Under China’s transition toward high-quality financial development, this study investigates the tail dependence among media sentiment, investor attention, and stock market returns using the M-Clayton Copula. Comparative model fitting tests demonstrate that the M-Clayton Copula outperforms both single Copula models and M-Copula models in characterizing asymmetric negative dependence structures. It is particularly effective in capturing both upper-lower and lower-upper tail dependencies, thereby providing a more comprehensive analysis of their interdependencies. The empirical results reveal three key findings. First, a significant positive tail dependence exists between media sentiment and stock market returns, suggesting that they tend to move together under extreme conditions, with notable asymmetry in the strength of co-movements. Second, media sentiment and investor attention exhibit negative tail dependence, with the lower-upper tail dependence coefficient exceeding its upper-lower counterpart, suggesting higher probability of rising investor attention following media sentiment decline than vice versa. Third, a pronounced negative tail dependence emerges between stock market returns and investor attention, particularly showing strong lower-upper tail correlation, implying substantial likelihood of increased investor attention subsequent to market downturns.

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.

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.

The low-altitude economy (LAE) refers to economic activities conducted within airspace below 1,000 meters. Drawing on related theories on socio-technical transitions, LAE can be understood as a future regime challenging the dominant urban mobility paradigm. As an emerging field, it has yet to be systematically examined through an empirical study, especially about local response. In this paper, we construct an Integrated Local Support Index (ILSI) based on the number of relevant local policies and the level of public interest measured by the Baidu search index. Private sector readiness is measured by the LAE Development Scale (DS) based on the registered capital of relevant enterprises locally. Focusing on the top 50 cities in China’s LAE sector, we conduct a comprehensive empirical study to explore the relationships between DS, ILSI, and other natural and socio-economic factors between 2012 and 2023. The dynamic interactions of key stakeholders (local government, foreign capital, and talents) are analysed by game theory. The findings suggest that the ILSI, education level, and foreign investment have significant positive impacts. Wind speed is identified as a negative factor for LAE development. The game theory analysis further reveals that the three positive factors tend to foster efficient and stable growth when working synergistically. This implies that enhancing local government support could trigger chain reactions that attract more investment and talents, thereby accelerating LAE development. Projecting to the future, local LAE DS in 2026 is predicted via a panel time-series model with random effects. This study provides both empirical evidence and governance strategies for decision-makers navigating the socio-technical transition of the LAE.

High-quality development of water conservancy (HDWC) is of critical value for safeguarding the stability of production systems, livelihoods, and ecosystems. As the digital revolution intersects with China’s “dual carbon” targets, development of water conservancy must transition from traditional engineering approaches to data-driven ecological models. Drawing on institutional logic theory and employing dynamic qualitative comparative analysis across 30 Chinese provinces, this study examines how digital ecology facilitates the HDWC. The findings reveal that none of digital government, digital infrastructure, digital economy, digital capability, or digital society constitutes a necessary condition for the HDWC. Instead, it is the result of the combined effects of multiple institutional logics. Five configurations leading to HDWC are identified and categorized into four types: government-market-driven model, government-market-society-driven model, market-society-driven model, and government-society-driven model. The consistency of the configurations significantly increased during the study period. Furthermore, their distribution showed substantial regional differences. There are two configurations that inhibit the HDWC, namely the government-market-absence type and the market-society-absence type. Digital society emerges as a critical factor. This research uncovers multiple pathways through which digital ecology can empower HDWC, providing valuable insights for optimizing regional digital environments.

Understanding the impact of local government debt on economic development has emerged as a focal issue for both academic research and policymakers. This study adopts a financing structure perspective and utilizes panel data from 214 cities and 3,228 A-share listed companies in China (2017–2023) to empirically investigate the impact of local government debt on corporate “shift from virtual to real economy” and its underlying mechanisms. The expansion of local government debt significantly promotes enterprises “shift from virtual to real economy”. The positive impact of local government debt on enterprises” transition from financialization to the real economy is more pronounced among firms in first tier and new first-tier cities, non-state-owned enterprises, and labor-intensive industries. Further analysis indicates that local government debt drives capital reallocation from financial investments to real investments by alleviating corporate financing constraints. This study proposes policy recommendations including optimizing debt fund allocation, further optimizing the financing environment, implementing differentiated regulatory measures. These suggestions provide both a theoretical foundation and practical references for synergistically advancing debt governance and real economy revitalization.

Data-factor marketization (DFM) serves as a critical driver for cultivating manufacturing-enterprise new-quality productive forces (ME-NQPF), fundamentally supporting China's transition toward high-quality economic development. Integrating matched panel data from A-share listed Chinese manufacturing firms (2011–2022) with the staggered establishment of regional data trading platforms as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework to identify the causal impact of DFM on ME-NQPF. Empirical results demonstrate that DFM significantly enhances ME-NQPF, a finding that remains robust across alternative specifications and endogeneity treatments. Mechanism analysis identifies enterprise digital transformation as a pivotal mediator in this relationship, while competitive intensity is found to positively moderate the productivity gains from data marketization. Heterogeneity analysis further indicates that these effects are most pronounced among non-state-owned enterprises, technology-intensive sectors, and firms situated in China's eastern and central regions. These findings suggest that institutionalizing data-factor markets and accelerating digital integration are effective mechanisms for optimizing resource allocation and sustaining advanced industrial productivity.

In this paper, we design two chapters to discuss trade dynamics with heterogeneous fluctuations, contributing new insights to macroeconomic issues related to international trade. In the first chapter, we model general exchange rate fluctuations through stochastic processes and analyze the impact of heterogeneous price shocks on export competitiveness. We find that monetary policy and innovation both show positive effects on export trade, while monetary policy stabilizes exchange rate fluctuations to comprehensively boost provincial export competitiveness, innovation reduces its reliance on exchange rate mechanisms. The optimal policy according to exchange rate fluctuations aims to solve the wealth distribution of exporters, and it suggests that optimal policy should promote dynamic transitions in trade patterns rather than maintain existing comparative advantages in heterogeneous trade structures. In the second chapter, we model labor market fluctuations and the ability to utilize production factors through stochastic processes, and we analyze the impact of heterogeneous aggregate production shocks on general international trade. We find that labor market fluctuations only benefit international trade under the cooperation policy. Moreover, for both sanction and cooperation policy scenarios, positive shocks (i.e., shocks where average wage growth in the labor market exceeds unemployment) strengthen their impact on import trade while weakening their impact on export trade, and vice versa. Regarding the theories proposed in these two chapters, we prove them through empirical analyses using the provincial data of China.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.

We use nearly a decade’s worth of panel data from European Union Chamber of Commerce in China business confidence surveys to analyse the deteriorating outlooks of EU firms in China from 2017 to 2025. All firms in China currently face challenges including slow profit growth and deflation. These circumstances have contributed to a rare drop of foreign direct investment into China over the last two years. However, certain challenges are particularly acute for foreign firms, including those from the EU. According to survey results, business sentiment among EU firms operating in China has never been bleaker. Respondents view their profitability, growth opportunities and competitiveness negatively, while fewer respondents than ever plan to expand their Chinese operations. Moreover, significant shares of respondents report recent increases in political pressure from the Chinese state and media, while nearly a third of respondents say they are siloing their Chinese operations, meaning separating them from other global activities. Disaggregated by size, sector, and years of operation in China, insightful differences emerge between the business strategies of EU firms. We broadly classify these into four categories: doubling-down, hedging, hibernating and ready to exit. EU policymakers should consider how to address the challenges EU firms in China face, such as asset-heavy sectors being ‘stuck’ in China and smaller firms lacking the capacity to operate at a loss in China’s market. The EU might need to facilitate transitions for these companies, helping them to reduce exposure to China and diversify into other emerging markets.