Evaluation of carbon emission reduction and sustainable carbon finance potential in Photovoltaic power generation by Coupling Artificial Intelligence and life cycle model

With a typical designed lifespan of 25 years, Photovoltaic (PV) power has become a promising alternative solution to traditional thermal power generation due to its significant long-term clean electricity production. However, few efforts have been made to thoroughly evaluate the prospects of integrating PV into the emerging carbon financial markets to maximize both economic and ecological benefits. Given the burgeoning AI and remote sensing, this study proposed a systematic and scalable methodological framework of “Remote Sensing Extraction − Life Cycle Assessment − Carbon Finance Potential Analysis” to comprehensively investigate the scale, distribution, carbon footprint, carbon reduction, and economic performance of PV. With urgent energy transition needs and a vibrant carbon finance market, the Yangtze River Delta region of China was then selected for a case study. The results indicate that the proposed multi-method framework exhibits a satisfactory accuracy-efficiency balance, with over 94 % extraction accuracy, rapid cloud platform-based processing, and a convenient modular evaluation workflow. With a PV construction area of 577 km² in 2020, the region will achieve an emission reduction benefit of 367 Mt CO₂ and a carbon financial potential of RMB 805.65 billion over a 25-year PV life cycle. Moreover, the distinct spatial pattern of these previously little-recognized substantial benefits demonstrates the necessity of leveraging PV-driven yields according to specific local conditions. By quantifying the multifaceted contribution of the PV industry, this interdisciplinary study aims to facilitate pioneering practices of market expansion and climate response among traders and policymakers, thereby enabling an economically beneficial and environmentally sustainable pathway.