Understanding high-emitting households in the UK through a cluster analysis

Abstract

Anthropogenic climate change is a global problem that affects every country and each individual. It is largely caused by human beings emitting greenhouse gases into the atmosphere. In general, a small percentage of the population is responsible for a large amount of emissions. This paper focuses on high emitters and their CO₂ emissions from energy use in UK homes. It applies a cluster approach, aiming to identify whether the high emitters comprise clusters where households in each cluster share similar characteristics but are different from the others. The data are mainly based on the Living Cost and Food survey in the UK. The results show that after equivalising both household emissions and income, the high emitters can be clustered into six groups which share similar characteristics within each group, but are different from the others in terms of income, age, household composition, category and size of the dwelling, and tenure type. The clustering results indicate that various combinations of socioeconomic factors, such as low-income single female living in an at least six-room property, or high-income retired couple owning a large detached house, could all lead to high CO₂ emissions from energy use at home. Policymakers should target each high-emitter cluster differently to reduce CO₂ emissions from energy consumption at home more effectively.

Keywords

cluster analysis / emissions reduction / energy use / high emitters / household energy consumption / socioeconomic factors

References

[1] Intergovernmental Panel on Climate Change (IPCC). AR5 climate change 2014: impacts, adaptation and vulnerability. 2017–01–09, available at IPCC website

[2] United Nations. Paris Agreement. 2017–04–28, available at unfccc website

[3] Her Majesty’s Stationary Office (HMSO). Climate Change Act 2008. London: TSO, 2008

[4] Her Majesty’s Stationary Office (HMSO). The Carbon Budget Order 2009. Statutory instruments Number 1259. London: HMSO, 2009

[5] Her Majesty’s Stationary Office (HMSO). The Carbon Budget Order 2011. Statutory instruments Number 1603. London: HMSO, 2011

[6] Her Majesty’s Stationary Office (HMSO). The Carbon Budget Order 2016. Statutory instruments Number 785. London: HMSO, 2016

[7] Anderson K. Talks in the city of light generate more heat. Nature, 2015, 528(7583): 437 doi:10.1038/528437a

[8] Pye S, Li F G N, Price J, Fais B. Achieving net-zero emissions through the reframing of UK national targets in the post-Paris Agreement era. Nature Energy, 2017, 2(3): 17024

[9] Department for Business, Energy and Industrial Strategy (BEIS). Final UK greenhouse gas emissions national statistics: final estimates of UK greenhouse gas emissions from 1990. London: BEIS, 2018

[10] Department for Business, Energy and Industrial Strategy (BEIS). Energy consumption in the UK. London: BEIS, 2016

[11] Ares E. Feed-in Tariffs. London: House of Commons. 2012

[12] Her Majesty’s Stationary Office (HMSO). The Electricity and Gas (Energy Companies Obligation) (Amendment) Order 2014. Statutory instruments Number 1131. London: HMSO, 2014

[13] Department of Energy and Climate Change (DECC). The Green Deal: A Summary of the Government’s Proposals. URN: 10D/996. London: DECC, 2010

[14] Foulds C, Powell J. Using the Homes Energy Efficiency Database as a research resource for residential insulation improvements. Energy Policy, 2014, 69: 57–72

[15] Dowson M, Poole A, Harrison D, Susman G. Domestic UK retrofit challenge: barriers, incentives and current performance leading into the Green Deal. Energy Policy, 2012, 50: 294–305

[16] Balcombe P, Rigby D, Azapagic A. Investigating the importance of motivations and barriers related to micro-generation uptake in the UK. Applied Energy, 2014, 130: 403–418

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

[18] Druckman A, Jackson T. The carbon footprint of UK households 1990–2004: a socio-economically disaggregated, quasi-multi-regional input-output model. Ecological Economics, 2009, 68(7): 2066–2077

[19] Baiocchi G, Minx J, Hubacek L. The impact of social factors and consumer behaviour on carbon dioxide emissions in the United Kingdom: a regression based on input-output and geo-demographic consumer segmentation data. Journal of Industrial Ecology, 2010, 14: 50–72

[20] Büchs M, Schnepf S. Who emits most? Association between socio-economic factors and UK households’ home energy, transport, indirect and total CO₂ emissions. Ecological Economics, 2013, 90: 114–123

[21] Lenzen M, Murray S A. The ecological footprint–issues and trends. ISA Research Paper 01–03. Sydney: University of Sydney, 2003

[22] Kenny T, Gray N F. A preliminary survey of household and personal carbon dioxide emissions in Ireland. Environment International, 2009, 35(2): 259–272

[23] Jones C M, Kammen D M. Quantifying carbon footprint reduction opportunities for US households and communities. Environmental Science & Technology, 2011, 45(9): 4088–4095

[24] Vringer K, Blok K. The direct and indirect energy requirements of households in the Netherlands. Energy Policy, 1995, 23(10): 893–910

[25] Bhattacharjee S, Reichard G. Socio-economic factors affecting individual household energy consumption: a systematic review. In: The ASME 5th International Conference on Energy Sustainability, Washington, D.C., USA, 2011

[26] Pachauri S. An analysis of cross-sectional variations in total household energy requirements in India using micro survey data. Energy Policy, 2004, 32(15): 1723–1735

[27] Kadian R, Dahiya R P, Garg H P. Energy-related emissions and mitigation opportunities from the household sector in Delhi. Energy Policy, 2007, 35(12): 6195–6211

[28] Gough I, Abdallah S, Johnson V, Ryan-Collins J, Smith C. The distribution of total greenhouse gas emissions by households in the UK, and some implications for social policy. London: Centre for Analysis of Social Exclusion, 2012

[29] Palmer J, Terry N, Armitage P, Godoy-Shimizu D. Savings, beliefs and demographic change. Cambridge Architectural Research Limited, Element Energy, and Loughborough University, 2014

[30] Thumim J, White V. Distributional impacts of personal carbon trading: a report to the department for environment, food and rural affairs. London: Department for Environment, Food and Rural Affairs, 2008

[31] Fahmy E, Thumim J, White V. The distribution of UK household CO2 emissions. Interim Report. JRF programme paper: Climate change and social justice, York, 2011

[32] Chancel L, Piketty T. Carbon and inequality: from Kyoto to Paris. Paris: Paris School of Economics, 2015

[33] Oxfam. Carbon emissions and income inequality. Technical Note. Oxford, 2015

[34] Element Energy. Further analysis of data from the household electricity usage study: consumer archetypes. Final Report for DECC and Defra. Cambridge: Element Energy limited, 2013

[35] Morrison B M, Gladhart P M. Energy and families: the crisis and the response. Journal of Home Economics, 1976, 68: 15–18

[36] Kaza N. Understanding the spectrum of residential energy consumption: a quantile regression approach. Energy Policy, 2010, 38(11): 6574–6585

[37] Lenzen M. Energy and greenhouse gas cost of living for Australia during 1993/94. Energy, 1998, 23(6): 497–516

[38] Sultana S, Pourebrahim N, Kim H. Household energy expenditures in North Carolina: a geographically weighted regression approach. Sustainability, 2018, 10(5): 1511

[39] HM Revenue and Customs. Deductions: General rules: main types of expenses: Landlord’s Energy Savings Allowance (LESA). 2017–04–28, available at gov.uk website

[40] Office for National Statistics (ONS). Living cost and food survey volume A: introduction January-December 2012. London: ONS, 2013

[41] Office for National Statistics (ONS). Input-output supply and use tables for 1997 to 2013. London: ONS, 2013

[42] Gough I. Carbon mitigation policies, distributional dilemmas and social policies. Journal of Social Policy, 2013, 42(2): 191–213

[43] Brand C. Transport and carbon emissions analysis. Project Paper Number 2. Oxford: Environmental Change Institute, University of Oxford, 2013

[44] OECD. OECD framework for statistics on the distribution of household income, consumption and wealth. OECD publishing. 2017–04–28, available at keepeek.com website

[45] Yohanis Y G, Mondol J D, Wright A, Norton B. Real-life energy use in the UK: how occupancy and dwelling characteristics affect domestic electricity use. Energy and Building, 2008, 40(6): 1053–1059

[46] Utley J I, Shorrock L D. Domestic energy fact file 2008. BRE Environment, DECC and EST, 2008

[47] The Scottish Government. Scottish house condition survey: key findings 2010. Edinburgh: The Scottish Government, 2010

[48] Pullinger M, Browne A, Anderson B, Medd W. Patterns of water: the water related practices of households in southern England, and their influence on water consumption and demand management. Final Report of the ARCC-Water/ SPRG Patterns of Water Projects. Lancaster: Lancaster University, 2013

[49] Lamb W F, Steinberger J K, Bows-Larkin A, Peters G P, Roberts J T, Wood F R. Transitions in pathways of human development and carbon emissions. Environmental Research Letters, 2014, 9(1): 014011

[50] Field A. Cluster analysis. 2017–04–28, available at website of statisticshell.com

[51] Everitt B S, Landau S, Leese M, Stahl D. Cluster Analysis. 5th ed. Chichester: John Wiley & Sons Ltd., 2011

[52] AEA. 2012 Guidelines to Defra/DECC’s GHG conversion factors for company reporting. Produced for the Department for Environment, Food and Rural Affairs (Defra) and the Department of Energy and Climate Change (DECC) (Version 1.0), London, 2012

[53] Department of Energy and Climate Change (DECC). Quarterly energy prices. URN: 12D/276D. London: DECC, 2012

[54] The Consumer Council. Comparative domestic cost of gas v oil report. Belfast: The Consumer Council, 2013

[55] Department of Energy and Climate Change (DECC). Quarterly energy prices. URN: 14D/276C. London: DECC, 2014

[56] Department of Energy and Climate Change (DECC). Annual fuel poverty statistics report. URN: 14D/146 . London: DECC, 2014

[57] Mooi E, Sarstedt M. A concise guide to market research: the process, data and methods using IBM SPSS statistics. Berlin Heidelberg: Springer-Verlag, 2011

[58] IBM Knowledge Centre. SPSS statistics two-step cluster algorithms. 2017–04–28, available at ibm.com website

[59] Okazaki S. What do we know about mobile Internet adopters? A cluster analysis. Information & Management, 2006, 43(2): 127–141

[60] Druckman A, Chitnis M, Sorrell S, Jackson T. Missing carbon reductions? Exploring rebound and backfire effects in UK households. Energy Policy, 2011, 39(6): 3572–3581

[61] Oke T R. Inadvertent climate modification. In: Boundary Layer Climates. London: Routledge, 1987

[62] Lo C P, Quattrochi D A, Luvall J C. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International Journal of Remote Sensing, 1997, 18(2): 287–304

[63] Shove E. Comfort, Cleanliness and Convenience: the Organisation of Normality. Oxford and New York: Berg, 2003

[64] Gram-Hanssen K. Standby consumption in households analyzed with a practice theory approach. Journal of Industrial Ecology, 2010, 14(1): 150–165

[65] Maller C, Horne R, Dalton T. Green renovations: Intersections of daily routines, housing aspirations and narratives of environmental sustainability. Theory and Society, 2012, 29: 255–275

[66] Strengers Y, Nicholls L, Maller C. Curious energy consumers: humans and nonhumans in assemblages of household practice. Journal of Consumer Culture, 2016, 16(3): 761–780

[67] Butler C, Parkhill K A, Shirani F, Henwood K, Pidgeon N. Exploring the dynamics of energy demand through a biographical lens. Nature and Culture, 2014, 9(2): 164–182

[68] Sorrell S, Dimitropoulos J. The rebound effect: microeconomic definitions, limitations and extensions. Ecological Economics, 2008, 65(3): 636–649

[69] Chitnis M, Sorrell S, Druckman A, Firth S K, Jackson T. Who rebounds most? Estimating direct and indirect rebound effects for different UK socioeconomic groups. Ecological Economics, 2014, 106: 12–32

[70] Sorrell S, Dimitropoulos J, Sommerville M. Empirical estimates of the direct rebound effect: a review. Energy Policy, 2009, 37(4): 1356–1371

[71] Boardman B. Home truths: a low-carbon strategy to reduce UK housing emissions by 80% by 2050. ECI Research Report 34. Oxford: Environmental Change Unit, University of Oxford, 2007

[72] UK Government. Winter fuel payment. 2019–03–01, available at website of gov.ukeligibility

Acknowledgments

This work was funded by the School of Mechanical, Aerospace and Civil Engineering and the Sustainable Consumption Institute at the University of Manchester.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11708-019-0647-6 and is accessible for authorized users.

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2019 The Authors (2019). This article is published with open access at link.springer.com and journal.hep.com.cn