Climate change presents a global threat, prompting nations to adopt low-carbon development pathways to mitigate its potential impacts. However, current research lacks a comprehensive framework capable of integrating multiple variables and providing dynamic optimization capabilities. This article focuses on designing pathways for developing a low-carbon economy to tackle climate challenges. Specifically, we construct a low-carbon economy model that incorporates economic, environmental, social, energy, and policy factors to analyze the drivers of economic growth and carbon emissions. We utilize economic model predictive control and tracking model predictive control to optimize development pathways aligned with various low-carbon targets, creating and validating a comprehensive framework for low-carbon policy design using historical data from China. This study highlights significant advantages in analyzing low-carbon pathways through advanced techniques like hierarchical regression and model predictive control, providing a robust framework that enhances our understanding of causal relationships within the LCE system, captures system feedback, dynamically optimizes pathways, and accommodates diverse policies within a comprehensive low-carbon economy system.

The co-holding of mutually interdependent pairs of heterogeneous blocks can provide firms with stable financial support. The interdependence of heterogeneous blockholders’ investment decision has become an important frontier in the financial literature on large shareholders. In this paper, we study the interdependence of heterogeneous blocks in China. We find significant positive interdependence among blockholders of the same type. In heterogeneous block pairs, the financial–private pair shows positive interdependence. The findings are in contrast to those observed in the US. Under the mixed-ownership reform in China, our findings suggest the potential for cooperation among multiple blocks of the same type rather than between heterogeneous strategic partners.

Central bank digital currency (CBDC) is intended to boost financial inclusion and limit threats to bank stability posed by private cryptocurrencies. Our study examines the impact of implementing CBDC on the bank stability of two countries in Asia and the Pacific, the People’s Republic of China (PRC) and India, that initiated research on CBDC within the last ten years (2013 to 2022). We construct a bank stability index by utilizing five dimensions, namely capital adequacy, profitability, asset quality, liquidity, and efficiency, using a novel “benefit-of-the-doubt” approach. Employing panel estimation techniques, we find a significant positive impact of adopting CBDC on bank stability and a moderating role of monetary policy. We also find that the effect is greater in India, a lower-middle-income country, than in the PRC, an upper-middle-income nation. We conclude that by taking an accommodative monetary policy stance, adopting CBDC favors bank stability. We confirm our results with various robustness tests by introducing proxies for bank stability and other model specifications. Our findings underscore the potential of adopting CBDC, when carefully managed alongside appropriate monetary policy, for enhancing bank or overall financial stability.

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.

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.

How enterprises can leverage frontier technologies to achieve synergy between environmental governance and high-quality development has become a critical issue amid the deepening global push for sustainable development and the green economic transition. Based on micro-level data of Chinese enterprises from 2009 to 2023, this study systematically examines the impact of artificial intelligence (AI) on corporate green governance performance and explores the underlying mechanisms. The findings reveal that AI significantly enhances green governance performance at the enterprise level, and this effect remains robust after accounting for potential endogeneity. Mechanism analysis shows that AI empowers green transformation through a dual-path mechanism of “cognition–behavior,” by strengthening environmental tendency and increasing environmental investment. Further heterogeneity analysis indicates that the positive effects are more pronounced in nonheavy polluting industries and state-owned enterprises, suggesting that industry characteristics and ownership structure moderate the green governance impact of AI. This study contributes to the theoretical foundation of research at the intersection of digital technology and green governance, and provides empirical evidence and policy insights to support AI-driven green transformation in practice.

This study examines how full-time external supervisors affect corporate irregularities using listed Chinese state-owned enterprises (SOEs) as a research sample. We find that full-time external supervisors restrain corporate irregularities. This outcome continues to hold after accounting for potential endogeneity concerns. Further mediating effect analysis shows that full-time external supervisors mitigate corporate irregularities by curbing managers' opportunistic behavior. Additionally, the heterogeneity analysis demonstrates that the impact of full-time external supervisors on corporate irregularities varies significantly across different types of SOEs and internal control environments. Overall, this paper enriches and expands the literature on the effectiveness of full-time external supervisors in emerging economies and provides new insights for dealing with corporate irregularities.

This research examines the impact of short selling on the financing cost of corporate bonds using panel data from Chinese A-share listed companies spanning the period from 2007 to 2022. The study aims to investigate the potential cross-market information spillover effects within the short selling system. The findings indicate that short selling significantly reduces the financing cost of corporate bonds, with a more pronounced effect observed under greater short selling forces. The robustness of the results is confirmed by controlling for various potential influencing factors and addressing the endogeneity issue through Propensity Score Matched Difference in Differences (PSM-DID) methodology. Moreover, the research reveals that the alleviation of information asymmetry serves as the primary mechanism through which short selling exerts its impact, particularly in regions with well-developed financial markets and favorable legal environments. This study offersa novel perspective of short selling in China and it sheds light on its cross-market spillover effects. By effectively enhancing resource allocation efficiency in capital markets, short selling emerges as a potent tool for mitigating information disparities between bond investors and enterprises.

In the mid-1990s, the concept of smart growth emerged in the United States as a critical response to the phenomenon of suburban sprawl. To promote sustainable urban development, it is necessary to further investigate the principles and applications of smart growth. In this paper, we proposed a Smart Growth Index (SGI) as a standard for measuring the degree of responsible urban development. Based on this index, we constructed a comprehensive 3E evaluation model—covering economic prosperity, social equity, and environmental sustainability—to systematically assess the level of smart growth. For empirical analysis, we selected two medium-sized cities from different continents: Wuhu County, China, and Colima, Mexico. Using an improved entropy method, we evaluated the degree of smart growth in recent years and analyzed the contributions of various policies to sustainable urban development. Then, guided by the ten principles of smart growth, we linked theoretical insights to practical challenges and formulated a development plan for both cities. To forecast long-term trends, we employed trend extrapolation based on historical data, enabling the prediction of SGI values for 2020, 2030, and 2050. The results indicate that Wuhu demonstrates a greater potential for smart growth compared with Colima. We also simulated a scenario in which the population of both cities increased by 50 percent and then re-evaluated the SGI. The analysis suggests that while rapid population growth tends to slow the pace of smart growth, it does not necessarily exert a negative impact on the overall trajectory of sustainable development. Finally, a study on the application of Transit-Oriented Development (TOD) theory in Wuhu County was conducted. Based on this analysis, we proposed several policy recommendations aimed at enhancing the city’s sustainable urban development.

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.