Closing the Energy Efficiency Gap: A study linking demographics with barriers to adopting energy efficiency measures in the home

This paper presents a study which linked demographic variables with barriers affecting the adoption of domestic energy efficiency measures in large UK cities. The aim was to better understand the ‘Energy Efficiency Gap’ and improve the effectiveness of future energy efficiency initiatives. The data for this study was collected from 198 general population interviews (1.5–10 min) carried out across multiple locations in Manchester and Cardiff. The demographic variables were statistically linked to the identified barriers using a modified chi-square test of association (first order Rao–Scott corrected to compensate for multiple response data), and the effect size was estimated with an odds-ratio test. The results revealed that strong associations exist between demographics and barriers, specifically for the following variables: sex; marital status; education level; type of dwelling; number of occupants in household; residence (rent/own); and location (Manchester/Cardiff). The results and recommendations were aimed at city policy makers, local councils, and members of the construction/retrofit industry who are all working to improve the energy efficiency of the domestic built environment.

[1]  Manfred Lenzen,et al.  A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan , 2006 .

[2]  Sarah C. Darby,et al.  Smart metering: what potential for householder engagement? , 2010 .

[3]  M. Friendly Mosaic Displays for Multi-Way Contingency Tables , 1994 .

[4]  A. Jaffe,et al.  The energy-efficiency gap What does it mean? , 1994 .

[5]  S. Mullainathan,et al.  Behavior and Energy Policy , 2010, Science.

[6]  Lukas Weber,et al.  Some reflections on barriers to the efficient use of energy , 1997 .

[7]  Loren Lutzenhiser,et al.  A cultural model of household energy consumption , 1992 .

[8]  N. Lior Sustainable energy development: The present (2009) situation and possible paths to the future , 2010 .

[9]  L. Yardley,et al.  Research Methods for Clinical and Health Psychology , 2003 .

[10]  A Agresti,et al.  Modeling a Categorical Variable Allowing Arbitrarily Many Category Choices , 1999, Biometrics.

[11]  Michael J. Kelly,et al.  Retrofitting the existing UK building stock , 2009 .

[12]  R. Madlener,et al.  ENERGY REBOUND AND ECONOMIC GROWTH: A REVIEW OF THE MAIN ISSUES AND RESEARCH NEEDS , 2009 .

[13]  R. Rajagopal,et al.  Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior , 2013 .

[14]  Paul Upham,et al.  Public Attitudes to Environmental Change: a selective review of theory and practice, A Research Synthesis for The Living Within Environmental Change Programme. , 2009 .

[15]  Christopher R Bilder,et al.  Testing for Marginal Independence between Two Categorical Variables with Multiple Responses , 2004, Biometrics.

[16]  A. Scott,et al.  The Analysis of Categorical Data from Complex Sample Surveys: Chi-Squared Tests for Goodness of Fit and Independence in Two-Way Tables , 1981 .

[17]  W. Nordhaus The "Stern Review" on the Economics of Climate Change , 2006 .

[18]  Michael Nye,et al.  Re-materialising energy use through transparent monitoring systems , 2008 .

[19]  Janet Stephenson,et al.  Energy cultures: A framework for understanding energy behaviours , 2010 .

[20]  D. Roland Thomas,et al.  Testing for Association Using Multiple Response Survey Data: Approximate Procedures Based on the Rao-Scott Approach , 2004 .

[21]  Dan Nettleton,et al.  Multiple Marginal Independence Testing for Pick Any/C Variables , 2000 .

[22]  M. Luoranen,et al.  Differences in perception: How the experts look at energy efficiency (findings from a Finnish survey) , 2013 .

[23]  J. Fereday,et al.  Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development , 2006 .

[24]  D. R. Thomas,et al.  A Simple Test of Association for Contingency Tables with Multiple Column Responses , 2000, Biometrics.

[25]  A. Scott,et al.  On Chi-Squared Tests for Multiway Contingency Tables with Cell Proportions Estimated from Survey Data , 1984 .

[26]  W. Abrahamse,et al.  How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings? , 2009 .

[27]  Denise A. Guerin,et al.  Occupant Predictors of Household Energy Behavior and Consumption Change as Found in Energy Studies Since 1975 , 2000 .

[28]  Keith Baker,et al.  Improving the prediction of UK domestic energy-demand using annual consumption-data , 2008 .

[29]  Thomas M. Loughin,et al.  Testing for Association in Contingency Tables with Multiple Column Responses , 1998 .

[30]  Michael Nye,et al.  Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors , 2010 .

[31]  Richard E. Boyatzis,et al.  Transforming Qualitative Information: Thematic Analysis and Code Development , 1998 .

[32]  Charles Neame,et al.  Towards a contemporary approach for understanding consumer behaviour in the context of domestic energy use , 2007 .