This paper examines the impact of green mergers and acquisitions (M&As) on firms’ transition to green exports. We develop a “Technology-Qualification” theoretical framework and conduct the empirical analysis using a matched dataset of Chinese listed manufacturing firms and customs records. The findings show that green M&As significantly promote firms’ green exports, and this effect remains consistent across a series of robustness test. Mechanism analysis reveals that green M&As promote green exports through two key channels: green innovation spillovers and green qualification spillovers. Further heterogeneity analysis indicates that the positive impact of green M&As on green exports is more pronounced among firms with stronger operational performance, weaker green foundations, and those involved in processing trade. In addition, green M&As not only stimulate green exports but also prevent the entry of polluting products and reduce the exit of green product, thereby driving a green-oriented dynamic restructuring of firms’ export structure. This paper offers micro-level insights into how firms can navigate the dual challenges of enhancing green production capabilities and overcoming barriers to green trade during their transition to green exports.

Background Economic stability depends on the ability to foresee financial risk, particularly in markets that are extremely volatile. Unbalanced financial data is difficult for traditional Support Vector Machine (SVM) models to handle, which results in subpar crisis detection capabilities. In order to improve financial risk early warning models, this study combines Gaussian SVM with stochastic gradient descent (SGD) optimisation (SGD-GSVM). Methods The suggested model was developed and assessed using a dataset from China's financial market that included more than 2,000 trading days (January 2022–February 2024). Missing value management, Min-Max scaling for normalising numerical characteristics, and ADASYN oversampling for class imbalance were all part of the data pretreatment process. Key evaluation metrics, such as accuracy, recall, F1-score, G-Mean, AUC-PR, and training time, were used to train and evaluate the SGD-GSVM model to Standard GSVM, SMOTE-SVM, CS-SVM, and Random Forest. Results Standard GSVM (76% accuracy, 1,200s training time) and CS-SVM (81% accuracy, 1,300s training time) were greatly outperformed by the suggested SGD-GSVM model, which obtained the greatest accuracy of 92% with a training time of just 180 seconds. Additionally, it showed excellent recall (90%) and precision (82%), making it the most effective and efficient model for predicting financial risk. Conclusion This work offers a new method for early warning of financial risk by combining SGD optimisation with Gaussian SVM and employing adaptive oversampling for data balancing. The findings show that SGD-GSVM is the best model because it strikes a balance between high accuracy and computational economy. Financial organisations can create real-time risk management plans with the help of the suggested technique. For additional performance improvements, hybrid deep learning approaches might be investigated in future studies.

We collect the geographical dataset of bank physical branch in China from 2008 to 2023, obtaining the 261,382 branches. Through careful data processing, we calculate the bank branch closure at city-level and merge it with regional entrepreneurship in China. With the panel dataset at city-industry-year level, we find that bank branch closure (BBC) significantly reduces neighbor entrepreneurship, which is proxied by the number of new firm entry. In mechanism analysis, we document that bank branch closure affects entrepreneurship through the financing channel and mobility channel. We also find that commercial bank branch closure plays a crucial role in affecting entrepreneurship. The reduction effect of BBC is more pronounced for those observations located in geographical intersections, coastal lines. Further, we explore the impact of BBC on the direction of entrepreneurship, showing that there is less new firm formation in manufacture industry after the BBC. In addition, we show that BBC may contribute to the entrepreneurship failure as well. Our findings may shed light on the policy makers, bank owners and those who want to form a new firm.

This paper examines how profits from mutual funds’ participation in initial public offerings (IPOs) shape fund performance, investor flows, and market stability in China. Using comprehensive fund–IPO matched data from 2016 to 2023, we decompose fund returns into an IPO-lottery component and residual performance. At the aggregate level, IPO allocations add 2.05% to annualized excess returns; net of IPOs, excess return is −0.35% per year. At the individual level, the contribution of IPO profits varies substantially across funds and is most pronounced among mid-sized funds, inflating perceived managerial skill. Funds with higher IPO-driven gains attract greater inflows despite the absence of performance persistence, leading to capital misallocation. At the market level, IPO-profit-induced trading (PIT) predicts short horizon price run-ups that dissipate and reverse over subsequent months, while raising both total and idiosyncratic volatility. Overall, IPO profits temporarily enhance reported performance but erode market stability by propagating non-fundamental shocks through secondary markets.

The influence of informal institutions such as history and culture on corporate behavior has been widely recognized, but few studies have been analyzed from the perspective of the ruling party culture. Based on the data of the old revolutionary base areas (ORBA) in China, this paper makes an empirical test on the role of Red Culture in promoting enterprises green innovation. First, this paper finds that the stronger the Red Culture in the region where the enterprise is located, the higher the level of green innovation.Secondly, in the samples with high political sensitivity and less cultural conflict, the promoting effect of Red Culture is more obvious. This paper not only expands the relevant literature on the influence of informal system on enterprise green innovation, but also enriches the research on the influence of Chinese unique culture on enterprise management decision-making.

This study investigates the impact of early leadership experiences on innovation performance in small and medium-sized enterprises (SMEs) in China. Using Enterprise Survey for Innovation and Entrepreneurship in China (ESIEC) cross-sectional datasets, it examines the mediating role of psychological traits and the moderating effect of age in this relationship. The analysis employs fixed effects models to control for regional and industry-specific unobserved characteristics. Results indicate a significant positive relationship between early leadership experiences and innovation, with psychological traits mediating this relationship strongly in younger entrepreneurs. For older entrepreneurs, early leadership has a more direct and stronger impact on innovation. These findings underscore the importance of early leadership development in education phase and suggest that the influence and pathways evolve with age, offering particular insights into the formation and application of social and human capital in the entrepreneurial journey

This paper develops an SOE (small open economy) dynamic general equilibrium model to study the impact of climate change on the current account. By calibrating the model to Chinese economy, we find the following results. First, the current account-output ratio improves in the first decade following an increase in global temperature caused by climate change. It then deteriorates in the following next three decades. Second, the overall current account-output ratio dynamics in response to climate change is neither affected by the types and stringency of climate policies, nor by the levels and growth rates of temperature increases. Third, the impact of an increase in temperature from 1.28 ℃ to 1.5 ℃ relative to the pre-industrial periods (1850-1900) on the current account-output ratio is equivalent to that of an approximate 0.14% permanent decline in TFP. Finally, although the current account-output ratio is likely to deteriorate in the first year when temperature increases instantly, it might not be true if the coefficient of relative risk aversion, or interest rate premium is larger, or debt sensitivity to interest rate is smaller.

As China’s Zero-COVID policy has come to an end and travel restrictions have been removed, the country’s mobility patterns are very likely to become more heterogeneous than during the pandemic. Human mobility is a key mechanism through which economic activities emerge and viruses spread. It can bring both advantages and challenges to cities with different characteristics. This paper investigates intra-city mobility trajectories of 368 Chinese cities within a non-linear time-varying latent factor framework to uncover the evolution of heterogeneity in local travel behavior amidst that China has been approaching the turning point of the post-pandemic new normal. To this end, we compiled a novel panel on a weekly basis, using the latest Baidu Mobility Data and the risk-level data released by the State Council of the People’s Republic of China. We further examine the effects of exposure to high COVID-19 risk in the city on commuting behavior between May 17, 2021 and June 26, 2022. Our results provide stylized facts on stratified local travel across China: first, the 368 cities can be categorized into six clusters based on their mobility dynamics, and second, the gaps in intra-city mobility tend to narrow within each cluster but widen between different clusters. Moreover, exposure to high COVID-19 risk has a stronger impact on home-workplace commuting rates than on dining-, leisure, and recreational travel rates, persistently dampening commuting behavior. In addition, divisions in intra-city travel strength and commuting behavior between western regions and the rest of China are evident. In sum, this paper suggests that the daily life and economic activities which depend heavily on human mobility are recovering at different rates across China.

Digital transformation provides enterprises a catalyst for new growth. This study delves into the correlation between digital transformation and corporate innovation from 2016 to 2020 based on a sample of Chinese A-share listed companies. It seeks to understand the underlying mechanisms and pathways of this relationship. Our research suggests that digital transformation significantly bolsters a company’s innovation capabilities. The mediating mechanisms indicate that the degree of digital transformation in enterprises supports this enhancement in various ways. Firstly, it lowers production costs. Secondly, it strengthens positive market expectations. Thirdly, it aids in managing operational risks effectively. All these factors collectively augment the innovation capacities of enterprises. Further analysis shows that digital transformation can successfully counterbalance the negative influences of economic policy uncertainty on corporate innovation. These insights offer a theoretical basis for elevating the level of digital transformation in enterprises and achieving superior-quality development more effectively.

This paper conducts a regression analysis using data from Chinese listed companies between 2008 and 2023, and finds the following: First, financial support significantly increases firms’ customer dependence; second, financial support indirectly promotes the enhancement of customer dependence by reducing firms’ excess administrative expenses; third, further heterogeneity analysis reveals that the impact of financial support on customer dependence varies significantly depending on whether the firm is audited by a Big Four accounting firm, with the effect being more pronounced in firms not audited by a Big Four firm.