Chapter 17
A Framework to Achieve Large Scale Energy Savings for Building Stocks through Targeted Occupancy Interventions1

Aslihan Karatas1, Allisandra Stoiko2 and Carol C. Menassa1

1Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA

2Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

1 Contents of this chapter originally appeared in the following article: Karatas, A., Stoiko, A., and Menassa, C. (2016) A framework for selecting occupancy-focused energy interventions in buildings. Building Research and Information, Special Issue: Building governance and climate change: regulation and related policies, Taylor and Francis. 44(5–6), 535–551. This chapter is reprinted with permission of the publisher (Taylor & Francis Ltd, http://www.tandfonline.com).

Chapter Menu

Introduction

Objectives

Review of Occupancy-Focused Energy Efficiency Interventions

Role of Occupants' Characteristics in Building Energy Use

A Conceptual Framework for Delivering Targeted Occupancy-Focused Interventions

Case Study Example

Discussion

Conclusions and Policy Implications

Objectives

  • • To become familiar with occupants' energy use characteristics and their major impact on building energy use.
  • • To introduce a multilevel framework for occupancy energy use intervention strategies in buildings.
  • • To become familiar with the MOA methodology to collect occupancy energy use characteristics data.
  • • To introduce a link between occupants' energy use characteristics and energy policy tools.

17.1 Introduction

In the United States, the existing building sector accounts for 40% of the total energy consumption by the built environment [1]. UNEP [2] reported that the existing building sector represents an excellent opportunity to achieve large-scale energy use reductions in a cost-effective manner through efficiency and conservation strategies. The objective is to alleviate economic, environmental, and social problems associated with diminishing natural resources and global warming. To maintain a large-scale energy use reduction in the existing building sector, researchers have emphasized the need for decision-makers (e.g., policy-makers) to carefully analyze the effect of individual occupant behaviors and their determinants on the energy consumption of buildings to ensure that they can design effective energy policy tools [3, 4].

Energy policy tools aimed to achieve large-scale energy savings for a stock of buildings (e.g., university campus, community, city) should integrate occupancy-focused interventions that implement knowledge-based solutions, persuasion, reward/penalty systems, and technological solutions [5, 6]. To achieve this, energy policy tools can be designed based on one of two basic ways that intend to (i) directly affect individual behaviors through inducement tools and regulation tools (i.e., incentives and/or sanctions) and (ii) affect the environment in which individual behaviors are manifested through knowledge tools that indirectly influence behavior (i.e., knowledge based and/or persuasion) [7]. Regulatory tools, often referred to as government command and control systems, are intended to change behavior by forcing people to obey the law or other regulations without providing a promise of a positive incentive. Inducement tools aim to motivate an individual through the promise of a reward or penalty to behave in a certain way without the level of government coercion inherent in regulations. On the other hand, knowledge tools are intended to change individual behavior based on the provision of information. These methods strive to achieve a desired outcome through continuous monitoring of how individual or group behaviors change [7].

Despite the fact that policy analysts often suggest knowledge tools as a starting point for policy intervention because of their noncoercive nature [7], energy policy tools are mostly designed as either regulatory tools (e.g., occupancy-focused interventions that implement technological solutions such as HVAC scheduling and resets according to outside conditions) [8–11] or inducement tools (e.g., occupancy-focused interventions that implement reward/penalty systems such as varying energy costs with on- and off-peak consumption) [10, 12, 13].

The aforementioned approach for designing energy policy tools often leads to three main challenges for decision-makers (e.g., policy makers) when applied in the context of energy reduction in the built environment. First, complying energy policy tools with the regulations and incentives mostly resulted in inefficiencies due to higher costs of implementation of appliance standards, building energy codes, financial incentives, and public sector energy leadership programs [5, 7, 14, 15].

Second, energy policy makers often ignore the significant impact of occupants on the energy use in buildings by assuming that all occupants have the standard behavior pattern (e.g., a fixed set-point room temperature preference) [16–19]. Azar and Menassa [17] carried out a comprehensive analysis using energy simulation to study the impact of nine occupancy-related actions (e.g., after-hours equipment and light use) on energy use in commercial buildings of different sizes and located in 10 different weather climates across the United States. The results from these individual impacts were found to be as high as 30%. The combined effects of some of the occupancy-related actions resulted in an increase in energy use in the building in excess of 50%. Other studies such as Moezzi et al. [20], Sanchez et al. [21], and Webber et al. [22] support these results and emphasize that significant energy reductions can only be achieved if building occupants are engaged in the process.

Third, Hand [7] highlighted that lack of information or capacity is mostly the primary barrier, which can be overcome by knowledge tools that relay the appropriate information to the occupants. The main difference in this case is that it is unnecessary to incentivize or sanction the target in order to elicit the desired behavior. Therefore, when designing policy measures aimed at reducing energy use in a large stock of buildings, it is important to identify the diverse energy use characteristics of occupants that significantly contribute to environmental problems and the factors (e.g., occupancy-focused interventions) that make sustainable behavioral patterns attractive [4].

To address and overcome the aforementioned challenges in the development of large-scale energy policy tools, this study focuses on the development of a conceptual framework that proposes multilevel building energy use intervention strategies. This is achieved by examining the fundamental differences among occupancy-focused intervention strategies and identifying which interventions are more effective based on the occupants' characteristics and energy use profiles. To accomplish this, the framework presented in this study adopted MOA approach from the consumer and social marketing field to establish an analogy that enables occupancy-focused intervention strategies, in this case considered as advertisements, to encourage the building occupants to adopt the desired energy use characteristics. This framework assists decision-makers to propose cost-effective large-scale energy policy tools to deliver energy efficiency occupancy-focused interventions and to evaluate the benefits and effectiveness of different energy policy tools before the actual implementation on a large stock of buildings.

17.2 Objectives

The aim of this chapter is to address the identified research gap in the development of energy policy tools (i.e., their higher costs for building occupants and managers and ignoring the impact of occupants' impact on buildings energy consumption) and present a conceptual framework for assisting policy makers in designing effective energy policy tools. This framework proposes a multilevel building energy use intervention strategy that is capable of systematically evaluating the effects of occupancy-focused interventions on the occupants' behavior and also designing energy policy tools tailored to various behavioral and energy use characteristics of occupants. To accomplish this, the proposed framework is developed to answer the following questions:

  1. 1. How can occupants' energy use profile be measured and classified before and after the implementation of energy policy tools? The answer to this question helps determine the energy use profiles (i.e., occupants' situational characteristics pre- and postexposure to any intervention) to evaluate the benefits and effectiveness of different energy policy tools before actual implementation on a large stock of buildings (e.g., in a residential community, city, or campus environment).
  2. 2. What type of building energy use intervention strategies can be selected by decision-makers to achieve the required energy reductions at lower costs based on the occupants' energy use characteristics? This supports decision-makers in formulating effective energy policy tools to achieve cost-effective occupancy-focused intervention programs that increase the attractiveness of pro-environmental behavior and encourage a more sustainable behavior pattern.
  3. 3. How can the proposed framework be implemented in real-life examples to achieve large-scale energy reductions by delivering interventions to the occupants through the most effective energy policy tools? The results of a case study of a real building demonstrate the capabilities of the proposed framework in evaluating the intervention strategies and designing effective energy policy tools based on the occupants' energy use characteristics.

17.3 Review of Occupancy-Focused Energy Efficiency Interventions

To engage occupants in reducing energy use in a stock of buildings (e.g., university campus, community, city), energy policy tools should integrate occupancy-focused interventions that implement knowledge-based solutions, persuasion, reward/penalty systems, and technological solutions [5, 6]. Existing occupancy-focused interventions are investigated based on a comprehensive literature review related to knowledge translation theory [23–29], social marketing theory [30–32], and law and public policy [33, 34]. Accordingly, a detailed description of each intervention is provided below.

17.3.1 Knowledge-Based Interventions

Knowledge-based interventions involve the presentation of informative messages to invoke voluntary behavior change. It attempts to raise awareness about the benefits of changing a behavior without presenting an explicit reward or penalty. Through outlets like posters, articles, videos, and brochures, researchers have attempted to convey information to influence consumers in contexts such as encouraging vegetable consumption, stair use, and sunscreen use [35–37]. Sahota et al. [37] found that students increased vegetable consumption by 0.3 servings per day after 1 year of healthy living education and class-wide discussions. Armstrong et al. [35] determined that sunscreen use increased to 1.6 days/week after adults viewed a video about the sun's effect and the importance of sunscreen and read a pamphlet with similar information. This method has generally experienced moderately positive results, with modest improvements in the desired behavior.

In energy use context, several studies used knowledge-based interventions to reduce energy use in buildings and were divided into four main categories. The first category used information distribution outlets (e.g., posters, videos, brochures) to influence occupants [38–42]. For example, Hayes and Cone [39] created a poster that described methods to reduce electricity consumption and gave out consumption statistics. Results showed that the distribution of energy consumption facts and reduction guidelines does not appear to effectively influence occupants. Zografakis et al. [43] used schoolwork and a project, including field visits and other hands-on approaches, to educate students on energy-efficient behavior. Both students and their parents consequently behaved in more energy-efficient ways, including turning off lights and closing the windows when the heater is on, but some benchmarks experienced no significant changes. While information distribution is a widely tested form of education, it has provided mixed results on its own.

The second category considered interactive programs with more personalized approaches to information delivery [44, 45]. McMakin et al. [45] created site-specific video programs to motivate military families to conserve energy. Combined with other awareness materials, the program resulted in a 3% reduction in gas use and 7% reduction in energy use after 1 year. However, Geller [44] found that motivated individuals attending a 3-h workshop on energy conservation did not tend to apply skills either immediately or 6 weeks later. This implies that the short-term delivery of information may not be effective, and an extended program may yield better results.

The third category considered feedback methodology that is based on comparing current energy use with historical use [46, 47]. External feedback provides consumers with personalized evaluation and a means to monitor progress. These studies generally concluded that even if feedback method is an effective knowledge-based method for building energy reduction, it may become less effective in changing behaviors over an extended period of time (e.g., 2 years).

Finally, the last category focused on peer comparison of monthly or quarterly energy use [48–50]. Allcott [48] determined that energy use decreased approximately 2% with these reports and began to regress between quarterly reports. Ayres et al. [51] discussed studies that experienced energy savings of 1.2–2.1%. One of the studies, however, showed no statistical difference between monthly or quarterly reports. Ayres et al. [51] concluded that peer comparison is more effective in decreasing energy use than energy consumption information alone. Furthermore, participants' energy use dropped 16% after the first peer comparison period and 32% after the second peer comparison, both relative to baseline levels. These studies generally concluded that peer comparison is a successful means to reduce energy use.

17.3.2 Persuasion Interventions

Persuasion involves providing rewards to encourage a favorable behavior. Researchers studied persuasion interventions to incite voluntary changes through incentives like money and fast food [52, 53]. Flora and Flora [54] studied students who participated in the “Book It!” program for free pizza or had financial incentives from parents if the students read. They determined that these forms of persuasion increased the amount of reading and may have helped increase enjoyment of reading and literacy. John et al. [53] provided financial incentives for weight loss in obese veterans and determined that incentives provided greater weight loss. However, follow-ups after a maintenance period revealed no significant net weight loss between financial incentive and control groups. These studies show that persuasion appears to be an effective strategy to encourage people to adopt a desired behavior, but the behavior may be reduced if a persuasion method is later removed. In the context of energy use, several studies have been conducted to study the effect of incentives for reducing energy use. These are divided into two main categories.

The first category considered monetary incentives, both small and large, to decrease energy use [55–60], as well as their respective long-term effects. Katzev and Johnson [58] provided occupants with $3.00–$10.00 for reducing energy use during 2-week periods. They measured no significant difference in energy use between control groups during the study or follow-up. Winett et al. [61] provided households with variably high ($0.30 per 1% reduction) and low ($0.013 per 1% reduction) rewards for decreased kilowatt-hours of electricity compared to the average. Initially, both high- and low-reward groups saved energy, with the high-reward group decreasing 16% more than the low-reward group. After receiving the reward, however, there was a larger increase in energy use by the high-reward group than the low-reward group. This larger relapse indicates that high-reward groups may become less intrinsically motivated than low-reward groups. While monetary incentives appear to be an effective persuasion method to reduce energy use, low-to-medium monetary rewards could lead to more sustained motivation for the occupants.

The other category of studies considered pledging campaigns as incentives to encourage sustainable energy conservation behavior [52, 60, 62]. These studies found that pledging to an action, such as by signing a promise, resulted in higher commitment. Schick and Goodwin [63] reported that in a voluntary “pledge to save,” participants decreased energy use by a factor of three when compared with non-pledgers. Boyce and Geller [62] observed that signing a pledge to give thank-you cards to others for pro-environmental behavior resulted in significantly increased activity. These promise signers gave 3.0 cards per week compared with 0.5 cards per week. This indicates that pledging is an effective persuasion method to encourage occupants to reduce energy use.

17.3.3 Penalty Interventions

Penalty interventions consist of negative consequences, often legal, that discourage an unfavorable behavior. It provides a set of rules that result in sanctions and penalties for noncompliance. For example, Farchi et al. [64] examined a point system used to discourage risky driving behavior. They studied Italy's highly publicized new driving point system, where drivers began with 20 points and lost points for various traffic violations, ultimately having their licenses revoked. Results were significant where the emergency department visits decreased by 12% over 3 years. Additionally, several studies have focused on the influence of penalties in the context of energy conservation.

For example, multiple studies considered dynamic building control by altering energy costs with on- and off-peak consumption [12, 13, 65]. These studies observed that increased price of on-peak electricity reduced the consumption of energy during that period. Heberlein and Warriner [65] determined that consumers with higher price ratios (8:1) used less on-peak electricity than those with lower price ratios (2:1). Therefore, it appears that increasing energy cost during on-peak periods may be very effective in reducing energy use particularly by increasing the difference in ratio between on- and off-peak costs.

17.3.4 Technology Interventions

Technology interventions consist of tools and systems that automatically solve problems without continued human influence. It is often dictated by laws and other regulations that require nonvoluntary changes for compliance. Research suggests that careful building design, including incorporation of building automation systems (BASs), can dramatically reduce energy use [8, 10, 11, 66–68]. Mathews et al. [68] determined that the most lucrative BAS strategies include heating and air conditioning scheduling according to outside conditions, air bypass control on cooling coils, and reset and setback control. By optimizing these systems, predicted annual energy savings were 66%, which would result in a 30% reduction in a building's total energy consumption. Ruzelli [10] reported actual total savings of 10–15% in buildings featuring a general BAS.

Existing buildings have many options to decrease energy use by retrofitting and replacing electrical components [69, 70]. Harvey [70] observed an average energy savings of 68% in buildings changing from noncondensing to condensing boilers. Furthermore, Environmental and Energy Study Institute [69] asserts that compact fluorescent light bulbs use 66% less energy, while modern energy-efficient appliances use up to 40% less energy than their non-energy-efficient equivalents.

17.3.5 Building Energy Use Interventions in Energy Policy Design

To achieve large-scale energy reductions in a stock of buildings, aforementioned interventions (i.e., knowledge based, persuasion, penalty, technology) should be delivered efficiently and cost-effectively through energy policy tools (i.e., knowledge, inducement, and regulatory tools). From a policy making perspective, relaying the appropriate information to the target is mostly the major barrier to obtain the desired behavior [7]. Therefore, knowledge-based interventions can be delivered through knowledge energy policy tools that enable or encourage voluntary behavior change of the occupants with minimum economic and environmental costs, as shown in Figure 17.1. Persuasion interventions can also be delivered through knowledge energy policy tools to change the occupants' energy use behavior based on the given information and in the desired manner. This will also provide voluntary behavior change for the occupants to produce and maintain energy use reduction in buildings over time.

Illustration of Framework for designing effective energy policy tools.

Figure 17.1 Building energy use intervention strategies.

If extreme energy use patterns are observed among the building occupants, lower-level interventions such as knowledge based and persuasion might not be sufficient to effectively reduce the energy use. Therefore, there is a need to supplement knowledge-based methods with interventions from higher levels of interventions (i.e., penalty, technology), which result in higher economic and environmental costs, as shown in Figure 17.1. Penalty interventions can be delivered through inducement energy policy tools, since they mainly aim to motivate individuals through the promise of a reward or penalty to change their behavior in the desired manner. Moreover, technology interventions can be delivered through regulatory energy policy tools, since they are intended to change individuals' behavior through legislative command and control systems [7] that render the individual behavior obsolete. In both cases, the focus is mostly on involuntary (forced) behavior change of the occupants, at increased economic costs.

17.4 Role of Occupants' Characteristics in Building Energy Use

Policies aimed that to engage occupants in energy reduction, strategies should simultaneously investigate possible determinants of their energy use characteristics (e.g., knowledge, attitudes) and the effectiveness of interventions [71, 72]. To identify the role of occupants' characteristics in building energy use and their impact on the interventions, the proposed framework in this study attempts to measure three main metrics of occupancy characteristics: motivation, opportunity, and ability. These measures are often used in consumer and social marketing field to determine product popularity and potential for sale and accordingly design marketing strategies. In this case, the analogy assumes intervention strategies as advertisements enticing the building occupants to adopt certain energy use characteristics.

Several studies from consumer and social marketing studied MOA model to identify the consumers' information processing level and accordingly main influential factors in encouraging consumers to adopt desired behavior [73–79]. Maclnnis et al. [76] proposed an MOA model to explain certain outcomes influenced by the extent of brand information processing from advertisements. Based on this model, consumers' MOA levels have a great impact on the level of processing brand information during advertisements or after exposure to ads. Hastak et al. [80] emphasized the important mediation role that MOA plays in determining the communication effectiveness of ads in consumer research.

Consumers loyal to a particular product usually have high MOA levels that help facilitate adoption of this product. Moreover, Bigné et al. [81] applied an MOA model to explore the key drivers of online airline ticket purchases intentions and also to identify which perceived channel benefits are more effective for consumers when using the Internet to purchase airline tickets. Results from this study highlighted that the MOA model is a useful methodology to predict consumers' ticket purchasing intentions that explains 55% of variations in adopting a desired behavior.

In consumer and social marketing field context, motivation is defined as a goal-directed arousal to engage consumers in the desired behavior to process brand information in the advertisement [72, 82–84], opportunity as executional factors (e.g., exposure time to ads) that are not in the control of consumers to enable desired actions [76, 79, 81, 85], and ability as consumers' perception of their capacity to access the brand information and interpret this information to create new knowledge structures [74, 76, 79, 81, 86].

In light of the aforementioned multidisciplinary approach combining social sciences and marketing, this study investigated an analogy between MOA characteristics of people to process brand information in their environment and MOA levels where occupancy-focused intervention strategies can be regarded as advertisements enticing the building occupants to adopt certain energy use characteristics. By using this analogy, multilevel intervention strategies and their related energy policy tools can be developed to reduce energy use in buildings based on identified MOA characteristics of building occupants. In energy policy designing context, the MOA level of occupants can be measured to identify the role of occupants' characteristics in building energy use and accordingly design the occupancy-focused intervention strategies and energy policy tools to be effective. A detailed explanation for the adopted methodology of occupants' energy use MOA characteristics for this presented framework is provided below:

The motivation (M) level of an occupant refers to a particular occupant's perceived personal relevance in terms of needs, goals, values, and the level of involvement with the information (e.g., external stimuli) presented in the energy intervention strategy. A strong link indicates a high M level and potential for the occupant to consider adopting the intervention strategy.

The opportunity (O) level of an occupant represents an important precondition for both motivation (M) and ability (A) and is directly related to the immediate environment of the occupants and how that affects the availability, accessibility, and time allocated for comprehension of the energy use knowledge.

The ability (A) level of an occupant measures a given occupant's proficiencies in interpreting energy use knowledge. This ability is largely dependent on the occupant's prior knowledge about energy use and conservation acquired through experience (e.g., asking occupants to turn light off before leaving their offices).

17.5 A Conceptual Framework for Delivering Targeted Occupancy-Focused Interventions

When designing energy policy measures to reduce building energy use, it is important to identify occupants' behaviors that significantly contribute to environmental problems and then identify the factors that make sustainable behavioral patterns attractive [4]. Therefore, the framework in this study proposes a multilevel intervention strategy targeted toward the diverse human characteristics to sustain energy use reduction in large building stocks over time. To achieve this, the presented framework for designing effective energy policy tools includes five main stages (see Figure 17.2): (i) measuring the occupants' preexposure MOA level; (ii) clustering occupants' preexposure MOA level and identifying their energy use profiles (i.e., occupants' situational characteristics prior to any intervention); (iii) measuring occupants' postintervention exposure MOA levels; (iv) clustering postexposure MOA levels and identifying energy use profiles (i.e., occupants' situational characteristics after any intervention) to determine the effectiveness of intervention strategies; and (v) identifying the link between occupants' MOA levels and the multilevel energy efficiency intervention strategies and accordingly energy policy tool types (i.e., knowledge tools, inducement tools, and regulation tools).

Scheme for MOA levels of occupants.

Figure 17.2 Framework for designing effective energy policy tools.

17.5.1 Measuring the Impact of Occupancy Characteristics on Building Energy Use

This stage of framework development focuses on measuring the occupants' energy use profile to evaluate the impact of occupancy characteristics on building energy use. Therefore, a set of metrics were identified based on MOA energy use characteristics of occupants and organized in Tables 17.1–17.3.

Table 17.1 Metrics for measuring occupancy motivation level for energy conservation

Metrics of construct Preexposure measures Postexposure measures
Measures of motivation (M)
Self-related knowledge (internal stimuli)
Needs
Goals
Values
Energy use knowledge (external stimuli)
Level of energy use
Impact and consequences		 Assess self-awareness about the importance of energy use knowledge
Measure desire to receive energy use knowledge
Detect norms of avoiding energy use knowledge (e.g., not interested in attending workshops or receiving e-mails)		 Assess self-awareness about the importance of energy use knowledge
Measure desire to process (continue to receive) energy use knowledge
Detect norms of avoiding energy use knowledge (e.g., still not interested in attending workshops or receiving e-mails)

Table 17.2 Metrics for measuring occupancy opportunity level for energy conservation

Metrics of construct Preexposure measures Postexposure measures
Measures of opportunity (O)
Availability
Amount of information
Information format
Modality
Rate of exposure to information		 Determine availability:
Availability of energy conservation control system (e.g., indoor lighting control)
Office condition level (e.g., physical characteristics of offices)
Determine number of times:
Attend awareness seminars
Read information on general advertisement boards (self-reported)
Read e-mails (ask for response with a blank e-mail)
Discuss with peers (self-reported)		 Measure information recall by recording:
Number of arguments about impact of energy use on global environment
Number of arguments about impact of energy use on building footprint
Number of arguments related to the benefits of conservation methods

Table 17.3 Metrics for measuring occupancy ability level for energy conservation

Metrics of construct Preexposure measures Postexposure measures
Measures of ability (A)
Energy use prior knowledge
Impact
Consequences
Conservation strategies		 Measure extent conservation strategies are used (e.g., estimate number of times consciously turn lights off at end of day)
Measure subjective knowledge of energy use relative to average person
Perceived effectiveness of intervention strategy to reduce impacts/consequences
Measure actual knowledge (i.e., factual information):
Terminology
Possible impacts/consequences
Criteria to evaluate impacts/consequences		 Measure improvement in actual knowledge (i.e., factual information):
Terminology
Possible impacts/consequences
Criteria for evaluating impacts/consequences
Factors related to conservation strategies
Perceived effectiveness of conservation strategies to reduce impacts/consequences

The motivation (M) level of an occupant can be measured as the perceived link between an occupant's needs, goals, and values (self-related knowledge) and the level of involvement with the energy use information (e.g., level, impact, and consequences), as shown in Table 17.1. M is a key factor if lower levels of intervention (e.g., knowledge based) and accordingly knowledge energy tools are to be successful. High M level of an occupant can be identified by the occupants' desire to adjust room temperature (e.g., setting the office heating point to a lower temperature during unoccupied hours) and lighting system (e.g., turning off the office lights when not in use) and adopt energy conservation strategies (e.g., bringing an extra jacket instead of setting heating point to higher degrees). Lack of motivation implies that occupants will resist any change and will require higher levels of intervention (e.g., penalties) and energy policy tools (i.e., inducement and regulatory tools) to improve their M level and get involved in energy conservation.

The opportunity (O) level of occupants can be measured by factors such as the amount of information, the format of the information (e.g., organized by energy reduction target like heating set point and the impact of changing that on energy use or by measures to avoid changing the heating set point like wearing layers), the modality (e.g., information presented in workshops gives occupants short period of time to process vs. having the information available on bulletin boards affording occupants' continuous opportunities to attend to and comprehend), and how often the information available is (e.g., weekly e-mails) (see Table 17.2).

The ability (A) level of occupants can be measured by evaluating their prior knowledge about energy use, its impact, and consequences as well as knowledge about possible conservation strategies (see Table 17.3). The probability that occupants will process energy reduction information from intervention strategies is directly related to this preexisting knowledge. The A level of occupants can also be measured by perceived energy consumption knowledge level and actual level of knowledge on energy consumption facts. One set of studies has shown that people need to have sufficient ability (e.g., self-efficacy) before they can actively care enough to take environmentally responsible actions that benefit others [87].

The identified metrics presented in Tables 17.1–17.3 enable decision-makers to measure occupants' preexposure MOA level and energy use profiles (i.e., occupants' situational characteristics prior to any intervention) and postexposure MOA levels (i.e., occupants' situational characteristics after any intervention) to determine the effectiveness of intervention level (e.g., knowledge based, persuasion, or combination) to use for a given energy reduction strategy (e.g., encouraging occupants to turn office lights off when not in use).

17.5.2 Clustering Occupants' MOA Levels and Energy Use Profiles

In this stage of the framework development, occupants' preexisting MOA levels are classified into main target clusters relative to their potential to process energy use characteristics. Azar and Menassa [16] identified the different occupants' energy use characteristics as “high energy consumers” that represent occupants that overconsume energy, “medium energy consumers” that represent occupants making minimal efforts toward energy savings, and “low energy consumers” that represent occupants that use energy efficiently. Based on these identified characteristics, this study classified the occupants' MOA levels into three categories (see Figure 17.3): “prone to change” (i.e., occupants who are willing to adopt energy reduction strategies immediately), “unable to change” (i.e., occupants who are willing to adopt energy reduction strategies immediately but do not have the necessary knowledge and tools to do that), and “resistant to change” (i.e., occupants who are unwilling to adopt energy reduction strategies regardless of whether they have the necessary knowledge and tools or not). As shown in Figure 17.3, each MOA level of an occupant is measured using the “MOA scale” that ranges from 1 (represented in green color as high motivation level) to 0 (represented in red color as low motivation level).

Scheme for Occupants' energy use characteristics: (a) before intervention, (b) after intervention.

Figure 17.3 MOA levels of occupants.

After identifying occupants' preexposure energy use characteristics, the effectiveness of intervention strategies is determined by measuring the occupants' postexposure MOA level. The actual energy use data before and after the application of the intervention to a group of occupants can be compared to develop effectiveness distributions for the four levels of intervention strategies relative to the overall preexisting MOA level (i.e., prone, unable, or resistant) in the building. Occupants in a certain energy use profile of the building stock are expected to adopt and implement the intervention strategy based on the effectiveness level of interventions. The effectiveness of each strategy is expected to be highest when it corresponds to a certain MOA level (e.g., persuasion is most effective when overall MOA level is in the medium range).

17.5.3 Identifying Multilevel Building Energy Use Intervention Strategies

This stage of the framework development focuses on linking the occupants' MOA levels to the multilevel energy efficiency intervention strategies, and accordingly energy policy tool types (i.e., knowledge tools, inducement tools, and regulation tools), as shown in Table 17.4. First, the occupant MOA levels (e.g., “prone to react” that refers to the occupant who has self-interest and consistent with societal goals and is willing to adopt energy reduction strategies without additional reinforcement) are presented in Table 17.4. Then, these MOA levels were linked to energy policy tools and their related four energy efficiency intervention strategies (i.e., knowledge based, persuasion, penalties, and technology). Subsequently, these intervention strategies were classified as the benefits and solutions presented to target (e.g., adding choices with comparative advantage and favorable cost–benefit relationships), the expected benefits/costs (e.g., sanctions, penalties, and legal consequences to behave in a certain way without the level of government coercion inherent in regulations for noncompliant energy use behavior), the expected occupant reactions of the target to each intervention (e.g., un-coerced choice behavior that refers to the change of energy use behavior voluntarily), and the time to achieve expected benefits (e.g., direct and timely exchange for desired energy use behavior that refers to direct reinforcement by the government command and control systems), as shown in Table 17.4.

Table 17.4 Characteristics of intervention strategies from building energy conservation perspective

Occupants' MOA characteristics Energy policy tools Intervention Benefits and solutions presented to target Expected benefits/costs Occupant reaction Time to achieve benefits
MOA = Prone
Strong self-interest and consistent with societal goals
Merely uniformed occupant
No additional reinforcement necessary		 Knowledge tools Knowledge based: attempts to teach and create awareness about benefits of a particular behavior
Objective: influence knowledge, attitudes, and beliefs		 Suggests benefits but does not deliver them explicitly
Does not add new choices (uses existing choices)
Target is required to initiate quest/solution to achieve benefit		 No explicit reward/penalty Un-coerced free choice behavior
Voluntary compliance		 Promise of future potential payback
Unable to reinforce directly
MOA = Unable to slightly resistant
Strong self-interest but insufficiently consistent with societal goals and reinforcement in self-interest		 Persuasion: offers reinforcing incentives/consequences
Objective: invite voluntary exchange		 Offers benefits they want
Adds choices with comparative advantage and favorable cost–benefit relationships
Target receives solutions through well-known distribution channels		 Positive reward/punishment delivered when exchange transaction is completed Un-coerced free choice behavior
Voluntary exchange (self-monitoring – self-sanctioning)		 Direct and timely exchange for desired behavior
Direct reinforcement
Expects free market exchange
MOA = Resistant
Existing self-interest cannot be overcome with additional rewards through exchange		 Inducement tools Penalties: prescribes a body of rules of action/conduct
Objective: coerce and threaten to achieve nonvoluntary compliance		 Forces benefits by providing external motivation in the absence of internal one
Adds proffered choices
Target bound by legal force		 Sanctions, penalties, and legal consequences for noncompliance Coerced behavior
Nonvoluntary compliance		 Direct and timely exchange for desired behavior
Direct reinforcement
Regulation tools Technology: control behavior change and referred to governmental authority and legitimacy
Objective: coerce and threaten to achieve nonvoluntary compliance		 Law or other costly regulations without requiring a promise of a positive incentive

Accordingly, Table 17.4 provides a guideline for policy makers to design cost-effective and efficient energy policy tools to deliver multilevel building energy use intervention strategies based on identified energy use characteristics of occupants in large building stocks. For example, if the majority of occupants in a large building stock are identified as “prone” to conserve energy, then decision-makers should focus on designing knowledge energy policy tools to deliver knowledge-based interventions for the occupants. These interventions are based on teaching and creating awareness about benefits of energy reduction strategies that only provide suggestions for the occupants and comply with voluntarily behavior change to adopt energy reduction strategies.

17.6 Case Study Example

Data was collected from employees of an energy efficiency consulting company that occupies a single floor in an 85,000 ft2 building located in Madison, Wisconsin. The offices are equipped with an intelligent BAS with a centralized monitoring and control of the indoor environment to maintain the operational performance of the facility and comfort of building occupants. This BAS system provides occupants with different levels of occupancy control over building environment. For example, unlike the occupants in single offices, not all surveyed occupants had the capability to control their environment through individual thermostats. In both cases, the opportunity level of the occupants is expected to vary.

To identify occupants' MOA level preexposure to occupancy-focused interventions, an online survey was distributed to the occupants in the case study building. This survey includes 39 questions and focuses on evaluating occupants' control level on energy systems (e.g., indoor lighting control), office environment conditions, energy conservation motivation level, and energy conservation knowledge level. Previous studies (e.g., medical field, ticket purchasing website) found out that motivation is directly associated with most behaviors [77, 81]. However, opportunity and ability affect behaviors only when motivation is present and therefore moderate the impact of motivation on behaviors. Thus, the survey is developed by stating a set of research hypotheses and their relevant measures to investigate occupants' energy use characteristics through assessing their MOA level on adopting energy-saving behaviors. These hypotheses are designed based on the extended context of MOA levels of occupants in energy use characteristics and incorporated with a set of measures that is identified based on a comprehensive literature review.

The company surveyed had a total of 39 employees. Of those employees, 19 responded to the online survey with a response rate of 49%. Occupants' energy use characteristics and preexposure to occupancy-focused interventions were identified using k-means clustering analysis performed in MATLAB (2014). Framework implementation was accomplished in three steps: (i) all survey questions are categorized to measure M, O, or A; (ii) M, O, and A for each respondent are determined as the average of the responses to the corresponding questions; (iii) k-means was used to cluster the occupants into three main clusters. Each of the resulting clusters had the number of occupants shown in Figure 17.4a with the centroids (i.e., the average M, O, and A of the respondents in each cluster, respectively) as follows: for resistant to react (M = 0.12, O = 0.33, A = 0.27), for the unable to react (M = 0.73, O = 0.20, A = 0.87), and for prone to react (M = 0.89, O = 0.80, A = 0.84).

Scheme for Criteria for evaluating intervention strategies.

Figure 17.4 Occupants' energy use characteristics: (a) before intervention, (b) after intervention.

These results can be interpreted as follows:

  1. i. 13 occupants (i.e., 68% of total respondents) with prone-to-react behavior are those with flexible behavior characteristics and are willing to adopt energy reduction strategies without additional reinforcement. These occupants have very high MOA levels when measured on a scale of 0–1. This result is expected given the company profile that the respondents work for. For example, an occupant in this group has self-motivation to adjust the plug loads when not in use to conserve energy, is satisfied with the thermal comfort and lighting quality levels at his/her office environment, and has a high level of perceived self-knowledge capacity on energy conservation.
  2. ii. Three occupants (i.e., 16% of total respondents) with unable-to-react behavior have self-interest to adopt energy reduction strategies voluntarily but do not have the necessary ability and/or occupancy control level to do that (e.g., no/low control on thermostat settings in his/her office room). This is reflected in the low average O level and very high M and A levels that occupants in this category had. Even if occupants in this group have similar M and A levels to occupants with prone-to-react behavior, their lower O level makes them unable to react to change their energy conservation behavior. For example, an occupant in this group is not able to turn off the lights when not in use due to the lack of control on lighting, even if he/she has self-motivation to adjust lights when not in use and a high level of knowledge capacity on energy conservation strategies about lights. In this particular case, there are not much behavioral interventions that can be done to change the profile of these respondents to prone to react.
  3. iii. Three occupants (i.e., 16% of total respondents) with resistant-to-react behavior are reluctant to adopt energy reduction strategies. Occupants in this category had very low M level with moderate-to-low O and A levels. For example, occupants in this category are identified as unwilling to turn off their computers when not in use while exhibiting strong knowledge about the methods to reduce plug loads in their office space.

Due to the higher percentage of prone-to-react occupants in this case study, the conceptual framework presented here proposed that energy savings can be achieved effectively by delivering knowledge-based interventions to the occupants. Accordingly, the proposed intervention strategy for this case study is evaluated using three criteria: attributes (e.g., offers occupant alternative to changing heating set point), consequences (e.g., I will have to bring an extra jacket, I will be rewarded for turning lights off with a gift card to my favorite store), and time and cost of implementing the intervention (e.g., knowledge-based intervention is the cheapest as it does not require an exchange of rewards or penalties for achieving required change and can be implemented immediately), as shown in Figure 17.5.

Venn diagram for sustainable development based on the three pillars of sustainability: social, environmental, and economic.

Figure 17.5 Criteria for evaluating intervention strategies.

Moreover, occupants' behavior change after delivering the education interventions is also investigated to highlight the impact of intervention on the occupants' behavior. Azar and Menassa [88] used simulation analysis and estimated the occupancy conversion rate from extreme (i.e., resistant) to low (i.e., prone) users at 10% when discrete interventions are used (e.g., educational campaigns). This will result in an increase in the number of prone-to-react occupants. Accordingly, this study assumed 10% occupancy energy use profile conversion rate to analyze the energy use behavior change after exposure to education intervention. The results from this analysis are presented in Figure 17.4b. In this case, two resistant-to-react occupants are converted to prone-to-react occupants by increasing their motivation level through education interventions. Additional interventions (e.g., rewards like gift cards) can be used to entice the remaining resistant occupant to adopt more conservative energy use behavior.

17.7 Discussion

In energy policy making, common approaches, such as assuming that all occupants have the standard behavior pattern (e.g., a fixed set-point room temperature) and presuming that people are rational agents making considered decisions based on available information and resources, have resulted in unintended consequences of individual behaviors and diminished the impacts and effectiveness of interventions [3, 89]. To overcome these devastating consequences, it is important to systematically evaluate the effects of interventions and their impacts on the occupants' behavior in a stock of buildings [71, 72]. Therefore, this study proposed a conceptual framework that provides a linkage between energy policy tools and their related multilevel building energy use intervention strategies to systematically evaluate and change the environmental behavior of occupants and accordingly achieve large-scale energy reductions. Using this framework, occupancy-related actions (e.g., after-hours equipment use; occupied and unoccupied hours; cooling and heating temperature set points) can be examined, and well-tuned interventions to change the relevant occupants' behaviors can be provided efficiently and cost effectively.

Moreover, the set of criteria presented in Figure 17.5 can be used for all four intervention strategies (i.e., education, persuasion, penalties, and technology) to guide decision-makers in the selection of intervention strategies by providing them with research-based evidence of their effectiveness to achieve the required energy reductions given the occupants' MOA characteristics. Accordingly, decision-makers can determine (i) how occupants' energy use profile in a stock of buildings can be measured and clustered pre- and postimplementation of energy policy tools and (ii) how to design cost-effective energy policy tools to deliver multilevel energy efficiency occupancy-focused interventions that promote actions for improving sustainable behavior pattern. The conceptual framework presented in this study demonstrates a scalable, adaptable, and expandable approach able to support decision-makers (e.g., energy planners, local administrators) in identifying the most effective energy policies and strategies.

17.8 Conclusions and Policy Implications

The objectives of this study are to present a framework that defines the relationship between occupants' energy use characteristics and the effectiveness of occupancy-focused intervention strategies. This approach will lead to the design of cost-effective and efficient energy policy tools for large-scale energy savings in building stocks (e.g., university campus, community, and city). To achieve this, a comprehensive literature review was conducted on intervention strategies that aim to engage occupants in reducing energy use in buildings. Subsequently, an analogy was investigated between MOA characteristics of people to process brand information in their environment and MOA levels where occupancy-focused intervention strategies can be regarded as advertisements enticing the building occupants to adopt certain energy use characteristics. Then, a conceptual framework was presented that proposes a multilevel intervention strategy tailored to various occupants' energy use characteristics to maintain energy use reduction in buildings. The presented framework for designing effective energy policy tools was developed in three steps: (i) quantitatively measuring occupants' preexposure MOA level and energy use profiles (i.e., occupants' situational characteristics prior to any intervention) and postexposure MOA levels to determine the effectiveness of intervention level (e.g., knowledge based, persuasion, or combination) to use for a given energy reduction strategy (e.g., encouraging occupants to turn office lights off when not in use); (ii) clustering occupants' pre- and postexposure MOA levels and energy use profiles; and (iii) linking occupants' MOA levels to the multilevel energy efficiency intervention strategies and accordingly energy policy tool types (i.e., knowledge tools, inducement tools, and regulation tools). This framework will assist researchers and policy makers in (i) systematically evaluating the effects of occupancy-focused interventions on occupants' behavior and (ii) designing and implementing energy policy tools targeted to diverse human energy use characteristics to deliver occupancy-focused intervention programs efficiently and cost effectively.

This framework will also help in encouraging pro-environmental behavior for occupants in a stock of buildings and providing a more sustainable behavior pattern.

Currently the authors are testing the implementation of the proposed framework in real case study stock of buildings. Data from the occupants of actual buildings and their energy consumption are being collected from several buildings at the University of Michigan campus. Then, the effectiveness of intervention strategies on different occupants' characteristics will be investigated to validate the proposed framework on a large-scale building stock. It is also noteworthy to mention that the proposed framework in this chapter mainly focuses on the collective impact of individuals on energy use of large building stocks. Authors' future study will also consider the effect of the interactions between occupants by understanding their underlying social network topology (SNT), which defines the relationships between occupants and the strength of their influence on each other based on the frequency of interaction and interdependence. Understanding SNT will help decision-makers to analyze how energy knowledge diffuses between occupants and how this diffusion contributes to enhance the impact of the chosen intervention strategy on improving the postexposure MOA levels and the building's energy consumption.

Questions

  1. 1 Why is it important to consider occupants' energy use characteristics when considering energy reduction interventions in buildings?

  2. 2 Name the different levels of occupancy-focused interventions in buildings.

  3. 3 What is the origin of the motivation, opportunity, and ability model?

  4. 4 What are the motivation metrics in the context of building occupants?

  5. 5 What are the opportunity metrics in the context of building occupants?

  6. 6 What are the ability metrics in the context of building occupants?

Acknowledgment

The authors would like to acknowledge the financial support for this research received from the US National Science Foundation (NSF) CBET 1407908 and 1349921. Any opinions and findings in this chapter are those of the authors and do not necessarily represent those of NSF.

References

  1. 1 EIA (2014) How Much Energy Is Consumed in Residential and Commercial Buildings in the United States?
  2. 2 UNEP (2014) Sustainable Buildings and Climate Initiative Promoting Policies and Practices for Sustainability, UNEP.
  3. 3 Schweiker, M. and Shukuya, M. (2010) Comparative effects of building envelope improvements and occupant behavioural changes on the exergy consumption for heating and cooling. Energy Policy, 38, 2976–2986.
  4. 4 Von Borgstede, C., Andersson, M., and Johnsson, F. (2013) Public attitudes to climate change and carbon mitigation—Implications for energy-associated behaviours. Energy Policy, 57, 182–193.
  5. 5 Azar, E. and Menassa, C.C. (2014) A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks. Energy Policy, 67, 459–472.
  6. 6 Carrico, A.R. and Riemer, M. (2011) Motivating energy conservation in the workplace: An evaluation of the use of group-level feedback and peer education. Journal of Environmental Psychology, 31, 1–13.
  7. 7 Hand, L.C. (2012) Public Policy Design and Assumptions About Human Behavior, Western Political Science Association's Annual Conference.
  8. 8 Akridge, J.M. (1998) High-albedo roof coatings--Impact on energy consumption (No. CONF-980123--), American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA.
  9. 9 Jollands, N., Waide, P., Ellis, M. et al. (2010) The 25 IEA energy efficiency policy recommendations to the G8 Gleneagles Plan of Action. Energy Policy, 38, 6409–6418.
  10. 10 Ruzelli, A. (2010) Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building. ACM, Zurich, Switzerland, p. 93.
  11. 11 Wong, N.H., Cheong, D., Yan, H. et al. (2003) The effects of rooftop garden on energy consumption of a commercial building in Singapore. Energy and Buildings, 35, 353–364.
  12. 12 Braun, J.E. (1990) Reducing energy costs and peak electrical demand through optimal control of building thermal storage. ASHRAE Transactions, 96, 876–888.
  13. 13 Kouveletsou, M., Sakkas, N., Garvin, S. et al. (2012) Simulating energy use and energy pricing in buildings: The case of electricity. Energy and Buildings, 54, 96–104.
  14. 14 Levine, M., Urge-Vorsatz, D., Blok, K., Geng, L., Harvey, D., Lang, S., Levermore, G., Mehlwana, A.M., Miragedis, S., and Novikova, A. (2007) Residential and Commercial Buildings. Climate change 2007; Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the IPCC. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  15. 15 Lopes, M.A.R., Antunes, C.H., and Martins, N. (2012) Energy behaviours as promoters of energy efficiency: A 21st century review. Renewable and Sustainable Energy Reviews, 16, 4095–4104.
  16. 16 Azar, E. and Menassa, C. (2011a) An agent-based approach to model the effect of occupants' energy use characteristics in commercial buildings. Computing in Civil Engineering, American Society of Civil Engineers (ASCE), Miami, FL, 536–543.
  17. 17 Azar, E. and Menassa, C.C. (2011b) Agent-based modeling of occupants and their impact on energy use in commercial buildings. Journal of Computing in Civil Engineering, 26, 506–518.
  18. 18 Mahdavi, A. and Pröglhöf, C. (2009). Toward Empirically-Based Models of People's Presence and Actions in Buildings, Proceedings of building simulation, pp. 537–544.
  19. 19 Tanimoto, J., Hagishima, A. (2009) Total Utility Demand Prediction Based on Probabilistically Generated behavioral Schedules of Actual Inhabitants.
  20. 20 Moezzi, M., Iyer, M., Lutzenhiser, L., and Woods, J. (2009) Behavioral assumptions in energy efficiency potential studies, California Institute for Energy and Environment (CIEE), Oakland, Calif.
  21. 21 Sanchez, M., Webber, C., Brown, R. et al. (2007) Space heaters, computers, cell phone chargers: How plugged in are commercial buildings? Lawrence Berkeley National Laboratory.
  22. 22 Webber, C.A., Roberson, J.A., McWhinney, M.C. et al. (2006) After-hours power status of office equipment in the USA. Energy, 31, 2823–2838.
  23. 23 Backer, T.E. (1991) Knowledge utilization The third wave. Science Communication, 12, 225–240.
  24. 24 Bzdel, L., Wither, C., and Graham, P. (2004) Knowledge Utilization Resource Guide. Knowledge Utilization and Policy Implementation.
  25. 25 Estabrooks, C.A., Thompson, D.S., Lovely, J.J.E., and Hofmeyer, A. (2006) A guide to knowledge translation theory. Journal of Continuing Education in the Health Professions, 26, 25–36.
  26. 26 Kaufman, L. and Rousseeuw, P.J. (2009) Finding groups in data: an introduction to cluster analysis, John Wiley & Sons.
  27. 27 Norcross, J.C., Koocher, G.P., and Garofalo, A. (2006) Discredited psychological treatments and tests: A Delphi poll. Professional Psychology: Research and Practice, 37, 515.
  28. 28 Spring, B., Walker, B., Brownson, R. et al. (2008) Definition and competencies for evidence-based behavioral practice (EBBP), Counsel for Training in Evidence-Based Behavioral Practice, Evanston, IL.
  29. 29 Wensing, M., Bosch, M., and Grol, R. (2010) Developing and selecting interventions for translating knowledge to action. Canadian Medical Association Journal, 182, E85–E88.
  30. 30 Grier, S. and Bryant, C.A. (2005) Social marketing in public health. Annual Review of Public Health, 26, 319–339.
  31. 31 Kotler, P., Roberto, N., and Lee, N. (2002) Social marketing: Improving the quality of life, Sage Publications, Thousand Oaks, Calif.
  32. 32 Peattie, K. and Peattie, S. (2009) Social marketing: A pathway to consumption reduction? Journal of Business Research, 62, 260–268.
  33. 33 Hedlund, J. (2000) Risky business: safety regulations, risk compensation, and individual behavior. Injury Prevention, 6, 82–89.
  34. 34 Houston, D.J. and Richardson, L.E. (2007) Risk compensation or risk reduction? Seatbelts, state laws, and traffic fatalities. Social Science Quarterly, 88, 913–936.
  35. 35 Armstrong, A.W., Idriss, N.Z., and Kim, R.H. (2011) Effects of video-based, online education on behavioral and knowledge outcomes in sunscreen use: a randomized controlled trial. Patient Education and Counseling, 83, 273–277.
  36. 36 Blamey, A., Mutrie, N., and Tom, A. (1995) Health promotion by encouraged use of stairs. BMJ, 311, 289–290.
  37. 37 Sahota, P., Rudolf, M.C., Dixey, R. et al. (2001) Randomised controlled trial of primary school based intervention to reduce risk factors for obesity. BMJ, 323, 1029.
  38. 38 Agha-Hossein, M., Tetlow, R., Hadi, M. et al. (2014) Providing persuasive feedback through interactive posters to motivate energy-saving behaviours. Intelligent Buildings International Journal, 7, 1–20.
  39. 39 Hayes, S.C. and Cone, J.D. (1977) Reducing residential electrical energy use: Payments, information, and feedback. Journal of Applied Behavior Analysis, 10, 425–435.
  40. 40 Hutton, R.B. and McNeill, D.L. (1981) The value of incentives in stimulating energy conservation. Journal of Consumer Research, 8, 291–298.
  41. 41 Marans, R.W. and Edelstein, J.Y. (2010) The human dimension of energy conservation and sustainability: a case study of the University of Michigan's energy conservation program. International Journal of Sustainability in Higher Education, 11, 6–18.
  42. 42 Midden, C.J., Meter, J.F., Weenig, M.H., and Zieverink, H.J. (1983) Using feedback, reinforcement and information to reduce energy consumption in households: A field-experiment. Journal of Economic Psychology, 3, 65–86.
  43. 43 Zografakis, N., Menegaki, A.N., and Tsagarakis, K.P. (2008) Effective education for energy efficiency. Energy Policy, 36, 3226–3232.
  44. 44 Geller, E.S. (1981) Evaluating energy conservation programs: Is verbal report enough? Journal of Consumer Research, 8, 331–335.
  45. 45 McMakin, A.H., Malone, E.L., and Lundgren, R.E. (2002) Motivating residents to conserve energy without financial incentives. Environment and Behavior, 34, 848–863.
  46. 46 Dolan, P. and Metcalfe, R. (2013) Neighbors, Knowledge, and Nuggets: Two Natural Field Experiments on the Role of Incentives on Energy Conservation, Centre for Economic Performance, LSE.
  47. 47 Van Houwelingen, J.H. and Van Raaij, W.F. (1989) The effect of goal-setting and daily electronic feedback on in-home energy use. Journal of Consumer Research, 16, 98–105.
  48. 48 Allcott, H. (2011) Social norms and energy conservation. Journal of Public Economics, 95, 1082–1095.
  49. 49 Ayres, I., Raseman, S., and Shih, A. (2012) Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage. Journal of Law, Economics, and Organization, 29, 992–1022.
  50. 50 Siero, F.W., Bakker, A.B., Dekker, G.B., and Van Den Burg, M.T. (1996) Changing organizational energy consumption behaviour through comparative feedback. Journal of Environmental Psychology, 16, 235–246.
  51. 51 Ayres, I., Raseman, S. and Shih, A. (2009) Evidence from Two Large Field Experiments that Peer Comparison Feedback can Reduce Residential Energy Usage. 5th Annual Conference on Empirical Legal Studies Paper. Available at: http://ssrn.com/abstract=1434950. Accessed on: May 05, 2016.
  52. 52 Burn, S.M. and Oskamp, S. (1986) Increasing community recycling with persuasive communication and public commitment. Journal of Applied Social Psychology, 16, 29–41.
  53. 53 John, L.K., Loewenstein, G., Troxel, A.B. et al. (2011) Financial incentives for extended weight loss: a randomized, controlled trial. Journal of General Internal Medicine, 26, 621–626.
  54. 54 Flora, S.R. and Flora, D.B. (2012) Effects of extrinsic reinforcement for reading during childhood on reported reading habits of college students. The Psychological Record, 49, 1.
  55. 55 Alahmad, M.A., Wheeler, P.G., Schwer, A. et al. (2012) A comparative study of three feedback devices for residential real-time energy monitoring. IEEE Transactions on Industrial Electronics, 59, 2002–2013.
  56. 56 Darby, S. (2006) The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays, 486, 2006.
  57. 57 Handgraaf, M.J., Van Lidth de Jeude, M.A., and Appelt, K.C. (2013) Public praise vs. private pay: Effects of rewards on energy conservation in the workplace. Ecological Economics, 86, 86–92.
  58. 58 Katzev, R.D. and Johnson, T.R. (1984) Comparing the effects of monetary incentives and foot-in-the-door strategies in promoting residential electricity conservation. Journal of Applied Social Psychology, 14, 12–27.
  59. 59 McClelland, L. and Cook, S.W. (1980) Promoting energy conservation in master-metered apartments through group financial incentives. Journal of Applied Social Psychology, 10, 20–31.
  60. 60 Whitsett, D.D., Justus, H.C., Steiner, E., Duffy, K. (2013) Persistence of Energy Efficiency Behaviors over Time: Evidence from a Community-Based Program.
  61. 61 Winett, R.A., Kagel, J.H., Battalio, R.C., and Winkler, R.C. (1978) Effects of monetary rebates, feedback, and information on residential electricity conservation. Journal of Applied Psychology, 63, 73.
  62. 62 Boyce, T.E. and Geller, E.S. (2001) Encouraging college students to support pro-environment behavior effects of direct versus indirect rewards. Environment and Behavior, 33, 107–125.
  63. 63 Schick, S. and Goodwin, S. (2011) Residential Behavior Based Energy Efficiency Program Profiles, Bonneville Power Administration.
  64. 64 Farchi, S., Chini, F., Rossi, P.G. et al. (2007) Evaluation of the health effects of the new driving penalty point system in the Lazio Region, Italy, 2001–4. Injury Prevention, 13, 60–64.
  65. 65 Heberlein, T.A. and Warriner, G.K. (1983) The influence of price and attitude on shifting residential electricity consumption from on-to off-peak periods. Journal of Economic Psychology, 4, 107–130.
  66. 66 Erickson, V.L., Achleitner, S., and Cerpa, A.E. (2013) POEM: Power-Efficient Occupancy-Based Energy Management System, Proceedings of the 12th international conference on Information processing in sensor networks. ACM, pp. 203–216.
  67. 67 Erickson, V.L. and Cerpa, A.E. (2010) Occupancy Based Demand Response HVAC Control Strategy, Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building. ACM, pp. 7–12.
  68. 68 Mathews, E., Botha, C., Arndt, D., and Malan, A. (2001) HVAC control strategies to enhance comfort and minimise energy usage. Energy and Buildings, 33, 853–863.
  69. 69 Environmental and Energy Study Institute (2006) Energy-Efficient Buildings: Using Whole Building Design to Reduce Energy Consumption in Homes and Offices.
  70. 70 Harvey, L.D. (2009) Reducing energy use in the buildings sector: measures, costs, and examples. Energy Efficiency, 2, 139–163.
  71. 71 Abrahamse, W., Steg, L., Vlek, C., and Rothengatter, T. (2005) A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25, 273–291.
  72. 72 Steg, L. and Vlek, C. (2009) Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29, 309–317.
  73. 73 Buurma, H. (2001) Public policy marketing: Marketing exchange in the public sector. European Journal of Marketing, 35, 1287–1302.
  74. 74 Celsi, R.L. and Olson, J.C. (1988) The role of involvement in attention and comprehension processes. Journal of Consumer Research, 15, 210–224.
  75. 75 Machleit, K.A., Madden, T.J., and Allen, C.T. (1990) Measuring and modeling brand interest as an alternative ad effect with familiar brands. Advances in Consumer Research, 17, 223–230.
  76. 76 Maclnnis, D.J., Moorman, C., and Jaworski, B.J. (1991) Enhancing and measuring consumers' motivation, opportunity, and ability to process brand information from ads. Journal of Marketing, 55, 32–53.
  77. 77 Moorman, C. (1990) The effects of stimulus and consumer characteristics on the utilization of nutrition information. Journal of Consumer Research, 17, 362–374.
  78. 78 Polonsky, M.J., Binney, W., and Hall, J. (2004) Developing better public policy to motivate responsible environmental behavior–an examination of managers' attitudes and perceptions towards controlling introduced species. Journal of Nonprofit & Public Sector Marketing, 12, 93–107.
  79. 79 Rothschild, M.L. (1999) Carrots, sticks, and promises: a conceptual framework for the management of public health and social issue behaviors. Journal of Marketing, 63, 24–37.
  80. 80 Hastak, M., Mazis, M.B., and Morris, L.A. (2001) The role of consumer surveys in public policy decision making. Journal of Public Policy & Marketing, 20, 170–185.
  81. 81 Bigné, E., Hernández, B., Ruiz, C., and Andreu, L. (2010) How motivation, opportunity and ability can drive online airline ticket purchases. Journal of Air Transport Management, 16, 346–349.
  82. 82 Govindaraju, R., Hadining, A.F., and Chandra, D.R. (2013) Physicians' Adoption of Electronic Medical Records: Model Development Using Ability–Motivation–Opportunity Framework, Information and Communication Technology, Springer, pp. 41–49.
  83. 83 Richins, M.L. and Bloch, P.H. (1986) After the new wears off: the temporal context of product involvement. Journal of Consumer Research, 13, 280–285.
  84. 84 Zaichkowsky, J.L. (1985) Measuring the involvement construct. Journal of Consumer Research, 12, 341–352.
  85. 85 Hallahan, K. (2001) Enhancing motivation, ability, and opportunity to process public relations messages. Public Relations Review, 26, 463–480.
  86. 86 Parra-Lopez, E., Gutierrez-Tano, D., Diaz-Armas, R.J., and Bulchand-Gidumal, J. (2012) Travellers 2.0: motivation, opportunity and ability to use social media Social Media in Travel, Tourism and Hospitality, Ashgate, pp. 171–185.
  87. 87 Geller, E.S. (1995) Actively caring for the environment - an integration of behaviorism and humanism. Environment and Behavior, 27 (2), 184–195.
  88. 88 Azar, E. and Menassa, C.C. (2015) Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings. Energy and Buildings, 97, 205–218.
  89. 89 Prendergrast, J., Foley, B., Menne, V., and Isaac, A.K. (2008) CreATures of HABiT?, The art of behaviour change, The Social Market Foundation, London.
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