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Achieving economic development and ecological protection simultaneously is an inevitable selection for sustainable development in today's world, so it is crucial to improve eco-efficiency (EE). According to Chinese panel data at the provincial level between 2010 and 2020, this research explores the nexus between green finance (GF) and EE. The results denote that GF can significantly improve EE, and the higher the level of EE, the stronger the effect of improvement. The upgrading of industrial structure, optimization of energy structure, enterprises' concern for environmental protection and the public's attention to the environment are all favorable factors that can enhance the promotion effect of GF on EE. Additionally, this facilitation can only be played under a good external environment and mature internal conditions. Our findings can provide new insights for improving EE by developing GF. [ABSTRACT FROM AUTHOR]

Using data on Chinese non-financial listed firms covering 2009 to 2022, we explore the effect of supply chain transparency on stock price crash risk. Two proxies for supply chain transparency are constructed using the number of supply chain partners' names and the proportion of their transactions disclosed in annual reports. The results reveal that enhancing supply chain transparency can decrease crash risk, specifically by mitigating tax avoidance and earnings management. Moreover, the analysis suggests that this risk-reduction effect is more prominent in companies where managers are more incentivized to hide negative information and investors possess superior abilities to acquire information. Interestingly, supplier transparency is more influential in mitigating crash risk than customer transparency. These findings emphasize the significance of supply chain transparency in managing financial risk. [ABSTRACT FROM AUTHOR]

This study offers additional evidence on the self-employment of rural-to-urban migrants from the perspective of cultural barriers in China. By employing seven waves of the China Migrants Dynamic Survey data from 2010 to 2016, we find that cultural distance has a negative and significant effect on the self-employment of rural-to-urban migrants in eight subdivision industries. In particular, such negative effect is greater in the service sectors. [ABSTRACT FROM AUTHOR]

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security. [ABSTRACT FROM AUTHOR]

The digital economy has a significant influence on the financial behavior of enterprises. According to the panel data of A-share listed enterprises in the manufacturing industry from 2010 to 2020, we discuss how manufacturing enterprises allocate their financial assets in the process of digitalization from the perspective of operating profit and risk taking. The results reveal that ICT investment significantly reduces financial asset allocation, while digital transformation improves the financial asset allocation of manufacturing enterprises. Because ICT investment increases operating profit from the main business, manufacturing enterprises reduce the allocation of short-term and long-term financial assets. We thus propose two phenomena: precautionary saving and substitutional investment. As digital transformation increases risk taking, financial assets are elevated by manufacturing enterprises for the precautionary saving motivation, boosting the short-term financial assets in their portfolios. These findings are further supported by the heterogeneity of innovation characteristics and ownership. [ABSTRACT FROM AUTHOR]

The digital transformation of global economic and social activities brought about by advanced information and communication technology (ICT) may have a profound impact on sustainable economic development. This research measures the digitalization of the economy and society in cities of China based on Internet-related indicators and digital financial development and investigates the economic and environmental effects of their digitalization. The empirical results show that digitalization has significantly improved green economic efficiency (GEE), and this finding remains valid after a series of robust analyses. However, there is regional heterogeneity in the improvement effect of GEE. The green effect of digitalization is significant only in the cities with a high degree of economic servitization, capital-abundant cities, and eastern cities. Although digitalization has promoted the green development of the urban economy, it also has a negative spatial spillover effect on the GEE of neighboring cities. One explanation is that cities are shifting polluting industries to other areas during the process of digital transformation. [ABSTRACT FROM AUTHOR]

This study investigates the impact of the COVID-19 pandemic on economic uncertainty and its spillover network based on social network analysis. The study constructs the inter-provincial Chinese economic uncertainty spillover network using a mixed frequency dataset and the provincial social network using microblog user data. Furthermore, the temporal exponential random graph model is used to analyze the impact of COVID-19 and social network during three periods. The results show that the COVID-19 pandemic significantly affects China's provincial economic uncertainty and social network significantly hinders economic uncertainty spillover networks. The inhibitory effect of social networks on uncertainty spillover network has regional heterogeneity, which is more significant in provinces severely affected by the pandemic and strictly controlled. [ABSTRACT FROM AUTHOR]

Using about 73,000 individuals' data in China, this research, for the first time, analyzes the impact of labor transfer (LT) caused by foreign product demand (FPD) on human capital investment. Two-stage least squares estimation with the instrumental variable is applied and produced three findings. First, the FPD-LT model illustrates that with FPD increasing, more labor transfers from the agricultural sector to the non-agricultural sector. Second, working in the non-agricultural sector requires higher-level skills than in the agricultural sector. LT from agricultural sector to non-agricultural sector motivates people to invest more in human capital and promotes gender equality in human capital investment. Third, labor transferring from the agricultural sector to the non-agricultural sector enhances individuals' income, leading to the increase of children's human capital investments. The better-educated people make greater investment in their children's human capital, exacerbating intergenerational inequality. [ABSTRACT FROM AUTHOR]

Green bonds are adopted to exclusively finance or refinance green projects and thus is an important policy instrument for sustainable development. The development of these eco-friendly projects benefits the reduction of carbon emission. What impacts do green bonds have on carbon emission intensity and how? This issue needs to be revealed, including the dynamic spatial interactive rules and regional heterogeneity. This is especially true for China, with its vast territory and a short history of green bonds. Different with existing literature, this paper collects the data of the amount of green bond issued in each province in China rather than the policy dummy variable of green bond. The spatio-temporal interactions of both the impact of green bonds and the related mechanisms are studied. A dynamic spatial Durbin model (DSDM) combined with a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model reveals the negative impacts of green bonds on carbon emissions and the spatio-temporal interactions. On this basis, the mediation model is introduced to clarify the three impact mechanisms of green bonds and find the predominant role of technology mechanism. In addition, different characteristics in spatial interactive rules and impact mechanisms of green bonds are found in various regions of China. Finally, the study proposes some policy recommendations on how to effectively reduce carbon emissions with green bonds. [ABSTRACT FROM AUTHOR]

Carbon emissions are a global issue, especially among manufacturers, and China is facing many problems to lower such pollutants due to its goal of attaining carbon neutrality. This research applies a mathematical model to achieve the desired goal of decreasing CO 2 with the innovation of carbon neutrality tools and by moving towards high quality under the load capacity factor (LCF). We adopt a simulation-based optimization approach at different carbon peaks and manipulate various scenarios in order to find the best decisions for carbon neutrality tools. The results indicate that the green innovation fund, CO 2 emission tax, and CO 2 emission cap have significant roles in maximizing LCF. First, the green innovation fund plays a crucial part in reducing carbon emissions. Second, a CO 2 emission tax allows manufacturers to produce less than the green innovation fund and CO 2 emission cap. Third, the CO 2 emission cap strategy conversely allows manufacturers to produce more than the CO 2 emission tax strategy. This study provides reference for policymakers to achieve carbon neutrality and for the government to determine the best carbon-neutral tools under carbon peak. Lastly, the findings help manufacturers maximize LCF and profit and clean up the environment. [ABSTRACT FROM AUTHOR]

This research investigates the role of China's exchange rate policy uncertainty in manufacturing corporate performance using the fixed effect model and quantile regression model. The findings indicate that an exchange rate policy fluctuation constrains better-performing corporates, but is adverse for those with inferior performance. Government subsidy appears to explain the positive impact, and the effect is enhanced or alleviated somehow by corporate governance, growth, and financial constraints. Exchange rate policy fluctuation also significantly pushes corporates to adjust and upgrade their business, which improves their performance accordingly. Additionally, positive shocks significantly stimulate corporate performance, while negative shocks including global financial crisis and COVID-19 epidemic hinder corporate development. Overall, the risk confrontation ability of Chinese manufacturing enterprises presents a positive upward trend. The China government should thus issue more positive exchange rate policies that could send positive signals to the market and increase the openness of its manufacturing industry. More corporate-friendly policies can be launched, such as adjusting the amount of subsidies for manufacturing enterprises in a timely manner and helping manufacturing enterprises, especially more mature ones, deal with exchange rate policy uncertainty risks. [ABSTRACT FROM AUTHOR]

The world's largest food producer, China, is now facing increasing pressure to solve food security and is committed to forging new prospects for global food security and sustainable agricultural development. As food security is receiving greater attention, this research takes panel data from 31 provinces and cities in China from 2011–2020 to examine the spatial spillover effect of the digital economy on food security. The study conclusions are as follows. (1) Improvement of the digital economy significantly promotes the development of China's food security. (2) The digital economy has an inhibitory effect on food security in local regions and promotes food security in neighboring regions. (3) Heterogeneity exists within the spatial spillover effects of the digital economy on food security, and such differences often vary based on the status of regional economic development, natural geographical location, and land function. Therefore, policy suggestions proposed herein cover three aspects: accelerating infrastructure investment and construction, improving the empowering capability of the digital economy, and implementing different digital economy strategies to better support food security in China. [ABSTRACT FROM AUTHOR]

This research examines the role of corporate investment inefficiency and abnormal investment behaviors on the effectiveness of monetary policy in China's transitional economy during 2001–2017. Using panel data regression models, we explore corporate investment inefficiency and its response to money supply and interest rates. We find that firms tend to overinvest and react abnormally to interest rates. Overall, the interest rate transmission mechanism is hindered and monetary policy becomes muddled. Our finding suggests that regulating the money supply or interest rates cannot help in coping with the dilemma between economic and financial stability until extensive financial reforms are made. Finally, we draw implications for overall financial reforms. [ABSTRACT FROM AUTHOR]

Starting from Chinese A-listed firms' loan announcements, this research creatively constructs a dynamic, variant-linkage, and more comprehensive banking network in China during 2007 and 2016. Exploiting techniques from the literature on complex networks, we find that China's banking network exhibits more clustering, more coherence, higher centrality, and even more heterogeneity. Empirical results show that the above network features, especially the heterogeneity of the network, have a great impact on financial systemic risk. A network with higher clustering coefficient, higher coherence, lower centrality, and greater heterogeneity is associated with a lower financial systemic risk. A range of policy measures can be drawn from our results, including macro-prudential policy, increasing network stability, and applying surcharges for systemically important financial institutions so as to minimize financial systemic risk. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID‐19) has already devastated the world, and the economy becomes the most critical challenge for any country worldwide. The increasing uncertainty of the COVID‐19 outbreak has made stock markets in China more turbulent and less predictable. Under the current exceptional circumstances, the hospitality industry suffered the most due to the travel restrictions. This research thus assesses the dynamic relationship among the COVID‐19 outbreak, macroeconomic fluctuations and hospitality stock returns based on a structural VAR framework from 13 January to 11 May 2020, in China. Evidence reveals that macroeconomic fluctuations and hospitality stock returns are significantly affected by shocks from the COVID‐19 outbreak. An unanticipated positive change of the COVID‐19 explosion triggers an addition in exchange rates and causes a reduction in the stock market and hospitality industry returns. For the impacts of the exchange rate, findings reveal that a surprise increase in exchange rates (currency depreciation) exerts a significant negative influence on stock market returns. Additionally, a positive change of stock market returns is linked to a decline in exchange rates and a rise in hospitality industry returns. Therefore, knowledge of these relationships can enable policymakers to evaluate and implement effective policies to stabilize the stock markets and help investors to make appropriate investment strategies. [ABSTRACT FROM AUTHOR]

The growing carbon crisis threatens sustainable development. This paper verifies that the rise of green finance contributes to overcoming this challenge. The benchmark results show that green finance can directly drive decarbonization of the economy. High-level openness, government support, human capital and urbanization are prerequisites of this effect, and green finance is more effective in promoting decarbonization in areas with high carbon intensity. Moreover, green finance drives economic decarbonization by promoting economic growth, technical innovation, industrial upgrading, and renewable energy transition. Internet availability, financial level and higher education have moderating roles in boosting this effect. Furthermore, the implementation of green finance policy also contributes to decarbonization. The robustness of the basic result is proven with several methods, thus strengthening the reliability of the basic conclusion in this paper. Based on the above main findings, targeted policy recommendations concerning how to further develop green finance are proposed in this paper. • Green finance can directly drive economic decarbonization. • Some high-level external circumstances are the prerequisite of that effect. • Green finance can drive economic decarbonization through mediating paths. • Moderating paths are Internet availability, financial level and education. • Green finance policy makes contributions to the economic decarbonization. [ABSTRACT FROM AUTHOR]

Digital financial inclusion (DIFI) exerts vital influence on poverty alleviation in China, thus developing digital financial inclusion may provide new approaches to inclusive growth and create more possibilities for the sustainable development with extending the coverage and usage concerning financial services. This research explores how poverty alleviation is impacted by digital financial inclusion derived from the provincial data of China from 2011 to 2019. The results show that digital financial inclusion can make considerable contributions to poverty alleviation significantly. We conduct heterogeneity analysis with quantile regression conjointly, which reveals that digital financial inclusion has divergent effects on users with diverse poverty levels. The results of mediation regression demonstrate that the income level and income distribution play a vital role in poverty alleviation. Moreover, based on spatial Dubin model, digital financial inclusion enhances poverty alleviation with a spillover effect, which correlates with a "U" non-linear relation, laying a hidden danger of increasing poverty. Finally, we carry out IV regression to enhance the robust results, proving the reliability of this research's basic conclusion. [ABSTRACT FROM AUTHOR]

This research proposes a theoretical framework to illustrate the incentives behind the exponential growth in asset size of China's large state‐owned enterprises (SOEs) in the past few decades, with results presenting three types of incentives that led to their expansion. (1) The business empire building effect. (2) The state predatory effect. (3) A state benevolent effect in which the state is also concerned with "social welfare" in terms of increasing labor hiring through a larger asset size of SOEs for the sake of social stability. The empirical evidence is broadly consistent with the theories proposed herein. [ABSTRACT FROM AUTHOR]

Due to the unique contribution it makes to both environmental and financial performances, green innovation has become a key factor driving organizations to achieve sustainable development. Policy change presents a good opportunity to discover the drivers of corporate behavior. Considering the way in which anti-corruption policy can affect firms' innovation environment and innovation decisions, this research takes China's anti-corruption policy as a shock condition and conducts a quasi-experiment to discuss the impact of anti-corruption policy on the sensitivity of green innovation to executive incentives in China. The main findings reveal that anti-corruption policy significantly improves the sensitivity of green innovation to equity incentives and decreases the sensitivity to control rights incentives, but does not influence the sensitivity to reputation incentives. These findings show the integrated impact of an exogenous environment and endogenous governance upon green innovation. We also reveal the difference of this integrated impact in the context of various natures of ownership. Specifically, in private enterprises, anti-corruption policy can optimize the sensitivity of green innovation to equity incentives, while in state-owned enterprises the sensitivity of green innovation to reputation incentives combined with equity incentives or with control rights incentives significantly improves. The findings presented in this paper reveal the micro-economic effects of anti-corruption policy on green innovation and instruct firms to optimize executive incentive arrangements to green development under the background of China's anti-corruption campaign. [ABSTRACT FROM AUTHOR]

The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume‐volatility nexus by decomposing and reconstructing the trading activity into short‐run components that typically represent irregular information flow and long‐run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out‐of‐sample realized volatility (RV) forecasts. This finding is robust both in one‐step ahead and multiple‐step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume‐volatility linkage to EMD‐HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy. [ABSTRACT FROM AUTHOR]

This research applies the Data Envelopment Analysis-Slack Based Measure (DEA-SBM) model and Global Malmquist–Luenberger (GML) index to estimate the quality of economic growth (QEG) in 30 provinces of China during the period 2011–2019. The results are that both digital finance and QEG have significant temporal and spatial differentiation characteristics. Moreover, the regional disparities of digital finance show a significant decline, and the regional disparities of QEG exhibit an upward trend. In terms of research methods, the relational data analysis paradigm and the quadratic assignment procedure help examine the impact of various factors, including digital finance on the regional disparities in QEG. The findings cover the following four aspects. First, from the perspective of the full sample, the regional disparities of digital finance have a negative effect on the regional disparities of QEG. The influence of the narrowing regional disparities of digital finance on widening the regional disparities of QEG is realized by widening the regional disparities in technological progress. The regional disparities of the coverage breadth and usage depth of digital finance have a negative effect in the regional disparities of QEG. Among the factors, marketization is the primary factor affecting the regional disparities of the QEG. Second, during the '12th Five-Year' and '13th Five-Year' periods, the regional disparities of digital finance have played a negative role on the regional disparities of QEG, but the impact intensity is lower. Third, narrowing regional disparities of digital finance can expand regional disparities of QEG in the east and central regions. Finally, in terms of different years the role of digital finance in regional disparities of QEG demonstrates a downward trend. This study has important academic and practical significance for the sustainable development of China's economy and has theoretical and practical implications for the formulation and implementation of digital finance related policies in the future. It also provides a basis for narrowing the regional disparities in QEG in China and for promoting regional coordinated development. [ABSTRACT FROM AUTHOR]

Carbon emission is a global issue, and China is facing many problems to resolve it, especially in emission‐generating companies (EGCs). To maintain sustainable development, it is necessary to reduce carbon emissions for the well‐being of society, and carbon neutrality can play an important role. With China targeting the goal of carbon neutrality to reduce carbon emissions, this research aims to bridge the gap in the literature by considering carbon neutrality instruments in a duopoly market. The study sets up a mathematical model of the duopoly game to achieve desired objectives and then applies a simulation‐based optimization technique to examine the mathematical model under different experiments. After manipulating various scenarios to find the optimal values of carbon neutrality instruments, the results indicate that green bond provision, emission tax, and emission quota play a vital role in carbon neutrality. Different combinations of carbon neutrality instruments reduce various levels of carbon emissions. We further show that a combination of emission tax upon EGCs plays a key role in reducing carbon emissions during the fifth scenario. In the second scenario, a combination of green bond provision and emission tax reduces carbon emissions, but is lower than the fifth scenario. This research offers a reference for policymakers to move towards carbon neutrality, and it is beneficial for the government to decide the optimal values of carbon neutrality instruments. [ABSTRACT FROM AUTHOR]

This article employs the non-linear autoregressive distributed lag model in analysing the environmental impact of tourism in China from 2002Q1 to 2018Q4, while controlling for non-renewable energy consumption, trade openness, urbanisation, per capita GDP and its quadratic form. The approach permits the simultaneous testing of non-linearities in the short and long run. This is achieved through negative and positive partial sum decompositions of the regressor of interest. Findings from the article indicate the following: (i) long-run co-movements exist between tourism activities and environmental performance in China, (ii) tourism activities considerably damage the environment in China, (iii) the environment responds to tourism in an asymmetric manner, such that the damage caused by increasing tourism is smaller than the positive environmental effect of decline in tourism. Thus, less tourism protects the environment faster than more tourism damages it. A supplementary non-parametric test for non-linear causality was also performed. The result further provides strong evidence of a non-linear feedback causality between tourism activities and environmental performance in China in the first moment (mean) and second moment (variance). Tourism (environmental performance) is thus a significant predictor of China's environmental performance (tourism). [ABSTRACT FROM AUTHOR]

Building a stronger modern socialist country is China's second centenary goal, and ecological beauty is the innovation of the connotation of modernization. How to realize the drive toward modernization with green development is an urgent topic. Based on the municipal panel data of the Yangtze River Economic Belt from 2009–2019 in China and selecting the air circulation coefficient and river network density as instrumental variables, this research examines the impact mechanism of green development on modernization. The results show the following: (1) green development significantly promotes modernization; (2) green development indirectly promotes the improvement of modernization through technological innovation, foreign capital flow, and talent attraction; and (3) the promotion of green development to modernization is affected by the urban size, and medium-sized cities benefit most. The study results provide empirical support and a decision-making basis for promoting the win-win situation of green development and modernization construction. [ABSTRACT FROM AUTHOR]

Under the influence of geopolitical conflicts, extreme weather, and other factors, global food security has been severely impacted. Cultivated land ecological security in China is vital for guaranteeing local food security and sustainable development. Therefore, using provincial data from 2003 to 2020, this research establishes an index system based on the pressure-state-response (PSR) model and assesses its cultivated land ecological security via the entropy weight method. A series of measurements such as Dagum's Gini coefficient and spatial Markov chain are then adopted to reveal changes in regional differences and spatial-temporal characteristics of cultivated land ecological security in China. The findings indicate that this ecological security has risen from insecure to critically secure, but the overall gap has gradually widened, mainly stemming from interregional differences. Cultivated land ecological security exhibits positive spatial clustering characteristics and the phenomenon of club convergence. The amount of water resources, the degree of soil erosion control, and the strength of local financial expenditure on agriculture are the main factors hindering its cultivated land ecological security, and the threats mainly come from the response criterion layer. Therefore, it is essential to strengthen measures for cropland ecological protection, to formulate targeted strategies by combining the regional characteristics, to narrow the intra-regional gap, and to bring into play the spatial radiation effect of high-level provinces. • This paper probes the overall status of China's cultivated land ecological security. • The overall gap of cultivated land ecological security stems from inter-regional gap. • The cultivated land ecological security in China shows a club convergence phenomenon. • The factors affecting China's cultivated land ecological security mainly come from the response criterion layer. [ABSTRACT FROM AUTHOR]

This study addresses critical gaps in supply chain management (SCM) by integrating emission-risk minimization (ERM), green purchasing (GP), and profit maximization (PM). The research focuses on the optimal behaviors of manufacturers, agents, and retailers within the SCM framework to achieve carbon neutrality and reduce carbon dioxide emissions (CO 2 e). This study considers Guangdong province, China, a region facing challenges in optimizing energy systems and meeting CO 2 e reduction targets. Simulation-based optimization techniques within mathematical models are employed. A design of experiment (DOE) method was used to explore the dynamics of key variables in the SCM environment. Results reveal optimal behaviors for manufacturers, agents, and retailers, demonstrating the ideal values for profit and pricing decisions. Manufacturers optimize production quantity, achieving CO2e reduction and PM through ERM. Agents exhibit a strong commitment to GP practices, enhancing PM and carbon-neutral goals. Retailers get more PM than manufacturers and agents, contributing to a clean environment. Interestingly, retailers make contributions to the clean environment without considering ERM and GP in SCM. The study contributes novel insights by addressing the identified gap in SCM research, emphasizing the joint consideration of ERM, GP, and PM. This research assists manufacturers, agents, and retailers in terms of PM for economic objectives. It cleans the environment through carbon-neutral SCM in society. • The sustainability of carbon neutral SCM has been investigated in China. • The study was analyzed by using SBO under ERM and GP. • PM for stakeholders has been extracted to keep the environment clean. • Optimal behavior of carbon neutral indicators was found to PM under SCM. [ABSTRACT FROM AUTHOR]

In the domain of global socio-economic dynamics, the pivotal role of food production (FP) cannot be overstated. It constitutes the bedrock of fundamental human necessities while being inexorably linked with critical aspects such as economic expansion, societal equilibrium, and geopolitical interplays. The multifaceted nature of FP appears through its direct impact on macroeconomic indicators and its latent influence on international relations and policy-making. This critical nexus underscores the imperative for a comprehensive understanding and strategic management of food production processes to sustain and enhance global socio-economic stability and growth. Investigating the nexus between green finance (GF) and FP is pivotal for ensuring national security and promoting sustainable economic development. This research thus paper examines GF's impact on FP in China as outlined in the 2017 green finance policies. It employs the difference-in-differences (DID) method and analyzes a balanced panel dataset comprising 282 cities in China from 2009 to 2020. The results show that GF has an inhibitory effect on FP, and this remains valid after a range of reliability checks, including parallel trend assessments and placebo tests. Further findings of transmission mechanism analysis indicate that non-agricultural factors and financial exclusion are critical for GF's inhibitory effect on FP. Heterogeneity analyses show that GF's inhibitory effect on FP is more pronounced in the main food-producing areas, the main food-marketing areas, and cities with better infrastructure. The empirical results of this paper comprehensively illustrate GF's impact on FP and how it contributes to the achievement of food security objectives. [ABSTRACT FROM AUTHOR]

Energy plays a vital part in stimulating economic progress, and the shift towards a cleaner energy system is highly significant for ensuring the sustainable development of the economy. China's energy structure urgently needs to be transitioned. The fast advancement and implementation of artificial intelligence (AI) has provided a new and important tool for promoting the transition of energy structure. So, what is the relationship between the application of artificial intelligence and the transition of the energy structure? This research introduces artificial intelligence into the energy sector, focusing on the relationship between artificial intelligence and energy transition. Since nonlinear models are better able to study the complex effects and phase differences of artificial intelligence. Using China's provincial panel data spanning from 2006 to 2019, this study employs nonlinear modeling to explore the stage differences in the process of AI in facilitating energy structure transformation. This paper derives the following findings based on empirical research. First, there is a U-shaped relationship between artificial intelligence and the transition of energy structure. Specifically, before the inflection point, the initial application of artificial intelligence, artificial intelligence may adversely impact energy transition. When the inflection point is passed, AI will help facilitate the energy transition. Second, the U-shaped relationship between AI and energy transition is more pronounced in coastal and non-resource-based regions. Third, energy intensity, government investment in science and technology, and informatization will moderate the U-shaped relationship between artificial intelligence and energy transition, changing the steepness of the original U-shaped relationship and even reversing it. Hence, it is imperative to effectively utilize the technological benefits of artificial intelligence through the development patterns and distinctive features of different regions, thereby facilitating the smooth transition of the energy structure. [Display omitted] • The role of artificial intelligence in the energy transition process is studied. • Stage differences in the role of AI are analyzed. • The nonlinear relationship between AI and energy transition is investigated. • Provide insights into the factors affecting the relationship between AI and energy transition. • This research helps the government to advance the energy structure transition. [ABSTRACT FROM AUTHOR]

The existing literature mainly focuses on the impact of information communication technologies on carbon emissions, but little attention has been paid to the role of the digital economy in transporting carbon emissions. This paper calculates the index of the digital economy through the entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and constructs the panel data of 30 provinces in China from 2008 to 2017. By extending the Stochastic Impacts by Regression on Population, Affluence and Technology model, this study explores the potential linear and non‐linear relationship between digital economy and carbon emissions in the transport sector. Empirical results show that the digital economy has the alleviating effect on carbon emissions in the transportation sector, and a change in the digital economy of one unit standard deviation resulted in a 6.14% reduction in carbon emissions. In terms of sub‐regions, the digital economy has a significant negative impact on transport carbon emissions in the eastern and central regions, while it is insignificant in the western regions. This paper further investigates the threshold effect of urbanization on the relationship between the digital economy and transportation‐related carbon emissions. The digital economy accelerates the transport sector's carbon emissions in the low urbanization stage, while it reduces the carbon emissions in the high urbanization stage. [ABSTRACT FROM AUTHOR]

This study applies the event‐analysis method and takes three Chinese listed textile and apparel companies that are representative of the upstream, midstream, and downstream of the textile value chain as research objects. By tracking the Baidu index trend of the keyword "trade war" to identify the 'time window' for each iconic event, we apply the autoregressive distributed lag approach to examine the impact of important landmark events on the performance of these companies during the period of Sino–US trade friction in 2018. We find that the impact diminished over time. Additionally, compared with upstream companies, midstream and downstream companies were hurt more. However, the risks were generally controllable. [ABSTRACT FROM AUTHOR]

This research presents a full picture of the rationale behind China's infrastructure investment under the Belt and Road Initiative (BRI), which was formally initiated in 2013. In this paper, we argue that the main reason for China to conduct infrastructure investment under BRI is to strategically respond to the emergence of the "New Normal," which pushes the country to sustain economic growth through further structural transformation. We come up with three relevant factors for why infrastructure projects under BRI could be conducive to structural transformation in China's economy, as it (1) provides a much better alternative to the existing poor logistic conditions and can create accessibility among regions, (2) enables the smooth flow of factor endowments of production that significantly reduce production costs, and (3) indirectly strengthens the influence of the debt provider's home currency. This paper also provides three theoretical pillars: the comparative advantage following (CAF) and defying (CAD) development strategies, the late development theory with antineoliberalism characteristics, and the new international division of labor, from which these three factors might potentially explain how BRI could enhance the structural transformation of this second‐largest economy in the world. [ABSTRACT FROM AUTHOR]

This research studies the non‐linear relationship between interest rates and exchange rates for China and the United States using the rolling‐window method. We also investigate uncovered interest rate parity (UIP) and the capital market theory for the whole time period and subperiods so as to reconsider various economic connections between China and the United States. The results suggest that the effect of the latter's interest rate adjustment on China/U.S. exchange rate volatility is stronger than that of China's interest rate adjustment. Moreover, changes in the China/U.S. exchange rate have a slightly stronger effect on the U.S. interest rate than on China's interest rate. Our findings reveal that the interest rate parity theory does not hold for the entire sample period but may hold in subperiods. The results provide a reference for the steady implementation of RMB internationalization. [ABSTRACT FROM AUTHOR]

There are different types of margin requirements for margin buying and this paper focuses on setting both initial and maintenance margin levels. By using the data of stock portfolio returns over the period from March 31, 2010 to December 31, 2020, the research computes and compares margins derived by several margin setting methods using extreme value theory (EVT) for margin buying in China. Important findings are summarized as follows. First, the VaR-x method generates more accurate forecasts of both unconditional and conditional margin levels than the parametric and the Hill non-parametric methods particularly given lower probabilities of margin violation. This is robust to different portfolios, market conditions and sample periods. Second, margins derived actually vary over time, becoming higher (lower) when market volatility increases (decreases). The findings have important economic and practical implications. [ABSTRACT FROM AUTHOR]

Given China' rapid transformation, its economy is currently experiencing a change from a rugged development style to a sustainable development style with green development being critical to this transformation. Increasing green total factor productivity (GTFP) is now considered one of the significant signs of economic growth. With increasingly stricter environmental laws and regulations and a broad application of innovation capability (ICY) in modern life, this research investigates the impact mechanisms of both environmental regulations (ER) and ICY on GTFP, offering robust empirical results from panel data of 30 provinces in China covering the period 2006–2017. The results indicate that both ICY and ER effectively promote GTFP, but compared to ER, ICY has a heterogeneous effect on GTFP. Moreover, ICY plays a mediating role in ER and GTFP, while ER promotes GTFP through ICY. Accordingly, the paper puts forward some suggestions, such as to optimize and improve the ER policy, enact an innovation-driven development strategy, widely use ICY technology, and strengthen monitoring and supervision. [ABSTRACT FROM AUTHOR]

This research assesses the effect of short sales on investment efficiency in China by applying the DID method. Results show that short sales can improve investment efficiency and that the effect is more pronounced for companies with low information transparency, low governance capacity, low audit quality, and high management performance pressure. Evidence reveals that the impact of short sales varies under different market sentiments. Our findings support the beneficial role of short sales on the information and governance environment. Governments and investors can thus pay more attention to short sales, as they help in their role of accelerating information dissemination. [ABSTRACT FROM AUTHOR]

Using the data of China's service trade and diplomatic events with 45 countries, this paper examines the effects of diplomatic relations on service trade based on the gravity model. Through an empirical analysis, the results proved that good diplomatic relations can significantly promote the export of China's service trade and that the effects of diplomatic relations on trade have a clear distinction regarding different political positions. Our results support the view that diplomatic relation does an important role in international trade. [ABSTRACT FROM AUTHOR]

This research investigates the dynamic correlation between China's policy uncertainty and the crude oil markets (i.e. domestic and international markets) using monthly time-series data from May 2003 to December 2018. To consider the non-linear and dynamic properties, we adopt the time-varying parameter structural vector autoregression model (TVP-SVAR) to estimate the dynamic correlations between the time series. By employing four categorical policy uncertainty indices, i.e. monetary policy uncertainty index (MYPU), fiscal policy uncertainty index (FLPU), exchange rate policy uncertainty index (EXER), and trade policy uncertainty index (TEPU), our results reveal that the correlation between China's policy uncertainty and real crude oil returns is time-varying and non-linear. Specifically, the independence between policy uncertainty and oil returns varies more constantly and at a high degree while the time-varying interaction between policy uncertainty and global oil production (global economic activity) varies at a lower frequency and at a low level. Furthermore, regarding the associations between categorical policy uncertainty and global oil returns, the average correlation of EXER (negative) is the strongest one, followed by FLPU (negative), then MYPU (positive), and finally TEPU (negative). Moreover, it is noteworthy that WTI (Daqing) crude oil prices are utilized to increase the robustness of our conclusions. Finally, we generally confirm the dynamic and negative correlation between China's geopolitical risk and crude oil returns. It is evidently clear from the results that investors should pay close attention to the policy uncertainty to avoid the adverse impact of specific policy uncertainty on crude oil returns. [ABSTRACT FROM AUTHOR]

This research first adopts three indicators to measure the systemic risk of different financial industries in China. Second, we employ the Time Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-SV-VAR) model to investigate the time-varying relationship among COVID-19 epidemic, crude oil price, and financial systemic risk. The results herein not only help us grasp the current level of systematic risk in China, but also can assist at improving the early warning risk indicators and enhance the risk management system. Lastly, this research can also help investors to make reasonable asset planning. [ABSTRACT FROM AUTHOR]

This research constructs a simple dynamic model to illustrate the micro‐mechanism of industrial upgrading along the global value chains. Our model predicts that as firms move up from downstream to upstream stages, (a) there is higher profitability if and only if the following three conditions are satisfied. First, the increasing rate of sunk cost (including R&D expenditure) over sequential stages of production cannot be sufficiently large (endogenous sunk cost effect). Second, the decreasing rate of change of intermediate input demand with respect to the price set by firms at a production stage cannot be sufficiently high (intermediate input price effect). Third, the decreasing rate of change of intermediate input demand with respect to the pricing dynamics over the sequential stages of production cannot be sufficiently large (sequential pricing uncertainty effect); (b) total cost is lower if and only if the decreasing rate of change of input demand with respect to the price is sufficiently large; (c) output is higher if and only if and the decreasing rate of change of input demand with respect to the price is not sufficiently large; and (d) the price decreases. We show that the empirical patterns revealed in China are consistent with our model's predictions. [ABSTRACT FROM AUTHOR]

This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed‐Data Sampling (GARCH‐MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH‐MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH‐MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and (3) high‐frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH‐MIDAS in volatility forecasting. [ABSTRACT FROM AUTHOR]

Outward foreign direct investment (OFDI) in an open economy has gradually become an important source of green innovation (GI). With the rapid development of China's OFDI, this research studies the impact of OFDI on the country's GI, employing panel data of 30 provinces from 2006 to 2017. We first use the Super-SBM model to measure GI performance and then test the impact of OFDI on GI with the system GMM model. Evidence finds that the negative impact of OFDI on GI is not significant on the whole, but the results of regional regression show that impact of OFDI on GI exhibits obvious regional differences. We then utilize the dynamic threshold panel model to determine the non-linear relationship between OFDI and GI through the perspective of environmental regulation in order to avoid the bias caused by ignoring the impact of institutional factors and time dynamic change. After dividing environmental regulations into command control environmental regulation and market incentive environmental regulation, the research results show that the double threshold effects of both environmental regulations are significant. Command control environmental regulation does not play a role in promoting the effect of OFDI on GI. When the intensity of market incentive environmental regulation is low, OFDI negatively affects GI. Moreover, only when the market incentive regulation shows high intensity can OFDI significantly promote GI. With the continuous growth of China's OFDI, it is therefore necessary to determine the appropriate environmental regulation to improve the reverse spillover effect of OFDI enterprises on the country's GI. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce the idea of shift-share analysis, where local inflation is decomposed into the national factor, structural stickiness (with sectoral and regional aspects), and a localized pricing power effect in order to gain a better understanding of the inflation dynamics. We apply price-level data in China as a dual economy, which has historically been separated into urban and rural areas. The results show that sectoral differences in inflation are the most important factor explaining inflation in China, while local pricing power must be seriously considered in many sectors in rural areas. [ABSTRACT FROM AUTHOR]

This research investigates the role of high-frequency data in volatility forecasting of the China stock market by particularly feeding different frequency return series directly into a large number of GARCH versions. The contributions of this research are as follows. 1) We provide clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data. 2) We incorporate different distribution assumptions in GARCH models to capture the stylized facts of high-frequency data. The result shows that: 1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; 2) non-normal distributions such as skewed student-t and generalized error distribution are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; and 3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models such as HAR and ARFIMA at volatility forecasting. [ABSTRACT FROM AUTHOR]

This research explored the effects of the coronavirus disease (COVID-19) outbreak on stock price movements of China's tourism industry by using an event study method. The results showed that the crisis negatively impacted tourism sector stocks. Further quantile regression analyses supported the non-linear relationship between the government's responses and stock returns. The results present that the resurgence of the virus in Beijing did bring about a short-term negative impact on the tourism industry. The empirical results can be used for future researchers to conduct a comparative study of cultural differences concerning government responses to the COVID-19. [ABSTRACT FROM AUTHOR]

Frequent recent episodes of geopolitical risk have spurred scholarly interest in its economic and environmental implications. Compared with previous research, this study constructed a panel data set of China's 30 provinces/municipalities to assess how geopolitical risk affects the development of renewable energy. The results of both linear and nonlinear analysis reveal that the effect of geopolitical uncertainty is not alike and is region- and state-dependent. Compared to its effect in China's eastern and central regions, geopolitical risk exerts a negative effect on renewable energy development in the western region. This negative effect tends to be more pronounced in areas characterized by relatively weak economic and social conditions. We also observe a threshold effect between them, as geopolitical risk typically weakens (enhances) renewable energy development when the economic condition is at a lower (higher) level. Finally, a sound financial system oriented toward "going green" and a stronger environment of technological innovation lessen the harmful effect of geopolitical risk. These findings provide targeted insights for policymakers seeking to improve local economic conditions, promote energy transition, balance regional energy inequality, and realize sustainable development. [ABSTRACT FROM AUTHOR]

As population aging becomes the new demographic norm in China, its workforce structure is changing, and its demographic dividend is about to disappear. Artificial intelligence (AI) has been experiencing rapid progress in the last few years, and it is becoming an important tool to address the impact and challenges of an aging population. Therefore, this research introduces population aging and artificial intelligence into agricultural production, focusing on the effects of population aging on food security and the function played by artificial intelligence in it. From an empirical study conducted based on provincial panel data, the following conclusions arise. First, population aging in rural China has not negatively impacted food security, so there is no need to be overly pessimistic about the inevitable aging of the rural population. Second, AI has a favorable moderating function on the effects of population aging on food security. Third, the moderating effect of AI is heterogeneous. Compared with other provinces, AI can play a strongly positive moderating effect in central and west regions and major grain-selling areas. Based on the above findings, this paper proposes targeted policy recommendations on protecting food security in the context of artificial intelligence and population aging. • Artificial intelligence is introduced into the field of agricultural production. • Food security is investigated considering the aging of the agricultural workforce. • The positive moderating role of AI on the influence of aging population on food security. • This study contributes to ensuring food security in the context of aging population and artificial intelligence. [ABSTRACT FROM AUTHOR]

The increasingly critical ecological environment in the major grain-producing regions (MGPRs) of China has attracted widespread attention, but there is limited research at the city level. This study presented a land ecological security (LES) evaluation index system based on the pressure, state, response (PSR) model. It used the panel entropy weight method, kernel density function method, Moran index model, and spatial econometric model to measure LES of 158 cities in MGPRs from 2005 to 2020, and analyzed the spatial and temporal evolutionary trends, spatial divergence characteristics, and influencing factors of LES. The results are as follows. (1) From 2005 to 2020, the overall trend of urban LES level was rising, but it experienced an N-shape fluctuation trajectory of "rise-fall-rise". The state index and response index were on an upward trend, while the pressure index had a certain degree of decline. (2) LES was characterized by the spatial distribution of "high in the north and low in the south" and "high in the east and low in the west", while spatial heterogeneity became increasingly significant, with greater spatial differences within the northern cities and within the central cities. (3) There was a significant spatial spillover effect of LES, but the effect decreased with the expansion of spatial divergence. The spatial association patterns mainly exhibited "high-high" (HH) and "low-low" (LL) agglomerations. (4) The response layer was the main obstacle subsystem, while green space per capita, environmental governance investment, precipitation per capita, arable land per capita, and agricultural development were the main obstacle factors. (5) Economic development, fiscal decentralization, industrial advancement, and innovation input were the positive factors of LES, whereas financial development and industrial development were the negative factors of LES. The findings provide effective policy reference for the sustainable utilization of urban land resources. • Constructed an evaluation index system for land ecological security at the city level. • The land ecological security was generally on the rise, but with increasing spatial differences. • There was a significant spatial spillover effect of land ecological security. • The paper conducted obstacle factor identification and influencing factor analysis. [ABSTRACT FROM AUTHOR]

The trend of global warming has sparked a growing concern about the impact of climate change on food security. China is a vast country with different climate situations in each region. This study examines the impact of climate change on food security in China using panel data from 31 provinces and cities (1984–2020). Results show that climate change significantly affects food security, bringing more uncertainty risks. Agricultural policies can mitigate this impact by reducing production costs and improving farmers' risk resistance and motivation. Regional heterogeneity exists, with climate change having significant moderating effects on food security in non-food-producing regions and rainfall having significant effects in north and central regions. Relevant policy implications are suggested for strengthening climate change resilience, improving system design, and coordinating regional agricultural production. • A long-term evaluation of food security in China from 1984 to 2020. • Empirically analyze the influence of climate change in terms of temperature, rainfall, and sunshine. • Agricultural policies can ensure food security and reduce the impact of climate change on it by serving as a moderating effect. • Heterogeneity analysis of climate change on food security with regional differences. [ABSTRACT FROM AUTHOR]

As climate warming intensifies, reducing greenhouse gas emissions has become a national consensus for many countries. Green finance plays an important role in promoting energy efficiency and lowering such emissions, but there is scant literature that analyzes the impacts and mechanisms of green finance on energy efficiency. Based on China's provincial panel data from 2010 to 2018, this research measures the green finance index using the panel entropy weighting method and systematically evaluates the relationship between green finance and energy efficiency using the fixed-effects model. The results show the following. (1) Green finance significantly contributes to energy efficiency and remains the same after a series of robustness tests such as endogeneity treatment, replacement of explanatory and interpreted variables, and replacement of regression models. (2) Green technology innovation and energy structure optimization are the transmission mechanism of green finance for energy efficiency, which means that green finance improves energy efficiency by promoting green technology innovation and energy structure optimization. (3) There is regional heterogeneity, temporal heterogeneity, and industry heterogeneity in the impact of green finance on energy efficiency. The positive effects of green finance on energy efficiency are more pronounced in resource-dependent regions, regions with irrational industrial structure, north regions, industrial sectors, and after 2013. The conclusions of this paper offer some policy recommendations, including promoting the development of green finance, building a cooperation platform for green finance and technological innovation, enhancing the depth and breadth of renewable energy utilization, and building differentiated green finance strategies according to local conditions. [ABSTRACT FROM AUTHOR]

China is now the world's largest energy consumer, but severe air pollution problems have brought greater pressure to the production and development of its domestic economy. As an unavoidable result of air pollution, PM2.5 emissions are increasing. Previous literature has focused more on the impact of PM2.5 on the micro-level such as resident health and company location, yet macro-pattern studies between PM2.5 and innovation are inadequate. To bridge this gap, our research uses a spatial dynamic panel data model to systematically investigate the internal and external effects of PM2.5 concentration on innovation in China during the period 2001–2016. After forming a dataset of real-time PM2.5 concentration from satellite detection and using an innovation index instead of patents, we find a stronger spatial linkage between PM2.5 concentration and innovation. Thus, PM2.5 inhibits regional innovation significantly, and this result still exists after using the air mobility index as an instrument variable to alleviate endogenous problems. Lastly, PM2.5 concentration in neighboring regions also impedes local innovation considerably, indicating a spatial ripple effect of PM2.5. [ABSTRACT FROM AUTHOR]