Against the backdrop of escalating global carbon emission reduction urgency and China’s "dual carbon" goals, individual carbon trading (ICT) has emerged as a key measure to tap into consumer-side emission reduction potential. However, the current ICT mechanism remains in the exploratory stage, plagued by limited access to formal trading channels, strict entry barriers, and low public penetration, only 5 carbon exchanges in China allow individual participation, with fewer than 10,000 total participants. Focusing on mechanism integrity and integrating the carbon inclusive system, this study explores the influencing factors of ICT operation and constructs targeted optimization strategies. Based on Mechanism Design Theory, the Theory of Planned Behavior, and Value-Belief-Norm Theory, a theoretical model covering five core ICT components was established. Using data from 1,200 questionnaires across 15 Chinese cities, multiple linear regression and hierarchical regression were employed to analyze the impacts of institutional fairness, technical usability, social atmosphere, and individual cognition. Results show: (1) Institutional fairness increases quota compliance by 38% through transparent rules; (2) User-friendly technical tools boost engagement by 1.8x, enhancing accounting accuracy and platform efficiency; (3) Social atmosphere moderates the effect of individual cognition on incentive conversion, particularly strongly in rural areas; (4) Individual cognition mediates the relationship between social atmosphere and trading platform efficiency. Additionally, optimizing mechanism components is more critical for long-term operation than single incentives. This study proposes a multi-dimensional optimization framework, providing theoretical and policy references for improving ICT integrity and advancing "dual carbon" goals.

Exploiting China’s 300 billion RMB Special Central Bank Lending Facility for the Clean and Efficient Use of Coal as a quasi-natural experiment across 12,816 firm-year observations from 2018 to 2023, we show that subsidized transition credit increased targeted coal and thermal power firms’ aggregate carbon emissions by 14.18 log points. We establish this backfire as a Financial Jevons Paradox, whereby credit-induced scale expansion overwhelms unit efficiency gains. Although the facility raised total factor productivity and stabilized emission in-tensity, it lowered debt costs, expanded bank loans, and induced production-biased patenting, proving that transition finance requires binding aggregate emission caps.

We examine whether and how the cultural background of corporate chairpersons, specifically clan culture, influences firm-to-firm lending decisions. Using hand-collected data on Chinese entrusted loans, a major segment of the country’s shadow banking sector, we find that firms led by chairpersons from prefectures with stronger clan culture are more likely to originate entrusted loans and charge lower spreads. We identify distinct mechanisms governing affiliated versus nonaffiliated transactions. In affiliated lending, our evidence suggests an in-group favoritism channel: culturally embedded chairpersons support affiliated parties despite higher ex-post default risk. Conversely, non-affiliated lending operates via an informational channel, where the positive impact of clan culture is more pronounced when geographic barriers are low, and manifests in larger size dispersion. Overall, our findings highlight the dual role of cultural norms in shadow banking, demonstrating that clan culture facilitates credit allocation while generating sharply different efficiency implications across relationship-based and arm’s-length markets.

This paper investigates the sales seasonality premium (Gustavo et al., 2020) in the Chinese stock market. We document a significant sales seasonality premium in the cross section of stocks listed on Chinese market. A long-short strategy of buying low-sales stocks and shorting high-sales season stocks generate a monthly return of 0.84% with a Newey-West t statistic of 2.94. The return spreads between low-sales season stocks and high-sales season stocks are robust to well-known anomalies and are larger in magnitude among large-cap companies. We also find that the return spreads are more pronounced within industries with relatively fixed patterns in product demand. The sales seasonality premium in the Chinese stock market is found to be a combined result of investor rationality and irrationality.

The global financial system is currently navigating a profound transformation driven by digitalization and the rise of decentralized financial innovations. In response, central banks are increasingly exploring or implementing Central Bank Digital Currencies (CBDCs) as a sovereign digital evolution of fiat money. This study investigates the dual nature of CBDCs, evaluating whether they represent a strategic opportunity to modernize the economy or a systemic threat to existing financial stability. Employing a qualitative methodology, the research analyzes official policy frameworks from the IMF and BIS alongside diverse real-world case studies, including China’s e-CNY, the Bahamas’ Sand Dollar, Nigeria’s eNaira, and the European Central Bank’s Digital Euro. The findings suggest that while CBDCs offer significant benefits—such as enhanced payment efficiency, reduced transaction costs, and improved financial inclusion—they also introduce critical risks. These include the potential disintermediation of commercial banks, heightened cybersecurity vulnerabilities, and concerns regarding individual data privacy and government surveillance. The study concludes that the successful integration of CBDCs is not merely a technical challenge but a social and strategic one. Adoption is heavily dependent on infrastructure, digital literacy, and public trust. Ultimately, the research highlights that there is no "one-size-fits-all" model; the future of money will be shaped by how effectively individual nations balance technological innovation with the preservation of financial architecture.

This paper develops a delegation-based framework to explain how institutional design shapes pricing incentives under risk. Using China’s 2016 IPO reform—which abolished prefunding requirements and transferred payment obligations from investors to underwriters—as a natural experiment, we show that introducing subscription-payment risk (SPR) renders underwriter’s partial residual claimants with respect to unpaid allocations. Building on Baron’s (1982) delegation model, we argue that the reform amplifies information asymmetry and induces underwriters to adopt more conservative pricing strategies to manage perceived payment risk. Empirically, IPOs exposed to SPR exhibit greater underpricing and lower offer prices, particularly when investor bids reflect stronger valuation pessimism. The effect tends to be less pronounced for reputable underwriters and when foreign institutional investors participate. Overall, the evidence demonstrates how risk redistribution and institutional frictions jointly shape underwriter behavior and pricing efficiency in primary equity markets.

This study examines the role of artificial intelligence (AI) adoption in the market for corporate control using a sample of Chinese listed firms from 2011 to 2021. We construct a novel firm-level AI Index through textual analysis of annual reports and find that AI adoption significantly enhances both the likelihood of becoming an acquisition target and the valuation premiums commanded in M&A transactions. Specifically, a one-standard-deviation increase in the AI Index is associated with a significant increase in the probability of being acquired and higher deal premiums measured by price-to-earnings multiples. We identify two channels through which AI adoption creates value recognized by the M&A market: an efficiency channel, whereby AI reduces agency costs and improves profitability, and an innovation channel, evidenced by increased high-quality patent output. The persistence of these effects over time further suggests that AI adoption generates substantive improvements in firm fundamentals rather than serving as a transitory informational signal. Importantly, we document significant heterogeneity across ownership structures: the positive effects of AI adoption are substantially weaker for State-Owned Enterprises (SOEs) than for non-SOEs. Our findings contribute to the literature on digital transformation and corporate finance by demonstrating that AI adoption serves as a value-relevant firm attribute that shapes outcomes in the market for corporate control.

We examine the unintended consequences of bankruptcy court establishment on corporate behavior. Using data on Chinese listed firms from 2009 to 2019 and a staggered difference-in-differences model, we find that the establishment of bankruptcy courts increases accrual earnings management by about 17% among high bankruptcy risk firms relative to low-risk firms. While bankruptcy courts improve bankruptcy efficiency and justice, reduce local government intervention, and accelerate the exit of zombie firms, they also induce greater earnings management. This effect is driven mainly by survival pressure and managerial reputation concerns, rather than by efforts to correct external evaluations. Consistent with this interpretation, we do not observe improvements in long-term operations, governance, performance, or real earnings management. Overall, this paper enriches the literature on earnings management from the perspective of judicial governance and on the economic consequences of creditor-friendly bankruptcy institutions.

This paper identifies the marginal investors in China’s stock market and examines their impact on stock pricing. To clearly distinguish between the equity constraint channel and the debt constraint channel, we construct the capital ratio factor and the debt constraint factor for banks and securities companies, the two most critical financial intermediaries in China’s stock market. Our results demonstrate that banks indeed serve as marginal investors and influence stock market efficiency primarily through the equity capital constraint channel. Furthermore, we find that the bank capital ratio factor significantly explains stock mispricing in China, with the single-factor model based on bank equity capital producing substantially smaller pricing errors compared to traditional multi-factor models.

We find that firm-specific social media tone influences leveraged trading. A more positive tone predicts greater next-day net margin purchasing, driven predominantly by sentiment. High margin purchasing following positive social media tone consistently yields inferior performance over both short and long horizons. Short sellers are collectively more sophisticated. They capitalize on fluctuations in social media tone, both positive and negative, through strategies that adjust to different tone windows and holding periods. While experienced, rational short sellers can swiftly profit from temporary negative sentiment, high short selling following persistently high social media tone is highly profitable over longer horizons.