurity shocks test whether regional trade networks can maintain physical food availability when established sourcing channels are disrupted. This narrower dimension of food security is termed food-supply resilience. This study treats the Regional Comprehensive Economic Partnership (RCEP) as a policy-relevant regional supply space and examines whether its agri-food networks contain short-run callable capacity for import-vulnerable economies. The analysis advances the claim that connectivity is not capacity: trade links support resilience only when they connect vulnerable importers to suppliers with callable surplus, stable ties, and usable access. Methods This study constructs 126 commodity-year networks for six agri-food categories over 2001–2021 using CEPII-BACI trade data, with FAOSTAT quantities used as an independent cross-source check. Import vulnerability is measured through supplier dependence, concentration, and tie stability. Supplier substitution is assessed through regional supplier persistence, lower-bound callable surplus, gross-export upper-bound coverage, and shock-specific access conditions. Post-2021 observations are used as descriptive monitoring checks rather than as agreement-effect estimates. Results Regional Comprehensive Economic Partnership agri-food networks became more connected, but resilience profiles diverged by commodity. Rice was the only distributed-supplier profile. Milk-powder dairy and palm oil combined large regional surplus with exporter cores concentrated around New Zealand and Australia, and Indonesia and Malaysia, respectively. Soybean and pork remained weak regional-substitution cases, while selected tropical and subtropical fruit was retained only as a compositional regional-trade profile. China showed a centrality paradox: it became more central where regional alternatives already existed but remained peripheral in soybean and pork, where extra-regional dependence mattered most. Lower-bound and gross-export upper-bound checks confirmed pervasive regional substitution gaps in soybean and pork across eligible importer profiles, not only for China. The Philippines-rice case showed the contrasting profile of an import-vulnerable economy backed by substantial regional surplus. Discussion The shock episodes support the diagnostic. Rice export restrictions, Indonesia’s palm-oil export ban, and the African swine fever pork shock show that regional networks support food-supply resilience only when vulnerable importers are connected to suppliers with callable surplus, stable ties, and usable access. Regional agreements can reduce frictions and improve coordination, but they do not automatically create deployable supply. Food-security governance should therefore monitor commodity-importer vulnerability, callable surplus, gross-export upper bounds, tie persistence, and access readiness rather than treating network connectivity as resilience.","引言 政策、市场和生物安全冲击检验了区域贸易网络在既有采购渠道中断时能否维持实体粮食可获得性。这一较窄维度的粮食安全被称为粮食供应韧性。本研究将《区域全面经济伙伴关系协定》（RCEP）视为具有政策相关性的区域供应空间，并考察其农食网络是否对进口脆弱型经济体包含短期可调用能力。分析提出，连通性不等于能力：贸易联系只有在将脆弱进口方连接到拥有可调用盈余、稳定联系和可用准入的供应方时，才能支撑韧性。方法 本研究利用CEPII-BACI贸易数据构建了2001—2021年间六大农食类别的126个商品—年份网络，并使用FAOSTAT数量数据作为独立的跨来源校验。进口脆弱性通过供应方依赖、集中度和联系稳定性来衡量。供应方替代通过区域供应方持续性、下限可调用盈余、总出口上限覆盖率和冲击特定准入条件来评估。2021年后的观察结果被用作描述性监测检查，而非协定效应估计。结果 RCEP农食网络变得更为连通，但韧性特征因商品而异。大米是唯一呈现分布式供应方特征的商品。奶粉乳制品和棕榈油分别将大规模区域盈余与集中在新西兰和澳大利亚、印度尼西亚和马来西亚的出口核心相结合。大豆和猪肉仍是区域替代较弱的案例，而部分热带和亚热带水果仅作为构成性区域贸易特征得以保留。中国表现出中心性悖论：在区域替代品已经存在的地方，中国变得更为中心，但在大豆和猪肉这些域外依赖最为重要的领域，中国仍处于边缘。下限和总出口上限检查证实，在符合条件的进口方特征中，大豆和猪肉普遍存在区域替代缺口，不仅限于中国。菲律宾—大米案例则展示了另一种特征：一个进口脆弱型经济体得到大量区域盈余的支撑。讨论 冲击事件支持了这一诊断。大米出口限制、印度尼西亚棕榈油出口禁令和非洲猪瘟猪肉冲击表明，区域网络支撑粮食供应韧性","2026-09-16T00:00:00Z",80,{"impact":19,"substance":17,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":66},"基于CEPII-BACI与FAOSTAT构建126个商品-年份网络，提出“连通性不等于可调用产能”的诊断框架，对大豆、猪肉等进口脆弱品类有实质结论，但属学术论文，产业落地影响有限。",[68],{"name":10,"url":60},[70,71,27,72,28,73],"粮食安全","大豆","供应链韧性","猪肉",[75,76],"供应链韧性 农产品贸易 粮食安全 大豆","供应链韧性 农产品贸易","供应链韧性农产品贸易粮食安全大豆-2762","10.3389\u002Ffsufs.2026.1924874",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":36,"oa_url":94,"card":95,"direction":99,"ingested_from":52},"W7213434130",[82,84,87,89,91],{"name":83,"orcid":9},"Guo Ru",{"name":85,"orcid":86},"Bolu Wei","https:\u002F\u002Forcid.org\u002F0009-0003-4854-2036",{"name":88,"orcid":9},"Mingrong Zeng",{"name":90,"orcid":9},"Pan Liu",{"name":92,"orcid":93},"Bixiang Shi","https:\u002F\u002Forcid.org\u002F0000-0003-2034-1710","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1924874\u002Fpdf",{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"基于RCEP农食贸易网络，诊断进口脆弱性、供应商替代与短期可调用产能。","用CEPII-BACI 2001–2021年126个商品-年份网络，FAOSTA","RCEP连通性增强但韧性分化，大豆和猪肉普遍存在区域替代缺口。","农业人工智能与决策模型","可构建可调用剩余与准入条件的动态预警模型，识别替代缺口商品。","2026-09-17T23:30:08.362367Z",{"id":103,"title":104,"url":105,"summary":106,"summary_zh":9,"content":107,"source_name":108,"source_url":9,"published_at":109,"category":110,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":117,"tags":119,"search_phrases":124,"slug":127,"view_count":36,"doi":9,"paper":9,"created_at":128},2563,"中国农业科学院 AI赋能农产品贸易跨境有害生物风险管控 植保所登Artificial Intelligence in Agriculture","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F6e1f3ee4b85d402c9b490a571ade9af0.htm","植保所农业入侵生物预防与监控创新团队利用人工智能技术有效破解跨境有害生物多阶段风险评估与管控决策衔接脱节难题，相关研究成果发表在农业人工智能期刊上。研究团队首次解析了柑橘害虫随跨境贸易传入的243条风险路径和98个关键风险节点，将多阶段定量风险评估信息与大语言模型等生成式人工智能相结合，构建了证据可追溯、结论可解释的智能风险研判与辅助决策应用系统。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fcaas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 \n\n![Image 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 2](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[![Image 3](https:\u002F\u002Fcaas.cn\u002Fstatic2022\u002Fimages\u002Fmenu_logo.png)](https:\u002F\u002Fcaas.cn\u002Findex.htm)\n\n*   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Agriculture**_）上。\n\n随着全球贸易持续加速，柑橘等重要果品进口量攀升，植物有害生物随贸易“搭车”入境的风险日益加剧。然而，跨境植物有害生物伴随果品贸易“传入—定殖—扩散”的多阶段风险尚不明确。\n\n研究团队首次解析了柑橘害虫随跨境贸易传入的243条风险路径和98个关键风险节点，精准锁定介壳虫、实蝇等高风险类群及其关键来源地与传入地区，发现我国南方柑橘主产区面临多种害虫共同传入与定殖的复合威胁。\n\n在此基础上，该研究将多阶段定量风险评估信息等数据与大语言模型等生成式人工智能相结合，突破通用大语言模型缺乏领域知识的固有局限，构建了证据可追溯、结论可解释的智能风险研判与辅助决策应用系统，为跨境有害生物风险管控提供科技支撑。\n\n该研究得到国家重点研发计划等项目支持。\n\n相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.07.004\n\n(单位: 中国农业科学院植物保护研究所)\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fcaas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [[科学网] 弯刺蔷薇缘何成为月季的抗寒育种亲本](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F71f0fd687f5a4cbcb5d8e14dd8ca5ab7.htm)2026-09-11  \n*   [[科学网] 高效引导编辑系统创制双抗黄瓜新种质](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F918aff6c268a4196880a4514b117bf34.htm)2026-09-11  \n*   [[总台中国乡村之声]《乡村FM》东北粮仓的秋粮冲刺](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F2a01860de3d54f759501beb9fb666fee.htm)2026-09-11  \n*  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2026-09","2026-09-13T01:00:00Z","报道",76,{"impact":17,"substance":113,"depth":114,"authority":114,"freshness":115,"relevant":22,"comment":116},16,15,8,"国家级科研机构在AI农业期刊发表跨境有害生物风险管控成果，专业性与权威性突出，但正文信息有限，属细分领域科研进展。",[118],{"name":108,"url":105},[120,121,122,123,28],"农业人工智能","植物保护","智慧植保","跨境有害生物",[125,126],"农业人工智能 跨境有害生物 农产品贸易 智慧植保","农业人工智能 跨境有害生物","农业人工智能跨境有害生物农产品贸易智慧植保-2563","2026-09-16T00:03:46.695280Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":9,"content":134,"source_name":135,"source_url":9,"published_at":136,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":137,"score_detail":138,"sources":143,"tags":145,"search_phrases":149,"slug":152,"view_count":153,"doi":9,"paper":9,"created_at":154},420,"Structural Evolution and Determinants of the Agricultural Machinery Trade Network Among RCEP Members","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070","Xinyi Li、Wenqi Wang、Meng Zhang发表于Sustainability 2026, 18(16), 8070, DOI: 10.3390\u002Fsu18168070。基于15个RCEP成员国2004-2023年双边农机贸易数据,构建有向加权网络,运用社会网络分析(SNA)和二次指派程序(QAP)考察结构演变、节点位置、社区格局及双边贸易联系因素。网络密度从0.467增至0.962,平均路径长度从1.533降至1.038,中介中心度从0.064降至0.021,表明联系更紧密、潜在传输路径更短、对中介节点依赖降低。2020年后中国中心度最高,日本、韩国、澳大利亚、新加坡及主要东盟经济体保持互补地位;QAP结果显示贸易联系与GDP差异、共同语言、接壤、汇率差异正相关,与水资源差异、贸易结构差异、制度距离负相关。","## 1. Introduction\n\nAgricultural machinery provides an essential material and technological foundation for food security, higher agricultural productivity, and the timely completion of farm operations [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B1-sustainability-18-08070),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B2-sustainability-18-08070),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B3-sustainability-18-08070)]. Unlike conventional approaches to mechanization that emphasize labor substitution and scale expansion, sustainable agricultural mechanization gives greater weight to economic viability, environmental compatibility, social inclusion, and adaptation to local farming systems. Appropriate mechanization can reduce drudgery, improve land and labor productivity, support conservation tillage, post-harvest handling, and agro-processing, and thereby enhance the operational efficiency of food systems [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B2-sustainability-18-08070),[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B4-sustainability-18-08070),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B5-sustainability-18-08070),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B6-sustainability-18-08070)].\n\nThe sustainability effects of agricultural machinery are determined not by machinery inputs alone but by the production technologies and practices they embody. Precision seeding, variable-rate input application, water-efficient irrigation, reduced or zero tillage, energy-efficient drying, and digital control can lower the use of fuel, water, fertilizers, and pesticides while maintaining or increasing output [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B2-sustainability-18-08070),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B7-sustainability-18-08070)]. International trade can broaden access to relevant equipment, components, and technical knowledge. The extent of diffusion nevertheless depends on equipment prices, financing conditions, skills training, maintenance capacity, and the compatibility of machinery with local crop types, farm sizes, water conditions, and climate risks [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B4-sustainability-18-08070),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B5-sustainability-18-08070),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B6-sustainability-18-08070),[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B8-sustainability-18-08070)].\n\nRCEP entered into force on 1 January 2022. Its member economies differ substantially in manufacturing capacity, agricultural production structures, and market demand [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B9-sustainability-18-08070),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B10-sustainability-18-08070),[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B11-sustainability-18-08070),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B12-sustainability-18-08070),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B13-sustainability-18-08070)]. Available industrial and trade patterns suggest that Japan and the Republic of Korea have relatively strong capabilities in high-value equipment, precision components, and intelligent control systems; China combines a broad manufacturing base with scale and cost advantages; Singapore is well positioned to support logistics organization and supply-chain services; ASEAN members represent expanding markets for scale-appropriate agricultural machinery; and Australia and New Zealand have demand associated with large-scale cropping and livestock production. These production-demand complementarities provide an economic and functional basis for regional agricultural machinery trade and potential cross-border technology flows [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B8-sustainability-18-08070)].\n\nRegional trade integration generates both efficiency gains and concentration risks. Dense agricultural machinery trade ties and specialized supply systems can reduce procurement costs, shorten delivery times, and facilitate technological learning. Conversely, excessive dependence on a single country or a small number of producers of critical components may allow localized shocks to spread rapidly through the trade network [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B14-sustainability-18-08070),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B15-sustainability-18-08070)]. The sustainability of agricultural machinery trade therefore depends not only on cost and efficiency improvements but also on diversified sources of supply, interoperable technical standards, inventories of critical components, maintenance capacity, and coordination across communities. Because agricultural machinery is a durable capital good, sustainable governance should extend across the product lifecycle, including environmental performance assessment, durable and repairable design, component reuse, remanufacturing, recycling, and regulated end-of-life treatment [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B16-sustainability-18-08070),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B17-sustainability-18-08070),[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B18-sustainability-18-08070),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B19-sustainability-18-08070)].\n\nAgainst this background, this study addresses three questions. How has the structure of the agricultural machinery trade network among RCEP members evolved? How have the relative positions of individual members changed within the regional network? How are economic, resource, geographical, cultural, and institutional differences associated with bilateral agricultural machinery trade ties, and what possible implications follow for sustainable technology diffusion and regional supply-chain resilience? This study provides significant insights into the development of the agricultural machinery trade network within the RCEP region, the influencing factors, the promotion of sustainable technology diffusion, and the regional supply chain resilience of agricultural machinery.\n\n## 2. Literature Review\n\nThe literature relevant to this study comprises three strands. The first examines the structure and evolution of international trade networks. As global trade has shifted from linear bilateral relationships toward interactions among multiple actors, conventional trade analysis has become less able to capture structural dependence among countries. Complex network analysis and social network analysis have therefore been widely applied to international trade. These studies typically treat countries as nodes and bilateral trade flows as directed weighted edges, and investigate the topology and evolution of global trade networks in terms of connectivity, node centrality, and community structure [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B20-sustainability-18-08070),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B21-sustainability-18-08070),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B22-sustainability-18-08070),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B23-sustainability-18-08070),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B24-sustainability-18-08070),[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B25-sustainability-18-08070),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B26-sustainability-18-08070),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B27-sustainability-18-08070)]. Related research on international economic integration and the dynamics of global trade communities has documented core–periphery structures and regional differentiation [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B28-sustainability-18-08070),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B29-sustainability-18-08070),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B30-sustainability-18-08070)]. Within the RCEP context, recent studies have examined the structural evolution and formation mechanisms of manufacturing trade networks [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B31-sustainability-18-08070),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B32-sustainability-18-08070)]. This work provides an important methodological foundation, but network research remains limited for agricultural machinery, which simultaneously functions as a capital good, a carrier of technology, and an agricultural input.\n\nThe second strand concerns agricultural mechanization and sustainable development. Early research focused mainly on the economic effects of machinery through labor substitution, productivity growth, and technology adoption [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B1-sustainability-18-08070)]. More recent work has expanded to resource use, climate adaptation, social inclusion, and food-system transformation [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B2-sustainability-18-08070),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B3-sustainability-18-08070)]. Farm size and land fragmentation directly affect machinery adoption and operating efficiency [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B33-sustainability-18-08070)], whereas machinery-service outsourcing and specialization can ease the fixed-investment constraints faced by smallholders [[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B34-sustainability-18-08070)]. Studies further show that scale-appropriate equipment, leasing services, and cooperative use can improve smallholder access to technology, although diffusion still depends on financing, skills training, local repair networks, and supportive institutions [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B4-sustainability-18-08070),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B5-sustainability-18-08070),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B6-sustainability-18-08070),[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B8-sustainability-18-08070)]. Regarding environmental outcomes, precision agriculture can improve input-use efficiency and offer emissions-reduction potential [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B7-sustainability-18-08070)]. Durable design, repair, remanufacturing, and material recovery are also integral to sustainable lifecycle governance of agricultural machinery [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B16-sustainability-18-08070),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B17-sustainability-18-08070),[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B18-sustainability-18-08070),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B19-sustainability-18-08070)].\n\nThe third strand addresses trade in agricultural machinery products and its determinants. Existing studies examine trade scale and product structure [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B35-sustainability-18-08070)], export flows and trade potential [[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B36-sustainability-18-08070),[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B37-sustainability-18-08070),[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B38-sustainability-18-08070)], intra-industry trade [[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B39-sustainability-18-08070)], developments in agricultural machinery markets in major advanced economies [[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B40-sustainability-18-08070)], and the comparative competitiveness of agricultural machinery products from China and India [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B41-sustainability-18-08070)]. Research on export competitiveness evaluates Chinese agricultural machinery products in terms of comparative advantage, market share, export technological sophistication, and product quality. It generally finds continued growth in China’s agricultural machinery exports but substantial heterogeneity in competitive advantage, quality tier, and market performance across products [[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B42-sustainability-18-08070),[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B43-sustainability-18-08070),[44](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B44-sustainability-18-08070),[45](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B45-sustainability-18-08070)]. Trade-policy shocks can also affect agricultural machinery trade through import costs, product substitution, and supply-chain adjustment [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B46-sustainability-18-08070)]. Overall, this literature relies largely on gravity models, constant-market-share models, and competitiveness indices to explain bilateral trade volumes or export performance, while paying less attention to the overall regional structure of agricultural machinery trade from a relational-network perspective.\n\nTaken together, the literature provides a strong basis for understanding trade-network structures, the effects of agricultural mechanization, and trade in agricultural machinery, but three gaps remain. First, trade-network studies focus mainly on global trade or manufacturing as a whole, whereas research on agricultural machinery trade concentrates on export competitiveness, trade potential, and bilateral determinants. The overall evolution of the RCEP agricultural machinery trade network has not been systematically identified. Second, few studies examine agricultural machinery trade simultaneously through the lenses of structural readiness for technology diffusion and exposure to supply-chain concentration. Consequently, they reveal little about how connectivity, centralization, and community structure may enable cross-border technology flows while also creating concentration risk. Third, conventional regression models cannot adequately account for the structural dependence inherent in relational data between countries. This article attempts to provide a supplement. This study makes three contributions: first, it constructs a directed weighted agricultural machinery trade network among RCEP member countries from 2004 to 2023, characterizing its evolution at the levels of the overall network, nodes, and communities; second, it evaluates this network as critical infrastructure facilitating technology flow, supply chain coordination, and inclusive mechanization, exploring its potential links to sustainable technology diffusion and regional supply chain resilience; third, it employs QAP methods to identify statistical associations between factors such as economic scale, agricultural resources, geography and culture, exchange rates, trade structure, and institutional distance, and bilateral trade relationships.\n\n## 3. Evolution of the Agricultural Machinery Trade Network Among RCEP Member States\n\n### 3.1. Data and Product Coverage\n\nThis study uses bilateral agricultural machinery trade data for 2004–2023 from the United Nations Comtrade Database (UN Comtrade) [[47](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B47-sustainability-18-08070)]. Because statistical definitions and product coverage differ across members, this study follows the function- and production-stage-based classification adopted in related research [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B46-sustainability-18-08070)] and groups six-digit HS products into nine categories: tillage and land preparation, planting and fertilizing, harvesting, post-harvest handling, primary processing of agricultural products, agricultural transport, livestock machinery, agricultural power machinery, and other agricultural machinery ([Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#table_body_display_sustainability-18-08070-t001)). This classification broadly covers the pre-production, production, and post-production stages of agriculture and permits the network analysis to capture trade channels through which machinery, components, and related technical knowledge may move.\n\n**Table 1.** Classification and HS Codes of Agricultural Machinery Products.\n\n### 3.2. Node Selection and Network Construction\n\nIn the trade network, countries constitute nodes and bilateral trade relationships form directed edges; trade among multiple countries therefore creates a directed weighted network [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B20-sustainability-18-08070),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B21-sustainability-18-08070),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B22-sustainability-18-08070),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B23-sustainability-18-08070)]. The sample comprises all 15 RCEP members: China, Japan, the Republic of Korea, Australia, New Zealand, and the ten ASEAN members. Bilateral 15 × 15 trade matrices are constructed from data for 2004–2023. To represent changes across stages of network development, 2004, 2010, 2015, 2020, and 2023 are selected as benchmark years. A bilateral trade value of USD 1 million is used as the edge threshold to identify economically substantive capital-goods relationships and to prevent low-frequency, very small, or one-off transactions from mechanically inflating network density. The cutoff is an operational definition of the high-value trade backbone, not a minimum level required for technology diffusion. It may understate fragmented participation by smaller economies, including Laos, Cambodia, Myanmar, and Brunei; consequently, findings concerning peripheral participation and network density are conditional on this threshold. Alternative cutoffs could change the number of low-value ties and should be examined in future sensitivity analyses. Network visualization and indicator calculation are performed in Gephi 0.9.2 and UCINET 6, respectively [[47](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B47-sustainability-18-08070),[48](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#B48-sustainability-18-08070)].\n\n### 3.3. Evolution of Agricultural Machinery Trade Network Patterns\n\nGephi is used to visualize the agricultural machinery trade network among RCEP members. Nodes represent member states, and node size reflects a country’s share of total regional agricultural machinery trade. Directed edges represent export relationships and point from the exporter to the importer; edge thickness reflects bilateral trade flows. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#fig_body_display_sustainability-18-08070-f001) presents the network structures in 2004, 2010, 2015, and 2023. China, Japan, the Republic of Korea, Singapore, Thailand, Vietnam, Malaysia, and Australia display relatively high node weights in the selected benchmark years, whereas Laos, Myanmar, Cambodia, Brunei, and New Zealand display relatively low weights under the USD 1 million threshold. The visualized network therefore exhibits a marked hierarchy in high-value trade participation.\n\n**Figure 1.** Evolution of the Agricultural Machinery Trade Network, 2004, 2010, 2015, and 2023. Note: In [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8070#fig_body_display_sustainability-18-08070-f001), from left to right, these represent the years 2004, 2010, 2015, and 2023.\n\nChina’s observed position in the regional agricultural machinery trade network strengthened over the sample period. In 2004, China already had substantial trade ties with the Republic of Korea, Japan, Thailand, Vietnam, and Malaysia, with the China–Republic of Korea relationship particularly prominent. China’s node size subsequently expanded, and its connections with Japan, the Republic of Korea, Vietnam, Malaysia, Thailand, and Singapore became markedly thicker. By the later part of the sample period, a larger share of high-value trade flows involved China, which is consistent with an expanding hub position in regional agricultural machinery imports, exports, and industrial-chain linkages.\n\nJapan, the Republic of Korea, and Singapore retained comparatively important network positions throughout the sample period. Japan and the Republic of Korea consistently ranked among the members with high node weights and maintained strong trade ties with China, Vietnam, Malaysia, and Thailand. In conjunction with the industrial patterns discussed in the literature, these network positions are consistent with important roles in high-value equipment and component supply. The China–Republic of Korea link was promi","MDPI Sustainability 2026-08-07","2026-08-07T00:00:00Z",72,{"impact":19,"substance":139,"depth":19,"authority":140,"freshness":141,"relevant":22,"comment":142},20,12,4,"论文聚焦RCEP农机贸易网络，数据详实，对区域农业合作有参考价值，但时效性一般。",[144],{"name":135,"url":132},[27,146,147,148,72],"农机贸易","社会网络分析","可持续技术扩散",[150,151],"可持续技术扩散 社会网络分析 供应链韧性 农机贸易","可持续技术扩散 社会网络分析","可持续技术扩散社会网络分析供应链韧性农机贸易-420",2,"2026-08-11T23:57:03.318282Z"]