Artificial Intelligence-Enabled Low-Carbon Transition in Shipping: A Systematic Bibliometric Review

Abstract

Introduction: International shipping faces the dual imperative of decarbonization and the maintenance of safety and service reliability. However, evidence concerning the conditions under which artificial intelligence (AI) generates verifiable carbon benefits remains fragmented. Methods: This review examines 479 English-language articles and reviews retrieved from the Web of Science Core Collection and Scopus and published from 2016 to 23 August 2026 through bibliometric analysis, science mapping, auxiliary document-level thematic coding, and full-text synthesis of six representative reviews and one perspective. A post hoc domain-validation sensitivity analysis used two independently specified deterministic rule sets to test whether broad search terms altered the main conclusions. Results: Publication output accelerated markedly after 2022, with 277 papers (57.83%) published during 2022–2025 and a further 137 records already indexed in the partial year 2026. The two screening rules agreed on 96.87% of records (Cohen’s kappa = 0.753). A conservative sensitivity subset of 437 records, obtained through a strict rule-based title-abstract screen and removal of one retracted and one withdrawn record, reproduced the principal temporal, source-journal, and leading-keyword patterns. Machine learning remained the most frequent keyword, while recent studies increasingly addressed deep learning, ship energy efficiency, port operations, federated learning, and energy management. Discussion: Based on these findings, the review advances an evidence-informed AI-to-Carbon Value Chain (AICV) conceptual synthesis comprising data observability, model credibility, decision executability, system coordination, and carbon verification. This synthesis is interpretive rather than a validated causal framework. Future research should prioritize carbon-ready benchmarks, calibrated physics-informed and causal models, human-in-the-loop field evaluation, network-level coordination, and auditable well-to-wake assessment. Review registration and appraisal: This review was not registered, and no formal protocol was prepared. Because no effect-size synthesis was undertaken, formal study-level risk-of-bias, reporting-bias, and certainty assessments were not applied. Funding: The review received no external funding.