Search Results (295)

The rapid development of digital technology is reshaping the global economic landscape. However, its impact on firms’ capacity utilization rate (CUR), particularly through technological innovation, remains unclear. This study investigates this issue by developing an endogenous growth model that connects digital technology to CUR. The empirical analysis is based on data from Chinese A-share manufacturing firms. The methods employed include quantile regression, instrumental variable techniques, and various tests to explore underlying mechanisms. CUR is calculated using a special model that looks at random variations, and digital transformation is assessed using text analysis powered by machine learning. The findings indicate that digital transformation significantly enhances CUR, especially for firms with average capacity utilization levels, but has a limited effect on low- and high-end firms. Moreover, technological innovation mediates this relationship; however, factors like “double arbitrage” (involving policy and capital markets) and “herd effects” tend to prioritize quantity over quality, which constrains innovation potential. Improvements in CUR lead to enhanced firm performance and productivity, generating industry spillovers and demonstrating the broader economic externalities of digitalization. This study uniquely applies endogenous growth theory to examine the role of digital transformation in optimizing CUR. It introduces the “quantity-quality” technology innovation paradox as a crucial mechanism and highlights industry spillovers to address overcapacity while offering insights for fostering sustainable economic and social development in emerging markets. Full article

As the fundamental physical carrier for human production and socio-economic endeavors, enhancing urban land green use efficiency (ULGUE) is crucial for realizing sustainable development. To effectively enhance urban land green use efficiency, this study systematically examines the intrinsic relationship between industrial policies and ULGUE based on panel data from 286 Chinese cities (2010–2022), employing an integrated methodology that combines the Difference-in-Differences (DID) model, Super-Efficiency Slacks-Based Measure Data Envelopment Analysis model, and ArcGIS spatial analysis techniques. The findings clearly demonstrate that the establishment of the “Made in China 2025” pilot policy significantly improves urban land green use efficiency in pilot cities, a conclusion that endures following a succession of stringent evaluations. Moreover, studying its mechanisms suggests that the pilot policy primarily enhances urban land green use efficiency by promoting industrial upgrading, accelerating technological innovation, and strengthening environmental regulations. Heterogeneity analysis further indicates that the policy effects are more significant in urban areas characterized by high manufacturing agglomeration, non-provincial capital/non-municipal status, high industrial intelligence levels, and less sophisticated industrial structure. This research not only provides valuable policy insights for China to enhance urban land green use efficiency and promote high-quality regional sustainable development but also offers meaningful references for global efforts toward advancing urban sustainability. Full article

From the perspective of credit ratings, this paper studies the impact of air pollution on corporate credit costs and the impact mechanism. Based on 2007–2022 data on A-share listed companies in the Chinese capital market, this paper uses a two-way fixed effects model to examine the impact of air pollution on corporate credit costs and the impact mechanism. The results show that air pollution increases the credit costs for enterprises because air pollution affects the sentiment of rating analysts, leading them to give more pessimistic credit ratings to enterprises located in areas with severe air pollution. The moderating effect analysis reveals that the effect of air pollution on the increase in corporate credit costs is more pronounced for high-polluting industries, manufacturing industries, and regions with weaker bank competition. Further analysis reveals that in the face of rising credit costs caused by air pollution, enterprises tend to adopt a combination strategy of increasing commercial credit financing and reducing the commercial credit supply to cope. Although this response behavior alleviates corporations’ own financial pressure, it may have a negative effect on supply chain stability. This paper provides new evidence that reveals that air pollution is an implicit cost in the capital market, enriching research in the fields of environmental governance and capital markets. Full article

In light of the persistent environmental degradation driven by fossil fuels, developing new energy sources is essential for achieving sustainability. The recent surge in electric vehicle adoption has underscored the significance of new energy batteries. However, the supply chains of new energy battery manufacturers face multiple sustainability risks, which impede sustainable practice adoption. To tackle these challenges, leanness philosophy is an effective tool, and Industry 5.0 enhances its efficacy significantly, further mitigating sustainability risks. This study integrates the supply chain, leanness philosophy, and Industry 5.0 by applying quality function deployment. A novel four-phase hybrid MCDM model integrating the fuzzy Delphi method, DEMATEL, AHP, and fuzzy VIKOR, identified five key sustainability risks five core leanness principles, and eight critical Industry 5.0 enablers. By examining a Chinese new energy battery manufacturer as a case study, the findings aim to assist managers and decision-makers in mitigating sustainability risks within their supply chains. Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

Achieving the dual carbon goal and addressing escalating environmental challenges requires that manufacturing enterprises in China must pursue sustainability via green innovation strategies. A key rationale for green innovation is to overcome boundaries and acquire knowledge through boundary-spanning search. Additionally, leveraging artificial intelligence (AI) capabilities provides technical support throughout the innovation process. Thus, both boundary-spanning search and AI capabilities are crucial for achieving sustainability objectives. Drawing on organizational search and knowledge management theories, this paper aims to analyze how dual boundary-spanning search affects sustainability performance and green innovation. It also examines the moderating role of AI capabilities and constructs a moderated mediation model. We analyzed questionnaire data collected from 171 Chinese manufacturing companies over a 13-month period, employing hierarchical regression and bootstrap sampling methods using SPSS 27.0. Our findings reveal that both prospective and responsive boundary-spanning searches significantly enhance corporate sustainability performance. Furthermore, green innovation acts as a positive partial mediator between dual boundary-spanning search and corporate sustainability performance. Notably, AI capabilities positively moderate the relationship between dual boundary-spanning search and green innovation. They also strengthen the mediating effect of green innovation on the link between dual boundary-spanning search and corporate sustainability performance. Based on these findings, more resources should be allocated to boundary-spanning search while encouraging enterprises to pursue green innovation and develop AI capabilities. These efforts will provide robust support for sustainability performance in the manufacturing sector. Full article

With the rapid advancement of artificial intelligence and smart manufacturing technologies, the penetration of industrial robots into Chinese markets has profoundly reshaped the structure of the labor market. However, existing studies have largely concentrated on the employment substitution effect and the diffusion path of these technologies, while systematic analyses of how industrial robots affect labor resource allocation efficiency across different regional and industrial contexts in China remain scarce. In particular, research on the mechanisms and heterogeneity of these effects is still underdeveloped, calling for deeper investigation into their transmission channels and policy implications. Drawing on panel data from 280 prefecture-level cities in China from 2006 to 2023, this paper employs a Bartik-style instrumental variable approach to measure the level of industrial robot penetration and constructs a two-way fixed effects model to assess its impact on urban labor misallocation. Furthermore, the analysis introduces two mediating variables, industrial upgrading and urban innovation capacity, and applies a mediation effect model combined with Bootstrap methods to empirically test the underlying transmission mechanisms. The results reveal that a higher level of industrial robot adoption is significantly associated with a lower degree of labor misallocation, indicating a notable improvement in labor resource allocation efficiency. Heterogeneity analysis shows that this effect is more pronounced in cities outside the Yangtze River Economic Belt, in those experiencing severe population aging, and in areas with a relatively weak manufacturing base. Mechanism tests further indicate that industrial robots indirectly promote labor allocation efficiency by facilitating industrial upgrades and enhancing innovation capacity. However, in the short term, improvements in innovation capacity may temporarily intensify labor mismatch due to structural frictions. Overall, industrial robots not only exert a direct positive impact on the efficiency of urban labor allocation but also indirectly contribute to resource optimization through structural transformation and innovation system development. These findings underscore the need to account for regional disparities and demographic structures when advancing intelligent manufacturing strategies. Policymakers should coordinate the development of vocational training systems and innovation ecosystems to strengthen the dynamic alignment between technological adoption and labor market restructuring, thereby fostering more inclusive and high-quality economic growth. Full article

At present, there is a lack of in-depth knowledge of the effects of heavy metal-related industries (HMIs) in China on the environment. Hunan Province, as a representative gathering place of HMIs, is among the regions in China that are the most severely polluted with heavy metals. This paper selected Hunan Province as the study area to analyze the development trend, characteristics of pollution emissions, and environmental impacts of seven HMIs based on emission permit information data from Hunan Province. The results of this study show that (1) from 2000 to 2022, the number of heavy metal-related enterprises in Hunan Province increased overall. Among the seven industries, the chemical product manufacturing industry (CPMI) had the largest number of enterprises, whereas the nonferrous metal smelting and rolling industry (NSRI) had the highest gross industrial product (27.6%). (2) HMIs in Hunan Province had significant emissions of cadmium (Cd), arsenic (As), and hydargyrum (Hg) from exhaust gas and wastewater. Heavy metal-related exhaust gas and wastewater outlets from the NSRI constituted 43.9% and 35.3%, respectively, of all outlets of the corresponding type. The proportions of exhaust gas outlets involving Cd, Hg, and As from the NSRI to total exhaust gas outlets were 44.27%, 60.54%, and 34.23%, respectively. The proportions of wastewater outlets involving Cd, Hg, and As from the NSRI to total wastewater outlets were 61.13%, 57.89%, and 75.30%, respectively. (3) The average distances of heavy metal-related enterprises from arable land, rivers, and flooded areas in Hunan Province were 256 m, 1763 m, and 3352 m, respectively. Counties with high environmental risk (H-L type) were situated mainly in eastern Hunan. Among them, Chenzhou had the most heavy metal-related wastewater outlets (22.7%), and Hengyang had the most heavy metal-related exhaust gas outlets (23.1%). The results provide a scientific basis for the prevention and control of heavy metal pollution and an enhancement in environmental sustainability in typical Chinese areas where HMIs are concentrated. Full article

This study aims to reveal the influence mechanism of innovation competition on the high-quality development of the manufacturing industry in Chinese local governments. Additionally, the study provides a theoretical basis for understanding how governments’ investment in science and technology breaks through key technological bottlenecks, enhances the innovation ability of enterprises, and promotes the high-quality development of the manufacturing industry. Based on balanced panel data of 269 prefecture-level and above cities in China from 2008 to 2021, the entropy value method is used to construct a comprehensive evaluation index of manufacturing development quality, and a two-way fixed-effect panel model is employed for the empirical analysis. The findings reveal that (1) for every 1% increase in local government investment in science and technology, the manufacturing high-quality development index will increase by 0.261%, indicating that local governments’ innovation competition significantly promotes the quality of manufacturing development; (2) enterprise innovation capacity plays a mediating role between government competition and manufacturing quality improvement; (3) the combined mechanism of innovation drive and promotion tournament results in a significant spatial strategic interaction of local governments’ innovation competition and a positive spillover effect on neighboring regions. Therefore, this study suggests that local governments implement different science and technology innovation investment strategies to optimize the allocation of innovation resources according to the regional manufacturing technology level. Full article

The Belt and Road Initiative (BRI) prioritizes infrastructure investment to enhance regional connectivity and foster sustainable economic development. Therefore, this empirical study aims to examine the impact of the BRI, specifically through Chinese foreign direct investment (CFDI) on sustainable growth in Bangladesh. The study employs the Mann–Kendall trend analysis and the generalized method of moments (GMM). For the Mann–Kendall trend analysis, sectoral FDI and output data from four major industrial sectors, obtained from Bangladesh Bank and CEIC for the period 1996–2020, are used to analyze trends in industrial development. Additionally, to assess the BRI’s role in sustainable development, this study compares green gross domestic product (GGDP) and gross domestic product (GDP) using a GMM analysis of CFDI inflows across 16 industrial sectors from 2013 to 2022, sourced from various databases. Findings reveal that CFDI significantly contributes to domestic industrial growth, particularly in the manufacturing and construction sectors. Although Bangladesh joined the BRI in 2016, a notable surge in CFDI appears from 2011–2012, partially driven by Bangladesh’s economic liberalization policies, and reflects early strategic investment consistent with China’s expanding economic diplomacy, which was later formalized under the BRI framework. The two-step system GMM results demonstrate that CFDI has a stronger impact on GGDP (0.0350) than on GDP (0.0146), with GGDP showing faster convergence (0.6027 vs. 0.1800), highlighting more robust and rapid sustainable growth outcomes. This underscores the significant Chinese investment in green sectors in Bangladesh. The study also demonstrates that the BRI supports the achievement of Sustainable Development Goals (SDGs) 7 (green energy) and 9 (sustainable infrastructure). These insights offer valuable direction for future research and policy, suggesting that Bangladesh should prioritize attracting green-oriented CFDI in sectors like energy, manufacturing, and construction, while also strengthen. Full article

Under the impetus of the global urbanization, the synergistic relationship between smart city policies and green innovation capabilities has emerged as a critical agenda for achieving sustainable development goals. While existing studies have explored the techno-economic effects of smart cities, systematic evidence remains scarce regarding their pathways and heterogeneous impacts on green growth. This study investigates the influence of smart city pilot policies on urban green growth trajectories and their heterogeneous characteristics. Leveraging panel data from 293 Chinese prefecture-level cities, we employ a multi-period difference-in-differences (DID) model with two-way fixed effects to control for unobserved city-specific and time-specific factors, complemented by robustness checks including parallel trend tests, placebo tests, and alternative dependent variable specifications. Data sources encompass the China City Statistical Yearbook, CNRDS, and CSMAR databases, covering core metrics such as green patent applications and grants, industrial upgrading indices, and environmental regulation intensity, with missing values being addressed via mean imputation. The findings demonstrate that smart city pilot policies significantly enhance green innovation levels in treated cities, with effects exhibiting pronounced spatial and resource-based heterogeneity; there are notably stronger impacts in non-resource-dependent cities and eastern regions. Mechanism analysis shows that policies are driven by a dual effect of industrial upgrading and environmental regulation. The former is manifested by the high substitution elasticity of the digital economy for traditional manufacturing, while the latter is reflected in the rising compliance costs of polluting enterprises. This research advances a cross-nationally comparable theoretical framework for understanding green transition mechanisms in smart city development while providing empirical benchmarks for policy design in emerging economies. Full article

Low-carbon restructuring serves as a critical strategy for enterprises to achieve the “dual-carbon” target and foster sustainable development, whereas supply chain resilience is essential for maintaining competitiveness in complex environments. Based on the data of Chinese A-share listed companies in the manufacturing industry from 2011 to 2023, this paper empirically examines the relationship between low-carbon restructuring, R&D investment, and supply chain resilience. This study reveals a U-shaped relationship between low-carbon restructuring and supply chain resilience, with an inflection point at approximately 2.34. R&D investment significantly strengthens supply chain resilience and positively moderates the relationship by accelerating technological synergies and optimizing resource allocation. Further analysis shows that heavily polluted industries face more pressure in the early stage of low-carbon restructuring compared to non-heavily polluted industries, but R&D investment has a more significant moderating effect on heavily polluted industries. The prediction results based on the Holt–Winters model show that the level of low-carbon restructuring in China’s manufacturing industry will increase steadily in the next seven years, with an average annual growth rate of about 0.021. These new findings are important for managers and researchers to improve supply chain resilience during the low-carbon transition process. Full article

The advancement of digital technology is driving high-quality and sustainable development in China’s manufacturing sector. This study investigates the role of digitalization in this transformation, using provincial panel data from China spanning 2011 to 2022. A comprehensive theoretical framework is developed to examine both the direct effects of digitalization and its indirect pathways through innovation factor mobility and industrial structure upgrading. The results show that digitalization significantly enhances high-quality manufacturing development, supported by robust empirical evidence. Mechanism analysis confirms that digitalization accelerates innovation diffusion and structural optimization, promoting sustainable industrial transformation. Heterogeneity analysis reveals stronger effects in central and western regions, and in areas with lower economic development and human capital levels. The findings provide valuable insights for aligning digitalization strategies with sustainable development objectives, particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). While grounded in the Chinese context, this study offers lessons for other emerging economies pursuing sustainable industrial modernization. Full article

The digital economy has profoundly reshaped industrial organizational structures and the spatial distribution of cooperative agglomerations in the manufacturing and productive service sectors. To support the coordinated and sustainable development of China’s industries, it is essential to clarify how the digital economy influences industrial cooperative agglomeration. This study first constructs a comprehensive index system capturing the quality, quantity, and synergy of industrial cooperative agglomeration, enabling an evaluation of collaborative agglomeration levels across 30 Chinese provinces. Second, the relationship between the digital economy and industrial collaborative agglomeration is examined using both static and dynamic spatial panel models. Finally, the paper investigates regional disparities in this relationship across eastern, central, and western China. The results reveal the following findings: (1) The digital economy has a significant inhibitory effect on industrial collaborative agglomeration overall. (2) Dynamic spatial lag model results show an inverted U-shaped relationship, where the digital economy initially promotes but later inhibits industrial agglomeration, with notable temporal lags and spatial spillover effects. (3) In eastern China, digital economy growth suppresses local agglomeration while promoting it in neighboring regions; in the central region, it enhances local agglomeration but dampens it in adjacent areas; and in the western region, the relationship is nonlinear and U-shaped. Full article

China’s leading development of a complete battery value chain for electric vehicles (EVs) is restructuring the global automotive sector. In contrast with the normal point of view, which emphasizes the role of industrial policy, this article argues that the competitive advantage of China’s EV battery industry lies in firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork carried out at an EV battery cluster in south China, this paper identifies the Chinese EV battery industry’s core competency and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability, which allows them to supply vehicle manufacturers (OEMs) with lithium-ion batteries of stable and consistent quality at competitive prices. This competency is acquired by firms through technological learning at the workshop level while making use of the experiences they have accumulated while mass producing batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge of battery manufacturing on a large scale is also facilitated by the local industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings have significant implications for battery and car makers in global competition as well as for national and local governments which aim to promote EV battery development. Full article