Climate change presents a severe challenge to the operation of rural commercial banks. Employing the panel data of rural commercial banks in China, this study delves into the impact of climate change on bank risk-taking behavior and explores the underlying mechanisms. We note that (1) extreme climate events significantly increase the risk-taking level of rural commercial banks; (2) the agricultural natural risk mechanism and agricultural market risk channel are validated; (3) heterogeneity analysis shows that high temperatures and extreme precipitation have a greater impact on banks in monsoon regions, while those in non-monsoon regions are more affected by low temperatures. Moreover, extreme temperatures and precipitation are significant factors in both non-grain and grain areas. Additionally, long-term temperature variations induce a passive increase in the risk-taking level. These findings provide valuable insights for rural commercial banks to better address climate risks and for central banks to implement targeted measures to enhance banking stability.

Recent disruptions have made supply chain resilience a central concern for firms and policymakers, yet the financial-policy foundations of resilience remain insufficiently understood. This study examines whether policy-driven industry–finance cooperation strengthens corporate supply chain resilience and through which financial channels this effect operates. Using the China Industry–Finance Cooperation (IFC) Pilot Policy as an exogenous policy shock, we analyze Chinese A-share listed manufacturing firms from 2014 to 2023. The SDID estimates show that the IFC Pilot Policy increases firms’ supply chain resilience by 0.061 standard deviations, and this result remains stable across multiple robustness checks. Mechanism tests indicate that the policy improves resilience by reducing debt financing costs, promoting supply chain finance, and enhancing real investment efficiency. Further heterogeneity analysis shows that the effect is stronger among technology-intensive firms and firms located in less favorable business environments. Theoretically, this study links industry–finance cooperation to supply chain resilience through a “funding cost–chain cash flow–capital use” framework. Practically, it shows that better coordination between financial services and industrial needs can help firms strengthen their capacity to withstand supply chain risks.

This study investigates the impact of Artificial Intelligence (AI) policy on firms' supply chain disruption risk. Prior research has focused on the effect of AI technology on supply chain risk, while paying less attention to the role of AI-related policies. Using data on Chinese listed firms from 2012 to 2024 and exploiting the implementation of China's National AI Innovative and Application Pilot Zones as a quasi-natural experiment, we find that AI policy significantly reduces firms' supply chain disruption risk. Our analysis reveals that the risk-mitigating effect operates through higher innovation investment and lower operating costs, and the aboveeffect is more pronounced for non-state-owned enterprises, smaller firms, and firms with more concentrated supply chains. Further analysis indicates that the risk-mitigating effect of AI policy generates spillovers along supply chains. This study offers practical insights for policy design to integrate technological progress with supply chain risk management.

Against the backdrop of global efforts to combat climate change and promote energy transition, the development of renewable energy has become a key path to achieving sustainable development. Based on cross-country data from 60 countries spanning 2001 to 2021, this study employs fixed effects and quantile regression methods to systematically examine the impact of stock markets on renewable energy development and its dynamic evolution. The findings reveal that stock market development has a statistically significant positive effect on renewable energy growth, particularly in developed economies and non-resource-dependent countries. Quantile regression results further indicate a non-linear relationship: at the lower stages of renewable energy development, the marginal effect of the stock market shows a decreasing trend, but once the development level surpasses a certain threshold, the marginal effect turns to an increasing trend, indicating that the support effect of the stock market on the renewable energy industry has a phased feature of nurturing period and acceleration period. Mechanism tests show that the stock market can promote the development of renewable energy by alleviating financing constraints and enhancing the efficiency of financial institutions and markets. This study not only provides new empirical evidence for understanding the interactive relationship between capital markets and energy transition, but also offers references for countries to formulate differentiated renewable energy development policies.

This study examines whether debt financing affects firm labor employment. Using Chinese listed companies between 2007 and 2021, we identify an inverted U-shaped relationship between debt financing and labor employment. Increased debt financing tends to boost labor employment. However, this positive influence gradually diminishes until a critical point is reached and the relationship turns negative thereafter, suggesting that there is a best balance between debt financing and labor employment. This inverted U-shaped association is also influenced by firm profitability and financial risk, with the former steepening the relationship and the latter making it smoother. Moreover, the impact of debt financing on labor employment is more pronounced in large firms, non-state-owned enterprises, labor-intensive companies, and firms with high solvency. Our findings imply that companies can effectively harness the positive effects of debt financing on labor employment by strategically adjusting their capital and labor resources.

Studying the interrelationship among artificial intelligence, supply chain and energy market is crucial to achieving sustainable development. The research uses the TVP-SV-VAR methodology to recognise the ever-changing correlation among the artificial intelligence index (AII), global supply chain pressure indicator (GSCPI) and global energy-related uncertainty index (GEUI). In light of quantitative discussions, it is presented that AII exerts negative influences on GSCPI and GEUI, highlighting that the development of artificial intelligence would facilitate global supply chain stability and energy sustainability; in turn, GSCPI and GEUI have positive and adverse influences on AII. Through comparing, the correlation between AII and GEUI is only reflected in the short term, whereas the interrelationship between AII and GSCPI could be observed in the short-, medium- and long-run situations. In addition, GSCPI exerts positive and negative influences on GEUI in the short, medium and long runs, whereas the positive effects of GEUI on GSCPI means the uncertainty in the energy market might destroy the supply chain across the globe. In the context of a new round of scientific and technological revolution and industrial transformation, this study will provide significant recommendations to maintain global supply chain stability and energy sustainability by applying artificial intelligence technology.

Understanding the key drivers of systematic risk is important for effective operations management and decision-making within a firm. This study explores the determinants of systematic risk based on firm-specific features, including the firm basic characteristics, profitability, liquidity, operational efficiency, and growth, using a large-scale sample of Chinese listed companies. In contrast to previous studies, we use various machine learning algorithms to predict systematic risk and visualize the permutation importance and influence of firm-specific features. We find that, first, the light gradient boosting machine (LightGBM) model performs best in predicting systematic risk. Second, the firm's founding age, initial public offering age, internationalization, cash ratio, and gross margin significantly contribute to the prediction of systematic risk. Interestingly, our results suggest that the firm's basic characteristics factors are more important than other categories in the prediction of systematic risk. Overall, our findings offer important theoretical contributions to the systematic risk literature and have significant implications for risk management practices and investor decision-making.

A plethora of studies are available on the linkage of natural resources, economic growth, and financial development. However, studies on the different financial indices are lacking in the literature concerning sustainable development; hence, to cover the gaps in the literature, this study evaluates the nexus of natural resources with the trilemma of financial development, markets, and institutions in China from 1983 to 2021. The study also checked the role of government expenditures (GEXEDU), renewable electricity (EREC), and economic growth (GDP). Several econometric strategies have been deployed as time series data, including ADF with break years to check the unit root in the data and Bayer-Hanck for each predicted variable to ensure the long-run equilibrium of explanatory variables with the predicted one. FMOLS and CCR are utilized for primary estimations, while the robust least squares method is used for robustness checks. The outcomes indicate that all variables are found static at level except the financial development index and renewable electricity, whereas long-run cointegration has been confirmed between all models. The primary outcomes reveal that GDP stimulates financial development and its three indices, while natural resources have a resource curse in financial development multidimensional and institutional indices while blessing exists on the financial markets index. Moreover, the negative effect of government education expenditures is present only in the financial markets index, while renewable electricity has a positive and significant influence on financial development multidimensional and market indices. Moreover, renewable electricity reduces the financial markets index in the short and long run. Besides, robustness check analysis provides similar outcomes that are robust and valid. Relevant policies regarding the nexus of natural resources and financial development indices are suggested.

The carbon and fossil energy markets have been significantly affected by the COVID-19 pandemic. This article examines the nonlinear correlation and the spillover effect between the global fossil energy markets and the European Union carbon market prior to and following COVID-19. The nonlinear correlation is captured by the localized Gaussian correlation approach, and the spillover effect is evaluated using a quantile-based Granger causality analysis method. Empirical evidence demonstrates that COVID-19 strengthens the correlation between the carbon and oil markets. Conversely, the correlation between the carbon and coal markets is weakened by COVID-19. Furthermore, COVID-19 only resulted in the contagion between the carbon and Texas gas markets. Finally, the spillover effect between the European Union carbon market and fossil energy markets changes from unidirectional to bidirectional. This article provides inspiration for investors to adjust their portfolios to reduce investment risks and meaningful information for policymakers to maintain market stability.

Given that risk-taking is an essential channel for companies to obtain high returns and realize value enhancement, the goal of this study is to holistically explore the determinants of corporate risk-taking using various machine learning algorithms. Based on the data from Chinese listed companies between 2010 and 2019, we document that the adaptive boosting (AdaBoost) model makes better predictions of corporate risk-taking. We further visualize the importance and influence of the firm basic characteristics, firm performance, and chief executive officer (CEO) characteristics and discover that in the AdaBoost model, the firm basic characteristics, and performance factors, such as the firm’s fixed asset investments, size, and return on equity, are important in predicting corporate risk-taking, while CEO characteristics are less important. Finally, the role of variables in corporate risk-taking varies among large and small enterprises. Overall, our findings deepen the comprehension of what drives corporate risk-taking and provide a potential way for real-world firms seeking to adjust their risk-taking level.

Analysing the green bond's hedging performance against cryptocurrency uncertainty is essential to maximising investment returns. The study innovatively uses the TVP-SV-VAR process to capture the time-dependent connection among cryptocurrency uncertainties and the green bond markets in China and the U.S. The quantitative outcomes present that cryptocurrency policy uncertainty (CPU) has favourable effects on China's green bond (CGB), often evidencing the property of China's green bond to hedge against cryptocurrency uncertainty. However, CPU's adverse and insignificant impacts on CGB refute this opinion. CPU has negative effects on U.S. green bonds (USGB) most of the time, which underlines that the U.S. green bond market is not an effectual safe haven against cryptocurrency uncertainty. By comparison, China's green bond has better hedging performance than the U.S. one regarding the uncertainties in the cryptocurrency market. Additionally, various time points accompanied by massive undulations in CPU and critical incidents exhibit interrelations among CPU, CGB and USGB. Further, we replace CPU with cryptocurrency price uncertainty (CPRU) to examine the robustness and confirm that the reported results and corresponding discussions are credible. Under the severe cryptocurrency risks, the conclusion offers significant implications to investors and relevant policymakers.

This paper explores how corporate financial portfolio influences distress risk. We define distress risk as a dummy variable determined by whether firms need external subsidies to repay the interest payable. Spanning our analysis with 3,698 listed firms in China between 2007 and 2019, our findings are twofold. First, financial portfolio is associated with less distress risk. Second, the impact is more pronounced for firms with higher levels of liquidity of financial portfolio. We provide evidence that corporate financial portfolio prevents distress risk by reducing financial expenses and by improving investment income. Our findings post a challenge to the existing view in China that financial portfolio would harm corporate operation. The implication is that companies could allocate more liquid financial assets than illiquid ones to mitigate forewarned risk.

The building sector accounts for a major portion of China's total energy use and energy-related CO2 emissions. Promoting the development of green buildings (GBs) via the application of green financial instruments (GFIs, including green fiscal investment, green credit, green insurance, and green bonds) is crucial for China to achieve carbon mitigation goals. This study explores the effect and mechanism of GFIs on supporting the development of GBs using time series econometrics. Results show that the overall green finance system exhibits significant supporting effects on the development of GBs. Among four types of GFIs, green fiscal investment plays a critical and an indispensable role. The effects of alternative GFI combinations on GB development are compared. The combination of green fiscal investment, insurance, and credit shows the most satisfactory supporting effect. In this combination, insurance plays a bridging role, as it better ensures that GBs' actual energy performance meets expected performance, thus promoting green credit financing for GBs. However, the results also indicate that the current green finance market is still in a government-driven pattern. To facilitate its transformation to a market-driven pattern, policies should focus on designing more targeted GFI-combination products, bridging GBs' performance gaps, and updating GBs' rating system.

The COVID-19 pandemic created unprecedented challenges for communities and economies around the world. Based on 13 leading global stock indices, the event study method is adopted in this research to explore the impact of the COVID-19 pandemic on the performance of the stock market indices in the short term. Regression results show that the global stock markets performed poorly in response to the COVID-19 pandemic. The findings of the event study imply that the stock markets reacted rapidly and negatively to the COVID-19 pandemic when lockdown restrictions were announced to contain the spread of the novel coronavirus. The Asian stock indices experienced more negative abnormal earnings than the stock indices of the countries outside Asia. Moreover, investor sentiments act as a wedge between financial investment decisions, returns, and fear of uncertainty caused by the pandemic. Furthermore, the panic experienced by investors may be an effective transmission channel through which the COVID-19 outbreak affects the returns on the stock market indices.

Supply chain finance (SCF) has attracted considerable attention being an innovative business model that allows firms, especially small- and medium-sized enterprises (SMEs), to convert illiquid assets into cash without incurring additional liabilities. However, its effects on SME performance and risk have been insufficiently studied. The competitiveness of SMEs depends on performance enhancement and risk mitigation. Thus, this study constructs a scaled-decile rank transformation of account receivable turnover to gauge the degree to which a supplier implements SCF, thereby examining the relationship between SCF, performance, and risk. We collect data on 4,679 SMEs from the Chinese manufacturing sector. Thereafter, hierarchical linear regression, a complex form of multiple linear regression analysis, is employed to test the hypotheses. The results indicate that an SME’s SCF adoption positively impacts its performance but negatively impacts its risk. To further explore cross-sectional variability, we investigated the buyer-supplier relationship’s moderating role. Results show that an increase in customer concentration strengthens both the positive effects of SCF on performance and the negative effects of SCF on risk. Overall, our study contributes to the literature on the interface of operations and finance in supply chains by exploring the multiple facets of SCF adoption and highlighting the moderating role of buyer-supplier relationship in SCF and SME competitiveness. Finally, we provide managerial implications for SMEs and financial service providers by validating the value of SCF implementation and the buyer-supplier relationship management in forging competitive advantages.

Due to the spread of coronavirus disease 2019 (COVID-19), business environmental uncertainty, a restricted flow of personnel and materials, and changes in consumer demand have impacted business operations and financial performance. This study investigates the relationship between COVID-19, customer concentration, and sustainable growth based on data from listed companies in China. The results reveal that COVID-19 has had a negative impact on sustainable growth, but customer concentration can mitigate this negative association. By emphasizing the moderating effect of customer concentration on the relationship between COVID-19 and sustainable growth, this study provides new insights for firms who are seeking to mitigate the negative shocks of COVID-19 and promote sustainable development.

本文基于劳动力分配的视角,为金融要素如何影响实体经济活动提供了一些新的经验证明.首先,从规模和效率两个路径阐述了行业产出与劳动力配置的关系.在此基础上进一步考虑了银行信贷在劳动力配置中的直接和间接影响作用.基于中国制造业2003-2015年25个子行业的行业面板数据的实证检验结果表明,生产规模和生产效率与劳动力分配分别表现出正的和负的相关关系.银行信贷会影响行业间的劳动力配置,主要是借助于规模渠道和效率渠道对劳动力分配产生同向的加强作用.信贷扩张可以提高就业率并带动经济增长,但不利于全要素生产率水平的提高.

本文从事后激励的角度,构建了一个关于房地产个人贷款违约与银行反应策略的博弈模型,对中国房地产价格下跌的诱发机制以及家庭和银行的最优决策进行了理论分析.在此基础上选择35个大中城市作为研究样本,利用面板数据回归模型对相关理论进行了实证检验,结果表明,家庭收入下降和房地产贷款违约是诱发房地产价格下跌的关键因素.提高购房首付比,降低房地产贷款价值比以及保持房地产贷款市场结构的适度集中,既可以抑制房地产价格过快上涨,也可以预防房地产价格发生暴跌风险.当房地产贷款出现违约时,为了避免房地产价格进入下降螺旋,银行的最优策略不是取消房地产抵押品的赎回权,而是采取积极的信贷措施以稳住房地产价格.贷款市场份额占比越高的银行越有激励这样做.

融资约束是企业生产经营过程中所面临的主要风险之一.对于一个有效的市场机制,这种风险应该被市场价格所发现,或者融资约束风险会对资本市场的定价产生显著的影响.通过构建融资约束指数对企业面临的融资约束风险进行了描述和测度,借助CAPM模型和三因子定价模型对融资约束风险的存在性、独立性进行了相关检验.研究发现,融资约束会提升公司的系统风险.融资约束作为独立因子虽然不能普遍解释所有上市公司的股票收益率,但是对于规模较小,公司特质信息含量高的企业,融资约束因子的解释效果显著.

本文对金融危机对我国上市公司投资的影响及信贷刺激政策的有效性进行了实证检验和分析.研究发现,金融危机主要通过市场需求冲击和提高投资者的预防性动机对企业投资支出产生负面影响,信贷供给冲击对我国上市公司的投资没有产生显著影响.金融危机期间所采取的信贷刺激政策对上市公司而言没有发挥显著作用.本文进一步对信贷政策的长期效果进行了检验,从一个完整的经济周期来看,良好的盈利前景和持续的信贷供给是增加企业投资水平的有效保证.但是经济下行期,企业投资对信贷刺激的敏感性降低,应避免采取过激的政策并诱发其他经济不良后果的可能性.