NEEA Report: CFL System Dynamics Model Development Report-CFL... · Web viewWord of Mouth (WOM):...

February 6, 2011REPORT #40206

NEEA Report: CFL System Dynamics Model DevelopmentPrepared by:Aaron Ingle21095 S. Wisteria Rd, West Linn, OR 97068

Northwest Energy Efficiency [email protected]

TABLE OF CONTENTS

EXECUTIVE SUMMARY..............................................................................................................i1 INTRODUCTION........................................................................................................................1

1.1 Project Purpose and Goals...................................................................................................11.2 Project Scope and Assumptions...........................................................................................21.3 A Brief Review of System Dynamics...................................................................................31.4 Project Background and Methodology...............................................................................3

1.4.1 Background......................................................................................................................31.4.2 Model feature selection....................................................................................................31.4.3 Model Development.........................................................................................................41.4.4 Socket or Bulb Model......................................................................................................51.4.5 Household adoption states model.....................................................................................61.4.6 Major market feedback loops...........................................................................................61.4.7 Exogenous events and interventions................................................................................81.4.8 Model Testing and Calibration.........................................................................................81.4.9 Sensitivity Analysis..........................................................................................................91.4.10 Plausibility Evaluation...................................................................................................9

2 OBSERVATIONS - MODEL DEVELOPMENT, TESTING, AND CALIBRATION.........92.1 Incandescent Bulb Average Life........................................................................................102.2 Unable to Calibrate Model with 2004 Data Set from KEMA Report............................122.3 Modeling the CFL Market without Interventions...........................................................132.4 Current State of the Market...............................................................................................15

3 MODEL SENSITIVITY ANALYSIS.......................................................................................163.1 Relative importance of Interventions................................................................................173.2 Relative importance of Market Drivers............................................................................173.3 Relative sensitivity to CFL and Incandescent Bulb Life.................................................18

4 PLAUSIBILITY OF MARKET MECHANSIMS FOR EXPLAINING 2009 SALES........204.1 A ‘gap’ between innovativeness segments.........................................................................204.2 ‘Paradox of choice’ effect resulting from increased decision complexity......................244.3 A ‘gap’ between limited adoption and full adoption........................................................294.4 Exogenous effect of 2008-2009 recession...........................................................................314.5 Exogenous effect of a ‘stall’ in new housing growth........................................................32

5 SYNTHESIS, CONCLUSIONS, AND RECOMMENDATIONS.........................................345.1 Synthesis of Findings from Analysis..................................................................................34

5.1.1 State of the NW CFL Market.........................................................................................345.1.2 Market Forces at Play.....................................................................................................34

5.2 Conclusions, Implications, and Recommendations..........................................................355.3 Summary of Data Needs.....................................................................................................365.4 Relationship of system dynamics CFL model to ACE model..........................................375.5 Use of system dynamics models for planning/prediction in other markets...................38

REFERENCES..............................................................................................................................39APPENDIX A: ORIGINAL CFL MODEL REPORT..............................................................40APPENDIX B: SCOPE OF MODEL DEVELOPMENT.........................................................58APPENDIX C: ADDITIONAL DOCUMENTATION OF THE CFL MODEL....................77

NEEA Report: CFL System Dynamics Model Development© 2011 NEEA – All Rights Reserved

EXECUTIVE SUMMARY

The adoption curve for compact fluorescent light bulbs (CFLs) in the Northwest changed dramatically in 2009, with sales decreasing dramatically from 2008 levels. The question of primary interest is therefore: what structural market factors created this sales decrease, and what are the implications for future sales trends?

NEEA funded this effort to model CFL adoption in the Northwest, with three goals. The original intent of the effort was to work towards development of a generalized model that would be useful in portfolio management for evaluating adoption rates of emerging technologies. Additionally, NEEA wanted to explore whether this type of tool could be useful in providing a graphical view for communicating market effects and intervention strategies. Finally, NEEA recognized that the unique sales changes occurring in the CFL market in 2009 provide a ‘natural experiment’ with the possibility of being able to trace these changes back to market factors generating these changes—gaining a greater understanding of the current market and likely future trajectory. With the significant data available for the CFL market, NEEA requested that the contractor focus on this final objective, while secondarily pursue the other two objectives.

The contractor developed a system dynamics model of CFL adoption and calibrated it to reflect available market data and historic adoption trends. The contractor ran various simulations to evaluate the current state of the market and the plausibility of three mechanisms as causes of the 2009 sales decrease. The contractor used the experience of these simulations to identify challenges and opportunities regarding the possibility for developing a system dynamics-based generalized adoption model for adoption curve prediction across various technologies and markets. Finally, the contractor compared this method to NEEA’s ACE modeling technology to identify strengths and weaknesses of the system dynamics modeling approach for applications in market adoption, and to place the system dynamics method in NEEA’s current modeling context.

Significant model findings on the state of the Northwest CFL market, made with moderate reliability based on the significant market data available and referenced to inform this portion of the model, include:

If the historical adoption trend is to continue, the majority of future unique CFL sales will come from households moving from ‘limited adoption’ (approximately 10 CFLs/household) to higher levels of adoption

The conversion of these limited adopter households to higher levels of adoption appears to be a particularly difficult portion of the adoption process, indicating that satisfaction with existing bulbs is not high enough to drive rapid additional adoption

While the model findings demonstrated neither significant market segmentation nor any effect on sales, it is apparent that if this segmentation does exist, the innovators and early adopters are likely already saturated. This indicates that future sales growth is in the hands of the majority segment(s).

While the model findings did not demonstrate that decision complexity is inhibiting sales of CFLs, it does appear that the CFL purchase decision has been increasing in complexity. If this continues, it is possible that this may hinder future sales

Therefore, with moderate reliability, the modeling exercise supports the conclusion that the current challenge in the CFL market is to convince marginally satisfied, not-that-innovative

NEEA Report: CFL System Dynamics Model Development i© 2011 NEEA – All Rights Reserved

households to navigate an increasingly complex retail experience to convert large numbers of lower-priority sockets to CFLs before their existing incandescent bulbs burn out. Specific recommendations derived from this finding are to:

Target future interventions toward converting limited adopter, less innovative households to higher levels of adoption

Gain a better understanding of the particular decision process these limited adopter, less innovative households follow when deciding to further adopt

Consider actions which help simplify or streamline the CFL purchase

In addition, the contractor found that readily available market information, while substantial for the CFL market, is still insufficient to fully inform the model. The primary challenge for the project was in empirically supporting assumptions regarding the relative influence on the CFL market from various market feedback loops, historic interventions by NEEA and partner utilities, and exogenous events such as the 2000-2001 electricity crisis, and more recently the 2008 onset of the ‘Great Recession’. This highlights a challenge as well for applying this approach to understanding and predicting adoption for other technologies and markets—the data necessary to develop informed models may not be generally available. An assessment of this issue, across various markets, would be a useful avenue for NEEA to consider in planning whether to continue or expand this effort towards system dynamics modeling of adoption.

Finally, the modeling project does not conclusively identify a ‘smoking gun’ cause for the 2009 sales decrease. Each of the three mechanisms evaluated may plausibly result in some decrease in sales, or decrease in the adoption trend, over this time period. However, these mechanisms do not plausibly explain the size and immediacy of the actual sales decrease. NEEA and the contractor have proposed a variety of other potential explanatory mechanisms, and continued exploration is possible.

NEEA Report: CFL System Dynamics Model Development ii© 2011 NEEA – All Rights Reserved

1 INTRODUCTION

The Northwest (Washington, Idaho, Oregon, and Montana) compact fluorescent light bulb (CFL) market shifted significantly in 2009, changing from rapid adoption to a significant drop in CFL sales, as shown in Figure 1. Figure 1: Northwest CFL Sales vs. Time (NEEA ACE Model, July 6, 2010)

Annual Northwest CFL Sales

-

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

CFL

Sale

s/ye

ar

Certainly, the question of whether (and when) CFL adoption will resume a steep upwards trend is relevant to energy efficiency programs in the Northwest, as CFL adoption has been a significant success story for these programs, including the Northwest Energy Efficiency Alliance (NEEA) and their utility partners, and has been built into expectations for future demand management.

1.1 Project Purpose and Goals

NEEA funded this effort to develop a system dynamics model of the Northwest CFL market in order to achieve three major goals. The original intent of the effort was to work towards development of a generalized model that would be useful in portfolio management for evaluating adoption rates of emerging technologies. Therefore, this project provides a possible first step towards a system dynamics-based tool for portfolio management across various innovations and markets. The ideal tool for this purpose might use a handful of parameters regarding the market, relatively accessible to the project manager, to generate a predicted adoption curve consistent with the most relevant market and technology attributes. NEEA expects that the experience of modeling the CFL market will support an evaluation of this approach and of the suitability of system dynamics models for this purpose. NEEA proposed using the CFL market for this project because it is relatively well documented, with significant market adoption data readily available to inform the modeling exercise. NEEA also wanted to explore whether this type of tool could be useful in providing a graphical view for communicating market effects and intervention strategies. The system dynamics

NEEA Report: CFL System Dynamics Model Development Page 1 © 2011 NEEA – All Rights Reserved

modeling method has been identified as a possible addition to NEEA’s modeling toolbox for particular use in representing and communicating the important market structures and feedback dynamics which affect adoption curves and which determine the relative effectiveness of various interventions. This project is intended to provide experience with system dynamics and to enable further evaluation of the potential benefits (and drawbacks) of this technology for enabling, representing, and communicating this type of ‘operational thinking’ (Sterman 2000), compared to the ACE models currently in use at NEEA.

Finally, NEEA recognized that the unique sales changes occurring in the CFL market in 2009 provide a ‘natural experiment’ with the possibility of being able to trace these changes back to market factors generating these changes—gaining a greater understanding of the current market and likely future trajectory. With the significant data available for the CFL market, this project focused on this final objective, while secondarily addressing the two objectives described above.

Therefore, the primary purpose of this current effort is to incorporate the 2009 Northwest CFL sales data (shown in Figure 1 above) and associated market research findings (KEMA 2010, USDOE Energy Star 2009, and Sandahl et al., 2006) into a system dynamics CFL adoption model updated and expanded from a model previously developed by the contractor. The goal was to understand the changes to the market that have led to decreased sales of CFLs starting in 2009. An understanding of these market changes would be useful in understanding the likely durability of the changes, as well as to identify and test possible intervention strategies for restoring the positive growth of CFL sales and market adoption.

1.2 Project Scope and Assumptions

The model developed for this project was by necessity focused on the particularities of the NW market. A variety of assumptions simplified the modeling process. The contractor has highlighted these simplifications in the body of the report when pertinent. These assumptions were necessary for the completion of the project in the absence of complete market data. The contractor considered the nature of these assumptions when weighing the confidence in the implications and conclusions drawn. Therefore, the results presented in the report should not be taken as fact, but rather as the outcome of a complex modeling process based on a wide range of assumptions, and informed as best as practicable by empirical market data.

In general, the intent of the model is to reflect the market for medium screw base lamp (MSBL), ENERGY STAR® CFLs. This represents the great majority of the CFL market. In practice, the data sources referenced to inform and calibrate the model only intermittently differentiate between the market characteristics; therefore, the numbers do not necessarily consistently reflect this distinction.

The system dynamics approach involves a basic assumption of homogeneity unless modeled otherwise. Therefore, for example, the model treats households as homogenous in terms of number of sockets (36 per household) and adoption behavior, except when segmenting with different innovativeness characteristics, for example. This assumption enables a great deal of useful abstraction, but it is as an important limitation of the method when applied to a heterogeneous population of process.


Additionally, as described in the project purpose, the contractor developed the model to focus on understanding the 2009 CFL sales decrease and not to predict future market trends (nor is it capable of such). Therefore, while a number of the model runs continue into the future, these runs are intended to better illustrate the behaviors of interest rather than to provide any prediction of future market trends.

1.3 A Brief Review of System Dynamics

System dynamics is a general method for thinking about and modeling complex systems of various sorts. This method is useful in a wide range of arenas including markets, supply chains, biological processes, governmental policies at various levels, and so forth (see Sterman 2000 for a wide variety of example applications). System dynamics models utilize a visual diagramming method to define the feedback structure of a system, and generate a set of simultaneous first order differential equations to represent the system. After definition of an initial state, the model generates a numerical solution to the equation set showing behavior of the system over time. System dynamics is most applicable to understanding and predicting the behavior of systems that unfold over time, and for resolving strategic or policy questions regarding these systems. More information on system dynamics is available through a variety of sources; Sterman 2000 provides an exhaustive treatment.

1.4 Project Background and Methodology

1.4.1 Background The Northwest Energy Efficiency Alliance (NEEA) has requested this report as an extension of a prior effort completed by the contractor to develop a ‘system dynamics’ model of northwest compact fluorescent light bulb (CFL) sales. For reference, Appendix A includes the report for this original project. The current effort represents a significant increase in the scope of the model as well as an expanded time frame, specifically to capture the dramatic market change observed in 2009 CFL sales compared to prior years.

The model was developed to reproduce the reported CFL sales figures prior to 2009, while including a set of different configurations enabling the contractor to evaluate the ‘plausibility’ of various market ‘structures’ that have been hypothesized to be driving the 2009 sales decrease. The contractor based the model on the Bass Diffusion Model (Sterman 2000), a generic framework that is widely used to model/capture/explain the dynamics of product/technology adoption and diffusion. To reflect the market characteristics of most interest to NEEA as well as to more broadly reflect certain market dynamics described in the Diffusion of Innovations literature, the key resource of which is Everett Rogers’ Diffusion of Innovations (2003), the contractor expanded the model significantly. Appendix A includes additional theoretical background, along with a comparison of the relative characteristics of the Bass Diffusion Model and Diffusion of Innovation theory. Appendix B (list of features) includes details on whether and how the contractor utilized these theoretical frameworks in the final CFL model.

1.4.2 Model feature selection


At the beginning of the model development process, the contractor reviewed with NEEA a set of potentially relevant market structures and dynamics, including proposed mechanisms for explaining the 2009 sales decrease. From this presentation, NEEA and the contractor selected a subset of these features to be included in the CFL market model. Because of the nature of the modeling enterprise, it is critical to achieve the correct level of model abstraction to achieve tangible results that are meaningful and representative of the target system (the CFL market) while being expedient and efficient. This market feature selection exercise served to formally define the level of abstraction and scope of the model—clearly defining what items were most important and useful to include. From this exercise, NEEA and the contractor selected the following basic model features to be most important to the current project (Appendix B describes these in more detail in):

Basic S-Shaped Adoption Behaviors (bass diffusion model backbone) Differentiation of conversion (Incandescent -> CFL) vs. replacement (CFL -> CFL)

sales CFL Measure Life of 5.1 years, Realistic CFL Replacement mechanics Stocking of CFLs in Households Market Size vs. Willingness to Pay Simplification Promotions/Interventions

In terms of market features that could potentially explain the 2009 sales decrease, NEEA and the contractor selected three endogenous market features (from the list) for evaluation (Appendix B describes these in more detail):

Two Innovativeness Segments with an adjustable ‘gap’ possible between them Multiple Adoption-Decision States with the potential for a ‘gap’ between limited and

full adoption states ‘Decision Complexity’/’paralysis of choice’ hypothesis, including the effect of

competitionAdditionally, due to the method of construction of the model, the contractor found it was additionally possible to easily consider two other exogenous forces potentially affecting CFL sales:

The 2008-2009 ‘Great Recession’ The associated effect on housing construction and socket growth.

These two effects are included in the analysis that follows.

1.4.3 Model DevelopmentOnce NEEA and the contractor selected this set of market features, the contractor proceeded with model development. The final model consists of four major subsystems, shown in Figure 2. These subsystems interact with each other to drive the overall market behavior. For example, the ‘household adoption states model’ captures households as they move through various levels of awareness and adoption, and drives how many sockets are filled with CFLs and incandescent bulbs, which is managed in the ‘socket/bulb model’. These household adoption behaviors drive several major market feedback loops, such as word-of-mouth generated by adopters, and a strengthening ‘CFL industry’ as sales increase, which drives a greater ability of the industry to generate additional sales. Finally, exogenous inputs are treated in the model, reflecting events such as the 2000-2001 electricity crisis, and the resulting effect on household adoption behavior and major feedback loops. Interventions performed by NEEA, utilities, and other partners are also modeled as exogenous influences on the modeled market.


FIGURE 2: Overall Model Organization in Four Layers

The following sections briefly describe each of the four model subsystems, starting at the bottom and working up. Appendix C describes these model subsystems in greater detail.

1.4.4 Socket or Bulb ModelThis base level of the model represents the state of each socket—either filled with a CFL or an incandescent bulb. The simplifying assumption is that sockets are not left empty, nor are they left with burned out bulbs, or unused bulbs. This was modeled using two parallel ‘burnout replacement’ cycles—one for CFLs, and the other for incandescent bulbs. For each cycle, the contractor utilized a third order delay structure to generate a burnout distribution similar to those reported in the ACE model documentation (NEEA 2009). This was necessary to generate a reasonably representative accounting of the differences between incandescent and CFL burnout behavior. The model assumes that homeowners replace sockets in the incandescent cycle with another incandescent when their incandescent burns out. This is true, except for a certain number of incandescent bulbs that the homeowner are replaces with CFLs based on conversion of the socket; this socket then moves into the CFL cycle. The model repeats the same process for the CFL cycle. The total number of sockets in this cycle grows with housing/socket growth. Figure 3 presents the basic burnout replacement structure from the model.

Figure 3: CFL and Incandescent (INC) Burnout Replacement Cycles:

Newly Placed INCs

INCs Age 1Step

INCs Age 2Step

INC Aging RateStep 1

INC Aging RateStep 2

INC BurnoutReplacement Rate

Newly Placed CFLs

CFLs Age 1 Step

CFLs Age 2Step

CFL Aging RateStep 1

CFL Aging RateStep 2

CFL BurnoutReplacement Rate

INC to CFLConversion Rate

CFL to INCReversion RateCFL BURNOUT

REPLACEM ENTINC BURNOUT

REPLACEM ENT


Socket/Bulb Model

Household Adoption States Model

Major Feedback Loops (aka ‘market forces’)

Exogenous Inputs

Socket/Bulb Model

Household Adoption States Model

Major Feedback Loops (aka ‘market forces’)

Exogenous Inputs Interventions Events

CFL Incandescent

Interventions Events

CFL Incandescent

1.4.5 Household adoption states modelThe intent of this model subsystem is to represent the major adoption stages or states which a household may exhibit, and the major ways that households move between these stages. Also, it is useful to have a representation which maps to the household-level data presented in the various data sources. The contractor developed The stages and flows presented in Figure 4 to reflect these market features:

FIGURE 4 Adoption stages model, including modeled average number of CFLs per household (HH)

While the model fixed the trial adopter, limited adopter, and full adopter household stages, it adjusted the number of CFLs per household at each of these stages as part of the calibration process. In particular, the model adjusted these values to allow appropriate sales figures while also meeting ‘ever adopter’ numbers.

Households are given the ability to flow both ways through the process, with the exception that once a household has trial adopted, they cannot move back to the ‘aware’ and ‘unaware’ stocks. This generally assumes that all households who have trialed CFLs will remain ‘aware’ of CFLs as well, even if they subsequently reject the technology. This assumption may modestly alter ‘awareness’ numbers compared to the actual market, but likely not significantly. The final model calibrates the flows connecting these stocks relative to the other flows, to generate the appropriate CFL sales and other known market data. At the same time, these flows are driven dynamically by various modeled market variables (captured under ‘Major Market Feedback Loops’ below) including the state of the technology (and subsequent satisfaction), positive and negative word of mouth, positive and negative mass media exposure, and direct experience with the technology from prior adoption.

1.4.6 Major market feedback loops The contractor identified and included a number of major feedback mechanisms in the model. Figure 5 (Policy Diagram) illustrates the major model features at a very high level. The contractor selected the feedback loops based on the definition of features in the model specification. In addition, the contractor included other feedback loops to generate additional behaviors necessary for the model to function over the full variety of cases.


PositivelyAware PotentialAdopter HHs

Trial AdopterHHs

Full AdopterHHs

Rejector HHs

UnawarePotentialAdopter

HHs

LimitedAdopter HHs

NegativelyAware PotentialAdopter HHs

Avg 2.5 CFLs/HH

Avg 0 CFLs/HH

Avg 9.5 CFLs/HH

Avg 35 CFLs/HH

Figure 5: ‘Policy diagram’ showing major feedback loops in CFL market model (‘+’ represents positive or reinforcing feedback loops, while ‘-‘ represents negative or balancing feedback loops)

Socket Penetrationof CFLs

CFL TechnologyPerformance per Unit

Cost

CFL AdoptionRate

Satisfactionw/CFLs

CFLMeasure

LifeConsumerInnovativeness

Effect of2008/2009Recession

Positive MassMedia

Negative MassMedia

Excitement overNew Techology

Effect of 2001Electricity Crisis

CFL Sales

CFL BurnoutReplacement Sales

INC Sales

CFL Threat toExisting Industry

+

-

++ ++

+

PositiveWord ofMouth

+

-+-

+

+

InterventionSpending onMass Media

+

NegativeBacklash fromInterventionSpending

+

+

+

-

+

+

Developmentof Improved

Incandescents

+ Development ofAlternate Bulb

Technologies (LEDetc)

+

Interventionsto ReduceCFL Price

Interventions toImprove CFLTechnology

++

Economies of Scaleand Learning Curves

+

Strength of CFLIndustry

+

+

+

Proliferation ofCFL Brands and

Models

Greater DecisionComplexity

+++

+

-

-

-

CFL IndustryEstablishment

(+)

WOM (+)

CFLAnticipatory

PositiveMass

Media (-)

INC IndustryEntrenchment

(-)

Paradoxof

Choice(-)

INC industryspending on adsto compete with

CFLs

+

Number ofSatisfiedAdopters

+

+

Satisfaction-driven

additionaladoption

+

+

-

SatisfactionDriven

additionalAdoption

(+)

Several of the major feedback loops present in the model are identified (in red) in the policy diagram. A brief description of each of these follows:

Word of Mouth (WOM): this mechanism captures the dynamic where current adopters talk to non-adopters about the technology and increase the likelihood that non-adopters will then adopt, at least when the word of mouth is positive. WOM can create a positive feedback dynamic, driving a market towards adoption until the number of potential adopters becomes depleted. The CFL model formulation allowed both positive and negative word of mouth, depending on the adopter’s experience with the technology.

Satisfaction-Driven Additional Adoption: this mechanism reflects the dynamic where adopters trial the innovation and have a highly satisfactory experience, convincing them to continue to adopt further. In the CFL market, because there are various levels of adoption possible, this effect is potentially significant.

Paradox of Choice: Section 4.2 describes this in detail. Incandescent (INC) Industry Entrenchment: This mechanism captures the dynamic

where, as the CFL technology begins to demonstrate success in the market, the pre-existing incandescent bulb industry recognizes the risk posed to their business and reacts in multiple ways to prevent CFL adoption. Incandescent bulb industry reactions include investing in their own marketing/advertising campaigns, using their influence


with retailers for shelf space and product placement, investing more in the development of the incandescent technologies to generate new competitive models.

CFL Industry Establishment: This mechanism reflects the idea that for the early CFL industry, success breeds success. With early successful sales, the CFL industry can re-invest some of the income into research and development, scaling up production, and marketing and advertising. As sales continue to increase, the influence of the CFL industry continues to increase as well.

CFL Anticipatory Positive Mass Media: This is an interesting dynamic describing the ability for the mass media to identify a gap between the potential of a technology, and the current awareness and adoption of the technology. For example, mass media is likely to be attracted to new and exciting technologies, (such as LEDs today), especially if the media is targeting innovative individuals. This type of media exposure can then drive adoption that closes the gap between the potential adoption and actual adoption of the technology.

1.4.7 Exogenous events and interventionsExogenous Events--In terms of exogenous events, the 2000-2001 electricity crisis was explicitly modeled, as an increase in media attention relevant to CFL awareness and adoption. The contractor modeled this media spike as starting in early 2000, peaking in early 2001, and then declining more gradually until becoming negligible in 2004. Appendix C details this curve.

Market Interventions—The contractor modeled three major market interventions by NEEA and the Northwest utilities. In the actual market, a much wider variety and specificity of interventions are possible. For the purposes of this model, the reality was greatly simplified. The three categories, with basic descriptions, are as follows:

Mass media interventions—this intervention captures efforts to educate, inform and convince consumers to purchase CFLs. These activities both raise awareness and influence the purchase decision. In this model formulation, retail and retailer effects are not modeled separately; some mass media interventions may flow through these channels and not just major media sources.

Price subsidies—this intervention captures efforts to reduce the price of CFLs, and is modeled as a base average price curve (assuming no interventions) and then the intervention-based price savings. This category captures coupons, giveaways, manufacturer-focused incentives, and other types of price-reducing interventions.

Investments in research and development—the model assumes a basic product development curve without intervention, and then an ability for additional strategic investments in CFL research and development to have ‘accelerated’ this curve. The assumption is that this additional spending did not create new potential for the technology (change its development trajectory), but instead just moved the technology along this trajectory more quickly.

Appendix C describes the specific modeling of these market interventions in detail in.

1.4.8 Model Testing and CalibrationThe contractor debugged and tested the completed model to ensure that it captures the intended model features. The model was then calibrated, by adjustment of model parameters, to


simultaneously reflect the historical CFL sales figures through 2008 (see Figure 1) while also reflecting measured awareness and ‘ever adoption’ of CFLs for a subset of years, and to match the distribution of households across levels of adoption in 2006-2007. The contractor extracted these calibration points from the KEMA market research report (2010) and the Energy Star CFL Market Profile (USDOE, 2009). APPENDIX C includes details of the calibration process and the reference market behaviors used for this calibration.

The calibrated models show behaviors approximately reflective of the actual market, but have a large number of degrees of freedom, meaning that there are likely multiple model ‘calibration states’ that would reflect the market behavior. Therefore, this report does not claim that the specific calibrated state (and the model factors most significant in determining the behavior) closely match the actual CFL market. However, certain areas of the model are more constrained by the available data. For these areas, the model supports stronger conclusions about the ‘state of the CFL market’. For example, market data about ‘awareness of CFLs’ and ‘ever having purchased CFLs’ provides significant support for the modeled distribution of households across adoption stages.

In the calibration process, the contractor identified three major deviations between the model behavior and other data sources. Additionally, the contractor used the market model to gain some general understanding of the likely current state of the CFL market. Section 2 presents these deviations, findings, and their implications.

1.4.9 Sensitivity AnalysisThe contractor then utilized the calibrated model to perform a sensitivity analysis which evaluated the sensitivity of model dependent variables (End 2011 Total Unique CFL Sales and End 201l CFL Market Share) to changes in the key input parameters driving the model dynamics (the independent variables). This analysis enabled measurement of the relative strength of various factors and feedback loops in generating model outputs (‘the model drivers’), and measurement of the sensitivity of the modeled market’s behavior to parameters with known uncertainty (for example, the CFL measure life). Section 3 presents the results of this sensitivity analysis

1.4.10 Plausibility EvaluationNext, the mechanisms that had been identified as possible ‘causes’ for the observed 2009 sales decrease were sequentially applied to the calibrated market model. The behavior of the calibrated model with each mechanism in place was evaluated for whether this mechanism was a ‘plausible’ cause of the 2009 sales decrease. The contractor used the following criteria for this evaluation:

-Ability to represent market behavior through 2008-Ability to endogenously represent the 2009 sales decrease-Overall plausibility of the mechanism, the parameters used, etc.

Section 4 presents the results of the ‘plausibility evaluation’.

2 OBSERVATIONS - MODEL DEVELOPMENT, TESTING, AND CALIBRATION


Section 1.4 and in Appendix C describe the development, calibration, and testing of the model.. In the process of this development, the contractor identified irreconcilable differences between the model and the reference data. This section explores these deviations in detail.

Additionally, the contractor reviewed the state of the modeled market and developed a series of observations about the modeled market. Where the contractor achieved an appropriate level of confidence, the contractor applied these observations to the actual NW CFL market.

2.1 Incandescent Bulb Average Life

The contractor noted a basic inconsistency between the incandescent sales numbers generated by the CFL model as compared to the sales numbers reported in the ACE model documentation, given the same assumed total number of sockets, etc. The ACE model reported the median incandescent bulb life to be 1.14 years, based on a 1000 hour bulb life and 2.4 hours per day on (NEEA, 2009). To generate the sales numbers reported in the ACE model documentation, the model required a median INC bulb life of 2.22 years. This represents a significant deviation between the ACE documentation and the model.

The ACE model document supports this deviation through basic calculations of the mean incandescent bulb life from the Total Sockets and Total Incandescent Sales numbers. For years 1997-2000, rates of CFL adoption were very low (below 1%) and had only displaced a small portion of incandescent sales; therefore, the model can safely ignore CFL sales. Doing so, the calculated mean incandescent bulb life for these four years was approximately 2.4 years. While the incandescent burnout distribution is likely somewhat skewed, this skew does not explain the difference between a 1.14 year median life and 2.4 year mean life. Therefore, instead of using the 1.14 year reported median life, the contractor calibrated the model to reproduce the ACE estimated incandescent bulb sales for the years 1997-2000, prior to significant CFL adoption. This calibration resulted in use of a median incandescent bulb life of 2.22 years. Figures 6 and 7 show the incandescent sales numbers generated by the calibrated CFL model (using 1.14 and 2.22 year incandescent bulb median life), compared to the numbers generated by the ACE model. For the 2.22 year model, the sales begin to deviate significantly only as CFL adoption behavior begins to deviate significantly from the ACE model assumed adoption curve in the last few years.

Figure 6: Incandescent bulb sales modeled using 1.14 year median incandescent bulb life (red) vs ACE modeled sales (blue)


200 M

100 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

ACE Total NW INC Sales Calibration Reference : 1 Segment CALSCALED Estimated INC Purchase Rate : 1 Segment CAL

Figure 7: Incandescent bulb sales modeled using 2.22 year median incandescent bulb life (red) vs ACE modeled sales (blue)

This inconsistency is significant and could represent an error in the model, differing assumptions, or a difference in actual bulb usage. The contractor tested the model for errors and reviewed in detail the portion of the model generating this behavior. Several explanations for the inconsistency are possible if we assume that incorrect input parameters generated the inconsistency: Incorrect total number of sockets; the total number of sockets would be nearly half that

reported in the ACE documentation, resulting in approximately 18-20 sockets/household instead of the reported 35 or more sockets per household

Incorrect INC sales numbers, which are estimated from the model; actual sales (assuming 1.14 year life is correct) would be nearly double the reported sales.

Significantly longer incandescent bulb life. The 1000 hour rating might then be interpreted as almost 2000 hours median life.

Significantly lower average bulb usage. This could mean that all bulbs are used on average significantly less (1.2 hours instead of the 2.4 hours used in the ACE report), or that the most used bulbs/sockets are on 2.4 hours per day, but the majority of sockets receive much less usage, bringing the average down significantly.

Implications—For the purposes of the model, the contractor used the 2.22 year median life to achieve adequate calibration. This choice did not significantly affect the model behavior with the exception of calculated CFL market share, a key market characteristic of interest to NEEA. However, because incandescent bulb sales are not directly measured in the current market (as evidenced by the ACE model treatment, and reporting on CFL sales only), CFL market share is a calculated number, based on significant assumptions, and is not currently a value to be trusted from this model. The same might be the case for the target market.

One way in which market behavior is potentially sensitive to the incandescent burnout/retirement rate is when considering the CFL conversion rates generated by consumers in a ‘burnout replacement’ behavior mode. This behavior is when a household is replacing incandescent bulbs with CFLs as the incandescent bulbs burn out. Considering a 1.14 year median incandescent bulb life, a typical household would replace nearly half of their incandescent bulbs in a year. After one year in ‘burnout replacement’ mode, this household (starting with no CFLs) would have already achieved ‘limited adoption’ and be moving towards full adoption. One would expect a relatively rapid conversion of nearly all sockets to CFLs given this behavior. The longer 2.22 year median incandescent bulb life, on the other hand, would result in much slower ‘burnout replacement’ adoption rates.


80 M

40 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

ACE Total NW INC Sales Calibration Reference : 1 Segment CALSCALED Estimated INC Purchase Rate : 1 Segment CAL

This ‘burnout replacement’ adoption mode is potentially relevant to the market in two (hypothetical) cases. First, limited adopters might be likely to fall into a ‘burnout replacement’ mode of CFL adoption; Section 4 discusses this. Second, it is hypothesized that after the implementation of the EISA federal legislation which phases-in a bulb efficacy standard starting in 2012, which will eliminate certain standard incandescent bulbs from the market, many households will simply continue replacing their current incandescent bulbs as they burn out, except now they will be replaced with CFLs (or alternative bulb technologies). If this is the behavior mode, what CFL adoption rates would be expected? This adoption curve is, again, quite sensitive to the incandescent bulb life.

Data needs—The data points that would help resolve this inconsistency have been identified above. Measuring actual incandescent bulb sales from the NW market would potentially help resolve this issue, and would help support CFL market share claims. Secondarily, having empirically supported average incandescent lifetimes in homes would potentially help resolve this issue.

2.2 Unable to Calibrate Model with 2004 Data Set from KEMA Report

The first round of model calibration attempted to rely on the survey results reported in KEMA’s 2010 market research report for the years 2004, 2005, 2006, and 2010 for fraction of all households that had ever purchased CFLs, that were aware of CFLs, that were unaware of CFLs, and that were either aware of CFLs or had ever purchased CFLs. As described in Appendix C, the contractor modeled these data points onto the modeled household adoption process to generate CFL sales numbers over time. Calibration consisted of modifying various model parameters until the model would replicate both the KEMA results and the CFL sales results. However, the model was unable to replicate both data sets simultaneously, and was particularly unable to replicate the major market changes indicated between 2004 and 2005 in the KEMA report: a rapid rise in ‘ever purchased’ rates and a rapid decline in ‘unaware households’. A review of the literature describing market changes in this time frame did not generate any major market events or exogenous events which would explain this shift. The contractor developed a separate model that forced the ‘ever purchased’ factor to conform to this data, and with this, the model generated sales numbers significantly exceeding the actual market sales. Finally, a separate set of data for ‘total aware and ever purchased’ households spanning 2001-2003 was identified (Sandahl et al., 2006). These results showed a very high fraction of households to be either aware or have already purchased a CFL in the 2001-2003 data, seemingly in conflict with the KEMA data, which indicated a lower value in 2004, then rising to values similar to the other data set.

Based on these various indicators, the contractor determined that it was most likely that the KEMA data for 2004 was erroneous or otherwise non-representative. The contractor dropped this data from the calibration and instead used the PNNL and remaining KEMA data to calibrate the model.

Implications--The necessity to eliminate this portion of the data was regrettable, and does somewhat reduce the contractor’s confidence in model results. Further, if this data were accurate, it would indicate significant events/market changes in the 2004-2005 time frame that could lend


significant insight into the dynamics of this market—whether due to endogenous or exogenous behaviors. If the explanation were exogenous, this would also lend significant insight into the type of exogenous events affecting the market, and the sensitivity of the market to such events. Indeed, this type of market shift would be similar to the one that is the primary focus of this report, the dramatic decrease in CFL sales between 2008 and 2009.

Data needs--Additional data to support removing or keeping the KEMA 2004 data would be useful. Alternately, information on relevant market events in 2004-2005 would be useful. Finally, similar data sets covering other time frames would be very useful.

2.3 Modeling the CFL Market without Interventions

The contractor calibrated both the historical market interventions and the major feedback mechanisms driving CFL adoption curves using two adoption curves. The model uses actual CFL sales numbers as the basis for the behavior of the market with all interventions ‘on’. Then, the contractor disabled these interventions (representing the NW market as if no utility, NEEA, or other partner interventions in the CFL market had taken place) and compared the resulting CFL sales curve (net of retirements) to the ‘Baseline CFL Sales Net of Retirements’ numbers reported in the ACE model documentation (NEEA, 2009). Figure 8 shows these two reference adoption curves. For calibration, the contractor performed these two comparisons while adjusting the sensitivity of the market to interventions and while adjusting the gain on the major feedback mechanisms on the model.

Figure 8: Actual CFL Sales through 2009 (red curve) and ACE Model ‘Baseline CFL Sales Net of Retirements’ (blue curve)

This process ended up being effective in generating an adoption curve that is representative of observed sales, but not effective in terms of replicating the ACE ‘Baseline Sales’ without interventions. Figures 9 and 10 shows these two comparisons:

Figure 9: Calibration of CFL Model (red curve) to Actual NW Sales (blue curve)


40 M

20 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

ACE Baseline CFL Sales net Retirements Calibration Reference : 1 Segment CALNEEA Reported NW CFL Sales vs Time : 1 Segment CAL

Figure 10: Calibration of CFL Model (red curve) to ACE Model Sales Net Retirements (blue curve), Assuming No Interventions

As can be seen, the non-intervention run shows CFL sales responding strongly to the 2000/2001 electricity crisis that affected California and the Northwest, but then showing a more gradual ramp-up in sales. This indicates a weaker set of endogenous positive feedback mechanisms driving the increasing sales of CFLs in this model compared to the ACE model baseline.

Because the contractor did not know the specific mechanisms used to generate the ACE model, an assessment of the impact of this deviation on the confidence in model results is not possible. The consultant is of the opinion that both models are by necessity strongly dependent on assumptions in generating this behavior; therefore, the discrepancy is likely the result of a difference of assumptions rather than from a model error.

Implications--This particular discrepancy highlights the inherent arbitrariness of this portion of the model. This results in a low confidence in conclusions from the model relating to 1) the sensitivity of the NW market to interventions, and 2) the relative strength of various market feedback mechanisms, whether working to maintain the status quo, and driving the market towards greater CFL adoption. Other portions of the model, more directly related to adoption mechanics, awareness, and adoption levels observed in the calibrated market model, are largely independent of this arbitrariness.

Data needs--A variety of data would support this portion of the calibration process. In particular, more confidence in the NW CFL sales baseline (without interventions) would be useful in this calibration. This is likely unachievable, but alternately, information on 1) NW market sensitivity to interventions and 2) other sales baselines such as in other regions of the US, could be used to


40 M

20 M

01997 2001 2005 2009

Time (Year)

Bul

bs/ Ye

ar

NEEA Reported NW CFL Sales vs Time CFL Purchase Rate

40 M

20 M

-8001997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

ACE Baseline CFL Sales net Retirements Calibration Reference : 1 Segment CALSCALED ESTIMATED CFL Sales Net Retirements : 1 Segment CAL

support this section. While US market data is available, the contractor did not use it in this analysis.

2.4 Current State of the Market

Despite the model discrepancies described above, a variety of observations can be made of the modeled system with at least moderate confidence that they accurately portray the actual NW market.

High rate of awareness—it is clear from the data provided from KEMA, and the model state, that awareness of CFLs is currently very high in the Northwest.

High rate of ‘ever adoption’—it is similarly clear from this data and from the model, that a high proportion of households have ‘trialed’ CFLs in their homes, either currently or sometime in the past.

Low rates of rejection and non-adoption—it is apparent that there are relatively low rates of CFL rejection and unaware households. It is difficult to directly assess the rates of rejection (currently having no CFLs installed, after having previously trialed CFLs) compared to the rates of aware non-adopters, but in total these numbers are modest.

Rates of ‘limited adoption’ are relatively high, while rates of ‘full adoption’ are relatively low—in essence, historic CFL sales indicate a certain number of sockets have been converted from incandescent bulbs to CFLs. The model provides an indication of how these conversions are distributed within households, and with moderate confidence indicates that ‘limited adoption’ households are relatively common while ‘full adoption’ households remain relatively scarce. Figure 11 illustrates this.

Figure 11: Distribution of households by level of CFL adoption, 2005 and 2008

Distribution of Households by Level of Adoption--2005 and 2008

0

0.1

0.2

0.3

0.4

0.5

Fraction of AllHHs have No

CFLs

Fraction of AllHHs are Trial

Adopters

Fraction of AllHHs are Limited

Adopters

Fraction of AllHHs are Full

Adopters

Prop

ortio

n of

All

Hous

ehol

ds

2005

2008

Implications--The various factors described above indicate that recent unique CFL sales growth has been driven to a significant degree by growth in the ‘limited adopter’ segment, but that sales from this segment are near, at, or past peak, and will soon decline. This implies an increased dependence for future unique CFL sales growth on the conversion of limited adopters to full adoption. The modeled proportion of unique CFL sales generated by movement of homes between adoption levels is illustrated in figure 12. This graphic compares the modeled proportion of sales generated from the three adoption levels in 2005 and 2008 to show how much the market changed over these three years. The same data is traced over time in Figure 13,


showing how new full adopters increasingly dominate unique CFL sales after approximately 2007.

Figure 12: Comparison of unique CFL purchases by adoption level for 2005 and 2008

Unique CFL Sales by Adoption Level--2005 and 2008

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

New Trialing HHPurchases

New Lim Adopter HHPurchases

New Full Adopter HHPurchases

Prop

ortio

n of

All

Uniq

ue C

FL

Sale

s

2005

2008

Figure 13: Unique CFL purchases by adoption level, change over time (new trialing households: blue curve, new limited adopters: red curve; new full adopters: green curve)

Data needs-- Improved market data could strengthen model implications regarding the state of the market. In particular one data set which would be very informative, and may already have been gathered by KEMA to generate their 2010 market research, is the current level of adoption (number of CFLs currently installed in sockets) on a household by household basis. The contractor used this data to generate a distribution similar to that in Figure 11 above. This data would confirm (or refute) the modeled distribution of households and potentially strengthen the proposal that most future sales will come from households moving to full adoption.

3 MODEL SENSITIVITY ANALYSIS

The contractor completed a sensitivity analysis of the CFL market model in part to evaluate the relative importance of various model mechanisms in determining the modeled market behavior. However, due to relative lack of direct market data supporting the function of many of these


Unique CFL Sales by Adoption Level (not scaled to represent actual market)

400

200

01997 2000 2003 2006 2009 2012

Time (Year)

NO

T TO SC

ALE

Bul

bs/

Year

new cfls purchased by new trialing hhs new cfls purchased by new limited adopter hhs : new cfls purchased by new full adopter hhs :

mechanisms, the conclusions generated in this section, unless otherwise noted, are of low confidence.

3.1 Relative importance of Interventions

The profile versus time of the three market interventions modeled (price supports, investment in technology development, and mass media/marketing spending) was determined based on an interpretation of available data sources, as documented in Appendix C. The calibration process determined the sensitivity of the market to these interventions (in other words, the effectiveness of the interventions). The relative importance of the interventions to the final state of the market (in this case, the total unique CFL sales and CFL market share at the end of 2011) was measured by turning off each of these interventions in turn. Figure 14 presents the results.

Figure 14: Relative influence of the three modeled interventions on the CFL market; 0 = no effectInfluence on Unique Sales

Influence on Market Share

Mass Media Intervention Spending .41 .37CFL Price Subsidies .20 .14CFL Research and Development Subsidies .05 .03

This table indicates that the modeled market is very sensitive to the ‘mass media’ spending intervention, moderately sensitive to price supports, and relatively insensitive to interventions accelerating development of the technology, measured both in terms of total unique CFL sales or ultimate (end 2011) CFL market share.

Implications—the contractor has low confidence in these results of the modeled market. Based on the data available, no direct interpretation or calibration was possible regarding the relative sensitivity of the market to these interventions. Additionally, these interventions represent a very significant simplification of the actual variety of interventions undertaken in this market. The contractor expects these various interventions to influence the market in ways (and with relative effectiveness) not captured in this simplified version. Therefore, conclusions from this portion of the model are of low confidence. With this qualification, the primary implication of this aspect of the model is that the interventions, in the modeled market, vary significantly, in how they drive market adoption compared to endogenous market behaviors. Therefore, if these conclusions can be trusted, the CFL market could be considered relatively passive and driven by intervention, as opposed to an active market with adoption driven by strong market actors or forces. 3.2 Relative importance of Market Drivers

In addition to the interventions described above, several market drivers were specifically included in the model. These market drivers were primarily endogenous, with the exception of the effect of the 2000/2001 electricity crisis, which was modeled as an exogenous influence on potential adopter ‘innovativeness’ over this time period. The contractor evaluated the relative influence of these drivers on unique CFL sales in the sensitivity analysis, presented in Figure 15.

Figure 15: Relative influence of major market drivers on the CFL market; 0 = no effect; 0 = no effect


Influence on Unique Sales

Influence on Market Share

CFL Industry Establishment .42 .47CFL Anticipatory Positive Mass Media .40 .34Incandescent Industry Entrenchment .29 .26Positive Mass Media Generated by Exogenous Events .12 .09Word of Mouth .08 .07

This table clearly indicates that, in the modeled market, word-of-mouth has a very weak influence, while the 2000/2001 electricity crisis has a somewhat stronger, but still modest influence. On the other hand, ‘CFL anticipatory positive mass media’, ‘CFL industry establishment FB loop’, and the ‘incandescent industry/entrenchment feedback loop’ (described in more detail in Section 1.4.6) each have a very significant effect on CFL adoption curves.

Implications--Again, it is not possible to say, based on the data used to inform this model, whether these indeed are the major market forces driving the adoption curves. Instead, this particular model only represents a set of forces which could plausibly generate this behavior (through 2008, at least). Therefore, any conclusions drawn concerning these particular market forces are of low confidence. 3.3 Relative sensitivity to CFL and Incandescent Bulb Life

The contractor also evaluated the sensitivity of the model to changes in both CFL and incandescent bulb average life. CFL measure life is contested and likely significantly lower than the bulb rated hours would indicate (see Jump et al., 2008, for example). Indications from this modeling exercise also raise concerns in the reliability of the incandescent average life used for the modeling. Therefore, the contractor ran both bulb life numbers through a sensitivity analysis. Findings from this analysis indicate that, in general terms, most model results are relatively insensitive to changes in these parameters. However, three caveats are in order. First, CFL market share is quite sensitive to variations in incandescent bulb life, specifically because of the large differences in incandescent sales generated from changes in this parameter. However, CFL sales (and socket saturation) are relatively insensitive. Second, CFL sales (by the end of 2011) are decreasingly unique, so while unique sales are not sensitive to CFL measure life, total CFL sales (and thus market share) are relatively sensitive to this. See Figure 16.

Figure 16: Relative sensitivity of CFL Market to CFL measure life and incandescent median life; 0=no effect

Sensitivity of Unique Sales

Sensitivity of Market Share

CFL measure life (median life) .04 .36Incandescent bulb median life .02 .63

The third caveat is that the current model does not take into account the experienced life of bulbs in the socket. Therefore, changes in CFL life do not effect the adoption decision in terms of cost-benefit calculations or in consumer disappointment with early burnouts. Instead, the changes in bulb life only affect the bulb burnout and replacement rates generated by the model.


Implications--One general implication of this model insensitivity is that the modeled CFL market is largely NOT driven by burnout-replacement (either incandescent or CFL burnouts) sales. Given the relatively long life of CFLs, replacement sales currently make up a disproportionately small share of the total CFL market. It is significant, however, that the CFL market has seemed to thus far been mostly detached from the incandescent burnout-replacement market. The prospects for CFL sales as direct replacements for incandescent burnouts is addressed elsewhere in this report.

Data Needs (for the sensitivity analysis as a whole)--Data needs suggested by this sensitivity analysis highlight a significant portion of the model that would benefit from increased market data. Particularly of benefit would be increased data on the 1) sensitivity of the market to particular interventions and 2) identification of and evaluation of the strength of various major CFL market drivers. These particular data deficiencies are also relevant to the challenge of generalizing the modeling approach employed here to other markets-this type of data, central to the modeling approach, is likely to be scarce in most markets.


4 PLAUSIBILITY OF MARKET MECHANSIMS FOR EXPLAINING 2009 SALES

The contractor modeled each of the following postulated market mechanisms and explored possible explanations or causes for the 2009 sales decrease:

A ‘gap’ between innovativeness segments ‘Paradox of choice’ effect resulting from increased decision complexity A ‘gap’ between limited adoption and full adoption Exogenous effect of 2008-2009 recession Exogenous effect of new housing ‘stall’

Based on the following criteria, the contractor evaluated the plausibility of each of the modeled market mechanisms for explaining the 2009 decrease in sales :

Ability to represent market behavior through 2008 Ability to endogenously represent the 2009 sales decrease Overall evaluation of plausibility of the case, various parameters, etc.

Additionally, for each mechanism, the question is asked whether it would be possible to confirm the presence of the mechanism with additional market data? Finally, the contractor evaluated the implications of each of these mechanisms for the current market.

4.1 A ‘gap’ between innovativeness segments

Description of Postulated Mechanism--Diffusion of innovation theory (see, for example, Rogers 2003) argues that, when an innovation is being adopted in a new market, the innovation typically is first adopted by the most innovative individuals who are driven to adopt for different reasons than later adopters. This theory suggests the presence of five main adoption segments, as depicted in Figure 17. Moore, in Crossing the Chasm, describes the possibility of gaps, present between segments (1991), resulting from different sensitivities to product and market characteristics (price, performance, quality, knowledge requirements, complexity, product maturity), and different communication channel (such as word of mouth) effectiveness between the segments. Figure 18 illustrates this for a gap between early adopters and the early majority.

Figure 17: The five ‘innovativeness segments’: innovators, early adopters, early majority, late majority, and laggards (from Rogers, 2003)

Figure 18: The ‘innovativeness gap’, in this case between early adopters and the early majority (from Moore, 1991)

The contractor developed the innovativeness segmentation structure to model two separate segments of adopters, each exhibiting their own set of characteristics, adopting independently while potentially influencing each other to a determined degree. For example, with this model


both segments could be equally affected by external mass media, without the two segments influencing each other by word of mouth (WOM), though members of each segment are influenced via WOM by the other segment members. While market segmentation into five groups has been widely accepted (Rogers 2003), this current model implementation captures only two segments for modeling simplicity. These two segments are sufficient to model a single ‘gap’ between segments, of variable strength. In practice for the CFL model, this gap was not a direct factor, as WOM was very weak in the modeled market, and no other mechanism for the gap (for example a mass media ‘gap’) has been proposed for this market. Still, the ability to differentiate segments in terms of innovativeness or rate of progression through adoption stages is significant and useful as described below. Figure 19 shows a simple causal loop diagram representation of a two-segment market with only mass media and word-of-mouth driving adoption. The ‘gap’ between segments represents a failure of WOM to cross between segments.

Figure 19: Innovativeness Segmentation with ‘Gap’

Segment 1Adoption

Segment 2Adoption

MassMedia

Segment 2WOM

Segment 1WOM

+

+

+

+++

++

GAP

The distribution of households between the two segments was adjustable in the model. The contractor separately calibrated two separate model configurations, with the gap placed such that:

‘50/50’ Model--50% of households are in each segment ‘1/3-2/3’ Model--1/3 of the households are in the more innovative segment (‘segment

1’) and 2/3 of households are in the less innovative segment (‘segment 2’) Because each configuration requires separate calibration (to ensure that the model reproduced the appropriate levels of awareness, ‘ever adoption’, total CFL sales, and other calibration points), this model development was labor-intensive, and the contractor stopped developing the model after these two alternatives were developed. However, it would be possible to evaluate alternate gap locations using this model.

For these two segmentation configurations, the contractor differentiated the segments primarily in terms of innovativeness and resulting rapidity of adoption. The ‘gap’ effect illustrated above refers to a failure for the early success to feed later success. One example would be if word-of-mouth (WOM), generated first as innovators adopt and then spreading to early adopters, failed to convince the early majority to adopt because of differences in what they valued in the innovation, or because the majority individuals tended to talk to other majority individuals and not innovators and early adopters. In the model developed here, word-of-mouth is quite weak and a very minor factor in determining the adoption curve. Therefore, while the contractor modeled a WOM gap it was not an effective mechanism for generating an actual ‘gap’ in the adoption process. Therefore, while the model strongly differentiated the two segments in terms of innovativeness, the model did not evaluate the originally intended ‘gap’ mechanism.


Evaluation of Plausibility--In terms of overall plausibility, the 1/3-2/3 innovativeness gap model came the closest to endogenously reflecting 2009 behavior while also matching historical data. However, instead of generating a downturn in 2009, the model still showed sales growth, just more slowly, as shown in Figure 20. Underlying this slower growth starting in 2009-2011 in this model is a peaking of new conversion sales in this time frame. Figure 21 illustrates this.

Figure 20: 1/3-2/3 Innovativeness gap model (grey), compared to actual reported sales and 1-segment model

Figure 21: New conversion (unique) CFL sales in 1/3-2/3 innovativeness gap model compared to 1-segment model

As can be seen, in the 1/3-2/3 model (red curve in Figure 21), CFL sales net of retirements (primarily conversion of sockets from incandescent bulbs to CFLs) shows a gradual peaking in approximately 2010. It is plausible, if a two-segment model was developed with a smaller, more innovative initial segment, that this peak would be earlier still, possibly timed to correspond with the 2008 peak and 2009 sales decline. Still, in this circumstance the peak would need to be


CFL Sales vs Time40 M

20 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/ Ye

ar

NEEA Reported NW CFL Sales vs Time : 1 Segment CAL

SCALED CFL Purchase Rate : 1 Segment CALSCALED CFL Purchase Rate : 2 Segments One Third CAL

40 M

30 M

20 M

10 M

01997 2000 2003 2006 2009 2012

Time (Year)

B ul bs / Ye ar

SCALED ESTIMATED CFL Sales Net Retirements : 1 Segment CALSCALED ESTIMATED CFL Sales Net Retirements : 2 Segments One Third CAL

CFL Sales Net Retirements

dramatic enough to draw down total sales, which includes replacements of existing CFLs, which are growing, as shown in Figure 22 below (the blue curve).

Figure 22: Modeled CFL total sales (red), replacements (blue), and unique sales (green)

Even if this peak in CFL conversions were not dramatic enough to drive down total sales, it would indicate a weakness in the market in this particular model that could exacerbate other factors.

Implications--The sort of innovativeness segmentation modeled here is generally plausible in the CFL market, and could be contributing to the changes observed in the recent market. However, it is unlikely that this sort of segmentation is the sole cause or explanation for the 2009 sales decrease.

It is also plausible that other factors beside innovativeness could divide an otherwise relatively homogenous market, creating a similar effect. For example, ‘politicization’ of a technology (the entry of a particular technology into the political debate with major parties and/or organizations picking sides on whether it should be adopted or not) might theoretically polarize a market. Some households might adopt more quickly and some households more slowly based on political alignment and not their innate ‘innovativeness’. This polarized market would show a similar shift in the adoption curve as the positively inclined group becomes saturated and market growth begins to depend on the disinclined group.

The 1/3-2/3 model generates an interesting peak in the ‘unique sales’ curve (or slowing of growth in the total sales curve) as the socket conversion sales from the more innovative segment peaks around 2008-2009, as these innovators and early adopters begin to become saturated (see Figure 23). From this point, growth in conversions is dependent on the second segment.

Figure 23: ‘Unique Sales’ generated by segment 1 (red) and segment 2 (green), and total (blue)


CFL Sales and Retirements40 M

20 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/

Yea

r

SCALED CFL Burnout Replacement Rate : 2 Segments One Third CAL

SCALED CFL Purchase Rate : 2 Segments One Third CAL

SCALED ESTIMATED CFL Sales Net Retirements : 2 Segments One Third CAL

New CFL Conversion Sales by Segment400

200

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

Avg Rate of Socket Conversion to CFLs : 2 Segments One Third CALAvg Rate of Socket Conversion to CFLs Segment 1 : 2 Segments One Third CALAvg Rate of Socket Conversion to CFLs Segment 2 : 2 Segments One Third CAL

Based on this result, we can infer with moderate confidence that for the actual NW CFL market, to the degree that any innovative segments (i.e. ‘innovators’ and ‘early adopters’) are present in the market, they are very likely to now be relatively saturated in terms of level of adoption and ability to generate increases in CFL sales. Current and future CFL growth is therefore likely to be more in the hands of the majority segment.

Data Needs-- It may be relatively difficult to elicit the CFL-adoption innovativeness characteristics of households by talking to them after the fact, as there are many factors mixed up in the question of whether to adopt. It may be possible to look at the larger population of adopters and identify an innovativeness gap based on one metric. As formulated here, innovativeness primarily affects the overall rate of adoption. Therefore, one measure would be the time from first trialing CFLs to achieving a set level of adoption, perhaps the ‘limited adoption’ stage. A distribution of this measure, over the population of adopters, might evidence a bi-modal distribution that could indicate an innovativeness gap. However, this is not trivial, nor is it likely to be definitive.

4.2 ‘Paradox of choice’ effect resulting from increased decision complexity Description of Postulated Mechanism--The ‘paralysis of choice’ mechanism was developed based on the theory of Barry Schwartz (2004, 2005), who argues that excess decision complexity or number of choices for consumers when they are faced with a purchase decision can result in two adverse effects:

A decreasing ability to choose at all, with a increased chance of defaulting to the status quo choice (in this case, the incandescent bulb)

A decrease in the resulting satisfaction with the choiceFigure 24 presents a causal loop diagram of the this mechanism representing, at a high level, how this was captured in the the model. This becomes a full feedback loop when a postive ‘Rate of CFL Adoption’ (on the far right) drives an increase in ‘CFL Market Success’ (on the far left). This mechanism actually represents a balancing feedback loop, reigning in growth in CFL sales as the effect strengthens.


Figure 24: Paradox of Choice Causal Loop Diagram

Development ofImproved Incandescents

Development of AlternateBulb Technologies (LED

etc)

Proliferation of CFLBrands and Models

Consumer Choice++

+

ConsumerExpectations

CFL TechnologyPerformance per Cost

ConsumerSatisfaction

Rate of CFLAdoption

+

+

-

+

+

DecisionComplexity + Rate of Consumer

Defaulting to StatusQuo

-CFL Market

Success

+

+

+

For the CFL market, this mechanism was postulated as one potential explanation for the 2009 sales decrease, because of several observed market changes potentially driving an increase in the ‘decision complexity’ faced by the consumer:

The advent of initial LED models and strong hype around the future potential of this technology

The advent of new incandescent technologies which have a longer life and higher efficacy/efficiency

A proliferation of CFL models, brands, and manufacturers (see KEMA 2010) Penetration of the market beyond 60W MSBL models to provide CFLs for various

specialty applicationsThe ‘paradox of choice’ argument would suggest that the experience of purchasing a new CFL bulb has become much more complex, increasing the likelihood that consumers will instead default to what they know—traditional incandescent bulbs. And, even when they successfully purchase CFLs, they have the nagging idea in their head that they maybe didn’t make the best choice.

This effect does not consider direct competition for sales. For example, market share of LED bulbs to date appears to be very low in this market and does not significantly affect CFL sales numbers. Despite very low market share, however, the ‘idea’ of LEDs may still significantly complicate the CFL purchasing decision, creating a new option for consumers, to ‘wait and see’ how quickly LED prices drop and the technology advances.

‘Decision complexity’ refers to the number of available choices (for example styles, brands, and underlying technologies of light bulbs suitable for a particular socket) and the level of knowledge/information necessary to make a good choice. Increasing competition in the lighting marketplace, from new technologies such as LEDs and higher-efficiency incandescents and from a proliferation of brands and specialty bulbs, has increased both the degree of choice and the complexity of the bulb purchasing decision.

The contractor modeled the ‘paradox of choice’ effect by synthesizing a trend in the ‘complexity’ of the CFL bulb decision process based on limited data on the factors listed above. Based on a variety of assumptions on the details of the effect and of the market, the contractor modeled a


trend curve approximately representative of the increasing complexity of the purchase decision over time. Appendix C presents the details of this process. The contractor represented the decision complexity for this modeling exercise in ‘bits’, calculated as log2(number of choices). The contractor proposed this measurement of decisions on the logic that a decision with a ‘complexity’ of two bits (four choices) can be broken down into two separate yes/no decisions. Figure 25 presents the trend curve generated to represent the increasing complexity of the purchase decision.

The contractor generated the first curve as an exogenous input to the model. However, to make this complexity responsive to changes in the market success of CFLs (as indicated in the causal loop diagram above) the contractor then made the effect endogenous to the model (such that the model recreated the curve via internal model dynamics and feedback). This endogenous effect was formulated as resulting from the interplay between CFL success in the market, incandescent manufacturer response in terms of new product development, and third-party new product development (for example of LEDs) driven by the success of CFLs as a ‘proof of market’. Figure 26 compares this endogenous effect, in the NW market model case, to the exogenous effect developed from market data.

Figure 25: Original exogenous choice complexity

EXOGENOUS Choice Complexity vs Time8

4

01997 2000 2003 2006 2009 2012

Time (Year)

bits

EXOGENOUS Choice Complexity vs Time : 2 Segments One Third CAL


Figure 26: Comparison of Endogenous (final) and Exogenous (original) representations of choice complexity

One aspect of the mechanism that is of particular relevance is the likely presence of a ‘threshold effect’—increasing choice, below this threshold, is unlikely to have any detrimental effect on consumer ability to make decisions. Indeed, this type of increase of choice is generally celebrated in the consumer culture. However, once this threshold is crossed, the effect kicks in an as choice increases, CFL adoption is increasingly inhibited.

The presence of the threshold is (in this case) useful in the modeling exercise, as we can see that the effect does not significantly affect the market until the threshold is crossed. In Figure 27 shows a significant change in sales in approximately 2006.

Figure 27: ‘Paradox of Choice’ effect on CFL sales, illustrated by comparing sales without the effect (blue) to sales with the effect (red)

Evaluation of Plausibility-- Figure 27 shows that even with a discrete onset threshold and a gain strong enough to stall the market this effect still comes on relatively gradually. To create the kind of sales decline seen in the NW market in 2009, the effects driving this increase in complexity would have to be very large. While the recently rising awareness and excitement around LEDs might be such an effect, this seems unlikely to have penetrated consumer decision-making to this


8

4

01997 199

92001 200

32005 200

72009 2011

Time (Year)

bits

ENDOGENOUS Choice Complexity

EXOGENOUS Choice Complexity

Endogenous and ExogenousChoice Complexity

CFL Sales vs time 40 M

20 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/ Ye

ar

CFL Purchase Rate CFL Purchase Rate Paradox of Choice

extent, particularly the less innovative consumers that likely play a significant part in current sales (see section on Innovativeness Segmentation).

On the other hand, a gradual-onset stall effect as modeled here is at least somewhat plausible in the market. Figure 28 compares this ‘paradox of choice’ adoption curve to actual sales.

Figure 28: Actual CFL Sales (blue) compared to modeled CFL sales with paradox of choice (red)

Using this comparison, the actual market sales in 2007-2008 appear to be anomalously high, instead of 2009 sales being anomalously low. This alternate perspective, while unlikely, provides an interesting subject for analysis. It is possible that particular events in 2007-2008 could cause this sort of sales ‘bubble and bust’. Examples of possible events include the 2007 Walmart ‘100 million CFLs’ sales promotion (which resulted in Walmart selling more than 40% of the CFLs sold in the US according to the Energy Star 2009 Market Profile) or a rising general awareness of energy efficiency in the consumer market as a result of spiking oil and gasoline prices in the 2005-2008 time frame (which finally collapsed in mid-2008 as the recession took effect). However, the contractor doesn’t know the relative effect of Walmart’s sales promotion in the Northwest, nor to what degree energy efficiency awareness affects CFL sales.

From this alternate perspective, the ‘paradox of choice’ effect driving a stall in CFL sales is potentially plausible. However, it is difficult to evaluate the strength of this effect. The ‘stall’ shown in Figure 28 above represents quite a significant effect on purchase decisions; weaker effects are generally more plausible, and may therefore be only partially contributing to the 2009 sales decline. In addition, if this effect has not yet begun to affect this market, it may yet. On the other hand, the market may eventually undergo significant consolidation, simplifying the purchase decision. Alternatively, retailers may take matters into hand and change their stocking patterns or product presentation to make the experience easier for the consumer.

Implications--To the degree that the ‘paradox of choice’ effect is a significant current drag on CFL sales, this would suggest a distinct set of actions targeted at simplifying and streamlining the consumer decision process, while also marketing CFLs more effectively against upcoming technologies like LEDs. Retailers likely would have a large influence in this, as would manufacturer packaging/presentation. Strong brand recognition can have a positive effect in giving the consumer a reliable brand to fall back on.


CFL Sales vs Time40 M

20 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/ Ye

ar

NEEA Reported NW CFL Sales CFL Sales: Paradox of Choice

Certain market segments could be more sensitive to decision complexity as well, for example less innovative households, or those looking to move to ‘full adoption’. The effect may be more plausible for these segments than for the most innovative households.

It is also interesting to consider the possible effects that the coming EISA regulations could have on this decision complexity. Elimination of the ‘status quo’ incandescent bulb models from the market may both reduce total decision complexity and eliminate the fallback choice, but manufacturers have also reacted to this legislation by introducing more efficient incandescent bulbs. It is difficult to predict how these factors will change the complexity of the consumer decision.

Data Needs--While it is theoretically possible to research the consumer purchasing decision to determine whether consumers perceive and react to this proposed complexity, this is likely a very messy proposition. It might be possible to compare different retail outlets with different presentation, shelf spaces and shares, numbers of models, and technologies represented, to see how CFL sales perform as a function of these variables (and whether sales correlate with a ‘decision complexity’ calculated from these variables). Again, this is not a trivial task.

4.3 A ‘gap’ between limited adoption and full adoption

Description of Postulated Mechanism--To a significant degree, the contractor captured a ‘gap’ between limited and full adoption in all models, as the contractor used the same multi-stage adoption process for each of the calibrated models. The relative rate of progression through each stage was calibrated to generate the appropriate sales data, as well as to approximately match the distribution of household adoption levels provided in the Energy Star CFL Market Profile (2009)—as measured for the US in 2008, but applied to the CFL market (because of the higher rates of adoption) to 2006-2007. The assumption implicit in the use of this calibration point is that the Northwest market is similar to the US market as a whole, except approximately two years ahead.

The basic mechanism proposed here is the idea that there is a (relative) failure in the market to convert trial and limited adopter households into full adopter households. In other words, by this explanation the market has been successful in convincing households to trial CFLs and then get these trialing households to more adopt on a limited basis, but then failed to convince these limited adopters to continue to increase their level of adoption by proactively replacing nearly all of their incandescent bulbs with CFLs.

Indeed, this modeling exercise shows (with at least moderate confidence) this effect in the market. Rates of trialing in the market are very high, and rates of full adoption are necessarily very low based on CFL sales and CFL socket penetration numbers (likely currently between 25% and 30%).

Evaluation of Plausibility--In the models run, tracing new socket conversion sales by (new) adoption level provides an illustrative means of seeing how the market changes over time. Figure 29 below, for the one segment model, provides an example. In the early years (until approximately 2003) households trying CFLs for the first time dominated unique CFL sales. After 2003, the majority of these sales were to trialing households increasing their levels of


adoption (to become ‘limited adoption’ households). Now, since approximately 2007, it appears that new ‘full adopters’ have begun to dominate new CFL conversion sales.

Figure 29: CFL conversion sales by adoption level

New CFL Conversion Sales by Adoption Level400

200

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

new cfls purchased by new trialing hhs : 1 Segment CALnew cfls purchased by new limited adopter hhs : 1 Segment CALnew cfls purchased by new full adopter hhs : 1 Segment CAL

Thus, the market appears to be, in the last few years, particularly dependent on convincing households to become ‘full adopters’. Therefore, the question of whether there is a ‘gap’ between limited and full adoption becomes quite relevant.

Several possible mechanisms could explain such a difficulty in convincing households to fully adopt CFLs. One explanation is to consider the direct experience of the household with the CFL technology and the subsequent satisfaction of the household. It is feasible that for highly superior technologies, given a very positive initial experience, the consumer would quickly go out and buy many more, rapidly converting to full adoption. In this way, that initial positive experience triggers a positive/reinforcing feedback loop driving towards full adoption. Based on observation of the current CFL market, this ‘satisfaction-driven additional adoption’ feedback loop (included in the Policy Diagram, Figure 5) appears to be generally weak or absent, at least for the majority of households. Two possible explanations for this weak satisfaction feedback are either that households are not highly satisfied with the CFL technology, or that they are highly satisfied, but the satisfaction does not cause a decision to rapidly convert their homes. The satisfaction data reported in KEMA’s 2010 market research report does not seem conclusive on this issue.

The ‘burnout replacement’ mode is an alternative to the ‘rapid replacement’ mode of CFL adoption, where homeowners may remove and replace many incandescent bulbs prior to burn out. A homeowner in this mode might be convinced as to the superiority of CFLs, but still prefer to replace their incandescent bulbs only as they burn out. It seems plausible that a household that has already made the effort to reach a ‘limited adopter’ status, having replaced a number of their most utilized bulbs, might feed good about their adoption and less motivated to go out and buy a bunch more CFLs and spend the time to replace their remaining bulbs. Instead, they might default to this ‘burnout replacement’ mode. If this behavior is common enough in the higher adoption stages, it could evidence as a ‘gap’.


Implications--The ‘full adoption gap’ hypothesis is likely to be a part of the reality of the CFL market going forward, at least in some form. The ‘full adoption decision’ seems to be particularly difficult, while the market becomes increasingly dependent on just this decision. Therefore, the focus of future market interventions will likely need to be on converting ‘limited adopters’ into full adopters.

Data Needs--Certainly, there is speculation on various possible decision heuristics and behavior modes above. Market research focusing specifically on limited adopters and their particular adoption decision process might be informative.

4.4 Exogenous effect of 2008-2009 recession

Description of Postulated Mechanism--One of the most significant events in the 2008-2009 time frame was the ‘Great Recession’ which caused a dramatic effect in the global economic markets, and which also likely caused large changes in consumer behavior. In terms of direct impact on the NW CFL market, it is postulated that the recession could potentially have caused an effect on consumers which mimics a reduction in the overall ‘innovativeness’ of the consumer, through mechanisms like changes in (implicit) discount rates, increased aversion to high up-front costs, decreased propensity for risk-taking, etc. This effect was simplistically and qualitatively modeled as a temporary reduction in consumer innovativeness and therefore in the overall rate of adoption behavior. This modeled recession effect on innovativeness is shown in Figure 29, and the subsequent effect on CFL sales is shown in Figure 30.

Figure 29: Fractional effect of 2008-2009 recession on household new adoption behavior

Figure 30: Modeled CFL sales with effect of 2008-2009 recession (red), compared to actual NW CFL sales (blue)


1

0.5

01997 2000 2003 2006 2009 2012

Time (Year)

Fractional effect on adoption rates

Effect of 2008-2009 Recession on Rate of New Adoption

CFL Sales with effect of recession40 M

20 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

Of note, CFL sales continue to trend upwards in this model during the recession (after the initial drop) because of increasing CFL burnouts and replacements, as well as the continuing growth in this particular model in the housing stock, and therefore in the total number of sockets.

Evaluation of Plausibility and Implications--Overall, the recession is a plausible cause of the 2009 CFL sales decrease, due to the obvious intensity of the recession and overall effect on the economy. However, it is difficult to determine with any certainty to what degree, if any, the recession helped cause the sales decrease. While possible, the contractor has not pursued further work defining a particular and plausible mechanism for this economic effect or calibrated this effect to a measured economic indicator.

Data needs--Data reflecting overall consumer ‘innovativeness’ over time might help support this explanation if they were to show a significant suppression of innovativeness over the time frame of the recession. Similarly, if 2009 sales decreased in other similar technologies, this would present a stronger case for a large-scale exogenous influence such as the recession. Alternatively, as mentioned above, identification of an economic indicator associated with a particular decision process in the model could provide a much more compelling case for this event as a cause of the sales decrease.

4.5 Exogenous effect of a ‘stall’ in new housing growth

Description of Postulated Mechanism-- Similar to (and related to) the effect of the recession described above, the rate of new housing (and likely, new socket) growth has suffered as new home construction has faltered. The model captures the historical growth in housing stock and sockets, with the assumption that current socket share of incandescent bulbs and CFLs fill new sockets. The contractor took the housing and socket growth numbers from the ACE model documentation (NEEA, 2009), which also extrapolates these trends through 2015. These numbers do not reflect recent changes in the housing market, so the contractor did not include them in the model. However, the ‘housing stall’ effect described here is modeled by stopping new housing/socket growth in the model at a certain time. The contractor modeled this effect to trigger in mid-2008, with sales returning slowly starting in 2011. This modeled effect does not capture the more extreme case of a housing ‘contraction’, where the total number of households and homes is reduced through household consolidation, abandonment of homes, retirement of housing stock, etc.

Based on the ‘housing stall’ scenario, the effect on CFL sales in the model is very modest, such that the decline in mid 2008 is nearly undetectable (see Figure 31):


Figure 31: Modeled CFL sales with a stall in housing growth, compared to actual NW CFL sales

Evaluation of Plausibility--Therefore, it seems unlikely that this effect had a significant contribution to the observed 2009 CFL sales decrease, unless it was much stronger and caused a significant contraction in the total number of households in the Northwest. It is likely, however, that this sort of contraction would have already become apparent in other ways (besides simply effecting CFL sales).

Implications--This type of effect is unlikely to have significantly contributed to the observed 2009 sales decrease.

Data Needs--Updated data reflecting recent housing and socket trends would provide clear evidence on this issue.


CFL Sales with a ‘Housing Stall’40 M

20 M

01997 2000 2003 2006 2009 2012

Time (Year)

Bul

bs/Y

ear

NEEA Reported NW CFL Sales

Modeled CFL Sales

5 SYNTHESIS, CONCLUSIONS, AND RECOMMENDATIONS

5.1 Synthesis of Findings from Analysis

Certain of the implications of the modeling, as identified in prior sections of the report, taken together inform a view of the current state of the NW CFL market and of the major forces influencing this market. The next two sections synthesize these findings into a coherent view.

5.1.1 State of the NW CFL Market

This characterization is generally of moderate reliability, unless identified otherwise, since the data supports most aspects reasonably well.

Northwest households appear to have high rates of awareness of CFLs, and high rates of households have trialed CFLs at some point in the past. There also appear to be low rates of households rejecting CFLs, whether having trialed in the past, or not (low reliability). Similarly, levels of ‘limited adoption’ of CFLs by households are relatively high, indicating that new conversion sales generated from households in the trialing to limited adoption range are near or past their peak. Additionally, to the degree that the market is segmented, the innovators and early adopters are likely highly saturated—such that new conversion sales are now mainly driven by the majority segment(s). The above factors drive the conclusion that the easy sales have been mostly made; the task now is to convert limited adopters to much higher levels of adoption.

The contractor has identified several challenges to this next task. The satisfaction of limited adopters with the CFL technology does not appear to be driving limited adopters to rapidly adopt further, whether because households are only marginally satisfied or because of a disconnect between satisfaction and further adoption. It is also probable, as mentioned above, that these households now predominantly represent the majority segments, which typically have higher standards, a generally slower adoption decision process, who and may be significantly more sensitive to decision complexity and therefore susceptible to the ‘paradox of choice’. Certainly the variety of bulb models and awareness of alternative technologies has increased dramatically in the last few years (KEMA 2010), also driving up the complexity of the CFL purchase decision.

Therefore, with moderate reliability, the modeling exercise supports the conclusion that the current challenge in the CFL market is to convince marginally satisfied, not-that-innovative households to navigate an increasingly complex retail experience to convert large numbers of lower-priority sockets to CFLs before their existing incandescent bulbs burn out.

5.1.2 Market Forces at Play

Based on analysis of the calibrated model, it is possible to postulate what market forces and feedback mechanisms might be strong or weak in the actual NW CFL market. However, these interpretations are relatively subjective and not strongly supported or refuted by the data, and therefore are of low reliability.


In general, the modeled market is relatively passive—the behavior of the model is very sensitive to interventions, and less so to endogenous forces. Certain key feedback mechanisms that often drive technology adoption appear to be relatively weak in this market: word of mouth is very weak in the current model, as is adoption driven by direct prior experience with CFLs (after trialing and limited adoption). A third driver, stronger in the model but difficult to assess in the NW market, is the build-up of the CFL industry and it’s ability to drive further increases in sales, for example through influencing retailers and by marketing to consumers. Other forces are clearly present and influential in the market: economies of scale, learning curves, and product development investments have driven prices down and performance and quality up. However, these forces may be mostly played-out: prices have been relatively stable, and product performance, while slowly improving, does not appear likely to undergo further transformative change.

It also appears that exogenous forces might have played a significant role in the rate of adoption of CFLs. Of note is the 2000/2001 electricity crisis, and the associated peak in sales. More recent events, such as the 2007 Wal-Mart CFL promotion, the recent ‘oil bubble’ peaking in mid-2008, and the 2008-2009 recession, could have had a significant impact on CFL sales.

These factors lead to a conclusion, though of low reliability, that the NW CFL market is relatively passive—driven more by interventions and exogenous events than by internal forces. This may mean that transformation of this market is unlikely to proceed faster than driven—either by a strong CFL industry, by enhanced retailer support, by continued utility and partner intervention, or by the implementation of federal mandates and the resulting rate at which homeowners replace burnt-out incandescent bulbs with CFLs or other compliant bulbs.

5.2 Conclusions, Implications, and Recommendations

This modeling project did not conclusively identify a ‘smoking gun’ cause for the 2009 sales decrease. Each of the three mechanisms evaluated may plausibly result in some decrease in sales, or decrease in the adoption trend, over this time period. However, model does not plausible explain the size and immediacy of the actual sales decrease by these mechanisms. A variety of other potential explanatory mechanisms may be at play in this market, and continued exploration is possible.

However, based on what the contractor understand about the current market, the implications identified above can help guide future interventions. The contractor bases these implications, and associated recommendations, on an improved understanding of the current state of the market, of the market forces possibly at play, and of the plausibility of the various postulated mechanisms explored in the context of the 2009 sales decrease. Figure 32 summarizes the major implications, with associated recommendations and level of confidence identified where appropriate.


Figure 32: Summary of Major RecommendationsReport Section

Implication Recommendation

2.1 It is possible that households adopt a burnout-replacement behavior mode in a couple of different circumstances [low confidence]

Gather data to improve understanding of incandescent bulb burnout rates, to inform understanding of adoption rate expected in this behavior and benefits of intervention

2.3, 3.1, 3.2

The sensitivity of the CFL market to interventions, and the relative strengths of various market feedback mechanisms are poorly understood, limiting effectiveness of system dynamics modeling [high confidence]

Further evaluate whether this type of information is available at all, to determine usefulness of system dynamics approach as a basis for a general market evaluation/analysis tool

2.4, 4.3 The model indicates that the major source of future unique CFL sales is from households moving from limited adoption to higher levels of adoption [moderate confidence], and that this is a particularly difficult stage in the adoption process [moderate confidence]

-Improve ability to target future interventions at this particular adoption process-Gather market research to support a better understanding of the particular decision process when moving from limited to full adoption

4.1 Innovators and Early Adopters, to the degree that such segmentation exists in the market, are likely relatively saturated, and future sales growth will be in the hands of the majority[moderate confidence]

Future interventions should be targeted at ‘majority’ consumers instead of at innovators or early adopters

4.2 The paradox of choice may be affecting the current CFL market, and actions to reduce decision complexity in the CFL purchase may be beneficial [low confidence]

Reacting to the ‘paradox of choice’ suggests a distinct set of actions targeted at simplifying and streamlining the consumer decision process, while also reducing the confusion or stasis caused by awareness of upcoming technologies

4.4 Exogenous events may have a significant effect on CFL market and adoption dynamics [moderate confidence]

Development of methods for identifying and quantifying the effect of these exogenous events could be useful for improving market intervention strategies

5.3 Summary of Data Needs

An additional finding from the project indicates that readily available market information, while substantial for the CFL market, is still insufficient to fully inform the model. The primary challenge for the project was in empirically supporting assumptions regarding the relative influence on the CFL market from various market feedback loops, historic interventions by NEEA and partner utilities, and exogenous events such as the 2000-2001 electricity crisis, and more recently the 2008 onset of the ‘Great Recession’. The various additional data needed to support either the model itself or the analysis and conclusions drawn from the model are presented in Figure 33, along with a priority identified based on the likely usefulness of this particular data set to informing the conclusions or raising the level of confidence associated with the findings.


Figure 33: Summary of Major Data NeedsReport Section

Summary of Data Needed Priority of Data/Likely Benefit of Having Data

2.1 -Actual incandescent bulb sales from the NW market OR-Empirically supported average incandescent lifetimes in homes

Medium—would support market share claims and resolve inconsistency in model/data

2.2 -Additional data to support awareness and ‘ever adoption’ numbers in the 2004-2005 time frame-Similar data across all modeled years

Low—may help eliminate model inconsistency, improve confidence

2.3 -Information on NW market sensitivity to interventions-More details on the assumptions in generating the ACE model baseline (market with no interventions)

Low—may help calibrate the model better, improve confidence

2.4 Current level of adoption (number of CFLs currently installed in sockets) on a household by household basis.

High—would improve understanding of the current state of the market and key market segments

3.3 Identification of the key CFL market drivers, and evaluation of their relative strength

High—would improve understanding of the major market forces and confidence in model; necessary for generalizing model to other markets

4.2 Additional research into the complexity of the consumer purchasing decision, either by focusing on the consumer decision process or by performing a modified shelf space study to look to see to what degree the complexity of presentation affects sales

Low—a difficult task, but might yield significant insight on the ‘paradox of choice’ mechanism

4.3 Market research focusing specifically on limited adopters and their particular adoption decision process

Medium—could help inform interventions targeted at this particular adoption barrier

4.4 -Overall consumer ‘innovativeness’ data over time-Identification of ‘indicator markets’ reflecting similar sensitivity to exogenous forces as the CFL market

Medium—could allow a better isolation of exogenous effects on the market

4.5 Updated data reflecting recent housing and socket trends Low—would help isolate housing effect on CFL market

5.4 Relationship of system dynamics CFL model to ACE model

As has been described in various sections above, the contractor developed the system dynamics CFL model based on input and output data sets from the ACE model, as documented in the report (NEEA 2009). The actual ACE model was not available to the contractor during development of the system dynamics model. Figure 34 shows a comparison of these two models.


Figure 34: Summary of relationship between the ACE CFL model and the system dynamics CFL model

ACE CFL Model SD CFL Model (as developed)Purpose of model Accounting for CFL intervention

effectiveness-Understand/identify effect of market structure and market forces on adoption curve-Tool for explaining/communicating effects of market forces-Provide evaluation of possibility for portfolio-level prediction of adoption curves

Characteristics necessary to serve main purposes

-Accounting accuracy-Defensibility-Appropriate level of abstraction

-Basic accounting capabilities-Detailed modeling of selected market structures and market forces-Other market forces highly abstracted or excluded

Predictive capability Non-predictive; may extrapolate various market trends

Ideally, would have predictive capability; current model is non-predictive

Helps explain or communicate effect of market forces

No direct ability to visualize market forces or dynamics

-Able to visualize selected market structures and forces; however these may be quite complex and require abstraction for effective communication

Exposing for review the mechanisms driving/inhibiting adoption

-Mechanism for generation of adoption curve not clear from documentation, however contractor does not have enough information to evaluate this

Detailed review of mechanisms possible. Actual mechanisms are quite complex; it is very challenging to fully inform the model with market data, to avoid large assumptions

5.5 Use of system dynamics models for planning/prediction in other markets

Prediction in general, and particularly prediction of non-linear adoption dynamics of markets, is a very challenging proposition. One trajectory for further development of system dynamics models for NEEA would be in pursuit of a generalized market assessment or portfolio management tool which, given a handful of inputs, would indicate a likely adoption curve (possibly including tolerance bands) for a technology. This exercise has shed light on the challenges with this proposal, as well as potentially beneficial avenues for further research.

As described above (section 5.2 and earlier), this CFL system dynamics modeling exercise was unable to positively identify the major market forces driving or inhibiting adoption of CFLs. There are a variety of such market forces that could come into play in different markets. Without a clear identification and quantification of the forces in play in a market, it is unlikely that the system dynamics modeling approach will provide a sufficiently predictive capability for NEEA’s needs.

On the other hand, this exercise took a small number of these factors (the effect of innovativeness segmentation, the ‘paradox of choice’ effect, and the effects of a multi-stage adoption process with a possible challenge in reaching full adoption) and identified a possible ‘signature pattern’ for the effect of each of these on the basic adoption curve. These signatures could help identify 1) when in the adoption curve this effect occurs, 2) the particular effect on the adoption curve, 3) the likely duration of the effect, and 4) the most effective interventions associated with this effect. It may be possible to extend this approach to a variety of other market forces/factors. However, beyond simply modeling these effects, it would be necessary to confirm these ‘signature patterns’ across various markets and with other research in the field.


Developing a portfolio of the most common market forces/effects/pathologies, informed by system dynamic modeling combined with research across various markets, is a very large but potentially useful task for furthering NEEA’s ability to strategically intervene in markets. To achieve this goal, it would be simultaneously necessary to develop a capacity to identify, isolate, or even predict the presence of these effects. It is likely that these market forces appear in various combinations, confounding identification or isolation. For the forces evaluated in this research, the data necessary for clear identification is generally elusive.

REFERENCES

Jump, C., Hirsch, J.J., Peters, J., and D. Moran. 2008. Welcome to the Dark Side: The Effect of Switching on CFL Measure Life. 2008 ACEEE Summer Study on Energy Efficiency in Buildings.

KEMA. May 18, 2010. 2009-10 Residential Lighting Market Research Study. Prepared for the Northwest Energy Efficiency Alliance by KEMA, supported by ECONorthwest.

Moore, Geoffrey A. 1991. Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers. HarperBusiness.

NEEA (Northwest Energy Efficiency Alliance). June 23, 2009. 2008 ACE Model and Cost-Effectiveness Analysis Key Assumptions: ENERGY STAR Lighting.

Rehley, Mark. 2010. “NEEA Energy Star Northwest CFL Sales By Year, July 2010”. Personal Communication, July 6, 2010.

Rogers, Everett M. 2003. Diffusion of Innovations, Fifth Edition. Free Press.Sandahl, LJ, Gilbride, TL, Ledbetter, MR, Steward, HE, and C Calwell. May 2006. Compact

Fluorescent Lighting in America: Lessons Learned on the Way to Market. Pacific Northwest National Laboratory, prepared for the USDOE under contract DE-AC05-76RL0 1830.

Schwartz, Barry. 2004. The Paradox of Choice: Why More is Less. New York: Harper Collins.Schwartz, Barry. 2005. “Barry Schwartz on the Paradox of Choice.” TED: Ideas worth spreading.

http://www.ted.com/talks/barry_schwartz_on_the_paradox_of_choice.html USDOE (United States Department of Energy) ENERGY STAR. March 2009. Big Results,

Bigger Potential: CFL Market Profile. http://www.energystar.gov/ia/products/downloads/CFL_Market_Profile.pdf

Sterman, John D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World.


http://www.energystar.gov/ia/products/downloads/CFL_Market_Profile.pdf

http://www.ted.com/talks/barry_schwartz_on_the_paradox_of_choice.html

APPENDIX A: ORIGINAL CFL MODEL REPORT

Development of a System Dynamics Model forAnalyzing Interventions in

Energy Efficiency Technology Adoption

Aaron IngleMarch 20, 2010


APPENDIX A: Development of a System Dynamics Model for Analyzing Interventions in Energy Efficiency Technology Adoption

Introduction

The Northwest Energy Efficiency Alliance (NEEA) is a non-profit organization that organizes and implements ‘market transformation’ activities geared towards sustainable, cost-effective increases in energy efficiency in the Northwest. To achieve these market transformations, NEEA actively seeks out high-leverage interventions in their target markets—which include consumer, commercial, and industrial energy-consuming activities and technologies.

NEEA has experienced successful and unsuccessful interventions and seeks to elicit patterns from these experiences to refine their application of interventions in the future. Particularly, they are interested in refining their choice and timing of interventions among the many possible alternatives.

In partnership with NEEA, this project involved development of a preliminary and rough system dynamics model of a historical case study of a successful NEEA project, the development and adoption of compact fluorescent light bulbs (CFLs) in the Northwest. The model was utilized for a preliminary evaluation of the leverage of various intervention points in the adoption of CFLs, focusing particularly on the sensitivity of energy savings to the timing of the interventions.

The original intent of the modeling project was to compare the successful CFL program with another, less successful program (Verdiem Surveyor network energy management software)—looking for similarities and differences between leverage points and interventions. The original study questions were as follows: What differences can be seen in the lever points between the models of CFL adoption and

Surveyor adoption? Do the lever points show different effectiveness and/or different time windows of

opportunity? Are the levers/interventions consistent between the two systems, or unique? Could the intervention pattern from the ‘successful’ project have been applied to the

‘unsuccessful’ project? If so, to what effect? Do the models suggest any interventions for either system that could be beneficial, but were

not applied?However, the development of the first model took longer than expected, and I have not developed the second model. Therefore, the primary project output is an analysis of the sensitivity of leverage points within the CFL adoption model, addressing primarily the second bullet point.

A detailed description of the CFL model, including the development process, assumptions, and final formulation, is presented in Attachment 1. The CFL model requires further development, calibration, and verification and validation. It specifically is missing important input to inform the parameters, structure, and boundaries of the model. As such, results from the study questions are for illustrative purposes only; the model CFL adoption dynamics do not sufficiently represent the target system to draw specific answers to the study questions. However, the study generated interesting results in illustrating, in this model adoption system, ‘time windows’ of intervention for multiple system variables when considering both the absolute effectiveness of the intervention and the cost-effectiveness of the intervention. The model also shows certain interventions having a large short-term benefit and smaller longer-term benefits, illustrating the importance of time-scale on the evaluation of the effectiveness of interventions.

Model Application to Study Questions

Study DesignDespite the model being not sufficiently well formulated and tested to accurately represent the CFL adoption behaviors of interest, I studied the sensitivity of adoption behaviors in this market NEEA Report: CFL System Dynamics Model Development Page 41 © 2011 NEEA – All Rights Reserved


to different interventions, particularly focusing on the sensitivity of outcomes to the timing of the intervention. To measure this sensitivity, I used as a dependent variable throughout all runs the ‘Energy Savings’ associated with CFL adoptions compared to the primary alternative, the continued use of equivalent incandescent bulbs. This calculation assumes that adopters stay adopted, and represent an average 60W incandescent bulb or 15W CFL equivalent, with usage of 2.34 hours per day, 365 days a year. This yields an energy savings per bulb of 38kWh/year. I rounded this to 40kWh/year in the model. In the publication, “NEEA Success Story—Removing Barriers to Drive Compact Fluorescent Lights over Tipping Point”, NEEA presents an energy savings from CFL-related interventions of 4 billion kWh as of 2008. Comparing the NW baseline model run with the US Average baseline model run, I calculated a difference of 5.25 billion kWh using the method described above. This ballpark result represents an adequate confirmation of this measure for its use as a study dependent variable. In addition, I calculated this estimated Energy Savings from 2008 to 2017 across each of the various runs. I did this to allow differentiation of the shorter-term and longer-term benefits of various interventions. Certain interventions show a significant short-term benefit that is not significant in the longer term. This is a potentially differentiation when evaluating potential interventions.

For this study, I evaluated the sensitivity of Energy Savings to several interventions. These interventions, and their associated parameters, are described in Figure 1.

Figure 1: Description of five intervention cases studiedIntervention

Points How to Achieve Baseline Value

Study Parameter Values Intervention Requirements Associated

Variable Change

Case 1

Price Supports

Decrease in price by 50%; step function used

1 (unitless)

t=2000, 2004, 2008, 2012

model as 2 yr. intervention different start times (multiplier difference--captures changing

scale of intervention)

Price modifier

Case 2

Awareness increasing

Reduction in TC by 50% during increased awareness

effort3 yrs t=2000, 2004, 2008,

2012model as 2 yr. intervention

different start timesMarket Awareness

TC

Case 3

Coupon Campaign Changes

changes in timing of campaign (amplitude

already checked)

1 (unitless)

1997, 1999 (baseline), 2001, 2003, 2007,

2011

model after current NW intervention--different start

times

Coupon Campaign 0

Case 4

Product characteristics interventions

Years gained on product maturity--modeled as a leap

forward in state of technology by the indicated

number of years

0 yearsLeap forward at t= 1997, 2000, 2004,

2008, 2012

model as stepwise intervention (absolute not multiplier),

different start times

Product Attribute Modifier (years

technology 'leaps forward')

Case 5

Advertising supplements

Increase in advertising investment by 3X during

intervention10000

Increase by 300% at t=2000, 2004, 2008,

2012

Model as 2 yr. intervention different start times

Fixed minimum investment rate

Study ResultsThe results of the five study cases are presented in Figure 2 below.



Figure 2: Results of five intervention cases studied

Associated Variable Change

Parameter Value

Time Frame

Baseline Energy

Savings-2008

(million kWh)

Baseline Energy

Savings-2017

(million kWh)

Run Energy Savings-2008 (million kWh)

Run Energy

Savings-2017

(million kWh)

Calculated Relative

Cost associate

d w/ interventi

on

%Change in Energy Savings-

2008

% Change in Energy Savings-

2017

Case 1

Price modifier (1.0 baseline) 0.5

2000-2002 8700 57000 9400 59500 52 8 42004-2006 8700 57000 10700 65000 55 23 142008-2010 8700 57000 8700 65000 175 0 142012-2014 8700 57000 8700 62000 220 0 9

Case 2

Market Awareness

TC (3.0 baseline)

1.5

2000-2002 8700 57000 8700 57000 N/A 0 0

2004-2006 8700 57000 9400 60500 N/A 8 62008-2010 8700 57000 8700 59000 N/A 0 4

2012-2104 8700 57000 8700 58000 N/A 0 2

Case 3

Coupon Campaign 0 1

1997-2000 8700 57000 10500 58500 450 21 31999-2002 (baseline) 8700 57000 8700 57000 320 0 0

2001-2004 8700 57000 7000 55000 160 -20 -42003-2006 8700 57000 5100 54000 105 -41 -52007-2010 8700 57000 950 47000 95 -89 -182011-2014 8700 57000 900 43000 95 -90 -25

Case 4

Product Attribute Modifier (years

technology 'leaps

forward'-0 baseline)

2

1997 - end 8700 57000 9250 63000 N/A 6 11

2000 - end 8700 57000 9250 63000 N/A 6 11

2004 - end 8700 57000 9000 62000 N/A 3 9

2008 - end 8700 57000 8700 59000 N/A 0 4

2012 - end 8700 57000 8700 57500 N/A 0 1

Case 5

Fixed minimum

investment rate (10000 baseline)

30000

2000-2002 8700 57000 8700 57000 N/A 0 02004-2006 8700 57000 9000 57500 N/A 3 12008-2010 8700 57000 8700 57500 N/A 0 12012-2014 8700 57000 8700 57000 N/A 0 0

This study shows a number of interesting results. In general, a positive % Change in Energy savings represents that the intervention has a positive effect, with greater results indicating a greater effect. Within each Case, these % Change results can be compared to identify the time frame of intervention with the greatest effect. In Cases 1, 2, and 5, the time frame with greatest effect was a 2004-2006 intervention. For Cases 3 and 4, the earlier intervention had greater effects, dropping off over time. In general, this drop-off occurred around the 2000-2004 time frame.

Additionally, for Cases 1 and 3, the interventions are related to subsidizing CFL purchases (in different forms) and therefore are amenable to an estimated cost function. While comparisons across the two Cases are not meaningful, within each Case the runs can be compared. For example, in Case 1, half of the CFL price is subsidized and homeowners using the subsidy purchase a known number of bulbs. Therefore, the model can calculate the total cost of the subsidy. This cost calculation shows for Case 1, a generally increasing cost of intervention, reinforced by early interventions having a greater effect than later interventions. Case 3 shows a decreasing cost of intervention over time but also a decreasing effectiveness. Therefore, the sweet spot for this intervention is likely to be in the middle stages where cost is lower but benefit is higher. Also interesting for this case, the short-term benefits of an early intervention are significant, but in the longer-term, the benefits of an early intervention are not significantly greater than an intermediate, lower cost intervention.



Also of note coming out of this study was the general ineffectiveness of the advertising campaign interventions (Case 5). However, I believe this is a result of model calibration, and is not necessarily reflective of actual CFL adoption conditions. This calibration effect also accounts for the relative effectiveness of the ‘Coupon Campaign’ approach compared to the other approaches—because advertising is calibrated to be relatively ineffective as a ‘jump start’ to the adoption process, instead the ‘Coupon Campaign’ provides the primary jump start to word of mouth-driven adoption. Because it is the trigger, the Coupon Campaign generates the greatest range of responses. This calibration effect again highlights that this study is primarily useful for comparison of results within a particular Case, not between Cases.

Study Interpretation and Implications

The study, from an overall perspective, seems to generally reflect what I would expect from the nonlinear adoption process, and the assumption implicit in NEEA’s business model—there may be intervention ‘windows’ where interventions are more transformative than at other times. Based on a cursory study using a not-too-trustworthy model, this window generally seems to be associated with the time period immediately preceding the major adoption ‘explosion’. In the CFL model, this time frame was approximately 2000-2006.

The addition of cost-effectiveness as a criterion for this ‘window’ adds complication to this picture. For example, for product attributes, an earlier investment in R&D to improve the attributes is better. However, it may be that early progress on development is more expensive (or risky) than later investment, and as such the window for intervention for an organization like NEEA would be not too early in the development cycle.

It also may be that for some innovations, the cost vs. benefit window of intervention is larger, while for other innovations, this window is smaller or non-existent. This model serves to hint at an interesting phenomenon possibly worth more research.

Some Cases also show short-term benefits not captured in the long term (transient benefits), or vice versa. While these results are an interesting additional criterion to apply to deciding between potential interventions, also important and not considered are discount rates and risks or uncertainties associated with the time frames. For example, extrapolating CFL adoption rates to 2017 is a task fraught with assumptions—for example, of lack of competitive substitutes, changes in regulatory landscape, electricity prices, etc. If the risk of change is high, a higher discount rate would be applied valuing short-term benefits over longer-term energy savings. Similarly, if an organization is judged on short to intermediate-term results (as most organizations are), results in these time frames take on increased importance.

Returning to the original study questions regarding comparison of the CFL and Verdiem programs, one interesting difference I found in developing the original model formulations of the two systems was in the organizational context of the adopter. I would guess that this context makes a significant difference in the adoption decision process. For example, an IT system administrator potentially considering adoption of the Verdiem software has a very different position and influence within an organization than a head of household deciding on purchasing CFL lamps. The larger IT organizations, with hierarchical decision processes and a potential risk presented by adoption to network stability or their job effort, likely proceed through a quite different adoption decision process than consumers. This would be one significant difference between CFL and Verdiem adoption (and intervention) processes.



References

Canseco, J., Johnson, K., Siminovitch, M. February 25, 2009. Memo: California Compact Fluorescent Lamp (CFL) Market Overview, California Informal Working Group on Lighting. Available: http://www.californiaenergyefficiency.com/docs/lighting/CFLMarketOverview.pdf

Moore, Geoffrey A. 1991. Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers. HarperBusiness.

NEEA Success Story—Removing Barriers to Drive Compact Fluorescent Lights over Tipping Point. Available (accessed 2/4/10): http://www.nwalliance.org/successstories/docs/NEEA%20Success%20Story%20CFL.pdf

NEEA Webinar: CFL Market Update and Next Steps. November 5, 2009. Available (accessed 2/4/10): http://www.nwalliance.org/research/documents/CFL_Slide_Presentation_111009.pdf

Northwest Energy Efficiency Alliance, Website: http://www.nwalliance.org Quantec, LLC. Surveyor Network Energy Manager Market Progress Evaluation Report, No. 2.

Prepared for NEEA. Report #E05-136, January 19, 2005. Available (accessed 2/4/10): http://www.nwalliance.org/research/reports/136.pdf

Rogers, Everett M. 2003. Diffusion of Innovations, Fifth Edition. Free Press.Sterman, John D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex

World.Valente, Thomas W. 1995. Network Models of the Diffusion of Innovations. Hampton Press,

Inc.Verdiem Surveyor Long-Term M&T, draft report March 15, 2008. Received by e-mail, 2/2/2010,

from Mark Rehley, Operations Manager, Emerging Technology, NEEA.


http://www.nwalliance.org/research/reports/136.pdf

http://www.nwalliance.org/

http://www.nwalliance.org/research/documents/CFL_Slide_Presentation_111009.pdf

http://www.nwalliance.org/successstories/docs/NEEA%20Success%20Story%20CFL.pdf

http://www.nwalliance.org/successstories/docs/NEEA%20Success%20Story%20CFL.pdf

http://www.californiaenergyefficiency.com/docs/lighting/CFLMarketOverview.pdf


ATTACHMENT 1: MODEL DEVELOPMENT AND FORMULATION

A.1 Background

A.1.1 General Description of Situation Modeled

Each of the two targeted systems are related to the adoption (or non-adoption) of an energy-efficiency product in the Northwest within the last ten years, driven by market forces and additional interventions by NEEA and others.

A.1.2 Reference Behavior Pattern (RBP) for Systems Modeled

A.1.2.1 Compact Fluorescent Light bulbs (CFLs)

NEEA and partners Bonneville Power Administration (BPA) and Energy Trust of Oregon, starting in approximately 1997, undertook to shepherd the emerging CFL technology as a means to significantly increase home lighting energy efficiency. They assessed the technology and found four major barriers to adoption (from NEEA Success Story—Removing Barriers to Drive Compact Fluorescent Lights over Tipping Point):

Quality concerns including premature failures High prices Unconventional bulb size and shape Lack of public awareness

They then undertook various interventions to address and overcome these barriers, including supporting R&D activities and third party quality testing, followed by a coupon campaign and partnership with retailers to stock and promote the bulbs. In parallel, NEEA worked with manufacturers to increase production. Figure 3 below presents an account of NEEA’s various intervention activities.

Figure 3: NEEA Interventions into CFL Adoption

From: NEEA Webinar: CFL Market Update and Next Steps. November 5, 2009.



The adoption barriers and intervention points described above are necessary features of a system dynamics model of this case study. Figures 4 and 5 capture some of the adoption behaviors of interest.

Figure 4: Northwest and US CFL market share, 2001-2008, from NEEA Webinar 11/5/09

Figure 5: Incremental CFL Sales vs. Investment, from NEEA Success Story-- Removing Barriers to Drive Compact Fluorescent Lights over Tipping Point

A.1.2.2 Verdiem Network Energy Management Software

While I did not model this system, I considered it in the development of the generic adoption model. The following section presents the RBPs.

In the interest of reducing the rate of growth of energy consumption resulting from the proliferation of computer systems in the Northwest, from 2001 to 2003 NEEA undertook a partnership with Verdiem, Inc. to support the development and adoption of their Surveyor software. This software enables central administrator remote control of the power management functions of personal computers connected to the network, and addresses an under-served opportunity for improved energy efficiency in networked environments.

NEEA identified four major barriers to adoption of the Surveyor software (from Surveyor Network Energy Manager Market Progress Evaluation Report, No. 2):

Lack of product that is aggressively marketed to help reduce the unnecessary on-time of networked computers

Lack of knowledge on the part of customers about the potential energy savings from controlling networked computers

Reluctance of network administrators to adding software to their server



Lack of brand recognition in the marketplace of both Verdiem and the Surveyor software

NEEA, in partnership with Verdiem, then undertook various interventions to address and overcome these barriers, including software feature development, support for product introduction, and support for the viability of the Verdiem operation.

Figure 6 captures some of the adoption behaviors of interest. Figure 7 presents the overall PC market trends relevant to energy consumption.

Figure 6: Total and adjusted sales of Surveyor, 2002-2007. Adjusted sales reflect average deployment delay of one month, from Verdiem Surveyor Long-Term M&T, draft report March 15, 2008

Figure 7: Actual and forecasted market sales for PCs by type, as of 2007, from Verdiem Surveyor Long-Term M&T, draft report March 15, 2008

A.2 Developing the Model

A.2.1 Selection of the Bass Diffusion Model ‘Backbone’

The behavior of primary interest in these two case studies is the adoption dynamic for the new technology. Therefore, I selected the Bass diffusion model as a ‘backbone’ for the model. This classic model characterizes market adoption dynamics as resulting from the combined effect of ‘word of mouth’ generated by current adopters, and ‘advertising’ or other externally generated factors, and replicates the ‘S-shaped’ curves typically associated with adoption dynamics.

However, each case study involves a number of additional dynamics and factors affecting the adoption process, and therefore required significant extensions to the Bass diffusion model framework. Additionally, the two case studies are quite distinct and differed in which extensions were necessary to capture the behaviors and interventions of interest. Therefore, extensions to the ‘backbone’ model were in order.

A.2.2 Extending the Bass Diffusion Model—Determining Model Scope

To evaluate which extensions were necessary or desirable, I reviewed the literature on Diffusion of Innovation theory, and in parallel attempted to tap into NEEA’s expertise with these adoption systems. By comparing these two understandings with the benefits and limits of the Bass model, in the context of these case studies, I hoped to ‘triangulate’ a model formulation with a reasonable compromise between theoretical validity and incorporation of NEEA’s knowledge,



while not straying any further than necessary from the simplicity of the Bass Diffusion Model. Figure 8 presents this process.

Figure 8: Determining model scope by comparing various perspectivesCharacteristic of Interest

Bass Diffusion Model DOI Theory NEEA perspectives Final Approach

Differentiation of innovativeness of adopting population

Homogeneous adopter pool; assuming no differentiation, but progression of adoption mimics the behavior of a continuum of innovativeness (Rogers’)

-Rogers (2003) describes as continuum of innovativeness

-Moore (1991) presents idea of gaps between distinct adopter groups

-Appear to distinguish between adopter groups

Unless case study provides strong indication of differentiation, particularly gaps between groups, Bass Diffusion assumption of homogenous pool is retained

Multi-stage adoption decision process

Bass diffusion model is basically a 1-step diffusion process, combining various stages together into one

-Rogers (2003) describes a 5-step adoption decision process, starting with knowledge or awareness of the innovation, proceeding to persuasion, decision, implementation, and confirmation

-Emphasize lack of potential adopter awareness in case studies-Some anecdotal indication that retention of current adopters could have been problematic in early CFL period

In an effort to capture interventions made to boost awareness of the innovation prior to/separate from adoption, decided to add an ‘awareness step’. Decided for now not to add the possibility of reverting from adoption back to non-adoption

Market size -Basic bass diffusion model captures a fixed market; all population pool is either potential adopter, or current adopter

-Sterman (2000) describes extension of bass diffusion model to allow changing market size based on product attributes

-Not clear Utilized Sterman’s (2000) approach, developing market size logic associating product price and attributes with total potential pool of adopters

Relevance of specific innovation attributes

Basic Bass Diffusion Model formulation is ‘atttribute-free’—no specific innovation characteristics are captured

Rogers (2003) describes five key innovation attributes:-Relative advantage-Compatibility-Complexity-Observability-Trialability

-Both case studies present emphasis on development of characteristics of innovation to facilitate adoption

Attributes of innovation clearly a key aspect of adoption process I am capturing; determined to focus on a combined indicator of the innovation characteristics, along with price, to keep model simple

R&D effect on product attributes

Basic Bass Diffusion Model formulation is ‘atttribute-free’—no specific innovation characteristics are captured

Rogers (2003) describes the five key innovation attributes as changeable, not fixed in time

-Case studies emphasize interventions to change product attributes through R&D-Differentiate between three stages of development/maturity for innovations (development, standardization, diffusion)

R&D module was developed to capture change in product attributes with investment in R&D; however, based on the complexity of this function along with the overall complexity of the model, this endogenous component was replaced with a TABLE function representing the basic behavior, without feedback relationships.

External events Not specifically addressed Theoretical contributions not considered for this decision

-Case studies each show dependence of adoption behaviors on external events, particularly 2008/2009 recession for CFLs

Decided to capture these major external events exogenously as a means to improve model ability to match historical behaviors; also may be possible to explore interplay between market interventions and ‘external’ conditions

Differentiation of various influences on adoption decisions

Bass diffusion model characterizes adoption decisions based on combined effects of WOM and advertising; network aspects are captured (contact rate and various populations) but more specific decision process is excluded

Rogers (2003), with the 5 stage process, 5 key innovation attributes, and predisposing factors such as innovativeness, felt needs (problems needing solutions), etc., presents a much richer set of factors affecting the decision process

-NEEA references a variety of barriers and interventions, reflecting a wide range of influences on adoption decisions-Differentiate push/pull influence on adoption decisions

Decided to incorporate these where feasible in the model, particularly focusing on capturing the effects of the interventions actually made in the two case studies. A large number of additional interventions are available; modeling these is another project

Network aspects of adoption process

Bass diffusion model, by assuming a homogeneous population, effectively

Valente (1995) and Rogers (2003) detail a variety of network effects on adoption, particularly the roles

Case studies do not seem to draw on network effects in particular

Decided to not consider these effects in current modeling



assumes away more complex network effects

of opinion leadership, personal networks, change agents

A.2.3 Generic model formulation

A.2.3.1 Basic model components

Based on the above assessment, I developed a series of extensions to the bass diffusion ‘backbone’. Figure 9 lists these components.

Figure 9: Model extensions developedModel Subsystem/ComponentPrice of the innovation and an attribute capturing the (combined, net) benefits associated with adoption of the innovationMarket size, sensitive to price and innovation attributesProduct R&D effect on product attributesAddition of ‘unaware potential adopters’ stock to Bass diffusion backboneAdoption decision process, barriers and interventionsMajor external events (2008-2009 recession) effect on adoption behaviors

A preliminary conceptual generic system diagram, developed at the beginning of the design process, is included as Figure 10 for reference.

Figure 10: Preliminary conceptual generic system diagram. Red boxes indicates model components originally considered.

PotentialAdopters

CurrentAdoptersRate of Adoption

Adoption fromWord of Mouth

Adoption fromAdvertising andExternal Events

AdvertisingEffectiveness

ProductPrice

Factors drivingWord of Mouth

ProductAttributes

Market Potential

CompetitiveLandscape

Current MarketSize

MarketGrowth

CurrentProductDesign

R&DInvestment

Economies ofScale

Supply andDemandBalance

External Events

ProductionCapacity

Investment inFactors ofProduction

<Rate ofAdoption>

MARKET SIZE

BASS DIFFUSIONMODEL

SUPPLY ANDDEMAND

MANUFACTURINGAND SUPPLY CHAIN

R&D

EXTERNAL EVENTS(EXOGENOUS)

A.2.3.2 Developing Bass Model Extensions

I developed the model components identified in Figure 9 as follows:

Price and Innovation Attributes



I captured price in the model first endogenously by attempting to drive price as a function of economies of scale, R&D investment, etc. However, it was possible to capture an estimated price trend for CFLs based on published data from California and the U.S. This negated the immediate need to endogenize price; however, further development of the model should include calibration of the price factors to match the observed market.

However, literature only indirectly alludes to innovation attributes. I developed a single attribute focusing on capturing the relative benefit offered by the innovation, compared to the baseline/preexisting technologies. For example, the model compares the relative benefits of CFLs to the baseline technology, incandescent bulbs. Other innovation attributes (quality, compatibility, complexity, observability, trialability) are to some degree embedded in this attribute, and R&D is not likely to significantly improve them. However, specific interventions are possible to improve these characteristics, for example opening software to ‘beta testing’ and free limited duration trials, to improve trialability. Therefore, these other product attributes can be captured in the context of specific interventions, and are only developed to the degree necessary to capture the interventions for these particular case studies.

The product price and relative benefit are combined together to influence the adoption decision process. This combined benefit/cost criteria, which changes as the product is developed, is a main contributor to adoption dynamics.

I designed the model to change he product characteristics endogenously based on R&D investment; however, to reduce complexity during the model calibration stage, this feedback was eliminated, and a TABLE-based formulation for the ‘relative advantage’ attribute was developed. This simplification sacrifices some of the endogenous representation of the model, effectively shrinking the model boundary, but simplifies the model considerably.

Market sizeThe market size functionality captures the dynamic whereby the pool of potential adopters for an innovation is a function of the innovation price and relative benefit. A product with a high price relative to its benefit is unlikely to be ever adopted by a large market; only with a low price and high benefit is the actual market able to reach its potential. This market potential is, in turn, a function of the number of adopters who would ever adopt the product, at any price. For example, somebody without significant land is unlikely to ever purchase a tractor, even if the tractor was very inexpensive. A market size relationship was developed as a function of price/attributes (units—dollars per unit benefit). This shows that the higher the benefits per dollar, the greater the market share captured. The ‘Tech Demand Curve’ function, Figure 13, captures this relationship for CFLs. Research and DevelopmentI initially developed the innovation R&D function to have an effect on price and innovation attributes, such that R&D investment serves to decrease prices and increase the relative benefit of the innovation. Instead, I modified both price and product attributes to utilize Table functions, so this module was no longer necessary.

‘Unaware potential adopters’This was initially incorporated as an additional stock in the Bass Diffusion Model ‘backbone’, prior to ‘Aware Potential Adopters’ and ‘Current Adopters’. However, after modeling this, I found that the complexity increase in understanding and controlling the model behaviors outweighed the reward for the present model purpose. I decided that it would be possible to more simply capture lack of ‘awareness’ as a delay in the correction of the market size to the market potential driven by the market potential module. This delay was formulated as a 3 year time constant. I evaluated the sensitivity of model behavior to this time (results under ‘Sensitivity NEEA Report: CFL System Dynamics Model Development Page 51 © 2011 NEEA – All Rights Reserved


Analysis’ section) and reduced this parameter to represent an awareness-raising intervention (results under ‘Intervention Analysis’ section).

Modeling the adoption decision processIn addition to the component-specific interventions already reviewed in this report, a number of additional barriers and interventions affect the adoption-decision process within the individual decision-maker. To capture this effect in the model, I borrowed a ‘susceptibility’ metaphor from similar epidemiological models (SIR, etc). ‘Susceptibility’ reflects the likelihood that an aware potential adopter will choose to adopt a particular innovation after ‘contact’ by WOM or influence by advertising, and is a function of the relative benefit, price, and a variety of other innovation characteristics, as well as the ‘felt need’ of the individual and their decision-making authority within an organization context, for example within a family or a business.

Finally, the susceptibility incorporates the balance between the risk/uncertainty inherent to an adoption decision, and the risk tolerance of the potential adopter. Rogers (2003) describes the adoption decision process as a means of using a social network to mitigate/resolve the uncertain individual benefits associated with adopting an innovation. This process involves leveraging one’s social network to gather knowledge and experiences on how the innovation will work for oneself; susceptibility captures this knowledge level available in the social network, in combination with the uncertainty posed by the innovation (its newness, etc.) and the level of risk tolerance/adverseness of the adopter.

Figure 14 presents the final formulation for ‘susceptibility’ as a function of residual risk for the CFL model. The residual risk, in turn, is primarily a function of the Benefit/Cost ratio for the innovation, such that an innovation with a high relative benefit and low cost has a high residual risk, leading to a high susceptibility value.

Major external eventsExternal events can be selectively captured exogenously as influences on the model parameters, for example in time preference levels (the 2008-2009 recession). With model development, the effect of the 2008-2009 recession was modeled as affecting the adoption decision process, and reasonably meets recent CFL sales data showing a strong drop-off in purchases. However, when running the ‘intervention study’, I decided that the recession behaviors are a complicating factor that do not add significant benefit to run in the study. Therefore, exogenous events do influence the results.

A.2.4 Tailoring the Model to CFLs

The data informing the CFL model was from NEEA documents (www.nwalliance.org), discussions with NEEA personnel, and from various internet sources. However, I proceeded with model development and calibration with insufficient data to fully inform the model. A number of assumptions regarding model structure and parameters were necessary to calibrate the model.

Specific model formulations, and TABLE functions in particular, were utilized to tailor the model to CFLs. Figure 11 to 14 present the primary model table functions and inputs vs. time used in the CFL model. I generated the product price input (Figure 11) from data from the literature for U.S. average CFL prices (Canseco et al, 2009), adjusted proportionally to match 2008 average NW CFL price of $4.06(NEEA Webinar: CFL Market Update and Next Steps, 2009).


http://www.nwalliance.org/


Figure 11: Product Price vs. Time InputProduct Price

20

15

10

5

01997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017

Time (Year)

dolla

rs

Product Price : Current

Figure 12: Product Attributes vs. Time InputProduct Attributes

40

35

30

25

201997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017

Time (Year)Product Attributes : Current

Figure 13: Tech Demand Curve TABLE Figure 14: Susceptibility vs. Residual Risk TABLE

Based on the assumptions indicated above, at this point the model is speculative and, while potentially representing interesting system behaviors, is not representative of this historical case. Nevertheless, an ‘intervention study’ was completed to demonstrate the potential of this approach in assessing the potential leverage of various intervention options in the historical CFL market; the results from this study are not currently sufficiently grounded in an understanding of the real market to be anything but speculative.

A.2.5 Tailoring the Model to Verdiem

While the original project intent was to include the Verdiem program to compare and contrast with the CFL program, the complexity of the CFL modeling prevented me from proceeding to detailed modeling of the Verdiem project. Additionally, while significant data is available on CFLs, much less was available for Verdiem, and the model would have been significantly less grounded than the already very speculative CFL model.

A.3 Summary of Final Model Formulation

Attachment 2 presents a stocks and flows diagram of the CFL model.

The CFL system was modeled from a time frame of 1997 to 2017. The initial NEEA intervention started in approximately this time frame, and the simulations are run to 2017 to allow a long-term time frame for evaluating the effect of interventions on long-term energy savings. I focused the modeling on adoption within the Northwest, the extent of NEEA intervention, though I also developed an ‘average U.S.’ model to use for calibration.



Figure 8 suggests a variety of model assumptions. Specifically to the CFL model, additional assumptions include: A socket-based CFL adoption model. Specifically, the adopters in the CFL model are the

light ‘sockets’, not simply the individuals making the adoption decision. This approach was taken to address the socket-saturation issue whereby one person may ‘adopt’ CFLs for any number of lights in their household. However, this assumption creates non-intuitive adoption network numbers, and is somewhat counterintuitive.

Assuming that there are approximately 360 million ‘sockets’ in the NW compatible with CFLs. However, assuming a 6 year average replacement life, full CFL market share would be approximately 60 million bulbs.

A.4 Model Calibration, Verification, and Validation

A.4.1 Model Calibration

My approach to model calibration relied heavily on the availability of two adoption curves for CFLs, one representing the Northwest states, the other representing the US average. I calibrated the model to utilize the ‘Coupon Campaign’ intervention from ~2000 to 2002 as a means for differentiating the behavior of the two models—with this intervention, the model approximately follows the NW trend (Figure 15).

Figure 15: Northwest adoption trend (green line) from NEEA Webinar 11/5/09 and model behavior, baseline with ‘Coupon Campaign’ intervention at 1.0

Market Share NW, US0.8

0.6

0.4

0.2

01997 2000 2003 2006 2009 2012 2015

Time (Year)REF US CFL Market Share : CurrentCFL Market Share of Total Potential : CurrentREF NW CFL Market Share : Current

Figure 16: Average U.S. adoption trend (blue line) from NEEA Webinar 11/5/09, and model behavior (red line) at U.S. baseline, with ‘Coupon Campaign’ intervention at 0.3

Market Share NW, US0.8

0.6

0.4

0.2

01997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017

Time (Year)REF US CFL Market Share : CurrentCFL Market Share of Total Potential : CurrentREF NW CFL Market Share : Current

With this intervention switched ‘mostly off’ to a level of 0.3, the model trend approximates that of the U.S. average CFL adoption (Figure 16). To achieve this result, I tuned a variety of parameters as necessary. In general, the degrees of freedom of the model were significantly greater than that necessary for the behavior, such that there appeared to be multiple ways to achieve (approximately) similar behaviors. This complicated the calibration process, and I’m less confident in the validity of the model. It helped when I stripping away some of the model complexity, but the changes didn’t fully resolve the model complexity.

A.4.2 Model Verification and Validation

While I completed other basic functional tests, the centerpiece of my model verification activities was a sensitivity analysis. This sensitivity analysis selected a number of the key model parameters and subjected each of them to a +/-20% variation to identify the response of the adoption behavior to this perturbation. For the dependent variable (DV) for the analysis, I used the ‘Energy Savings’ associated with CFL adoptions compared to equivalent incandescent bulbs. Section 3.1 describes this calculation, performed within the model. Attachment 3 presents the NEEA Report: CFL System Dynamics Model Development Page 54 © 2011 NEEA – All Rights Reserved


results of the sensitivity analysis. These results show that the adoption behaviors are most sensitive to price, the relative advantage characteristic of the innovation, and the ‘coupon campaign’ intervention strength. In general, adoption behaviors are relatively insensitive to the other parameters, with Energy Savings changed by 0-6% by a parameter change of 20%.

This stability of the model is an important characteristic, but this is not sufficient to verify the model. In general, the limitations in model formulation and data availability limited my ability to perform more extensive calibration, verification, and validation activities. The ability of the model to reasonably reflect NW and US adoption patterns, based a difference in a single intervention, described previously in the ‘Calibration’ section, helps to demonstrate some level of validity for the model behaviors. However, there certainly are other differences between the NW and US markets, including a variety of other interventions by NEEA (see Figures 1 and 3). As such, it is not fair to test for the validity of the model at this stage.



Attachment 2: CFL Adoption Model Stocks and Flows Diagram

AwarePotentialAdopters

CurrentAdoptersRate of

Adoption

Adoption fromWord of MouthAdoption from

Advertising andExternal Events

AdvertisingEffect

ProductPrice

ProductAttributes

Current SocketPotential

CurrentSockets in

Market

Susceptibilityto Adoption

Contact Rate

MarketAwareness TC

ReplacementTime

Total SalesRate

ReplacementSales

AdoptionSales

FractionalEffect of

Felt Need

TechnicalPotential of

Product

Total Populationof Adopting

Entities

PopulationGrowth Rate

AdoptionNetwork

Size

UltimateFractionalSocketPotential

Current FractionalSocket Potential

RelativeAdvantage of

Innovation

<ProductAttributes>

<Product Price>

SocialNetwork

Conditions

BaselineInnovativeness

of PotentialAdopter

Effect ofObservability ofInnovation onContact Rate

RiskTolerance of

PotentialAdopter

Susceptibilityvs ResidualRisk Table

Risk fromAdoption Need Felt as

a fraction ofRelative

Advantage

TimePreference

<Susceptibility toAdoption>

Innovativenessof PotentialAdopter in

Org Context

DecisionmakingRank in

Organization

Market GrowthRate

Persuasivenessof Advertising

Contacts

Trust or Qualityof AdvertisingInfo Sources

NumberPeople in

OrganizationPotentially

Affected byAdoption

MacroeconomicConditions

ReferenceRisk of

Innovation

Current Sockets inMarket as Fractionof Potential Sockets

CFL PriceRelative to 2007

Price v TimeTable

<timestep>

REFERENCECFL Price

2007 CFLPrice

<Intervention toSubsidize

Advertising Effort>

<Adoption Ratefrom Coupon

CampaignSubsidy>

<Tech DemandCurve>

Total EnergySavings from

CFLs vsIncandescents

Rate of EnergySavings

<CurrentAdopters>

Avg Rate ofEnergy Savingsper CFL Bulb

ALT ProductAttributes Table

<timestep>

Alt ProductAttributes vs Time

Manufacturer andRetailer Advertising

Spending

PerceivedRelative

Advantage

Adjustment ofPerceived Rel Adv

Perceived RelAdv Adj TC

Fixed MinimumInv Rate

DirectAdoption

Price Modifier

Product AttributeModifier



Attachment 3: Results of Sensitivity Analysis


APPENDIX B: SCOPE OF MODEL DEVELOPMENT

CFL Adoption Model Change Matrix

September 8, 2010

Aaron Ingle


APPENDIX B: CFL Adoption Model Change Matrix

Summary Prioritization of CFL Model Changes (Behaviors to Capture):

CFL Market (the Target System) Behavior of InterestProposedPriority

Basic S-Shaped Adoption Behaviors No GapConversion (Incandescent -> CFL) vs. Replacement (CFL -> CFL) Sales No GapMeasure Life and Realistic CFL Replacement 1Innovativeness Segmentation (1 gap modeled) 2Multiple Adoption-Decision States (more complex approach) 3‘Decision Complexity’/’paralysis of choice’ hypothesis, including the effect of competition 4Market Size vs. Willingness to Pay Simplification 5Promotions/Interventions 6Stocking of CFLs in Households 7PROPOSED SCOPE OF MODEL REVISION—TO INCLUDE ITEMS ABOVE THIS LINEDirect Competition for CFL Sales 8Effect of new housing/construction changes on CFL sales (from meeting discussions) 9Decreasing Incandescent Burnout Negative Feedback Loop 10Increasing ‘Payback Time’ Negative Feedback Loop 11Changing Consumer ‘Time Preference’ 12Retail Sales Channel Effects 13Consumer ‘Knowledge Gap’ regarding CFL Technology 14Rising Awareness and Misinformation of CFLs as a Health Threat 15Competing Technologies Exacerbate ‘Knowledge Gap’ 16Changing Household Mobility and CFL Sales 17EISA Legislation Backlash 18Difference between ‘Perceived Value’ vs. ‘Actual Value’ of CFLs 19Competing Technologies Decrease ‘Perceived Value’ of CFLs 20CFL Supply Chain Dynamics, Supply-Demand, and Intervention Effects 21Consumer ‘Energy Awareness’ Fluctuations 22New Competitive Technologies Make CFLs No Longer Innovative 23Endogenous (vs. Exogenous) CFL Price and Technological Development 24‘Stranding’ of Old CFLs at Market Periphery 25Retail Channels and Market Segmentation Effects 26EISA Legislation and Incandescent Stockpiling 27

Definition of Terms Used in Change Matrix:--Word-of-mouth (WOM): The idea that spread of awareness and acceptance of a technology within a market is a social phenomenon, with consumer to consumer communication (word of mouth) playing an important role in the success or failure of adoption. This mechanism is central to Diffusion of Innovations and the ‘Bass Diffusion Model’, --‘Trialing’ behavior: This refers to trying-out the technology prior to more significant adoption. In the case of CFLs, because the barriers to trying out CFLs are relatively low (cost, availability, compatibility, etc.), trialing behavior is likely to be common.



--‘Burnout-replacement’ behavior: This describes the potential adoption behavior whereby one waits to replace a particular incandescent bulb (with a CFL) until it has burned out. --‘Down-watting’ behavior: My term for the potential behavior whereby one replaces a higher-wattage standard incandescent bulb with a lower wattage standard incandescent bulb, thereby reducing energy consumption (and also reducing light output); indicated as a behavior by a retailer representative interviewed in KEMA 2010 study.



Detailed PROPOSED Prioritization of CFL Model Changes (Behaviors to Capture):

CFL Market (the Target System) Behavior of Interest

Current Model Features Capturing Behavior of Interest

Gap?

Description of Additional Model Features Needed to Capture Behavior

Benefits, Challenges of Including, Related Assumptions, and Additional Data Required if Including

Degree of Difficulty (1=easy, 3 difficult) Recommendation

Priority of

closing gap

Basic S-Shaped Adoption BehaviorsBasic market behaviors (s-shaped adoption curves, positive feedbacks by word-of-mouth (WOM), price, etc)

‘Bass Adoption’ w/word of mouth and advertising currently used as backbone of model

NO N/A Minimum model recommended to capture basic market behaviors; provides a flexible ‘backbone’ on which to build more sophisticated model. Without additional features, however, the bass adoption model is inadequate to capture more subtle market behaviors (such as incomplete adoption)

Assumes a ‘market’ with word of mouth, some connectedness between actors

1 No change No Gap

Conversion (Incandescent -> CFL) vs. Replacement (CFL -> CFL) SalesSales resulting from CFLs replacing incandescent bulbs (conversion) represent new adoption, while CFLs replacing CFLs (replacement) represents maintained adoption behavior. Two distinct behaviors have different meanings and importance.

Model currently treats conversion sales (incandescent -> CFL) as distinct from replacement sales (CFL -> CFL)

NO N/A Basic market behavior and important for interpretation; in combination with accurate measure life and CFL -> CFL replacement modeling (below), allows potential to capture and evaluate ‘natural peak’ behavior in market, where sales of CFLs to replace incandescent bulbs exceeds sustainable CFL sales numbers from CFL -> CFL replacement, as a result of the much longer useful life of a CFL bulb than an equivalent incandescent bulb

1 No change No Gap





Gap?




Priority of

closing gap

Measure Life and Realistic CFL ReplacementMeasure life reflects the actual average life of newly installed CFLs (5 years); CFL -> CFL replacement (from burnout of CFLs) proceeds with a distinct delay pattern:

Current model uses a naïve delay structure and a 6yr measure life

Approximate current modeled delay pattern (1st order delay):

YES Modify measure life to 5 years and develop a more sophisticated CFL -> CFL replacement model including a higher-order delay (possibly 2nd or 3rd order) approximating the CFL bulb life distribution observed in actual use

--Allows significantly more accurate accounting for CFL replacement behaviors, while not adding much complexity to model--Should be possible to select an appropriate delay function based on existing data (for example, Corina Jump, James J. Hirsch, Jane Peters, and Dulane Moran, Welcome to the Dark Side: The Effect of Switching on CFL Measure Life (2008 ACEEE Summer Study on Energy Efficiency in Buildings)

1 Recommend changing measure life to 5 years and adding higher order delay replacement to model

1


CFL Measure Life = 5yrs

Time

Prob

abili

ty o

f C

FL B

urno

ut

CFL Measure Life = 5yrs

Time

Prob

abili

ty o

f C

FL B

urno

ut




Gap?




Priority of

closing gap

Innovativeness SegmentationSegmentation of adopters in terms of innovativeness, as described by Rogers (Diffusion of Innovations, 2003) and many others:

Gaps may be present between segments as described by Moore (Crossing the Chasm, 1991), resulting from different sensitivities to product and market characteristics (price, performance, quality, knowledge requirements, complexity, product maturity), and different communication channel effectiveness between the segments

Current bass diffusion model does not segment adopters, but assumes a continuum of innovativeness without gaps, approximating Rogers conception.

YES Innovativeness segmentation; 2 or more segments depending on interpretation of where the gap(s) are that are affecting adoption dynamics (additional segmentations exist).

Each in the chart below, represents a distinct market segment. Each row represents a possible innovativeness segmentation model:

Modeling this segmentation would require a separate bass adoption ‘backbone’ for each segment, tuned with particular sensitivities, and interconnected at various points representing communication channel effectiveness

Allows evaluation of hypothesis that CFL adoption has hit a ‘gap’ or ‘chasm’ between segments

Assumes segments are distinct in some characteristics.

Data characterizing different segments and the communication between segments useful to calibrate model; segmented sales data also potentially useful

3 (due to systemic effect)

Recommend developing innovativeness segmentation structure, starting with 2 segments (modeling one gap, adjustable to any point in the innovativeness continuum), and evaluating the effect on model behavior and complexity

If necessary, add additional segmentation

2





Gap?




Priority of

closing gap

Multiple Adoption-Decision StatesMultiple adoption-decision states, possibly including --being unaware of CFLs --aware but haven’t tried--tried but only partially adopted (~1-5 CFLs)--replaced the most visible or most used bulbs (~6-15 CFLs?), but further adoption is limited by some factor(s) or constraint(s) such as price, CFL attributes, confusion, health concerns, incompatibilities, specialty sockets, etc--replacing incandescent bulbs only when they burn out ‘burnout replacement’ mode --mostly or fully adopted (>15-20 bulbs?)--aware but haven’t tried and rejecting CFLs--have tried and rejected (and possibly removed) CFLs

Adoption-decision state might effect propensity for advocating for/against the technology (via WOM), and receptiveness to WOM from those in same or different states

Current model utilizes a 2-stage adoption-decision process representing awareness (1st stage) and adoption (2nd stage); current model also deals at the ‘socket-level’, ignoring the grouping of sockets within households and the household-based dynamics of adoption

YES Multi-stage adoption process, with possibly 3 or more stages depending on interpretation; change model to reflect a household grouping of adoption (rather than socket-by-socket as originally modeled); possibly may need a co-flow structure to manage both household and socket-level adoption dynamics at one time

Possible Basic Household Adoption States Model

Possible Complex Household Adoption States Model

Seems to capture observed market behaviors (awareness, differential adoption, burnout replacement vs. immediate replacement, etc.)

Assumes distinct adoption stages

Higher-resolution data from KEMA 2010 survey would be very useful to confirm nature of behavior and distribution of adopters between categories

3 (due to systemic effect)

Recommend directly developing more complex multi-stage household adoption process (lower model below). Simple process: multi-stage household adoption process including differentiation between non-adopters households, partial/trial adopter households (1-5 CFLs), full-adopter households (>15-20 CFLs), and rejection behaviors. Also includes a co-flow structure to facilitate tracking of socket saturation.Complex process: also includes ‘burnout replacement’ behavior mode and ‘limited adoption’ mode, and awareness (without adoption).

3


PotentialAdopters

Trial Adopters(1-5 CFLs)

Full Adopters(>15-20 CFLs)

Rejectors




Gap?




Priority of

closing gap


AwarePotentialAdopters

Trial Adopters(1-5 CFLs)

Full Adopters(>15-20 CFLs)

Rejectors

UnawarePotentialAdopters

Retry Rate BurnoutReplacer

(6-15 CFLs)

LimitedReplacer

(6-15 CFLs)

Rate of IncandescentBurnout

Rate of OvercomingAdoption-Limiting Factors

Direct Adoption




Gap?




Priority of

closing gap

‘Decision Complexity’ / ‘Paralysis of Choice’ hypothesis, including the effect of competition‘Paralysis of choice’ (Barry Schwartz) refers to the idea that an increasing amount of choice can result in 1) decreasing ability to choose at all, and 2) decreasing satisfaction with the choice after the fact. ‘Decision complexity’ refers to the number of available choices (for example styles, brands, and underlying technologies of light bulbs suitable for a particular socket) and the level of knowledge/information necessary to make a good choice. Increasing competition in the lighting marketplace, from new technologies such as LEDs and higher-efficiency incandescents and from a proliferation of brands and specialty bulbs, has increased both the degree of choice and the complexity of the bulb purchasing decision.

Decision complexity and paralysis of choice is not reflected in the current model

YES Would require capturing the effect of increasing choices over time and the relative complexity of the adoption decision on the movement of adopters through the decision process. ‘Paralysis of Choice’ could be modeled with a simple model following Barry Schwartz’s mechanism. ‘Decision complexity’ could be captured based on exogenous inputs from KEMA 2010 survey, including proliferation of CFL models, alternative technologies (LEDs and higher-efficiency incandescents), and other inputs including the improvement of the CFL technology.

Different innovativeness groups may have greater tolerance for choice or decision complexity.

May also be a mechanism for influence by education, knowledge, or in-store support campaigns by third parties, retailers and manufacturers

This ‘paralysis of choice’ and ‘decision complexity’ may drive ‘trialing’ and ‘burnout replacement’ behavior modes and a ‘wait and see’ attitude among potential adopters.

3 Recommend capturing ‘decision complexity’ in the model, focusing on the effect of viable competition/alternatives on decision complexity.

4





Gap?




Priority of

closing gap

Market Size vs. Willingness to Pay SimplificationThe potential market for CFLs is related to their price and technological characteristics; as CFLs get less expensive, the market size can be seen as expanding; or, within a fixed market size, the probability of adoption can be seen as increasing

Current model has ‘market size’ dependent on price and technology characteristics, somewhat redundant with or overlapping the consumer willingness to pay (WTP) function, which reflects consumer price sensitivity, but is not well supported by data

YES Modify model structure to consolidate the representation of market dependence on price and technological characteristics to be captured by a single function (WTP). Supporting data for the WTP function would also be desirable

Change should simplify the model by eliminating a redundancy, and may increase the correspondence to real-world data, allowing evaluation of consumer sensitivity to price changes and to incentive programs.

Better economic data on WTP would be very useful.

2 Recommend removing the ‘market size’ formulation and instead capturing the behavior in the consumer willingness-to-pay function.

Recommend finding supportive data for this function

5


Price

Improving Technology Characteristics

Ado

ptio

n Pr

obab

ility

/W

illin

gnes

s to

Pay

Ado

ptio

n Pr

obab

ility

/W

illin

gnes

s to

Pay




Gap?




Priority of

closing gap

Promotions/InterventionsPromotions have had a major historical effect on the CFL market in the NW and elsewhere, and continue to have a significant effect on sales as documented in KEMA 2010 study.

Current model has simple promotional structures which capture the basic influence of certain interventions on adoption dynamics; the specific interventions currently captured are price supports, awareness campaigns, coupon campaign, and product improvements pace

YES Additional model development required to capture historical promotions accurately, to capture the effects of promotions more accurately (including side effects, influence on different adopter groups, differentiating upstream, downstream, midstream interventions, etc.), and/or to expanding to additional interventions

To some degree, improvements are necessary to accurately capture the market, and will also allow evaluation of various promotion mechanisms

3 Recommend further development of previously modeled promotional structures (price supports, awareness campaigns, coupon campaigns, product improvements pace) for historical accuracy, side effects, upstream/downstream effects

Do not recommend capturing additional intervention categories unless deemed to be very significant in current market; additional promotional structures would be useful to model in a later revision

6

Stocking of CFLs in HouseholdsHouseholds stocking some proportion of CFLs, as reported in KEMA 2010 study

Current model does not consider household stocking of CFLs

YES Model would require a stock representing the average number of CFLs stored unused in each household, along with a decision process determining the average size of the stock and responsiveness to other factors

Observed market behavior (KEMA 2010); necessary to differentiate sales rates from actual socket penetration

2 Add feature to model. Behavior is clearly demonstrated in data (KEMA 2010), and has a significant effect on purchasing behavior and on energy consumption.

7

PROPOSED SCOPE OF MODEL REVISION—TO INCLUDE ITEMS ABOVE THIS LINE





Gap?




Priority of

closing gap

Direct Competition for CFL SalesSales of alternative technologies in the marketplace (LEDs, improved-efficiency incandescents, and possibly ‘down-watting’?) may be directly taking socket-share and reducing sales of CFLs

Competing technologies are not considered in the current model

YES Modeling of competition for sales may require modeling of adoption curves for each competitive technology

Allows evaluation of effects of competing technologies

2 OPTIONAL; I think effect is likely small and do not recommend addition, but it is a viable channel for competition to effect CFL sales; if desired this can be added in this round. (+15 hours if added to scope).

8

Effect of new housing/construction changes on CFL salesConstruction of new housing means new sockets coming online over time. These sockets may be filled with CFLs purchased through various sales channels. Specialty wholesalers utilized by major home-builders should be excluded from CFL sales data, but it is possible that smaller builders purchase bulbs through conventional channels, and that therefore changes in new housing completions have an immediate effect on CFL sales.

New housing/construction is not directly considered in the current model, however growth in the total number of sockets in the market is enabled

YES New housing completions and the associated socket growth would need to be captured (exogenously); these drivers, along with the fraction of new sockets filled with CFLs through conventional sales channels, would allow the model to capture this effect

Allows isolating the effect of new housing completions on CFL sales numbers, and potentially eliminating this effect as a possible explanation for the observed drop in CFL sales.

1 OPTIONAL; can evaluate whether this is a potentially significant effect based on housing construction data and CFL sales; recommend not modeling at this time. (+5 hours if evaluation added to scope, not including modeling)

9





Gap?




Priority of

closing gap

Decreasing Incandescent Burnout Negative Feedback LoopAssuming that a significant number of households are currently in a ‘burnout replacement’ mode of adoption, and assuming that the most highly utilized incandescent bulbs are preferentially replaced (assuming they burn out earlier and assuming people are more likely to replace more utilized bulbs prior to burnout), then the less utilized sockets are more likely to retain incandescent bulbs, and the average life of these incandescent bulbs increase because of the low utilization. This should slow down the ‘burnout replacement’ behavior (and associated sales) as CFL socket saturation increases

Current model does not capture burnout-replacement behavior, and does not capture feedbacks potentially slowing CFL socket saturation (such as the mechanism described here)

YES Model would need to have incandescent burnout rates modeled as a decreasing (vs constant) function of CFL penetration in household sockets, along with explicit modeling of burnout-replacement adoption behavior for at least some segment of the population

Allows evaluation of a possible negative feedback mechanism that could slow socket penetration

1 Recommend running calculations (or finding existing calculations) to evaluate whether this is a potentially significant behavior

If significant, recommend capturing in model

10


Decreasing

CFL Socket Saturation

Inca

ndes

cent

B

urno

ut R

ate

Constant




Gap?




Priority of

closing gap

Increasing ‘Payback Time’ Negative Feedback Loop‘Payback time’ in switching from incandescent to CFL is dependent on the usage pattern of the bulb. Again assuming selective replacement of the most highly utilized incandescent bulbs with CFLs (same assumption as above), increasing CFL saturation of household sockets would be associated with increased payback times for additional incandescent bulb replacements. If this is perceived by the household, household incentive to replace additional bulbs may decrease as CFL socket saturation increases

Current model does not capture preferential replacement behavior, and assumes a constant ‘payback time’ and associated willingness-to-pay across all sockets

YES Model would need to have the incandescent cost/benefit function modeled as a decreasing (vs constant) function of CFL penetration in household sockets

Allows evaluation of a possible negative feedback mechanism that could slow socket penetration

1 Recommend running calculations (or finding existing calculations) to evaluate whether this is a potentially significant behavior

If significant, recommend capturing in model

11


Constant

Increasing

CFL Socket Saturation

CFL

Pay

back

Tim

e




Gap?




Priority of

closing gap

Changing Consumer ‘Time Preference’Economic factors effect household purchasing decisions. As CFLs represent a form of investment (compared to incandescent bulbs) with higher initial cost but lower lifetime costs, time preference (or discount rate) captures the consumer’s relative preference for these factors. Exogenous economic events, such as the recent recession, can significantly change the time preference of consumers and resulting purchasing behaviors. This in effect means that the consumer willingness-to-pay function may change over time due to exogenous economic factors.

A control representing consumer time preference is present in current model, effectively altering the currently time-constant consumer willingness-to-pay (WTP) function, but has not been calibrated or tested and is not utilized

YES Additional calibration and testing of the existing model feature is necessary at a minimum, and incorporating and external economic indicator to be determined, but possibilities include NW unemployment rates, economic optimism indicators, etc.

Allows evaluation of potential exogenous economic effects

Would benefit from additional research data isolating this effect; may be able to extrapolate from general economic indicators

2, likely to be

difficult to isolate data for

calibration

Recommend selecting relevant economic indicator and calibrating the current model mechanism if reliable data can be found to estimate the magnitude of this effect on CFL purchasing behavior

12

Retail Sales Channel EffectsRetail sales channels have important impacts on consumer awareness, education, and adoption decisions. Shelf space allocations (compared to alternatives) and degree of visibility or push at retailers may have a large impact on sales (Wal-Mart’s 100 million CFL campaign in 2007 may be an example of this). Interventions intended to increase retailer push may have relatively high leverage if this link is currently weak (for example, if retailer profit from CFL sales is inadequate to encourage greater push.

The retail sales function, and associated economic and promotion-based drivers for retailers to ‘push’ CFLs, are not currently directly captured in the model

YES Model would require factors linking retailer visibility or ‘push’ to the adoption-decision process. These relationships would require calibration and testing.

Additional factors linking retailer ‘push’ behaviors to CFL economics and promotional campaigns may be desirable to capture the behavior endogenously; alternately, historical data (such as shelf surveys) may be sufficient to define an exogenous ‘retail push’ factor

Allows evaluation of effects of sales channels, particularly as a limiting factor in adoption or as an avenue for high-leverage promotions. Also may enable evaluation of the effects of promotions on retailer behaviors.

May allow evaluation of ‘Walmart Effect’ hypothetical scenario related to Wal-Mart’s 2007 ‘100 Million Bulb’ campaign.

Data supporting this relationship would be very useful, and may be available in existing shelf-space evaluations (for example, KEMA 2010)

2 Recommend adding retail sales channel effects to the model. Shelf space study data (for example KEMA 2010 study) may be useful in calibrating historical behaviors

113





Gap?




Priority of

closing gap

Consumer ‘Knowledge Gap’ regarding CFL TechnologyConsumer knowledge of CFL technology potentially lags actual bulb development; lack of knowledge (or misinformation) can result in lost sales, inadequate CFL replacement behavior (not replacing all of the bulbs that could be effectively replaced), or negative purchase experiences (purchasing the wrong size or color CFLs); lack of consumer knowledge (or misinformation) can be mitigated by application guidance and educational activities

Current model does not directly consider consumer knowledge of CFL technology, however to some degree this knowledge gap is implicit in the diffusion-of-innovation model

YES Separate stock representing consumer knowledge/ understanding of CFL tech; potential for ‘misinformation’ to contribute either positively or negatively to adoption decision

Allows evaluation of complexity/knowledge/confusion hypothesis

2 Recommend capturing as a second priority

14

Rising Awareness and Misinformation of CFLs as a Health ThreatIncreasing consumer awareness of the small amount of mercury in CFL bulbs is in many cases accompanied by the potential for over-reaction. This issue creates a tradeoff which complicates the adoption decision process

The current model only considers product attributes in aggregate instead of dealing with specific attributes

YES Decompose ‘product attributes’ to capture the specific role of important attributes, such as health concerns with the product. Formulate model relationships to capture the effects on the adoption decision process and on word of mouth from consumer awareness and misinformation regarding the attribute(s). Model would require a separate ‘knowledge’ stock (see previous line) for each product attribute considered.

Allows evaluation of the effect of product attributes separately.

Requires separate development of the potential effects of each attribute; would benefit from detailed data (may be extracted from KEMA 2010).

2 (health attribute

only)

Recommend capturing together with ‘Knowledge Gap’ (prior line) and ‘Competing Technologies Exacerbate Knowledge Gap’ (next line) as these items are closely related.

15

Competing Technologies Exacerbate ‘Knowledge Gap’Presence of competing technologies in the marketplace or in development (LEDs, improved-efficiency incandescents, and possibly ‘down-watting’?) may be creating too many choices, or raising the knowledge required for consumers, resulting in confusion or a ‘wait and see’ attitude--stalling potential sales

Competing technologies are not considered in the current model, and consumer knowledge of CFL technology in comparison to alternative technologies is not captured

YES Modeling would require separately modeling the state of CFL technology (complexity, etc) in relationship to alternative technologies and in comparison to consumer understanding/knowledge of the technology; the relationships between these factors will help determine the results of the adoption-decision process


2 Recommend not modeling in next revision, but considering for future revisions

16





Gap?




Priority of

closing gap

Changing Household Mobility and CFL SalesConsumer CFL purchases may be affected by their mobility or movement from home to home. If adopters are likely to leave CFLs behind when they move between houses (and purchase and install a large set in the new home), then any changes in mobility (as a result of large changes in the housing market over the last few years) can potentially significantly effect CFL sales. However, do not yet have anything but anecdotal data that changes in mobility have occurred

This factor is not considered in the current model

YES Define and calibrate a relationship between consumer CFL purchases and (exogenous) household mobility, representing behavior resulting from current adopter households moving to new homes, and non-adopters moving into CFL-outfitted homes


Currently assumes household moves are likely to increase CFL sales, and that recent trends are towards decreased mobility.

3 As second priority, recommend finding and evaluating pre-existing household mobility data to estimate largest possible impact of this effect. If this is significant, then modeling (and finding data to support model calibration) is recommended, again as a second-priority activity

17

EISA Legislation BacklashEISA legislation (banning certain incandescent models starting in 2012) may trigger backlash by households and/or retailers, and provide momentum to competitive technologies such as more efficient incandescent bulbs

Impacts of legislation not captured in the current model; competitive technologies not captured in the current model

YES Model would require modeling of adoption dynamics of competitive technologies and changes in these dynamics due to EISA legislation

Allows evaluation of possible impacts of EISA legislation

Does not appear to be driving a large consumer backlash based on low awareness of EISA in the marketplace (per KEMA 2010 study), but may be significantly driving retailer or manufacturer behaviors

2 Not recommended for this round of model development

18

Difference between ‘Perceived Value’ vs. ‘Actual Value’ of CFLsConsumer perception of the ‘value’ presented by CFLs is distinct from the actual value, and is the result of various factors including ‘trialing’ behavior, WOM, level of knowledge, access to true bulb costs, competitive technologies, etc.

Current model has a stock representing ‘Perceived Relative Advantage’ of the CFL technology, but this structure is not adjusted in a realistic manner adequate to represent the behavior of interest

YES Model would need a more sophisticated logical structure capturing the changes of consumer perception of the ‘value’ of CFLs

Allows evaluation of ‘lagging perception’ hypothesis, allows evaluation of side effect of promotions on perceived value

2 Leave model as-is for next round of changes; however, this change will be useful to evaluate side effects of certain promotions

19

Competing Technologies Decrease ‘Perceived Value’ of CFLsPresence of competing technologies in the marketplace or under development (LEDs, improved-efficiency incandescents) may reduce the perceived value that consumers attribute to CFLs

Competing technologies are not considered in the current model

YES Modeling would require definition and calibration of a relationship between the presence of competitive technologies and consumers’ perceived value placed on CFLs

Allows evaluation of effects of competing technologies.

Data supporting this relationship would be very useful to inform the model.

2 Recommend not modeling in next revision, but considering for future revisions

20





Gap?




Priority of

closing gap

CFL Supply Chain Dynamics, Supply-Demand, and Intervention EffectsCFL bulb manufacturing and distribution (supply) must keep pace with sales; otherwise shortages will result, with resulting inability of households to purchase bulbs, and/or increases in prices. These dynamics can also generate large swings or cycles in the market. Also, large promotional campaigns can trigger or exacerbate these issues.

Supply chain dynamics are also useful for characterizing various market intervention mechanisms, from upstream to downstream. These interventions have different impacts on the supply chain and on side-effects such as free ridership.

Supply chain dynamics are not captured in the current model

YES Modeling would require capturing supply-demand relationships representing the manufacturer-retailer balance and/or retailer-consumer balance, calibrated to historical data

Allows model to reflect supply-demand dynamics and challenges with supply and growth; possibly useful for scenarios where supply issues are significant

3 This appears to be an important aspect of the market, particularly in capturing promotion effects and side-effects. However, it also appears complex and challenging to do well and is recommended for consideration in the next round

21

Consumer ‘Energy Awareness’ FluctuationsConsumers can be argued to have some degree of ‘energy awareness’, a knowledge of the energy consumption associated with various activities and the potential costs associated with that consumption. This energy awareness varies widely among consumers, and is subject to influence by educational campaigns and by exogenous factors such as the California ‘electricity crisis’, the more recent oil/gasoline price ‘bubble’, and the gulf oil spill. This ‘energy awareness’ may affect CFL purchasing behavior. At the same time, however, energy is complex and oil and electricity are only indirectly linked. Therefore, utilizing a single ‘energy awareness’ factor may be too oversimplified to be useful.

No ‘energy awareness’ factor is currently represented in the model

YES Addition of a ‘consumer energy awareness factor’, dependent on exogenous economic events (oil/gasoline price ‘bubble’, CA electricity crisis), along with calibration of the effect of these economic events on this awareness factor


Would benefit from additional research data isolating this effect; may be able to extrapolate from general economic indicators

2 (likely to be

difficult to find data

for calibration

Do not recommend adding this to current model if data is not available; however, if data can be easily found, the modeling of this effect appears to be straightforward

22





Gap?




Priority of

closing gap

New Competitive Technologies Make CFLs No Longer InnovativePresence of competing technologies in the marketplace or under development (LEDs, improved-efficiency incandescents, and possibly ‘down-watting’?) may have replaced CFLs as a ‘cutting-edge technology’, possibly decreasing the appeal to early adopters but possibly increasing the perceived ‘maturity’ of the technology and perhaps its appeal to the majority

Competing technologies are not considered in the current model, and multiple innovativeness segments are not considered in the current model, and the perceived maturity of the technology is not considered

YES Modeling would require a separate factor capturing the perception of the maturity of CFL technology as effected by the presence of competitive technologies and with different innovativeness segments responding distinctly to this factor


2 Recommend not modeling; relationship described is tenuous and unlikely to be significant

23

Endogenous (vs. Exogenous) CFL Price and Technological DevelopmentChanging CFL price and technology characteristics affect the adoption decision process; increased (or decreased) adoption may also effect changes in CFL price and technological development

Current model uses changing market price and technology characteristics over time, driven exogenously by functions based on price data and estimated product performance changes over time

YES May be desirable to endogenously capture feedback mechanisms driving down CFL prices over time and driving technological development

Endogenous generation of these behaviors is necessary if the coupling between the NW market and CFL price/development is sufficiently strong such that feedback effects are significant in both directions (e.g., increased NW sales create economies of scale which drive down prices which cause greater sales…)

1 Recommend leaving as-is (exogenous); upgrading to capture behaviors endogenously is a lower priority--I think it is useful, however pretty good market data exists for the current arrangement, (particularly for price) and I am guessing that the NW market is a relatively small portion of the overall CFL market, such that any effects captured here would be small

24





Gap?




Priority of

closing gap

‘Stranding’ of Old CFLs at Market PeripherySlow turnover of bulbs, combined with fast product development cycles, can strand older model bulbs (with lower performance and higher prices) on retail shelves at the market periphery (for example, small rural hardware stores), preventing placement of newer, more competitive models

CFL model turnover and stocking patterns are not captured in the current model

YES May require developing separate stocks to differentiate current state-of-art pricing and technology from actual pricing and technology in retailers. This would allow a lag at the retailers, which would be a function of CFL sales rate, and the rate of change of prices and CFL technology. The pricing and technology at the retailer would determine actual adoption decisions

Allows evaluation of effects of slow bulb turnover and stranding of older bulbs in retailers, along with interventions as performed historically to remove these old bulb models from shelves

2 Do not recommend capturing currently; a known problem earlier in the CFL adoption cycle, but less likely to be a problem currently given higher overall sales and more mature product (slower pace of development)

25

Retail Channels and Market Segmentation EffectsFocusing on a different aspect of retail sales, certain channels may more effectively sell to certain consumer segments, or may more effectively facilitate different portions of the adoption-decision process (e.g., ‘trialing’ vs. bulk purchasing). Also, knowledgeable sales-people may be more available in some retailers to support the consumer—more important for less knowledgeable consumers (or more complex technologies).

The retail sales function, with various sales channels, is not currently captured in the model

YES Modeling would require defining the relationship of various retail sales channels’ to innovativeness segments and possibly also adoption-decision segments

Allows evaluation of sales channel effects

3 Not recommended for next round of modeling. Sales channel effects, if existing in the marketplace, are not emphasized in the literature and prior studies, and are likely to have only minor significance

26

EISA Legislation and Incandescent StockpilingEISA legislation (banning certain incandescent models starting in 2012) may trigger stockpiling of incandescent bulbs by households

Impacts of legislation and potential stockpiling of incandescent bulbs are not captured in current model

YES Model would require modeling of existing incandescent bulb stocks and changes in this behavior due to EISA legislation

Allows evaluation of possible impacts of EISA legislation;

Does not appear to be affecting current CFL sales based on low awareness of EISA in the marketplace (per KEMA 2010 study).

2 Not recommended for this round of model development

27


APPENDIX C: ADDITIONAL DOCUMENTATION OF THE CFL MODEL


APPENDIX C: Additional Documentation of the CFL Model

Introduction

The body of the report provides an overview of the development process for the CFL model. The full specification of the CFL model is quite detailed and beyond the scope of this report and appendix. However, I’ve provided an additional layer of detail here, focusing on the following model features:

Adoption Decision Process Socket growth Market interventions 2000-2001 Western electricity crisis Stocking of CFLs Satisfaction with CFL purchases Calibration reference data and results

CFL and incandescent bulb life/burnout Ever adopter and awareness rates CFL socket penetration Distribution of CFLs adoption levels across households

Decision complexity trend generation

Adoption Decision Process

A basic description of the adoption decision process is useful as reference for understanding how various model factors influence adoption. Within this model, the ‘adoption decision’ refers to the movement of households between adoption states. For example, the movement of aware households to become a trialing household is the result of an adoption decision. Therefore, the model reflects the adoption decision in the logic driving the ‘flows’ between the various adoption state ‘stocks’.

Because the model treats the adoption decision as an increase or decrease in adoption level, replacement sales (buying a bulb to replace a burned out bulb) are, by default, treated as automatic in the model and not considered in the decision logic. Therefore, the model doesn’t treat high or low burnout replacement rates (due to longer or shorter bulb life) as influencing decision rates. Obviously, this is an assumption of the model, and probably highly accurate. In certain situations, households may enter, for example, a ‘burnout replacement’ mode whereby as incandescent bulbs burn out, the homeowner replaces them with CFLs. The model doesn’t current capture this behavior.

Instead, the model bases the decision process on average rates of movement between adoption levels. I based these adoption rates on several factors:

Inherent ‘difficulty’ of the decision, or time to make this adoption decision relative to decisions at the other stages; for example, the decision to move from limited adoption to full adoption was necessarily modeled as a more difficult or slow decision on average than the decision to trial adopt



Effect of mass media, driving increased level of adoption if the media is on balance positive in nature, or driving decreased level of adoption (or rejection) if the media is on balance negative in nature; the strength of this effect is proportional to the overall strength of the mass media (positive and negative)

Effect of word of mouth (generally weak in the model), driving increased level of adoption if the word-of-mouth is on balance positive in nature, or driving decreased level of adoption (or rejection) if the word-of-mouth is on balance negative in nature; the strength of this effect is proportional to the overall strength of the word of mouth (positive and negative)

Perception of the technology based on direct experience, for those who have previously adopted (factor does not affect the decision to trial adopt); higher average satisfaction (satisfaction >1) with the technology will drive faster rates of additional adoption, and lower average satisfaction (satisfaction <1) drive slower rates of adoption, and higher rates of rejection. This satisfaction with the technology is sensitive to changes in the performance and price of the technology, as described in other parts of this appendix.

These four major factors drive the rates of households adopting (and rejecting) CFLs in this model, and drive growth of awareness (both positive and negative awareness). As mentioned above, a more sophisticated model would also account for how the rates of bulb burnout affect adoption behaviors.

I modeled interventions to affect mass media and technology performance and price. The 2000-2001 Western electricity crisis was modeled to affect the ‘difficulty’ of the adoption decision, by changes in general innovativeness or risk-taking, as well as to create significant mass media attention.

Socket Growth

The Northwest market has a constantly changing number of light bulb sockets in active use—with a functioning bulb and getting some ‘on time’ over whatever time frame is of interest. In general, the trend over the model time period has been towards increasing sockets in the Northwest, as new homes have been built, number of households has increased, and the average number of sockets in each home have increased, according to the ACE model 2008 documentation (NEEA 2009). However, the trend in socket growth in recent years, with the housing slump and other economic events, are uncertain. Therefore, the model assumes (except in model runs where this is noted to be otherwise) that the socket growth trend indicated in the ACE model documentation continues through the end of the model run.

A additional assumption is made for the model that the average number of sockets per household is fixed at 36. Calculations made from the ACE model documentation show this average changing over time as new homes with more sockets come on to the market. These calculations show a change from 35 sockets/household in 1997, to 36 sockets/household in 2003, to approximately 38 sockets per household by 2010. For simplicity of modeling, I fixed this number at the 2003 value, 36 sockets/household. It is felt that the small error likely from this estimation is not significant to the overall representativeness of the model.



Based on these two assumptions, the ‘Total Households Net Energy Star New Homes’ figures reported in the ACE model documentation were used for ‘Total HHs Assumed’ in the model, as reported in Figure C-1. The ‘Total Sockets’ used in the model and reported in Figure C-1 was calculated based on the simple assumption of 36 sockets per household.

Figure C-1: Modeled total Northwest sockets and households, converted from ACE model 2008 documentation based on fixed 36 sockets/household

YearTotal Sockets,

Assuming 36/HHTotal HHs Assumed

1997 168,316,092 4,675,4471998 170,201,412 4,727,8171999 172,107,900 4,780,7752000 174,035,700 4,834,3252001 176,058,612 4,890,5172002 178,168,824 4,949,1342003 180,369,252 5,010,2572004 182,662,848 5,073,9682005 185,052,816 5,140,3562006 187,542,288 5,209,5082007 190,134,648 5,281,518

Est 2008 192,833,388 5,356,483Est 2009 195,642,108 5,434,503Est 2010 198,564,480 5,515,680Est 2011 201,604,392 5,600,122Est 2012 204,765,876 5,687,941

For the scenario ‘Exogenous effect of a ‘stall’ in new housing growth’ evaluated in the main report section 4.5, it is assumed that the growth in households and sockets stops at mid-2008 levels, and remains fixed until 2011. Figure C-1 depicts this modified growth trend.

Market Interventions

As described in the main report section 1.4.7, I modeled three market interventions representing actions by NEEA and the utilities. In the actual market, a much wider variety and specificity of interventions are possible. Mass media interventions capture efforts to educate, inform, and convince consumers to purchase CFLs. These activities both raise awareness and influence the purchase decision. I modeled retail and retailer effects separately; some mass media interventions may flow through these channels and not just major media sources. Price subsidies capture efforts to reduce the price of CFLs, and I modeled them based on an average price curve (assuming no interventions) and then the intervention-based price savings. This category captures coupons, giveaways, manufacturer-focused incentives, and other types of price-reducing interventions. Investments in research and development reflect spending to improve the characteristics or performance of the technology, including quality.

Data provided in the ACE 2008 model documentation (NEEA 2009) was utilized to develop an estimate of total spending by utility and NEEA programs for each year from 1997 the present, and to extrapolate this spending going forward. Various literature sources identified active programs



for each year.(KEMA 2010, Sandahl et al. 2006). Figure C-2 presents the total spending numbers, and the programs identified for each year.

Figure C-2: Calculated total incentive spending per year and active programs

YearTotal Incentive

Spending/Yr Description of Program in Progress1997 $1,278,065 Lightwise--NEEA and Utilities--Mfg rebate $5/bulb, in-store-promos, increased availability (started 1996)1998 $2,483,771 Lightwise--NEEA and Utilities--Mfg rebate $5/bulb, in-store-promos, increased availability (started 1996)

DOE/PNNL CFL RnD development sub-CFLs1999 $3,036,587 Lightwise--NEEA and Utilities--Mfg rebate $5/bulb, in-store-promos, increased availability (started 1996)

DOE/PNNL CFL RnD development sub-CFLs2000 $2,179,768 Energy Star Lighting (DOE branding, Utility rebates)

Coupon Campaign Q4 2000 through 2001 (BPA)--6$/bulb; 8.6 million bulbs (7 million sold, 39% w/coupons)NEEA Retailer Focus; advertising/marketing support (320 retailers)DOE/PNNL CFL RnD development sub-CFLsPEARL (Bulb Evaluation) program founding sponsorship by NEEA

2001 $25,475,168 Energy Star Lighting (DOE branding, Utility rebates)Coupon Campaign Q4 2000 through 2001 (BPA)--6$/bulb; 8.6 million bulbs (7 million sold, 39% w/coupons)National Change a Light Change the World Campaign (Fall)NEEA Retailer Focus; advertising/marketing support (~1000 retailers)DOE/PNNL CFL RnD development sub-CFLs

2002 $5,182,900 Energy Star Lighting (DOE branding, Utility rebates)NEEA Retailer Focus; advertising/marketing support (~1400 retailers)NEEA Mgmt of BetterBulbsDirect.comETO Direct Install program

2003 $8,050,420 Energy Star Lighting (DOE branding, Utility rebates)NEEA/Osram Sylvania Earth Day Promo (Advertising, $3/bulb mail-in rebates) ~$500,000 funds NEEA and mfgs/retailersNEEA Retailer Focus; advertising/marketing support (~1100 retailers)NEEA Mgmt of BetterBulbsDirect.comETO Direct Install program

2004 $6,965,738 Energy Star Lighting (DOE branding, Utility rebates)NEEA Retailer Focus; advertising/marketing support (~1100 retailers)NEEA Supports CFL Quality research and NPDNEEA Mgmt of BetterBulbsDirect.comETO Direct Install program

2005 $10,732,999 Utility/NEEA Manufacturer Price Buydown (BPA Fall Campaign)Savings with a TwistFall Change a Light (assume redundant with above)NEEA upstream incentives to CFL manufacturers; inclusive of urban, rural, big-box and nonETO Direct Install program

2006 $12,572,870 Savings with a TwistFall Change a LightNEEA upstream incentives to CFL manufacturersNEEA promotions (more focused)

2007 $19,218,467 Savings with a TwistFall Change a LightSpring Specialty Change a Light (BPA)NEEA upstream incentives to CFL manufacturersNEEA promotions (more focused)

2008 $19,218,467 Spring Specialty Change a Light (BPA)2009 $19,168,467 Spring Specialty Change a Light (BPA)

2010 est $18,193,467 Programs not identified2011 est $18,193,467 Programs not identified2012 est $18,193,467 Programs not identified

From here, I allocated total spending between the three intervention categories based on an interpretation of the focus of programs occurring during that year. These allocations were then fed into the model, with adjustment adjustments in some cases for efforts known to have higher leverage (for example, manufacturer incentives compared to retailer incentives) or which could have had effects on both mass media and price reduction (for example, a coupon campaign). I did not confirm these total spending numbers, and they may vary significantly from actual spending. Figure C-3 below documents these estimations and calculations.



Figure C-3: Modeled intervention spending by category (mass media, research and development, and price subsidies) and effect of price subsidy spending on average CFL purchase price

Year

Actual Modeled

Intervention Spending on Mass Media

Modeled Intervention

Spending on CFL quality and

Research and Development

Modeled Intervention Spending on

CFL Price Subsidies

Estimated Effect of

Subsidies on CFL Price

(Calculated and/or stated)

$/bulb

Overall Avg Price Paid by Consumers including Incentives

$/bulb

Estimated Baseline CFL Price--No Incentives

$/bulb1997 $639,033 $0 $639,033 4.39 13.61 181998 $620,943 $1,241,886 $620,943 5.41 12.59 181999 $759,147 $1,518,294 $759,147 4.17 12.83 172000 $726,589 $1,453,179 $0 - 13.66 13.662001 $8,433,138 $0 $19,656,000 3.40 6.26 9.662002 $2,718,975 $711,625 $584,100 0.15 6.51 6.662003 $3,453,105 $376,200 $1,740,105 0.45 5.55 62004 $2,206,184 $482,150 $1,425,801 0.28 5.22 5.52005 $4,653,599 $0 $4,653,599 0.95 4.26 5.212006 $6,286,435 $0 $6,286,435 0.82 3.89 4.712007 $9,609,234 $0 $9,609,234 0.71 4.07 4.782008 $9,609,234 $0 $9,609,234 0.38 4.24 4.622009 $9,584,234 $0 $9,584,234 0.42 3.76 4.182010 $9,096,734 $0 $9,096,734 0.28 3.90 4.182011 $9,096,734 $0 $9,096,734 0.25 3.93 4.182012 $9,096,734 $0 $9,096,734 0.26 3.92 4.18

Figure C-3 above also provides the modeled CFL price over time, with and without modeled intervention spending. I used reported prices from KEMA (2010) as the primary basis for this calculation, but these prices only covered a portion of the data set, and so this price trend is treated as a gross approximation only. Additionally, the use of average prices is a significant assumption in the model; in reality, the market shows a diversity of bulb models, retail channels, and prices not particularly well captured by a single average price. The calculated intervention spending on price subsidies, together with reported CFL sales numbers, was used to back-calculate, for certain years, what the average baseline CFL price would have been if incentives were not present. For other years, I interpolated or extrapolated this baseline price from other values.

In terms of the research and development intervention, the model assumes a basic product development curve without intervention, and then an ability for additional strategic investments in CFL research and development to ‘accelerate’ this curve. The assumption is that this additional spending does not create new potential for the technology (change its development trajectory), but instead just moved the technology along this trajectory more quickly. Figure C-3 reports intervention spending on research and development. The base technology performance curve (without intervention), and the technology performance with the ‘modeled actual market’ interventions, are presented in Figure C-4. These curves show how I modeled the intervention as an acceleration of product development.

Figure C-4: Attributes of CFLs with (red curve) and without (blue curve) research and development intervention spending; units on y-axis are arbitrary (higher values imply a higher-performance product)



Avg Attributes of CFLs Entering Market60

40

201997 2000 2003 2006 2009 2012

Time (Year)Avg Attributes of CFLs Entering Market : 1 Segment CAL-no rndAvg Attributes of CFLs Entering Market : 1 Segment CAL

2000-2001 Western Electricity Crisis

The only exogenous event that I explicitly modeled in the base configuration is the 2000-2001 energy crisis affecting the western states. I found details of the overall effects of this event in various places, for example in Sandahl et al’s report (2006). I captured this event in two ways: as a significant spike in mass media attention relevant to CFL adoption, and (in combination) a significant increase in overall innovativeness of the population relating to a more generalized awareness of or predisposition towards energy-saving measures. In reality, this event likely also caused a variety of other changes in the market. For simplicity, I modeled these two dimensions. Based on the rising nature of awareness of the crisis through 2000, and a subsequent gradual decline in the crisis (rather than a dramatic ending), the effect of the crisis was modeled as shown in Figures C-5 and C-6.

Figure C-5: Effect of 2000-2001 Western Energy Crisis on adopter innovativeness; 1 = normal innovativeness, <1 = increasing innovativeness Innovativeness of HHs Segment 1

1

01997 2000 2003 2006 2009 2012

Time (Year)Innovativeness of HHs Segment 1 : 1 Segment CAL-no rndInnovativeness of HHs Segment 1 : 1 Segment CAL

Figure C-6: Strength of mass media attention generated by 2000-2001 Western Energy Crisis; units reflect relative effect on adoption (not scaled) Strength of Positive Mass Media Generated by Exogenous Events

0.4

0.2

-4e-0091997 2000 2003 2006 2009 2012

Time (Year)Strength of Positive Mass Media Generated by Exogenous Events : 1 Segment CAL-no rndStrength of Positive Mass Media Generated by Exogenous Events : 1 Segment CAL

The model considers these effects are independent of the exogenous interventions—and are therefore present in all model runs.

Stocking of CFLs

The recent KEMA report (2010) identified Stocking of CFLs as a significant behavior, with a significant fraction of all purchased CFLs in storage instead of use. I captured this storage behavior as a propensity to store 40% of the number of CFLs proportional to the level of CFL socket saturation in the house. So, a house with 10 CFLs installed is assumed to have 4 CFLs in



storage. These stocking purchases are included in the total purchase numbers, but not in ‘conversion’ or ‘unique’ purchase rates. I checked the numbers of stored bulbs against the KEMA report numbers and found them to be reasonably representative. While the stocking proportion has changed slightly over time, it has remained sufficiently close to 40%.

Satisfaction with CFL Purchases

The model makes the assumption that, after purchasing the CFL, households determine a ‘satisfaction’ with the CFL purchase based on a combination of the current performance of the technology and the price. The ‘paradox of choice’ effect described in Section 4.2 may degrade this satisfaction. The model averages this purchase satisfaction and accumulates it in a stock representing the whole population of adopters. The model uses this in future decision-making of this group to further adopt CFLs. Therefore, the model shows that the average satisfaction values have inertia and are significantly delayed compared to changes in the price and performance of technology on retail shelves. When households who have previously adopted CFLs make a decision of whether to purchase additional CFLs, they do so partially based on this average satisfaction with CFLs across the whole population.

The price and CFL performance trends over time are combined to calculate a ‘relative state of CFL technology and market’ variable that reflects the attractiveness of the CFL compared to incandescent bulbs. A ‘1’ value represents parity, while a value greater than 1 represents CFLs as the superior choice (yielding, on average, higher satisfaction). This number is equivalent in scale and meaning to the ‘satisfaction’ metric described above. The satisfaction generated by current purchases, or the ‘relative state of CFL technology and market’, in comparison to the average satisfaction for the entire adopter population, is plotted in Figure C-7 below. This figure demonstrates the time lag between technology improvements and price reductions, and these improvements being directly experienced by adopters. As can be seen in this figure, the model makes the simplifying assumption that CFLs are directly comparable to and substitutable for incandescent bulbs (this is a significant assumption). As parity between the two technologies is assumed where the y-value = 1, the model is calibrated to have new CFLs equivalent to incandescent bulbs in mid-2001, and on average adopters perceive this to be the case by approximately 2004. These dates represent a significant point of reference for the market as a whole, and were determined in the calibration process and based on a general understanding of the state of the technology over time. Again, the dynamics represented in this figure are approximate and not well understood for this market.

Figure C-7: Modeled average satisfaction with the technology among all adopters (green) lags modeled satisfaction with the current (state-of-the-art) technology and price (blue); 1 = satisfaction equivalent to that generated by an



incandescent purchase. In the model, y-axis values are converted to log10.: satisfaction of 10 is equivalent to a dissatisfaction of 0.1. Selected Variables4

2

01997 1999 2001 2003 2005 2007 2009 2011

Time (Year)

Satis

units

/Hou

seho

ld

Avg Satisfaction from HH Experience with Current Tech and Market : 1 Segment CAL-no rndAvg Satisfaction from HH Experience with Current Tech and Market : 1 Segment CALAvg Satisfaction of Ever Adopter HHs : 1 Segment CAL-no rndAvg Satisfaction of Ever Adopter HHs : 1 Segment CAL

CFL Model Calibration

Model calibration required a significant set of comparisons between model behaviors and data on the actual market. Each of these comparisons is presented below for the 1-Segment model comparison. I performed an additional calibration for the two 2-segment models. These calibrations followed the same process and show very similar results, so I didn’t present them are in this appendix.

CFL and Incandescent Bulb Life/BurnoutAs described in the main text of the report, Sections 1.4.4 and 2.1, I calibrated the bulb life to match the distributions presented in the ACE 2008 model documentation (NEEA 2009). A third order material delay structure was used to create the life curves presented in figures C-8 and C-8 below for incandescent bulbs and C-10 and C-11 for CFL bulbs. As explained in Section 2.1 of the main report, significant changes to the median incandescent bulb life were then necessary to generate incandescent bulb sales results meeting the numbers presented in the ACE 2008 model.

Figure C-8: CFL burnout distribution from ACE model 2008 documentation (NEEA 2009).

0%

20%

40%

60%

80%

100%

0 5 10 15Year

Retir

emen

t Rat

e

Raw DataManual Fit

Figure C-9: CFL burnout distribution utilized in model

CFL Burnout Distribution100

75

50

25

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time (Year)Total CFLs Burned out : Current

Figure C-10: Incandescent burnout distribution from ACE model 2008 documentation (NEEA 2009)

Figure C-11: Incandescent burnout distribution based on 1.14 year median life as indicated in ACE model 2008 documentation (NEEA 2009)—not utilized in



0%

20%

40%

60%

80%

100%

0 5 10 15Year

Retir

emen

t Rat

e

Raw Data

Manual Fit

model.Incandescent Burnout Distribution

100

75

50

25

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time (Year)Total INCs Burned Out : Current

As indicated in Section 2.1, the incandescent burnout distribution presented in figure C-11 was not utilized, as a 2.22 year median incandescent bulb was utilized instead based on calibration to total incandescent bulb sales. The resulting burnout distribution has the same form, just an extended time frame.

Calibration of Ever Adopter and Awareness RatesKEMA’s market research report (2010) provides data points for rates of ever adoption (households ever having purchased a CFL) and awareness (households aware of CFLs). I assumed the difference between these to be the set of households aware of CFLs but having never purchased one. The report gives values for the portion of the total population falling into these categories at a few points in time, primarily 2004, 2005, 2006, and 2010. As detailed in the main report, Section 2.2, I felt this data from 2004 to be inaccurate or otherwise not representative of the market and eliminated it from the calibration curves. Instead, I used additional data from Sandahl et al (2006) to inform certain data points in 2001-2003. Based on these available data points, un-awareness and ever adoption curves were generated representing modeling time frame by interpretation, extrapolation, and some creative guessing over the effect of the 2000-2001 Western electricity crisis. I used these curves to calibrate the modeled adoption behaviors. The 1 segment model behaviors compared to these curves are presented in Figures C-12 and C-13.

Figure C-12: Fraction of households unaware of CFLs—Calibration curve (blue) and modeled (red)Fraction of HHs Unaware CALIBRATION

1

0.5

01997 2000 2003 2006 2009 2012

Time (Year)

fract

iona

l

Fraction of HHs Unaware Calibration Reference : 1 Segment CAL-no rndFraction of HHs Unaware : 1 Segment CAL-no rnd

Figure C-13: Fraction of households ever having purchased CFLs—Calibration curve (blue) and modeled (red)Fraction of HHs Ever Adopters CALIBRATION

1

0.5

01997 2000 2003 2006 2009 2012

Time (Year)

fract

iona

l

Fraction of HHs Ever Adopters Calibration Reference : 1 Segment CAL-no rndFraction of All HHs are Ever Adopters : 1 Segment CAL-no rnd



These curves show a decently close calibration. There is a small point of departure in approximately the 2006-2007 time frame. I don’t know whether this deviation in between the model behavior and market data is significant.

Socket Penetration CalibrationKEMA’s 2010 market research provides socket saturation estimates based on reported household results for several years. I used these values as calibration points for the model. As can be seen in Figure C-14 below, these values do not perfectly equate, but 2004-2006 data is similar (within a 1-year time frame). I expect data in 2010 to deviate, as the modeled adoption curve does not include the drop-off of sales (and adoption) in 2009.

Figure C-14: Socket penetration calibration; modeled values compared to available KEMA 2010 reported values

Time (Year) Modeled KEMA 20101997 0.001998 0.001999 0.002000 0.002001 0.012002 0.032003 0.042004 0.05 0.072005 0.07 0.102006 0.09 0.112007 0.122008 0.172009 0.232010 0.29 0.23

Distribution of CFL Adoption Across Households Another important point of calibration used in the model was the distribution of households by level of adoption. This distribution was not available for the Northwest in the current data, for example, KEMA’s market research (2010); however, if I could get the raw data I could calculate these in the future from KEMA’s raw data. Therefore, I used national distribution data from the Energy Star CFL Market Profile (USDOE 2009) as a calibration point for the model (Figure C-15). I assumed that the 2008 data available at the national level was approximately applicable to 2006 proportions in the Northwest. This assumption was based on empirical results (it was necessary for adoption levels in the Northwest to reach certain levels to generate the known total CFL sales), and based on the understanding that the Northwest is significantly ahead of national averages in terms of socket penetration.

Additionally, the simplified adoption stages that I chose for the model (non-adopter, trial adopter, limited adopter, and full adopter) do not directly correlate with the binning used for the national data; therefore, I updated the calculation to re-bin the data into the model bins. Some interpretation/estimation was necessary for this re-binning, and the original data would be necessary to generate an accurate binning. This estimated distribution, along with the distribution generated by the model for 2006, is presented in Figure C-16. As can be seen, these results are approximately similar, but not completely representative. As the source distribution is relatively



interpretive in nature, it was felt that additional model calibration to bring these distributions closer would not enhance the ability of the model to represent actual NW CFL market adoption.

Figure C-15: Estimated national distribution of CFL ownership in households, 2008 (graphic from USDOE ENERGY STAR, 2009)

Figure C-16: Estimated NW distribution of CFL ownership in households, 2006—interpreted from 2008 national data (blue), vs. modeled (red)

Estimated NW Distribution of Household CFL Ownership (2006)

0%5%

10%15%20%25%30%35%40%45%50%

0 2.5 9.5 35Average CFLs Installed Estimated 2006 Market

Calibrated Model Result

CFL Adoption Decision Complexity

This effect is described in Section 4.2 of the main report. Of additional interest, beyond what was previously described, is the calculations and assumptions used to generate the curve representing the increasing complexity of the CFL adoption decision over time (Figure 25 in the main report). The model generated this trend based on an analysis of the following factors:

The advent of initial LED models and strong hype around the future potential of this technology

The advent of new incandescent technologies which have a longer life and higher efficacy/efficiency

A proliferation of CFL models, brands, and manufacturers (see KEMA 2010) Penetration of the market beyond 60W MSBL models to provide CFLs for various

specialty applicationsAs ‘decision complexity’ refers to the number of available choices (for example styles, brands, and underlying technologies of light bulbs suitable for a particular socket) and the level of knowledge/information necessary to make a good choice, the number of choices available was the primary factor which informed this analysis. The total number of bulb models was used as a proxy for this, as calculated from KEMA 2010. Additionally, the decision between basic bulb technologies (incandescent, CFL, LED, and high-efficacy incandescent) was modeled as an additional factor, with the decision between 4 technologies being modeled as 1 bit more complex than the decision between two technologies. These calculations are shown in Figure C-17 below.



Figure C-17: CFL purchase decision complexity calculations (some data from KEMA 2010, other data calculated or extrapolated)Description 2005 2006 2008 2009# CFL Models 8 10 18 22CFLs as a % of Total Lighting Shelf Space 0.14 0.16 0.22 0.24% of Total Lighting Shelf Space 0.4 0.4 0.4 0.4Calculated total # bulb models, all technologies 22.9 25.0 32.7 36.7Complexity (assuming half all bulb models are relevant to decision (bits) 3.5 3.6 4.0 4.2

# Viable Technologies (Incandescent, CFLs, etc.) 2.0 2.0 3.0 4.0Complexity added by decision between technology types (bits) 1.0 1.0 1.6 2.0

Total Decision Complexity (bits) 4.5 4.6 5.6 6.2


NEEA Report: CFL System Dynamics Model Development Report-CFL... · Web viewWord of Mouth (WOM):...

Documents

Transcript of NEEA Report: CFL System Dynamics Model Development Report-CFL... · Web viewWord of Mouth (WOM):...