A spatial panel analysis of carbon emissions, economic growth and high-technology industry in China

How urban agglomeration improve the emission efficiency？A spatial econometric analysis of the Yangtze River Delta urban agglomeration in China

Based on panel data from 31 provinces in China covering the beef cattle industry from 2009 to 2022, this paper constructs a framework for carbon emission measurement and systematically analyzes the spatial and temporal evolution of carbon emissions, the spatial agglomeration effect, and its driving factors in the beef cattle industry using life cycle assessment, Kernel density estimation, Moran’s index, and the spatial Durbin model. The study found that: (1) The total carbon emissions of China’s beef cattle industry exhibit a steady growth trend, with significant regional distribution differences. Emissions grow at a slower rate in the eastern region, while the emission levels in the central and western regions, particularly in the western region, are significantly higher than the national average.1 (2) Carbon emissions exhibit “high-high” and “low-low” spatial agglomeration patterns. Emission reduction is effective in the eastern region, while the central region is gradually catching up. The western region remains the core of high emissions. (3) Carbon emission dynamics indicate a trend of spreading from high-emission regions to peripheral areas, with medium- and small-scale farming regions having greater potential for emission reduction. (4) Improvements in environmental governance, mechanization, and education significantly reduce carbon emissions per unit of beef, driving emission reductions in neighboring regions through spatial spillover effects. Large-scale farming and urban–rural income disparities positively impact carbon emissions, while the role of scientific research inputs in emission reduction remains insignificant in the short term. This study provides a theoretical basis for promoting low-carbon development and regional synergy in the beef industry, suggesting the strengthening of research, development, and promotion of low-carbon technologies, improving the mechanism for regional synergy in emission reduction, and promoting the development of integrated crop-livestock systems to support the realization of the “dual-carbon” goal and the high-quality development of agriculture in the future.

This research attempted to investigate the impact of urbanization and spatial agglomeration on carbon emissions. To achieve the goal, the dynamic panel data model was employed to explore the nonlinear relationship between urbanization and carbon emissions for 166 cities in China taking the period 2005-2015, and the Gini coefficient of urban population size distribution in 15 urban agglomerations were calculated to analyze whether the spatial agglomeration of cities contributed to environmental protection. The results show that there is an inverted U-shaped curve between urbanization and carbon emissions; high-level urbanization development helps reduce carbon emissions; the spatial agglomeration of cities can contribute to carbon reduction to a certain extent based on the empirical results of the spatial agglomeration promotes the early arrival of the inflection point in the inverted U-shaped relationship between urbanization and carbon emissions; and the improvement of urban agglomeration level can present an abatement effect on carbon emissions at a lower urbanization level, which enhances the positive environmental effect of urbanization development compared with the decentralized urban distribution model. Furthermore, there is a significant U-shaped relationship between spatial agglomeration and carbon emissions, which indicates that the scientific planning of urban clusters will achieve economies of scale and agglomeration effect, thereby reducing carbon emissions. These findings contribute to complement the existing literature as well as provide some implications related to sustainable urban development for policymakers.

Impact of environmental regulation intensity on the efficiency of sustainable economic growth in the European Union

Collaborative governance (co-governance) is a crucial pathway and essential strategy for ensuring ecological security and high-quality development in the Yellow River Basin (YRB), which faces complex ecological challenges amid unbalanced regional development. This study employs a three-layer driving chain analytical framework to systematically investigate barriers to ecological co-governance in the YRB through a tiered approach. Firstly, spatial econometric methods are utilized to analyze spatial agglomeration, network strength, and spatial spillover effects, clarifying macro-level associations and driving mechanisms of ecological collaborative governance. Subsequently, tripartite evolutionary game analysis is conducted to explore the intrinsic dynamic logic of barrier factors emerging from micro-level interactions among key actors, supplemented by core driving layer theoretical analysis to investigate obstacles in the co-governance system. The results indicate that, (1) From a spatial measurement perspective, the effectiveness of ecological governance in the YRB exhibits significant spatial correlation, agglomeration, and interaction effects, with insufficient digital governance levels and structural flaws in the green industrial sector identified as key apparent-level barriers. (2) Under stable collaborative conditions, the vertical governance system operates efficiently; parameter sensitivity analysis and model robustness tests reveal that environmental protection costs in production, environmental regulation intensity, and supervision intensity are critical and sensitive parameters, significantly influencing the speed of strategic change and convergence, with the model demonstrating strong robustness. (3) Critically, divergent governance concepts and conflicting regional interests, rooted in disparities in core governance awareness and interests, constitute the most fundamental barriers to ecological co-governance.

The high technology (high-tech) industry of China has gained a key strategic position in the Chinese economic goals. In this positioning, foreign direct investment (FDI) and technological innovation have emerged as strong pillars of the high-tech industry. However, there are growing concerns of carbon emission from this industry which is still debatable. In this context, this study measures the effect of FDI and technology innovation on carbon emissions in the high-tech industry from 28 provinces of China. The study uses the provincial data for China over the period 2000-2018. In addition to examining unit root properties, structural breaks, and cointegration, this study uses quantile regression for estimating long-run relationships among study variables. The findings reveal the negative impact of FDI on carbon emissions. Technology innovation positively impacts in the initial three quantiles, whereas negatively impacts in the next six quantiles. These results indicate that FDI and technology innovation have shaped the energy intensity in the high-tech industry, which causes fluctuation in carbon emissions over time. After controlling the effects of urbanization, energy intensity, and economic growth, this study recommends that policymakers should emphasize on the heterogeneous effects of FDI and technology-lead emissions at different quantiles during the process of CO2 emission reduction.

Transportation is an important part of social and economic development and is also a typical high-energy and high-emissions industry. Achieving low-carbon development in the transportation industry is a much-needed requirement and the only way to achieve high-quality development. Therefore, based on the relevant data of 30 provinces in China from 2010 to 2018, this research uses the static panel model, panel threshold model and spatial Durbin model to conduct an empirical study on the impact and mechanism of digital innovation on carbon emissions in the transportation industry, and draws the following conclusions. (1) Carbon emissions in the transportation industry have dynamic and continuous adjustment characteristics. (2) There is a significant inverted U-shape non-linear relationship between the level of digital innovation and carbon emissions in the industry. In regions with a low level of digital innovation, the application of digital technology increases carbon emissions in this industry, but as the level of digital innovation continues to increase its application suppresses carbon emissions, showing an effect of carbon emission reduction. (3) The impact of digital innovation on carbon emissions in the transportation industry has a spatial spillover effect, and its level in one province significantly impacts carbon emissions in other provinces’ transportation industry through the spatial spillover effect. Therefore, it is recommended to further strengthen the exchange and cooperation of digital innovation in the transportation industry between regions, improve the scale of digitalization in this industry, and accelerate its green transformation through digital innovation, thus promoting the green, low-carbon, and sustainable development of China’s economy.

The sustainability of the ecological environment has been greatly threatened. Based on carbon emissions and combined with the panel data of 30 provinces in China from 2003 to 2020, this paper studied the various mechanisms of industrial structure optimization and population agglomeration on carbon emissions. The fixed effect model, panel threshold model and spatial spillover model are used to study the direct and indirect effects of industrial structure optimization and population agglomeration on carbon emissions, and the robustness of the results is tested in various ways. In terms of direct effects, the industrial structure optimization has a significant negative effect on carbon emissions, and the significance level is 1%. Population agglomeration has a significant positive effect on carbon emissions, with a significance level of 1%. In terms of indirect effects, 1) by adding the cross term of industrial structure optimization and population agglomeration, it is proved that population agglomeration can promote the carbon emission reduction effect of industrial structure optimization. 2) Population agglomeration was used as the threshold variable to verify the interval effect of industrial structure optimization on carbon emission reduction. The results show that the industrial structure optimization has a double threshold effect of population agglomeration on carbon emissions, and the threshold values are 2.1137 and 5.9263, respectively. And the larger the population agglomeration interval, the weaker the inhibition effect of industrial structure optimization on carbon emissions. 3) The industrial structure optimization, population agglomeration and carbon emissions have significant spatial spillover effects. The industrial structure optimization in neighboring areas has a significant inhibitory effect on carbon emissions, and the population agglomeration in neighboring areas has a significant promoting effect on carbon emissions.

As the processes of globalization and localization deepen, spatial externalities of economic growth are becoming increasingly apparent. The agglomeration mechanisms and spillover effects of China’s regional economic growth are also gradually gaining attention. Nevertheless, there is a continuing lack of research at the prefecture and county levels. As a result, building on the foundations of new economic geography and centered on the concept of market potential, this paper used spatial econometrics and panel data from Chinese counties to calculate inequality in the economic growth of counties at the prefecture level for the period 1992–2013. It also investigated the agglomeration versus economic inequality trade-off as well as quantitatively measuring spatial spillover effects at the county and prefecture level in China. The results showed that economic agglomeration, represented by market potential, had a significant influence on economic growth at the prefecture level in China. In addition, economic agglomeration exacerbated regional economic inequality, but economic inequality within a controllable range was found to have a positive influence on economic growth. Thus, there is a trade-off between economic growth and economic agglomeration. Economic growth at the prefecture level in China is not yet free of the effects of basic factors of production, and direct spillover effects, represented by market potential, have the most significant and strongest positive influence on economic growth. Moreover, it was found that the economic growth of prefectures was inseparable from the random impacts of surrounding prefectures and that it was also affected by indirect spatial spillover effects. On the whole, the rational use of the benefits of regional economic agglomeration and spillover effects, the gradual removal of market barriers, and the transformation of the development of prefecture-level economic growth will be the keys to prefecture-level economic development in the future.

Spatiotemporal evolution and key driver analysis of ozone pollution from the perspectives of spatial spillover and path-dependence effects in China

High-quality tourism development under the “double carbon” target (the peaking of carbon emissions and achievement of carbon neutrality) is an important path to achieving low-carbon emissions in the tourism industry and is vital for improving the industry’s carbon emissions efficiency. Using spatial and temporal panel data for 11 prefecture-level cities in Jiangxi Province from 2000 to 2020, a spatial Durbin model and a threshold model were constructed to assess the spatial spillover and threshold effects that high-quality tourism development has on the carbon emission efficiency of the tourism industry. The three key results were as follows. (1) There is a non-linear relationship between the carbon emission efficiency of tourism and the high-quality development trend of tourism, with differences in spatial distribution. (2) Coordinated development, green development, and open development all have significant positive direct effects on the carbon emission efficiency of tourism. Innovation-driven and coordinated development have a positive spillover effect on the carbon emission efficiency of tourism. In contrast, green development, open development, and shared results have a negative spatial spillover effect. (3) When the scale of the tourism economy crosses the first threshold in the second stage and the structure of tourism investment crosses the second threshold in the third stage, the ability of the tourism quality development to enhance the tourism carbon emission efficiency is the largest. When the tourism investment structure and tourism carbon emission intensity cross a single threshold, the role of the tourism quality development level in enhancing the tourism carbon emission efficiency decreases. Accordingly, three types of countermeasures are proposed: solving development problems, tapping into positive spillovers, and scientifically describing the impact of thresholds. The ultimate goal of this is to provide theoretical references and innovative ideas for promoting green, low-carbon, and high-quality development of tourism in Jiangxi Province and elsewhere.

Unbalanced and inadequate development in China has resulted in significant temporal and spatial differences in carbon intensity, impeding the achievement of carbon reduction targets. This paper explores the spatial distribution and convergence of China's provincial carbon intensity during 2000-2017 and its influencing factors employing spatial panel techniques. The spatial distribution analysis supports the existence of significant spatial agglomeration and radiation effects in China's provincial carbon intensity, and several provinces play key roles in the spatial distribution of carbon intensity, which are an important focus of carbon emission reduction policies. The results of spatial convergence estimation support that China's provincial carbon intensity presents significant spatial absolute and conditional convergence, and after considering regional differences, the spatial convergence speed is significantly accelerated. Meanwhile, economic level, urbanization, energy consumption structure, and industrial structure have significant spatial radiation effects on carbon intensity, and carbon intensity itself also has a spatial diffusion effect, indicating that carbon emission reduction requires multi-regional coordinated actions. This paper examined the spatial distribution and convergence of China's provincial carbon intensities over 2000-2017. The empirical findings verified the spatial agglomeration and radiation effects, as well as the absolute and conditional spatial convergence of China's provincial carbon intensities, which supports the policy-making related to the carbon reduction in China.

Regional disparity, spatial spillover effects of urbanisation and carbon emissions in China

Industrial agglomeration, urban characteristics, and economic growth quality: The case of knowledge-intensive business services

Carbon emissions reduction is crucial to global climate governance and sustainable development. By 2060, China envisioned being carbon-neutral, and it has adopted a series of policies and measures for environmental management, especially in the main stream of Yangtze River basin, where China’s carbon emissions are centered. The spatial distribution characteristics and agglomeration effects of carbon dioxide (CO2) emissions in the main stream of Yangtze River basin are analyzed from 2010 to 2019 based on the perspective of local (city and state) administrative regions, and uses the spatial Durbin model to examine the influencing factors and spatial spillover effects of carbon emissions. The findings discovered from the extensive research are as follows: First, carbon emissions in the main stream of Yangtze River basin present a fluctuating upward trend, and CO2 emissions in the lower reaches are significantly higher than those in the middle and upper reaches, which are closely related to the economic volume. Secondly, carbon emissions have a significant positive spatial correlation among prefecture-level cities, and carbon emissions show a high-high concentration in downstream regions and low-low concentration in upstream regions. Thirdly, regional economic development level, secondary industry development level, and population density have considerable influence on CO2 emissions, among which the Kuznets hypothesis is evidenced by the interaction between economic progress and carbon emissions. Therefore, strengthening regional cooperation efforts and collaborating to promote low-carbon development are the vital ways to achieve carbon emissions reduction.