Impact of household transitions on domestic energy consumption and its applicability to urban energy planning

Abstract

The household sector consumes roughly 30% of Earth’s energy resources and emits approximately 17% of its carbon dioxide. As such, developing appropriate policies to reduce the CO₂ emissions, which are associated with the world’s rapidly growing urban population, is a high priority. This, in turn, will enable the creation of cities that respect the natural environment and the well-being of future generations. However, most of the existing expertise focuses on enhancing the thermal quality of buildings through building physics while few studies address the social and behavioral aspects. In fact, focusing on these aspects should be more prominent, as they cause between 4% and 30% of variation in domestic energy consumption. Premised on that, the aim of this study was to investigate the effect in the context of the UK of household transitions on household energy consumption patterns. To achieve this, we applied statistical procedures (e.g., logistic regression) to official panel survey data comprising more than 5500 households in the UK tracked annually over the course of 18 years. This helped in predicting future transition patterns for different household types for the next 10 to 15 years. Furthermore, it enabled us to study the relationship between the predicted patterns and the household energy usage for both gas and electricity. The findings indicate that the life cycle transitions of a household significantly influence its domestic energy usage. However, this effect is mostly positive in direction and weak in magnitude. Finally, we present our developed urban energy model “EvoEnergy” to demonstrate the importance of incorporating such a concept in energy forecasting for effective sustainable energy decision-making.

Keywords

urban energy planning / household transitions / smart cities / energy forecasting / household projection / serious gaming

References

[1] Allison P D (2006). Fixed effects regression methods in SAS. Thirty-first Annual SAS. https://www.researchgate.net/profile/Paul_Allison/publication/228767539_Fixed_effects_regression_methods_in_SAS/links/0fcfd50e5de875b37b000000.pdf

[2] Bartiaux F, Gram-Hanssen K (2005). Socio-political factors influencing household electricity consumption: A comparison between Denmark and Belgium. ECEE 2005 Summer Study, 1313–1325

[3] Bramlett M D, Mosher W D (2012). Cohabitation, marriage, divorce, and remarriage in the United States. Vital health statistics, 23(22): 1–32

[4] Brounen D, Kok N, Quigley J M (2012). Residential energy use and conservation: Economics and demographics. European Economic Review, 56(5): 931–945

[5] CIA (2015). The world factbook. https://www.cia.gov/library/publications/the-world-factbook/fields/2212.html

[6] DECC (2015a). Energy Consumption in the UK (2015). https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/449102/ECUK_Chapter_1_-_Overall_factsheet.pdf

[7] DECC (2015b). RPI: fuel & light: electricity (Jan 1987=100) - Office for National Statistics.https://www.ons.gov.uk/economy/inflationandpriceindices/timeseries/dobx.

[8] DECC (2015c). Annual Fuel Poverty Statistics Report

[9] Druckman A, Jackson T (2008). Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model. Energy Policy, 36(8): 3177–3192

[10] Du R Y, Kamakura W A (2006). Household life cycles and lifestyles in the United States. Journal of Marketing Research, 43(1): 121–132. 

[11] Frederiks E R, Stenner K, Hobman E V (2015). The socio-demographic and psychological predictors of residential energy consumption: A comprehensive review. Energies, 8(1): 573–609

[12] Genjo K, Tanabe S I, Matsumoto S I, Hasegawa K, Yoshino H (2005). Relationship between possession of electric appliances and electricity for lighting and others in Japanese households. Energy and Building, 37(3): 259–272

[13] Gill Z M, Tierney M J, Pegg I M, Allan N (2010). Low-energy dwellings: The contribution of behaviours to actual performance. Building Research and Information, 38(5): 491–508

[14] Goodman A, Greaves E (2010). Cohabitation, marriage and child outcomes.http://www.ifs.org.uk/comms/comm114.pdf

[15] Greene W W H (2012). Econometric analysis. 97. 

[16] Guerra Santin O, Itard L, Visscher H (2009). The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy and Buildings, 41(11): 1223–1232

[17] Hoaglin D C, Iglewicz B, Tukey J W (1986). Performance of some resistant rules for outlier labeling. Journal of the American Statistical Association, 81(396): 991–999

[18] ISER (2016). British Household Panel Survey (BHPS) - Institute for Social and Economic Research (ISER).https://www.iser.essex.ac.uk/bhps

[19] JRF (2005). The effect of parents’ employment on outcomes for children/JRF.https://www.jrf.org.uk/report/effect-parents-employment-outcomes-children

[20] Kornbrot D (2005). Point biserial correlation. Wiley StatsRef: Statistics Reference Online

[21] Krüger A, Kolbe T H (2012). Building Analysis for Urban Energy Planning using Key Indicators on Virtual 3D City Models – the Energy Atlas of Berlin. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXII Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS), Melbourne

[22] Longhi S (2014). Residential energy use and the relevance of changes in household circumstances. ISER Working Paper Series

[23] Mansouri I, Newborough M, Probert D (1996). Energy consumption in UK households: Impact of domestic electrical appliances. Applied Energy, 54(3): 211–285

[24] NCT (2014). Barriers remain for parents returning to work. https://www.nct.org.uk/press-release/barriers-remain-parents-returning-work

[25] ONS (2013a). Divorces in England and Wales: 2013. http://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/divorce/bulletins/divorcesinenglandandwales/2013#children-of-divorced-couples

[26] ONS (2013b). Home ownership and renting in England and Wales – detailed characteristics

[27] ONS (2014). Families in the labour market. http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/dcp171776_388440.pdf

[28] ONS (2015a). Chapter 5: Financial wealth, wealth in Great Britain, 2012 to 2014. http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/dcp171776_428639.pdf

[29] ONS (2015b). Births by parents’ characteristics in England and Wales: 2014. http://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/livebirths/bulletins/birthsbyparentscharacteristicsinenglandandwales/2014

[30] Santamouris M, Kapsis K, Korres D, Livada I, Pavlou C, Assimakopoulos M N (2007). On the relation between the energy and social characteristics of the residential sector. Energy and Building, 39(8): 893–905

[31] Skelton C (2013). Soft City Culture and Technology: The Betaville Project. Springer Publishing Company, Incorporated

[32] Sonderegger R C (1978). Movers and stayers: The resident’s contribution to variation across houses in energy consumption for space heating. Energy and Building, 1(3): 313–324

[33] Steg L, Vlek C (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3): 309–317

[35] Van Raaij W F, Verhallen T M M (1983). A behavioral model of residential energy use. Journal of Economic Psychology, 3(1): 39–63

[36] WhatPrice (2017). Advantages and disadvantages of UK house types. http://www.whatprice.co.uk/building/uk-house-types.html

[37] Whiting S (2010). Socio-demographic comparison between those UK families with up to two children and those with three or more. Population Matters. http://populationmatters. org/documents/family_sizes. pdf

[38] Wiesmann D, Lima Azevedo I, Ferrão P, Fernández J E (2011). Residential electricity consumption in Portugal: Findings from top-down and bottom-up models. Energy Policy, 39(5): 2772–2779

[39] Zhou S, Teng F (2013). Estimation of urban residential electricity demand in China using household survey data. Energy Policy, 61: 394–402

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2017 The Author(s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)