Search Results (898)

Green finance can theoretically direct capital toward low-carbon sectors, but systematic city-level empirical evidence is still limited for the Yangtze River Delta region. Using panel data of 41 prefecture-level cities from 2010 to 2024, this paper employs year-fixed-effects, mediation, and moderation models to examine the impact of green finance on carbon emission intensity. The findings are as follows. First, green finance significantly reduces carbon emission intensity. A one-standard-deviation increase in the green finance index lowers carbon intensity by about 23.6% of the sample mean, and this result is robust. Second, green technology innovation contributes about 30% and industrial structure upgrading contributes about 7%, serving as two key mediating pathways. Third, industrial pollution level positively moderates the abatement effect: the more polluted a city, the stronger the marginal emission reduction effect of green finance. Fourth, the emission reduction effect is more pronounced in low-income cities, while the moderating role of urbanization level is not significant. This paper reveals the transmission mechanisms and boundary conditions of the emission reduction effect of green finance, providing empirical evidence for designing regionally adapted green finance policies in the Yangtze River Delta. Full article

Against the backdrop of accelerating digitalization and intelligent transformation, intelligent manufacturing has emerged as a key driver of green transition in manufacturing. However, evidence on its effects and the mechanisms underlying corporate green innovation remains limited. Using panel data of Chinese A-share manufacturing firms from 2011 to 2023, this study exploits the pilot policy of intelligent manufacturing as a quasi-natural experiment and employs a difference-in-differences (DID) approach. Results indicate that intelligent manufacturing significantly enhances firms’ green innovation, with robust evidence across multiple checks. Mechanism analysis shows that this effect operates through an integrated dynamic capability channel, whereby firms strengthen their adaptive capability, absorptive capability for green knowledge and digital technologies, and innovation capability through technological integration, thereby improving green innovation. Moreover, intellectual property protection strengthens this mechanism by increasing innovation returns and enhancing the capability-to-innovation conversion efficiency. Heterogeneity results suggest stronger effects in non-high-tech firms, non–heavily polluting industries, and technology-intensive firms, reflecting differences in digital readiness and resource reconfiguration capacity. Overall, this study provides causal evidence on the green effects of intelligent manufacturing, clarifies internal mechanisms, and highlights institutional and firm-level heterogeneity, with implications for digital-driven green transformation and policy design. Full article

This study examines when green innovation contributes to pollution abatement by analyzing how financial globalization and different forms of innovation jointly shape ecological pressure across European Union (EU) countries over the period 1992–2021. The findings show that financial globalization consistently increases ecological pressure, with stronger effects at upper quantiles (0.8–0.9). Technological innovation exhibits a nonlinear pattern: general RD increases ecological pressure at lower quantiles (0.1–0.4), but this effect becomes insignificant and then negative at higher quantiles (0.7–0.9). In contrast, environmental innovation (EI) reduces CO₂ emissions at middle and upper quantiles (0.5–0.8), suggesting a stronger environmental contribution under medium-to-high ecological pressure conditions. Overall, the results demonstrate that the environmental impact of innovation depends on both the type of innovation and the prevailing level of ecological pressure. Specifically, general R&D and environmental innovation exhibit different environmental effects across lower and upper quantiles, suggesting that environmentally oriented innovation policies may be more effective under higher ecological pressure conditions. Full article

This study examines whether digital infrastructure contributes to sustainable development by improving green total factor productivity (GTFP)—a comprehensive measure that jointly evaluates economic output and environmental performance—in the Yangtze River Economic Belt. We exploit the staggered implementation of the “Broadband China” pilot policy as a quasi-natural experiment and estimate its effects using panel data for 107 prefecture-level cities from 2010 to 2022. The empirical strategy combines a staggered difference-in-differences design with an event study framework. The baseline results show that the average treatment effect for the full sample is positive but not statistically significant at conventional levels under standard TWFE estimation; however, the Sun–Abraham interaction-weighted estimator confirms a significant positive effect (ATT = 0.080, p < 0.05), and the Goodman-Bacon decomposition shows that the TWFE estimate is driven primarily by clean comparisons (91% weight, 0% negative weights). Further analysis reveals substantial regional heterogeneity. The estimated effect is significantly positive in the central region (0.171, p < 0.05), positive but not significant in the eastern region (0.097), and negligible in the western region (−0.042). A similar pattern emerges across income groups: digital infrastructure generates significant gains in GTFP in high- and middle-income cities, whereas the effect is not identifiable in low-income cities. These results remain robust to propensity score matching, placebo tests, alternative specifications, and alternative measures. Exploratory mechanism analysis provides limited evidence that technological innovation and industrial upgrading mediate the effect of digital infrastructure on GTFP within the sample period, though the causal interpretation of mediation is constrained by the sequential ignorability assumption. The findings suggest that the environmental returns to digital infrastructure depend on local complementary conditions, especially human capital, institutional capacity, and industrial foundations. These results imply that digital infrastructure policy should be differentiated across regions rather than implemented uniformly. By demonstrating that the environmental returns to digital infrastructure are conditional on local complementary conditions, this study contributes to the sustainability literature by providing a framework for quantifying and monitoring the sustainability impacts of digital infrastructure policies, with implications for sustainable development strategies in developing economies. Full article

Intelligent manufacturing has become a new driving force for the comprehensive green transformation and development of the lithium industry, representing both an intrinsic requirement and a strategic direction for promoting high-quality development in the sector. This study examines whether intelligent manufacturing can effectively enhance the green total factor productivity of the lithium industry from the dual perspectives of internal motivation and external pressure, based on relevant data from Chinese A-share listed lithium companies between 2010 and 2023. The study finds that: (1) Intelligent manufacturing can significantly enhance the green total factor productivity of the lithium industry. (2) Heterogeneity analysis indicates that the level of regional environmental regulations and the intensity of green competition within the industry are positively correlated with the extent of improvement in the lithium industry’s green total factor productivity. (3) Mechanism analysis reveals that intelligent manufacturing influences green total factor productivity through two pathways: green technological innovation and ESG disclosure. Furthermore, the intrinsic incentive effect of green technological innovation is stronger than the extrinsic pressure driven by ESG disclosure. (4) Further analysis reveals that the “Intelligent Manufacturing Pilot Project” policy and the “Comprehensive Green Transformation of Economic and Social Development” policy provide strong support and driving force for the intelligent manufacturing and green development of the lithium industry. Full article

As green and sustainable development has become a central policy objective, identifying new drivers of urban green total factor productivity (GTFP) is of growing importance. This study examines whether data-element development is associated with improvements in urban GTFP and whether this relationship varies across different urban contexts. Using balanced panel data for 270 Chinese prefecture-level and above cities from 2011 to 2021, we construct a city-level data-element development index and employ a two-way fixed-effect framework to conduct the empirical analysis. The results show that data-element development is positively associated with urban GTFP, and this finding remains stable across a series of robustness checks. Further mechanism analyses provide evidence consistent with partial mediation through green technology innovation. The moderation analysis indicates that the GTFP-enhancing effect of data-element development is stronger in cities with higher levels of human capital. Heterogeneity analyses show that the positive effect is more pronounced in Eastern cities, higher-tier cities, and cities with stronger environmental regulation. The findings offer system-oriented policy implications for cities seeking to leverage data elements to improve GTFP, emphasizing coordinated governance across data circulation, human capital, green innovation conversion, and environmental regulation under differentiated urban conditions, thereby supporting more effective pathways for urban green productivity improvement. Full article

China has achieved rapid industrial development, but at the same time, the problems of tightening energy supply and environmental degradation have become increasingly prominent. Improving green total factor productivity (GTFP) has become a central task in China’s industrial transformation. Using industrial input–output data for 30 Chinese provinces from 2008 to 2020, this paper innovatively extends the Hsieh–Klenow framework by introducing land, energy and environmental pollution as input factors, and combines it with a spatial Durbin model to quantify industrial GTFP losses caused by energy misallocation and factor price distortions and to examine their spatial spillover effects. The results show that resource misallocation has generated significant inter-provincial industrial GTFP losses, with loss rates ranging from 13.96% to 32.57%, among which energy misallocation is the most important source. By comparing effective GTFP under a scenario without energy distortions with the observed GTFP, it can be found that eliminating energy misallocation would increase the average level of China’s inter-provincial industrial GTFP by about 40% during the sample period. Spatially, GTFP losses exhibit significant clustering, mainly concentrated in the northwest region, particularly in the Yellow River Basin, while lower losses are observed in the Yangtze River Basin and the southeastern coastal regions. In addition, industrial GTFP losses show significant spatial correlation and spillover effects. Environmental regulation can reduce local GTFP losses, but it may increase losses in neighboring regions through mechanisms such as industrial relocation or pollution spillovers. To support the sustainable development of China’s industry, these findings indicate the need for more efficient energy allocation, faster technological upgrading and talent investment, as well as stricter and more targeted environmental regulation. Full article

As a core policy tool for promoting green economic transformation and high-quality development, green finance has significantly optimized the allocation of resources for corporate green innovation, thereby inevitably influencing the efficiency of green innovation in the manufacturing sector. Using the “Green Finance Policy Pilot Program” as a case study, this study employs a multi-period difference-in-differences (DID) model and robustness tests to examine the impact of green finance policies on the green innovation efficiency of Chinese manufacturing firms. The results indicate that green finance policies help enhance the green innovation efficiency of manufacturing firms. Mechanism analysis reveals that green finance policies enhance firms’ green innovation efficiency by alleviating financing constraints for green innovation, improving the quality of environmental information disclosure, and promoting collaborative green technology innovation. Heterogeneity results indicate that the positive correlation between green finance policies and firms’ green innovation efficiency is particularly pronounced among large-scale firms and firms in regions with high levels of green development. This study not only enriches the literature on the micro-level effects of green finance but also provides valuable insights for governments and firms seeking to enhance green innovation efficiency. Full article

Against the backdrop of frequent adjustments and iterations in global climate policies, the issue of policy uncertainty surrounding corporate energy structure upgrades has become increasingly prominent. A key concern for achieving global green sustainable development is how to efficiently advance corporate low-carbon transition. In view of this, we construct the energy structure low-carbon transition at the enterprise level, and explore the influence and mechanism of climate policy uncertainty on the energy structure low-carbon transition of enterprises from the perspective of enterprise willingness and ability. The research findings indicate: (1) Corporate energy structure low-carbon transition is substantially impeded by climate policy uncertainty, and this conclusion is upheld by a battery of robustness and endogeneity analyses. (2) Climate policy uncertainty inhibits corporate energy structure low-carbon transition by reducing management’s long-term behavior, lowering green technology innovation levels, and weakening effective investment. (3) According to heterogeneity analysis, non-state-owned businesses, areas with lax environmental regulations, and businesses with poor climate risk awareness are more affected by the inhibiting impact caused by climate policy uncertainty. In addition to offering theoretical underpinnings and helpful advice for governments looking to create stable climate policies and enhance climate governance systems, this paper gives fresh perspectives on the fundamental reasoning behind corporate energy structure decarbonization. Full article

As an emerging environmentally sustainable consumption paradigm, digital consumption plays a pivotal role in synergistic pollution and carbon reduction. Based on provincial-level panel data from China covering from 2013 to 2024, this study adopts the two-way fixed effect model, the mediating effect model, and the threshold model to examine the impact and mechanisms of digital consumption in promoting the synergistic effect of pollution and carbon reduction and provide a theoretical framework and practical guidance. The results demonstrate that digital consumption exerts a statistically significant synergistic effect on pollution and carbon reduction, even after endogenous and robustness tests. This effect is more pronounced in regions with supportive digital policy environment, favorable carbon control policy, lower degrees of industrialization, and higher levels of environmental protection. Industrial structure upgrading and green technology innovation are two primary transmission channels through which digital consumption enhances the synergistic effect of pollution and carbon reduction. This effect is significantly stronger when industrial structure upgrading and green technology innovation exceed the threshold values of 3.6253 and 2.0374, respectively. Full article

This study investigates the spatial association network structure of Green Transportation Development (GTD) in China to support coordinated regional development. Based on panel data from 30 major Chinese cities over the period 2011–2020, an entropy weighting method is used to evaluate urban GTD levels, while social network analysis (SNA) and the Quadratic Assignment Procedure (QAP) are employed to identify the spatial network topology, clustering patterns, and driving factors of GTD. The results show that GTD exhibits significant intercity spatial associations. The overall network structure is relatively stable and exhibits a loose hierarchical pattern, with network density fluctuating between 0.232 and 0.277. Shanghai, Yinchuan, and Nanjing play prominent roles in the core–periphery structure. Block modelling further classifies the network into four functional groups: “net spillover,” “bilateral spillover,” “net benefit,” and “broker” blocks. In 2020, the network contained 214 association ties, of which 176 were inter-block ties, indicating evident cross-block spillover effects but relatively weak intra-block communication. The QAP regression results further reveal that geographical distance inhibits network formation, whereas differences in economic development and transport-related employment promote intercity GTD associations; differences in technological innovation exert a negative effect. These findings suggest that policymakers should reduce administrative barriers, formulate differentiated GTD policies, strengthen regional linkages, and promote intercity cooperation based on complementary advantages to improve the overall performance of GTD. Full article

Against the backdrop of economic growth and the transition toward a green, low-carbon economy, the coordinated advancement of digital intelligence and green development has become a crucial pathway to promoting high-quality industrial development. Using panel data from 30 Chinese provinces from 2013 to 2023, this study employs two-way fixed-effects models, mediation models, and threshold regression models to conduct an empirical analysis. The results indicate that, during the sample period, both the level of digital intelligence and industrial green transformation efficiency increased steadily across Chinese provinces, exhibiting significant temporal and spatial heterogeneity. A significant positive relationship is identified between the two variables, with industrial structure and green technological innovation serving as mediating mechanisms. The effect of digital intelligence on industrial green transformation efficiency exhibits a nonlinear pattern, with the provincial average enterprise size functioning as the threshold variable. Furthermore, this effect exhibits significant regional heterogeneity. These findings provide novel empirical evidence on the relationship between digital intelligence and industrial green transformation. Full article

Green total factor productivity (GTFP) plays an important role in urban sustainability. In the context of rapid advances in artificial intelligence (AI), this study examines whether and how the accumulation of AI technologies affects urban GTFP. We construct a city-level AI development index based on patent data. Using panel data on 120 Chinese prefecture-level cities from 2010 to 2023, we employ two-way fixed-effects models, instrumental-variables estimation, and multiple robustness checks to identify the impact of AI on urban GTFP. The results show that AI significantly improves urban GTFP. A one-standard-deviation increase in AI development is associated with an increase of approximately 0.75 in GTFP, which represents a substantial improvement within the distribution of GTFP. Mechanism analysis indicates that this effect operates primarily through green innovation and industrial upgrading. The positive impact is more pronounced in cities with stronger economic and institutional foundations. Full article

Digital infrastructure is increasingly regarded as a key enabler of economic modernization and urban sustainability, but its sustainability implications depend on whether digitalization guides innovation activities toward greener technological directions. Against the backdrop of China’s “dual carbon” goals and the deepening of low-carbon transformation, this study examines the relationship between digital infrastructure development and the green orientation of urban innovation from the perspective of innovation structure. Using panel data for 284 prefecture-level cities in China from 2011 to 2023, we measure the share of green innovation by the proportion of green invention patents in total granted patents, and use broadband Internet access users per 100 residents, denoted as InternetRate, as a proxy for digital infrastructure development. A two-way fixed effects model is employed to investigate the empirical relationship between the two. The results show that digital infrastructure development is significantly negatively associated with the relative share of green innovation within total innovation. This finding remains robust to alternative functional-form specifications, extreme-value treatment, alternative measures of digital infrastructure, and alternative measures of green innovation structure, and remains directionally consistent in a supplementary instrumental-variable test. Decomposition of scale effects indicates that this negative association reflects the relatively faster expansion of non-green innovation rather than an absolute contraction in green innovation, suggesting a structural reallocation pattern within urban innovation activities. Heterogeneity analysis shows that the negative association is mainly concentrated in cities with lower levels of economic development and higher text-based environmental governance attention, and is more pronounced in cities with a lower degree of industrial servitization. Moderation analysis further shows that this negative association becomes weaker in cities with stronger local green fiscal support. Spatial analysis indicates that the share of green innovation exhibits significant spatial dependence; however, the association between digital infrastructure development and innovation structure is mainly localized, with no significant spatial spillover detected. These findings contribute to sustainability research by showing that digital infrastructure does not automatically improve the green composition of innovation and that sustainable digital transformation requires complementary green fiscal support, environmental governance, and industrial upgrading policies. Full article

Amid the dual pressures of the global energy transition and green technology upgrading, corporate environmental responsibility increasingly depends on interactions among firms rather than on isolated firm-level resources. From a systems perspective, this study focuses on the inter-firm green innovation linkages within the new energy sector, where knowledge diffusion, technological learning, and governance signals are jointly shaped by network structure. Using quarterly panel data from 52 listed Chinese new energy firms from 2018Q1 to 2023Q2, we employ the Adaptive Elastic Net Generalized Method of Moments approach to reconstruct a green innovation network from the observed dynamics of the panel data, and examine how firms’ positions within the network affect their environmental responsibility. The results show that the network exhibits a clear core–periphery spillover structure. Inter-firm ties are more likely to form when firms are located in the same province and when target firms have higher green patent citation impact and more executives with environmental backgrounds. Higher network centrality is associated with better corporate environmental responsibility, especially among firms facing intense market competition, state-owned firms, and non-key environmental regulatory units. These findings suggest that green innovation networks can alleviate innovation imbalances and strengthen informal inter-firm governance mechanisms in emerging green industries. Full article