Spatiotemporal evolution and spatial differentiation of carbon emission intensity in the Chinese transport sector.

Regional differences and driving factors analysis of carbon emission intensity from transport sector in China

The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector: Evidence from dynamic panel quantile regression

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.

Studies on the CO2 emissions from the transportation sector in China are increasing, but their findings are inconclusive. The main reason is that the spatial correlation of CO2 emissions from the regional transportation sector has been ignored in examinations of the driving factors of CO2 emissions from this sector. In this paper, new emission factors are adopted to calculate the CO2 emission levels from the transportation sector in Chinese provinces. By fully considering the spatial correlation of regional CO2 emissions and based on a two-way Durbin model incorporating both spatial and temporal fixed effects, the driving factors of CO2 emissions from the transportation sector in China are studied. The CO2 and spatial regression results for the transportation sector in China suggest the following: 1) Most of the regions with the highest CO2 emissions from the Chinese transportation sector are located on the east coast; they have gradually expanded over time to include the central and western regions. 2) The CO2 emissions from the transportation sector are higher in South China than in North China, and the regions with higher CO2 emissions have gradually shifted from north to south. 3) Transportation activity intensity, urbanization level, technological level, industrial structure and per capita GDP greatly impact CO2 emissions from the transportation sector in each province of China. Among these factors, transportation activity intensity, urbanization level, and per capita GDP exert not only direct effects but also indirect effects, whereas technological level and industrial structure exert only direct effects.

The problem of global warming caused by the increase in carbon emissions poses a serious challenge to the sustainable development of human society, and the search for reasonable methods of carbon reduction has become an important issue around the world.This study employs kernel density estimation to dynamically analyze the temporal evolution patterns of carbon emissions across 249 Chinese cities from 2005 to 2018. Spatial autocorrelation analysis is utilized to characterize and interpret the spatiotemporal dynamics of carbon emissions. Additionally, both Traditional Markov Chain and Spatial Markov Chain models are applied to investigate the spatial-temporal transition pathways of urban carbon emissions.The carbon emission levels of most Chinese cities show a convergence trend, and the emission intensities of some cities significantly deviate from the main distribution intervals. Global spatial autocorrelation results show significant spatial heterogeneity of carbon emissions in 249 cities in different years. Spatial Markov Chain analysis shows that the transfer of carbon emission intensity is closely related to the region, and there is a spatial spillover effect, and the regional background strengthens the “club convergence effect”, and the neighboring state has a guiding effect on the direction of carbon emission transfer. The study of the spatio-temporal evolution law and spatial spillover effect of carbon emission intensity will be conducive to the further development of carbon emission reduction measures.

Under the “double carbon” target, it is important to reduce carbon emissions in each region. Using exploratory spatial data analysis (ESDA), the center of gravity method, and spatial econometric models, we analyzed the characteristics and spatial spillover effects of carbon emission intensity in counties in Northeast China from 2000 to 2020 and made recommendations to the government for more reasonable carbon reduction strategies in order to achieve sustainable development. The results were as follows: (1) Since 2000, the carbon emission intensity in Northeast China has increased after first declining, and the carbon emission intensity in the western and northern regions of Northeast China has increased faster than Northeast China’s average. (2) After 2000, the spatial aggregation of carbon emission intensity has improved in Northeast China. (3) Northeast China’s carbon emission intensity has a positive spatial spillover effect. Through the feedback mechanism, the growth in population size, the rise in economic development level, the level of industrialization as well as the rise in living standard, the land use structure dominated by arable land and construction land, and the increase in urbanization level in the region will cause the carbon emission intensity in the surrounding areas to increase. An increase in public expenditures leads to a decrease in carbon emission intensity in the adjacent area. (4) When the vegetation cover exceeds its threshold value, it can have a larger inhibitory influence on carbon emission intensity. To summarize, each county in Northeast China is a carbon emission reduction community, and policymakers must consider the spatial spillover effect of carbon emission intensity when developing policies.

Social and economic growth in developing countries has heightened the awareness of environmental challenges, with carbon emissions emerging as a particularly pressing concern. However, the impact of economic development on carbon emission intensity has rarely been considered from the perspective of economic agglomeration, and the relationships and mechanisms between the two remain poorly understood. We analyzed the impact of economic agglomeration on carbon emission intensity and its spatial spillover effect in Guangdong Province, the most economically advantaged province of China, based on a spatial weight matrix generated using geographic proximity, exploratory spatial data analysis (ESDA), and the spatial Durbin model. Between 2000 and 2019, economic agglomeration and carbon emission intensity in Guangdong Province exhibited persistent upward trajectories, whereas between 2016 and 2019, carbon emission intensity gradually approached zero. Further, 80% of the province’s economic output was concentrated in the Pearl River Delta region. Strong spatial autocorrelation was observed between economic agglomeration and carbon emission intensity in the cities, and the economic agglomeration of the province had a parabolic influence on carbon emission intensity. Carbon emission intensity peaked at an economic agglomeration level of 1.2416 × 109 yuan/km2 and then gradually decreased. The spatial spillover effect of the openness degree on carbon emission intensity was positive, while GDP per capita and industrial structure had negative effects. Further, the economic agglomeration effects of Guangdong Province increased the carbon emission intensity of major cities and smaller neighboring cities. The stacking effect of economic agglomeration between cities also affected the carbon emission intensity of neighboring cities in the region. During the period of rapid urban development, industrial development and population agglomeration increased resource and energy consumption, and positive externalities such as the scale effect and knowledge spillover were not well reflected, resulting in greater overall negative environmental externalities relative to positive environmental externalities.

How does the concentration of spatial allocation of urban construction land across cities affect carbon emission intensity in China?

Establish of air pollutants and greenhouse gases emission inventory and co-benefits of their reduction of transportation sector in Central China

It is of much importance to clarify the impact of technological innovation on carbon emission intensity for the low-carbon transformation of China's economy. This study, based on the panel data of 30 Chinese provinces and municipalities from 2010 to 2020, measures and analyzes the carbon emission intensity and the level of technological innovation, establishing a spatial econometric model to study the spatial spillover effect and a panel threshold model to analyze the nonlinear influence of technological innovation level on carbon emission intensity. The findings are as follows: First, the overall carbon emission intensity in China shows a decreasing trend from 2010 to 2020, with the average dropping from 3.09 in 2010 to 1.98 in 2020; Second, the spatial autocorrelation results reveal that the level of technological innovation and carbon emission intensity in China are obviously aggregated in the global spatial distribution pattern. Third, the regression results of the spatial econometric model show that the direct effect of technological innovation on carbon emission intensity is significantly negative at the level of 1%, that is, the improvement of the technological innovation in a certain area has a significant inhibitory effect on carbon emission intensity. Fourth, based on the level of economic development, there is a significant three-threshold effect of the level of technological innovation on carbon emission intensity in China, and the influence of the level of technological innovation on carbon emission intensity varies in the direction of existence and coefficient values within different threshold intervals. As economic development reaches the third interval, the technological innovation level has the most significant inhibition on carbon emission intensity. These findings enriches the research of the nonlinear relationship between technological innovation and carbon emission intensity, clarifies the spatial spillover effect and threshold effect between among them, and provides inspiration for better promote the low-carbon transformation of economy.

From now until 2030, China will be in a sprint to achieve reductions of 40–45% in carbon emission intensity by 2020 and 60–65% by 2030 compared to 2005; rigid requirements have thus been imposed for controlling carbon emission intensity. In this study, a spatial Durbin model that integrates a spatial lag model and a spatial error model is used to measure the degree of influence held by the energy consumption structure and other factors over carbon emission intensity and the spatial spillover effect. The results show that there is a spatial demonstration effect on the reduction in interregional carbon emission intensity in China. While the carbon emission intensity in the adjacent region decreases by 1%, the carbon emission intensity in this region will decrease by 0.05%, indicating that China’s regional low-carbon development model is also applicable to neighboring provinces and plays a large role in driving and demonstrating a low-carbon economy. Every additional 1% improvement toward optimizing the energy consumption structure enables the carbon emission intensity of the region to decrease by 0.21%; further, there is a positive spatial spillover effect driving carbon emission intensity decreases in neighboring areas of 0.25%. Industrial structure, energy intensity, energy price, and level of openness are the main factors influencing regional carbon emission intensity. According to the “14th Five-Year Plan,” there is an urgent need to optimize the energy consumption structure in the medium and long term and give full play to its ability to contribute to declines in carbon emission intensity.

How does ecological civilization construction affect carbon emission intensity? Evidence from Chinese provinces’ panel data

China’s transport industry has made rapid progress, which has led to a great amount of carbon emissions. However, it is still unclear how carbon emissions from the transport sector are punctuated by shifts in underlying factors. This paper aims to examine the process of China’s carbon emissions from the transport sector as well as its major driving forces at the provincial level during the period of 2000 to 2015. We first estimate the carbon emissions from the transport sector at the provincial level based on the fuel and electricity consumption using a top-down method. We find that the carbon emission per capita is steadily increasing across the country, especially in the provinces of Chongqing and Inner Mongolia. However, the carbon emission intensity is decreasing in most provinces, except in Yunnan, Qinghai, Chongqing, Zhejiang, Heilongjiang, Jilin, Inner Mongolia, Henan and Anhui. We then quantify the effect of socioeconomic factors and their regional variations on carbon emissions using a panel model. The results show that the development of secondary industry is the most significant variable for carbon intensity at both the national and regional levels, while the effects of the other variables vary across regions. Among these factors, population density is the main factor of the increasing carbon emissions per capita from the transport sector for both the whole country and the western region, whereas the consumption level per capita of residents and the development of tertiary industry are the primary drivers of per capita carbon emissions for the eastern and central regions.

Influencing mechanisms of renewable energy development on carbon emission intensity in China

The Bohai Economic Rim (BER) is an important economic Rim in north China. Since the implementation of the Beijing−Tianjin−Hebei Coordinated Strategy in 2014, the provinces have become more closely connected in economic development and environmental governance. This paper investigates the dynamics and spillover effects of carbon emission intensity in the BER before and after removing the common factors, and analyzes the reasons for the difference. In this study, the serial dynamics characteristics and spatial spillover effects of the carbon emission intensity of provinces were analyzed in the BER provinces between 2000 and 2019. Based on the Moran index and the spatial Durbin model, the provincial carbon emission intensity and influence factors were examined. CD (Correlation Dependence) tests were then applied, with the test results indicating that the carbon intensities had strong spatial correlation. Therefore, the dynamic spatial Durbin common factor model was introduced, characterizing the dynamic characteristics of the carbon emission intensity and the spatial spillover effect in the BER. The consequences obtained are as follows: (1) The carbon emission intensities in the BER were influenced by the energy intensity, urbanization level, economic growth, and population density. There was a significant spatial spillover effect between a province and its neighboring provinces. (2) The carbon emission intensities of the provinces exhibited a strong correlation. (3) The reason for the strong carbon emission intensity correlation is associated with environmental protection policies that are similar and the common external development environment. Combining the above findings and study conclusions, the authors offer the following policy suggestions: (1) optimize the energy structure; (2) improve the industrial structure; (3) construct a regional collaborative governance mechanism for carbon emissions; and (4) formulate a precise policy. This study is crucial for reducing regional carbon emissions, promoting the transition to a green economy and society, and achieving the “carbon peaking” and “carbon neutrality” targets in China.