Abstract:Grounded in the strategic orientation of China’s “double carbon” goal, the manufacturing industry urgently needs to transform environmental regulatory pressures into drivers of green innovation in order to build a sustainable and credible corporate green image. Drawing upon institutional theory and the resource-based view from a stakeholder perspective, this study constructs a logical framework of “environmental regulation→green innovation→green image.” Using panel data from listed firms in China’s heavily polluting manufacturing sectors between 2014 and 2021, the study finds the following: 1) Both command-and-control and market-based environmental regulations enhance firms’ green image, with the former exerting a stronger influence on government-perceived green image, and the latter having a more pronounced effect on non-government-perceived green image; these effects also exhibit non-linear characteristics; 2) Green product, process, and organizational innovation serve as key mediating mechanisms linking environmental regulation and green image; and 3) Ethical leadership positively moderates the impact of environmental regulation on green image, while environmental ethics culture and green human capital further enhance the effect of environmental regulation on green innovation. Overall, this study reveals the internal mechanisms and boundary conditions through which environmental regulation shapes the corporate green image, offering theoretical insights for promoting firms’proactive engagement in green and low-carbon innovations in response to China’s carbon neutrality goals.

Abstract:Exploring the industrial pollution agglomeration effect of regional integration addresses the collaborative implementation of regional coordinated development and ecological civilization construction strategies. By matching the pollution data of Chinese enterprises from 1998 to 2012 to the city level, this paper uses the regression discontinuity method and the difference-in-differences model to investigate the overall effect of regional integration on industrial pollution agglomeration, as well as the differences between incumbent and entrant cities, from the perspectives of the boundary and enlargement effects in China’s Yangtze River Delta (YRD) region. In particular, two mechanisms of the pollution agglomeration effect: economic agglomeration and industrial division are examined. The results show a clear discontinuity in industrial pollution agglomeration (i.e., a positive industrial pollution agglomeration boundary effect) at the boundary of the YRD region. Fortunately, the enlargement of the YRD region significantly inhibits industrial pollution agglomeration, and such an inhibiting effect varies with different types of cities. In detail, the inhibiting effect on the agglomeration of chemical oxygen demand and sulfur dioxide is significantly stronger in incumbent cities than in entrant cities. The mechanism analysis reveals that economic agglomeration promotes industrial pollution agglomeration, whereas the expansion of the YRD region curbs it by reducing economic agglomeration. Meanwhile, although industrial diversification in the YRD region increases industrial pollution agglomeration, the enlargement reduces industrial pollution agglomeration by inhibiting industrial specialization.

Abstract:As emerging technologies such as big data, cloud computing, and artificial intelligence become pervasive in the financial sector, the academic community has extensively investigated the impact of digital finance on firm behavior at the micro level. This study draws on data from listed firms on the Shanghai and Shenzhen A-share markets between 2011 and 2018 to empirically assess how digital finance influences corporate earnings management and to explore the mechanisms underlying this relationship. The results demonstrate that digital finance significantly curtails earnings management, thereby evidencing a strong governance effect. Further examination reveals that this governance impact is more pronounced in regions with stringent financial regulations and among companies possessing robust internal controls. An in-depth analysis of the developmental stages, structural aspects, and regional dispersion of digital finance shows that the governance implications of its “utilization depth” become more marked during periods of dynamic development, also displaying notable regional disparities. Tests of underlying mechanisms indicate that digital finance primarily affects earnings management by enhancing information supervision and reducing financing constraints. The analysis of economic outcomes suggests that the governance effects of rapid digital finance development substantially improve corporate performance and value. This study not only broadens the understanding of corporate governance in the era of big data but also advances research on the interplay between digital finance and earnings management. These insights offer valuable references for optimizing corporate governance structures and refining financial regulatory frameworks amid economic transformation.

Abstract:Facing the challenges of large-dimensional characteristics and the limitations of ad-hoc sparsity assumption, this paper constructs a portfolio of systematic risk factors with stronger asset pricing capability and broader representativeness for the A-share market. Drawing on the implicit factor structure with reference to the cutting-edge research in the field of large-dimensional factor statistical inference, this study conducts a comprehensive deconstruction and analysis of the corresponding factor characteristics and their associated risk compensation structure. The results indicate that: 1) Compared with the CH-〖KG*4〗4 and Fama-French multi-factor pricing models, the large-dimensional high-frequency RP-PCA factor portfolio captures more systematic risk characteristics and exhibits better pricing performance along with a more robust time-varying loading structure; 2) The A-share market contains five stable systematic risk factors. Apart from the market factor, the other four can be approximated by the portfolios of six specific sub-sectors, predominantly represented by the finance sector; 3) Systematic risk in the A-share market is not fully priced, and there is significant asymmetry in risk exposure. Overall, investors tend to be more concerned about risks when the market rises while individual stocks fall, as well as during simultaneous declines in both the market and individual stocks.

Abstract:With the increasing complexity of customer demand, quality improvement faces significant challenges, as personalized and customized production patterns are frequently adopted and large-scale factors are involved in complex production. Aiming at the challenges posed by large-scale factors and small sample sizes in complex and customized production, this paper proposes a bootstrap-based factor screening method, which serves as the first stage of quality improvement. Firstly, a one-order polynomial model is adopted to fit the input-output relationship without assuming a specific distribution for the random term, accommodating the situation of small sample size. Secondly, the sequential bifurcation (SB) procedure is modified according to the distribution-free response model. Thirdly, three significance testing methods for factor effects are proposed, based on Students’ t test and enhanced through bootstrapping and bias correction procedures. Finally, Monte Carlo simulations are employed to compare the proposed significance testing methods with the classic Student’s t test under small sample sizes, and to verify the effectiveness and robustness of the proposed screening methods in both small-scale and large-scale factor scenarios.

Abstract:Unmanned heavy transport vehicles have been widely adopted in various industry scenarios, prompting a shift from individual to fleet intelligence. However, managing a large fleet of unmanned vehicles presents significant challenges, including road congestion, vehicle conflicts, and deadlock situations. This study introduces a novel two-stage online algorithm to address these issues, specifically focusing on automated guided vehicles in container terminals. The algorithm redefines the traditional static vehicle routing problem into two manageable sub-problems. In the initial stage, an improved A* algorithm is designed to identify the most efficient route for each vehicle. This improvement not only aims at optimizing traffic flow but also takes into account the dynamic nature of network workload and the likelihood of conflicts between vehicles. The subsequent stage introduces a dynamic vehicle grouping algorithm followed by a deadlock-free path planning algorithm. These algorithms are crucial for categorizing vehicles into clusters and streamlining the vehicles’movement within their respective clusters. The simulation demonstrates that the new algorithm surpasses traditional static vehicle routing methods by 20〖WTXT〗%〖WTBZ〗 in terms of efficiency and achieves an 80〖WTXT〗%〖WTBZ〗 reduction in computation time. Remarkably, it supports the real-time operation of 100 to 500 vehicles without any incidents of deadlock. The implications of these findings are significant, offering valuable insights for the future implementation of large-scale unmanned vehicle systems in complex industrial environments.

Abstract:In recent years, e-commerce enterprises have begun leveraging digital platforms to explore agricultural supply chain business. This paper considers the loss aversion behavior of farmers and compares three financing models: Bank financing, platform direct financing, and platform guarantee financing. The results show that the platform’s choice of financing model is highly dependent on farmers’ loss aversion and financial constraints. For the platform, when farmers exhibit high loss aversion and low capital scarcity, platform-guarantee financing is optimal; Otherwise, platform-direct financing is optimal. For both the farmers and the whole supply chain, platform-guarantee financing is optimal. Overall, when farmers exhibit high loss aversion and low capital scarcity, platform-guarantee financing can realize a win-win situation for both the platform and the farmers and improve the efficiency of the agricultural supply chain. The results of the study provide an explanation for the practical application of platform-guarantee financing, and offer a reference for managerial decision-making in similar businesses.

Abstract:In response to adverse shocks, managers of money market funds (MMFs) typically resort to fire sales to prevent concentrated redemptions. However, such behavior may trigger risk contagion among MMFs, potentially leading to a systemic crisis. This paper proposes a fire sales risk contagion model based on the “fund-asset” bipartite network and measures the total spillover loss, systemic vulnerability and systemic importance of MMFs under a unified framework. Meanwhile, this paper examines the effects of fund characteristics, fund liquidation strategies, and risky asset prices on systemic risk. Using bond market transaction data and Chinese MMFs’ financial data, our findings reveal that the interconnectedness between MMFs and the bond market serves as a core driver of systemic risk generation. Ignoring spillover effects caused by fire sales significantly underestimates the systemic risk. Higher similarity in asset portfolios leads to increased vulnerability and systemicness of the MMFs. Fund size significantly contributes to the systemicness of the MMF. The price levels of risky assets arising from different liquidation rules account for the disparities in systemic risk exposure among MMFs. These results provide crucial insights for financial regulatory authorities to implement appropriate interventions against risk contagion among MMFs, and enhance the evaluation and supervision framework for important MMFs.

Abstract:Both researchers and practitioners in the areas of inventory management and production planning focus on dynamic lot sizing and forecast horizons. With the rise of intelligent manufacturing, big data, cloud computing and artificial intelligence (AI), new challenges and opportunities have emerged in studies of dynamic lot sizing and forecast horizons. Starting from the study of dynamic lot sizing, forecast horizon and their interface, this paper reviews the current state of the literature in this area and provides detailed comments. Based on these current situations and comments, this paper extracts ten hot topics in the research area on dynamic lot sizing and forecast horizons, and then analyzes the current situations, future research directions, and opportunities of these hot topics one by one.

Abstract:Since online reviews are crucial to the purchasing decisions of consumers, merchants typically manipulate online reviews to increase product sales. Review manipulation is therefore becoming a hot topic in both economics and management literature. However, studies on review manipulation are still in the “pre-paradigm” stage: The research perspectives are diverse, topics are fragmented, and there lacks a widely accepted theoretical framework. On key scientific issues, such as the definition, identification, and governance of review manipulation, there is no consensus. This study reviewed 363 studies published in prestigious journals. Firstly, review manipulation is redefined by considering the evolution of its characteristics and manners. Secondly, this study builds a theoretical framework of review manipulation based on topic analysis. The framework includes six parts: The definition and identification, antecedents, consequences, mechanisms, situational factors, and intervention conditions of review manipulation. Finally, this study summarizes the limitations and controversies and proposes future research directions. Theoretically, this study advances the development of review manipulations. This study has practical implications for the government and e-commerce platforms to effectively curb review manipulation and foster a fair online shopping environment.