The digital economy and corporate credit risk: An empirical study based on Chinese new energy enterprises

Many industries put on a show of sustainability to draw in investors even though they are not financially viable. This study examines how real-earnings management (REM) moderates the relationship between credit risk (CR) and financial sustainability (FS). Real earnings management (REM) uses three techniques to measure earnings management: cash flow, overproduction, and discretionary spending. The distance-to-default approach of the KMV model, as an inverse proxy, is used in the current study to enumerate CR. Panel data of non-financial listed companies from 2013 to 2021 is used in this study. This study used PROCESS macro to construct bootstrap confidence intervals to estimate the model and “simple slope analysis” to visualize the model. The findings demonstrate a significant negative relationship between credit risk and financial sustainability. Real earnings management as a moderator weakens the relationship between financial sustainability and credit risk. This study aids investors in integrating sustainability into their investment process and helps them make wise choices. In addition, the results of this study might assist managers in adjusting cash flow patterns, real earnings management practices, and financial sustainability to reduce credit risk.

Existing literature has documented a lot of research efforts on the issues of corporate diversification discount and diversification premium, most of them trying to explain the rationales why firms diversify or focus, especially to unveil the benefits and costs of their diversification decisions. However, few of them explore the relationship between corporate diversification and corporate credit risk. It is the purpose of this study to investigate the effects of corporate diversification on both credit risk and firm value using American firms engaged in mergers and acquisitions from 1980 to 2005. In this study, we calculate a firm's one year default probability according to Black-Scholes (1973) and Merton (1974) and we follow both Berger and Ofek (1995) and Mansi and Reeb (2002) to calculate the excess firm values. We obtain several interesting findings. First, we observe that according to levels of credit quality, the changes in default probability move in different directions when firms diversify. Default probabilities of risky firms decline when diversifying, which is consistent with coinsurance effect and risk effect, while, on the contrary, those of safe firms increase, which violates the hypothesis of coinsurance effect. It can be explained by a strong risk effect suggested by Billett, King, and Mauer (2004). Second, market values of equity move in the same direction with default probabilities. It is consistent with the option theory based credit model originated by Black-Scholes (1973) and Merton (1974). We find that risky firms bear diversification discount and safe firms enjoy diversification premium when we only consider the wealth changes of stockholders. Finally, we provide empirical evidences for wealth transfer effect and show that the directions of the transfer are opposite according to the credit quality of diversifying firms.

In the context of the digital age, the digital economy, as a new economic model that the Chinese government is currently committed to developing, has played a positive role in driving consumption and creating employment opportunities. However, the differential development characteristics of the digital economy are becoming increasingly evident. The level of digital infrastructure and the application of digital facilities in China’s eastern regions are superior to those in the central and western regions. The increasing level of differential development in the digital economy will further accelerate the cross-regional mobility of labor. For the more developed eastern regions in China, in terms of the digital economy, the ability to empower employment is relatively high, which can create more job opportunities and attract a larger labor force seeking employment opportunities. In contrast, the central and western regions face slower development in the digital economy and relatively insufficient employment-empowering capacity, leading to labor force outflow. Proper cross-regional labor mobility can enhance the efficiency of labor resource allocation. However, excessive labor force mobility can lead to imbalanced labor resource allocation, causing job shortages and reduced employment quality in regions with an excess of labor force, while labor loss regions face labor shortages and talent drain, resulting in a loss of economic vitality in those regions. Therefore, clarifying and addressing the various negative impacts brought about by the differential development of the digital economy are crucial for improving the overall employment quality in the digital economy era. However, there is currently limited research focused on the influence of differential development levels of the digital economy on employment quality. This study delves into the impact of the differential development levels of the digital economy on employment quality and analyzes the underlying mechanisms. Based on panel data from 31 provinces and cities in mainland China from 2011 to 2020, this study uses the entropy method to calculate both the employment quality index and the digital economy index. Building upon the digital economy index, the Gini coefficient of the digital economy development level in various regions in China is calculated using the Gini coefficient formula. Subsequently, a two-way fixed-effects model empirically analyzes the impact of China’s differential development levels in the digital economy on employment quality. The research finds that the improvement in China’s differential development level in the digital economy significantly reduces employment quality. After re-calculating the Gini coefficient and the employment quality index using principal component analysis, it is found that the Gini coefficient of the digital economy still has a significantly negative impact on the employment quality index. After conducting 2SLS regression using instrumental variables, it is confirmed that there is still a significant negative correlation between the Gini coefficient of the digital economy and the employment quality index. According to the regression results, for every 1% increase in the Gini coefficient of the digital economy, the employment quality index will decrease by 0.111% to 0.361%. Through a regression analysis of the mechanism of action, it is found that the industrial structure plays an intermediary role in the impact of the differential development levels of the digital economy on employment quality. The improvement in the differential development levels of the digital economy is unfavorable for the transformation and upgrading of the industrial structure in the central and western regions, as well as the rational development of China’s overall industrial structure, thereby affecting the improvement of employment quality. Based on the above empirical results, the following policy recommendations are proposed: 1. The Chinese government should increase fiscal support for digital infrastructure construction in the central and western regions, continuously narrowing the gap in digital economy development levels between regions. 2. Regional governments in China should actively guide the healthy upgrading of industrial structures based on the actual conditions of each region. 3. In the digital economy era, the government should introduce relevant labor protection and social security policies based on the characteristics of emerging professions to further improve the employment quality of workers in the digital economy era.

Most emerging economies are in a transition period, and an imperfect institutional environment will lead to inefficient supervision mechanisms in the digital economy. Based on data representing the experiences of China’s listed commercial banks from 2013 to 2021, this study empirically examines the impact of the digital economy on credit risk by using a panel fixed-effect model. The findings indicate that the development of the digital economy helps to reduce credit risk faced by commercial banks. Institutional variables, such as financial supervision and the legal institutional environment, have significant mediation effects of the digital economy. By increasing the intensity of financial supervision and improving the legal institutional environment, the digital economy can reduce the credit risk faced by commercial banks. Interestingly, the effect of the digital economy on credit risk is not uniform across all banks. State-owned commercial banks benefit more from the digital economy in terms of reducing their credit risk than on non-state-owned banks. The inhibitory effect of the digital economy on the credit risk of commercial banks is significant in eastern China but not in central and western China.

Based on the perspective of the innovation value chain, this paper divides the technological innovation of new energy enterprises into research and development and results transformation stages, uses the panel data of listed Chinese new energy enterprises from 2016 to 2021 to test the impact of urban digital economy development on the two-stage innovation efficiency, and examines the moderating role of organizational inertia and entrepreneurship on the relationship between the two. The study found that the development of the digital economy has significantly improved the efficiency of enterprises’ two-stage innovation, but its role in promoting the stage of research and development is more obvious; Organizational inertia and entrepreneurship have a positive moderating effect on the relationship between the two; Further research found that there are regions, urban class and population size, and enterprise nature differences in the impact of digital economy development on the efficiency of two-stage innovation of enterprises.

Purpose The integration of environmental, social and governance (ESG) factors into credit risk assessment has gained prominence among investors and corporations. However, the divergence in ESG ratings across agencies introduces additional uncertainty for corporate credit risk assessment. This study investigates the effect of ESG rating divergence on corporate credit risk, including the mediating role of credit ratings and the moderating role of country ESG performance. Design/methodology/approach Based on a panel dataset of 562 publicly listed firms from 2017 to 2022, the study employs fixed effects regressions to investigate the relationship between ESG rating divergence and credit risk, measured by credit default swap (CDS) spreads and probability of default. Two path analyses assess the mediating role of credit ratings, while interaction terms analyze the moderating effect of country-level ESG performance. Findings The empirical results indicate that ESG rating divergence increases corporate credit risk, particularly pronounced in the environmental pillar. Credit ratings only partially mediate this relationship, suggesting that ESG divergence exerts a direct effect on credit risk. Moreover, higher country ESG performance mitigates the impact of ESG divergence. Originality/value This is the first study to examine whether credit markets price ESG rating disagreement across developed and emerging regions using credit default swap spreads as a forward-looking, market-based measure of credit risk. It further advances the literature by testing credit ratings as a mediating channel and by assessing whether country-level ESG performance moderates the relationship between ESG rating divergence and corporate credit risk.

In the digital intelligence economy era, enterprises' marketing model tend to be precise and refined. As the forerunner and leader of 5G mobile phones, Huawei mobile phones have both risks and opportunities in the era of the digital economy. On the one hand, Huawei’s traditional marketing strategies for mobile phones need to be adjusted and improved. On the other hand, the development of the digital economy has broadened Huawei’s market development space of mobile phones. This study focuses on Huawei smartphones as the primary subject of investigation, meticulously analyzing their marketing strategies within the framework of the SWOT (Strengths, Weaknesses, Opportunities, and Threats) model. It delves into four distinct dimensions: the strengths and weaknesses inherent to Huawei's marketing approach, along with the opportunities and threats it faces in the market. Furthermore, this research proposes concrete strategies for refining Huawei's product marketing tactics, offering valuable insights for enterprises seeking to navigate and thrive in the digital economy era.

The global industrial chain and energy supply chain are being reconfigured at an accelerated pace, and the uncertainty of China’s energy supply security is growing significantly. Empowering energy supply chains through the digital economy (diec) has a positive effect on accelerating the transformation of China’s energy supply structure. This paper discusses the effect and mechanisms of the digital economy on energy supply chain efficiency (esce). Specifically, based on the panel data of 112 energy enterprises in China from 2011 to 2019, energy supply chain efficiency and digital economy at the enterprise level were evaluated through three-stage DEA and content analysis, respectively. A two-way fixed effects model and mediation effect mode were adopted to investigate the nexus of diec and esce. The results show that the digital economy improves energy supply chain efficiency, and the conclusion holds water even after a series of robustness tests and endogenous treatment. Meanwhile, its promotion effect is more significant among large enterprises, non-state enterprises and enterprises in high market-oriented regions. The main impact mechanisms are regional industrial agglomeration and technological innovation of enterprises. Based on the above conclusions, it is suggested to take advantage of the industrial aggregation effect and technological innovation effect of the digital economy to further improve the efficiency of the energy supply chain for the purpose of maintaining energy supply security.

Financial deregulation and operational risks of energy enterprise: The shock of liberalization of bank lending rate in China

The study reviews the literature analysing the relationship between ESG factors and corporate credit risk using bibliometric and systematic literature review methods, processing scientific publications published between 2020 and 2024. Of these, 61 relevant studies are identified and examined in detail, focusing primarily on research trends and empirical results. Due to stricter regulations and market interest, the number of articles focusing on ESG and credit risk has increased significantly. According to the majority of publications, better ESG performance reduces credit risk. Our analysis revealed several research gaps: the examination of the relationship between ESG and credit risk in the small and medium-sized enterprise sector, the impact of climate risks on corporate credit risk and credit portfolios, region-specific analyses, and the application of innovative artificial intelligence and machine learning-based methodological frameworks are areas that could generate significant added value in both academic and market contexts.

Nowadays, banks operate in a highly dynamic environment where substantial vulnerability to credit risk exposures threatens their performance by affecting the quality of bank portfolios and increasing their vulnerability to insolvency. In this context, the paper reviews the existing literature and finds no studies investigating the determinants of retail and corporate credit risk using machine learning techniques to enhance the predictive performance of bank credit risk exposures. Consequently, the paper aims to utilize machine learning algorithms, regression analysis, and classification models to identify the most effective predictive model that can improve banks' credit risk prediction capabilities. It will cover the period from 2011 to 2023 and analyze a sample of 26 banks operating in Egypt. Additionally, it classifies credit risk into retail and corporate categories to develop more robust predictive models tailored for the retail and corporate sectors of bank credit risk management, thereby underscoring the paper’s novelty. The findings showed that the Random Forest and Kernel SVM can be used to improve the prediction of corporate and retail credit risk by utilizing bank-specific factors like profitability, liquidity, income diversification, capital, asset size, and operating efficiency, as well as macroeconomic factors like external debt, inflation, exchange rate, GDP, interest rate, and foreign direct investment. Doi: 10.28991/ESJ-2025-09-02-025 Full Text: PDF

The emergence of a new generation of information technology represented by big data, artificial intelligence, and mobile internet marks the arrival of the era of big data-driven digital economy. In the era of digital economy, enterprises face greater financial risks and need to constantly break through and innovate existing management models. Through the construction and linkage of industry-finance integration, enterprises can fully grasp market demand and continue to adjust. The article starts from the significance of industry-finance integration in the digital economy, and combines with the problems of low information infrastructure level, low business and financial process, weak internal organization and human technology, and poor information communication in the process of industry-finance integration. It puts forward ideas and ideas for industry-finance integration, and hopes to provide reference and reflection for the industry-finance integration of companies in the digital era.

Amid growing economic uncertainty and rising corporate default risks, effective legal reforms are essential for financial stability and sustainable business practices. This study examines the impact of China’s security interest system reform on corporate credit risk, highlighting its implications for corporate social responsibility (CSR) and sustainable development. Using a Difference-in-Differences approach and data from Chinese listed firms, the results show that the reform significantly reduces credit risk, particularly for asset-light, risk-averse, and growth-stage enterprises. Lower credit risk alleviates financing constraints, enabling firms to allocate more resources to Environmental, Social, and Governance (ESG) activities and long-term sustainability initiatives. The findings reveal that legal reforms in secured transactions not only serve as risk management tools but also function as institutional mechanisms that foster CSR engagement and contribute to building a sustainable economic cycle. This research fills a gap in linking legal system reform, credit risk mitigation, and CSR, offering practical insights for future policy design in sustainable finance and green innovation.

Since the 1990s, with the rapid development of ICTs and internet economy, more and more Chinese enterprises have begin to integrate ICTs into the whole processes of their businesses, and many of these enterprises have carried on some organizational innovations to face the challenge of Internet economy. Based on a case study in a Chinese top enterprise named Haier Group, this paper explores the impact of Internet economy and ICTs on organizational innovation, and tries to analyze the new characters of organizational innovation in the internet economy, and the complex relations among application of ICTs, organizational innovation and enterprise performance. Finally, the authors summarize some valuable results, which may be useful for other enterprises to learn from.

Purpose: In early 2018, the corporate non-performing loan ratio began to climb steadily, showing some threats to the financial health of the Egyptian banking system. Therefore, the determinants of corporate and retail credit risks in the banking sector of Egypt during 2013-2020 were studied in the present work to provide better insight into how the macroeconomic and microeconomic determinants affect the level of credit risks. Methodology: the present research utilized the Dickey-Fuller test to assess the stationarity of the panel data and then employed the generalized method of moments (GMM) for data analysis. Findings: it was found that asset size, loans-to-deposits, inflation, gross domestic product (GDP), and lending interest rate were negatively associated with the corporate credit risk, while the capital adequacy ratio, foreign direct investment (FDI), and public debt were positively related. Moreover, the loans-to-deposits ratio was negatively associated with retail credit risk, while the capital adequacy ratio was positively related. Originality/Value: In this context, classifying the credit risk into corporate and retail credit risk was very crucial as it illustrated that the corporate credit risk was more sensitive to the determinants than the retail ones.