Applications of artificial intelligence in carbon management and carbon footprint research: a bibliometric review

Abstract

This paper presents a systematic review of the current research landscape in the field of “Artificial Intelligence (AI) + Carbon”. Utilizing bibliometric and visual analysis methods, it identifies and examines key research themes, regional distribution patterns, and evolutionary trajectories within this domain. Findings indicate that a coherent thematic structure centered on carbon emissions has emerged, in which AI technologies play a critical role across various dimensions, including carbon monitoring, simulation, and system optimization. Geographically, research efforts exhibit distinct developmental pathways influenced by divergent national energy strategies and governance frameworks. China leads in applied research and implementation, whereas the United States predominates in foundational theoretical innovations. Other nations engage in context-specific explorations tailored to local priorities. Keyword network analysis reveals a profound coupling between technological capabilities and application scenarios. Temporally, the field has evolved from initial exploratory integrations toward more systematic and holistic approaches, reflecting a growing synergy between technological advances and carbon management imperatives. This review not only offers a structured understanding of the intellectual architecture and emerging hotspots in AI applications for carbon management and footprint research but also provides a foundation for fostering international collaboration and guiding future scholarly and practical endeavors.

Keywords

Artificial intelligence / watershed water pollution / regulatory challenge / SETO loop

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