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COVID-19 has seriously impacted China's real economy and directly threatened the survival and healthy development of enterprises. Based on the data of Chinese A-share listed enterprises in Shanghai and Shenzhen from 2017 to 2021, this paper empirically examines the impact and mechanism of COVID-19 on enterprises financial distress. The findings are as follows: (1) The impact of COVID-19 significantly increases the risk of enterprises financial distress. (2) There are differences in the effect of COVID-19 on the financial situation of enterprises with different characteristics, with significant negative impacts on enterprises in industries such as mining and manufacturing, as well as smaller enterprises and those located in the central and western regions. (3) Digital transformation and government subsidies can alleviate the negative effects of COVID-19 on enterprises financial distress. The paper provides a basis for enterprises to recover from financial difficulties as soon as possible, enhance competitiveness, and realize sustainable development. [ABSTRACT FROM AUTHOR]

The high cost of financing is one of the reasons that restrict the development of the real economy. The soundness and development of the capital market can help reduce the cost of corporate financing. Based on the quasi-natural experiment of the margin financing and short selling system, this paper takes A-share listed companies in Shanghai and Shenzhen stock exchanges from 2010 to 2020 as research samples, and uses a difference in difference model to study the impact and internal mechanism of relaxation of short selling on the cost of equity financing of enterprises. The research indicates that the reduction of short-selling control is significantly negatively related to the company's equity financing cost, and the relaxation of short selling can reduce the cost of equity financing by improving the company's transparency. Further research indicates that the relaxation of short selling restrictions has a more significant effect on reducing the cost of equity financing in private companies and companies with a high level of analyst coverage. This paper enriches the literature in related fields such as margin trading and short selling, equity financing cost, and also provides some theoretical references for government department to strengthen the supervision of financial markets and promote financial system reform. [ABSTRACT FROM AUTHOR]

• Industrial robots can significantly promote firm productivity. • The main mechanism comes from the improvement of human capital structure and innovation of firms. • Industrial robots are more likely to increase the total factor productivity of non-state owned and small-scale firm. • Industrial robots are more likely to increase the total factor productivity in firms that have political affiliations and low market concentration. • Industrial robots are more likely to increase the total factor productivity in firms that low financialization, and high managerial competence. Industrial robotics has become a driving force in the development of high-quality manufacturing enterprises. This paper investigates the impact of industrial robotics on the total factor productivity of enterprises and its mechanism of action through the use of data from A-shares of Chinese companies listed on the Shanghai and Shenzhen exchanges from 2007 to 2019. We found that the use of industrial robots significantly increases the total factor productivity of enterprises, findings that still hold when Bartik instrumental variables are constructed. The mechanism of action suggests that industrial robot application improves both the human capital structure and the innovation capability of enterprises, thus increasing total factor productivity. Heterogeneity analysis shows that industrial robot applications are more likely to increase total factor productivity in firms that are non-state-owned and small scale and have political affiliations, low market concentration, low financialization, and high managerial competence. The findings of this study will help to accurately assess the productivity effects of industrial robotics, and the government should continue to promote the integration of industrial robotics and manufacturing development and actively cultivate independent innovation capabilities and professional skills in the field of industrial robotics in order to increase total factor productivity in enterprises. [ABSTRACT FROM AUTHOR]

With the launch of the Shanghai-Hong Kong and the Shenzhen-Hong Kong Stock Connect programs, the correlations among the three stock markets have been more complex. Therefore, this paper applies the Minimum Spanning Tree and Sliding Window methods to analyze the complex correlations of the 38 financial institutions in the three markets. Based on closing prices ranging from January 1, 2011 to December 31, 2021, three conclusions are obtained. The top 20 institutions in the network were mainly banking institutions from 2011 to 2013, while the node strength of non-banking financial institutions tended to increase from 2014 to 2021. The network shows different structures over time, even the changes in the three correlation coefficients generally show the same trend. The degree correlation between any two layers is positive, but the similarity shows different characteristics. These results would help to enrich multi-layer network theory and reveal the evolutionary characteristics of the correlation between financial institutions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid two-stage robustness approach to portfolio construction under data uncertainty. In the first stage, a stock's efficiency performance from candidate stocks is assessed and selected using an integrated dynamic slack-based measure data envelopment analysis model. We discuss the stability of efficiency estimates using the leave-one-out method. In the second stage, a "robust" stable and scaled mean-variance-Entropic Value-at-Risk model is used to determine the optimal weights allocated to qualified stocks in the presence of proportional transaction costs. The proposed method reduces computational complexity, increases robustness, and provides a comprehensive evaluation of stocks under different financial decisions, thereby increasing conservatism in the investment process. We demonstrate the applicability of the proposed hybrid two-stage approach to stock data from the Shenzhen and Shanghai Stock Exchanges. Results show that with increasing required returns, the proposed method improves the capital amount for investment and lowers transaction costs at the expense of additional risk. The study concludes by comparing the computational performance of the proposed approach to that of existing methods. [ABSTRACT FROM AUTHOR]

Corporate innovation legitimacy is the key guarantee for listed companies to obtain returns from innovation activities. The purpose of this paper is to investigate the effect of analyst coverage on corporate innovation legitimacy based on effective supervision hypothesis and market pressure hypothesis. Moreover, it examines the moderating effect of analyst coverage characteristics, individual corporate behavior, external governance mechanism on the relationship between analyst coverage and corporate innovation legitimacy. The hypotheses are tested using a data set of Shanghai and Shenzhen A-share listed companies from China Research Data Service Platform (CNRDS) database, Hexun.com and CSMAR database. To overcome sample selection bias and reverse causality, this paper conduct robustness test using propensity score matching, lag variable, substitution variable and sample reduction to ensure the reliability of the research conclusions. Findings show that analyst coverage can effectively improve the innovation legitimacy of listed companies. In addition, based on the heterogeneity of the above relationship in different contexts, further research finds that the depth of analyst tracking, and the assessment of analyst optimism strengthen the positive impact of analyst coverage on innovation legitimacy. The implementation of green social responsibility brings a good reputation for the company, thereby helping analysts to focus on improving the legitimacy of innovation; In the market environment with relatively perfect external governance mechanism, the stronger the effect of analyst coverage on corporate innovation legitimacy. Prior studies mostly considered the R&D investment and other factors to overlook how other dimensions influence companies from the perspective of quantity. The research conclusion confirms the significance of analyst coverage as an external corporate governance mechanism, further enriches the theoretical research framework of innovation legitimacy, and has certain reference value for the improvement of innovation quality of listed companies. [ABSTRACT FROM AUTHOR]

Using the data of Shanghai and Shenzhen A-share listed companies from 2012 to 2020, this paper empirically analyzes the impact of digital finance development on enterprise independent innovation and the transmission mechanism of trade credit and government subsidies. The results show that the development of digital finance significantly promotes enterprise independent innovation, and trade credit and government subsidies play a mediating effect in the relationship between digital finance and enterprise independent innovation. And they also play a moderating role in the process of different dimensions of digital finance influencing enterprises' independent innovation. Finally, the heterogeneity analysis shows that the innovation incentive effect of digital finance is stronger for private enterprises and small and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

Probabilistic site characterization is the cornerstone of data-centric geotechnics, which underpins the digitalization in geotechnical engineering. The essential aspect of probabilistic site characterization is to reasonably infer the probability density distributions of geotechnical parameters, based on field and laboratory data. In this paper, a clay property database, SZ-CLAY/11/5130, was established by collecting geotechnical investigation reports from 52 engineering projects in Shenzhen city. This SZ-CLAY/11/5130 database was first compared with the global clay database CLAY/10/7490 and the Shanghai clay database SH-CLAY/11/4051, in terms of their sample ranges, correlations, and scatter plots. Then, the multivariate probability distribution of eight clay parameters was established using the training records selected from SZ-CLAY/11/5130, to characterize the generic correlations between multiple clay parameters. Based on Bayesian theory, the posterior distributions of clay mechanical parameters from a construction site in Shenzhen city were updated using both the site-specific correlations and the generic correlations given by SZ-CLAY/11/5130. The constructed multivariate probability density distribution is necessary for this updating step. [ABSTRACT FROM AUTHOR]

• This study assesses the impacts of near-real-time air pollution on ES. • It has differences in the reduction in ES based on near-real-time and average data. • The cities with better air quality have larger differences in the reduction in ES. • The near-real-time reduction in ES can reveal potential ecological risks. • This study can facilitate more high time-efficient ecosystem management. Air pollution is impacting ecosystem services (ES). This paper proposes an emergy-based method framework to assess the dynamic impacts of near-real-time air pollution on ES at different temporal-spatial scales. The paper presents the cases of Shenzhen, Shanghai, Beijing and Baoding, China, investigates the impacts of air pollution on the ES in 2020. In particular, we compared the reduction in ES evaluated using either average (R1) or near-real-time (R2) air pollution data. The results indicate that the differences between R1 and R2 range from about 9% (for Baoding) to almost 45% (for Shenzhen), thus implying the underestimation of the impacts of air pollution on ES based on average data. The ratios of the reduction in ES to the average ES per hour are near-real-time dynamic, with the values of 0–231.62%, 0–59.42%, 0–50.51%, and 0–35.26% in Shanghai, Baoding, Beijing and Shenzhen respectively. Although the air quality in Shanghai is aggregately better than that in Baoding in 2020, the reduction in ES caused by air pollution in Shanghai is more than twice as that of in Baoding, emphasizing the necessity of investigating the impacts of near-real-time air pollution to properly reveal and assess the potential ecological risks. The ratio of the annual total reduction in ES to the annual total ES, near-real-time reduction in ES to the average ES per hour, and near-real-time reduction in ES to the near-real-time ES in Beijing are 7.22%, 0–50.51% and 0–219.27% respectively, highlighting the significance of near-real-time monitoring of air pollution and ES to reveal the potential or hidden impacts of air pollution on ES, and further to facilitate more high time-efficient and fine ecosystem management and conservation. [ABSTRACT FROM AUTHOR]

To achieve its carbon peak and neutrality targets, China is in need of balancing environmental and economic sustainability; In addition, whether environmental regulations can gain economic dividends while protecting the environment is particularly important. Based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2021, this study takes China's environmental fee-to-tax as a quasi-natural experiment, and adopts the difference-in-difference-in-differences model to evaluate the impact of environmental tax on enterprises' total factor productivity (ETFP). The results show that the environmental fee-to-tax significantly hinders the growth of ETFP. Further, this negative impact mainly stems from the fact that the reform prompts enterprises to choose strategic innovation and conduct greenwashing behavior. Furthermore, when considering enterprises' heterogeneity, we find that enterprises with large scale, high investment efficiency, state ownership, high market competitiveness and located in western regions are more resistant to policy shocks. This paper provides reliable empirical evidence and useful policy implications for further formulating environmental tax policies to accomplish the "win-win" goal for both the environment and the economy. [ABSTRACT FROM AUTHOR]

Stimulating enterprises to implement environmental management system certification (EMSC, that is to say ISO14001 certification) to the greatest extent and resolving their obstacles in time are the basis for promoting green development of enterprises. Using the full samples of Shanghai and Shenzhen A-share listed industrial enterprises from 2010 to 2019, and employing the Logit model and Propensity Score Matching method (PSM), this paper empirically analyzes the motivations and obstacles for Chinese enterprises to implement EMSC certification, further explores the transformation between advantages and disadvantages under certain conditions, and the differentiation characteristics among enterprises under different social responsibility, property rights and enterprise size. The results show that: ① There is a "U"-shaped relationship between corporate social responsibility and EMSC, and the social responsibility of non-state-owned enterprises could realize the transformation from obstacle to motivation earlier than that of state-owned enterprises. ② There is an inverted "U"-shaped relationship between environmental regulation and EMSC, precisely speaking, this inverted "U"-shaped relationship only exists in non-state-owned enterprises, large-scale enterprises and enterprises with high social responsibility. ③ The public environmental awareness could promote enterprises to implement EMSC, and would strengthen the impact of environmental regulations on enterprises' implementation of EMSC. ④ Based on Propensity Score Matching method, it is concluded that Chinese enterprises indeed improve their environmental performance rather than their financial performance by EMSC. The conclusions provide helpful practical guidance for comprehensively revealing the motivations and obstacles for China's enterprises to implement EMSC. [ABSTRACT FROM AUTHOR]

Intelligent forecasting model of stock price is an effective way to obtain ideal investment returns. Due to the impact of quantitative transactions, traditional forecasting methods face challenges and pressures. How to find a reliable forecasting model and improve the forecasting accuracy will be a scientific problem worthy of further discussion. This paper proposes an intelligent forecasting model of stock price based on neighborhood rough set (NRS) and multivariate empirical mode decomposition (MEMD) by means of the ideal of "granular computing" and "decomposition ensemble". Firstly, the multiscale permutation entropy (MPE) method is conducted to determine decision features, which can improve the stability of permutation entropy. Then, a new feature selection method, fusing mutual information (MI) and the NRS, is proposed to automatically capture and select the condition features from the stock market. Subsequently, aiming on revealing the more detailed feature information and maintaining relevance between features, the MEMD is utilized to simultaneously decompose all features. Finally, the decomposed features are inputted into the long short-term memory (LSTM) network to train the intelligent forecasting model and provide the forecasting results of stock price. The validity of our proposed model is assessed through the stocks of Shanghai and Shenzhen markets. The results show that our proposed intelligent forecasting model of stock price makes a beneficial attempt and discussion for the integration of "granular computing" and "decomposition ensemble". And it will provide scientific support and reference for investors' actual investment decisions. [ABSTRACT FROM AUTHOR]

• Introduces a hybrid forecasting framework to identify enterprise financing risk. • Develops a feature selection module to identify the optimal input variables. • Proposes an improved ELM prediction model for enterprise financing risk. • Demonstrates the framework's reliability in forecasting enterprise financing risk. A scientific framework that can effectively forecast enterprise financing risks can both promote enterprise management and reduce the cost of risk for financial institutions. This study constructs a novel hybrid forecasting framework for enterprise financing risk incorporating modules for data preprocessing, feature selection, forecasting, and evaluation. Specifically, the data preprocessing module mainly realizes the prescreen financing risk indicators and solves the forecasting challenge created by imbalanced data; The feature selection module based on binary grey wolf optimization is designed to intelligently identify optimal financing risk indicators; The forecasting module based on the improved extreme learning machine model established in this paper achieves higher forecasting accuracy; and the evaluation module provides reasonable and scientific evaluations of the proposed hybrid forecasting framework by using the data from small and medium-sized enterprises (SMEs) in China and all listed enterprises with Shanghai and Shenzhen A-shares. Using the SMEs dataset as an example, the Type-2 error value of the developed hybrid forecasting framework is 0.1765, which is 70.24% lower than the average result of the other models; the G-mean value of the framework is 0.8566, which is 40.56% higher than the average result of the other models. Based on the results, the proposed hybrid forecasting framework outperforms other comparative models and is a reliable tool for forecasting enterprise financing risk. [ABSTRACT FROM AUTHOR]

• Energy technology patents are used to characterize energy technology innovation. • LCCP policy can make energy technology innovation generate clean direction. • LCCP policy has produced crowding out effect on other technological innovation. • The quality of energy technology innovation has not been improved. Energy technology composed of renewable and fossil energy is not entirely beneficial to the environment. Consequently, we believe that the direction of energy technology innovation should be clean to achieve sustainable development. This paper takes the low-carbon city pilot as a quasi-natural experiment, employs the difference-in-difference method and the energy technology patent data with A-share listed enterprises in Shanghai and Shenzhen from 2007 to 2019 to explore the incentive effect, mechanism, and heterogeneity of low-carbon city pilot on the direction of energy technology innovation. We found that (1) the low-carbon city pilot has an incentive effect on the direction of energy technology innovation by promoting renewable energy technology, which indicates that the clean direction of energy technology innovation is taking shape. (2) The incentive effect of low-carbon city pilot on energy technology innovation is not the leverage effect superimposed on the established innovation activities of enterprises, but the crowding out effect on other technological innovation. This means that the pilot policy is suspected of negatively the productivity of enterprises. (3) The quality of energy technology innovation has not been stimulated, renewable energy technologies that are easy to generate high economic benefits are favored by enterprises, and environmental information disclosure profits to improve the policy effect of low-carbon city pilot. This study provides implications for the effective implementation of environmental policy and the synergistic of renewable and fossil energy technologies. [ABSTRACT FROM AUTHOR]

• Digital finance can promote the value of strategic emerging enterprises. • The promotion of the depth of the use of digital finance is more significant. • Mechanisms: "supplementary funds", "reducing risk" and "promoting innovation". • Administrative mandatory environmental regulation has a regulatory effect. This paper uses the data of A-share strategic emerging enterprises in Shanghai and Shenzhen Stock Exchanges from 2011 to 2018 to explore the impact of digital finance on the value of strategic emerging enterprises. It is found that the development of digital finance has a structural driving effect on the value of strategic emerging enterprises. It shows that the driving impact caused by the depth of use of digital finance is more evident than the coverage of digital finance. At the same time, it is more apparent in the low degree of marketization, nonstate-owned enterprises, and central and western regions, highlighting the inclusive characteristics of digital finance. Mechanistic research shows that digital finance can enhance the value of strategic emerging enterprises through three paths: supplementary funds, reducing risk, and promoting innovation, thereby realizing a sustainable society. In particular, the impact of administrative mandatory environmental regulations on digital finance and the importance of strategic emerging enterprises presents an inverted U-shaped relationship. [ABSTRACT FROM AUTHOR]

• Knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities. • The quality of this output has increased over time Chinese cities improve the quality of their knowledge output before increasing the quality of technology. • Only Beijing, Shanghai & Shenzhen compete globally in terms of quality of output – other Chinese cities do not. • This can be a mechanism for increasing regional inequality within China. Are Chinese cities becoming smarter in terms of the knowledge and technologies they produce? For decades, the widely held believe in the global North is that China merely copies, imitates and only incrementally improves ideas. Recently, this believe is increasingly being challenged. In this paper I quantify this process. Using data from about 6,1 million patents and 60 million academic publications I examine the complexity of technologies and knowledge developed in major Chinese cities and compare these to those produced in other global cities. The results show that (1) knowledge and technologies produced in Chinese cities has increasingly taken a greater share of total output of global cities; (2) the quality of this output has increased over time; (3) Chinese cities improve the quality of their knowledge output before increasing the quality of technology; and (4) only Beijing, Shanghai and Shenzhen compete with other global cities in terms of the quality of output, suggesting emerging regional inequality within China linked to knowledge and technology production. Not only is the size of the Chinese economy increasing, so is the quantity and quality of its knowledge and technology output. [ABSTRACT FROM AUTHOR]