Leveraging artificial intelligence (AI)―a cutting-edge technological tool―to drive the new quality transformation of Chinese manufacturing is a crucial foundation for China’s steady advancement of the new real economy, as well as an inevitable requirement for China to align with contemporary economic and technological trends. This study constructs a multi-sectoral equilibrium model to theoretically analyze the focal points of the new quality transformation in Chinese manufacturing and the impact AI has on it, followed by corresponding empirical tests. The results indicate that (1) AI has a positive impact on the qualitative transformation of China’s manufacturing sector; a one-unit increase in a firm’s AI level leads to a 0.171-unit increase in the sector’s qualitative transformation level. (2) This impact exhibits heterogeneity at the firm, industry, and regional levels. At the firm level, the impact varies depending on firm size, digitalization level, operational performance, internal control strength, and governance quality. At the industry level, the impact varies depending on technology intensity, industrial structure, strategic importance, and green development level. At the regional level, heterogeneity is reflected in geographical location, natural resource endowments, and the degree of urban agglomeration. (3) Artificial intelligence promotes the new quality transformation of Chinese manufacturing through the following mechanisms: reducing time lag costs and transaction costs in market penetration mechanisms; enhancing the quality of cutting-edge factor combinations and key core technologies in advanced innovation mechanisms; and improving resource utilization and operational management efficiency in lean production mechanisms.

Against the backdrop of China’s new carbon neutrality work plan for 2024 and a new phase in the country’s urban development, this study applies a comprehensive evaluation indicator system we have created to evaluate and analyze green and low-carbon development in major Chinese cities. The evaluation system covers five sectors: progress toward carbon neutrality, energy transition, industrial upgrade, green development, and governance effectiveness. It also includes such innovative indicators as comparative advantage in carbon emissions, which reflects fairness and combines scoring by experts and equal weighting. The results show: (1) In 2024, the green and low-carbon development composite index for 337 cities reached an average of 80 points, a significant increase from 2023 (76.7 points), and a noteworthy achievement. (2) Regional disparities in the green and low-carbon development composite index persist. The eastern region led with 83.6 points, followed by the central region (81.6 points), western region (79.0 points), and northeastern region (75.0 points); southern cities (83.3 points) consistently outperformed northern cities (77.4 points), though the gap has slightly narrowed. (3) Pilot cities outperformed non-participating cities significantly. Beijing, Shenzhen, and other pilot cities scored higher, confirming the effectiveness of the pilot initiatives; other cities saw serious unevenness in their green and low-carbon development. (4) The core weaknesses varied from region to region. Northern cities scored notably lower (15.3 points) on progress toward carbon neutrality than southern cities (22.3 points). (5) Transition risks followed certain patterns. Economic scale was positively correlated with the level of green and low-carbon development. Economically underdeveloped cities were trapped in a vicious cycle in which low GDP led to high transition risks, which led to low green and low-carbon development level, which made growing GDP and green and low-carbon development even more difficult.

As an important level in the administrative system, counties have abundant natural and agricultural resources and play a key role in promoting green development strategies. This article uses the entropy method to construct a comprehensive evaluation index system for green and low-carbon development from five dimensions: green innovation, green coordination, green efficiency, green openness, and green sharing. It uses econometric methods such as Moran’s index and kernel density estimation to empirically analyze the level of green and low-carbon development in Chinese counties, differentiation characteristics, and spatial effects. Furthermore, geographically weighted regression model is introduced to explore and analyze the driving factors of various variables on the differences of green development in Chinese counties. The study finds the following: From a temporal perspective, the overall level of green development in Chinese counties shows a fluctuating upward trend; From the perspective of spatial evolution, the green development of Chinese counties presents a regional distribution characteristic of “moderate distribution in west, high in the east and low in west”; The level of green development in counties has a significant spatial positive correlation. The driving factors of economic development level, green technology innovation, and government support mainly have a positive impact on the green development of Chinese counties, while the quality of human resources mainly has a negative impact. The research results emphasize that there is obvious spatial heterogeneity in the impact of each driving factor. By implementing differentiated green development strategies to alleviate resource and environmental problems between counties, deepening the concept of green and low-carbon development, optimizing the industrial layout structure of counties, and adhering to the strategy of innovation driven development, suggestions can help counties achieve sustainable development goals.

Abstract The convergence of digitalization and greening is emerging as a focal point for scholars. Existing research has predominantly examined the linear effects of these two factors at the provincial and municipal levels, with scant attention paid to their non-linear relationship or to the heterogeneity within urban agglomerations. This study employs a panel regression with two-way fixed effects based on data from 280 Chinese cities spanning 2011 to 2019. The results indicate that in the short term, the negative externalities and “digital divide” arising from digitalization may impede urban green development. Over the medium to long run, once digitalization reaches maturity it overcomes these frictions and becomes a net driver of green progress. Specifically, a 1 % increase in the level of digital economic development will elevate the level of urban green development by 0.0159 %. The promotion of urban green development by the digital economy exhibits heterogeneous effects, with key urban agglomerations, economically developed cities in the eastern region, and high-tier cities exhibiting positive effects. The implementation of the policy of “Innovative Cities” has also significantly enhanced the urban green development of pilot cities. Finally, this study proposes policy recommendations to strengthen the digital economy development and implement green development strategies tailored to local conditions.

Evaluation of the spatial effect of urban green development can provide spatial clues for solving urban governance problems. The existing research lacks an assessment of urban green development from the perspective of productivity. In this study, 285 cities in China from 2008 to 2020 were selected as research objects. An indicator system for urban green development was innovatively constructed from a productivity perspective. Meanwhile the spatial autocorrelation effects of its development under the adjacency relationship weight matrix (ARWM), geographical distance weight matrix (GDWM), and economic distance weight matrix (EDWM) were clarified. Spatial Durbin Models (SDM) partial differential method was used to decompose the spatial effects of the influencing factors of urban green development, and the spatial correlation effects and spatial spillover effects of urban green development were clarified. The findings reveal that: (1) From the perspective of spatial correlation, urban green development has global spatial autocorrelation, which indicates that the level of urban green development is affected by at least 13% of cities with similar economic levels, 84% by cities with close geographical distance, and 63% by adjacent cities. (2) From the perspective of spatial spillover effect, urban green development has significant spatial spillover effect under ARWM and GDWM, specifically, the level of economic development under different spatial weight matrices has a significant spillover effect on urban green development. There are spillover effects on the impact of industrial structure, innovation level, infrastructure conditions, government intervention and foreign trade openness on urban green development under the influence of the ARWM. There is a significant spillover effect of environmental regulation on urban green development under the influence of EDWM.

Based on data on energy consumption per unit of GDP in Zhejiang Province from 2000 to 2023, this paper constructs an exponential regression model to quantitatively analyze the evolution trend of green development. The results indicate that energy consumption per unit of GDP in Zhejiang Province exhibits a significant exponential downward trend, with a goodness of fit of 0.9573, reflecting a continuous improvement in energy utilization efficiency. Key policy milestones—such as the “Eight-Eight Strategy” implemented in 2003 and the energy-saving policies introduced during the 12th Five-Year Plan period—are highly consistent with stages of accelerated energy-intensity reduction, highlighting the strong role of policy interventions. Forecasting results suggest that energy consumption per unit of GDP will continue to decline in the future, although the rate of decline is expected to slow, indicating that improvements in energy efficiency are entering a stage of diminishing marginal returns. This study provides empirical evidence for evaluating green development performance in Zhejiang Province and offers quantitative support for future policy optimization.

The neoclassical theory posits that an economic system often presents multiple stable equilibria in response to short-term shocks, rather than a sole equilibrium. Contrary to the conventional β-convergence approach that emphasises the convergence of equilibrium across all nations, the club convergence methodology uncovers the diverse trajectories of green economic growth among different countries. Utilising global green economic growth data from 2010 to 2020, this paper distinguishes four types of 'club convergence' and delves into the potential factors shaping the emergence of these clubs. The results indicate that energy vulnerability, the human development index, and GDP per capita enhance a country's probability of joining high green economic growth clubs, whereas per capita carbon emissions, the unemployment rate, and the urbanisation rate diminish the likelihood of belonging to these high-growth clubs. Additionally, this paper presents substantial evidence on how spatial spillover effects and temporal effects collaboratively impact the formation of various clubs. Through these analyses, this paper furnishes novel theoretical perspectives on the geographical distribution of green economies and their determinants, and advocates for the formulation of differentiated green economy policies in accordance with each country's economic structure and green development level.

This paper focuses on exploring the role of digital finance in corporate technological innovation, how it influences technological innovation, and the manifestation of green development in this process. The study centers on China's listed automotive manufacturing enterprises and is based on data from Chinese A-share automotive manufacturing companies listed from 2011 to 2023, examining the mechanism by which digital finance affects technological innovation and the moderating effect of green development level. The research results obtained in this paper show that: (1) Digital finance can promote technological innovation in enterprises, and the effect is obvious. It achieves this by optimizing the allocation of resources to make them more reasonably distributed and reducing the cost required for enterprise financing. In this way, digital finance can effectively enhance the technological innovation capabilities of automotive manufacturing enterprises. (2) Its core mechanism is mainly to alleviate the constraints that enterprises encounter in financing, provide very crucial financial support for the innovation activities of typical capital-intensive industries such as the automotive manufacturing industry, and enable these innovation activities to proceed smoothly. (3) The level of green development of enterprises will positively regulate this relationship. In the automotive manufacturing industry, where the pressure of green transformation is relatively high, the better the environmental performance of those enterprises, the more obvious the impact of digital finance.

Against the background of global climate change and China's "dual carbon" goals, the development of green finance in the Guangdong-Hong Kong-Macau Greater Bay Area has strategic demonstration significance for the whole country. However, there is a significant imbalance within the region. This study focuses on 9 inland cities in the Greater Bay Area from 2016 to 2022. It builds an evaluation system covering 7 dimensions including green credit and green investment, uses the entropy method to calculate the green finance index, and combines the GTWR model to explore the spatio-temporal heterogeneity of 5 core influencing factors. The findings indicate that green finance in the Greater Bay Area exhibits notable spatial and temporal differences. In terms of time, the index rises with fluctuations, and the growth rate increases after 2019. Guangzhou and Shenzhen have become a "dual-core" driving force. In terms of space, the pattern has changed from "isolated dual-core" to "dual-core and dual-belt". The eastern bank of the Pearl River Estuary is more strongly driven by the coordination of Guangzhou and Shenzhen. Green patents have a continuous positive driving effect. Industrial structure and economic development mainly have negative impacts. The impacts of government intervention and energy consumption intensity are different between the eastern and western banks of the Pearl River Estuary. In addition to providing empirical support for developing distinct green finance policies and advancing regional coordinated development, this study also closes the theoretical gap in the spatio-temporal differentiation of regional green finance.

This paper takes 13 prefectural-level cities in Beijing-Tianjin-Hebei as research samples, constructs a green development evaluation index system for urban agglomerations from the four dimensions of scientific and technological innovation, economic growth, green life and green "double carbon", and applies the entropy value method, ArcGIS natural fracture classification method and other tools to measure the level of green development from 2011-2020 and analyze its spatial and temporal evolution characteristics.Using the entropy value method, ArcGIS natural fracture classification method and other tools, we measured the level of green development from 2011 to 2020 and analyzed its spatial and temporal evolution characteristics.The study found that: (1) in the evaluation system, scientific and technological innovation, green "double carbon" and economic growth have the highest weight on the level of green development; (2) the average growth rate of the level of green development in Beijing-Tianjin-Hebei cities was 6.75% in 2011-2015, and it rose to 8.88% in 2016-2020; (3) the level of green development in the Beijing-Tianjin-Hebei(3) Beijing-Tianjin-Hebei city cluster gradually forms a spatial distribution pattern of "high - high agglomeration, low - low agglomeration".

This paper studied the spatial characteristics of tourism economic differences in the context of sustainable environment. This paper first introduced the impact of agricultural waste pollution on the environment and economic development, as well as the spatial characteristics and influencing factors of tourism economic differences that do not consider environmental factors. Then, taking nine provinces as the research object, this paper studied the efficiency driving mechanism of tourism industry and the spatial characteristics of tourism economic differences under the sustainable environment. The outcomes of the experiment demonstrated that environmental contamination would lower the tourism sector's economic efficiency. The study findings indicate that there was a 0.011 drop in the Theil index of tourist industry efficiency, a narrowing of the regional difference in tourism green development level, and a progressive rise in the regional efficiency difference as a significant contributing factor to the total regional difference.

Spatial and temporal evolutionary patterns and impact mechanisms of green development in arid zones: Evidence from Xinjiang, China

The measurement, spatial-temporal evolution and influencing factors of urban green and low-carbon development level

To analyze the development of green finance, the weights of each green finance indicator are determined by the entropy value method. To analyze the role mechanism of green financial development on economic growth and to get the relationship between the two more clearly, the study optimizes the parameters of BP network with genetic algorithm and constructs correlation analysis model. The scores of green financial development in some regions show that the development level of the Yangtze River Delta is the highest, with an average score of 62.4. The heavy industries in North China and Northeast China are well developed and have a greater demand for green financial products, with an increase of 12.7% and 11.9% respectively. The economic growth of the corresponding regions shows that the economic development level of Chang San is higher and that of the Northeast is lower. The results of the GA-BP model training show that the convergence of the algorithm is fast and the curve increases rather than decreases in the validation set, indicating that the model is well trained and the error has reached the minimum value. The mean square error of the correlation analysis model is below 0.02 and the fit coefficient R 2 is above 0.85, indicating the high accuracy of the results and the superior fit. The empirical evidence finds that the higher the level of green finance development, the better its economic development, and after a certain peak, the promotion effect diminishes. At present, some regions need to increase the proportion of green financial industries and accelerate industrial restructuring to achieve a green economy.

Purpose Supply chain green gap refers to the disparity in the green development levels between a focal firm and its upstream suppliers and downstream customers. Drawing on the institutional isomorphism perspective within the supply chain context, this study aims to understand how supply chain green gap influence firms’ green behavior and the strategic adoption of greenwashing. Design/methodology/approach Using a sample of Chinese A-share listed companies from 2012 to 2023, the authors empirically test the impact of supply chain green gap on corporate greenwashing and its mechanisms. Distinguishing the relative positions of firms and supply chain partners in terms of green development, the authors further discuss whether and how firms conduct greenwashing in different ways. Findings Supply chain green gap leads to firm greenwashing. When a firm’s level of green development is below the level of supply chain partners, it tends to engage in “talking more.” Conversely, when a firm’s green development exceeds the level of supply chain partners, it tends to engage in “doing less.” Supply chain green gap exacerbates information asymmetry, increases transactional volatility and creates oversight gaps, all of which contribute to the firm greenwashing. Furthermore, supply chain concentration and supply chain instability amplify these effects, while environmental sensitivity and environmental regulation help mitigate the occurrence of greenwashing. Practical implications This study highlights how supply chain green gaps induce firm greenwashing via asymmetric institutional pressures, enriching institutional theory. Aligning green strategies with supply chain partners and enhancing regulatory coordination can reduce symbolic compliance and corporate greenwashing. Originality/value The authors introduce an innovative concept of the supply chain green gap and present heterogeneous viewpoints on corporate greenwashing through the application of an institutional isomorphism framework.

As carbon emissions in China continue to rise and the cost advantage in the global value chain diminishes, enterprise transformation and upgrading has emerged as a new engine for economic growth. By implementing low carbon incentive policies, the government aims to spur corporate self‑innovation and phase out obsolete capacity, thereby boosting resource use efficiency and curbing environmental pollution. This paper examines the impact of China’s low carbon incentive policies on enterprise transformation and upgrading, with a particular focus on the role and mechanisms of urban environmental policy in this process. Employing a multi-period difference in differences approach, we analyze how the low carbon city policy affects the transformation and upgrading of Chinese listed firms. The results show that the low carbon city policy significantly enhances enterprise transformation and upgrading at the 1% level: participation in the low carbon city policy raises the composite index of enterprise transformation and upgrading by 0.012. We further explore the moderating role of enterprise green development level by incorporating it into our model of low carbon city policy effects. The findings reveal that firms exhibiting higher green total factor productivity, as well as those adopting green innovation and green management practices, display stronger adaptability to the low carbon city policy. Finally, both heterogeneity and dynamic analyses indicate that, over the medium to long term, the low carbon city policy continues to promote enterprise transformation and upgrading. In sum, the low carbon city policy not only provides exogenous momentum for enterprise transformation and upgrading but also interacts synergistically with firms’ green development to guide them toward more efficient and sustainable transformation and upgrading.

Carbon emissions are increasingly becoming a focal point of green policies and lifestyle choices. China’s “dual carbon” policy mandates the expansion of its digital economy to decrease carbon emissions. This study utilizes panel data from 30 Chinese provinces from 2011 to 2023 to investigate the influence of the digital economy on reducing carbon emissions through mediation and threshold effect models. The following conclusions have been made: The digital industry’s expansion hinders carbon emissions, leading to a progressive reduction in carbon emissions as the digital economy advances. The digital economy has a significant threshold effect on carbon emissions, showing a non-linear relationship between the impact of the digital economy on carbon emissions. By utilizing green finance as a mediating factor, the digital economy can address the issue of carbon emissions by improving the level of green finance development, thereby reducing regional carbon emissions. Geographical variations affect how the digital economy impacts carbon emissions, with varying degrees of influence across different locations. To achieve the “dual carbon” development goals, it is essential to carefully analyse the digital economy’s impact on reducing carbon emissions, enhancing the theoretical framework for environmental pollution management, and maximizing the digital economy’s capabilities.

The purpose of this study is to effectively establish an evaluation framework for green development in the tourism industry. A novel evaluation index system for green tourism development based on “society-economy-environment” that incorporates environmental protection values is constructed firstly. Subsequently, a comprehensive assessment framework was established using a combination of entropy weight and Complex Proportional Assessment (COPRAS) methods. Taking Zhejiang Province as an example, the numerical results show that between 2013 and 2023, the level of green development in Zhejiang Province’s tourism industry followed an “N” pattern of “rise-fall-rise,” with overall performance improving. However, the four specific dimensions of development exhibit characteristics of “strong economy, weak ecology, and a need to strengthen investment and environmental awareness.” Finally, several valuable suggestions are proposed, such as strengthening crisis management and optimizing investment factors, to promote the improvement of green development levels.

As China’s agriculture transitions toward high-quality development, reconciling agricultural green transformation with improved farmers’ income quality has become critical. This study seeks to investigate the effects of agricultural green development on the quality of farmers’ income from three dimensions: direct impact, structural influence, and heterogeneous characteristics. Leveraging provincial panel data from China spanning the period 2011 to 2022, a mixed-methods research design is adopted to conduct empirical analysis. First, the entropy weight method is applied to evaluate the comprehensive development level of agricultural green development and the quality of farmers’ income, along with their respective temporal variation features. On this basis, a two-way fixed effects model is then constructed to examine three core issues: the overall impact of agricultural green development on farmers’ income quality, as well as the structural heterogeneity and spatial heterogeneity characteristics inherent in this impact relationship. The results show that agricultural green development has significantly promoted farmers’ income quality in China, with improved resource utilization efficiency and output quality being the core driving factors, while environmentally friendly practices exhibit a negative effect in the short term. Specifically, agricultural green development significantly enhances income adequacy and structure but has a short-term inhibitory effect on income growth, with no significant impact on knowledge-based income. Regional heterogeneity analysis shows the strongest positive effect in the western region, followed by the eastern region, a negative impact in the northeastern region, and an insignificant effect in the central region. The income-increasing effect of green development is more significant in regions with poor natural resource endowments and low fiscal support for agriculture but is weakened in regions with high market vitality. This study provides a theoretical and practical basis for formulating differentiated agricultural green development policies and improving farmers’ income quality. These findings enrich the theoretical interface between agricultural green transformation and rural income improvement and offer actionable, region-specific policy insights by clarifying the constraints, key links and heterogeneous effects involved.

ABSTRACT Promoting green development along the Belt and Road Initiative (BRI) is an important initiative for global environmental governance and an inevitable choice for leading the economic and social development in a green, low-carbon and recycling-oriented manner. This article analyzes the impact of the BRI on the green innovation development along the routes and neighbouring regions by using the Super-SBM model, the dynamic-spatial difference-in-difference (DS-DID) model and the GMM estimation method. It is found that: (1) The level of green innovation development in different regions varies greatly, but the overall positive spatial correlation is stable. (2) The BRI can effectively improve the green innovation development in the regions along the routes, and has the ‘spatial and temporal spillover effect’ of ‘taking neighbours as partners and making progress together’. (3) The BRI can promote the green innovation development of the regions along the routes and neighbouring areas through industrial development and scientific and technological progress. This article puts forward suggestions for reference from the aspects of balancing regional competition with overall objectives, coordinating pilot projects and joint development, taking into account both domestic and international ‘double-cycling’, and focusing on the precise implementation of the powers and responsibilities of local governments.