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As China's green financial system continues to improve and sustainable finance steadily expands, it has effectively promoted the green and low-carbon transformation and upgrading of the domestic capital market. Against the backdrop of the routine development of green investment and financing activities, environmental, social, and governance (ESG) risk management has become a core tool for financial institutions to ensure compliance and control green credit risks, directly determining the quality of risk management across various green financial services. Traditional ESG assessment systems primarily rely on static corporate disclosures and manual expertise for risk evaluation, resulting in a relatively coarse overall approach that struggles to adapt to the new industry landscape following the full implementation of low-carbon policies. These systems commonly suffer from inherent shortcomings such as delayed data updates, strong subjectivity in human judgment, reactive risk mitigation, and insufficient coverage of risk dimensions, making them inadequate to meet today’s demand for refined, high-quality green finance development. Leveraging cutting-edge fintech technologies—including big data, blockchain, and artificial intelligence—financial institutions can achieve quantitative risk assessment, dynamic monitoring, and end-to-end closed-loop management of ESG risks, significantly enhancing the intelligence and precision of green risk governance. However, in practice, multiple real-world barriers still hinder the effective application of fintech in ESG risk management. Issues such as significant disparities in data quality, structural biases in algorithms, lack of standardization in industry-wide ESG evaluation frameworks, and lagging regulatory evolution substantially diminish the enabling power of digital technologies. Based on current industry practices, this paper systematically identifies key application scenarios of fintech in ESG risk management, deeply analyzes critical implementation bottlenecks, and proposes targeted optimization pathways to provide practical guidance for Chinese financial institutions in building standardized, digitalized ESG risk control systems.
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Jieqiong Li (2026) studied this question.
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