AI-Driven Green Supply Chain Management: Assessing Adoption, Implementation Gaps, and Environmental Performance

Abstract

 Artificial Intelligence (AI) is increasingly transforming Green Supply Chain Management (GSCM) by enabling intelligent decision-making, logistics optimization, resource efficiency, and environmental improvement. This study examines the role of AI adoption in GSCM among logistics-intensive organizations in Western Maharashtra. Specifically, the study assesses the level of AI adoption, examines the effect of technology investment on AI adoption and environmental performance, and investigates the mediating role of AI adoption in the relationship between technology investment and environmental performance. Primary data were collected from 131 organizations across major logistics-intensive sectors in Western Maharashtra. Descriptive analysis, cluster analysis, regression, mediation analysis, and moderated mediation analysis were employed. The findings identified three distinct levels of AI adoption: low (35.1%), medium (40.5%), and high (24.4%). AI adoption demonstrated a significant positive effect on carbon reduction, with carbon reduction increasing from 4.28% in low-AI organizations to 12.42% in high-AI organizations. Technology investment also increased significantly across AI-adoption levels, from 2.41% to 5.58%. Mediation analysis confirmed that AI adoption significantly mediates the relationship between technology investment and carbon reduction, with an indirect effect of 0.125 (95% CI: 0.068–0.192), indicating partial mediation. Further analysis showed that organizational readiness strengthens the translation of technology investment into AI adoption. The study concludes that AI adoption represents an important organizational capability through which technological investment can contribute to improved environmental performance and more sustainable GSCM practices.