Analysis of China's carbon emission driving factors based on Kaya model

2013 ◽  
Author(s):  
Liu Xuezhi ◽  
Hou Pengfei ◽  
Qiao Yu ◽  
Zheng Yanyan
Keyword(s):  
Carbon Emission ◽  
Driving Factors

scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

Analysis of driving factors and allocation of carbon emission allowance in China

2019 ◽  
Vol 673 ◽  
pp. 74-82 ◽  
Author(s):  
Ang Yu ◽  
Xinru Lin ◽  
Yiting Zhang ◽  
Xia Jiang ◽  
Lihong Peng
Keyword(s):  
Carbon Emission ◽  
Driving Factors ◽  
Emission Allowance

Is global carbon inequality getting better or worse? A decomposition analysis of carbon inequality in intraincome and interincome groups

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rongrong Li ◽  
Qiang Wang ◽  
Yi Liu ◽  
Rui Jiang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Income Group ◽  
Driving Factors ◽  
Theil Index ◽  
High Income Group ◽  
Middle Income ◽  
Content Type ◽  
Middle Income Group ◽  
Global Carbon

PurposeThis study is aimed at better understanding the evolution of inequality in carbon emission in intraincome and interincome groups in the world, and then to uncover the driving factors that affect inequality in carbon emission.Design/methodology/approachThe approach is developed by combining the Theil index and the decomposition technique. Specifically, the Theil index is used to measure the inequality in carbon emissions from the perspective of global and each income group level. The extended logarithmic mean Divisia index was developed to explore the driving factors.FindingsThis study finds that the inequality in carbon emissions of intraincome group is getting better, whereas the inequality in carbon emission of interincome group is getting worse. And the difference in global carbon emissions between income groups is the main source of global carbon emission inequality, which is greater than that within each income group. In addition, the high-income group has transferred their carbon emissions to upper-middle income group by importing high-carbon-intensive products to meet the domestic demand, while lower-middle-income group do not fully participate in the international trade.Practical implicationsTo alleviate the global carbon inequality, more attention should be paid to the inequality in carbon emission of interincome group, especially the trade between high-income group and upper-middle income group. From the perspective of driving factors, the impact of import and export trade dependence on the per capita carbon emissions of different income groups can almost offset each other, so the trade surplus effect should be the focus of each group.Originality/valueIn order to consider the impact of international trade, this study conducts a comprehensive analysis of global carbon emissions inequality from the perspective of income levels and introduces the import and export dependence effect and the trade surplus effect into the analysis framework of global carbon emission inequality drivers, which has not been any research carried out so far. The results of this paper not only provide policy recommendations for mitigating global carbon emissions but also provide a new research perspective for subsequent inequality research.

Dynamic Change and Analysis of Driving Factors of Carbon Emissions from Traffic and Transportation Energy Consumption in Jilin Province

2014 ◽  
Vol 472 ◽  
pp. 851-855 ◽  
Author(s):  
Biao Gao ◽  
Qing Tao Xu ◽  
Yu Bo Li
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Growth Stage ◽  
Dynamic Change ◽  
Driving Factors ◽  
Jilin Province ◽  
Energy Structure ◽  
Estimation Model ◽  
Transportation Energy

Based on the traffic and transportation energy consumption, the carbon emissions of traffic and transportation energy consumption are obtained by using the estimation model of carbon emissions from 1999 to 2011 in Jilin Province, and the dynamic changes and the Environmental Kuznets Curve (EKC) of carbon emissions are analyzed. The result indicates that the carbon emission of traffic and transportation energy consumption increased continuously from 99.3750×104 t to 331.8255×104 t between 1999 and 2011 in Jilin Province, the change process is divided into three stages which include the stage of the stationary growth phase, accelerated growth stage and slow growth stage, the large consumption of diesel energy is the main reason of the rapid growth in carbon emissions. The EKC of carbon emission shows the inverted U shape roughly and the turning point appeared in 2011, after 2011, carbon emissions will decrease along with the economic growth. Based on the STIRPAT model, the study reveals that elasticity coefficients of driving factors such as population, per capita GDP, the unit GDP energy consumption, the investment of traffic and transportation, city rate, the number of private cars are 0.23440.2202-0.22470.16570.2864 and 0.2163, respectively. Jilin Province must implement effective measures to change the existing development mode of traffic and transportation, change the energy structure, and increase the innovation of scientific and technological, to strive for the realization of negative growth in carbon emissions of traffic and transportation energy consumption.

scholarly journals Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia

2020 ◽  
Vol 12 (8) ◽  
pp. 3101 ◽  
Author(s):  
Xiaoqing Zhu ◽  
Tiancheng Zhang ◽  
Weijun Gao ◽  
Danying Mei
Keyword(s):  
Spatial Pattern ◽  
East Asia ◽  
Carbon Emissions ◽  
Industrial Development ◽  
Carbon Emission ◽  
East Asian ◽  
Driving Factors ◽  
Material Consumption ◽  
Mixed Use ◽  
Urban Rural

Urban-intensive areas are responsible for an estimated 80% of greenhouse gas emissions, particularly carbon dioxide. The urban–rural fringe areas emit more greenhouse gases than urban centers. The purpose of this study is to analyze the spatial pattern and driving factors of carbon emissions in urban–rural fringe mixed-use communities, and to develop planning methods to reduce carbon emissions in communities. This study identifies mixed-use communities in East Asian urban–rural fringe areas as industrial, commercial, tourism, and rental-apartment communities, subsequently using the emission factor method to calculate carbon emissions. The statistical information grid analysis and geographic information systems spatial analysis method are employed to analyze the spatial pattern of carbon emission and explore the relationship between established space, industrial economy, material consumption, social behavior, and carbon emission distribution characteristics by partial least squares regression, ultimately summing up the spatial pattern of carbon emission in the urban–rural fringe areas of East Asia. Results show that (1) mixed-use communities in the East Asian urban–rural fringe areas face tremendous pressure to reduce emissions. Mixed-use community carbon emissions in the late urbanization period are lower than those the early urbanization. (2) Mixed-use community carbon emission is featured by characteristics, such as planning structure decisiveness, road directionality, infrastructure directionality, and industrial linkage. (3) Industrial communities produce the highest carbon emissions, followed by rental-apartment communities, business communities, and tourism communities. (4) The driving factor that most affects the spatial distribution of carbon emissions is the material energy consumption. The fuel consumption per unit of land is the largest driver of carbon emissions. Using the obtained spatial pattern and its driving factors of carbon emissions, this study provides suggestions for planning and construction, industrial development, material consumption, and convenient life guidance.

scholarly journals Driving Factors and Future Prediction of Carbon Emissions in the ‘Belt and Road Initiative’ Countries

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5455
Author(s):  
Lili Sun ◽  
Huijuan Cui ◽  
Quansheng Ge
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Driving Factors ◽  
Peak Time ◽  
Belt And Road Initiative ◽  
Belt And Road ◽  
Future Prediction ◽  
Global Carbon ◽  
The Belt And Road

‘Belt and Road Initiative’ (B&R) countries play critical roles in mitigating global carbon emission under the Paris agreement, but their driving factors and feasibility to reduce carbon emissions remain unclear. This paper aims to identify the main driving factors (MDFs) behind carbon emissions and predict the future emissions trajectories of the B&R countries under different social-economic pathways based on the extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The empirical results indicate that GDP per capita and energy consumption structure are the MDFs that promote carbon emission, while energy intensity improvement is the MDF that inhibits carbon emission. Population, as another MDF, has a dual impact across countries. The carbon emissions in all B&R countries are predicted to increase from SSP1 to SSP3, but emissions trajectories vary across countries. Under the SSP1 scenario, carbon emissions in over 60% of B&R countries can peak or decline, and the aggregated peak emissions will amount to 21.97 Gt in 2030. Under the SSP2 scenario, about half of the countries can peak or decline, while their peak emissions and peak time are both higher and later than SSP1, the highest emission of 25.35 Gt is observed in 2050. Conversely, over 65% of B&R countries are incapable of either peaking or declining under the SSP3 scenario, with the highest aggregated emission of 33.10 Gt in 2050. It is further suggested that decline of carbon emission occurs when the inhibiting effects of energy intensity exceed the positive impacts of other MDFs in most B&R countries.

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