How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes

被引：86
作者：
Roman-Collado, Rocio [1 ,2 ,3 ]
Cansino, Jose M. [1 ,2 ,3 ]
Botia, Camilo [1 ]
机构：
[1] Univ Seville, Seville, Spain
[2] Univ Autonoma Chile, Providencia, Chile
[3] Dept Econ Anal & Polit Econ, Avda Ramon y Cajal 1, Seville 41018, Spain
来源：
ENERGY | 2018年 / 148卷
关键词：
Energy consumption; Tapio index; Elasticity decoupling; LMDI decomposition; Colombia; NINO-SOUTHERN-OSCILLATION; CO2; EMISSIONS; LMDI DECOMPOSITION; ECONOMIC-GROWTH; AGGREGATE ENERGY; DRIVING FORCES; DIVISIA INDEX; MAIN DRIVERS; INTENSITY; POPULATION;
D O I：
10.1016/j.energy.2018.01.141
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
A decoupling elasticity analysis and a two-level decomposition analysis of energy consumption in Colombia from 2000 to 2015 are developed. Firstly, the decoupling elasticity approach is used to analyse the importance of energy consumption changes in relation to the GDP changes. Then, a Logarithmic Mean Divisia Index analysis is carried out, decomposing the changes in energy consumption into four effects: Population, Activity, Structural and Intensity. Secondly, a decoupling index determines the main drivers of the inhibiting effect on energy consumption. The results show that the Population and Activity effects contribute to the country's increase of energy consumption, while the Intensity effect and, to a lesser extent, the Structure effect help to decrease it. From a sectoral perspective, variations in the energy consumption are mainly caused by the Transport and Industrial sectors. In the light of the results obtained, current decoupling-oriented measures are steps in the right direction, but more efforts should be made because until now they have not been effective. New policy recommendations are provided. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：687 / 700
页数：14
相关论文
共 81 条
  • [1] Decomposition analysis of the variations in residential electricity consumption in Brazil for the 1980-2007 period: Measuring the activity, intensity and structure effects
    Achao, Carla
    Schaeffer, Roberto
    [J]. ENERGY POLICY, 2009, 37 (12) : 5208 - 5220
  • [2] Aiwen Z., 2013, TECHNOLOGY EC, V1, DOI [10.3969/j.issn.1002-1980X.2013.01.019.019, DOI 10.3969/J.ISSN.1002-1980X.2013.01.019.019]
  • [3] CO2 emissions of Turkish manufacturing industry: A decomposition analysis
    Akbostanci, Elif
    Tunc, Gul Ipek
    Turut-Asik, Serap
    [J]. APPLIED ENERGY, 2011, 88 (06) : 2273 - 2278
  • [4] Structural analysis of electricity consumption by productive sectors. The Spanish case
    Alcantara, Vicent
    del Rio, Pablo
    Hernandez, Felix
    [J]. ENERGY, 2010, 35 (05) : 2088 - 2098
  • [5] Drivers in CO2 emissions variation: A decomposition analysis for 33 world countries
    Andreoni, Valeria
    Galmarini, Stefano
    [J]. ENERGY, 2016, 103 : 27 - 37
  • [6] Energy decomposition analysis: IEA model versus other methods
    Ang, B. W.
    Liu, Na
    [J]. ENERGY POLICY, 2007, 35 (03) : 1426 - 1432
  • [7] Carbon emission intensity in electricity production: A global analysis
    Ang, B. W.
    Su, Bin
    [J]. ENERGY POLICY, 2016, 94 : 56 - 63
  • [8] LMDI decomposition approach: A guide for implementation
    Ang, B. W.
    [J]. ENERGY POLICY, 2015, 86 : 233 - 238
  • [9] Properties and linkages of some index decomposition analysis methods
    Ang, B. W.
    Huang, H. C.
    Mu, A. R.
    [J]. ENERGY POLICY, 2009, 37 (11) : 4624 - 4632
  • [10] The LMDI approach to decomposition analysis: a practical guide
    Ang, BW
    [J]. ENERGY POLICY, 2005, 33 (07) : 867 - 871
123456789