When do energy-efficient appliances generate energy savings? Some evidence from Canada

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Energy Policy 36 (2008) 34–46 www.elsevier.com/locate/enpol

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When do energy-efficient appliances generate energy savings? Some evidence from Canada Denise Young Department of Economics, University of Alberta, Edmonton, AB, Canada T6G 2H4 Received 28 June 2007; accepted 10 September 2007 Available online 22 October 2007

Abstract Improvements in the energy efficiency of household appliances have the potential to decrease residential energy use, but these reductions accrue gradually over time as newer appliances replace older models. SHEU-2003 data are used to examine appliance replacement patterns in Canada for refrigerators, freezers, dishwashers, clothes washers and clothes dryers. The data indicate that the ages at which appliances are replaced tend to be lowest for dishwashers and highest for freezers, with over 40% of freezers in use for more than 20 years before being retired. The life spans of Canadian appliances are compared to the underlying assumptions regarding appliance lifetimes used in models of residential energy demand. We find that Canadian appliance retirement patterns differ from those assumed in the previous literature. Socioeconomic factors related to appliance replacement are also examined. We find that replacement patterns can be sensitive to household characteristics such as income, providing evidence that there may be scope for targeted policies aimed at inducing earlier replacements of older household appliances with new energy-efficient models. r 2007 Elsevier Ltd. All rights reserved. Keywords: Energy efficiency; Appliances; Hazard models

1. Introduction The energy efficiency of major household appliances has improved significantly in recent years. These advances in energy efficiency are due in part to regulatory efforts that have led to the imposition of standards for household appliances and in part to improvements in technology that would have arisen in the absence of such standards (Kim et al., 2006; Koomey et al., 1999; OEE, 2005). The extent to which these improvements in energy efficiency will have an impact on overall energy demand, and subsequently on the environment, depends on how quickly the newer and more efficient models replace older models in household use. In other words, the rates at which households replace various appliances have important implications for the realization of household energy demand savings in response to technological improvements. The Survey of Household Energy Use (SHEU)-2003, conducted by Statistics Canada on behalf of Natural Tel.: +1 780 492 7626; fax: +1 780 492 3300.

E-mail address: [email protected] 0301-4215/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2007.09.011

Resources Canada’s Office of Energy Efficiency (OEE), provides the type of data needed to empirically examine the rate at which newer appliances enter into household use in Canada (OEE, 2006a). The survey offers an overview of Canadian residential energy use and contains several questions pertaining to the current and replacement ages for five major household appliances: refrigerators, freezers, dishwashers, clothes washers and dryers. This information allows us to examine the replacement patterns for a variety of major household appliances in Canadian households. In particular, the appliance age data from the survey make it possible to gauge whether or not the (often linear) retirement and survival curves that have been posited for the purpose of forecasting appliance replacement rates in previous studies (Hwang et al., 1994; Interlaboratory Working Group (IWG), 2000; Koomey et al., 1999; Lu, 2006; Meyers et al., 2003, 2005) are appropriate for use in Canadian models of household energy demand. Furthermore, by combining the appliance replacement information with household demographic and socioeconomic data available in the survey, we are able to examine which factors, if any, are related to the timing of appliance

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replacements. This information is useful for the design of appropriate policy strategies that can be implemented in order to induce households to modify the rates at which they replace their older appliances with newer models that embody energy-efficient technologies. In the next section, we examine the recent literature with a focus on assumptions that have been made regarding appliance lifetimes and retirement rates and on socioeconomic factors that impact the uptake of energy-efficient appliances by households. The pertinent data from the SHEU-03 survey of Canadian households are described in Section 3, including a presentation of summary statistics for appliance retirement ages, survival rates and empirical survival curves. In Section 4, we examine the impacts of socioeconomic factors on appliance replacement rates. Section 5 provides a discussion of the policy implications of our results. Section 6 concludes. 2. Literature review In a bid to curb residential energy consumption, many countries have imposed energy efficiency standards on newly manufactured household appliances (Koomey et al., 1999; Lu, 2006; OEE, 2005). As a result, major gains have been realized in terms of the energy usage characteristics of appliances that are currently being sold on the market. For example, over an approximately two decade period from 1980 to 2002, the average efficiency of a 20–22 ft3 ‘‘freezer on top’’ model refrigerator increased by 150% in the US (Kim et al., 2006). Measurements from Canada indicate that most major household appliances, with the exception of electric ranges, have seen dramatic improvements in energy efficiency over the 1990–2003 period (OEE, 2005). The impacts of these technological improvements on energy-use and the environment as new appliances move into household use have been the subject of a substantial body of literature. For the purposes of this study, two strands of this literature are especially relevant. One deals with the estimation of appliance lifetimes and the incorporation of appliance lifetime information into the modeling of aggregate energy savings from the increased energy efficiency of household appliances. The other involves the socioeconomic factors that influence the rate at which households decide to replace appliances with more energyefficient models. 2.1. Appliance lifetimes The impacts of energy efficiency standards for household appliances on energy demand and on the environment have been considered in several detailed studies (Brown et al., 2001; Geller et al., 1998; IWG, 2000; Koomey et al., 1999, 2001; Meyers et al., 2003, 2005). Assumptions regarding appliance lifetimes enter into these models in two ways. Appliance lifetime assumptions are used to determine the horizon over which the life-cycle costs (LCC), cost of

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conserved energy (CCE), and/or annualized net dollar savings (ANS) corresponding to newer appliances that embody energy efficiency standards are calculated. In combination with a selected discount rate and forecasted fuel prices, the appliance lifetime is used in the calculation of the discounted sum of cost savings that can be expected from the purchase and installation of a new technology. These calculations, which are used to determine whether or not it makes economic sense for a given new technology to be introduced into the market, are then factored into the modeling process. Details regarding LCC calculations can be found in Chapter 7 of USDOE (2000), while information on CCE and ANS calculations can be found in IWG (2000) and Koomey et al. (1999), among others. Appliance lifetime information is also crucial for the construction of retirement curves. These are used to model the turnover of appliances, whereby households eventually discard older, less-efficient models and replace them with new models that meet energy-efficient standards. This information, in conjunction with information on expected penetration rates and numbers of new households, is used to determine the expected numbers of appliances of various vintages in place at any given point of time. When this is combined with information on household appliance energy use by vintage, energy savings from the introduction of new energy-efficient technologies can be calculated. Retirement functions used in most US studies are based on information provided by the US Department of Energy (USDOE) in a series of Technical Support Documents (TSDs) (USDOE, 1995, 2000). The USDOE uses a variety of information sources and modeling techniques to determine average appliance lifetimes. For refrigerators and freezers, historical shipments reported by industry trade associations are analyzed via a Residential Energy Demand Model in order to calculate an expected average lifetime (USDOE, 1995). For clothes washers, a much more complex modeling approach is used. The market for clothes washers is divided into four segments, including existing houses wherein the clothes washer may or may not require repairs in any given year. If repairs are required, the household may decide to either replace or repair (and thereby extend the life of a clothes washer by 6 years). The models used are calibrated based on industry shipment data, market information from the Association of Home Appliance Manufacturers (AHAM) and Clothes Washer Consumer Analysis (USDOE, 2000). The USDOE is currently examining whether or not the appliance lifetime results for a dishwashers (and a few other appliances not considered in our study) are still valid (USDOE, 2006). Table 1 provides estimated average lifetimes for the five appliances considered in our study. Included in Table 1 are both the values used in IWG (2000) that are based on USDOE TSDs and the average lifetimes used by the Canadian Appliance Manufacturer Association (CAMA) in the context of a study on ‘‘White Goods’’ disposal. Except for the case of refrigerators, the latter figures are based on the length of first ownership from the ‘‘26th

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1.2

Table 1 Estimated average appliance lifetimes

1

USDOE (years)

CAMA

Refrigerator Freezer Dishwasher Clothes washer Dryer

19 19 13 14 17a

16 11 8 12 13

Source: USDOE ages are from IWG (2000), Appendix C1; CAMA ages are from CAMA (2005). a This age is not from an USDOE TSD. For details see IWG (2000).

0.8 Survival

Appliance

0.6 0.4 0.2 0 -0.2

0

5

10

15

20 Age

25

30

35

40

Fig. 1. Linear survival curve for average appliance age of 21.

Annual Portrait of the US Appliance Industry’’ (CAMA, 2005). To the extent that appliances move into the used market after first-use, the CAMA figures will underestimate lifetimes. The information on average lifetimes, regardless of the source, must be combined with further assumptions in order to generate retirement curves for the individual appliances. It is generally assumed that old appliances are discarded when a new model is purchased.1 A further simplifying assumption of linearity is usually imposed (Brown et al., 2001; Hwang et al., 1994; IWG, 2000; Koomey et al., 1999; Lu, 2006). For example, IWG (2000) posit that all appliances are retired somewhere between 2/3 and 4/3 of the average life span. In other words, it is assumed that no appliance is retired before 2/3 of the average lifespan for that category of appliance and all appliances are retired by 4/3 of the average lifespan. This type of assumed retirement behavior can also be expressed in terms of ‘survival’ rates, whereby all appliances survive until they reach 2/3 of the average lifespan and no appliances survive beyond 4/3 of the average lifespan, with a linear function capturing the retirement rates in between these two extremes. Fig. 1 illustrates this type of survival curve for an appliance whose average lifespan is 21 years. These linear retirement or survival curves can be specified in terms of an average appliance life in conjunction with a set of parameters that determine the minimum and maximum life span, or the minimum and maximum life spans can be specified directly. While Koomey et al. (1999), IWG (2000) and Lu (2006), for example, select the former approach, Hwang et al. (1994) opt for the latter. Although no explicit retirement or survival equation is used in their models per se, Kim et al. (2006) allow for a maximum life span of 20 years for a refrigerator. Truttmann and Rechberger (2006) choose a simpler specification whereby all appliances are used over a fixed lifespan and retired at the end of either 10 or 15 years, depending on the scenario under consideration. 1 Not all ‘replaced’ appliances have necessarily been retired from use. CAMA (2005) estimate that as many of 10% of discarded appliances enter the re-sale market. Furthermore, a significant proportion of households in the SHEU-2003 data set retain an older model refrigerator for secondary use.

Examples of the use of non-linear retirement functions can also be found in the literature. In Meyers et al. (2003) and Meyers et al. (2005), appliances are assumed to have useful lifetimes of 10–20 years and retirement functions are constructed under the assumption that lifetimes are normally distributed around their means, with standard deviations selected such that almost all units retire within a few years of the average. Whether or not these (typically) linear specifications, based on appliance lifetime estimates from industry data and demand modeling, adequately capture actual household behavior is an empirical question. One of the ways in which it can be addressed is in the context of duration models based on household-level data. Duration models have been applied to the US Department of Energy’s 1990 Residential Energy Consumption Survey (RECS) data, in an attempt to look at the factors that determine replacement of electric space-heating equipment and central airconditioners. The Weibull specifications used by Fernandez (2001) match up reasonably well with average industry predictions of average life spans for these appliances, but the study was somewhat hampered by the fact that only information on appliances currently in-use were available in the survey. A major advantage of the SHEU-2003 survey is that it asks respondents about both the ages of appliances currently in use and, if there have been replacements of any major appliances, the ages of previous appliances when they were replaced. These data allow us to have a more thorough examination of duration models for a variety of major household appliances in Section 4. 2.2. Socioeconomic factors influencing appliance replacement decisions Although newer appliances tend to be less costly to operate since they use less energy,2 there are many factors that enter into the decision to replace an older less efficient 2 To the extent that households opt to replace smaller-capacity appliances with larger-capacity models with added energy-using features or use energy-efficient appliances more intensively (rebound effect), the savings from the retirement of older models may be limited.

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model. For a typical household, any gains in performance and energy efficiency must be weighed against the time and monetary costs involved in ‘retiring’ the old appliance and purchasing and installing a new model in the home. In many jurisdictions, the time costs of searching for a more energy-efficient appliance has been reduced through government initiatives. Initiatives such as the ‘‘EnerGuides’’ labeling system and ‘‘Energy Stars’’ designations make it easy for consumers to quickly identify which models perform best in terms of energy consumption. See, for example, OEE (2006b). The monetary gains from switching to a more energyefficient appliance will depend on (expected) future energy prices. With the exception of natural gas clothes dryers and water-heating technologies, the most important energy price to be considered is that for electricity. Energy price considerations are expected to be most important in the case where the purchaser and the user of the appliance are the same agent. In the case of appliances purchased by landlords for the use of tenants who pay their own utilities, the purchase price will tend to have a stronger influence than electricity prices. Disposable income is also expected to influence appliance purchase decisions, with higher income households more likely to make an ‘early’ replacement, possibly purchasing a more luxurious model with more (energy-using) features (IWG, 2000; Meyers et al., 2003, Schipper and Hawk, 1991). While the ‘use’ stage of an appliance’s life cycle will account for the majority of the energy consumed, the bulk of the related household expenses occur with the purchase price of the appliance. It has been estimated that an optimal life cycle for a refrigerator, from an energy-use or global-warming perspective, might lead to a household buying and retiring six or seven models over the course of 35 years. The optimal life cycle based on private household cost objectives would have the same household buying and retiring only two models over that time period (Kim et al., 2006). In the next two sections we will examine the lifetimes and retirement patterns for major household appliances in Canada, as well as the socioeconomic factors that influence the timing of appliance replacement. This will be followed by a discussion of the policy implications of these results. 3. Appliance replacement in Canada: summary statistics and empirical survival curves from the SHEU-2003 data 3.1. Summary statistics The SHEU-2003 was conducted by Statistics Canada in 2004 on behalf of Natural Resource Canada’s OEE (OEE, 2006a). Households from across the country (excluding the northern territories) were surveyed about several facets of their energy and appliance use over the 2003 calendar year. Of particular interest for this study are the questions related to appliance replacement activities and respondent demographics.

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Information on retirement ages for appliances was gathered for refrigerators, freezers, dishwashers, clothes washers and dryers. Respondents were asked for the approximate ages at which various appliances were replaced and whether or not an appliance was still working when it was replaced. For households who had replaced an appliance, the possible categories that a respondent could choose from for the age of the appliance at replacement were: (1) (2) (3) (4) (5) (6) (7) (8)

3 years or less; 4–5 years; 6–10 years; 11–15 years; 16–20 years; 21–25 years; 26 years or more; don’t know/refuse.

For all appliances except refrigerators, all respondents indicated that the old appliance had failed. In order to produce retirement or survival statistics that are comparable to the assumptions used in the models discussed in Section 2, we only report replacement ages for refrigerators that were replaced due to failure. From Table 2 we see that, for each of the five appliances, there have been retirements in all seven of the age groups. For all appliances, the most common age at replacement is either 11–15 years (dishwashers, dryers) or 16–20 years (refrigerators, freezers, clothes washers). These ‘most common’ replacement ages match up quite well with the ‘USDOE’ figures presented in Table 1 for three appliances: refrigerators, freezers and dishwashers. The ‘most common’ replacement age for clothes washers is at least 2 years higher than the USDOE figure, while for clothes dryers it is at least 2 years lower. With the exception of dryers, the ‘most common’ replacement ages exceed the average ‘‘life expectancies’’ cited in CAMA (2005). The pattern of retirement ages varies across appliances. Dishwashers constitute the appliance that is most likely to be replaced within 5 years of purchase. Dishwashers also constitute the least likely appliance to survive beyond 20 years in household use. Clothes washers and dryers exhibit similar patterns to each other in terms of retirement ages, with the bulk of these appliances being replaced after 11–20 years of use. The appliances most likely to survive beyond 20 years of use are refrigerators and freezers. Over 30% of all refrigerators that were replaced due to failure were used for over 20 years before a newer model was purchased. For freezers, over 40% survived for more than 20 years, with over 20% surviving more than 25 years. These results match up reasonably well with some, but not all, of the assumptions in Hwang et al. (1994) regarding minimum and maximum replacement ages on which they base their appliance retirement functions for the US. They assign the lowest minimum (0 years) and maximum (25 years) ages to dishwashers and the highest minimum (11 years) and maximum (31 years) ages to freezers. Their

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38 Table 2 Ages of other appliances at replacement in Canada Approximate age

Refrigerators

Freezers

Numbera

Percentb

3 years or less 4–5 years 6–10 years 11–15 years 16–20 years 21–25 years 26 years or more

38 23 86 203 249 154 131

4.3 6.9 16.6 39.6 67.8 85.2 100.0

Total

884

Number 8 13 31 39 78 53 59

Dishwashers Percent 2.8 7.5 18.5 32.4 60.1 79.0 100.0

281

Number 22 19 74 95 75 31 10

Percent 6.7 12.6 35.3 64.4 87.4 96.9 100.0

326

Clothes washers

Dryers

Number

Percent

Number

Percent

3.6 6.9 21.2 48.6 77.7 90.9 100.0

36 26 86 221 209 125 87

4.6 7.8 18.7 46.7 73.2 89.0 100.0

45 41 178 341 362 164 113 1244

790

Source: based on data from SHEU-2003. a Number ¼ number of replacements in age group. b Percent ¼ cumulative percentage of appliances retired in this age range or earlier.

assumptions regarding the relative minimum retirement ages for clothes washers and dryers (2 years and 9 years, respectively) and maximum retirement ages for these two appliances (25 years for washers, 30 for dryers) are somewhat at odds with the Canadian data, which show retirement age distributions for washers and dryers which are very similar to each other. While these assumptions may or may not be valid for US households, their use in a Canadian model could lead to earlier predicted energy gains from the introduction of some more energy-efficient appliances, such as clothes washers, than would be supported by the SHEU-2003 data. This will be discussed further in Section 5.

Table 3 Life Table for Canadian refrigerators (replacements due to failure)a,b Approximate age (tj)

Nj

Cj

Rj

Xj

Sj

3 years or less (t1) 4–5 years (t2) 6–10 years (t3) 11–15 years (t4) 16–20 years (t5) 21–25 years (t6) 26 years or more (t7)

1864 1710 1609 1316 924 510 261

116 78 207 189 165 95 130

1806 1671 1505 1221 841 462 196

38 23 86 203 249 154 131

1.0000 0.9790 0.9655 0.9103 0.7590 0.5344 0.3565

(0.000) (0.003) (0.004) (0.007) (0.011) (0.014) (0.015)

Source: based on data from SHEU-2003. a Values in parentheses are standard errors. b N=Entered; C=Censored; R=At Risk; X=Exited; S=Survival.

3.2. Life Tables and empirical survival curves The summary statistics presented in Table 2 provide some basic information on the ages of appliances that were replaced. They do not incorporate any of the available information from SHEU-2003 on the ages of appliances that were still in use. The information on the age of appliances at replacement can be combined with survey information on the current ages (at the time of the survey) of appliances for households that had not yet replaced their older models. The combined data can be used to compute ‘‘Life Tables’’ and empirical survival and (Kaplan–Meier) hazard curves (Greene, 2002). Details regarding the construction of the Life Tables and Survival curves can be found in Appendix A. 3.3. Discussion of Life Table results Our Life Table information is presented in Tables 3 and 4. In order to understand the various components of the Life Table, consider the case of refrigerators, which is presented in Table 3. We have data on the ages of refrigerators for 1864 households who are either still using their first refrigerator or have replaced a refrigerator due to failure. Of these households, 884 had replaced their

original refrigerators before the survey year and 980 were still using their ‘original’ refrigerators. Of the 1864 appliances, 38 were replaced at an age of ‘3 years or less’ and 116 of the ‘original’ refrigerators were in the same ‘3 years or less’ age category. By definition, the survival proportion for the first age category is 1 (since all refrigerators survive for at least 0–3 years). This survival proportion, which is depicted in Fig. 2, falls to 0.9790 (or 97.9%) for ages of 4 or 5 years, and remains at over 0.90 (or 90%) until we reach ages of 16–20 years or older where the survival proportion is 0.7590 (or 75.9%). By the time we reach the largest age category, the survival rate for refrigerators has dropped to a little over 0.35. Figs. 2–6 illustrate the empirical survival curves (based on the values in Table 5), along with the linear survival curves used in IWG (2000) and Hwang et al. (1994).3 Freezers exhibit the highest longevity of all of the household appliances considered. The empirical survival rate 3 The SHEU-2003 survival curves are plots of the values from the Life Tables. The ‘‘Hwang et al.’’ curves are based on the age ranges reported in Hwang et al. (1994). These are: refrigerators (7–29 years), freezers (11–31 years), dishwashers (0–25 years), clothes washers (2–25 years) and dryers (9–30 years). The ‘‘IWG’’ survival curves are based on the average ages reported in IWG (2000), which are presented in Table 1.

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Table 4 Empirical survivals for other appliances in Canadaa Approximate age (tj) 3 years or less (t1) 4–5 years (t2) 6–10 years (t3) 11–15 years (t4) 16–20 years (t5) 21–25 years (t6) 26 years or more (t7) N Exits

Freezers 1.000 (0.000) 0.9961 (0.001) 0.9893 (0.002) 0.9706 (0.004) 0.9398 (0.006) 0.8524 (0.011) 0.7616 (0.015) 2157 281

Dishwashers

Clothes washers

1.000 (0.000) 0.9801 (0.004) 0.9604 (0.006) 0.8664 (0.012) 0.6944 (0.018) 0.4783 (0.024) 0.3126 (0.029) 1192 326

1.000 (0.000) 0.9785 (0.003) 0.9576 (0.004) 0.8557 (0.008) 0.6259 (0.012) 0.3382 (0.013) 0.1783 (0.011) 1244 932

Clothes dryers 1.000 (0.000) 0.9807 (0.003) 0.9656 (0.004) 0.9089 (0.007) 0.7295 (0.012) 0.5104 (0.015) 0.3281 (0.016) 1940 790

Source: based on data from SHEU-2003. a Values in parentheses are standard errors.

Fig. 5. Empirical and linear survival curves for clothes washers. Fig. 2. Empirical and linear survival curves for refrigerators.

Fig. 6. Empirical and linear survival curves for dryers.

Fig. 3. Empirical and linear survival curves for freezers.

Fig. 4. Empirical and linear survival curves for dishwashers.

does not drop below 0.95 until we reach the 16 to 20 year age range and falls only to 0.76 by the time we reach the 26 years or more category. For the remaining appliances (dishwashers, clothes washers and dryers), survival rates are greater than 0.95 for age ranges up to 10 years, and fall steadily over the older age ranges. Survival rates for clothes washers drop more quickly than those for dishwashers and dryers. For dryers and even for dishwashers, the survival rate is still above 0.3 by the time we reach the 26 year or more category. Overall, these graphs indicate that the linear survival curves used in previous energy demand modeling exercises may be reasonably accurate for the lower age ranges. However, the linear survival curves used in the US studies could overestimate the replacement rates of appliances in

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Table 5 Tests for life table differences across stratified samples (p-values) Refrigerators

Freezers

Dishwashers

Clothes washers

Dryers

0.190 0.331

0.105 0.078

0.398 0.381

0.056 0.000

0.634 0.328

Stratified by household size Log-rank 0.647 Wilcoxon 0.553

0.527 0.504

0.505 0.218

0.001 0.002

0.015 0.000

Stratified by incomea Log-rank Wilcoxon

0.008 0.078

0.003 0.039

0.001 0.000

0.012 0.000

Stratified by region Log-rank Wilcoxon

0.872 0.645

Source: based on data from SHEU-2003. a Based on fewer observations due to missing values for income.  Significant at 10%.  Significant at 5%.  Significant at 1%.

the older age ranges if applied to Canada. These results will be discussed further in Section 5. 4. Socioeconomic influences on appliance replacement rates in Canada

the extent to which retirement ages are affected by household characteristics. Another way to look at these impacts is through a parametric specification of appliance survival rates. 4.2. Parametric duration (survival) analysis

4.1. Stratified Life Table tests A further examination of the Life Table statistics can provide some basic information regarding whether or not socioeconomic factors influence the ages at which households replace appliances. For each appliance, we test for differences across stratifications of the Life Table data. Details of the testing procedure can be found in Greene (2002). Our stratifications are constructed according to (i) household size; (ii) household location and (iii) household income. Based primarily on the categories available in the data set, we divide household size into 4 categories (1–2 persons, 3–4 persons, 5–6 persons, 7 or more persons), location into five regions (Atlantic, Quebec, Ontario, Prairies, British Columbia) and annual income into five categories (‘less than $20,000’, ‘$20,000–$39,999’, ‘$40,000–$59,999’, ‘$60,000–$79,999’, and ‘$80,000 or more’). The results of the tests are presented in Table 5. According to these tests, the retirement ages for all appliances except refrigerators appear to be sensitive to income. For clothes washers and dryers retirement ages also vary across household size. Sensitivity of retirement ages to location appears to apply only to clothes washers, and possibly freezers. Although these homogeneity tests provide some preliminary information about whether or not a household’s socioeconomic situation may influence the turnover of appliances, they do not control for other factors when testing for the impact of any given socioeconomic characteristic and they do not allow for quantification of

Standard statistical ‘duration model’ techniques allow us to model appliance ‘survival’ in terms of a broad set of household characteristics that might be expected to impact the decision to retire an appliance. The specific set of socioeconomic variables included in our analysis is based on a consideration of factors discussed in the literature review, while taking into account data availability. For interested readers, a brief overview of the technical details of the parametric analysis and detailed tables of results can be found in Appendix A. The factors that we include in our model can be grouped into three broad categories. We attempt to control for location-specific factors through the inclusion of provincial dummies (the excluded province is British Columbia). One of the location-specific factors that we would ideally like to control for is the price of energy. Unfortunately, this is not available in the data set. Furthermore, given that ‘retired’ appliances were replaced at various points in time, those decisions were not necessarily based on prices in place at the time of the survey. To the extent that the relevant prices vary systematically across provinces, price effects may be partially picked up through the provincial dummies. We control for income/lifestyle with a set of annual income category dummies (the excluded category is under $20,000), a set of dwelling-type dummies (the excluded type is single detached), a dummy for whether or not the dwelling is owner-occupied, and an urban/rural dummy (urban ¼ 1). We also control for the demand for appliance services in the household through the inclusion of household size, the number of children in the household, and a

ARTICLE IN PRESS D. Young / Energy Policy 36 (2008) 34–46 Table 6 Summary of results for parametric survival models Appliance

Significant covariates

Refrigerator Owner-occupied (+) Freezer Mobile home (), urban (+), household size (), no. of children (+) Dishwasher Owner-occupied (), household size () Clothes Income (), urban (+), household size (), no. of children washer (+) Dryer At-home (+), household size () Source: Appendix A.

dummy for whether or not there is usually somebody at home during the day. Table 6 contains a summary of the statistically significant (at 10% or less) variables (excluding provincial dummies) and the sign of their impacts on the age of the appliance at replacement. Once we control for other characteristics (which is not the case in our Life Table stratification tests), the only appliance whose survival is sensitive to income is clothes washers. For most income categories, the absolute size of the coefficient (and therefore the effect) increases with the income category. This indicates that the tendency for higher-income households to replace clothes washers earlier increases with the level of income. The one exception is the highest income level category. It is interesting to note that, for the case of clothes washer replacements, in all cases the survey respondent indicated that the appliance was replaced due to ‘failure’. The evidence that replacements ‘due to failure’ are incomesensitive may reflect the fact that appliance ‘failure’ can be a subjective evaluation that takes into account, among other things, the cost of repair to correct the ‘failure’. Households with lower incomes may be willing to pay a repair bill that requires a smaller cash outlay than would be needed for a replacement purchase. Those with higher incomes may simply elect to acquire a new appliance. The fact that this phenomenon is not observed for all appliances may be related to the costs and feasibility of repairs, the price of the appliance, and the expected postrepair lifetime of the appliance. These are all factors that are likely to vary across appliance type. With the exception of refrigerators, the larger the household, the lower is the expected age at replacement for major household appliances. This result is of greater statistical significance (5% vs. 10%) for clothes washers and dryers, two appliances that tend to be used more intensively by larger households. For clothes washers, this effect is partially offset if the household composition includes more children. In households where somebody is generally home during the day, dryers tend to be replaced at older ages, possibly due to an increased tendency to use other, more time intensive, methods to dry clothes in these households.

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Overall, the results indicate that the household characteristics that affect the decision to replace an appliance vary across the five appliances considered in our study. For one appliance (clothes washers) replacement behavior is income-sensitive. The most common factor influencing appliance replacement decisions appears to be demographics, as larger households tend (or need) to replace appliances at more frequent intervals than smaller ones. Policy implications of our results will be discussed in Section 5. 5. Policy implications Our empirical results, presented in the previous two sections, provide information on the location and shape of appliance survival curves as well as on the socioeconomic factors that impact the timing of appliance replacement. Especially at the older age ranges, the empirical survival curves derived from Canadian household data differ from those used in previous studies in the US. That is, many appliances remain in household use far beyond the oldest assumed age at which an appliance might retire in these studies. This impacts the analysis of energy savings from improved technologies in two ways. Firstly, if households form their expectations of future appliance lifetimes on the performance of previous appliances, they will make any assessment of the benefits of purchasing an energy-efficient appliance based on a longer stream of benefits (i.e., energy savings) than currently assumed. These longer lifetimes for appliances, if incorporated into residential energy modeling exercises, will affect any LCC or CCE calculations in a positive way, as the impacts of lower energy usage will be considered over a longer time horizon. Energy-efficient appliances will therefore be more likely to be successfully introduced into the market within these residential energy demand models. On the other hand, the tendency of households to hold onto their current appliances for longer periods of time than allowed for in previous studies means that, in Canada at least, many households continue to use older inefficient models beyond the dates assumed in these models. This leads to a delay in the introduction of the energy savings that result from retirements. The net effects of longer appliance lifetimes on estimated overall energy demand in models of the residential sector can only be determined by running the models with longer expected ages and with survival curves that correspond more closely to the empirical ones. Our parametric analysis of appliance survival indicates that there are identifiable factors that influence the age at which an appliance is replaced. While the age at replacement is not sensitive to income for most appliances, households with higher incomes tend to replace clothes washers at a lower age than low-income households. If it is indeed the case that lower income households are holding on to older clothes washers due to financial constraints

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related to the magnitude of cash outflows required to replace an appliance, this implies that there may be scope for policies that provide financial incentives for the replacement of their current clothes washers with more energy-efficient models, leading to lower residential energy use. The fact that clothes washers have income-sensitive retirement rates that are likely to be affected by financial incentives is especially promising from a policy perspective given a societal interest in generating energy savings in the residential sector. According to CAMA (2004), clothes washers constituted one of the top two household appliances (second only to refrigerators) in terms of total energy use in Canada in 2003 (the year of the SHEU survey data). CAMA figures indicate that if all households in Canada were to switch to the most efficient Energy Stars makes of clothes washers, the resulting savings would be about 800 MW. Similarly, in studies based on US data, standards on clothes washers have been found to be one of the biggest contributors to energy savings (Meyers et al., 2003). These findings from our parametric survival analysis provide some support for Canadian programs that are already in place.4 There are several programs that offer financial incentives for the replacement of major household appliances with Energy Stars appliances. And many of these programs specifically target clothes washers. Programs targeting clothes washers include rebate offers from Hydro-Que´bec, Manitoba Hydro, Newmarket Hydro, the City of Victoria, the City of Toronto, and the Yukon Government. The Yukon program is unique in that it offers rebates that range from $75 to $125 on the purchase of an Energy Stars clothes washer, with the higher rebate amount applying to households located in communities where diesel fuel is used to generate electricity. The latter program addresses the fact that while the energy savings due to the purchase of a new appliance are the same regardless of location, the environmental impact will depend on the way in which the electricity used by the appliance is generated. Although our data do not allow us to perform an overall cost–benefit analysis regarding these programs, it appears likely that current programs targeting clothes washers are aimed in the right direction. They are focused on one of the two appliances that use the most electricity and whose replacement is the most sensitive to income, and therefore likely to be sensitive to price incentives. The Yukon Government program is exceptionally promising given that it makes replacement especially attractive in areas where there are greater environmental benefits to be realized.

6. Conclusions The appliance replacement patterns for Canadian households differ somewhat from those assumed in most models of residential energy demand that examine the impacts of energy efficiency improvements on aggregate energy use. Our results indicate that the linear survival curves do not match up well with the Kaplan–Meier empirical survival curves from the SHEU-2003 data, especially for higher age ranges, with higher maximum lifetimes evident in the empirical survivals. The overall implications of the differences between the survival curves currently used in residential energy modeling exercises and our empirical curves are unclear. On the one hand, the fact that more households than are currently assumed hold on to older appliances means that the mix of appliance vintages in residential energy models may include too few older appliances and too many newer appliances at any point in time. On the other hand, the longer expected appliance lifetimes from the empirical survival curves would tend to make the purchase of a new appliance more attractive within these models since this increases the time horizon over which energy cost savings accrue for a household. Our observed replacement patterns from SHEU-2003 are not, however, set in stone. An examination of the relationship between replacement rates and household demographics indicates that if there is some scope for targeted policies aimed at inducing households to replace some appliances earlier. And in fact, many current programs appear to be aimed in the right direction. As long as the decrease in residential energy demand from earlier replacement more than offsets the costs of running these programs and the costs associated with an increased use of energy and materials in the production and transportation of the new appliances, there could be a net benefit from such policies. The success of policies aimed at achieving energy savings through the replacement of appliances with more energyefficient models will depend on several factors, including the choice of features in the replacement model and whether or not new appliances that are purchased actually replace an appliance or, instead, remains in use along with the newer model in the household. This latter phenomenon is especially relevant for refrigerators. The SHEU-2003 data indicate that a significant proportion of Canadian households continue to use a refrigerator as a secondary ‘beer fridge’ upon the purchase of a new refrigerator. The determinants and implications of this phenomenon are the focus of another study. Acknowledgements

4

A list of current programs is available on the Office of Energy Efficiency (Natural Resources Canada) website. The URL on September 3, 2007 was http://www.oee.nrcan.gc.ca/energystar/english/consumers/ rebate.cfm.

Funding of this project by Natural Resources Canada (NRCan) through the Canadian Building Energy End-Use Data and Analysis Centre (CBEEDAC) is gratefully acknowledged. The author would like to thank participants

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at the CBEEDAC/CABREE discussion series and at the Canadian Economics Association Annual Meetings (Halifax, 2007) and two anonymous referees for their comments and suggestions. Appendix A A.1. Life Table analysis Life Tables provide an ‘actuarial’ snapshot of appliance lifetimes. Using standard actuarial methods, the frequencies of ‘exits’ of appliances from household use at various ages can be used to calculate, among other things, the probability that an appliance will ‘survive’ (i.e., not be replaced) given its current age. These ‘survival’ probabilities can be plotted graphically in what is commonly referred to as a ‘Kaplan–Meier’ survival curve. These in turn can be compared to the ‘survival’ rates that are implied by linear retirement curves (discussed in Section 2) that are often posited in the literature. Associated with the survival curve is the hazard rate, which is a measure of the probability that an appliance will be replaced soon given its current age. While not directly relevant for our comparison of appliance lifetimes based on SHEU-2003 data to the values used in the literature, the shape of the empirical hazard function can be useful in the choice of a functional form for our parametric hazards used in Section 4. Before examining the exact formulas used for the Life Tables, survival curves and hazard curves, it should be noted that some minor modifications to the SHEU-2003 data are necessary for the calculation of the Life Table statistics. Appliance replacement ages are recorded according to age-range categories (described in Section 3), but the current age of a still in-use original appliance is recorded either (i) in years or (ii) according to the same categories as used for the replacement ages. For the purposes of producing our Life Tables and associated survival and hazard curves, any ages recorded in years are converted to the appropriate category. In our graphs of the empirical survivals, the mid-point of each category is used. Based on the distribution of reported ages for appliances currently in use, the ‘mid-point’ assigned to the final category is 30. Note also that for all appliances except refrigerators, all survey respondents who have ‘retired’ an appliance claim that they have done so because the appliance had failed. It might reasonably be assumed that in these cases, the ‘retired’ appliance is no longer in use. For refrigerators, however, many households ‘replaced’ an appliance that was still in working order. For the purposes of this paper, we calculate our Life Table statistics based on an ‘exit’ being defined as replacement due to the failure of the original refrigerator. We do this in order to make our results as comparable as possible to the assumptions made in the models discussed in Section 2. All Life Table information is calculated using the software package LIMDEP according to the methodology

43

of Cutler and Ederer (1958). For each of the seven possible discrete appliance age groups, tj, (t1 ¼ 1–3 years, t2 ¼ 4–5 years, y, t7 ¼ 26 years or more) the Life Table information presented consists of: Nj ¼ the number of observations that ‘enter’ age group tj (i.e., the number of appliances that survive to at least tj before replacement); Cj ¼ the number of censored observations in age group tj (i.e., the number of observations that correspond to appliances whose age corresponds to tj and were not replaced); Rj ¼ the number of appliances in the ‘risk set’ for age group tj (i.e., a measure of the number the number of appliances in age group tj that are ‘at risk’ of being replaced); in LIMDEP, this is measured as Rj ¼ NjCj/2; Xj ¼ the number of observations that exit (i.e., the number of appliances in the age group tj that are replaced at that age); Sj ¼ the empirical survival rate (i.e., the cumulative proportion of appliances surviving); this is measured as [1(Xj1/Rj1)]Sj1, vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u j1 uX ðS j Þ ¼ S j t ½ðX k =Rk Þ=ðRk ð1  ðX k =Rk ÞÞÞ k¼1

where S1 ¼ 1 and s.e. is the standard error of the empirical survival rate. The empirical (Kaplan–Meier) hazard curves rates, which are useful for selecting a functional form of the parametric hazard function are generated in LIMDEP based on the following formula: H i ¼ 2ðX i =Ri Þ=ðwð2  ðX i =Ri ÞÞ, where w is the interval width. A.2. Parametric duration analysis A parametric analysis of appliance survival requires the selection of an appropriate functional form. Parametric studies of appliance duration can be couched in terms of either ‘survivals’ or a related concept: ‘hazards’. In the context of household appliances, the survival function, S(t) is defined as the probability that an appliance will last at least t years before failure/replacement. Technically, the survival function is simply 1F(t), where F(t) is the cumulative distribution function (CDF) for the random variable ‘appliance life’. The hazard rate is a measure of the likelihood that an appliance will fail (or be replaced) at age t, given that it has survived to an age of at least t. Mathematically, the hazard rate is defined as h(t) ¼ f(t)/ S(t), where f(t) is the probability density function (PDF) corresponding to F(t). Given the relationships between the survival function and the corresponding CDF, PDF and hazard functions, it suffices to select a functional form for any one of these functions.

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In practice, the shape of the (Kaplan–Meier) empirical hazard, which can be derived from Life Table information, can prove to be useful in making a choice of functional form for the parametric hazard. When a hazard function slopes upward, the hazard function is said to exhibit positive duration dependence. In this case, the likelihood that an appliance will fail at age t, conditional on having survived up to age t, increases with t. A downward sloping hazard function exhibits negative duration dependence. While an ‘exponential’ specification of the functional form implies a flat hazard, a ‘Weibull’ specification can exhibit either positive or negative duration dependence (but not both), and a ‘log-logistic’ or ‘log-normal’ specification will lead to a ‘hill-shaped’ hazard that exhibits positive duration dependence for small t, and negative duration dependence for large t (Greene, 2003).

An examination of the empirical (Kaplan–Meier) hazards for the major household appliances covered in the SHEU-2003 data set (not shown) indicates that all of the appliances, with the exception of dishwashers, exhibit purely positive duration dependence. Dishwashers, however, have a hill-shaped empirical hazard. As a result, we can rule out an exponential functional form for all of the appliances considered. For refrigerators, freezers, clothes washers and dryers, we use Weibull specifications. For dishwashers we also consider ‘loglogistic’ and ‘log-normal’ specifications of the hazard/ survival functions. All three hazard specifications considered in our empirical specifications (Weibull, log-logistic and lognormal) can be most compactly expressed in terms of their associated survival functions.

Table A1 Parametric hazards (Weibull)a,b Refrigerators Constant

Freezers

3.1013*** (0.1020)

3.6424*** (0.1468)

Provincial dummies NFLD PEI NS NB QUE ONT MAN SASK AB

0.1543* (0.0816) 0.0812 (0.1971) 0.0136 (0.0864) 0.0319 (0.0838) 0.0239 (0.0647) 0.0311 (0.0644) 0.0522 (0.0831) 0.1222 (0.1017) 0.0330 (0.0813)

Income dummies $20,000–$39,999 $40,000–$59,999 $60,000–$79,999 $80,000 or more

0.0351 0.0334 0.0828 0.0454

Dwelling type dummies Double Row or terrace Duplex Low-rise apartment Mobile Home

0.0684 0.0051 0.0917 0.0248 0.2348

Other dummies Urban At home Owner occupied

0.0032 (0.0362) 0.0349 (0.0336) 0.1672*** (0.0629)

Household size variables Size No. of children

0.0087 (0.0192) 0.0175 (0.0271)

0.0538* (0.0313) 0.0890* (0.0470)

2.5085*** (0.0619) 1726 1072.00 34.36*

2.6415*** (0.1146) 1977 607.08 48.12***

Scale parameter p N Log-likelihood LR (overall) a

3.2231*** (0.0771)

Dryers 3.3413*** (0.1082)

0.3110*** (0.1072) 0.0512 (0.1959) 0.0511 (0.1230) 0.0197 (0.1263) 0.0348 (0.0987) 0.0736 (0.0911) 0.0022 (0.1259) 0.1686 (0.1346) 0.0885 (0.0990)

0.1266** (0.0556) 0.0828 (0.1462) 0.0477 (0.0594) 0.0117 (0.0580) 0.0527 (0.0449) 0.0398 (0.0440) 0.0662 (0.0753) 0.0112 (0.0733) 0.0272 (0.0539)

0.0254 (0.0818) 0.3027** (0.1252) 0.0558 (0.0729) 0.0361 (0.0776) 0.0737 (0.0619) 0.0702 (0.0581) 0.1096 (0.0878) 0.0408 (0.0882) 0.1254* (0.0681)

(0.0506) (0.0547) (0.0654) (0.0623)

0.0343 (0.0815) 0.1646* (0.0955) 0.1141 (0.1063) 0.0367 (0.0967)

0.0838* (0.0435) 0.1015** (0.0462) 0.1184** (0.0514) 0.0894* (0.0482)

0.0253 0.0092 0.0315 0.0082

(0.0715) (0.1394) (0.1047) (0.0750) (0.1681)

0.2007 (0.1388) 0.0940 (0.2157) 0.1097 (0.2114) 0.1008 (0.1582) 0.3113** (0.1339)

0.0832 0.0388 0.0736 0.0222 0.0514

0.1546 (0.0940) 0.0419 (0.1272) 0.2005* (0.1142) 0.0019 (0.1039) 0.0373 (0.1079)

0.1144** (0.0570) 0.0002 (0.0447) 0.1282 (0.1160)

Values in parentheses are standard errors. ***significant at 1%; **significant at 5%; *significant at 10%.

b

Clothes washers

(0.0541) (0.1120) (0.0822) (0.0658) (0.0986)

(0.0568) (0.0583) (0.0649) (0.0611)

0.0768*** (0.0269) 0.0353 (0.0252) 0.0373 (0.0536)

0.0146 (0.0358) 0.0898*** (0.0338) 0.0299 (0.0788)

0.0588*** (0.0131) 0.0370** (0.0184)

0.0648** (0.0178) 0.0386 (0.0236)

2.5729*** (0.0545) 2035 1152.07 73.58***

2.5244*** (0.0656) 1818 1010.68 58.08***

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Table A2 Parametric hazards: dishwashersa,b Log-logistic

Log-normal

3.4261*** (0.2396)

Constant Provincial dummies NFLD PEI NS NB QUE ONT MAN SASK AB

0.1213 0.0231 0.0521 0.0977 0.0216 0.0137 0.0304 0.1761 0.0407

(0.1936) (0.2860) (0.1633) (0.1704) (0.1161) (0.1149) (0.1667) (0.1603) (0.1307)

0.1256 0.1764 0.1010 0.1117 0.0320 0.0732 0.1159 0.2173 0.0344

(0.2224) (0.2651) (0.1857) (0.1937) (0.1318) (0.1248) (0.1752) (0.1838) (0.1518)

0.1150 0.0240 0.0579 0.0803 0.0847 0.0011 0.0134 0.1046 0.0284

(0.1716) (0.2976) (0.1474) (0.1599) (0.1044) (0.1048) (0.1484) (0.1348) (0.1180)

Income dummies $20,000–$39,999 $40,000–$59,999 $60,000–$79,999 $80,000 or more

0.1601 0.0232 0.0412 0.0254

(0.1269) (0.1263) (0.1428) (0.1337)

0.1875 0.0237 0.1179 0.0161

(0.1455) (0.1408) (0.1657) (0.1551)

0.1023 0.0379 0.0679 0.1136

(0.1127) (0.1142) (0.1279) (0.1199)

Dwelling type dummies Double Row or terrace Duplex Low-rise apartment Mobile home

0.0052 0.1710 0.0174 0.3466 0.3614

(0.1945) (0.2900) (0.2086) (0.2234) (0.3578)

0.0903 0.2690 0.1028 0.4802 0.5177

(0.1698) (0.3409) (0.2161) (0.2672) (0.4744)

0.0479 0.2261 0.0684 0.2992 0.3394

(0.1891) (0.3094) (0.2241) (0.2250) (0.3389)

Other dummies Urban At home Owner occupied

0.1102 (0.0698) 0.0489 (0.0703) 0.4069** (0.1793)

0.0827 (0.0805) 0.0454 (0.0808) 0.3607* (0.1888)

0.0748 (0.0609) 0.0559 (0.0614) 0.4613** (0.1962)

0.0729* (0.0376) 0.0663 (0.0482)

0.0810* (0.4458) 0.0751 (0.0533)

0.0494 (0.0332) 0.0452 (0.0441)

2.6160*** (0.1088) 1103 549.62 42.86***

1.3047*** (0.0462) 1103 571.28 42.00***

2.2194*** (0.0938) 1103 541.19 46.6***

Household size variables Size No. of children Scale parameter p N Log-likelihood LR (overall) a

3.4627*** (0.2608)

Weibull 3.6747*** (0.2442)

Values in parentheses are standard errors. ***significant at 1%; **significant at 5%; *significant at 10%.

b

Specification Survival (as a function of t) Weibull

e½ðltÞ

p



Expected duration (as a function of covariates) 0

E½tjxi  ¼ e½xi b G½ð1=pÞ þ 1; G: gamma funtion

Log-normal F[p ln(lt)]; E½lnðtÞjxi  ¼ x0i b; F: normal CDF Log-logistic 1/[1+(lt)p]

E½lnðtÞjxi  ¼ x0i b;

In each specification, the survival function is defined in terms of two basic parameters. The parameter p is a scale parameter, while the parameter l is a location parameter. The effects of household income and other household characteristics can be entered into the specification by making l a function of these ‘covariates.’ If we let xi be the vector of characteristics corresponding to household i ,

they can be entered into the survival function by specifying the location parameter as li ¼ exp½x0i b. The interpretation of the parameters on the household characteristics differs from the case of a linear regression model. The parameters (b) do not directly represent the impacts of the individual household characteristics on the life-span of a household appliance. For any particular specification, the impact of any individual characteristic can be determined based on the corresponding formula for the expected value of duration of ln(duration). In the case of the Weibull specification, the impact of the kth household characteristic on the conditional mean of the life-span of an appliance is a multiple of the associated parameter bk, while for the other specifications, the impact of the kth household characteristic on the conditions mean of the log of duration, ln(t), is given by bk. Table A1 reports the results for a Weibull hazard/ survival specification for all appliances except dishwashers. Table A2 presents the results for dishwashers.

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