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 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.

Energy Consumption and Structural Reformation in Chinese Northeast Old Industrial Base

2013 ◽  
Vol 448-453 ◽  
pp. 4281-4284 ◽  
Author(s):  
Shao Bo Liu
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Energy Utilization ◽  
Energy Structure ◽  
Energy Technology ◽  
Industrial Base ◽  
Old Industrial Base ◽  
Emissions Intensity

Using IPCC methodology, the carbon emissions of Chinese Northeast Old Industrial Base is calculated, and the energy's synthesized impact on carbon emissions intensity is presented. The resulting shows that the carbon emissions in the three northeast provinces decreased 52.87% from 2000 to 2010, of which, Liaoning, Jilin and Heilongjiang are individually 60.09%, 45.47% and 54.14% lower. The implications are that the energy structure is one of the main factors in carbon emission in the Old Industrial Base of Northeast China, and its industrial structure is changing greatly due to energy consumption carbon emission. To adjust optimally the energy and industrial structure, and to develop the energy technology to promote energy utilization are recommended.

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 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.

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.

Carbon Emission of Mining Industry’s Energy Consumption in Heilongjiang Province

2015 ◽  
Vol 1092-1093 ◽  
pp. 1597-1600
Author(s):  
Zhong Hua Wang ◽  
Xin Ye Chen
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Mining Industry ◽  
Calculation Model ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Heilongjiang Province ◽  
Low Carbon

The need to reduce carbon emission in Heilongjiang Province of China is urgent challenge facing sustainable development. This paper aims to make explicit the problem-solving of carbon emission to find low carbon emission ways. According to domestic and foreign literatures on estimating and calculating carbon emissions and by integrating calculation methods of carbon emissions, it was not possible to consider all of the many contributions to carbon emissions. Calculation model of carbon emissions suitable to this paper is selected. The carbon emissions of energy consumption in mining industry are estimated and calculated from 2005 to 2012, and the characteristics of carbon emission are analyzed at the provincial level. It makes the point that carbon emissions of energy consumption in mining industry can be reduced when we attempt to alter energy consumption structure, adjust industrial structure and improve energy utilization efficiency.

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.

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 Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

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