Skip to main content
Applied EnergyVolume 121, 15 May 2014, Pages 10-19

Index decomposition analysis of residential energy consumption in China: 2002-2010(Article)

  Save all to author list
  • aSchool of Economics and Management, Changchun University of Science and Technology, Changchun, China
  • bInternational Centre for Integrated Assessment and Sustainable Development (ICIS), Maastricht University, Maastricht, Netherlands
  • cUNU-MERIT, Maastricht University, Maastricht, Netherlands

Abstract

Residential energy consumption in China increased dramatically over the period of 2002-2010. In this paper, we undertake a decomposition analysis of changes in energy use by Chinese households for five energy-using activities: space heating/cooling, cooking, lighting and electric appliances. We investigate to what extent changes in energy use are due to changes from appliances and to change in floor space, population and energy mix. Our decomposition analysis is based on the logarithmic mean Divisia index technique using data from the China statistical yearbook and China energy statistical yearbook in the period of 2002-2010. According to our results, the increase in energy-using appliances is the biggest contributor to the increase of residential energy consumption during 2002-2010 but the effect declines over time, due to energy efficiency improvements in those appliances. The second most important contributor is floor space per capita, which increased with 28%. Of the four factors, population is the most stable factor and energy mix is the least important factor. We predicted electricity use, with the help of regression-based predictions for ownership of appliances and the energy efficiency of appliances. We found that electricity use will continue to rise despite a gradual saturation of demand. © 2014 Elsevier Ltd.

Author keywords

ChinaGrowth in floor space and electric appliancesIndex decomposition analysisResidential energy consumption

Indexed keywords

Engineering controlled terms:Electric appliancesEnergy efficiencyFloorsHeatingHousing
Engineering uncontrolled termsChinaDecomposition analysisEnergy efficiency improvementsFloor spaceIndex decomposition analysisLogarithmic meanPopulation and energyResidential energy consumption
Engineering main heading:Energy utilization
GEOBASE Subject Index:demand-side managementelectricity supplyelectronic equipmentregression analysisresidential energystatistical analysis
Regional Index:China

Funding details

  • 1

    In this study, we exclude REC for transport because of the focus on house-based energy use through our floor space variable. This is done by deducting petrol and diesel oil consumption which accounts for 8.41% from total REC. Here we suppose petrol and diesel is consumed only by passenger vehicles, which is supported by previous studies [12,13] . According to our limited knowledge, this is the first study which decomposes REC of China from such perspective.

  • ISSN: 03062619
  • CODEN: APEND
  • Source Type: Journal
  • Original language: English
  • DOI: 10.1016/j.apenergy.2014.01.070
  • Document Type: Article
  • Publisher: Elsevier Ltd

  Nie, H.; International Center for Integrated Assessment and Sustainable Development, Maastricht University, P.O. Box 616, Netherlands;
© Copyright 2020 Elsevier B.V., All rights reserved.

Cited by 99 documents

Zhang, L. , Yang, M. , Zhang, P.
De-coal process in urban China: What can we learn from Beijing's experience?
(2021) Energy
Zhang, M. , Chen, Y. , Hu, W.
Exploring the impact of temperature change on residential electricity consumption in China: The ‘crowding-out’ effect of income growth
(2021) Energy and Buildings
Lamb, W.F. , Wiedmann, T. , Pongratz, J.
A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018
(2021) Environmental Research Letters
View details of all 99 citations
{"topic":{"name":"Divisia Index; Logarithmic Mean; Energy Intensity","id":4801,"uri":"Topic/4801","prominencePercentile":99.67056,"prominencePercentileString":"99.671","overallScholarlyOutput":0},"dig":"c6130d091594ffd385dc049fceed563d332d6d4fdf16a5446b8158084f6dc74b"}

SciVal Topic Prominence

Topic:
Prominence percentile: