Auxiliary heating in the residential sector

Auxiliary heating in the residential sector J. M. Reilly and S. A. Shankle

The contribution of auxiliary heating equipment toward meeting residential energy demand is estimated using conditional demand estimation techniques. Auxiliary heating equipment, excluding fireplaces is present in 22% of US single family homes heated primarily with gas, 22 % of homes heated primarily with electricity, and 40 % of homes heated primarily with oil. Wood stoves are the most prevalent form of auxiliary heating equipment, present in 8% of all single family homes. Among US regions, auxiliary heating equipment contributed as much as 75% of heating requirements in the case of wood stoves in homes in the South heated primarily with oil. The use of auxiliary heating equipment can be seen as a response to fuel price and availability uncertainties, its presence increasing demand price responsiveness. Keywords: Residential

energy demand;

The explicit recognition of technology/appliance choice is the basis for many recent residential energy models [3,5,6,8,9, 10, 12,13, 14,163. The rationale for such models is that fuel switching is limited by the household energy using capital in place and generally available technology; eg the existence of an oil furnace limits heating energy demand to oil while the existence of a gas furnace limits heating energy demand to gas. As a result, the stock of energy using equipment limits the substitution possibilities in the short run to altering the intensity with which the existing equipment is used. In the long run, the residential user can alter the capital stock. Explicit modelling of this distinction allows for the treatment of the separate factors that influence decisions on the intensity of use of equipment and on the replacement of capital stock. All existing detailed

Reilly is with the Economic Research Service, US Department of Agriculture, Washington, DC 20005-4788, USA. S. A. Shankle is at the University of Washington. J. M.

The research was conducted while the authors were employed with Pacific Northwest Laboratory. The authors are grateful to Barry McNutt, US Department of Energy, for his encouragement and support for investigating the role of auxiliary heating equipment. Final manuscript

received

0140-9883/88/010029-15

5 June 1987.

$03.00 0

1988 Butterworth

Conditional

demand

analysis;

Home heating

models assume, however, that the residential fuel user has a single heating unit that uses only one fuel. Multi-fuel capability equipment is one important response of private agents to varying and uncertain fuel prices. Such capabilities have significant implications for public policy. Those fuel users who are able to switch easily incur far lower economic and welfare losses in a time of shortage. As a result, economy-wide losses are lower when fuel is short and the economy is better able to take advantage of short-term drops in fuel prices. In addition, the ability to switch fuel rapidly greatly increases the demand elasticity for a fuel. By doing so, it makes it much more difficult for fuel suppliers to create a sustained increase in the price of the fuel. Lower prices benefit all consumers whether or not they have dual fuel capability. This private sector response can greatly reduce the need for government action to allocate fuels or control price increases and can act as a substitute for a fuel stockpile in a shortage situation. These benefits are not without cost as dual fuel capability requires additional capital expenditure. Multi-fuel capability in the residential sector has not been examined in the past, largely on the assumption that it would rarely be economic for a household to purchase redundant equipment to allow rapid fuel switching. In fact, low capital cost auxiliary heating

& Co (Publishers)

Ltd

29

Auxiliary

heating in the residential sector: .J. M. ReiUy and S. A. Shankk

equipment does exist and several innovative technological responses have occurred that allow residences to have multi-fuel capability in space heating at a relatively low cost. Portable electric, LPG, and kerosene heaters are among the low cost auxiliary technologies that can provide the ability to fuel-switch and may further increase energy savings by allowing zonal heating. Fireplace inserts allow households to turn fireplaces into a contributor to heating requirements. Wood-burning furnace add-ons allow the household to heat the house through the existing central furnace duct-work with firewood, offsetting gas or oil use. Such multi-fuel capability has been built into some new furnaces allowing wood/coal/oil, wood/coal/gas, and other combinations. Such devices allow the household to alter fuel use as convenience, fuel availability and fuel price dictate. Electric heat pump/gas furnace combinations, while a dual fuel capability, do not allow resident to easily alter the mix of fuels; while such combinations have implications for the future share of fuel use they do not necessarily increase the ability of the household to respond to changing relative prices. This paper examines the prevalence of dual fuel capabilities in the residential sector of the USA and estimates the intensity of use of this equipment. The Residential Energy Consumption Survey (RECS) data collected by the US Department of Energy are utilized. The following section describes the types of auxiliary and dual fuel capability equipment reported in the RECS data and shows the prevalence ofthis equipment by housing type and US Census Region. We then discuss the conditional demand analysis technique as an approach to derive estimates of the intensity of use of auxiliary equipment, followed by a report of results of applying this technique. Finally, we provide suggestions for incorporating dual fuel capability into residential fuel forecast models and implications of the findings for policy.

(05) Built in Electric Units (Permanently Ceiling, or Baseboard) (06) Floor. Wall, or Pipeless (07) Room Heater (08) Heating Stove Burning (09)

Portable

(21)

Other.

What

(01) (02) (03) (04) (OS) (06) (07) (08) (21) The

19. Are any of these types of equipment additiorl to your main equipment?

as a

used in your home in

(01) Hot water pipes running through a slab floor (radiant heating) (02) Steam or Hot Water System with Radiators or Convectors (03) Central Warm Air Furnace with Ducts to Individual Rooms (04) Heat Pump

30

Wood.

Coal,

or Coke

Heaters

fuel is used by this additional

11 categories

equipment?

of technology

and

nine

categories

99 possible combinations of fuel and technology. Some of these combinations, however, are mutually exclusive. For example, a heating stove burning wood, coal, or coke by definition does not use electricity or solar collectors. Additional combinations, steam and hot water pipes, fuelled by electricity, while not definitionally impossible are not extant technologies. Some combinations are not very different for our purposes. Finally, some technologies were rarely used as auxiliary equipment. As a result it was possible to reduce the 99 possible combinations to 15 by collapsing several categories of equipment and fuel. Our initial analyses focused on the 15 possible allow

Table 1.

Auxiliary technologies.

Auxiliary technology

Auxiliary heating data are available in RECS result of the following questions ([Z], p 143):

Furnace

Gas from underground pipes LPG Gas (bottled or tank gas) Fuel Oil Kerosene or Coal Oil Electricity Coal or Coke Wood Solar Collectors Other.

of fuel

Fuel”

Technologyh

or gas heat pump

01.02

Ccnlral oil furnace Central electric furnace Central coal. coke. or wood furnace Electric heat pump

03, 04 05

01, 02, 03, 04. 06 01, 02, 03. 06 01, 02, 03, 06

Central

Types and prevalence of residential auxiliary heating technologies

in Wall,

Fireplace(s)

(10)

22.

Installed

gas furnace

06,07

01. 02, 03, 06

Electric wall units Gas heaters or gas ‘other’

05 05 01,02

04 05

Oil heaters or oil ‘other’ Wood or coal stoves Gas fireplace Wood/coal/coke fireplaces Gas portable heater Oil portable heater Electric portable heaters Other

03, 04 08 01,02 09 01,02 03. 04 05 21

07, 21 07, 21 06.07 09 06, 07 IO 10 10 08.21

a From question 22 of RECS, see text. h From question I9 of RECS, see text.

ENERGY

ECONOMICS

January

1988

Auxiliary Table 2.

heating in the residential sector: J. M. Reilly and S. A. Shankle

Prevalence of multi-fuel capability (% of households within category). Single family, electricity”

All homes, all fuels

Auxiliary technology Wood/coal fireplace Wood or coal stove Portable electricity heater Gas heater Electric wall units Portable oil heater Gas furnace Gas fireplace Oil heater Electric heat pump Oil furnace Electric furnace Wood/coal furnace Other Total Percent Numberb

Single family, oil”

Single family, gas”

13.8 4.9 7.3 2.5 3.1 0.6 1.6 0.7 0.7 0.4 I.6 0.5
28.9 12.1 4.0 1.5 1.2 0.6 0.4 0.4 1.0 I.2 0.2 0.4 0.0 0.0

24.1 4.5 6.9 3.4 3.2 0.8 1.0 1.3 0.3 0.2
18.7 17.8 11.6 1.0 4.1 2.1 0.0 0.0 1.9 0.3 0.0 0.2 0.2 0.3

37.8 6204

51.9 520

45.8 2193

58.2 584

a Fuel used for primary space heating requirements. b Number of households in the RECS sample.

technology/fuel combinations in Table 1. Table 2 shows the prevalence of each of these technologies, for all homes and for single family homes broken down by primary heating system fuel. Table 3 gives the regional breakdown for single family homes. The estimated percentages are weighted to provide unbiased estimates for the population. For all households, 37.8% have auxiliary heating. Depending on the primary

Table 3.

heating fuel, between 45 and 58% of single family homes have some type of auxiliary heating capability including fireplaces. Those with oil heat as the primary heat source show the highest proportion of homes with auxiliary heating capability, possibly reflecting the fact that oil prices have been most volatile thereby providing the most economic incentive for dual-heating capability.

Regional prevalence of auxiliary beating equipment.

Region

Total households

Total with auxiliary (“/.)

Electricity

USA Northeast North Central South West

520 38 47 259 176

48.1 65.8 42.6 39.0 59.1

0.4 0.0 0.0 0.8 0.0

1.2 0.0 2.1 0.8 1.7

1.5 2.6 4.3 1.5 0.6

12.1 21.1 17.0 9.7 12.5

28.8 42.1 17.0 21.6 39.8

4.0 0.0 2.1 4.6 4.5

Gas

USA Northeast North Central South West

2193 239 739 605 610

43.2 39.3 36.1 39.8 56.6

I .o 0.4 1.5 1.3 0.5

3.2 2.1 2.6 2.8 4.9

3.4 0.8 2.7 7.4 1.1

4.5 2.9 6.1 2.1 5.6

24.1 21.8 18.8 18.2 37.2

6.9 11.3 4.5 7.9 7.2

USA Northeast North Central South West

584 313 88 127 56

53.3 52.4 48.9 50.4 71.4

0.0 0.0 0.0 0.0 0.0

4.1 3.8 4.5 3.1 7.1

1.0 0.3 1.1 3.1 0.0

17.8 18.8 17.0 12.6 25.0

18.7 18.5 18.2 14.2 30.4

11.6 10.9 8.0 17.3 8.9

ECONOMICS

January

Primary beating fuel

Oil

ENERGY

1988

Central gas furnace (%)

Electric wall units (%)

Gas heater (%)

Wood stove (%)

Fireplace (%)

Electric portable heater (X)

31

Auxiliary

heating in the residential sector: J. M. Reilly and S. A. Shankle

The total auxiliary heating figure includes homes with fireplaces. Because fireplaces may actually increase use of the primary heating fuel they are more appropriately categorized as having aesthetic value rather than value as auxiliary heating equipment. Subtracting fireplaces from the total leaves between 2 1.7 and 39.5 % of homes with auxiliary heating. In all homes, the comparable figure is 24%. Notably, this calculation increases the difference between households with oil as the primary heating source and other households. It also eliminates the difference between all homes and single family homes. Apart from fireplaces, the most prevalent auxiliary heating equipment types in single family homes are wood stoves, present in 8.1% of homes, and portable electric heaters, present in 7.3 % of homes. The fact that fireplaces have a lower penetration rate in oil heated homes than in gas or electrically heated homes and that wood and coal stoves have a higher rate may well reflect an income effect. For all homes, electric portable heaters are the most prevalent. Households heated primarily with gas furnaces favour portable electric heaters for auxiliary equipment whereas households heated primarily with electricity and oil favour wood stoves. Electric wall units and gas heaters (3.1% and 2.7% respectively, in all single family homes) are the next most popular auxiliary heating devices. Interestingly, 1 % of single family homes with central gas furnaces view these furnaces as auxiliary equipment and report gas heaters as the primary heating device in the home. Some regional differences in the prevalence of auxiliary technologies are apparent. Among electrically heated homes, a higher percentage of homes in the Northeast have auxiliary technologies (65.8 %) than those in the West (59.1%), with North Central (42.6%) and Southern (39.0%) homes falling behind. In both gas and oil heated homes, those in the West are much more likely to have auxiliary technologies (56.6% of gas heated homes, 71.4 % of oil heated homes) than those in the rest of the country. There is little variation among the Northeast, North Central and South regions in gas and oil heated homes. Among gas heated homes in these three regions, the overall percentage of homes with at least one of the auxiliary technologies is 38 %. Among oil heated homes in the Northeast, North Central and South the overall percentage is 51%. It is difficult to distinguish any clear regional patterns for individual auxiliary equipment types. Overall, wood stoves are relatively unpopular in the South but, among gas heated homes, wood stoves are most popular in the North Central, among electrically heated homes, wood stoves are most popular in the North East, and among oil heated homes, wood stoves are most popular in the West.

32

Conditional demand analysis The primary problem in attempting to proceed econometrically in estimating energy use by heating technology is that no sufficiently detailed database exists. The Residential Energy Consumption Survey (RECS) data developed by the Department of Energy provide a data set with generally sufficient detail; it includes appliance stocks, for example. The remaining obstacle in this data set is the lack of detail on energy use by end-use technology. For example, billing data provide a measure of total electricity use by the household but, lacking costly metering of use for each appliance, electricity use for home heating is not separated from electricity use for refrigeration or hot water heating. Parti and Parti [21] develop a methodology for estimating the demand functions for the unobserved, endogenous consumption variables. Following Parti and Parti note total consumption of fuel j (Ej) is identically equal to the sum of all appliance fuel demands: E,=

i,

Eji

(1)

i= I, . . . n

i=O

where Eji is technology i usingfuelj, E, being the use of fuel j by all unspecified technologies or appliances and n being the number of identified services. Further, suppose that Eji = J1:(X)

(2)

given that the appliance is owned, where X represents a vector ofexplanatory factors such as fuel price, income, etc. Letting Ai represent a dummy variable equal to I if the appliance is owned and zero if not, total demand for fuel j can be represented as Ej = i& ‘,(x(X))

(3)

If a linear form is chosen for J the estimated becomes

Ej=

i

f

i=O k=O

equation

bik(XkAi)

(4)

with A, and X, equal to 1. A few problems arise in estimating Equation (4). First, the set of explanatory variables (the vector X) is likely to be similar across appliance demands. Thus, colinearity among variables is a potential problem. Second, estimation of individual appliance demands requires that the data set contains observations of households both with and without the appliance, essentially a colinearity problem as well. Third, even though a fairly large overall sample may exist it is likely that some sample cells will be small.

ENERGY

ECONOMICS

January

1988

Auxiliary heating in the residential sector: J. M. Reilly and S. A. Shankle

Parti and Parti restrict estimates of income and price elasticities to be equal for most services other than heating, cooling, and hot water heating. The second problem suggests the need to examine the data to ensure that differential appliance ownership for each subgroup for which an equation is estimated exists. In problem cases it is necessary to include the appliance/ technology as part of the other category. The third problem makes it impossible to estimate econometrically fuel demand for technologies that have been inadequately sampled. The conditional demand analysis technique has been used extensively in residential demand models.’ Conditional demand based models, like other detailed residential models, assume that fuel choice occurs with heating equipment choice. As a result, the conditional fuel demand equation depends only on the own price of the fuel. Admitting the existence of auxiliary equipment using a different fuel than the primary fuel or the existence of dual fuelled equipment violates this assumption. Thus the justification for excluding crossprice effects in the estimated equation is eliminated. That is, the vector X in Equation (2) should include the own-fuel price and other fuel prices. Further, those households that use auxiliary heating equipment to augment primary heating should, other things equal, use less fuel with the primary equipment than those households without such equipment; some of the heating load will be assumed by the auxiliary equipment. Failure to include the auxiliary equipment will bias the fuel use estimate for primary equipment downward in thecase where only a primary technology exists and upward for households with primary and auxiliary equipment. Auxiliary equipment can be included directly as one of the technologies in Equation (2). A slight difference exists in Equation (3); for auxiliary technologies of different fuels than the primary fuels the Ai associated with the auxiliary equipment technology is the vector product of Aj and A,, where A, is the primary heating equipment dummy and A, is a dummy equal to 1 if the auxiliary technology is present and 0 otherwise. Interpretation of the coefficients and estimated ‘fuel

‘The yearly average price (annual bill divided by annual use) is used for all fuels. Taylor [22] points out the problems in using average price where average and marginal prices differ as for electricity. The marginal price captures the normal price effect whereas the average price captures the income effect of block rates. (See also Adams and Rockwood [I] and Henson [15]). The RECS data set includes the rate schedules for electricity and natural gas. We attempted to obtain marginal prices and estimate the equations with marginal prices and average prices. Unfortunately the data on the block rate structures were incomplete, making it necessary to attempt to estimate the block structure for missing values. The resultant marginal prices performed very poorly in estimated equations. All reported results are those using average prices.

ENERGY

ECONOMICS

January 1988

use’ for auxiliary equipment is also somewhat different; for auxiliary equipment using different fuel than the primary equipment the estimated ‘fuel use’ will be negative, reflecting the reduction in the primary fuel due to use of the auxiliary equipment. Thus, the estimates are not directly fuel use of the auxiliary equipment but rather savings of the primary fuel. Differential conversion efficiencies of fuels immediately suggest that the relationship between primary fuel saved and secondary fuel used will not be one-to-one. In addition, the nature in which auxiliary equipment is typically used suggests that the estimate of primary fuel saved will exceed the actual secondary fuel use. Portable heaters allow the primary unit to be set very low and only rooms in use need be heated to a comfortable level. This should result in a net reduction in heating fuel requirements. Non-portable auxiliary equipment is likely to have the same effect if it is zonal (heats a limited number of rooms) and is situated (as might be expected) in heavily used rooms or where higher temperatures are desired. For auxiliary equipment using the same fuel as the primary equipment, an equipment equation (Equation (2)) based on Ai should be included. A separate equipment equation using Al, can also be included. In this case the interpretation of fuel use based on Ai will be the net change in primary fuel use due to using the auxiliary technology. Households might elect to have an auxiliary technology using the same fuel as the primary technology if the auxiliary technology is portable or zonal thereby allowing them a net reduction in fuel use by only heating rooms while they are in use. If this effect is not present estimated ‘fuel use’ will be zero. The A, based fuel use estimate should be positive and reflect actual use of the fuel by the auxiliary technology in households with other primary fuels. For purposes of providing unbiased estimates of the primary technology, inclusion of the A, based equation is optional. It is equivalent to having another appliance, unrelated to heating. Inclusion of A,, however, should increase the explanatory power of the equation. In general, it would be possible to estimate direct use of fuel for auxiliary technologies as a separate type of energy-using equipment in its own-fuel equation. Our interest, however, is the impact of owning this equipment on the primary technology. In addition, many of the interesting auxiliary technologies are based on poorly measured or unmeasured fuels (eg firewood for wood stoves or solar insolation for solar collectors). Thus, there are no good estimates of fuel use for these technologies. Moreover, policy questions in the USA are directly related to commercial fuels. Use of renewables are of interest because they offset commercial fuel use. The methodology employed here estimates this 33

Auxiliary heating in the residential sector: J. M. Reilly and S. A. Shankle

offset directly, implicitly accounting for differences conversion efficiencies and other differences.

in

Intensity of use of auxiliary heating technologies Several variants of Equation (4) were estimated using generalized least squares in an attempt to work around limitations of the available sample data and the conditional demand technique and problems endemic to residential energy and econometric analysis. The RECS data are a sample of 4 294 households across the continental USA including owner occupied and rental dwellings and single family, multi-family, and mobile homes. As a result, the sample is extremely heterogeneous with different vintages of homes, different building practices, different styles and insulation characteristics, located in different climates, and with different household characteristics. There are numerous hypotheses leading to correlation of variables of interest (primary and auxiliary heating) with other variables and therefore exclusion of such variables would lead to biased estimates. For example, fireplace ownership may be highly correlated with income and if income is positively related to energy use then exclusion of the income variable will lead to a upward bias in estimates of the intensity of fireplace fuel use. As another example, the economic benefit of an auxiliary technology (and its fuel use) may be related to the climate and local conditions. Auxiliary technology may be more economic in cold climates and may therefore be correlated with heating degree days. Fuel use by the primary technology is also correlated with heating degree days. Thus, exclusion of heating degree days would lead to an upward bias in the estimate of fuel use by the auxiliary technology. The climate relationship, however, may not be as simple as this. Homes in climates with many days where only a little heat is needed may be able to make the best use of auxiliary technologies because the existence of auxiliary technologies may allow these households to turn off the primary heat for most of the year. This type of climate need not be directly related to heating degree days and to the extent it is related is likely to be associated with moderate heating degree days rather than very low or very high heating degree day climates. Thus, the relationship is not linear. Other hypotheses are possible. The RECS data set are very rich in that they contain many variables one might include in such hypotheses in some form. We have attempted to address these issues by including forms of the variables likely to be correlated with

34

variables of interest and by subsetting the sample to create a more homogeneous sample. Exploratory runs suggested the need to restrict ourselves to single family homes. With that restriction, we began with a national equation for each fuel with only the technology dummies as explanatory variables. We added additional variables and computed separate estimates for Census regions in an attempt to improve the model specification. The picture that emerged from this process is that for some auxiliary technologies we can be relatively confident about the sign and general magnitude of their contribution to space heating in the residential sector. Results for other auxiliary technologies are less clear; variability in the estimates and their significance may be the result of real differences between regions or one of the specifications may be more appropriate than the others. In some cases the unclear results are fairly obviously due to small cell size, making the estimates particularly subject to spurious correlation with any number of variables. Among the additional explanatory variables are fuel price,* the number of people in the household, household income, census region, heating degree days, and cooling degree days.3 Included technologies are primary heating equipment, cooling equipment, hot water ‘A variety of other variables might be included which relate IO the housing shell efficiency. eg insulation. storm windows, storm doors, building materials used, etc. These have not been included. ‘Ownership of multiple appliances of a given type (eg IWO or more refrigerators) can be addressed in several ways. The most general approach is to treat each as a separate appliance, ie a separate set of parameters is estimated for the first refrigerator. second refrigcramr. etc. Such an approach greatly expands the number of variables in the demand equations and at some level of ownership suffers from small sample cell size. Two less general approaches probably provide satisfactory resolution in estimates. One is lo include a variable. number of appliances of each type minus one. In cases where only one of theappliances exists in the household this variable IS zero and the demand equation reduces lo the simple (no multiple ownership) equation. This approach implies that energy use for the lirst appliance tn multiple ownership household is the same as for single ownership households and that additional appliances add a fixed and equal amount of energyuse for each additional appliance. The fixed amount is different (eg less) than the amount predicted for the tirst appliance. A second alternative is to form the variable, number ofappliances of each type. and assume that the number of appliances household did not atTect energy use per appliance (E):

in a

E = I(X) and NE = N/(X) where N is number of appliances and X is a vector of explanatory variables. The second alternative is appropriate for an appliance like the refrtgerator where, if two are used, the existence of one probably does not affect energy use of the other. The first alternative is more appropriate for an appliance like the colour television, where the family may have a primary set but also has a second or third set for occasions when viewing conflicts arise.

ENERGY

ECONOMICS

January

1988

Auxiliary heating in the residential sector: J. M. Reilly and S. A. Shankle

heating equipment, and other appliances4 Two forms of electric heating technologies are identified, heat pump and electric resistance.5 Electric resistance is primarily base board or other zonal electric heating. Appliances and water heating technologies are included because they use relatively large amounts of fuel. Because presence of the appliance in a household may be correlated with presence of auxiliary heating equipment, bias could be introduced in the estimated equations if appliances were omitted. The largest energy consuming appliances are based on Parti and Parti’s [21] estimates. We do not separate frost free from non-frost free refrigerators and we do not separate out dishwashers, black and white television, or clothes washers since these have relatively small demands for electricity. Natural gas appliances and technologies are similar to those for electricity with obvious exceptions.

Results The results of two model formulations are reported. Model one represents the simplest model formulation,

4 Because the heat pump serves both heating and air conditioning needs, it requires special consideration. One approach for providing separate estimates is to let E be electricity demand by heat pumps, unobserved but fitting into the conditional demand framework, let E, be electricity demand for heating with the heat pump and let E, be demand For cooling. Then let

where Xi are explanatory variables and CDD and HDD are heating and cooling degree days. Supposing that LJ and jj(i= I, 2, 3) are linear, gives ”

E=

qjxi + i:

f:

j= 1

qj[xicDD]

+

,=I

x a3jCX,HDDl ,=

1

The a,j represent income, price, and other impacts common to heating or cooling with a heat pump and aijr i = 2. 3, provide estimates of the separate effects of heating and cooling with a heat pump. Restricting the n,j = 0 gives

E,= c ali[X,CDD] i= 1 and

E,= i Q[X~HDD] r=, and

E = E, + Eh thereby providing separate estimates for heating and cooling with heat pumps. Since we are not looking for separate estimates of E, and E,,, a formulation which includes CDD and HDD only as separate variables, not as multiplicative variables is sufficient for our purposes. ’ Fully regional models were estimated but statistical significance of parameters was weak, apparently due to small sample cell size.

ENERGY

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January

1988

including only technology dummies as explanatory variables. Model two includes a complete set of variables including price, income, climate, and regional variables. Tables 4, 5, and 6 give the estimated coefficients for the regression equations. Many of the auxiliary technologies listed in Table 2 existed in very few households and were excluded from the estimated equations Sufficient numbers of wood and coal stoves, fireplaces, and electric portable heaters existed to be included in the models of all of the three fuel types. Statistical

significance

of auxiliary fuel use

In model one the significance of the auxiliary technologies is directly given by the significance of the technology dummy. Whether fuel used by auxiliary technologies in model two is statistically significant is given by the group parameter tests in Table 7. Most of the variables related to primary heating, cooling, and major appliances are statistically significant and of the expected sign. Results for the auxiliary heating technologies are mixed. Wood stoves and fireplaces are most consistently significant and of the expected sign.

Estimated contributions requirements

to primary heating

Table 8 indicates the estimated contribution of auxiliary heating to home heating requirements. The strongest result is the significant contribution of wood stoves in homes where they are present. Wood stoves are estimated to reduce primary heating requirements from 10% in gas heated homes to 50% in homes heated with oii (model two national results). Results concerning fireplaces are also consistent and expected. Existence of a fireplace increases fuel demands of the primary heating technology by approximately 20% across the fuels (model 2, national average). Increased primary fuel use with fireplaces is expected due to the fact that the fireplace provides an avenue of heat escape in the home, The results concerning portable electric heaters are most difficult to explain. While significant at the 95 % level for homes heated primarily with electricity and natural gas, the estimated fuel use results (Table 8), show mixed results. For electrically heated homes the existence of electric portable heaters tends to increase electricity use by a small amount in the national average. One might expect electric portable heaters to decrease electricity use if they are used to heat primary living areas while lowering the primary thermostat. On the other hand, families may use electric portable heaters to increase air temperature in bathrooms or

35

Auxiliary heating in the residential sector: J. M. Reilly and S. A. Shank/e Table 4.

Model results:

electricity. Model I

Variables

Model 1

Model 2

Variables

Intercept Heat pump HDD’ Income Price Northeast North Central South Resistance heat HDD Income Price Northeast North Central South Central air Window air Wood stove HDD’ Income Northeast North Central South

8 257.683 14019.130”

11 561.339 50 199.474” - 1.955 0.18gh - 2 152 405.208’ 3 366.637 -4773.799 4004.213 27 764.668” 1.557” 0.487 -2039251.756” 21 387.891” 17021.318” 8 473.720” 12 305.377” 2 968.488” 2 164.539 0.489 -0.113 -2 102.014 -20261.705” -7 161.503

Fireplace HDD Income Northeast North Central South Portable electric HDD’ Income Price North Central South Water heater Refrigerator@ Freezers” Rangesd Clothes dryersd Television? Household’ Northeast North Central South Price

24 936.169”

11625.400” 2 565.333” I 682.269

Note: All variables refer to own-fuel variables unless otherwise a Significant at a 99 % level. “Significant at a 95% level but not a 99% level ’ Heating degree days. d Number of appliances of listed type. e Number of members in household.

noted;

other living areas without lowering the thermostat, or portable electric heaters may serve to heat basements or other home areas not served by a central electric system. As such they would be expected to increase electricity use. The estimated increase in gas use for homes with portable electric heaters is obviously inconsistent with an interpretation of expected change in gas use in a particular home due to use of a portable electric heater. The positive and significant coefficient estimates suggest a consistent correlation between purchase of electric portable heaters and home characteristics excluded from our regression equation. For example, it may indicate that household residents living in poorly insulated, draughty homes choose to improve comfort levels through the use of portable electric heaters. Even though such homes may be reducing their use of gas (and possibly reducing overall energy use and acting optimally to minimize heating costs including capital costs), the gas use of the home may still be high compared to a neighbouring home with insulation, weatherstripping, storm windows and doors, and a high efficiency furnace. The latter home may have no need for a portable heater.

36

eg price is own-price,

10783.131”

-6730.718

12911.982” 2 965.966’ 5 355.962’ 2 177.716’ 6036.239” 4442.661”

appliances,

Model 2 -4

156.802 3.916 - 0.243’ 580.057 -3 760.122 2 837.272 19 297.638 - 5.609 0.468 -544731.221 70 708.8 12 -4001.499 10 430.184” 2418.696’ 3 997.382” 1 870.284” 4 784.552” 3 636.769 3 190.258” 2361.811’ 2361.811 3 555.661’ - 579 775.299”

hot water, etc.

Overall the portable electric heater results suggest that such devices are used more to increase comfort and/or expand the heated area than actively to conserve fuel or provide for fuel-switching capability. The existence of the equipment does provide a latent ability to fuel-switch if necessary due to fuel curtailments. Electric wall units were included as auxiliary technologies in the gas and in the oil models. The results are similar to those for portable electric heaters. In the primary gas equations, gas heaters and central gas furnaces as auxiliary heating devices were also included. The fuel use estimates for central gas furnaces reported as auxiliary heating devices call into question the accuracy of reporting the equipment as auxiliary equipment because the estimated fuel use is nearly as high as for those homes that use a gas furnace as the primary heat source. Gas heaters show mixed results. As in the case of electric portable heaters, these results may indicate an average of diverse uses of such equipment. The regional results show some interesting and expected results. Small cell size, however, is an increasing problem for the regional estimates. As a proportion of primary fuel use, households in the South and West

ENERGY

ECONOMICS

January

1988

Auxiliary

Table 5.

heating in the residential sector: J. M. Rei1l.v and S. A. Shankle

Model results: gas.

Variables

Model 1

Model 2

Variables

Model 1

Intercept Furnace HDD Income Price Northeast North Central South Central air Furnace (auxiliary) HDD’ Income Price Northeast North Central South Electric wall unit HDD’ Income Electric price Northeast North Central South Gas heater HDD’ Income Price Northeast North Central South

30281.610 59 023.758”

39 313.604 18 055.503b 9.671” 0.090 -8 123240.136” 68 638.501’ 31069.802” - 2 960.387” 31989.347’ -2960.157 - 14.334 4.001” - 254 930.259 -49024.981 50 747.013 -48915.213 -2927.617 -2.977 0.804” -499 027.007 44 582.829 34 245.267 -18981.185 -35 134.554 1.867 1.215’ -927 778.073 - 8 038.502 10929.185 23 332.499

Wood stove HDD’ Income Northeast North Central South Fireplace HDD’ Income Northeast North Central South Portable electric HDD’ Income Electric price Northeast North Central South Water heater Refrigerator.@ Ranges” Clothes dryersd Household’ Northeast North Central South Price

-2065.483

41368.630” 32 962.01 3b

-3 972.888

-2868.186

Note: Ail variables refer to own-fuel variables unless otherwise a Significant at a 99% level. b Significant at a 95% level but not a 99% level ’ Heating degree days.

Table 6.

noted; eg price e is own-price, d Number ’ Number

10 172.461”

2049.693

28629.717” 873.551 2 302.990 b 9 798.828 a.

appliances,

Model 2 20 217.665 - 5.788 0.144 - 10009.389 12 095.225 10 228.444 - 19 629.088b 2.330 0604’ -7409.2816 3 887.9678 603.0133 8437.126 - 3.975 1.000’ -501515.209 2852.918 23912.946 -9 365.200 17 467.853” - 7 730.307 2 939.704b 2 430.949 4 8 18.203’ - 23 936.392” - 3 245.807 -3496.561 - 1054 636.946”

hot water, etc.

of appliances of listed type. of members in household.

Model results: fuel oil. Model 1

Variables Intercept Furnace HDD’ Income Price Northeast North Central South Electric wall unit HDD’ Income Electric price Northeast North Central South Wood stove HDD’ Income Northeast North Central South

32 746.951 51054.356”

25 149.791 b

17 020.792”

Model 2

Variables

-29 102.251 127013.249 4.113b 0.140 - 16 050 745.299 56 052.676b 26518.038 40932.001 - 114 282.025 19.947b 0.68 1 - 1040 404.380 - 11414.073 33971.547 42 394.184 29 476.889 - 7.924b -0.376 10937.564 33754.131 -18332.505

Fireplace HDD’ Income Northeast North Central South Portable electric HDD’ Income Electric price Northeast North Central South Water heater Householdd Northeast North Central South Price

Note: All variables refer to own-fuel variables unless otherwise a Significant at a 99 % level. b Significant at a 95 % level but not a 99 % level.

ENERGY

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January

1988

noted;

Model 1 4805.314’

I 358.624

57 555.949’

Model 2 5 599.286 - 2.990 0.360 23 753.095 4 743.558 -9 420.888 17 247.287 -4.646 0.197 -651 135.179 33 626.546 63 398.253 - 11 177.469 43821.443” 4 587.388” - 36405.873 -19302.169 - 36 169.498 9066515.316

eg price is own-price, appliances, hot water, etc. c Heating degree days. d Number of members in household.

37

Auxiliary heating in the residential sector: J. M. Reilly and S. A. Shankle Table 7.

Technology group significance tests.

Primary fuel

Technology group

DF

F value

PR > F

Electricity

Wood stoves Fireplaces Electric portable

5 5 6

2.06 9.19 3.95

0.0673 0.0001 0.0006

heaters

Gas

Central gas furnace, auxiliary Electric wall units Gas heaters Wood stoves Fireplaces Electric portable heaters

6 6 6 5 5 6

3.03 2.26 0.78 1.31 1.83 2.37

0.0059 0.0349 0.5819 0.2580 0.1030 0.0275

Oil

Electric wall units Wood stoves Fireplaces Electric portable heaters

6 5 5 6

2.10 I .82 I .03

0.05 19 0.1064 0.3968 0.1907

Table 8.

1.46

Contribution to primary fuel use (Btu x 103). Model 2 NC

Electricity technologies

Model 1 USA

USA

NE

Electric heat pump, primary Electric resistance, primary Wood stoves Fireplaces Electric portable heaters

14019.130 24936.169 1682.269 10783.131 -6730.718

17 679.578 27 256.618 -4 106.697 5 819.789 111.083

15 073.260 40 565.659 - 1348.618 13715.112

11 821.901 36436.960 - I6 563.291 6 933.809 59 423.487

16714.231 19 150.160 -6041.878 - 309.292 1713.101

22 993.142 27641.575 1 6 14.098 8 820.646 - 5 068.975

59 023.758 32 962.013 - 3 972.888 -2868.186 - 2 065.483 10 172.461 2 049.693

50 604.400 41 118.259 8721.052 10453.119 - 5 337.884 13919.464 10713.434

106 639.004 -46 118.766 50 659.397 - 5 524.289 -23821.704 14402.118 -552.914

81414.378 80 865.240 I9 349.772 17 742.067 - 4 226.670 21 171.021 23 663.621

9 733.654 7 190.340 - 11249.510 10652.648 10261.165 IO 666.599 10495.313

28 978.047 16 795.008 6478.332 - 5 773.446 - 8 983.993 10920.725 8 355.042

5 1054.356 25 149.791 - 17 020.792 14805.314 1358.624

55 740.508 22 991.523 -27903.714 10 940.795 1819.751

70 597.732 20 083.420 - 26 883.683 22 484.307 I1 488.986

51 162.428 53501.551 - 10296.587 - 830.524 35 496.945

41864.725 3 692.426 -31794.413 -6036.721 -20607.451

12681.930 I1 400.694 - I8 797.473 617.703 - 8 175.924

W

S

Natural gas technologies Central gas furnace, primary Central gas furnace, auxiliary Electrical wall units Gas heaters Wood stoves Fireplaces Electric portable heaters Fuel oil technologies Central oil furnace, primary Electric wall units Wood stoves Fireplaces Electric portable heaters

tend to use auxiliary equipment much more intensively than in the Northeast and North Central regions and primary heating demands less overall. The milder climate associated with these regions reduces heating demands in general but apparently increases the potential to use auxiliary equipment. In such climates there are likely to be many days where moderate use of auxiliary equipment can completely eliminate use of primary heating equipment.

38

Auxiliary demand

heutiny and the price responsiveness

oj

Aggregate econometric estimates of the energy demand tend to indicate that the price elasticity ofenergy demand increased after 1973. Price elasticity estimates based on pre-1973 data show very little price responsiveness. Based on such estimates, analysts were pessimistic about the economy’s and consumer’s ability to

ENERGY

ECONOMICS

January

1988

Auxiliary heating in the residential sector: J. M. Reil1.v and S. A. Shankle

conserve energy or switch fuels in response to the price increases of the 1970s. As the 1970s unfolded, however, a greater than expected ability to conserve energy and switch fuels was exhibited by the economy. The 1980s provided striking evidence of the increasing price responsiveness of energy demand as the oil price increases of 1979-80 led to an oil glut and an inability of oil producers to maintain prices. Numerous changes in every sector of the economy contributed to the improved ability to respond to rising prices. Greater penetration of dual fuel heating capabilities in the residential sector are one potential source of increased price responsiveness. While the cross section data of this study do not by themselves indicate whether auxiliary heating equipment is more prevalent today than 10 years ago, evidence on sales of wood stoves, kerosene heaters, portable electric heaters, and other equipment indicates that many homeowners purchased such equipment to supplement or partially substitute for their primary equipment. Table 9 provides estimates of the partial price elasticity attributable to each energy demand component of the conditional demand equations. The estimates are indicative of the increased price elasticity due to auxiliary equipment.

Conclusions A significant share of households in the USA have some form of heating capability beyond a reported primary heating capability. A total of 37.8% of all homes have auxiliary heating equipment and 24% have auxiliary heating capability other than fireplaces. Wood stoves, portable electric heaters, gas heaters, and electric wall units are the most prevalent types of auxiliary equipment. Wood stoves, when present, are the most intensively used auxiliary heating equipment and offset from lO-50% of primary fuel use. Existence of a fireplace consistently increases primary fuel use by 20%, reflecting heat loss through the chimney. Portable electric heaters, gas heaters, and electric wall units show mixed results, reflecting the diverse ways in which these units are used in the household.

Table 9.

While this study is based completely on cross section data, it appears likely that the significant share of homes with auxiliary heating capability may reflect one response to rising and variable fuel prices. In

particular, auxiliary heating equipment (other than fireplaces) is most prevalent in homes heated primarily with oil, the fuel that has shown the most price increases and variability over the past 10-15 years. As such it represents an important response to uncertainty and variability in fuel prices. It suggests that fuel price responsiveness may have increased over time, that many households are better prepared to weather fuel shortages, and that the auxiliary heating capability represents a significant private ‘oil stockpile’ should the country face a boycott or shortage in the future. Finally, the ability to switch rapidly to other fuels should provide a brake on fuel prices during periods of short supplies, benefitting all consumers. The above results have important implications for energy policy and models supporting energy policy. Aggregate econometric estimates of elasticity of response of consumers to energy prices have, in general, shown a greater response since the various price shocks of the 1970s. Dual fuel heating capability is one explanation for this increasing responsiveness. Econometric models based on time series data may underestimate price responsiveness for projection purposes if the elasticity estimates are based on an average of data with less dual fuel capability. Variable coeIhcients models may be able to capture some of this effect to the extent it represents a secular trend. Detailed models that explicitly identify the energy-using equipment, whether the parameters of the model are econometrically estimated or estimated using enginering/cost data, also suffer from the failure to represent auxiliary heating equipment. Engineering/ cost minimization models will tend to overestimate use of the primary heating technology and therefore overestimate fuel use for the primary technology. Such models will further underestimate the degree of fuel switching possible in the short term. The a priori direction of bias in cross-section econometric models that fail to incorporate auxiliary heating equipment is, in general

Price elasticities of demand.

Elasticity component

Electricity, beat pump

Electricity resistance

Gas

Oil

Primary heating Auxiliary heating total Other appliances

-0.927 -0.233 - 0.326

-0.782 -0.233 - 0.326’

-0.367 -0.226 - 0.059

- 1.481 -0.327 0.839

“Theestimated equation, as formulated. restricts the respective elasticities. When electric resistance is the primary heating technology elasticities are restricted to be equal to those when a heat pump is the primary heating technology.

ENERGY ECONOMICS

January 1988

39

Auxiliary

heating in the residential sector: J. M. Reilly and S. A. Shankle

unknown. First, the analysis presented here suggests the need to incorporate cross prices in the estimated equations: the typical assumption that the price of alternative fuels is important only in choosing equipment and not in utilizing the equipment is incorrect given that dual fuel capability exists. Failure to incorporate cross prices will bias parameter estimates; the direction of bias will depend on the particular pattern of relative price changes occurring in the historical data set. If future prices are exogenous inputs and the pattern of relative prices coincidentally mirrors the pattern in the historical data set then no bias in aggregate forecasted fuel use, from this source, will occur. Further penetration of dual fuel heating than that present in the cross-section data, however, could provide bias in forecasts even if the pattern of forecasted prices is the same. The most significant error may result from conditional demand studies utilizing the results to estimate heating requirements. In particular, one would expect a consistent downward bias in heating fuel demand because the estimated heating use would exclude heating demand supplied by secondary heating equipment (whether supplied by the primary fuel or another fuel). The primary policy implication of fairly widespread dual fuel capability is to change the notion that residential consumers can do little in the short run in response to a fuel price increase other than turn the thermostat down and suffer. Further, the results provide evidence of the mechanism by which energy price elasticities may have increased over time. As such, it increases the confidence that the economy has improved its ability to respond to price shocks as well as having improved the efficiency with which fuel is used. This improved ability to respond to price shocks is not free, however. A reserve of capacity is idle, serving as an insurance against fluctuating prices. In this regard, the estimates of use of auxiliary equipment in 198 1 represent decisions to idle primary equipment to some degree based on relative prices existing in 1981. As such, the estimates do not represent a maximum capability of the auxiliary equipment. Finally, auxiliary heating capability is not permanent and some of the technologies (electric space heaters) may have shorter lives than primary equipment. If stable energy prices exist for several years, the capability to respond to a price shock quickly is likely to diminish.

References Itnpuct ofimproved Building Thermal Eflciency~ on Residential Energy Demand, Pacific Northwest Laboratory, Richland, WA,

2

3

4

5

6

7

8

9

40

Paper No MIT-EL 79-049WP, Cambridge, MA, 1979. R. S. Hartman, A Note on the Use af Aggregate Data in Individual Choice Models.. Discrete Consumer Choice Among Alternative Fuels for Residential Appliances,

10

11

12

13

14

15

16

17

1 R. C. Adams and A. D. Rockwood, 1979.

T. Amemiya, ‘Qualitative response models: a survey’, Journal of Economic Literature, Vol XIX. December 1981, pp 1483-1536. Arthur D. Little Inc, Estimation of Conservation Penetration,for the Southern California Gas Company Service Area, 1981-1986, Report to the Southern California Gas Co, Los Angeles, CA, 198 1. G. Copper and A. Scott, ‘The economics of house heating: further findings’, Eneryy Economics, Vol 4, No 2, pp 134-138. Cambridge Systematics Inc. The Residential End-Use Energy Planning &stem, Final Report No EPRI RP 1211-2, The Electric Power Research Institute, Palo Alto, CA, 1981. Charles River Associates Inc, Consumer Choice and Market Penetration of New Technologies: Applicatian qf a Discrete Choice Model, Report No CRA-423, Boston, MA, 1980. J. C. Franke et al, Energy Eficient Appliances Case Study, draft, Pacific Northwest Laboratories, Richland, WA, 1985. R. S. Hartman, Discrete Consumer Choice Among Alternative Fuels and Technologies for Residential Energy-Using Appliances, Energy Laboratory Working

18

MIT Energy Laboratory Working Paper, No MIT-EL 80-018WP, Cambridge, MA, 1980. R. S. Hartman, Consumer Choice and Alternative Fuels and Appliance Technologies: An Analysis af the Eflects of Alternative Conservation Strategies, MIT Energy Laboratory Working Paper No MIT-EL 82-036WP, Cambridge, MA, 1982. R. S. Hartman, ‘The appropriateness of conditional logit for the modeling of residential fuel choice’, Land Economics, VoI 58, No 4, November 1982, pp 478487. R. S. Hartman, ‘The importance of technology and fuel choice in the analysis of utility-sponsored conservation strategies for residential water heating’, The Energy Journal, Vol 5, No 3, July 1984, pp 99-118. R. S. Hartman and W. Wallace, Assessment of the Appropriate Methods of Incorporating Appliance Engineering Analyses and Data Into Residential End-Use Demand Models, Report No EPR EA 4146, The Electric Power Research Institute, Palo Alto, CA, 1982. J. A. Hausman, ‘Individual discount rates and the purchase and utilization of energy-using durables’, Bell Journal of Economics, Vol 10, No 1, Spring 1979, pp 33354. S. E. Henson, ‘Electricity demand elasticities under increasing block rates’, Southern Economic Journal, Vol 51, No 1, 1984, pp 147-156. E. Hirst and J. Carney, The ORNL Engineering-Economic Model of Residential Energy Use, Oak Ridge National Laboratory Report No ORNL/CON-24, Oak Ridge, TN, 1978. R. C. Johnson and D. L. Kaserman, ‘Housing market capitalization of energy saving durable good investments’, Economic Inquiry, Vol XXI, July 1983, pp 374-386. G. G. Judge, W. E. Griffiths, R. Carter Hill and T-C Lee, The Theory and Practice of Econometrics, John Wiley, New York, 1980, pp 5833620.

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1988

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20

21

H. G. McDougall, J. D. Claxton, J. R. Brent Ritchie and C. D. Anderson, ‘Consumer energy research: a review’, Journal of Consumer Research, Vol 8, No 3, Fall 1981, pp 343-354. K. Neels, ‘Reducing energy consumption in housing: An assessment of alternatives’, International Regional Science Review, Vol 7, NO 1, Spring 1982, pp 69-81. M. Parti and C. Parti, ‘The total and appliance-specific conditional demand for electricity in the household sector’, Bell Journal of Economics, Vol 11, No 1, Spring

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1988

heating in the residential sector: J. M. Reil1.v and S. A. Shankle

22

23

1980, pp 309-321. L. D. Taylor, ‘The demand for electricity: a survey’, The Bell Journal of Economics,Vol6, No 1,Spring 1975, pp 75-112. US Department of Energy, Residential Energy Consumption Survey: Consumption and Expenditures April I981 Through March 1982. Part I: National Data, Energy Information Administration DOE/EIA-0321/ 1(81), Washington, DC, 1983.

41