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The research explored the temp-spatial evolution characteristics for the spatial mismatch and vulnerability among the tourism-economy-ecosystem along the Yangtze River Economic Belt by using research methods including the spatial mismatch and vulnerability based on the panel data from 2004 to 2018. The results are as follows. (1) The overall mismatch along the Yangtze River Economic Belt presented a polarized development trend within which Shanghai, Jiangsu, and Zhejiang showed a typical positive mismatch and other provinces showed a negative mismatch; the overall spatial mismatch index tended to be more severe, the phenomenon of antagonism occurred more frequent and the situation of mismatch became more critical. (2) The overall vulnerability index along the Yangtze River Economic Belt presented a fluctuating trend and the healthy, risky, and severe development trends coexisted within the development process of each province’s vulnerability index. (3) The nine provinces and two cities could be decided into 3 typical categories of planned development regions by the comprehensive spatial mismatch and vulnerability index: Shanghai, Jiangsu, Zhejiang, and Hubei belonging to developing regions; Hunan, Chongqing, and Sichuan belonging to development and protection regions; Anhui, Jiangxi, Guizhou, and Yunnan belonging to protective regions. In the end, the paper puts forward specific suggestions on how to eliminate the spatial mismatch and vulnerability situation in the Yangtze River Economic Belt from both macro and micro perspectives, so as to provide theoretical reference for the high-quality sustainable development of the Yangtze River Economic Belt and other major river basins. The research explored the temp-spatial evolution characteristics for the spatial mismatch and vulnerability among the tourism-economy-ecosystem along the Yangtze River Economic Belt by using research methods including the spatial mismatch and vulnerability based on the panel data from 2004 to 2018. The results are as follows. (1) The overall mismatch along the Yangtze River Economic Belt presented a polarized development trend within which Shanghai, Jiangsu, and Zhejiang showed a typical positive mismatch and other provinces showed a negative mismatch; the overall spatial mismatch index tended to be more severe, the phenomenon of antagonism occurred more frequent and the situation of mismatch became more critical. (2) The overall vulnerability index along the Yangtze River Economic Belt presented a fluctuating trend and the healthy, risky, and severe development trends coexisted within the development process of each province’s vulnerability index. (3) The nine provinces and two cities could be decided into 3 typical categories of planned development regions by the comprehensive spatial mismatch and vulnerability index: Shanghai, Jiangsu, Zhejiang, and Hubei belonging to developing regions; Hunan, Chongqing, and Sichuan belonging to development and protection regions; Anhui, Jiangxi, Guizhou, and Yunnan belonging to protective regions. In the end, the paper puts forward specific suggestions on how to eliminate the spatial mismatch and vulnerability situation in the Yangtze River Economic Belt from both macro and micro perspectives, so as to provide theoretical reference for the high-quality sustainable development of the Yangtze River Economic Belt and other major river basins. [ABSTRACT FROM AUTHOR]

Big data, a vital driving force of high-quality economic development in China, improves both the speed and efficiency of information exchange and knowledge dissemination. Existing studies have conducted a series of discussions on the dividend effect of big data development, but few have analyzed it from the perspective of corporate green development. Based on the theoretical analysis of the impact of big data development on the internal mechanisms of corporate green technology innovation and on the regulatory role of intellectual property protection, in light of the exogenous policy shock from the ‘National Big Data Comprehensive Pilot Zones,’ this paper studied the A-share listed companies in Shanghai and Shenzhen by adopting the difference-in-differences model (DID) to evaluate the impact of big data development on corporate green technology innovation and the moderating effect of intellectual property protection between them. The research results showed that the development of big data significantly promoted corporate green technology innovation, and that the conclusion still held after a series of robustness tests. Heterogeneity tests showed that the promoting effect of big data development on corporate green technology innovation was more observable in large-scale enterprises, state-owned businesses, and companies with high-level access to data from local governments. The mechanism test indicated that the development of big data could promote corporate green technology innovation by improving the intensity of regional environmental regulation, easing the financing constraints of enterprises, and improving their human capital level. The moderating effect test showed that intellectual property protection played a significant positive moderating role between big data development and corporate green technology innovation. Accordingly, the government should improve the big data development policies to promote deep integration of big data and the real economy in a consistent manner, reinforce the system design concerning corporate green technology innovation guided by policies of big data development, and combine big data development with green development goals such as achieving peak carbon emissions and carbon neutrality. Meanwhile, policies should be refined based on different types of enterprises in different regions so that a long-term mechanism can be established to support corporate green innovation development. It is also necessary to strengthen the construction of an intellectual property protection system so as to accelerate corporate green technology innovation and green development more effectively. [ABSTRACT FROM AUTHOR]

Under the background of economic system reform and the continuous transformation of financial functions, the implementation of financial fine management has become the mainstream direction of reducing cost, increasing efficiency and sustainable development of feed enterprises. The paper takes the t-3 financial data of 20 feed listed enterprises in Shenzhen and Shanghai in 2020 as the research sample to explore the applicability of PCA-SVM model to financial fine management of feed enterprises. It is found that PCA-SVM model effectively integrates the advantages of PCA and SVM model, eliminates the linear correlation of sample data, reduces dimension and significantly enhances the classification ability and generalization of financial fine management of feed enterprises. And PCA-SVM model has good applicability for measuring the financial fine management level of high-dimensional data and small sample feed enterprises, which can provide reference for financial managers. Therefore, feed enterprises should strengthen the application of modern information network technology, improve the technology embedding system, strengthen the whole process control and improve the application level of PCA-SVM model in financial fine management. [ABSTRACT FROM AUTHOR]