Buy, Lease, or PPA? Drivers of the Mode of Consumer Energy Technology Adoption

Varun Rai1, D. Cale Reeves1, Robert Margolis2

1The University of Texas at Austin2 National Renewable Energy Laboratory

33rd USAEE/IAEE North American Conference, Pittsburgh27 October 2015

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Acknowledgements• This work was funded by the National Renewable Energy

Laboratory (NREL) (Subcontract # XGG-3-23326-01).

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Information in Big Ticket Adoption DecisionsFocus on BOTH Social and Economic Factors

• Information and the costs of acquiring it are central to understanding individual decision-making (Nelson, 1970).

• Behavioral factors (such as risk aversion, anchoring, decision heuristics, etc.) and social factors (such as norms, trust-based information networks, etc.) are of special interest because of the way they expose different individuals to different sets of information (Dietz, 2010; Kemp & Volpi, 2008; Stern, 1992), thereby giving rise to the bounded rationality of individual decision-makers (Wilson & Dowlatabadi, 2007).

• An important question (in disaggregated modeling) is: how and from whom do individual decision-makers seek the necessary information and form expectations about technological trajectories to aid their decision-making?

• We address this question in the context of residential solar PV adoption process.

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Household-level Heterogeneity

• Much of the current work in solar PV aggregates household-level heterogeneity in behavioral drivers and underlying information processes.

• Understanding household-level heterogeneity is important for:• Estimating demand (Gillingham et al., 2014), • Building detailed spatio-temporal models of adoption (Rai & Robinson

2015; Robinson & Rai, 2015; Zhang et al. 2014 ), and• Identifying and addressing information gaps and other barriers faced by

different customer segments (Rai & Beck, 2015).

• A couple of modeling examples...

5Source: Souyris, Rai, Duan, and Balakrishnan, In preparation, 2015.

Key Inputs for Disaggregated Demand Models

Integrated Decision-Making Framework Based on Deep Data and Predictive Modeling

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• Household-level Data Adopter and non-adopter

Surveys Appraisal district rolls

Solar program data Installer surveys

• Multi-method Econometric analyses Financial modeling GIS integration Agent-based modeling (ABM) Dynamic discrete choice modeling

Source: Rai & Robinson, Environmental Modelling & Software, 2015.

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Hardware costs have been declining rapidly, thereby spurring demand.

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Information in the Adoption of Residential Solar PV: Available Everywhere, but What to Trust?

• However, system cost is not the only barrier faced by solar adopters; potential adopters also face various informational barriers, leading to high indirect costs during the information search process, which can be quite intensive for a capital-intensive durable like a solar PV system.

Umm?!

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Peer Effects in the Adoption of Residential Solar PV

• Bollinger & Gillingham (2012) [zip-code level data, CA; CSI data until 2011]: Peer effects increase the probability of adoption.

• Rai & Robinson (2013) [household-level data, TX; data until 2012]: Peer effects accelerate adoption by about 2 to 5 months (~ 30% - 60%).

• Other papers: Grazziano & Gillingham (2014); Noll, Dawes, & Rai (2014);...

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Length of Decision Period (Months)

8.9 (Mean)6 (Median)

$ Not Very Important

Peer Effects

Neighborhood Contact

Lease

4.72.72.31.4

Adj. R2 = 0.24 P < 0.0001 All IV p < 0.01

Source: Rai & Robinson (2013), Environmental Research Letters, 8(1), 014044.

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Drivers of the Growth of Third-Party Ownership?

Source: GTM Research, 2015.

Residential Third-Party Ownership (TPO) Penetration and Installations by Ownership Type

• Is TPO cannibalizing the conventional “bought” model?• Are there any key differences in the preferences/attributes of buyers vs.

leasees?

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Mode of Adoption: Buy vs. LeaseWhat Barriers Does the Leasing Model Address?

• Using aggregate data, Drury et al. (2012) posit three characteristics that explain the increasing trend toward third-party ownership (TPO), which includes leasing and power purchase agreements (PPA), of residential solar PV in California: • it removes some of the complexities and uncertainties associated with

adopting a new technology;• it reduces the upfront financial burden – both overnight costs and costs

associated with securing financing; (found demographic differences too)• it re-frames the financial benefits of adoption as a simple-to-perceive

monthly savings.

• Rai & Sigrin (2013) find that...

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22% 24% 26%0%

5%

10%

15%

20%

25%

Buyers Leasers

Implied Discount Rate (%)

Per

cen

tag

e o

f S

amp

le (

%)

Long-Term Focused

Short-Term Focused

Leasing is Preferred By Customers Who Have a Tighter Cash Flow Situation. Buyers Use a MEAN Discount Rate 8 – 14% Lower Than Leasers

• In the early phases in a market the TPO model is able to penetrate the “information ready but cash poor” market segments.

Source: Rai, V., Sigrin, B. (2013). Diffusion of environmentally-friendly energy technologies: Buy vs. lease differences in residential PV markets. Environmental Research Letters. 8(1), 014022 (1-8).

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Research Questions

• Where do potential adopters get their information from during the research (i.e., information gathering) period, and especially what is the role of peer effects and installers (i.e., the supply side) in this process?• Previous analyses use early CSI data before 2012• No household-level analysis exists for California

• What drives the decision to buy versus lease solar?• No household-level analysis exists for California• To our knowledge, no disaggregated study to date looks at the

effects of O&M and financial factors on the mode of adoption

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Data• We use a dataset composed of a survey of households in northern

California that chose to adopt solar PV systems.

• Between April and June of 2014 the survey was sent to 2131 customers, of which a total of 380 completed responses were received (18% response rate).

• The survey collected information from respondents across eight broad categories: system details, purchase/leasing/power purchase agreement (PPA) details, decision-making process, financial aspects, sources of information, post-installation evaluation compared to prior expectations, environmental attitude, and demographics.

• These data are matched by customer to the solar program dataset, which contains system-level details of the individual PV systems such as nameplate capacity, date of interconnection, total system cost, and rebate received.

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Neighborhood-level peer effects play a significant role in providing valuable information to potential adopters of solar

• Of the 170 respondents that had pre-existing PV systems in their neighborhoods, 49 (29%) agreed or strongly agreed that, “Solar systems in the neighborhood motivated me to seriously consider installing one.”

• 22 of these 170 respondents (13%) further recognized the importance of neighbors as information pathways in their decision by agreeing or strongly agreeing that without PV systems in their neighborhood they would not have chosen to install.

• Thus peer effects impacted the decision-making of about 30% of the respondents (exposed to peer effects) and fully determined the behavior of 13% (who would not have adopted solar otherwise).• Note: Peer influence on behavior is typically underreported (Nolan et al., 2008).

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Peer effects important, but may not be a necessary condition for adoption

• However, the majority (87%) of these 170 respondents disagree that they would not have installed solar PV systems had there been no previously installed PV systems in their neighborhood.

• Adopters who received door-to-door marketing, value information from PV-owning neighbors less. (Fisher’s exact test for independence)• Direct marketing appears to mitigate the impact of peer effects.

• Overall, this suggests that peer effects translate into a higher motivation and confidence for non-adopters, but that peer effects may not be a necessary condition for adoption.

Or, does the marginal utility of direct marketing increases in the presence of peer effects? (Open question)

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Determinants of Adoption: Buy or Lease?

• The relationship of technological uncertainty and financial returns with the decision to pursue third-party ownership (given that the respondent had third-party ownership options available) is explored in a logit model.

• Survey used to operationalize aspects of technological uncertainty (“concerns about O&M”) and financial returns (“importance of financial returns”), along with several other control variables.

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Determinants of Adoption: Buy or Lease?

• Overall, when controlling for neighbor contact, decision period, decision-making factors, spark events, and relevant demographics, importance of financial returns and concerns about O&M are significant determinants of mode of adoption.

• Ceteris paribus, respondents that regard O&M concerns as very important or extremely important, have about 40-50% higher odds of pursuing TPO.

• Ceteris paribus, respondents that value financial returns as very important or extremely important are about 25% less likely to pursue TPO. • These findings describe a subset of adopters that are financially savvy and

approach the decision to install solar PV largely as an investment vehicle, and find buying as the right mode to do so.

• This group appears to be evaluating the benefit-cost of solar over a longer time horizon than the 15-20 typical in TPO contracts. Thus this group appears to be using a lower (implied) discount rate.

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Determinants of Spatio-Temporal Patterns in Adoption

• Financial aspects of the solar-adoption decision performs well in predicting the rate and scale of adoption

• Accounting for agent-level attitude and social interactions are critical for predicting spatial and demographic patterns of adoption with high accuracy

Fit to minimize cumulative

Source: Rai & Robinson, Applied Energy, 2015.

Workforce Training and Reinforcement:90% engagement / 90% accuracy

Data Proof of knowledge and skill retentionReinforce critical information without taking time away from workMultiple interactions daily

Residential EE & Solar Adoption Behavior: An Online Gamification Study

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Main Findings• Disaggregate, household-level data show that peer effects are

important in the adoption of solar PV in CA. However, • Peer effects do not appear to be a necessary condition for adoption, • Direct marketing appears to substitute for peer effects.• Substitution or complementarity of peer effects and direct marketing

needs further investigation.

• Different business models address important barriers associated with PV adoption differently and hence unlock different segments of potential adopters.

References• Rai, V. and Beck, A. L. (2015). Public perceptions and information gaps in solar energy in

Texas. Environmental Research Letters. 10, 074011 (1-9). • Robinson, S. A. and Rai, V.. (2015). Determinants of spatio-temporal patterns of energy

technology adoption: An agent-based modeling approach. Applied Energy. 151, 273-284. • Rai, V. and Robinson, S. A. (2015). Agent-based modeling of energy technology adoption:

Empirical integration of social, behavioral, economic, and environmental factors. Environmental Modelling & Software. 70, 163-177.

• Noll, D., Dawes, C. and Rai, V. “Solar community organizations and active peer effects in the adoption of residential PV,” Energy Policy, 67:330–343, 2014.

• Rai, V. and Robinson, S. A. Effective Information Channels for Reducing Costs of Environmentally-Friendly Technologies: Evidence from Residential PV Markets, Environmental Research Letters, 8(1), 014044(1-8), 2013.

• Blackburn, G., Magee, C., and Rai, V. Solar Valuation and the Modern Utility's Expansion into Distributed Generation, The Electricity Journal, 26(11), 18-32, 2014.

• Rai, V. and Sigrin, B. Diffusion of Environmentally-friendly Energy Technologies: Buy vs. Lease Differences in Residential PV Markets, Environmental Research Letters, 8(1), 014022 (1-8), 2013.

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Solar “Spark” Events: Beginning of the Decision Process

• Direct marketing by a solar company was the most popular spark event, followed closely by planning for retirement and a recent increase in electricity rates.

• These three spark events each operated on roughly 30% of the respondents. Comparatively, both of the two neighbor-related spark events “seeing a neighbor install solar panels” and “conversation with a neighbor who had solar panels” combined only sparked less than 15% of the respondents.

• However, looked at alone this metric underestimates the prevalence of neighbor-related spark events. One really needs to look at this metric conditional upon a respondent having access to a neighbor with solar PV already installed.

• Of the subset of respondents that report having at least one solar PV system in their neighborhood (170), 27 reported their interest was sparked by seeing a neighbor install a system (16%), 26 report that their interest started after a conversation with a neighbor (15%), and 12 (7%) report that their interest was sparked by both seeing a neighbor install a system and having a conversation with a neighbor.

• Thus, conditional upon access to neighborhood peer effects, the two neighbor-related spark events combined are nearly as common as direct marketing, retirement planning, and increasing electricity rates.

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Solar “Spark” Events: Beginning of the Decision Process

• Strong peer effects observable as PV installation density

increases

• Under certain conditions, peer effects also accelerate adoption

decisions.• Policy outcomes could vary significantly depending on these

effects

No PV Dense Communities With PV Dense Communities

Word-of-Mouth/Peer Effects

Source: Rai & Robinson (2013), “Effective information channels for reducing costs of environmentally-friendly technologies: evidence from residential PV markets,” Environmental Research Letters, 8(1), 014044 (2013).

SCO-driven active peer-effect process. The dotted arrows from the top layer represent the observation that SCOs can form organically, even without the input of resources from other entities.

Solar Community Organizations (SCOs)

Source: Noll, Daniel, Dawes, Colleen, Rai, Varun. (2014). Solar community organizations and active peer effects in the adoption of residential PV. Energy Policy. 67, 330-343.

ScenarioVery

ConservativeConservative Baseline Optimistic

Very Optimistic

Electricity Cost Growth 0.0%/yr 2.6%/yr 2.6%/yr 3.3%/yr 5.0%/yr

System Life 20 yrs 20 yrs 25 yrs 25 yrs 25 yrs

System Loss Rate 0.75%/yr 0.5%/yr 0.5%/yr 0.5%/yr 0.25%/yr

Maintenance Costs 0.5% /yr 0.25% 0.25%/yr 0.15%/yr 0%/yr

Inverter Replacement

Cost$0.95/W $0.95/W $0.7/W $0.7/W None

Electricity Plan After PV Adoption

Keeps same REP and plan

post-installation; no outflows

Adopts solar plan if offered

by current REP

Adopts solar plan if offered

by current REP; min. 7.5¢/kWh outflow

Adopts plan with max. value

among current market solar plans or BAU

plan

Same as Scenario 4

SCENARIO APPROACH TO PARAMETER UNCERTAINTYScenarios Approach to Parameter Uncertainty

Buyers:Mean: $6.2/WSD: $1.4/W

Leasees:Mean: $8.3/WSD: $0.53/W

3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 1010.5 11

010203040506070

BoughtLeased

Coun

t

Installed Costs (No Rebates)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 60

10

20

30

40

50BoughtLeased

Coun

t

Cost of Ownership (After Rebates)Buyers:Mean: $2.6/WSD: $0.95/W

Leasees:Mean: $0.70/WSD: $0.13/W

Installed Cost vs. Cost of Ownership

Source: Rai & Sigrin, “Diffusion of environmentally-friendly energy technologies: buy versus lease differences in residential PV markets,” Environmental Research Letters, 8(1), 014022 (2013).

MEASUREMENT OF IMPLIED NET PRESENT VALUE (NPV)

This consumer is indifferent to paying $3000 more for the system– the implied NPV!

0 5 10 15 20 25 300

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Consumer-Reported Payback Period (years)

Mod

eled

Pay

back

Per

iod

(yea

rs)Too Optimistic

Too Pessimistic

BUYERS ARE OPTIMISTIC IN PAYBACK PERIOD ESTIMATE

BASELINE SCENARIO

Buyers Are Optimistic In Payback Period Estimate

HOWEVER BUYING MAKES SENSE UNDER OPTIMISTIC ASSUMPTIONS

0 5 10 15 20 25 300

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BoughtLeased

Consumer Reported Payback Period (years)

Mod

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Pay

back

Per

iod

(yea

rs)

VERY OPTIMISTIC SCENARIO

However, Buying Makes More Sense Under Optimistic Assumptions

The University of Texas at Austin 32

Perception and Attitude of Solar Non-Adopters

• Huge gap between attitude (5.31/7) and perceived affordability (3.15/7) of solar– Perception of ability to afford solar low (3.15 on a 7-pt Likert scale)– Solar perceived as expensive due to incomplete information about

performance, leasing, and incentives• Only 16% reported awareness of any incentives

– Addressing info gap could open up large potential demand

• Customer awareness of the cost of solar has not caught up with– Available incentives and rebates, declining prices, and lease options that

are quickly increasing the affordability of solar energy• Descriptive norms significant for solar models

– Respondents feel more knowledgeable and confident with energy conservation than with solar energy; thus, looking to others for information and/or confirmation has greater benefit

– Consistent with recent literature: Bollinger & Gillingham (2012); Rai & Robinson (2013)

Experiment Overview• Use initial survey to capture existing

attitudes and intentions regarding energy conservation and solar, as well as other controls

• Create two randomized cohorts:– Control– Treatment (Gamified information)

• Employ trivia-style mobile gaming platform to succinctly deliver key information to the Game cohort

• Use final survey to capture changes in attitudes and intentions regarding energy conservation and solar, and perceived effectiveness of gamified platform

33Q1 2015 Progress Report DOE SunShot SEEDS: UT Austin

Game Platform: Ringorang®

• A clue gives a little hint for players new to energy topics

• A question conveys actionable or educational information

• An insight provides more context or information about the topic < 1min

• A “learn more” link to a web site for additional research or information on incentives

• A sliding scale for points based on how quickly you answer

• A leaderboard to compete with other players

34Q1 2015 Progress Report DOE SunShot SEEDS: UT Austin

Energy Games: Impact• The interactive nature of a trivia game tests respondents

perceived knowledge– More “aha” moments (vs. say a newsletter) with the

gamified version Higher perceived affordability

• Awareness of incentives significantly increased, which indicates that incentive programs may not be well publicized for passive audiences

• Likelihood of calling to request a solar quote increased following the game. This is one of the key factors to influence as it is a critical and necessary hurdle in the solar adoption process

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