Mergers and acquisitions (M&A) are widely regarded as a critical instrument for industrial consolidation and capacity rationalization. Utilizing a dataset of China’s major national and regional steel enterprises from 1998 to 2013, this study empirically evaluates the efficacy of M&A in resolving over1capacity. We document a striking heterogeneity in outcomes: while M&A generally alleviates overcapacity, this effect is exclusively driven by inter-provincial transactions; intra-provincial mergers show no significant impact. Mechanism analysis reveals that inter-provincial M&A facilitates the transfer of control rights, effectively insulating firms from local government intervention. In contrast, intra-provincial mergers are undermined by local intervention through mechanisms of "internal control" and "external defense." Furthermore, despite the reduction in capacity, we find that inter-provincial mergers suffer from incomplete post-merger integration. There is a persistent lack of governance coordination, and no convergence in capacity management performance is observed between the merging entities over time. These findings offer critical policy implications for mitigating local protectionism and enhancing integration efficiency to resolve industrial overcapacity.

This paper explores the impact of the Integrated Government Service Platform (IGSP) reform, a large-scale digital regulatory initiative in China, on firms’ investment efficiency. Using a firm-level panel dataset of Chinese listed firms, we find that the IGSP reform significantly enhances firms’ investment efficiency. Mechanism analyses indicate that the IGSP enhances firms’ investment efficiency by improving governance service quality and convergence of governance services across cities within a province, thereby reducing economic policy uncertainty and reinforcing market expectations. The heterogeneity analyses reveal that the effect is more pronounced for firms without political connections, those operating in high business volatility and high-tech industries, and regions with stronger informal institutions. Finally, the IGSP encourages firms to expand their investment activities to other cities within the same province. This finding is fully consistent with the underlying mechanisms identified in this study. Our findings provide novel evidence on the economic consequences of digital regulatory integration and offer policy implications for advancing service-oriented and efficiency-driven governance in developing economies.

Digital technologies are often portrayed as disruptive levers for decarbonisation, yet many digital green initiatives improve transparency and efficiency without redesigning how environmental value is authorised, coordinated, and appropriated. We address this puzzle by theorising green digital business model innovation as the evolution of venture-level configurations under multi-domain selection in policy-steered sustainability transitions. Building on the activity-system view of business models and on innovation-studies, we define business model radicality as coupled reconfiguration across content, structure, and governance rather than technology novelty alone. We further theorise a cross-domain convergence requirement: configurations are retained only when they align simultaneously with institutional role allocations, market interfaces, technological infrastructures, and ecological verification demands. A comparative longitudinal study of three blockchain-enabled initiatives in China’s energy-finance nexus shows that radical disintermediation is screened out early when any single domain withholds authorisation or credible settlement, whereas incumbent-compatible configurations are retained through sequencing, interpretive pruning, and boundary-spanning alignment work. The study contributes to innovation studies by treating business models as meso-level selection objects, sharpening configuration-level radicality, and explaining why digital green opportunities often remain incremental in regulated transition systems.

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.

This study constructs a three-dimensional space model based on fund investment styles, using a sample of open-end equity and mixed funds from 2005 to 2021 to measure the degree of style convergence. The research explores how style convergence impacts fund managers’ window dressing behavior. The results indicate that, after accounting for the effects of fund performance, style convergence exacerbates window dressing behavior among fund managers. Specifically, this is reflected in fund managers increasing their holdings in winning stocks and selling off losing stocks, which indirectly highlights the intense competition within China’s open-end fund industry. The findings remain robust after a series of endogeneity and robustness tests. Further analysis reveals that style convergence contributes to the risk of client attrition, thereby intensifying the agency problem within the fund industry. The window dressing effect due to style convergence is particularly pronounced in funds managed by individuals with lower educational backgrounds, lower investment skills, smaller family sizes, and lower institutional investor ownership. The paper offers valuable insights into the agency problems arising from investment style convergence and provides guidance for mitigating fund managers' self-interested behavior.

The disparities in green transition have led to the call for a ‘just transition’. However, the large differences in energy efficiency across different regions have been identified as a primary hazard to the just transition. This study examines whether transferring low-carbon technology can improve the efficiency of energy, enhancing the overall energy efficiency, and marketing a sustainable and equitable energy future. In this paper, we utilize the Undesirable-SE-SBM model to estimate the energy efficiency of China's 30 provinces during 2012 to 2022, and empirically tested the impact of low-carbon technology transfer on the convergence of total-factor energy efficiency by convergence analysis. The results showed that: (1) There is evidence of σ convergence and absolute β convergence in the eastern and western regions, but not in the central region. (2) Low-carbon technology transfer can accelerate the convergence of total factor energy efficiency. Lagging regions that adopt low-carbon technologies can catch up with the advanced regions' level of total-factor energy efficiency. (3) There is regional heterogeneity in the effect of low-carbon technology transfer on the accelerating convergence of total factor energy efficiency. The western region experiences the most significant acceleration, followed by the eastern and central regions.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

Since September 1, 2014, the renminbi (RMB) offshore market in Taiwan has been started on according a cross-strait MOU. A completed RMB market in the Chinese Economic Area therefore has been established. Due to political and economic disruptions, such as the aftermath of the global tsunami, mainland China’s stock market crash and RMB exchange rate reform in 2015, as well as failure of the Service Trade Agreement between Taiwan and mainland China in 2016, the arbitrage opportunities among the three RMB markets can be explored. This paper evaluates the convergence and divergence of RMB market returns by the sigma-convergence (or log t) test, which provides a more precise indication for market return convergence than does the traditional unit root test. Policy implications for the RMB arbitrage are also provided.

This paper applies a variety of short‐run and long‐run time series techniques to data on a broad group of Asia‐Pacific stock markets and the United States extending to 2010. Our empirical work confirms the importance of crises in affecting the persistence of equity returns in the Asia‐Pacific region and offers some support for contagion effects. Post‐Asian financial crisis quantile regressions yield substantial evidence of long‐run linkages between the Shanghai market, the US market and many regional exchanges. Cointegration is particularly prevalent at the higher end of the distribution. Our results suggest that the enormous growth of the Shanghai market in the new millennium has been accompanied by a meaningful level of integration with other regional and world markets in spite of ongoing capital controls.