Paul McLauchlin Thesis

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A Business Case for Microscale Renewable Energy Deployment
in Rural Alberta, Canada: Partnerships, Resources, and Incentives
for Public Policy Success
Paul Adam McLauchlin
Subject Area: Masters of Business Administration
Specialization: Finance
Supervisor: Dr. Panayiotis Savvas
Words: 16290
Submitted: September 1st, 2016
Dissertation submitted to the University of Leicester in partial fulfilment of the requirements of
the degree of Master of Business Administration

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Table of Contents

Table of Contents.................................................................................................................2
List of Acronyms................................................................................................................5
Glossary ...............................................................................................................................6
Executive Summary...........................................................................................................7
1. Introduction .................................................................................................8
1.1. Potential for Investment of Rural Alberta, Canada.....................................................9
1.2. Barriers and Motives for Household Investment......................................................10
1.3. Research Questions...................................................................................................11
2. Literature Review on Theory and Empirical Analysis ..........................13
2.1. Introduction...............................................................................................................13
2.2. Theories of the Role of Incentives and Motives for Investment...............................13
2.2.1. Planned Behaviour and Renewable Energy Choices...................................16
2.2.2. Autarky, Own Power, and Willingness to Pay............................................17
2.2.3. Environmental, Social, and Intangible Drivers for Investment...................17
2.2.4. Summary of Theoretical Framework ..........................................................18
2.3. Empirical Studies Residential Investment in MG.....................................................19
2.3.1. Empirical Studies Involving Barriers and Motives .....................................19
2.3.2. Environmental Attitudes, Energy Attitudes, and Willingness to Pay .........21
2.4. Hypotheses and Conclusions ....................................................................................23
3. Data and Methods .....................................................................................25
3.1. Survey Design and Data Collection..........................................................................25
3.1.1. Survey: Baseline Household, Knowledge, Actions, and Intentions............25
3.1.2. Survey: Best-worst Scaling .........................................................................26
3.1.3. Survey: Scenarios and Willingness to Pay..................................................26
3.1.4. Survey: Attitudes Towards Energy .............................................................27
3.1.5. Survey: Attitudes Towards the Environment ..............................................27
3.2. Participant Identification...........................................................................................28
3.3. Analysis Methods......................................................................................................28
4. Analysis and Results..................................................................................30
4.1. Participants, Knowledge, Actions, and Intentions....................................................30
4.2. Microgenerators........................................................................................................37
4.3. Barriers and Motives.................................................................................................38

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4.4. Choices of Incentives, Investment Scenarios, and Willingness to Pay.....................40
4.5. Energy Attitudes .......................................................................................................46
4.6. Environmental Attitudes...........................................................................................47
4.7. Summary of Hypotheses Tests as Drivers for Investment........................................49
4.8. Business Case for Rural Solar...................................................................................51
4.9. Summary Business Case for Rural Solar..................................................................53
5. Discussion and Conclusions......................................................................54
5.1. Summary...................................................................................................................54
5.2. Theoretical implications............................................................................................59
5.3. Practical implications................................................................................................59
5.4. Limitations................................................................................................................60
5.5. Directions for future research ...................................................................................60
5.6. Reflections ................................................................................................................61
References …………………………………………………………………………….63
APPENDIX A: Population and Energy Use.....................................................................73
APPENDIX B: Barriers and Motivations for Microgeneration........................................75
APPENDIX C: Correlations .............................................................................................76
APPENDIX D: NEP Statistics..........................................................................................77
APPENDIX E: ROI and IRR Calculations.......................................................................78
APPENDIX F: Microgenerators.......................................................................................90
APPENDIX G: Energy Attitudes.......................................................................................94
APPENDIX H: Questionnaire ...........................................................................................95
APPENDIX I: Dissertation Proposal..............................................................................115
APPENDIX J: Best-Worst Analysis...............................................................................127
APPENDIX K: Results from Descriptive Statistics for Questions..................................133
APPENDIX L: Likelihood of Investment........................................................................138
Table of Figures
Figure 1. Motives Best-Worst Standardized Scoring .......................................................39
Figure 2. Barriers Best-Worst Standardized Scoring........................................................40
Figure 3. Types of MG systems considered (N=137).......................................................41
Figure 4. Likelihood of Investment 2 to 3 Years (N=137) ...............................................42
Figure 5. Incentives Best-Worst Scoring (N=137) ...........................................................43
Figure 6. Energy Attitudes (N=112).................................................................................47
Figure 7. Environmental Attitudes (N=87).......................................................................49
Figure 8. Likelihood of Investment 5 Years (N=137) ....................................................138
Figure 9. Likelihood of Investment 10 Years (N=137) ..................................................138
Figure 10. Likelihood of Investment with Costs and Efficiency Half in 5 years (N=111)139

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Figure 11. Likelihood of Investment with Costs and Efficiency Half in 10 Years (N=112)139

List of Tables
Table 1: General Participants’ Location and Knowledge.................................................31
Table 2: Climate Change Awareness and Attitudes .........................................................33
Table 3: Knowledge of Renewables, Willingness to Pay and Spending ..........................35
Table 4: Energy Efficiency...............................................................................................37
Table 5: Role and Preferred Type of Incentive.................................................................41
Table 6: Investment and Incentive Scenarios ...................................................................45
Table 7: Energy Use in the Province of Alberta...............................................................73
Table 8: Population of Rural Alberta Canada...................................................................73
Table 9: Farms Alberta, Canada .......................................................................................74
Table 10: Microgenerators Alberta, Canada.....................................................................74
Table 11: Energy Independence and when you would likely invest ................................76
Table 12: NEP Statistics Uncorrected...............................................................................77
Table 13: IRR Calculations 6kw System..........................................................................78
Table 14: Scenario Calculations for Price Per kwH income.............................................81
Table 15: Annual Income Scenarios Given Price of Power Increases 6kW Array ..........83
Table 16: Payback, NPV, IRR and ROI for different Price Scenarios 6Kw ....................84
Table 17: Payback Scenarios .............................................................................................85
Table 18: 3 kW System......................................................................................................86
Table 19: Energy Attitude Statistics .................................................................................94

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List of Acronyms
Term Components of the term
KW
KWH
MG
MW
Kilowatt
Kilowatt Hour
Microgeneration
Megawatt
PV
RE
UK
US
Photovoltaic
Renewable/ Alternative Energy
United Kingdom
United States

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Glossary
Term Definition
Levelized Cost of
Electricity
A lifecycle cost of electricity (per kilowatt hour as Kwh) as the
“minimum per Kwh that an electrical generator would require to
break even over the entire lifecycle of the generator”
(Reichelstein and Yorkston, 2013:118)
Micro-renewable
and Microgeneration
Small scale generation of heat and electric power by individuals.
As per the Alberta Microgeneration regulation:
1. Exclusively uses sources of renewable or alternative
energy
2. Is intended to meet all or a portion of the customer’s
electricity needs
3. Has a nominal capacity not exceeding 1 Megawatt
4. Is located on the customer’s side or site owned by or
leased to the customer that is adjacent to the customer’s
site.
Alberta Regulation 203/2015 Alberta Government Electric
Utilities Act: Microgeneration Regulation Pages 2-3.
Renewable or
Alternative Energy
Solar, wind, hydro, fuel cell, geothermal, biomass or other
generation sources
Large
Microgeneration
Macrogeneration
generation of electric energy from a microgeneration
generating unit with a total nominal capacity of at least
150 kW but not exceeding 1 MW
Generation of electricity from renewable sources of roughly over
1MW

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Executive Summary
The Alberta Provincial Government is beginning to shift the energy generation mix in favour
of renewable energy. With this shift comes the potential increase in residential or homeowner
domestic power generation in the form of micro-renewable (MG). Specifically, there are
possibilities of household investment in MG due to available land base, high energy use,
favourable regulatory frameworks, and attitudes and behaviours of rural residents. With this
shift, an analysis of willingness to invest, possibilities of production, and the business case for
solar power in Rural, Alberta has not been assessed in the literature.
Despite provincial goals for renewable energy, there are multiple barriers and motives for
households considering renewable energy investments. Modifying but replicating similar studies
undertaken in maturing or matured markets, this study identified key areas worthy of
investigation regarding MG including knowledge, actions, intentions, barriers, motives, and
energy and environmental attitudes of a random selection of rural populations. The study used a
survey that included nominal, binomial, and ordinal questions, scenarios, and opinions, and was
given in both an online and paper-based format.
The study found that although there is a knowledge of and interest in MG household investment,
there still exist motives and barriers to participants. The highest rated motivations identified by
participants included make the home more self-sufficient, protect against higher future energy
costs, save or earn money from lower fuel bills, and protect the home against power outages.
Barriers included costs too much to buy, trustworthy information is difficult to find, system
performance is unreliable, and disruptions or hassle of operation. Very few participants were
interested in the investment scenarios investigated, but there was interest in scenarios “fixing”
the utility prices of power for 10 years. The study’s investigation found that the business case
for renewables does not require incentives, and based upon future expected utility price
increases, is within acceptable conservative investment returns based upon the internal rate of
return and under different payback assessments.

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1. Introduction
In recognizing the role of conventional carbon-based power generation (in the province of
Alberta, Canada) as a source of greenhouse gas, the Alberta provincial government is
beginning to shift the energy generation mix in favour of renewable energy (RE; Alberta
Government, 2016). With this shift comes the potential increase of residential or homeowner
domestic power generation in the form of micro-renewable electricity generation (hereafter
referred to as micro-renewable and microgeneration [MG]). Specifically, there are
possibilities of household investment in MG due to available land base, high energy use,
favourable regulatory frameworks, and attitudes and behaviours of rural residents. With this
shift, an analysis of willingness to invest, possibilities of production, and the business case for
solar power in rural Alberta has not been assessed in the literature. The following sections
include the context for the research which includes; the intention to reflect on other
jurisdictions, how motives and barriers will be assessed and the research questions
investigated by this project.
Other Canadian and international jurisdictions have used household investment and
installation of MG as a component of their renewable energy goals (sometimes referred to as
domestic goals). For example, the UK domestic solar goal was “1,000,000” rooftop
installations by 2015 (Government of the UK, 2016), Ontario, Canada’s goals were “100,000”
rooftops by 2014 (Government of Ontario, 2009), India’s National Solar Mission was to add
200 MW of rooftop solar between 2015 and 2016 (Economic Times of India, 2016), and the
Government of Germany has installed 9 gigawatts of PV Capacity (Property Wire, 2010;
Wirth, 2015). These more mature renewable markets have encouraged and incorporated
household MG as a component of their renewable energy strategy by use of a variety of
policy programs and instruments that have typically included generous incentives and long-
term policy support. Moreover, in terms of the comparisons of overall societal benefit in the
long run, renewable energy, when compared to conventional sources in terms of “financial,
technical, environmental and socio-economic-political” criteria rank higher and many
jurisdictions have made significant commitments based upon these assessments (Stein,
2013:641).

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1.1. Potential for Investment of Rural Alberta, Canada
As can be seen in Appendix A: Table 7, 18% of electricity use is by residential consumers in the
province of Alberta, with 17% of the 1,405,894 electrical customers being rural (AUC, 2016). A
further 3% (83,816 customers) of energy use in the province of Alberta is identified as
agriculture related (AUC, 2016). As a land use type, 43,234 farms in the province of Alberta
have an average of 1,168 acres that represent a viable area for the Government of Alberta to
pursue a residential and agricultural renewable energy plan at the household scale (Appendix A:
Tables 8 and 9). Thus, rural Alberta has significant land base, power use, and potential investors
for micro-renewable energy.
The province of Alberta presently has few, if any, renewable energy incentives outside of
pilot programs, however, they have established CO2 reduction commitments and a well-
published phase-out of traditional power generation by coal (Alberta Government, 2016).
With this in mind, by looking at the experience of established government programs and
incentives in other jurisdictions such as Germany (Hoppmann et al., 2014; Weiss, 2014;
Wirth, 2015), the United Kingdom (Walker, 2012) and the State of California (Dong et al.,
2014), one can look to the potential motives and barriers micro-renewable investors and
local/regional governments may have had or currently have (Holtorf et al., 2015). These early
adopting and mature renewable markets have potential transferrable experiences for the
burgeoning Alberta, Canada renewable program.
Rural Alberta provides the possibility of residential investment in MG as the capacity of solar
power is significant from a production and capacity standpoint in the region (NRCAN, 2016).
Renewable energy is best generated where it is used, and specifically agricultural users tend to
have significantly larger power use requirements than their urban neighbours. Heinonen and
Junnila (2011) found in rural Finland that “electricity dominates the total energy consumption
in the rural areas” (P:1245) and is analogous to the rural Alberta, Canada situation. Rural
local government participation may have the potential to become an economic development
opportunity while supplying sustainable energy, requiring very little change in land use policy

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(Luger, 2007), as a method for providing incentives (Dong et al., 2014; Hoppmann et al.,
2014), or potentially as Public Private Partnerships (Vining and Boarman, 2006). The
business case for micro-renewables goes beyond just financial as the renewable energy
systems provide intangible benefits that can become part of an overall economic development
scenario analysis (Shrivastava, 1995).
The role of rural communities in the potential contribution to renewable energy, when
coupled with the inherent advantage of rural land bases, has been evaluated in other Canadian
jurisdictions (Mosher and Corscadden, 2012), but not for the potential impact to the province
of Alberta. In an analysis of wind turbines and farms in Nova Scotia, Canada, Mosher and
Corscadden (2012) found that three policy objectives should be sought by policy makers:
maximizing generation, offsetting greenhouse gas emissions, and minimizing costs to
consumers (see also Balcombe et al., 2014; Holtorf et al., 2015). However, this is not without
complexity as 50% of rural and urban Albertans, in the wake of a new carbon tax, oppose the
switch to renewable energy (Mainstreet, 2015; Mildenberger et al., 2016).
1.2. Barriers and Motives for Household Investment
Despite the national and provincial goals for renewable energy, multiple barriers and motives
exist—even in mature renewable markets—for households considering renewable energy
investments. These barriers take the form of endogenous factors, such as “awareness of the
technology” and “environmental consciousness,” and exogenous factors, such as “costs, market
structure and regulatory frameworks” (Islam and Meade, 2013:522). In the UK, for example,
Allen et al. (2007) noted that despite a favourable incentive environment, the actual payback
period was uncompetitive early in the adoption process. In many cases the incentive structures,
whether generous or not, do not fully incorporate the positive externalities (reduction of CO2) of
competitive energy sources (increased CO2), thus do not provide a cost-competitive alternative in
many jurisdictions (Islam and Meade, 2013). Further, delays in adoption, even with the trend in
reductions in installation costs, could become a barrier as investors anticipate further cost
reductions of technology over time (Jaffe and Stavin, 1994). Additionally, delays may be a

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result, as Bauner and Crago (2015) modelled using the option value decision rule, because the
values of benefits must exceed the investment cost when based upon factors that address
uncertainty of benefits, as investors may wait for resolution of uncertainty. Guidolin and
Mortarino (2010), in looking across multiple countries’ programs, deduced that due to a time
delay in returns of investment, adoption of renewable technologies—specifically solar—is seen
as a risky endeavour by households. Moreover, Guidolin and Mortarino (2010) found that other
deterrents exist for adoption due to complexity of installation and operation, and concurred with
Jager (2006) that immature markets are typified with unknowledgeable investors. Jurisdictions
that incorporate significant policy-based incentives tend to increase “diffusion” of renewable
technologies despite the aforementioned barriers (Guidolin and Mortarino, 2010; Islam and
Meade, 2013).
1.3. Research Questions
This study seeks to identify, by way of surveys, how rural Albertans feel regarding possibilities
of household investment in micro-renewable energy. Key questions that this investigation seeks
to resolve are:
 What are the potential barriers for deploying micro-renewable energy as seen by rural
municipalities, service providers, and potential participants?
 What are the motivations of households’ (investors) decisions about where to install
micro-renewables in rural Alberta?
 What effect does the relative importance of motivations and barriers have on the business
case for micro-renewable energy?
 What potential production possibilities exist in the development of micro-renewable
energy projects in rural Alberta?
 What performance measures, results, and opportunities exist in developing micro-
renewable projects in rural Alberta as a means of meeting local, regional, and provincial
goals for energy and climate change strategies and policies?
The methodology employed in this study used online and paper-based surveys of mostly rural
participants geographically distributed throughout all of the province of Alberta, Canada. In

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using simple yes or no queries, Likert-like questions, and best-worst scaling methodologies,
this study sought to understand the current situation in rural Alberta for the potential of
investment in renewable technologies, and specifically solar photovoltaic. The topic will be
of interest to policy makers, municipal leaders, service providers, and the general public in
that it will provide insight into the interest, knowledge, intentions, and characteristics of
households, and identify barriers and motives for investment. This topic was chosen because
it provides a unique opportunity to use current research as potential leverage to influence
future strategies that may facilitate policy success. A final goal of this investigation is to
determine the business case for rural MG and provide insight into the potential opportunities
it can provide to meet provincial and federal climate change objectives, as well as provide for
the needs of rural Alberta households.

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2. Literature Review on Theory and Empirical Analysis
2.1. Introduction
What follows is a review of the literature surrounding the theories of the roles of incentives,
planned behaviour, and theories of choice. Sarzynski et al. (2012) looked at effectiveness of
different forms of incentives relative to the deployment of “solar capacity,” which was further
supported by those results seen by Kwan (2012). Barr and Gilg (2007) postulated how theories
of planned behaviour affect environmental actions, including renewable investment, by people.
These theories of planned behaviour are further manifested in those decisions related to personal
choices as theorized by Stern et al. (1993) based upon levels of environmental concern and
behaviour (see also Aldrich et al., 2007). Subsequent to this is a review of the empirical studies
surrounding willingness to pay, as well as motives and barriers for household (residential)
renewable investments as seen in other comparative investigations.
2.2. Theories of the Role of Incentives and Motives for Investment
A risk of climate change to world economies, societies, and the environment due to CO2
emissions from the burning of fossil fuels has been confirmed (IPCC, 2009). In response,
European, Asian, and American jurisdictions have adopted incentive mechanisms to promote
private investment (residential) in renewable energy technologies to meet national climate
change objectives (Brown et al., 2011). This “decarbonisation” of the electricity market has
been an ongoing trend internationally with only marginal—if any—actions in the province of
Alberta, Canada to promote renewable energy. The primary motive of governments in the
adoption of incentives for renewable energy is to level cost parity of traditional electrical
generation market costs in comparison to higher costs of renewable technologies (Darling et
al., 2011; Reichelstein and Yorkston, 2013; Branker et al., 2011; Stein, 2013). The overall
motive of government involvement is “correct[ing] negative externalities” by using incentives
for “achieving dynamic efficiency by stimulating technical change” (Menanteau et al.,
2003:800). The foundation for the use of incentives is based upon a comparison of renewable

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energy to conventional energy as a mechanism to bridge the gap between the comparative
levelized cost of electricity (LCOE).
The levelized cost of electricity (LCOE) has been used as a mechanism to compare standard
generating plants (e.g. coal, gas, diesel) to renewables as described by Reichelstein and
Yorkston (2013) and by Branker et al. (2011). LCOE is a lifecycle cost of electricity (per
kilowatt hour as Kwh) as the “minimum per Kwh that an electrical generator would require to
break even over the entire lifecycle of the generator” (Reichelstein and Yorkston, 2013:118).
This analysis allows for a comparison and rationale for the inclusion or exclusion of
renewable electricity generation sources in a pairwise fashion and based upon a variety of
scenarios (Branker et al., 2011). What LCOE comparisons have shown is that there exists a
notable need to bridge this gap by the use of subsidies as nations and jurisdictions pursue their
carbon emission reduction goals. The benchmark analysis is found in the Lazard Report,
whereby they “compare the cost of generating energy from conventional and alternative
technologies” (Lazard, 2016). What the Lazard report shows is that the LCOE, without
incentives and the incorporation of externalities (tangibles such as carbon pricing, and
intangibles such as well-being and social conscience), is not at parity when comparing
residential installations to non-renewable conventional power generation (Lazard, 2016).
The role of incentives and the issues that can be created can be exemplified in the case of
Rooftop Solar (an urbanized RE strategy), which has issues related to the higher LCOE when
compared with utility scale solar PV and wind (Lazard, 2016). Not only is the LCOE much
higher when compared to conventional power, but in some jurisdictions it has the issues of
“potentially adverse social effects in the context to net metering regimes” where there exists a
potential of “high income homeowners benefiting” disproportionately while relying on the
grid, causing a cost transfer “to the relatively less affluent” (Lazard, 2016:1 executive
summary). An example of this issue is in Arizona’s rooftop strategy, which allows for
revenue streams (net metering) as a production-based incentive program that has favoured the
more affluent (Hertzog, 2013). This has been deemed a “Reverse Robin Hood Effect” by the
Institute for Energy Research (2013), whereby affluent ratepayers can afford the investment in
MG, which causes a cascading burden on the system ultimately borne by all the ratepayers.

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Thus, incentives, while designed to increase the investment and adoption of MG, can have a
net negative effect on the system by burdening less affluent with the benefits gained by the
investor (CPUC, 2013). This however, is not without controversy as the CPUC report has its
share of critics and opposition, but as is mirrored in Arizona, there does exist a “cost shift”
that is borne by the system, which is ultimately borne by the ratepayers.
Production-based incentives take the form of feed-in tariffs (FIT) or similar credit or
compensation programs that provide a premium for MG power generation back to the
producer. FIT programs provide a minimum tariff per kWh based upon time; in the US there
are production tax credits, which can be leveraged against a tax base; and finally, there is a
quota system, which creates tradeable certificates that are sellable in the marketplace (Stram,
2016). Investment-based incentives have been based upon tax credits (e.g. California), grants
(e.g. Holland), tax exemption (e.g. Arizona, Maine), accelerated depreciation, interest-free
loans (e.g. Australia), and loan guarantees (e.g. U.S. Department of Energy). Both California
and Germany have the most mature and established incentive history and have been the model
by which many other governments have learned about policy development, societal uptake,
the role of incentives, and the impact of wider uptakes on the electricity infrastructure (Weiss,
2014; CPUC, 2013).
Many jurisdictions have implemented incentives but have often failed to meet their renewable
energy targets. An example of these failures was stated by Walker (2012) in the UK
renewables obligation, in not meeting their targets regardless of incentive types or methods.
Balcombe et al. (2013) have identified capital costs, regardless of incentive method, as the
greatest barrier to private investment in renewable energy by households (also seen by Scarpa
and Willis, 2010; Maalla and Kunsch, 2008; Palm and Tengvard, 2011). A lack of investment
has been a struggle in other countries that have progressed further along towards their
renewable targets.
As mentioned previously, the role of incentives is to correct the negative externalities of fossil
fuels. What comes with that is the need to bridge the internal costs (price per Gj) while
recognizing the overall goals of reducing these externalities (air pollution and carbon emissions).

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Welsch and Ferreira (2014) found that in fact the use of renewable energy has the effect of an
increased level of “well-being.”
2.2.1. Planned Behaviour and Renewable Energy Choices
Barr and Gilg (2007) and Arkesteijn and Oerlemans (2005) have identified a model of
“planned behaviour” after Ajzen and Fishbein (1977) for personal environmental choices
(investments). This model of “planned behaviour” has proposed that the drivers of behaviour
are attitudes (Ajzen, 1988), subjective norms (Tarkiainen and Sundqvist, 2005) and
perceptions of behavioural control (Ajzen, 1991). This behaviour manifests itself as a
household’s “intentions” that “subsequently lead to actual behaviour” in making investment
choices (Leenheer et al., 2011:5622). Upon identifying the potential drivers of this
willingness to pay for the adoption of renewable technology for power generation, one may
implement a successful incentive program (Banfi, 2005). Balcombe et al.’s (2013) review of
18 relevant studies summarized the expected motivations and barriers for the adoption of PV
systems. The household choices for investment and the related factors that cause households
to conserve, innovate, and invest in MG included motivations and barriers related to finance,
the environment, security of supply, uncertainty and trust, and inconvenience and impact on
the resident (Balcombe et al., 2013:656). As Stern (1992) has put forward, the theories of
psychology that influence behaviours related to energy conservation and choices are based
upon attitudes and “household knowledge” regarding the costs of renewable choices.
One assumption made in this study is that rural communities, based upon geographic,
demographic, economic, and societal norms, can be identified as a group that would be highly
likely to invest in renewable opportunities (Mosher and Corscadden, 2012). Rural community
participation has been shown to reveal a positive socioeconomic potential for renewable
adoption at regional and local scales (del Rio and Burguillo, 2008). The study tests the
assumption that given the “ideal” conditions, there will be a reasonably high level of intention
of adoption of renewable energy opportunities by rural participants with the right policy and
incentive development.

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2.2.2. Autarky, Own Power, and Willingness to Pay
Another driver of renewable energy is the concept of autonomy associated with the distributed
self-generation of power. Autarky is the concept of self-sufficiency at a national level, however,
it has relevance in the context of a rural community (Muller et al., 2011). In the context of
renewable energy, individuals can have drivers for the investment based upon a drive for self-
sufficiency and a separation from reliance on grid-based systems at the community level
(Walker, 2008). In fact, as Muller et al. (2011) have stated, this self-reliance on energy provides
a sustainable development vision and framework, which regions and individuals can use as a
catalyst of acceptance (Wustenhagen et al., 2007).
Taking autarky one step further at the individual level is the concept of “Own Power,” which is
the ultimate level of energy self-sufficiency (Muller et al., 2011; Leenheer et al., 2011). This
action and the motives behind it forms another component of understanding for the choice of
individuals (households) to invest in renewable energy (Leenheer et al., 2011). Scarpa and
Willis (2010) showed that because of the pressures of “supply side” finances (the existing low
cost of carbon-based power generation) that even if one has the drive for own power, they rarely
act on it. Conversely, it is only with early adopters that the drive for own power is enough to
change and incentivize the household (Scarpa and Willis, 2010). It is those with strong
“attitudes towards the environment” and a “lower reputation of energy companies” that the own
power drive exists (Leenheer et al., 2011:5623–5624). Individual motives such as attitudes
towards the environment (like Dunlap’s NEP, discussed later in this section) remain drivers for
individual investment; the literature has shown that these drivers dominate motives for household
energy use and by extension, generation (Poortinga et al., 2004; Van Raaij and Verhallen, 1983).
2.2.3. Environmental, Social, and Intangible Drivers for Investment
We have looked at financial, behavioural, and independent drivers for investment in MG and
need to consider the societal, environmental, and other intangible theories of what motivates
individuals to invest or, just as significantly, to not invest. These drivers take three forms:
affinity for energy, affinity for technology, and affinity for the environment. More specifically,
the drivers of those who invest in renewable energy technologies as early adopters and the

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motives that drive these adopters can be a litmus test to the underlying drivers and the diffusion
of innovation by their investment behaviour (Rogers, 1995). Rogers’s (1995) diffusion theory of
innovation holds some promise towards understanding the role of environmental, social, and
other intangible drivers for investment.
In terms of renewable energy in an undeveloped or immature market, one can look at the
prospects of innovation diffusion as per Rogers (1995). Early adopters (innovators) have distinct
drivers and characteristics as householders that are important to consider: “they have high social
status, financial liquidity, advanced education, and are more socially forward” when compared to
laggards at the end of Rogers’s adoption spectrum (Rogers, 1995). This has been seen in Sauter
and Watson’s (2007:2270) findings that “domestic investments” in MG are different from other
energy choices that are driven by “social acceptance;” rather they are driven by “active
acceptance” by the household. This then transfers the concept of choice as it relates to energy
choices from passive support for renewable energy, to the active support of the household as an
energy producer. These motives and actions are important for understanding the energy affinity
and environmental affinity of households.
2.2.4. Summary of Theoretical Framework
The theoretical framework for this study has resulted in the postulation of a conceptual model of
the behaviour of potential investors. Incentives as a mechanism for “leveling” of the cost of
renewable energy generation promotes investment by households by increasing the diffusion of
renewable energy investment by households. Households see incentives as a requirement in
order to stimulate investment and make choices for renewable energy generation. Attitudes
towards energy and the environment provide a basic driver of behavioural choices in investment.
When a household is exposed to or educated as it relates to energy and environmental choices,
the “subjective norm” of the household can be a driver for investment. Finally, autarkial motives
and perceptions of control over choices and use of energy are a considerable motive for some
households to invest in renewable energy.

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2.3. Empirical Studies Residential Investment in MG
The literature regarding the topic of residential investment in renewable energy has involved a
series of studies that have looked at the barriers and motives of household investment in MG.
These studies have also focused on own power and willingness to pay. It is against this backdrop
of what drives and deters investment that the lack of extensive adoption in many jurisdictions—
even with significant incentives—can be analyzed.
2.3.1. Empirical Studies Involving Barriers and Motives
Consumer uptake for MG has been low in many EU countries, the UK, many of the States in the
US, and in several Canadian jurisdictions (Walker, 2012; CPUC, 2013; Wirth, 2015; Islam and
Meade, 2013). As Balcombe et al. (2013) found in the UK, despite government incentives and
support while showing a 10,000% increase in residential solar PV, from 2008 to 2012 they still
have only yielded less than 0.2% of UK energy demand by domestic users (further investigated
by Walker, 2012). Balcombe et al. (2013) undertook an extensive literature review of 18 relevant
studies related to the motives and barriers for adoption of renewable energy investments of a
variety of technologies including solar, wind, combined heat power, biomass, solar thermal,
photovoltaic, fuel cells, and heat pumps. Their findings provided a summary of motives and
barriers related to the adoption of MG categorized as Financial, Environmental, Security of
Supply, Uncertainty and Trust, Inconvenience, and Impact on Residence (Balcombe, 2013:658).
Financial motives were identified as a key barrier for investment by Scarpa and Willis (2010) in
their study of 1,279 United Kingdom homeowners; a choice experiment for estimating the
willingness to pay for a variety of MG technologies. Their study involved the use of a logit
model allowing them to regress the decisions to adopt a technology related to ancillary and
capital costs of installation and operation (Scarpa and Willis, 2010). Caird et al. (2008) in
interviewing 111 randomly selected individuals who had sought advice on energy efficiency or
renewable energy as a defined “greener” population also saw “up front costs” as a barrier to
adoption.

20
Jager’s (2006) study in Holland involved 197 photovoltaic adopters via interviews and closed
ended questionnaires using Likert scales, which was similar to Kierstead’s (2007) study of 91
photovoltaic adopters. Kierstead identified that adopters were wealthier and better educated in
their study population, where Jager identified that “independence” was as great a driver as
environmental attitudes. Interestingly, Jager (2006:1936) stated that even with Dutch
government incentives that “covered about 90% of the costs of a PV system, the resulting break-
even period of about 3 years,” uptake was still marginal, and in fact the Dutch government
abandoned the system for lack of uptake.
In looking at MG, Goto and Ariu (2009:6) found “low energy cost, health, usability, low risk of
system failure, and fast disaster recovery.” Interestingly, Goto and Ariu (2009) found that
motivations among 3,431 Japanese households (closed-ended questions with Likert scales)
multivariate regression analysis found that preferences of households were not based upon CO2
emissions but had particular emphasis on energy cost and added values, for example usability
and health. Faiers and Neame (2006) completed a study of 43 early adopters and 350 early
majority that were surveyed by an agreement scale survey comparing motivation and perception
traits of “early adopters” and the “early majority” in an effort to assess what barriers would need
to be crossed to have the early majority adopt. Faiers and Neame (2006) concluded that
renewable energy systems are unattractive, unaffordable, and grant levels are not high enough.
As Faiers and Neame (2006:1804) further postulated, Rogers’s (1995) diffusion theory of
innovations and the further incorporation of Moore’s (1999) identified a chasm between early
adopters and the early majority such that “systems are not visually intrusive, are maintenance
free, add value to properties, will not affect the visual landscape, and installation is easy.”
A study analogous to the one undertaken in this project was by Arkesteijn and Oerlemans
(2005:183), as their project timing was uniquely “before the liberalization of the Dutch green
electrical market, creating a unique database of residential (non-)users.” The Arkesteijn and
Oerlemans (2005) study involved adopters and non-adopters using random telephone surveys
choosing “green” and “grey” households (n=250 each). Arkesteijn and Oerlemans (2005) looked
at early adoption of green power by households and found that investors saw the “higher
involvement” and “capital requirements” as barriers to MG as it was much easier to have this
energy supplied by a green energy retailer. Moreover, Arkesteijn and Oerlemans (2005:195)

21
typified early adopters as “knowledgeable about the use and background of sustainable energy
and who often take a positive position on environmental and related issues” with the converse
being true of non-adopters. Early adopters were also typified by Arkesteijn and Oerlemans
(2005) as not being interested in the visibility of their choices and instead were seen as an
“autonomous” group driven by principle.
Prior to the opening of the Green market in Holland, the Arkesteijn and Oerlemans (2005) study
showed that the lack of information on price, ease of use, and education of what green power is
are all barriers that need to be eliminated in order to induce investment or adoption. Ben Maalla
and Kunsch (2008), in looking at combined heat power (CHP) system adoption via modelling,
showed that using “natural economic forces” is not sufficient to induce investment in a very
capital-intensive technology, and that diffusion is only possible by the use of appropriate
incentives that include both capital and “enduring” financial assistance. Richards et al. (2012),
in evaluating barriers to wind power in Saskatchewan, found technological and political barriers
dominated concerns by those interviewed. Richards et al. (2012) identified that the primary
common roots of these barriers are lack of knowledge and drivers for increasing understanding
and action.
2.3.2. Environmental Attitudes, Energy Attitudes, and Willingness to Pay
Personal belief in and the importance of the environment has been the focus of many studies on
the drivers in action or investment in renewable technologies. This driver of environmental
attitudes has a measure that ultimately results in a willingness to pay for or invest in renewable
energy choices. Batley et al. (2001) looked at consumer attitudes for the purchase of green
energy and a measure of environmental attitudes, identifying a direct correlation between
environmental beliefs and willingness to pay. Looking at populations’ environmental attitudes,
energy attitudes, and willingness to pay are all correlated measures in the literature related to
renewable energy investment.
The New Environmental Paradigm (NEP) scale is an often-used measure of environmental
attitudes (Dunlap et al., 2000; Albrecht et al., 1982). If used appropriately, it has become a

22
powerful tool for the assessment of environmental attitudes of groups of people and can be
compared to choices, decisions, and willingness to pay for environmental goods and services
(Hawcroft and Milfont, 2010). In an analysis of choice experiments in New Zealand, Ndebele
and Marsh (2014) found a direct correlation between NEP scales and willingness to pay for
Green Energy, in that those identified on the NEP scale as having a “strong environmental
attitude” will pay twice as much as those scoring lower. These results are confirmed by Amador
et al. (2013:955) in a Spanish study, which determined that along with other factors, “concern for
greenhouse gas (GHG) emissions” resulted in “engaging in energy saving actions” with positive
effects on willingness to pay.
Willingness to pay a premium over and above standard prices for goods and services for
environmental benefit has been examined in the literature specifically for MG and retail green
power. Borchers et al. (2007) determined that depending on the source of power generation,
households were willing to pay a premium for “green electricity.” This has been further
supported by Longo et al. (2008), Bergmann et al. (2006), and Ek (2005), showing that
households are willing to pay not only for “greener” electricity, but in fact are willing to pay for
better stewardship and environmental protection. The level of this willingness has been tested by
Scarpa and Willis (2010), showing that the transition from “green” retail electricity to MG has
limitations on the households’ willingness to pay. Scarpa and Willis (2010) found that the larger
capital cost of more efficient boilers, solar, wind, ground, or air source heat pumps exceeded
households’ willingness to pay. A factor of relatively three units of currency for one unit of
currency was the standard willingness to pay by households (Scarpa and Willis, 2010), and when
compared to a household “time horizon of 3 to 5 years,” “this does not correlate to the time
horizon of 10 plus years for many of these technologies.” Scarpa and Willis (2010) concluded
that while households have shown a willingness to pay for renewable and efficient technologies,
there is a large discrepancy in the cost of these technologies over what households are willing to
pay.
In the findings of their rural and urban environmental concern study, Huddart-Kennedy et al.
(2009:309) showed that rural demonstrated comparably higher scores for altruistic values,
priority of the environment, active recycling, and stewardship behaviour. In a study of

23
renewable energy investments in Scotland, Bergmann et al. (2008) found that rural residents,
when compared with their urban counterparts, had a higher willingness to pay for attributes such
as wildlife impacts, air pollution, and job creation. As Bergmann et al. (2008) have also stated,
the interesting findings of this study is that in the case of wind power, rural residents are most
impacted by these projects, but still exhibit a willingness to pay for and support their
environmental and social benefits.
Environmental attitudes and the willingness to pay have another factor related to the energy
attitude of households. This attitude manifests itself in two ways: the relationship of the
household to power use and technology, and attitudes and opinions about retailers of energy.
The energy autarky and the drivers of this willingness to pay and generate one’s own power have
been assessed in the literature. Leenheer et al. (2011), in looking at Dutch households, found
that 40% of them wish to generate their own power. Two groups are represented by this data and
the motives to generate one’s own power: “generating savers” (21%), who wish to generate to
save money, and the “generating users” (19%), who are not driven to save (Leenheer et al.,
2011:5627). While environmental drivers in Leenheer et al.’s (2011) study are foremost in
motives, the affinities with power and technology and attitudes or perceptions of energy
companies are also drivers of this motive and intention. These drivers, interestingly enough,
eclipsed the primary economic driver identified by Scarpa and Willis (2010). This difference
between Scarpa and Willis (2010) and Leenheer et al. (2011) likely had shown that the
measurable drivers of environmental concern, energy attitude, and the resultant willingness to
pay can be high enough to generate household intentions that may be limited by capital
availability/priorities rather than willingness.
2.4. Hypotheses and Conclusions
Based upon a review of the literature, five main hypotheses have been postulated related to the
motivations and barriers for household investment in renewable energy. One primary driver
(Hypothesis one) is that the main driver for household investment in MG is based upon
decreasing the household’s carbon footprint: Environmental Concern. Trends in decarbonizing
of the energy market and the incorporation of carbon taxes will drive up the price of power that

24
investors may counteract by generating power themselves (Hypothesis two), therefore, a motive
will be to: Offset Higher Market Prices. An appreciation of technology by households
(Hypothesis three) and the reputation of innovation will be a driver for future and present
investors in MG. Hypothesis four has been a long-term trend in the increase of power bills,
ancillary charges, and anticipation of further perceived “victimization” by monopolies; thus
Hypothesis four is an investment response to offset: Monopolization of Power Purchase Choices.
Hypothesis five is based upon individual choices and motives for autarkical motives: Reliability
and Self Reliance.
The theoretical framework for this study has shown that MG is perceived as a capital- and
resource-intensive exercise for the average household. While incentives have been designed as
both financial and regulatory/technical assistance to promote MG, the actual level of investment
in many comparable jurisdictions has been less than planned or expected. Behaviour, motives,
knowledge, attitudes, and willingness all have a role to play in household choices for investment.
What the literature has shown is that the majority of households, for often similar reasons, have
environmental, social, economic, and personal drivers that have promoted their willingness to
invest in MG, and depending on the household acceptance, there are many barriers that still exist
that can prevent the uptake of participation. The situation in Alberta as having low current
uptake and little to no incentives is an interesting opportunity to assess this willingness and
intention, and to identify barriers early on in order to design and implement successful policy.

25
3. Data and Methods
This study was designed as a hybrid of both a choice study and a survey of current
behaviours, knowledge, and actions of survey participants. The unique timing for this study
prior to the development of incentive programs for efficiency and generation and details of
policy implementation in the province of Alberta, Canada, allows us to determine the situation
prior to a government strategy. Categories of the study involved four main areas of
investigation: (a) establishing what individuals are doing now and what they know (adoption
of renewable energy is marginal at best); (b) determining households’ intentions to
participate; (c) a replicate investigation (modified) of the relative importance of motivations
and barriers related to MG choices; and (d) the establishing of household attitudes to the
environment and energy, with the final result of the determination being households’
willingness to pay given standardized mock scenarios.
3.1. Survey Design and Data Collection
The study is modelled upon the methodology of Balcombe et al. (2014) using a “Best-Worst
Scaling” (BWS; Vermeulen et al., 2010; Louviere et al., 2013, 2008) of the criteria identified by
Balcombe et al. (2013; Appendix B), albeit with modifications. This model has been chosen
because it represents the results of the comprehensive review by Balcombe et al. (2013) that
compiled research on the motivations and barriers in many jurisdictions with varying incentive
scenarios for micro-renewables. The surveys were designed to be as accessible and as easy as
possible for participants to respond to, did not ask for personal information such as gender,
income, and age, and was not stratified. The survey was in both a paper and online format, the
latter of which was developed to be delivered using Qualtrics online software and was
specifically sent to rural residents (including towns with populations under 10,000 persons).
3.1.1. Survey: Baseline Household, Knowledge, Actions, and Intentions
This portion of the survey is intended to assess the household’s current situation as it relates to
location, awareness of climate change, attitudes towards climate change, knowledge of

26
renewable energy, and energy efficiency choices. Additionally, the survey assessed whether
individuals had purchased renewable energy systems, what they chose, and their level of
satisfaction with their system. If individuals had not purchased systems, survey questions
investigated what technology they may have considered, to what stage they may have
investigated a choice, and timeframes for MG choice intentions.
3.1.2. Survey: Best-worst Scaling
The majority of the survey was developed into a BWS method that includes “five choices for
motivations” with four motivations, and “seven choice tasks for barriers” with five barriers
(Balcombe et al., 2013:406). Appendix B provides the base for creating a list of 12 “choice
sets” with four or five items per choice. This method was selected as it allows this research
project to account for the hierarchical representations of values in choices “over large sets of
independent items” (Balcombe et al., 2013:407). The methodology selected allows
respondents to make judgements by extreme comparisons, which results in ratio-scaled results
with better discrimination (Vermeulen et al., 2010). When compared with ranking based
methods (Likert, for example) that have issues such as scale bias and are difficult for
discrimination of a large number of items, ratio-scaled results offer an advantage (Balcombe
et al., 2013:407; Cohen and Orme, 2004).
3.1.3. Survey: Scenarios and Willingness to Pay
The researcher developed the survey questions to identify the willingness to invest based upon
different subsidy scenarios. Scenarios loosely followed Scarpa and Willis (2010), but instead of
choice experiments, investment decisions were based upon a blend of capital requirements and
potential returns on investment. The scenarios were based upon Islam and Meade (2103), with
the exception of attributes related to feed-in tariff (which has not been proposed in Alberta), and
involved up-front grants when compared to increases in subsidies spread over time at varying
levels. Choice models were eliminated as the Ontario, Canada model has very generous feed-in
tariffs and the study population lacks knowledge of or experience with the possibilities of feed-in
tariffs. The lack of exposure in Alberta to any incentive program made it difficult to adopt
choice experiments. The primary motive behind the chosen scenario model in the survey is a test

27
of whether the province of Alberta may fall victim to the “Reverse Robin Hood Effect.” This
effect occurs when the capital requirements of MG incentives make it only available to the
wealthy. Although this study did not assess demographic drivers to solar adoption, there are
opportunities to design incentives to use government-backed loans and payment systems. Islam
and Meade (2013) found that there was a direct connection between income levels and adoption
in Ontario, Canada, which highlights the issue of the “effect.” Data from this portion of the
survey is to be used to provide the econometric analysis that forms the basis of this study in order
to determine the possibilities of the best form of incentives and policies for MG.
3.1.4. Survey: Attitudes Towards Energy
The survey also assessed the attitudes and interest levels of participants from energy and power
companies. This portion of the survey was modified after Leenheer et al. (2011), including
modification based upon Maignan (2001), and regional modifications for language and value
statements. The questions were designed to assess affinity with technology, energy prices, and
power companies’ reputations. The scale of the assessment mirrored the Likert agreement
spectrum of analysis used in the New Ecological Paradigm (NEP) for possibilities of comparison
and correlations. Reasons for this were to compare survey results of Energy attitude agreement:
“I want to be energy independent,” and see if they are correlated with NEP statements such as
“We are approaching the limit of the number of people the Earth can support” or juxtaposed to a
dominant social paradigm statement such as “Humans were meant to rule over the rest of
nature.”
3.1.5. Survey: Attitudes Towards the Environment
This portion of the survey involves the use of the New Ecological Paradigm (NEP) as developed
by Dunlap (2008). The intent of this component of the survey is to provide a context of
individual measures of environmental concern as a comparable spectrum of agreement with
standardized questions. The NEP questions are developed to assess the spectrum of agreement
with statements that are categorized as measures of both the dominant social paradigm (DSP)
and the NEP. These two measures are polarized by a belief in the dominance of humans and

28
their needs (DSP) versus the NEP, which rejects this anthropocentrism and believes in the
important role and required protection of nature.
3.2. Participant Identification
The survey was in two forms: an online Qualtrics-based survey and a paper version that exactly
replicated the questions found in the online version. Identifying sources of participants involved
the use of provincial, regional, and municipal contacts to randomly distribute the survey on
behalf of the project. Additionally, three different data sources were identified for this
investigation: (a) Utility Service Providers; (b) Rural Municipal Administration; and (c) Rural
and Urban Landowners. Geographical distribution of participants is based upon the six zones of
the Alberta Association of Municipal Districts and Counties (AAMDC). Participants included
rural service providers’ key contacts within the zones, rural municipalities’ representatives of the
identified zones, and agricultural societies found within the zones. Participants were emailed
and invited to participate, then followed up with to ensure participation.
3.3. Analysis Methods
The majority of the survey responses were categorized as nominal and ordinal. In order to
present the outcomes of data collection for the responses, SPSS was used to present the
frequency distribution of responses from the surveys. For Likert scale data, using SPSS, the
mean was calculated for a representation of the average response based upon the population
studied. Included in this analysis the median was calculated as a measure of central tendency
(using SPSS) as a representation of the “likeliest” of the responses (Kostoulas, 2016). For Likert
scale data, in addition to mean and median, the interquartile range was calculated as a “measure
of dispersion” using SPSS for those pertinent responses (Kostoulas, 2016).
Preference choice assessments (BWS) are analyzed using random utility theory as postulated
by Louviere et al. (2008; 2013) and Finn and Louviere (1992). Results are statistical analyses
of the best-worst comparisons (Allenby et al., 2005). The method employed was a
modification of the MaxDiff method, as described by Cohen and Orme (2004), in order to

29
determine results of the best-worst choice being made by respondents (Cohen and Orme,
2004).
The analysis was mirrored on Finn and Louviere (1992); based upon this design, all the
motivations showed up four times in five choice tasks in the survey, and barriers showed up
three times in seven choice tasks. Level of importance is calculated by subtracting the
number of times a barrier or motive was least important from the number of times it was most
important in all choice sets. Therefore, it is determined that the level of importance of each
barrier or motivation depends on the number of respondents and the frequency that an
attribute appears in the choice sets. From this the level of importance is transformed into a
standard score (Finn and Louviere, 1992). Standardization allows comparison between
different groups of respondents where the number differs in each choice group.
The formula is thus,

Count Countw

Where,
Countbest is the number of times a barrier or motive was most important
Countworst is the number of times a barrier or motive was least important
n is the number of surveys
freq is the frequency of the appearance of each barrier or motive in the questions

30
4. Analysis and Results
This section of the research will provide an interpretation and presentation of the results
of this study in reflection to findings in the literature. This analysis looks at four parts of
the study: (a) location, knowledge, actions, and intentions of the household; (b) BWS
barriers and intentions; (c) energy attitudes; and (d) attitudes towards the environment.
Finally, the results from this investigation will be further formulated and compared to a
financial business case for MG using photovoltaic installations as a model.
4.1. Participants, Knowledge, Actions, and Intentions
The first part of the study data is based on identifying where the participants come from. This
study was intended to focus on rural opportunities, however, there was room in the sample
numbers for input from urban dwellers for the purposes of comparison. Data from the surveys
show that 86.9% of the survey population is rural (as defined to include towns, villages, and
hamlets under 10,000 in population) and 85.4% own their home (Table 1). Acceptance of urban
participants was based on findings of Huddart-Kennedy et al. (2009:309), revealing that “results
showed few differences between rural and urban residents on indicators of” environmental
concern.

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Table 1: General Participants’ Location and Knowledge
Parameter Status %
Resident Status
N=137
Rent 14.6%
Own 85.4%
Property Location
N=137
Rural (includes towns and
hamlets)
86.9%
Urban 13.1%
Climate Change Awareness
Median: 1 IQR: 1
N=137
Yes 69.3 %
No 6.6 %
Do not Agree 24.1%
Does Climate Change concern you?
Median: 1 IQR: 1
N=137
Yes 35.8%
No 49.6%
Do not Agree 14.6%
How important is it that we act on
Climate Change now?
Mean: 3.85 SD: 1.584 Variance:
2.508
Median: 4 IQR: 3 N=137
Extremely Important 8.8%
Very Important 16.1%
Moderately Important 13.9%
Slightly Important 21.9%
Not at all Important 21.2%
Climate Change is not
happening
18.2%
Data regarding climate change is interesting as the media has been inundated with
information on new federal and provincial climate change discussions, policies, and
prospective actions. When one looks at the survey question: “How important is it that we
act on climate change now?” we see in Table 1 that only 8.8 % of those surveyed feel it is
extremely important. When one looks at the responses to this, 61.3% of the respondents

32
believe action on climate change is slightly important, not important at all, or that climate
change is not happening (Median 4 Slightly Important, IQR 3). This result is higher than
the University of Montreal’s (2016) study when asking adults “if the earth is getting
warmer because of human activities.” In the province of Alberta, 28% agreed, compared
to the national average of 44% in Canada.
When looking at Table 2 regarding CO2 reduction and carbon tax knowledge, current
policy announcements regarding the Government of Alberta’s climate leadership plan
seems to have had the penetration one would expect (Alberta Government, 2016a).
Where the federal commitments for CO2 reduction were known to 77.4% of those
surveyed, a nearly equal 75.2% knew the Alberta commitments for CO2 reduction. One
of the Alberta Government (2016b) pillars of CO2 action is a carbon tax (levy) on all
fuels, natural gas, and coal for every Albertan, with an increase in carbon prices above
targets for large emitters of $20 per tonne of CO2. When looking at the implications for
the Alberta carbon tax coming into force in January 2017, a majority of those surveyed
have indicated an understanding of the implications, however, the connection to this
carbon levy and Alberta’s actions on CO2 are disjointed. From the perspective of
willingness to pay for renewable energy over and above the new Alberta carbon tax, a
majority of those surveyed were not willing to pay more (20.4%) as seen in Table 2. One
has to wonder how this tax may change behaviour post-January 2017 as more than half of
respondents believe that they are doing enough to for CO2 reduction by paying the tax if
one extrapolates as to their intention in the answer of “No [they would not pay more]”.
Conversely, the carbon tax has been identified to be working in the province of British
Columbia and as the Globe and Mail (2014) has said, “[the carbon tax has] been
extraordinarily effective in tackling the root cause of carbon pollution: the burning of
fossil fuels.” Beck at al. (2016) found that rural British Columbia was overburdened by
the carbon tax, but redistribution balanced the situation.

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Table 2: Climate Change Awareness and Attitudes
Question Response %
Are you aware of CO2 reduction targets set by the
federal government?
N=137
Yes 77.4%
No 22.6%
Are you aware of CO2 reduction targets set by the
provincial government?
N=137
Yes 75.2%
No 24.8%
Are you aware of the implications of the Alberta
carbon tax on energy use that is coming into force in
2017?
Mean: 2.43 SD: 1.327 Variance: 1.762
Median: 2.5 IQR: 1
N=137
A Great Deal 30.7%
A lot 29.2%
A Moderate Amount 17.5%
A little 11.7%
None at all 10.9%
Would you be willing to pay more for renewable
energy over and above the new Alberta carbon tax?
N=137
Yes 20.4%
No 79.6%
Included in the survey was further assessment of the knowledge of survey participants. Table 3
provides a presentation of the results of the questions related to knowledge, barriers for solar
installation, and a willingness to pay. Interestingly, knowledge of renewable energy technology
was average, slightly above average, and moderately above average; 8%, 30.7%, and 22.6%
respectively. This result shows that the subject population perceives their knowledge to be
decidedly higher than what would be expected in a province with so little renewable power
generation at present. As of January 2015 there are only 1,147 MGs in the province of Alberta,
with a combined capacity of 6.6 megawatts representing 0.04% of provincially installed capacity
(AUC, 2015). With a population of 4.08 million people, only 0.03% of the population had MG
in 2015.

34
From Table 3 we can also see the primary factors that would prevent those surveyed from
installing solar energy technology. Affordability and inconvenience were the most frequently
chosen factors from the list presented in the survey, at 38% and 21% respectively. As stated
previously, lack of understanding of the convenience factor of renewable energy is expected in a
province that has so little solar installation when compared with other jurisdictions. As the
province moves into the promotion or incentivising of MG, it will become apparent that
education will be an important factor in program success. Willingness to pay was assessed in a
simple question of “[What] would you rather spend your money on?” with home improvement
yielding more than half of the responses (60.6%). This result will be compared with a later
question about choice and willingness to pay, but in this case, the result simply shows a
discretionary spending choice that prioritizes home improvement (short-term gain) over
installing and maintaining renewable energy, which can be seen as a longer term investment
(Jager, 2006).

35
Table 3: Knowledge of Renewables, Willingness to Pay and Spending
Question Response %
How aware are you of renewable energy power
generation technologies such as solar or wind?
Mean: 3.08 SD: 1.29 Variance: 1.677
Median: 3.00 IQR: 2
N=137
Far Above Average 8%
Moderately above average 30.7%
Slightly above average 22.6%
Average 29.2%
Slightly below average 5.1%
Moderately below average 2.2%
Far below average 2.2%
What factors would prevent you from installing
solar energy technology?
N=137
Affordability 38%
Inconvenience 21%
Lack of Knowledge 16%
Lack of Interest 10%
Technology Distrust 16%
Would you rather spend your money on?
Mean: 1.53 SD: .718 Variance: 0.516
Median: 1 IQR: 1
N=137
Home Improvement 60.6%
Maintenance 26.3%
Installing Renewable Energy 13.1%
Table 4 shows a summary of the data collected on energy efficiency. A large majority of
surveyed individuals acknowledged that they have energy efficient purchases in their home
(86.1%). This is not surprising as the Energy Star program is promoted by Natural Resources,
Canada at a federal level that supports the federal government’s Energy Efficiency Regulations
(Government of Canada, 2016). These regulations cover a significant number of appliances that
can be sold in Canada and must meet federal energy efficiency standards in order to be imported

36
or manufactured in Canada. This program has also resulted in consumer education promoting
choice in more energy efficient white goods (washers, driers etc.), and has now extended into
brown goods (DVD players, TVs, etc.). With 78.8% of survey respondents making energy
efficient choices when they are making purchases (expectedly white goods and electronics), it is
a potential indicator that the federal energy efficiency regulations have taken hold. Household
willingness to make these types of purchases may represent that consumer education of energy
efficiency from an appliance purchase standpoint has been successful. Canada has eliminated
the manufacturing and importation of 40, 60, 75, and 100 watt incandescent light bulbs and it is
therefore expected that a majority of lighting in homes is LED or compact fluorescent
(Government of Canada, 2016). Thus, the results from the survey show the gradual expected
decrease in other forms of lighting as stock depletes in existing incandescent bulbs. Our survey
also looked at sodium halide or halogen yard lights that have longer life expectancies then
incandescent bulbs and have slowly been replaced by new LED technology. Only 34.4% of
respondents said they had this lighting technology and will likely follow the aforementioned
trend of the technological shift.

37
Table 4: Energy Efficiency
Question Response %
Do you have any energy efficient purchases in your home? Yes 86.1%
No 13.9%
Have you made purchases in consideration of their energy efficiency? Yes 78.8%
No 21.2%
Is your clothes drier? Gas 15.3%
Electric 78.8%
Clothesline 5.8%
Do you have a fridge or freezer that is older than 15 years in your
home?
Yes 35.8%
No 64.2%
Have you any LED or compact fluorescent or fluorescent lighting in
your home?
If Yes, what percentage? Average: 61.85% SD: 34.179
Yes 90.5%
No 9.5%
4.2. Microgenerators
In the survey there were questions directed to the experiences and technology used by those
individuals with MG technologies. However, with only 1,147 microgenerators in the entire
province, only a few respondents could even answer these questions. A summary of the results
has been placed in Appendix F for consideration, but will not be analyzed as part of this study.

38
4.3. Barriers and Motives
The barriers and motives were assessed by participants in a best-worst scaling methodology after
Balcombe et al. (2013), following methodology put forth by Balcombe et al. (2014) and Finn and
Louviere (1992). The results are separated into motivation importance scores from the survey
data, and barriers importance scores by the process of standardization.
The standardized scores for the motivations are found in Figure 1. The four motivational
attributes of make the home more self-sufficient, protect against higher future energy costs, save
or earn money from lower fuel bills, and protect the home against power outages were the
highest motivations. These results proved similar to Balcombe et al. (2014), with the exception
that there was a considerable difference between the scoring of help improve the environment
and protect against power outages. As Balcombe et al. (2014) has stated, the relative importance
of motives only matters for the top four, and thus these are the results that require further
discussion.
The scoring of the motive help improve the environment was not in the top four of the
motivations, and in this study it was second last (i.e. seventh). The placing of environmental
motivations will be reflected elsewhere in the study and is the likely result of immaturity in the
renewable diffusion in Alberta, socioeconomic difference from a carbon-based resource
economy, and an overall difference in environmental and energy attitudes.

39
Figure 1. Motives Best-Worst Standardized Scoring
Results for the barriers for investment best-worst standardization is found in Figure 2. These
results are somewhat similar to those of Balcombe et al. (2014) in that MG technology costs too
much to buy, trustworthy information is difficult to find, system performance is unreliable, and
disruptions or hassle of operation rank in the top four. An interesting difference between the
data in this study and that in Balcombe et al. (2014) is found when looking at experiential
barriers such as disruption or hassle of operation. However, this confirms the non-financial
barrier identified by Snape et al. (2015) as a prominent barrier in the addition of heat pump
adoption in the UK.
‐0.4 ‐0.3 ‐0.2 ‐0.1 0 0.1 0.2 0.3
Show my environmental commitment to others
Help Improve the environment
Increase the value of my home
Use an innovative and high technology system
Protect the home against power outages
Save or earn money from lower fuel bills
Protect against future higher energy costs
Make the home more self sufficient/ less dependent on energy
companies
Motives for Microgeneration Investment: Best‐Worst Standard Score

40
Figure 2. Barriers Best-Worst Standardized Scoring
4.4. Choices of Incentives, Investment Scenarios, and Willingness to Pay
Within the survey results, the survey participants were asked what types of renewable system
they had been considering. Of the choices made, a majority chose solar photovoltaic, solar
thermal, geothermal, wind turbines, and 37% of respondents were not considering any at all
(Figure 3). Additionally, participants were asked what stage they had gotten to in their
consideration. Most respondents (52.6%) have undertaken some initial investigation, while
21.2% have talked to others who have installed.
‐2 ‐1.5 ‐1 ‐0.5 0 0.5 1 1.5
Neighbour disapproval/annoyance
Take up too much space
Home/location is not suitable
Would not look good
Lose money if I moved home
Energy not available when I need it
Environmental benefits are too small
High maintenance costs
Hassle of installation
Cannot earn enough/save enough money
Disruption or hassel of operation
System performance or reliability not good enough
Hard to find trustworthy information/advice
Costs too much to buy/install
Barriers for Investment in Microgeneration: Best‐Worst Standard
Score

41
Figure 3. Types of MG systems considered (N=137)
Table 5: Role and Preferred Type of Incentive
Question Response %
Has the lack of incentives/support prevented
you from installing a system?
Mean: 2.96 SD: 1.716 Median: 3 IQR: 4
N=137
A great deal 35.0
A lot 10.9
A moderate Amount 11.7
A little 10.2
None at all 32.1
Preferred type of Incentive?
Mean: 2.46 SD: 0.814 Median: 3 IQR: 1
N=137
Capital Grant 20.4
Maintenance 13.1
Installing Renewable Energy 66.4
In looking at the likelihood of installation, a majority (27%) felt it was extremely unlikely that
they were going to install a system they were considering within the next 10 years (Appendix L:
Figure 9). Projections over five years indicate that the efficiency and ease of installation of solar
panels and other forms of MG will contribute to their costing half what they do now.
Respondents were given that information and asked how likely they would be to move to
0 10 20 30 40 50 60 70
None
Solar Photovoltaic
Solar Thermal
Wind turbine
Ground Source Heat Pump
Airsource Heat Pump
Biomass wood boiler
CHP (combined heat power system)
Microhydroelectric

42
renewables in the five- and ten-year timeframes because of it. When asked if they were going to
install in the next five, or ten years, a majority of respondents felt it extremely unlikely for all
three timeframes (19% and 16% respectively; Appendix L: Figures 10 and 11).
Figure 4. Likelihood of Investment 2 to 3 Years (N=137)
0
10
20
30
40
50
60
1 Extremely
Likely
2 Moderately
Likely
3 Slightly Likely4 Neither Likely
or unlikely
5 Slightly
Unlikely
6 Moderately
Unlikely
7 Extremely
Unlikely
Two or Three Years

43
Figure 5. Incentives Best-Worst Scoring (N=137)
In evaluating the best-worst scenarios of potential government involvement in incentives for
renewable energy (Figure 5) in a standardized comparison, the respondents identified long-term
yearly rebates, feed-in tariff scenarios, and grants—in that order—as the best scenarios. When
asked about incentives, the participants considered grants (35.71%) as the most important
method of support, along with long-term yearly rebates (53.57%). Respondents were not
interested in regulatory support nor feed-in tariffs alone as incentives for inducing them to invest
in MG. Interestingly, when participants were asked if incentives or support had prevented them
from installing a system, the results were split in terms of none at all (32%) and a great deal
(35%; Table 5), but they felt that a combination of grants and a feed-in tariff system (66%)
would be the best form of support.
Respondents were provided with investment scenarios that provided schemes based on those
found in other jurisdictions (Table 6; Islam and Meade, 2013). The levers of incentive models
changed in each scenario by making changes in capital cost incentives (one-time payment of
20%) with only 24.8% willing to pay; an increase in capital cost incentives (30% over 10 years
‐0.3 ‐0.25 ‐0.2 ‐0.15 ‐0.1 ‐0.05 0 0.05 0.1 0.15 0.2 0.25
Regulatory Support
Technological Assistance
Material Support
Installatin Support
Tax Rebates
Grants
Feed in Tariff
Long Term Yearly Rebates
Incentives Best‐Worst Standard Scoring

44
with the same capital requirement of household investment of $10,000) resulted in only 4.4% to
29.2% willing to pay. Even in a scenario with an increase in incentive of 50% over 10 years and
a decrease in capital requirements of the resident, a majority of respondents would not invest
(60.6%). This scenario was tested as a dummy situation after Horowitz and McConnell’s (2002)
and Sayman and Öcüer’s (2005) findings as the measure of disparity between willingness to
accept and willingness to pay. What this may indicate is that even when given an unrealistic
incentive and price point, the population has not accepted MG as worthy of investment at all (i.e.
there is no willingness to accept, therefore there is no willingness to pay). Fixing the price of
power in the scenario to below current rates with no capital incentive did induce an increase in
potential household investment at a resident capital cost of $10,000 with 56.9 % willing to
invest, but when asked if they were willing to invest $25,000, the number of potential investors
dropped to 29.9%. The possibility of a government-backed 10-year loan in an identical scenario
did not induce more respondents to be willing to pay and actually had a 0.9% decrease. The
survey respondents had very little interest in assuming debt as a mechanism to fix their price of
power for the 10 years being proposed.
When we look at the results from the scenarios we must consider two key pieces of information:
what are the intentions of the participants, and how does that manifest in behaviour to invest?
Due to the low level of diffusion of renewable technologies in Alberta at present, the level of
knowledge is likely low, which is not represented in the energy attitudes assessment of this
study. As the saying goes, the more you know, the more you know what you do not know. An
understanding of costs, production, maintenance, installation, and regulatory processes is not
likely high in the subject population and was not assessed directly in this investigation. Without
the knowledge of what people may know, one can infer from the results that there is a likelihood
of behavioural economics at play here. One thing is clear from the business case of renewable
energies: the payback period is prolonged, which the participants likely know. What can be
postulated as a likely impact on the responses put forward by the participants is the concept of
“hyperbolic discounting,” which, in its simplest form, is a manifestation of “present bias”, as
seen by Thaler (1981). In the simplest terms, this situation arises when the investor sees the
return on investment far enough away in the horizon that the interest to invest is discounted or
lost. The time horizons will be further discussed in the business case analysis, however, for the

45
purposes of the results seen here we can clearly state that the return on investment for capital is
worth less to the participants when compared to the opportunity to decrease, or at least fix the
price of power. Hyperbolic discounting and quasi-hyperbolic discounting provide an additional
overarching consideration related to the situation of global warming and CO2 reduction (Karp,
2004). As with the household choice of renewables, considerations of the actions, cost, and
behaviour responsible for global warming at a societal level are “discounted” due to the
timeframes for success being so far in the future (Karp, 2004). The societal, as with the
household decisions related to actions to abate global warming have timeframes that exceed,
behaviourally, those that the typical individual considers in day-to-day financial decisions (Karp,
2004).
Table 6: Investment and Incentive Scenarios
An interesting corollary to this situation is forced savings and the idea, as Thaler and Benartzi
(2004) have put forward, of behavioural inducement to saving. The outlaying of capital for
future savings is the key behavioural choice that renewables, with or without incentives, induce.
If we look at the results from the study and correlate the responses, overall there is a negative
response to long-term incentives (capital payments over time), as opposed to short-term capital
incentives that decrease upfront costs. The most interesting result related to an assessment of
behavioural choices from this study relates to the “dummy” scenario where an extremely
generous incentive program with less household investment did not induce an increase in
investment by participants. When you compare this to the results from increased capital from
Incentive Scenario Capital
from
Resident in
Canadian
Dollars
Yes No
20% of initial capital costs $10,000 24.8% 75.2%
30% of initial capital over 10 years $1,0000 29.2% 70.8%
50% of capital costs over 10 years $6,250 39.4% 60.6%
Fix price of power at 0.10 CND per kWH for 10
years
$10,000 56.9% 43.1%
Fix price of power at 0.10 CND per kWH for 10
years
$25,000 29.9% 70.1%

46
households and a fixing of the price of power, it is evident that the subject population in this
survey resembles many of the subject populations that Thaler and Benartzi (2004) have assessed.
The comparison is valid in that the motive is the fixing of the price of power, and the solution is
to provide a mechanism where revenue from a system can go against a debt or principle
regardless of the consumer’s behaviour. Thus a hybrid solution or possibility of a policy
incentive exists where there is a business case to provide homeowners with a static price of
power, with any variability and surplus acting to decrease capital debt. This possibility is in
keeping with the Thaler and Bernatizi (2004) model of forced savings, which is in keeping with a
concept of “prescriptive programs” that can use incentives to modify economic decisions. The
extent of how this may be used to induce investment is outside of the scope of this dissertation
but is worthy of further investigation.
4.5. Energy Attitudes
This portion of the survey looked at the characteristics related to energy attitudes of participants.
It looked at four main components: the survey participants’ affinity for and understanding of
energy, energy aptitude, desires for energy use, and opinions of energy supply companies.
Figure 6 provides a summary of the results from the survey (Appendix E for Statistics). What
can be interpreted when one looks at the figure is that survey participants believe strongly that
power will be more expensive in the future, and that they have a poor view of the social
responsibility of energy companies. A majority of the respondents felt comfortable with their
knowledge of technology, are handy, and have a good understanding and care for the energy they
use. As a representation of the motivations for MG, a majority of the respondents felt energy
prices are too high, will continue to rise, and have a strong motivation to be energy independent.
Based on the outcomes of the energy attitude ordinal results, it is evident that the respondents to
the survey are ideally motivated and interested in MG possibilities. It is very likely that, as seen
in the best-worst analysis, there is a gap in information, resources, knowledge, and experience
available to participants that allows them to act on these motivations for looking at MG. Factors
related to autonomy as a primary driver have been seen by Fisher (2004), and when one
correlates the response “I want to be energy independent” with actions, “likeliness of investing”
in two to three-, five-, and ten-year timeframes (Appendix C), there exists a correlation at all

47
timeframes relating to intention to invest (all greater than 0.01 significant correlation using
Spearman’s rho statistical analysis using SPSS).
Figure 6. Energy Attitudes (N=112)
4.6. Environmental Attitudes
As with the energy attitude analysis in the previous section, respondents’ answers to the New
Ecological Paradigm questions are found in Figure 7. The NEP assesses attitudes associated
with “balance of nature, limits to growth,” and perceptions of “man over nature” (Alibeli and
White, 2011:1; Dunlap et al., 2000). It must be noted that in total there were 50 refusals for this
portion of the survey, thus only 64% of participants even answered this portion. The New
Ecological Paradigm assumes and tests a worldview of “anti-exceptionalism” of humans, “anti-
31%
39%
38%
54%
21%
18%
12%
18%
38%
45%
21%
5%
39%
22%
39%
40%
44%
26%
36%
30%
37%
41%
34%
45%
44%
32%
27%
30%
35%
38%
35%
44%
13%
21%
17%
9%
27%
15%
30%
26%
14%
13%
32%
31%
20%
18%
20%
10%
5%
7%
4%
4%
10%
13%
11%
7%
2%
5%
13%
21%
4%
16%
4%
5%
6%
7%
4%
2%
5%
13%
13%
4%
3%
5%
6%
13%
2%
5%
2%
1%
ENERGY PRICES HAVE RISEN STRONGLY…
THERE ARE MANY ADDED COSTS TO MY POWER BILL THAT I DO NOT …
I THINK THE PRICE OF ENERGY IS TOO HIGH…
I EXPECT ENERGY PRICES TO RISE IN THE NEAR FUTURE…
I HAVE A GOOD KNOWLEDGE OF TECHNOLOGY…
I AM HANDY AND CAN DO MOST RENOVATION AND BUILDING PROJECTS …
I HAVE EXPERIENCE INSTALLING TECHNOLOGY IN MY HOME…
I KNOW HOW MUCH ENERGY AN APPLIANCE USES.…
I BUY PRODUCTS THAT ARE ENERGY EFFICIENT PURPOSELY.…
I PAY GOOD ATTENTION TO MY ENERGY USE…
ENERGY COMPANIES PROVIDE GOOD SERVICE…
ENERGY COMPANIES ARE WELL MANAGED…
ENERGY COMPANIES ONLY WANT TO MAKE PROFIT…
MY POWER IS RELIABLE AND I DO NOT WORRY ABOUT BLACKOUTS.…
I WANT TO BE ENERGY INDEPENDENT…
I AM TRYING TO SAVE ENERGY WHEN I CAN…
ENERGY ATTITUDES
Strongly agree Somewhat agree Neither agree nor disagree Somewhat disagree Strongly disagree

48
anthropocentricism,” limits to growth, the balance of nature, and the present world situation as
an ecocrisis (Erdoan, 2009). As seen in Appendix D, for the means of the questions asked, the
average uncorrected consolidated mean for the respondents is 2.73, which represents a
respondent group that does not support NEP sentiments. In fact, if you compare this mean of
2.73 of a five-point assessment of the NEP and compare to Hawcroft and Milfonts’s (2010)
assessment of similar studies worldwide, this is notably low. When looking at similar Canadian
assessments of NEP, such as by McFarlane et al. (2006) showing NEP means of 3.71, 3.87, and
3.67 with SD of 0.64, 0.60, and 0.60 respectively, this study consolidated mean of 2.73, with an
SD of 0.399, is much lower than what has been seen in the literature by many sample
populations (Ndeble and Marsh, 2014). For the results in this study, Cronbach alpha for
reliability is 0.306 and with standardization based on missing results (refusals) is 0.344, which is
lower than the 0.6 suggested as a measure of reliability in the literature; thus, one must be
cautious about reading too much into these results.

49
Figure 7. Environmental Attitudes (N=87)
4.7. Summary of Hypotheses Tests as Drivers for Investment
Five hypotheses were posed by this study for the assessment of motives and barriers for
renewable energy development in rural Alberta, Canada. The first hypothesis, Environmental
Concern, has been diminished as a motive by the results of this study, as seen in respondents’
opinions on climate change concern (not concerned: 49.6% plus 14.6% do not agree), action on
climate change as not needed or not at all important (more than half believe it is slightly
important, not important, or not happening at all), and with 79.6% not interested in paying more
for renewable energy over and above the pending carbon tax. When looking at the best-worst
analysis, “help improve the environment” was almost last (seventh) out of the eight primary
11%
9%
18%
22%
18%
29%
36%
13%
39%
33%
11%
7%
12%
9%
15%
26%
33%
31%
34%
36%
39%
23%
23%
39%
21%
28%
20%
35%
21%
21%
31%
26%
25%
24%
23%
16%
24%
22%
14%
15%
25%
37%
31%
28%
28%
15%
22%
18%
14%
13%
14%
14%
31%
7%
17%
14%
13%
18%
21%
21%
16%
9%
7%
6%
10%
2%
3%
11%
1%
14%
22%
24%
3%
22%
16%
IF THINGS CONTINUE ON THEIR PRESENT COURSE, WE WILL SOON EXPERIENCE A
MAJOR...
HUMANS WILL EVENTUALLY LEARN ENOUGH ABOUT HOW NATURE WORKS TO BE ABLE
TO CO...
THE BALANCE OF NATURE IS VERY DELICATE AND EASILY UPSET
HUMANS WERE MEANT TO RULE OVER THE REST OF NATURE.
THE EARTH IS LIKE A SPACESHIP WITH VERY LIMITED ROOM AND RESOURCES.
THE SO‐CALLED “ECOLOGICAL CRISIS” FACING HUMANKIND HAS BEEN GREATLY
EXAGGER...
DESPITE OUR SPECIAL ABILITIES, HUMANS ARE STILL SUBJECT TO THE LAWS OF NATU...
THE BALANCE OF NATURE IS STRONG ENOUGH TO COPE WITH THE IMPACTS OF
MODERN I...
PLANTS AND ANIMALS HAVE AS MUCH RIGHT AS HUMANS TO EXIST.
THE EARTH HAS PLENTY OF NATURAL RESOURCES IF WE JUST LEARN HOW TO DEVELOP
T...
HUMANS ARE SERIOUSLY ABUSING THE ENVIRONMENT
HUMAN INGENUITY WILL ENSURE THAT WE DO NOT MAKE THE EARTH UNLIVABLE
WHEN HUMANS INTERFERE WITH NATURE IT OFTEN PRODUCES DISASTROUS
CONSEQUENCES
HUMANS HAVE THE RIGHT TO MODIFY THE NATURAL ENVIRONMENT TO SUIT THEIR
NEEDS
WE ARE APPROACHING THE LIMIT OF THE NUMBER OF PEOPLE THE EARTH CAN
SUPPORT
NEW ENVIRONMENTAL PARADIGM
Strongly agree Somewhat agree Neither agree nor disagree Somewhat disagree Strongly disagree

50
motives for renewable energy investment. Therefore, hypothesis 1 of environmental concern as
a primary motive has been quashed. This differs from what was seen in Japan and Germany in
the early 1990s where global warming concerns had driven early adoption in those countries
(Guidolin and Mortarino, 2010). Hypothesis 2 speculated that the drive would be an offset of
higher market prices. In the energy attitude assessment portion of the survey, it was clearly
reported that the majority believe power prices are too high and will become higher in the future.
Additionally, the motives for investment in the best-worst analysis identified that the second
highest motive behind being less dependent on energy companies was to “protect against higher
future energy prices.” Thus, hypothesis 2 has been supported by the findings.
Hypothesis 3 stated that a driver for innovation and technology will induce rural households to
invest in MG. In the best-worst analysis, the “use of an innovative and high-tech system” was a
mid-point motive (fifth of eight motives). Accordingly, in the best-worst analysis of barriers, the
second and third greatest barrier to investment was “hard to find trustworthy advice,” and
“system performance and reliability.” Lack of knowledge, inconvenience, and technology
distrust were all mostly equal as factors that had prevented respondents from installing solar
technology. In the energy attitudes results, the respondents did feel they had a good knowledge
of technology, but it is obvious from the previous answers that hypothesis 3 has been mildly
undermined as a driver for investment. It does make sense that this situation exists in an
immature market as based on the Bass Model (Bass, 1969) related to the extent of “external
information sources” and the role of “social interactions,” which have not induced a general
awareness of the technologies at the stage of market maturity in Alberta, Canada. Hypothesis 4
involves the driver of households to offset the monopolization of power companies. The energy
attitudes portion of the survey identified energy independence and the motives of energy
companies to only want to profit to be statements that were strongly agreed on by most of the
respondents. When coupled with the statements of added costs on power bills, the strongly
rising power bills, and expectations of energy prices to rise in the near future, additionally being
strongly agreed on by most participants, we can say that hypothesis 5 is a supported driver for
household investment in MG. The autarkical drivers of energy independence as hypothesis 5
had been assessed in the energy attitudes results, with most of the respondents agreeing with the

51
statement, “I want to be energy independent.” The best-worst analysis of motives placed “make
my house more self-sufficient” as the primary motive for household investment in MG.
4.8. Business Case for Rural Solar
Based on the scenario analysis discussed in section 4.4 it seems evident, at this early stage, that
the potential uptake may be marginal without improper policy development. Three requirements
must be in place in order for proper execution of a renewable energy program: the resources or
technology, the finances, and the policy. The resources/technology pieces are in place; solar
availability is very good to excellent in Alberta (Cansia, 2014), and the finance and policy pieces
are the subject of this section of the study.
The methodology for analysis of the business case for rural solar is based on Swift (2013).
Information used in the analysis is based on the following criteria: cost of electricity (present and
future) that the system saves, availability of sunlight (also known as solar insolation), system
costs and performance, and financial incentives (Swift, 2013:138–139). This truncated formula
based on Swift’s (2013) is due to the lack of: federal income tax credits, provincial tax credits,
and any upfront utility rebates or incentives. Note that Growing Forward is a pilot program in
the province, but it has not been funded adequately to identify it as an actual incentive program,
thus it has not been included in this assessment (Alberta Government, 2016).
LCOE for Alberta is calculated to be 0.205 CDN dollars per kWh for the delivery of
conventional power as of July 2016. Due to fluctuating power pool prices, impending carbon
taxes, and changes in the generation and distribution of energy in the province, this value will be
stale-dated immediately upon printing. However, at the time of writing, the Alberta Power Spot
Pool price for electricity is the lowest it has been in 20 years. From April to June of 2016 the
Alberta Power Pool Price was $15.00 per megawatt-hour, which is the lowest rate since 1996
(AESO, 2016). This overall trend, which is likely to be reversed by 2018, has significant
implications for the existing business model for renewable energy and for LCOE. In 2017, via a
carbon tax, there will be an implication for externalities to become part of the power pool price at
both the generation stage and at the distribution end (Alberta Government, 2016).

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