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With the rapid development of technologies such as cloud computing and big data, various levels of government departments in the country have successively introduced digital subsidy policies to promote enterprises' digital transformation. However, the effectiveness of these policies and their ability to truly achieve policy objectives have become pressing concerns across society. Against this backdrop, this paper employs a moderated mediation effects model to empirically analyze the incentive effects of financial subsidies on the digital transformation of A-share listed manufacturing companies in the Shanghai and Shenzhen stock markets from 2013 to 2022. The research findings indicate a significant promotion effect of financial subsidies on the digital transformation of manufacturing enterprises, especially demonstrating a notable incentive impact on the digital transformation of large enterprises, non-asset-intensive enterprises, technology-intensive enterprises, and non-labor-intensive enterprises. However, the incentive effect on the digital transformation of small and medium-sized enterprises (SMEs), asset-intensive enterprises, non-technology-intensive enterprises, and labor-intensive enterprises is not significant. Notably, the expansion of financial subsidies positively influences the augmentation of R&D investment within manufacturing enterprises, subsequently providing indirect encouragement for their digital transformation. Additionally, the incorporation of the degree of marketization implies its potential to moderate both the direct and indirect impacts of financial subsidies on enterprise digital transformation. This study enriches the research on the mechanism of the role of financial subsidies in digital transformation and provides empirical evidence on how market participation influences the effects of financial subsidies, thereby assisting policymakers in comprehensively understanding the impact of financial subsidy policies on different types of enterprises. [ABSTRACT FROM AUTHOR]

Science and technology and skills have increasingly become the driving force to lead the development of knowledge economy. With the changes in the demand for knowledge workers and the location of enterprises, a new type of innovation space—innovation districts—has emerged. Countries have begun to identify and nurture innovation districts. Therefore, how to accurately identify innovation districts in cities has become an important research topic. The existing research on identifying innovation districts is mainly based on a qualitative description method at the element level. However, whether there are other potential innovation districts in urban space can be identified by quantitative identification. Therefore, the purpose of this paper is to establish an innovation district identification framework and a case study of the framework. The identification framework includes spatial identification and factor identification. In spatial identification, the identification index system is constructed based on the spatial location, range limit, and the surrounding area of innovative assets. In factor identification, the identification index system is constructed based on innovative assets, physical assets, and network assets, and Kendall Square and Boston innovation districts are used as the reference basis to determine whether the identified districts meet the standards of the constructed innovation districts. In empirical case research, spatial identification identified the Gaoxin South District (GXSD) spatial range. In factor identification, it was found that GXSD does not fully meet the standard of innovation district identification. This paper argues that the framework is essential for urban managers, planners, and urban designers to identify and evaluate high-quality innovation districts. [ABSTRACT FROM AUTHOR]

The health significance of walking has become increasingly prominent. To better understand how factors influence behavior is a necessary basis for effective behavioral intervention. In view of the complexity of human behavior, this paper explores the influencing factors of pedestrian walking and their influencing mechanism under different behavioral purposes from the socio-ecological perspective, based on the empirical data of Shenzhen. The analysis reveals that: factors in each dimension interact to have a joint influence on walking activities, among which the influences of behavioral habit, physical environment, and policy environment are more prominent; the differentiation of influencing factors under different behavioral purposes is relatively obvious in physical environment dimension; the comprehensive performance of factors is more prominent in physical environment dimension, which is effective, especially under the intervention of policy environment factors; behavioral intervention practices should be integrated with the respective characteristics of behavior psychology, social environment, physical environment, and policy environment, so as to maximize the intervention effects. [ABSTRACT FROM AUTHOR]

As the cradle of scientific research institutions and the cultivation of innovative talents, high-level universities are of certain theoretical and practical significance to investigate the impact of high-level universities on the innovation of local enterprises under the realistic background of the revival of machine learning. This paper takes A-share listed enterprises in Shanghai and Shenzhen stock markets from 2013 to 2019 as samples, and empirically examines the relationship between high-level universities and local enterprise innovation from the perspective of "double first-class" universities. The study found that high-level universities have a significant role in promoting the innovation of local enterprises, and a series of robustness tests have also confirmed the conclusions of this paper. Mechanism analysis shows that high-level universities promote local enterprise innovation mainly by promoting human capital output and school-enterprise cooperation. Heterogeneity analysis shows that executive gender, government intervention, and differences in regional development levels have significant heterogeneity effects. This paper has obvious policy implications: China should guide high-level universities to cultivate high-quality talents, cultivate positive school-enterprise cooperation, promote the sharing of innovative resources, and promote the high-quality development of the real economy. [ABSTRACT FROM AUTHOR]

Affected by the fluctuation of the market and economic environment during the epidemic period, the capital pressure of companies has increased sharply, which increases the possibility of risk transmission in the same industry and poses new challenges to the operation and financing of companies. From the perspective of preventive motivation of corporate cash holding, we creatively use the industry working capital shortfall as the explanatory variable to construct an Extended Cash Holding Model and a Cash Holding Value Regression Model. Taking the panel data of A-share listed companies in Shanghai and Shenzhen as samples, this paper uses the Classical Linear Regression Model and Fixed Effects Regression Model to study the relationship between industry working capital shortfall and cash holdings in the same industry, as well as the relationship between industry working capital shortfall and cash holding value. The empirical results show that industry working capital shortfall has an important impact on the cash holding level within the same industry, and the cash holding level is significantly and positively correlated with industry working capital shortfall. Moreover, this study also reveals that the industry working capital shortfall has a dual impact on cash holdings. Specifically, the higher the risk of industry working capital shortfall, the lower the value of company cash holdings. The conclusions of this paper not only extend the research on cash holding but also provide support and reference for companies to optimize the cash holding value. [ABSTRACT FROM AUTHOR]

Affected by the fluctuation of the market and economic environment during the epidemic period, the capital pressure of companies has increased sharply, which increases the possibility of risk transmission in the same industry and poses new challenges to the operation and financing of companies. From the perspective of preventive motivation of corporate cash holding, we creatively use the industry working capital shortfall as the explanatory variable to construct an Extended Cash Holding Model and a Cash Holding Value Regression Model. Taking the panel data of A-share listed companies in Shanghai and Shenzhen as samples, this paper uses the Classical Linear Regression Model and Fixed Effects Regression Model to study the relationship between industry working capital shortfall and cash holdings in the same industry, as well as the relationship between industry working capital shortfall and cash holding value. The empirical results show that industry working capital shortfall has an important impact on the cash holding level within the same industry, and the cash holding level is significantly and positively correlated with industry working capital shortfall. Moreover, this study also reveals that the industry working capital shortfall has a dual impact on cash holdings. Specifically, the higher the risk of industry working capital shortfall, the lower the value of company cash holdings. The conclusions of this paper not only extend the research on cash holding but also provide support and reference for companies to optimize the cash holding value. [ABSTRACT FROM AUTHOR]

Charging station planning is critical in the implementation of public transport electrification. However, in cities, there is insufficient experience and knowledge of the factors that influence the utilization of charging stations, particularly charging stations aimed at serving a fleet of electric taxis. Shenzhen is one of the pioneers in promoting electric taxis. In this paper, we collect large-scale datasets from Shenzhen and provide a data-driven space–time analysis of the relationship between charging station utilization and urban form and demand for taxi services. We use a Random Forest Regression model to explore these relationships and apply a Shapley value method to interpret the results. We find that demand for taxi services, measured as hourly pick-up and drop-off densities, have a non-linear relationship with utilization. Metro station density positively correlates with utilization, whereas the relationships between population density, land-use entropy, road density, and bus station density are more complicated. [ABSTRACT FROM AUTHOR]