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.

We examine the relationship between government data opening and corporate leverage manipulation. Leveraging the staggered establishment of province-level open government data platforms in China as a quasi-natural experiment, we find that government data opening significantly reduces corporate leverage manipulation. This policy effect is more pronounced among firms with low capital intensity, firms with limited analyst coverage, and those located in regions with low banking competition. Mechanism analysis suggests that government data opening affects the cost-benefit tradeoff of corporate leverage manipulation by alleviating financing constraints and enhancing corporate governance, thereby mitigating leverage manipulation practices. Our research contributes to the literature on government data opening, highlighting its role in reducing leverage manipulation and offering valuable implications for mitigating significant financial risks.

The global AI inference market—reaching approximately $90–100 billion annually and growing at 18% CAGR—operates without organized exchange infrastructure. We document three market failures: resource misallocation (80% of China’s newly built compute capacity sits idle), price opacity (100-fold price dispersion across providers of equivalent quality), and unhedged risk exposure (85% of enterprises miss AI cost forecasts by more than 10%). Following the market design tradition of Roth [2002] and Budish et al. [2015], we propose the AI Service Right (ASR) as a transferable property right on AI compute and the AI Service Unit (ASU) as a quality-adjusted, cross-platform unit of account grounded in hedonic price theory [Rosen, 1974]. The ASU is modality-neutral: billing prices across text, image, video, and speech modalities are unified via eq-token conversion factors (κimg ≈ 2,667 eq-tokens per image; κvid ≈ 2,667 per second of video; κspc ≈ 7 per second of audio), and modality-appropriate benchmark sets (MMLU/HumanEval for language; FID/CLIP Score for image; FVD/CLIPSIM for video; MMBench for multimodal) supply the quality in dex via PCA. We design a hybrid secondary market architecture synthesizing mechanisms from four orthogonal market traditions: foreign exchange markets (cross-platform exchange rates and PPP-analog arbitrage via the ASU); equity markets (Central Limit Order Book, market making, clearing); electricity markets (Compute Locational Marginal Pricing for spatial scarcity signals); and decentralized finance (Automated Market Maker for long-tail liquidity). We establish nine formal propositions: bilateral trading is generically inefficient; Compute Locational Marginal Pricing decomposes nodal prices into system marginal cost, capacity congestion, and bandwidth premia; no-arbitrage equi librium holds with capital constraints (extending Shleifer and Vishny 1997); the ASR market Pareto-improves over bilateral trading; market prices are more in formative under ASR; the hybrid CLOB-AMM architecture weakly dominates either mechanism alone; platform adoption admits multiple equilibria with a coordination trap; financialization may improve or reduce price informativeness depending on speculator-hedger composition; and a hedonic micro-foundation justifies the ASU definition. Calibrated agent-based simulation (500 steps, 30 Monte Carlo runs) provides computational validation: the hybrid architecture reduces price dispersion by 90% relative to bilateral trading, and order-of-magnitude welfare estimates suggest enterprise procurement cost savings of 0.2–20% (net of ASR transaction costs; see Table 7) and potential TFP gains from compute reallocation of up to $29.9 billion annually. We propose a phased implementation roadmap from shadow ledger to full financialization, and we engage critically with the concern that financialization may not reduce intermediation costs [Philippon, 2015].

In dynamic housing markets, a sale can affect not only the transacting parties, but also other buyers who had considered the property. We develop a dynamic sequential search model in which property exit creates imperfect recall and signals tighter market conditions, generating transaction cascades. Using data from a major Chinese housing platform, we exploit quasi-random sales of previously inspected properties as option-loss shocks. Option loss raises affected buyers’ purchase probability by 67%, with stronger effects in tighter markets and among buyers with larger choice sets. A back-of-the-envelope quantification suggests that these cascades accounts for about 30% of observed market-level transaction activity. Option loss also reduces the number of property visits, broadens search criteria, and is associated with higher transaction prices. The results highlight imperfect recall in dynamic search as a microlevel channel through which housing market activity can be amplified.

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.

Data-factor marketization (DFM) serves as a critical driver for cultivating manufacturing-enterprise new-quality productive forces (ME-NQPF), fundamentally supporting China's transition toward high-quality economic development. Integrating matched panel data from A-share listed Chinese manufacturing firms (2011–2022) with the staggered establishment of regional data trading platforms as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework to identify the causal impact of DFM on ME-NQPF. Empirical results demonstrate that DFM significantly enhances ME-NQPF, a finding that remains robust across alternative specifications and endogeneity treatments. Mechanism analysis identifies enterprise digital transformation as a pivotal mediator in this relationship, while competitive intensity is found to positively moderate the productivity gains from data marketization. Heterogeneity analysis further indicates that these effects are most pronounced among non-state-owned enterprises, technology-intensive sectors, and firms situated in China's eastern and central regions. These findings suggest that institutionalizing data-factor markets and accelerating digital integration are effective mechanisms for optimizing resource allocation and sustaining advanced industrial productivity.

We investigate how the loss of informative salient signals in financial markets influences stock return volatility, using the 2024 intraday disclosure reform of the mainland China-Hong Kong Stock Connect program as a natural experiment. The reform eliminated the real-time disclosure of northbound capital (NC) flows on trading platforms, rendering NC trading information invisible to Chinese investors during market hours. We find that the removal of NC signals induces increased investor belief dispersion and intensifies informed trading, thereby amplifying intraday volatility in NC-eligible stocks. Moreover, this effect is more pronounced for stocks with higher investor attention, indicating that attentive investors suffer stronger anchor loss when NC signals disappear. In contrast, lottery-type stocks and stocks with alternative NC trading clues exhibit weaker volatility responses, since the presence of strong alternative signals reduces the effect of NC signal loss. These findings highlight the informational role of insightful salient signals in stabilizing stock returns.

We study the role of superstition in China’s peer-to-peer lending market by ex-amining whether lenders time their bids according to “lucky hours” from the Chinese farmer’s calendar. Loans funded during lucky hours perform better—but only because the platform lists higher-rated loans at those times. This pattern is consistent with a screening mechanism: highly risk-averse lenders place greater value on both true risk reductions and auspicious-day signals, so the platform maximizes surplus by bundling the two—listing low-risk loans on auspicious days. Moreover, listing safer loans at lucky hours can further boost proffts because biased beliefs decay more slowly under asymmetric (bad-news-heavy) learning.

The governmental governance environment significantly influences real corporate investment. Based on the data of listed A-share enterprises from 2010-2020,we adopt a heterogeneous timing difference-in-differences method to examine the impact of Open government data (OGD) on real corporate investment by leveraging the launch of OGD platforms. It is found that OGD significantly promotes real corporate investment. This conclusion remains robust after a series of tests for robustness and endogeneity, including parallel trend, placebo, heterogeneity treatment effect, and replacing variable. The analysis of the impact mechanism reveals that OGD influences real corporate investment by reducing enterprise uncertainty and alleviating financing constraint. The heterogeneity analysis indicates that OGD exerts a more pronounced investment promotion effect on non-state-owned enterprises, without political affiliations, regions characterized by intense government intervention, and areas exhibiting low social trust. This study contributes both conceptual insights for advancing the real economy with higher quality and practical recommendations to support the modernization of national governance structures and administrative effectiveness.

While observational studies have long suggested a positive correlation between social relationships and online transactions, surprisingly little research demonstrates a causal link. Effects identified in observational data generally conflate the Information effect, which bears the counterfactual causal interpretation, with the Homophily/environment effect. Against this background, this study conducted a pioneering a randomized field experiment design to isolate the Information effect of friendship disclosure from confounding homophily factors. We exploit a rare opportunity to conduct a field experiment on a large Chinese online second-hand platform, in which we manipulate buyer and seller’s awareness of their preexisting friendship ties. We provide the first empirical evidence that the effect of revealing friendship information between transaction parties turns out to be insignificant. We demonstrate that reliance on observational estimates of the “total effect” of friendship significantly overstates the benefits of providing friendship information in online marketplaces. Our findings contribute to a better understanding of social commerce and highlight the potential fallacy of relying on observational data in business studies.