walter scheib thesis_spatial aspects of energy efficiency
TRANSCRIPT
Examining the Spatial Aspects of Residential Energy Efficiency: GIS and
Survey Analysis in Boulder County, Colorado
__________
A Thesis
Presented to
The Faculty of Natural Sciences and Mathematics
University of Denver
__________
In Partial Fulfillment
of the Requirements for the Degree
Master of Arts
__________
by
Walter S. Scheib IV
June 2015
Advisor: Dr. E. Eric Boschmann
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Author: Walter S. Scheib IV Title: Examining the Spatial Aspects of Residential Energy Efficiency: GIS and Survey Analysis in Boulder County, Colorado Advisor: Dr. E. Eric Boschmann Degree Date: June 2015
ABSTRACT
The completion of residential energy efficiency upgrades leads to multifaceted
benefits including cost and energy savings, increased household comfort, health
benefits, and reduced CO2 emissions. As a result of these benefits, state and local
energy efficiency programs across the United States are striving to increase the
widespread adoption of energy efficiency upgrades by homeowners. Many program
strategies for widespread adoption are informed by case studies of other successful
energy efficiency programs. These program strategies would benefit from the
additional insight provided by spatial analysis, but a spatial perspective is currently
underutilized by energy efficiency programs across the United States. This thesis
research examines Boulder County, Colorado’s EnergySmart residential energy
efficiency program using a mixed-methods approach that combines GIS cluster
analysis, spatially targeted survey research, and demographic analysis. Research
findings include a GIS cluster analysis technique for targeting future energy
efficiency upgrades, survey results that provide an overview of attitudes towards
energy efficiency in Boulder County divided by cluster type, political ideology and
other demographic characteristics, the identification of active peer effects as an
important factor in widespread adoption of energy efficiency upgrades, and the
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identification of specific demographic groups that are currently underserved by
energy efficiency upgrade programs in Boulder County.
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CONTENTS 1. Introduction and Problem Statement .........................................................................1
2. Research Questions ....................................................................................................4
3. Literature Review .......................................................................................................6
3.1 Energy Efficiency ................................................................................................6
3.2 Innovation Diffusion and Peer Effects: The Spread of Technology Adoption at the Neighborhood Level ..........................................................................................10
3.3 Social Exclusion .................................................................................................15
3.4 Literature Gaps and Conclusion .........................................................................17
4. National Scope of Energy Efficiency ......................................................................19
5. Study Area: Boulder County, Colorado ...................................................................24
6. Overview: Research Methods and Analysis ............................................................29
7. Spatial Analysis .......................................................................................................31
8. Survey Analysis .......................................................................................................54
8.1 Survey Overview ...............................................................................................54
8.2 Survey Design and Implementation ...................................................................54
8.3 Survey Results: General Findings ......................................................................56
8.4 Survey Results: Cluster Analysis and Energy Efficiency Upgrades .................74
8.5 Energy Efficiency and Peer Effects ...................................................................77
8.6 Survey Results: Peer Effects ..............................................................................78
8.7 Survey Analysis Conclusions ............................................................................85
9. Demographic Variation of EnergySmart Upgrades .................................................86
9.1 ANOVA Analysis ..............................................................................................87
9.2 Demographic Analysis: Conclusion ..................................................................98
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10. Discussion ..............................................................................................................99
11. Recommendations and Next Steps .......................................................................109
12. Conclusion ...........................................................................................................114
Bibliography ..............................................................................................................117
Appendix A-1: Survey Results ..................................................................................125
Appendix A-2: Open-Ended Survey Question Responses (Coded) ..........................139
Appendix B: Maps of Randomly Selected Clusters for Survey Distribution ............147
Appendix C: Survey Distribution Discussion ............................................................151
Appendix D: Observed Housing Types by Cluster ....................................................156
Appendix E: Survey Distribution Envelope ..............................................................166
Appendix F: Survey Introduction Letter ....................................................................167
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LIST OF TABLES Table 1: Demographic characteristics of Boulder County as compared to the United States as a whole ..........................................................................................................25 Table 2: Data layers used for GIS cluster analysis ......................................................32 Table 3: Energy efficiency upgrades completed through the EnergySmart program ..33 Table 4: Total number of clusters and residential parcels within each cluster zone ....48 Table 5: Cluster types within Boulder County Municipalities ....................................49 Table 6: EnergySmart upgrades completed within each cluster type ..........................49 Table 7: Percentage of homes in Boulder County municipalities that have completed an EnergySmart upgrade ..............................................................................................50 Table 8: Average Home Age by Cluster Type .............................................................50 Table 9: Homeownership duration of survey respondents ..........................................57 Table 10: Monthly utility bill by type of energy efficiency upgrade completed .........64 Table 11: Type of energy efficiency upgrade completed ............................................66 Table 12: Completion of energy efficiency audit by number of years living in home (< 1 year to 15 years) ...................................................................................................67 Table 13: Completion of energy efficiency audit by number of years living in home (16 years to > 25 years) ................................................................................................67 Table 14: Count of coded free responses .....................................................................68 Table 15: Count of coded free responses .....................................................................71 Table 16: Count of coded free responses, divided by cluster type (High High, High Low Clusters) ...............................................................................................................72 Table 17: Count of coded free responses, divided by cluster type (Low High, Not Significant Clusters) .....................................................................................................73
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LIST OF FIGURES Figure 1: Map of the Better Buildings Neighborhood Program energy efficiency grantees across the U.S. (Energy.gov) .........................................................................20 Figure 2: EnergySmart upgrades completed by quarter (2011-2014) .........................34 Figure 3: Location and clustering of EnergySmart energy efficiency upgrades in Boulder County, CO ....................................................................................................35 Figure 4: Results of ‘Average Nearest Neighbor’ spatial statistical analysis ..............36 Figure 5: Areas zoned ‘Residential’ within the Cities of Boulder, Lafayette, Longmont, Louisville and Superior, and unincorporated Boulder County .................38 Figure 6: 1/8th square mile clusters .............................................................................40 Figure 7: 1/16th square mile clusters ...........................................................................41 Figure 8: Example of High-High clusters ....................................................................42 Figure 9: Example of High-Low clusters .....................................................................43 Figure 10: Example of Low-High clusters ...................................................................44 Figure 11: Example of Not Significant clusters created using Local Moran’s I spatial clustering tool ...............................................................................................................45 Figure 12: All Local Moran’s I cluster types within Boulder County .........................46 Figure 13: Detailed view of Local Moran’s I cluster analysis in northeast Boulder County ..........................................................................................................................47 Figure 14: Detailed view of Local Moran’s I cluster analysis in northeast Boulder County ..........................................................................................................................48 Figure 15: Survey sampling technique ........................................................................52 Figure 16: Awareness and rating (out of 100) for various energy terms .....................58 Figure 17: Awareness of energy terms divided by income ..........................................59 Figure 18: Rating of energy terms by income .............................................................60 Figure 19: Awareness of energy terms by political identification ...............................61 Figure 20: Rating of energy terms by political affiliation ...........................................61 Figure 21: Rating of energy terms divided by energy efficiency upgrade type completed .....................................................................................................................63 Figure 22: Number of upgrades completed by type of energy efficiency upgrade completed .....................................................................................................................65 Figure 23: Method of completing energy efficiency upgrade, divided by cluster type75 Figure 24: Question responses divided by cluster type ...............................................79 Figure 25: Active peer effects--question responses divided by cluster type ...............80 Figure 26: Passive peer effects--question responses divided by cluster type ..............82 Figure 27: Question responses divided by cluster type ...............................................83 Figure 28: ‘High ratio’ and ‘low ratio’ Block Groups in Boulder County ..................89 Figure 29: ANOVA results (race): Hispanic/Latino ....................................................91 Figure 30: ANOVA results (race): Asian ....................................................................92 Figure 31: ANOVA results (race): African American .................................................92 Figure 32: ANOVA results: Median income ...............................................................93 Figure 33: ANOVA results: Median home value ........................................................94 Figure 34: ANOVA results: Households with children under age 18 at home ...........95 Figure 35: ANOVA results (education): Bachelor’s degree ........................................96
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Figure 36: ANOVA results (education): High School degree .....................................97 Figure 37: ANOVA results (education): Associate’s degree .......................................97
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1. INTRODUCTION AND PROBLEM STATEMENT
Energy efficiency is a key element of sustainability that contributes to the
reduction of carbon emissions, leads to lower energy bills, and allows for a more
comfortable living environment. An increase in a home’s energy efficiency leads to a
better lifestyle due to a reduction in utility bill costs and increased comfort in the home.
Currently, there are numerous energy efficiency upgrade programs offered
through local sustainability programs, local governments, and utilities, which work to
offset the additional up-front costs of residential energy efficiency upgrades. However,
these programs are not realizing the massive potential for residential energy efficiency
upgrades in the United States (Zimring et al. 2011). To meet this potential, residential
energy efficiency programs in the United States must move beyond small-scale pilot
projects and bring residential energy efficiency to full-scale implementation by
completing energy efficiency upgrades in a large percentage of homes nationwide. In
order to accomplish this goal, energy efficiency programs must be able to effectively
target large numbers of households.
Numerous energy efficiency program case studies have focused primarily on the
program management and marketing aspects of these programs, but geographic insight
into programmatic improvement has been lacking, despite the potential of Geographic
Information Science (GIS) as an effective analysis approach for evaluating program
outcomes and targeting specific homeowners for future energy efficiency upgrades. This
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thesis research project analyzes residential energy efficiency upgrade data from Boulder
County, Colorado’s EnergySmart residential energy efficiency program by conducting a
combination of spatial clustering analysis, survey analysis, and demographic analysis to
analyze the spatial distribution of energy efficiency upgrades in Boulder County.
First, GIS cluster analysis was used to identify spatial trends related to
EnergySmart upgrades. Next, this GIS cluster analysis was used to spatially target an
energy efficiency survey for Boulder County homeowners. This spatially-targeted survey
allowed for a comparison of energy efficiency awareness, implementations rates, and
impacts of peer effects across different geographic subsections of Boulder County.
Finally, a Census Block Group level demographic analysis was completed to help the
EnergySmart program understand what demographic groups are currently not completing
energy efficiency upgrades and identify areas of the county in which these groups live.
1.1 Research Significance
The results from this research contribute to academic literature related to energy
efficiency, innovation diffusion, peer effects, and social exclusion, while also providing
the EnergySmart program with detailed survey results and spatial targeting techniques
that can be used to increase homeowner participation in energy efficiency upgrades. This
research also addresses a gap in the academic literature related to peer effects and energy
efficiency. The influence of peer effects on the adoption of solar photovoltaic (SV)
systems by homeowners has been examined, but the influence of peer effects on energy
efficiency upgrades has not yet been studied. Furthermore, many case studies of energy
efficiency have been completed by Federal, State and local energy efficiency programs
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across the United States, but the spatial aspects of energy efficiency upgrades are largely
ignored in these studies, and certainly none of these case studies combine cluster analysis
and spatially targeted survey research at the household level.
Bringing the benefits of residential energy efficiency to all Boulder County
homeowners will lead to increased energy and utility bill savings, reduced greenhouse
gas emissions, and improved health and quality of life. Furthermore, increasing the
energy efficiency of homes is the first step to a net zero energy housing stock, through a
combination of energy efficiency and renewable solar, wind and geothermal measures.
Although, it is important to note that net zero homes are not completely off the grid
(Sewalk and Throupe 2013). Studying energy efficiency upgrades from a geographic
perspective can also provide other energy efficiency programs across the United States
with useful and actionable information that may lead to increased levels of residential
energy efficiency upgrades nationwide.
This thesis research paper will first discuss the research questions that were used
to frame the spatial analysis portions of the study. Next, a literature review will discuss
relevant literature related to energy efficiency, innovation diffusion, peer effects, and
social exclusion. Third, the three major research methods used for this study will be
detailed along with subsequent analysis resulting from the implementation of these
methods. The analysis and results will then be discussed in the context of prior academic
and governmental research. And finally, recommendations will be provided to the
EnergySmart program along with a list of next steps to ensure that these
recommendations can be easily implemented.
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2. RESEARCH QUESTIONS In order to properly address issues related to the spatial distribution of energy
efficiency upgrades in Boulder County, homeowner views on energy efficiency, and the
demographic characteristics of homeowners completing energy efficiency upgrades in
Boulder County, several major research questions were asked at the outset of this study.
These questions are addressed though different methods of analysis throughout this
paper. The first two questions help understand the current picture of energy efficiency
upgrades in Boulder County by examining the spatial distribution of upgrades and
identifying neighborhoods with high or low clusters of upgrades:
Q1. What is the spatial distribution of EnergySmart residential energy
efficiency upgrades in Boulder County?
Q2. Are there certain areas of Boulder County that exhibit clustering of
EnergySmart upgrades?
Beyond just identifying clusters with high and low instances of energy efficiency
upgrades, a survey questionnaire was distributed to Boulder County homeowners in the
areas with high and low instances of efficiency upgrades. Major questions that will be
addressed by the questionnaire include:
Q3. How can EnergySmart better market itself to Boulder County
homeowners?
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Q4. Are Boulder County homeowners knowledgeable of available energy
efficiency services and discounts? If yes, how did they become
knowledgeable of these services (energy efficiency program marketing,
word of mouth etc.)?
Q5. Does sufficient knowledge of energy efficiency programs lead to action
(the completion of an upgrade)?
Q6. How do peer effects impact the spread of energy efficiency technology
at the neighborhood level?
Q7. Are there Boulder County homeowners with certain demographic
characteristics that should be targeted as an attempt to reduce exclusion
from residential energy efficiency upgrades?
The methods used to address these questions will be fully detailed in the Research
Methods and Analysis section of this paper.
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3. LITERATURE REVIEW
3.1 Energy Efficiency 3.1.1 Overview of Modern Energy Efficiency in the United States
Energy efficiency is an effective method for providing cost savings to energy
users while also reducing energy use and carbon emissions. Energy efficiency first
became part of official U.S. energy policy during the oil embargo of the 1970s with the
enactment of the Energy Policy and Conservation Act of 1975 (EPCA) and has been a
part of U.S. energy policy at varying levels ever since (Gillingham, Newell, and Palmer
2006; Dixon et al. 2010; Vine et al. 2012; Barbose et al. 2013).
Energy efficiency measures can be applied to all of the major energy-consuming
sectors of the U.S. economy, including commercial, industrial, transport, and residential.
This literature review will focus primarily on the residential sector, which currently
accounts for 37 percent of all electricity consumption, and 22 percent of all primary
energy consumption in the U.S. (U.S. Energy Information Administration 2011).
Currently, major residential energy efficiency upgrade strategies include
household appliance standards, financial incentive programs, and informational and
voluntary programs (Gillingham, Newell, and Palmer 2006; Hoicka, Parker, and Andrey
2014).
A growing focus on climate change has reinvigorated the need for energy
efficiency after a lull during the 1990s (Parker, Rowlands, and Scott 2003; Dixon et al.
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2010). This increased focus was apparent in the Energy Policy Act of 2005 (EPAct05)
and The Energy Independence and Security Act of 2007 (EISA), which contained over
200 energy efficiency and conservation provisions between them (Dixon et al. 2010). The
energy savings potential at the household level is dramatic—a typical house that has
completed a comprehensive energy efficiency upgrade will consume 42.5 percent less
energy annually than a house built to code that does not have energy efficiency measures
in place (Sadineni, France, and Boehm 2011). Because of this great potential energy
savings, there are worries that large energy bill savings will lead to a ‘rebound effect’
where household energy consumption levels rise—or rebound—to pre energy efficiency
savings levels due to less worry about cost (Greening, Greene, and Difiglio 2000). While
this is a concern, the multifaceted benefits of energy efficiency far outweigh this potential
drawback, especially for disadvantaged members of the population.
A holistic, nationwide energy efficiency policy for the commercial, industrial,
transport and residential sectors in the U.S. would have massive impacts: energy savings
worth more than $1.2 trillion, a reduction of end-use energy consumption in 2020 by 9.1
quadrillion BTUs, and the prevention of over 1 gigaton of greenhouse gas emissions
annually (Alcott and Greenstone 2012). With the residential sector accounting for 22
percent of current primary energy consumption in the U.S. (U.S. Energy Information
Administration 2011), residential energy efficiency can make a large contribution if a
holistic approach is adopted.
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3.1.2 Recent Legislation
In reality, residential energy efficiency efforts are increasing, but not yet at the
potential savings level described above. However, there is reason for optimism; by 2025,
spending on just utility customer-funded residential energy efficiency programs alone is
expected to double from 2010 spending levels to a figure of $9.5 billion annually
(Barbose et al. 2013). In addition, the Better Buildings Neighborhood Program (BBNP),
which funded EnergySmart Boulder and forty other energy efficiency programs
nationwide, was made possible by the American Recovery and Reinvestment Act of
2009. More details on BBNP are provided below in the ‘National Scope of Energy
Efficiency’ section. The increase in funding from both the public and private sectors
shows the growing significance placed upon residential energy efficiency in the U.S.
Unfortunately, other legislative efforts at the Federal level have not been as
successful. For example, The American Clean Energy and Security Act of 2009, was
passed by the House, but it eventually failed in the Senate. The original version of this
bill contained residential energy efficiency language that called for all homeowners to
upgrade their houses before sale to comply with 2012 IECC energy efficiency standards.
This part of the legislation did not consider the massive upgrade costs a homeowner
would incur for a whole house retrofit, in order to bring their house up to code. Recent
research focusing on a case study of 114 homes in the Denver area calculated that it
would cost an average of $22,901 to bring a house up to code before sale). This provision
was scrapped from the final version of the bill due to intense lobbying from the National
Association of Realtors (Sewalk and Throupe 2013).
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In May 2014, the Energy Savings and Industrial Competitiveness Act of 2014,
which surprisingly had bipartisan support in both the Senate and the House, died in the
Senate after amendments concerning the Keystone XL Pipeline and EPA climate change
regulations were introduced and led to disagreement between Democrats and
Republicans. Lack of momentum on the Federal level is disheartening, but this leaves
room for state and local programs to devise innovative energy efficiency programs for
their communities.
3.1.3 The Social Element of Energy Efficiency Programs
This literature review has detailed the multifaceted benefits of energy efficiency
related to cost and energy savings, and the reduction of greenhouse gas emissions, but has
not yet touched on the social benefits of energy efficiency upgrades, which include
improved levels of health and quality of life for residents.
It has been found that conducting energy efficiency upgrades has a positive effect
on health within the household. A study of 248 households in Boston, Chicago and New
York City, which completed residential energy efficiency upgrades coupled with indoor
environmental quality improvements, found that these upgrades led to improved general,
respiratory, cardiovascular and mental health (Wilson et al. 2014). In addition, a study of
1350 low-income households in New Zealand that completed insulation retrofits found
that these households experienced a warmer and drier environment, which led to
improved self-reported ratings of health, a lower number of visits to general practitioners,
and lower numbers of hospital admissions for respiratory issues (Howden-Chapman et al.
2007).
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Quality of life is impacted by high energy bills in several ways. First, cold
weather causes increased seasonal financial stress due to cold winter weather, and it also
causes higher mortality rates amongst elderly populations (Aylin et al. 2001). Second,
disadvantaged families face higher levels of food insecurity during times of high winter
heating costs or high summer cooling costs (Nord 2003, as cited in Frank et al. 2006).
This shift of financial resources from food to home heating or cooling was confirmed by
a study that found impoverished families reduce their caloric intake by 10 percent in the
winter months; whereas, there was no caloric reduction amongst families with more
financial resources (Bhattacharya et al 2003, as cited in Frank et al 2006). Overall, this
research shows that the benefits of energy efficiency extend beyond just cost and energy
savings by increasing the comfort of a house and the overall quality of life of residents.
3.2 Innovation Diffusion and Peer Effects: The Spread of Technology Adoption at the Neighborhood Level
In order to examine social exclusion from energy efficiency upgrades in Boulder
County, there needs to be an understanding of how awareness and adoption of energy
efficiency measures spread from household to household within neighborhoods.
Innovation diffusion and neighborhood-level peer effects must be considered when
analyzing the spatial distribution of energy efficiency upgrades at the metropolitan scale,
and to also gain a geographic understanding of how the ideas and technologies associated
with energy efficiency spread at the neighborhood scale.
3.2.1 Innovation Diffusion
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The study of innovation diffusion strives to understand how new ideas and the
technologies associated with these ideas spread across time and space. Breaking down the
term innovation diffusion, an innovation is an idea that is new to an individual, or at least
perceived to be new, and diffusion is the spread of an idea from its originator to those
meant to use or adopt the idea (Rogers 1962). In the social sciences, innovation diffusion
is a well-studied concept as demonstrated by Rogers (1962), who summarizes over 500
publications on the topic and identifies similar key elements of analysis found within
many of these publications. The four key elements of innovation diffusion are: “(1) the
innovation, (2) its communication from one individual to another (3) in a social system
(4) over time” (Rogers 1962, 12). Rogers’ research helped lay the foundation for
identifying these factors, and spurred on more study of innovation diffusion from varying
academic perspectives (Noll, Dawes, and Rai 2014).
Unfortunately, the spatial elements of innovation diffusion were largely ignored
by Rogers and other social scientists before him. The geographic study of innovation
diffusion is tied to Hägerstrand’s seminal work Innovation Diffusion as a Spatial Process
(1967)1, which stressed the importance of the spatial aspects of innovation diffusion by
identifying how information flows through a hierarchy of networks at varying scales.
Hägerstrand also used quantitative methods to study innovation diffusion, including
pioneering work with Monte Carlo simulation models. Key geographic contributions
include the cartographic visualization of the distribution of a phenomenon using dot
1 This work was originally published in Swedish in 1953. Ironically enough, Hägerstrand was a victim of poor information diffusion, as the language barrier prevented his 1953 work from being widely disseminated to an English speaking audience until Allen Pred translated it to English in 1967. Hägerstrand’s 1953 Swedish publication was briefly cited by Rogers (1962) but the impact of Hägerstrand’s research was not fully realized until it was translated into English.
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distribution and proportional circles for quantitative visualizations. Key spatial
characteristics of innovation diffusion identified by Hägerstrand include: (1) initial
agglomerations, with a concentrated and small set of initial adopters, (2) the outward
radial dissemination of the initial agglomeration and the formation of secondary
agglomerations, and (3) saturation, where growth ceases (Hägerstrand 1967).
One area of Hägerstrand’s research particularly relevant to the diffusion of energy
efficiency technologies is what he terms complementary elements. These are
technological innovations which are already commonly used, and their diffusion from
initial adopters cannot be traced. However, the distribution of complementary elements
from a single point in time can be traced. Hägerstrand relates this to working with
“individual links in the chain of perpetual change without having any possibility of
determining the developments which have led up to the situation to be analyzed.”
(Hägerstrand 1967, 13). General household elements specified by Hägerstrand include
plumbing, refrigerators, and electric ranges. When investigating the diffusion of energy
efficiency technology, which includes the upgrade of appliances such as refrigerators, it
is important to pick a specific timeframe, or link in the chain, to start from when
examining the spatial distribution of these household technologies. Although it took some
time to be recognized by innovation diffusion scholars, Hägerstrand’s work is extremely
influential and much cited, as demonstrated by Persson and Ellegard (2012), who
conducted an analysis of scholarly literature citing Hägerstrand over both time and space.
In subsequent years, research by Brown (1968a, 1968b, 1975, 1990) broadened
the spatial study of innovation diffusion by examining the supply side of innovation
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diffusion. This involved studying how and where agencies locate themselves in order to
maximize the diffusion of their ideas and technologies to consumers. Innovations are
distributed through agencies, so the location of agencies is key in the subsequent pattern
of product dispersal (Brown 1975). In addition to profit motivated diffusion, non-profit
groups, including federal, state and local governments, can also employ similar tactics
when determining where to locate in order to dispense their services to the most
taxpayers or other clients as possible. While it is outside the scope of this study,
examining the location of various energy efficiency program offices in relation to
effectiveness of the program would be an interesting way to apply Brown’s supply side
analysis of innovation diffusion to a non-profit agency.
There is no question that the works of Hägerstrand and Brown played a crucial
role in bringing geography to the forefront of the study of innovation diffusion; however,
these approaches are somewhat lacking for analysis of the spread of energy efficiency
technologies at the neighborhood level. First, Hägerstrand (1967) mentions avoidance of
analyzing the dispersion of technologies where adoption would be impeded to a
considerable degree by economic or technical factors. Cost and technical understanding
are two key factors in the adoption of energy efficiency technologies, and can act as
barriers to adoption; therefore, any study addressing energy efficiency upgrades must
strive to overcome cost and technical barriers. Brown’s research (1968a, 1968b, 1975,
1990) also has some limitations in the context of my study. First, the supply-side focus of
Brown’s innovation diffusion research does not focus on the adoption behavior of
customers, which is an important factor in my research. Furthermore, Brown’s regional
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scale analysis does not lend itself to an investigation of neighborhood level innovation
diffusion.
Due to these limitations, the study of innovation diffusion at the neighborhood
level needs to include an analysis of how peer effects can lead to increased information
and higher levels of adoption of energy efficiency measures amongst early-majority and
late-majority adopters.
3.2.2 Peer Effects
Peers are typically neighbors, friends, or roommates, depending upon the focus of
the study; information flow through peer influence is known to enhance innovation
diffusion (Noll, Dawes, and Rai, 2014). The two main categories of peer effects are
active peer effects and passive peer effects. Active peer effects involve direct contact and
conversation with a peer, while passive peer effects involve indirect influence, such as
seeing a neighbor outfitting their house with a specific energy efficient product (Rai and
Robinson 2013). Beyond this basic overview of peer effects, Scott and Carrigan (2011)
provide a comprehensive literature review of previous peer effects research and typology.
Several recent studies on peer effects and the neighborhood level diffusion of
residential solar photo-voltaic (PV) technology (Rai and Robinson 2013; Islam 2014;
Noll, Dawes, and Rai 2014) can be applied to energy efficiency programs. Specific
methods and survey questions from these studies will be reproduced in the context of
energy efficiency for my research project, and are further detailed in the Methods section
and in the survey questionnaire (Appendix B).
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3.3 Social Exclusion
Social exclusion is a perspective that illustrates inequality by going beyond the
typical measures of inequality. Some scholars assert that there is not an agreed upon
definition of social exclusion (Levitas 1998; Marsh and Mullins 1998; Marsh 2004;
Arthurson and Jacobs 2004), while others have provided broad definitions: “an individual
is socially excluded if (a) he or she is geographically resident in a society and (b) he or
she does not participate in the normal activities of citizens in that society” (Burchardt,
LeGrand, and Piachaud 1999, 230), and “a situation in which certain members of a
society are separated from much that comprises the normal ‘round’ of living and working
within that society” (Gregory et al. 2009, 691). Despite this disagreement over
definitions, it can be agreed upon that social exclusion embraces a multi-dimensional
approach to inequality that involves economic, social and political processes (Bhalla and
Lapeyre 1997, Somerville 1998). These three major processes produce “a sense of social
isolation and segregation from the formal structures and institutions of the economy,
society, and the state” (Somerville 1998, 762). Social exclusion has primarily academic
roots, but it has also become a major policy focus for addressing poverty throughout
Europe (Levitas 1998, Marsh 2004, Arthurson and Jacobs 2004, Beland 2007). In
addition, this perspective has been used to examine issues of housing and transportation,
which has led to a more spatial understanding of social exclusion (Madanipour 1998, as
cited in Arthurson and Jacobs 2004; Somerville 1998; Watt and Jacobs 2000; Marsh
2004).
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3.3.1 Social Exclusion and Housing
Social exclusion has been used as a tool to study and address transportation issues
(e.g. Church, Frost, and Sullivan 2000; Hine and Grieco 2003; Cass, Shove, and Urry
2005; Boschmann and Kwan 2008; Preston 2009), but it is also a relevant perspective for
addressing housing inequality. In fact, “transport policy may only be a secondary tool to
reducing social exclusion, with policies concerning employment, income, housing, social
care, health and education of greater primary importance…” (Preston 2009, 141).
Examining the housing and transport issues of social exclusion has led to a more
spatial and geographic thinking about how place of residence relates to social exclusion.
Examples include: different forms of social exclusion as spatially discernable
(Madanipour 1998, as cited in Arthurson and Jacobs 2004), studying clusters of social
exclusion (Hine and Grieco 2003), and the spatial element of social exclusion in the
United States, which typically involves “exclusive” spaces such as neighborhoods, clubs,
or prep schools (Silver and Miller 2003).
Social exclusion policy has also evolved and transitioned from a focus on the
individual to a more spatial focus (Watt and Jacobs 2000) that looks to address places as
a whole. This is due to the assertion that, when studying social exclusion, area effects
carry more weight than individual circumstances (Marsh 2004).
Moving from the neighborhood scale to the household scale, social exclusion
takes on a different form within the home. Equitable distribution of housing is certainly
important on the metropolitan and neighborhood scales, but at the household scale, the
ability to access specific goods and services that allow for sufficient upkeep and
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maintenance of a house is a major facet of household social exclusion as well (Somerville
1998). Exclusion from access to residential energy efficiency programs is twofold. First,
it denies important service and upkeep to the home, which negatively impacts the
finances, quality of life, and health of residents. Second, energy efficiency programs are
generally offered as either fully or partially-publically funded services, and exclusion
from this type of service is one of the primary concerns related to the social aspect of
social exclusion. Energy efficiency programs primarily focus on widespread adoption and
CO2 emissions reduction, but it is critical for programs to also include disadvantaged
populations that would benefit most from energy efficiency upgrades.
3.4 Literature Gaps and Conclusion This study draws upon a diverse cross-section of literature from both academic
and governmental research. Incorporating spatial analysis into these areas of literature
will fill a gap that currently exists related to using a combination of GIS techniques and
survey analysis to enhance energy efficiency programs.
The gap can be seen when looking at current energy efficiency program case
studies across the United States. A comprehensive summary of fourteen energy efficiency
program case studies at state and local levels provides details related to the program
management and marketing aspects of energy efficiency programs, but there is no
mention of spatial analysis as a technique for enhancing the impact of energy efficiency
programs (Fuller et al. 2010). In addition, one of the Better Buildings Neighborhood
Program’s three major goals is identification of the most effective approaches to
completing energy efficiency upgrades, but again, there is no mention of spatial analysis
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as a means for accomplishing this goal (Department of Energy, Building Technologies
Office 2013).
Despite the lack of recognition of spatial analysis by many energy efficiency
programs, two recent academic studies have used spatial analysis to effectively examine
various facets of energy efficiency programs in Phoenix, Arizona and Los Angeles,
California. The first study focused on commercial energy efficiency upgrades completed
by businesses in downtown Phoenix (Dalrymple, Melnick, and Schwartz 2014). The use
of Local Moran’s I cluster analysis in this study was closely emulated for by my study of
residential energy efficiency in Boulder County.
The second study focused on changes in energy use at the Block Group level in
Los Angeles, before and after the implementation of residential energy efficiency
upgrades (Sun 2014). Informal interviews were conducted as part of the Los Angeles
study, but no formal survey analysis was conducted. These studies are important first
steps in establishing spatial analysis as a useful tool in exploring energy efficiency
upgrades; however, neither study spatially targets survey research using cluster analysis
the way this study of Boulder County does.
Studying energy efficiency through the lenses of peer effects, GIS cluster analysis
and social exclusion will create new linkages between these study areas while also
providing new strategies for ensuring that large numbers of homeowners in Boulder
County are able to easily take advantage of energy efficiency programs.
19
4. NATIONAL SCOPE OF ENERGY EFFICIENCY: THE BETTER BUILDINGS NEIGHBORHOOD PROGRAM
The American Recovery and Reinvestment Act of 2009 (ARRA) designated $508
million in grants to fund 41 state and local residential energy efficiency programs across
the country, including two in the Front Range region, Denver Energy Challenge and
EnergySmart Boulder (see Figure 1below) (Department of Energy 2013). This program,
called the Better Buildings Neighborhood Program (BBNP), was managed by the
Department of Energy’s Building Technologies Office and was actively funded by
ARRA grants from 2010 until this funding was depleted in 2013. Many BBNP grantees
continued to operate after Federal funding was depleted, including Boulder’s
EnergySmart program.
20
Figure 1: Map of the Better Buildings Neighborhood Program energy efficiency grantees across the U.S. (Energy.gov)
Below is a list of the Better Buildings Neighborhood Program’s nationwide
accomplishments between 2010 and 2013 (Energy.gov):
• Upgraded more than 105,000 residential and commercial buildings to be more energy efficient
• Performed more than 240,000 residential and commercial energy assessments • Developed sustainable energy efficiency upgrade programs, approximately three-
fourths of which will continue through at least 2014 without additional DOE funding
• Saved consumers $730 million in estimated lifetime energy savings, in addition to improving the comfort of their homes and buildings
• Trained more than 5,000 home performance workers to enhance their skills • Completed more than $780 million worth of energy upgrades • Leveraged more than $440 million in private capital and federally funded
revolving loan funds. Grantees of the BBNP program were widely distributed across the United States, and
varied in size—ranging from statewide programs in California to more rural county-wide
21
programs areas such as Rutland County, VT, population 60,086 (U.S. Census 2014).
Despite a wide geographic distribution of programs with varying boundary and
population sizes, preliminary analysis of the BBNP program shows that the geographic
location of a program or the size of the grantee (statewide, large urban, small urban, rural,
etc.) did not have a major effect on the success of the program (U.S. Department of
Energy, Building Technologies Office 2013). Instead, existing program infrastructure,
program organization, and the availability of financing were the major determining
factors for success. According to a conversation with a key BBNP staff member (2015,
February 26. Telephone interview), the most successful BBNP grantee programs were the
ones that already had some sort of existing energy efficiency program in place. This
existing infrastructure made it much easier for these grantees to build on the momentum
of existing programs by quickly implementing energy efficiency programs using grant
money from BBNP. Important types of existing infrastructure include the necessary
internal staff to offer home energy assessments, process paperwork, and assisting
homeowners who are interested in completing upgrades. Beyond this internal structure,
programs that had an existing network of home performance contractors (contractors who
are certified to complete energy efficiency upgrades) were able to easily connect
homeowners with established contractors who had a proven record of completing energy
efficiency upgrades. The many factors involved in completing an energy efficiency
upgrade can be overwhelming for homeowners, so a significant number of the grantees
(an exact number will not be available until DOE publishes its final BBNP report) put
‘energy advisors’ in place to assist homeowners with all steps of the energy efficiency
22
upgrade process. Preliminary Better Buildings Neighborhood Program research
conducted by DOE shows that providing homeowners with one consistent person (an
energy advisor) to keep in touch with throughout the upgrade process helps increase
accountability and also helps minimize the barriers related to energy efficiency upgrades.
As previously mentioned, the geographic or population sizes of grantee programs
were not major factors for determining program success, but these geographic and
demographic factors impacted the strategies used by grantees to facilitate energy
efficiency upgrades. For example, larger statewide programs such as California had to
deal with the complexity of managing a statewide program while working with counties
and cities in the state to ensure impactful results. Despite increased complexity, larger
grantees benefitted from the ability to make financing available to homeowners who
completed energy efficiency upgrades through these programs. DOE is still trying to
quantify how important having financing available is, but according to BBNP staff,
preliminary findings show that homeowners are more positively influenced to complete
an energy efficiency upgrade when they know financing is available. Whether or not
homeowners actually take advantage of loan programs, they see the grantee program as
more legitimate if they know the program is backed by a bank, which is willing to lend
money in order for the homeowner to complete energy efficiency upgrades (2015,
February 26. Telephone interview).
It is important to note that more rural grantee programs, such as Rutland County, VT,
were still successful despite their smaller size and lack of financial means available to
major urban or statewide programs (Department of Energy, Rutland County Case Study
23
2011). As exhibited by the examples above, the BBNP energy efficiency upgrade model
worked across regional boundaries and with grantee programs of varying sizes.
Beyond the organizational and management structures of BBNP grantees, the
political leanings of homeowners is an important factor which requires some nuance
when marketing energy efficiency upgrades in more conservative areas of the county.
According to research by the Center for Sustainable Energy (Treadwell 2015), focusing
on the positive environmental impacts of energy efficiency or solar PV is not a line of
messaging that resonates well with political conservatives. Instead, messages focusing on
preventing waste (whether it be money or energy), health improvements (less asthma for
kids), increased comfort in the home, and empowerment to take control over your energy
bill are all messages that resonate well with homeowners that do not have environmental
motivations or more liberal political leanings. The effects of political affiliation on
energy efficiency upgrade completion will be further discussed in the survey results
section of this paper.
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5. STUDY AREA: BOULDER COUNTY, COLORADO
EnergySmart is Boulder County’s BBNP grantee program; this program is
operated by the Boulder County Commissioner’s Sustainability Office, who I have
partnered with on this thesis research. The Sustainability Office provided me with
address-level EnergySmart energy efficiency upgrade data that was used to target the
survey research for this study. In addition, I have met regularly with Sustainability Office
staff members throughout the completion of this project. Ultimately, the findings from
this project will be used by the Sustainability Office to effectively target and market
future EnergySmart residential energy efficiency upgrades.
Since its inception in 2009, EnergySmart has completed over 11,000 energy
efficiency upgrades in Boulder County (EnergySmartYes.com). This accounts for about
10 percent of all energy efficiency upgrades completed by the 41 Better Buildings
Neighborhood Program grantees nationwide, which makes EnergySmart one of the most
successful BBNP grantee programs (Energy.gov). And according to the upgrade data
provided by EnergySmart, 4,747 of the approximately 11,000 upgrades were completed
on owner occupied, single family homes. This success is owed both to the operation
strategies detailed in the national scope discussion in the previous section, and the
importance that is placed on sustainability and the environment by Boulder County
residents.
25
Beyond being the study site for this thesis research, Boulder County can be
considered a unique case for sustainability in the United States. Boulder County is often
seen as a leader in sustainability because it is led by the City of Boulder’s progressive
traditions related to the environment and, more recently, leadership associated with CO2
emissions reduction strategies. The City of Boulder makes up almost one-third of
Boulder County’s population (US Census Bureau 2014) and helps drive a vision that
leads to the City of Boulder either being seen as an inclusive, forward-thinking utopia for
sustainability, or as a demographically homogeneous area facing increasing issues of
affordability and wage inequality (BBC 2014). Boulder County has major demographic
variations as compared to nationwide averages of the United States, which can be seen in
Table 1 below.
Table 1: Demographic characteristics of Boulder County as compared to the United States as a whole. Source: US Census Quick Facts (Derived from American Community Survey Data and Census of Housing and Population), Federal Elections Commission, and Boulder County Elections.
Despite these differences, planned emissions reduction goals in Boulder County
(and the City of Boulder specifically) may be reflective of emissions reduction plans that
are implemented in cities, states and counties across the US in coming years due to
Demographic Characteristic
Boulder County United States
% White alone (2013) 78.6% 62.6%
Median Household Income (2009-2013)
$67,956 $53,046
Median Home Value (owner occupied units, 2009-2013)
$350,900 $176,700
% voted Democratic (Obama/Biden) in 2012 Presidential Election
69.69% 51.06%
26
President Obama’s recent commitment to a 26-28 percent cut in CO2 emissions below
2005 levels by 2025 (The White House, Office of the Press Secretary 2015). This Federal
commitment makes studying issues of energy efficiency in Boulder County important,
because Boulder County is already thinking well beyond 2025.
Boulder County has a climate change resilience plan in place (Boulder County
Commissioner’s Sustainability Office 2012) and this plan recognizes the need for
leadership from, and collaboration with, cities and municipalities throughout the county
in order to accomplish the county’s resilience and climate change goals. The challenge of
leadership on the issue of climate change has been accepted by the City of Boulder,
whose city council has recently examined the feasibility of an 80% reduction of CO2
emissions by 2050 (using 2005 emissions as the baseline). The study acknowledges
challenges related to this goal, but found that it is achievable through major changes to
Boulder’s energy systems over the next thirty five years (Brautigam et al. 2014). This
goal is on par with some of the most aggressive CO2 emissions reduction efforts in the
world. For example, the European Union (EU) recently proposed this same 80% by 2050
goal (European Commission 2015). Partially as a result of these aggressive efforts, EU
member states hold nine of the top ten rankings for the 2014 Climate Change
Performance Index, while the US ranks 43rd (Burck, Marten, and Bals 2014). In
December 2014, the US entered into an emissions reduction agreement with China that
calls for a 26-28% reduction in CO2 emissions by 2025 (using 2005 emissions levels as a
baseline). This is a significant agreement because the US and China account for 45% of
worldwide CO2 emissions, but the agreement does not set targets for a more aggressive
27
goal like 80% emissions reduction by 20502 (The White House, Office of the Press
Secretary 2014). Despite a lack of commitment to long-term goals at the Federal level,
there are cities and states around the US that are setting similar 80% by 2050 goals,
including New York City (Mayor’s Office of Long-Term Planning and Sustainability
2014), Austin, TX (Austin Energy 2014), the state of California (California
Environmental Protection Agency 2014), and the state of Maryland (Maryland
Department of the Environment 2014).
If the City of Boulder makes the 80% by 2050 goal official, this places the city in
rare company with other US cities and states that are officially pursuing such aggressive
goals. This goal cannot be accomplished without county-wide participation, especially
related to transportation and energy systems, which are not confined to the City of
Boulder alone. In order for the city to reach this goal, it will require efforts across all
sectors:
“…achieving reductions of this magnitude will require broad energy system changes that include but are larger than switching electricity sources. It is also evident that the scale of action will require broad participation of all sectors of the community and a comprehensive community energy vision that aligns an energy system transition with core community values, benefits and aspirations.” (Brautigam et al. 2014, pg. 2)
Energy efficiency will play an important role in reducing emissions, as residential
energy efficiency (for both rental and owner-occupied homes) accounts for almost 14%
of all CO2 emissions reductions required to meet the 2050 goal (Brautigam et al. 2014).
To accomplish this reduction, efficient targeting and outreach related to home energy 2 The White House says this agreement keeps the US economy on ‘the right trajectory’ to make 80% emissions reductions by 2050, but it does not specifically commit the US to meeting this goal.
28
upgrades completed the county-wide EnergySmart program is required. EnergySmart is
expected to play a large role in helping not just the City of Boulder, but all of Boulder
County, reduce CO2 emissions. Energy efficiency is also a case where individuals are
empowered to make a difference in emissions reductions. Other sectors, such as
transportation for example, allow personal empowerment though alternative
transportation modes such was biking or walking, but easy use of these transportation
modes or public transit is largely dependent upon major planning projects. In addition, a
major shift to a renewable energy grid is required to make plug-in vehicles
environmentally friendly. Many of these efforts are underway in Boulder County, but
while residents wait for these larger scale projects to become a reality, it is important to
take tangible actions at the household level that will contribute to Boulder’s CO2
emissions reduction goals.
Sections throughout the rest of this paper will detail methods for targeting future
energy efficiency upgrades using GIS computer mapping, analyze survey results, and
provide recommendations related to how EnergySmart can contribute to reducing CO2
emissions in Boulder County, while also providing many other benefits related to energy
efficiency.
29
6. OVERVIEW: RESEARCH METHODS AND ANALYSIS
There are three major methods associated with this thesis research project. The
first section details the GIS methods that were used to map the addresses of all
EnergySmart energy efficiency upgrades completed at owner occupied households within
Boulder County. Major steps included gathering GIS shapefiles and EnergySmart
upgrade data, conducting cluster analysis of EnergySmart upgrades in Boulder County,
and targeting of the survey using this cluster analysis.
The second section provides an overview of the survey instrument and analyzes
survey results. This portion of the research involved creation of a survey with questions
relating to homeowner awareness and attitudes towards energy efficiency, testing of the
survey with twenty Boulder County homeowners, adaption of the paper survey into an
online survey, and random distribution of the survey to 1,000 Boulder County
homeowners in specifically defined spatial clusters. After Boulder County homeowners
completed the survey, statistical and open-ended coding analysis of survey results was
completed.
And third, in order to examine demographic groups that were underrepresented by
survey respondents, Block Group level demographic data was examined using analysis of
variation (ANOVA) statistical analysis. Steps for this demographic analysis included GIS
analysis of Block Group level demographic data from the US Census, identification of
block groups with a low ‘EnergySmart upgrade to total homeowner’ ratio, and ANOVA
30
statistical analysis to identify demographic inequality related to energy efficiency
upgrades.
This combination of cluster analysis, survey analysis and demographic analysis
allows for a deeper understanding of the spatial and attitudinal dynamics related to
residential energy efficiency upgrades in Boulder County.
31
7. SPATIAL ANALYSIS GIS computer mapping was a key component for identifying spatial trends related
to EnergySmart upgrades, and it was also used to target survey distribution. This section
discusses the data used and spatial analysis methods employed to target the survey based
upon cluster analysis of EnergySmart energy efficiency upgrades in Boulder County. The
cluster analysis techniques detailed below also help answer the following research
questions:
Q1. What is the spatial distribution of EnergySmart residential energy
efficiency upgrades in Boulder County?
Q2. Are there certain areas of Boulder County that exhibit clustering of
EnergySmart upgrades?
7.1.1 GIS Data Layers and Spatial Distribution of EnergySmart Upgrades
The GIS data layers below in Table 2 were the key data layers used to complete
the cluster analysis and then randomly target distribution of the survey.
32
Data Layer Data Type Data Source Function EnergySmart Upgrades
Points EnergySmart Boulder
Addresses of owner occupied households that completed energy efficiency upgrades through EnergySmart
Land Use and Zoning (City of Boulder, Boulder County (unincorporated areas), Lafayette, Longmont, Louisville, Superior)
Polygons Cities of Boulder, Lafayette, Longmont, Louisville and Superior; Boulder County
Used to isolate areas within each municipality (and unincorporated areas of Boulder County) that are zoned residential; used as boundary for Local Moran's I cluster analysis
Boulder County Land Parcels
Polygons Boulder County Used to identify specific household addresses for survey distribution
Boulder County Block Groups
Polygons US Census Block groups with demographic data. Used to complete ANOVA statistical analysis
Table 2: Data layers used for GIS cluster analysis
The ‘EnergySmart Upgrades’ layer started as a list of addresses, in Excel format,
provided for this thesis research by EnergySmart staff. Whenever a homeowner
completed an energy efficiency upgrade through the EnergySmart program, an
EnergySmart staff member would record key data about the upgrade. Important data
fields included: homeowner address, date of sign-up for the EnergySmart program, date
of completion of energy efficiency upgrades, and type of upgrade completed. When
summarizing the most common types of upgrades completed, six of the top eight
upgrades completed are considered building envelope (building walls, roof and windows)
33
upgrades (Table 3). This is important because reducing wasteful air leakage is an
important first step before making more expensive upgrades such as a more efficient heat
pump, air conditioner, or other appliances.
Upgrade Type Count
Ceiling/Attic insulation 1772
Air-sealing (professional) 1755
Window replacement 1023
Floor/Crawlspace insulation 909
Gas furnace 763
Wall insulation 448
Furnace or boiler tune-up 382
Duct repair/sealing 378
DIY Weather-stripping 354
Refrigerator replacement 352
Dishwasher replacement 278
Air conditioner replacement 256
Water heater replacement 239
Clothes washer replacement 220
Air conditioner tune-up 209
Table 3: Energy efficiency upgrades completed through the EnergySmart program
In addition, the number of upgrades completed by quarter (Figure 2) shows a
strong number of upgrades completed until early 2013, when the number of upgrades
completed per quarter drops off significantly.
34
Figure 2: EnergySmart upgrades completed by quarter (2011-2014)
Two possible reasons for this drop-off include a loss in program funding or
market saturation amongst highly-motivated early adopters. Issues related to the
transition from early adopters of energy efficiency programs to widespread adoption will
be further discussed later in this paper.
Before entering this data into ArcGIS to conduct cluster analysis, the data had to
be ‘cleaned’ to ensure consistent address formatting and then geocoded as address level
points before it was usable in ArcGIS. Since this data was gathered by multiple staff
members, some addresses were entered in different formats, which required time
consuming formatting adjustments in order for the data to be properly geo-located by
ArcGIS. A map of all EnergySmart upgrades by address can be seen below in figure 3.
Upgrade locations largely mirror areas with residential housing within Boulder County,
Kernel Density Analysis shows visual evidence of clustering within certain areas of the
county (red areas of Figure 3 below indicate clustering of EnergySmart upgrades).
35
Figure 3: Location and clustering of EnergySmart energy efficiency upgrades in Boulder County, CO
Kernel Density Analysis only provides visual confirmation of clustering; in order
to statistically confirm the presence of clustering, Average Nearest Neighbor cluster
analysis was used. Average Nearest Neighbor tests for evidence of statistically significant
clustering by comparing the distribution of all EnergySmart upgrade points against a
random distribution of points. The null hypothesis used by Average Nearest Neighbor is a
random distribution of points, but as can be seen by the analysis results below (Figure 4),
the distribution of EnergySmart upgrades falls within the rejection region for this null
hypothesis. The z-score of -76.79 and p-value of 0.00 signify statistically significant
clustering of EnergySmart upgrade points in parts of Boulder County.
36
Figure 4: Results of ‘Average Nearest Neighbor’ spatial statistical analysis
Average Nearest Neighbor is useful for determining the presence of clustering on
a county-wide level, but more localized cluster analysis needed to be implemented in
order to target the survey research. This process is detailed in the discussion of other data
layers below.
37
The other four layers are from various public data sources; these sources are
detailed previously in Table 2. The ‘Land Use and Zoning3’ data layers provided a
boundary for the cluster analysis, while also ensuring that the cluster analysis would only
be conducted in areas that were zoned as residential. Zoning data for the Cities of
Boulder, Lafayette, Longmont, Louisville and Superior, along with unincorporated
Boulder County was used to identify the geographic boundaries for survey distribution
within Boulder County4.
‘Select by Attributes’ queries were used in ArcGIS to remove all non-residential
zoning parcels from the cluster analysis area. This ensured that the cluster analysis would
not accidently identify any areas zoned industrial, for example, as areas with low
clustering of residential energy efficiency upgrades. Any sort of residential housing,
including areas zoned for mixed use development, was included in the residential zoning
layers; although, only single family, owner-occupied homes were surveyed.
After all non-residential zoning areas removed, the remaining residential zones
were merged into one layer, which provided the geographic extent for the cluster analysis
(Figure 5 below).
3 Some municipalities called this data ‘zoning’ and others called it ‘land use’, but they are the same thing. 4 Mountain towns such as Nederland and Lyons, along with other areas of Boulder County that are west of the foothills were not included in the cluster analysis or survey research.
38
Figure 5: Areas zoned ‘Residential’ within the Cities of Boulder, Lafayette, Longmont, Louisville and Superior, and unincorporated Boulder County
7.1.2 Local Cluster Analysis Local Moran’s I Cluster Analysis (referred to from here on as just Local
Moran’s), a tool in the ArcGIS Spatial Statistics Toolbox, was used to analyze potential
clustering of EnergySmart upgrades in Boulder County. Local Moran’s determines the
presence or absence of clustering by counting the number of EnergySmart upgrade points
within a specified geographic zones. These zones were created using the Fishnet tool in
ArcGIS. This tool created squares of a specified area to overlay the residential zoning
areas of Boulder County. A similar study of commercial energy efficiency upgrades in
Phoenix, Arizona (Dalrymple, Melnick, and Schwartz 2014) used 1/16th square mile
39
fishnet overlays to conduct Local Moran’s analysis. This overlay size was sufficient for
examining dense commercial developments in downtown Phoenix, but the larger parcel
sizes and lower density of single family developments associated with this project made
neighborhood level analysis using 1/16th square mile boundaries difficult. Therefore,
larger 1/8th square mile zones were used because this spatial scale more readily captures
full segments of a neighborhood (full streets, cul de sacs, etc.), while the 1/16th zones
only capture several land parcels within each zone and inadvertently divide
neighborhoods into small subsections that do not reflect the character of the
neighborhood as well as 1/8th square mile zones do. An example of this can be seen in the
two maps below; the first map (Figure 6) shows a neighborhood divided by 1/8th square
mile cluster zones, and the second map shows the same neighborhood divided into 1/16th
square mile cluster zones (Figure 7).
40
Figure 6: 1/8th square mile clusters created using Local Moran’s I spatial clustering tool
41
Figure 7: 1/16th square mile clusters created using Local Moran’s I spatial clustering tool
Using square polygons of varying sizes to divide up neighborhoods with amorphous
street designs will lead to undesired divisions at whatever spatial scale is used to
aggregate data due to the modifiable aerial unit problem (MAUP) (Fotheringham and
Wong 1990; Gatrell et al. 1995; Flowerdew, Manley and Sabel 2008). However, for this
study, 1/8th square mile zones were able to best divide residential neighborhoods for
cluster analysis.
Next, a Spatial Join was performed in ArcGIS in order to count the total number
of EnergySmart upgrades within each 1/8th square mile zone. Local Moran’s clustering
uses the total count of EnergySmart upgrade points within each 1/8th square mile zone to
calculate a statistical z-score, which determines if there is a presence or absence of
42
clustering. Four different cluster types were found when conducting Local Moran’s
Cluster Analysis for EnergySmart upgrades in Boulder County5:
High-High Clusters (Figure 8 below): A single 1/8th square mile zone that has a high
positive z-score (>1.96), and is neighbored by other zones with high positive z-scores
(>1.96). This high z-score indicates the presence of statistically significant clustering of
EnergySmart upgrades within neighboring 1/8th square mile zones.
Figure 8: Example of High-High clusters created using Local Moran’s I spatial clustering tool
5 There was no presence of the fifth type of clustering: Low-Low clustering, which indicates an absence of clustering within neighboring 1/8th square mile zones.
43
High-Low Clusters (Figure 9 below): A single zone with a high positive z-score (>1.96)
that is surrounded by neighboring zones with z-scores lower than 1.96. This indicates one
zone with a high presence of clustering surrounded by zones with a lack of clustering.
Figure 9: Example of High-Low clusters created using Local Moran’s I spatial clustering tool
44
Low-High Clusters (Figure 10 below): A single zone with a low negative z-score (< -
1.96) that is surrounded by neighboring zones with z-scores higher than 1.96. This
indicates one zone with a lack of clustering surround by zones with a high presence of
clustering.
Figure 10: Example of Low-High clusters created using Local Moran’s I spatial clustering tool
45
Not Significant Clusters (Figure 11): Zones with z-scores between 1.96 and -1.96.
These z-scores indicate a random distribution of points, which means there is no
statistically significant clustering taking place in these zones.
Figure 11: Example of Not Significant clusters created using Local Moran’s I spatial clustering tool
An overview of all cluster types identified in Boulder County can be seen below
in Figure 12. In addition, detailed figures of northeast and southeast Boulder County are
included so the cluster types can be viewed at a more localized scale (Figures 13 and 14).
46
Figure 12: All Local Moran’s I cluster types within Boulder County
47
Figure 13: Detailed view of Local Moran’s I cluster analysis in northeast Boulder County
48
Figure 14: Detailed view of Local Moran’s I cluster analysis in northeast Boulder County
The Local Moran’s Cluster Analysis resulted in the following counts of each
cluster type in Boulder County, along with the number of individual land parcels within
each cluster type (Table 4).
All
Zones
High-High Zones
High-Low
Zones
Low-High Zones
Low-Low
Zones
Not Significant
Zones
Cluster Count 6,190 333 45 104 0 5,708 Residential Parcel Count 97,615 15,091 1,502 2,519 0 87,528
Table 4: Total number of clusters and residential parcels within each cluster zone
49
The number of cluster types within each Boulder County municipality can be seen below
in Table 5.
Cluster Type
High-High High-Low Low-High
Not Significant
Mun
icip
ality
Boulder 98 5 19 1,215 Lafayette 18 4 1 484 Longmont 69 10 6 1,418 Louisville 16 4 1 325 Superior 11 0 0 196 Unincorporated Boulder County 121 22 77 2,070
Total 333 45 104 5,708
Table 5: Cluster types within Boulder County Municipalities
Despite there being only 333 High-High cluster zones, over 1,000 EnergySmart
upgrades were completed in High-High cluster zones (Table 6). In addition, no
EnergySmart upgrades took place in the 104 Low-High cluster zones.
Cluster Type EnergySmart Upgrade
Points (Count) High-High 1,037 High-Low 103 Low-High 0 Not Significant 2,004 None6 462
Table 6: EnergySmart upgrades completed within each cluster type
6 EnergySmart upgrades in the ‘None’ category are upgrades that were completed in locations west of the Front Range foothills, in towns such as Lyons and Nederland. These locations are outside of the designated study area.
50
When examining the location of EnergySmart upgrades by municipality, the most
upgrades were completed in the City of Boulder; however, these upgrades only accounted
for 6.6% of all owner occupied homes in the City of Boulder. Unincorportated Boulder
County had the highest ‘Energy Smart Upgrade to total homeowner ratio’ (23.6%), as can
be seen below in Table 7.
Total Energy Smart
Upgrades
Total Owner Occupied Homes
Percentage of all owner occupied homes that have completed an
EnergySmart upgrade Unincorporated
Boulder County 313 1,329 23.6% Boulder 1,830 27,758 6.6% Superior 90 3,169 2.8% Louisville 219 9,089 2.4% Longmont 535 23,474 2.3% Lafayette 178 11,282 1.6%
Table 7: Percentage of homes in Boulder County municipalities that have completed an EnergySmart upgrade
The average home age within each cluster type shows that homes in the Not
Significant clusters are the oldest, on average, while homes in the High-Low cluster are
the most recently constructed (Table 8 below).
Cluster Type Average Home Age High-High 1971 High-Low 1982 Low-High 1974 Not Significant 1966
Table 8: Average Home Age by Cluster Type
As will be detailed later in the survey results, homeowners in the Not Significant
clusters have completed energy efficiency upgrades through a home energy audit at a
51
much lower rate than homeowner in the High-High and High-Low clusters. The
combination of low home energy audit rates and old housing stock means that homes in
the Not Significant clusters should be targeted for energy efficiency upgrades.
Another factor to consider beyond average home age are recent amendments to
the residential building code in Boulder County. Several new building codes have been
adopted in Boulder County in recent years, with new adoptions in 2010 (2006
International Building Code adopted), 2011 (2009 International Building Code adopted),
and 2013 (2012 International Building Code adopted). Houses built or renovated since
2010 can be considered relatively energy efficient due to the code requirements from
Boulder County, but houses that were built before 2010 or not renovated since 2011 are
likely not built to such stringent standards. Owner of these houses may think that the
recent construction of renovation of their home renders energy efficiency upgrades moot,
but Boulder County housing codes have become much more stringent in recent years, and
additional energy efficiency upgrades may be beneficial.
In addition to providing the findings detailed above, GIS cluster analysis was also
used to target survey distribution. The targeting techniques are detailed in the next
section.
7.1.3 Survey Targeting
The residential land parcels (97,615 total) detailed above in Table 4 served as the
survey population. From this population, 250 land parcels from each of the four cluster
types were randomly selected (1,000 parcels total) to receive the survey. The following
steps were conducted to select the random 1,000 residential parcels:
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1) Use the ArcGIS Sampling Design Tool (designed by Buja 2013) to select 15
random 1/8th square mile zones from each cluster type (High-High, High-Low,
Low-High, Not Significant), for a total of 60 1/8th square mile zones.
2) Within the 15 randomly selected zones, 250 randomly selected parcels were
selected using the Sampling Design Tool. This process was repeated for each of
the four cluster types until 1,000 randomly selected parcels were identified.
This process is visualized below for the High-High cluster zones:
Figure 15: Survey sampling technique
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If a cluster was selected that contained a large number of apartment buildings or
other multi-family housing, this cluster was eliminated, and another random re-sample
was completed to select a new cluster.
In order to identify the survey results by cluster type, four identical versions of the
survey were created. Each cluster type, containing 250 households, received a version of
the survey that could be geographically identified by cluster once the results started
populating the survey software interface. A detailed discussion of the on-the-ground
survey distribution experience can be found in Appendix C: Survey Distribution. One
important survey distribution note: originally only 1,000 surveys were going to be
distributed, but on the first day of survey distribution, I focused more on distributing as
many surveys as possible. This meant just dropping the letter at front doors rather than
ringing the doorbell at each home and trying to engage potential respondents. This
technique resulted in a low response rate (around 10%) and a high number of disqualified
responses because renters were attempting to take the survey and getting disqualified.
This meant that the High-High cluster zone only had 24 completed surveys, even after
waiting several weeks for responses to come in. In order to ensure 30 complete surveys in
the High-High cluster zones, I had to distribute an additional 50 surveys in two randomly
re-sampled High-High cluster zones. Therefore, a total of 1,050 surveys were distributed.
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8. SURVEY ANALYSIS
8.1 Survey Overview A total of 1050 surveys were distributed to households throughout Boulder
County. Of these 1050 surveys, there were 107 refusals that could not be distributed
elsewhere in the cluster, nine surveys were disqualified because the respondent was not
the homeowner, and seven surveys were only partially completed (partials not included in
results). Respondent replied to the survey through an online survey questionnaire. A total
of 152 surveys were completed, which is a response rate of 16.4 percent7. The survey
consisted of 27 questions, and the average completion time for the online survey was 10
minutes and 18 seconds, which is just slightly longer than the predicted ten minute
completion time.
8.2 Survey Design and Implementation 8.2.1 Survey Design
The survey distributed for this research contained Likert scale questions, binary
yes/no questions, rating scales for various terms and groups, demographic and housing
stock questions, and several open ended qualitative questions, which allowed respondents
7According to Carley-Baxter et al. (2009), there is no consensus on the importance of response rate for surveys, many other factors need to be considered. 16.4% may be considered a low response rate, but surveys with similar response rates to mine have been deemed acceptable for publishing in scholarly journals. Furthermore, my door to door survey method made it difficult to send reminders to households. I did not have email addresses for homeowners, and sending reminders by mail to 1,050 households would have been cost prohibitive and time consuming. Nulty (2008) also suggests that three follow-up reminders be sent to potential survey respondents. If this had been feasible for my survey, it would have likely boosted the response rate by 10% or more.
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to give detailed responses in their own words. Several survey questions also included
survey logic, which automatically directed respondents to specific follow-up question
depending upon their answer to the previous question. In addition, one section of
questions was based upon survey questions asked in similar peer effects studies focusing
on the adoption of residential solar PV technology at the neighborhood level; specifically,
questions pertaining to awareness of other energy efficiency upgrades in the
neighborhood (Rai and Robinson 2013), upgrade time-probability and intent (Islam
2014), and contingent valuation in relation to the environmental benefits of energy
efficiency upgrades (Hanemann 1994) were included. Gideon’s (2012) seven steps to
survey questionnaire writing provided helpful formatting guidelines, as did feedback
from EnergySmart staff members, who provided personal feedback on a draft version of
the survey and also distributed a paper version of the survey to twenty Boulder County
homeowners for testing and feedback.
8.2.2 Survey Implementation
An online survey format was chosen as the best distribution option for several
reasons. First, respondent comments related to the paper test survey (distributed to twenty
Boulder County residents in August 2014) overwhelmingly said the survey logic
directions were wordy and confusing to follow. An online survey allows for automation
of the survey logic process and brings respondents to the proper next question
automatically. Second, the survey software automatically tabulates survey results, while
paper survey results would have to be manually tabulated. Third, buying postage for
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1,000 surveys would have cost approximately $500, while the online survey software
only costs $33 per month.
Only two homeowners refused to take the survey due to a lack of Internet access;
these homeowners did not want a survey mailed to them either. In addition, the survey
introduction letter also included an option to complete a paper survey by contacting the
researcher. Two homeowners contacted me and requested to complete a paper survey
instead of the online survey. I mailed a paper version of the survey to these homeowners,
along with a stamped return envelope. One of the two homeowners completed and
returned the survey. Respondents did not experience many technical issues with the
online survey; only about five potential respondents contacted me with issues related to
accessing the survey link. To remedy this issue, I emailed them a hyperlink to the survey
and all respondents were then able to complete the survey. Overall, an online survey was
the easiest way for respondents to complete the survey, and it also provided response data
in a pre-tabulated, easily manageable format.
8.3 Survey Results: General Findings 8.3.1 Survey Demographics
Survey respondents were 85 percent white, well educated (88 percent have at least
a Bachelor’s degree or higher; my survey did not ask about Associate’s degrees), high
income (50 percent had a total household income of over $100,000), and 56 percent
identified as either ‘somewhat liberal’ or liberal’. The average age of survey respondents
was 56 years old. When comparing the survey respondent demographics to all
homeowners in Boulder County, 92 percent of Boulder County homeowners are white,
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46 percent of owner occupied households had an income of $100,000 or higher, 87
percent of homeowners have at least an Associate’s degree or higher (US Census), and 46
percent are registered Democrats (Boulder County Elections Division). The median age
of Boulder County homeowners was not available from the 2013 ACS, but a 2011 survey
of Boulder County homeowners also had an average homeowner age of 56 years (US
Forest Service, Rocky Mountain Research Station).
It was not my goal to achieve a representative sample of all Boulder County
homeowners since I was focusing on specific clusters within the county, but it is
important to note that none of these demographic characteristics vary too widely from the
county as a whole.
8.3.2 Housing Stock The average year built for respondents’ homes is 1984, and the median square
footage of these homes is 2,700 square feet. Fifty-one percent of respondents are part of a
two person household, with four person households being the second most common
response (18.4 percent). Term of homeownership varied widely, as can be seen in Table 9
below.
How long have you owned this home? Percentage Less than a year 5.9%
1 year to 5 years 19.1% 6 years to 10 years 23.0% 11 years to 15 years 17.1% 16 years to 20 years 10.5% 21 years to 25 years 11.8% More than 25 years 12.5%
Table 9: Homeownership duration of survey respondents
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In addition, only 3% of respondents plan to sell their home within the next five years.
8.3.3 Energy Efficiency: Awareness and Attitudes
Survey respondents were asked to rate their awareness and opinion of four energy
terms (Figure 16 below). Of the four terms presented to survey respondents, EnergySmart
had both the lowest frequency of awareness (49 percent) and lowest opinion rating (68 of
100—note: all ratings are out of 100). Respondents’ attitudes toward the generic term
‘residential energy efficiency’ was 15 points higher than EnergySmart (83 of 100), and
ENERGY STAR, a well-known energy efficiency program for appliances and personal
electronics, scored 11 points higher (79 of 100).
Figure 16: Awareness and rating (out of 100) for various energy terms
Boulder County first offered residential energy efficiency audits and upgrades
through the Center for ReSource Conservation in 2006, while the EnergySmart program
has only been an option for homeowners since 2010 (Hampton, Hummer, and Wobus
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2012). This is one possible reason why ‘residential energy efficiency’ has a higher level
of awareness as compared to EnergySmart.
8.3.4 Attitudes by Income There is a greater awareness of both EnergySmart and ‘residential energy
efficiency’ in general by respondents with a yearly household income under $100,0008
(Figure 17 below). It is not surprising that the income group that pays a higher percentage
of their income to utility bills would be more aware of programs that can be used to
reduce their monthly utility bills.
Figure 17: Awareness of energy terms divided by income
Despite EnergySmart having higher levels of awareness amongst households with
under $100,000 in yearly income (56 percent), the program is rated lower by this same
under $100,000 income group (a rating of 62 out of 100). On the other hand, households
earning over $100,000 gave the program a 74 out of 100 rating (Figure 18). Furthermore,
8 $100,000 was used as the dividing line in order to have a statistically significant number of responses for both groups.
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the generic term ‘residential energy efficiency’ was rated about equally between the two
income groups, with ratings of 83 and 82 for the ‘income under $100,000’ and ‘income
over $100,000’ groups respectively. Due to this rating difference, it is possible that the
affordability of the program is a barrier for households with incomes under $100,000.
Figure 18: Rating of energy terms by income
8.3.5 Attitude by Political Identification Fifty-three percent of respondents who identified as politically liberal were aware
of the EnergySmart program, while 50 percent of political moderates, and only 40
percent of political conservatives were aware of the program (Figure 19 below). On the
other hand, awareness of the generic term ‘residential energy efficiency’ equal amongst
political liberals and conservatives (60 percent), while 81 percent of moderates were
aware of the term residential energy efficiency.
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Figure 19: Awareness of energy terms by political identification
Liberals also rate EnergySmart 39 points higher than conservatives (77 vs. 38)
(Figure 20 below). There is also a 14 point gap between liberals and conservatives for
their ratings of the generic term ‘residential energy efficiency’ (86 vs. 72).
Figure 20: Rating of energy terms by political affiliation
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Response to the survey’s open-ended question about perceived benefits of energy
efficiency9 provides some insight into how the EnergySmart program, and residential
energy efficiency in general, can be marketed in a manner that appeals to homeowners of
all political orientations. First, when sorting the open-ended responses by political
orientation (liberal, moderate, conservative), cost savings on utility bills is the most
appealing benefit of energy efficiency to all three groups. Cost savings dominates the
responses for conservative and moderate homeowners, accounting for 76 percent and 73
percent of all coded responses respectively, and then there is a drop-off in response
diversity for these two groups. This is not as much of a concern for moderates because
they still rate Energy Smart and ‘residential energy efficiency’ highly, but the low ratings
by conservatives are still an area that needs to be improved through increased marketing
of energy efficiency’s multifaceted benefits.
Specifically, marketing should focus on messages related to ‘increased comfort in
the home’ and ‘waste reduction’. Increased comfort was the third most common response
by both conservatives and liberals, which shows that having a warmer home in the winter
and a cooler home in the summer is a benefit of energy efficiency that resonates widely.
The Center for Sustainable Policy (2015) has found that marketing energy efficiency and
solar PV as ways to reduce waste is a message that resonates well with conservatives;
however, only two of the 139 responses to the ‘most appealing benefit of energy
efficiency…’ question mention reducing waste. It is important for EnergySmart to
9 Question text: “Based upon what you know about residential energy efficiency, what is the most appealing benefit of an energy efficiency upgrade to you personally? If you do not know, please state that you do not know.” The coded responses to this question are discussed in more detail later in the analysis section.
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increase awareness of waste reduction as a benefit of energy efficiency, especially
amongst conservatives, by giving this term a more prominent position in marketing
materials.
8.3.6 Completion of a Home Energy Audit
When the responses are sorted by homeowners who did or did not complete
energy efficiency upgrades by way of a home energy audit, respondents who have
completed an audit have similar levels of awareness for both EnergySmart and the term
‘residential energy efficiency’ (62 percent awareness vs. 66 percent awareness,
respectively). Homeowners who completed an audit also give EnergySmart a rating of 79
(Figure 21 below), which is 13 points higher than the rating given by respondents who
did not complete energy efficiency upgrades through a home energy audit.
Figure 21: Rating of energy terms divided by energy efficiency upgrade type completed
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The increase in identification and rating by respondents who completed energy
efficiency upgrades by way of a home energy audit suggests that these respondents had a
positive experience with the home energy audit process. In addition, respondents who
completed energy efficiency upgrades through a home energy audit saved an average of
10 percent on their monthly utility bills, as compared to those who completed energy
efficiency upgrades independently, and they saved 12 percent compared to homeowners
who did not conduct any energy efficiency upgrades (Table 10 below).
Conducted Energy Efficiency upgrades?
Utility bill cost per 100 square feet
Utility bill price increase (vs. upgrade w/audit)
Yes, w/audit $4.96/100sf
Yes, independently $5.44/100sf 10% higher
No upgrade $5.64/100sf 12% higher
Table 10: Monthly utility bill by type of energy efficiency upgrade completed
Homeowners who completed energy efficiency upgrades through an audit also
completed a slightly higher number of upgrades on average as compared to those who
completed energy efficiency upgrades independently (Figure 22).
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Figure 22: Number of upgrades completed by type of energy efficiency upgrade completed
Home energy audits focus on a wider array of potential upgrades than a
homeowner may typically think of completing on their own, which is one reason why
more upgrades may be completed by homeowners who upgraded through an audit.
In addition, survey results show that homeowners who completed upgrades
though an audit were more likely to complete the most cost effective types of upgrades.
As the ‘Percent Difference’ row in Table 11 below shows, homeowners who upgraded
independently are not completing important upgrades such as ‘air and duct sealing’ and
‘insulation in attics or walls’. These two upgrades are elements of the building envelope
(walls, windows and roofs) that are typically not thought about by homeowners, because
they are not visible and are not electronic or mechanical elements that break down or
require servicing after years of use. These important elements of energy efficiency are
largely ‘out of sight, out of mind’ for homeowners and may not be improved unless a
home energy audit suggests this course of action.
4.2
3.5
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Yes, w/Audit Yes, Independently
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Energy efficiency upgrade
completed?
Air and duct
sealing
Insulation in attic or
walls
LED or CFL
lighting
New air conditioning
unit
New heat
pump
New water heater
New windows
ENERGY STAR/ other
appliances
Yes, w/Audit 59% 97% 86% 7% 3% 31% 52% 52%
Yes, Independently
20% 38% 80% 36% 8% 50% 44% 54%
Percent Difference
+39% +59% +6% -29% -4% -18% +8% -3%
Table 11: Type of energy efficiency upgrade completed
If a home does not have the proper barrier between the indoor and outdoor
environments, it does not matter how efficient the home’s air conditioning unit, heat
pump or other elements are, unnecessary amounts of energy will still be wasted if the
building envelope is not properly insulated and sealed.
8.3.7 Home Energy Audit and Number of Years Living in Home
Homeowners who have lived in their home for 16 years or longer are the most
likely to have completed a home energy audit (Tables 12 and 13 below). After a house
has been lived in for a long period of time, it typically needs substantial energy-related
repairs or upgrades (whether it be an old piece of equipment such as hot water heater, or
drafty insulation and windows), so an audit and upgrade is an appropriate course of
action to complete these significant repairs. EnergySmart should also encourage
homeowners who have recently moved into their homes to complete a home energy audit.
Just after moving into a home is a good time to complete major energy efficiency
upgrades through a financing plan, because the energy savings will lead to the investment
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paying for itself over time and then becoming monthly savings on utility bills, after the
upgrade is fully paid for.
Number of Years Living in Home
< 1 year
1 year to 5
years
6 years to 10 years
11 years to 15 years
Audit Completed?
(count)
Yes 2 9 10 6
No 5 19 24 19
% Yes 40% 47% 42% 32%
Table 12: Completion of energy efficiency audit by number of years living in home (< 1 year to 15 years)
Number of Years Living in Home
16 years to 20
years 21 years to
25 years > 25 years Audit
Completed? (count)
Yes 6 7 9
No 10 11 10
% Yes 60% 64% 90%
Table 13: Completion of energy efficiency audit by number of years living in home (16 years to > 25 years)
8.3.8 Most Appealing Benefits of Energy Efficiency Upgrades Survey respondents were given the opportunity to provide an open-ended, text
response to the question:
‘Based upon what you know about residential energy efficiency, what is the most appealing benefit of an energy efficiency upgrade to you personally? If you do not know, please state that you do not know.’
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These free response questions were individually evaluated and placed into coded
response categories10. The coded responses can be seen below in Table 14.
Coded term Count Cost savings/Lower energy bills 94
Environment 40
Increased comfort in home 24
Reduced carbon footprint/emissions 22
Reduced energy use/consumption 12
I don’t know 9
The right thing to do 4
Personal satisfaction 3
Benefits our children/family 3
Combining with renewable energy 2
Conserving resources 2
Reduce waste 2
A better future 1
Use of new technologies 1
Health benefits 1
Table 14: Count of coded free responses
According to the coded responses, ‘cost savings/lower energy bill’ is by far the
most appealing benefit of energy efficiency to Boulder County homeowners.
Environmental benefits accounting for the second highest response may be a result of
Boulder County’s unique demographics, and may not be a benefit that has broad appeal
nationwide.
10 If a respondent listed multiple reasons, the multiple reasons were split out and coded separately. This meant it was possible for respondents to have multiple coded responses.
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Only receiving nine ‘I don’t know’ responses is a strong indicator of respondents’
high awareness of the benefits of residential energy efficiency. It is encouraging that high
numbers of homeowners in Boulder County know why they find energy efficiency
appealing, but they may be unsure of the best or most cost-effective ways to complete
energy efficiency upgrades.
Furthermore, there is a need to increase awareness of the waste reduction benefits
and health benefits related to increased energy efficiency. Recent research has found that
eliminating energy or cost waste is a compelling argument for more conservative
homeowners who are considering installing solar PV on their roofs (Treadwell 2015). It
is possible that this sort of messaging can be used for energy efficiency as well, since
energy efficiency also works to eliminate cost and energy waste. The health benefits
associated with residential energy efficiency include improved air quality (Wilson et al.
2014) and lower numbers of hospital admissions for respiratory issues (Howden-
Chapman et al. 2007). Families with young children may have reduced amounts of time
or money available for energy efficiency upgrades, so it is important for EnergySmart to
detail the health benefits of energy efficiency, which may encourage families with young
children to complete upgrades.
8.3.9 Measuring Awareness of Energy Efficiency Tax Breaks/Incentives
This section of survey analysis addresses the following research question: Q4. Are Boulder County homeowners knowledgeable of available energy
efficiency services and discounts? If yes, how did they become
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knowledgeable of these services (energy efficiency program marketing,
word of mouth etc.)?
Survey respondents who responded ‘Yes’ to the question ‘Are you aware of any
financial incentives currently available to you if you decided to complete an energy
efficiency upgrade?’ were given the opportunity to provide a free response to the
question:
‘How did you hear of these energy efficiency tax breaks and/or incentives?’
The coded responses varied considerably, as can be seen below in Table 15.
Coded term Count Internet 14 Energy efficiency company/manufacturer 13 Media/News (medium not specified) 12 Xcel 11 Newspaper 10 Tax Filing/IRS 10 Radio 9 Mailings 8 Utility bill 8 Sales person 6 Energy Audit 5 City of Boulder 4 Friends 3 Co-workers/at work 3 General knowledge 3 Word of mouth 2 Boulder County 2 Works in the EE/solar industry 2 Magazines 2 Television 2
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City of Longmont 1 Family member 1 EnergySmart 1 School 1 Federal/State Program 1
Table 15: Count of coded free responses
It is apparent that survey respondents have received information on rebates and tax
breaks from a broad number of sources, since no one coded response category was
overwhelmingly the most common. However, breaking these responses out by cluster
type (Table 16 and 17 on the next page) provides more insight.
First, the ‘energy efficiency installer/manufacturer’ coded response (highlighted
in green--Table 16 and 17 on the next page) is a top tier response (ranked either first or
tied for first) in the High-High and High-Low categories, but it drops to a second tier
response (not ranked first or tied for first) for the Low-High and Not Significant clusters.
Installers and manufacturers of energy efficiency products should be encouraged to
discuss tax rebates and other financial incentives with homeowners because there are
groups of homeowners, as can be seen from responses in the Low-High and Not
Significant clusters, who could benefit from increased marketing for energy efficiency
installers and manufacturers.
Second, the ‘Internet’ (highlighted in blue--Table 16 and 17 on the next page) is a
top three response in all four cluster types. This means that homeowners are either
seeking out information about financial rebates related to energy efficiency or they are
seeing ads related to financial rebates. To capture searches on financial rebates,
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EnergySmart should ensure that information related to tax incentives or other rebates is
easily available on their website. Homeowners should not have to wait until they are
filing their taxes to find out that the energy efficiency upgrades they completed can be
written off.
Table 16: Count of coded free responses, divided by cluster type (High High, High Low Clusters)
Coded word Count Coded word CountEnergy efficiency company/manufacturer 6 Internet 3Xcel 5 Utility bill 3Internet 3 Energy efficiency company/manufacturer 3City of Boulder 2 Mailings 2Mailings 2 Newspaper 2Newspaper 2 Media/News (medium not specified) 2Sales person 2 Xcel 1Works in the EE/solar industry 2 Boulder County 1Media/News (medium not specified) 2 Tax Filing/IRS 1City of Longmont 1 EnergySmart 1Word of mouth 1 General knowledge 1Family member 1 Magazines 1Boulder County 1 Federal/State Program 1Friends 1 City of Boulder 0Utility bill 1 City of Longmont 0Energy Audit 1 Word of mouth 0Co-workers/at work 1 Family member 0Tax Filing/IRS 1 Radio 0General knowledge 1 Sales person 0Magazines 1 Friends 0School 1 Energy Audit 0Radio 0 Co-workers/at work 0EnergySmart 0 Works in the EE/solar industry 0Federal/State Program 0 School 0Television 0 Television 0
High High Clusters (23 Respondents) High Low Clusters (17 Respondents)
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Table 17: Count of coded free responses, divided by cluster type (Low High, Not Significant Clusters)
Now that the overall impressions of energy efficiency and the benefits of home
energy audits have been examined, the geographic elements of the survey will be
discussed. These elements of the survey provide insight into how EnergySmart can
spatially target future upgrades and more effectively market energy efficiency upgrades
through the use of GIS cluster analysis, active peer effects and other strategies.
Coded word Count Coded word CountMedia/News (medium not specified) 6 Newspaper 4Xcel 4 Internet 4Internet 4 Tax Filing/IRS 4Tax Filing/IRS 3 Sales person 2Mailings 2 City of Boulder 2Newspaper 2 Mailings 2Sales person 2 Utility bill 2Utility bill 2 Energy Audit 2Energy Audit 2 Co-workers/at work 2Energy efficiency company/manufacturer 2 Energy efficiency company/manufacturer 2Television 2 Media/News (medium not specified) 2Word of mouth 1 Xcel 1Friends 1 Radio 1General knowledge 1 Friends 1City of Boulder 0 City of Longmont 0City of Longmont 0 Word of mouth 0Family member 0 Family member 0Radio 0 Boulder County 0Boulder County 0 EnergySmart 0Co-workers/at work 0 General knowledge 0EnergySmart 0 Works in the EE/solar industry 0Works in the EE/solar industry 0 Magazines 0Magazines 0 School 0School 0 Federal/State Program 0Federal/State Program 0 Television 0
Low High Clusters (20 Respondents) Not Significant (24 Respondents)
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8.4 Survey Results: Cluster Analysis and Energy Efficiency Upgrades
This survey analysis section addresses the following research question: Q5. Does sufficient knowledge of energy efficiency programs lead to action
(the completion of an upgrade)?
The original goal of the cluster analysis conducted as part of this research was to
target survey distribution based upon different cluster types (detailed above in the Spatial
Analysis section); however, survey results show that this cluster analysis technique is
useful for effectively targeting future energy efficiency upgrades in Boulder County.
Examination of responses to the question “Have you completed any energy efficiency
upgrades for your home?” shows clear division based upon which cluster the respondent
is located in.
8.4.1 Method of completion for energy efficiency upgrade: audit or independent upgrade? As Figure 23 shows, 26.5 percent of High-High cluster respondents and 28.2
percent of High-Low cluster respondents completed energy efficiency upgrades as a
result of a home energy audit, while only 11.8 percent of Low-High and 11.1 percent of
Not Significant cluster respondents completed energy efficiency upgrades as a result of a
home energy audit.
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Figure 23: Method of completing energy efficiency upgrade, divided by cluster type
To ensure that this division by cluster is statistically significant, Chi square tests and
Fisher’s exact tests (used only when values under five were present) were used to
examine the results. The results concluded that there is a difference between the High-
High/High-Low and Low-High/Not Significant clusters11 at the 0.10 significance level.
This means that the probability of completing an efficiency upgrade by way of a home
energy audit is statistically different depending upon which type of cluster a home is
located in (High-High/High-Low vs. Low-High/Not Significant).
Two important conclusions can be drawn from the responses to the question above
(Figure 23). First, it confirms that the cluster analysis worked as planned. The clustering
was meant to examine differences in attitudes towards residential energy efficiency
between areas with high clusters of EnergySmart upgrades and areas with low clusters of
EnergySmart upgrades, and the divide in energy efficiency upgrades completed by audit
11 The High-High cluster was tested against both the Low-High and Not Significant clusters, and High-Low cluster was tested against both the Low-High and Not Significant clusters (four total tests).
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between the High-High/High-Low and Low-High/Not Significant cluster areas made it
easier to examine these differences.
Second, the cluster analysis can be used to target future marketing efforts by
EnergySmart. Survey responses show that the Low-High and Not Significant clusters
have completed energy efficiency upgrades by way of audits at a much lower rate than
the High-High and High-Low clusters. There are 52,033 residential parcels in the Low-
High and Not Significant clusters, or approximately 32,780 owner occupied homes (using
Boulder County’s 63 percent homeownership rate). According to the survey results, it is
likely that only 10 to 12 percent of the owner-occupied parcels in these two cluster types
have completed an energy efficiency upgrade with a home energy audit. This leaves
almost 30,000 homes that should be targeted for energy efficiency upgrades in the Low-
High and Not Significant clusters alone. EnergySmart should focus their door to door
and/or direct mail marketing of energy efficiency upgrades on households within these
two cluster areas. A large number of homes (almost 30,000), combined with a low rate of
energy efficiency upgrades completed by way of home energy audit, means there is a
large potential for effective marketing and completion of audits and subsequent upgrades.
Respondents from the Low-High and Not Significant clusters have a positive view of
residential energy efficiency (ratings of 82 and 84 out of 100 respectively), and are
motivated to complete energy efficiency upgrades for reasons beyond just cost savings
(over 85 percent of respondents from both groups say they would spend at least 5 percent
more money on energy efficient products in order to reduce their CO2 emissions).
However, these homeowners may not actively seek out home energy audits, so it is
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important for EnergySmart to actively conduct outreach to homeowners in these cluster
areas.
Now that a county-level examination of how GIS cluster analysis can be used to
target marketing across Boulder County has been completed, analysis will shift to the
neighborhood scale and examine how peer effects impact energy efficiency upgrades at a
more localized scale.
8.5 Energy Efficiency and Peer Effects
This survey analysis section addresses the following research question: 6. How do peer effects impact the spread of energy efficiency technology at
the neighborhood level?
As detailed in the peer effects literature review section, researchers have
investigated the impacts of peer effects on solar energy, with a focus on passive peer
effects in particular (Rai and Robinson 2013; Islam 2014; Noll, Dawes, and Rai 2014).
This thesis research expands the research beyond renewable energy and into the realm of
energy efficiency. Furthermore, understanding the ways in which peer effects impact
energy efficiency is important for achieving a holistic view of CO2 emissions reductions
at the household level, whether it be through energy efficiency, renewable energy, or a
combination of the two.
Attempting to measure whether or not social interactions have an impact upon
consumer decisions is complicated due to correlated unobservables between peers
(Manski 1993). This means it can sometimes be unclear if a social interaction influenced
adoption of a new technology, or if homophily of homeowners living in the same
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neighborhood (similar socio-demographic and behavior characteristics) led to the
purchase adoption of a new technology (Noll, Dawes, and Rai 2014). This issue can be
addressed by conducting survey research, which helps determine if social interaction was
a source of motivation for adopting a new technology.
In the case of energy efficiency upgrades in Boulder County, questions from a
peer effects study of PV adoption (Rai and Robinson 2013) were altered to focus on
energy efficiency and peer effects rather than solar PV and peer effects. The survey
population examined by Rai and Robinson (2013) consisted of homeowners who had
already completed solar PV upgrades, while this thesis research survey just focuses on
homeowners, with no requirement of prior energy efficiency or solar PV upgrade
completion. Due to the survey population differences, it is not useful to compare the
results of these two surveys; however, this survey of Boulder County homeowners helped
draw several conclusions about peer effects and energy efficiency.
8.6 Survey Results: Peer Effects
There are not high levels of agreement related to the statement ‘Energy efficiency
upgrades completed by other homes in my neighborhood motivated me to seriously
consider completing energy efficiency upgrades in my own home’ (Figure 24 below).
However, it is important to note that living in an area with a higher number of home
energy upgrades (the High-High cluster) makes it more likely that a homeowner will be
positively influenced to consider completing energy efficiency upgrades on their home as
compared to homeowners in one of the other four clusters.
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Figure 24: Question responses divided by cluster type
One possible reason why the High-Low cluster does not have a higher number of
‘Agree’12 responses has to do with the isolation of many High-Low clusters. While
distributing surveys, I observed that almost all High-Low clusters were surrounded by
open space of some sort. Living in a lower density area may limit the amount of
interaction homeowners have with their neighbors since most High-Low cluster
homeowners have less neighbors directly surrounding them. The results in Figure 24
above show that neighbors are at least somewhat influenced to complete energy
efficiency, but does not answer the question of whether active or passive peer effects had
a larger influence on the completion of energy efficiency upgrades in Boulder County.
8.6.1 Active Peer Effects
To examine active peer effects, which involve direct, in-person interaction (Noll,
Dawes, and Rai 2014), homeowners were asked their level of agreement related to the
following statement: ‘Talking to other neighbors or friends who completed energy
12 ‘Agree Total’ combines the ‘Strongly Agree’ and ‘Agree’ responses, and ‘Disagree Total’ combines the ‘Strongly Disagree’ and ‘Disagree’ responses.
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efficiency upgrades was useful in my decision to complete energy efficiency upgrades on
my own home’. This question specifically focuses on active peer effects by examining
whether or not direct interaction (talking) with neighbors and friends played a role in
their completion of energy efficiency upgrades.
A higher percentage of respondents in the High-High, High-Low, and Low-High
clusters agreed that talking to neighbors or friends was useful, as compared to
respondents in the Not Significant clusters, where only 5 percent of respondents agreed
with this statement (Figure 25).
Figure 25: Active peer effects--question responses divided by cluster type
It is surprising to see the Low-High clusters have agreement levels similar to the
High-High and High-Low clusters, due to little similarity on other questions throughout
the survey. However, it is important to keep in mind that homeowners in the Low-High
cluster are still in close proximity to areas that have higher than average clustering of
EnergySmart energy efficiency upgrades. As detailed in the Methodology section, each
1/8th square mile Low-High cluster is surrounded by at least several areas with higher
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than average clustering (‘High’ clusters). It is possible that one neighborhood contains
both low and high clusters, which may lead to increased interaction with other residents
who may live in a different cluster type. On the other hand, residents of Not Significant
clusters may not have the opportunity for localized interaction with residents in areas
with high amounts of clustering. As with the previous question, relatively low
proportions of ‘Agree’ responses across all four cluster types suggest that active peer
effects may have a moderate role in motivating homeowners to complete energy
efficiency upgrades, but are not the primary deciding factor.
Rewording of the active peer effects survey question in future research may also
help achieve a clearer view of how energy efficiency upgrades are affected by active peer
effects. Specifically, turning this into a two part question would be useful. Part one
would ask the respondent if they have talked to a friend or neighbor about energy
efficiency upgrades, and part two would ask if this conversation was useful in the
respondent’s decision to complete energy efficiency upgrades. The two part question
would first measure the how many respondents have had direct conversations and
interaction about residential energy efficiency, and second, it would measure how useful
this interaction was in leading to an energy efficiency upgrade. This two question format
may help provide additional insight for all types of peer effects research.
8.6.2 Passive Peer Effects The survey also adopted a passive peer effects question from Rai and Robinson’s
solar PV research (2013) to examine the effects of passive peer effects on energy
efficiency upgrades: ‘Seeing the results of energy efficiency upgrades in other homes in
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my neighborhood gave me the confidence that it would be a good decision to make
energy efficiency upgrades on my own home’. The results, split by cluster type, can be
seen below (Figure 26).
Figure 26: Passive peer effects--question responses divided by cluster type
The translation of this question from a solar PV study to an energy efficiency
study led to inconclusive results. Vagueness related to the term ‘seeing’ was the main
problem with this question. Using this term works for solar panels since they are on a
homeowner’s roof and easily visible. However, energy efficiency upgrades are not as
visual as solar panels on a neighbor’s roof, and many times, energy efficiency upgrades
may not even be noticeable to an outside observer. The term ‘seeing’ related to energy
efficiency is much more vague; a neighbor may see workers or a contractor’s truck when
the energy efficiency products are being installed, but it is also possible that ‘seeing’ was
a result of a conversation with a neighbor (active peer effects) where the neighbor
showed or demonstrated the energy efficient product. The passive peer effects question
wording used in this survey was not in-depth enough to parse out this sort of distinction.
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This question should be re-worded for any future research on energy efficiency
and passive peer effects; specifically, the addition of a follow-up question that requires
the respondent to specify how they saw the technology in question would help provide
more useful data. It is quite possible they saw the technology during a conversation with
a friend or neighbor, which enters this interaction into a gray area between active and
passive peer effects.
8.6.3 Peer Effects: Conclusion
The vast majority of survey respondents planned on making energy efficiency
upgrades regardless of interactions with friends of neighbors (Figure 27 below), but they
may not be choosing the best method of energy efficiency upgrades, which is by way of a
home energy audit.
Figure 27: Question responses divided by cluster type
In order to spread knowledge of energy efficiency upgrades beyond early adopters
and to the general public, EnergySmart should encourage discussion of energy efficiency
(active peer effects) as part of a neighborhood-level grassroots strategy for increasing the
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number of energy efficiency upgrades completed by way of a home energy audit.
Specifically, households that completed energy efficiency upgrades through an audit
should be encouraged to:
1) Share the positive experiences related to EnergySmart’s energy advisors assisting
them through the process.
2) Share the multifaceted benefits they have experienced since completing energy
efficiency upgrades. Key benefits that homeowners should be encouraged to share
include lower utility bills, increased comfort in the home, and energy savings.
3) Increase the use of visual cues related to energy efficiency upgrades. A residential
energy efficiency marketing study found that social influence through visual cues,
such as door hangers (literature left on a homeowner’s doorknob) detailing how other
homeowners in the neighborhood are making efforts to reduce their energy
consumption, led to more energy conservation than similar marketing strategies that
focused on cost savings, the environment, or social responsibility (Cialdini and
Schultz 2004). This strategy should be extended beyond just energy conservation to
focus on energy efficiency as well.
These strategies will help homeowners become more informed about home
energy audits, and may encourage them to complete energy efficiency upgrades by way
of an audit instead of independently. In addition, more insightful peer effects research
may result from enhanced question wording. The wording suggestions provided in the
paragraphs above may help provide deeper insights into how knowledge of energy
efficiency is shared at the neighborhood level.
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8.7 Survey Analysis Conclusions
Survey respondents are aware of energy efficiency in general, and they are also
largely aware that discounts and tax breaks for energy efficiency programs exist.
However, there is not widespread awareness of the EnergySmart program (only 49
percent of respondents were able to identify the program by name). EnergySmart needs to
take advantage of the fact that Boulder County homeowners are motivated to complete
energy efficiency upgrades, for both cost savings and environmental reasons by focusing
on how easy the audit and upgrade process can be if completed with one of
EnergySmart’s energy advisors. Beyond the convenience factor, EnergySmart should
advertise the utility bill cost savings and comprehensive upgrade possibilities (such as
insulation and attic sealing) that may not be possible for homeowners who complete
energy efficiency upgrades without an audit. EnergySmart should also consider the use of
peer effects-related marketing; specifically, encouraging homeowners to join their
neighbors in completing energy efficiency upgrades, or encouraging homeowners who
have already completed a home energy audit to discuss the benefits with neighbors. A
more extensive list of suggestions related to the survey results is discussed in the
Recommendations and Next Steps section of this paper.
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9. DEMOGRAPHIC VARIATION OF ENERGYSMART UPGRADES
Additional statistical analysis was completed after the survey research in order to
examine demographic groups that were underrepresented by the survey respondents. This
analysis was completed at the Census Block Group geographic level; it combined the
locations of EnergySmart upgrades with 2013 American Community Survey (ACS) 5
year (2009-2013) demographic data for Boulder County homeowners, or with 2013 ACS
demographic data of all Boulder County residents when homeowner demographic data
was not available.
First, a ratio was calculated for each block group by dividing the number of
EnergySmart upgrades completed in the block group by the total number of owner
occupied homes in the same block group. Next, ‘low ratio’ block groups were identified
by selecting the block groups with an ‘EnergySmart upgrade to homeowner ratio’ that
fell in the bottom 20 percent of all block groups. The same process was used to identify
‘high ratio’ block groups, which are block groups with a ratio in the top 20 percent. Ten
percent was used as a cut-off for identifying the ‘low ratio’ and ‘high ratio’ block groups
because including more block groups would lead to an unmanageable number of homes
for EnergySmart to focus on. Specifically, the bottom 20 percent, or ‘low ratio’, block
groups already contain over 14,500 homes. And focusing on less than 20 percent of block
groups would neglect homeowners across Boulder County that can benefit from energy
efficiency upgrades.
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Each block group contained 2013 ACS demographic data related to race,
educational attainment, income, children under the age of 18 living at home, home value,
and age of home. To identify statistically significant differences between demographics
of the ‘high ratio’ and ‘low ratio’ block groups, analysis of variation (ANOVA) statistical
tests performed using JMP statistics software. The ANOVA analysis tested for
statistically significant variation between the demographic compositions of the ‘low ratio’
and ‘high ratio’ block groups. If there was a statistically significant difference (an F-ratio
outside the bounds of ±1.96 and a p-value < 0.05) between the means of the ‘low ratio’
and ‘high ratio’ block groups, this suggests that the demographic group in question is
either under or overrepresented within the block group. This analysis will help
EnergySmart understand what demographics are currently underserved by the program,
and allow the program to not only focus on the ‘low ratio’ block groups in general, but
focus outreach on specific demographic groups within these block groups.
9.1 ANOVA Analysis
This statistical analysis addresses the following research question: 7. Are there Boulder County homeowners with certain demographic
characteristics that should be targeted as an attempt to reduce exclusion
from residential energy efficiency upgrades?
As previously discussed, the survey results came from a relatively homogeneous
group of respondents that were primarily white, high income, and well-educated; it is
important to look beyond these groups to Boulder County citizens that are
underrepresented in the survey and may be excluded from EnergySmart efficiency
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upgrades as well. As was detailed in the Methods section, a ratio was calculated for each
block group by dividing the number of EnergySmart upgrades completed in the block
group by the total number of owner occupied homes in the same block group. Next, ‘low
ratio’ block groups were identified by selecting the block groups with an ‘EnergySmart
upgrade to homeowner ratio’ that fell in the bottom 20 percent of all block groups. The
same process was used to identify ‘high ratio’ block groups, which are block groups with
a ratio in the top 20 percent.
9.1.1 Geographic Distribution
When looking at the geographic distribution of the ‘low ratio’ and ‘high ratio’
block groups (Figure 28), all of the high ratio block groups are concentrated in the City of
Boulder, while the low ratio block groups are distributed throughout the county.
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Figure 28: ‘High ratio’ and ‘low ratio’ Block Groups in Boulder County
Longmont and Lafayette are two specific municipalities where multiple low ratio
block groups are present. These low ratio block groups in eastern Boulder County are of
particular interest because EnergySmart is attempting to expand its presence in the
eastern areas of Boulder County.
In Longmont, the individual low ratio block group with the largest number of
owner occupied homes has an average year built of 2000, which is approximately 25
years younger than the average owner-occupied home in Boulder County. This means
that homeowners may not deem energy efficiency upgrades necessary yet and could be
one possible reason for the low number of upgrades in this area. Furthermore, the
Longmont low ratio block groups have a median home value of $150,993, which is 62
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percent lower than the County-wide median home value of $393,271. In addition, the
median income of $52,282 in these block groups is 31 percent lower than the median
income for all of Boulder County ($75,740).
In the Lafayette low ratio block groups, the average year built for owner occupied
homes is 1990, which is approximately 15 years younger than the average owner
occupied home in Boulder County. Similar to Longmont, the low ratio block groups in
Lafayette have a 59 percent lower median home value than the rest of the county
($161,074 vs. $393,271) and a 36 percent lower median income than the rest of the
county ($48,740 vs. $75,740).
The young average home age in these Longmont and Lafayette low ratio block
groups could mean that homeowners are not yet considering energy efficiency upgrades,
but many of these homes were built before recent code adoption, so energy efficiency
upgrades may still be beneficial to these homeowners. However, financial constraints
may be a barrier to homeowners in these block groups; it is possible that homeowners
with lower median home values or lower median incomes may not see the benefit of a
large financial investment into energy efficiency. This is a challenge that EnergySmart
may find when encouraging homeowners in these block groups to complete energy
efficiency upgrades.
Since the low ratio block groups are not clustered in a specific part of Boulder
County, the ANOVA analysis discussed below will provide insight into which specific
demographic groups within the low ratio block groups should be focused upon as
priorities for energy efficiency upgrades.
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9.1.2 Race by Owner Occupied Status
ANOVA analysis finds that there is a disproportionately low number of
Hispanic/Latino homeowners live in the ‘low ratio’ block groups (p-value = < 0.0001)
(Figure 29 below).
Figure 29: ANOVA results (race): Hispanic/Latino
In addition, the difference of means for Asian (Figure 30) and African American
(Figure 31) homeowners between the two block group areas was not statistically
significant when conducting ANOVA tests.
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Figure 30: ANOVA results (race): Asian
Figure 31: ANOVA results (race): African American
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However, the results for both of these demographics have p-values less than 0.05,
with Asian households at p = 0.036 and African American households at p = 0.368.
These values lead to less confidence in failing to reject the null hypothesis that the means
are not significantly different at the 95 percent confidence interval.
These results show that EnergySmart should increase outreach to minority
homeowners in Boulder County, especially Hispanic and Latino homeowners. Working
with community organizers or other community ‘gatekeepers’ may be an effective
technique to reach minority homeowner groups.
9.1.3 Income and Home Value
There is no statistically significant variation between median income by total
Boulder County population (figure 32) (median income by owner occupied housing units
was not available at the block group level).
Figure 32: ANOVA results: Median income
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However, median home value of all owner occupied homes shows a different
story. The median home value for ‘high ratio’ block groups is much higher (Figure 33
below) than median home value for ‘low ratio’ block groups ($521,209 vs. $256,436),
which is a statistically significant difference and has a large f-ratio of 59.9 (p-value <
0.0001).
Figure 33: ANOVA results: Median home value
EnergySmart should work to target owner occupied homes with lower median
values throughout Boulder County, but specifically within the low ratio block groups.
This is an important group to target because it is possible that homeowners with lower
median value homes also have lower median incomes, and middle income groups should
be a primary focus of energy efficiency upgrade programs. In addition, median home
value is typically lower in block groups outside of the City of Boulder; therefore,
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focusing on homeowners with lower median home values will also engage more
homeowners from eastern Boulder County.
9.1.4 Families with Children Living at Home
The low ratio block groups also have disproportionately more families (from the
total population, not owner occupied homes) with children under the age of 18 living at
home (Figure 34). As previously mentioned, children are particularly vulnerable to
respiratory health issues related to the air quality inside a home (Wilson et al. 2014).
Therefore, it is important for EnergySmart to engage with families that have children
under the age of 18. This can be accomplished by working with schools and youth-
oriented organizations (Girl Scouts, Boy Scouts, etc.) to make parents more aware of the
health issues that can be minimized though energy efficiency upgrades.
Figure 34: ANOVA results: Households with children under age 18 at home
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9.1.5 Education
When comparing the high ratio and low ratio block groups, there is no statistically
significant difference in the between the number of people (total Boulder County
population) with Bachelor’s degrees (Figure 35).
Figure 35: ANOVA results (education): Bachelor’s degree
However, there is a disproportionate amount of people with only high school
degrees or Associate’s degrees in the ‘low ratio’ block groups (Figures 36 and 37).
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Figure 36: ANOVA results (education): High School degree
Figure 37: ANOVA results (education): Associate’s degree
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Unfortunately, there may not be an easy way to directly target homeowners with
lower levels of education, but finding other demographic characteristics the correlate with
lower education homeowners in the county may be a good starting point.
9.2 Demographic Analysis: Conclusion
A limitation of the survey research portion of this study is the lack of insight
related to minority demographics and energy efficiency upgrades in Boulder County. A
demographic comparison at the block group level does not allow for focused targeting at
the neighborhood level; however, this analysis helps identify which demographic groups
are more likely to live in the 20 percent of block groups that have the lowest ratio of
EnergySmart upgrades completed to homeowners. Targeting groups such as Latino
homeowners, homeowners with lower median home values, homeowners with younger
children, and homeowners with lower educational attainment will help ensure that
EnergySmart reaches groups that may otherwise be excluded from completing energy
efficiency upgrades.
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10. DISCUSSION
This Boulder County research contributes to a more comprehensive understanding
of residential energy efficiency upgrades, peer effects, and the household elements of
social exclusion by combining spatial analysis and survey analysis. Furthermore, this
study both informs previous academic literature and provides insight that will allow
EnergySmart to effectively target future energy efficiency upgrades.
10.1 Energy Efficiency Programs at the State and Local Levels
Case studies from fourteen energy efficiency programs around the United States
have been summarized by Fuller et al. (2010), but as previously mentioned, none of these
case studies include spatial analysis. This section will discuss how these case study
findings from across the country relate to the spatial and survey analysis of energy
efficiency upgrades in Boulder County.
There has been increasing attention and funding given to energy efficiency
programs in recent years, as the half-billion dollar Better Buildings Neighborhood
Program demonstrates. However, concern about high energy use is not a pressing issue
for most homeowners. Instead, focus groups and market research have found that issues
related to energy efficiency, such as health, comfort, energy security are better ways to
engage homeowners about energy efficiency. Open ended responses in the Boulder
County study listed comfort and health as two major benefits of energy efficiency, but
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cost savings and the environment still ranked as the top two benefits of energy efficiency.
This suggests that it may be beneficial to first raise energy efficiency awareness through
issues of health and comfort, and then focus on the major benefits of cost savings and
environmental benefits as important selling points.
Around the United States, information-based campaigns have been used to
increase homeowners’ understanding of energy efficiency; however, it has been found
that information about energy efficiency does not necessarily translate to action (Fuller et
al. 2010). In addition, the adoption of solar PV technology faces a similar ‘information to
action’ gap (Rai and Robinson 2013). This is not as much of an issue in Boulder County,
as 86 percent of respondents have completed an energy efficiency upgrade of some sort,
which indicates Boulder County homeowners have taken action on completing energy
efficiency upgrades. Despite this willingness to take action, the type of action taken is of
concern. Only 19 percent of survey respondents completed an energy efficiency upgrade
by way of a home energy audit, which is the most effective way to complete energy
efficiency upgrades. Therefore, Boulder County needs to focus on directing homeowners
towards the most appropriate actions to take related to energy efficiency upgrades.
10.2 Full Scale Implementation of Energy Efficiency Upgrades in Boulder County
EnergySmart has been one of the most successful BBNP grantees, which means
Boulder County is no longer in the early adoption phase for energy efficiency upgrades.
It is now time for Boulder County to bring the EnergySmart program to full scale
implementation in order to meet resiliency and emissions goals established by the County
and the City of Boulder respectively.
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Spatial analysis can play a major role in full scale implementation by providing
effective targeting techniques for future energy efficiency upgrades. One of the Better
Buildings Neighborhood Program’s three major goals is identification of the most
effective approaches to completing energy efficiency upgrades in the United States.
Current research for this goal relates to program management and marketing techniques
(Department of Energy, Building Technologies Office 2013), but very little spatial
research related to energy efficiency programs has been conducted.
10.3 Spatial Analysis of Energy Efficiency Programs
BBNP’s goal of identifying effective approaches can be informed by this cluster
analysis research in Boulder County, along with other spatial studies of energy efficiency
upgrades that have been completed in Phoenix, AZ and Los Angeles, CA. A discussion
comparing the results of these three studies will lead to a better understanding of the
spatial aspects of energy efficiency upgrades.
First, the Phoenix study of commercial energy efficiency (Dalrymple, Melnick,
and Schwartz 2014) used the same cluster analysis technique (Local Moran’s I) that was
used for this Boulder County study. In both studies, survey respondents in areas that
exhibit high amounts of clustering said far more often that they heard about the energy
efficiency programs from contractors than from local word of mouth or any other
sources. For both commercial and residential energy efficiency programs, this shows the
importance of energy efficiency contractors. Encouraging contractors to conduct sales
outreach in areas with low clustering of energy efficiency upgrades may be an effective
way to encourage early adopters to complete both residential and commercial energy
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efficiency upgrades. However, as of 2015, Boulder County is soon to surpass the early
adoption phase and will need increased levels of community engagement to accomplish
mass adoption of energy efficiency upgrades. Community-oriented techniques for
widespread adoption of energy efficiency upgrades will be further discussed in the peer
effects section.
The second study examined the geographic coverage of energy efficiency
programs in Los Angeles while also analyzing neighborhood level changes in energy use
before and after energy efficiency upgrades were completed (Sun 2014). When
comparing energy use in neighborhoods before and after energy efficiency upgrades were
completed, there are neighborhoods in Los Angeles that exhibit hot spots of improved
energy efficiency and neighborhoods that still exhibit cold spots with little improvement
in energy efficiency. Energy use measurement was not an element of the Boulder County
research, but there are certainly areas that exhibit clustering and areas that exhibit a lack
of clustering of energy efficiency upgrades in Boulder County, which is related to energy
use. In addition, neighborhoods that participated in energy efficiency programs in Los
Angeles were primarily higher income and white, which reflects results of previous
research in the San Francisco Bay area (Action Research 2010) and also the survey
demographics of this research in Boulder County.
Survey research was not a major focus on the energy use study in Los Angeles,
but informal interviews were conducted with homeowners in two neighborhoods. Fifty
four percent of respondents in both Los Angeles neighborhoods knew about local energy
efficiency programs, as compared to 49 percent of Boulder County survey respondents
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who were aware of EnergySmart. Interestingly, respondents in the more homogenous
Northridge, Los Angeles neighborhood, which consists mainly of single family homes,
became aware of energy efficiency upgrades through similar avenues that Boulder
County survey respondents did (notices in utility bills, online, energy efficiency
contractors). On the other hand, the other neighborhood studied, Boyle Heights, mainly
consists of multi-family housing units. Residents in this neighborhood primarily learned
about energy efficiency programs through community events and word of mouth. The
Boulder County survey only focused on single family homes, so the results from the Los
Angeles study prompt additional questions in Boulder County. Specifically, should
EnergySmart examine the possibility of different outreach strategies for different
neighborhood types? For example, it may be useful to continue strategies related to utility
bill inserts and contractor outreach in neighborhoods consisting of single family homes,
but a more community-oriented approach may be useful in neighborhoods consisting of
multi-unit residences.
The use of GIS cluster analysis for investigating the effectiveness of energy
efficiency programs, and gaining further understanding into where clustering of upgrades
occurs, is an analysis technique that has been largely overlooked by energy efficiency
programs in the past. However, the studies discussed above show that cluster analysis can
successfully lead to enhanced evaluation of energy efficiency programs.
10.4 Peer Effects and Innovation Diffusion
Once a community progresses beyond the early adoption stage (the first 15
percent of adopters), where contractors and utility bill inserts play an important role in
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encouraging energy efficiency upgrades, localized community aspects become a more
important element with potential to lead to widespread uptake of energy efficiency
upgrades. Past research shows that evidence of energy efficiency program participants
sharing their positive experiences with friends and neighbors (active peer effects) is a
sign of program success. As one energy efficiency program director in Long Island, NY
said, “Success is when participants become proselytizers” (Fuller et al. 2010, 23). This
sort of proselytization can be seen in responses to the active peer effects questions in
Boulder County. More so than in any other cluster type, respondents in High-High
clusters indicate that interactions with friends and neighbors were useful motivating
factors in completing energy efficiency upgrades of their own. A much higher percentage
of energy efficiency upgrades through home energy audits were completed in High-High
clusters, and now it seems that homeowners in this cluster type are sharing the benefits
with their friends and neighbors. These results in Boulder County also help confirm
similar peer effects research conducted on household adoption of solar PV. The use of
technology by others is an important source of knowledge for potential adopters, and the
information generated through the use of solar PV is a positive externality due to positive
influences exerted on neighbors and friends (Islam 2014; Rai and Robinson 2013).
Beyond interaction with neighbors, interaction with trusted community networks
has made a positive impact related to adoption of solar PV. In addition, concern for
nature has played a major role in the founding of many neighborhood solar organizations
(Noll, Dawes, and Rai 2014). Survey results demonstrate that Boulder County
homeowners express concern for nature and see environmental protection as a major
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benefit of energy efficiency upgrades. Therefore, extension of the solar neighborhood
organization strategy to Boulder County may be a good strategy for increased community
interaction related to energy efficiency upgrades. Rallying around environmental
concerns may be a way for neighborhood and community groups in Boulder County to
motivate more homeowners to complete energy efficiency upgrades.
Positive feedback from early adopters is critical to adoption of an innovation by
early and late majorities of the population. If the first 15 percent of adopters have a
negative experience with an innovation, or with the energy efficiency upgrade process in
general, momentum can stall and widespread adoption becomes very challenging (Rogers
1983; Fuller et al. 2010). Boulder County is likely close to this 15 percent tipping point of
energy efficiency upgrade completion, but the positive influence of peer effects and a
high rating of the EnergySmart program by homeowners who have completed a home
energy audit (a rating of 79 out of 100 vs. 66 out of 100 for homeowners who have not
completed an audit) shows that EnergySmart was been well received by early adopters.
High cost can be a barrier for widespread adoption of certain technologies. For
example, the large financial commitment required for residential solar PV installation has
led to slow diffusion of residential solar installation in the US. Homeowners are aware of
the environmental benefits of renewable energy generation, but expense is a major point
of hesitation (Islam 2014). This does not appear to be the case for energy efficiency
upgrades in Boulder County. Eighty-six percent of survey respondents said they are
willing to pay at least 5 percent more money for energy efficient products that will help
reduce CO2 emissions, and 22 percent of respondents are willing to pay 20 percent more
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money to reduce CO2 emissions. It is not clear if this willingness to pay a premium for
CO2 emissions reduction would translate to a willingness to pay for expensive rooftop
solar PV as well, but it is important to note that cost of energy efficiency upgrades is not
a major barrier for most Boulder County homeowners.
Boulder County appears to be on the right track towards widespread adoption, due
to positive feedback from early adopters and few cost-related concerns. However, it is
critical that individual homeowners and community organizations continue to discuss the
benefits of energy efficiency upgrades with friends and neighbors in order to encourage
widespread adoption of energy efficiency technologies by Boulder County homeowners.
10.5 Social Exclusion and Housing Despite success in Boulder County, it is important to broaden the focus of
EnergySmart beyond the ‘low hanging fruit’ by ensuring that certain groups of
homeowners are not excluded from the benefits of energy efficiency upgrades.
Demographic characteristics of Boulder County survey respondents reflect the
nationwide trend of wealthier, less diverse, and older homeowners being more likely to
participate in energy efficiency upgrade programs. It is important for energy efficiency
programs to balance energy savings goals, which are easier to accomplish by primarily
engaging the demographic groups mentioned above, with the social obligation to reach
vulnerable members of the community (Fuller et al. 2010).
Unfortunately, there were few lower income or minority respondents to the
Boulder County survey. Lack of insight into these groups based upon survey
demographics led to completion of the ANOVA demographic analysis, which helped
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generally identify where minority homeowners who are not taking advantage of the
EnergySmart program live within Boulder County, but it does not provide any insight
into potential information gaps and other barriers these groups face related to energy
efficiency. In addition, the most recently published EnergySmart Progress Report (2012)
does not address demographic inequality related to EnergySmart upgrades, but inequality
is concern that EnergySmart should address with future research. There are certainly
homeowners in Boulder County who live in poor housing conditions and could benefit
from basic energy efficiency upgrades. Past research has indicated that energy efficiency
upgrades completed on houses in poor conditions lead to more comfortable and healthy
homes for residents (Sommerville 1998; Howden-Chapman et al. 2014). If EnergySmart
hopes to address this issue, the identification of where disproportionately
underrepresented groups of homeowners live, which was accomplished by the ANOVA
analysis, is an important first step. After identifying specific locations to focus on,
additional research and community forums are next steps that should be completed in
order to find effective strategies to reach these groups of homeowners. The overall goal
of CO2 emissions reduction must be kept in focus, but not at the expense of vulnerable
members of the Boulder County community.
10.6 Discussion Conclusion
EnergySmart was one of the most successful Better Building Neighborhood
Program Grantees (Hampton, Hummer, and Wobus 2012), but much work is still needed
to reach full scale implementation of this program. Ensuring that all homeowners in
Boulder County have access to home energy audits and energy efficiency upgrades is a
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step towards reducing inequality and exclusion in Boulder County while also ensuring
that the residential buildings sector meets its CO2 emissions reduction goals, which are
part of larger emissions reduction goals for the City of Boulder and Boulder County.
Beyond expanding the literature related to energy efficiency, spatial exclusion and peer
effects, a goal of this research was to provide specific suggestions to the EnergySmart
program about how it can overcome barriers related to the completion of residential
energy efficiency upgrades through the use of GIS targeting and marketing strategies.
These suggestions are detailed below in the Recommendations and Next Steps section.
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11. RECOMMENDATIONS AND NEXT STEPS
As part of the EnergySmart program, homeowners in Boulder County completed
4,747 upgrades in homes across Boulder County between 2010 and 2013. This strong
effort that made EnergySmart one of the most successful Better Buildings Neighborhood
Program grantees in the country. However, 4,747 homes only represents 6 percent of all
owner-occupied households in Boulder County (76,101 total owner occupied homes). For
residential energy efficiency to make an impact related to CO2 emissions reductions from
buildings, an effort must be made to expand the number of home energy audits and
energy efficiency upgrades completed by Boulder County. In particular, EnergySmart
must move beyond primarily completing energy efficiency upgrades for the ‘low hanging
fruit’ of Boulder County—homeowners and neighborhoods that actively sought out
upgrades through the EnergySmart program. While this may seem like a daunting
challenge, there is reason to be optimistic.
According to survey results, homeowners in Boulder County are largely aware of
the benefits related to energy efficiency and are motivated to reduce their negative impact
on the environment. However, several major barriers to upgrade completion still exist:
1. Homeowners may think they can complete all necessary energy efficiency
upgrades themselves, without professional assistance.
2. Homeowners may think the home energy audit and upgrade process is too
complicated.
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3. Homeowners may not have time to actively seek out an audit and upgrade.
EnergySmart can use both spatial analysis and refined marketing techniques to more
effectively engage with Boulder County homeowners to break down these barriers, while
also making a large contribution to reducing CO2 emissions from residential buildings.
11.1 Use of Spatial Analysis to Effectively Target Energy Efficiency Upgrades
Survey results show that homeowners in the Low-High and Not Significant
clusters zones have completed home energy audits and energy efficiency upgrades by
way of audits at a much lower rate as compared to homeowners in the High-High and
High-Low cluster zones. Targeting the Low-High and Not Significant cluster zones for
the next round of EnergySmart upgrades is a useful strategy for the following reasons.
There are 52,033 residential parcels in the Low-High and Not Significant clusters, or
approximately 32,780 owner occupied homes (using Boulder County’s 63 percent
homeownership rate), and according to the survey results, it is likely that only 10 to 12
percent of the owner-occupied parcels in these cluster zones have completed an energy
efficiency upgrade with a home energy audit. This leaves almost 30,000 homes that
should be targeted for energy efficiency upgrades in the Low-High and Not Significant
cluster zones alone.
Furthermore, the Census Block Groups with the lowest 20 percent of
‘EnergySmart upgrade to homeowner’ ratios were identified, along with specific
demographic groups within these block groups that are disproportionately represented in
these ‘low ratio’ block groups. There are over 18,000 owner-occupied households in
these block groups, which should be prioritized by EnergySmart for home energy audits
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and efficiency upgrades. Lists of neighborhoods and household addresses for these block
groups can be produced by use of the GIS database and maps designed for this thesis
research project.
11.2 Refine Marketing and Outreach Techniques Based on Survey Results
This section addresses the following research question: Q3. How can EnergySmart better market itself to Boulder County
homeowners?
The survey of Boulder County homeowners also provided insights that can be
used to more effectively market EnergySmart. Based on these results, several marketing
and outreach strategies are suggested below:
1. Work with local energy efficiency product retailers and contractors to raise
awareness of home energy audits. Homeowners are probably not thinking about
energy efficiency upgrades when they need to make an emergency replacement of
a hot water heater or air conditioning unit, for example. This means it is important
for retailers and contractors to discuss energy efficient replacement options with
homeowners and detail the multifaceted benefits related energy efficient products.
This can help turn the struggle of an emergency repair into an opportunity for cost
and energy savings in the future.
2. Focus on the cost savings and cost effectiveness benefits of completing energy
efficiency upgrades through a home energy audit. As the survey open-ended
responses indicate, cost savings is the most commonly acknowledged benefit of
energy efficiency, so focusing on cost savings and cost effectiveness benefits
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related to home energy audits is important. Survey respondents who conducted
energy efficiency upgrades as a result of a home energy audit have, on average, a
10 percent less expensive monthly utility bill than homeowners who completed
energy efficiency upgrades on their own13. In addition, home energy audits
encourage important upgrades such as insulation and attic sealing, which form a
proper barrier between the indoor and outdoor environments and save
homeowners significant amounts of money on heating and cooling costs.
3. Appeal to a broad range of homeowners by focusing on the multifaceted
benefits of energy efficiency upgrades. Increased comfort within the home is a
benefit that resonated well with both liberal and conservative survey respondents,
which shows this benefit has broad appeal even to homeowners who are skeptical
about the need to reduce energy use.
4. Frame energy efficiency as a way to eliminate cost and energy-related waste.
This is another strategy that has worked well with homeowners of varying
political views (Treadwell 2015), but it was sparsely mentioned in free response
questions as one of the most appealing benefits of energy efficiency.
5. Encourage homeowners who have completed energy efficiency upgrades to
discuss the benefits with their friends and neighbors. Active peer effects were
not a key factor in survey respondents’ decisions to complete energy efficiency
upgrades, but they still play a role. Almost one-third of High-High cluster
respondents agreed that talking to neighbors about energy efficiency upgrades
13 And 12% less expensive than homeowners who had completed no energy efficiency upgrades at all.
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was useful in their decision to complete energy efficiency. It’s possible that
conversations may encourage homeowners who were previously unsure about
energy efficiency upgrades to consider upgrades of their own.
The combination of updated marketing strategies and spatially targeted outreach will
help EnergySmart produce impactful CO2 emissions reduction, while also allowing
homeowners in Boulder County to experience utility bills cost savings, increased comfort
and increased levels of health within their homes.
11.3 Next Steps
The strategies detailed above will help EnergySmart target future energy
efficiency upgrades, but proper communication of these recommendations to
EnergySmart staff is important. During the Summer of 2015, I will present these results
to the Boulder County Commissioner’s Sustainability Office. This will allow me to
answer any questions their staff may have about the GIS targeting techniques, survey
results, or statistical analysis. If desired, I will also train staff members to use the GIS
geodatabase, conduct additional analysis of their own, or produce their own targeted
address lists. Ideally, this geodatabase will be a living map that is updated periodically as
more EnergySmart upgrades are completed. This will allow Boulder County to
effectively track the progress of residential energy efficiency upgrades at the county and
neighborhood scales for years to come.
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12. CONCLUSION
As detailed throughout this paper, energy efficiency upgrades are important for a
multitude of reasons. At the household scale, energy efficiency upgrades provide cost and
energy savings, increased levels of comfort, improved health, and a way for individuals
to reduce their impact on the environment. These individual efforts also have the
potential to lead to significant reductions in household CO2 emissions at the county,
state, and national scales.
Within Boulder County, understanding how residential energy efficiency
upgrades are clustered provides spatial insight that had not been previously undertaken by
the EnergySmart program. Furthermore, survey research provides insight into attitudes
and actions taken in relation to energy efficiency within the different cluster types. These
two analyses will help EnergySmart target the locations of new upgrades while also
providing additional insight into energy efficiency marketing techniques. And finally, it
is important to balance the goal of widespread adoption with the social obligation to
reach vulnerable members of society who would strongly benefit from energy efficiency
upgrades. This concern was addressed by ANOVA demographic analysis in order to
identify groups of Boulder County residents and homeowners who may not be currently
benefitting from energy efficiency upgrades.
Despite the positive contributions of this research, it is important to note several
limitations related to both the scope of the project and specific characteristics of Boulder
115
County itself. First, the use of cluster analysis to target future energy efficiency upgrades
must be tested in other areas of the United States in order to examine whether or not this
technique is broadly replicable. This is challenging because not all energy efficiency
programs have access to GIS software or staff members who can conduct cluster analysis
using GIS. However, the demonstrated value of cluster analysis in Boulder County and
the ease of replicability (assuming the user has GIS experience) for the cluster analysis
used in this study may lead to consideration of spatial analysis techniques by other energy
efficiency programs. Second, the largely homogenous character of Boulder County
homeowners may limit the broad applicability of the survey results. Due to the
demographic makeup and political views of homeowners surveyed, the results may not
be reflective of US homeowners as a whole, and the suggested marketing strategies may
not resonate as well with residents outside of Boulder County. Third, residential energy
efficiency upgrades are an important priority in areas with large numbers of residential
buildings, but it may be more effective for dense urban areas with large numbers of
commercial buildings to focus on commercial energy efficiency upgrades because
commercial buildings in cities are responsible for a larger share of energy use and CO2
emissions as compared to residential buildings. For example, completing energy
efficiency upgrades for one large commercial building may have the same CO2 emissions
reduction effect as completing an energy efficiency upgrades in many homes. However,
this should not discount the importance of residential energy efficiency upgrades,
especially in areas with many suburban homes, due to the multifaceted benefits
experienced by homeowners beyond just CO2 emissions reductions. Finally, this study
116
briefly addresses inequality related to energy efficiency upgrades, but a more detailed
study of inequality and energy efficiency upgrades is needed both in Boulder County and
in the United States as a whole. As this research in Boulder County demonstrates,
surveying sufficient numbers of minority homeowners is challenging, but it should be
attempted because demographic inequality related to the completion of energy efficiency
upgrades currently exists in Boulder County.
Despite the limitations detailed above, this research has demonstrated the
effectiveness of combining GIS cluster analysis, survey research, and demographic
analysis to evaluate energy efficiency upgrades in Boulder County, Colorado. This sort of
multi-method approach to energy efficiency analysis is currently underutilized both in the
academic literature and in practice by energy efficiency programs throughout the country.
The insight provided by this research has the potential to effectively target future energy
efficiency upgrades in Boulder County and lead to more widespread adoption of similar
spatial analysis techniques by other energy efficiency programs across the United States.
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BIBLIOGRAPHY Alcott, H. and Greenstone, M. 2012. Is there an energy efficiency gap? Journal of
Economic Perspectives. 26 (1): 3-28. Allard, S. 2004. Access to social services: The changing urban geography of poverty and
service provision. Washington D.C.: Brookings Institution Press. Andrich, M.A., Imberger, J., Oxburgh, E.R. 2013. Inequality as an obstacle to sustainable
electricity and transport energy use. Energy for Sustainable Development 17 (4): 315-325.
Austin Energy. ‘Austin Climate Protection Plan’.
http://austinenergy.com/wps/portal/ae/about/environment/austin-climate-protection-plan/!ut/p/a1/jVJdT9swFP0tPMRvadwEtoBkTW5YS9pBCbA25AW57m3qKrWN7TTArycpDxsTHVjy5z3n2Pf4BkWQB4VkO1EyJ5RkVbcvvj3gMA4vEhymo-9hjOkoGUxOZlf9a4xbwP3fgOnP6TlOZ9MZnU4SPEqiL_IPNIo_44-_ (last accessed 27 March 2015). Austin, Texas.
Aylin, P., Morris, S., Wakefield, J., Grossinho, A., Jarup, L., and Elliott, P. 2001.
Temperature, housing, deprivation and their relationship to excess winter mortality in Great Britain, 1986-1996. International Journal of Epidemiology. 30 (5): 1100-1108.
Barbose, G. L., Goldman, C. A., Hoffman, I. M., and Billingsley, M. 2013. The future of
utility customer-funded energy efficiency programs in the USA: Projected spending and savings to 2025. Energy Efficiency. 6 (3): 475-493.
Bell, S., Wilson, K., Bissonnette, L., Shah, T. 2013. Access to primary health care: Does
neighborhood of residence matter? Annals of the Association of American Geographers 103 (1): 85-105.
Bhattacharya, J., DeLeire, T., Haider, S., Currie, J. 2003. Heat or eat? Cold-weather
shocks and nutrition in poor American families. American Journal of Public Health. 93 (7): 1149-1154.
118
Boschmann, E. E., and Kwan, M. 2008. Toward socially sustainable urban transportation: Progress and potentials. International Journal of Sustainable Transportation, 2 (3): 138-157.
Boulder County Commissioner’s Sustainability Office. Boulder County Climate Change
Preparedness Plan. http://www.bouldercounty.org/doc/sustainability/ccpp.pdf (last accessed 5 April
2015). Boulder County, Colorado. Boulder County Elections Division. ‘Voter Registration Status’
http://www.bouldercounty.org/elections/register/pages/votregstats.aspx (last accessed 21 March 2015) Boulder County, Colorado.
BBC News. ‘BBC Pop Up explores Boulder's affordable housing crisis’
http://www.bbc.com/news/world-us-canada-29240704 (last accessed 2 April 2015). London, England.
Brautigam, J., Driskell, D., Richstone, S., Bailey, H., Hagelin, C., Huntley, S., KenCarin,
B., Koehn, J., Gichon, Y., Crandall, K., Tupper, K., and Vasatka, L. Study Session: Climate-Energy Framework and Program Updates. Presented to the City of Boulder City Council, 12 November 2014.
Brown, L.A. 1968a. Diffusion dynamics: A review and revision of the quantitative theory
of the spatial diffusion of innovation. Lund: The Royal University of Lund, Sweden. Brown, L.A. 1968b. Diffusion processes and location: Conceptual framework and
bibliography. Philadelphia: Regional Science Research Institute. Brown, L.A. 1975. The market infrastructure context of adoption: A spatial perspective
on the diffusion of innovation. Economic Geography 51 (3): 185-216. Brown, L.A. 1990. Innovation diffusion: Retrospect and prospect. Geography 42: 153-
164. (Journal of the Korean Geographical Society) Buja, K. 2013. Sampling Design Tool for ArcGIS. Available at
http://www.arcgis.com/home/item.html?id=ecbe1fc44f35465f9dea42ef9b63e785. Burchardt, T. Le Grand, J., and Piauchaud, D. 1999. Social exclusion in Britain 1991-95.
Social Policy and Administration. 3 (33): 227-244. Burck, J., Marten, F., and Bals, C. 2014. The climate change performance index: Results
2014. Bonn: Germanwatch.
119
Carley-Baxter, L., Hill, C., Roe, D., Twiddy, S., Baxter, R., and Ruppenkamp, J. 2009. Does response rate matter? Journal editors use of survey quality measures in manuscript publication decisions. Survey Practice, 2(7): 1-7.
California Environmental Protection Agency. ‘Climate Change Programs’.
http://www.arb.ca.gov/cc/cc.htm (last accessed 27 March 2015). Sacramento, California.
Cass, N., Shove, E., and Urry, J. 2005. Social exclusion, mobility and access. The
Sociological Review. 53 (3): 539-555. Champ, P., Brenkert-Smith, H., and Webster, J. 2011. U.S. Forest Service’s Rocky
Mountain Research Station. Boulder County, Homeowner Mitigation & Preparedness, and the Fourmile Canyon Fire.
Church, A., Frost, M., Sullivan, K. 2000. Transport and social exclusion in London.
Transport Policy. 7 (3): 195-205. Cialdini, R., and Schultz, W. 2004. Understanding and motivating energy conservation
through social norms. Prepared for the William and Flora Hewlett Foundation. Dalrymple, M., Melnick, R., and Schwartz, M. (ed.). 2014. Energize Phoenix: Energy
efficiency on an urban scale. Phoenix: Arizona State University Global Institute of Sustainability.
Dawkins, C.J., Shen, Q., and Sanchez, T.W. 2005. Race, space, and unemployment
duration. Journal of Urban Economics 58 (1): 91-113. Denver Energy Challenge. 2014. ‘How it works’ http://www.denverenergy.org/ (last accessed 12 March 2014). Denver, CO. Department of Energy Staff Member (2015, February 26). Telephone interview. Dixon, R. K., McGowan, E., Onysko, G., and Scheer, R. M. 2010. US energy
conservation and efficiency policies: Challenges and opportunities. Energy Policy. 38 (11): 6398-6408.
EnergySmart. 2014. ‘About’ http://www.EnergySmartYes.com (last accessed 13 May 2014). Denver, CO.
European Commission. ‘Roadmap for moving to a low-carbon economy in 2050’. http://ec.europa.eu/clima/policies/roadmap/index_en.htm (last accessed 30 March 2015). Brussels, Belgium.
120
Flowerdew, R., Manley, D., Sabel, C. 2008. Neighborhood effects on health: Does it matter where you draw the boundries? Journal of Social Science & Medicine. 66(6): 1241-1255.
Fotheringham, A., Wong, D.W. 1990. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A. 23 (7): 1025 – 1044.
Fuller, M., Kunkel, C., Zimring, M., Hoffman, I., Lindgren Soroye, K., Goldman, C. 2010. Driving Demand for Home Energy Improvements: Motivating Residential Customers to Invest in Comprehensive Upgrades That Eliminate Energy Waste, Avoid High Bills, and Spur the Economy. California: Lawrence Berkeley National Laboratory.
Gatrell, A., Bailey, T., Diggle, P., Rowlingson, B. 1996. Spatial Point Pattern Analysis and Its Application in Geographical Epidemiology. Transactions of the Institute of British Geographers. 21 (1): 256-274.
Gideon, L. (ed.). 2012. Handbook of Survey Methodology for the Social Sciences. New York: Springer.
Gillingham, K., Newell, R., and Palmer, K. 2006. Energy efficiency policies: A retrospective examination. Annual Review of Environment and Resources. 31: 161-192.
Greening, L., Greene, D., Difiglio, C. 2000. Energy efficiency and consumption—the rebound effect—a survey. Energy Policy. 28: 389-401.
Gregory, D., Johnson, R. Pratt, G., Watts, M., Whatmore, S. (eds.). 2009. The Dictionary of Human Geography. New York: Wiley-Blackwell.
Hagerstrand, T. 1967. Innovation diffusion as a spatial process. Trans. A. Pred. Chicago: University of Chicago Press.
Hampton, J., Hummer, J., and Wobus, N. 2012. EnergySmart Program Progress Review. Boulder: Navigant, Inc.
Hanemann, W.M. 1994. Valuing the environment through contingent valuation. The Journal of Economic Perspectives. 8 (4): 19-43.
Hine, J., Grieco, M. 2003. Scatters and clusters in time and space; implications for delivering integrated and inclusive transport. Transport Policy. 10 (4): 299-306.
Hoicka CE, Parker P, Andrey J. 2014. Residential energy efficiency retrofits: How program design affects participation and outcomes. Energy Policy. 65:594–607.
121
Hoicka, C. E., and Parker, P. 2011. Residential energy efficiency programs, retrofit
choices and greenhouse gas emissions savings: A decade of energy efficiency improvements in waterloo region, Canada. International Journal of Energy Research. 35 (15): 1312-1324.
Howden-Chapman, P., Matheson, A., Crane, J., Viggers, H., Cunningham, M., Blakely,
T., Cunningham, C., Woodward, A., Saville-Smith, K., O’Dea, D., Kennedey, M., Baker, M., Waipara, N., Chapman, R., Davie, G., Thompson, Petticrew. 2007. Effect of insulating existing houses on health inequality: Cluster randomised study in the community. British Medical Journal. 334 (7591): 460-464.
Islam, T. 2014. Household level innovation diffusion model of the photo-voltaic (PV)
solar cells from stated preference data. Energy Policy. 65: 340-350.
Kodras, J. 1997. The changing map of American poverty in an era of economic restructuring and political realignment. Economic Geography 73 (1): 67-93.
LeDoux, T.F., Vojnovic, I. 2013. Going outside the neighborhood: The shopping patterns and adaptions of disadvantaged consumers living in the lower eastside neighborhoods of Detroit, Michigan. Health & Place 19: 1-14.
Leete, L., Bania, N., Sparks-Ibanga, A. 2012. Congruence and coverage: Alternative
approaches to identifying urban food deserts and food hinterlands. Journal of Planning Education and Research 32 (2): 204-218.
Madanipour, A. 1998. Social exclusion and space. In Social Exclusion in European
Cities: Processes, Experiences, and Responses, ed. Madanipour, A., Cars, G., Allen, J. London: The Stationary Office.
Manski, C. 1993. Identification of endogenous social effects: The reflection problem. The
Review of Economic Study. 60 (3): 531-542.
Maryland Department of the Environment. ‘Climate Change Maryland’. http://climatechange.maryland.gov/news-and-events/governor-omalley-issues-executive-order-to-expand-commission-on-climate-change/ (last accessed 28 March 2015). Annapolis, Maryland.
New York City, Mayor’s Office of Long Term Planning and Sustainability. One City: Built to Last. http://www.nyc.gov/html/builttolast/assets/downloads/pdf/OneCity.pdf (last accessed 24 March 2015). New York City, New York.
Noll, D., Dawes, C., Rai, V. 2014. Solar community organizations and active peer effects in the adoption of residential PV. Energy Policy. 67: 330-343.
122
Nord, M. Keeping warm, keeping cold, keeping food on the table: Seasonal food insecurity and costs of heating and cooling. Paper from Economic Research Service, US Department of Agriculture. Presented at: Annual Meeting of the National Association for Welfare Research and Statistics: July 13-16, 2003; San Diego, CA.
Nulty, D. 2008. The adequacy of response rates to online and paper surveys: What can be done? Assessment & Evaluation in Higher Education, 33(3): 301-314.
Parker, P., Rowlands, I., and Scott, D. 2003. Innovations to reduce residential energy use and carbon emissions: An integrated approach. Canadian Geographer-Geographe Canadien. 47 (2): 169-184.
Parks, V. 2004. Access to work: The effects of spatial and social accessibility on unemployment for native-born black and immigrant women in Los Angeles. Economic Geography 80 (2): 141-172.
Pasten, C., Santamarina, J.C. 2012. Energy and quality of life. Energy Policy 49: 468-
476.
Persson, O., and Ellegard, K. 2012. Torsten Hagerstrand in the citation time web. The Professional Geographer 64 (2): 250-261.
Preston, J. 2009. Epilogue: Transport policy and social exclusion-some reflections. Transport Policy 16 (3): 140-142.
Rai, V. and Robinson, S. 2013. Effective information channels for reducing costs of environmentally-friendly technologies: Evidence from residential PV markets. Environmental Research Letters 8: 1-8.
Rogers, E. 1962. Diffusion of Innovations. (1st ed.). New York: The Free Press. Rogers, E. 1983. Diffusion of Innovations. (3rd ed.). New York: The Free Press.
Sadineni, S., France, T., Boehm, R. 2011. Economic feasibility of energy efficiency measures in residential buildings. Renewable Energy 36: 2925-2931.
Scott, J., Carrington, P. (Eds.). 2011. The SAGE handbook of social network analysis. London: SAGE Publications.
Sewalk, S., and Throupe, R. 2013. The feasibility of reducing greenhouse gas emissions in residential buildings. The Journal of Sustainable Real Estate 5(1): 35-61.
Silver, H., Miller, S. M. 2003. The European approach to social disadvantage. Indicators. 2 (2): 1-17.
123
Somerville, P. 1998. Explanations of social exclusion: Where does housing fit in? Housing Studies. 13 (6): 761-780.
Stanley, J., and Vella-Brodrick, D. 2009. The usefulness of social exclusion to inform social policy in transport. Transport Policy. 16 (3): 90-96.
Treadwell, T. 2015. ‘Political Ideology Effects on Purchasing Solar Photovoltaics’. The Center for Sustainable Energy. San Diego, CA.
U.S. Census. 2013. Population QuickFacts: Boulder County, Colorado. http://quickfacts.census.gov/qfd/states/08/08013.html (last accessed 13 May 2014).
Washington, D.C. U.S. Census. 2013. Population QuickFacts: Rutland County, Vermont. http://quickfacts.census.gov/qfd/states/50/50021.html (last accessed 7 April 2015).
Washington, D.C. U.S. Census. 2014. Population QuickFacts: USA.
http://quickfacts.census.gov/qfd/states/00000.html (last accessed 12 March 2014). Washington, D.C.
U.S. Department of Energy (DOE). 2014. Better Buildings Neighborhood Program
Partners. http://energy.gov/eere/better-buildings-neighborhood-program/better-buildings-neighborhood-program-partners#top (last accessed 11 February 2014). Washington, D.C.
U.S. Department of Energy (DOE). 2013. Preliminary energy savings impact evaluation:
Better Buildings Neighborhood Program. Washington, D.C.: US DOE/Research Into Action, Inc.
U.S. Department of Energy (DOE). 2011. Spotlight on Rutland County, Vermont: How
local ties lead to local wins. http://energy.gov/sites/prod/files/2014/01/f6/cs_rutland_drivingdemand.pdf (last accessed 4 April 2015) Washington, D.C.: US DOE
U.S. Energy Information Administration (EIA). 2011. Annual Energy Review “Table
2.1a: Energy Consumption Estimates by Sector, Selected Years, 1949- 2011.” http://www.eia.gov/totalenergy/data/annual/pdf/sec2_6.pdf
Vine, E., Hall, N., Keating, K. M., Kushler, M., and Prahl, R. 2012. Emerging issues in the evaluation of energy-efficiency programs: The US experience. Energy Efficiency. 5 (1): 5-17.
124
Watt, P., and Jacobs, K. 2000. Discourses of social exclusion - an analysis of bringing Britain together: A national strategy for neighbourhood renewal. Housing Theory and Society. 17 (1): 14-26.
The White House. ‘Fact Sheet: U.S.-China Joint Announcement on Climate Change and
Clean Energy Cooperation’ https://www.whitehouse.gov/the-press-office/2014/11/11/fact-sheet-us-china-joint-announcement-climate-change-and-clean-energy-c (last accessed 25 March 2015). Washington, D.C.
The White House. ‘Fact Sheet: U.S. Reports its 2025 Emissions Target to the UNFCCC’
https://www.whitehouse.gov/the-press-office/2015/03/31/fact-sheet-us-reports-its-2025-emissions-target-unfccc (last accessed 6 April 2015). Washington, D.C.
Weiss, C.W., Purciel, M., Bader, M., Quinn, J.W., Lovasi, G., Neckerman, K.M., Rundle,
A.G. 2011. Reconsidering access: park facilities and neighborhood disamenities in New York City. Journal of Urban Health: Bulletin of the New York Academy of Medicine 88 (2): 297-310.
Wilson, J., Dixon, S. L., Jacobs, D. E., Breysse, J., Akoto, J., Tohn, E., Isaacson, M.,
Evans, A., and Hernandez, Y. 2014. Watts-to-wellbeing: Does residential energy conservation improve health? Energy Efficiency. 7 (1): 151-160.
World Commission on Economic Development (WCED). 1987. Our Common Future
(The Brundtland Report). Oxford: Oxford University. Zhang, X., Lu, H., Holt, J.B. 2011. Modeling spatial accessibility to parks: a national
study. International Journal of Health Geographies 10 (31): 1-14. Zimring, M., Borgeson, M.G., Hoffman, I., Goldman, C., Stuart, E., Todd, A.,
Billingsley, M. 2011. Delivering Energy Efficiency to Middle Income Single Family Households. California: Lawrence Berkeley National Laboratory.
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APPENDIX A-1: SURVEY RESULTS
Note: Survey results are aggregated in the 'ALL' column and sorted by cluster type in the next four columns (HH, HL, LH, NS)
1. Are you one of the homeowners of this residence? Value All HH HL LH NS Yes 100.0% 100.0% 100.0% 100.0% 100.0% No 0.0% 0.0% 0.0% 0.0% 0.0%
2. How long have you owned this home for?
Value All HH HL LH NS Less than a year 5.9% 5.9% 2.6% 5.9% 8.9% 1 year to 5 years 19.1% 26.5% 7.7% 14.7% 26.7% 6 years to 10 years 23.0% 26.5% 23.1% 29.4% 15.6% 11 years to 15 years 17.1% 11.8% 20.5% 23.5% 13.3% 16 years to 20 years 10.5% 8.8% 12.8% 2.9% 15.6% 21 years to 25 years 11.8% 8.8% 18.0% 2.9% 15.6% More than 25 years 12.5% 11.8% 15.4% 20.6% 4.4%
3. Do you plan on selling your home in the next five years? Value All HH HL LH NS Yes 3.3% 2.9% 7.7% 2.9% 0.0% No 64.5% 61.8% 64.1% 61.8% 68.9% I'm not sure 32.2% 35.3% 28.2% 35.3% 31.1%
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4. Below, you are asked to rate your opinion of some terms and organizations, with a rating of 100 meaning a VERY HIGH OPINION; rating of 0 meaning a VERY LOW OPINION; and 50 meaning a NOT PARTICULARLY LOW OR HIGH OPINION. You can use any number from 0 to 100, the higher the number, the higher the opinion you have of that term or organization. If you have never heard of that term or organization, please leave the box blank:
4.1 EnergySmart Boulder
All HH HL LH NS
ID % 49% 65% 36% 41% 47% Rating 68 76 70 76 57
4.2 Residential energy efficiency'
All HH HL LH NS
ID % 65% 56% 59% 76% 69% Rating 83 77 88 84 82
4.3 ENERGY STAR
All HH HL LH NS
ID % 89% 82% 90% 88% 93% Rating 79 79 78 81 78
4.4 'Renewable energy' (solar, wind and geothermal energy)
All HH HL LH NS
ID % 89% 85% 90% 91% 91% Rating 86 90 84 83 86
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5. Has a home energy audit been completed for your home? All HH HL LH NS Yes 32.2% 38.2% 41.0% 26.5% 24.4% No 64.5% 55.9% 59.0% 70.6% 71.1% I'm not sure 3.3% 5.9% 0.0% 2.9% 4.4%
6. Have you completed any energy efficiency upgrades for your home? All HH HL LH NS Yes, as a result of a home energy audit 19.1% 26.5% 28.2% 11.8% 11.1% Yes, but I completed the energy efficiency upgrades independently of a home energy audit 66.5% 58.8% 59.0% 76.5% 71.1% No, I have not completed any energy efficiency upgrades 7.9% 8.8% 7.7% 5.9% 8.9% other 6.6% 5.9% 5.1% 5.9% 8.9%
7. Please check any of the following energy efficient upgrades you have completed: Value All HH HL LH NS Air and duct sealing 28.7% 31.0% 33.3% 26.7% 24.3% Insulation in attic or walls 51.2% 48.3% 66.7% 43.3% 46.0% LED or CFL lighting 82.2% 69.0% 87.9% 80.0% 89.2% New air conditioning unit 29.5% 24.1% 33.3% 43.3% 18.9% New heat pump 7.0% 3.5% 9.1% 6.7% 8.1% New water heater 45.7% 55.2% 48.5% 43.3% 37.8% New windows 45.7% 41.4% 60.6% 43.3% 37.8% Upgraded to ENERGY STAR or other energy efficient appliances 54.3% 58.6% 42.4% 60.0% 56.8% Other 30.2% 34.5% 15.2% 33.3% 37.8%
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8. Please state your level of agreement or disagreement with the following statement: Energy efficiency upgrades completed by other homes in my neighborhood motivated me to seriously consider completing energy efficiency upgrades on my own home. Value All HH HL LH NS Agree strongly 0.8% 3.5% 0.0% 0.0% 0.0% Agree somewhat 10.9% 24.1% 6.1% 10.0% 5.4% Neutral 34.9% 24.1% 42.4% 40.0% 32.4% Disagree somewhat 11.6% 17.2% 12.1% 3.3% 13.5% Disagree strongly 38.0% 27.6% 36.4% 43.3% 43.2% I don't know 3.9% 3.5% 3.0% 3.3% 5.4%
Agree Total 11.7% 27.6% 6.1% 10.0% 5.4% Neutral 34.9% 24.1% 42.4% 40.0% 32.4% Disagree Total 49.6% 44.8% 48.5% 46.6% 56.7% I don't know 3.9% 3.5% 3.0% 3.3% 5.4%
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9. Please state your level of agreement or disagreement with the following statement: Seeing the results of energy efficiency upgrades in other homes in my neighborhood gave me the confidence that it would be a good decision to make energy efficiency upgrades on my own home. Value All HH HL LH NS Agree strongly 3.1% 6.9% 0.0% 0.0% 5.4% Agree somewhat 10.9% 17.2% 9.1% 13.3% 5.4% Neutral 32.6% 34.5% 36.4% 40.0% 21.6% Disagree somewhat 13.2% 13.8% 15.2% 3.3% 18.9% Disagree strongly 37.2% 27.6% 36.4% 40.0% 43.2% I don't know 3.1% 0.0% 3.0% 3.3% 5.4%
Agree Total 14.0% 24.1% 9.1% 13.3% 10.8% Neutral 32.6% 34.5% 36.4% 40.0% 21.6% Disagree Total 50.4% 41.4% 51.6% 43.3% 62.1% I don't know 3.1% 0.0% 3.0% 3.3% 5.4%
10. Please state your level of agreement or disagreement with the following statement: Without first seeing energy efficiency upgrades in other homes in my neighborhood, I would not have made energy efficiency upgrades on my own home. Value All HH HL LH NS Agree strongly 2.3% 3.5% 6.1% 0.0% 0.0% Agree somewhat 0.0% 0.0% 0.0% 0.0% 0.0% Neutral 14.0% 13.8% 18.2% 10.0% 13.5% Disagree somewhat 8.5% 17.2% 6.1% 6.7% 5.4% Disagree strongly 73.6% 65.5% 69.7% 80.0% 78.4% I don't know 1.6% 0.0% 0.0% 3.3% 2.7%
Agree Total 2.3% 3.5% 6.1% 0.0% 0.0% Neutral 14.0% 13.8% 18.2% 10.0% 13.5% Disagree Total 82.1% 82.7% 75.8% 86.7% 83.8% I don't know 1.6% 0.0% 0.0% 3.3% 2.7%
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11. Please state your level of agreement or disagreement with the following statement: Talking to other neighbors or friends who completed energy efficiency upgrades was useful in my decision to complete energy efficiency upgrades on my own home. Value All HH HL LH NS Agree strongly 3.9% 10.3% 3.0% 3.3% 0.0% Agree somewhat 16.3% 20.7% 18.2% 23.3% 5.4% Neutral 25.6% 27.6% 27.3% 16.7% 29.7% Disagree somewhat 14.7% 13.8% 18.2% 10.0% 16.2% Disagree strongly 37.2% 24.1% 33.3% 46.7% 43.2% I don't know 2.3% 3.5% 0.0% 0.0% 5.4%
Agree Total 20.2% 31.0% 21.2% 26.6% 5.4% Neutral 25.6% 27.6% 27.3% 16.7% 29.7% Disagree Total 51.9% 37.9% 51.5% 56.7% 59.4% I don't know 2.3% 3.5% 0.0% 0.0% 5.4%
Note: Questions 12, 13, 18, 19, 20, 21 were only for homeowners who had not completed energy efficiency upgrades. This was a low number of respondents, so the results from these questions is not statistically significant. The number of responses from each group is shown below the percentage of responses for each group. For example, n=3 means only 3 people responded to this question in that particular cluster type.
12. If you plan to complete energy efficiency upgrades on your home, indicate how many years from now your household will complete energy efficiency upgrades (please select one time period only) Value ALL HH HL LH NS Less than a year from now 25.0% 33.3% 0.0% 50.0% 25.0% 1 year to 2 years from now 16.7% 0.0% 0.0% 50.0% 25.0% 3 years to 5 years from now 16.7% 66.7% 0.0% 0.0% 0.0% More than 5 years from now 0.0% 0.0% 0.0% 0.0% 0.0% I'm not sure when I plan to complete energy efficiency upgrades on my home 16.7% 0.0% 66.7% 0.0% 0.0%
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I do not plan to complete energy efficiency upgrades at any point 25.0% 0.0% 33.3% 0.0% 50.0%
n=12 n=3 n=3 n=2 n=4
13. Please check any of the following energy efficient upgrades you plan to complete: Value ALL HH HL LH NS Air and duct sealing 25.0% 0.0% 100.0% 50.0% 0.0% Insulation in attic or walls 62.5% 33.3% 100.0% 100.0% 50.0% LED or CFL lighting 25.0% 0.0% 0.0% 50.0% 50.0% New air conditioning unit 25.0% 33.3% 0.0% 50.0% 0.0% New heat pump 12.5% 0.0% 0.0% 50.0% 0.0% New water heater 0.0% 0.0% 0.0% 0.0% 0.0% New windows 37.5% 66.7% 0.0% 50.0% 0.0% ENERGY STAR or other energy efficient appliances 12.5% 0.0% 0.0% 50.0% 0.0% Other 12.5% 0.0% 0.0% 0.0% 50.0%
14. Do you know any neighbors who have completed energy efficiency upgrades on their homes? Value All HH HL LH NS Yes 53.3% 61.8% 48.7% 58.8% 46.7% No 36.8% 35.3% 43.6% 29.4% 37.8% I'm not sure 9.9% 2.9% 7.7% 11.8% 15.6%
15. Do you know anyone outside of your neighborhood who has completed energy efficiency upgrades on their homes? Value All HH HL LH NS Yes 59.9% 67.7% 43.6% 64.7% 64.4% No 32.9% 29.4% 46.2% 26.5% 28.9% I'm not sure 7.2% 2.9% 10.3% 8.8% 6.7%
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16. Are you aware of any financial incentives currently available to you if you decided to complete an energy efficiency upgrade? Financial incentives include cost rebates, grants or loans for energy-efficiency improvements, direct income tax deductions, or sales tax exemptions. Value All HH HL LH NS Yes 55.3% 67.7% 43.6% 58.8% 53.3% No 33.6% 26.5% 43.6% 29.4% 33.3% I'm not sure 11.2% 5.9% 12.8% 11.8% 13.3%
18. Please state your level of agreement or disagreement with the following statement: Talking to neighbors or friends who have completed energy efficiency upgrades would be useful in my decision making process about whether or not to complete an energy efficiency upgrade on my own home. Value All HH HL LH NS Agree strongly 16.7% 0.0% 0.0% 0.0% 50.0% Agree somewhat 41.7% 66.7% 33.3% 100.0% 0.0% Neutral 8.3% 33.3% 0.0% 0.0% 0.0% Disagree somewhat 8.3% 0.0% 33.3% 0.0% 0.0% Disagree strongly 16.7% 0.0% 0.0% 0.0% 50.0% I don't know 8.3% 0.0% 33.3% 0.0% 0.0%
n=12 n=3 n=3 n=2 n=4
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19. Please state your level of agreement or disagreement with the following statement: Talking to a neighborhood association with detailed information on energy efficiency upgrades would be useful in my decision making process about whether or not to complete an energy efficiency upgrade on my own home. Value All HH HL LH NS Agree strongly 8.3% 0.0% 33.3% 0.0% 0.0% Agree somewhat 33.3% 66.7% 0.0% 0.0% 50.0% Neutral 16.7% 33.3% 0.0% 50.0% 0.0% Disagree somewhat 16.7% 0.0% 66.7% 0.0% 0.0% Disagree strongly 16.7% 0.0% 0.0% 0.0% 50.0% I don't know 8.3% 0.0% 0.0% 50.0% 0.0%
n=12 n=3 n=3 n=2 n=4
20. Please state your level of agreement or disagreement with the following statement: Talking to energy efficiency program staff or spokespersons with detailed information energy efficiency upgrades would be useful in my decision making process about whether or not to complete an energy efficiency upgrade on my own house. Value All HH HL LH NS Agree strongly 16.7% 0.0% 0.0% 50.0% 25.0% Agree somewhat 58.3% 100.0% 100.0% 50.0% 0.0% Neutral 8.3% 0.0% 0.0% 0.0% 25.0% Disagree somewhat 0.0% 0.0% 0.0% 0.0% 0.0% Disagree strongly 16.7% 0.0% 0.0% 0.0% 50.0% I don't know 0.0% 0.0% 0.0% 0.0% 0.0%
n=12 n=3 n=3 n=2 n=4
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21. Please state your level of agreement or disagreement with the following statement: Talking to professional energy efficiency contractors with detailed information energy efficiency upgrades would be useful in my decision making process about whether or not to complete an energy efficiency upgrade on my own home. Value All HH HL LH NS Agree strongly 16.7% 0.0% 0.0% 50.0% 25.0% Agree somewhat 50.0% 33.3% 100.0% 50.0% 25.0% Neutral 8.3% 33.3% 0.0% 0.0% 0.0% Disagree somewhat 8.3% 33.3% 0.0% 0.0% 0.0% Disagree strongly 16.7% 0.0% 0.0% 0.0% 50.0% I don't know 0.0% 0.0% 0.0% 0.0% 0.0%
n=12 n=3 n=3 n=2 n=4
22. How much more money (as a percentage) would you be willing to pay to purchase energy efficient products that will limit excess energy use and environmentally damaging carbon dioxide (CO2) emissions? Value ALL HH HL LH NS 5% more money than for a non energy efficient product 16.5% 20.6% 12.8% 17.7% 15.6% 10% more money than for a non energy efficient product 27.0% 26.5% 33.3% 20.6% 26.7% 15% more money than for a non energy efficient product 20.4% 17.7% 23.1% 26.5% 15.6% 20% more money than for a non energy efficient product 22.4% 26.5% 10.3% 23.5% 28.9% No more money than for a non energy efficient product 13.8% 8.8% 20.5% 11.8% 13.3%
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25. What would you say is your average monthly utility bill cost? Value ALL HH HL LH NS Less than $50 0.7% 2.9% 0.0% 0.0% 0.0% $51 to $75 5.9% 8.8% 5.1% 2.9% 6.7% $76 to $100 16.5% 23.5% 7.7% 17.7% 17.8% $101 to $125 21.7% 26.5% 15.4% 20.6% 24.4% $126 to $150 15.1% 14.7% 15.4% 17.7% 13.3% $151 to $175 11.8% 8.8% 23.1% 2.9% 11.1% $176 to $200 7.2% 5.9% 7.7% 5.9% 8.9% More than $200 14.5% 5.9% 18.0% 23.5% 11.1% I'm not sure 6.6% 2.9% 7.7% 8.8% 6.7%
26. In what year was your home built? If you do not know the year, please estimate. (Answers should be a four digit year. Example: 1994)
Value ALL HH HL LH NS Average year built 1984 1978 1985 1979 1991
27. Approximately how many square feet is your home? (Write a number in square feet for this answer. Example: 1264)
Value ALL HH HL LH NS Average SF 2744 2551 2867 2770 2768
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28. Including you, how many people currently live in your home? Value ALL HH HL LH NS One person (just me) 8.6% 8.8% 10.3% 11.8% 4.4% Two people 50.7% 47.1% 48.7% 41.2% 62.2% Three people 12.5% 17.7% 15.4% 11.8% 6.7% Four people 18.4% 14.7% 18.0% 23.5% 17.8% Five people 8.6% 11.8% 7.7% 5.9% 8.9% More than five people 0.0% 0.0% 0.0% 0.0% 0.0% I'd rather not say 1.3% 0.0% 0.0% 5.9% 0.0%
29. In what year were you born? (Answers should be a four digit year. Example: 1955) Value ALL HH HL LH NS Average Age 56 54 56 62 53
30. What is the highest level of education you’ve achieved? Value ALL HH HL LH NS Some high school 0.0% 0.0% 0.0% 0.0% 0.0% High school graduate 3.3% 0.0% 7.7% 0.0% 4.4% Some college 6.6% 11.8% 0.0% 8.8% 6.7% College graduate 34.9% 20.6% 43.6% 35.3% 37.8% Beyond undergraduate college (Master’s degree, PhD, MD, law school, etc.) 53.3% 67.7% 48.7% 47.1% 51.1% I'd rather not say 2.0% 0.0% 0.0% 8.8% 0.0%
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31. What is your total yearly household income before taxes? Value ALL HH HL LH NS Under $35,000 4.7% 8.8% 7.9% 3.0% 0.0% $35,000 to $55,000 4.0% 8.8% 2.6% 6.1% 0.0% $55,000 to $75,000 7.3% 5.9% 10.5% 6.1% 6.7% $75,000 to $100,000 12.7% 11.8% 18.4% 12.1% 8.9% $100,000 to $125,000 12.0% 8.8% 10.5% 12.1% 15.6% $125,000 to $150,000 8.7% 11.8% 5.3% 3.0% 13.3% $150,000 or more 29.3% 32.4% 29.0% 18.2% 35.6% I'd rather not say 21.3% 11.8% 15.8% 39.4% 20.0%
Middle income range (35-75k) 11.3% 14.7% 13.1% 12.2% 6.7%
32. When it comes to most political issues, do you consider yourself conservative, somewhat conservative, moderate, somewhat liberal or liberal? Value ALL HH HL LH NS Conservative 10.5% 0.0% 12.8% 14.7% 13.3% Somewhat conservative 9.2% 2.9% 15.4% 8.8% 8.9% Moderate 17.1% 8.8% 25.6% 17.7% 15.6% Somewhat liberal 21.1% 41.2% 10.3% 11.8% 22.2% Liberal 34.9% 41.2% 30.8% 35.3% 33.3% I'd rather not say 7.2% 5.9% 5.1% 11.8% 6.7%
Total Conservative 19.7% 2.9% 28.2% 23.5% 22.2% Total Moderate 17.1% 8.8% 25.6% 17.7% 15.6% Total Liberal 56.0% 82.4% 41.1% 47.1% 55.5%
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33. What is your race?
Value ALL HH HL LH NS American Indian or Alaska Native 0.7% 0.0% 0.0% 0.0% 2.2% Asian 2.6% 2.9% 2.6% 0.0% 4.4% Black or African American 0.0% 0.0% 0.0% 0.0% 0.0% Caucasian or White 85.5% 82.4% 92.3% 76.5% 88.9% Native Hawaiian or Other Pacific Islander 0.0% 0.0% 0.0% 0.0% 0.0% Hispanic or Latino 0.7% 0.0% 2.6% 0.0% 0.0% Other 0.7% 0.0% 2.6% 0.0% 0.0% I'd rather not say 9.9% 14.7% 0.0% 23.5% 4.4%
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APPENDIX A-2: OPEN-ENDED SURVEY QUESTION RESPONSES (CODED)
Question 17: How did you hear of these tax breaks/incentives? Note: this question was asked of respondents who answered ‘Yes’ to the previous question: ‘Are you aware of any financial incentives currently available to you if you decided to complete an energy efficiency upgrade? Financial incentives include cost rebates, grants or loans for energy-efficiency improvements, direct income tax deductions, or sales tax exemptions.’
Code Coded word Count 11 Internet 14 23 Energy efficiency company/manufacturer 13 24 Media/News (medium not specified) 12 1 Xcel 11 8 Newspaper 10
19 Tax Filing/IRS 10 9 Radio 9 7 Mailings 8
16 Utility bill 8 13 Sales person 6 17 Energy Audit 5 2 City of Boulder 4
14 Friends 3 18 Co-workers/at work 3 21 General knowledge 3 5 Word of mouth 2
12 Boulder County 2 22 Works in the EE/solar industry 2 25 Magazines 2 28 Television 2 4 City of Longmont 1 6 Family member 1
20 EnergySmart 1
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26 School 1 27 Federal/State Program 1
Question 17: How did you hear of these tax breaks/incentives? Responses broken out by cluster type:
Coded word Count Coded word Count Coded word Count Coded word CountEnergy efficiency company/manufacturer 6 Internet 3 Media/News (medium not specified) 6 Newspaper 4Xcel 5 Utility bill 3 Xcel 4 Internet 4
Internet 3Energy efficiency company/manufacturer 3 Internet 4 Tax Filing/IRS 4
City of Boulder 2 Mailings 2 Tax Filing/IRS 3 Sales person 2Mailings 2 Newspaper 2 Mailings 2 City of Boulder 2Newspaper 2 Media/News (medium not specified) 2 Newspaper 2 Mailings 2Sales person 2 Xcel 1 Sales person 2 Utility bill 2Works in the EE/solar industry 2 Boulder County 1 Utility bill 2 Energy Audit 2Media/News (medium not specified) 2 Tax Filing/IRS 1 Energy Audit 2 Co-‐workers/at work 2
City of Longmont 1 EnergySmart 1Energy efficiency company/manufacturer 2
Energy efficiency company/manufacturer 2
Word of mouth 1 General knowledge 1 Television 2Media/News (medium not specified) 2
Family member 1 Magazines 1 Word of mouth 1 Xcel 1Boulder County 1 Federal/State Program 1 Friends 1 Radio 1Friends 1 City of Boulder 0 General knowledge 1 Friends 1Utility bill 1 City of Longmont 0 City of Boulder 0 City of Longmont 0Energy Audit 1 Word of mouth 0 City of Longmont 0 Word of mouth 0Co-‐workers/at work 1 Family member 0 Family member 0 Family member 0Tax Filing/IRS 1 Radio 0 Radio 0 Boulder County 0General knowledge 1 Sales person 0 Boulder County 0 EnergySmart 0Magazines 1 Friends 0 Co-‐workers/at work 0 General knowledge 0School 1 Energy Audit 0 EnergySmart 0 Works in the EE/solar industry 0Radio 0 Co-‐workers/at work 0 Works in the EE/solar industry 0 Magazines 0EnergySmart 0 Works in the EE/solar industry 0 Magazines 0 School 0Federal/State Program 0 School 0 School 0 Federal/State Program 0Television 0 Television 0 Federal/State Program 0 Television 0
High-‐High Clusters (23 Respondents) High-‐Low Clusters (17 Respondents) Low-‐High Clusters (20 Respondents) Not Significant Clusters (24 Respondents)
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Question 23: If you have not completed an energy efficiency upgrade on your home, what are the major reasons for not completing one? Note: there is a low number of respondents because respondents who have already completed energy efficiency upgrades were not asked this question.
ALL Responses (Count:12) Code Coded Word Count
3 Money/financial constraints 4 2 Time constraint 3 4 Home already efficient enough 3 1 Lazy 1 5 See no need to upgrade 1 6 I don’t know enough about it 1
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Question 24: Based upon what you know about residential energy efficiency, what is the most appealing benefit of an energy efficiency upgrade to you personally? If you do not know, please state that you do not know.
Code Coded word Count 3 Cost savings/Lower energy bills 94 1 Environment 40 7 Increased comfort in home 24 8 Reduced carbon footprint/emissions 22 5 Reduced energy use/consumption 12 4 I don’t know 9
13 The right thing to do 4 6 Personal satisfaction 3
11 Benefits our children/family 3 9 Combining with renewable energy 2
10 Conserving resources 2 15 Reduce waste 2 2 A better future 1
12 Use of new technologies 1 14 Health benefits 1
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Question24: Based upon what you know about residential energy efficiency, what is the most appealing benefit of an energy efficiency upgrade to you personally? If you do not know, please state that you do not know.
Responses broken out by cluster type:
Code Coded word Count Code Coded word Count3 Cost savings/Lower energy bills 18 3 Cost savings/Lower energy bills 271 Environment 6 1 Environment 97 Increased comfort in home 5 7 Increased comfort in home 78 Reduced carbon footprint/emissions 5 8 Reduced carbon footprint/emissions 55 Reduced energy use/consumption 2 13 The right thing to do 42 A better future 1 5 Reduced energy use/consumption 34 I don’t know 1 4 I don’t know 26 Personal satisfaction 1 6 Personal satisfaction 29 Combining with renewable energy 1 15 Reduce waste 2
10 Conserving resources 0 14 Health benefits 111 Benefits our children/family 0 2 A better future 012 Use of new technologies 0 9 Combining with renewable energy 013 The right thing to do 0 10 Conserving resources 014 Health benefits 0 11 Benefits our children/family 015 Reduce waste 0 12 Use of new technologies 0
HH (Responses: 32) HL (Responses: 38)
Code Coded word Count Code Coded word Count3 Cost savings/Lower energy bills 20 3 Cost savings/Lower energy bills 291 Environment 13 1 Environment 118 Reduced carbon footprint/emissions 6 7 Increased comfort in home 87 Increased comfort in home 4 8 Reduced carbon footprint/emissions 65 Reduced energy use/consumption 2 5 Reduced energy use/consumption 54 I don’t know 1 4 I don’t know 411 Benefits our children/family 1 10 Conserving resources 22 A better future 0 11 Benefits our children/family 26 Personal satisfaction 0 12 Use of new technologies 19 Combining with renewable energy 0 2 A better future 0
10 Conserving resources 0 6 Personal satisfaction 012 Use of new technologies 0 9 Combining with renewable energy 013 The right thing to do 0 13 The right thing to do 014 Health benefits 0 14 Health benefits 015 Reduce waste 0 15 Reduce waste 0
LH (Responses: 34) NS (Responses: 44)
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Question24: Based upon what you know about residential energy efficiency, what is the most appealing benefit of an energy efficiency upgrade to you personally? If you do not know, please state that you do not know.
Responses broken out by political orientation:
Code Coded word Count Percentage3 Cost savings/Lower energy bills 22 76%1 Environment 5 17%7 Increased comfort in home 5 17%4 I don’t know 4 14%8 Reduced carbon footprint/emissions 2 7%2 A better future 1 3%5 Reduced energy use/consumption 1 3%
14 Health benefits 1 3%6 Personal satisfaction 0 0%9 Combining with renewable energy 0 0%
10 Conserving resources 0 0%11 Benefits our children/family 0 0%12 Use of new technologies 0 0%13 The right thing to do 0 0%15 Reduce waste 0 0%
Conservative (Count: 29)
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Code Coded word Count Percentage3 Cost savings/Lower energy bills 19 73%8 Reduced carbon footprint/emissions 6 23%1 Environment 5 19%5 Reduced energy use/consumption 4 15%4 I don’t know 2 8%6 Personal satisfaction 2 8%
13 The right thing to do 1 4%2 A better future 0 0%7 Increased comfort in home 0 0%9 Combining with renewable energy 0 0%
10 Conserving resources 0 0%11 Benefits our children/family 0 0%12 Use of new technologies 0 0%14 Health benefits 0 0%15 Reduce waste 0 0%
Moderate (Count: 26)
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Code Coded word Count Percentage3 Cost savings/Lower energy bills 46 55%1 Environment 28 33%7 Increased comfort in home 15 18%8 Reduced carbon footprint/emissions 12 14%5 Reduced energy use/consumption 9 11%4 I don’t know 2 2%6 Personal satisfaction 2 2%9 Combining with renewable energy 2 2%
11 Benefits our children/family 2 2%13 The right thing to do 2 2%15 Reduce waste 2 2%12 Use of new technologies 1 1%2 A better future 0 0%
10 Conserving resources 0 0%14 Health benefits 0 0%
Liberal (Count: 84)
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APPENDIX B: MAPS OF RANDOMLY SELECTED CLUSTERS FOR SURVEY DISTRIBUTION
High-High Clusters
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High-Low Clusters
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Low-High Clusters
150
Not Significant Clusters
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APPENDIX C: SURVEY DISTRIBUTION DISCUSSION
Pre-Distribution Planning Pre-planning a route for survey was very important to keeping on track and organized
while distributing the surveys. After the fifteen random clusters were selected in ArcGIS,
they were ordered in a manner that allowed for the most efficient driving rout from
cluster to cluster. Survey distribution would typically start in southeast Boulder County
(Superior or Louisville, which was closest to Denver) and then head north throughout the
day.
Next, for all four cluster types the random sample of 250 parcels was exported to an
Excel Sheet. This sheet was first sorted by clusters one through fifteen, and then was
sorted by street addresses within each cluster. Once on the ground in each cluster, this
organization technique allowed for efficient and ordered survey distribution.
Pre-planning also led to the identification and removal of two clusters that were
condominium buildings. This saved time because it eliminated unnecessary visits to these
clusters while on the ground distributing surveys. To remedy this situation, two new
random cluster selections were selected to replace the condominium clusters. Overall,
pre-planning and organization was key to distributing surveys in a timely manner.
On the Ground: Survey Distribution Once in the car, I used my Google Maps phone app to navigate between clusters. I
would then park on a street and distribute surveys to each parcel that was on my list.
Sometimes I could stay parked in one spot and distribute surveys to all parcels within the
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cluster by not having to move my car, but sometimes it was more efficient to drive
around the block to continue distribution.
Upon arriving at each address, I would ring the doorbell and give a quick ‘pitch’ (see
wording below) if someone answered the door. If nobody was at the door, I would leave
the survey information packet (see Appendices F and G for the envelope and survey
introduction) at their front door. The standard 4 1/8” x 9.5” envelope allowed the packet
to be easily placed in the doorhandle or doorknob area.
There are several efforts I made to ensure a high response rate from homeowners:
1. DU branding was important to survey distribution. I wore a DU Department of
Geography & the Environment collared shirt that I borrowed from the
Department. I noticed that one of the first thing people did after answering the
door was look at my shirt. This helped them realize that I was not a salesman
before I even asked them to take the survey. In addition, the survey envelope and
the survey information letter contained DU Department of Geography & the
Environment logos, and contact information for myself, the department, and Dr.
Eric Boschmann (my advisor).
2. Face-to face engagement was another important element for increasing the
response rate. On the first day of survey distribution, I focused more on
distributing as many surveys as possible. This meant just dropping the letter at
front doors rather than ringing the doorbell at each home and trying to engage
potential respondents. This technique resulted in a low response rate (around 10
percent) and a high number of disqualified responses because renters were
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attempting to take the survey and getting disqualified. More face to face
interaction would have led to me knowing these were renters and not giving them
the survey. For subsequent distributions, I rang every doorbell and properly honed
my pitch, which helped increase response rates (16.4% overall)
3. Proper wording for the survey pitch was also important. The pitch I found most
effective (and used at every house once I had honed it in) was worded as follows:
“Hi, my name is Walter, I’m a student researcher at the University of Denver. I’m conducting research on energy efficiency in homes, and I was just wondering if you’d have about 10 minutes over the next few days to complete an online survey related to the research.” At first, I did not include the “over the next few days” part in the pitch. People
thought I was asking them to complete a survey right then while I waited at their
door. Once I realized this issue, I re-worded the pitch to put emphasis on the fact
that it was an online survey and could be completed at their leisure over the next
few days. This element of flexibility led to more survey acceptances from
homeowners who were initially going to refuse to respond if they had to complete
the survey at the while I was at the door. After a homeowner responded
affirmatively to taking the survey, I would hand them the envelope and say that
directions and a link to the survey are enclosed. It is also interesting to note that a
good number of people were home and answering their doors on weekdays. This
was true for both genders; I did not get a disproportionate number of males or
females answering the door. It is possible that increased rates of teleworking in
recent years may have contributed to this. Overall, it took approximately twelve to
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fifteen hours to distribute 250 surveys, which meant I spent close to sixty hours
distributing surveys.
Survey Refusals There were several major types of refusal categories: not interested, too busy over
the coming days, I’m a renter, I don’t have internet (only two homeowners refused due to
a lack of internet; they did not want a survey mailed to them either). There were also four
households that weren’t able to participate due to the person answering the door not being
able to speak English. Unfortunately, I did not have foreign language versions of the
survey available. Some houses also had ‘No Soliciting’ signs. I did not approach these
houses or leave a survey packet for them to look at. Two potential respondents also
contacted me and Dr. Eric Boschmann with concerns about the authenticity of the survey
due to recent fraud and identity theft issues is the US. After assuring them the survey was
academic research, and not fraudulent, these two participants completed the survey.
People who refused to take the survey were all polite. I never felt threatened or
uncomfortable when someone refused to take the survey.
When a randomly selected parcel refused to take the survey or if they had a ‘No
Soliciting’ sign, I would attempt to distribute the survey to a neighbor directly next door
to the refusal household. Some clusters had multiple refusals that could not be distributed
elsewhere in the cluster, so they were not distributed; this was the case for 107 of the
1050 total surveys.
Diversity of Housing Types in Survey Clusters I completed brief field notes about the character of the neighborhood and housing
style for each cluster. It is not within the scope of the project to do an architectural
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analysis of the housing stock, but the neighborhoods I visited were diverse in
architecture, square footage, and era built. My field notes, which include general housing
characteristics for each cluster, are available in Appendix E.
Post-Survey Distribution Online surveys allowed for instant results and saved a large amount of money on
postage. There was no need to wait days or weeks to be mailed. Furthermore, having
response rates quickly available was critical to changing my survey distribution technique
after the first day of survey distribution by increasing attempts of face-to-face
engagement. In addition, one respondent requested to see the results of the survey, which
I provided to them through email in PDF format.
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APPENDIX D: OBSERVED HOUSING TYPES BY CLUSTER Below are field notes related to the different types of housing observed in each cluster while distributing surveys. The house style was gathered from a home typology index (Realtor.org), and the approximate square footage for homes in the cluster was aggregated from the real estate website Zillow.com
HH Cluster Sample
Cluster City/town Style Approx.
year built
Approx. square footage
Other Observations
1 Boulder Split level 1960s 1000 sf
some homes have been retrofitted/rebuilt
2 Louisville Colonial early 1990s 2500 sf
3 Boulder Single level ranch 1950s 1200 sf
Near CU; seemed to be a decent number of renters in the cluster
4 Boulder Split level 1960s 1000 sf
5 Boulder Masonry late 1980s 3500 sf
6 Boulder Victorian late 1800s 2000 sf
neighborhood is in the foothills of Boulder. Some homes had been turned into duplexes
157
7 Boulder Victorian early 1900s 1200 sf
Near CU; seemed to be a decent number of renters in the cluster
8 Boulder Single level ranch late 1950s 1500 sf
9 Boulder Contemporary early 1970s 2000 sf
Denser development: homes had shared driveways and small yards
10 Boulder Craftsman early 1990s 2000 sf
This neighborhood was a mix of condos and single family homes (only surveyed single family homes). Some traces of new urbanist design: denser development, garages behind some homes
11 Boulder Split level early 1970s 1800 sf
12 Gunbarrel Split level early 1970s 1500 sf
158
13 Niwot Single level ranch early 1970s 2000 sf
14 Niwot Rambler Mid 1990s 2000 sf
15 Longmont Mediterranean early 2000s 2000 sf
Dense development; all homes looked the exact same
16 (re-sample) Lafayette Rambler Mid 1990s 2500 sf
17 (re-sample) Lafayette Split level early 1980s 1000 sf
HL Cluster Sample
Cluster City/town Style Approx. year
built
Approx. square footage
Other Observations
1 Louisville Rambler early 1990s 1700 sf Near an area of open space
2 Louisville Rambler early 1990s 2200 sf
159
3 Eldorado Springs
all 6 homes in this cluster either refused to take the survey or had 'No Soliciting' signs
4 Boulder
Single level ranch early 1980s 2500 sf
some homes in this neighborhood were upgraded/rebuilt; near an area of open space
5 Boulder
Single level ranch Mid 1960s 2000 sf
6 Boulder Split level early 1970s 1800 sf
Surrounded by open space, school bus depot and a power plant
7 Boulder Varying styles
mid to late 2010s 5000+ sf
very large custom built houses on large >1 acre lots; open space behind many of the homes
8 Lafayette Rambler early 1990s 2500 sf
9 Lafayette A-frame late 1990s 4500 sf
Large custom homes located on relatively small lots; golf course community
160
10 Lafayette
Single level ranch mid 1970s 1500 sf
Surrounded by open space
11 Longmont
Single level ranch early 1970s 1200 sf
many homes in need of upkeep; surrounded by a school, Longmont Public Works building (has a large parking lot), and open space
12 Longmont
Single level ranch early 1970s 1000 sf
13 Longmont Split level Late 1970s 1200 sf
some homes near a lake/park (open space)
14 Longmont Dutch Colonial Mid 1980s 3000 sf
15 Boulder
Single level ranch early 1980s 4000 sf
near a lake (open space)
LH Cluster Sample
Cluster City/town Style Approx.
year built
Approx. square footage Other Observations
161
1 Louisville Split level
early 1980s 1000 sf
Based on casual observation, this cluster had a higher amount of homes with rooftop solar panels than any other cluser
2 Lafayette
Single level ranch and split level late 1970s 1000 sf
3 Boulder
Single level ranch and split level late 1960s 2000 sf
4 Boulder
Single level ranch and split level late 1960s 2000 sf
5 Boulder
Single level ranch and split level mid 1970s 3000 sf
Golf course community
6 Boulder
Single level ranch and split level mid 1970s 3000 sf
Golf course community
7 Boulder
Single level ranch late 1960s 2500 sf
Golf course community
8 Boulder
Single level ranch
early 1970s 2500 sf
162
9 Longmont
Single level ranch and split level mid 1970s 2000 sf
10 Longmont Colonial Mid 1990s 1000 sf
This was a dense neighborhood (small yards and closely spaced homes) with a mix of single family homes, duplexes and condos (only distributed to single family homes)
11 Longmont Rambler early 1990s 2500 sf
12 Longmont Condo building
Once I determined these were condos, I did not distribute surveys here
13 Boulder
Single level ranch and split level mid 1970s 3000 sf
Golf course community
14 Boulder Rambler late 1980s 3000 sf
15 Boulder
Single level ranch and split level
early 1970s 1000 sf
163
NS Cluster Sample
Cluster City/town Style
Approx. year built
Approx. square footage
Other Observations
1 Longmont
Single level ranch
early 1970s 1500 sf
2 Longmont Craftsman mid 2000s 2000 sf
New urbanist development; closely spaced homes, garages in an alley behind house
3 Longmont Colonial late 1990s 1800 sf
Closely spaced homes, small lots; many, but not all, people in this cluster were of retirement age (possibly a quasi-retirement community)
4 Longmont Rambler Late 2000s 3000 sf
5 Niwot Rambler Mid 1990s 2800 sf
6 Niwot
Single level ranch
late 1970s 1500 sf
164
7 Boulder
Single level ranch
mid 1960s 1400 sf
cluster had a very rural feel to it; spaced far apar; multiple homes with livestock
8 Boulder Contemporary
mid 1980s 1500 sf
9 Boulder
Single level ranch and split level
Late 1960s 1000 sf
Close to CU; some renters in this cluster
10 Boulder Rambler Early 1990s 2800 sf
11 Boulder
Single level ranch
Mid 1930s
1000-2000 sf (size varied in this cluster)
Cluster along a main road; not really a neighborhood
12 Louisville
Single level ranch and split level
late 1970s 1300 sf
13 Lafayette Craftsman mid 2000s 2000 sf
New urbanist development; closely spaced homes, garages in an alley behind house. Quite a few homes had solar panels on the roof
165
14 Louisville
Mixed (single level ranch, remodeled Victorians, tear downs that were new large homes)
mid 1950s to mid 2000s
1500 sf to 3000 sf
This was a highly variable neighborhood. Near old town Lewisville, so there were still some old houses, with newer remodels and rebuilds dotted around
15 Superior Rambler Late 1990s 2000 sf
166
APPENDIX E: SURVEY DISTRIBUTION ENVELOPE Note: envelope was printed in black and white
167
APPENDIX F: SURVEY INTRODUCTION LETTER Note: The survey link provided to respondents was dependent upon which of the four cluster areas they were in. All four surveys were identical, but having different links allowed me to divide responses by cluster zone. The survey introduction letter was printed in black and white.