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.

We conducted a nationwide field experiment with 4,800+ Chinese-listed companies, randomly raising ESG concerns to their management teams via high-visibility and high-stakes online platforms. Tracking the full impact-generating process, we find that companies respond to our concerns by providing high-quality answers, publishing ESG reports, and making commitments to investors. Over time, Environmental (E) inquiries boost stock valuations, while Governance (G) concerns prompt skepticism. Productive and opaque firms are more likely to respond, consistent with a signaling model where costly ESG actions signal firm quality under information asymmetry. Overall, ESG actions are likely driven by profit-oriented signaling rather than values-based motives.

The commodity and financial characteristics of carbon emission allowances play a pivotal role within the Carbon Emission Trading Scheme (CETS). Evaluating the effectiveness of the scheme from the perspective of carbon price is critical, as it directly reflects the underlying value of carbon allowances. This study employs a time-varying Difference-in-Differences (DID) model, utilizing data from publicly listed enterprises in China over the period from 2010 to 2023, to examine the effects of carbon price level and stability on Total Factor Productivity (TFP). The results suggest that both an increase in carbon price level and stability contribute to improvements in TFP, particularly for heavy-polluting and non-stateowned enterprises. Mechanism analysis reveals that higher carbon prices and stability can stimulate corporate engagement in green innovation, activate the Porter effect, and subsequently enhance TFP. Furthermore, optimizing the system environment proves to be an effective means of strengthening the scheme's impact. The study also finds that allocating initial quotas via payment-based mechanisms offers a more effective design. This research highlights the importance of strengthening the financial attributes of carbon emission allowances and offers practical recommendations for increasing the activity of trading entities and improving market liquidity.

This paper investigates the impact of environmental awareness on local fiscal revenue in China. We exploit the unexpected release of the environmental documentary Under the Dome in early 2015 as an exogenous shock on residents preferences. The generalized difference-in-difference estimation shows that on average, a one standard deviation increase in the exposure to the documentary would reduce the government land sale revenue by 21.45 billion CNY. Consistent with the “vote with feet” mechanism in Tiebout model, after the release of this film, residents increase awareness of air pollution and express higher mobility intention. Local government also raises environmental investment as a response. This indicates the value of market in constraining the behavior of local governments in authoritarian states.

With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this limitation, we incorporate prospect theory to model the decision-making process of bounded rational GenCos operating under risk. A bilevel stochastic model is developed to simulate strategic bidding in the spot market. In addition, a distributional re-inforcement learning algorithm is proposed to tackle the decision-making challenges faced by bounded rational GenCos with risk considerations. The proposed model and algorithm are validated through simulations using a 27-bus system from a region in eastern China. The results demonstrate that the algorithm effectively captures market uncertainties and learns the distribution of GenCo’s profits. Furthermore, simulated bidding strategies for various types of GenCos highlight the applicability of prospect theory to describe bounded rational decision-making behavior in electricity markets.

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.

In this paper, we present a rational expectation equilibrium model in which fundamental and ESG traders hold heterogeneous valuations towards the risky asset. Trading occurs based on private information and price signal which is determined by a weighted combination of these diverse valuations. Our findings indicate that higher level of ESG rating disagreement increases ESG information uncertainty, thereby reducing trading intensity among ESG traders and attenuating the price informativeness about ESG. We further discover that allowing fundamental traders access to ESG information increases the coordination possibilities in the financial market, leading to multiple equilibria exhibiting characteristics of strategic substitutability and complementarity. Additionally, through measuring the ESG rating disparities among four prominent agencies in China, we deduce that ESG rating disagreement negatively impacts price informativeness by decreasing stock illiquidity.

Environmental disclosure in emerging markets remains poorly understood, despite its critical role in sustainability governance. Here, we analyze 42,129 firm-year environmental disclosures from 4,571 Chinese listed firms (2008-2022) using machine learning techniques to characterize disclosure patterns and regulatory responses. We show that increased disclosure volume primarily comprises boilerplate content rather than material information. Cross-sectional analyses reveal systematic variations across industries, with manufacturing and high-pollution sectors exhibiting more comprehensive disclosures than consumer and technology sectors. Notably, regional rankings in environmental disclosure volume do not align with local economic development levels. Through examination of staggered regulatory implementation, we demonstrate that market-based mechanisms generate more substantive disclosures compared to command-and-control approaches. These results provide empirical evidence that firms strategically manage environmental disclosures in response to institutional pressures. Our findings have important implications for regulatory design in emerging markets and advance understanding of voluntary disclosure mechanisms in sustainability governance.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.

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.