This study examines the influence of extreme weather events on stock market behavior in China, focusing on the Shanghai and Hong Kong Stock Exchanges. This article’s hypothesis is that local weather affects individual investors in Shanghai more significantly due to their short-term, speculative trading habits. In contrast, institutional investors in Hong Kong are less influenced by short-term considerations due to their long-term strategies and access to resources. The Glosten-Jagannathan-Runkle Generalized AutoRegressive Conditional Heteroskedasticity (GJR-GARCH) estimator can test the hypothesis under different market conditions and volatility clustering. The analysis utilizes daily financial and meteorological data from January 1, 2009, to December 31, 2023. The GJR-GARCH estimator incorporates variables such as air pressure, humidity, sunshine hours, and temperature. The results show that extreme weather has a more pronounced effect on the Shanghai market than the Hong Kong market. Furthermore, extreme weather events influence stock turnover and volatility more than stock returns, reflecting shifts in investment behavior. The hypothesis is further tested to determine whether it remains valid during bull and bear markets, which are emotionally charged periods. The hypothesis still holds, albeit with less pronounced effects. Thus, extreme weather can impact stock market performance, with the composition of investors playing a significant role.

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 frequent occurrence of extreme weather events not only poses serious challenges to global economic growth and financial stability but also affects firms negatively across multiple dimensions. Using a sample of Chinese A-share listed firms from 2006-2016, this study aims to explore the effect of climate risk on firm export sophistication. The findings show that climate risk inhibits firm export sophistication, with the results varying depending on firm and industry types. Specifically, climate risk (i) inhibits export sophistication for firms with low government subsidies more than for firms with high government subsidies; (ii) restraints export sophistication for firms in high-tech industries rather than for low-and medium-tech industries; and (iii) reduces export sophistication for firms in low-marketization regions more than for firms in high-marketization regions. In addition, channel analysis shows that climate risk inhibits firm export sophistication by increasing financial constraints and reducing human capital.

Climate change and the escalation of extreme weather events jeopardize every corner of the globe. This paper investigates the impact of extreme high temperatures on the spread of newly issued municipal corporate bonds (MCBs) in China, which serves as a crucial instrument for local governments to meet the financial demands. We find that relative to the reference temperature range of 16 ◦C–20 ◦C, the issuing spread of MCBs increases by 2.48 basis points for each extra day where the mean temperature surpasses 32 ◦C. The findings highlight the risk-increasing effects of extreme temperatures in financial markets.