Investigation for the Decomposition of Carbon Emissions in the USA with C-D Function and LMDI Methods

被引:17
作者
Jiang, Rui [1 ]
Li, Rongrong [1 ,2 ]
Wu, Qiuhong [3 ]
机构
[1] China Univ Petr East China, Sch Econ & Management, Qingdao 266580, Peoples R China
[2] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China
[3] Xinjiang Univ Finance & Econ, Sch Business Adm, 449 Middle Beijing Rd, Urumqi 830011, Xinjiang, Peoples R China
关键词
combined decomposition technique; perfect decomposition; labor input; fixed asset investment; GREENHOUSE-GAS EMISSIONS; ENERGY-CONSUMPTION; CO2; EMISSIONS; ECONOMIC-GROWTH; DRIVING FORCES; INPUT-OUTPUT; DECOUPLING ANALYSIS; DIOXIDE EMISSIONS; GHG EMISSIONS; CHINA;
D O I
10.3390/su11020334
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Residual problems are one of the greatest challenges in developing new decomposition techniques, especially when combined with the Cobb-Douglas (C-D) production function and the Logarithmic Mean Divisia Index (LMDI) method. Although this combination technique can quantify more effects than LMDI alone, its decomposition result has residual value. We propose a new approach that can achieve non-residual decomposition by calculating the actual values of three key parameters. To test the proposed approach, we decomposed the carbon emissions in the United States to six driving factors: the labor input effect, the investment effect, the carbon coefficient effect, the energy structure effect, the energy intensity effect, and the technology state effect. The results illustrate that the sum of these factors is equivalent to the CO2 emissions changes from t to t-1, thereby proving non-residual decomposition. Given that the proposed approach can achieve perfect decomposition, the proposed approach can be used more widely to investigate the effects of labor input, investment, and technology state on changes in energy and emission.
引用
收藏
页数:15
相关论文
共 63 条
[11]   A survey of index decomposition analysis in energy and environmental studies [J].
Ang, BW ;
Zhang, FQ .
ENERGY, 2000, 25 (12) :1149-1176
[12]   A new energy decomposition method: perfect in decomposition and consistent in aggregation [J].
Ang, BW ;
Liu, FL .
ENERGY, 2001, 26 (06) :537-548
[13]  
[Anonymous], SUSTAINABILITY BASEL
[14]  
[Anonymous], SUSTAINABILITY BASEL, DOI DOI 10.3390/SU9050793
[15]  
[Anonymous], 2017, SUSTAINABILITY BASEL, DOI DOI 10.3390/SU9061013
[16]  
[Anonymous], 2019, Databases, Tables, and Calculators by Subject
[17]  
BEA, US BUR EC AN FIX ASS
[18]   Drivers of greenhouse gas emissions in the Baltic States: A structural decomposition analysis [J].
Brizga, Janis ;
Feng, Kuishuang ;
Hubacek, Klaus .
ECOLOGICAL ECONOMICS, 2014, 98 :22-28
[19]   Main drivers of changes in CO2 emissions in the Spanish economy: A structural decomposition analysis [J].
Cansino, Jose M. ;
Roman, Rocio ;
Ordonez, Manuel .
ENERGY POLICY, 2016, 89 :150-159
[20]   Driving forces of Spain's CO2 emissions: A LMDI decomposition approach [J].
Cansino, Jose M. ;
Sanchez-Braza, Antonio ;
Rodriguez-Arevalo, Maria L. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 48 :749-759