This study aims to elucidate the behaviors of the Shanghai and Shenzhen stock exchanges during extreme volatilities—China’s 2015 Stock Market Crash and the 2020 COVID-19 pandemic. Using topological data analysis (TDA), the study identiffes early warning signals within the Shanghai–Hong Kong (SHHK) and Shenzhen–Hong Kong Stock (SZHK) -Stock Connect markets. This timeliness ensures proactive market stabilization and portfolio adjust-ments. The results also reveal that the interconnected market signals are more stable, supporting multidimensional crisis detection and offering valu-able tools for policymakers and investors to effectively mitigate ffnancial risks.

This study examines the evolution and spillover effects of informational efficiency across carbon markets following the launch of China ’s national carbon emissions trading system (NCET). Using a time-varying parameter VAR model, we analyze efficiency transmission among the National Carbon Emission Allowance (CEA), six China’s pilot markets, and the European Union Allowances (EUA). The results reveal substantial heterogeneity in efficiency dynamics. Since early 2023, the CEA and Shenzhen have shown improved efficiency and stability, while the EUA and other pilot markets have experienced declines in efficiency and increased volatility. Despite progress in domestic markets’ efficiency, the EUA remains the primary source of efficiency spillover effects, followed by the CEA, Shenzhen, and Beijing, whereas other pilot markets—particularly Shanghai—act mainly as net recipients. Spillover intensity increases significantly during major regulatory periods, especially around China’s annual “Two Sessions,” highlighting the influence of policy signals on market linkages. These findings offer empirical insights into the time-varying transmission of efficiency under institutional reform and inform the coordinated design of carbon trading policies.

In view of the divergence of existing research results on the relationship between ESG and investment returns, this paper constructs an S-score metric, which comprehensively measures corporate sustainability performance. It further tests the applicability of a sustainability-based investment strategy using this metric in China's A-share market. Using Shanghai and Shenzhen A-shares from May 2016 to April 2024 as the research sample, the S-score is constructed across five dimensions: Profitability, Growth Opportunities, Investment Efficiency, Risk Mitigation, and ESG Performance. The S-score is calculated using Z-score standardization and entropy weighted. Strategy effectiveness was tested through univariate grouping, bivariate grouping, and Fama-Macbeth regression, further examining strategy performance under varying market conditions, holding periods, and information environments. The study finds that the S-score demonstrates significant discriminative power for cross-sectional stock returns. The hedge portfolio based on this metric achieved an annualized excess return of 7.943% after adjusting for the China three-factor (CH-3) model. Its predictive power remains robust after controlling for variables such as market capitalization and book-to-market ratio, delivering significant positive returns across bull and bear markets, extreme pandemic conditions, and holding periods of up to eight years. From a behavioral finance perspective, this paper reveals that explanations such as the gradual diffusion of information and investors' limited attention span help elucidate the profitability of the S-score strategy. The findings demonstrate the effectiveness of Sustainable Investing strategies in China's A-share market, indicating that ESG-integrated factor investing can optimize resource allocation. This research contributes empirical evidence on Sustainable Investing in emerging markets, providing insights for policy formulation and practical implementation while supporting the virtuous cycle between Sustainable Investing and long-termism.

This study employs a Triple-Difference (Triple-DID) model, utilizing balanced panel data at the district level from Beijing, Shanghai, Shenzhen, and Hangzhou between 2018 and 2024, to critically evaluate the effectiveness of the Multi-School Zoning Policy (MSZP) in suppressing the capitalization of educational resources into housing prices and promoting educational equity. The research explicitly accounts for spatial and institutional heterogeneity as well as household strategic behavior.The results indicate that: (1) MSZP significantly reduced the average housing price premium associated with elite school districts by 15.2%, with the strongest effect observed in Beijing and the weakest in Hangzhou; (2) The policy's effectiveness diminishes as the spatial concentration of high-quality educational resources increases, highlighting persistent structural inequalities; (3) In areas characterized by resource monopolization and strong institutional inertia, the policy's suppressive effect on educational capitalization and its gains in educational equity are both constrained.The findings suggest that MSZP alone cannot fully overcome the "spatial lock-in" effect of high-quality educational resources. Achieving lasting equity requires complementary deeper institutional innovations, such as robust cross-district teacher rotation, transparent resource allocation mechanisms, and adaptive zoning algorithms. This research offers quantitative evidence for optimizing policy and institutional tools in the pursuit of comprehensive urban education reform.

Suzhou and Shenzhen are among the top cities in China by GDP, and both have performed exceedingly well in terms of cultivating technological industries and attracting foreign investment. This is in spite of the fact that neither city is a provincial capital nor a centrally administered city like Shanghai and Beijing. Yet, the two cities embody very different administrative models with respect to their relationship with the provincial and central governments. Shenzhen, in particular, has a closer relationship with the central government than almost any non-centrally administered city in China, whereas Suzhou is a city that remains closely in coordination with the provincial government even as its economy has grown by leaps and bounds. This begs the question of which city's model will prevail moving forward: the Shenzhen model, typified by "re-centralization" of power, or the Suzhou model, which represents more of the conventional regional decentralization model that has been prevalent in China since the 1980s. The article attempts to argue that even though Shenzhen is of pivotal importance to the central government's policies, it will remain an outlier for the time being so as to avoid disturbing the delicate balance between the central and provincial governments, barring an unforeseen economic or political crisis.

The role of Market-incentive environmental regulation (MIER) within the framework of environmental governance is patently evident. While extant literature lauds the advantageous outcomes attributed to the environmental protection tax (EPT) which as a representative of MIER, our empirical inquiry presents a contrasting narrative. By employing the sophisticated Difference-in-Difference-in-Difference (DDD) methodology and utilizing data from A-share listed firms in Shanghai and Shenzhen from 2015-2022, our investigation reveals a significant decrease in firms’ total factor productivity (TFP) following the implementation of EPT. Our core assertion is fortified through the discernment of two plausible mechanisms, namely, the production downsizing effect and the production capital crowding-out effect. Building upon this revelation, we delve into the nuanced pathways through which firms can strategically mitigate the impacts of EPT, encompassing the enhancement of human capital, amplification of research and development (R&D) investments, and fortification of overall firm resilience. Heterogeneity analysis discloses a notably heightened impact of EPT on TFP of state-owned enterprises (SOEs), larger enterprises and enterprises located in eastern regions. Ultimately, an approximately cost-benefit analysis conclusively demonstrates that the benefits derived from EPT far surpass the costs incurred by the concomitant industrial output reduction, which further illustrates the rationale for the implementation of EPT.

As China’s population ages, more older workers are participating in the labor market, including a significant number of older migrant workers moving to urban areas. However, surprisingly little research has been done on their destination city and employment patterns. This paper addresses this gap by investigating the impact of city-level and industry specialization on the employment prospects of older migrant workers. Using both individual- and city-level data, we find that unlike prime-age migrant workers, older migrant workers have higher employment probabilities in relatively less-developed lower-tier Chinese cities than in better-developed high-tier cities like Shanghai, Beijing, Shenzhen, or Guangzhou. This phenomenon is driven by industry specialization, particularly in the construction sector, which fosters a dense labor market and facilitates higher job-finding rates. Additionally, construction firms and real estate developers in lower-tier cities are more willing to offer better wages than those in high-tier cities, which aligns with older migrant workers’ relatively moderate education profile and wage preferences over housing costs.

In order to clarify the effect and mechanism of servitization level and digital transformation on the performance of listed sporting goods manufacturing enterprises in China, the index of the degree of digital transformation is constructed based on the data of 31 sporting goods manufacturing enterprises listed on Shanghai and Shenzhen A shares and the New OTC Market in China, taking the proportion of service business income in enterprise operating income as the index of servitization level, by analyzing the semantic expression of national policy related to digital economy and collecting "digital" category keywords in enterprise annual report with the help of crawler technology, then, the influence of servitization level and digital transformation on enterprise performance is discussed, and whether digital transformation plays a moderating effect between servitization and enterprise performance is tested. The results show that the servitization level suppresses the performance of listed sporting goods manufacturing enterprises, and there is a "Servitization Paradox" phenomenon. The degree of digital transformation has a positive U-shaped impact on enterprise performance, and at the same time, digital transformation has a weak positive moderating effect on servitization level and enterprise performance.

We examine the effect of the identification of high-tech enterprises on corporate innovation from the perspective of supplier relationship management. We use data from the Shanghai and Shenzhen A-share listed companies in China from 2007 to 2020 as samples and a time-varying difference-in-differences (DID) method. The results show that the identification of high-tech enterprises significantly promotes corporate innovation. The potential mechanism is that there is a sharp decline in the concentration of suppliers, the occupation of suppliers’ trade credit, and the inventory cost of enterprise after getting the identification of high-tech enterprises. Further analyses show that the enterprise identified as a high-tech enterprise tends to be more innovative due to the improvement of the supplier relationship management, leading to a better operating performance. Overall, our findings indicates a positive implementation effect of the policy of identification of high-tech enterprises. This paper not only contributes to the research about the economic consequences of the identification of high-tech enterprises from the perspective of supplier relationship management, but also enriches the existing literature on the effect of the supply relationship on corporate innovation, and supplier relationship management from the perspective of identification of high-tech enterprises. In summary, this study provides a theoretical basis and policy reference for the evaluation of the implementation effect of the policy of identification of high-tech enterprises, and the strengthening of supplier relationship management .

Taking the national carbon market and seven local carbon markets in China, we use DEA model to measure market efficiency, and then classify them by hierarchical cluster method. Efficiency of the national carbon market and local carbon markets of Beijing, Shenzhen, Hubei and Shanghai are leading, while Guangdong is in the middle; Chongqing and Tianjin are left behind. Room for improvement and scale returns are further analyzed, and suggestions for each carbon market are proposed finally.