An Empirical Analysis of the Impact of ESG Management Strategies on the Long-Term Financial Performance of Listed Companies in the Context of China Capital Market

In the evolving landscape of China’s capital markets, the integration of Environmental, Social, and Governance (ESG) considerations has become increasingly crucial for investors and decision-makers. Traditional financial performance metrics often fall short in capturing the multidimensional and long-term impacts of ESG factors. This study introduces a novel computational framework that combines domain-adapted pre-trained language models with structured financial regression analysis, aiming to empirically assess the correlation between ESG disclosures and long-term financial performance. This approach allows for the simultaneous processing of both structured and unstructured ESG data, using graph-based modeling and reinforcement learning to guide sustainability aligned policy optimization. Our empirical results show that firms with consistent and well-structured ESG strategies exhibit significantly superior long-term financial outcomes compared to those with weak or inconsistent ESG engagement. This study not only confirms the value of ESG engagement in enhancing financial resilience but also offers practical recommendations for investors, regulators, and corporate decision-makers, emphasizing consistent disclosure, sector-aligned ESG investment, and proactive adaptation to policy shifts.