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.

China faces significant environmental challenges, including reducing pollutants, improving environmental quality, and peaking carbon emissions. Industrial restructuring is key to achieving both emission reductions and economic transformation. This study uses the Environmentally Extended Multi-Regional Input-Output model and multi-objective optimization to analyze pathways for China’s industrial transformation to synergistically reduce emissions. Our findings indicate that under a compromise scenario, China’s carbon emissions could stabilize at around 10.9 billion tonnes by 2030, with energy consumption controlled at approximately 5 billion tonnes. The Papermaking sector in Guangdong and the Chemicals sector in Shandong are expected to flourish, while the Coal Mining sector in Shanxi and the Communication Equipment sector in Jiangsu will see reductions. The synergy strength between carbon emission reduction and energy conservation is highest at 11%, followed by a 7% synergy between carbon emission and nitrogen oxide reduction. However, significant trade-offs are observed between carbon emission reduction and chemical oxygen demand, and ammonia nitrogen reduction targets at -9%. This comprehensive analysis at regional and sectoral levels provides valuable insights for advancing China’s carbon reduction and pollution control goals.

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.

Using firm-customer matched data from 2005 to 2020 in China, we examined the spillover effects and mechanisms of green innovation (GI) among supply chain partners. Results show a positive association between customers' GI and their supply firms' GI, indicating spillover effects in the supply chain. Customers' GI increase from the 25th to the 75th percentile leads to a significant 19% increase in supply firms' GI. Certain conditions amplify the spillover effect, including customers with higher bargaining power, operating in less competitive industries, and supply firms making relationship-specific investments or experiencing greater customer stability. Geographic proximity and shared ownership further enhance the spillover effect. Information-based and competition-based channels drive the spillover effect, while customers with higher GI encourage genuine GI activities by supply firms. External environmental regulations, such as the Chinese Green Credit Policy and Environmental Protection Law, strengthen the spillover effect, supporting the Porter hypothesis. This research expands understanding of spillover effects in the supply chain and contributes to the literature on GI determinants.

We construct firm-level indicators to capture the engagement in the Belt and Road Initiative (BRI, henceforth) via textual analysis. We find that higher firm engagement in BRI predicts higher stock returns in the subsequent 12 months. The top 10% high-BRI firms have 12.42% higher annual returns than bottom 10% low-BRI firms in China A-Share market. Additionally, two fundamental channels of increased earnings and reduced liabilities explain the higher expected returns of high-BRI firms. Furthermore, we reveal that the phenomenon is more pronounced among non-state-owned enterprises. For large-cap firms, BR Report is a more effective indicator for predicting future stock returns, while BR Beta performs better for small-cap firms. These findings contribute to the measurement of firm engagement in BRI and its impact on the stock market.

We examine the performance of actively managed Chinese mutual Funds over the period 2002-2020. Using the bootstrap-based false discovery technique, we find that 19.25% of Chinese actively managed mutual funds produce positive-alpha, which contrasts with existing studies documented by others in developed markets. Our findings survive a battery of robustness tests. Unlike in developed markets, equilibrium accounting may not hold in China as the Chinese stock market is dominated by retail investors instead of mutual funds, and thus the mutual funds in China can be more skilled at the expense of the retail investors. We find supportive evidence of the applicability of the bootstrap-based false discovery rate method by conducting simulations.

We apply various test portfolios and alternative statistical methodologies to evaluate the performance of eleven prominent asset pricing models. To compile the test portfolios, we construct 105 anomalies in China and apply the 23 significant anomalies as test assets for model comparison. The results indicate that in the time-series test and anomalies explanation, the Hou et al. (2019) five-factor q model exhibits the best overall performance. The pairwise cross-sectional R^2s and the multiple model comparison tests affirm that the Hou et al. (2019) five-factor q model, the Fama and French (2018) six-factor (FF6) model and the Kelly et al. (2019) five-factor Instrumented Principal Component Analysis (IPCA5) model stand out as the top performers. Notably, the performance of the five-factor q model is insensitive to variations in experimental design.

This study examines whether sell-side analysts have the ability to disseminate information consistent with anomaly prescriptions in China. I adopt 192 trading-based and accounting-based anomaly signals to identify undervalued and overvalued stocks. Analysts tend to give more (less) favorable recommendations and earnings forecasts to undervalued (overvalued) stocks. On analyzing the information content, I find that analyst recommendations and earnings forecasts are consistent with accounting-based information rather than trading-based information. Analysts make recommendations and earnings forecasts consistent with anomalies, especially when firms experience relatively bad firm-level information. Additionally, undervalued (overvalued) stocks are associated with high (low) analyst coverage. The results indicate that analysts may contribute to mitigating anomaly mispricing and improving market efficiency in China.

Using daily news tone data between 2017 and 2020, we examine whether news tones can predict stock returns in Chinese A-share market. We first document that the news tones significantly and positively predict the cross-sectional stock returns over next day and over the next 12-weeks. When we separate the news into online news and paper news, the online news exhibit strong predictive power for future returns, while the printed news only displays marginal predictive power. We hypothesize that the online news is more related to firm fundamentals, while the paper news is more linked to political aspects of firm information. Our results using earnings surprises and SOE subsamples provide supportive evidence for the hypothesis.

Over the past 150 years, finance has evolved into one of the world’s most globalized, digitized and regulated industries. Digitalization has transformed finance but also enabled new entrants over the past decade in the form of technology companies, especially FinTechs and BigTechs. As a highly digitized industry, incumbents and new entrants are increasingly pursuing similar approaches and models, focusing on the economies of scope and scale typical of finance and the network effects typical of data, with the predictable result of the emergence of increasingly large digital finance platforms. We argue that the combination of digitization, new entrants (especially BigTechs) and platformization of finance – which we describe as FinTech 4.0 and mark as beginning in 2019-2020 – brings massive benefits and an increasing range of risks to broader sustainable development. The platformization of finance poses challenges for societies and regulators around the world, apparent most clearly to date in the US and China. Existing regulatory frameworks for finance, competition, data, and technology are not designed to comprehensively address the challenges to these trends to broader sustainable development. We need to build new approaches domestically and internationally to maximize the benefits of network effects and economies of scope and scale in digital finance while monitoring and controlling the attendant risks of platformization of finance across the existing regulatory silos. We argue for a principles-based approach that brings together regulators responsible for different sectors and functions, regulating both on a functional activities based approach but also – as scale and interconnectedness increase – addressing specific entities as they emerge: a graduated proportional hybrid approach, appropriate both domestically in the US, China and elsewhere, as well as for cross-border groups, building on experiences of supervisory colleges and lead supervision developed for Globally Systemically Important Financial Institutions (G-SIFIs) and Financial Market Infrastructures (FMIs). This will need to be combined with an appropriate strategic approach to data in finance, to enable the maximization of data benefits while constraining related risks.