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The transition to sustainable development has become a global priority, with energy transformation serving as a key component. In this context, green financial instruments play a crucial role in shaping corporate energy-saving behaviors and promoting sustainable development. This paper examines the impact and mechanism of China's first green credit policy (GCP) on energy consumption intensity (ECI) of manufacturing firms in China, using a difference-in-difference (DID) approach with micro-level data (2004-2009). We find that GCP significantly reduces enterprise ECI, primarily by promoting green technological innovation and capital renewal. We also learn that the effect of GCP varies with firm and industry characteristics, such as financial dependence, size, ownership, capital intensity, and industrial chain position. Moreover, the outcomes suggest that the effect of GCP is stronger in regions with higher degrees of financial development and marketization. This study contributes to the literature on green finance, environmental regulation, and energy efficiency by providing novel evidence on how GCP influences ECI from a micro perspective., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. This manuscript proposes a credit default prediction model based on TabNeT-Stacking. First, use the PyTorch deep learning framework to construct an improved TabNet structure. The multi-population genetic algorithm is used to optimize the Attention Transformer automatic feature selection module. The particle swarm algorithm is used to optimize the hyperparameter selection and achieve automatic parameter search. Finally, Stacking ensemble learning is used, and the improved TabNet is used to extract features. XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Category Boosting), KNN (K-NearestNeighbor), and SVM (Support Vector Machine) are selected as the first-layer base learners, and XGBoost is used as the second-layer meta-learner. The experimental results show that compared with original models, the credit default prediction model proposed in this manuscript outperforms the comparison models in terms of accuracy, precision, recall, F1 score, and AUC (Area Under the Curve) of credit default prediction results.

In line with Sustainable Development Goals, firms are increasingly incorporating Environmental, Social, and Governance (ESG) considerations in their investment strategies. The effect of firms' climate change risk (FCCR) on firms' Value (FV), and how such investment engagements moderate this effect, is a prominent subject of debate among scholars, investors, and policymakers. To examine these dynamics, we analyze a dataset of 1771 United States (US)-listed firms from 2006 to 2021 to quantify the effect of FCCR on FV. We use the generalized method of moments model to achieve our objectives. The major findings are summarized as follows: First, FCCR has a negative and significant effect on FV. Second, ESG investments positively and significantly influence FV. Third, ESG investments significantly moderate the FCCR-FV relationship. We confirm our estimations are robust under different estimations strategies. Finally, this article provides a fresh perspective on risk management with significant policy implications for investors, managers, and regulators in the US. We suggest that ESG investing is an important strategic catalyst for US firms., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

With the deepening of the diversification and openness of financial systems, financial vulnerability, as an endogenous attribute of financial systems, becomes an important measurement of financial security. Based on a network analysis, we introduce a network curvature indicator improved by Copula entropy as an innovative metric of financial vulnerability. Compared with the previous network curvature analysis method, the CE-based curvature proposed in this paper can measure market vulnerability and systematic risk with significant advantages.

Since the onset of the COVID-19 pandemic, energy price predictability has worsened. We evaluate the effectiveness of the two machine learning methods of shrinkage and combination on the spot prices of crude oil before and during the COVID-19 epidemic. The results demonstrated that COVID-19 increased economic uncertainty and diminished the predictive capacity of numerous models. Shrinkage methods have always been regarded as having an excellent out-of-sample forecast performance. However, during the COVID period, the combination methods provide more accurate information than the shrinkage methods. The reason is that the outbreak of the epidemic has altered the correlation between specific predictors and crude oil prices, and shrinkage methods are incapable of identifying this change, resulting in the loss of information., (© 2023 Elsevier B.V. All rights reserved.)

We investigated the effect of pegylated recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF) on lymphocytes and white blood cells of patients with malignant tumors. After PEG-rhG-CSF treatment, the count of lymphocytes increased in 66 cases, remained unchanged in 2 cases, and decreased in 20 cases. The difference in lymphocyte count before and after treatment was statistically significant ( P < 0.001). White blood cell changes were positively correlated with lymphocyte changes ( r = 0.36, P = 0.001). In the subgroup with increased white blood cells ( n = 80), there were 62 cases with increased lymphocytes, 1 case with unchanged lymphocytes, and 17 cases with decreased lymphocytes after PEG-rhG-CSF treatment. There was significant difference in the count of lymphocytes and white blood cells ( P < 0.001). In the subgroup with 6 mg of PEG-rhG-CSF ( n = 66) and the subgroup with 3 mg of PEG-rhG-CSF ( n = 22), the changes of white blood cell and lymphocyte counts before and after treatment were statistically significant ( P < 0.001). The two were positively correlated in the 6 mg PEG-rhG-CSF subgroup, with correlation coefficient r = 0.34 ( P = 0.002). PEG-rhG-CSF can increase the count of lymphocytes and white blood cells in patients with malignant tumors, and the increase of lymphocytes is positively correlated with the increase of white blood cells., Competing Interests: Conflict of interest: Authors state no conflict of interest., (© 2023 the author(s), published by De Gruyter.)

The economic and environmental consequences of bad banking practices have aroused much attention. In China, banks are at the center of shadow banking activities through which they avoid regulation and support environmentally unfriendly businesses such as fossil fuel companies and other high-pollution enterprises. In this paper, we study the impact of bank's engagement in shadow banking activities on its sustainability by using annual panel data of Chinese commercial banks. The result shows that bank's engagement in shadow banking activities has a negative impact on its sustainability and the negative impact of bank's engagement in shadow banking activities is more pronounced for city commercial banks and unlisted banks which are less regulated and lack corporate social responsibility (CSR). Furthermore, we explore the underlying mechanism of our findings and prove that bank's sustainability is impeded because it transforms high-risk loan into shadow banking activities which are less regulated. Finally, by using difference-in-difference (DiD) approach, we find that bank's sustainability improved after the financial regulation on shadow banking activities. Our research provides empirical evidence that the financial regulation on bad banking practices is beneficial for bank's sustainability., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Financial activity plays an important role in the analysis of environmental and ecological implications. Previous research shows that shadow banking activities, informal and less regulated financial activities, have become an important part of financial systems after the 2008 financial crisis. However, the impact of shadow banking activities on carbon dioxide emissions has been ignored in previous research. In this paper, we use the panel annual data of 30 Chinese provinces over the period 2013-2020 to study the effect of shadow banking activities on carbon dioxide emissions. The result shows that shadow banking activities have a positive impact on carbon dioxide emissions, and the result is still robust by using dynamic panel regression with general method of moments (GMM) method. Heterogeneity analysis shows that the impact of shadow banking activities on carbon emissions is more pronounced for the secondary industry, which proves that shadow banking activities support more for environmentally unfriendly businesses. Furthermore, by using difference-in-difference (DiD) method, we examine the causal effect of shadow banking regulation policy on carbon dioxide emission reduction and demonstrate that the shadow banking regulation can reduce carbon dioxide emissions. Our research contributes to environment protection and achieving the goal of low-carbon economy by exploring the role of shadow banking activities and financial regulation in carbon dioxide reduction., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The study intends to investigate the role of managerial personality traits on working capital management of Chinese SMEs with the mediating role of overconfidence behavior. Data from the Chinese SMEs managers is collected through a close-ended survey questionnaire using multi-stage cluster sampling and structural equation modeling is applied for empirical analysis. The results shown that extroversion, openness to experience, and agreeableness traits determines overconfidence behavior among the managers. While, conscientiousness and neuroticism traits were found insignificant with overconfidence behavior. Overconfidence behavior significantly mediated during COVID-19 between managerial personality traits (e.g., extroversion and agreeableness traits) and working capital management. Limitted time remained a major limitation in completing this study. The study extended the knowledge by investigating the working capital management practices during COVID-19 in Chinese SMEs and contributed by presenting multiple practical implications for effective working capital management., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.)

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic., Competing Interests: None., (© 2021 Elsevier Inc. All rights reserved.)

As an important measure to enhance the allocation of land resources and achieve moderate scale operation in China, land transfer has an irreplaceable role in giving full play to the social and economic value of land. This paper uses data from the China Family Panel Studies in 2018 and applies OLS, PSM and mediator effects to study the impact of land transfer on household income. The results of the study show that: firstly, participation in land transfer can significantly increase the income level of farming households, but the impact of different land transfer acts on household income varies. Secondly, the age and physical condition of the household head, the number of agricultural and non-agricultural members in the household and the poverty status of the household all have a negative effect on household income. On the other hand, the education level of the household head, the household size and the presence of village officials in the household, agricultural subsidies, land titling and land size all have a positive effect on household income. Thirdly, the impact on household income for households that did not participate in land transfer is greater than for those that did. The results of the mediator effect test show that land transfer has a significant effect on household farm income, and that farm income has a full mediator effect, but all have a positive significant effect on household income., Competing Interests: The authors have declared that no competing interests exist.

The coronavirus (COVID-19) outbreak in the China has exposed small- and medium-sized enterprises (SMEs) to a variety of challenges, some of which are potentially life-threatening to their sustainability. Therefore, this study aims to investigate the macroeconomic lockdown effects of COVID-19 on small business in China. A survey questionnaire with 313 participants was used to collect the data. In this study, the SEM technique was used to analyse model. The data have been gathered for the study from the managers and employees of Chinese SMEs. The findings of the study show that COVID-19 has a significant negative impact on financial performance, operational performance, profitability, access to finance, and customer satisfaction. According to the study's findings, external support aids have a greater impact on SMEs' ability to survive and thrive through innovation than on their actual performance. The findings of this study have a number of important practical consequences for small- and medium-sized business owners, governments, and policymakers., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

As a consequence of the COVID-19 pandemic outbreak, most commodities experienced significant price drops, which were expected to continue well into 2020. As a result, the Markov switching model is used to study the influence of policy uncertainty and the COVID-19 pandemic on commodity prices in the USA. Commodity markets are stimulated by economic policy uncertainty, according to results from a two-state Markov switching model. In both high and low regimes, economic policy uncertainty (EPU) influences the commodity market, according to the study's findings. However, in the high regime, EPU has a greater influence on the energy and metal sectors. EPU has different influences on commodity markets in high- and low-volatility regimes, according to this study. There is a wide range of correlations between COVID-19 outcomes and EPU and how the prices of natural gas, oil, corn, silver, soybean, copper, gold, and steel respond to these tremors, in both high- and low-volatility tenure. Oil and natural gas, on the other hand, are unaffected by shifts in COVID-19 death rates under either regime. Results show that in both high- and low-volatility regimes, the demand and supply for most commodities are responsive to historical prices., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Innovation has been a major growing driver of sustainability. The topic addressed in this study is a much-required transition to environmental and social sustainability considering the role of innovation in pacing up those changes. Digital evolution has greatly helped in dealing with climatic changes and promoting sustainability. This has helped the entrepreneurial organizations to adopt innovative approaches to tackle the inflexible challenges. Few developed and developing countries are at the forefront regarding technological innovation that encounter significant challenges in terms of innovation and adoption of new technologies and there is still a study vacuum as to whether the influence of technical innovation on achieving social and environmental sustainability differs depending on the stage of sustainability. This quantitative study has explored these effects collecting data from the SME's (small and medium enterprises). The findings of the study show that attitude toward technological innovation has a strong role to play in organizational innovation, digital entrepreneurship, environmental and social sustainability. Organizational innovation has been found a strong mediator between technological innovation and sustainability while digital entrepreneurship could not find significant results as mediator. This study will be useful for the countries and organizations involved in adopting new technologies considering their organization's role in achieving an overall eco-friendly and social sustainability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xiao and Su.)

We provide a first view of vulnerable informal economy after the blows from COVID-19, using transaction-level business data of around 80 million offline micro businesses (OMBs) owners from the largest Fintech company in China and employing machine learning method for causal inference. We find that the OMBs activities in China experienced an immediate and dramatic drop of 50% during the trough. The businesses had rebounded to around 80% of where they should be seven weeks after the COVID-19 outbreak, but had remained at this level until the end of our time window. We find a larger disruption to the OMBs in urban areas, the female merchants and the merchants who were not grown up in the places where they conducted businesses. We discuss the implications for policy support to the most vulnerable, and highlight the importance to take full advantage of digital development to follow up the informal economy., (© 2021 Elsevier Inc. All rights reserved.)

In the post-epidemic era, green finance plays a more significant role in supporting the "green recovery" of the economy, so it is necessary to evaluate the implementation effect of previous green financial policies. In 2017, the green finance reform and innovation pilot zone set up in five provinces and autonomous regions made an exploration in the development of green finance. From the perspective of micro-enterprises, can this policy play a beneficial policy effect in the long run? Based on the quasi-natural experiment of green finance pilot, using the data of A-share listed companies, this paper empirically tests the impact of pilot policies on the long-term value of green enterprises in pilot areas. It is found that, compared with non-pilot zones, the green finance pilot enables a significant increase in the Tobin Q-measured value of green enterprises in the pilot zones. Heterogeneity analysis shows that green finance pilot has a more significant impact on non-state-owned enterprises, enterprises in traditional industries, large enterprises, and enterprises in the eastern region of China. Green finance pilot zone can achieve better policy effects in areas with stronger environmental impact regulation and higher financial development levels. The mechanism test shows that the green finance pilot affects the long-term value of green enterprises through the capital market effect improving the stock trading activity of enterprises and through the real effect improving the operational efficiency and profitability of enterprises. From the perspective of micro-enterprises, this paper enriches the research on the development effect of green finance and provides theoretical support for the effect evaluation of green finance pilot policies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hu, Li, Li, Liu, Wang and Zheng.)

Objective: As elderly parents today will share a longer life with their children than ever before, the spillover effects of children's human capital on parents' well-being become increasingly important. This study investigated whether children's schooling leads parents to give up smoking and whether the effects were moderated by their education or child-parent contact frequency., Methods: Using data from the China Health and Retirement Longitudinal Study, we conducted an instrumental variable (IV) analysis using China's compulsory schooling reform in the 1980s as a natural experiment., Results: The IV estimates suggest that elderly parents of more highly educated children are more likely to quit smoking. Moreover, the effects are more significant among parents who had not finished primary school and also slightly stronger among parents who live close to their children or meet their children frequently., Discussion: Our findings add to current evidence regarding spillover effects of education on smoking cessation. A child's education may exert an impact through the spillover of health knowledge., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Detecting the adverse effects of major emergencies on financial markets and real economy is of great importance not only for short-term policy reactions but also for economic and financial stability. This is the lesson we learnt from the COVID-19 pandemic. This paper focuses on the risk spillover effect of the COVID-19 on Chinese energy industry using a high-dimensional and time-varying factor-augmented VAR model. The results show that the net volatility spillovers of the pandemic remain positive to all underlying energy sectors during January to June of 2020 and February to April of 2021. For the former sub-period, the volatility spillover of the COVID-19 is not only the highest, but also lasts longest for oil exploitation sector, followed by the power and gas sectors. While for the latter sub-period, the COVID-19 has relatively higher volatility spillovers to the power, coal mining and petrochemical sectors. These findings suggest that the COVID-19 has significant risk spillover effects on Chinese energy sectors, and the effects vary among different energy sub-sectors and across different periods of time., (© 2021 Elsevier B.V. All rights reserved.)

This research aimed to examine the effects of paternalistic leadership on the safety participation of high-speed railway drivers. Survey data were collected from 601 drivers in major Chinese rail companies. Structural equation modeling was conducted to analyze the influence of paternalistic leadership on safety participation via leader-member exchange (LMX). The results indicated that moral leadership directly promotes safety participation. Besides, benevolent leadership was positively associated with safety participation. Also, LMX partially mediates the positive relationship between benevolent leadership, moral leadership, and safety participation. Therefore, paternalistic leadership promotes the safety participation of high-speed railway drivers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhang, Liu, Pan and Guo.)

The link between CO2 emission and its possible determinant, namely financial development, foreign direct investment, economic growth, and energy consumption, gives a significant environmental research place. This study attempts to determine the nonlinear link between CO2 emissions and its determinants in China for the period 1985-2019. Based on the theoretic links and foundation, we develop an asymmetric autoregressive distributed lag (ARDL) model using annual data in this study. The findings of the nonlinear ARDL suggest that domestic credit from the banks (DCB) has a negative effect on environmental pollution in the short and long run, while domestic credit to the private sector (DCP) has an insignificant negative role in environmental pollution in China. The results show that positive change in DCB causes a decrease in CO2 emissions, while negative change increases it in the short and long term. The coefficient for a positive shock in DCP is a negative, and negative shock in DCP has a significant positive impact on environmental pollution in the short and long run. This means that nonlinear ARDL financial development effects on environmental pollution have deviated from the linear ARDL estimates. Empirical findings also support the presence of asymmetric long-run cointegration among the indicators. The findings also show that energy consumption and real GDP have a significant positive impact on CO2 emissions in the long-run, while foreign direct investment (FDI) improves the environment. Based on the empirical findings, the study endorses the suitable channelization of the finance market towards clean energy projects without compromising economic growth.

This paper investigates the effect of firm-level operating flexibility on stock performance during the COVID-19 outbreak in China. We find that firm-level operating flexibility is significantly positively correlated with the cumulative abnormal stock returns that occurred during the event window, and this positive relation is more pronounced in firms in the provinces most affected by the epidemic. This positive relation is also more obvious in firms that have relatively fewer fixed assets. Therefore, our results provide direct empirical evidence that the real options embedded in operating flexibility played an important role during the COVID-19 outbreak., (© 2020 Elsevier Inc. All rights reserved.)

Since 2013, China has implemented a large-scale initiative to systematically deploy solar photovoltaic (PV) projects to alleviate poverty in rural areas. To provide new understanding of China's targeted poverty alleviation strategy, we use a panel dataset of 211 pilot counties that received targeted PV investments from 2013 to 2016, and find that the PV poverty alleviation pilot policy increases per-capita disposable income in a county by approximately 7%-8%. The effect of PV investment is positive and significant in the year of policy implementation and the effect is more than twice as high in the subsequent two to three years. The PV poverty alleviation effect is stronger in poorer regions, particularly in Eastern China. Our results are robust to alternative specifications and variable definitions. We propose several policy recommendations to sustain progress in China's efforts to deploy PV for poverty alleviation.

By introducing net entropy into a stock network, this paper focuses on investigating the impact of network entropy on market returns and trading in the Chinese Growth Enterprise Market (GEM). In this paper, indices of Wu structure entropy (WSE) and SD structure entropy (SDSE) are considered as indicators of network heterogeneity to present market diversification. A series of dynamic financial networks consisting of 1066 daily nets is constructed by applying the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model with a threshold adjustment. Then, we evaluate the quantitative relationships between network entropy indices and market trading-variables and their bilateral information spillover effects by applying the bivariate EGARCH model. There are two main findings in the paper. Firstly, the evidence significantly ensures that both market returns and trading volumes associate negatively with the network entropy indices, which indicates that stock heterogeneity, which is negative with the value of network entropy indices by definition, can help to improve market returns and increase market trading volumes. Secondly, results show significant information transmission between the indicators of network entropy and stock market trading variables.

Consider a class of power-transformed and threshold GARCH ( p , q ) (PTTGRACH ( p , q ) ) model, which is a natural generalization of power-transformed and threshold GARCH(1,1) model in Hwang and Basawa [2004. Stationarity and moment structure for Box-Cox transformed threshold GARCH(1,1) processes. Statistics & Probability Letters 68, 209-220.] and includes the standard GARCH model and many other models as special cases. We first establish the asymptotic normality for quasi-maximum likelihood estimators (QMLE) of the parameters under the condition that the error distribution has finite fourth moment. For the case of heavy-tailed errors, we propose a least absolute deviations estimation (LADE) for PTTGARCH ( p , q ) model, and prove that the LADE is asymptotically normally distributed under very weak moment conditions. This paves the way for a statistical inference based on asymptotic normality for heavy-tailed PTTGARCH ( p , q ) models. As a consequence, we can construct the Wald test for GARCH structure and discuss the order selection problem in heavy-tailed cases. Numerical results show that LADE is more accurate than QMLE for heavy-tailed errors. Furthermore, the theory is applied to the daily returns of the Hong Kong Hang Seng Index, which suggests that asymmetry and nonlinearity could be present in the financial time series and the PTTGARCH model is capable of capturing these characteristics. As for the probabilistic structure of PTTGARCH ( p , q ) model, we give in the appendix a necessary and sufficient condition for the existence of a strictly stationary solution of the model, the existence of the moments and the tail behavior of the strictly stationary solution., (Copyright © 2007 Elsevier B.V. All rights reserved.)