The literature has extensively explored the relationship between trade and envi-ronment, with most studies focusing on how trade affects the environment. However, our research takes a different approach by examining how air pollution affects firms’ exports. We use Chinese export and pollution data from 2000 to 2007 at the firm and county levels. By using fine particulate matter (PM2.5) concentrations as a proxy for air pollution and employing thermal inversion as an instrumental variable, we ffnd that a 1% increase in PM2.5 leads to a 0.89% reduction in firms’ exports. We also observe this negative effect of air pollution on entry and exit (i.e., extensive margins). Our mechanism analysis identiffes two channels through which air pollution affects exports. First, air pollution decreases exports by reducing firm productivity. Second, air pollution induces stringent environmental regulations, which reduces exports as firms need to increase abatement costs or reduce production to meet the environment standards.

This study explores how the application of democratic rule in land reform decision-making determines villagers’ political trust towards different levels of the government in China. Based on analyses of a two-period household survey data we find that in China’s most recent Collective Forest Tenure Reform, the use of democratic rule improves villagers’ trust for town and county cadres, whereas the impact on trust towards village cadres is only significant for the democracy involving all the villagers or households in a village. This pattern of trust is partly explained by our findings that the democratic process helped decrease the unresolved inter-village forestland disputes whilst there seems no such impact on the within-village land disputes. Heterogeneity analyses show that democratic decision-making has a more pronounced effect in improving trust for villagers with lower income, and those without affiliation with the Chinese Communist Party (CCP) or to the village committee.

The discussion on the innovative effects of intellectual property pledge financing is a mainstream trend. In this context, this study has improved the existing research from several aspects, such as broadening the dimensions of innovation, adding dynamic analysis, refining multidimensional mediation mechanisms, and employing unique samples. Ultimately, we come to the following conclusions: (1) Intellectual property pledge financing suppresses enterprise innovation, especially innovation quality, but this pattern will be broken by raising the threshold of innovation conditions. The reason is that strict innovation conditions can lead to a poor innovation foundation for enterprises, which are rarely affected by the fluctuation of funds obtained from intellectual property pledge financing. (2) Intellectual property pledge financing has a non-linear effect on firm innovation, characterized by an increase followed by a decrease, suggesting that intellectual property pledge financing in current China can only provide a temporary stimulus for firm innovation. (3) The relationship between intellectual property pledge financing and enterprise innovation is strongly moderated by the ownership, type, and size of the enterprise, with the inhibitory effect of intellectual property pledge financing on enterprise innovation occurring mainly in state-owned enterprises, high-tech enterprises, and small enterprises, while its positive effects are more pronounced in private enterprises, non-high-tech enterprises, and medium-sized enterprises. (4) Financing constraints, internal incentives, external supervision, and signaling mechanisms are indeed key pathways through which intellectual property pledge financing affects firm innovation, especially when we analyse these mechanisms using dynamic models.

Between high-quality and high-efficiency green innovation, which can truly reduce stock price crash risk? We use data from Chinese listed companies from 2010 to 2022 to study the impact mechanism and effect of radical and incremental green innovation stock price crash risk. Results show that radical green innovation can significantly reduce stock price crash risk, and this effect is more evident than the incremental one. Radical green innovation can improve information efficiency and enhance risk management, thus reducing stock price crash risk. Besides, among companies held by trading institutions and with low analyst coverage, the inhibitory effect is more evident.

This study investigates the link between market arbitrage risk and liquidity risk pricing in a conditional asset pricing framework. We estimate comparative models both at the portfolio and firm level in the Chinese A- and B-shares to test behavioral hypotheses with respect to foreign ownership restrictions and market segmentation. Results show that conditional liquidity premium and risk betas exhibit pronounced asymmetry across share classes which could be attributed to differentiated levels of market mispricing. Specifically, stocks with a greater degree of information asymmetry and retail ownership are more sensitive to liquidity risks when the market arbitrage risk increase. Further policy impact analysis shows that China’s market liberalization efforts, contingent upon its recent stock connect programs, conditionally reduce the price of liquidity risk for connected stocks.

This paper is the first to examine the evolution of price discovery in agricultural commodity markets across the four distinct phases determined by trade friction and trade policy uncertainty. Using cointegrated vector autoregressive model and common factor weights, we report that corn, cotton, soybean meal, and sugar (palm oil, soybean, soybean oil, and wheat) futures (spot) play a dominant role in price discovery during the full sample period. Moreover, the leadership in price discovery evolves over time in conjunction with changes in trade friction phases. However, such results vary across commodities. We also report that most of the agricultural commodity markets are predominantly led by futures markets in price discovery during phase Ⅲ, except for the wheat market. Our results indicate that taking trade friction into consideration would benefit portfolio managements and diversifying agricultural trade partners holds significance.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

Facing external shocks, maintaining and stabilizing imports is a major practical issue for many developing countries. We first document that sourcing market switching (SMS) is widespread for Chinese firms (For 2000-2016, SMS firms account for 76.29% of all import firms and 96.30% of total import value). Then we use Chinese firm-level data to show that SMS can significantly mitigate the negative impacts of international uncertainty on imports, which further stabilizes firm employment and innovation, leading to increases in national and even world welfare. Possible motivations for SMS include stabilizing import supply, lowering import tariffs, raising the real exchange rate, and increasing product switching. We also find that the effects of SMS vary by the type of uncertainty, firm ownership, productivity, credit constraints, trade mode, and product features.

ESG is emerging as a new benchmark for measuring a company's sustainable development capabilities and social impact. As a measure of ESG performance, ESG ratings are increasingly receiving attention from companies, the general public, and government institutions, and are becoming an important reference factor influencing their decision-making. This paper investigates the impact of corporate ESG ratings on Total Factor Productivity (TFP) and its mechanisms of action. Focusing on listed companies in China, we find that higher ESG ratings contribute to improving a company's TFP, and this conclusion remains valid after robustness tests and addressing endogeneity issues. Further exploration into the reasons behind this result reveals that ESG ratings can be seen as a signal that a company sends to the outside world, representing its overall performance. Higher ESG ratings enhance a company's TFP by reducing market financing constraints and obtaining government subsidies. Heterogeneity analysis shows that the positive impact of ESG ratings on TFP is more pronounced for companies with higher levels of attention, reputation, and audit quality. Additionally, we explore whether ESG ratings can serve as a predictive indicator for measuring a company's TFP. This hypothesis was tested using machine learning algorithms, and the results indicate that models incorporating ESG rating indicators significantly improve the accuracy of predicting a company's TFP capabilities.

This paper examines the impact of ESG rating divergence on stock price delays in the context of the Chinese capital market. We find that ESG rating divergence significantly increases the stock price delays. Mechanism analysis results suggest that ESG rating divergence affects stock price delays by reducing information transparency and firm internal control quality. Heterogeneous analysis results indicate that the impact of ESG rating divergence on stock price delays is more pronounced in high-tech firms and when investor sentiment is high.