Integrating generative artificial intelligence into green logistics: A systematic review and policy-oriented research agenda

Abstract

In light of mounting environmental issues, the logistics industry plays a critical role in promoting sustainability. While generative artificial intelligence (GAI) has the potential to revolutionize green logistics, several barriers still prevent its widespread adoption. In existing literature, little is known about applications, drivers, enablers, critical barriers, and challenges associated with implementing GAI along with green logistics. To fill this gap, this study aims to systematically identify and assess the existing body of knowledge on the GAI and green logistics nexus, drawing on a systematic literature review carried out in compliance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) protocol. The study identifies 34 key GAI-driven green logistics applications, 23 drivers and enablers, and 38 major barriers and challenges. The findings illustrate that GAI-driven green logistics applications, such as risk assessment and mitigation, decision support and real-time environmental response, resilience testing and scenario planning, are essential for developing sustainable logistics ecosystems. Organizational readiness, stakeholder collaboration, and supportive regulatory frameworks emerge as crucial enablers, while lack of digital infrastructure, investment costs, and regulatory gaps constitute significant barriers. The study proposes a decision-making framework to prioritize policy initiatives that could promote GAI adoption in green logistics. This research fills current knowledge gaps and has significant implications for supply chain stakeholders, scholars, and policymakers aiming to support sustainable and cutting-edge logistics systems.