Factors Of Carbon Emission Intensity Research Articles

Spatial Differences and Influencing Factors of Carbon Emission Intensity in China’s Urban Agglomerations toward the Carbon Neutrality Target

It is of great scientific value to study the spatial differences and influencing factors of carbon emission intensity (CEI) in urban agglomerations (UAs), and it also has reference significance for China in formulating energy-saving and emission-reduction policies to achieve the target of carbon neutrality. Taking 165 prefecture-level cities in 19 UAs in China from 2007 to 2019 as the research object, this study investigated the spatial differences of CEI in UAs using exploratory spatial data analysis and explored the influencing factors of CEI via Geodetector. The results showed the following: (1) The CEI of the UAs showed a downward trend. (2) The CEI of the UAs has typical spatial agglomeration characteristics, where the North comprises mainly high-high and low-high types, whereas the South is primarily high-low and low-low types. (3) The influencing factors of CEI have undergone a transformation from industrial structure to population urbanization.

Spatial network structure and influencing factors of carbon emission intensity in Guangdong-Hong Kong-Macao greater bay area

Introduction: In response to China’s ambitious dual-carbon goals, this study investigates the spatial correlation and influencing factors of carbon emission intensity within the Guangdong-Hong Kong-Macao Great Bay Area (GBA), a region pivotal for the nation’s energy conservation and emission reduction efforts. Through a comprehensive analysis encompassing the period from 2000 to 2020, this research aims to delineate the spatial dynamics of carbon emissions and identify actionable insights for regional low-carbon development.Methods: Utilizing an integrated methodology comprising spatial autocorrelation analysis, Social Network Analysis (SNA), and the Quadratic Assignment Procedure (QAP), the study analyzes carbon emission data alongside socio-economic variables. These methodologies allow for a nuanced exploration of the spatial correlation structure and the determination of factors influencing carbon emission intensity across the GBA.Results: Findings reveal a cyclical fluctuation in the spatial network of carbon emissions from 2000 to 2020, characterized by varying degrees of cohesion among cities, indicating significant opportunities for spatial optimization. A “core-periphery” pattern emerges, with economically robust cities such as Hong Kong and Macao at the core, and less developed cities like Huizhou and Jiangmen on the periphery. Cities like Guangzhou and Shenzhen play crucial mediator roles. The QAP analysis further identifies six major influencing factors: geographic spatial proximity, economic development level, urbanization rate, industrial configuration, level of technological innovation, and environmental protection efforts, with the latter four having a markedly positive impact on spatial relevance.Discussion: The study’s insights underscore the importance of understanding the spatial dynamics of carbon emissions and the role of socioeconomic factors in shaping these patterns. For policymakers and stakeholders in the GBA, these findings highlight the necessity of targeted intervention strategies that consider both the unique position of cities within the spatial network and the broader socio-economic context. This approach can significantly contribute to achieving China’s dual-carbon objectives, promoting energy conservation, and facilitating the transition to a low-carbon economy.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China.

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Regional differences and driving factors of carbon emission intensity in China's electricity generation sector.

As an industry with immense decarbonization potential, the low-carbon transformation of the power sector is crucial to China's carbon emission (CE) reduction commitment. Based on panel data of 30 provinces in China from 2000 to 2019, this research calculates and analyzes the provincial CE intensity in electricity generation (CEIE) and its spatial distribution characteristics. Additionally, the GTWR model based on the construction explains the regional heterogeneity and dynamic development trend of each driving factor's influence on CEIE from time and space. The main results are as follows: CEIE showed a gradual downward trend in time and a spatial distribution pattern of high in the northeast and low in the southwest. The contribution of driving factors to CEIE has regional differences, and the power structure contributes most to the CEIE of the power sector, which promotes regional CE. Concurrently, most provinces with similar economic development, technological level, geographic location, or resource endowment characteristics show similar spatial and temporal trends. These detections will furnish broader insights into implementing CE reduction policies for the regional power sector.

Serial Dynamics, Spatial Spillover and Common Factors of Carbon Emission Intensity in China’s Bohai Economic Rim

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.

Spatial disequilibrium and influencing factors of carbon emission intensity of construction land in China

Revealing the spatial disequilibrium and influencing factors of carbon emission intensity of construction land (CEICL) is of great significance for improving the sustainable utilization of urban land, and helping to achieve global green and low-carbon development. This study estimates the CEICL of 285 cities in China from 2008 to 2019. Then, this paper analyzes the spatial disequilibrium of CEICL, and discusses the spatial heterogeneity of the influencing factors at different levels of CEICL. The results show that the CEICL has an inverted “U-shape”, increasing first and decreasing later, with most cities concentrating on 0.3 million tons/km2 and some cities breaking through 3 million tons/km2. Also, the results imply a relatively large gap in CEICL among cities, despite a gradually narrowing trend of this gap. Among regions, the average annual difference of CEICL is about 0.1–0.4 million tons/km2 for cities of the same scale. Within regions, CEICL decreases with increasing city scale, and the agglomeration characteristics of high-high and low-low values within regions are significant and stable, and the spatial lock-in effect is strong. Moreover, the results infer a negative spatial spillover effect in CEICL in China, and the significant spatial heterogeneity effect of the seven factors (i.e., population agglomeration intensity, industrial structure upgrading, land development intensity, land investment intensity, economic output level, technological R&D and innovation, and ecological resource endowment) on CEICL in cities of different regions and scales. This study provides a scientific basis and empirical support for developing differentiated and feasible carbon reduction policies based on cities' own characteristics.

Multi-scale variations and impact factors of carbon emission intensity in China.

China's carbon emissions have developed swiftly in recent decades, which will not only affect the nation's own sustainable development, but have a potentially negative impact on global climate stability. Given that socioeconomic development is susceptible to regional heterogeneity and geographic scales, a systematic exploration of spatiotemporal variations of carbon emission intensity (CEI) and their drivers across different levels is conducive to enacting more reasonable and efficient measures for emission reduction. However, there is still a lack of comprehensive analysis of these issues. In this paper, we attempted to quantify and compare the spatiotemporal evolution and spatial spillover effects of impact factors on CEI from nighttime light imagery and socioeconomic data at two China's administrative levels by utilizing the variation coefficient, spatial autocorrelation model and spatial econometric methods. The results showed that the spatiotemporal variations of CEI were greater at the prefecture level compared to the provincial level during 2000-2017. There were significant positive spatial autocorrelation of CEI at two administrative levels, and self-reinforcing agglomeration was more substantial at the prefectural level than that provincial level. While the local spatial clustering of CEI of each administrative level altered with scale dependence, the binary spatial structure (High-High and Low-Low) of CEI remained relatively steady in China. Various driver factors not only had direct effects on local CEI, but had spatial spillover effects on neighboring areas. Our findings illustrate that China's CEI is sensitive to the space-time hierarchy of multi-mechanisms, and suggest that "proceed in the light of local conditions" strategies can assist the Chinese government for CEI mitigation.

Spatial-Temporal Pattern and Driving Factors of Carbon Emission Intensity of Main Crops in Henan Province

Agriculture is the national economy’s primary industry, and its carbon emissions (CE) are one of the most significant factors influencing the environment. As a large agrarian province, reducing the carbon emission intensity (CEI) of agricultural is of great practical significance to the sustainable development of agriculture in Henan province. In this paper, the CEI of rice, maize, and wheat from 2001 to 2020 in 18 prefecture-level cities in Henan province was calculated, and its spatial-temporal evolution patterns were analyzed. The Spatial Dubin model was used to study the impact mechanism and spatial spillover effect of the main crops’ CEI. As a result, the following was determined: (1) The CEI of main crops in 18 cities of Henan province showed an inverted “V” shape, whereas the geographical distribution showed an oblique “T” shape mainly in the north and west. (2) The CEI of main crops was significantly different under different factors. Technical efficiency, agricultural openness, urbanization level, agriculture production agglomeration, and agriculture fiscal expenditure negatively impact the main crops’ CEI. The structure of the food industry and the cost of water for agriculture and forestry positively affect the CEI of main crops. (3) The spatial spillover effects of agricultural openness, production technology efficiency, environmental protection, and fiscal expenditure spread to the surrounding areas through factor flow, technology spillover, and policy spread. The efficiency of production technology and fiscal expenditure on environmental protection have a demonstrative effect, and the degree of agricultural openness has a siphon effect. Based on the research results, we should strengthen agriculture technology extension and investment and gradually improve technical efficiency. Agriculture should be financially supported by the government. We will actively promote the optimization of the structure of the grain industry by promoting orderly urbanization, strengthening the sharing of factors among regions, and reducing the CEI of main crops.

Study on Measurement and Driving Factors of Carbon Emission Intensity From Energy Consumption in China

Study on Measurement and Driving Factors of Carbon Emission Intensity From Energy Consumption in China

Driving Factors of Carbon Emission Intensity for China's Planting: A Combination of LMDI and PDA

It is important to explore the driving factors of the carbon emission intensity (CI) for China's planting under the dual pressures of adequate food supply and carbon neutrality. Previous studies separately investigate the impact of technical or structural factors on the total carbon emissions of China's agricultural sector, but few studies assess the comprehensive effects of these two. To this end, this paper incorporates the production-theoretical decomposition analysis (PDA) into the logarithmic mean Divisia index (LMDI) and decomposes the changes of CI into seven components, namely, two technical effects, four structural ones, and one regional layout effect. Based on the panel data of the agricultural sector for 31 provinces in China from 2001 to 2018, the contribution rates of the seven components to the changes of China's planting CI are computed. The results indicate that China's planting CI presents a downward trend with an average annual decreasing rate of 11.4% over the whole study period. The improvement in technical efficiency (TEFF) plays a dominant role in the decline of CI for planting with a contribution rate of 83.19%, followed by the output structure (OS) change (27.28%). In contrast, technical change (TECH) (8.00%) promotes the increase of CI. Further, the effects present significant regional heterogeneities. Specifically, TEFF contributes the highest share to the decline of CI for producing-sales balance areas (BA), and OS plays the greatest role in the decrease of CI for main grain-sales areas (MCA) during the entire study period. Accordingly, some policy recommendations are put forward on how to reduce the CI of China's planting.

Carbon emission intensity and biased technical change in China's different regions: a novel multidimensional decomposition approach.

The decomposition analysis has been employed to discover the driving factors of carbon emission intensity, but the current studies assume that production functions are under the condition of the neutral technical change. Grounded on biased technical change production theory, this paper proposes a novel multidimensional decomposition approach which combines production-theory decomposition analysis (PDA) and index decomposition analysis (IDA). This novel approach can illustrate how energy structure effect, element substitution effect, efficiency change effect, input biased technical change, output biased technical change and magnitude of technical change affect carbon emission intensity of China's 30 provinces. The results indicate that during the 11th FYP and 13th FYP, output biased technical change and the magnitude of technical change are the critical factors in China's carbon emission intensity, while other four drivers increase carbon emissions. But, during the 12th FYP, the role of six drivers has been reversed contrasting 11th FYP and 13th FYP. In addition, we also explore the impact of each driver from the perspective of regional heterogeneity.

Spatial effects and influencing factors of carbon emission intensity in the Yangtze River Delta urban agglomeration: An industrial transfer-based perspective

Spatial effects and influencing factors of carbon emission intensity in the Yangtze River Delta urban agglomeration: An industrial transfer-based perspective

Spatiotemporal heterogeneity, convergence and its impact factors: Perspective of carbon emission intensity and carbon emission per capita considering carbon sink effect

Clarifying the spatiotemporal heterogeneity, convergence and its impact factors of carbon emissions is not only beneficial to the formulation of differential carbon reduction policies, but also to achieve regional coordinated development. As the second largest source of carbon emissions, agricultural carbon emissions have attracted extensive attention practically and academically. The current study used the panel data of 31 provinces in China from 1997 to 2017, combining with the carbon emission accounting formula, Theil index method and convergence model, to analyze the spatiotemporal heterogeneity, convergence and its impact factors of carbon emission intensity (CEI) and carbon emission per capita (CEPC) of planting industry considering carbon sink effect. The results revealed that the total carbon emission shows a downward trend during the investigation, while evident differences exist in different regions and dimensions mainly caused by the inter-regional differences. The CEI Theil index demonstrated gradient decreasing pattern of “western (0.14) > central (0.09) > eastern (0.05) > northeast (0.03)” and the CEPC Theil index showed the distribution characteristics of “northeast (0.09) > central (0.08) > western (0.08) > eastern (0.05)”. Convergence existed in the whole country and in the four regions when taking CEI as the estimation index, while different divergence characteristics appeared (except central) when CEPC is applied. The impact effects of initial CEI/CEPC, rural GDP per capita, agricultural financial support level, agricultural mechanization degree, agricultural structure, rural population size and urbanization level have spatiotemporal heterogeneity.

Spatial econometric analysis of carbon emission intensity in Chinese provinces from the perspective of innovation-driven.

This study estimates the carbon emission intensity of China's provinces during the period from 2000 to 2015. First, the temporal and spatial pattern evolution of China's carbon emission intensity was analyzed using spatial statistics. Then, from an innovation-driven perspective, combining the data of innovative technologies and scale factors to construct a spatial panel model to explore the main influencing factors of carbon emission intensity and its spatial spillover effect. The results show that: China's provincial carbon emission intensity has obvious spatial agglomeration characteristics, and regional differences are improving, and the spatial spillover effect of some influencing factors is obvious; innovation indicators such as the number of patent authorizations, technical market turnover, and foreign direct investment, and GDP have a significant negative impact on carbon intensity, and the effects of general scale variables such as urbanization rate, energy consumption, and population density on carbon intensity are significantly positive.

Spatial spillover effect and driving forces of carbon emission intensity at the city level in China

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.

Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method

Uncovering the driving factors of CO2 emission intensity declining is important for China. This paper improves the logarithmic mean Divisia index technique, which includes energy density and energy consumption intensity, to explore the driving factors of carbon emission intensity (CI) in 29 Chinese provinces from 1995–2012. The main results are: (1) energy consumption intensity plays a more important role than carbon emission density (CD) for a rapid decrease in CI during the research period, so a much room is left for a significant CD reduction through carbon emission reduction technology, energy structural reduction, and energy consumption proportional reduction. (2) The decrease in energy consumption technology and energy structure in secondary industries contributes the most reduction in energy consumption intensity. (3)The energy consumption proportions of secondary and tertiary industries are the two most important drivers to decrease CD. (4) During the research period, the energy consumption proportions of secondary industries result in the most decrease in CD, whereas the energy consumption proportions of tertiary industries cause the most increase in CD.

Analysis of Influencing Factors on Regional Carbon Emission Intensity in China-Based on Empirical Research with Provincial Panel Data

This thesis analyzes the influencing factors of carbon emission intensity based on the five types of panel data in 31 provinces (cities and autonomous regions) throughout China collected in China’s “12th Five-Year Plan”, and has conclusions as follows: the carbon intensity in various regions is mainly influenced by energy intensity, economic growth, proportion of the secondary industry, and fiscal expenditure. The carbon intensity basically maintains positive correlation with energy intensity and economic growth, while the correlation between carbon intensity and industrial structure, and that between carbon intensity and fiscal expenditure varies from each other. There are different industrial structures and corresponding changing laws in various provinces and cities. The favorable influences on carbon intensity by fiscal expenditure are not remarkable, and yet the relationship between proportion of fiscal expenditure and carbon intensity still shows positive correlation in most of the provinces. In the long run, the major way of realizing energy saving and emission reduction for various provinces still lies in adjustment and optimization of both industrial structure and energy consumption structure.

从中碳强度指数看上海碳减排的足迹<br>Shanghai Carbon Emission Reduction Footprint, by SINO Carbon Intensity Index

中碳强度指数是为全国节能减排行业提供一个以单位GDP二氧化碳排放量为载体的指数系统，可有效反应时间序列上地区碳排放强度的变化。本文从产业结构、能源强度、能源结构、政策及经济环境等几个方面，探讨了2005至2011年上海市碳排放强度的影响因素，并在此基础上提出了促进上海碳减排活动的建议。分析结果表明，上海市的节能目标和低碳政策，对能源结构调整、产业结构优化和能源强度下降都起到了推动作用，进而促成了近几年来上海市碳排放强度和上海市碳强度指数(Xsh,y)的持续下降。而经济危机、“4万亿元刺激经济方案”、以及2010年上海世博会等重大经济事件和活动，也作为独立的外部因素对Xsh,y起到了重要影响。 SINO Carbon Intensity Index is an index system which is expressed as the carbon dioxide emis- sion per GDP and is designed to promote emission reduction. The index can effectively reflect the changes on carbon emission intensity in different administrative regions in time series. This paper discusses the influenc- ing factors of carbon emission intensity of Shanghai from 2005 to 2011, based on industrial structure, energy intensity, energy structure and policy & economic environment. It outlines activities accordingly to advance emission reductions in Shanghai. The result shows that the energy-saving target and low-carbon policy of Shanghai promoted the adjustment of energy structure, the optimization of industrial structure and the de- creasing of energy intensity; and then pushed forward the declining of carbon emission intensity of Shanghai and Xsh,y in recent years. While the economic crisis, “4 Trillion Yuan Economic Stimulus Plan”, Expo 2010 ShanghaiChinaand other major economic events and activities were the independent external factors which influenced the Xsh,y obviously.

从中碳强度指数看上海碳减排的足迹Shanghai Carbon Emission Reduction in General, by SINO Carbon Intensity Index

中碳强度指数是为全国节能减排行业提供一个以单位GDP二氧化碳排放量为载体的指数系统，可有效反应时间序列上地区碳排放强度的变化。本文从产业结构、能源强度、能源结构、政策及经济环境等几个方面，探讨了2005至2011年上海市碳排放强度的影响因素，并在此基础上提出了促进上海碳减排活动的建议。分析结果表明，上海市的节能目标和低碳政策，对能源结构调整、产业结构优化和能源强度下降都起到了推动作用，进而促成了近几年来上海市碳排放强度和上海市碳强度指数(Xsh,y)的持续下降。而经济危机、“4万亿元刺激经济方案”、以及2010年上海世博会等重大经济事件和活动，也作为独立的外部因素对Xsh,y起到了重要影响。SINO Carbon Intensity Index is an index system which is expressed as the carbon dioxide emission per GDP. The index can reflect the changes on carbon emission intensity in different administrative regions in time series. This paper discusses the factors of carbon emission intensity of Shanghaifrom 2005 to 2011, based on industrial structure, energy intensity, energy structure and policy & economic environment. The result shows that the energy-saving target and low-carbon policy of Shanghai promoted the energy restructuring, the optimization of industrial structure and the decreasing of energy intensity; and then pushed forward the declining of carbon emission intensity of Shanghai and Xsh,y in recent years. Major economic events and activities, such as the economic crisis, “4 Trillion Yuan Economic Stimulus Plan” and Expo 2010 Shanghai China, etc., were the independent external factors that influenced the Xsh,y obviously.

A research on influencing factors of carbon emission intensity

The dual challenge of economic development and environmental destruction has aroused public attention recently.According to the cross-sectional data of 112 independent economic entities and econometric analysis,based on statistical analysis and econometrics,a complex quantitative model was established to elucidate the carbon emissions per capita and carbon emissions per unit GDP affected by the factors of per capita income,urbanization rate,industrialization rate,and proportion of service industry.Finally a four-stage and five-type model was generalized which reflected inner relationship between carbon intensity and socio-economic development.Accompanied by socio-economic development,the increase of carbon intensity has a convergence trend in the long range.The key to reduce carbon intensity lies in decrease of luxury consumption mainly conducted by developed economic entities and their technical aid to the low income economic entities,especially in energy technology and advanced technology aspects.Meanwhile,areas in the quick development of industrialization should pave a new industrial path rather than the following predecessors,to improve energy structure,accelerate technological transformation and abandon outmoded production technology.Moreover,optimizing settlement distribution,constructing compact cities and proceeding modern service industries should also be on their list.