WORKING PAPERS. The Impact of Technological Change and Lifestyles on the Energy Demand of Households


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1 ÖSTERREICHISCHES INSTITUT FÜR WIRTSCHAFTSFORSCHUNG WORKING PAPERS The Impact of Technologcal Change and Lfestyles on the Energy Demand of Households A Combnaton of Aggregate and Indvdual Household Analyss Kurt Kratena, Ina Meyer, Mchael Wüger 334/2009
2 The Impact of Technologcal Change and Lfestyles on the Energy Demand of Households A Combnaton of Aggregate and Indvdual Household Analyss Kurt Kratena, Ina Meyer, Mchael Wüger WIFO Workng Papers, No. 334 February 2009 Emal addresses: 2009/035/W/0
3 The mpact of technologcal change and lfestyles on the energy demand of households: a combnaton of aggregate and ndvdual household analyss Kurt Kratena, Ina Meyer, Mchael Wüger * Abstract: Ths paper deals wth ntegratng technology as well as lfestyles as drvng forces of energy demand nto a model of total prvate consumpton. Prvate consumpton s determned by economc varables lke ncome and prces as well as these other factors. Technology s represented by varables measurng the effcency of households' captal stocks and lfestyles by socodemographc varables. Data for both types of varables are avalable n cross secton consumer surveys and n tme seres data of aggregate consumpton. The cross secton surveys provde nformaton on ncome and socodemographc factors relevant for energy demand (characterstcs of buldng and populaton densty). The tme seres data contan nformaton on prces and ncome as well as the energy effcency emboded n household applances. The fnal model of consumpton conssts of a consstent combnaton of both tme seres and cross secton nformaton nto one comprehensve econometrc model. Ths model descrbes demand for energy and nonenergy commodtes n an almost deal demand system (AIDS) and s used n ex post smulaton exercses to solate the mpact of technologcal and socodemographc varables on the demand for gasolne/desel, heatng and electrcty. Key words: household energy demand, rebound effects, effcency of applances, lfestyles JEL Code: D11, D13, Q53 * Austran Insttute of Economc Research (WIFO), P.O. Box 91, A1103 Venna (Austra), correspondng author:
4 1. Introducton Energyconsumpton of households ncludng fuel use for passenger transport, households electrcty and heat consumpton are growng rapdly despte of technologcal progress. It s well known that households energy demand s determned by a complex nterplay of economc (ncome, prces), technologcal (energy effcency) and socodemographc varables (households lfestyles). As technologcal change leads to hgher effcency of energy usng applances and therefore has a ceters parbus dampenng effect on energy demand, other factors must have compensated ths mpact. One counterbalancng factor s the well known rebound effect of effcency mprovements (Khazzoom, 1980 and 1989, Berkout, et.al., 2000) based on the mechansm that effcency mprovements lower the prce of the servce of energy. Hgh ncome growth and low or fallng energy prces and ther mpact on demand can also compensate for effcency mprovements. A thrd factor s socodemographc change that brngs about changes n lfestyles that are relevant for households energy demand. A large body of lterature on households energy demand exsts (see: Madlener, 1996). Most of the lterature s characterzed by ts partal analytcal nature, the use of sngle equaton estmaton and the applcaton of cross secton or panel data sets. (e.g. Larsen and Nesbakken, 2004; Holtedahl and Joutz, 2004; Hondroyanns, 2004). Some recent studes cover the whole resdental energy demand (Labandera et al., 2006) and only a few the whole energy relevant consumers demand (e.g. Brännlund et al., 2007). Technologcal nformaton s ntegrated nto some studes wthn the concept of a synthess between economc and engneerng models (Larsen and Nesbakken, 2004) or as a combnaton of bottomup and topdown modellng (Rvers and Jaccard, 2005). The mportance of the stock of applances s taken nto account n
5 some studes for sngle energy categores as heatng (Nesbakken, 2001) and some studes also set up models of total consumpton by applyng system estmaton technques (e.g.: Baker, et.al., 1989), whch allows capturng a large varety of cross prce effects between dfferent energy commodtes as well as energy and nonenergy commodtes. Therefore a full account of technologcal progress and socodemographc households characterstcs n a model coverng dfferent energy and nonenergy commodtes s to our knowledge stll mssng. Another problem of studes usng mcroeconomc data for estmaton (mostly panel data sets) s that n most cases the varance n prces s very small compared to the varance n household ncome and household characterstcs. Ths s due to the fact that the tme seres dmenson s usually much smaller spannng only a few years than the households' dmenson usually spannng thousands of households and can be overcome by nterpolaton technques and the use of other nformaton to enlarge the tme seres dmenson of the data set, as s done n Labandera, et.al., We start from consderng the tme seres nformaton based on consumers' expendture from Natonal Accounts n Austra as an adequate source for prce nformaton. Beng aware of conceptual problems of dfferent Household Budget Surveys concernng the consstency wth Natonal Accounts we do not opt for the methodology of constructng a panel data set lke Labandera, et.al. (2006). Instead we use a dfferent methodology of combnng cross secton and tme seres data sources, whch should be equvalent to the constructon of a panel data set by nterpolaton technques or by a combnaton of data sources. In the lterature we fnd only very few studes that combne cross secton and tme seres models by an econometrc methodology lke Nchèle and Robn (199, and Bardazz and Barnaban (2001). Both approaches use estmaton results from ether household survey data or tme seres data and nsert them n the other model respectvely. We follow ths lne of research and use as a
6 crteron for nsertng results from one model nto the other the comparatve advantage n nformaton. For ncorporatng technologcal factors we use data on the effcences of the stock of applances classfed by types of energyusng applances. The socodemographc factors are taken nto account from a rch dataset of a Household Budget Survey coverng about 3,500 households. The nfluence of ncome and socodemograhc varables on consumpton s descrbed well n the cross secton data set, due to the large varance across types of households n dfferent regons. The paper s structured as follows: We present the methodologcal approach of modellng households demand for energy and nonenergy commodtes n the cross secton, the tme seres and the lnked model n Secton 2. The tme seres data and cross secton data used to model energy demand patterns are lad down n Secton 3. Secton 4 summarses the model results n terms of elastctes and shows the results of two model smulatons concernng the mpact of technologcal and socodemographc factors on households energy demand. Conclusons wll be presented n Secton The model of consumers demand The structure of the model dstngushes between aggregate household consumpton, expendture of households for captal goods, and expendture for heatng/electrty and transport energy as well as for other goods and servces. Energy commodtes are used by consumers for the producton of servces (heatng, lghtng, communcaton, transport etc.). These servces are demanded by households and requre nputs of energy flows, E and a certan captal stock, K. The man characterstc of ths stock s the effcency of convertng an energy flow nto a servce level:
7 S E = (1) η ES In (1) E s the energy demand for a certan fuel and S s the demand for a servce nversely lnked by the effcency parameter (η ES ) of convertng the correspondng fuel nto a certan servce. For a gven converson effcency, a servce prce p S (margnal cost of servce) can be derved, whch s a functon of the energy prce and the effcency parameter: p S p η E = (2) ES These prces of servces (p S ) become arguments of the vector of commodty prces n the overall consumpton model (p ). The budget shares of energy demand can be defned as the tradtonal energy cost share or as the servce share : p E p S E S. C C We proceed by applyng the cost functon of the AIDS model (Deaton, Muellbauer (1980)) C(u, p ): logc ( u, p ) = (1 u)log( a( p )) + u log( b( p )) (3) wth the translog prce ndex for a(p ): log a( p ) = α + α log p γ log p log p approxmated n our case by the 0 k k k k j j k j Stone prce ndex: log P * = w k log p k, the CobbDouglas prce ndex for b(p ): k β k log b( p ) = log a( p ) + p and the level of utlty, u. As the level of utlty u s an β 0 k k argument of the expendture functon, an ndrect utlty functon can be derved: U logc( u, p) loga( p ) = βk β 0 p k k (4)
8 Applyng Shephard s Lemma to the cost functon (3), nsertng the ndrect utlty functon (4) and allowng for addtonal technologcal and socodemographc factors captured n the vector of varables Z, gves the well known budget share equatons for the nondurable goods: w C = α + p Z γ log + β log + ξ j j (5) j P For the tme seres (T) verson of (5) due to the low varance n factors captured n the vector of Z the parameter ξ cannot be dentfed and therefore the model reduces to: w T T T T T T C = α + γ log p + β log j j (6) T j P For the cross secton verson (C) the model n (11) can be wrtten as: w C C r l m C C C = α + β log + ξ dum + ξ dum + C u u s s ξ dum k k (7) P u = 1 s= 1 k = 1 In (7) the Stone prce ndex P C s equal to 1 and the dummy varables capture the socodemographc varables u (constructon year of buldng), s (area of dwellng) and k (populaton densty). U The followng expressons for ncome ( ε ) and uncompensated prce elastctes ( ε ) wthn j AIDS can be derved (Green and Alston, 1992): β ε = +1 (8) w ε γ β w U j j = j δ (9) j w
9 Va the Slutsky equaton the followng general relatonshp holds between the compensated K U K U ( ε ) and the uncompensated elastcty ( ε ): ε = ε + ε w. The compensated elastcty j j measures the pure prce effect and assumes that the household s compensated for the ncome effect of a prce change. Applyng the Slutsky equaton n the case of AIDS yelds for the compensated elastcty: j j j γ β w = ε w (10) j w K j j ε δ + j j In (9) and (10) δ j s the Kronecker delta wth δ j = 0 for MDQGδ j = 1 for =j. Our methodolgy of lnkng the models n (6) and (7) conssts of estmatng both approaches and combnng results of each model for the ncome term, whch s a determnng factor n both models. Income s the lnk varable of both models and we use the advantage of the cross secton over the tme seres nformaton concernng number of observatons (3,500 households) and hgher varance across dfferent household types. As results from the estmatons we use the elastctes representng a relatve measure of the propertes of each demand system (cross secton and tme seres). The elastctes of both models are used together wth the budget shares to derve parameter restrctons. The already mentoned qualty of the cross secton nformaton concernng ncome leads us to take n a frst step the ncome elastcty of the cross secton model as gven. We use ths C elastcty ε to derve ncome parameters β of the lnked model, whch are consstent wth T * the budget shares w of the tme seres model: T C ( 1) w T T * β = ε (11)
10 Addtonally we use ths nformaton to correct the ncome effect wthn the uncompensated own prce elastcty. As stated above the uncompensated own prce elastcty can be splt up n a term measurng the substtuton effect (= the compensated prce elastcty) and a term measurng the ncome effect (= the ncome elastcty tmes the budget share). As prce nformaton s the comparatve advantage of the tme seres model we take the substtuton effect (= the compensated prce elastcty) measurng the pure prce nduced effect on demand as gven. Ths leads us to derve new values for the prce parameter γ usng the value for β from equaton (11): T T * ( ε + 1 β ) w T T * γ + = (12) Ths procedure ensures that the orgnal own prce elastcty of the tme seres model becomes consstent wth the ncome nformaton of the cross secton level wthn the AIDS T * model approach. The new parameter γ followng from ths adjustment dffers from the orgnal γ from the tme seres model by a budget share weghted dfference n the ncome parameters of both models: T ε γ T * T = γ + T * T ( β β ) w T (13) The fnal step conssts of settng up the lnked model wth the parameters derved n (11) and (12) and usng estmaton results of the cross secton model for the socodemographc varables. The estmaton results for the parameters ξ (wth = u, s, k) measure the total mpact of a certan household characterstc and therefore had to be transformed nto a relatve measure by takng the dfference σ (wth = u, s, k) from the average mpact ξ. That allows usng relatve varables for the socodemographc varables n terms of the shares of each household type wd (wth = u, s, k) concernng each socodemographc varable:
11 w = α + n r l m T * T T * h γ log p + γ log p + β log + wd wd j j σ + u u σ + s s = 1 j u= 1 s= 1 k = 1 C P σ wd (14) k k The demand for energycommodty E s determned by the level of servce demand S and energy effcency for the applance usng the relevant energy carrer (η ) as well as energy effcency for the other applances (η j ). Addtonally demand for energycommodty E s determned by the dstrbuton of households across the demographc varables: constructon year of the buldng, useful area of dwellng, and populaton densty. The frst varables can also be seen as measurng effcency of the entre heatng system and the second varable as a measure for servce demand. 3. Data sources The commodty classfcaton n ths model ncludes: () servces for prvate transport (va nput of gasolne/desel): F () servces for heatng (va nput of sold fuels, ol, gas, dstrct heatng): H () servces for electrcty usng applances (va nput of electrcty): H_E (v)food and beverages, tobacco: FO (v) clothng and footwear: CL (v) other (nonenergy) commodtes: OTH The econometrc model s appled to prvate consumpton data of Austra ( ) takng nto account the estmaton results of the cross secton model. Tme seres data on prvate consumpton n current prces and the correspondng prce ndces are drectly taken from prvate household sector data n Natonal Accounts of Austra (n COICOP classfcaton).these data are then extended wth nformaton on converson effcency of
12 household applances comprsng ndces of effcency of captal stocks for major energyusng applances, n the sector of heatng, electrcty and passenger car transport. For electrcal applances and heatng (ncludng water heatng) equpment the major data source was the ODYSSEE database ( for the hstorcal sample from 1990 to The exact procedure of arrvng at effcency ndcators by fuels s descrbed n Kratena, Meyer and Wueger (2008). The data stock for the technologcal characterstcs of the regstered car fleet n Austra from 1990 to 2007 s based on the regstered car fleet publshed by Statstcs Austra (2007) and on energy relevant technologcal parameters for cars as descrbed n Meyer and Wessely (2008). The relevant nformaton for the model lned out n secton 2 conssts of the gap between the energy and the servce prce brought about by effcency gans. Table 1 contans these results for the three energy usng household consumpton categores. It s obvous (as already lned out n Kratena, Meyer and Wueger, 2008) that consderable technologcal progress has been acheved concernng energy effcency of households. Table 2 shows the descrptve statstcs of the varables n the tme seres model. >>>>>>Table 1: Energy and servce prces for gasolne, heatng and electrcty, >>>>>>Table 2: Descrptve statstcs of varables, In order to lnk the tme seres wth the cross secton dmenson t was also necessary to collect data on those socodemographc varables that consttute the relevant household characterstcs n the cross secton. For the varables u (year of constructon of the buldng) and s (useful area of dwellng) the data could be drectly taken from statstcs of Austran households (Mkrozensus).
13 Table 3 and 4 reveal that consderable shfts have occured n the household structure between 1990 and 2006 concernng the lvng condtons of households. In the case of the constructon year of the buldng the sum of the shares of buldngs before 1980 has decreased by almost 18 percentage ponts between 1990 and In the case of the useful area of dwellngs the share of households wth dwellngs larger than 110 m2 has ncreased by 10 percentage ponts between 1990 and >>>>>>>>> Table 3: Shares of households (n %) by constructon year of buldng >>>>>>>> Table 4: Shares of households (n %) by useful area of dwellng For the varables k (populaton densty) we had data n the populaton census 1991 and 2001 and a subsample of households from household statstcs (Mkrozensus). Both sources have been used to nterpolate the populaton census data between 1990 and The results n terms of shares of households are shown n table 5. Hgh populaton densty ncludes households lvng n areas of more than 50,000 nhabtants and wth a densty of more than 500 nhabtants per km 2, mddle densty s defned by the same characterstc of total nhabtants (50,000) plus more than 100 and less than 500 nhabtants per km 2. All other household are grouped together n the thrd category. Table 5 reveals that the shfts between household groups were much smaller for ths varable than for the other two. >>>>>> Table 5: Shares of households (n %) by populaton densty of regon
14 Fnally these data sets had to be complemented by the consumers expendture data of Household Budget Survey 2004/2005 from Statstcs Austra coverng nformaton about expendture and lvng standard of prvate households. The Household Budget Survey 2004/2005 was mplemented as a random sample survey from 09/2004 to 09/2005 wth a net samplesze of 8,400 households and a response rate of 42%. For the socodemographc varables we were nterested n we could fnally use a sample of 3,500 households. The expendture classfcaton of the Household Budget Survey s a more dsaggregated level of COICOP than for the data n Natonal Accounts. Due to methodologcal and conceptual changes n Household Budget Surveys from 2004/2005 on we could not use nformaton of former surveys n a consstent manner. Ths s the man reason why we were forced to combne the estmaton of one crosssecton model wth the tme seres model. 4. Emprcal results In a frst step we estmated the budget share equatons (7) of the cross secton model. Table 6 shows the correspondng parameter estmates. These results enable us to calculate n a frst step the ncome elastctes descrbed n Table 7. Usng the mean value of the budget shares of the tme seres data (1990 to 2006) we derved the parameter β usng equaton (11). T * >>>>>>>> Table 6: Parameter estmates from crosssecton estmaton The uncompensated prce elastcty of the tme seres model was derved by estmatng the system of budget shares (6), where accordng to the homogenety restrcton n AIDS one equaton can be dropped and the estmaton results are robust wth respect to the choce of
15 equaton that s dropped (n our case t was the aggregate of other nonenergy commodtes). Estmatng the tme seres model we appled the SUR (Seemngly Unrelated Regresson) estmator and mposed the symmetry restrctons. Another general restrcton n demand systems (concavty of cost functon) and the ntroducton of lnear tme trends (descrbng preferences and tastes) has been treated as n Kratena, Meyer, Wueger (2008). The new T * parameter γ then drectly follows from equaton (13). >>>>>>> Table 7: Inputs from tme seres and cross secton estmaton for the lnked model The relatve measures σ for the mpact of the socodemographc varables descrbed n secton 2 can also be drectly taken from the results of the cross secton estmaton and are lsted n Table 8. >>>>>> Table 8: Relatve mpact of socodemographc varables on budget shares The second step conssted n settng up the tme seres estmaton of the lnked model (equaton (14)) usng the nputs lsted n Table 7 and 8. The estmated parameter values together wth the data for the budget shares are, n a next step, used to calculate uncompensated as well as compensated prce elastctes and ncome elastctes.table 9 shows the values for the calculated elastctes wth the sample mean of the budget shares. The estmaton procedure of the lnked model reproduces the ncome elastcty of the cross secton and the own prce elastctes of the tme seres model consstent wth the ncome elastcty of the cross secton model. We can use the uncompensated prce elastcty as a drect measure of the (prcenduced) rebound effect of energy effcency mprovements. Accordng to our result ths would
16 gve a rebound effect for gasolne (automotve fuels) of 48%, for heatng fuels of 27% and for electrcty of about 12%. Comparng these results wth other studes referred n the surveys of Greenng, Greene (1997) and Greenng, et.al. (2000) they can be characterzed as lyng at the upper bound of the range found n the lterature. For heatng (ncludng water heatng) rebound effects found n the lterature are between 10% and 30% (Greenng, et.al., 2000). They are slghtly hgher for coolng and lower for prvate car transport. Therefore, the rebound effect for prvate car transport dentfed here for Austra (48%) s sgnfcantly above the results found n the lterature. As lad down n Kratena, Meyer, Wueger (2008), ths can partly be explaned wth the ncrease of crossborder fuel shoppng durng the 1990es from consumers of the neghbourng countres. The cross prce elastctes between the energy commodtes have a postve sgn ndcatng a substtutve relatonshp wth the excepton of the cross prce elastctes between heatng and electrcty, whch show a negatve sgn. Changes n effcency lead to changes n the prce system and therefore to demand reactons n all energy categores. >>> Table 9: Uncompensated and compensated prce elastctes In secton 3 we found that n the sample analysed here ( ) consderable mprovements n the effcency of household's captal stock as well as mportant changes n the lfestyles of households measured by the dstrbuton of households across socodemographc characterstcs  have occured. At the same tme the energy demand of households has ncreased, so that the querston arses how and n whch order of magntude the dfferent factors have slowed down or accelerated ths development. Our model can be used to trace back energy demand changes to changes n
17 effcency (technology) and these lfestyle changes. For ths purpose we carred out two smulaton exercses assumng that () the dstrbuton of households across the socodemographc varables would have remaned constant at the level of the startng year (1990) over the observaton perod and () the technolgy (effcency) of households captal stocks would have been constant at the level of the startng year (1990) over the observaton perod. The frst smulaton yelds results about the mpact of lfestyle changes on households energy demand and the second smulaton measures the mpact of technologcal change on energy demand. Table 10 shows that for socodemographc varables the largest mpact between 1990 and 2006 has been on electrcty demand, whch would have been by 11% lower than the actual level n 2006 wthout changes n the lvng condtons of households. For heatng ths mpact stll amounts to more than 7%, where for gasolne/desel lvng condtons captured by our socodemographc factors seem to have a neglgble effect (only less than 1%). The sngle socodemographc factors may have dfferent drectons of mpact on energy demand as n the case of heatng: the ncrease n the average area of dwellng between 1990 and 2006 ncreased energy demand by almost 11%, wheras the age structure of buldngs decreased energy demand by 4%. As mentoned before the age structure of the buldngs mght also be nterpreted as an ndcator of technology. The sngle socodemographc factors show the same drecton of mpact on electrcty demand but wth dfferent ntenstes, where as expected  the average area of dwellng shows by far the largest mpact. In general the changes n socodemographc factors between 1990 and 2006 have ncreased energy demand. On the other hand the smulaton results sow that technologcal changes have reduced energy demand consderably, especally for gasolne/desel and heatng. Wth constant technology of 1990 demand for gasolne/desel would have been by 18% hgher and for heatng by about
18 16%. The mpact s consderably smaller for electrcty (8.6 %). Due to the former mentoned cross prce and ncome effects changes n the effcency of one category have an mpact on all energy demands. Ths can be seen especally n the case of electrcty and heatng, whch are complementary goods. Therefore the effcency effect of heatng on electrcty s n opposte drecton and vce versa. Table 10 also allows nterpretng the relatve mpact of technologcal and socodemographc factors on energy demand. For gasolne/desel the technologcal change s much more mportant than the socodemographc change. For heatng the socodemographc factors have about half of the mpact of technologcal change and as mentoned above the socodemographc varable 'constructon year' also contans technologcal effects. Therefore the total mpact of the effcency of the heatng system could be approxmated by the technology effect of household equpment (17.6%) plus the constructon yeareffect (4%). For electrcty we fnd a more balanced relatonshp between the effcencyeffect and the effect of socodemography. >>>>>> Table 10: Change n energy demand 2006 wth constant socodemography and constant technology of Conclusons In ths paper a consstent lnk between tme seres and cross secton estmaton n one comprehensve econometrc model for households' energy demand has been presented. The methodologcal nnovaton of our approach conssts of usng the comparatve advantage of both data sets n terms of varance n prces and ncome from estmaton results. Ths s done
19 by estmatng both types of models (tme seres and cross secton) and dervng useful parameter restrctons for the lnked model. Therefore we use the nformaton of both data sets n a smlar way as n the constructon of a panel data set. Ths approach enables us to nclude not only ncome and prces as varables of household demand, but also technologcal and socodemographc factors. Therefore we can calculate the mpact of a large varety of factors of nfluence on energy demand of households wth specal emphass on technologcal change and lfestyles. An addtonal mportant feature of our model s the descrpton of total prvate consumpton va a demand system, so that mportant repercussons and feedbacks between dfferent energy and nonenergy commodtes can be taken nto account. An ex post smulaton exercse for Austra ( ) shows that technologcal and lfestyle change has a sgnfcant nfluence on energy demand of household. In general socodemographc (lfestyle) change has ncreased energy demand n the past, especally for electrcty, whereas technologcal change has dampened energy demand growth, especally for gasolne/desel. In the case of gasolne/desel and heatng the mpact of technologcal change on energy demand was large enough to compensate for the demand drvers wthn the lfestyles of households. As energy demand n these two categores (gasolne/desel, heatng) has also ncreased between 1990 and 2006, ths must be assgned to the development of ncome and prces or other socodemographc vraables not captured n our analyss. In the case of electrcty the socodemographc varables taken nto account here to measure lfestyles had a larger energy ncreasng mpact on demand that could not be compensated for by the ncrease n effcency of applances.
20 References Baker, P., Blundell, R.W. and J.Mcklewrght, 1989, Modellng Household Energy Expendtures Usng McroData, Economc Journal (99), Berkhout, P.H., Muskens, J.C. and Velthusjen, J.W., 2000, Defnng the rebound effect, Energy Polcy, 28, Brännlund, R., Ghalwash, T., and Nordström, J., 2007, Increased Energy Intensty and the Rebound Effect: Effects on Consumpton and Emssons, Energy Economcs, 29(1), pp Conrad, K., and Schröder, M., 1991, Demand for durable and nondurable goods, envronmental polcy and consumer welfare, Journal of Appled Econometrcs, 6. Deaton, A, and Muellbauer, J., 1980, An almost Ideal Demand System. Green, R. D. and Alston, J. M., Elastctes n AIDS Models. Amercan Journal of Agrcultural Economcs, 72, 1990, Greenng, L.A., Greene, D.L., Energy use, techncal effcency, and the rebound effect: a revew of the lterature. Report to the Offce f Polcy Analyss and Internatonal Affars, US Department of Energy, Washngton, DC, December. Greenng, Lorna A., Greene, Davd L., Dfglo, Carmen, Energy effcency and consumpton the rebound effect a survey. Energy Polcy 28, Holtedahl, P., and Joutz, F. L., 2004, Resdental Electrcty Demand n Tawan, Energy Economcs, 26(2), pp Hondroyanns, G., 2004, Estmatng Resdental Demand for Electrcty n Greece, Energy Economcs, 26(3), pp Khazzoom, J. D., 1980, Economc Implcatons of Mandated Effcency n Standards for Household Applances, The Energy Journal, 1, pp Khazzoom, J. D., 1989, Energy Savngs from More Effcent Applances: A Rejonder, The Energy Journal, 10, pp Kratena, K., Meyer, I., Wueger, M., 2008, Modellng the Energy Demand of Households n a Combned TopDown/BottomUp Approach, WIFO Workng Papers, 321, Labandera, X., Labeaga, J. M., and Rodrguez, M., 2006, A Resdental Energy Demand System for Span, The Energy Journal, 27 (2), pp Larsen, B. M., Nesbakken, R., Household Electrcty EndUse Consumpton: Results from Econometrc and Engneerng Models, Energy Economcs, 2004, 26(2), pp Madlener, R., 1996, Econometrc Analyss of Resdental Energy Demand: A Survey, Journal of Energy Lterature, II (2), Nesbaken, R., 2001, Energy Consumpton for Space Heatng: A DscreteContnous Approach, Scandnavan Journal of Economcs, (103), Nchèle, V. and J.M. Robn, 1995, Smulaton of Indrect Tax Reforms Usng Pooled Mcro and Macro French Data, Journal of Publc Economcs (56), Rvers, N., and Jaccard, M., 2005, Combnng TopDown and BottomUp Approaches to EnergyEconomy Modelng Usng Dscrete Choce Models, The Energy Journal, 26(1), pp
21 Table 1: Energy and servce prces for gasolne, heatng and electrcty, Gasolne Gasolne Heatng Heatng Electrcty Electrcty energy prce servce prce energy prce servce prce energy prce servce prce Table 2: Descrptve statstcs of varables, Mean Maxmum Mnmum Std. Dev. Budget shares Food Clothng Gasolne/Desel Heatng Electrcty Other Prce ndces Food Clothng Gasolne/Desel Heatng Electrcty Other Total expendture Stone Prce ndex
22 Table 3: Shares of households (n %) by constructon year of buldng before to 1980 after Table 4: Shares of households (n %) by useful area of dwellng up to 60 m2 60 to 110 m2 more than 110 m
23 Table 5: Shares of households (n %) by populaton densty of regon hgh mddle other
24 Table 6: Parameter estmates from crosssecton estmaton Food Clothng Gasolne/Desel Heatng Electrcty log (C/P) 0,068 0,0050,0250,0240,015 standard error 0,003 0,002 0,001 0,001 0,000 tvalue 24,660 2,09022,19025,07035,240 Constructon before ,000 0,000 0,000 0,000 0,000 standard error 0,000 0,000 0,000 0,000 0,000 tvalue..... Constructon ,014 0,006 0,0010,002 0,000 standard error 0,004 0,003 0,002 0,001 0,001 tvalue 3,580 1,810 0,6101,550 0,420 Constructon after ,022 0,009 0,0010,007 0,000 standard error 0,004 0,003 0,002 0,001 0,001 tvalue 5,760 2,790 0,3705,1000,690 Space to 59 m20,021 0,016 0,0040,0210,010 standard error 0,005 0,004 0,002 0,002 0,001 tvalue 4,660 3,780 2,12013,33014,210 Space 60 m² m² 0,008 0,0070,0010,0090,005 standard error 0,003 0,003 0,001 0,001 0,000 tvalue 2,750 2,9300,4608,9509,960 Space > 110 m² 0,000 0,000 0,000 0,000 0,000 standard error 0,000 0,000 0,000 0,000 0,000 tvalue..... hgh pop. Densty 0,717 0,019 0,249 0,228 0,145 standard error 0,023 0,021 0,010 0,008 0,004 tvalue 31,140 0,910 26,080 28,660 39,500 mddle pop. Densty 0,727 0,015 0,251 0,232 0,149 standard error 0,023 0,020 0,009 0,008 0,004 tvalue 31,930 0,740 26,580 29,540 41,070 low pop. Densty 0,739 0,009 0,256 0,236 0,151 standard error 0,023 0,020 0,009 0,008 0,004 tvalue 32,810 0,470 27,400 30,390 42,090 R 2, adjusted 0,843 0,520 0,751 0,654 0,799 Table 7: Inputs from tme seres and cross secton estmaton for the lnked model Income Uncompensated elastcty w Parameter β * prce elastcty Parameter γ * cross secton tme seres lnked model tme seres lnked model Food Clothng Gasolne /Desel Heatng Electrcty
25 Table 8: Relatve mpact of socodemographc varables on budget shares Food Clothng Gasolne /Desel Heatng Electrcty constructon year buldng σ 0,00970,00430,0007 0,00180,0007 σ 0,0013 0,0008 0,0005 0,0010 0,0005 σ 0,0084 0,0035 0,00020,0028 0,0002 area of dwellng σ 0,0159 0,0097 0,00120,01290,0069 σ 0,0010 0,00020,0018 0,0009 0,0003 σ 0,01500,0098 0,0006 0,0120 0,0066 populaton densty σ 0,0128 0,00640,00270,00740,0051 σ 0,0006 0,00040,0010 0,0007 0,0012 σ 0,01340,0068 0,0037 0,0067 0,0040 Table 9: Uncompensated and compensated prce elastctes Uncompensated prce elastctes Food Clothng Gasolne /Desel Heatng Electrcty Food 0,1111 0,2601 0,15100,05680,1037 Clothng 0,46061,59530,0473 0,0294 0,0363 Gasolne 0,79060,08660,4750 0,1238 0,1844 Heatng 0,3496 0,1460 0,16660,26990,3819 Electrcty 0,4591 0,1979 0,30500,47600,1241 Compensated prce elastctes Food Clothng Gasolne /Desel Heatng Electrcty Food 0,0389 0,2966 0,16500,04610,0952 Clothng 0,58931,53010,0223 0,0485 0,0515 Gasolne 0,84900,05700,4635 0,1325 0,1913 Heatng 0,3117 0,1651 0,17400,26420,3774 Electrcty 0,4183 0,2186 0,31290,47000,1193
26 Table 10: Change n energy demand 2006 wth constant socodemography and constant technology of 1990 Socodemographc varables Gasolne/Desel Heatng Electrcty Total mpact of whch constructon year effect area of dwellng effect populaton densty effect Technologcal varables Total mpact of whch effcency, transport effcency, heatng effcency, electrcty
27 2009 Österrechsches Insttut für Wrtschaftsforschung Medennhaber (Verleger), Hersteller: Österrechsches Insttut für Wrtschaftsforschung Wen 3, Arsenal, Objekt 20 A1103 Wen, Postfach 91 Tel. (43 1) Fax (43 1) Verlags und Herstellungsort: Wen De Workng Papers geben ncht notwendgerwese de Menung des WIFO weder Kostenloser Download:
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