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<p>The integration of Artificial Intelligence (AI) into Green Supply Chain Management (GSCM) is accelerating as firms seek to enhance decision-making, resource efficiency, and environmental performance. This study conducts a scientometric analysis of 1448 Scopus-indexed articles (1999–2025) using VOSviewer and Bibliometrix to map the evolution, key contributors, and emerging themes within AI-driven GSCM. Three dominant thematic clusters are identified: (1) Circular Economy and Industry 4.0, (2) Blockchain Integration, and (3) Sustainability and Reverse Logistics. Results show rapid growth in machine learning, predictive analytics, blockchain, and multi-objective optimization, reflecting a shift toward digitally enabled sustainability solutions. AI’s role in reverse logistics, waste management, and carbon-emissions reduction is growing, yet challenges remain related to data governance, interoperability, scalability, and organizational readiness. The findings extend theoretical understanding by highlighting the relevance of Dynamic Capabilities, Technology–Organisation–Environment (TOE), and socio-technical transition perspectives in explaining AI-enabled transformations in sustainable supply chains. Practically, the study outlines best practices for AI integration, including forecasting, logistics optimization, blockchain-based traceability, and circular economy performance metrics. Future research should address ethical concerns, regional adoption disparities, and the need for governance frameworks to support responsible and scalable AI deployment in GSCM.</p>

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AI-driven green supply chain management: a scientometric review of global research trends

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Alhamzah F. Abbas,
  • Yudi Fernando,
  • Thoo Ai Chin,
  • Noraindah Abdullah Fahim

摘要

The integration of Artificial Intelligence (AI) into Green Supply Chain Management (GSCM) is accelerating as firms seek to enhance decision-making, resource efficiency, and environmental performance. This study conducts a scientometric analysis of 1448 Scopus-indexed articles (1999–2025) using VOSviewer and Bibliometrix to map the evolution, key contributors, and emerging themes within AI-driven GSCM. Three dominant thematic clusters are identified: (1) Circular Economy and Industry 4.0, (2) Blockchain Integration, and (3) Sustainability and Reverse Logistics. Results show rapid growth in machine learning, predictive analytics, blockchain, and multi-objective optimization, reflecting a shift toward digitally enabled sustainability solutions. AI’s role in reverse logistics, waste management, and carbon-emissions reduction is growing, yet challenges remain related to data governance, interoperability, scalability, and organizational readiness. The findings extend theoretical understanding by highlighting the relevance of Dynamic Capabilities, Technology–Organisation–Environment (TOE), and socio-technical transition perspectives in explaining AI-enabled transformations in sustainable supply chains. Practically, the study outlines best practices for AI integration, including forecasting, logistics optimization, blockchain-based traceability, and circular economy performance metrics. Future research should address ethical concerns, regional adoption disparities, and the need for governance frameworks to support responsible and scalable AI deployment in GSCM.