A central premise of mandatory disclosure regulation is that greater transparency reduces information asymmetry and lowers borrowing costs. We challenge this premise by examining industry-level operational disclosure - a regulatory form that reveals horizontally comparable information across peer firms rather than refining individual firm fundamentals. Exploiting the staggered introduction of mandatory industry-specific disclosure guidelines by Chinese stock exchanges between 2013 and 2019, we find that enhanced industry disclosure significantly widens bond credit spreads by approximately 54 basis points - the opposite of what standard disclosure theory predicts. This counterintuitive effect is more pronounced in non-homogeneous industries, among smaller firms, and for bonds restricted to institutional investors. Mechanism tests confirm two opposing channels: disclosure reduces information asymmetry while simultaneously intensifying product market competition by exposing strategically sensitive operational metrics. Our evidence challenges the one-size-fits-all approach to disclosure regulation and highlights that the competitive implications of disclosed information - not merely its quantity - shape credit risk pricing.

The China Securities Regulatory Commission is allowing firms to use externally financed funds for share repurchases, a recent measure to enable listed companies address valuation pressures and protect investors; however, its implications for corporate marketing decisions remain unclear. Using an event sample of Chinese listed firms that conducted open-market repurchases between 2009 and 2024, this study empirically examines how this market activity financed by different sources influence marketing decisions and explores the underlying mechanisms. The findings show that compared with firms using internal cash for repurchases, those relying on debt financing are inclined to resist myopic marketing decisions, and this negative relationship is pronounced under high analyst coverage and when privately owned listed firms are controlled by family entrepreneurs. These results remain robust after replacing the dependent variables and applying propensity score matching. Overall, this study shows that debt financing to support share repurchases has a long-term beneficial governance impact as it improves earnings quality and protects investor interests, and offers a new perspective on the relationship between financing-based repurchases and marketing myopia, and provides useful policy insights for evaluating the effectiveness of China’s refinancing regulations related to share repurchases, while guiding further refinement.

The Full Guaranteed Purchase System began to replace the subsidy system as the core policy for promoting renewable energy integration, designating grid companies as the sole entity responsible for the physical integration of renewable energy. Meanwhile, the Tradable Green Certificates system provides environmental benefits for renewable power generators through market mechanisms. Therefore, exploring the strategic choices of various entities under the dual policy interventions, and uncovering the mechanisms for realizing electricity energy value and green value under different integration models, is of great significance for advancing China’s renewable energy integration. Based on China’s actual conditions, this study integrates the features of both policies and constructs a three-party evolutionary game model involving government, power generators, and grid enterprises to simulate their interactions and identify key factors influencing strategic choices. The results show that active government regulation effectively encourages positive strategies from both generators and grid companies, and that active power integration by grid companies further promotes green power generation. A “stepwise complementary” relationship exists among reputation gains, reputation losses, and regulatory costs: higher reputation gains can offset decision-making resistance arising from increased regulatory costs. Green power generation costs and innovation costs have significant negative effects on strategic choices, while the guaranteed purchase price has a significant positive effect on generators’ active strategies. The penalty parameter plays a key positive role in grid companies’ active strategies, and the guaranteed purchase price significantly influences their active integration behavior. This paper provides recommendations for motivating all entities actively participate in the consumption process.

Against the backdrop of global green development and China’s sustainable economic transition, many firms exaggerate green-transition disclosures to cater to national strategies and capital market preferences, leading to a severe "Say–Do Gap". Based on signaling theory, this study uses the phased establishment of environmental courts in 208 prefecture-level cities as a quasi-natural experiment, adopting a staggered DID design with 2007–2023 panel data of Chinese A-share listed firms for empirical tests. Results show widespread corporate green pandering, with improved disclosure not translating into actual carbon reduction. Environmental courts effectively curb this behavior, with environmental litigation risk as the core mediating channel. Heterogeneity tests reveal stronger deterrence in regions with weaker regulation/heavier pollution and polluting firms with stronger environmental technology. This study enriches literature from a judicial deterrence perspective and provides implications for substantive corporate green transition.

Breaking away from being “large but not strong” and accelerating the internal “dual circulation” reform to integrate the manufacturing and service industries is a daunting challenge. This study examines how environmental regulations and financial instruments can simultaneously drive servitization evolution and green transformation. Utilizing the Green Credit Guidelines (GCG) policy rollout by China in 2012 as a quasi-natural experiment, we analyze 2007-2021 data from A-share listed manufacturing corporations through DID model to evaluate the policy ramifications and investigate servitization direction. The results show that: (1) While GCG generally promotes overall servitization, it biases firms toward traditional rather than modern servitization pathways. (2) Contrary to typical innovation compensation effects, GCG induces short-sight in managerial decisions, favoring quick wins over innovation-driven progress. These results highlight why firms have tended to advance traditional servitization while constraining modern servitization efforts. (3) Heterogeneity analysis shows stronger policy impacts in firms with domestically-oriented executives and domestic ownership, where both overall and traditional servitization are significantly enhanced.

This study challenges democratic citizenship theory by examining altruistic behaviour in authoritarian China. We find that financial resources influence prosocial engagement primarily through psychological mediators such as long-term planning and self-discovery, rather than directly providing material means for participation. Communist Party membership shapes specific domestic ideological orientations, but not attitudes toward market regulation or income equality, while cultural tradition emerges as a stronger predictor of certain political attitudes than party affiliation. Cluster analysis identifies three distinct citizen profiles: (1) future-oriented altruists; (2) status-driven contributors; and, (3) short-termpragmatists, revealing motivational heterogeneity within authoritarian publics. These findings suggest that authoritarian citizenship operates through psychological mediation and plural motivational pathways fundamentally different from democratic societies, with potential applicability to other single-party regimes where states actively structure prosocial behaviour. The study contributes to comparative authoritarianism literature by theorizing how regime type moderates the relationship between socio-economic resources, political identity, and civic engagement.

With the rapid development of the global shipping industry, maritime traffic continues to grow, and maritime traffic risks are becoming increasingly prominent, posing serious threats to economic development, the ecological environment, and public safety. In this context, this study develops an environmental data-driven dynamic Bayesian network (DBN) model to simulate the dynamic evolution process of maritime traffic risks from the massive data of complex shipping systems. Firstly, based on the systems theoretic accident model and processes (STAMP) accident causation analysis framework, risk influencing factors (RIFs) are identified through the analysis of maritime accident report systems. Secondly, addressing the dynamic nature of maritime risks, a novel transition probability matrix (TPM) learning mechanism integrating environmental data is proposed, constructing a DBN model capable of characterizing temporal risk performance. Finally, a case study of typical routes along the Chinese coast reveals that risk performance evolution exhibits significant spatiotemporal heterogeneity across sea areas, with the East Sea and South China Sea regions being the most prominent. Their fluctuations are highly correlated with seasonal meteorological and hydrological changes, and the distribution of accident risks is also closely associated with extreme weather events such as typhoons. Sensitivity analysis validates the model's reliability. This study provides a quantitative tool for the dynamic risk management of intelligent shipping systems and offers policy insights for intelligent maritime transportation safety regulation.

This paper investigates how environmental regulation induces firm digitalization. We construct a digital index based on textual analyses and find that after the implementation of the program, pilot firms' digitalization increased relative to that of a group of carefully matched control firms, which is opposite to the findings in the extant literature on technology adoption. This increase cannot be fully explained by regional unobservables, firms' own innovation, firm selection, or other policies. The results are robust when we consider firm subsidiaries. The increase in digitalization is not due to regulatory arbitrage, and the industry-level concentration of digitalization changes little.

Recent technological advancements have enabled the emergence of business organizations fully managed by algorithms, such as decentralized autonomous organizations (DAOs) or through artificial intelligence (AI), as observed in China’s online food delivery sector. These organizations are collectively referred to as algorithm-managed firms (AMFs). Given machines’ capabilities in data collection and analysis, human directors are increasingly being replaced by algorithms or AI in specific sectors. This article contends that algorithms can effectively take over human directors’ managerial, monitoring, and mediating roles. The diminishing role of human directors raises certain concerns of stakeholder protection. Unlike human directors, algorithm directors or managers would not consider stakeholders’ interests unless clearly instructed to do so. However, the algorithm supplier and the AMFs may lack the incentives to fully consider stakeholders because they do not always internalize the social costs. To address the challenges of AMFs, policymakers need to consider different regulation strategies. First, they must choose between command-and-control regulations and target-based regulations. Command-and-control regulations often do not work well because regulators lack enough information or control over complex algorithms. Instead of setting detailed technical rules, policymakers should adopt target-based regulations that let the algorithm balance various interests and regulate its own operations. Second, policymakers should decide between entity-based and algorithm-based regulations. Algorithm-based regulation is more suitable because it prevents companies from passing costs onto society. The state could consider regulating the composition of the board of directors of the algorithm supplier to ensure that they incorporate the concerns of stakeholders’ interests in the development of the algorithm. Additionally, corporate law doctrines that protect creditors and other stakeholders, such as piercing the corporate veil and limiting liability for corporate torts, must be revisited and modified because their foundational assumptions no longer align with the realities of AMFs.

As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.