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.

How do market-design reforms affect secondary-market liquidity in an emerging equity market? We examine China’s ChiNext reform, which combined registration-based IPO issuance with changes in secondary-market trading rules. Using firm-month observations and high-frequency order-book data, we compare incumbent ChiNext firms with Main Board firms in a difference-in-differences framework. The results show that the reform significantly improves secondary-market liquidity, with consistent evidence across alternative DID estimators and liquidity measures. Dynamic estimates show comparable pre-reform movements and increasingly favorable liquidity outcomes after implementation. Cross-sectional analysis indicates stronger liquidity improvements among larger and more profitable firms. Microstructure results show narrower quoted spreads, broadly stable displayed depth, and a shift in order flow toward buyer-initiated trading. Overall, the findings demonstrate that the integrated ChiNext reform package improved secondary-market liquidity and reshaped the cross-sectional distribution of liquidity gains, highlighting the market-quality consequences of institutional reform in emerging equity markets.

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.

Capital market internationalization is widely regarded as an important pathway to improving resource allocation efficiency and enhancing governance quality, while simultaneously imposing higher requirements on corporate sustainability and social responsibility. However, its impact on firms’ internal income distribution remains subject to academic debate. This study treats the inclusion of China’s A-shares in the MSCI Emerging Markets Index as a landmark exogenous shock of capital market internationalization. Based on a sample of Chinese A-share listed firms from 2014 to 2022, we manually compile firm-level MSCI inclusion data and construct a multi-period difference-in-differences model for empirical testing. The results indicate that firms’ inclusion in the MSCI Index significantly increases their labor income share. The mechanism analysis reveals that this promoting effect operates mainly through three channels: alleviating financing constraints, fostering innovative development, and strengthening investor governance. Further heterogeneity analysis shows that the effect is more pronounced among technology-intensive firms, firms under greater competitive pressure, and firms with higher degrees of internationalization. By incorporating income distribution outcomes into the framework of capital market internationalization, this paper enriches the evidence on the economic and social effects of internationalization and provides policy implications for emerging economies to advance high-level capital market opening and optimize income distribution.

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.

This study investigates how the development of digital finance influences the geographical distribution of corporate supply chains using data from Chinese A-share listed companies from 2010 to 2023. We examine whether digital finance enables firms to overcome traditional geographical constraints and adopt different supply chain distribution strategies. The analysis identifies two primary mechanisms through which digital finance influences supply chain geography: governance effects, which operate through enhanced risk management and information transparency, and financing effects, which function through alleviated capital constraints and trade credit provision. We further explore heterogeneous impacts across four dimensions: regional economic development, regional digital infrastructure, industry market competition, and enterprise lifecycle stages. By examining the geographical distribution of supply chains as an outcome of digital finance development, this study provides novel evidence on the micro-governance implications of digital finance. Our findings contribute to understanding how digital finance fundamentally changes the geographical constraints that have historically shaped supplier selection decisions and enables firms to develop more flexible supply chain configurations.

A critical barrier to labor mobility arises from institutional constraints that im-pose discriminatory costs on migrants. Using China’s hukou system as a case study,we construct a novel, outcome-based measure of mobility frictions that infers thesediscriminatory costs. We document a systematic relationship between our frictionmeasure, migrants’ decisions to leave behind families (“partial migration”), remit-tances, and expenditure patterns. Our estimated spatial general equilibrium modelencompasses these features and examines how mobility frictions interact with tradeliberalization to shape migration, inequality, and welfare. Trade-exposed regionsbenefft from attracting migrants, while high-friction regions experience muted laborreallocation and smaller welfare gains.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm, we provide evidence that both extreme events (COVID-19 quarantines) and everyday contexts (geographic locations) trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to substantial losses for the representative investor's portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which contextual cues affect financial markets.

We document a striking anomaly in international capital flows that we term "sanction arbitrage": U.S. investors exploited the 2014 sanctions on Russia by significantly increasing holdings in Russian equities while Rest-of-World (ROW) investors fled. We rationalize this behavior through a simple game-theoretic model where the sanctioning government faces a trade-off between geopolitical objectives and domestic welfare, effectively creating a protective shield for domestic investors and driving out ROW investors. Empirically, we confirm that pre-sanction U.S flows negatively predicted subsequent sanction designations. Consequently, U.S. investors internalized this protection to act as opportunistic buyers, absorbing fire-sale assets from exiting foreign investors and capturing significant excess returns from Russian stock holdings. These findings reveal that "smart" sanctions designed to preserve market access can inadvertently generate wealth transfers from foreign to domestic agents.

This study investigates the role of contextual cues in memory-based decision-making within high-stakestrading environments. Using trade records from a large Chinese brokerage firm and a novel dataset on COVID-19 quarantines, we find that quarantine periods trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to an annualized loss of approximately 70 percentage points for the representative investor’s portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides causal evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which COVID-19 policies affect financial markets.