Driving Factors of Carbon Emissions in China's Municipalities: A LMDI Approach

2021 ◽  
Author(s):  
Yuanxin Liu ◽  
Yajing Jiang ◽  
Hui Liu ◽  
Bo Li ◽  
Jia-hai Yuan
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Industrial Structure ◽  
Driving Factors ◽  
Policy Implications ◽  
Growth Effect ◽  
Structure Effect ◽  
Energy Structure ◽  
Intensity Effect ◽  
Population Effect

Abstract China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities directly under the Central Government, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that promote the growth of carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits the growth of carbon emissions, followed by energy structure effect. The paper thereby puts forward policy implications for China's economic policies.

Framework for accounting for tourism carbon emissions in China: An industrial linkage perspective

2020 ◽  
pp. 135481662092489
Author(s):  
Jianping Zha ◽  
Rong Fan ◽  
Yao Yao ◽  
Lamei He ◽  
Yuanyuan Meng
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Tourism Industry ◽  
Broad Sense ◽  
Structure Effect ◽  
Energy Structure ◽  
Narrow Sense ◽  
Intensity Effect ◽  
Carbon Emission Reduction

Understanding tourism carbon emissions and their influencing factors from the perspective of industrial linkages can inform policy-making in the development of sustainable tourism. Based on a combination of the environmental input–output (I-O) model and structural decomposition analysis, this article develops a novel framework for analyzing the industrial linkage pathways of China’s carbon emissions linked to tourism and identifying the driving factors affecting change in carbon emissions embodied in the supply chain. Results reveal that most carbon emissions linked to China’s broad-sense or narrow-sense tourism industry derive from some critical upstream industries, that is, indirect carbon emissions resulting from the intermediate production processes. Significant differences exist in the industrial linkage pathways of carbon emissions between tourism subsectors; thus, emission reduction policies for the broad-sense or narrow-sense tourism industry should be formulated based on these key interindustrial linkage pathways. The direct energy consumption intensity effect and energy structure effect are beneficial to carbon emission reduction, while the I-O structure effect reverses the effect on carbon emission reduction from negative to positive.

scholarly journals Decomposition of China’s Carbon Emissions Intensity from 1995 to 2010: An Extended Kaya Identity

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Li ◽  
Qing-Xiang Ou
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Logarithmic Mean ◽  
Development Path ◽  
Optimal Outcomes ◽  
Emissions Intensity ◽  
Kaya Identity

This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2emissions trends in China. Change in CO2emissions intensity is decomposed from 1995 to 2010 and includes measures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China’s emission reduction targets in 2020 are to be reached. This requires a change in China’s economic development path and energy consumption path for optimal outcomes.

scholarly journals Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method

2019 ◽  
Vol 16 (20) ◽  
pp. 4004
Author(s):  
Junliang Yang ◽  
Haiyan Shan
Keyword(s):  
Energy Consumption ◽  
Sulfur Dioxide ◽  
Emission Reduction ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Structure Effect ◽  
Index Method ◽  
Intensity Effect ◽  
Energy Mix ◽  
Sulfur Dioxide Emissions

The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th “Five-Year Plan” period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004–2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized.

scholarly journals Research on driving factors of China’s carbon emissions based on structural decomposition

2021 ◽  
Vol 248 ◽  
pp. 02026
Author(s):  
Hua Gao ◽  
Zhoujie Huang
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Final Demand ◽  
Driving Factors ◽  
Structure Effect ◽  
Influence Coefficient ◽  
Intensity Effect ◽  
Demand Structure ◽  
Demand Effect ◽  
Main Factor

After further processing the input-output tables of 2007, 2012 and 2017, the carbon emissions are decomposed into four driving factors: energy intensity effect, Leontief technology effect, final demand structure effect and final total demand effect through IO-SDA model. The results show that the energy intensity effect has a significant negative effect, which is the main factor to promote the reduction of carbon emissions. The Leontief technical effect and the final total demand effect are positive effects, and the total final demand effect is the main factor leading to the increase in carbon emissions, and the effect of the final demand structure effect is not significant. In addition, the results of the influence coefficient and the inductance coefficient show that: metal smelting and rolling manufacturing, petroleum processing and coking and nuclear fuel processing, coal mining and processing, and oil and gas mining and processing industries are high-energy-consuming industries, but the status of the basic industry makes it possible to formulate energy-saving policies only in terms of technological progress.

scholarly journals Carbon Emissions in the Xinjiang Production and Construction Corps and Driving Factors

2021 ◽  
Vol 9 ◽  
Author(s):  
Meng Wang ◽  
Lei Feng ◽  
Pengfei Zhang ◽  
Gaohang Cao ◽  
Hanbin Liu ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Emission Inventory ◽  
Driving Factors ◽  
Hot Water ◽  
Total Carbon ◽  
Energy Structure ◽  
Petroleum Processing ◽  
Carbon Emission Reduction

Xinjiang production and Construction Corps (XPCC) is an important provincial administration in China and vigorously promotes the construction of industrialization. However, there has been little research on its emissions. This study first established the 1998-2018 XPCC subsectoral carbon emission inventory based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission inventory method and adopted the logarithmic mean Divisia indexmethod (LMDI) model to analyze the driving factors. The results revealed that from 1998 to 2018, the total carbon emissions in the XPCC increased from 6.11 Mt CO2 in 1998 to 115.71 Mt CO2 in 2018. For the energy structure, raw coal, coke and industrial processes were the main contributors to carbon emissions. For industrial structure, the main emission sectors were the production and supply of electric power, steam and hot water, petroleum processing and coking, raw chemical materials and chemical products, and smelting and pressing of nonferrous metals. In addition, the economic effect was the leading factor promoting the growth of the corps carbon emissions, followed by technical and population effects. The energy structure effect was the only factor yielding a low emission reduction degree. This research provides policy recommendations for the XPCC to formulate effective carbon emission reduction measures, which is conducive to the construction of a low-carbon society. Moreover, it is of guiding significance for the development of carbon emission reduction actions for the enterprises under the corps and provides a reference value for other provincial regions.

scholarly journals An Empirical Study of Carbon Emission Impact Factors Based on the Vector Autoregression Model

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7797
Author(s):  
Wei Fan ◽  
Xi Luo ◽  
Jiabei Yu ◽  
Yiyang Dai
Keyword(s):  
Economic Growth ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Vector Autoregression ◽  
Linear Regression Analysis ◽  
Growth Effect ◽  
Impact Factors ◽  
Energy Structure ◽  
Annual Data ◽  
Intensity Effect

It is important to effectively reduce carbon emissions and ensure the simultaneous adjustment of economic development and environmental protection. Therefore, we used Kaya identity to screen the factors influencing carbon emissions and conducted preliminary qualitative analyses, including grey relation analysis and linear regression analysis, on important variables to establish a vector autoregression (VAR) model based on their annual data to empirically analyze the influencing factors of carbon emissions. The results showed that economic growth effect, energy intensity effect and embodied carbon in foreign trade were the key factors affecting carbon emissions, among which the economic growth effect contributed the most. Accordingly, we propose countermeasures including technological innovation to reduce energy intensity, the development of new energy sources to improve energy structure, acceleration of industrial structure transfer, and optimization of trade structure.

scholarly journals The Regional Inequity of CO2 Emissions per Capita in China

2017 ◽  
Vol 9 (7) ◽  
pp. 228 ◽  
Author(s):  
Ting Liu ◽  
Wenqing Pan
Keyword(s):  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Theil Index ◽  
Energy Structure ◽  
Co2 Emission Reduction ◽  
Index Method ◽  
Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

scholarly journals The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

2020 ◽  
Vol 12 (4) ◽  
pp. 1428 ◽  
Author(s):  
Na Lu ◽  
Shuyi Feng ◽  
Ziming Liu ◽  
Weidong Wang ◽  
Hualiang Lu ◽  
...  
Keyword(s):  
Construction Industry ◽  
Carbon Emissions ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Energy Structure ◽  
Chinese Government ◽  
Low Carbon ◽  
Industry Agglomeration ◽  
Technology Innovations ◽  
Growth Energy

As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

scholarly journals Analysis of Factors Influencing Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China

2020 ◽  
Vol 12 (3) ◽  
pp. 1089
Author(s):  
Jiancheng Qin ◽  
Hui Tao ◽  
Chinhsien Cheng ◽  
Karthikeyan Brindha ◽  
Minjin Zhan ◽  
...  
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Economic Structure ◽  
Energy Structure ◽  
Intensity Effect ◽  
Entire Energy ◽  
Key Factor ◽  
Main Factors

Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990–2016. Afterwards, we analyzed the contribution rate of sectors’ energy intensity effect and sectors’ economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different stages. During 1990–2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001–2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011–2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry’s economic structure and transportation’s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc.

Regional Economic Growth and Carbon Emissions - A Case Study of Anhui Province

2013 ◽  
Vol 869-870 ◽  
pp. 1056-1062
Author(s):  
Xue Qin Wang ◽  
Cheng Xin Wang ◽  
Yun Wei Du ◽  
Jia Lu Shi
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Energy Saving ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Reduction Process ◽  
Anhui Province ◽  
Energy Structure ◽  
Analysis Model ◽  
Promotion Policy

This essay tends to probe into the decoupling relationship between economic growth and carbon emissions through structuring the decoupling analysis model. The results show that: In recent years, the decoupling relationship between economic growth and carbon emissions in Anhui province has improved. Through the research about some intermediate variables, we find that the change trend of energy consumption elastic elasticity of carbon emissions and the one of GDP elastic elasticity of carbon emissions are basically the same. Meanwhile, Anhui province is relatively backward in the energy-saving and emission reduction process, carbon emissions growth and energy consumption growth did not achieve effective decoupling, which reflects that this province still has some defects in the adjustment of energy structure, energy saving and emission reduction technology promotion policy etc.

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