ARTIFICIAL INTELLIGENCE IN GREEN LOGISTICS AND ENVIRONMENTAL PERFORMANCE: A BIBLIOMETRIC ANALYSIS OF GLOBAL RESEARCH TRENDS (2016-2026)

RENUGALA M SHOLLUNAYAGAM; Azian  Ibrahim; Joann  Lim; Sangita  Jeyaram; Fuziah  Ismail

doi:10.51200/lbibf.v24i1.7798

Authors

RENUGALA M SHOLLUNAYAGAM Faculty of Business and Management, Southern University College (Southern UC), Johor Bahru, Malaysia
Azian Ibrahim Faculty of Industrial Management, University Malaysia Pahang Al-Sultan Abdullah (UMPSA), Pahang, Malaysia
Joann Lim Faculty of Business and Management, Southern University College, Johor, Malaysia
Sangita Jeyaram Faculty of Business and Management, Southern University College, Johor, Malaysia
Fuziah Ismail Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor, Malaysia

DOI:

https://doi.org/10.51200/lbibf.v24i1.7798

Abstract

The environmental impact of logistics operations has accelerated the adoption of Artificial Intelligence (AI) to enhance operational efficiency and environmental performance. However, research on AI-enabled green logistics remains fragmented, limiting a comprehensive understanding of its intellectual development. This study presents a bibliometric analysis of AI-enabled green logistics research using 1,929 peer-reviewed journal articles indexed in Scopus between 2016 and May 2026. Following data cleaning with OpenRefine, performance analysis and science mapping were conducted using VOSviewer and Scopus analytical tools, including citation, keyword co-occurrence, and co-authorship analyses. The findings indicate rapid publication growth since 2020, with machine learning, intelligent transportation systems, optimisation, emission reduction, and energy efficiency emerging as dominant research themes. The United States, China, and the United Kingdom lead global research collaboration, although the field remains conceptually fragmented with limited theoretical integration. This study provides a comprehensive knowledge map, identifies key research gaps, and proposes future research directions for AI-enabled green logistics and environmental performance.