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Do homes that are more energy efficient consume less energy?: A structural equation model for England's residential sector


  • Kelly, S.


Energy consumption from the residential sector is a complex sociotechnical problem that can be explained using a combination of physical, demographic and behavioural characteristics of a dwelling and its occupants. A structural equation model (SEM) is introduced to calculate the magnitude and significance of explanatory variables on residential energy consumption. The benefit of this approach is that it explains the complex relationships that exist between manifest variables and their overall effect through direct, indirect and total effects on energy consumption. Using the English House Condition Survey (EHCS) consisting of 2531 unique cases, the main drivers behind residential energy consumption are found to be the number of household occupants, floor area, household income, dwelling efficiency (SAP), household heating patterns and living room temperature. In the multivariate case, SAP explains very little of the variance of residential energy consumption. However, this procedure fails to account for simultaneity bias between energy consumption and SAP. Using SEM its shown that dwelling energy efficiency (SAP), has reciprocal causality with dwelling energy consumption and the magnitude of these two effects are calculable. When nonrecursivity between SAP and energy consumption is allowed for, SAP is shown to have a moderately negative effect on energy consumption but conversely, homes with a propensity to consume more energy have a higher SAP rating and are therefore more efficient.

Suggested Citation

  • Kelly, S., 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model for England's residential sector," Cambridge Working Papers in Economics 1139, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1139

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    References listed on IDEAS

    1. Shorrock, LD & Dunster, JE, 1997. "The physically-based model BREHOMES and its use in deriving scenarios for the energy use and carbon dioxide emissions of the UK housing stock," Energy Policy, Elsevier, vol. 25(12), pages 1027-1037, October.
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    Cited by:

    1. Giovanni Marin & Alessandro Palma, 2015. "Technology Invention and Diffusion in Residential Energy Consumption. A Stochastic Frontier Approach," Working Papers 2015.104, Fondazione Eni Enrico Mattei.
    2. repec:eee:enepol:v:109:y:2017:i:c:p:520-529 is not listed on IDEAS
    3. Kelly, Scott & Shipworth, Michelle & Shipworth, David & Gentry, Michael & Wright, Andrew & Pollitt, Michael & Crawford-Brown, Doug & Lomas, Kevin, 2013. "Predicting the diversity of internal temperatures from the English residential sector using panel methods," Applied Energy, Elsevier, vol. 102(C), pages 601-621.
    4. Longo, L. & Colantoni, A. & Castellucci, S. & Carlini, M. & Vecchione, L. & Savuto, E. & Pallozzi, V. & Di Carlo, A. & Bocci, E. & Moneti, M. & Cocchi, S. & Boubaker, K., 2015. "DEA (data envelopment analysis)-assisted supporting measures for ground coupled heat pumps implementing in Italy: A case study," Energy, Elsevier, vol. 90(P2), pages 1967-1972.
    5. Dineen, D. & Ó Gallachóir, B.P., 2017. "Exploring the range of energy savings likely from energy efficiency retrofit measures in Ireland's residential sector," Energy, Elsevier, vol. 121(C), pages 126-134.
    6. repec:eee:enepol:v:110:y:2017:i:c:p:246-256 is not listed on IDEAS
    7. Hårsman, Björn & Wahlström, Marie H., 2014. "Residential energy consumption and conservation," Working Paper Series in Economics and Institutions of Innovation 388, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    8. Stutterecker, Werner & Blümel, Ernst, 2012. "Energy plus standard in buildings constructed by housing associations?," Energy, Elsevier, vol. 48(1), pages 56-65.
    9. Huebner, Gesche M. & Hamilton, Ian & Chalabi, Zaid & Shipworth, David & Oreszczyn, Tadj, 2015. "Explaining domestic energy consumption – The comparative contribution of building factors, socio-demographics, behaviours and attitudes," Applied Energy, Elsevier, vol. 159(C), pages 589-600.
    10. Estiri, Hossein, 2014. "Building and household X-factors and energy consumption at the residential sector," Energy Economics, Elsevier, vol. 43(C), pages 178-184.
    11. Comodi, Gabriele & Cioccolanti, Luca & Renzi, Massimiliano, 2014. "Modelling the Italian household sector at the municipal scale: Micro-CHP, renewables and energy efficiency," Energy, Elsevier, vol. 68(C), pages 92-103.
    12. Taylor, Nicholas W. & Jones, Pierce H. & Kipp, M. Jennison, 2014. "Targeting utility customers to improve energy savings from conservation and efficiency programs," Applied Energy, Elsevier, vol. 115(C), pages 25-36.
    13. Lin, Boqiang & Yang, Fang & Liu, Xia, 2013. "A study of the rebound effect on China's current energy conservation and emissions reduction: Measures and policy choices," Energy, Elsevier, vol. 58(C), pages 330-339.
    14. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    15. Galvin, Ray & Sunikka-Blank, Minna, 2016. "Quantification of (p)rebound effects in retrofit policies – Why does it matter?," Energy, Elsevier, vol. 95(C), pages 415-424.
    16. Lin, Boqiang & Liu, Xia, 2012. "Dilemma between economic development and energy conservation: Energy rebound effect in China," Energy, Elsevier, vol. 45(1), pages 867-873.
    17. Estiri, Hossein, 2015. "The indirect role of households in shaping US residential energy demand patterns," Energy Policy, Elsevier, vol. 86(C), pages 585-594.
    18. Rosenberg, Eva, 2014. "Calculation method for electricity end-use for residential lighting," Energy, Elsevier, vol. 66(C), pages 295-304.
    19. Burman, Esfand & Mumovic, Dejan & Kimpian, Judit, 2014. "Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings," Energy, Elsevier, vol. 77(C), pages 153-163.
    20. Dineen, D. & Rogan, F. & Ó Gallachóir, B.P., 2015. "Improved modelling of thermal energy savings potential in the existing residential stock using a newly available data source," Energy, Elsevier, vol. 90(P1), pages 759-767.
    21. Zhao, Dong-Xue & He, Bao-Jie & Johnson, Christine & Mou, Ben, 2015. "Social problems of green buildings: From the humanistic needs to social acceptance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1594-1609.
    22. Zhang, Tao & Siebers, Peer-Olaf & Aickelin, Uwe, 2012. "A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK," Energy Policy, Elsevier, vol. 47(C), pages 102-110.
    23. Pan, Wei & Garmston, Helen, 2012. "Compliance with building energy regulations for new-build dwellings," Energy, Elsevier, vol. 48(1), pages 11-22.
    24. repec:gam:jeners:v:11:y:2018:i:5:p:1180-:d:145117 is not listed on IDEAS
    25. Huebner, Gesche M. & Shipworth, David, 2017. "All about size? – The potential of downsizing in reducing energy demand," Applied Energy, Elsevier, vol. 186(P2), pages 226-233.

    More about this item


    Residential; energy; modelling; SAP; structural equation efficiency;

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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