Search Results (716)

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study utilizes a sample comprising Chinese A-share listed enterprises over the period from 2012 to 2024 to construct an enterprise climate transition risk index using text analysis methods. It empirically investigates this index’s impact on enterprise green innovation by adopting panel data analysis method to construct a fixed effects model and further examines the moderating roles of institutional investors’ shareholding and enterprise environmental uncertainties in response to climate transition risks. The research findings indicate the following: First, climate transition risks significantly enhance enterprise green innovation. The validity of this conclusion persists following a series of robustness and endogeneity tests, including replacing the explained variable, lagging the explanatory variable, controlling for city-level fixed effects, and applying instrumental variable methods. Second, both institutional investors’ shareholding and enterprise environmental uncertainties exert a significant positive regulatory effect on the relationship between climate transition risk and green innovation, indicating that external monitoring and heightened risk perception jointly enhance enterprises’ responsiveness in driving green innovation. Thirdly, heterogeneity analysis indicates that the positive impact of climate transition risks on green innovation is notably amplified within non-state-owned enterprises and manufacturing enterprises. By examining the dual regulatory mechanisms of ‘external monitoring’ and ‘risk perception’, this study broadens the study framework on the relationship between climate risks and enterprise green innovation, offering new empirical evidence supporting the applicability of the ‘Porter Hypothesis’ within the context of climate-related challenges. Furthermore, it provides valuable implications for policymakers in refining climate information disclosure policies and assists enterprises in developing forward-looking green innovation strategies. Full article

(1) Background. The rapid development of the digital economy provides a new perspective for enhancing industrial chain resilience. This study examines how manufacturing firms’ digitalization affects their industrial chain resilience, drawing on resource dependence and dynamic capability theories, and explores spillover effects on upstream and downstream enterprises. (2) Data and Methods. Using panel data from Chinese listed manufacturing firms (2011–2023), we employ ordinary least squares (OLS) models to analyze the relationship, its mechanisms, and heterogeneity. We further match firms with their suppliers and customers to identify spillover effects. (3) Results. Digitalization significantly improves resilience, particularly by enhancing supply–demand matching and competitive capabilities. Effects are stronger for small, labor-intensive, and high-environment, social and governance (ESG) firms. Bargaining power and governance capability are key channels. Spillover effects are heterogeneous, with a stronger impact on downstream customers. (4) Discussion. The positive impact of digitalization varies by firm characteristics, and spillovers differ across the chain. These findings offer precise insights and policy implications for leveraging digitalization to strengthen industrial chain resilience. Full article

The center of mass is a key parameter characterizing the mass distribution of an object, and its measurement holds significant importance in high-tech fields such as aerospace, the defense industry, and precision manufacturing. With modern engineering demanding ever-increasing spacecraft flight stability, control precision, and precision measurement requirements, the accuracy, efficiency, and adaptability of center of mass measurement have also become research hotspots. This paper systematically reviews current mainstream measurement techniques, including static and dynamic methods, while analyzing their respective advantages and sources of error. By comparing Chinese and non-Chinese achievements in center of mass measurement equipment development and engineering applications, it identifies existing challenges and issues in the field and outlines future trends in center of mass measurement technology. Full article

High-end manufacturing supply chains are increasingly exposed to disruption risks and environmental uncertainty, yet how Industrial Internet (II) empowerment builds supply chain resilience (SCR) and when such benefits are most pronounced remain unclear. Grounded in the resource-based view and ambidextrous innovation logic, this study investigates whether II empowerment—captured by connectivity capability (CC), integration capability (IC), and analytics capability (AC)—enhances SCR through supply chain collaborative innovation (SCCI), including supply chain breakthrough innovation (SCBI) and supply chain incremental innovation (SCII), and whether environmental uncertainty (EU) conditions these relationships. Survey data from 293 Chinese high-end manufacturing firms were analyzed using structural equation modeling and bootstrapped mediation tests, supplemented by moderated regression analysis. The results indicate that CC, IC, and AC all directly and positively affect SCR. CC and AC significantly promote SCBI, whereas the effect of IC on SCBI is not significant; meanwhile, CC, IC, and AC all significantly foster SCII. Both SCBI and SCII are positively associated with SCR. SCBI mediates the effects of CC and AC (but not IC) on SCR, while SCII mediates the effects of all three II dimensions. Furthermore, EU strengthens the impacts of CC, AC, SCBI, and SCII on SCR, whereas the IC × EU interaction is not significant. These findings clarify the innovation-based mechanisms and boundary conditions of II-enabled resilience and offer actionable implications for high-end manufacturers seeking resilient supply chains under uncertainty. Full article

Against the dual demands of green transformation and digital integration in the manufacturing industry, green value co-creation has become a core pathway for enterprises to achieve sustainable development. However, the role of artificial intelligence (AI) in driving green value co-creation remains under explored, especially in the context of Chinese manufacturing. To enrich this research, this study aims to investigate the impact of AI development on corporate green value co-creation and its intrinsic mechanism. This study draws on panel data of listed manufacturing enterprises listed on China’s Shanghai and Shenzhen A share markets spanning the period 2015–2024, and employs multiple regression and negative binomial regression as research methodologies to empirically examine the impact of AI development on corporate green value co-creation and its underlying mechanisms. The results demonstrate that: AI development exerts a significantly positive effect on manufacturing enterprises’ green value co-creation, which is achieved by enhancing firms’ technological spillover capacity and total factor productivity (TFP); financing constraints negatively moderate the aforementioned relationship, while corporate influence plays a positive moderating role; heterogeneity analysis reveals that this impact is more pronounced for enterprises under voluntary regulation, state-owned enterprises (SOEs), and high-pollution enterprises. This study elucidates AI’s role and mechanism in corporate green development at the micro level, provides empirical evidence for related research, and offers practical insights to promote enterprise AI advancement and green value co-creation. Full article

Minimum wage is an important tool for reducing income inequality and supporting social welfare. Consequently, governments around the world have established minimum wage systems. As such, minimum wage policies connect distributive justice with the economy’s capacity to sustain broad-based welfare over time, placing the equity–efficiency trade-off at the center of societal sustainability. However, the micro-level impact of the minimum wage system on firms has always been an important topic for scholars. This study uses panel data from listed Chinese manufacturing firms over a period from 2005 to 2021 to construct an indicator of the minimum wage standards implemented in the firm locations. Employing the multiple linear regression model, this paper empirically examines the effects of minimum wage on labor productivity. The empirical findings demonstrate that minimum wage significantly reduced the sample firms’ labor productivity. Moreover, the negative impact of the minimum wage was primarily concentrated among non-state-owned firms, labor-intensive firms, firms operating in industries characterized by intense product market competition, firms situated in regions with strong legal protections, firms with comparatively low average employee wages, and export-oriented firms. Subsequently, this study delves into the mechanism through which minimum wage negatively affects labor productivity. We find that implementation of minimum wage leads to a reduction in corporate investment, indicating that there is no significant substitution relationship between capital and labor. These adjustment margins provide microfoundations through which statutory wage floors can influence the resilience and inclusiveness of development, indicating that the pace and design of wage increases should balance income protection with the preservation of productive capacity to support sustainable human development—grounded in steady productivity growth, equitable income distribution, and stable firm investment. Our findings contribute to a better understanding of the mechanism through which minimum wage affects labor productivity in theory, while concurrently furnishing policy insights for the optimization of the minimum wage system and maintaining sustainable societal development in practice. Full article

This study examines the role of green information technology capital (GITC) and knowledge source on firms’ green competitive advantage (GCA), with the mediating role of ambidextrous green innovation strategy (AGIS), and the moderating role of technological orientation (TO). Research employed partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze data gathered from 367 respondents from Chinese manufacturing firms. The results revealed a significant direct effect of GITC and knowledge sources on GCA, whereas AGIS partially mediated the relationships. Moreover, TO significantly moderates the impact of GITC on AGIS, whereas it does not moderate the relationship between knowledge sources and AGIS. fsQCA results revealed that a varied combination of GITC, knowledge sources, and AGIS dimensions, along with TO, can lead to high GCA. This study advances the literature by offering insightful perspectives on enhancing GCA by leveraging organizational resources to stimulate AGIS. Full article

As public attention to environmental issues grows, enterprises have begun implementing environment-centered business management. Achieving environmental sustainability requires the participation of all organizational members. This study was conducted in Chinese food manufacturing small and medium-sized enterprises located in Guangdong and Jiangsu provinces, China, and employed a three-wave, time-lagged survey design to collect and match data from team leaders and employees. Hierarchical linear modeling was used to test the cross-level hypotheses, and the indirect effect was assessed using Bayesian multilevel mediation analysis. Using cross-level data from both team leaders and team members, this study examines how green transformational leadership impacts employees’ pro-environmental behavior. In addition, this study examines the mediating role of employee value–action barriers and the moderating role of green brand image. The results indicate that (1) green transformational leadership positively influences employee pro-environmental behavior, (2) employee value–action barriers mediate the relationship between green transformational leadership and employee pro-environmental behavior, and (3) green brand image moderates both the correlation between green transformational leadership and employee pro-environmental behavior and the relationship between employee value–action barriers and employee pro-environmental behavior. These findings provide empirical support for the application of social learning theory and offer managerial insights into how managers can more effectively enhance their employees’ pro-environmental behavior. Future research may further test the robustness and applicability of these relationships in other industries and in different regional and national contexts. Full article

The Chinese government has promoted intelligent, green, and integrated transformation to advance sustainable manufacturing. Central to this strategy is the Service-Oriented Manufacturing Demonstration (SOMD) policy, which aims to deepen manufacturing-service integration. However, its regional spillovers and transmission mechanisms remain unclear. Using China’s county-level panel data from 2015 to 2023, we exploit the staggered national rollout of the SOMD policy as a quasi-natural experiment, employing a staggered difference-in-differences (DID) design. We find that the policy significantly increases both the number and share of new manufacturing firms among total business entries by fostering diversified agglomeration of producer services and reducing manufacturers’ operational costs. This effect is highly context-dependent and occurs only when new producer service firms constitute 60% to 98% of all new service entrants. Moreover, we identify a sustainability trade-off, as it stimulates regional economic activity through manufacturing entry but suppresses overall business formation. These findings suggest that achieving balanced sustainable manufacturing requires moving beyond narrow sectoral growth targets toward fostering an integrated industrial ecosystem that strengthens both manufacturing resilience and service-sector dynamism. Full article

Within the context of China’s green economy aimed at sustainable development, research on the linkage between water resources and industry has garnered considerable attention in the academic community. However, the impact of total body water (TBW) transfer and allocation embodied in the labor force—the primary economic actors—has not been addressed in the economic sector. On methodology, the “EEIO-SDA-SPD-II” (ISSI) model employed in this study encompasses measurements methods, such as an environmentally extended input–output model (EEIO), structural decomposition analysis (SDA), structural path decomposition (SPD), and the imbalance index (II), to explore the crucial paths, driving factors, and distribution of water transfer in TWB spanning 15 Chinese industries between 2007 and 2022. The findings indicate that the shifts in TBW in the manufacturing sector are more discernible when viewed through the lens of social driving factors. The construction business exhibits the most significant increase in male total body water (MTBW), whereas the education sector reflects the rapid growth in female total body water (FTBW). Pertaining to final demand, domestic consumption constitutes the primary contributor category to the increase in TWB, followed by fixed capital formation and exports. According to the SPD results, the construction sector exerts the greatest influence on the transfer of MTBW, while the education sector is characterized by the highest path coefficient value for FTBW. In contrast, the manufacturing sector shows the most pronounced initial path. Based on the imbalance index analysis, agriculture derives the greatest economic gains from TBW input, whereas the education sector yields the lowest. Full article

The recent rise of trade protectionism has complicated the relationship between trade and innovation in some countries. This paper evaluates the impact of U.S. export control on the innovation performance of Chinese manufacturing listed firms. Based on firm-level invention patent data from 2015 to 2023, we find that firms subject to export control substantially expand their patent application activities. However, the quantitative expansion coincides with a deterioration in patent quality, as evidenced by the fast-track granted rate. Further analysis suggests that the divergence between firms’ internal innovation preferences, as reflected in management’s innovation awareness, knowledge width and technological trajectory, and their external R&D investment, underlies the innovation quantity–quality tension. Moreover, the decline in innovation quality is primarily concentrated in technological fields not favored by Chinese industrial policy and among state-owned enterprises, suggesting strategic balancing of innovation decisions in response to government intervention. This study provides further insights into the comprehensive impact of trade shock on innovation and contributes to the literature on the potential technological externalities of the U.S.–China trade conflict. Full article

Green innovation holds significant importance for achieving sustainable development goals. Artificial intelligence has emerged as the primary force behind a new wave of technological and industrial transformation. Using data on Chinese A-share listed manufacturing firms from 2012 to 2023, this study examines the influence of AI policy on corporate green innovation. A chain mediation model is used to identify and test the specific pathway through which this influence operates. The results reveal three findings: First, AI policy has a significantly positive influence on corporate green innovation. Second, industrial agglomeration and knowledge diversity serve as chain mediators, playing the role of transmitting the effect of AI policy to corporate green innovation. Third, AI policy more effectively stimulates green innovation in specific contexts, particularly among SMEs, non-SOEs, high-tech industries, and competitive sectors. This study deepens our understanding of how AI policy can promote corporate green innovation, providing important insights for advancing the coordinated development of green and intelligent manufacturing. Full article

Background: Dulaglutide, a GLP-1-IgG4 Fc fusion, is a long-acting GLP-1 receptor agonist used for type 2 diabetes therapy and other emerging indications. It is produced commercially in Chinese hamster ovary (CHO) cells. The supply of the original drug is now limited in some regions, so creation of highly productive biosimilar manufacturing platforms is important. Methods: Two expression plasmids (p1.1-Tr2-Dul, p1.2-GS-Dul) encoding dulaglutide were sequentially transfected into apoptosis-resistant CHO 4BGD cells. Two-step transgene amplifications with methotrexate (MTX), followed by methionine sulfoximine (MSX) selection and subsequent cell cloning pipeline, were employed. Candidate clonal cell lines were selected using fed-batch culturing and long-term productivity testing. Results: Transfection with a second plasmid encoding glutamine synthetase (p1.2-GS-Dul) and selection with MSX resulted in a further ~30% increase titer in polyclonal population even after MTX-driven amplification. Top clone 4BGD/Dul #73 reached 1.05 g/L product titer in fed-batch culture (qP up to 22 pg·cell⁻¹·day⁻¹) and remained stable for 69 days in medium without MTX/MSX. Size exclusion-high-performance liquid chromatography showed ≥95% monomer; EC₅₀ of the purified GLP-1-Fc in a GLP-1R/CRE-Luc assay was 52 pM for the obtained product versus 76 pM for the original reference drug. Conclusions: The sequential transfection and dual-marker selection approach enables the efficient generation of a robust, high-yield, and glutamine-independent CHO producer, representing a productive strategy suitable for industrial biosimilar development. Full article

In recent years, the impacts of the new scientific and technological revolution on the industrial system and production modes have begun to emerge. Digital transformation is gradually being integrated into the production behaviors of manufacturing enterprises and has become a component of the micro-economy. We aim to find better methods for measuring digital transformation and to analyze its impact on both market performance and innovation performance within manufacturing enterprises. To achieve our goals, we employ an empirical study to examine the influence of digital transformation on market and innovation performance using panel data for Chinese listed manufacturing enterprises from 2012 to 2021. We emphasize digital transformation efficiency and affirm its role in relieving financing constraints. Our study shows that digital transformation effectively improves both the market and innovation performance of manufacturing enterprises. Moreover, it mitigates the financing constraint dilemma, resulting in performance enhancement. Heterogeneity analysis indicates that digital transformation has a more significant promotional effect on the market and innovation performance of large-scale and mature enterprises. Our research offers fresh perspectives for better understanding digital transformation, enriching the body of work on the impact of digital transformation in manufacturing enterprises and its underlying mechanisms. Full article

Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R&D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development. Full article