Environmental, social and governance (ESG) goes beyond its function as a business to maximize profits for the shareholders to work for societal purposes. Meanwhile, the green credit policy in China is still in its infancy, and the impact of green loans on the efficiency of commercial banks is significantly different. In this context, this paper details the company’s performance in crucial aspects such as low-carbon operations, eco-friendly financial innovation, a sustainable economic system, data security and the development of organizational capabilities to provide a sustainable development paradigm for supply chain finance technology peers. Based on ESG portfolio, we found that adding ESG holdings to a company affects its compliance with delivery or environmental rules, and anode and cathode of ESG combined Dual Carbon (DC) are presented in terms of emission levels. Our further research indicates the implementation of Green Credit Guideline has a positive impact on ESG performance of both green and polluting firms in comparison with others. The result was fully supported by different methods and models including PSM-DID (Propensity Score Matching-Differences-in-Differences), QDID (Quantiles Differences-in-Differences), and Kernel approaches, which can provide more implications and references for policy makers. Investors, politicians, and other essential stakeholders perceive ESG as a strategy to protect enterprises from future risks.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

The banking sector is a pillar of the world’s economic fabric and is today facing a major revolution due to the demands of sustainable development objectives and the evolution of sustainable finance tools. This article analyses the impact of green credit on commercial banks’ performance based on data from 10 commercial banks in China between 2012 and 2022. The study found that in the short term, the implementation of green credit has a positive effect on the income level of commercial banks’ intermediate activities and a moderating effect on their return on total assets and non-performing loan ratio.