scholarly journals Increased inequalities of per capita CO2 emissions in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Yang ◽  
Yun Hao ◽  
Chao Feng
Keyword(s):  
Industrial Structure ◽  
Climate Mitigation ◽  
Energy Structure ◽  
Level Energy ◽  
Inequality Decomposition ◽  
Inequality Index ◽  
Decomposition Approach ◽  
Per Capita ◽  
Capita Output ◽  
Change Mitigation

AbstractDesigning inter-regional and inter-provincial responsibility-sharing mechanisms for climate change mitigation requires the knowledge of carbon distributions. This study is the first to use a two-sector (i.e., productive and household sectors) inequality decomposition approach to examine the regional, provincial, and national inequalities of per capita CO2 emissions (CPC) in China, as well as their determinants. We show that the CPC inequality index in China increased from 1.1364 in 2000 to 2.3688 in 2017, with the productive sector accounting for 91.42% of this expansion and households responsible for the rest. The production-side per capita output level, energy efficiency, energy structure, and industrial structure explain 69.01%, 12.81%, 5.57%, and 4.03% of these inequalities, respectively. Further, the household per capita energy consumption and energy structure explain only 8.12% and 0.46%, respectively. Therefore, future responsibility-sharing mechanisms for climate mitigation need to be formulated taking mainly the productive sector into account.

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.

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 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.

scholarly journals Projections and Recommendations for Energy Structure and Industrial Structure Development in China through 2030: A System Dynamics Model

2019 ◽  
Vol 11 (18) ◽  
pp. 4901 ◽  
Author(s):  
Song Han ◽  
Changqing Lin ◽  
Baosheng Zhang ◽  
Arash Farnoosh
Keyword(s):  
Energy Consumption ◽  
System Dynamics ◽  
Industrial Structure ◽  
Simulation Software ◽  
Utilization Efficiency ◽  
Energy Structure ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Per Capita

In this research, we established a System Dynamics Model named “E&I-SD” to study the development of the energy structure and industrial structure in China from 2000 to 2030 using Vensim Simulation Software based on energy economy theory, system science theory and coordinated development theory. We used Direct Structure Test, Structure-oriented Behavior Test, and Behavior Pattern Test to ensure the optimal operation of the system. The model’s results showed that the indicators of total energy consumption, total added value of GDP after regulation, energy consumption per capita, and GDP per capita were on the rise in China, but emissions per unit of energy showed a downward trend. Separately, the model predicted average annual growth rates in China through 2030. Based on these findings, we proposed important policies for China’s sustainable development. Firstly, short- and long-term policy measures should be implemented to replace fossil fuels with clean energy. Secondly, the utilization efficiency of raw coal should be appraised future. The planning should provide for steady development and improvement of the primary, secondary, and tertiary sectors. Thirdly, the mid- and long-term plans for development and management of various industrial sectors and the corresponding energy consumption should be based on technological trends. Finally, a market-oriented pricing mechanism for energy should be established in China as soon as possible.

The Scenario Analysis of Carbon Emissions Based on Improved IPAT Model in China

2012 ◽  
Vol 616-618 ◽  
pp. 1484-1489 ◽  
Author(s):  
Xu Shan ◽  
Hua Wang Shao
Keyword(s):  
Carbon Dioxide ◽  
Economic Development ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Energy Structure ◽  
Low Carbon ◽  
Policy Scenario ◽  
Social And Economic Development ◽  
Energy Environment ◽  
Development Goals

The coordination development of economy-energy-environment was discussed with traditional environmental loads model, combined with "decoupling" theory. Considering the possibilities of social and economic development, this paper set out three scenarios, and analyzed quantitatively the indexes, which affected carbon dioxide emissions, including population, per capita GDP, industrial structure and energy structure. Based on this, it forecasted carbon dioxide emissions in China in future. By comparing the prediction results, it held that policy scenario was the more realistic scenario, what’s more it can achieve emission reduction targets with the premise of meeting the social and economic development goals. At last, it put forward suggestions to implement successfully policy scenario, from energy structure, industrial structure, low-carbon technology and so on.

On the climate change mitigation potential of CO2 conversion to fuels

2017 ◽  
Vol 10 (12) ◽  
pp. 2491-2499 ◽  
Author(s):  
J. Carlos Abanades ◽  
Edward S. Rubin ◽  
Marco Mazzotti ◽  
Howard J. Herzog
Keyword(s):  
Climate Change ◽  
Climate Change Mitigation ◽  
Liquid Fuels ◽  
Climate Mitigation ◽  
Co2 Conversion ◽  
Mitigation Potential ◽  
Alternative Systems ◽  
Change Mitigation

Proposed utilization schemes producing liquid fuels from captured CO2 offer fewer climate mitigation benefits at higher costs than alternative systems.

scholarly journals Analyzing the Role of the Permanent and Temporary Shocks in Peru Using the Co-Movements Approach

2021 ◽  
Vol 13 (11) ◽  
pp. 115
Author(s):  
Cesar R. Sobrino
Keyword(s):  
Empirical Work ◽  
Capita Consumption ◽  
Quarterly Data ◽  
Common Trend ◽  
The Common ◽  
Per Capita ◽  
A Minor ◽  
Capita Output ◽  
Per Capita Consumption

In this study, we use the co-movements approach to examine the role of permanent (common trend) and temporary (common cycle) shocks on per capita output, per capita consumption, and per capita investment in Peru, a small open commodity-based economy. Using quarterly data from 1993: Q1 to 2019: Q1, the effects of the temporary shocks are short-lived, and, on average, are a minor source of the variations of macro time series, over 10 quarters. This evidence suggests that the main source of per capita output and per capita consumption variations is the common trend shock which must be related to the 1990s reforms. Moreover, per capita output and per capita consumption are less responsive to unfavorable (favorable) common cycle shocks than per capita investment is. This outcome indicates that per capita investment has a much more volatile cycle than per capita private output and per capita consumption which is consistent with a previous empirical work.

scholarly journals Economic Development

2019 ◽  
Author(s):  
Dhina Vadyza
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Income Distribution ◽  
Urban Areas ◽  
Gini Index ◽  
Production Factor ◽  
Real Economy ◽  
Long Run ◽  
Per Capita ◽  
Capita Output

Economic growth is a process of increasing per capita output that occurs continuously in the long run. Economic growth is one indicator of the success of development. Increasingly increasing economic growth usually increases people's welfare. While economic development is an effort to increase per capita income by processing potential economic forces into the real economy through investment, increasing knowledge, increasing skills, using technology, adding management skills and organizing.Economic growth is also related to the increase in "per capita output". The theory must include theories about GDP growth and theories about population growth. Then the third aspect is economic growth in a long-term perspective, that is, if for a long period of time the per capita output shows an increasing tendency.The distribution of income distribution in Indonesia is increasingly uneven. This can be seen from the increasing Indonesian Gini Index. As is known, the Gini index measures the income distribution of a country. The size of the Gini index Between 0 (zero) to 1 (one), the Gini index Equal to 0 (zero) indicates the index that the income distribution is perfectly equal, while the Gini index is 1 (one ) shows that the income distribution is totally uneven. Based on the data, the Indonesian Gini index continues to increase from year to year.The state of income distribution in Indonesia since 1970 can be said not to improve, this is caused by many factors, including the First production factor market (input market) which is the increase in labor supply which results in excess labor, low labor wages and limited employment opportunities in urban areas resulting in unemployment and urban slums.Second, land ownership. Land distribution is the main determinant of the extent of poverty and income distribution.

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