{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:14:19Z","timestamp":1768878859308,"version":"3.49.0"},"reference-count":77,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Social Science Projects in China (Evaluation of the Development Potential of the Deep Blue Fisheries Industry under Climate Change)","award":["No.21&ZD100"],"award-info":[{"award-number":["No.21&ZD100"]}]},{"name":"Major Social Science Projects in China (Evaluation of the Development Potential of the Deep Blue Fisheries Industry under Climate Change)","award":["CARS-47-G29"],"award-info":[{"award-number":["CARS-47-G29"]}]},{"name":"China Modern Agricultural Industry Technology System Support Project","award":["No.21&ZD100"],"award-info":[{"award-number":["No.21&ZD100"]}]},{"name":"China Modern Agricultural Industry Technology System Support Project","award":["CARS-47-G29"],"award-info":[{"award-number":["CARS-47-G29"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system\u2019s risk resistance capacity and providing decision support for achieving the \u201cdual carbon\u201d goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of \u201csteady-state maintenance\u2013dynamic adjustment\u2013continuous evolution\u201d from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of \u201cmultiple conjunctural causality\u201d in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the \u201ccarbon reduction\u2013supply stability\u2013efficiency improvement\u201d goals, transforming them from policy vision to operational reality.<\/jats:p>","DOI":"10.3390\/systems13070615","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T13:59:11Z","timestamp":1753106351000},"page":"615","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6486-5554","authenticated-orcid":false,"given":"Daqing","family":"Wu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Tianhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Hangqi","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China"}]},{"given":"Shousong","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s43979-022-00014-8","article-title":"Facilitating Full and Effective Implementation of the Paris Agreement for Carbon Neutrality Vision","volume":"1","author":"Xu","year":"2022","journal-title":"Carbon Neutrality"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Asha, L.N., Dey, A., Yodo, N., and Aragon, L.G. 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