Does government intervention affect CO2 emission reduction effect of producer service agglomeration? Empirical analysis based on spatial Durbin model and dynamic threshold model

The impact of public fiscal expenditure on industrial transformation and upgrading: An inverted U-shape evidence from China

High-quality economic development relies on industrial transformation and upgrading. To promote industrial transformation and upgrading, efficient fiscal expenditures are undoubtedly important as pillars of national governance. However, in the context of the market economy, the government's excessive intervention in industrial development will lead to the "promotion tournament" of officials and the "beggar-thy-neighbor" local protectionism, resulting in the convergence of regional industrial structure, which will bring uncertain impact on the upgrading of regional industrial structure. Thus, this study empirically assesses how public fiscal expenditure impacts industrial transformation and upgrading as well as the mechanism by developing a spatial econometric model using the panel data of 250 Chinese cities from 2007 to 2020 and further discusses the differential impact from the perspective of urban scale. The findings disclose that public fiscal expenditure serves a crucial role in facilitating industrial transformation and upgrading, but their relationship resembles an inverted U. Therefore, an optimal scale of public fiscal expenditure exists. Heterogeneity findings reveal that the promoting effect of public fiscal expenditure on industrial transformation and upgrading decreases with the expansion of the city scale. The role mechanism implies that public fiscal expenditure indirectly leverages industrial transformation and upgrading through promoting technological innovation, reducing resource dependence, and expanding scale economies. The conclusion provides a theoretical and practical framework for the government to optimize public fiscal expenditure, promote the transformation and upgrading of China's industrial structure, and ultimately attain high-quality development.

Explaining and modeling the impact of industrial co-agglomeration on regional economic growth in China: integrated a quality concern of night-time light perspective

The double-wheel drive of manufacturing industries and producer services industries is one of the key pathways to promote high-quality development relying on a modern industrial system. This paper explores the impacts of industrial co-agglomeration on regional economic growth in China with systematic consideration of static, dynamic, and spatio-temporal perspectives based on panel data for 285 prefecture-level cities from 2003 to 2020, employing the consolidated night-time light data. The empirical results show that industrial co-agglomeration significantly accelerates regional economic growth, especially high-tech intensity producer services industries, information industries, and finance industries. In addition, its spatial spillover effects are evidently established, which are characterized by cyclic accumulative, feedback features, and distance attenuation. Carrying out robustness tests, the preliminary regression results are verified. The heterogeneous influences are established across cities with different geographical locations, innovation capacities, and resource endowments. The further mechanism analysis indicates that industrial upgrading, technological progress, and efficiency enhancements account for the main channels for a sharp promotion in regional economic growth over the sample period. Furthermore, the government moderates this process more significantly than market forces do, especially when it comes to macroexogenous shocks that are regulated by the government. The findings of this study recommend policymakers to give full play to the positive externalities of industrial co-agglomeration and accelerate the industrial co-agglomeration process in a reasonable manner, so as to promote high-quality economic growth in China.

Exploring the impacts of producer services agglomeration on manufacturing carbon emissions: Empirical evidence from China

This study employs panel data from 30 provinces and cities in China from 2004 to 2019 to empirically estimate the relationship between producer services agglomeration (PSA) and manufacturing carbon emissions. The findings suggest that such agglomeration is beneficial for lowering carbon emissions in manufacturing, and this conclusion passes multiple robustness tests. Heterogeneity analysis results show that PSA in the east and west regions significantly lowers manufacturing carbon emissions, while its impact in the central region is not significant. High-end PSA is beneficial for cutting carbon emissions in manufacturing, but the inhibitory effect of middle- and low-end PSA is not significant. PSA significantly suppresses carbon emissions from capital- and technology-intensive manufacturing, while it has little impact on carbon emissions from labor-intensive manufacturing. Further analysis reveals that PSA has a dual-threshold impact based on absorptive capacity and a single-threshold effect based on infrastructure level on manufacturing carbon emissions. As the absorption capacity crosses the second threshold or the infrastructure level crosses the first threshold, the inhibition effect of PSA on manufacturing carbon emissions begins to become prominent and shows a trend of enhancement. Our research findings provide theoretical and empirical bases for lowering carbon emissions in the manufacturing sector and fostering its ascent to the highest position of the value chain.

Assessing the impact of industrial intelligence on urban carbon emission performance: Evidence from China

With the growing emphasis on sustainable development, there has been increasing attention given to measures aimed at promoting environmental improvements and reducing carbon emissions, including the adoption of intelligent industry. Recent studies have analyzed the influence of industrial intelligence on urban carbon emission performance while ignore the spatial spillover effects and lack in-depth discussion of the mechanisms, which reduces the reliability of the assessment of industrial intelligence's impact on carbon emission performance. To address this issue, the paper examines direct effect, spatial spillover effects, and mechanisms, utilizing a balanced panel data from 2008 to 2019 for 238 Chinese cities. The findings reveal that a 1 % improvement in industrial intelligence results in a 2.747 % enhancement of local carbon emission performance. Moreover, through the spatial spillover analysis, we determined that industrial intelligence has a notable negative impact on the carbon emission performance of surrounding areas. The mechanism analysis demonstrated that industrial intelligence affects the carbon emission performance of local and neighboring areas by influencing the agglomeration of productive services. Furthermore, our study illustrates that the industrial intelligence's enhancement effect on carbon emission performance shows more significantly in eastern, resource-dependent, and ordinary prefecture-level cities. Finally, endogeneity and robustness tests conducted yielded consistent conclusions. Our study provides a new perspective on industrial intelligence's carbon reduction effect and contributes to the development of policies related to industrial upgrading and green development.

Impact of artificial intelligence on carbon emission efficiency: evidence from China

Artificial intelligence (AI) has been extensively used as a revolutionary and versatile technology in various fields. However, scholars have not given substantial consideration to the impact of AI on the environment, particularly carbon emission efficiency (CEE). This study adopts the global super-efficiency slacks-based model to evaluate CEE of 30 provinces in China from 2006 to 2019. Thereafter, the current study investigates the impact mechanism of AI on CEE using the stochastic impact of population, affluence, and technology (STIRPAT) model. The empirical analysis provides the following valuable research findings. First, AI, represented by industrial robots, can significantly improve CEE. Second, AI can enhance CEE by promoting technological innovation and upgrading industrial structures. Lastly, the relationship between AI and CEE is influenced by marketization and government intervention.

Can industrial collaborative agglomeration improve carbon emission efficiency? Empirical evidence from China

Against the backdrop of low-carbon development, it is imperative to cultivate a modernized industrial system and new development model. Industrial collaborative agglomeration (ICA) between manufacturing and producer services may offer an opportunity to aid carbon reduction in this scenario. Using balanced panel data of China's 30 provinces and municipalities from 2008 to 2019, this paper attempts to investigate the influences of ICA on carbon emission efficiency (CEE), its heterogeneous effects, impact mechanisms, and spillover effects. Our main findings can be concluded as follows: (1) There is a U-shaped relationship between ICA and CEE; namely, ICA will inhibit first and then promote CEE. (2) The heterogeneity results further indicate that this U-shaped relationship is significant in the eastern area while there exists an inverted U-shaped relationship between ICA and CEE in the western area; however, the influence of ICA on CEE is not significant in the central area. (3) More deeply, the mechanism identification uncovers that industrial structure upgrading and green technological innovation are important channels through which ICA affects CEE. (4) Importantly, we unfold that ICA in the local area has spatial spillover effects; namely, it will influence CEE in neighboring areas, which also presents a U-shaped relationship. These findings provide not only new insights into understanding the environmental effects of ICA but also helpful inspiration for regional policymakers to scientifically formulate industrial development policies and effectively implement carbon emission control actions.

Exploring the low-carbon transition pathway of China's construction industry under carbon-neutral target: A socio-technical system transition theory perspective

The carbon emissions growth in the construction industry hinders the achievement of global carbon-neutral target, especially in China. Studies suggest that developing low-carbon technologies is an effective means of achieving the low-carbon transition (LCT) in the construction industry. However, these studies ignore the fact that the LCT is a complex and systemic issue that needs to consider the interaction of technical and non-technical factors. Thus, based on the socio-technical system transition theory, this study identified the influencing factors and constructs a dynamics model to simulate the dynamic changes of the LCT in China's construction industry under different scenarios. The results showed that multi-level factors coordinated to drive the LCT of the construction industry. Environmental factors played a weak role and the effectiveness of government intervention decreased with the transition process. On the contrary, technological and market factors were indispensable drivers and especially played a dominant role during the later stages of the transition. Finally, the LCT pathway of China's construction industry was proposed based on the results. These findings expand the boundary of theoretical research on industrial transition and provide a decision-making reference for the advancement of international carbon-neutral work.

Trade, FDI, and CO2 emissions nexus in Latin America: the spatial analysis in testing the pollution haven and the EKC hypotheses

Americas have a mix of developing and developed economies. Thus, the pollution haven hypothesis (PHH) is expected in the developing countries of Latin America. Using the spatial Durbin model, the present study investigates the effects of foreign direct investment (FDI), exports, and imports on emissions in 18 Latin American countries from 1970 to 2019, including economic growth and the financial market development (FMD) in the model. The environmental Kuznets curve (EKC) is validated, and the region is found in the first stage of the EKC. Hence, Latin American economic growth has environmental consequences. Exports have a positive impact on home and neighboring countries' CO₂ emissions and pollute the whole region, which validates the PHH. Imports could not affect the home economies but have positive environmental effects on neighboring economies and the entire Latin American region. The negative coefficient of imports is larger than the positive coefficient of exports. Therefore, the net effect of trade is environmentally pleasant in Latin America. Moreover, FDI has a statistically insignificant effect and the impact of FMD is positive on CO₂ emissions. The study recommends caring the exporting, financial, and economic activities for a sustainable environment in Latin America.

The impact of local government competition and green technology innovation on economic low-carbon transition: new insights from China

The government-led Chinese economic development system determines that local government competition is a significant factor affecting the economic low-carbon transition. Driving an economic development mode with green technology innovation as the core is the critical path to realizing an economic low-carbon transition. Consequently, it is of significant practical relevance to investigate the impact of local government competition and green technology innovation on the economic low-carbon transition under the government-led Chinese economic development system. This paper systematically explores the nexus between green technology innovation and economic low-carbon transition in terms of local government competition perspective using the system generalized method of moments, panel threshold model, and geographically weighted regression on the basis of a dataset of 30 provincial administrative areas in China from a period of 2006-2019. The results indicate that green technology innovation significantly promotes the economic low-carbon transition. Local government competition not only significantly dampens the economic low-carbon transition but also considerably inhibits the positive effect of green technology innovation on the economic low-carbon transition. A significant N-shaped association is evident between green technology innovation and the economic low-carbon transition when green technology innovation is applied as a threshold, while such association is insignificant when local government competition is used as a threshold. Compared with high-competition intensity areas, green technology innovation promotes economic low-carbon transition weaker in low- competition intensity areas, while local government competition inhibits economic low-carbon transition stronger. However, local government competition significantly inhibits the positive effect of green technology innovation on the economic low-carbon transition in low-competition intensity areas, while insignificant in high-competition intensity areas. The geographically weighted regression technique as a whole also verified the above results. Therefore, policymakers should not only increase research and development investment in green technologies, but also develop a regionally linked low-carbon emission reduction system to avoid ineffective competition among governments to facilitate the earlier fulfillment of the "dual carbon" goal.

Identifying impacts of industrial co-agglomeration on carbon emissions: Evidence from China

Based on panel data of 285 cities in China at the prefecture level and above from 2005 to 2020, this paper aims to study the nexus between industrial co-agglomeration and carbon emissions from dual perspectives including space and time. It adopts multiple approaches including a dynamic general method of moment, panel quantile regression model, panel threshold model, and dynamic spatial Durbin model. The non-spatial empirical results support the establishment of the threshold effect and the imbalance effect. The spatial empirical results indicate that industrial co-agglomeration poses a dramatic stimulating effect on urban carbon emissions, and its spatial spillover effect and spatial heterogeneity are conditionally established. Furthermore, heterogeneous effects are supported, such as the positive spillover effects of industrial co-agglomeration are more significant in western cities, resource-oriented cities, and non-low-carbon pilot cities. The heterogeneous influence of cost factors on industrial agglomeration and carbon emissions has also been partially confirmed. In terms of the channels and mechanism of action, the negative externalities of industrial co-agglomeration occupy a dominant position in the current status of economic development. The dynamic equilibrium between government intervention and marketization is a solid foundation for the optimization of carbon emission reduction paths.