The Chinese government’s ongoing endeavors to achieve the “low carbon goals” hold immense importance in global emissions reduction. Nonetheless, reducing emissions will inevitably cause economic losses. Therefore, the pursuit of green economic efficiency is regarded as an effective tool to mitigate the economic losses during emission reduction. Synchronously, the realization of green economic efficiency is essential for sustainable development. With the increasing awareness of regional green development, emphasis companies place on environmental, social, and corporate governance (ESG), which contributes to corporate ESG construction, could become a novel advantage in terms of attracting investors. Additionally, it could have a lasting impact on corporate green technological innovation, thereby enhancing the efficiency. Based on the data of A-share listed companies in China from 2009 to 2019, this study analyzes the effect of corporate ESG construction on the efficiency of regional green economy as well as its mechanism. The research findings demonstrate a significant positive impact of corporate ESG construction on the efficiency of regional green economies. Specifically, each unit improvement in corporate ESG construction is associated with an approximate 0.7% increase in the efficiency of the regional green economy. The conclusion can be drawn after robustness testing. Notably, the effect of corporate ESG construction is more pronounced for companies located in the eastern region, state-owned enterprises, and high-polluting industries. In terms of the underlying mechanism, corporate ESG construction facilitates regional green economic efficiency by fostering corporate green technological innovation. Furthermore, it is observed that environmental regulations have a negative moderating influence on corporate ESG construction, which in turn affects regional green economic efficiency. When examining the decomposed variables of regional green economic efficiency, the impact of corporate ESG construction on regional green scale efficiency is found to align with its overall effect on regional green economic efficiency. This study contributes to the existing research on corporate ESG construction and regional green economic efficiency, offering valuable insights to guide companies in enhancing both aspects. Building upon the conclusions drawn, we will provide policy recommendations from the perspectives of the company itself, corporate investors, and the government. These recommendations aim to facilitate improvements in corporate ESG construction and foster the enhancement of regional green economic efficiency.

This paper studies the impact of the implementation of smart city policy (SCP) on the development of low‐carbon economy (LCE) in China. For this purpose, we developed a nonconvex meta‐frontier data envelopment analysis (DEA) approach to measure LCE and used the differences‐in‐difference (DID) analysis method in the econometric model to empirically analyze the impact of SCP on LCE, using the dataset of 230 cities from 2005 to 2018. The results show that the implementation of SCP can significantly improve the LCE of cities, and the dynamic effect test presents that the promotion of smart cities to low‐carbon economy increases with time. In addition, SCP promotes the development of LCE by optimizing government functions and improving the efficiency of governance and the degree of implementation openness. But there is heterogeneity between different cities as follows: the implementation of SCP has a more significant effect on the promotion of LCE in central and western regions in China and large‐scale cities and cities without strict environmental protection planning. Finally, the robustness test verifies the reliability of the experimental data again and puts forward conclusions and policy recommendations.

With the rapid development of China’s Internet finance industry and the continuous growth of transaction amount in recent years, a variety of financial risks have increased, especially credit risk in the financial industry. Also, the credit risk evaluation is usually made by using the application card scoring model, which has the shortcomings of strict data assumption and inability to process complex data. In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. At the same time, an Internet lending company in China is taken as a case study to compare the performance of the traditional credit card scoring model and the proposed machine learning (ML) algorithm model. The results show that ML algorithm has a very significant advantage in the field of Internet financial risk control, it has more accurate prediction results and has no particularly strict assumptions and restrictions on data, and the process of processing data is more convenient and reliable. We should increase the application of ML in the field of financial risk control. The value of this paper lies in enriching the related research of financial technology and providing a new reference for the practice of financial risk control.