This paper examines how economic policy uncertainty (EPU) affects mergers and acquisitions (M&As) between companies with different levels of financing constraints. Existing literature overlooks the interactive effect of EPU and financing constraints on M&As, and empirical evidence regarding EPU's influence on financially constrained firms remains limited. China's unique ownership structure provides a valuable context for this analysis, as state-owned enterprises (SOEs) face fewer financing constraints than private firms. Using a 2007-2021 sample of Chinese listed state-owned enterprises (SOEs) and private companies, we find that high EPU decreases the likelihood of private firms acquiring SOEs, while increases the likelihood of private firms being acquired by SOEs. These results suggest that under high EPU, financially constrained firms experience greater survival pressure, limiting their capacity to alleviate constraints by acquiring less-constrained targets. Conversely, less-constrained firms enhance their bargaining power and are more likely to acquire financially stressed counterparts. EPU facilitates control transfers from high-constraint to low-constraint firms, contributing to long-term market returns and improving financial market allocation efficiency. Our study contributes to the literature by shedding light on how EPU shapes divergent M&A behaviors based on firms’ financing constraints.

This study examines how firm-specific characteristics and monetary tightening influence the composition and dynamics of trade credit received by Chinese listed firms. Using panel data, the analysis distinguishes among three sources of trade credit: related parties, non-related parties, and controlling shareholders. The findings reveal a clear asymmetry in firms’ financing responses to monetary tightening: while trade credit from non-related parties declines, credit from related parties—especially controlling shareholders—increases. This underscores the strategic role of intra-group financing in buffering firms against external financial shocks during periods of constrained liquidity. Moreover, firm-specific factors such as size, profitability, market power, and ownership have differing effects depending on the source of trade credit. These effects are most pronounced when the credit is extended from controlling shareholders, reflecting the influence of intra-group trust and reduced information asymmetries. The results also highlight a substitute relationship between bank credit and trade credit, which weakens when trade credit is sourced from related parties and disappears entirely in the case of controlling shareholders. By shedding light on the distinct mechanisms of intra-group trade credit in China’s underdeveloped financial system, this study contributes to a deeper understanding of corporate financing strategies of Chinese firms.

This study applies large language models (LLMs) to measure technological links and examines its predictive power in the Chinese stock market. Using the BAAI General Embedding (BGE) model, we extract semantic information from patent textual data to construct the technological momentum measure. As a comparison, the measure based on traditional International Patent Classification (IPC) is also considered. Empirical analysis shows that both measures significantly predict stock returns and they capture complementary dimensions of technological links. Further investigation through stratified analysis reveals the critical role of investor inattention in explaining their differential performance: in stocks with low investor inattention, IPC-based measure loses its predictive power while BGE-based measure remains significant, indicating that straightforward information is fully priced in while complex semantic relationships require greater cognitive processing; in stocks with high investor inattention, both measures exhibit predictability, with BGE-based measure showing stronger effects. These findings support behavioral finance theories suggesting that complex information diffuses more slowly in markets, especially under significant cognitive constraints, and demonstrate LLMs’ advantage in uncovering subtle technological connections that traditional methods overlook.

To address the challenges posed by renewable energy integration in power systems, China is advancing the development of Pumped Hydro Storage (PHS). However, the rapid growth of PHS installations, coupled with strict regulations and a high reliance on capacity compensation, has led to increasing financial burdens on other utilities. One solution is to reallocate the capacity compensation through market-based approaches to implement the “beneficiary-pays” principle. To achieve this goal, an operational policy named ’partial-regulated dispatch’ is proposed in this study. The analysis of this policy encompasses two crucial dimensions: the dispatch mechanism and business models. The dispatch mechanism evaluates PHS’s capacity contribution to grid stability, while the business models focus on enhancing PHS profitability to reduce dependency on capacity compensation while ensuring long-term economic sustainability. Furthermore, the flexibility of PHS is introduced as a criterion for assessing system security contributions, considering both individual unit vibration characteristics and multi-unit commitment strategies. The case study shows that through partial-regulated dispatch, PHS can reduce its reliance on capacity compensation by nearly 50% while ensuring its regulation service via flexibility compensation. This policy effectively balances economic viability with system support capabilities. Moreover, flexibility compensation provides PHS operators with a risk mitigation strategy in the complex power market environment. Under an appropriate operational strategy and policy incentives, the flexibility can be enhanced by nearly 30% in a fully marketized scenario, contributing to both system stability and operational efficiency.

The high-penetration integration of renewable energy requires huge demand for reliable capacity resources, and the coal-fired units are the main providers of the reliable capacity in China. This study proposes a future-oriented approach to facilitate coal-fired power’ transition through capacity market development. Focusing on China’s power market reform context, we propose a two-stage capacity market mechanism integrating annual capacity auctions and monthly capacity bidding, and design the procedural and transactional framework for coal-fired power participation. We further outline three market strategies including energy market trading, centralized capacity market trading, and renewable energy alliance leasing. Environmental costs are incorporated to construct revenue models and derive boundary conditions for coal-fired units’ decision-making. Research results reveal that current capacity prices fail to cover costs, requiring substantial market-driven price increases to achieve profitability. While stable capacity revenue can reduce medium-to-long-term and spot market prices, fostering competition between coal-fired power and renewable energy resources. However, coal-fired power remains highly sensitive to price volatility, demanding robust resilience to fluctuations. Carbon prices significantly influence capacity prices, yet excessive free carbon quota allocations weaken carbon price transmission effects, necessitating optimized quota ratios to enhance market responsiveness. Finally, policy implications are proposed according to the research results.

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.

Green bonds have emerged as a novel funding mechanism specifically aimed at addressing environmental challenges. Focusing on A-share listed companies in China that went public with bond issues domestically from 2012 to 2021, we reveal that companies with higher energy usage and better environmental disclosure quality are the most inclined to issue green bonds. Such issuance is identified as a pathway towards real green transformation, markedly boosting the green transformation index, green innovation efficiency, and ESG performance. Further analysis indicates that the effect of substantial transformation is particularly pronounced among companies in the eastern regions of China.

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 investigates the "stars effect" of recruiting overseas scholars as deans and its impact on academic output in China from 2001-2019. We find that appointing a returnee dean increases a department's English publications by 40% annually. This positive effect applies to both top-tier and non-top-tier journals, without crowding out Chinese publications. The magnitude of the effect correlates with the dean's international connections and the ranks of the destination and source institutions. Returnee deans enhance output through knowledge spillovers, expanded networks, and increased overseas personnel, but not additional research grants. Our findings demonstrate the positive role and extensive influence of power-granted talent initiatives in developing regions.