sou · GWB325 · 2009-01-01 00:00:00 — För biobränsle inklusive utvinning och förädling, transport, och distribution har bränslevärdet 1,08 i storskalig energiomvandling satts i enlighet . För mer förädlade biobränslen, som till exempel pellets, ska hänsyn även tas till att energibehovet för förädling är större än för mindre förädlade Utredningen använder en viktningsfaktor på 1,2 för slutlig användning av biobränslen. Vid jämförelser av enskilda byggnaders energieffektivitet måste även hänsyn tas till den individuella förbränningsanläggningens verkningsgrad för uppvärmning och tappvarmvatten.TPF27FPT Detta värde kan variera starkt på årsbasis för biobränslepannor. I små bio- bränsleanläggningar kan verkningsgraden variera mellan på årsbasis. I större biobränsleanläggningar ökar rökgas- kondensering anläggningarnas effektivitet. Detta har vägts in i den föreslagna genomsnittliga viktningsfaktorn för biobränsleanlägg- ningar. En viktningsfaktor på 1,5 bör användas för att jämföra en byggnad med individuell uppvärmning med biobränsle med en byggnad med annat uppvärmningssystem. Denna viktningsfaktor avspeglar nettoenergibehovet. Även när det gäller biobränsle kan ett marginalresonemang föras. När det gäller efterfrågan på biobränsle finns en konkurrens om råvaran från bland annat massa- och pappersindustrin och träskiveindustrin. Denna konkurrenssituation påverkar dock endast i ringa mån behovet av primärenergi för att framställa och förädla biobränsle. Utredningen använder därför en viktningsfaktor på 1,2 även för biobränsle på marginalen. Utredningen har i enlighet med ovanstående beskrivning analyserat och tagit fram viktningsfaktorer för el, fjärrvärme, fjärrkyla, oljeprodukter samt biobränsle, som utredningen använder i det fortsatta utredningsarbetet. En översikt över faktorerna redovisas i Tabell 2. 27TP PT Jämförelsen blir i annat fall missvisande mellan t.ex. en byggnad som är oljeuppvärmd och en byggnad som är el- eller fjärrvärmeuppvärmd. Tabell 2 Sammanställning av utredningens viktningsfaktorer för el, fjärr- värme, fjärrkyla, oljeprodukter samt biobränsle 28TP PT En viktningsfaktor på 1,5 bör användas för att jämföra en byggnad med individuell uppvärmning med en byggnad med annat uppvärmningssystem, se kapitel 8 29TP PT En viktningsfaktor på 1,5 bör användas för att jämföra en byggnad med individuell uppvärmning med en byggnad med annat uppvärmningssystem, se kapitel 9 Effects of Taxation on of energy taxes on energy demandUT................ -residential buildingsUT........................................... residential and other sectorUT................................. -series properties of the dataUT ............................. of energy savingsUT .................................... I: Econometric BackgroundUT............................... II: Description of Test StatisticsUT ........................ III: Econometric resultsUT .................................... This is a report of a study commissioned by the Energieffektivi- seringsutredningen in reply to EU directive (2006/32) on more efficient use of energy and energy services. According to this directive, member states shall reduce energy use by 2016 by in relation to their average during the five years previous to the directive. This reduction is to be achieved by the use of measures to improve energy efficiency, and the effects of measures carried out from 1991 can be taken into account. The aim of this study is to provide an estimate of these energy savings. According to the directive, both bottom-up and top-down approaches are to be used in the estimation of the effects of policy measures. This study applies a top-down or econometric approach. Using an econometric approach, only the effect of quantitative measures can be analysed. In particular, we are con- cerned with the effects on demand of various sorts of taxation measures on end-use energy demand. The effects of subsidies, regulation, etc. are thus not included. 1.2Effects of energy taxes on energy demand The impact of price-related energy policy will depend upon the extent to which consumers, i.e., households, industry, etc., respond to changes in energy prices. This is measured by the price-elasticity of energy demand. The elasticity may differ for different types of taxation, for example, individuals may respond differently to taxes affecting variable motoring costs than to taxes affecting fixed motoring costs. However, it is not the taxes per se that influence consumers choices, but the effect they have on the prices or costs experienced by the consumer. Since the tax is generally placed on the fuel directly, the fuel price elasticity is the appropriate measure to use. In some cases where the tax is not placed on the fuel itself, e.g., annual vehicle taxation, a separate elasticity may need to be used. Such issues primarily arise in the transport sector. It is well accepted that the price elasticity of energy demand varies by end-use and by sector. Commonly, the economy is divided into three sectors (transport, the residential and sector and industry) and end-use energy demand is separated into individual products (petrol, diesel, heating oil, gas, electricity, etc.). A further division can be made according to the process involved: heating, lighting, household appliances, industrial motive power. Similarly, the three main sectors can also be further disaggregated. For example, in the transport sector, we can consider person and goods transport, or motoring and other transport modes. The most suitable degree of disaggregation depends on the questions we are hoping to answer. For example, if we are interested in the impact of prices or taxation, more accurate estimates will be obtained if sectors/uses with different demand characteristics are treated separately. The empirical literature suggests that households are more sensitive to price changes than are firms. This is also in agreement with the premise that firms can pass on the price increases to consumers by increasing the price of their product. Of course this does not hold for highly competitive industries or industries that are export-directed and need to compete on the world market. With energy price increases, there will be a tendency to replace energy with other production factors, for example, with energy- saving capital. Because of the heterogeneity of industry and the more complicated models and data that are required, it is the most difficult sector for which to estimate energy demand and the most plausible estimates are based on individual industries. For this reason, and because of the reduced energy taxation in many industrial sectors in , the industrial sector is excluded from this study. Given the large share of transport sector in Swedish energy demand and the prevalence of taxation in energy policy in this sector, much of this report concentrates on the transport sector. The residential sector is also considered, albeit less thoroughly, mainly because of data limitations. The data have been provided by the and . All data are on an annual basis, and cover a time period from 1970 or 1983 to 2005/6. Data include energy use for road transport, the residential sector and for non-residential (the other sector). In the transport sector, data were also provided on passenger kilometres by rail, bus and domestic flights as well as average prices or fares. For road transport, information was also available on the vehicle stock, annual vehicle kilometres and average fuel use per kilometre. For and other , the data included energy use for heating and electricity use for non-heating purposes, the numbers of houses and flats and the total heated areas. Energy prices are defined as consumer prices inclusive of all taxes and VAT. For road transport, data were also available on average annual vehicle taxes and for the kilometre tax on diesel that was in effect prior to 1994. Other data included disposable house- hold income, GDP, the consumer price index and population. The transport sector can be divided into person and goods transport. Each of these is supplied by various modes. In , of person transport (in pkm, 2006) is carried out by car, by rail, by bus and by air. For domestic freight, of domestic freight is moved by road, by rail and by boat (air cargo is excluded owing to lack of data). Taxation policy in is most relevant in the road transport, in by private car and in goods transport by light and heavy goods vehicles. For this reason, and because of their dominance in transport energy use, this study is limited to these transport modes. In terms of energy use, the car is responsible for and goods vehicles for . As shown in Figure 1, petrol consumption in cars dominates, followed by diesel use in heavy goods vehicles, which is only about ¼ of the petrol used for cars. Compared to these, diesel use in cars, petrol use in heavy goods vehicles and fuel use in light goods vehicles are relatively insignificant. A number a trends in fuel use could be highlighted: an apparent levelling off of petrol use beginning in 1990; an increase in diesel use in car from the mid- nineties; a slow, but continual growth in diesel use in heavy goods vehicles; and a decline in fuel use in light goods vehicles in the mid- nineties combined with an equivalent increase in diesel use. Figure 1 Fuel use in road transport, cubic metres Diesel - light goods vehicles Diesel - heavy goods vehicles Petrol - light goods vehicles Petrol - heavy goods vehicles Of residential energy consumption in , around is used for heating and hot water while the remaining is electricity used for household electrical equipment. Of the total energy used for heating, is used in single-family dwellings and in multi-family dwellings or flats. The composition of energy sources used for heating differs considerably between these two residential forms. In apartment houses, district heating accounts for around of heating (measured in TWh). This has increased from about in the late seventies, when oil was the predominant heating source, accounting for . With the high oil prices of the seventies and the uncertainty over future prices and supplies, district heating expanded to cover a large proportion of apartment . This is clearly seen in Figure 2. From 1983, oil use declined by over , which was replaced by an increase in district heating. The superior efficiency of district heating means that, in terms of KWh, much less is required compared to oil. However, the comparison is not strictly valid, since losses accrue in conversion which are not included in the district heating figures here, while the figures for oil include such losses. The reduction in total energy use is also partially explained by improvements in insulation and other energy-saving measures. In terms of energy use per heated area, multi-unit dwellings have reduced consumption by 1/3 since the early eighties. Figure 2 Energy use for heating in multiple-unit dwellings, TWh The picture is very different in single-family houses, where the infrastructure for district heating is more costly owing to the lower degree of housing density. Instead, oil, which accounted for of heating in the seventies, was first replaced with electric heating, and later with bio-fuels and heat-exchange pumps. By 2005, oil had diminished to only of heating in single-family houses, with electricity (including heat-pumps) producing and bio-fuels . As for multi-family houses, there is a steady decline in total energy use since the late eighties, but we also note a substantial decline in oil use since 2000, coincident with a sharp rise in oil prices. Given the heating options in the two types of housing, it is preferable to examine the response to taxation measures for each separately. There is also a more important reason for this division: that is, that the decision-making process is very different in the two cases. In single-family houses, it is the home-owner who makes investment decisions concerning heating systems, while in apartment it is generally the freeholders who are only rarely the residents themselves. The owner or freeholder may not choose the system which will be most economical for the residents, as he/she does not bear the running costs. Because of this we would expect those living in single-family houses to be more responsive to taxation measures. Figure 3 Energy use for heating in single-family dwellings, TWh Energy consumption for heating purpose in this sector has fluctuated at slightly over for the last 20 years. The pattern in very similar to multi-unit residential : since the late seventies, oil has been replaced by district heating. Figure 4 Energy use in non-residential , TWh. Energy use, E, for road vehicles can be broken down in the following way: E = V × Where V is the number of vehicles, Km/V is the average number of kilometres driven per vehicle and E/Km is the energy use per kilometre. This last term, the specific energy use (S), is the inverse of vehicle fuel efficiency. Since the price elasticity is defined as Bilaga 5 SOU 2008:25 using the above relation, the elasticity can be written as EP = VP + KVP + SP This states that the price elasticity of fuel use is the sum of the price elasticities of vehicle ownership, kilometres driven and specific energy use. The price elasticity for energy use in vehicles can thus be estimated from the energy demand function or from the three individual demand functions for vehicles, vehicle use and specific energy use. Alternatively, since the first two terms on the right hand side of equation (1) are equal to the total kilometres driven by all vehicles V, we can instead estimate two demand functions: total kilometres driven and specific energy use. In this case the price elasticity for energy becomes EP = KP + SP The method used will depend on the data available and on the objective of the study. Separation into three demand equations gives more information on the mechanism of the response to price changes. For example, increased fuel prices can lead to a reduction in car use (fewer kilometres per vehicle), the purchase of more energy efficient vehicles (a reduction in the specific energy use) and/or a reduction in car ownership. The empirical literature indicates that these effects are not the same: the predominant effect appears to through a reduction in specific energy use, mainly by the purchase of smaller or more energy-efficient cars, with the impacts on car use and car ownership substantially smaller.TPF1FPT In fact, much of the evidence from other studies indicates that the effect on energy use is smaller than the effect on energy efficiency, which suggests a significant rebound effect, i.e. the reduction in fuel use per kilometre makes driving cheaper so that the number of kilometres driven increases in the long run. However, there is consensus in the empirical literature regarding the magnitude of the rebound effectTPF2FPT. Given the growth of the diesel car market over recent years, energy use by diesel and petrol cars are estimated as separate equations, but using a seemingly unrelated equations estimation procedure, which takes into consideration the correlation between 1TP PT See , Dargay and Hanly (2004) and other papers in the reference list. 2TP PT See, e.g., (1992). the error terms of the two equations. The long-run equilibrium demand for energy use or its components as specified in equation (1) in each of these segments can be expressed in a general form as = fP (PP , PD ,TP ,TD , I ) = fD (PP , PD ,TP ,TD , I ) where E*iB B is fuel use by petrol and diesel cars. This is assumed to be some function fi of the prices (including tax and VAT) of petrol (PPB )B and diesel (PDB B) and their respective vehicle taxes (TPB )B and diesel (TDB B) and income, I. The kilometre tax on diesel which existed until 1993 is included in the price of diesel. Since this tax is based on use, consumers will see it as any other use tax, and will respond to it in the same way as to the fuel price itself. In addition to the variables shown in equation (5), new car prices and the prices of alternative modes (rail and bus fares) were also included in the model, but were found to be non-significant in all cases and were thus omitted. The model in (5) can be interpreted as a reduced form model. In the structural specification, car ownership would appear in each of the equations. ownership, itself, however, is not exogenous with respect to the other explanatory variables, but is determined, in principle, by these variables. Substituting the exogenous variables (in this case, prices and income) for car ownership into the structural model results in the reduced form model shown in (5). The demand functions in (5) thus allow estimation of the effects of income, prices and taxes on both car ownership and use per car. The dynamic specification is discussed in the Section 3. The results presented here are based on direct estimates of the energy demand function. Estimates of the components of equation (1) indicate that that the effects of prices and taxation on car ownership and use have been minimal in , both being predominantly determined by income. No significant long-run relationship could be found between fuel prices and vehicle taxation and either the number of cars, the average kilometres driven per car or the total number of kilometres driven by car. In addition, estimates of the efficiency component on its own did not produce very reliable results: although the coefficients were of the correct sign, the standard errors were very large. surprising, and has been found in most studiesTPF FPT attempting to estimate fuel efficiency from time-series data for a single country. Since our results indicate that the VP = KVP = KP = 0 in equations (3) and (4), this implies that EP = SP , in other words, the price elasticity of specific energy use (per km) is equal to the elasticity of fuel use with respect to its price. Since efficiency is the inverse of specific energy use, the elasticity of fuel efficiency with respect to prices and taxation is the negative of the elasticity of fuel demand. This means that the effects captured by the fuel price elasticity are a measure of their effects on energy efficiency. This is supported by the finding that no significant relationship could be found between passenger journeys by rail, bus, coach or air and fuel prices or car taxation from the Swedish data, so that they have not induced a shift to other transport modes. 3.2The residential and other sector For the single-family dwellings, four equations are estimated for heating, one for each energy product, oil (O), electricity (E), bio- fuels (V) and district heating (F), and a separate equation for electricity for non-heating uses: ES*,O ES*,E ES*,F ES*,H = f F ,O (PO , PE , PF , I ) = fS,E (PO , PE , PF , I ) = fS,V (PO , PE , PF , I ) = fS,F (PO , PE , PF , I ) = fS,H (PE , I ) The price of bio-fuels is not included in the heating equations owing to lack of price data. Only the price of electricity is included in the final equation, since there are no substitutes for electricity for non-heating purposes. For each housing type aggregate energy was also estimated, but the results were difficult to interpret. The models were also estimated using a seemingly unrelated regression procedure, but this was found to have effect on the results. 3TP PT and Schipper (1997). For multi-unit dwellings and other , only 3 equations were estimated: oil, electricity and district heating. Bio-fuels are rarely used in multi-family for heating and the only approximations are available for household electricity use. 4 The econometric results In any given time period, actual demand could only be expected to be in equilibrium with respect to the prevailing costs, incomes etc. if complete adjustment to changes in these factors occurs within the time interval of the data or the forecasts (in our case, a year) or if the they have remained over a sufficiently long time for responses to have settled down. There are numerous reasons why complete adjustment is not achieved in a single period. These include persistence of habit, uncertainty and imperfect information regarding alternatives and price and costs of adjustment. The length of this time required for complete adjustment is determined empirically from the same evidence as produces short- and long- run elasticities. The literatureTPF4FPT generally finds orders of magnitude in the range 3-10 years. Since full adjustment does not occur within the same year as the change in the explanatory factors, the specification of the demand for energy can be written as a general autoregressive-distributed lag (ADL) model a(L)Ei ,t = b(L) Xt + t where (L) is a lag operator such that LrP P ytB B = ytB -rB for any variable y and a(L) = ar Lr so that demand in any year is influenced by the demand in the previous years as well as by the exogenous variables in previous years. The number of lags included is determined by r; in practice this needs to be limited depending on the time length of the available data. However, by including lags of the dependent variable 4TP PT See , Dargay and Hanly (2004) and other references. amongst the explanatory variables, all previous values of the exogenous variables are taken into consideration. The dynamic representation used in the study includes two lags of the dependent variable and three lags of price and income variables, so the dynamic model can be written as: = + 1Et 1 2 + 0 Xt + 1 Xt 1 In the empirical work, two special cases of the ADL model are also examined: the partial adjustment model () and the error- correction model (ECM). These are described in Appendix I. The modelling strategy is basically a general to specific approach. A general dynamic model is initially estimated, and lags and variables are consecutively excluded which are either implausible because of magnitude or sign or are insignificant in a statistical sense. In model selection, consideration is given to a number of diagnostic tests: measures of the models explanatory power, tests of assumptions regarding the error term (normality, non-autocorrelation, and homoskedasticity), tests for stability of the estimated relationship and tests for model specification. These tests are described in Appendix II. Residual plots of the preferred equations are also examined to give further insight into the suitability of the model specification. A elasticity specification (with all variables in logarithmic form) is assumed, since this is has an economically realistic interpretation and is generally found to be the preferred in empirical studies. We also assume that all explanatory variables – prices and income are exogenous. This is likely to be the case in the individual sectors studied. 4.2Time-series properties of the data Before embarking on the estimation of the econometric models, it is useful to explore the time-series characteristics of the data. It was apparent from visual inspection of the data that many of the variables are increasing or decreasing over time, which suggests that they may be non-stationary, or contain a unit root (see Appendix II). A non-stationary variable has a definite positive or negative trend over time, whereas a stationary variable fluctuates over time but returns to a mean value. which appear to increase or decrease over time can also be trend- stationary, i.e., they increase or decrease over time, but return to a trend. Whether the variables are stationary or not has important implications for econometric modelling. Non-stationarity can result in the problem of spurious regression, which occurs when a non-stationary variable is regressed on a completely unrelated non-stationary variable.