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 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 Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

2019 ◽  
Vol 11 (6) ◽  
pp. 1806 ◽  
Author(s):  
Xianrui Liao ◽  
Wei Yang ◽  
Yichen Wang ◽  
Junnian Song
Keyword(s):  
Energy Use ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Industrial Output ◽  
Contribution Rate ◽  
Index Method ◽  
Intensity Effect ◽  
Output Effect ◽  
Final Energy ◽  
Two Stages

With continuous industrialization and urbanization, cities have become the dominator of energy consumption, to which industry is making leading contribution among all sectors. Given the insufficiency in comparative study on the drivers of energy use across cities at multisector level, this study selected seven representative cities in China to quantify and analyze the contributions of factors to changes in final energy use (FEU) in industrial aggregate and sectoral levels by using Logarithmic Mean Divisia Index method. Disparities in the drivers of industrial FEU across cities were explicitly revealed within two stages (2005–2010 and 2010–2015). Some key findings are presented as follows. Alongside the increase in industrial output of seven cities within two stages, the variation trends in industrial FEU are different. Industrial output effect (contribution rate 16.7% ~ 184.0%) and energy intensity effect (contribution rate −8.6% ~ −76.5%) contributed to the increase in aggregate FEU positively and negatively, respectively. Beijing had the largest contribution share of industrial structure effect (−24.4% and −12.8%), followed by Shenyang and Xi’an. Contributions of energy intensity effect and industrial output effect for Chemicals, Nonmetals, Metals, and Manufacture of equipment were much larger than those of other sectors. The results revealed that production technological innovations, phase-out of outdated capacities of energy intensive industries, and industrial restructuring are crucial for reduction in industrial FEU of cities. This study also provided reference to reasonable industrial layout among cities and exertion of technological advantages from a national perspective.

scholarly journals Multiplicative Structural Decomposition Analysis of Spatial Differences in Energy Intensity among G20 Countries

2020 ◽  
Vol 10 (8) ◽  
pp. 2832
Author(s):  
Yang Wang ◽  
Meng Sun ◽  
Rui Xie ◽  
Xiangjie Chen
Keyword(s):  
Energy Consumption ◽  
South Korea ◽  
Energy Intensity ◽  
Decomposition Analysis ◽  
Embodied Energy ◽  
Structure Effect ◽  
Structural Decomposition Analysis ◽  
Intensity Effect ◽  
Input Structure ◽  
Spatial Differences

Comparing the spatial differences in the energy intensity of the Group of Twenty (G20) countries and identifying the factors that influence these differences can help the G20 countries formulate targeted policies to achieve energy conservation goals. This study analyzes the spatial differences in the G20 countries’ energy intensity at the aggregate and sectoral levels based on an input–output framework and reveals its driving factors by employing multiplicative structural decomposition analysis, obtaining the sectoral energy intensity, input structure, and final demand structure effects. The results show that: (1) the gap in aggregate energy intensity among the G20 countries tended to converge from 2000 to 2014 with the reducing energy intensity in Russia, India, China, and South Korea having great potential to reduce global energy consumption and improve global energy efficiency; (2) in 2014, the main driving forces for above-average energy intensity was the sectoral energy intensity effect in India, South Korea, and Canada, the input structure effect in Russia and China, and the final demand structure effect in Indonesia; (3) using the average of the G20 countries as a reference, the energy reduction potential of China, Russia, India, South Korea, Indonesia, and Canada is 62.75, 31.94, 21.24, 7.67, 1.47, and 0.81 exajoules (EJ), respectively. The embodied energy consumption decline in these countries was equivalent to 21.78% of the G20’s total energy consumption in 2014; and (4) the most important factor of the high embodied energy intensity of key sectors in India and South Korea is the sectoral energy intensity effect, while for Russia and China, it is the input structure effect.

Analysis of Energy Consumption in China and the Driving Factors Based on MRCI

2014 ◽  
Vol 521 ◽  
pp. 855-858 ◽  
Author(s):  
Hong Hai Sun ◽  
Yan Bin Sun ◽  
Yan Qiu Wang
Keyword(s):  
Energy Consumption ◽  
Energy Intensity ◽  
Structure Effect ◽  
Contribution Rate ◽  
Complete Structure ◽  
Output Analysis ◽  
Intensity Effect ◽  
Input Output Analysis ◽  
Analysis Theory ◽  
Consumption Saving

Using the complete structure of input output analysis theory and no residual decomposition method (MRCI) effect on the growth of energy consumption China and corresponding contribution rate of quantitative analysis of scale effect, the contribution rate of 100.99%, the structure of production effect, the contribution rate of 38.16%, per capita energy efficiency reached 9.05%, population scale contribution rate of 12.11%, the use of the structure effect reached 6.09%, distribution structure effect is up to 5.83%, pushing the energy consumption growth Chinese role; energy intensity effect contribution rate of-72.23% China, inhibit the growth of energy consumption, saving energy and reducing consumption of our country plays an important role.

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.

Study on the industrial structure optimization under constraint of energy intensity

2020 ◽  
pp. 0958305X2092159
Author(s):  
Xiongfeng Pan ◽  
Mengna Li ◽  
Chenxi Pu ◽  
Haitao Xu
Keyword(s):  
Energy Consumption ◽  
Computable General Equilibrium ◽  
Technological Progress ◽  
Energy Intensity ◽  
Industrial Structure ◽  
High Energy ◽  
Industrial Sector ◽  
Added Value ◽  
Tertiary Industry ◽  
Intensity Control

This study establishes a multi-sector dynamic computable general equilibrium framework that integrates energy intensity module to explore the reverse feedback effect of energy intensity control on industry structure. The results indicate that (1) the tightening effect of energy intensity constrains on the Industrial sector is most significant, followed by the Tertiary Industry, with the least impact on Agriculture; (2) when there is no technological progress in the departments, the change of industrial structure is mainly reflected in the sharp decline in the proportion of Industry and the significant increase in the proportion of Tertiary Industry. When technological progress exists in high energy-consumption departments, the tightening effect of energy intensity constraints on the industrial sector will be reduced; when there is technological progress in all departments, the industrial structure will have a smaller change, and the technology progress can alleviate the tightening effect of the energy intensity target on various sectors; (3) under the constraint of energy intensity, the high energy-consuming industry shifts to the Equipment Manufacturing with low energy-consumption and high-added value. The increasing proportion of Tertiary Industry mainly comes from two industries including Wholesale, Retail, Hoteling and Catering, and Transportation, Storage, and Post.

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.

Research on Energy Efficiency and Environmental Pollution in China: a Simultaneous Equations Approach

2011 ◽  
Vol 361-363 ◽  
pp. 974-977 ◽  
Author(s):  
Ying Nan Dong ◽  
Yu Duo Lu ◽  
Jiao Jiao Yu
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Economic Loss ◽  
Simultaneous Equations ◽  
Consumption Structure ◽  
The Relationship

This paper examined the relationship between the energy efficiency and the environmental pollution. By using the data of energy intensity and economic loss caused by environmental pollution (ELP) in China from 1989-2009, a simultaneous equations was developed. The result of two-stage OLS estimation suggested that the energy had exerted positive influences on the decreasing of the environmental pollutions. By enhancing the energy efficiency and adjusting the industrial structure and energy consumption structure, China is exploring a road for sustainable development in the energy conservation.

An Analysis of Changes of Carbon Emissions by the Farmers in Sichuan, China in 1997-2010

2013 ◽  
Vol 448-453 ◽  
pp. 4455-4460
Author(s):  
Juan Tan ◽  
Chang Quan Lin
Keyword(s):  
Energy Consumption ◽  
Population Size ◽  
Carbon Emissions ◽  
Rural Areas ◽  
Energy Intensity ◽  
Sichuan Province ◽  
Residential Areas ◽  
Direct Role ◽  
Energy Mix ◽  
Economic Level

On the basis of the extended Kaya identity and by means of LMDI, the writers analyze quantitatively the 4 factors including energy mix, energy intensity, economic level, and population size, etc. thus giving effects to changes of carbon emissions from livelihood energy consumption by the farmers in the rural residential areas in Sichuan Province from 1997 to 2010.The results show that the energy mix and energy intensity have brought negative effects to the growth of carbon emissions. These effects will come into being more and more obviously. Economic level played a direct role in growth of carbon emissions, which is considered to be their greatest and everlasting contributor in respect of growth of carbon emissions. The contribution of population size keeps pace with carbon emissions basically. These reflect Sichuan Province have made some achievements in developing new energy in the rural areas in recent years. The rural population have been maintained under stable control in Sichuan. Meanwhile, with its economic development, the rural residents livelihood have been improved gradually and the demand for energy consumption keeps on rising. Finally, carbon emissions have increased in the rural areas of Sichuan Province.

Exploration of the Development of Schemes to Achieve a District-Aggregate Energy-Intensity Goal - Taking the 12th Five-Year Period Energy Intensity Goal of Daxing District, Beijing as a Study Case

2014 ◽  
Vol 962-965 ◽  
pp. 1455-1460
Author(s):  
Xiang Qian Li ◽  
Li Juan Yang ◽  
Ling Ling Chen
Keyword(s):  
Energy Consumption ◽  
Gross Domestic Product ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Losses ◽  
Residential Energy ◽  
Initial Value ◽  
Study Case ◽  
Per Capita

The paper explored how to develop schemes to achieve a district’s energy consumption per gross domestic product (ECPGDP) target. It first analysed the available measures regarding the reduction of ECPGDP. These measures include optimising the industrial structure, reducing the energy intensity of different industries, reducing the per capita residential energy consumption, and reducing the energy losses. Next, the procedure and methods of developing schemes to achieve the target ECPGDP were proposed. The procedure contains five steps: determine the target ECPGDP, predicting the initial value of the ECPGDP, analysing the availability of different measures of reducing the ECPGDP, forming the schemes of achieving the target, and summarising the proposed schemes. Finally, the paper considered the 12th Five-Year period ECPGDP target of Daxing District, Beijing as a study case. In the case study, four quantitative schemes to achieve the target ECPGDP were considered.

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