Driving factors of carbon emissions in China: A joint decomposition approach based on meta-frontier

2019 ◽  
Vol 256 ◽  
pp. 113986 ◽  
Author(s):  
Bingquan Liu ◽  
Junxue Shi ◽  
Hui Wang ◽  
Xuelin Su ◽  
Peng Zhou
Keyword(s):  
Carbon Emissions ◽  
Driving Factors ◽  
Decomposition Approach ◽  
Joint Decomposition

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.

scholarly journals Research on driving factors of China’s carbon emissions based on structural decomposition

2021 ◽  
Vol 248 ◽  
pp. 02026
Author(s):  
Hua Gao ◽  
Zhoujie Huang
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Final Demand ◽  
Driving Factors ◽  
Structure Effect ◽  
Influence Coefficient ◽  
Intensity Effect ◽  
Demand Structure ◽  
Demand Effect ◽  
Main Factor

After further processing the input-output tables of 2007, 2012 and 2017, the carbon emissions are decomposed into four driving factors: energy intensity effect, Leontief technology effect, final demand structure effect and final total demand effect through IO-SDA model. The results show that the energy intensity effect has a significant negative effect, which is the main factor to promote the reduction of carbon emissions. The Leontief technical effect and the final total demand effect are positive effects, and the total final demand effect is the main factor leading to the increase in carbon emissions, and the effect of the final demand structure effect is not significant. In addition, the results of the influence coefficient and the inductance coefficient show that: metal smelting and rolling manufacturing, petroleum processing and coking and nuclear fuel processing, coal mining and processing, and oil and gas mining and processing industries are high-energy-consuming industries, but the status of the basic industry makes it possible to formulate energy-saving policies only in terms of technological progress.

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.

scholarly journals Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020

2016 ◽  
Vol 8 (10) ◽  
pp. 1018 ◽  
Author(s):  
Decai Tang ◽  
Tingyu Ma ◽  
Zhijiang Li ◽  
Jiexin Tang ◽  
Brandon Bethel
Keyword(s):  
Carbon Emissions ◽  
Jiangsu Province ◽  
Driving Factors ◽  
Trend Prediction
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