Copper resources are widely used in power networks and clean - energy tech like PV panels, wind turbines, and NEVs. Restricted by domestic resources, China's copper supply chain is vulnerable with risks. Based on six supply - chain stages, this paper builds an assessment system for China's copper supply - chain risks. By adopting an improved Benefit of Doubt (BOD) model, this paper has systematically evaluated the risks in the whole copper supply chain, revealing the trends and deep-rooted causes of these risks. The findings of this study reveal that: (1) The supply chain risk of China's copper resources presents a significant upward trend over the past 15 years; (2) The current supply chain risks in copper are mainly concentrated at the stages of import, production, and application; and the recycling risk has a great potential for reducing the copper supply chain risks in the future. Based on these findings, this paper proposes two policy recommendations: (1) Develop diversified channels for importing copper resources and optimize overseas investment patterns and; (2) Improve the domestic supply capacity of secondary copper resources and reduce the risks at the recycling stage.

Under the goal of carbon peaking and carbon neutrality, taking environment, social responsibility, and corporate governance (ESG) as the important investment factor has become an action guide and standard for capital market participants. The practice of the ESG concept is not only a new way for enterprises to form new asset advantages and realize green and low-carbon transformation, but also important access for promoting high-quality and sustainable development. Based on Chinese-listed companies within the period of 2009 to 2015, we investigate the impact of ESG responsibility performance on export performance as well as its mechanism. We theorize and find out show that ESG responsibility performance can significantly and stably promote enterprises’ export performance. Mechanism analysis shows that ESG can improve export performance by reducing financing costs and easing financing constraints, and the green technology innovation effect is also an important channel for ESG to affect export performance. Therefore, government should strengthen the supervision and incentive of ESG performance, encourage enterprises to improve their environmental, social and governance performance in order to adapt to the goal of carbon peak and carbon neutrality and promote the high-quality development of export trade. Future research may consider combining ESG accountability with other factors such as supply chain management, intermediate imports, and transnational spillovers to more fully understand its impact on export performance, so as to create more value for society.

The disparities in green transition have led to the call for a ‘just transition’. However, the large differences in energy efficiency across different regions have been identified as a primary hazard to the just transition. This study examines whether transferring low-carbon technology can improve the efficiency of energy, enhancing the overall energy efficiency, and marketing a sustainable and equitable energy future. In this paper, we utilize the Undesirable-SE-SBM model to estimate the energy efficiency of China's 30 provinces during 2012 to 2022, and empirically tested the impact of low-carbon technology transfer on the convergence of total-factor energy efficiency by convergence analysis. The results showed that: (1) There is evidence of σ convergence and absolute β convergence in the eastern and western regions, but not in the central region. (2) Low-carbon technology transfer can accelerate the convergence of total factor energy efficiency. Lagging regions that adopt low-carbon technologies can catch up with the advanced regions' level of total-factor energy efficiency. (3) There is regional heterogeneity in the effect of low-carbon technology transfer on the accelerating convergence of total factor energy efficiency. The western region experiences the most significant acceleration, followed by the eastern and central regions.

Background Economic stability depends on the ability to foresee financial risk, particularly in markets that are extremely volatile. Unbalanced financial data is difficult for traditional Support Vector Machine (SVM) models to handle, which results in subpar crisis detection capabilities. In order to improve financial risk early warning models, this study combines Gaussian SVM with stochastic gradient descent (SGD) optimisation (SGD-GSVM). Methods The suggested model was developed and assessed using a dataset from China's financial market that included more than 2,000 trading days (January 2022–February 2024). Missing value management, Min-Max scaling for normalising numerical characteristics, and ADASYN oversampling for class imbalance were all part of the data pretreatment process. Key evaluation metrics, such as accuracy, recall, F1-score, G-Mean, AUC-PR, and training time, were used to train and evaluate the SGD-GSVM model to Standard GSVM, SMOTE-SVM, CS-SVM, and Random Forest. Results Standard GSVM (76% accuracy, 1,200s training time) and CS-SVM (81% accuracy, 1,300s training time) were greatly outperformed by the suggested SGD-GSVM model, which obtained the greatest accuracy of 92% with a training time of just 180 seconds. Additionally, it showed excellent recall (90%) and precision (82%), making it the most effective and efficient model for predicting financial risk. Conclusion This work offers a new method for early warning of financial risk by combining SGD optimisation with Gaussian SVM and employing adaptive oversampling for data balancing. The findings show that SGD-GSVM is the best model because it strikes a balance between high accuracy and computational economy. Financial organisations can create real-time risk management plans with the help of the suggested technique. For additional performance improvements, hybrid deep learning approaches might be investigated in future studies.

We investigate the relationship between the intensity of intraday return reversals and future stock returns in the Chinese stock market. We find that a high frequency of positive overnight returns followed by negative daytime returns predicts one-month ahead returns positively. The analysis shows that daytime retail investors tend to overly sell their own rising stocks at market open, accepting lower stock prices in exchange for liquidity. As the price pressure attenuates, these stocks experience subsequent price increases, implying a positive relationship between return reversals and future returns.

We introduce a hypergraph-based approach to analyze information flow and investor attention transfers through news outlets in financial markets. Extending traditional graph models that focus on pairwise interactions, our hypergraph framework captures higher order relationships between firms that are simultaneously mentioned in the same news article. We develop a random walk based centrality framework that considers both the properties of the hyperedges (news articles) and the nodes (firms). This framework allows us to more accurately simulate investor attention flows and to incorporate different theories of investor behavior, such as category learning and investor attention theory. To demonstrate the effectiveness of our attention centrality, we apply it to the Chinese CSI500 market index from 2016 to 2021, where our centrality measures improve the prediction of future returns, with improvements ranging from 6.3% to 14.0% compared to traditional graph-based models. This improvement implies that our centrality measure can better capture investor attention transfers on the news hypergraph. In particular, we find that investors pay more attention to news that covers both a greater number of firms and firms on which the sentiments are more negative. Although we focus on financial markets in this research, our hypergraph framework holds potential for broader applications in information systems — for example, in understanding social or collaboration networks.

This paper investigates whether and how managers of highly polluting firms adjust their earnings forecast behaviors in response to the introduction of environmental legal institutions. Using the establishment of environmental courts in China as a quasi-natural experiment, our triple difference-in-differences (DID) estimation shows that environmental courts significantly increase the likelihood of management earnings forecasts for highly polluting firms compared to non-highly polluting firms. This association becomes more pronounced for firms with stronger monitoring power, higher environmental litigation risk, and greater earnings uncertainty. Additionally, we show that highly polluting firms improve the precision and accuracy of earnings forecasts following the establishment of environmental courts. Furthermore, we provide evidence that our results do not support the opportunistic perspective that managers strategically issue more positive earnings forecasts to inflate stakeholders‘ expectations subsequent to the implementation of environmental courts. Overall, our research indicates that environmental legal institutions make firms with greater environmental concerns to provide more forward-looking information, thereby alleviating stakeholders’ apprehensions regarding future profitability prospects.

Using a comprehensive dataset for Chinese listed companies from 2009 to 2021, we find that online media is negatively associated with cash dividend level, and the proportion of positive news has a negative moderating effect on this relationship. Our results support the "information intermediary" effect and exclude the "external governance" and "market pressure" effects. We further propose that online media weakens the positive relationship between cash dividends and past earnings (rather than the future), indicating that cash dividends contain signals of improvement in past earnings and are replaced by online news. We also find that only firms with more positive news pay dividends that have signaling effects, and there is a synergistic effect between positive news and dividend signal. Additional results show that the effect of online media on dividend policy is more pronounced than traditional media, which has almost no influence. Our main conclusions remain valid after addressing potential endogeneity issues and conducting various robustness tests.

This paper is the first to examine the evolution of price discovery in agricultural commodity markets across the four distinct phases determined by trade friction and trade policy uncertainty. Using cointegrated vector autoregressive model and common factor weights, we report that corn, cotton, soybean meal, and sugar (palm oil, soybean, soybean oil, and wheat) futures (spot) play a dominant role in price discovery during the full sample period. Moreover, the leadership in price discovery evolves over time in conjunction with changes in trade friction phases. However, such results vary across commodities. We also report that most of the agricultural commodity markets are predominantly led by futures markets in price discovery during phase Ⅲ, except for the wheat market. Our results indicate that taking trade friction into consideration would benefit portfolio managements and diversifying agricultural trade partners holds significance.

We investigate the effect of third-party certification for green bonds by analyzing its impact on issuer's future green innovation performances. We find that third-party certification for green bonds can significantly promote issuer's future green innovation performances. Furthermore, the promotion effect is more prominent in non-state-owned issuers, large issuers and heavy polluting issuers, and can be more significantly exerted by professional and reputable third-party certification agencies. Besides, third-party certification for green bonds can play the effect by reducing the issuer's tax expenditure, increasing the issuer's loan financing, and receiving a positive response in stock returns. But unexpectedly, it cannot play the effect by further reducing the credit spread of green bonds. Our findings indicate that independent external supervision can play a positive role in green bond issuance, but there is still a long way to go.