An Empirical Study on the Influence Factors of Regional Carbon Emissions in China

This study analyzed the spatiotemporal differences and driving factors of carbon emission in China’s prefecture-level cities for the period 2003–2019. In doing so, we investigated the spatiotemporal differences of carbon emission using spatial correlation analysis, standard deviation ellipse, and Dagum Gini coefficient and identified the main drivers using the geographical detector model. The results demonstrated that 1) on the whole, carbon emission between 2003 and 2019 was still high, with an average of 100.97 Mt. Temporally, carbon emission in national China increased by 12% and the western region enjoyed the fastest growth rate (15.50%), followed by the central (14.20%) and eastern region (12.17%), while the northeastern region was the slowest (11.10%). Spatially, the carbon emission was characterized by a spatial distribution of “higher in the east and lower in the midwest,” spreading along the “northeast–southwest” direction. 2) The carbon emission portrayed a strong positive spatial correlation with an imbalance polarization trend of “east-hot and west-cold”. 3) The overall differences of carbon emission appeared in a slow downward trend during the study period, and the interregional difference was the largest contributor. 4) Transportation infrastructure, economic development level, informatization level, population density, and trade openness were the dominant determinants affecting carbon emission, while the impacts significantly varied by region. In addition, interactions between any two factors exerted greater influence on carbon emission than any one alone. The findings from this study provide novel insights into the spatiotemporal differences of carbon emission in urban China, revealing the potential driving factors, and thus differentiated and targeted policies should be formulated to curb climate change.

Climate warming caused by carbon emissions is a hot topic in the international community. Research on urban industrial carbon emissions in China is of great significance for promoting the low-carbon transformation and spatial layout optimization of Chinese industry. Based on ArcGIS spatial analysis, Markov matrix and other methods, this paper calculates and analyzes the temporal and spatial evolution characteristics of industrial carbon emissions in 282 cities in China from 2003 to 2016. Based on the spatial Dubin model, the influencing factors of urban industrial carbon emissions in China and different regions are systematically analyzed. The study shows that (1) China’s urban industrial carbon emissions generally show a trend of first growth and then slow decline. The trend of urban industrial carbon emissions in the western, central, northeastern and eastern regions of China is basically consistent with the overall national trend; (2) In 2003, China’s urban industrial carbon emissions were dominated by low carbon emissions. In 2016, China’s urban industrial carbon emissions were dominated by high carbon emissions, and the spatial trend is gradually decreasing from the eastern region to the central region to the northeast region to the western region; (3) In 2003, the evolution pattern of China’s urban industrial carbon emissions was “low carbon-horizontal expansion” dominated by positive growth, and in 2016, it was “low carbon-vertical expansion” dominated by scale growth; (4) China’s urban industrial carbon emissions have spatial viscosity, and the spatial viscosity decreases with the increase of industrial carbon emissions. (5) In 2004, the relationship between urban industrial carbon emissions and gross industrial output value in China is mainly weak decoupling. In 2016, various types of decoupling regions are more diversified and dispersed, and strong decoupling cities are mainly formed from weak decoupling cities in southwest China and eastern coastal areas; (6) From a national perspective, indicators that are significantly positively correlated with industrial carbon emissions are urban industrial structure, industrial agglomeration level, industrial enterprise scale and urban economic development level, in descending order. Indicators that are significantly negatively correlated with urban industrial carbon emissions are industrial structure and industrial ownership structure, in descending order. Due to the different stages of industrial development and industrial structure in different regions, the influencing factors are also different.

With the acceleration of urbanization and industrialization, the issues of urban pollution emissions and carbon emissions have become increasingly prominent. The coupling coordination relationship between pollution emissions and carbon emissions has also become a key issue affecting sustainable urban development. This paper, based on a systems coupling perspective and social network analysis methods, examines the spatiotemporal characteristics and driving mechanisms of the coupling coordination between pollution emissions and carbon emissions in China. The findings that: (1) There are significant regional differences in the coupling coordination degree (CCD) of pollution and carbon emissions across various regions in China, exhibiting a gradient decreasing trend. The overall national improvement in coupling coordination is limited, indicating a need to strengthen synergistic governance of pollution reduction and carbon emission reduction. (2) The CCD between cities has undergone phased development from ‘barely coordinated—primary coordination—intermediate coordination’, with most cities still at the primary coordination stage. The central and western regions have yet to reach a more advanced coordination state. (3) Analysis of the driving mechanisms indicates that various complex factors, such as economic development, industrial structure, environmental regulation, and green technological innovation, significantly influence coupling coordination.

Analysis of the non-equilibrium and evolutionary driving forces of carbon emissions in China's construction industry

With accelerating urbanization, building sector has been becoming more important source of China’s total carbon emission. In this paper, we try to calculate the life-cycle carbon emission, analyze influencing factors of carbon emission, and assess the delinking index of carbon emission in China’s building sector. The results show: (i) Total carbon emission in China’s building industry increase from 984.69 million tons of CO2 in 2005 to 3753.98 million tons of CO2 in 2013. The average annual growth rate is 18.21% per year. Indirect carbon emission from building material consumption accounted to 96–99% of total carbon emission. (ii) The indirect emission intensity effect was leading contributor to change of carbon emission. The following was economic output effects, which always contributed to increase in carbon emission. Energy intensity effect and energy structure effect took negligible role to offset carbon emission. (iii) Delinking index show the status between carbon emission and economic output in China’s building industry during 2005–2006 and 2007–2008 was weak decoupling; during 2006–2007 and during 2008–2010 was expansive decoupling; and during 2010–2013 was expansive negative decoupling.

In order to study the relationship between transportation carbon emissions and economic growth, driving factors of carbon emissions, we established elastic decoupling model and LMDI decomposition analysis model of transport sector. Taking Beijing Tianjin Hebei region as an example, the empirical study was carried out.Results showed that Tianjin and Hebei were in weak decoupling state, Beijing was in expanded negative decoupling state. And the transportation output scale effect in Beijing, Tianjin and Hebei was the main factor to boost the growth of carbon emissions.Energy intensity effect has led to the reduction of carbon emissions in the transport sector in Tianjin and Hebei, but has led to the increase of transport carbon emissions in Beijing.In terms of the energy structure effect, the transportation energy structure effect of Beijing, Tianjin and Hebei have weak positive contributions or negative contribution to the overall of carbon emissions.

With the K-means clustering and Logistic model, we forecasted the carbon emissions in 30 provinces and autonomous regions in China from 2014 to 2023 based on the data of 30 provinces from 2005 to 2013. First, 5 indicators were selected, which include GDP, urbanization rate, the proportion of the second industry, the energy efficiency and the carbon emission intensity. Secondly, K-means cluster analysis method was used to divide the carbon emission into 5 types. Finally, the Logistic model of carbon emissions growth was built, to predict the carbon emissions these provinces from 2014 to 2023. It was found that the carbon emission of China from 2014 to 2023 is increasing continuously.

The two-wheel innovation drive comprising technological innovation alongside institutional innovation serves as a crucial driving force for reducing carbon emissions and promoting the realization of dual carbon goals. Based upon the effective measurement and construction of an index system for technological innovation and institutional innovation, this paper selects panel data from 30 provinces, municipalities, and autonomous regions in China spanning from 2005 to 2019. Employing a two-way fixed effects model, it empirically examines the impact of the two-wheel innovation drive on carbon emissions in China. The findings indicate that the two-wheel innovation drive exerts a substantial inhibitory effect on carbon emissions, with evidence of single threshold effect. Specifically, there is a phenomenon of diminishing marginal benefits observed after reaching a certain level. Technological innovation, as well as institutional innovation, each demonstrate an negative effect on carbon emissions. Besides, regional heterogeneity in this inhibition effect is evident, with the most significant impact observed in the western region, followed by the central region, while the effect is relatively weaker in the eastern region. Additionally, this paper constructs an interaction term to examine the moderating effect, affirming that with economic development, the two-wheel innovation drive can more effectively achieve carbon emission reduction.

It is of great significance to clarify the evolution trend and key influencing factors of China's rural energy carbon emissions in order to promote the green development of agriculture and rural areas and to realize the goal of "double carbon" on schedule. On the basis of clarifying the current situation of rural energy carbon emission in China and 30 provinces， this study focused on analyzing the evolution trend of rural energy carbon emission and key influencing factors by using the kernel density estimation method and the random forest model. The study showed that： ① China's total rural energy carbon emission was on an upward trend from 2005 to 2022， and their evolution could be broadly categorized into three phases： "fluctuating increase， relatively stable， and continuous increase，" and the carbon intensity increased by more than 160% during the study period. In terms of provinces， the total amount of rural energy carbon emission was greatest in Guangdong， with Shanghai showing the least， and the intensity was greatest in Tianjin， with Guangxi showing the lowest. ② During the investigation period， rural energy carbon emission intensity increased significantly in both the country and the southern and northern regions， and the distribution of provinces in the low-value region decreased significantly. The rural energy carbon emission intensity in both the country and the northern regions still showed some polarization at the end of the investigation period. ③Rural energy carbon emission was influenced by factors at the economic， social， and governmental levels. Among the factors at the economic level， the structure of agricultural industry had an inverted U-shaped effect on rural energy and carbon emissions. Among the factors at the social level， the aging of the rural population， the degree of mechanization of agriculture， and the increase in the level of rural human capital all led to an increase in rural energy and carbon emissions， while the increase in the level of urbanization could play a restraining role. Among the governmental factors， the increase in the level of financial support for agriculture will help to realize the carbon emission reduction of rural energy. The results of the study can provide scientific references for the construction of the optimization path of emission reduction and carbon sequestration in rural areas.

Based on the time series decomposition of the Log-Mean Divisia Index, this paper analyzes the driving factors of carbon emissions from energy consumption by introducing the indicators of energy trade in China during the period of 2000–2014. The carbon emissions are decomposed into carbon emission coefficient, population, economic output, energy intensity, energy trade, energy structure and industrial structure effect in the manuscript. The result indicates that economic activity has the largest positive effect on the variation of carbon emissions. The energy trade has a greatest opposite effect on carbon emission change. At the same time, China has achieved a considerable decrease in carbon emission mainly due to the improvement of energy intensity and the optimization of energy and industrial structure. However, the influences of those changes in energy intensity, energy and industrial structure are relatively small. In addition, through the analysis by using a suitable index of energy trade, it was found that improving the conditions of energy trade can effectively optimize the energy structure and reduce the carbon emission in China.

Clarifying the spatiotemporal characteristics of agricultural carbon emissions and influencing factors in China is crucial. A system for measuring agricultural carbon emissions was established, thus evaluating the level of carbon emissions in China and its provinces. Moreover, the dynamic evolution of agricultural carbon emissions in China and the regions on both sides of the Hu Line was analyzed, then investigated factors affecting agricultural carbon emissions by the LMDI model. The results indicate that the total amount and intensity of agricultural carbon emissions showed an upward and then a downward trend in China from 2001 to 2021. The peaks were 330.72 million tons and 1.98 tons\\ha, respectively. Agricultural carbon intensity in provinces was mostly Low-Low Cluster and the range of High-High Cluster has decreased. Inter-provincial disparities in agricultural carbon emissions were also gradually narrowing. These show that the effect of agricultural carbon emissions reduction was obvious in China. It is important to note that carbon emissions from energy consumption in agriculture and agricultural material inputs were substantial, accounting for about 95% of the total. Agricultural carbon emissions were restricted by the agricultural production efficiency, changes in industrial structure, rural population size, and agricultural industrial structure, but were promoted by the level of economy and urbanization. Therefore, we recommend enhancing inter-provincial synergistic collaboration to create agricultural carbon emissions reduction pathways with unique features. It is also essential to maximize agricultural production efficiency and grasp the direction of green and low-carbon. We also suggest that the Chinese government should accelerate the in-depth adjustment and transformation and upgrading of the industrial structure, thereby reducing agricultural carbon emissions at source.

Against the backdrop of the contradiction between economic development and the "dual carbon" policy, it is of great significance to explore the decoupling effect and driving factors of China's carbon emissions. Here, we used the Tapio decoupling model to characterize the decoupling state of China's carbon emissions and economic growth, optimizing and expanding the decoupling index based on the LMDI model, systematically exploring the driving factors and their contribution to carbon emissions decoupling, further predicting the driving factors based on the grey breakpoint model, and then exploring the main contradictions of carbon emission decoupling in China in the next few years. The results showed that the decoupling state of carbon emissions in various regions of China was mainly weak decoupling, with the western region performing the worst. Under the impact of the COVID-19 pandemic, most regions have shown a strong decoupling state, but the decoupling index of carbon emissions rebounded during the economic recovery stage. Energy efficiency and technological progress were the main driving forces for carbon emission decoupling, while economic growth was the main obstacle. The impact of fossil energy consumption structure and demographic factors was relatively small. In the next few years, the decrease in energy efficiency will weaken the role of the energy intensity effect in promoting carbon emission decoupling, and the decline in innovation efficiency will inhibit carbon emission decoupling.

Investigating the driving factors of carbon emissions in China's transportation industry from a structural adjustment perspective

The increase in carbon emissions has had great negative impacts on the healthy developments of the human environment and economic society. However, it is unclear how specific socio-economic factors are driving carbon emissions. Based on the multiscale geographically weighted regression (MGWR) model, this paper analyzes the impact mechanism of China's carbon emission data during 2010-2017. The results show that (1) during the study period, China's carbon emissions have obvious positive correlations in the spatial distribution, and the spatial autocorrelation of carbon emissions on the time scale has a further strengthening trend. (2) Compared with the results of the geographically weighted regression (GWR) model, the MGWR model is more robust, and the results are more realistic and reliable. The impacts of energy intensity, proportion of green coverage in built-up areas, and industrial structure on provincial carbon emissions are close to the global scale, and their spatial heterogeneity is weak. Other factors have spatially heterogeneous impacts on carbon emissions with different scale effects. (3) Except for proportion of green coverage in built-up areas, the industrial structure and trade openness have insignificant impacts on carbon emissions, but other variables have significant impacts. The total population, urbanization rate, energy intensity, and energy structure have positive impacts on carbon emissions, while the GDP per capita and foreign direct investment have negative impacts on it. This study shows that the main socio-economic factors have different degrees of impacts on carbon emissions with different scale, and we can refer to it to formulate more scientific measures to reduce carbon emissions.

In response to climate change, the reduction of carbon emissions during agricultural production has garnered increasing global focus. This study takes high-standard farmland construction (HSFC) implemented in 2011 as the standard natural experiment and adopts the continuous differences-in-differences (DID) model to explore the impact and internal mechanism of HSFC on agricultural carbon emissions based on a panel data of 31 provinces, municipalities, and autonomous regions in China from 2003 to 2021. The results show that HSFC can effectively reduce the carbon emissions in agricultural production, and the average annual reduction can reach 53.8%. The effects of HSFC on agriculture carbon emissions could be associated with reducing agricultural fossil energy consumption and reducing agricultural chemical use. Further, the heterogeneity study shows that the carbon reduction effect of HSFC was mainly reflected in non-major grain-producing areas, while there was no significant impact in major grain-producing areas. Policymakers should unswervingly continue to promote HSFC, considering their own economic and geographical conditions. This study can provide valuable information and references for developing countries similar to China to formulate policies on agricultural carbon reduction.