This opinion paper analyzes China’s new mandatory climate disclosure standards, which were formally launched between December 2024 and December 2025. We argue that China has not simply adopted the investor-focused model of the International Sustainability Standards Board (ISSB) or the European Union’s comprehensive double materiality approach. Instead, it has crafted a distinct “third way”—a new model of sustainability governance marked by a deliberate blending of global convergence and national priorities. Through a close, provision-level comparison, we identify eight specific provisions where China’s standard goes beyond both IFRS S2 (Climate-related Disclosures, issued by the International Sustainability Standards Board) and ESRS E1 (Climate Change, issued under the European Sustainability Reporting Standards), revealing a consistent rationale that treats corporate disclosure as an instrument of national governance, reaching institutional completeness by simultaneously issuing assurance standards, and maintaining developmental sensitivity through mechanisms like the proportionality principle. While considerable implementation challenges persist, this paper concludes that China’s approach presents a useful and reproducible model for other developing nations, positioning China as an active contributor to the evolving global standards architecture.

China’s green-finance expansion shifts attention from how much firms disclose to whether transition claims remain credible when verification tightens. Using a 2007–2022 panel of Chinese A-share listed firms (crash-risk measures available through 2021), we measure transition credibility by decomposing firms’ transition signals into verifiable actions, narrative disclosure, and realized environmental outcomes, and we propose a transparent narrative–action gap measure (WordsMinusActions = WordsIndex − ActionIndex) to capture ‘words without deeds.’ Exploiting the staggered rollout of China’s Central Environmental Protection Inspections (CEPI) as a plausibly exogenous increase in verification and enforcement intensity, we find that firms with high pre-CEPI narrative–action gaps experience a sharp increase in stock price crash risk around inspections: NCSKEW rises by about 0.09 in the inspection year and 0.20 in the following year (≈0.24 SD), and DUVOL rises by about 0.04 and 0.10 (≈0.27 SD) relative to low-gap firms. Consistent with enforcement discipline, these high-gap firms subsequently reduce disclosure intensity and increase verifiable green actions, narrowing the narrative–action gap after CEPI. The evidence supports governance that rewards verifiable action and consistency over disclosure volume in markets.

I distinguish renminbi movements against the dollar from renminbi-relative repricing around U.S. tariff announcements. Across 26 policy groups, offshore renminbi (CNH) minus a fixed four-currency basket averages 35.077 aligned log basis points. Official reference rates corroborate the relative pattern; pre-event factor adjustment leaves a positive pooled own-CNH residual. Earlier disclosures change the later-period own-CNH sign, while early-to-late attenuation persists. Own-currency event-minus-lead contrasts remain imprecise, and macro-news and dependence screens weaken precision. The evidence supports an RMB-relative association, not a uniformly timed causal depreciation response or verified executable offshore prices.

Firms frequently overstate digital transformation in public disclosures while committing less in observable investment, a phenomenon known as digital catering. We examine whether digital public financial infrastructure can discipline such symbolic behavior. Using a staggered difference-in-differences design on Chinese A-share listed firms over 2014--2024, we find that exposure to China's e-CNY pilot reduces digital catering by roughly 14% relative to the sample mean. The decline is driven almost entirely by reduced rhetorical digital transformation, while balance-sheet-based digital commitment rises only marginally: the e-CNY pilot is associated with substantially less digital talk, but not with a commensurate increase in digital walk in the short run. The effect operates through lower agency costs and reduced information asymmetry, and follows an amplification--substitution pattern---stronger among digital-sector and accounting-opaque firms, weaker where internal controls and institutional ownership are already strong. The evidence identifies financial-transaction verifiability as a novel source of discipline over corporate cheap talk: CBDC-related infrastructure constrains unsupported symbolic claims without immediately accelerating substantive digital investment.

A central premise of mandatory disclosure regulation is that greater transparency reduces information asymmetry and lowers borrowing costs. We challenge this premise by examining industry-level operational disclosure - a regulatory form that reveals horizontally comparable information across peer firms rather than refining individual firm fundamentals. Exploiting the staggered introduction of mandatory industry-specific disclosure guidelines by Chinese stock exchanges between 2013 and 2019, we find that enhanced industry disclosure significantly widens bond credit spreads by approximately 54 basis points - the opposite of what standard disclosure theory predicts. This counterintuitive effect is more pronounced in non-homogeneous industries, among smaller firms, and for bonds restricted to institutional investors. Mechanism tests confirm two opposing channels: disclosure reduces information asymmetry while simultaneously intensifying product market competition by exposing strategically sensitive operational metrics. Our evidence challenges the one-size-fits-all approach to disclosure regulation and highlights that the competitive implications of disclosed information - not merely its quantity - shape credit risk pricing.

Technology firms are often financed on narratives about products, contracts, customers, and technological progress well before these developments appear in accounting statements. We ask when such narratives become economically informative. Our central idea is that narratives should matter more once they can be linked to later verifiable outcomes rather than treated as stand-alone text.Using listed Chinese technology firms, we develop a validated corporate narrative framework for bank-affiliated investment, a setting in which investors must screen with soft information ex ante and then monitor hard realization and downside risk ex post. We use GPT-5.1 to extract business claims from management discussion, investor-relations records, exchange Q&A, and earnings-roadshow materials, and to label later claim–evidence pairs as support, partial support, conflict, duplicate, or irrelevant. We then connect these labels to official announcements, procurement awards, permits, project updates, and negative-event disclosures to construct a validated firm-month signal. The broad merged panel contains 592 firms and 30,169 firm-month observations; the main return tests use 576 firms and 18,230 firm-month observations over 2022–2024. A simple production rule that combines a low-narrative-premium component with hard-narrative and hard-event anchors, together with a separate downside-risk gate, delivers an implementable annualized long-short return of 8.93% in bank-invested firms after trading costs. The signal is much weaker in non-bank firms, predicts future gross-margin improvement more strongly than future ROE, and improves downside screening.

This paper examines the spillover effects of China’s Carbon Emissions Trading Scheme (CETS) on non-regulated firms’ bank loans. Using a sample of Chinese A-share listed firms and a staggered difference-in-differences design, we find that suppliers experience a significant decline in bank loans when their customers are included in the CETS. This effect is driven by reductions in firms’ cash flow and customer concentration. The negative effect of downstream CETS on suppliers’ bank loans is attenuated for suppliers with better environmental performance, more comprehensive carbon disclosure, and closer geographic proximity to customers. We also find that, in response to reduced bank credit, firms rely more heavily on trade credit. Overall, this study sheds new light on the unintended financial consequences of CETS policy on non-regulated firms.

Against the backdrop of global green development and China’s sustainable economic transition, many firms exaggerate green-transition disclosures to cater to national strategies and capital market preferences, leading to a severe "Say–Do Gap". Based on signaling theory, this study uses the phased establishment of environmental courts in 208 prefecture-level cities as a quasi-natural experiment, adopting a staggered DID design with 2007–2023 panel data of Chinese A-share listed firms for empirical tests. Results show widespread corporate green pandering, with improved disclosure not translating into actual carbon reduction. Environmental courts effectively curb this behavior, with environmental litigation risk as the core mediating channel. Heterogeneity tests reveal stronger deterrence in regions with weaker regulation/heavier pollution and polluting firms with stronger environmental technology. This study enriches literature from a judicial deterrence perspective and provides implications for substantive corporate green transition.

Purpose – Estimates disclosure of human capital management for Chinese listed companies. Investigate the patterns ofthe disclosure of human capital management acrossindustries and regions. Examine the determinants of human capital management disclosure in China. Examine the association between human capital management disclosure and firm performance. Design/methodology/approach – We employ natural language processing techniques on annual reports’ management discussion and analysissections.We construct exposuremeasuresforten human capitalmanagement dimensions and synthesize them into one comprehensive measure of human capital management disclosure. We conduct empirical analysis on the measure using a sample of Chinese listed companies during 2009–2022. Findings – We construct a measure of human capital management disclosure for 5,153 Chinese companies during 2009–2022. We find that firms with high HCM disclosure are more labor intensive and have more cash holdings and R&D expenditure but have lower sales growth, market-to-book ratio and leverage. HCM disclosure is associated with better future accounting performance but poor future market valuation. There are substantial variations in HCM disclosure across industries, geographic regions and ownership types. HCM disclosure has increased significantly during the COVID-19 pandemic. Social implications – The increased HCM disclosure and its association with firm operating performance and market valuation indicate the relevance of HCM in corporate management and underscore the need for more robust and standardized disclosure of HCM in China. Our findingssupport recent regulatory efforts by CSRC to enhance the transparency and accountability in HCM disclosures and advocate for more explicit and specific HCM disclosure requirements in the future. Originality/value – We propose a quantitative measure of human capital management disclosure, which can be modified to apply to other markets. We construct a comprehensive, ready-to-use dataset for HCM disclosure for Chinese listed companies and conduct descriptive analysis on the dataset. We identify the patterns of human capital management disclosure and its determinants in China.

This paper studies missing firm characteristics in the Chinese stock market and their implications for empirical asset pricing. Relative to the U.S. market, missing firm characteristics in China remain underexplored despite substantial differences in data availability and disclosure environments. Using a dataset of 106 firm characteristics from 1992 to 2021, we document a pronounced cliff-shaped pattern in missingness, with missing rates falling sharply after 2000. We then compare expectation-maximization (EM) and mean imputation (MN) in both univariate characteristic-sorted portfolios and machine-learning applications that combine many predictors. Results indicate that, in univariate analysis, the two methods produce very similar return spreads because they assign largely the same stocks to the extreme deciles. In machine-learning applications, however, EM-imputed data generally produce better-performing prediction-sorted portfolios than mean-imputed data. These findings provide new evidence on missing firm characteristics in a major emerging market and highlight the importance of imputation choices in machine-learning asset-pricing applications.