{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:32:02Z","timestamp":1784079122414,"version":"3.55.0"},"reference-count":92,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T00:00:00Z","timestamp":1765670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Social Science Foundation Project","award":["24BGL125"],"award-info":[{"award-number":["24BGL125"]}]},{"name":"Fundamental Research Funds for the Universities of Liaoning Province","award":["LJ112410143077"],"award-info":[{"award-number":["LJ112410143077"]}]},{"name":"Project of the Department of Science and Technology of Liaoning Province","award":["2025080034-JH4\/101"],"award-info":[{"award-number":["2025080034-JH4\/101"]}]},{"name":"Asian Research Centre of Liaoning University","award":["Y202313"],"award-info":[{"award-number":["Y202313"]}]},{"name":"Basic Scientific Research Project of Liaoning Provincial Department of Education","award":["LJ132510140001"],"award-info":[{"award-number":["LJ132510140001"]}]},{"name":"Shenyang Aerospace University Talent Introduction Research Fund","award":["22YB08"],"award-info":[{"award-number":["22YB08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019\u20132023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises\u2019 GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: \u201cAI technology innovation-driven (Configuration 1 and Configuration 2)\u201d and \u201cstrategic resource-driven (Configuration 3)\u201d; Configuration 1 combines research and development (R&amp;D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&amp;D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development.<\/jats:p>","DOI":"10.3390\/systems13121120","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T09:36:39Z","timestamp":1765791399000},"page":"1120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Path Exploration of Artificial Intelligence-Driven Green Supply Chain Management in Manufacturing Enterprises: A Study Based on Random Forest and Dynamic QCA Under the TOE Framework"],"prefix":"10.3390","volume":"13","author":[{"given":"Yifei","family":"Cao","sequence":"first","affiliation":[{"name":"College of Economics and Management, Shenyang Aerospace University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingfeng","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Economics and Management, Shenyang Aerospace University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Economics and Management, Shenyang Aerospace University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0227-0779","authenticated-orcid":false,"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Sunwah International Business School, Liaoning University, Shenyang 110136, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113043","DOI":"10.1016\/j.jenvman.2021.113043","article-title":"China carbon neutrality target: Revisiting FDI-trade-innovation nexus with carbon emissions","volume":"294","author":"Liu","year":"2021","journal-title":"J. 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