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While transportation infrastructure can accelerate innovation, no research has yet explored the link between intra-city subway operation and innovation. This study responds to this gap in the literature. The rapid, large-scale development of China’s subway system can serve as a ‘quasi-natural experiment’. Based on panel data on listed manufacturing companies from 2008 to 2020, this study employs the time-varying difference-in-differences method to explore the impact of subway operation on enterprise R&D investment and the impact’s heterogeneity under different enterprise characteristics. The results show that new subways have increased enterprise R&D investment and that the subway’s positive impact on R&D investment is more significant in enterprises with high employee compensation, non-state-owned enterprises, and large enterprises. Moreover, subway operation promotes enterprise R&D investment by attracting tech talent and promoting infrastructure convenience. [ABSTRACT FROM AUTHOR]

Compared with traditional manufacturing enterprises, intelligent manufacturing enterprises pay more attention to the investment of knowledge capital and technological capital. Taking 258 intelligent manufacturing listed companies in China from 2015 to 2020 as research samples, the paper selects the material capital, human capital, knowledge capital and technological capital of enterprises as the input variables of Cobb-Douglas production function. Considering that enterprises are often affected by spatial correlation, stochastic frontier panel model, spatial lag stochastic frontier panel model and dynamic spatial lag stochastic frontier panel model are constructed to measure capital allocation efficiencies of enterprises. The results show that all the factor capitals in the three models have a significant positive impact on enterprises' performance, and the dual lag effect of time and space is significant. Moreover, it is more reasonable to use the dynamic spatial lag stochastic frontier panel model to estimate the parameters and measure capital allocation efficiencies. The development of intelligent manufacturing industry has significant space-time spillover effect among provinces. About 52.98% of intelligent manufacturing enterprises have high capital allocation efficiencies, but 12.04% still need to further optimize capital allocation. The gap between the actual performance of the sample enterprises and efficiency frontier is mainly due to technical ineffectiveness. From a regional perspective, the top ten enterprises with high capital allocation efficiencies are all in the eastern region, but the average of capital allocation efficiency is the highest in the western region, followed by the eastern and central regions. [ABSTRACT FROM AUTHOR]

With the development of information technology, intelligent control technology is the comprehensive application of modern management techniques and methods. This paper mainly studies the intelligent financial decision support system based on data mining. This paper mainly introduces data mining technology, an intelligent financial decision support system and the application of data mining technology in an intelligent financial decision support system. The intelligent financial decision support system proposed in this paper uses a relational database to store massive business data, to improve the system expansion ability. By using mathematical model and data mining technology, an intelligent financial decision support system can automatically analyze data, discover the internal relationship between data, and mine the model that plays an important role in prediction and decision-making behavior, to establish a new business model, help decision-makers to make marketing strategies in line with the market and make correct decisions. The experimental results show that: the actual total profit of the company in 2019 is 43.37 million yuan, and the predicted total profit in 2019 is 43.38 million yuan. The similarity between the actual total profit in 2019 and the predicted total profit in 2019 is 99.98%. In 2019, the company’s sales revenue is 37.61 million yuan. The predicted sales revenue in 2019 is 37.62 million yuan, which is 99.97% similar to the actual sales revenue in 2019. The managers of the company can make marketing strategies and make correct decisions according to the sales revenue forecast in 2020. [ABSTRACT FROM AUTHOR]

The coordination relationship and the balance of development between green finance and environmental performance are crucial to guaranteeing sustainable development under circumstances of economic transition. The current paper demonstrates the construction of index systems pertaining to green finance and environmental performance. Based on a panel dataset of 30 Chinese provinces from 2008 to 2019, the coupling coordination degree model, spatial autocorrelation model, and spatial panel model are used to analyze the spatial-temporal characteristics and driving factors of the coordinated development of green finance and environmental performance across the country. The results show that, first, an overall upward trend in the green finance index appeared, with most high-value areas located in the eastern regions. In terms of environmental performance, the number of provinces with efficient environmental performance values rose from 3 to 17. These provinces were mainly distributed in the eastern and central regions. Second, the coupled coordination of green finance and environmental performance rose from mild incoordination to primary coordination during the study period, with marked spatial autocorrelation and a downward trend from east to west. Finally, industrial structure, economic development level, and environmental regulations were found to positively impact the coordinated development of green finance and environmental performance, while population density exerted an inhibiting effect on it. These effects differ across regions. The paper concludes with several policy implications, offered to guide the coordinated development of green finance and environmental performance. [Display omitted] • The provincial green finance and environmental performance are calculated. • The coordinated degree of green finance and environmental performance (GFEP) is estimated. • The driving factors of GFEP are identified. [ABSTRACT FROM AUTHOR]