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 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.

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 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.

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.

Research on Carbon Emissions Estimation and Decomposition of Industry in Hebei Province

2012 ◽  
Vol 518-523 ◽  
pp. 4941-4947
Author(s):  
Dong Heng Hao ◽  
Guo Zhu Li ◽  
Dian Ru Wang
Keyword(s):  
Carbon Emissions ◽  
Large Scale ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Hebei Province ◽  
Industrial Output ◽  
Factors Affecting ◽  
Industrial Enterprises ◽  
Industrial Energy ◽  
Major Factors

This paper estimated the carbon emissions of the large-scale industrial enterprises in Hebei Province, and analyzed their changing factors using the LMDI method. The results shows that major factors affecting the carbon emissions of industry in Hebei province are energy intensity, industrial structure and output changes . Seen from the absolute value , industrial output has the greatest impact on carbon emissions followed by energy intensity. Industrial structures has the least impact. The combined impact of industrial energy saving technological progress and industrial structure adjustment on carbon emissions is less than that of industrial output.

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.

Using LMDI Method to Analyze the Energy-Related CO2 Emissions in Guangdong Province, China

2013 ◽  
Vol 291-294 ◽  
pp. 1556-1561
Author(s):  
Ping Wang ◽  
Wan Shui Wu ◽  
Bang Zhu Zhu
Keyword(s):  
Co2 Emissions ◽  
Energy Intensity ◽  
Negative Impact ◽  
Scale Effects ◽  
Energy Structure ◽  
Per Capita Gdp ◽  
Lmdi Method ◽  
Greenhouse Gas Inventories ◽  
Per Capita ◽  
Population Scale

In recent years, Guangdong has achieved remarkable performance in economic development; meanwhile it is being faced with problems of increasing CO2 emissions. Following the IPCC Guidelines for National Greenhouse Gas Inventories, we estimated the energy-related CO2 emissions in Guangdong during the period of 1980-2010. We employed the logarithmic mean divisia index (LMDI) method to decompose the CO2 emissions into energy intensity, energy structure, per capita GDP and population scale effects. Besides, we deduced the calculation methods for the year by year effects, the accumulated effects and the contribution degrees. Using 1980 as the base year, the empirical results show that the accumulated effects of energy intensity and energy structure in 2010 are negative, while those of per capita GDP and population scale are positive. Per capita GDP is the chief positive influence on the CO2 emissions. Energy intensity is becoming more significant; however, its direction is instability. Population scale has a significant positive effect on the CO2 emissions. Energy structure has a negligible negative impact on the CO2 emissions. Some suggestions on CO2 emissions reduction in Guangdong are given based on the analysis.

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.

scholarly journals Decoupling of Carbon Emissions from Economic Growth: An Empirical Analysis Based on 264 Prefecture-Level Cities in China

2021 ◽  
Author(s):  
Wenmei KANG ◽  
Benfan LIANG ◽  
Keyu XIA ◽  
Fei XUE ◽  
Yu LI
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Economic Development ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Low Carbon ◽  
Per Capita Gdp ◽  
Plan Period ◽  
Per Capita

After setting the goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, it has become an irresistible trend for China to decouple carbon emissions from its economic growth. Since cities play a central role in reducing carbon emissions, the issues such as whether and when their carbon dioxide emissions can be decoupled from economic growth have become the focus of attention. Based on the carbon dioxide emissions of 264 prefecture-level cities in China from 2000 to 2017, this paper uses the Tapio decoupling index to measure the decoupling relationship between carbon emissions and economic growth of cities, analyzes the space–time evolution characteristics of carbon emissions and decoupling indexes by stages, and explores the relationship between carbon emissions and socio-economic development characteristics such as per capita GDP and industrial structure. The main conclusions drawn therefrom are as follows: (i) From 2000 to 2017, the city-wide carbon emissions rose from 2.484 billion tons in 2000 to 7.462 billion tons in 2017, registering a total increase of 200.40%. But the growth rate of carbon emissions within cities has been significantly reduced. (ii) As years passed by, the number of cities that achieved strong decoupling saw a significant increase, from zero in the 10th–11th Five-Year Plan period to 14 in the 12th Five-Year Plan period and the first two years of the 13th Five-Year Plan period, accounting for 5.3% of the total number of cities. (iii) There is an inverted U-shaped curve relationship between per capita carbon emissions and per capita GDP, which is consistent with the EKC curve hypothesis, but Chinese cities are still in the growth stage of the quadratic curve currently. The correlation between per capita CO2 emission and the proportion of the secondary industry was positive. The results of this study are expected to provide experience for the low-carbon development of cities in China and other developing countries, and provide references for the formulation and evaluation of policies and measures related to low-carbon economic development based on the decoupling model.

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