Homeowners’ Motivations to Invest in Energy-Efficient and Renewable Energy Technologies in Rural Iowa
Publication: ASCE OPEN: Multidisciplinary Journal of Civil Engineering
Volume 2
Abstract
Across the United States, rural households are more vulnerable to higher energy burdens (percentage of household income spent on energy bills). Energy-efficient and renewable energy technologies provide the means for improving energy performance and reducing the operating costs of residential buildings. While there is significant evidence regarding their benefits, the investment in these technologies can be significantly lower in rural areas. In this study, we analyze the adoption behaviors of rural households concerning five technologies: Smart thermostats, light-emitting diode (LED) lighting, weatherization, Energy Star-rated appliances, and rooftop solar panels. Previous research on the motivations and barriers underlying the adoption of these technologies has primarily focused on urban and suburban areas. This study adopted a qualitative approach in eight rural Iowa communities (Boone, Nevada, Pella, Solon, Swisher, Williamsburg, Kelley, and Elkhart), where 39 (N = 39) rural homeowners were interviewed. Our findings demonstrate that motivations to adopt energy-efficient and renewable energy technologies are driven by reduced energy costs, local availability of appliances or contractors, and environmental impact. By contrast, barriers to the adoption of these technologies are largely driven by high costs, insufficient information about the technologies, and the local availability of appliances or contractors. The qualitative data obtained from the interviews moves beyond survey-based data, providing inductive explanations for adoption behaviors among rural households. While the findings presented herein are designed to increase the awareness of energy-efficiency program administrators and policymakers, the paper does not suggest that the results and discussion are necessarily applicable to all rural regions. Rather, these results are primarily applicable to communities across rural Iowa.
Introduction
According to the American Housing Survey data, 21% of all occupied housing units in the United States are located in rural areas (USCB 2021). Throughout this paper, we use the US Census Bureau’s definition of rural areas as areas defined by populations, housing, and land not included within an urbanized area or urban cluster (USCB 2016). Despite consuming less energy per household than their urban counterparts, rural households face a disproportionately higher energy burden (Adua and Beaird 2018; Ross et al. 2018). Energy burden is calculated as the percentage of gross household income spent on energy costs (DOE 2017). On average, rural households face energy burdens that are 33% higher than the rest of the nation (Ross et al. 2018) and 42% higher than their metropolitan counterparts (Census Bureau 2017).
Energy-burdened households may lessen their energy burdens by adopting energy-efficient and renewable energy technologies [such as solar panels, light-emitting diode (LED) lights, or home insulation]. These technologies provide a means for improving energy performance and reducing operating costs. However, previous studies have identified a rural energy-efficiency gap (MacDonald et al. 2020). The rural energy-efficiency gap refers to the slower uptake of energy-efficient upgrades in smaller or more isolated communities even when these investments reduce household energy burdens. According to Ross et al. (2018), upgrades to more efficient energy technologies can lower household energy burdens by as much as 25%. Further, the adoption of energy-efficient technologies is shown to help rural utilities address aging infrastructure by reducing energy demand and avoiding the costs of constructing additional transmission, generation, and distribution infrastructure (Baatz 2015).
Considering these benefits, it is important to identify the motivations and barriers underlying the adoption of energy-efficient technologies among rural populations. Previous studies have demonstrated that adoption behaviors are associated with motivations and barriers (Nair et al. 2010; Kastner and Stern 2015; Abdmouleh et al. 2018; Xie et al. 2021). However, findings within the context of the rural US communities are minimal (MacDonald et al. 2020; Vega et al. 2022). We find that the majority of literature relies on urban population samples to determine the most significant factors in understanding adoption behaviors. In addition, most studies rely on large-scale survey methods, allowing for increased sample sizes but limited attributes and variables per participant. Therefore, the existing literature presents an opportunity to apply deductive interviewing analysis to our understanding of adoption behaviors specific to rural populations in the Midwest United States.
This paper introduces two primary research questions: (1) What motivates rural homeowners to invest in energy-efficient technologies? and (2) What barriers rural homeowners face in the adoption of energy-efficient technologies? To answer these questions, we interviewed 39 rural homeowners across 8 rural areas in Iowa, including Boone, Elkhart, Kelley, Nevada, Pella, Solon, Swisher, and Williamsburg. The participants were asked a series of questions related to the following five technologies: smart thermostats, LED lighting, weatherization, Energy Star-rated appliances, and rooftop solar energy. Our results identified three dominant categories of motivations and barriers underlying the adoption behaviors in this region. These categories are expressed as rationales that we identified using content analysis techniques.
The remainder of this paper includes a section on rural energy burdens that provides an overview of the energy needs and consumption patterns among rural populations. Next, we discuss the related literature on factors that impact residential adoption or uptake of energy-efficient technologies. Our methods include an outline of our data collection procedures, study sample demographics, and data analysis techniques. Our results present a table for each energy-efficient technology, the frequency of its adoption, and the primary rationales attached to each adoption behavior. Our conclusion presents a discussion of the potential insights provided by the data analysis and concluding recommendations for further studies.
Rural Energy Burden
Multiple factors contribute to an increased energy burden among rural populations, including population density, geographic terrain, and spatial remoteness (Michalski 2019). For example, power outages in rural areas tend to last longer than outages in densely populated regions because of the reliance on a single transmission supply line, lack of access to backup transmission resources, and increased service times (Li et al. 2014). It is not uncommon for rural populations to be situated within terrain that introduces barriers to energy access. For example, remote and/or hilly terrain can make grid extension uneconomical (Chauhan and Saini 2015) and increase the cost of fuel transportation. Rural populations tend to rely on petroleum fuels for heating their homes at a higher rate than their urban counterparts (MacDonald et al. 2020). In Iowa, approximately one in eight households use propane for heating (USCB 2019a), nearly triple the national rate. Iowa does not have any oil refineries located within the state and relies on pipelines to deliver petroleum products to residential consumers, making heating with petroleum costly.
Rural homeowners generally experience higher home cooling and heating costs due in part to an older building stock. Rural residential buildings in the United States are generally older than their urban counterparts (Muratori 2013; Johnston 2017) and face greater rates of disrepair. Rural housing generally consists of single-family homes constructed before the widespread implementation of building codes in the 1980s. Studies show that the implementation of energy codes is associated with a decrease in energy consumption (Jacobsen and Kotchen 2013; Kotchen 2017). Additionally, different states have different state codes, which set the standards for residential buildings. Some states may only enforce the adoption of these codes among municipalities with a population of more than 15,000 (Powers and Duties of Cities 2020).
These factors that contribute to increased costs and unreliable energy generation, transmission, and distribution are often exacerbated by socioeconomic variables. The low-income rural households face energy burdens nearly three times that of their higher-income counterparts (Ross et al. 2018). The average median income in rural areas is approximately 25% lower than the average median income in urban areas (Rural America at a Glance 2017). Previous research suggests that high-income homeowners are more likely to invest in technologies that increase the energy efficiency of their homes, while low-income homeowners are more likely to reduce energy consumption through behaviors on a day-to-day basis (Trotta 2018).
Given these characteristics, rural areas have unique energy needs that make it challenging for utilities, state energy offices, and other program implementers to deliver energy-efficient technologies or tools to rural customers. The results of this study can guide local policymakers and planners in providing more effective and representative programs to respond to the motivations and barriers underlying rural homeowners’ decisions to invest in energy-efficient technologies. The results of this study also contribute to and expand the literature surrounding the factors that impact residential uptake of energy-efficient technologies. Specifically, the study takes into account rural populations and the spatial context, which has received limited representation in the literature.
Literature Review
The current body of literature has provided evidence for a wide range of factors impacting residential adoption of energy-efficient and renewable energy technologies. These generally include household income and cost savings (Schleich 2019; Nair et al. 2010); environmental concerns (Ferreira et al. 2023; Schill et al. 2019; Mills and Schleich 2014; Di Maria et al. 2010); energy independence and security (Yemelyanov et al. 2019); government and financial incentives in the form of tax credits, rebates, and subsidies (Galarraga et al. 2016); education levels (Mills and Schleich 2012); and political affiliation (Gromet et al. 2013). These studies have primarily employed large-scale survey methods relying on participants from across multiple regions and countries. Studies that include a focus on rural populations generally find that cost savings (Aklin et al. 2018), improved livelihoods (Samad et al. 2013), and geographic/spatial variables (Vega et al. 2022) impact adoption behaviors. Here, we will briefly summarize the main findings of the literature with a narrowed focus on those factors that relate to rural populations and the five technologies examined in this study.
Smart thermostats are thermostats that rely on a wireless internet connection to control a household’s heating, ventilation, and air conditioning systems and may employ occupancy sensing and/or learning behavior (NEEP 2019). Most smart thermostat devices have the capacity to allow users to adjust temperatures remotely and set heating and cooling schedules. These functions have the potential to reduce energy consumption and household heating/cooling bills, and as a result, lower carbon emissions (Liang et al. 2013). Previous studies on smart thermostat adoption demonstrate that households are motivated by cost savings, remote control functionality (Koupaei et al. 2020), availability of energy usage feedback, and reduced environmental impact (Tu et al. 2021). Previous research also indicates that households are discouraged from adopting smart thermostats because of concerns about privacy and autonomy (Distler et al. 2020; Mamonov and Koufaris 2020). The initial costs and inadequate broadband access also pose barriers to the adoption of smart thermostat technologies (Ross et al. 2018). In 2016, the Federal Communications Commission (FCC) documented that 39% of rural Americans lacked access to the minimum broadband speeds identified as the FCC’s broadband benchmark goal (FCC 2016). Without adequate broadband access, rural households are less likely to adopt smart thermostat technologies, or meaningfully access energy-efficient technologies that rely on internet access.
Energy-efficient lighting refers to lighting technology that produces the highest lumen output with the least amount of power. Currently, one of the most commonly used energy-efficient lighting technologies is LED products. Prior research finds that consumers typically adopt LEDs to save energy and money, and/or reduce environmental impact (Roy et al. 2007; Caird et al. 2008). Furthermore, previous studies support the claim that households that tend to discount future savings (i.e., cost savings that are experienced over time as opposed to immediately) are less likely to adopt LEDs (Schleich et al. 2019; Olsthoorn et al. 2019). There is also evidence to suggest a disparity in LED adoption rates between high- and low-income households (Schleich 2019). These studies also suggest that adoption disparities across income groups not only exist for high-cost technologies but also for medium- and low-cost technologies such as small appliances and light bulbs.
Weatherization measures are used to reduce household energy costs and improve the health and safety of occupants (Schweitzer and Tonn 2003). The most common weatherization practices include replacement of temperature control systems, attic and wall insulation, and infiltration reduction (Fowlie et al. 2018). These practices are shown to reduce the cost of heating and cooling, and improve health outcomes (De Souza et al. 2019). Previous studies show that the single largest determinant of weatherization behaviors is interacting with others about energy issues (Southwell and Murphy 2014). People are more likely to participate in weatherization practices if they are encouraged by trusted community members such as school representatives, religious leaders, business leaders, politicians, and community organization representatives (Fuller 2010). Studies also show that weatherization programs, such as the Department of Energy’s Weatherization Assistance Program (WAP), experience greater success when framed as a social program rather than an energy-related program (Ternes et al. 2007).
However, some studies suggest that the financial returns to weatherization investments may not justify the costs incurred by safe and effective installation (Metcalf and Hassett 1999; Gerarden et al. 2015; Allcott and Greenstone 2017). In rural areas, investments in weatherization efforts are further hindered by limited utility program offerings, lack of information about existing programs, and a lack of trained contractor networks. In particular, rural areas face significant barriers to maintaining the necessary volume of work to support certified energy efficiency contractor networks (Ross et al. 2018).
In the context of this study, solar technology refers to residential solar photovoltaic (PV) generators. Residential users are largely motivated to adopt solar energy technologies to reduce electric bills, establish or maintain energy independence, and reduce environmental impact (Reindl and Palm 2021). Prior studies conducted in a US context have found increased adoption rates within states that have integrated solar into public energy assistance programs through rebates, net metering, tax credit, or feed-in tariff (FIT) policies. While these policies vary widely by state, they have appeared to increase the availability of solar energy to low-income and rural households where implemented (Ross et al. 2018). However, high installation and maintenance costs are cited as primary barriers to adoption among residential users. While the median household income of adopters of solar technology has decreased from $129,000 to $110,000 in the last decade (Barbose et al. 2021), this is still more than twice the median household income for rural counties, calculated at $44,212 (USDA 2017). In addition to high costs, weather patterns, geographic location, and shortages of contractors pose significant barriers to adoption (Palm 2018).
Energy Star-rated appliances refer to appliances labeled as energy-efficient products that meet strict energy-efficiency guidelines set by the US Environmental Protection Agency and the US Department of Energy. The label program aims to reduce US residential energy consumption through consumer awareness. Appliances with an Energy Star label on average have a higher fixed or upfront cost in comparison with conventional appliances. However, Energy Star appliances have reduced monthly costs compared with their conventional counterparts (ENERGY STAR Impacts, n.d.). Previous research has identified one of the most critical variables for both the awareness of and purchase of energy-saving appliances as whether the resident owns or rents the household (Murray and Mills 2011). Because renters often pay utilities separately from their rent, landlords experience little incentive to purchase more costly Energy Star appliances as the long-term energy savings are transferred to the renter (Murray and Mills 2011; Davis 2011). Previous research also shows that households with higher incomes are more likely to purchase energy-saving appliances (Murray and Mills 2011). Environmental concern and awareness also tend to increase with income (Mills and Schleich 2009). Further, consumers who reside in more recently built houses are also more likely to be aware of and purchase Energy Star-labeled appliances. Finally, the impact of rural environments on label awareness and appliance purchasing behavior requires further consideration. Murray and Mills (2011) found that geographic location does not significantly affect Energy Star appliance purchasing behavior. However, their study relied on the Residential Energy Consumption Survey (RECS), which distributes surveys by metropolitan statistical areas (MSAs). Similar studies have relied on samples that reside within a metropolitan statistical area (Hopkins et al. 2020; Wang et al. 2020). These data sets present significant geographic limitations. While many MSAs include rural territories, most sampled households are located within an urban context (USCB 2019b). Therefore, it is difficult to generalize previous findings to the rural context.
While the results of this paper may not be applicable to all rural regions across the United States, we believe our findings make significant contributions to the literature at present. In general, the current body of literature has left an opportunity to empirically contribute to the question of how US-based rural residency might impact the adoption of energy-efficient and renewable energy technologies. Relying on qualitative interviewing methods is another main contribution of this paper as it allows for increased attributes and observations per participant.
Methods
Study Design
Previous studies focused on adoption behaviors have primarily relied on quantitative survey methods that focus on a predetermined set of variables, such as attitudes, housing characteristics, or energy costs (Guta 2020; Cattaneo 2019). These methods allow for increased scale and statistical analysis on a small set of variables. In this study, we relied on qualitative interview methods and manual content analysis to produce a detailed description of locally specific adoption behaviors. Qualitative interviewing is a widely recognized and effective research method that provides an in-depth and nuanced understanding of a particular issue or research question (Neuman 2002; Levitt et al. 2018). It allows researchers to engage in a conversation with participants to explore their knowledge about a technology, its relevancy, and barriers (Connelly et al. 2014). In the context of energy research, qualitative interviewing allow for the analysis of personal narratives regarding the day-to-day use of energy consuming technologies (Bickerstaff et al. 2015).
An interviewing method used among small sample sizes does not necessarily provide a generalization of results across populations and regions. Rather, it allows for a focused or deeper understanding of a specific phenomenon. Interviewing methods produce knowledge and findings inductively over the course of the data analysis process. Scholars argue that sample size in qualitative research is less important than data saturation (Ritchie et al. 2013). In other words, the priority of the method is the accurate identification of patterns and themes within a single topic.
Data Collection
The data collected to support this analysis includes interviews with 39 rural households across Iowa. This sample represents eight rural communities across Iowa, including Boone, Elkhart, Kelley, Nevada, Pella, Solon, Swisher, and Williamsburg. Table 1 outlines the demographic profile of each community.
| Location | Population | Median annual income | Educational attainment | Employment rate (%) | Race |
|---|---|---|---|---|---|
| Boone | 12,469 | $60,854 | High school diploma, 35.2% Some college, 21.5% Bachelor’s degree or higher, 20.7% Associate degree, 16.5% No diploma, 6.0% | 71.3 | White, 93.8% Latinx, 4.4% Black or African American, 1.3% American Indian, 0.9% Pacific Islander, 0.5% Asian, 0.2% |
| Elkhart | 882 | $61,042 | High school diploma, 38.7% Some college, 23.4% Bachelor’s degree or higher, 17.8% Associate degree, 14.2% No diploma, 5.8% | 68.8 | White, 98.15% Other, 1.7% Black or African American, 0.1% Asian, 0.05% |
| Kelley | 369 | $101,250 | Bachelor’s degree or higher, 35.8% Some college, 30.5% High school diploma, 18.9% Associate degree, 12.3% No diploma, 1.8% | 85.4 | White, 93.2% Black or African American, 5.1% Latinx, 1.6% |
| Nevada | 6,737 | $60,144 | Bachelor’s degree or higher, 35.2% High school diploma, 24.7% Some college, 21.7% Associate degree, 13.0% No diploma, 5.2% | 64.6 | White, 93.4% Latinx, 5.1% Asian, 0.6% Black or African American, 0.4% |
| Pella | 10,279 | $75,848 | No diploma, 5.7% High school diploma, 27.8% Some college, 14.6% Associate degree, 8.4% Bachelor’s degree or higher, 43.6% | 68.1 | White, 94.8% Asian, 2.4% Latinx, 1.9% Black or African American, 1.1% |
| Solon | 2,690 | $83,897 | No diploma, 6.5% High school diploma, 19.8% Some college, 21.1% Associate degree, 14.4% Bachelor’s degree or higher, 38.1% | 71.3 | White, 98.7% Latinx, 0.8% Black or African American, 0.6% |
| Swisher | 1,054 | $89,375 | No diploma, 2.0% High school diploma, 27.9% Some college, 18.3% Associate degree, 16.6% Bachelor’s degree or more, 35.1% | 78.4 | White, 95.6% American Indian, 2.8% Latinx, 1.0% Black or African American, 0.8% |
| Williamsburg | 3,152 | $68,094 | No diploma, 3.6% High school diploma, 31.0% Some college, 15.9% Associate degree, 17.8% Bachelor’s degree or higher, 31.6% | 75.2 | White, 96.6% Latinx, 2.8% Asian, 0.6% |
Prior to participant recruitment, the approval of the Institutional Review Board (IRB) for the study was obtained from Iowa State University’s Office of Research Ethics. Our corresponding study number under the IRB is 20-423-00. Research participants were identified using snowball sampling methods relying on the use of flyers and participant referrals. Recruitment criteria required that the participants: (1) live in communities with a population <15,000; (2) are 18 years of age or older; and (3) identify as homeowners. Participants were offered a $25 gift card for their participation in the study.
The interviews were conducted remotely by phone, Webex, or Zoom. They lasted for between 30 and 60 min. The interviews were semistructured and narrative based. Participants were encouraged to share in detail their rationale behind each response, drawing on their lived experiences. In addition, they were asked a series of demographic questions. Table 2 provides our participants’ demographics. The participants were asked the same set of questions regarding smart thermostats, weatherization, LED lighting, solar energy, and Energy Star-rated appliances. These questions were intended to explore each participant’s understanding of the technology, its intended function, and whether they believed it was an energy-efficient technology. Finally, the participants were asked if they have adopted the technology or not, and to explain their decision-making rationale.
| Gender | Race | Avg. annual household income | Household size | Educational attainment |
|---|---|---|---|---|
| Female, 69.2% Male, 15.4% N/A, 15.4% | White, 87.2% N/A, 7.7% Asian, 2.6% | $104,799 | Family (no children), 51.3% Family (with children), 28.2% N/A, 12.8% Single, 7.7% | Bachelor’s degree or higher, 61.5% Some college, 18.0% High school diploma, 10.2% N/A, 10.2% |
Limitations
A limitation of this study can be found in sampling bias. A multicommunity sampling strategy was used to ensure adequate representation of rural Iowa communities and ideally a multiscalar application of results. While these results provide rich qualitative data and lend to productive, community-based policy recommendations, they are not wholly representative. In Iowa, the percentage of persons with a Bachelor’s degree or higher is 25.9%. The median household income is $61,836, and 31.4% of the households have children under 18 years of age (USCB 2021). Further, 50.3% of Iowans identify as female and 49.7% identify as male. The demographics represented in this study deviate as reflected in Table 2, particularly among the categories of gender, household income, household size, and educational attainment. Therefore, the demographics of this sample limit the generalizability of our findings. With these limitations in mind, we suggest our findings are primarily applicable to Iowa rural communities.
Analysis of Interview Findings
We drew on narrative inquiry methods to analyze our set of data. Narrative inquiry is a general term that refers to a number of different analytic approaches to textual data (Riessman 2008). Specifically, we relied on content analysis for identifying, analyzing, and reporting themes that extended across the entire set of our interviews. Content analysis is a well-suited approach for conducting exploratory work in an area where not much is already well documented or known (Green and Thorogood 2004) and allows for the quantification of qualitative data analyses (Grbich 2013). Because substantial research is lacking on the adoption of energy-efficient technology among rural households, we used inductive analysis to derive our coded categories directly from the text. Future studies may benefit from replicating our coded categories in varying contexts to compare categories at different regional scales or to test for theoretical saturation.
Content analysis methods are made up of several stages: preparation, organizing, and reporting. In the preparation stage, interview data were read and reread by one coder until thoroughly familiarized. In the organizing phase, the coder categorized each participant’s desire to adopt each type of technology. Desirability of adoption was divided into four categories: (1) participant has adopted the technology, or is motivated to adopt the technology; (2) participant desires to adopt the technology, but faces barriers; (3) participant has no desire to adopt the technology; and (4) participant has not heard of the technology, or has no concept of it. Next, the coder openly coded the participants’ rationales toward the adoption of each technology until clusters emerged. Clusters, or repeating rationales, were distributed into five overarching categories—economic, environmental, geographic/regional, informational, and social—as provided in Table 3. These five categories are made up of 21 codes, which represent the most frequent rationales underlying each category. Succinct and vivid statements were extracted to illustrate each of the 21 codes. Statements were selected on the basis of whether they contained a keyword used by the coder to organize rationales into categories.
| Adoption rationales | Codes |
|---|---|
| Economic Economic rationales are related to financial constraints or motives, or ability to pay for the adoption and installation of a certain technology | 1. Lowers energy costs 2. High upfront costs 3. Affordable |
| Environmental Environmental rationales are related to motivations to prevent the degradation of natural resources, or the conservation of natural resources | 1. Lowers energy consumption 2. Lowers emissions |
| Geographic/regional Geographic or regional-based rationales are related to the physical conditions that motivate or deter adoption | 1. Availability 2. Infrastructure 3. Uncomfortable to use 4. Necessitates retrofitting 5. Transportation barriers |
| Informational Rationales related to the consistency and accessibility of information and the capacity to apply it meaningfully | 1. No perceived benefits to adoption 2. Requires further information 3. Availability is unknown 4. Concerns with misapplication 5. Educational empowerment |
| Social Rationales related to relationships, community norms and attitudes, and policy | 1. Policy barriers 2. Lacks household decision-making power 3. Concerns with privacy or surveillance 4. Community influence 5. Policy empowerment |
Finally, in the reporting stage, the coder quantified the interview data by measuring the frequency of each category and code. The quantification of rationales here is intended to stand as a proxy for significant motivations and barriers to the adoption of energy-efficient technologies among rural homeowners.
Results and Discussion
Results from our content analysis are included in Table 4. This table reflects six categories of motivations and barriers among households by frequency. The most common motivations and barriers were defined by economic rationales. For example, 19.4% of all households cited lowering energy costs as a motivation for adopting energy-efficient technologies, whereas 12.4% of all households cited high upfront costs as a barrier to adoption. Overall, 35.4% of households cited economic rationales when making decisions about the adoption of energy-efficient technologies. Further, nearly 15% of households cited informational barriers to the uptake of energy-efficient technologies in general. However, this rate increases when we analyze informational barriers by individual technology. These results may suggest that adoption of certain energy-efficient technologies remain infeasible among middle- to high-income households due to informational barriers.
| Categories/codes | Keywords/statements | Relative frequency (%) |
|---|---|---|
| Economic | 35.4 | |
| Lowers energy costs | “Save”/It definitely saves us money. | 19.4 |
| High upfront costs | “Cost”/The initial cost [of the technology] is just more than what we can handle. “Expensive”/It’s not cost-effective in the long run because it’s so expensive to install. | 12.2 |
| Affordable | “Inexpensive”/It’s inexpensive to replace. | 3.8 |
| Environmental | 21.3 | |
| Lowers energy consumption | “Conserve”/You can turn it off and on when you aren’t at home and conserve energy. “Wasteful”/You’re going to have better control [over how much energy you’re using] and not be as wasteful. | 15.8 |
| Lowers emissions | “Renewable resources”/It puts less demand on non-renewables, less dependency on coal and oil and more through renewable resources. “Footprint”/It’s a carbon-neutral energy source, it helps reduce your overall footprint. | 5.5 |
| Geographic/regional | 19.0 | |
| Availability | “Convenient”/I think [buying the technology is] more of a convenience thing than an energy-efficient thing. “Options”/I don’t know that there’s really another option on the market at this point. | 10.6 |
| Infrastructure | “Internet”/The other reason we don’t have one, we don’t have wi-fi out here. We don’t have internet. We can’t get a router to run all that. | 3.8 |
| Uncomfortable to use | “Harder”/It’s harder for us as we get older to see using that kind of lighting. | 3.8 |
| Necessitates retrofit measures | “Wouldn’t work”/It wouldn’t work for our house yet. Maybe if I got a new furnace and central air. | 2.6 |
| Transportation barriers | “Drive”/You have to drive a ways to get to the store and get it, but it’s there. We’re not getting it here. | 2.6 |
| Informational | 14.6 | |
| No perceived benefits to adoption | “Unhelpful”/I don’t think it will help, I mean, a normal thermostat does as good a job as the other one. | 9.7 |
| Requires further information | “Learn”/I’m most interested in learning more about weatherization because I don’t really know what else we can do. “Learn”/I think that’s why we’ve never gotten one, just cause we could never be able to learn how to use it. “I don’t know”/I don’t know how available solar is in our area, maybe there’s just not people to install it around here. | 3.9 |
| Educational empowerment | “Read”/I read consumer reports and letters from Alliant all the time and we took them up on their energy audit. | 1 |
| Social | 9.6 | |
| Policy barriers | “Programs”/I would like to invest in that, but it would depend on the incentive programs. “Municipality”/It wasn’t feasible for us … but I could see it being a community buy-in, one that is owned by the municipality. | 3.4 |
| Lacks household decision-making power | “Spouse”/I think [the technology is] worthwhile. My biggest issue now is talking my spouse into being willing to get one. | 3.4 |
| Concerns with privacy/surveillance | “Watched”/It’s like I’m going to be watched. “Control”/They’re going to control everybody’s furnace. | 1.3 |
| Community influence | “Neighbors”/Just from talking with our neighbors who have already installed it and learning what it’s done for their home. | 1.3 |
| Policy empowerment | “Rebate”/We got a rebate from our electric company because they want us to buy Energy Star [appliances]. | 1 |
The least common motivations or barriers to adopting energy-efficient technologies were defined by social rationales. In this case, households were less likely to cite policy barriers or community influence as significant factors in their decision-making process. More commonly, rural households cited environmental rationales for their decision to adopt energy-efficient technologies. 21.3% of all households cited environmental rationales solely as motivators for adopting an energy-efficient technology.
Nineteen percent of all households cited barriers or motivations defined by geographic or regional-based rationales. These households most commonly referred to variables specific to rural landscapes and communities. For example, nearly 7% of all households claimed to have adopted a particular technology because it was the only option available to them in their local markets. By contrast, approximately 4% of all households cited a less developed energy infrastructure as a barrier to technology adoption. Most common among these barriers was a lack of adequate broadband infrastructure necessary for implementing the technology.
The results from the content analysis have also been organized by technology to reflect the variation in adoption rationales. Tables 5–9 include the participants’ attitude toward adoption and the rationale behind it. Demographic data are presented by status of adoption and includes the participant’s average annual household income, household size, gender identity, and highest level of educational attainment.
| Desire to adopt | Total (%) | Avg. annual household income | Household size | Gender | Educational attainment | Rationale (order of frequency) |
|---|---|---|---|---|---|---|
| Previously adopted, or motivated to adopt | 46.2 | $112,823 | Family (no children), 61.1% Family (with children), 27.8% Single, 5.5% N/A, 5.5% | Female, 66.7% Male, 27.8% N/A, 5.5% | Bachelor’s degree or higher, 61.1% Some college, 27.8% High school diploma, 5.5% N/A, 5.5% | Lowers energy costs, Ease of use, Lowers energy consumption, Necessitates retrofit measures, Affordable |
| Desire to adopt, faces barriers | 23.1 | $71,166 | Family (no children), 44.4% Family (with children), 33.3% N/A, 22.2% | Female, 77.7% Male, 11.1% N/A, 11.1% | Bachelor’s degree or higher, 77.8% High school diploma, 11.1% N/A, 11.1% | Necessitates retrofit measures, Lacks local access –requires lengthy transportation, High upfront costs, Concerns with misapplication |
| No desire to adopt | 23.1 | $127,655 | Family (no children), 44.4% Family (with children), 22.2% N/A, 22.2% Single, 11.1% | Female, 77.7% N/A, 22.2% | Bachelor’s degree or higher, 44.4% Some college, 22.2% N/A, 22.2% High school diploma, 11.1% | No perceived benefits to adoption, Concerns with privacy/surveillance, Concerns with misapplication |
| Has not heard of the technology | 7.7 | $57,500 | Single, 33.3% Family (with children), 33.3% Family (no children), 33.3% | Female, 100% | Bachelor’s degree or higher, 66.6% High school diploma, 33.3% | No concept of the technology |
| Desire to adopt | Total (%) | Avg. annual household income | Household size | Gender | Educational attainment | Rationale (order of frequency) |
|---|---|---|---|---|---|---|
| Previously adopted, or motivated to adopt | 69.2 | $110,217 | Family (no children) 48.1% Family (with children), 25.9% Single, 14.8% N/A, 11.1% | Female, 66.6% Male, 22.2% N/A, 11.1% | Bachelor’s degree or higher, 63.0% Some college, 18.5% High school diploma, 7.4% N/A, 7.4% | Lowers energy consumption, Affordable, Lowers energy costs, Ease of use, Widely available, Comfortable to use, Lacks household decision-making power |
| Desire to adopt, faces barriers | 17.9 | $101,517 | Family (no children), 57.2.0% Family (with children), 42.8% | Female, 100% | Bachelor’s degree or higher, 85.7% High school diploma, 14.3% | No perceived benefits, High upfront costs, Uncomfortable to use, Housing type unsuitable |
| No desire to adopt | 12.8 | $77,750 | Family (no children), 60% Family (with children), 20% N/A, 20% | Female, 80% N/A, 20% | Some college, 40% Bachelor’s degree or higher, 20% High school diploma, 20% N/A, 20% | Uncomfortable to use, No perceived benefits to adoption |
| Has not heard of the technology | 0.0 | N/A | N/A | N/A | N/A | N/A |
| Desire to adopt | Total (%) | Avg. annual household income | Household size | Gender | Educational attainment | Rationale (order of frequency) |
|---|---|---|---|---|---|---|
| Previously adopted, or motivated to adopt | 17.9 | $276,000 | Family (no children), 57.1% Family (with children), 28.6% N/A, 14.3% | Female, 85.7% N/A, 14.3% | Bachelor’s degree or higher, 66.6% Some college, 33.3% | Lowers energy costs, Policy empowerment, Reduces emissions, Community influence, |
| Desire to adopt, faces barriers | 69.2 | $194,818 | Family (no children), 55.5% Family (with children), 22.2% Single, 11.1% N/A, 11.1% | Female, 66.6% Male, 22.2% N/A, 11.1% | Bachelor’s degree or higher, 59.3% Some college, 14.8% High school diploma, 14.8% N/A, 11.1% | High upfront costs, Policy barriers, Infrastructure, Access is unknown, Lacks household decision-making power |
| No desire to adopt | 12.8 | $95,646 | Family (with children), 60% Family (no children), 20% N/A, 20% | Female, 100% | Bachelor’s degree or higher, 80.0% Some college, 20.0% | Infrastructure, No perceived benefits, High upfront costs |
| Has not heard of the technology | 0.0 | N/A | N/A | N/A | N/A | N/A |
| Desire to adopt | Total (%) | Avg. annual household income | Household size | Gender | Educational attainment | Rationale (order of frequency) |
|---|---|---|---|---|---|---|
| Previously adopted, motivated to adopt | 41.0 | $109,000 | Family (no children), 68.7% Family (with children), 18.7% N/A, 12.5% | Female, 68.7% Male, 25.0% N/A, 6.2% | Bachelor’s degree or higher, 75.0% Some college, 12.5% High school diploma, 6.2% N/A, 6.2% | Lowers energy costs, Lowers energy consumption, Improves household comfort, Lowers emissions, Educational empowerment |
| Desire to adopt, faces barriers | 30.8 | $96,222 | Family (with children), 33.3% Family (no children), 25.0% N/A, 25.0% Single, 10.0% | Female, 58.3% N/A, 25.0% Male, 16.7% | Bachelor’s degree or higher, 66.7% N/A, 25.0% Some college, 8.3% | Requires further information, High upfront costs, Lacks local access, Lacks household decision-making power |
| No desire to adopt | 15.4 | $114,764 | Family (no children), 83.3% Family (with children), 16.7% | Female, 100% | Some college, 50.0% Bachelor’s degree or higher, 33.3% High school diploma, 16.7% | No perceived benefits, Unnecessary, High upfront costs |
| Has not heard of the technology | 12.8 | $95,500 | Family (no children), 40.0% Family (with children), 40.0% Single, 20.0% | Female, 100% | Bachelor’s degree or higher, 40.0% High school diploma, 40.0% Some college, 20.0% | No concept of technology |
| Desire to adopt | Total (%) | Avg. annual household income | Household size | Gender | Educational attainment | Rationale (order of frequency) |
|---|---|---|---|---|---|---|
| Previously adopted, motivated to adopt | 69.2 | $194,608 | Family (with children), 44.4% Family (no children), 40.1% Single, 7.4% N/A, 7.4% | Female, 74.1% Male, 18.5% N/A, 7.4% | Bachelor’s degree or higher, 63.0% Some college, 22.2% High school diploma, 7.4% N/A, 7.4% | Lowers energy costs, Lowers energy consumption, Widely available, Lowers emissions, Policy empowered |
| Desire to adopt, faces barriers | 23.1 | $53,333 | Family (no children), 55.5% N/A, 33.3% Single, 11.1% | Female, 66.7% N/A, 22.2% Male, 11.1% | Bachelor’s degree or higher, 44.4% High school diploma, 22.2% N/A, 22.2% Some college, 11.1% | No perceived benefits, High upfront costs, Requires further information |
| No desire to adopt | 5.1 | $78,792 | Family (no children), 100% | Female, 100% | Bachelor’s degree or higher, 100% | Uncomfortable to use, No perceived benefits to adoption |
| Has not heard of the technology | 2.6 | N/A | Family (with children), 100% | Female, 100% | Bachelor’s degree or higher, 100% | No concept of the technology |
Smart Thermostats
Approximately 46% of all households had previously adopted or are motivated to adopt the smart thermostat technology. Key rationales identified among households who previously adopted smart thermostat technology included the belief that the technology lowers energy costs and is easy to use (or simplifies energy management). These findings confirm the motivations identified by previous literature (Tu et al. 2021). Additionally, these households believe that smart thermostats lower their consumption of energy and are an affordable investment. However, several households identified difficulties with remembering to manually change the settings on their thermostats and expressed a strong preference for an alternative that automated this task.
Areas in which our findings differentiated from the reviewed literature included barriers related to heating and cooling systems and an inability to purchase the technology in local stores. Households that faced barriers to the adoption of smart thermostats often cited the need to retrofit their heating or cooling systems to adopt the technology. Several households who reported not having central air or heating claimed to have found the technology appealing but believed it would not function in their homes. Other households claimed that smart thermostats were not locally accessible and required transportation from the nearest metropolitan area.
Few households who were motivated to adopt the technology expressed a fear that a smart thermostat might be too complicated to use and render them unable to control their heating or cooling system. Similarly, households that expressed no interest in adopting smart thermostats tend to perceive no benefits to using the technology. These households claim they lack enough information to understand the benefits of adoption or that manual thermostats are equally if not more effective than smart thermostat technology. For example, one household expressed, “A normal thermostat does as good a job as the other one. It’s just another technology.” These findings may support previous studies on the perceived usability of smart thermostats (Miu et al. 2019).
Other households expressed concerns with surveillance and data privacy. A common fear expressed among these households related to an erosion of self-determination or personal autonomy. One household stated, “One of the things that scares me a little bit about it is I don’t like letting my decisions be made by machines or other people.” While these findings support previous studies on consumer concerns with household privacy (Distler et al. 2020; Mamonov and Koufaris 2020), they did not represent the majority of rationales among our sample.
Demographics associated with smart thermostat adoption largely support previous studies (Trotta 2018). Households that had adopted smart thermostats or were motivated to adopt the technology had the highest rates of childless households (61.1%), and households that had not heard of smart thermostat technology reflected the lowest average annual incomes. However, one key difference among our findings included that households with no interest in adopting smart thermostat technology reflected the highest average annual incomes.
LED Lighting
Nearly 70% of the households had previously adopted or are motivated to adopt LED lighting. The remaining households either face barriers to adoption or express no interest in adopting LED lighting. The households that adopted LED lighting believe that the technology lowered their energy consumption. These households frequently connected their adoption of LED lighting to a sense of commitment to environmental conservation. In other words, by adopting LED lighting they believed they were acting in an environmentally conscious manner. The findings among these households largely support the previous findings (Roy et al. 2007; Caird et al. 2008).
Other key rationales expressed by households that previously adopted LED lighting included economic rationales, citing lower energy costs. Additionally, these households commonly expressed market-based rationales for their adoption behaviors, referring to LEDs as the only available option at local stores. Several households attributed their decision to adopt LED lighting to the inaccessibility of incandescent or alternative lighting. These findings may support policies surrounding incandescent lightbulb bans.
Households that face barriers to adopting LED lighting cited high upfront costs, the discomforting color of the light, and a lack of suitable light fixtures. The households with no interest in adopting LED lights tended to perceive no benefits in adopting them. For example, one household described LED lighting as having little impact on residential energy costs. “If you are in a shop, you know, a big huge building, maybe you’d see [lower energy costs]. But I don’t think in a house they would make much difference.” Other households claimed LED lighting products were excessively bright for both interior and exterior use. “If everybody had LED lights in their houses, it’d be awful. [It] would take away from the tranquility [of the neighborhood].”
Households with no interest in adopting LED lighting also represented the lowest average annual income. This demographic association supports the previous findings (Schleich, 2019). Other strong associations emerged among households that cited barriers to adopting LED lighting. These households were made up solely of female identifying participants.
Solar Energy
Nearly 70% of households desire to adopt solar energy technology, but face barriers to doing so. The common barriers among these households include high upfront costs, a lack of effective solar energy programs, and a lack of educational resources on solar adoption. Concerns with financing solar energy technology were frequently coupled with policy critiques. For example, one household explained, “I know that it’s the right thing to do environmentally, but I can’t afford it and I have the option of doing a group buy to get it at a lower upfront cost…but it still did not pan out and it did not fit in my budget.” These findings echo the barriers outlined in prior studies (Reindl and Palm 2021).
Additionally, we found these households tended to critique current solar adoption programs as inaccessible, ineffective, or unfair. “Policies suck, they do not promote the use of solar long term. They may subsidize the actual installation, but most people don’t even know [that] the electric companies are not required to pay a fair rate for anything that is produced above and beyond what we use. So, it’s not the greatest investment.” These rationales, which perceive solar energy programs as ineffective over the long term, deviate from the prior literature that has suggested programs are effective when implemented in rural regions (Ross et al. 2018). Further, these findings differ from research conducted in urban contexts, wherein residents frequently cite space constraints as a barrier to adoption (Mah et al. 2018).
Conversely, 17.9% of households cited lowered energy costs, effective solar adoption programs, reduced emissions, and community influence or engagement as motivations for adoption of solar energy technologies. One household credited their ability to adopt solar technology to a tax credit program. However, these households also represented the highest average annual income at $276,000—approximately $82,000 more per year than households that desire to adopt solar energy but face barriers.
Weatherization
Approximately 40% of households indicated they have adopted or were motivated to adopt weatherization measures. These households cite lower energy costs, reduced energy consumption, and improved household comfort as primary motivations.
Households (30.8%) that desire to adopt weatherization techniques but face barriers most frequently cite informational barriers. Often, these households were unable to identify strategies outside temporary solutions (i.e., plastic film insulation). These results may suggest an opportunity for increased rates of interactions related to energy assistance programs to encourage the diffusion of weatherization adoption among rural Iowa communities (Southwell and Murphy 2014). The households that identified more permanent weatherization techniques (i.e., window and door replacements) expressed an uncertainty around how to access replacement services or assistance programs. These findings largely validate the prior literature related to the adoption of weatherization techniques in rural areas (Ross et al. 2018; Krejci et al. 2016). However, further research is necessary to identify an effective approach toward increasing rates of social interaction in rural contexts.
Furthermore, these households viewed temporary weatherization techniques as more affordable energy-efficient measures. “[I used] weather stripping when I couldn’t afford to put new windows in. I’d use that plastic wrap, you know, with the tape and, you know, just trying to cut down on the draft coming in through the windowpanes before I could put in new windows.”
Around 15% of households expressed no desire to adopt weatherization technologies. These households frequently cited weatherization as unnecessary or ineffective in comparison with other energy efficiency strategies. Few demographic trends appeared to indicate a desire to adopt weatherization measures or not. The strongest demographic appeared to be educational attainment, with an association between higher educational rates and adoption behaviors.
Energy Star-Rated Appliances
Almost 70% of the households had previously adopted or were motivated to adopt Energy Star-rated appliances. Similar to the rationales underlying the adoption of LED lighting, most of these households cite a lack of alternative appliances available to them on the market. These households also claim that the adoption of Energy Star-rated appliances help to lower household energy costs and reduce energy consumption. These households represented the highest average annual income.
The households with no desire to adopt Energy Star-rated appliances expressed an uncertainty around the efficacy of the technology. These households expressed distrust in the Energy Star-rating system itself. One household claimed, “Sometimes to get these certifications, you know, things that make you look good, promotional type thing, you just simply apply, you pay a fee, and then you’ve got it.”
Contributions
The methods and findings included here present a productive avenue for rural households across Iowa to provide key insights as critical stakeholders to future policymakers. Based on these findings, it is important that rural policymakers consider a variety of approaches alongside income-based approaches to increase the adoption of energy-efficient technologies among the rural households. Policymakers should take into consideration rural communities’ access to both: (1) information about the technology and its relevance to users; and (2) contractors and labor necessary to the installation of some energy-efficient technologies. The qualitative interviewing approach is well-suited to capture information that may enable the expansion of energy-efficient households across Iowa’s rural communities.
Conclusion
Our data analysis reflects information about motivations and barriers underlying the adoption, or lack thereof, of energy-efficient technologies among the rural households in Iowa. Specifically, these findings provide detailed information regarding household attitudes toward five individual energy-efficient or renewable energy technologies. These data and findings are derived entirely from a US rural context.
Based on these findings, economic rationales are the leading indicators of adoption behavior among rural households across Iowa. Here, perceived energy cost savings predict household adoption behaviors. However, rationales vary widely by technology. For example, our results suggest that addressing informational barriers may lead to an increase in the adoption of smart thermostat technologies. This may include the implementation of regionally specific educational programs.
The adoption of rooftop solar energy may be increased among rural households by expanding policies that reduce the costs of investment in solar panel installation and maintenance, particularly among lower-income households. Despite representation among higher-income households, rooftop solar was still cited as far too costly for adoption. While reducing the upfront costs aligns with findings from previous studies, it is important to understand that the cost of installation is relatively higher across these regions given the lack of available installation labor.
Expanding weatherization assistance programs and increasing the availability of contractors may subsequently increase the adoption of weatherization measures among rural households. A significant barrier to weatherization among participants included the age of the building. Older residential buildings were cited as more costly and burdensome to weatherize. Several participants expressed a need for updated home insulation but chose temporary weatherizing measures instead to avoid the cost of installation labor or disruption in day-to-day activities.
Finally, our results suggest that addressing informational barriers around Energy Star-labeled appliances may increase adoption rates among rural households. Nearly one-third of the participants cited a desire to adopt Energy Star appliances, but could not due to barriers. The highest of these barriers included general information about how to access Energy Star appliances. Among the population sampled in this study, further educational programs may increase adoption rates.
These findings may be useful to rural policymakers as they consider how to improve rural housing stock and increase the uptake of energy-efficient and renewable energy technologies. Specifically, policymakers working within the context of rural Iowa might take into consideration residential access to information about the technology and access to its installation. Future researchers may also find this data useful in future applications of qualitative interviewing and inductive methods surrounding the research on improving the energy efficiency of rural housing stock.
Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abdmouleh, Z., A. Gastli, and L. Ben-Brahim. 2018. “Survey about public perception regarding smart grid, energy efficiency & renewable energies applications in Qatar.” Renewable Sustainable Energy Rev. 82: 168–175. https://doi.org/10.1016/j.rser.2017.09.023.
Adua, L., and A. Beaird. 2018. “Place-based inequality in ‘Energetic’ Pain: The price of residence in rural America.” Socius: Sociological Research for a Dynamic World 4: 1–18. https://doi.org/10.1177/2378023118803032.
Aklin, M., P. Bayer, S. P. Harish, and J. Urpelainen. 2018. “Economics of household technology adoption in developing countries: Evidence from solar technology adoption in rural India.” Energy Econ. 72: 35–46. https://doi.org/10.1016/j.eneco.2018.02.011.
Allcott, H., and M. Greenstone. 2017. Measuring the welfare effects of residential energy efficiency programs. Rep. No. w23386. Cambridge, MA: National Bureau of Economic Research.
Baatz, B. 2015. Everyone benefits: Practices and recommendations for utility system benefits of energy efficiency. Washington, DC: ACEEE.
Barbose, G., F. Sydney, O. Eric, and D. Naïm. 2021. “Residential solar-adopter income and demographic trends: 2022 update.” None, 1775422, ark:/13030/qt5vd6w51m. Berkeley, CA: Lawrence Berkeley National Laboratory (LBNL). https://doi.org/10.2172/1775422.
Bickerstaff, K., P. Devine-Wright, and C. Butler. 2015. “Living with low carbon technologies: An agenda for sharing and comparing qualitative energy research.” Energy Policy 84: 241–249. https://doi.org/10.1016/j.enpol.2015.04.015.
Caird, S., R. Roy, and H. Herring. 2008. “Improving the energy performance of UK households: Results from surveys of consumer adoption and use of low- and zero-carbon technologies.” Energy Effic. 1 (2): 149–166. https://doi.org/10.1007/s12053-008-9013-y.
Cattaneo, C. 2019. “Internal and external barriers to energy efficiency: Which role for policy interventions?” Energy Effic. 12 (5): 1293–1311. https://doi.org/10.1007/s12053-019-09775-1.
Census Bureau. 2017. “2017 American Housing Survey.” https://www.census.gov/programs-surveys/ahs/data.html.
Chauhan, A., and R. P. Saini. 2015. “Renewable energy based off-grid rural electrification in Uttarakhand state of India: Technology options, modelling method, barriers and recommendations.” Renewable Sustainable Energy Rev. 51: 662–681. https://doi.org/10.1016/j.rser.2015.06.043.
Connelly, K., K. Ur Rehman Laghari, M. Mokhtari, and T. H. Falk. 2014. “Approaches to understanding the impact of technologies for aging in place: A mini-review.” Gerontology 60: 282–288. https://doi.org/10.1159/000355644.
Davis, L. W. 2011. “Evaluating the slow adoption of energy efficient investments: Are renters less likely to have energy efficient appliances?. In The design and implementation of US climate policy, 301–316. Chicago, IL: University of Chicago Press.
De Souza, R., R. Evans-Agnew, and C. Espina. 2019. “Federal weatherization and health education team up: Process evaluation of a new strategy to improve health equity for people with asthma and chronic obstructive pulmonary disease.” J. Public Health Manage. Pract. 25 (1): E21–E24. https://doi.org/10.1097/PHH.0000000000000786.
Di Maria, C., S. Ferreira, and E. Lazarova. 2010. “Shedding light on the light bulb puzzle: The role of attitudes and perceptions in the adoption of energy efficient light bulbs.” Scott. J. Polit. Econ. 57: 48–67. https://doi.org/10.1111/j.1467-9485.2009.00506.x.
Distler, V., C. Lallemand, and V. Koenig. 2020. “How acceptable is this? How user experience factors can broaden our understanding of the acceptance of privacy trade-offs.” Comput. Hum. Behav. 106: 106227. https://doi.org/10.1016/j.chb.2019.106227.
DOE. 2017. “Low-income community energy solutions.” Energy.gov. Accessed December 22, 2022. https://www.energy.gov/eere/slsc/low-income-community-energy-solutions.
ENERGY STAR Impacts. n.d. Accessed December 22, 2022. https://www.energystar.gov/about/impacts.
FCC (Federal Communications Commission). 2016. Broadband progress report. Washington, DC: FCC.
Ferreira, L., T. Oliveira, and C. Neves. 2023. “Consumer’s intention to use and recommend smart home technologies: The role of environmental awareness.” Energy 263: 125814. https://doi.org/10.1016/j.energy.2022.125814.
Fowlie, M., G. Michael, and W. Catherine. 2018. “Do energy efficiency investments deliver? Evidence from the weatherization assistance program.” Q. J. Econ. 133 (3): 1597–1644. https://doi.org/10.1093/qje/qjy005.
Fuller, M. C. 2010. Driving demand for home energy improvements: Motivating residential customers to invest in comprehensive upgrades that eliminate energy waste, avoid high utility bills, and spur the economy. LBNL-3960E. Berkeley, CA: Lawrence Berkeley National Laboratory (LBNL).
Galarraga, I., L. M. Abadie, and S. Kallbekken. 2016. “Designing incentive schemes for promoting energy-efficient appliances: A new methodology and a case study for Spain.” Energy Policy 90: 24–36. https://doi.org/10.1016/j.enpol.2015.12.010.
Gerarden, T. D., R. G. Newell, R. N. Stavins, and R. C. Stowe. 2015. An assessment of the energy-efficiency gap and its implications for climate-change policy . Rep. No. w20905. Cambridge, MA: National Bureau of Economic Research.
Grbich, C. 2013. “Content analysis of texts.” In Qualitative data analysis: An introduction, 189–99. New York: SAGE Publications.
Green, J., and N. Thorogood. 2004. “Analysing qualitative data.” In Qualitative Methods for Health Research, 177–80. New York: SAGE Publications.
Gromet, D. M., H. Kunreuther, and R. P. Larrick. 2013. “Political ideology affects energy-efficiency attitudes and choices.” Proc. Natl. Acad. Sci. USA 110 (23): 9314–9319. https://doi.org/10.1073/pnas.1218453110.
Guta, D. 2020. “Determinants of household use of energy-efficient and renewable energy technologies in rural Ethiopia.” Technol. Soc. 61: 101249. https://doi.org/10.1016/j.techsoc.2020.101249.
Hopkins, E. A., A. T. Carswell, and K. R. Love. 2020. “Impacts of ENERGY STAR appliances on U.S. multifamily rents.” Fam. Consum. Sci. Res. J. 49: 37–51. https://doi.org/10.1111/fcsr.12372.
Jacobsen, G. D., and M. J. Kotchen. 2013. “Are building codes effective at saving energy? Evidence from residential billing data in Florida.” Rev. Econ. Stat. 95: 34–49. https://doi.org/10.1162/REST_a_00243.
Johnston, A. 2017. “Rural housing in modern America opportunities and challenges for affordable housing, fair housing, and community development in the new administration.” J. Affordable Housing and Commun. Dev. L. 26 (2): 281–304.
Kastner, I., and P. C. Stern. 2015. “Examining the decision-making processes behind household energy investments: A review.” Energy Res. Social Sci. 10: 72–89. https://doi.org/10.1016/j.erss.2015.07.008.
Kotchen, M. J. 2017. “Longer-run evidence on whether building energy codes reduce residential energy consumption.” J. Assoc. Environ. Resour. Econ. 4: 135–153. https://doi.org/10.1086/689703.
Koupaei, D. M., T. Song, K. S. Cetin, and J. Im. 2020. “An assessment of opinions and perceptions of smart thermostats using aspect-based sentiment analysis of online reviews.” Build. Environ. 170: 106603. https://doi.org/10.1016/j.buildenv.2019.106603.
Krejci, C. C., U. Passe, M. C. Dorneich, and N. Peters. 2016. “A hybrid simulation model for urban weatherization programs.” In Proc., 2016 Winter Simulation Conf., 1630–1641. New York: IEEE Press.
Levitt, H. M., M. Bamberg, J. W. Creswell, D. M. Frost, R. Josselson, and C. Suárez-Orozco. 2018. “Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA publications and communications board task force report.” Am. Psychol. 73 (1): 26. https://doi.org/10.1037/amp0000151.
Li, H., V. Singhvi, A. Maitra, S. Rajagopalan, B. Enayati, S. Santoso, and R. Patterson. 2014. “National grid microgrid feasibility evaluation: Case study of a rural distribution feeder.” In Proc., 2014 Institute of Electrical and Electronics Engineers Power and Energy Society General Meeting|Conf. & Exposition, 1–5. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).
Liang, L., W. Wenliang, L. Rattan, and G. Yanbin. 2013. “Structural change and carbon emission of rural household energy consumption in Huantai, Northern China.” Renew. Sustain. Energy Rev. 28: 767–776. https://doi.org/10.1016/j.rser.2013.07.041.
MacDonald, S., B. Winner, L. Smith, J. Juillerat, and S. Belknap. 2020. “Bridging the rural efficiency gap: Expanding access to energy efficiency upgrades in remote and high energy cost communities.” Energy Effic. 13: 503–521. https://doi.org/10.1007/s12053-019-09798-8.
Mah, D. N.-y., G. Wang, K. Lo, M. K. H. Leung, P. Hills, and A. Y. Lo. 2018. “Barriers and policy enablers for solar photovoltaics (PV) in cities: Perspectives of potential adopters in Hong Kong.” Renewable Sustainable Energy Rev. 92: 921–936. https://doi.org/10.1016/j.rser.2018.04.041.
Mamonov, S., and M. Koufaris. 2020. “It’s cool! analysis of factors that influence smart thermostat adoption intention.” In Proc., BLED 2020. Atlanta, GA: Association for Information Systems (AIS).
Metcalf, G. E., and K. A. Hassett. 1999. “Measuring the energy savings from home improvement investments: Evidence from monthly billing data.” Rev. Econ. Stat. 81 (3): 516–528.
Michalski, J. C. 2019. “Microgrids for micro-communities: Reducing the energy burden in rural areas note.” Mich.Tech. L Rev. 26 (1): [i]-174. https://doi.org/10.36645/mtlr.26.1.microgrids.
Mills, B. F., and S. Joachim. 2009. “Profits or preferences? Assessing the adoption of residential solar thermal technologies.” Energy Policy, 37 (10): 4145–4154. https://doi.org/10.1016/j.enpol.2009.05.014.
Mills, B., and J. Schleich. 2012. “Residential energy-efficient technology adoption, energy conservation, knowledge, and attitudes: An analysis of European countries.” Energy Policy 49: 616–628. https://doi.org/10.1016/j.enpol.2012.07.008.
Mills, B., and J. Schleich. 2014. “Household transitions to energy efficient lighting.” Energy Econ. 46: 151–160. https://doi.org/10.1016/j.eneco.2014.08.022.
Miu, L. M., C. M. Mazur, K. H. van Dam, R. S. C. Lambert, A. Hawkes, and N. Shah. 2019. “Going smart, staying confused: Perceptions and use of smart thermostats in British homes.” Energy Res. Social Sci. 57: 101228. https://doi.org/10.1016/j.erss.2019.101228.
Muratori, M. 2013. “Rural energy use and the challenges for conservation and efficiency.” In National agriculture & rural development policy center, edited by D. Albrecht, 147–162. New York: Taylor & Francis Group.
Murray, A. G., and B. F. Mills. 2011. “Read the label! Energy Star appliance label awareness and uptake among U.S. consumers.” Energy Econ. 33: 1103–1110. https://doi.org/10.1016/j.eneco.2011.04.013.
Nair, G., L. Gustavsson, and K. Mahapatra. 2010. “Factors influencing energy efficiency investments in existing Swedish residential buildings.” Energy Policy 38: 2956–2963. https://doi.org/10.1016/j.enpol.2010.01.033.
NEEP. 2019. The smart energy home: Driving residential building decarbonization. Boston, MA: Northeast Energy Efficiency Partnerships, Inc.
Neuman, W. L. 2002. “Social research methods: Qualitative and quantitative approaches.” Teach. Sociol. 30: 380. https://doi.org/10.2307/3211488.
Olsthoorn, M., J. Schleich, and C. Faure. 2019. “Exploring the diffusion of low-energy houses: An empirical study in the European Union.” Energy Policy 129: 1382–1393. https://doi.org/10.1016/j.enpol.2019.03.043.
Palm, J. 2018. “Household installation of solar panels–motives and barriers in a 10-year perspective.” Energy Policy 113: 1–8. https://doi.org/10.1016/j.enpol.2017.10.047.
Powers and Duties of Cities. 2020, November 24. “The Iowa legislature.” Accessed April 11, 2021. https://www.legis.iowa.gov/docs/code/364.17.pdf.
Reindl, K., and J. Palm. 2021. “Installing PV: Barriers and enablers experienced by non-residential property owners.” Renewable Sustainable Energy Rev. 141: 110829. https://doi.org/10.1016/j.rser.2021.110829.
Riessman, C. K. 2008. Narrative methods for the human sciences. New York: SAGE Publications.
Ritchie, J., J. Lewis, C. M. Nicholls, and R. Ormston. 2013. Qualitative research practice: A guide for social science students and researchers. New York: SAGE Publications.
Ross, L., A. Drehobl, and B. Stickles. 2018. The high cost of energy in rural America: Household energy burdens and opportunities for energy efficiency. Washington, DC: American Council for an Energy-efficient Economy.
Roy, R., S. Caird, and S. Potter. 2007. “People centred eco-design: Consumer adoption of low and zero carbon products and systems.” In Governing technology for sustainability, edited by J. Murphy, 41–62. London: Earthscan.
Samad, H. A., S. R. Khandker, M. Asaduzzaman, and M. Yunus. 2013. The benefits of solar home systems: An analysis from Bangladesh, policy research working papers. Washington, DC: World Bank.
Schleich, J. 2019. “Energy-efficient technology adoption in low-income households in the European Union—What is the evidence?” Energy Policy 125: 196–206. https://doi.org/10.1016/j.enpol.2018.10.061.
Schleich, J., X. Gassmann, T. Meissner, and C. Faure. 2019. “A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies.” Energy Econ. 80: 377–393. https://doi.org/10.1016/j.eneco.2018.12.018.
Schill, M., G.-W. Delphine, F. D. Mbaye, and B. Camilla. 2019. “Consumers’ intentions to purchase smart home objects: Do environmental issues matter?.” Ecol. Econ. 161: 176–185. https://doi.org/10.1016/j.ecolecon.2019.03.028.
Schweitzer, M., and B. Tonn. 2003. “Non-energy benefits of the US weatherization assistance program: A summary of their scope and magnitudeUS.” Appl. Energy 76 (4): 321–335. https://doi.org/10.1016/S0306-2619(03)00003-5.
Southwell, B. G., and J. Murphy. 2014. “Weatherization behavior and social context: The influences of factual knowledge and social interaction.” Energy Res. Soc. Sci. 2: 59–65. https://doi.org/10.1016/j.erss.2014.03.019.
Ternes, M. P., S. Martin, E. D. Bruce, L. S. Richard, and F. E. Joel. 2007. National evaluation of the weatherization assistance program: Preliminary evaluation plan for program year 2006.” Rep. No. ORNL/TM-2007/004, 932613. Oak Ridge, TN: Oak Ridge National Laboratory. https://doi.org/10.2172/932613.
Trotta, G. 2018. “Factors affecting energy-saving behaviours and energy efficiency investments in British households.” Energy Policy 114: 529–539. https://doi.org/10.1016/j.enpol.2017.12.042.
Tu, F. C., J. Schleich, and M.-C. Guetlein. 2021. “The heat is off! The role of technology attributes and individual attitudes in the diffusion of Smart thermostats—Findings from a multi-country survey.” Technol. Forecasting Social Change 163: 120508.
USDA. 2017. “Rural America at a glance.” Accessed December 22, 2022. https://www.ers.usda.gov/webdocs/publications/85740/eib182_brochure%20format.pdf?v=5538.2.
USCB (United States Census Bureau). 2016. “Defining ‘Rural’ areas.” Understanding and Using American Community Survey Data. Accessed December 22, 2022. https://www.census.gov/content/dam/Census/library/publications/2019/acs/ACS_rural_handbook_2019_ch01.pdf.
USCB (United States Census Bureau). 2019a. House heating fuel, table B25040, 2019 ACS 1-year estimates detailed tables. Washington, DC: United States Census Bureau (USCB).
USCB (United States Census Bureau). 2019b. Defining ‘rural’ areas, in: Understanding and using American community survey data: What users of data for rural areas need to know. Washington, DC: United States Census Bureau (USCB).
USCB (United States Census Bureau). 2021. “American housing survey: National, general housing data, all occupied units.” Accessed December 22, 2023. https://www.census.gov/programs-surveys/ahs/data/interactive/ahstablecreator.html?s_areas=00000&s_year=2021&s_tablename=TABLE1&s_bygroup1=1&s_bygroup2=1&s_filtergroup1=1&s_filtergroup2=4.
Vega, S. H., E. van Leeuwen, and N. van Twillert. 2022. “Uptake of residential energy efficiency measures and renewable energy: Do spatial factors matter?” Energy Policy 160: 112659. https://doi.org/10.1016/j.enpol.2021.112659.
Wang, X., W. Li, J. Song, H. Duan, K. Fang, and W. Diao. 2020. “Urban consumers’ willingness to pay for higher-level energy-saving appliances: Focusing on a less developed region.” Resour. Conserv. Recycl. 157: 104760. https://doi.org/10.1016/j.resconrec.2020.104760.
Xie, W., C. Chen, F. Li, B. Cai, R. Yang, L. Cao, P. Wu, and L. Pang. 2021. “Key factors of rural households’ willingness to pay for cleaner heating in Hebi: A case study in northern China.” Sustainability 13 (2): 633. https://doi.org/10.3390/su13020633.
Yemelyanov, O., A. Symak, T. Petrushka, O. Zahoretska, M. Kuisy, R. Lesyk, and L. Lesyk. 2019. “Changes in energy consumption, economic growth and aspirations for energy independence: Sectoral analysis of uses of natural gas in Ukrainian economy.” Energies 12 (24): 4724. https://doi.org/10.3390/en12244724.
Information & Authors
Information
Published In
ASCE OPEN: Multidisciplinary Journal of Civil Engineering
Volume 2 • December 2024
Copyright
This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.
History
Received: Mar 24, 2023
Accepted: Nov 6, 2023
Published online: Mar 18, 2024
Discussion open until: Aug 18, 2024
Published in print: Dec 31, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.