Mapping the Intersection of Artificial Intelligence and Sustainable Finance: A TCCM-Driven Bibliometric Analysis and Conceptual Framework

Artificial intelligence (AI) and sustainable finance are increasingly intersecting, yet re search at their intersection remains fragmented and theoretically underdeveloped. This study combines bibliometric analysis with the Theory–Context–Characteristics–Method ology (TCCM) framework to examine 61 PRISMA-screened articles retrieved from Web of Science. No publication-year restriction was applied; the included corpus spans 2021 to July 2025. Publication output within the corpus increased from seven articles in 2023 to fifteen in 2024, while the 2025 count covers only the period up to the July search date. China accounts for the largest share based on the corresponding author's country. Stake holder Theory is the most frequently applied theoretical lens (10 studies), followed by the Resource-Based View (7 studies) and Institutional Theory (5 studies). Within the thematic map, ESG and artificial intelligence occupy the motor-theme quadrant, whereas green fi nance lies in the emerging-or-declining quadrant. Building on the TCCM synthesis, the study develops a dual-level conceptual framework grounded in the Resource-Based View and Institutional Theory. The framework proposes that AI-related capabilities may con tribute to sustainability outcomes through absorptive capacity, green-innovation pro cesses, and supply-chain efficiency, and positions institutional quality, environmental un certainty, ownership type, and industry competitiveness as boundary conditions. Overall, the study provides a structured theoretical synthesis and a theory-informed agenda for future research at the AI-sustainable finance nexus.