This paper studies how air pollution shapes offline cashless spending in China. Using monthly transactions from 118,698 merchants in 332 cities from 2019 to 2023, we find that higher pollution raises cashless spending. Instrumental variable and regression discontinuity designs confirm a causal effect. The increase comes mainly from more frequent but smaller purchases and greater participation by new customers. Spending also rebalances from postponable durables toward high-frequency, proximity-based categories, while durables respond little. These results uncover a behavioral channel whereby poor air quality shifts the margins and the composition of offline cashless commerce.

This paper investigates how foreign institutional investors, specifically Qualified Foreign Institutional Investors (QFIIs), influence the investment strategies of Chinese mutual fund management companies (FMCs) in which they hold shares. By analysing panel data from 1,766 mutual funds managed by 44 foreign-invested FMCs in China between 2005 and 2021, we explore whether QFII-invested FMCs (Q-FMCs) learn more from their domestic counterparts (D-FMCs) than other foreign-invested FMCs (NQ-FMCs). Our findings show that Q-FMC-managed mutual funds exhibit portfolio allocations more closely aligned with local DFMCs than those managed by NQ-FMCs. This imitation is particularly pronounced when selecting new stocks, enhancing portfolio performance, but not when rebalancing existing positions. Additionally, Q-FMCs trade more actively than NQ-FMCs. Robustness checks confirm these results across various ownership structures, fund characteristics, market conditions, and regulatory changes. These findings highlight the dual role of QFIIs as both investors and learners in China’s evolving financial landscape, offering insights into how foreign capital integrates into emerging mutual fund markets, informing regulatory policy aimed at fostering cross-border financial development.

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.

This study examines the impact of stock market returns on the incidence of hypertension and related outpatient visits using daily data from both regular and major-illness outpatient visits for hypertension. We find that the number of hypertension consultations increases significantly as the stock market declines. Specifically, the effect of market downturns on outpatient visits is more prominent among the seniors and those with poor baseline health. While ambient temperature has a relatively weak effect on regular outpatient visits for hypertension (ROV), it explains a greater share of the variation in major-illness outpatient visits for hypertension (MOV). Both sets of findings suggest that the health effect of stock market volatility is immediate and transient. Using monthly data on MOV, we also find a significantly negative association between MOV and stock market returns, especially during periods of extreme volatility such as market crashes. These findings suggest that stock price declines may increase outpatient visits for hypertension through psychological stress or wealth-loss channels.

Traditional decision tree models, which rely exclusively on numerical variables, often encounter difficulties in handling high-dimensional data and fail to effectively incorporate textual information. To address these limitations, we propose the Integrated Multivariate Segmentation Tree (IMST), a comprehensive framework designed to enhance credit evaluation for small and medium-sized enterprises (SMEs) by integrating financial data with textual sources. The methodology comprises three core stages: (1) transforming textual data into numerical matrices through matrix factorization; (2) selecting salient financial features using Lasso regression; and (3) constructing a multivariate segmentation tree based on the Gini index or Entropy, with weakest-link pruning applied to regulate model complexity. Experimental results derived from a dataset of 1,428 Chinese SMEs demonstrate that IMST achieves an accuracy of 88.9%, surpassing baseline decision trees (87.4%) as well as conventional models such as logistic regression and support vector machines (SVM). Furthermore, the proposed model exhibits superior interpretability and computational efficiency, featuring a more streamlined architecture and enhanced risk detection capabilities.

Assessment in vocational education institutions is frequently organized around performance metrics—graduation rates, employment outcomes, and satisfaction scores—gathered too tardily to avert institutional dysfunction. In increasingly unstable policy situations, these models have become precarious: they quantify collapse more frequently than they avert it. This paper presents fuse logic as an innovative mechanism for risk-responsive governance in technical and vocational education and training (TVET). Utilizing systems control theory and the analogy of circuit breakers, fuse logic is a threshold-sensitive, dynamically activated assessment paradigm designed to disconnect institutional activities prior to complete failure. The research formulates a four-stage model—situational sensing, threshold definition, fuse activation, and adaptive reconfiguration—and implements it in a simulated scenario reflecting Chinese TVET trends. When critical metrics surpass risk thresholds (e.g., dropout rate, employment mismatch), fuse logic triggers systematic program shutdowns, stakeholder consultations, and conditional reintegration procedures.This study's contribution is in redefining evaluation from measurement to protection. It advocates a governance framework that permits temporary disconnection to maintain system integrity. Fuse logic enhances conventional quality assurance frameworks by providing an integrated, failure-tolerant layer of organizational resilience. The report concludes with a discussion on transferability, ethical considerations, and prospective avenues for implementation across varied educational systems.

We construct the China Crude Oil Volatility Index (CNOVX)—the first model-free, optionimplied measure of forward-looking oil price risk for China—using INE crude oil options from 2021 to 2024 and an adapted CBOE methodology that accounts for sparse strike availability via smooth interpolation and extrapolation. Our results show that CNOVX increases with trading activity in the futures market, declines with option volume, and is strongly predicted by the 30-day realized variance of the SC crude oil futures contract. External shocks, including the Russia–Ukraine conflict and the Geopolitical Risk Index, significantly elevate CNOVX levels. During the COVID-19 pandemic, mortality risk intensifies the volatility-amplifying role of futures trading and strengthens the volatility-dampening effect of options, while confirmed case counts have weaker influence. We further document a pronounced asymmetric leverage effect: negative futures returns raise CNOVX more than positive returns of equal size. However, volatility feedback effects are negligible, as changes in implied volatility respond primarily to contemporaneous market conditions. Overall, CNOVX serves as a timely and informative benchmark for monitoring risk in China’s evolving crude oil derivatives market, with valuable implications for investors, hedgers, and policymakers.

In this paper, we investigate alternative one-factor and two-factor continuous-time models with both affine and non-affine variance dynamics for the Chinese options market. Through extensive empirical analysis of the option panel fit and diagnostics, we find that it is necessary to include both the non-affine feature and the multi-factor structure. For performance evaluation, we examine various measures from both aggregate and dynamic perspectives. Our results are statistically significant.

This paper examines the interactional phenomenon where an institutional complainee initiates a complaint and becomes a co-complainant with their original complainant against a third party that is proposed to have caused grievances to both participants. Institutional complainees initiate their third-party complaints when their complainants repeatedly refuse to affiliate with their attempts to shift responsibility or their proposed solutions. This shift from being the complainee to being a co-complainant is regularly accomplished through practices in which the institutional complainee: 1) produces implicit counter-complaints; 2) partitions complainants and themselves as sharing similar identities; and 3) highlights and upgrades their own grievances. Once complainants affiliate with their complaints, institutional complainees attempt to end the complaint sequences. The interactions end with a sense of solidarity sustained between the participants, even though no satisfying solutions are offered to the original complainants. The findings suggest that institutional actors can make relevant their noninstitutional identities and go against what is expected of them as institutional actors to achieve the institutional task of directing blame away from their institutions. Recorded phone conversations between local residents and various institutional actors during COVID-19 lockdowns in China serve as data for this study.

Climate change presents a global threat, prompting nations to adopt low-carbon development pathways to mitigate its potential impacts. However, current research lacks a comprehensive framework capable of integrating multiple variables and providing dynamic optimization capabilities. This article focuses on designing pathways for developing a low-carbon economy to tackle climate challenges. Specifically, we construct a low-carbon economy model that incorporates economic, environmental, social, energy, and policy factors to analyze the drivers of economic growth and carbon emissions. We utilize economic model predictive control and tracking model predictive control to optimize development pathways aligned with various low-carbon targets, creating and validating a comprehensive framework for low-carbon policy design using historical data from China. This study highlights significant advantages in analyzing low-carbon pathways through advanced techniques like hierarchical regression and model predictive control, providing a robust framework that enhances our understanding of causal relationships within the LCE system, captures system feedback, dynamically optimizes pathways, and accommodates diverse policies within a comprehensive low-carbon economy system.