The objective of this paper is to analyze the problems of information asymmetry, mode lag, and high operational risk between banks and enterprises in the practice of supply chain finance business. In this paper, first, the concept and function of supply chain finance were elaborated, and the theoretical basis of supply chain financial risk control was studied. The implement of principal agent, information asymmetry theory, and self-compensated trade finance theory on realizing the risk avoidance in the actual development process was analyzed. Then, the business processes of the three basic financing models of supply chain finance and the shortcomings in the operation of each model were proposed. On this basis, risk sources and influencing factors of supply chain finance were analyzed, and operational risks were emphasized. Finally, a new financing mode of inventory pledge was designed based on the special function of Internet of Things (IoT) technology and the business process of inventory financing mode. The results showed that there was a big gap in the value of risk loss caused by various types of loss events in the operation risk of supply chain finance inventory pledge financing mode; among them, the biggest losses were from external fraud. By comparing the expected loss (ES) value of inventory pledge financing mode based on the IoT with the operation risk of traditional mode, it could be found that the supply chain finance mode based on the IoT technology effectively reduced the operation risk. Therefore, the system designed in this paper has certain reference and practical significance for financial risk management.

With the rapid development of technologies such as big data, intelligent data analysis and cloud computing, the application of Internet financial technology has become more and more extensive, and with the advent of the era of large asset management in the domestic wealth management industry, in order to improve the efficiency of financial services, traditional finance is needed. The products and services provided by the industry have been innovated, resulting in smart investment. Compared with traditional investment, smart investment as a new business model has the advantages of low threshold, low cost and high efficiency. However, as far as its nature is concerned, smart investment must first play the role of an investment adviser. Therefore, for enterprises or individuals who invest, the investment efficiency of smart investment is the most important. At present, the research on the efficiency analysis of smart investment, due to the improper selection of algorithm models or the lack of deep data mining, leads to the analysis of the investment efficiency of smart investment products is inconsistent with or even deviated from the actual situation. In view of these problems, this paper selects China Merchants Bank’s Capricorn Intelligence as the research object, and analyzes the investment efficiency of smart investment based on K-means cluster analysis and data mining technology. The results show that Capricorn has a certain randomness in the selection process of the fund, and chooses to reduce the rate of return in order to control the risk. The investment portfolio formulated for the customer has obvious timing. The results show that the machine learning based on K-means algorithm makes a concrete analysis of the investment efficiency of Capricorn Smart Investment, this method can also be used for the efficiency analysis of other smart investment products.

In the context of the rapid rise of the digital economy, comprehending its role in shaping urban-rural income dynamics has become increasingly critical. This study focuses on smart tourism cities (a hallmark of the digital economy) to examine how the digital economy influences the urban-rural income gap from a tourism perspective. Despite the growing prominence of the digital economy, its specific impact on urban-rural income disparities in tourism-dependent regions remains underexplored, motivating this investigation. This study selects 71 key tourism cities over the period 2012–2019 and employs advanced analytical methods, including mediating effects analysis, multi-period Difference-in-Differences (DID) testing, instrumental variable approaches, robustness testing, and heterogeneity analysis. The findings indicate that: first, the digital economy significantly reduces the urban-rural income gap in tourism cities; second, tourism serves as a key mediating variable in this process; third, after conducting an exogenous shock test using a multi-period DID econometric model based on the "Broadband China" initiative, the conclusions remain robust; fourth, the digital economy's impact on narrowing the income gap is particularly pronounced in second-tier and lower-tier cities, especially in third-tier and lower-tier cities, while no significant effect is observed in first-tier cities. In summary, the development of the digital economy effectively narrows the urban-rural income gap in tourism cities. Therefore, governments should prioritize enhancing the construction of smart and digital tourism cities to further promote balanced regional development.

Legal environmental regulation is not only an important tool for green technology innovation and energy efficiency improvement, but also a key measure for enterprise upgrading and high-quality economic development. Based on the panel data of China’s listed energy companies from 2010 to 2020, this paper finds that legal environmental regulation has a significant positive impact on enterprise green technological innovation by using SYS-GMM. And the threshold regression model is used to examine the nonlinear impact of legal environmental regulation on green technology innovation in this paper. The results show that the positive impact of legal environmental regulation on green output of energy enterprises is more prominent than that of non-R&D investment. In addition, the impact of legal environmental regulation on green technology innovation of energy enterprises has a threshold effect. That is, the loose legal environmental regulation (LER ≤ 399.656) has a negative impact on the green output of energy enterprises, while the impact of strict legal environmental regulation (LER > 399.656) is the opposite. Meanwhile, the loose legal environmental regulation (LER ≤ 491.291) and strict legal environmental regulation (LER > 491.291) have a negative impact on the non-R&D investment of energy enterprises. Finally, there are huge differences in green technology innovation capabilities of energy enterprises.

The concept of sustainable development has fostered significant advancements in environmental protection. However, the exploration of enterprise sustainable productivity remains nascent. Based on the data of Chinese listed companies from 2011 to 2022, this paper studies the impact of digital transformation on sustainable productivity. The marginal contribution includes: firstly, the framework of enterprise sustainable productivity is constructed and measured; secondly, it reveals the impact path from multiple aspects, such as innovation, human capital structure, financing constraints and operational efficiency, which enriches relevant literature; thirdly, in addition to robustness test, “broadband China” policy is used as an exogenous impact to verify the stability of the model; finally, from the perspective of enterprise ownership nature, scale and industry differences, the in-depth analysis is made to enhance the universality of the conclusion. Research finding that DT enhances SP through four key mechanisms: fostering innovation, optimizing human capital, easing financing constraints, and improving operational efficiency and the impact of DT on SP is more pronounced in state-owned enterprises, large-scale enterprises, and service-oriented firms.