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

2019 ◽  
Vol 11 (15) ◽  
pp. 4220 ◽  
Author(s):  
Jiancheng Qin ◽  
Hui Tao ◽  
Minjin Zhan ◽  
Qamar Munir ◽  
Karthikeyan Brindha ◽  
...  
Keyword(s):  
Scenario Analysis ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Gdp Per Capita ◽  
Energy Structure ◽  
Urbanization Level ◽  
Autonomous Region ◽  
Per Capita ◽  
Xinjiang Autonomous Region

The realization of carbon emissions peak is important in the energy base area of China for the sustainable development of the socio-economic sector. The STIRPAT model was employed to analyze the elasticity of influencing factors of carbon emissions during 1990–2010 in the Xinjiang autonomous region, China. The results display that population growth is the key driving factor for carbon emissions, while energy intensity is the key restraining factor. With 1% change in population, gross domestic product (GDP) per capita, energy intensity, energy structure, urbanization level, and industrial structure, the change in carbon emissions was 0.80%, 0.48%, 0.20%, 0.07%, 0.58%, and 0.47%, respectively. Based on the results from regression analysis, scenario analysis was employed in this study, and it was found that Xinjiang would be difficult to realize carbon emissions peak early around 2030. Under the condition of the medium-high change rates in energy intensity, energy structure, industrial structure, and with the low-medium change rates in population, GDP per capita, and urbanization level, Xinjiang will achieve carbon emissions peak at of 626.21, 636.24, 459.53, and 662.25 million tons in the year of 2030, 2030, 2040, and 2040, respectively. At last, under the background of Chinese carbon emissions peak around 2030, this paper puts forward relevant policies and suggestions to the sustainable socio-economic development for the energy base area, Xinjiang autonomous region.

scholarly journals Analysis of influencing factors of Chinese provincial carbon emissions based on projection pursuit model and Markov transfer matrix

2019 ◽  
Vol 11 (3) ◽  
pp. 406-423 ◽  
Author(s):  
Lei Wen ◽  
Linlin Huang
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Projection Pursuit ◽  
Energy Structure ◽  
Per Capita Gdp ◽  
Content Type ◽  
Factors Affecting ◽  
Optimal Projection ◽  
Per Capita

Purpose Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance. Design/methodology/approach This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix. Findings The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications. Originality/value This paper provides an insight into the current state and the future changes in carbon emissions.

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 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 Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jing-min Wang ◽  
Yu-fang Shi ◽  
Xue Zhao ◽  
Xue-ting Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Factors Affecting ◽  
Economic Output ◽  
Level Model ◽  
The Impact ◽  
The Relationship

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO2/energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

Measure of Carbon Emissions in Jiangsu Province and Analyses of Factors

2011 ◽  
Vol 71-78 ◽  
pp. 2262-2265
Author(s):  
Jian Hua ◽  
Jun Ren
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Jiangsu Province ◽  
Energy Structure ◽  
High Carbon ◽  
The Status ◽  
Per Capita ◽  
Emissions Intensity

We calculate the carbon dioxide emissions from the combustion of energy and production process of cement in Jiangsu Province from 1990 to 2009.Through the indicators such as carbon emissions intensity, per capita carbon emissions, we analyze the status and trends of carbon dioxide emissions in Jiangsu Province. Based on the factors of industrial structure, energy structure and high-carbon products, we give some suggestions.

scholarly journals Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3054 ◽  
Author(s):  
Zhen Li ◽  
Yanbin Li ◽  
Shuangshuang Shao
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Total Population ◽  
Industrial Structure ◽  
Global Climate ◽  
Primary Energy ◽  
Urbanization Level ◽  
Per Capita ◽  
Technology Level

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

scholarly journals Regional Heterogeneity of Carbon Emissions and Peaking Path of Carbon Emissions in the Bohai Rim Region

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chuanhui Wang ◽  
Mengzhen Zhao ◽  
Weifeng Gong ◽  
Zhenyue Fan ◽  
Wenwen Li
Keyword(s):  
Renewable Energy ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
High Energy ◽  
Energy Utilization ◽  
Total Carbon ◽  
Energy Structure ◽  
Bohai Rim ◽  
Carbon Peak

Taking the Bohai Rim region as the research object and based on the relevant data of energy consumption, GDP, and energy structure from 2000 to 2019, the total carbon emissions of the provinces and cities from 2020 to 2050 were predicted. The carbon peak situation of each province and municipality in the Bohai Rim region was also analyzed. A comparative analysis of the peaks among the provinces and cities has been carried out. The results show the following: (1) it is predicted that Beijing will reach its carbon peak before 2025. Tianjin is predicted to reach its carbon peak before 2030. Renewable energy development and utilization technologies in the two municipalities are crucial to achieving carbon peaks when energy intensity is already low. (2) Shandong and Shanxi have a heavy energy structure, are coal-minded, and have high energy intensity, while the replacement rate of renewable energy is relatively low. Shandong and Shanxi are predicted to reach carbon peaks around 2030. Liaoning also has the problem of heavy industrial structure, and it is predicted to reach the carbon peak before 2027. (3) Hebei itself relies on Beijing, and its renewable energy utilization technology is relatively advanced. It is predicted to reach the carbon peak before 2026. The energy intensity of Inner Mongolia has decreased rapidly, and it is predicted to reach the carbon peak before 2029. Therefore, according to the forecast results and the analysis of the similarities and differences among the provinces and cities, some specific suggestions for the optimization of the energy structure and the development of renewable energy in each province and city have been proposed in order to promote the comprehensive realization of the regional carbon peak goal in the Bohai Rim region.

scholarly journals The Decoupling of China's Economy-Carbon Emission and Its Driving Factors

2021 ◽  
Author(s):  
Peng Kuai ◽  
Yao Cheng ◽  
Shu'an Zhang
Keyword(s):  
Carbon Emission ◽  
Energy Intensity ◽  
Driving Factors ◽  
Gdp Per Capita ◽  
Energy Structure ◽  
Heterogeneity Effects ◽  
Per Capita ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
China’S Economy

Abstract Decoupling between economic growth and carbon emission is a global hot topic. This paper studies China’s economic-carbon decoupling and its driving factors. By using the panel data of 30 Chinese provinces from 2000 to 2016, the decoupling index of each province is calculated with the Tapio model. It is found that many provinces have progressed from no decoupling to weak decoupling and then strong decoupling. Then, the econometric models are used to explore the driving factors. Results show that energy structure is the most important factor, followed by GDP per capita and energy intensity, which all increase CO2 emission significantly. The results are robust when tested with GMM, PCSE and FGLS estimation and LMDI decomposition. Further, we conduct a comparative analysis regarding the temporal and spatial characteristics of the above three driving factors to identify their relationship with decoupling, four groups of regions that represent different economic features are selected for the analysis. Heterogeneity effects of the factors among the regions has been observed, based on this we provide targeted strategies for different regions.

A Comparative Study about Carbon Emissions

2012 ◽  
Vol 518-523 ◽  
pp. 1657-1663
Author(s):  
Chang Cai Qin ◽  
Shu Lin Liu ◽  
Yu Feng Wang
Keyword(s):  
Factor Structure ◽  
Carbon Emissions ◽  
Structure Factor ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Influence Factors ◽  
Energy Structure ◽  
Technical Factor ◽  
Scale Population

This article has introduced and evaluated the various methods of study on carbon emissions, and makes a comparison on the research conclusion by using these methods. We has classified the influence factors of carbon emissions into three primary factors such as technical factor, structure factor and scale factor, respectively including six secondary factors such as carbon emission intensity and energy intensity; energy structure and industrial structure; economic scale, population size.

Influencing Factors and Decoupling Analysis of Carbon Emissions in China's Manufacturing Industry

2021 ◽  
Author(s):  
baoling jin ◽  
ying Han
Keyword(s):  
Carbon Emissions ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Value Added ◽  
Carbon Intensity ◽  
Energy Structure ◽  
Investment Efficiency ◽  
Fixed Asset ◽  
Carbon Productivity ◽  
Fixed Asset Investment

Abstract The manufacturing industry directly reflects national productivity, and it is also an industry with serious carbon emissions, which has attracted wide attention. This study decomposes the influential factors on carbon emissions in China’s manufacturing industry from 1995 to 2018 into industry value added (IVA), energy consumption (E), fixed asset investment (FAI), carbon productivity (CP), energy structure (EC), energy intensity (EI), investment carbon intensity (ICI) and investment efficiency (IE) by Generalized Divisia Index Model (GDIM). The decoupling analysis is carried out to investigate the decoupling states of the manufacturing industry under the pressure of "low carbon" and "economy.” Considering the technological heterogeneity, we study the influential factors and decoupling status of the light industry and the heavy industry. The results show that: (1) Carbon emissions of the manufacturing industry present an upward trend, and the heavy industry is the main contributor. (2) Fixed asset investment (FAI), industry value added (IVA) are the driving forces of carbon emissions. Investment carbon intensity (ICI), carbon productivity (CP), investment efficiency (IE), and energy intensity (EI) have inhibitory effects. The impact of the energy consumption (E) and energy structure (EC) are fluctuating. (3) The decoupling state of the manufacturing industry has improved. Fixed asset investment (FAI), industry value added (IVA) hinder the decoupling; carbon productivity (CP), investment carbon intensity (ICI), investment efficiency (IE), and energy intensity (EI) promote the decoupling.

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