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The establishment of government investment funds serves as a crucial measure for governments at all levels to leverage their certification role and financial resources in attracting social capital to support enterprise development. This paper empirically examines the guiding certification effect and governance supervision effect of government investment funds on enterprise value enhancement, utilising panel data from listed companies and government investment fund investment event data spanning the period from 2011 to 2021. The research findings reveal that government investment funds significantly enhance the value of recipient enterprises. By leveraging their guidance and certification effects and governance supervision effects, these funds alleviate financing constraints, actively participate in corporate governance, and ultimately enhance corporate value. The impact of government investment funds is negatively moderated by the age and size of the enterprise, indicating that the "invest in early-stage and small businesses" investment strategy yields better results in promoting value enhancement. Furthermore, heterogeneity analysis demonstrates that government investment funds have a more pronounced impact on the value of non-heavily polluting industries, enterprises located in the eastern and southern regions of China, and non-state-owned enterprises. This article expands the research scope of government investment funds at the micro level, providing empirical evidence and theoretical support for optimising government investment funding policies and fostering the development of a modern capital market with distinctive Chinese characteristics. [ABSTRACT FROM AUTHOR]

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally. [ABSTRACT FROM AUTHOR]

This study examines the extent to which electricity shortage influences technical efficiency using data of 805 listed manufacturing companies in China from 2009 to 2020 collected from the CSMAR database. To achieve the objectives of this paper, first, a stochastic frontier analysis (SFA) is used to estimate the technical efficiency (TE) score of manufacturing companies. Subsequently, the TE score is used to evaluate the electricity shortage index and other factors that are postulated to affect enterprise productivity. Two estimation methods have been adopted including ordinary least squares (OLS), which is less robust to endogeneity and instrumental variable (IV) estimation, which turns out to be more robust to endogeneity in the data. The empirical results show that, under OLS estimation, electricity shortage has a significantly negative impact on the technical efficiency of the listed manufacturing companies. However, when IV regression is implemented to address endogeneity issues in the data, electricity shortages tend to have a significantly positive impact on the technical efficiency, underscoring the importance of capturing endogeneity in the data. Extending the baseline results, this study also finds that, while the size of an enterprise may have no bearing, state-owned companies are more likely to be negatively affected by electricity shortages compared to privately owned companies. These results have significant implications for industrial policy design in China in particular, and developing countries in general. Most importantly, the results of this study underscore the importance of policies and measures to promote a shift in the ownership structure towards the private sector. [ABSTRACT FROM AUTHOR]

Foreign direct investment (FDI) technology spillovers play an increasingly important role in a host country's development. Evaluating the positive effect of FDI inflows on green innovation is essential for correct city design. Based on the panel data of 262 cities in China from 2004 to 2018, we first analyzed the impact of FDI technology spillovers on green innovation in Chinese cities and then tested the threshold effect in four absorptive capacity factors: environmental regulation, economic growth, human capital, and industry size. Finally, we compared the time and space of two types of cities crossing the threshold from the perspective of innovative and non-innovative cities. The results show that FDI can significantly promote green innovation in Chinese cities and the promoting effect of FDI on green innovation has nonlinear characteristics, namely, such effects only make sense when absorptive capacity is above the threshold points. Additionally, among the four absorptive capacity factors, the development degrees of innovative cities are ahead of non-innovative cities; in particular, there is a significant difference between them in terms of economic growth. Local governments should develop reasonable policy combination tools according to the absorptive capacity characteristics of different cities to effectively promote the technology spillover effect of FDI and achieve coordinated ecological and economic development. [ABSTRACT FROM AUTHOR]

Many cities in China have built an advanced public transportation system (APTS) in recent years. These systems cannot carry out automated dispatches but rely on the dispatcher to ensure normal operations. Based on the theory of fuzzy comprehensive evaluation, this study created a hierarchical evaluation model to evaluate the work level of the dispatcher. Firstly, a hierarchical evaluation system was established based on the data of line complexity and dispatch workload provided by the APTS. Secondly, we assigned the weight of the evaluating indicator by combing an expert scoring method and the entropy weight method. We then transformed the quantitative data into qualitative evaluation data with a cloud model. Through a comparison with the standard cloud, the work level of an APTS dispatcher can be defined. The evaluation results for 12 cities in China showed that there was no obvious connection between the working level and bus company size, but the work level was always higher in large cities. We finally obtained the standard range of the evaluation indicator through cluster analysis. This method can be used as a reference to evaluate the work level of dispatchers in other cities, and can reflect the correlation between the work level and dispatching system construction, population and city size. [ABSTRACT FROM AUTHOR]