Farmland transfer in developing countries is characterized by informal contracts. Rich research typically attributes this phenomenon to the lack of property rights and rarely emphasizes the influence of land characteristics themselves. Utilizing plot-level data from 2017-2019 collected in Yangshan County, China, we evaluate the impact of land consolidation on the farmland transfer market transformation. We find that land consolidation has a positive impact on the transformation of farmland transfer markets, instead of its scale expansion. After land consolidation, the scope of transaction partners extends from relationship-based households to anonymous new agricultural operating entities; the transfer rent shifts from being free of charge to being paid; and the transfer duration shifts from short-term to long-term. Mechanism analysis reveals that land consolidation increases land value and reduces transaction costs by improving the endowment of farmland resources, thus driving the farmland transfer market transformation. Further exploration warns that market transformation caused by land consolidation may trigger non-grain farming, leading to a 15.2% reduction in the rice planting area and posing potential risks to food security. These findings enrich the existing studies that only focus on the impact of land rights reform, both theoretically and empirically. In practice, it has important policy implications for countries facing similar difficulties in the farmland transfer market as China.

Research Question/Issue: This study investigates whether and how political connections affect customer complaints in the Chinese market, using a comprehensive dataset from the country’s largest online complaint platform. Research Findings/Insights: Analyzing 22,644 firm-year observations from 2018 to 2023, we find that politically connected firms experience significantly fewer customer complaints. A one-unit increase in political connection strength is associated with a 9% reduction in complaints relative to the sample mean. This effect operates through two primary mechanisms: a reputation-motivation channel and a financial resource channel. The mitigating effect is more pronounced for firms in highly marketized regions, those with higher advertising expenditures, companies facing greater earnings pressure, and those with lower tangible asset ratios. Theoretical/Academic Implications: Our study contributes to the literature on political connections and corporate governance by demonstrating how political capital translates into tangible consumer experience advantages. It also advances research on the determinants of customer complaints by highlighting the role of internal governance mechanisms, particularly managerial political ties. Our findings support the Corporate Reputation and Financial Resource Hypotheses while challenging alternative explanations based on regulatory shielding or managerial complacency. Practitioner/Policy Implications: For corporate leaders, our results underscore the importance of reputation management and quality investment, particularly when political connections are absent. Policymakers should consider strengthening public monitoring institutions to reinforce reputational incentives across markets. Investors may use customer complaints as an indicator of product quality and operational stability in their investment decisions.

We propose the AI Narrative Gap as a novel firm characteristic—the systematic divergence between a firm’s AI strategic narrative intensity and its subsequent AI capital expenditure commitment—and document its capital market consequences. Using Chinese A-share listed firms from 2015 to 2022, we show that firms with a wider AI Narrative Gap attract significantly more optimistic and less accurate analyst earnings forecasts. These distorted expectations, in turn, predict lower subsequent stock returns, lower industry-adjusted abnormal returns, and weaker future accounting performance. A double-sort portfolio placing firms simultaneously in the highest tercile of the AI Narrative Gap and highest tercile of analyst optimism earns a mean return 22.8 percentage points below that of the lowest tercile on both dimensions (t = −5.10). The return reduction in the AI Narrative Gap coefficient is attenuated but not eliminated after controlling for optimism, consistent with a partial expectation-distortion channel. Collectively, these results establish the AI Narrative Gap as a cross-sectionally informative firm characteristic that captures the credibility of a firm’s AI strategic identity, with systematic implications for analyst expectations and asset prices.

This paper examines the impact of Exchange-Traded Fund (ETF) ownership on corporate earnings management. We find that ETF ownership is associated with a significant reduction in earnings management, and this result remains robust across a wide range of endogeneity tests and robustness checks. Further analyses reveal that ETFs exert a pronounced mitigating effect on sales manipulation, production manipulation, and expense manipulation. Mechanism tests indicate that ETFs curb earnings management by improving stock liquidity and strengthening external monitoring. We also find that the influence of ETFs is stronger in private firms, in firms with lower information transparency, and in firms with CEO duality, suggesting that ETFs serve as a more prominent external governance force when internal governance mechanisms are relatively weak. Overall, this study enriches the literature on the economic consequences of ETFs and provides new empirical evidence that financial innovation in emerging markets can help alleviate the information risk faced by investors.

Following the 18th National Congress of the Communist Party of China, the central government elevated the construction of ecological civilization to a central position within national strategy and introduced environmental governance indicators as mandatory criteria for evaluating officials, alongside GDP. These indicators served as an additional "threshold" for performance assessments. In the context of changes in the central government's development ideology and policies, this study utilizes matched data on the turnover of municipal party secretaries and local enterprise carbon emissions from 293 prefecture-level cities in China between 1990 and 2021. The research finds that turnovers of municipal party secretaries after the 18th National Congress have led to a significant reduction in carbon emissions from local enterprises, a trend that was not evident prior to the congress. This effect is more pronounced in situations where official turnover is primarily driven by promotion incentives, and less influenced by collusive behavior between the government and enterprises. Further analysis reveals that the decline in carbon emissions is more significant for private enterprises, non-heavy polluting enterprises, those located in the eastern region, and those in general prefecture-level cities, before and after municipal party secretary turnovers. This study enhances understanding of the relationship between the promotion incentives of Chinese officials and the carbon emissions of local enterprises, offering valuable insights for improving the official promotion assessment system and advancing local carbon reduction efforts.

How enterprises can leverage frontier technologies to achieve synergy between environmental governance and high-quality development has become a critical issue amid the deepening global push for sustainable development and the green economic transition. Based on micro-level data of Chinese enterprises from 2009 to 2023, this study systematically examines the impact of artificial intelligence (AI) on corporate green governance performance and explores the underlying mechanisms. The findings reveal that AI significantly enhances green governance performance at the enterprise level, and this effect remains robust after accounting for potential endogeneity. Mechanism analysis shows that AI empowers green transformation through a dual-path mechanism of “cognition–behavior,” by strengthening environmental tendency and increasing environmental investment. Further heterogeneity analysis indicates that the positive effects are more pronounced in nonheavy polluting industries and state-owned enterprises, suggesting that industry characteristics and ownership structure moderate the green governance impact of AI. This study contributes to the theoretical foundation of research at the intersection of digital technology and green governance, and provides empirical evidence and policy insights to support AI-driven green transformation in practice.

China faces significant environmental challenges, including reducing pollutants, improving environmental quality, and peaking carbon emissions. Industrial restructuring is key to achieving both emission reductions and economic transformation. This study uses the Environmentally Extended Multi-Regional Input-Output model and multi-objective optimization to analyze pathways for China’s industrial transformation to synergistically reduce emissions. Our findings indicate that under a compromise scenario, China’s carbon emissions could stabilize at around 10.9 billion tonnes by 2030, with energy consumption controlled at approximately 5 billion tonnes. The Papermaking sector in Guangdong and the Chemicals sector in Shandong are expected to flourish, while the Coal Mining sector in Shanxi and the Communication Equipment sector in Jiangsu will see reductions. The synergy strength between carbon emission reduction and energy conservation is highest at 11%, followed by a 7% synergy between carbon emission and nitrogen oxide reduction. However, significant trade-offs are observed between carbon emission reduction and chemical oxygen demand, and ammonia nitrogen reduction targets at -9%. This comprehensive analysis at regional and sectoral levels provides valuable insights for advancing China’s carbon reduction and pollution control goals.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.

We collect the geographical dataset of bank physical branch in China from 2008 to 2023, obtaining the 261,382 branches. Through careful data processing, we calculate the bank branch closure at city-level and merge it with regional entrepreneurship in China. With the panel dataset at city-industry-year level, we find that bank branch closure (BBC) significantly reduces neighbor entrepreneurship, which is proxied by the number of new firm entry. In mechanism analysis, we document that bank branch closure affects entrepreneurship through the financing channel and mobility channel. We also find that commercial bank branch closure plays a crucial role in affecting entrepreneurship. The reduction effect of BBC is more pronounced for those observations located in geographical intersections, coastal lines. Further, we explore the impact of BBC on the direction of entrepreneurship, showing that there is less new firm formation in manufacture industry after the BBC. In addition, we show that BBC may contribute to the entrepreneurship failure as well. Our findings may shed light on the policy makers, bank owners and those who want to form a new firm.

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.