Lifestyle factors in U.S. residential electricity consumption
Highlights
► Illustrates lifestyle analysis of residential electricity consumption. ► Lifestyle factors based on social and behavioral decisions and equipment use. ► Regression models using lifestyle factors account for 40% of consumption variance. ► Lifestyle factors are stable over time when applied to other data sets. ► Energy reduction opportunities are identified by segmentation analysis.
Introduction
The concept of “lifestyle” has been periodically addressed in relation to the social and behavioral aspects of energy consumption. Lifestyle is defined by Lutzenhiser and Gossard (2000) as “distinctive modes of existence that are accomplished by persons and groups through socially sanctioned and culturally intelligible patterns of action.” This definition implies specific clusters of social, demographic and behavioral patterns that influence expenditures, consumption, and ultimately, use of energy. The lifestyle concept has long been used in consumer research and advertising, as recognized lifestyle subgroups constitute distinct markets. The relevance of lifestyle to energy consumption is illustrated by findings that similarly structured households with identical physical shells are associated with widely varying energy usage (Socolow, 1977/78, Schipper et al., 1989, Lutzenhiser and Gossard, 2000). More detailed analysis of variables such as income and energy price shows relatively weak, and sometimes ambiguous or paradoxical relationships with consumption (Lutzenhiser, 1993, Herter and Wayland, 2010, Karanfil, 2009, O’Neill and Chen, 2002). We have reported elsewhere on preliminary investigations of lifestyle factors (Sanquist et al., 2010), and expand upon that work in this paper.
While the term “lifestyle” can be emotionally laden and sometimes associated with curtailment or deprivation in discussions of energy efficiency and conservation (Nader and Beckerman, 1978), our own and other research has shown it to be a useful concept for identifying specific energy reduction opportunities, based on quantitative analysis (Sanquist et al., 2010). Lifestyle may be broadly conceptualized as patterns of consumption influenced by decisions at various points across the lifespan, such as what profession to engage in, where to live, when (or whether) to marry and have children, and more proximal choices regarding what to purchase and how and when to operate energy consuming equipment. This conceptualization suggests that analysis of lifestyle and energy consumption needs to encompass not only the traditional demographic segmentation elements, but also information about what people own and how they use it.
This report is concerned with developing a quantitative, multivariate model of lifestyle factors in relation to U.S. residential electricity consumption. We focus on electricity consumption specifically for several reasons: (1) the proliferation of consumer electronics represents a growing source of electricity consumption that is likely reflected in social, cultural, demographic and behavioral measures (International Energy Agency, 2009); (2) the increasing interest in “behavioral wedges” to reduce energy usage through efficiency and conservation has clear implications for a range of residential electricity end uses (Dietz et al., 2009), and (3) the rapid development of “smart grid” technologies for changing consumption patterns through feedback can benefit from better knowledge of the lifestyle factors that influence energy consumption (Ehrhardt-Martinez et al., 2010).
Section snippets
Background
Lifestyle research in relation to energy consumption began to appear in the 1970s to describe broad clusters of activity, such as location of residence, type of car driven, etc. Anthropological and sociological analyses of lifestyle and energy consumption have been provided by Nader and Beckerman (1978), Lutzenhiser and Gossard (2000) and Wilhite et al. (1996). The common theme among these treatments is the linkage of value preferences to consuming behavior. Sobel (1981), for example, argues
Data
The Residential Energy Consumption Survey (RECS) is a national household energy survey conducted by the Energy Information Administration (EIA) of U.S. Department of Energy every three years. There have been 12 surveys since it was first conducted in 1978. This study is based on the 2005 survey, the most recent one at the time of this study.
The 2005 RECS collected data from 4382 households in housing units statistically selected to represent 111.1 million housing units in the U.S. that year (
Basic descriptive statistics
The average income for the sample of 2165 respondents is $54,345 (SD=35,330), and the average annual electricity usage in KWH is 12, 237 (SD=7416). Of particular note is the standard deviation in electricity usage. Between the low and high ends of the range, usage varies by a factor of four. Fig. 1 illustrates the range of electricity usage across the geographic regions sampled by RECS.
The geographic pattern maintains the high level of variability illustrated by the overall sample summary, and
Review and interpretation of results
The most noteworthy aspect of this analysis is the large proportion of variance in electricity consumption (42%) accounted for by the factors reflecting lifestyle patterns. These factors have relatively little to do with the physical shell of the residence, but instead portray social and behavioral predispositions and usage variation. The current work extends our understanding of the underlying patterns of consumption associated with socio-demographic variables, such as housing size, number of
Conclusions
This study has illustrated a factor analytic approach to lifestyle energy consumption research using data from the 2005 RECS. Five lifestyle factors account for more than 40% of the variance in electricity consumption, and provide independent measures of the influence of air conditioning usage, laundry usage, PC usage, television usage, and climate. Income adds negligibly to predictive power. We have shown that the lifestyle factors can be used to estimate prior-year consumption from similar
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