This paper conducts a scoping review of AI-related applications in low-carbon tourism based on representative studies from 2016 to 2025. A Python-assisted, rule-based literature screening pipeline was used to classify studies into carbon assessment, tourist behavior analysis, and intelligent decision support. The review shows that traditional methods such as carbon footprint accounting, STIRPAT, LMDI, system dynamics, and DEA remain dominant in tourism carbon studies, while AI-related methods such as machine learning, clustering, GIS-based analysis, recommendation algorithms, and optimization models are emerging in behavioral analytics and decision support. Based on these findings, an AI-enabled framework is proposed to integrate data acquisition, carbon accounting, intelligent modelling, application decision-making, and feedback optimization.

Yan Jinghui, Zhang Yanhua, An Xiang, Zhang Wendu. A Review of AI-Enabled Low-Carbon Tourism: From Carbon Assessment to Intelligent Decision Support. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 7: 52-57. https://doi.org/10.25236/AJCIS.2026.090707.

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