Personal and contextual influences on househould energy adaptations.

Abstract

Survey data on 478 residential electricity consumers in Massachusetts are used to examine the interactive effects of economic, demographic, structural, and psychological variables on four behaviorally distinct types of reported conservation response involving energy efficiency improvements or curtailment of the services energy provides. The causal model assumes that contextual variables (i.e., demographic, economic, and structural) may affect behavior indirectly through personal variables (e.g., attitudes, beliefs, norms) and that between personal variables, causality moves from the general through the specific to reported behavior. A path analysis incorporating these assumptions suggested that although behaviors that are relatively unconstrained for most households (such as temperature settings) are strongly influenced by norms, personal variables have much less influence on more constrained actions (such as major insulation activity). The effect of high and rising fuel price was stronger in producing economic sacrifice than in producing energy savings. Limits to generalizability are discussed.

Contributions of psychology to limiting climate change.

“…Black et al (1985) presented evidence that personal normative beliefs about energy conservation, which had explanatory power with reference to lower cost energy-saving behaviors (e.g., resetting thermostats, adding weather stripping), were not associated with behaviors that are strongly constrained by household infrastructure, home ownership, and financial cost (e.g., adding attic insulation, replacing inefficient furnaces). Contextual constraints can also reduce the relevance of psychological factors by pushing behavior strongly in pro-environmental directions.…”

“…However, the review did not disaggregate types of behavior to allow examination of whether different factors affect different types of ESB or evaluation of which factors are most important in terms of the aggregate impact of ESB. Some research has attempted to develop models that can be used for an integrated analysis across multiple behavioral types (e.g., Black et al, 1985;Stern, Black, & Elworth, 1983) or that can incorporate multiple theoretical perspectives (e.g, Harland, Staats, & Wilke, 2007;Matthies, 2003;Wall, Devine-Wright, & Mill, 2007). Considering that some very important ESBs have rarely been studied, it is premature to draw conclusions about the relative importance of psychological variables or theories for explaining ESB generally.…”

“…Realistic behavioral models for individual behavior show how internal factors (e.g., knowledge, feelings, values, attitudes) and external factors (physical and technological infrastructure; political, social, and cultural factors; economic incentives) combine to affect environmentally significant behavior (e.g., Black, Stern, & Elworth, 1985;Gardner & Stern, 2002;Kollmuss & Agyeman, 2002). For organizational and collective behavior, models may explicate the conditions under which organizations will undertake investments in energy efficiency (National Research Council, 2010b) or groups will fall prey to the commons dilemma (e.g., Kopelman, Weber, & Messick, 2002).…”

Resources Conservation and Waste Management Practices in Construction Industry

“…Then, he can either reject the consequences of the behaviour or his own responsibility in order to neutralize the norm (Schwartz, 1977). Besides, the model of the norm activation proved its efficacy for the study of several ecological behaviours: the important change in environmental attitudes (Heberlein, 1972), the emergence of an environmental ethic (Vandenbergh, 2005), the explanation of the individual consumption of energy (Black, et al, 1985) and so on.…”

Households transport-home energy conservation strategies in response to energy price policies: A stated adaptation experiment based on portfolio choices and cross effects designs

“…According to a literature review of in-home energy conservation studies, researchers found it is much easier to make a singular investment decision than to change daily behavior (Mills & Schleich, 2010). Moreover, energy saving resulting from technology adoption tends to have long-term effects, while behavioral measures may only have transitory effects (Barr, Gilg, & Ford, 2005;Black, Stern, & Elworth, 1985;Brechling & Smith, 1994;Dillman, Rosa, & Dillman, 1983;Ferguson, 1993;Long, 1993;Walsh, 1989). Third, to help respondents connect the hypothetic policy situation to their current, daily situation, a dedicated data collection system (SINA) was developed to collect detailed energy consumption information both in-home and out-of-home.…”