Although firms suffer from regional climate risk in their production and operation, they are still highly expected by the public to play a leading role in addressing regional climate risk. In this paper, we study how regional climate risk affects corporate social responsibility (CSR). By constructing regional climate risk indicators and employing the OLS method to conduct empirical analyses, we find that regional climate risk can significantly promote CSR. Furthermore, regional climate risk can suppress firm’s cash flow, thereby exerting internal pressure on firms to assume CSR. Meanwhile, regional climate risk can raise higher public expectations for firms, imposing external pressure on them to assume CSR. We suggest that external pressure from the public plays a dominant role in CSR decision-making. Besides, we confirm that CSR can achieve a win-win goal for both firms and the public by mitigating the damage of regional climate risk on the firm’s long-term performance. We provide a new perspective for studying firm’s motivation to assume CSR under the influence of regional climate risk.

We study the role of firm heterogeneity and imperfect competition for global production networks and the gains from trade. We develop a quantifiable trade model with two-sided firm heterogeneity, matching frictions, and oligopolistic competition upstream. More productive buyers endogenously match with more suppliers, thereby inducing tougher competition among them to enjoy lower input costs and superior performance. Transaction-level customs data confirms that downstream French and Chilean firms import higher values and quantities at lower prices as upstream Chinese markets become more competitive over time, with stronger responses by larger firms. Moreover, suppliers charge more diversified buyers lower mark-ups. Counterfactual analysis indicates that entry upstream benefits high-productivity buyers, while lower matching or trade costs benefit all buyers, with the biggest boost to mid-productivity buyers. All three shocks generate sizeable welfare gains, especially under package reforms. Global production networks thus mediate bigger effects and cross-border spillovers from industrial and trade policies.

The use of green finance to slow down global warming in support of sustainable development remains widely discussed. This study examines whether investment structure moderates the impact of green finance on the environment in China, one of the top carbon-emitting nations and the second-largest economy in the world. We primarily used the moments-quantile regression approach with fixed-effect models on panel data from 1992Q1 to 2020Q4. First, the results confirmed that green finance and public and private investments worked synergistically to lower CO2 emissions, especially in Central and Western China. However, there was no proof that green finance and foreign direct investment were complementary in reducing CO2 emissions in China, unlike the Central region. Second, green finance marginally lowered CO2 emissions in all provinces, mainly in Eastern and Western China; this reduction was largely dependent on private investment in the Western region’s most polluting areas and foreign direct investment in Eastern and Western China’s least polluting provinces. Third, the beneficial effect of green finance occurred at varying optimal thresholds and investment-related conditions across Chinese regions at different quantiles. Lastly, we showed that in contrast to the variable impacts of urbanization, oil prices, and economic growth across Chinese regions at different quantiles, renewable energy, and trade openness reduced CO2 emissions. In conclusion, the study makes some policy recommendations for China’s sustainable economic development, an important model from which other countries can tailor their investment strategies and environmentally friendly policies.

Owing to the liberalisation of financial markets, the impact of international capital flows on the Chinese stock market has become substantial. This study investigates the effects of economic policy uncertainty (EPU), geopolitical risk (GPR), consumer sentiment (CCI), macroeconomic fundamentals (MECI), and money supply (M2) on the correlations between the stock and exchange rate markets. The negative correlation between these two markets has become more pronounced in recent years. Moreover, EPU, GPR, CCI, and MECI negatively impact long-term stock-exchange rate correlations, while M2 has a positive impact. Portfolios of stock-exchange rates effectively reduce risk, especially when considering structural breaks.

Using Chinese data, we find that analysts’ earnings forecasts are more accurate and less biased when analysts are socially connected with the company’s signatory auditor. We also find that forecast performance improves following mandatory auditor rotations that result in new analyst-auditor connections and declines following mandatory rotations that terminate existing connections. We further find that our results become stronger when the information that auditors possess is likely to be more useful to analysts, that connected analysts have better career outcomes than unconnected analysts, and that investors and other analysts are more responsive to forecast revisions issued by connected analysts. Finally, we find that connected auditors provide higher quality audits to their connected clients and are more likely to retain those clients. Overall, our findings are consistent with connected analysts benefitting from private information obtained from their social connections with auditors by providing better earnings forecasts, and in turn, with auditors benefitting from information they receive from connected analysts by delivering higher quality audits that improve client retention.

The 2020 Holding Foreign Companies Accountable (HFCA) Act will force China-based firms to delist from U.S. exchanges if China fails to permit audit inspections during a two-year period. The Act also requires such firms, as soon as China blocks such inspections, to disclose ties to the Chinese party-state. We first explain why the delisting provisions, while well-intentioned, may well harm U.S. investors. We then turn to the disclosure provisions, explaining that they appear to be motivated by a desire to name-shame Chinese firms rather than to protect investors. While China-based firms do pose unique risks to U.S. investors, the Act fails to mitigate—and may well exacerbate—these risks.

Digital transformation empowers enterprises with new kinetic energy for high-quality development, can digital transformation enhance the transparency of capital market? This study constructs a corporate digital transformation index, and examines its impact on Chinese capital market transparency from the perspective of information senders. We find that corporate digital transformation significantly improves transparency, and this finding is more pronounced in non-SOEs, firms with low political connection, high industry environment uncertainty, and low regional marketization level. Channel tests show that lowering management myopia and increasing analyst attention are possible mechanisms. Furthermore, digital transformation improves stock liquidity by enhancing enterprises’ information transparency. Overall, our findings provide critical insights for improving transparency in China’s capital market.

The debate on whether legalization or prohibition is more effective in conserving species and curbing illicit black-market trade remains controversial, with insufficient evidence available. Here we investigate the effects of China’s ivory ban on elephant poaching. We find that the enaction of the total ivory ban corresponds with a sharp 50% decrease in poaching and a significant reduction of the number of seizure cases. Further, although China has taken restrictive measures in 2015 and signaled a total ivory ban in 2016, no occurrence of “last minute rush” in smuggling activity was found preceding the implementation of the ivory ban.

The rise of automated trading systems has made stock trading more accessible and convenient, reducing the link between traditional illiquidity measures and stock returns. However, empirical data in China’s stock market shows conflicting results. We find a significantly positive correlation between intraday illiquidity and future returns in China’s stock market. We offer that the pricing ability of this intraday illiquidity originates from the correlation between trading activity and intraday return. This finding provides compelling out-of-sample evidence for the debate regarding the pricing of the Amihud (2002) measure in the U.S. market. Additionally, we create an intradayreturn illiquidity factor that outperforms Liu, Stambaugh, and Yuan (2019) sentiment factors in China’s stock market.

Emissions data collection and management are crucial to operationalizing an emissions trading scheme (ETS). Regulators need high-quality data to allocate emissions allowances and monitor compliance. However, collecting such data can be costly, challenging various actors. Emitters may misreport data, weighing the cost against their interest, while governments may struggle with limited resources in managing compliance. Third-party verification is a solution but tends to be ineffectual and causes new problems unless with sufficient oversight and support. This quality-cost dilemma becomes even more complex in multi-level ETSs, as in China’s national ETS (NETS). Despite increased regulatory efforts to address data challenges, there remains a lack of in-depth legal analysis on the relationship between data quality and cost. This Article establishes a three-element analytical framework—data quality, cost concerns, and intergovernmental relations in data management—to shed light on the nuances of data regulation. Using China’s NETS as a case study, we gain a deeper understanding of the three elements in a specific jurisdiction and the legal institutions, practices, and challenges involved. Governments, emitters, and third-party verifiers each have unique roles and limitations in this process. We suggest legal and regulatory strategies for finding solutions. Our actor-centered analytical model and practical recommendations for the NETS can serve as a valuable guide for jurisdictions facing similar data challenges.