Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012

TL;DR: In this paper, the authors present a meta-analysis of information-based energy conservation experiments conducted to date, and find that pecuniary feedback and incentives lead to a relative increase in energy usage rather than induce conservation.

About: This article is published in Energy Policy.The article was published on 2013-10-01 and is currently open access. It has received 531 citations till now. The article focuses on the topics: Energy conservation & Conservation behavior.

Summary

1. Introduction

• The environmental impact of everyday activities is often invisible to consumers.
• Information strategies that aim at correcting this information asymmetry are increasingly common.
• Yet despite the accumulated experimental evidence, analyses of the effectiveness of such strategies have provided mixed results.
• The authors compare the impact of different types of information strategies on energy use to strengthen their understanding of energy conservation information-based strategies.
• The authors results also show that strategies providing individualized audits and consulting are comparatively more effective for conservation behavior than strategies that provide historical, peer comparison energy feedback and pecuniary feedback.

2. Understanding levers for energy conservation behavior

• The failure to engage in energy efficiency can be characterized as a market failure: individuals lack the relevant information or knowledge to engage in energy saving behaviors (DeYoung, 2000; Hungerford and Volk., 1990; Schultz, 2002) and acquiring such information is costly.
• Therefore detailed and immediate feedback is a frequently proposed solution to remedy wasteful energy use patterns (Van Houwelingen and Van Raaij, 1989).
• The authors first describe how information about individual energy usage such as historical feedback, and real time feedback, as well as information on saving approaches might facilitate conservation behavior.
• While these strategies aim at reducing the cost of acquiring information, they do not touch on the potential motivations that might trigger conservation.
• The authors then describe the potential effectiveness of information strategies based on social norms and pecuniary incentives.

2.1. Energy feedback

• Feedback can be described as “the mechanism that directs attention to a specific goal” (McCalley, 2006).
• The most common form of feedback informs participants about their own energy usage, often drawing comparisons to the past (e.g., Nielsen, 1993; Winett et al., 1979).
• If individuals perceive their own impact as negligible, they might not behave in a prosocial manner (Larrick and Soll, 2008).
• Making an individual more aware of their own energy usage may contribute to conservation.
• The authors therefore hypothesize the following: H1.

2.2. Information on problem solving strategies

• The implicit assumption behind the use of information strategies to reduce energy usage is that these strategies will result in a higher level of knowledge and therefore enable participants to change their behavior (Van Dam et al., 2010; Ouyang and Hokao, 2009).
• These preconditions to taking action may be enhanced if the person receives additional information on how to perform certain activities and on the outcomes of these activities.
• Information on conservation strategies will result in reduced energy use.
• The following information strategies frame the message to motivate behavior by focusing on pecuniary incentives or social norms.

2.3. Pecuniary strategies

• Pecuniary strategies represent another set of strategies commonly used in conservation behavior studies.
• Many energy conservation experiments inform participants about the financial expenses and/or savings potential associated with their energy usage (e.g., Bittle et al., 1979; Wilhite and Ling, 1995).
• Other studies change the price of electricity (e.g., Sexton et al., 1987), raising for example the price per kWh or introducing rate schedules that change with the time of day or demand levels.
• Furthermore, pecuniary strategies might not be effective if the monetary incentives are negligible.
• Based on the above discussion, the authors test the following hypothesis: H3.

2.4. The power of norms

• Comparative feedback provides comparisons to others (e.g., Alcott, 2011; Kantola et al., 1984; Schultz et al., 2007) and can also be called a motivational strategy, or nudge.
• Such strategies send non-price signals to participants that activate intrinsic and extrinsic motivation.
• These studies illuminate other motivations for changing energy use behavior.
• The effectiveness of social norms in bringing about conservation behavior is empirically supported by several studies.
• The authors therefore hypothesize the following: H4.

3.1. Data collection

• The authors used three complementary search strategies to identify relevant field studies for their analysis.
• Studies were selected for inclusion on the basis of four criteria.
• Second, the authors selected only those studies involving behavioral experiments in electricity usage.

3.2. Overview of meta-analysis methodology

• Meta-analysis is the art of calibrating and combining statistical evidence from separate studies into a single analysis to provide a quantitative, systematic overview of an empirical effect in the literature.
• The techniques for analysis generally result in increased statistical power – roughly equal to the sum of individual sample sizes – and can result in improved parameter significance and accuracy relative to primary studies alone (see Bijmolt and Pieters, 2001).
• This study uses meta-regression analysis (MRA) to estimate the effects of conservation strategies across many behavioral experiments.
• Heterogeneity in this context occurs when effect sizes in primary studies do not consistently converge to a central population mean, which is certainly the case in energy conservation studies with heterogeneous treatment effects (see Alcott, 2011; Costa and Kahn, 2010).
• A key advantage of meta-regression analysis is the ability to model excess heterogeneity in effect size distributions, particularly when combining empirical evidence across groups of studies.

3.3. The meta-regression model

• In equation (1), the authors adopt standard meta-analytic notation advocated by Stanley and Jarrell (1989).
• Βj is the ‘true value’ of the treatment effect, net of the biasing effect.
• The authors estimate Eq. (2) by generalized least squares (GLS) and use the square root of the sample sizes as analytical weights.
• This offers the advantage of adequately capturing variation in the estimated effect, correlation between effect sizes within the same study and any unobserved component.
• The authors meta-regression model mitigates known heteroskedasticity, provides analytical weights to studies with larger sample sizes, and is less sensitive to estimation bias from small sample studies.

3.4.1. Dependent variable

• The authors dependent variable is the reported “effect size” in percentage units.
• This is a normalized measure across all studies and is defined as the percent change in the treatment group minus the percent change in the control group.
• Effect sizes can take on both positive and negative values.
• A negative effect size estimate implies energy savings relative to a control group or baseline, whereas a positive effect size estimate implies energy increases relative to a control group or other baseline.

3.4.2. Independent variables

• The authors model the effect sizes as a function of study characteristics falling into one of three classes (i) feedback on energy usage feedback and problem solving strategies, (ii) pecuniary strategies, (iii) normative feedback, and (iv) study-level controls, such as weather or demographics.
• Energy feedback studies employ Usage Feedback: this means participants receive information about their own energy use as a self-comparison to their prior energy use (within subject comparison).
• Participants can also receive other monetary incentives for conserving energy or achieving certain consumption targets.
• Study-level controls include the following variables: Control Group indicates whether the study includes a control group as a measure of baseline consumption or treatment counterfactual.
• Weather Controls indicates if the study adjusts for the effects of weather, for example, using heating and cooling degree days.

4.1. Descriptive statistics

• Table 1 presents the means, standard deviations and percentages of all observations and Table 2 presents the correlations.
• Among the more significant correlations presented in Table 2, the authors observe that monetary savings information is strongly correlated with both individual and social comparison feedback strategies.
• In Table 1, the authors see that these comparative feedback studies now represent approximately 1/5 of all entries (23.7% of the observations and 20% of papers).
• Description Field observations Mean (%) St. Dev. Min Max.

4.2. Average treatment effect

• Across all studies (Table 3), the authors find the weighted average treatment effect to be −7.40%.
• These numbers are the most comprehensive field experimental figures to date.
• Interestingly, the average treatment effect differs between more and less methodologically rigorous studies.
• A savings effect of 1.99% is found for high quality studies that include statistical controls such as weather, demographics, and – most importantly – a control group.
• This suggests that savings effects may be overestimated in some of these studies.

4.3. Outcomes of different strategies

• Table 4 summarizes the results of the meta-regression model for the different classes of behavioral strategies.
• All of these, except for weather, are significant across specifications.
• Interestingly, energy usage increased relative to the control group for studies employing individual feedback strategies.
• While most energy savings tips are provided either in billing or website data, these results suggest that simply providing energy saving tips does not sufficiently motivate subjects to conserve.
• As Fig. 2 demonstrates, there is a difference in sampling distribution between studies that use individual feedback and those that use comparative feedback.

5. Discussion

• The authors study presents the first quantitative comparison of different information strategies used in studies targeted at energy conversation.
• One limitation of this approach is that financial benefits from saving energy are often quite small (Wolak, 2011).
• Bowles (2008) describes several conditions under which explicit financial incentives may be counterproductive, because selfinterest and prosocial motives are not separable, but interact.
• Many of the reviewed studies suffer from methodological problems.
• Conservation strategies may have different levels of intensity.

6. Conclusion

• The authors provide a comparison of the quantitative evidence on behavioral strategies targeting energy usage across various literatures in behavioral psychology, economics and related fields.
• This study represents the most comprehensive review of experimental energy conservation studies to date.
• Based on these results, the authors conclude that despite heterogeneous treatment effects, non-monetary, information-based strategies can be effective at reducing overall energy usage in controlled experimental studies.
• As the advent of new technologies such as smart meters reduces the cost of feedback and increases the quality and reliability of information provided, policy makers would be well-served to shift some means to these high-impact, relatively low-cost information programs, which can result in real savings.
• Such experiments should learn from the previous literature by following a few guiding principles with regard to the methodological rigor.

Figures

Table 4 Meta-regression results.

Table 1 Descriptive statistics.

Frequently asked questions

Q1. What are the contributions in "Information strategies and energy conservation behavior: a meta-analysis of experimental studies from 1975 to 2012" ?

This article offers the most comprehensive meta-analysis of information based energy conservation experiments conducted to date. The authors also find that the conservation effect diminishes with the rigor of the study, indicating potential methodological issues in the current literature. 

Q2. What are the future works mentioned in the paper "Information strategies and energy conservation behavior: a meta-analysis of experimental studies from 1975 to 2012" ?

While the authors controlled for these methodological factors in the meta-analysis to the best of their ability, future studies in this field should pay careful attention to these aspects to contribute to building a more solid basis of experimental evidence.