paul mclauchlin thesis
TRANSCRIPT
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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 energycompanies
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 ExtremelyLikely
2 ModeratelyLikely
3 Slightly Likely4 Neither Likelyor unlikely
5 SlightlyUnlikely
6 ModeratelyUnlikely
7 ExtremelyUnlikely
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).
52
Wire service providers in Alberta have a power distribution tariff, which is composed of a
transmission fee multiplier at $0.035 CDN per kwh and an energy price (per watt usage
multiplier) at $0.075 CDN per kwh as an average for residential regulated rates. In Alberta there
is a fixed fee distribution cost of $0.427 per day for residential users, but becomes a fixed fee for
rural installations of larger transformer services that range from $80.00 to $112.00 CDN per
month for rural service based on transformer size. These last figures are significant as rural
distribution prices are annually significantly larger compared with urban as far as a flat fee
associated with distribution charges. All of these numbers are averaged for the purposes of this
model.
Due to the size of the available land in rural Alberta, the sizing of a solar array is not as limited,
hypothetically, as that of a roof mount array in the city. A typical solar array on an urban or
acreage household is 1 to 3 kW, whereas there is a strong practicality of a 6 kW (6,000 watt)
installation on a rural farmstead or larger landholding. The current install price based on three
quotes obtained by the researcher average $2.50 to $4.00 per watt CDN for both hard and soft
costs. This per watt installation price is similar to other jurisdictions (BCSEA, 2016) and has
been averaged to $3.50 per watt installation price for purposes of modelling and fact that the
market in Alberta is immature so efficiencies have not been developed.
In Appendix E: Tables 13, 14, and 15 are the basis for the calculations in the summary in Table
16 for 6 kw systems. Additionally, based on the scenarios in the survey we have used a 3 kw
system for purposes of scale and payback calculations (Appendix E: Table 18). What Table 17
shows is that based on the installation of a PV system that is likely to be subjected to increases in
the price of power, the business case becomes apparent. This would occur without the need for
incentives, when the price of distributed power produced by conventional means increases based
on levelizing the cost of electricity. In other words, by the use of carbon pricing, the price of
power will increase in the near future and for 6kw systems, a 50% to 100% increase in the price
of power will have a discounted payback period of around 15 and 11 years respectively, based on
initial capital costs and decreasing of the price of power. When using the discounted payback
period, in consideration of the time value of money, results in a return on cash flow of 7.4% and
53
9.47% respectively, provides returns that exceed many current investment vehicles (see
Appendix E: Table 17). Additionally, for a 3,000-watt system, the same can be told for a similar
increase in the price of power (Appendix E: Table 18). It can therefore be speculated that with
expected viability in the business case for investment in systems, similar increases in the price of
power can be expected. This seems counterintuitive given the situation where Alberta Power
Pool prices are around $15.26 per megawatt at the time of writing, which is actually causing a
downward pressure on current retail prices. With power prices projected to increase to $40 to
$60 a megawatt in 2017 to 2018, it is expected that this pressure will translate into higher retail
distribution prices (EDC Associates, 2016). Moreover, macrogenerated wind power is being
deemed as the replacement for coal fire electricity in the Province of Alberta (Alberta
Government, 2016). If this is the case, EDC Associates have predicted that wind should increase
Power Pool prices to $60 to $85 (2016).
4.9. Summary Business Case for Rural Solar
What the business case identifies is that the net benefit from an investment standpoint is not as a
revenue stream to the grid, but becomes a recognizable investment opportunity “behind the
meter” for the purposes of self-consumption. From a financial investment standpoint, without
incentives, the installation of MG as photovoltaics is driven by self-consumption and the
economics are not at parity yet for a revenue stream or to achieve full autarky. Self-consumed
energy does not match the need for incentives as the overall net benefit is gained by the
homeowner. When one looks at the business case scenario and the role of incentives and the
incentive scenarios (Appendix E: Table 17), we can see that with the inclusion of incentives,
smaller systems have a faster payback period due to the size of capital requested by the
household. Of note is that these calculations are predicated on consumption and not on a
revenue model, as the actual revenue paid back to the grid requires larger systems, smaller
annual consumption, and at rates that equal retail values. At present in Alberta, MG power that
is returned to the grid is not at par with consumed electricity.
54
5. Discussion and Conclusions
What follows is a summary of the findings of this investigation including; a discussion of the
project conclusions, theoretical and practical limitations. Additionally, this section provides a
discussion of the project limitations, direction for future research and the project reflections of
the researcher.
5.1. Summary
The purpose of this paper was to identify the business case for the diffusion of microscale
renewable energy in rural Alberta, Canada. By looking at current actions and knowledge,
barriers and motives, willingness to pay, and energy and environmental attitudes, the business
case from a policy perspective was analyzed. This business case is based on the opportunities
available to policy decisions that can promote the investment and diffusion of renewable energy
technologies to rural Alberta. Current actions and attitudes of those individuals surveyed show
that they are aware of the discussion around climate change, but do not believe it is an issue that
needs to be addressed. Instead, it is apparent from this research project that the primary barrier
and motive for investment in renewable energy is financial in nature (cost as a barrier and
revenue as a motive). The motives to protect the environment or to show environmental
commitment to others do not rank high with survey participants in the best-worst comparisons of
motives. In addition to this finding, the best-worst comparisons of barriers are similar as the
participants felt they can’t save or generate enough money, and the costs are too high to install as
primary barriers. These results match the findings of Balcombe et al. (2013); Eleftheriadis and
Anagnostopoulou (2015); Jager (2006); Keirstead (2007); and Lenheer et al. (2011).
Autarkical motives dominated many of the respondents’ goals for incentives in investing in
renewable technologies. Energy independence, hedges against future energy prices, and an
overall dislike of ancillary transmission, delivery, and service charges have created an incentive
to become independent—at least in part—of the current electrical system in Alberta, Canada.
Rural Alberta has shown to be a cohesive group in terms of their opinions related to their drivers
for energy independence.
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The business case for microscale renewable energy in Alberta, Canada is in an extreme
predevelopment stage. This study does not point to a great demand at the household level in
rural Alberta to install MG energy. The new carbon tax in Alberta has potentially had an
opposite effect in the short term to not encourage individuals to invest in micro-renewables and
in fact has the potential, based on the results of this study, to have people “believe they have
done enough.” The nature of this type of study as a nominal and ordinal measurement of
motives and attitudes by way of survey did not lend itself to having dialogue with participants.
However, participants in various ways contacted this investigator to express their opinions, and
they are valid inputs to the conclusions of this study. On numerous occasions by phone, email,
and other communication methods, the investigator was informed of what was presented as a
“fatal flaw” in the study’s assumptions by their interpretation: the base notion that there needs to
be government involvement at all has been questioned. Rural Albertans, it was presented, are
proud, independent people who do not like government interference in their lives and most
importantly, in how and why they make energy choices. Further, in numerous cases it was said
that the government has no business being involved in renewable energy other than clearing the
way by eliminating bureaucracy and letting the market and rural Albertans take care of
themselves.
Policy development solutions for consideration are therefore mirroring what Goto and Ariu
(2009:1) proposed in looking at “efficient diffusion strategies (e.g. economic assistance policies,
consumer education and competition policies to encourage energy companies to develop more
valuable systems for consumers through their marketing activities), based on the preferences” of
potential household investors (see also Sadler, 2003).
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What follows is a summary of the key questions of this study and the key findings of the results.
What are the potential barriers for deploying micro-renewable energy as seen by rural
municipalities, service providers, and potential participants?
The barriers for deploying micro-renewable energy have been mirrored in this study as they have
been seen in most jurisdictions. The primary barriers to investment have been cost, lack of
information and knowledge, concerns over system performance, and perceptions related to the
hassle and complexity of the systems. At present, there exists a gap between what MGs are paid
for surplus power entering the grid, and the power pool price that is offered for renewable
energy. This would need to be corrected in order to support the business case.
What are the motivations of households’ (investors) decisions about where to install micro-
renewables in rural Alberta?
Motivations for investment in rural micro-renewables have been identified in the study as self-
sufficiency, protection against impending higher energy costs, earning or saving money, and
protection from power outages. What the study has shown is that the people of rural Alberta do
not see environmental motives as important for the development of renewable energy.
What effect does the relative importance of motivations and barriers have on the business case
for micro-renewable energy?
The motivations and barriers identified in this investigation set a framework for understanding
how best to deploy renewable energy at the micro and macro scales as they are closely
interrelated. Incentives will induce early adopters to invest, but pending increases in the price of
power by inclusion of a carbon tax will also induce the same early adopters. As this study has
shown, the business case is price sensitive and upward pressure on the price of power will itself
induce early adopters’ involvement. As the technology and cost of diffusion of renewable
technology moves through the process, later adopters, once the market matures, will be more
likely to invest.
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What potential production possibilities exist in the development of micro-renewable energy
projects in rural Alberta?
When analyzing the results of the study, the modelled system prices of 3kW and 6kW have the
potential for generating annual production of approximately 3,000kW and 8,000kW. If one
calculates 30% diffusion of solar to customers labelled as farms with 6kW arrays, they can yield
103,761.6 Mwh of annual electrical generation. Further, the 1,405,894 rural customers with 20%
installing 3kW arrays can produce another 843,536.4 Mwh annually. It takes approximately 0.4
kg of coal to yield a kilowatt hour of electricity and therefore this potential source of generation
has the ability to replace 378 million kilograms of coal annually. Coal has a significant social
cost and this cannot be discounted as a realistic opportunity to decrease that cost (Tamminen,
2006). Interestingly, when one considers the possible involvement of 294,000 households with
an annual use of electricity by the average Canadian household of 7,222 kWh, this production
would replace the energy use of 131,168 Alberta households entirely.
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?
In the short term, energy efficiency holds the greatest opportunity for rural Alberta (Knittel,
2014). By educating consumers, increasing energy efficiency, and identifying ways to reduce
energy demand, the system needs can be the best form of short-term policy choice to address the
provincial goals of climate change. It is also obvious from the results of this investigation that
the provincial goals for climate change response do not match those surveyed in rural Alberta.
The gaps in the provincial goals, societal goals, and individual goals are serious societal gaps. It
is therefore important to provide policies and programs that share a common language with rural
Alberta while meeting the overall goals of the provincial and federal governments without
alienating one another. This common language, in the short term, can be best seen in attempts to
lower household energy use, which should reduce household energy bills.
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In consideration of the role of government and the best policy decision to arrive at the most
effective system, the literature has evaluated the roles of programs for energy efficiency and
demand response (Behrangrad, 2015) as a mechanism for policy response. In fact, it is the
demand response, as a mechanism for reducing climate change, inducing behaviour change, and
promoting the better use of resources that should form a stronger policy piece than what has been
seen to date (Behrangrad, 2015). Smart meters and escalating rate systems based on use are two
such forms of mechanisms that could form a more effective program for inducing energy-
conserving behaviour and rewarding that behaviour by lowering household energy costs.
All policy decisions must be predicated on a few unique key issues that have been identified in
this study. The need to educate, to find common goals and language, and ultimately to respect
the fierce independence of rural Albertans is essential if rural residents are to become a partner in
Alberta’s renewable energy strategy. Rural Alberta has the land base needed for not only an MG
strategy, but is also the location for macro renewable projects such as wind, transmission, and
hydro development. Additionally, as high users of electricity per capita, they also have the ideal
scenario for distributed generation and use, but this should not be gained at the cost of
bureaucracy and respecting the individuality of rural Alberta. If rural Alberta does not believe in
the climate change “crisis,” no policy, however robust, will be accepted unless it bridges the
common interests. When or if an incentive program is put in place, based on the results of this
study, it will be accepted by only a small number of people and may be potentially at the cost of
a large number of people. As has been seen in other jurisdictions, one must be careful of this
“Reverse Robin Hood” scenario because the early adopters will likely be the ones with the
available capital to take advantage of incentives at the expense of others. In rural Ontario, a
model jurisdiction for what the future may hold for MG in rural Alberta, this situation has
resulted in a crisis of energy poverty due to the high cost of power distribution in rural Ontario,
coupled with compensatory costs to renewable energy diffusion and incentives. Individuals
without the means to invest but having the willingness to invest, could be left out of the
opportunity to generate their own power using MG. If increasing the amount of renewable
energy provides an overall increase in the price of power in the short to midterm, this burden will
be borne by all electrical rate payers and potentially more so by those with relatively lower
incomes and those living in rural areas (Stram, 2016).
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5.2. Theoretical implications
As an example of a jurisdiction that is statistically and policy-wise behind much of North
America and Europe in terms of the diffusion of renewable energy, this study has provided
insights into the societal and financial challenges to MG program development. Specifically,
rural Alberta participant’s have not been investigated regarding the barriers and motives for
participation and investment in MG energy and this study has been a first. In many ways the
subject population emulated the motives and barriers of other jurisdictions with the exception of
attitudes towards the justification of MG participation. With per capita production of CO2 higher
in Alberta than in any other Canadian jurisdiction (Environment Canada, 2013), however,
Albertans’ acceptance of climate change is the lowest in Canada (Insights West, 2016). This
juxtaposition is not without irony and shows the likely need for some level of attitudinal change.
The juxtaposition, and the drivers behind it, are worthy of further analysis as this studies findings
represents a definite outlier in terms of environmental attitudes in relation to motives for MG
diffusion. The relative impact of rural Albertans on Climate, when looking at identified barriers
and motives for investment, and the attitudes of Albertans has placed this study in a unique
standing as a worthy theoretical baseline for future investigations.
5.3. Practical implications
The use of best-worst scaling does prove to be an effective way to identify the primary motives
and barriers to investment decisions in a study design such as this. The ancillary questions
provided a further background that best-worst scaling does not provide, and allows for a better
understanding of the potential drivers for the decision to choose the best and the worst of the
factors identified. While the study survey was exhausting for participants in duration and
complexity of study, the characterization of a population regarding beliefs and attitudes provides
a potentially rewarding precursor to future investigations. The literature has concentrated on
innovators and early adopters and it is the subject of this study: the early and late majority that
has the greatest potential for understanding (Fischer, 2004). Policy and program development
and uptake, as seen in the literature, has not always been successful, and may not be until
Maloney’s chasm is crossed between innovators and imitators (Maloney, 2011). The bridging of
60
this chasm, either by education, garnering acceptance, incentives, or by easing of the
aforementioned barriers, can increase the diffusion of technology, encourage investment, and
increase penetration of program development.
5.4. Limitations
The study and survey occurred during a very difficult financial time in the province of Alberta.
The Conference Board of Canada has identified Alberta to be subject to one of the worst
recessions in the past 20 years (Conference Board of Canada, 2016). This is coupled with a new
left-of-centre government after 40 years of conservative government (right-of-centre), which has
not been accepted positively in all of rural Alberta. Federally, there has been the election of
another left-of-centre government after 10 years of conservative (right-of-centre) government,
and this has created a powder keg of emotions and strong opinions. For a jurisdiction with an
economy that has been driven by fossil fuel development and production, the society as a whole
has been affected greatly by global economic pressures on petroleum development, a move
against coal fire power generation, and the increased influence of larger urban centres on
government decision making. It is against this political and societal change that this survey was
circulated and the topic of renewable energy has the potential to be a victim of a backlash against
these significant changes and pressures.
5.5. Directions for future research
From the investigation it is apparent that while nominal and ordinal data collection provide the
ability to provide insights into the general trends in a given population, there exists more
potential insight by undertaking interviews and questionnaires. A few questions that can be
posed for further investigation include:
What would be the results using the same questions and methodology for strictly the
urban population?
What would be the results using the same questions for a rural or urban fringe
population?
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What are people’s investment expectations, returns on investment, and risk tolerance for
renewable technology?
What are rural populations’ attitudes in terms of other environmental measures such as
land, air, and water protection?
5.6. Reflections
Writing this dissertation was a more rewarding experience than imagined. This researcher had
entered the investigation with a clear view of the hypotheses, with a full expectation that the
researcher was correct, and interestingly, was rewarded in finding that the researcher was in fact
somewhat incorrect. This researcher’s general hypothesis was that rural Albertans were very
likely to be willing investors and participants in the development of MG. While this researcher’s
study did not necessarily disprove this, it did identify clearly that there are likely many
conditions and details that need to be shared in order to spark investment by the early majority.
Based on this study, one cannot discount rural Alberta as willing partners in Alberta’s renewable
energy goals, but when it comes down to what this partnership may look like, a common
language must be found. These common interests are related to the overall benefits to the
individual first, community second, and society last. The research has viewed renewable energy,
along with the environment, as a common good and common focus. Instead, the shift, while
with the same goals, comes down to the message, education, dialogue, and facts. Renewable
energy is not a panacea and cannot be seen as anything other than just another item on a list of
actions that we must all take as individuals for ourselves, for our community, and finally for the
good of all society. It is with this reflection that one can provide some insight on how to
improve further research.
Behavioural norms of a given population, how they make decisions, when they make decisions,
and why they make decisions is an area for exploration. The business case for renewable energy
is not sound at present as far as the diversion of household investment in pursuit of energy
independence, and ultimately, energy saving. Energy saving is the driver for the individual and
further understanding of what this actually is, in an empirical sense, is an area for further
62
exploration. When the empirical point of what energy savings means to the household is
identified, that is where the policy and incentive portions should meet. If incentives preclude
this tipping point, then Maloney’s chasm will not be crossed as the resources used to incentivize
innovators and early adopters will potentially, if poorly designed, prevent involvement of the
early majority (Maloney, 2011). It is therefore an area for future investigation to further delve
into the more detailed nuances of individual choices and motives using more involved
mechanisms such as interviews and questionnaires that can elicit the greater detail needed to
understand the underpinnings of what this study has shown. It is the early and late majority that
will make the greatest investment and with that, the greatest impact. In order for Alberta to
achieve sustainability as a final policy goal, “a balance of social and economic activities and the
environment” (Hofman and Li, 2009:407) is required, as well as more energy efficiency and
renewable energy. Finally, more “compulsory energy related policies need to be developed
(Hofman and Li, 2009:407).” These results, however, can only be achieved by approaching the
solution as a localized partnership and in the order prescribed by efficiency, renewable energy,
and then, and only then, compulsory means (Hofman and Li, 2009). How this entire Alberta,
Canada situation plays out will be socially, politically, and academically worthy of further
analysis.
63
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APPENDIX A: Population and Energy Use
Table 7: Energy Use in the Province of Alberta
Number of Customers
% Usage (GWh)
Year 2013 2014 2014 2010 2011 2012 2013 2014 % Average
Residential 1,373,960 1,405,894 83% 9,071 9,333 9,412 9,678 9,927 18% Farm 83,369 83,816 5% 1,708 1,828 1,800 1,836 1,865 3% Commercial 169,981 172,609 10% 13,748 14,207 14,596 14,778 15,155 27% Industrial 37,807 37,607 2% 27,076 27,294 27,474 27,838 28,432 52% Total* 1,665,117 1,699,926 51,603 52,662 53,283 54,131 55,379
Source AUC, 2016. Table 8: Population of Rural Alberta Canada
Population, urban and rural, by province and territory (Alberta) Alta.
Population Urban Rural Urban Rural Number % of total population
1961 1,331,944 843,211 488,733 63 37 1966 1,463,203 1,007,407 455,796 69 31 1971 1,627,875 1,196,250 431,615 73 27 1976 1,838,035 1,379,170 458,870 75 25 1981 2,237,724 1,727,545 510,179 77 23 1986 2,365,830 1,877,760 488,070 79 21 1991 2,545,553 2,030,893 514,660 80 20 1996 2,696,826 2,142,815 554,011 79 21 2001 2,974,807 2,405,160 569,647 81 19 2006 3,290,350 2,699,851 590,499 82 18 2011 3,645,257 3,030,402 614,855 83 17
Source: Statistics Canada (http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo62j-eng.htm)
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Table 9: Farms Alberta, Canada
Year Details Total 2011 Number of Census Farms 43234 2011 Average Farm Size (Acres) 1168 2011 Total Farm Area Reported (Acres) 50498834
Source: https://open.alberta.ca/opendata/census-farm-numbers-and-average-farm-size-alberta Table 10: Microgenerators Alberta, Canada
January Statistics Sites* Combined Capacity (megawatts)2015 1147 6.6 2014 888 4.5 2013 639 3.1 2012 355 1.3 2011 216 0.7 2010 122 0.4
Source: http://www.energy.alberta.ca/Electricity/microgen.asp
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APPENDIX B: Barriers and Motivations for Microgeneration
Motivations for Microgeneration (Modified from Balcombe et al. [2013])
Save or earn money from lower fuel bills
Help Improve the environment
Protect against future higher energy costs
Make the home more self-sufficient/ less dependent on energy companies
Use an innovative and high-tech system
Protect the home against power outages
Increase the value of my home
Show my environmental commitment to others
Barriers to Microgeneration
Costs too much to buy/install
Cannot earn enough/save enough money
Home/location is not suitable
Lose money if I moved away
High maintenance costs
System performance or reliability not good enough
Energy not available when I need it
Environmental benefits are too small
Take up too much space
Difficult to install
Would not look good
Neighbour disapproval/annoyance
Disruption or hassle of operation
Hard to find trustworthy information/advice
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APPENDIX C: Correlations
Table 11: Energy Independence and when you would likely invest
I want to be
Energy
Independent
Within next
two or three
years
Within five
Years
Within 10
Years
Spearman's rho
How likely are you
to invest in
microgeneration
within the following
time frames?
I want to be
Energy
Independent
Correlation Coefficient 0.252** 0.364** 0.352**
N 112 112 112
Within next
two or three
years
Correlation Coefficient 0.252** 0.622** 0.496**
N 112 137 137
Within five
Years
Correlation Coefficient 0.364** 0.622** 0.711**
N 112 137 137
Within 10
Years
Correlation Coefficient 0.352** 0.496** 0.711**
N 112 137 137
**. Correlation is significant at the 0.01 level (2-tailed).
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APPENDIX D: NEP Statistics
Q46–Q46. If, based upon projections that the price of solar panels and other forms of microgeneration, their efficiency, and ease of installation will make them cost half of what they do now in five years, how likely are you to move to renewables within the following time frames? Table 12: NEP Statistics Uncorrected
Q48b_1 Q48b_2 Q48b_3 Q48b_4 Q48b_5 Q48b_6 Q48b_7 Q48b_8 Q48b_9
Q48b_
10
Q48b_
11
Q48b_
12
Q48b_
13
Q48b_
14
Q48b_
15
N 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
50 50 50 50 50 50 50 50 50 50 50 50 50 50 50
Mean 2.98 2.89 2.47 2.64 2.61 2.22 2.26 3.06 1.92 2.57 3.07 3.28 2.66 3.25 3.02
Std. Error of
Mean 0.133 0.122 0.123 0.127 0.131 0.116 0.127 0.132 0.102 0.156 0.142 0.131 0.111 0.136 0.139
Median 3.00 3.00 2.00 3.00 2.00 2.00 2.00 3.00 2.00 2.00 3.00 3.00 3.00 3.00 3.00
Mode 3 2 2 2 2 2 1 4 1a 1 2 3 2 3 3
Std. Deviation 1.239 1.135 1.150 1.181 1.223 1.083 1.186 1.233 .955 1.452 1.328 1.227 1.032 1.269 1.294
Variance 1.534 1.289 1.322 1.395 1.497 1.173 1.406 1.520 .912 2.108 1.763 1.504 1.066 1.610 1.674
Range 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Sum 259 251 215 230 227 193 197 266 167 224 267 285 231 283 263
a. Multiple modes exist. The smallest value is shown
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APPENDIX E: ROI and IRR Calculations
Table 13: IRR Calculations 6kw System
SCENARIO 1 CURRENT PRICE OF
POWER
UNIT PRICE OF POWER $ 0.12 Per kwH CDN (calculated in Table 2) NOMINAL POWER 6000 WATTS PURCHASE PRICE $21,000.00 CDN VALUE OF NET INCOME
$16,684.47 25 Years Income does not equal purchase prices within 25 years
LCOE $ 0.21 CDN price of solar IRR BEFORE TAX -1.77 IRR AFTER TAX -1.8 YEAR 1 2 5 10 15 20 25 ENERGY PRODUCED (KWH)
8352.00 8310.24 8184.96 7976.16 7767.36 7558.56 7349.76
GROSS INCOME 1002.24 997.23 982.20 957.14 932.08 907.03 881.97 INSURANCE -105.00 -107.10 -113.66 -125.48 -138.55 -152.97 -168.89 MAINTENANCE -105.00 -107.10 -113.66 -125.48 -138.55 -152.97 -168.89 EBP 792.24 783.03 754.88 706.17 654.99 601.10 544.20 DEPRECIATION 1050.00 1050.00 1050.00 1050.00 1050.00 1050.00 0.00 NET INC. AFTER TAX
792.24 783.03 754.88 706.17 654.99 601.10 516.99
INCOME CUMUL. 792.24 1575.27 3868.24 7497.45 10875.81 13990.22 16684.47
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SCENARIO 2 50% Higher Price of Power UNIT PRICE OF POWER $ 0.15 Per kwH CDN (calculated in Table 2) NOMINAL POWER 6000 WATTS PURCHASE PRICE $21,000.00 CDN VALUE OF NET INOME
$22,516.88 25 Years Income equal purchase price 22.1 years
LCOE $ 0.21 CDN price of solar IRR BEFORE TAX 0.6 IRR AFTER TAX 0.6 YEAR 1 2 5 10 15 20 25 ENERGY PRODUCED (KWH)
8352.00 8310.24 8184.96 7976.16 7767.36 7558.56 7349.76
GROSS INCOME 1252.80 1246.54 1227.74 1196.42 1165.10 1133.78 1102.46 INSURANCE -105.00 -107.10 -113.66 -125.48 -138.55 -152.97 -168.89 MAINTENANCE -105.00 -107.10 -113.66 -125.48 -138.55 -152.97 -168.89 EBP 1042.80 1032.34 1000.43 945.45 888.01 827.85 764.69 DEPRECIATION 1050.00 1050.00 1050.00 1050.00 1050.00 1050.00 0.00 NET INC. AFTER TAX
1042.80 1032.34 1000.43 945.45 888.01 827.85 726.46
INCOME CUMUL. 1042.80 2075.14 5108.51 9946.68 14502.66 18763.39 22516.88
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SCENARIO 4 100% Higher Price of Power UNIT PRICE OF POWER $ 0.19 Per kwH CDN NOMINAL POWER 6000 WATTS Income equal purchase price 16.4 years PURCHASE PRICE $21,000.00 CDN VALUE OF NET INOME
$30,087.05 25 Years
LCOE $ 0.21 CDN price of solar IRR BEFORE TAX 3.3 IRR AFTER TAX 3.2 YEAR 1 2 5 10 15 20 25 ENERGY PRODUCED (KWH)
8352.00 8310.24 8184.96 7976.16 7767.36 7558.56 7349.76
GROSS INCOME 1586.88 1578.95 1555.14 1515.47 1475.80 1436.13 1396.45 INSURANCE -105.00 -107.10 -113.66 -125.48 -138.55 -152.97 -168.89 MAINTENANCE -105.00 -107.10 -113.66 -125.48 -138.55 -152.97 -168.89 EBP 1376.88 1364.75 1327.83 1264.50 1198.71 1130.20 1058.68 INCOME BEF. TAX 1376.88 1364.75 1327.83 1264.50 1198.71 1130.20 1058.68 DEPRECIATION 1050.00 1050.00 1050.00 1050.00 1050.00 1050.00 0.00 NET INC. AFTER TAX
1360.54 1349.01 1313.94 1253.78 1191.27 1126.19 1005.75
INCOME CUMUL. 1360.54 2709.54 6686.60 13076.70 19159.05 24921.24 30087.05
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Table 14: Scenario Calculations for Price Per kwH income
Scenario 1
Current July 16th,
2016 Power
Prices Annual Power Prices
Price of Use
Transmission Multiplier Subtotal
Distribution (rural Fixed)
Misc. Fixed Fees
Cost CDN $ $ 0.075 $ 0.035
$ 80.00 $ 8.00 Annual
Price per Kwh
Unit kwh kwh annual Month Fixed Fee $ 0.12
Annual kwH use 12,000 $ 900.00
$ 420.00 $ 1,416.00
$ 960.00 $96.00
$1,056.00 $cdn
Scenario 2 25% increase in price of power
Annual Power Prices
Price of Use
Transmission Multiplier Subtotal
Distribution (rural Fixed)
Misc. Fixed Fees
Cost CDN $ $ 0.095
$ 0.035 $ 80.00 $ 8.00 Annual
Price per Kwh
Unit kwh kwh annual Month Fixed Fee $ 0.13
Annual kwH use 12,000 $ 1,140.00 $ 420.00
$ 1,560.000 $ 960.00 $96.00
$1,056.00 $cdn
Scenario 3 50% increase in price of power
Annual Power Prices
Price of Use
Transmission Multiplier Subtotal
Distribution (rural Fixed)
Misc. Fixed Fees
Cost CDN $ $ 0.110 $ 0.035
$ 80.00 $ 8.00 Annual
Price per Kwh
Unit kwh kwh annual Month Fixed Fee $ 0.15
Annual kwH use 12,000 $ 1,320.00 $ 420.00
$ 1,740.000
$ 960.00 $96.00
$1,056.00 $cdn
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Scenario 4 100% increase in price of power
Annual Power Prices
Price of Use
Transmission Multiplier Subtotal
Distribution (rural Fixed)
Misc. Fixed Fees
Cost CDN $ $0.150 $ 0.035 $ 80.00 $ 8.00 Annual
Price per Kwh
Unit kwh kwh annual Month Fixed Fee $ 0.19
Annual kwH use 12,000 $ 1,800.00 $ 420.00 $2,220.000 $ 960.00 $96.00
$1,056.00 $cdn
***MONTHLY PV PERFORMANCE DATA, INCOME, AND RETURN ON INVESTMENT MODEL
LOCATION: Geographic Centre of Alberta
Array Tilt (deg): 45*
LOCATION: Edmonton, Alberta Array Azimuth (deg): 180
LAT (DEG N): 53.3 System Losses: 14
LONG (DEG W): 113.58 Invert Efficiency: 96
ELEV (M): 715 DC to AC Size Ratio: 1.1
DC SYSTEM SIZE (KW): 6 Average Cost of Electricity Purchased from Utility ($/kWh):
0.21*
MODULE TYPE: Standard Initial Cost 3.5 per Watt Installation Cost
ARRAY TYPE: Fixed (open rack)
*Idealized fixed tilt based upon latitude – 10% **Based upon regulated rates and fixed costs as a provincial average ***Calculated in part by using http://pvwatts.nrel.gov/ online calculator
83
Table 15: Annual Income Scenarios Given Price of Power Increases 6kW Array
Scenario Current 25% increase 50% increase 100% increase
Retail Rate Price per Kwh $ 0.12 $ 0.13 $ 0.15 $ 0.19
Month AC System Output(kWh)
Solar Radiation (kWh/m^2/day)
Plane of Array Irradiance (W/m^2)
DC array Output (kWh)
Annual Income
Annual Income
Annual Income
Annual Income
January 464.929 2.758 85.491 487.008 $ 58.44 $ 63.31 $ 73.05 $ 92.53
February 614.036 4.162 116.548 641.537 $ 76.98 $ 83.40 $ 96.23 $ 121.89
March 891.156 5.687 176.285 932.168 $ 111.86 $ 121.18 $ 139.83 $ 177.11
April 797.592 5.385 161.561 835.404 $ 100.25 $ 108.60 $ 125.31 $ 158.73
May 848.245 5.756 178.430 889.190 $ 106.70 $ 115.59 $ 133.38 $ 168.95
June 830.029 5.905 177.136 870.312 $ 104.44 $ 113.14 $ 130.55 $ 165.36
July 807.132 5.651 175.187 847.134 $ 101.66 $ 110.13 $ 127.07 $ 160.96
August 802.194 5.578 172.906 840.749 $ 100.89 $ 109.30 $ 126.11 $ 159.74
September 648.221 4.476 134.289 679.932 $ 81.59 $ 88.39 $ 101.99 $ 129.19
October 589.522 3.837 118.939 617.574 $ 74.11 $ 80.28 $ 92.64 $ 117.34
November 397.140 2.500 75.000 417.287 $ 50.07 $ 54.25 $ 62.59 $ 79.28
December 279.131 1.680 52.065 295.150 $ 35.42 $ 38.37 $ 44.27 $ 56.08
Total 7,969.327 53.374 1,623.837 8,353.444 $ 1,002.41 $ 1,085.95 $ 1,253.02 $ 1,587.15
84
Table 16: Payback, NPV, IRR and ROI for different Price Scenarios 6Kw
Current Price of Power
25% increase in Price of Power
50% increase in Price of Power
100% increase in Price of Power
$ 0.12 $ 0.13 $
0.15 $ 0.19
Annual Income Annual Income Annual Income Annual Income Annual Income of Produced Power $ 1,416.00 $ 1,560.00 $ 1,740.00 $ 2,220.00
Annual Fixed Fees $ 1,056.00 $ 1,056.00 $ 1,056.00 $ 1,056.00
Simple Payback (years) 20.95 19.34 16.76 13.23
Solar Payback (years) 14.58 13.46 11.67 9.21Discounted Pay-Back Period (3% Discount) Years 19.914 17.499 15.208 11.292
NPV ‐$4,198.37 ‐$2,798.23 $2.04 $5,602.58
IRR ‐1.8 ‐1 0.6 3.2
ROI Lifetime 49% 62% 86% 136%
Simple Pay‐back Scenarios 6kW versus 3kW Based Annual Production for Edmonton, Alberta, Canada Predicated on Consumption and not on Grid Revenue
85
Table 17: Payback Scenarios
6 kilowatt System Survey Results Jul‐16
25% increase 50% increase 100% increase
Kwh Price $0.12 $0.13 $0.15 $0.19
Annual Income given scenario $1,002.41 $1,085.95 $1,253.02 $1,587.15
No Incentive Simple Payback $21000 cost 14.58 13.46 11.67 9.21
20% Capital Incentive $16800 capital
24.8% Yes75.20% No 11.67 10.77 9.33 7.37
30% Capital Incentive $14700 capital Payback is annual rebate over 10 years
29.2% Yes70.8% No 10.21 9.42 8.17 6.45
50% Capital Costs by homeowner payback is annual Rebate over 10 years
Not Tested 7.29 6.73 5.83 4.61
3 kilowatt System
Jul‐16 25%
increase 50% increase 100% increase
Kwh Price $0.12 $0.13 $0.15 $0.19
Annual Income given scenario $478.00 $517.99 $599.00 $759.00
No Incentive Simple Payback $10,500 cost 7.29 6.73 5.83 4.61
20% Capital Incentive $8,500 capital
24.8% Yes75.20% No 5.90 5.45 4.72 3.73
30% Capital Incentive $7,350 capital 29.2% Yes70.8% No 5.10 4.71 4.08 3.22
50% Capital Costs by homeowner 29.9% No 70.1% Yes 4.34 4.01 3.47 2.74
86
Simple Payback= (System Cost/Cost of Energy)/ (annual electricity usage) does not include the time value of money Note: other incentives of feed-in tariff, or yearly rebates have similar effects
Table 18: 3 kW System
IRR for 3kW System
Scenario 1 Current Price Cost of power 0.12
Nominal power (kWp) 3
Purchase value (CAD) 10,500
Own Funds (CAD) 10,500 Present value of net income (CAD) 5,097
Levelised energy cost (CDN/kWh) 0.28
IRR (%) -5.3 1 5 10 20 25
Energy produced (kWh) 3,000.00 2,940.00 2,865.00 2,715.00 2,640.00 Gross income 360.00 352.80 343.80 325.80 316.80
Insurance -52.50 -56.83 -62.74 -76.48 -84.44 Maintenance -52.50 -56.83 -62.74 -76.48 -84.44
EBP 255.00 239.14 218.32 172.83 147.91 Income bef. tax 255.00 239.14 218.32 172.83 147.91
Depreciation 525.00 525.00 525.00 525.00 0.00 Net income after tax 255.00 239.14 218.32 172.83 147.91
Income cumul. 255.00 1235.58 2369.28 4306.78 5096.82
87
Scenario 3 50% Increase in Price of Power
Cost of power 0.15 Nominal power (kWp) 3
Purchase value (CAD) 10,500
Own Funds (CAD) 10,500 Present value of net income (CAD) 7,212
Levelised energy cost (CDN/kWh) 0.28
IRR (%) -2.9
Year 1 5 10 20 25 Energy produced (kWh) 3,000.00 2,940.00 2,865.00 2,715.00 2,640.00
Gross income 450.00 441.00 429.75 407.25 396.00 Insurance -52.50 -56.83 -62.74 -76.48 -84.44
Maintenance -52.50 -56.83 -62.74 -76.48 -84.44 EBP 345.00 327.34 304.27 254.28 227.11
Income bef. tax 345.00 327.34 304.27 254.28 227.11 Depreciation 525.00 525.00 525.00 525.00 0.00
Net income after tax 345.00 327.34 304.27 254.28 227.11 Income cumul. 345.00 1,681.08 3,249.03 6,021.28 7,211.82
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Scenario 4: 100% Increase in Price of Power
Cost of power 0.19 Nominal power (kWp) 3
Purchase value (CAD) 10,500
Own Funds (CAD) 10,500 Present value of net income (CAD) 10,032
Levelized energy cost (CDN/kWh) 0.28
IRR (%) -0.4
Year 1 5 10 20 25 Energy produced (kWh) 3,000.00 2,940.00 2,865.00 2,715.00 2,640.00
Gross income 570.00 558.60 544.35 515.85 501.60 Insurance -52.50 -56.83 -62.74 -76.48 -84.44
Maintenance -52.50 -56.83 -62.74 -76.48 -84.44 EBP 465.00 444.94 418.87 362.88 332.71
Income bef. tax 465.00 444.94 418.87 362.88 332.71 Depreciation 525.00 525.00 525.00 525.00 0.00
Net income after tax 465.00 444.94 418.87 362.88 332.71 Income cumul. 465.00 2,275.08 4,422.03 8,307.28 1,0031.82
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Payback, IRR and ROI for Different Price Scenarios 3Kw
CURRENT PRICE OF POWER
50% INCREASE IN PRICE OF POWER
100% INCREASE IN PRICE OF POWER
$ 0.12 $ 0.15 $ 0.19
Annual Income Annual Income Annual Income
ANNUAL INCOME OF PRODUCED POWER
$ 1,416.00 $ 1,740.00 $ 2,220.00
ANNUAL FIXED FEES $ 1,056.00 $ 1,056.00 $ 1,056.00
SIMPLE PAYBACK (YEARS) 7.29 5.83 4.61
IRR ‐5.3 ‐2.9 ‐0.4
DISCOUNTED PAYBACK PERIOD (3% DISCOUNT RATE) YEARS
8.52 6.756 5.177
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APPENDIX F: Microgenerators
Q21. What type(s) of renewable energy system(s) have you bought? Select all that apply
# Answer Bar Response %
1 None 3 37.50%
2 Solar photovoltaic (generating electricity)
5 62.50%
3 Solar thermal (heating water or air)
3 37.50%
4 Wind turbine 2 25.00%
5 Ground source heat pump
1 12.50%
6 Air sourceheat pump
1 12.50%
7 Biomass wood boiler
4 50.00%
8 CHP (combined heat and power)
t
2 25.00%
9 Micro hydroelectric
0 0.00%
Total 21 100.00%
Min Value Max Value Average Value
Variance Standard Deviation
Total Responses
Total Respondents
1 8 4.05 6.15 2.48 21 8
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Q23. Have you received any grants or incentives for the installation(s)?
# Answer Bar Response %
1 Yes 1 20.00%
2 No 4 80.00%
Total 5 100.00%
Min Value Max Value Average Value
Variance Standard Deviation
Total Responses
Total Respondents
1 2 1.80 0.20 0.45 5 5
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Q24. Would you buy one again?
# Answer Bar Response %
1 Extremely Likely 4 80.00%
2 Moderately Likely 0 0.00%
3 Slightly Likely 1 20.00%
4 Neutral/No opinion 0 0.00%
5 Slightly Unlikely 0 0.00%
6 Moderately Unlikely
0 0.00%
7 Extremely Unlikely 0 0.00%
Total 5 100.00%
Min Value Max Value Average Value Variance Standard Deviation
Total Responses
Total Respondents
1 3 1.40 0.80 0.89 5 5
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Q25. How satisfied with your system are you?
# Answer Bar Response %
1 Far exceeds expectations
0 0.00%
2 Exceeds expectations
2 50.00%
3 Equals expectations 2 50.00%
4 Short of expectations
0 0.00%
5 Far short of expectations
0 0.00%
Total 4 100.00%
Min Value Max Value Average Value
Variance Standard Deviation
Total Responses
Total Respondents
2 3 2.50 0.33 0.58 4 4
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APPENDIX G: Energy Attitudes
Table 19: Energy Attitude Statistics
Statistic Q47b_1 Q47b_2 Q47b_3 Q47b_4 Q47b_5 Q47b_6 Q47b_7 Q47b_8
Stat Stat Stat Stat Stat Stat Stat Stat
Mean 2.12 2.17 2.01 1.69 2.41 2.63 2.80 2.36
95% Confidence Interval for Mean
Lower Bound 1.91 1.94 1.81 1.51 2.21 2.38 2.58 2.17
Upper Bound 2.32 2.40 2.21 1.86 2.62 2.87 3.03 2.54
5% Trimmed Mean 2.02 2.08 1.90 1.58 2.35 2.58 2.78 2.29
Median 2.00 2.00 2.00 1.00 2.00 2.00 3.00 2.00
Variance 1.221 1.512 1.144 .883 1.199 1.660 1.421 1.006
Std. Deviation 1.105 1.229 1.070 .940 1.095 1.288 1.192 1.003
Minimum 1 1 1 1 1 1 1 1
Maximum 5 5 5 5 5 5 5 5
Range 4 4 4 4 4 4 4 4
Interquartile Range 2 2 2 1 1 2 1 1
Skewness 1.154 .854 1.106 1.528 0.588 0.606 0.454 0.756
Kurtosis 0.895 -0.184 0.853 2.125 -0.175 -0.756 -0.599 0.445
Statistic
Q47b_9 Q47b_11 Q47b_10 Q47b_12 Q47b_13 Q47b_14 Q47b_15 Q47b_16
Stat Stat Stat Stat Stat Stat Stat Stat
Mean 1.88 2.56 1.95 3.04 1.95 2.44 2.54 1.83
95% Confidence Interval for Mean
Lower Bound 1.71 2.35 1.73 2.84 1.77 2.22 2.31 1.67
Upper Bound 2.05 2.78 2.16 3.25 2.13 2.65 2.76 1.99
5% Trimmed Mean 1.79 2.51 1.83 3.05 1.87 2.38 2.48 1.75
Median 2.00 3.00 2.00 3.00 2.00 2.00 2.00 2.00
Variance 0.824 1.329 1.276 1.232 0.934 1.347 1.440 0.773
Std. Deviation 0.908 1.153 1.130 1.110 0.966 1.161 1.200 0.879
Minimum 1 1 1 1 1 1 1 1
Maximum 5 5 5 5 5 5 5 5
Range 4 4 4 4 4 4 4 4
Interquartile Range 1 1 1 2 2 1 1 1
Skewness 1.263 0.312 1.252 0.233 0.901 0.559 0.297 1.150
Kurtosis 2.144 -0.616 0.920 -0.771 0.462 -0.588 -0.920 1.311
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APPENDIX H: Questionnaire
The Study of: A Business Case for Microscale Renewable Energy Deployment in Rural Alberta, Canada: Partnerships, Resources and Incentives for Public Policy
Success
You are being asked to take part in a research study on Motivations and Barriers for Microscale Renewable Energy in Rural Alberta to Develop a Business Case for Microscale Renewable Energy Deployment in Rural Alberta, Canada: Partnerships, Resources, and Incentives for Public Policy Success.
Before you decide, it is important for you to understand why this study is being done and what it will involve. Please take time to read the following information carefully and decide whether or not you wish to take part. Ask me if there is anything that is not clear or if you would like more information.
WHAT IS THE STUDY ABOUT?
This study is looking at the Motivations and Barriers for microscale renewable energy in rural Alberta to create a business case for rural Alberta microscale renewable energy.
WHO IS DOING THIS STUDY AND WHY?
I am a student at the University of Leicester and am doing this study for my dissertation. I am supervised by Dr. Panayiotis Savvas at the University of Leicester School of Management.
WHY HAVE I BEEN CHOSEN?
I am inviting you to take part in this study as someone who I think would be able to provide some valuable opinions about Renewable Energy in Rural Alberta.
DO I HAVE TO TAKE PART?
It is up to you to decide whether or not you want to take part. You do not have to give a reason.
PARTICIPANTS’ RIGHTS
If you do decide to take part, you will be given this information sheet to keep. You may decide to stop being a part of the research study at any time without explanation. You have the right to ask that any data you have supplied to that point be destroyed. You have the right to omit or refuse to answer any question that is asked of you. You have the right to have your questions about the procedures answered. If you have any questions as a result of reading this information sheet, you should ask me, the researcher, before the study begins.
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WHAT HAPPENS NEXT IF I AGREE TO TAKE PART IN THIS STUDY?
You will need to fill out this questionnaire as best you can. This will take no more than 25 mins.
WHAT KIND OF INFORMATION WILL BE COLLECTED ABOUT ME?
In the Questionnaire I will ask you some information about why or why you have not undertaken mico-renewable energy projects, what you would be willing to pay for micro-renewable projects, and what you would need to make you want to undertake renewable energy projects. I will not ask questions regarding your finances, financial standing, etc.
CONFIDENTIALITY/ANONYMITY
Any information you supply to me will be treated confidentially in accordance with the 1998 Data Protection Act: your name and identifying affiliations will be anonymized in the analysis and any resulting publications, unless you give your explicit consent to identify you as a subject. Any information you provide will not be given to anyone else.
WHAT ARE THE BENEFITS IN TAKING PART IN THIS STUDY?
There is no payment for taking part in this study. This study will help understand the best way to improve access to solar energy in rural Alberta and will aid the creation of a rural plan for Alberta and Mico-renewable Energy.
ARE THERE ANY RISKS IN TAKING PART IN THIS STUDY?
There are no risks in taking part in this study.
WHAT HAPPENS WHEN THE STUDY FINISHES?
The results of the study will be presented in my dissertation.
FOR FURTHER INFORMATION
If you have further questions about the study, you are welcome to contact me at the following e-mail address: [email protected]
If you have any questions about the ethical conduct of this research please contact the School’s Ethics Officer at the following e-mail address: [email protected]
THANK YOU FOR TAKING THE TIME TO READ THROUGH THIS INFORMATION
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1. Do you rent or own your home?
2. How would you describe your property?
Acreage Hobby Farm Farm Landholding Commercial Industrial Other (specify)
3. Where is your home located? City Centre Urban Suburban Rural Town Hamlet Other (explain)
4. Are you aware of Climate Change?
Yes No Do not agree
5. Does Climate Change concern you?
Yes No
Comments:
6. How important is it that we act on Climate Change now? Very Important Important
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Not Important Climate Change is not Happening Do not Care
7. Are you aware of CO2 reduction targets set by the Federal Government?
Yes No
8. Are you aware of CO2 reduction targets set by the Provincial Government?
Yes No
9. Are you aware of the implications of the Alberta carbon tax on energy use that is coming
into force in 2017? Yes No
10. Would you be willing to pay more for renewable energy over and above the new Alberta
carbon tax? If yes, what dollar figure per month would you be willing to pay?
11. How aware are you of Renewable Energy Power Generation Technologies such as solar or wind?
Aware Slightly Aware Unaware
12. What factors would prevent you from installing Solar Energy Technology? Check all that
apply. Affordability Inconvenience Lack of Knowledge Lack of Interest Technology Distrust Other (explain)
13. Would you rather spend your money on: Home Improvement Maintenance
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Installing Renewable Energy Power Generation
14. Do you have any energy efficient purchases in your home? Yes No
15. Have you made purchases in consideration of their energy efficiency?
Yes No
16. Is your clothes drier?
electric gas
17. Do you have a fridge or freezer that is older than 15 years in your home?
Yes No
18. Have you any LED or Compact Fluorescent or Fluorescent lighting in your home? If yes,
what percentage of your home is using LED lights? Yes No
19. Do you have a sodium halide yard light?
Yes (how many?) No
20. Which of the following applies to you:
I own a microgeneration system I am currently thinking about owning a microgeneration system (go to Q27) I thought about it and decided not to buy a microgeneration system (go to Q27)
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21. What type(s) of microgeneration system(s) have you bought? None (skip to Q27) Solar photovoltaic (generating electricity) Solar thermal (heating water) Wind turbine ground source heat pump (geothermal) Air source heat pump Biomass/wood boiler CHP (combined heat and power) system Micro hydroelectric Other (specify)
22. What year was your system installed?
23. Have you received any grants or incentives for the installation(s)? Please describe
24. Would you buy one again?
Definitely would Probably would not Maybe Probably not Definitely not
25. Would you do anything different?
Nothing Do not know Yes, I would change something (explain)
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26. Motivations for buying a microgeneration system. From each of these lists please select which was the most important and which was the least important factor in your decision to buy a system.
Which is the MOST important, pick only one
Make the household more self sufficient/ less dependent on utility companies Protect the household against power outages Help improve the environment Use an innovative and high-tech system
Which is the MOST important, pick only one
Protect the household against power outages Make the household more self-sufficient and less dependent on utility companies Increase the value of my home Show my environmental commitment to others
Which is the LEAST important, pick only one Make the household more self-sufficient/ less dependent on utility companies Protect the household against power outages Help improve the environment Use an innovative and high-tech system
Which is the LEAST important, pick only one
Protect the household against power outages Make the household more self-sufficient and less dependent on utility companies Increase the value of my home Show my environmental commitment to others
27. Factors or motivations when you were thinking about buying a microgeneration system.
From each of these lists please select which was the most important and the least important factor in thinking about buying a system. Which is the MOST important, pick only one
Save or earn money from lower fuel bills and government incentives Protect against future higher energy costs Protect the household against power outages Make the household more self-sufficient/ less dependent on utility companies
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Which is the MOST important, pick only one
Show my environmental commitment to others Save or earn money from lower fuel bills and government incentives Protect against future higher energy costs Increase the value of my home
Which is the MOST important, pick only one
Use an innovative/ high-tech system Help improve the environment Save or earn money from lower fuel bills and government incentives Protect against future higher energy costs
Which is the LEAST important, pick only one
Save or earn money from lower fuel bills and government incentives Protect against future higher energy costs Protect the household against power outages Make the household more self sufficient/ less dependent on utility companies
Which is the LEAST important, pick only one
Show my environmental commitment to others Save or earn money from lower fuel bills and government incentives Protect against future higher energy costs Increase the value of my home
Which is the LEAST important, pick only one
Use an innovative/ high-tech system Help improve the environment Save or earn money from lower fuel bills and government incentives Protect against future higher energy costs
28. Things that have delayed you in buying a microgeneration system. What are the factors
that prevented you when you were thinking about buying a microgeneration system? From each of these lists please select which was the most important and the least important factor in thinking about buying a system.
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Which is the MOST important, pick only one Take up too much space Costs too much to buy/ install High maintenance costs Energy not available when I need it Cannot earn enough/ save enough money
Which is the MOST important, pick only one
Costs too much to buy/ install Cannot earn enough/ save enough money Lose money if I moved home System performance or reliability not good enough Home/ location not suitable
Which is the MOST important, pick only one
System performance or reliability not good enough Take up too much space Home/ location not suitable Would not look good Hard to find trustworthy information/ advice
Which is the MOST important, pick only one
Cannot earn enough/ save enough money Hard to find trustworthy information/ advice Would not look good Environmental benefits too small Hassle of installation
Which is the MOST important, pick only one
Lose money if I moved away Would not look good Environmental benefits too small Neighbour disapproval/ annoyance Disruption or hassle of operation
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Which is the MOST important, pick only one
Home/ location not suitable Hassle of installation Costs too much to buy/ install Neighbour disapproval/ annoyance Energy not available when I need it
Which is the MOST important, pick only one
High maintenance costs Lose money if I moved away Take up too much space Disruption or hassle of operation System performance or reliability not good enough
Which is the LEAST important, pick only one
Take up too much space Costs too much to buy/ install High maintenance costs Energy not available when I need it Cannot earn enough/ save enough money
Which is the LEAST important, pick only one
Costs too much to buy/ install Cannot earn enough/ save enough money Lose money if I moved home System performance or reliability not good enough Home/ location not suitable
Which is the LEAST important, pick only one
System performance or reliability not good enough Take up too much space Home/ location not suitable Would not look good Hard to find trustworthy information/ advice
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Which is the LEAST important, pick only one
Cannot earn enough/ save enough money Hard to find trustworthy information/ advice Would not look good Environmental benefits too small Hassle of installation
Which is the LEAST important, pick only one
Lose money if I moved away Would not look good Environmental benefits too small Neighbour disapproval/ annoyance Disruption or hassle of operation
Which is the LEAST important, pick only one
Home/ location not suitable Hassle of installation Costs too much to buy/ install Neighbour disapproval/ annoyance Energy not available when I need it
Which is the LEAST important, pick only one
High maintenance costs Lose money if I moved away Take up too much space Disruption or hassle of operation System performance or reliability not good enough
29. Is there anything else you would like to add about things that prevented you buying a microgeneration system?
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30. What types of microgeneration system have you been considering?
None Solar photovoltaic (generating electricity) Solar thermal (heating water/air) Wind turbine Ground source heat pump (geothermal) Air source heat pump Biomass/wood boiler CHP (combined heat and power) system Microhydroelectric, Other (please specify)
31. What stage of consideration have you got to? Initial investigation I have talked others who have installed I have been to see a system in action I received professional advice I received a quote from supplier/installer Other information:
32. How likely are you to install the system you are considering? Almost definitely Will Most likely Perhaps Most unlikely Will not Almost definitely not
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33. Alberta has no incentives provincially for residential micro-renewable energy at present.
What do you feel is the best way for the province to provide incentives for renewable energy?
Which is the MOST important, pick only one
Feed-in Tariff (Premium paid for power that is fed back into the system) Tax Rebates Grants Technological Assistance
Which is the MOST important, pick only one
Material Support (Parts of a Renewable Energy System) Installation Support Regulatory Support Long Term Yearly Rebates
Which is the MOST important, pick only one
Feed-in Tariff (premium paid for power that is fed back into the system) Installation Support Grants Technological Assistance
Which is the LEAST important, pick only one
Feed-in Tariff (premium paid for power that is fed back into the system) Tax Rebates Grants Technological Assistance
Which is the LEAST important, pick only one
Material Support (parts of a renewable energy system) Installation Support Regulatory Support Long-Term Yearly Rebates
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Which is the LEAST important, pick only one
Feed-in Tariff (premium paid for power that is fed back into the system) Installation Support Grants Technological Assistance
34. Are you familiar with any of the programs in other provinces for renewable energy?
Yes Vaguely No
35. Has lack of incentives/support prevented you from installing a system? Made no difference Made a little difference Made a large difference Stopped me completely
Why?
36. How likely are you to invest in microgeneration within the following time frames?
Time Frame Very Unlikely
Fairly Likely
No Idea Fairly Unlikely
Very Unlikely
Within next two or three years ☐ ☐ ☐ ☐ ☐
Within five Years ☐ ☐ ☐ ☐ ☐
Within 10 Years ☐ ☐ ☐ ☐ ☐
Not Likely
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37. Approximately how much would you spend on a microgeneration system? One Time, Amounts in $
Annually, Amounts in $
38. What is the best form of government support that would entice you to undertake a renewable energy project? Check one
Capital grant to allow for installation, energy paid back to installation at same rate of use
Feed-in Tariff as a premium paid on extra energy generated that enters the grid A combination of a and b.
Renewable Energy Scenarios
39. If provincial subsidies provided you with 20% of initial capital costs for microgeneration
installation, would you invest $10,000? Yes No
If yes, would you invest more? How much?
40. If provincial subsidies provided you with 30% of capital costs over 10 years for a
microgeneration installation, would you invest $10,000? Yes No
If yes, would you invest more? How much?
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41. If provincial subsidies provided you with 50% of capital costs over 10 years for a microgeneration installation, would you invest $10,000?
42. If you could fix your price per kwH (kilowatt Hour) at 10¢/kWh, (that takes into account
the costs of electricity supply, transmission, distribution, riders, etc.) for electricity for 10 years, would you be willing to invest in microgeneration? Current price of power on average is 20.5 ¢/kWh.
Yes No
If yes, would you pay $15,000?
Yes No
43. If you answered yes to question 42 and you were told it would cost you $25,000, would
you still invest? Yes No
44. If you answered no to question 42 and you were given access to a low interest loan
(government sponsored) for 10 years but the cost of the loan made the price of power 20¢/kWh for the 10 years, would you invest?
If you said no, why?
45. How long are you willing to pay for a loan for the prospect of fixing (keeping the same costs) or decreasing your power bill?
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46. If, based upon projections that the price of solar panels and other forms of microgeneration, their efficiency, and ease of installation will make them cost half of what they do now in five years, how likely are you to move to renewables within the following time frames?
Time Frame Very Unlikely
Fairly Likely
No Idea Fairly Unlikely
Very Unlikely
Within Five Years ☐ ☐ ☐ ☐ ☐
Within 10 Years ☐ ☐ ☐ ☐ ☐
Not Likely
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Your Thoughts on Energy and the Environment
47. Energy Prices and Power Companies. You are asked to indicate the strength of your agreement with each statement.
Item Strongly Agree
Mildly Agree
Unsure Mildly Disagree
Strongly Disagree
Energy prices have risen strongly ☐ ☐ ☐ ☐ ☐
There are many added costs to my power bill that I do not understand and I would like to reduce or eliminate
☐ ☐ ☐ ☐ ☐
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 myself ☐ ☐ ☐ ☐ ☐
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 ☐ ☐ ☐ ☐ ☐
I am trying to save energy when I can ☐ ☐ ☐ ☐ ☐
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 ☐ ☐ ☐ ☐ ☐
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48. The following questions are based upon the questions of the New Ecological Paradigm (NEP)1 that are used to measure levels of environmental concern amongst a group of people. You are asked to indicate the strength of your agreement with each statement.
Item Strongly
Agree Mildly Agree Unsure
Mildly Disagree
Strongly Disagree
We are approaching the limit of the number of people the Earth can support ☐ ☐ ☐ ☐ ☐
Humans have the right to modify the natural environment to suit their needs ☐ ☐ ☐ ☐ ☐
When humans interfere with nature it often produces disastrous consequences ☐ ☐ ☐ ☐ ☐
Human ingenuity will ensure that we do not make the Earth unlivable ☐ ☐ ☐ ☐ ☐
Humans are seriously abusing the environment ☐ ☐ ☐ ☐ ☐
The Earth has plenty of natural resources if we just learn how to develop them
☐ ☐ ☐ ☐ ☐
Plants and animals have as much right as humans to exist ☐ ☐ ☐ ☐ ☐
The balance of nature is strong enough to cope with the impacts of modern industrial nations
☐ ☐ ☐ ☐ ☐
Despite our special abilities, humans are still subject to the laws of nature ☐ ☐ ☐ ☐ ☐
The so-called “ecological crisis” facing humankind has been greatly exaggerated ☐ ☐ ☐ ☐ ☐
The Earth is like a spaceship with very limited room and resources ☐ ☐ ☐ ☐ ☐
Humans were meant to rule over the rest of nature ☐ ☐ ☐ ☐ ☐
The balance of nature is very delicate and easily upset ☐ ☐ ☐ ☐ ☐
Humans will eventually learn enough about how nature works to be able to control it
☐ ☐ ☐ ☐ ☐
If things continue on their present course, we will soon experience a major ecological catastrophe
☐ ☐ ☐ ☐ ☐
1 Dunlap, Riley E. (2008). The new environmental paradigm scale: From marginality to worldwide use. Journal of
Environmental Education, 40 (1), 3–18.
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49. Is there anything you would like to add regarding the issues this survey has addressed or the questions we have asked you?
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School of Management
APPENDIX I: Dissertation Proposal
Student Name: Paul Adam McLauchlin
Registration Number: 119048664119048664
Programme Title: MBA Finance
Module Title: MN7508 Dissertation Proposal
Proposed Dissertation Title: A Business Case for Micro-Renewable Energy Deployment in Rural Alberta, Canada: Resources and Incentives for Policy SuccessA Business Case for Micro-Renewable Energy Deployment in Rural Alberta, Canada: Resources and Incentives for Policy Success
Registration Expiration Date 31/10/2016
Word Count: 1794
The Proposal Specialism Finance Ethical Approval reference number 6209-pam28-schoolofmanagement6209-pam28-schoolofmanagement Dissertation Supervisor Dr. Panayiotis Savvas: Blackboard Discussion Title A Business Case for Micro-Renewable Energy Deployment in Rural Alberta, Canada: Partnerships, Resources and Incentives for Public Policy SuccessA Business Case for Micro-Renewable Energy Deployment in Rural Alberta, Canada: Partnerships, Resources and Incentives for Public Policy Success Word Count: 19
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Introduction
The business case for the public incentives and private investment of micro-renewable
(<100,0000 watts) energy projects, specifically Photovoltaic, in Alberta, Canada has not been
undertaken in the strictly rural context. At present there are very few participation or incentive
programs to support renewable energy in Alberta (Cansia, 2014). This study would seek to
answer the following:
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?
This study assumes that rural Alberta is expected to be a potentially effective participant in
renewable energy development and seeks to answer how motivations and barriers affect the
economic and public policy case for successful involvement of rural private investment.
Word Count: 196
Relation to previous research (Theoretical Framework)
This study seeks to identify the motives and barriers of potential participants in investing in
renewable energy technologies at the microscale by answering the research questions posed.
Most European and American jurisdictions have adopted incentive mechanisms to promote
private investment in renewable energy technologies to meet national climate change
117
objectives (Brown et al., 2011). The primary motive of governments to adopt incentives is to
bridge the 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, p. 800).
Barr and Gilg (2007) and Arkesteijn and Oerlemans (2005) have identified a model of
“planned behaviour” after Azjen and Fischbein (1977) for personal environmental choices
(investments). This model has proposed that the drivers of behaviour are attitudes, subjective
norms, and perceptions of control. This behaviour manifests itself as a household’s
“intentions” that “subsequently lead to actual behaviour” in making investment choices
(Leenheer, et al., 2011, p. 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.
At present, 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 (Henton, 2016; 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) and the State of California (Dong et al., 2014), this study
will identify what motives and barriers micro-renewable investors and local/regional
governments may have (Holtorf et al., 2015).
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; Maala and Kunsch, 2008; Palm and Tengvard, 2011). A lack of investment
118
has been a struggle in other countries that have progressed further along towards their
renewable targets, and this study seeks to understand if that may occur in Alberta as well.
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. Word Count: 523
119
School of Management
Proposed methods Qualitative research is the most appropriate method to use to conduct a study that considers
the motives and barriers of potential participants in investing in renewable energy at the
microscale. 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) of the criteria
identified by Balcombe et al. (2013). 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 scenarios for micro-renewables.
Questionnaires will be used as the primary source of information for this study to identify 1)
the motivations and barriers associated with the adoption of micro-renewables and, 2) the
“Willingness to Pay” for rural households (Scarpa and Willis, 2010).
Following the design outlined by Balcombe et al. (2013) a questionnaire will be developed
and modified to address the differences in regional culture and terminology specific to the
rural Alberta study area. A modified list of barriers and motivations used for the
questionnaire are found in Appendix B. The questionnaire will be developed into a BWS
survey method that includes “five choices for motivations” with four motivations and “seven
choice tasks for barriers” with five barriers (Balcombe et al., 2013, p. 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
120
and are difficult for discrimination of a large number of items, ratio-scaled results offer an
advantage (Balcombe et al., 2013, p. 407; Cohen and Orme, 2004).
Three different data sources will be identified for this investigation: 1) Utility Service Providers
(N=10), 2) Rural Municipal Administration (N=30), and 3) Landowners Rural and Urban
(N=90). Geographical distribution of participants will be based upon the six zones of the Alberta
Association of Municipal Districts and Counties (AAMDC). Participants will include, 1) rural
service providers’ key contacts within the zones 2) rural municipalities’ representatives of the
identified zones 3) Agricultural Societies found within the zones. Participants will be telephoned
and invited to requested to participate and followed up to ensure participation.
Results will be analysed using Hierarchical Bayes (HB) models that allow for the statistical
analysis of the best-worst comparisons (see Allenby et al., 2005). The method employed will be
the MaxDiff method, as described by Cohen and Orme (2004), in order to determine results of
the best-worst choice being made by respondents (Cohen and Orme, 2004). For the purposes of
data analysis, the researcher will enter paper survey results into Sawtooth Software Lighthouse
Studio 9.01 (see http://www.sawtoothsoftware.com).
Word Count: 499
121
Reflections
The largest barrier to this analysis is the current political landscape of the geopolitical
economic forces that result in variable prices of energy, and more specifically, the price of
carbon and power generation (AESO, 2016). Power prices, due to the change in the political
landscape of the province of Alberta and nationally in Canada, has had the greatest volatility
in recent memory (Henton, 2016). The current cost of generation and the energy rate
fluctuations for capital input calculations will also generate significant risks to the business
case analysis and an analysis of the investors willingness to pay.
As with any qualitative analysis by way of questionnaire, the quality of the data is only as
good as the question. By providing a clear, concise series of questions, the value of the
qualitative study will have the least possibility of providing “false correlations” or method
biases (Chang et al., 2010). In order to prevent some of the issues with qualitative
assessment, this study has been designed to be a replication of the Balcombe et al. (2013)
study. With study sample sizes and types of questions coming as close as possible to those in
the Balcombe study within context of regional variability (language, terminology, and
cultural) some of the issues related to study design are minimized.
Other assumptions and risks included in this study are:
Varying knowledge of questionnaire participants
Political announcements (geopolitical, regional and local) that can change or
influence participants (Alberta Government, 2016).
Participants’ financial situation based upon recession economics being felt
regionally at the time of writing
Researcher bias and ethical considerations (researcher is a prosumer with a
political position)
122
Addressing assumptions and risks involves the following undertakings:
Ensuring that the participants’ knowledge of renewable energy, the current
generation situation, and political acumen on the questionnaire topics are
assessed.
A flexible data collection methodology to ensure that if a key announcement is
made to have the ability to revisit (in a reasonable timeframe) the third data
collection methodology, only minor revisions will be necessary, if any.
Calculating running averages on financial variability functions, or generalizing
modifiers to assist in easy projections (e.g. normalizing the price of energy).
Ethical and bias considerations are to be addressed by providing for empirically
defensible data, repeatability, and a declaration of the status of the researcher.
Mindful of these, however, the overall risks in this study can be addressed by ensuring that
the business case, results, and conclusions pursue a relevant argument regardless of the
outcomes of these potential risks.
The researcher is currently one of only 890 microgenerators in the province of Alberta with
both solar and micro-wind installation. Additionally, the researcher is an elected rural
municipal official (County Reeve) who has potential bias considerations related to research
design and outcomes. With direct experience in micro-renewable energy economics,
regulatory approvals, and personal experience
(as a prosumer and as an elected official), this, if mitigated for ethical bias and academic
rigour, will be a strength in the ability of this dissertation to add to the body of knowledge.
Word Count: 501
123
School of Management
Timetable
McLauchlin 119048664 MBA Finance Dissertation
Task\Month* February
2016 March 2016
April 2016 May 2016
June 2016
Ethical Approval and Proposal Submission
Literature Review
Proposal Review
Methodology Modify Questionnaire Refinement
Questionnaire Circulate
Raw Data Collection
Data Analysis QA/QC Questionnaire Calculate
Draft Report
Data Review
Policy Review
Dissertation Break
Finalize Review
Submit Dissertation *statutory and other holidays inclusive.
Word Count: 56
124
Appendices
Appendix A: Formulas for Calculation of the Business Case for PV
LCOE: Basically the calculation is based upon the following formulas:
1. Cost structure = electricity output * cost of electricity Therefore the cost of the electricity of your LCOE can be defined as:
2. LCOE = (Cost Structure / Electricity Output)
Where,
125
With,
LCOE = Levelized Cost of Electricity
AO = Annual operations costs
DEP = Depreciation
PCI = Project Cost Minus Investment Tax
Credit
RV = Residual Value
LP = Loan Payment
DR = Discount Rate
INT = Interest Paid
TR = Tax Rate
N = Number of years for system
After Mercom Capital (2015) as modified from Darling et al. (2011) from Ecosmart (2015).
https://ecosmartsun.com/lcoe-definition/
ROI for microgeneration is derived from:
Where,
p = ROI period TRFi = Tariff of electricity paid at year i Ei = Energy produced in year i (derated by 20% over 25 years) OMi = O&M cost during year i CAPinit = Initial capital cost
From Ecosmartsun (2015) https://ecosmartsun.com/lcoe-definition/
See Raugei et al., 2013 for EROI as an expansion of this theory.
126
Appendix B: Barriers and Motivations for Microgeneration
Modified from Balcombe et al. (2013)
Motivations for Microgeneration
Save or earn money from decreased energy consumption
Improve the environment
Protect against future higher energy costs
Make the home more self-sufficient
Use an innovative and high-tech system
Protect the home against blackouts
Increase the value of the home
Show environmental commitment to others
Barriers to Microgeneration
Costs too much to buy and install
Cannot earn enough and save enough money
Home location is not suitable
Lose money if I moved away from the home
High maintenance costs
System performance or reliability not good enough
Energy not available when I need it
Environmental benefits are too small
Take up too much space
Difficult to install
Aesthetically unappealing
Neighbour disapproval or annoyance
Disruption or difficulty of operation
Hard to find trustworthy information and advice
127
APPENDIX J: Best-Worst Analysis
Formula is thus,
Where,
o Countbest is the number of times a barrier or motive was most important. o Countworst is the number of times a barrier or motive was least important. o n is the number of surveys. o freq is the frequency of the appearance of each barrier or motive is in the questions.
Motives Assessed 1 Save or earn money from lower fuel bills. 2 Help Improve the environment. 3 Protect against future higher energy costs. 4 Make the home more self-sufficient/ less dependent on energy companies. 5 Use an innovative and high technology system. 6 Protect the home against power outages. 7 Increase the value of my home. 8 Show my environmental commitment to others.
128
Motives Standard Scores Best sum
Motive countbest countworst n freq q1 q2 q3 q4 q5
1 27 8 26 6 8 9 10 27
2 14 17 26 5 6 8 14
3 22 19 26 6 7 8 7 22
4 29 10 26 6 13 10 6 29
5 6 34 26 5 5 1 6
6 12 24 26 6 3 4 5 12
7 13 10 26 3 5 8 13
8 9 9 26 3 8 1 9
27 27 26 26 26
129
worst sum freqn b-w b-w/freqn
Motive q1 q2 q3 q4 q5
1 2 3 3 8 156 19 0.121794872
2 5 7 5 17 130 -3 -0.023076923
3 7 4 8 19 156 3 0.019230769
4 4 4 2 10 156 19 0.121794872
5 15 10 9 34 130 -28 -0.215384615
6 3 6 15 24 156 -12 -0.076923077
7 10 10 78 3 0.038461538
8 9 9 78 0 0
27 27 26 26 25
130
Motives Results b-w/freqn
Save or earn money from lower fuel bills 0.121795 1.121795Make the home more self sufficient/ less dependent on energy companies
0.121795 1.121795
Increase the value of my home 0.038462 1.038462Protect against future higher energy costs 0.019231 1.019231Show my environmental commitment to others 0 1Help Improve the environment -0.02308 0.976923Protect the home against power outages -0.07692 0.923077Use an innovative and high technology system -0.21538 0.784615
Barriers Assessed 1 Costs too much to buy/install 2 Cannot earn enough/save enough money 3 Home/location is not suitable 4 Lose money if I moved home 5 High maintenance costs 6 System performance or reliability not good enough 7 Energy not available when I need it 8 Environmental benefits are too small 9 Take up too much space
10 Hassle of installation 11 Would not look good 12 Neighbour disapproval/annoyance 13 Disruption or hassle of operation 14 Hard to find trustworthy information/advice
131
rriers standard scores Best sum worst sum
countbest countworst n freq q1 q2 q3 q4 q5 q6 q1 q2 q3 q4 q5 q6 1 30 10 26 6 15 15 30 5 1 2 2 10
2 30 7 26 6 5 7 11 7 30 3 4 7
3 0 15 26 3 0 0 10 5 15
4 13 10 26 6 1 5 7 13 5 1 4 10
5 10 5 26 4 2 8 10 2 3 5
6 11 12 26 4 5 6 11 7 5 12
7 4 14 26 3 4 4 8 6 14
8 10 9 26 5 2 4 4 10 5 4 9
9 7 20 26 4 2 5 7 10 10 20
10 8 10 26 4 4 4 8 3 7 10
11 9 22 26 5 2 4 3 9 12 10 22
12 5 15 26 3 5 5 9 6 15
13 10 5 26 3 9 1 10 2 3 5
14 15 5 26 3 8 7 15 5 5
Barrier freqn b-w b-w/freqn 1 Costs too much to buy/install 156 20 0.128205
2 Cannot earn enough/save enough money 156 23 0.147436
3 Home/location is not suitable 78 -15 -0.19231
4 Lose money if I moved home 156 3 0.019231
5 High maintenance costs 104 5 0.048077
6 System performance or reliability not good enough 104 -1 -0.00962
7 Energy not available when I need it 78 -10 -0.12821
8 Environmental benefits are too small 130 1 0.007692
9 Take up too much space 104 -13 -0.125
10 Hassle of installation 104 -2 -0.01923
11 Would not look good 130 -13 -0.1
132
12 Neighbour disapproval/annoyance 78 -10 -0.12821
13 Disruption or hassle of operation 78 5 0.064103
14 Hard to find trustworthy information/advice 78 10 0.128205
Barriers Results b-w/freqn Cannot earn enough/save enough money 0.147 Costs too much to buy/install 0.128 Hard to find trustworthy information/advice 0.128 Disruption or hassle of operation 0.064 High maintenance costs 0.048 Lose money if I moved home 0.019 Environmental benefits are too small 0.008 System performance or reliability not good enough -0.01 Hassle of installation -0.02 Would not look good -0.1 Take up too much space -0.13 Energy not available when I need it -0.13 Neighbour disapproval/annoyance -0.13 Home/location is not suitable -0.19
Incentives Best Worst Analysis
standard scores Best sum worst sum freqn b-w b-w/freqn
countbest countworst n freq q1 q2 q1 q2 q3 q4 q5
Feed in Tariff 27 8 26 6 0 2 3 3 8 156 19 0.121795
Tax Rebates 14 17 26 5 6 6 5 7 5 17 130 -3 -0.02308
Grants 0 19 26 6 0 7 4 8 19 156 -19 -0.12179 Technological Assistance 23 10 26 6 13 10 23 4 4 2 10 156 13 0.083333
133
APPENDIX K: Results from Descriptive Statistics for Questions
Question N Minimum Maximum Mean
Std. Deviation
Q1 Q1. Do you rent or own your own home? 137 1 2 1.15 .354
Q2 Q2. How do you describe your property? 137 1 5 2.82 1.158
Q3 Q3. Where is your home located? 137 1 6 3.27 1.061
Q4 Q4. Are you aware of climate change? 137 1 3 1.55 .857
Q5 Q5. Does climate change concern you? 137 1 3 1.79 .680
Q6 Q6. How important is it that we act on Climate Change now?
137 1 6 3.85 1.584
Q7 Q7. Are you aware of CO2 reduction targets set by the Federal Government?
137 1 2 1.23 .420
Q8 Q8. Are you aware of CO2 reduction targets set by the Provincial Government?
137 1 2 1.25 .434
Q9 Q9. Are you aware of the implications of the Alberta carbon tax on energy use that is coming int...
137 1 5 2.43 1.327
Q10a Q10. Would you be willing to pay more for renewable energy over and above the new Alberta carbon...
137 1 3 1.80 .417
Q10b If yes, what dollar figure per month would you be willing to pay?
30 0 500 40.33 92.185
Q11 Q11. How aware are you of Renewable Energy Power Generation Technologies such as solar or wind?
137 1 7 3.08 1.295
Q12_1 Q12. What factors would prevent you from installing Solar Energy Technology? Check all that apply.‐Affordability
106 1 1 1.00 0.000
Q12_2 Q12. What factors would prevent you from installing Solar Energy Technology? Check all that apply.‐Inconvenience
57 1 1 1.00 0.000
Q12_3 Q12. What factors would prevent you from installing Solar Energy Technology? Check all that apply.‐Lack of Knowledge
44 1 1 1.00 0.000
Q12_4 Q12. What factors would prevent you from installing Solar Energy Technology? Check all that apply.‐Lack of Interest
27 1 1 1.00 0.000
Q12_5 Q12. What factors would prevent you from installing Solar Energy Technology? Check all that apply.‐Technology Distrust
44 1 1 1.00 0.000
134
Question N Minimum Maximum Mean
Std. Deviation
Q13 Q13. Would you rather spend your money on:
137 1 3 1.53 .718
Q14 Q14. Do you have any energy efficiency purchases in your home?
137 1 2 1.14 .347
Q15 Q15. Have you made purchases in consideration of their energy efficiency?
137 1 2 1.21 .410
Q16 Q16. Is your clothes drier? 137 1 3 1.91 .452
q17 Q17. Do you have a fridge or freezer that is older than 15 years in your home?
137 1 2 1.64 .481
Q18a Q18a. Have you any LED or Compact Fluorescent or Fluorescent lighting in your home?
137 1 2 1.09 .294
Q18b_1 Q18b. If yes to the prior question, what percentage of your home is using Energy Efficient light...‐Energy Efficient
119 0 100 61.85 34.179
Q19 A19. Do you have a sodium halide or halogen yard light?
137 1 2 1.66 .476
Q20 Q20. Which of the following applies to you:
137 1 3 2.53 .607
Q26b Which is the MOST important, pick only one
137 1 4 1.54 .795
Q30_1 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ None
37 1 1 1.00 0.000
Q30_2 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ Solar photovoltaic (generating electricity)
60 1 1 1.00 0.000
Q30_3 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ Solar thermal (hea ng water or air)
27 1 1 1.00 0.000
Q30_4 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ Wind turbine
23 1 1 1.00 0.000
Q30_5 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ Ground source heat pump (geothermal),
24 1 1 1.00 0.000
135
Question N Minimum Maximum Mean
Std. Deviation
Q30_6 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ Air source heat pump
6 1 1 1.00 0.000
Q30_7 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ Biomass wood boiler
24 1 1 1.00 0.000
Q30_8 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ CHP (combined heat and power) system
12 1 1 1.00 0.000
Q30_9 Q30. What types of microgeneration system have you been considering? Select all that apply . If...‐ Micro hydroelectric,
11 1 1 1.00 0.000
Q31_1 Q31. What stage of consideration have you got to?‐ Ini al inves ga on,
72 1 1 1.00 0.000
Q31_2 Q31. What stage of consideration have you got to?‐ I have talked others who have installed,
29 1 1 1.00 0.000
Q31_3 Q31. What stage of consideration have you got to?‐ I have been to see a system in action,
19 1 1 1.00 0.000
Q31_4 Q31. What stage of consideration have you got to?‐ I received professional advice,
19 1 1 1.00 0.000
Q31_5 Q31. What stage of consideration have you got to?‐ I received a quote from supplier/installer,
20 1 1 1.00 0.000
Q32 Q32. How likely are you to install the system you are considering?
137 1 5 3.68 1.294
Q33b Which is the MOST important, pick only one
137 1 4 2.14 1.065
Q33c Which is the MOST important, pick only one
137 1 4 3.04 1.175
Q33d Which is the Least important, pick only one 137 1 4 2.74 .866
Q33e Which is the Least important, pick only one 135 1 4 2.91 1.225
Q35 Q35. Has lack of incentives/support prevented you installing a system?
136 1 6 2.96 1.716
Q36_1 Q36. How likely are you to invest in microgeneration within the following time frames?‐Within next two or three years
137 1 7 5.33 1.871
136
Question N Minimum Maximum Mean
Std. Deviation
Q36_2 Q36. How likely are you to invest in microgeneration within the following time frames?‐Within 5 Years
137 1 7 4.74 2.121
Q36_3 Q36. How likely are you to invest in microgeneration within the following time frames?‐Within 10 Years
137 1 7 3.68 2.128
Q37a Q37a. Approximately how much would you spend on a microgeneration system? One Time, Amount in Do...
46 0 40000 7478.26 9383.174
Q37b Q37b. Approximately how much would you spend on a microgeneration system? Annually, Amount in Do...
49 0 10000 995.92 1690.994
Q38 Q38. What is the best form of government support that would entice you to undertake a renewable...
137 1 3 2.46 .814
Q39a Q39a. If Provincial subsidies provided you with 20% of initial Capital Costs for Microgeneration...
137 1 2 1.75 .434
Q39b Q39 b. If Yes in the prior question (Q39a), would you invest more with the same program that woul...
7 0 25000 5571.43 9271.051
Q40a Q40 a. If Provincial subsidies provided you with 30% of Capital Costs over 10 years for a Microg...
137 1 2 1.71 .456
Q90 Q40b. If Yes in question Q40a, would you invest more with the same program that would replace mor...
8 0 30000 8762.50 12093.438
Q41 Q41. If Provincial subsidies provided you with 50% of Capital Costs over 10 years for a Microgen...
137 1 2 1.61 .490
Q42 Q42. If you could fix your price per kwH (kilowatt Hour) at 10 ¢/kWh, (that takes into account t...
137 1 2 1.43 .497
Q43 Q43. If you answered yes to the previous question (Q42) and you were told it would cost you $25,0...
137 1 2 1.70 .460
Q44a Q44a. If you answered no to question 42 and you were given access to a low interest loan (governm...
105 1 2 1.71 .454
Q44b Q44b. Why did you answer as you did in Question 44a?
0
137
Question N Minimum Maximum Mean
Std. Deviation
Q45 Q45. How long are you willing to pay for a loan for the prospect of fixing (keeping the same cos...
18 0 42500 2364.28 10016.558
Q46_1 Q46. If, based upon projections that the price of solar panels and other forms of microgeneratio...‐Within 5 years
112 1 7 3.67 2.145
Q46_2 Q46. If, based upon projections that the price of solar panels and other forms of microgeneratio...‐Within 10 years
111 1 7 3.23 2.275
Valid N (listwise)
0
138
APPENDIX L: Likelihood of Investment
Figure 8. Likelihood of Investment 5 Years (N=137)
Figure 9. Likelihood of Investment 10 Years (N=137)
0
5
10
15
20
25
30
35
40
45
50
1 ExtremelyLikely
2 ModeratelyLikely
3 SlightlyLikely
4 NeitherLikely orunlikely
5 SlightlyUnlikely
6 ModeratelyUnlikely
7 ExtremelyUnlikely
Likelihood 5 Years
0
5
10
15
20
25
30
35
40
45
50
1 ExtremelyLikely
2 ModeratelyLikely
3 SlightlyLikely
4 NeitherLikely orunlikely
5 SlightlyUnlikely
6 ModeratelyUnlikely
7 ExtremelyUnlikely
Likelihood 10 Years
139
Figure 10. Likelihood of Investment with Costs and Efficiency Half in 5 years (N=111)
Figure 11. Likelihood of Investment with Costs and Efficiency Half in 10 Years (N=112)
0
5
10
15
20
25
30
1 ExtremelyLikely
2 ModeratelyLikely
3 Slightly Likely 4 Neither Likelyor unlikely
5 SlightlyUnlikely
6 ModeratelyUnlikely
7 ExtremelyUnlikely
Likelihood 5 years with Costs and Efficiency Half
0
5
10
15
20
25
30
35
40
45
1 ExtremelyLikely
2 ModeratelyLikely
3 Slightly Likely 4 Neither Likelyor unlikely
5 SlightlyUnlikely
6 ModeratelyUnlikely
7 ExtremelyUnlikely
Likelihood 10 years with Costs and Efficiency Half
140