Bridging the energy efficiency gap:
using bottom-up information in a
top-down energy demand model
Carl C. Koopmans
a,U, Dirk Willem te Velde
ba
CPB Netherlands Bureau for Economic Policy Analysis, Van Stolkweg 14, P.O. Box 80510, 2508 GM, The Hague, The Netherlands
bNational Institute of Economic and Social Research, London, UK
Abstract
Bottom-up modelers typically predict a lower energy demand and a higher energy efficiency than top-down modelers do, leading to the notion of the energy efficiency gap. This difference is often ‘explained’ by combining bottom-up information with unrealistically high discount rates. In this paper we combine the bottom-up and top-down approaches in an energy demand model. The model has a top-down structure, but we employ bottom-up information to estimate most of its parameters, using the discount rate that firms say they use. This new approach provides a partial reconciliation of top-down and bottom-up methods, which proves to be very useful for policy analysis.Q2001 Elsevier Science B.V. All
rights reserved.
JEL classifications:Q41; Q43; Q48
Keywords:Energy demand; Energy efficiency gap; Bottom-up information
1. Introduction1
Although the motivation has changed from the perceived energy crises in the 1970s to the environmental concerns of the 1990s, there is a sustained interest in
U
Corresponding author. Tel.:q31-70-3383456; fax:q31-70-3383350.
Ž .
E-mail address:[email protected] C.C. Koopmans .
1
This paper is based on work done at CPB Netherlands Bureau for Economic Policy Analysis in 1996. We are grateful for helpful comments from Lans Bovenberg, Maurice Dykstra, Andre de Jong and from´ participants of seminars at NIESR, CPB and the University of Utrecht.
0140-9883r01r$ - see front matterQ2001 Elsevier Science B.V. All rights reserved. Ž .
the relationship between economic growth and energy use. Crucial in this relation-ship is the evolution of energy efficiency, defined as the use of energy per unit of output. Top-down modelers investigating the observed behavior often predict that the volume of output increases much faster than energy efficiency, with energy use thus increasing. However, scientists studying bottom-up information on costs and performance of energy saving techniques argue that significant unexploited oppor-tunities exist for cost-effective investments in energy efficiency, thereby pointing to an energy efficiency gap. Consequently, bottom-up modelers suggest that energy use will increase less rapidly than top-down modelers do.
The energy efficiency gap has been the subject of much debate in the literature. This has provided a host of possible explanations for the existence of this gap. Many authors emphasize market failures, and conclude that energy use is ineffi-ciently large. Others point at non-market hurdles, which make seemingly inefficient
Ž .
outcomes efficient after all. For overviews, see Hassett and Metcalf 1993 , Jaffe
Ž . Ž . Ž .
and Stavins 1994a,b , Huntington 1994 , Metcalf 1994 , Sanstad and Howarth
Ž1994 , Scheraga 1994 , Howarth and Sanstad 1995 . Gillissen et al. 1995 and. Ž . Ž . Ž .
Ž .
Velthuijsen 1995 , discuss the Dutch situation in particular. Although many of these publications provide useful empirical information, the size and composition of the energy efficiency gap still remains to a large extent a ‘black box’ in quantitative terms.
Having two alternative approaches for predicting energy demand is confusing for environmental policy making. In this paper we introduce a new type of top-down energy demand model. Its parameters are estimated using bottom-up information,
Ž .
thereby taking into account the energy efficiency gap Koopmans et al., 1999 . The
Ž .
energy efficiency predictions of the model have a top-down aggregated nature, but they are consistent with bottom-up information. We note that previous at-tempts to combine bottom-up information and top-down models involved linking
Ž . Ž . Ž
bottom-up models or modules to top-down models or modules see e.g.
Ja-.
cobsen, 1998 . Our approach involves using bottom-up information within a top down energy demand model.
The new model uses the putty-semi-putty type of production function introduced
Ž .
by Fuss 1977 . In such vintage models, the substitution of factors of production is
Ž .
easier ex-ante than ex-post. Hence, following an unexpected energy price in-crease, the energy efficiency of new vintages will improve more than the energy efficiency of existing vintages. A price change achieves its full impact only after the capital stock has been completely renewed. Hence, price elasticities are larger in the long run than in the short run.
2. The bottom-up approach and the energy efficiency gap
In this section we review the literature about the bottom-up approach and the energy efficiency gap, with particular emphasis on the gap that exists between top-down and bottom-up modelers.
Analysis of bottom-up information usually involves drawing energy conservation
Ž . Ž
supply curves CSCs on the basis of individual energy saving techniques CSCs
.
were introduced by Meier et al., 1983 . Fig. 1 depicts an example of a CSC. Each step in the figure represents an energy saving technique that improves the energy efficiency without changing the level of output. Each technique saves an amount of energy per year against certain investment and maintenance costs. As costs and benefits are spread over more than 1 year, a discount rate is required to discount future costs and benefits. Bottom-up scientists often propose a real discount rate of 5]8%, based on market rates for loans. The figure shows a clear price sensitiveness of energy conservation and hence, of implied energy efficiency.
Fig. 1. Energy conservation supply curve.
To compare bottom-up information to top-down analysis, we need to link CSCs
Ž .
to economic production functions Huntington, 1994; Blumstein and Stoft, 1995 . An isoquant derived from a production function essentially displays the same thing
Ž .
as a CSC. Stoft 1995 derives a CSC from a production function and shows that this CSC is a conditional factor demand function, with the output or utility held constant. This implies that CSCs can only be used to analyze the substitution effects of factor price changes and not the output effect.
In Fig. 2 the isoquant or best-practice frontier depicts combinations of energy and other inputs that provide the same energy service. Point T represents the technical optimum for energy saving, but achieving this requires a lot of other inputs, mainly capital. Given the input prices, micro-economic theory can derive
Ž .
Fig. 2. Isoquant representation.
The position of point E depends on the ratio of the energy price to the price of other inputs PerPi. If the other input is capital, using more capital implies saving energy over a number of years in the future. In this situation, Pe is the mean discounted price of future energy use. For low discount rates, as often used by bottom-up modelers, point E is optimal. If a higher discount rate is used, Pe is lower and the optimal choice becomes point EU
. Implicit discount rates of 25% or
Ž .
more are often observed in reality Koomey and Sanstad, 1994 , although this does not tell us whether investors really utilize such high discount rates, or whether they use normal discount rates in a more complicated context than the simple cost-benefit approach used by bottom-up scientists.
Ž .
The observation that the actual situation point A in Fig. 2 seems sub-optimal lies at the heart of the debate between bottom-up and top-down modelers
ŽScheraga, 1994 . There is a rich literature on possible explanations for this energy.
Ž . Ž .
efficiency gap see introduction . In this paper we follow Jaffe and Stavins 1994a,b who distinguish between market failure and non-market failure explanations of the energy efficiency gap.
Market failures relate to limited information surrounding investments in energy efficiency. Information about energy efficiency is underprovided by ordinary mar-ket activity, due to the public goods nature of information. Once a firm creates information, it cannot prevent others from using the same information without paying for it. Similarly, the act of implementation itself creates a positive external-ity by providing useful information to other potential adopters. For instance, it may turn out that realized returns of investments fall short of the returns promised by engineers, so that the case for an energy efficiency gap may be weaker than
Ž
previously believed see Hassett and Metcalf, 1997 for this point in the case of attic
.
useful information in his hands remains unexploited. Such principal]agent prob-lems may arise for instance if tenants, and not the landlord, pay the energy bill.
Non-market failure explanations reflect why observed behavior is optimal, ex-plaining why it is rational to use a high discount rate or to wait and see. For instance, irreversible investments in energy efficiency whose benefits depend on uncertain, future energy prices require the use of high discount rates by risk-averse investors. Uncertainty may also explain why a wait-and-see policy can be optimal.
Ž .
Metcalf 1994 argues that the ability not to invest in energy efficiency until more
Ž
information is available increases the net present value of non-investment option
.
value . Another non-market failure explanation is that technologists’ estimates of costs of techniques often fail to incorporate transaction costs. Yet another non-market failure explanation is that most bottom-up calculations are based on the average firm. In fact, a technique may be profitable for an average firm in the sector, but not for all firms facing heterogenous cost functions. Finally, imperfec-tions in capital markets may impede investments. Some firms must pay substantial premia to obtain loans. If these firms are taking higher risks than others, then this is optimal from the lender’s point of view.
Fig. 3 summarizes the discussion, by pointing out which hurdles need to be overcome when incorporating bottom-up information on energy saving techniques
Ž .
in top-down models a move from E to A . Bottom-up analysts argue that a certain
Ž .
number of investments in energy efficiency are technically possible level T , a part
Ž .
of which is profitable level E . However, adjusting for non-market failures
Žrational use of high discount rates , means that fewer investments in energy.
Ž U
.
efficiency are profitable level E . Finally, after accounting for market failures, e.g. limited information, fewer investments will be achieved than are economically efficient. We have thus reached point A, representing the level of efficiency actually achieved.
One final notion about the energy efficiency gap is worth mentioning. Some argue that neither point A nor points E and EU
could be socially efficient. Such a social optimum can be attained if energy prices fully internalize the environmental damages of energy use, and after the abolishment of subsidies on energy use. However, in this paper we abstract from the socially optimal level of energy efficiency. We do not state which of the explanations of the energy efficiency gap call for government intervention. In this paper, we are merely interested in the behavioral modeling of investing in energy efficiency.
3. ICARUS
The bottom-up database ICARUS contains information on energy-saving tech-niques in the Netherlands. It tries to assess the possibilities for energy saving in
Ž .
2000 and 2015 De Beer et al., 1994 . ICARUS has been a basis for environmental policy making from the early 1990s, and has since been updated on a regular basis. Using ICARUS, scientists at the University of Utrecht have projected the energy use in 1990 on to 2000 and 2015 for given production growth at frozen efficiency, i.e. the same energy efficiency levels. On the basis of an average firm in a sector, they have subsequently identified costs and energy savings potentials of all avail-able energy savings techniques, which were not implemented in 1990. Life cycle costs include the initial investment costs, maintenance costs during the whole life-cycle and other costs. It is possible to calculate which techniques are profitable in 2000 and 2015 at given energy prices and discount rates. From this, we can
Ž .
compute energy intensity levels at the sectoral level see Fig. 4 for an example . We have focussed on the demand side of the energy market, thereby abstracting from the supply side options, such as cogeneration.
Ž . Ž .
Fig. 4. Energy-intensity 1990s100 in 2015 in ICARUS at different energy prices ps1990 price
Ž .
This bottom-up projection has a very serious limitation. ICARUS compares the
Ž .
actual situation in 1990 point A in Fig. 2, Fig. 3 , reflecting a partial implementa-tion of effective techniques, with a situaimplementa-tion of full implementaimplementa-tion of
cost-Ž .2
effective techniques in 2015 point E . Hence, predicting the location of point A for 2015, using only bottom-up information, is not possible. To make such a prediction we would require additional information about the distance between the points A and E in 2015. In the next section, we present a model that explicitly describes the difference between points A and E using vintages of capital goods and lagged behavioral reactions.
When we consider energy efficiency improvements over time, it matters whether existing capital can immediately be retrofitted after rapid price movements, or that only new techniques and production processes gradually penetrate by scrapping old ones. In the medium-term, retrofit is the most important mechanism. In the long run, new production processes and techniques determine energy efficiency by replacing existing capital that often has retrofit techniques added on.
Acknowledging the difference in techniques, we asked the makers of ICARUS to
Ž
supplement the database with information on the type of investment Van Vuuren,
. Ž
1996 . ICARUS now distinguishes between new or replacement techniques e.g. a
. Ž .
new energy efficient car and retrofit techniques e.g. wall insulation . A third category consists of good housekeeping, mainly reflecting labor intensive changes in energy management. As ICARUS was not designed to draw these distinctions originally, some matters of definition remain. Consider for instance a new electri-cal central heating pump in an existing house: Should one classify this as a completely new technique or as a retrofit technique? However, at the same time it
Ž .
sheds light on other issues: for instance, it appeared that retrofit on new future investments was not accounted for in ICARUS. Furthermore, it appeared that some of the techniques regarded as retrofit techniques could also be implemented in new vintages, suggesting that the energy-saving potential of replacement tech-niques should include part of the potential of retrofit techtech-niques.
Another addition to ICARUS concerns a more detailed picture of the availabil-ity of techniques over time. We have asked the makers of ICARUS to discern not only 2000 and 2015, but also 1995 and 2008. It appears that very few ‘new’ techniques become available after 2008. This raises the question of whether it is possible to look beyond 2010 on the basis of bottom-up information available in 1990.
With some simple assumptions, ICARUS allows us to empirically determine the magnitude of the energy efficiency gap in 1995. Suppose that investors use a 15% discount rate in analyzing saving techniques and assume that real energy-prices have been at the 1990 level. Then, ICARUS shows that 9% energy efficiency
Ž .
improvement excluding good housekeeping measures would have been profitable in the Netherlands in 1995 compared to 1990. However, empirical analysis shows
2
that only some 5% has been achieved in practice, implying an energy efficiency gap of some 4%.
4. NEMO
Ž
This section summarizes the main behavioral equations in NEMO Netherlands
.
Energy Demand Model , most of the parameters of which are derived from ICARUS. NEMO models the energy efficiency gap and tries to reconcile bottom-up and top-down approaches to energy efficiency.
The nature of ICARUS has led us to develop a vintage model of the putty]semi-putty type. In this model, ex-ante substitution possibilities of factors of production are higher than ex-post possibilities. Put differently, it is possible to retrofit existing vintages after a price increment, but its efficiency gain will be less than the addition of a new, highly efficient vintage while scrapping a less efficient one.
Ž
Fig. 5 shows a nested production structure of the KLEM type see, e.g. Berndt
. Ž
and Wood, 1975 . We assume that it is separable in its inputs. Energy fuel Ef and
. Ž .
electricity Ee and capital investments If and Ie aimed at energy saving are
Ž .
combined to produce capitalrenergy bundles Hf and He . These in turn are
Ž . Ž
combined with energy-saving labor Lf and Le , resulting in energy services Zf
. Ž .
and Ze . In the final stage, energy services are combined with materials M , labor
Ž . Ž .
not related to energy-saving L0 and vintages of capital not related to
energy-Ž . Ž .3
saving I0 to produce physical output Y . NEMO describes only the substitution
Fig. 5. Nested production structure of NEMO.
3
between capital, labor and energy in the production of energy services. Other
Ž . Ž .
models are required to predict output Y , energy services Zf and Ze and
Ž .
non-energy inputs M, I0, L0
For a formal derivation of energy use based on cost-minimization with CES
Ž .
production functions we refer to Koopmans et al. 1999 . The main aim of this paper is to show that the parameters of NEMO can be derived from ICARUS. For this aim, a mere representation of the behavioral equations is sufficient. However, it is worth pointing out one necessary simplification in the derivation due to data limitations. Starting with cost-minimization in nested CES production function, it is well known that the price elasticities are not constant, but depend on the level of factors of production. Although ICARUS contains information about the level of energy use, it does not contain information on the present level of capital
Ž .
investments aimed at energy-saving If and Ie . It gives only additional capital
Ž .
investments necessary to save energy. This required a log linearization of factor demand equations, yielding constant price elasticities that do not depend on levels of factor demand.4
The main equations of NEMO are summarized in Box 1. It describes ex-ante
w Ž . Ž .x
and ex-post efficiency of vintages Eqs. 1] 4 , pointing at differences in energy
Ž .
efficiency between new and existing vintages. The final equation 6 represents an
Ž .
index of energy efficiency of total capital stock Ft at time t in a sector by weighting together all vintages existing at timet, with total investments in the year
Ž .
in which the vintage was installed as weights total investments as proxy for Z . Scrapping of investments is assumed to be linear over a symmetric interval around
w Ž .x5 Ž . Ž .
the average lifetime of a vintage Eq. 5 . We will now describe Eqs. 1] 4 in turn, thereby explaining how each equation implicitly contributes to the energy efficiency gap.
Ž .
Eq. 1 defines the ex-ante energy efficiency of a new vintage added in year t relative to a vintage installed in year t0. Two factors affect the ex-ante efficiency. Firstly, performance improvements and lower equipment prices can be translated into a decreasing price of energy-saving investment per unit of energy saved. A decreasing price of energy-saving techniques leads to substitution of capital for energy, captured by the time trend a. Secondly, the relative energy price affects efficiency through elasticity b.
Trend parameter a is calibrated such that, at constant 1990 energy prices, simulated energy efficiency improvements in NEMO are equal to energy efficiency improvements in ICARUS. We have made two important adjustments to account for the remarks in the previous section. Firstly, on the basis of evidence provided in
4
Alternatively, we could have obtained log-linear equations by starting out with Cobb]Douglas production functions. However, we consider the Cobb]Douglas structure as very restrictive. Moreover, this would have yielded equations forErZwhich would not be log-linear in PErPZ, if we express PH as a weighted average of PEand PI.
5
Box 1 NEMOs main equations
e a
Ž .
Ex-ante fuel efficiency of the vintage added in yeart Ff ,t0s1 , exclusive of good housekeeping effects
ybf
Ideal ex-post fuel efficiency in yeartof the vintage installed in yeart, exclusive of good housekeeping effects
Ex-post fuel efficiency in vintagetin yeartafter actual retrofit through partial adjustment , exclusive of good housekeeping effects
Ex-post fuel efficiency including good housekeeping
yuf
Linear scrapping over interval amin,amax ctyts0 tyt-amin
Fuel efficiency index of total capital stock
t
where similar equations are used for electricity af,gf,hf,bf,df,uf)0strends resp. price elasticities
t,t0,tssubscripts denoting yeart, base yeart0and vintaget
Ž .
Van Vuuren 1996 , we assume that ICARUS’ energy saving potential is a better approximation for the year 2010 than for 2015. Secondly, we simulate a situation whereby the whole capital stock of 2010 has the efficiency of the vintage added in 2010, reflecting that ICARUS describes a situation of full penetration of available and cost-effective energy-saving techniques. The price elasticity b is calculated by fitting a function with constant price elasticity through the constructed points in Fig. 4.
The parameters a and b are estimated by evaluating ICARUS using a 15%
Ž .
discount rate. This is based on Velthuijsen 1995 , who investigated 303 firms and
Ž .
concluded that firms use on average a pay-back time of 5]6 years to evaluate energy saving techniques, which corresponds to a real discount rate of
approxi-Ž .
mately 15%. The fact that this is higher than market rates of interest 5]8% can be explained by the non-market failure factors mentioned above. The rest of the
Ž
energy efficiency gap the difference between implicit discount rates of 20]25%
.
and the 15% we use is described in NEMO, as a gradual penetration of new techniques in the capital stock.
Ž . Ž .
Eqs. 2] 4 describe ex-post efficiency of vintages, which may differ from the ex-ante efficiency of a vintage for several reasons. Firstly, during the lifetime of a vintage, retrofit investments may add capital aimed at saving energy in response to
w Ž .x
changes in energy prices and technical progress Eq. 2 . Ideal retrofit in year t
ŽGt.of a vintage installed in yeartdepends on technological change, reflected by time trend g, and on the relative energy price through elasticity d. In general, bottom-up information supports that g-a and d-b, showing that ex-post possibilities of substitution between factors are smaller than ex-ante possibilities, and hence the putty]semi-putty structure of production. The calculation of the parameters g and d follows similar methods to those for replacement techniques. In practice not all profitable retrofit investments are undertaken. This con-tributes to the energy efficiency gap. We describe this by a simple partial
adjust-w Ž .x
ment process Eq. 3 . The speed of adjustment depends on the rate of replace-ment of capital stock and other parameters reflecting e.g. competitive pressure. We calibrate « and c such that half of the profitable retrofit techniques penetrate within 2 years. Empirical information may lead to a review of these parameters.
Ž .
Eq. 3 also reflects that it is not possible to adjust to lower energy efficiency
Žhigher F. by decommissioning retrofit investment during the lifetime of the
w
vintage. Nevertheless, total retrofit investments can be reduced by scrapping Eq.
Ž .5 retrofitted vintages.x
The second reason for the difference between ex-ante and ex-post efficiency is
w Ž .x
that good housekeeping in the form of additional labor can save energy Eq. 4 . This may happen in the year in which the vintage is installed, or afterwards during the lifetime of the vintage. Bottom-up information on good housekeeping
‘invest-Ž .
ments’ is limited 40 techniques for 19 sectors . We assume that there is no trend
Ž .
Fig. 6. Explaining the energy gap modeled in NEMO in terms of the implicit discount rate in ICARUS; food and drink sector.
techniques were just not profitable in 1990. Good housekeeping will change only if the energy price changes.
We illustrate NEMO’s partial description of the energy gap in Fig. 6. We have analyzed the fuel efficiency improvement in the food and drinks sector according to
Ž
ICARUS with a 15% and 25% discount rate at various prices ps1990 fuel price;
.
fuel intensity in 1990s100 . We compare this to NEMOs predictions. The figure shows that the slopes of the curves are the same. Indeed, this follows from gearing price elasticities to ICARUS results. Secondly, at a 15% discount rate, NEMO predicts less energy efficiency improvements than ICARUS. This difference shows that NEMO predicts part of the energy efficiency gap. Thirdly, by analyzing
Table 1
NEMOs parameters compared to other studies
Ž .
Long-term efficiency trend % per year Long-term price elasticity Fuel Electricity Total Fuel Electricity Total
NEMO
Total y0.73 y1.42 y0.81 y0.30 y0.24 y0.29
Industry y0.49 y1.02 y0.56 y0.19 y0.14 y0.18
Historical studies
a b c d
Total y1.3 ,y0.93 y0.30 ,y0.62
c d
Industry y0.20 ,y0.27
a Ž .
Ministry of Economic Affairs 1996 ; 1960]1990.
b Ž .
Farla and Blok 1997 ; 1985]1994.
c Ž .
Groot 1988 ; 1965]1985.
d Ž .
ICARUS with a 25% discount rate, we may conclude that the part explained by NEMO corresponds to an additional discount rate of up to 10%.
We have outlined how we obtained trends and elasticities from the bottom-up information in ICARUS. Table 4 at the end of this paper contains trends and elasticities for 19 separate sectors, thereby distinguishing between fuel and electric-ity use, and the type of investment. For a detailed discussion of the parameters see
Ž .
Koopmans et al. 1999 . In this paper we will present some general findings and discuss the implicit discount rate in NEMO.
Ž .
Table 1 presents aggregated results over sectors from NEMO. The results show
Ž .
a long-term autonomous energy efficiency trend of approximately 0.8% per year and a long-term price elasticity of energy efficiency of approximately 0.3. The ‘raw’
Ž
ICARUS data suggests an energy efficiency improvement of 1.5]2% per year at a
.
15% discount rate , which is caused by the fact that ICARUS describes a hypothet-ical situation of full penetration of available and cost-effective energy saving techniques in 2015. This hypothetical situation will not occur in reality because actual energy efficiency lags behind technological possibilities.
Ž .
Table 1 also compares NEMOs parameters to results from top-down historical studies. One should however take caution in comparing the studies, for two reasons. Firstly, the historical studies, using sectoral data on energy use, exclude inter-sectoral effects but include intra-sectoral effects: if energy-intensive sub-sec-tors grow faster than energy-extensive sub-secsub-sec-tors, it leads to a decline in energy intensity of the sector as a whole. This effect is not accounted for by ICARUS or NEMO: these focus on efficiency. Secondly, this study investigates energy saving techniques at the energy demand side only, while the study by the Ministry of
Ž .
Economic Affairs 1996 includes energy efficiency improvements at both the supply and demand sides. Table 1 shows that NEMOs elasticities are compatible with results from other studies. NEMO’s trends, however, are lower. This might be caused by the differences described above. It might also indicate that new tech-nology for energy efficiency improvements will become available more slowly in the future than it did in the past.
5. Historical simulation
NEMO is not especially suited to simulate energy use in the past. Most of NEMOs parameters were estimated using technological possibilities for the period 1990]2010r2015.6 The parameters we obtained from this do not necessarily reflect technologies available before 1990. In fact, we would need a bottom-up database for, say, the period 1970]1990 to estimate parameters describing this period. As such a database is not available, we analyze the effects on energy efficiency of the
6
energy price increases of 1973r1974 and 1979r1980, and the price fall of 1985r1986, using NEMOs ‘1990]2010’ parameters.
Apart from energy prices, the volume of total fixed investments also strongly influences NEMOs outcomes. This captures business cycle effects. Historically, investments are negatively related to oil prices. The combination of real energy prices, investment levels and government policies determines NEMOs outcomes with respect to energy efficiency.
A first round of simulations confirmed the results from the previous section that NEMOs aggregate energy efficiency trend parameter is lower than in studies based on historical data. That is, predicted energy efficiency improvements on the basis of NEMO were lower than observed improvements over the period 1970]1990.
Ž .
This might be caused partly by intra-sectoral changes see above , but as we used very disaggregated data in measuring energy efficiency improvement, we expect this effect to be much smaller than the difference we found. Another explanation would be that the pace at which new technologies become available is substantially
Ž .
lower between 1990 and 2010 ICARUSrNEMO than between 1973 and 1995
Žhistorical data . To test this hypothesis, we did a second round of simulations in.
which we increased all trend parameters by 50%.
The outcomes of the second simulation are presented in Table 2. Energy efficiency increases strongly between 1973 and 1985, mainly as the result of the real energy price increases of 1974 and 1979r1980. The oil price shocks increase the total yearly efficiency improvement between 1973 and 1985 to 2.1% per year. We can compare this to historical data: between 1975 and 1985, the efficiency improvement was 2.0% per year. After the price fall of 1985r1986, the predicted rate of efficiency improvement falls back to 0.6% per year; the observed rate was 0.8% per year. The difference may be caused by government policies aimed at
Ž
energy efficiency. New policies which were introduced in the 1980s regulations for
Table 2
Energy efficiency, Netherlands, 1974]1995; NEMO simulation and historical data
1974]1985 1986]1995
a Ž .
NEMO simulation Efficiency improvement per year %
Households Fuel 3.3 1.4
Total final energy use 2.1 0.6
b
Historical data
Total final energy use 2 0.8
a Ž .
With trend parameters increased by 50% see text .
b Ž .
.
new-built houses, subsidies for energy-saving investments are not included in these predictions; they may have increased observed energy efficiency improvements.
On the whole, the second set of simulations with increased trends appear to describe energy efficiency reasonably well over the period 1970]1990. This suggests that the pace at which new technologies become available after 1990 is lower than in the 1970s and 1980s. Therefore, it may not be adequate to extrapolate historical trends of energy efficiency improvements into the future.
6. Policy analysis
One of the main examples showing a clear need for NEMO is the analysis of policy changes using scenario analysis. Until recently, energy scenarios were based either on bottom-up information, such as contained in ICARUS, or based on top-down econometric models. However, we have shown that both cases are insufficient to analyze the precise impact of, say, changes in energy taxes. For instance, bottom-up information does not include a proper vintage structure. Comparing situations of partial penetration now and full penetration in the future it is easy to overstate the economic energy savings potential. Furthermore, bottom-up information is likely to introduce lumpiness into the analysis. Measures are of a discrete nature and a small change of the energy price or other conditions may have the consequence that the whole sector switches techniques. In reality, the process is much more gradual. Top-down analyses, on the other hand, are based on past behavior and are no more than an extrapolation of past trends into the future. NEMO does not suffer from these disadvantages. It includes a vintage structure enabling it to compare two situations of partial penetration. It avoids lumpiness by constructing continuous elasticities from concrete measures. Furthermore, it does not extrapolate past trends as it explicity bases its trends on estimates by scientists and engineers. The following example shows the use of NEMO.
In the Netherlands, shifting taxes from labor and capital to energy and other
Ž .
‘environmental’ tax bases ‘greening’ the tax system is being used as an instrument of environmental policy. We analyzed two proposals for increases in energy taxes: one in which existing energy taxes are doubled for all users except energy-intensive
Ž .
industries variant 1 and one in which an existing tax is tripled for very small
Ž . Ž . Ž
energy users mainly private households only variant 2 Vermeend and Van der
.
Vaart, 1998 . These proposals increase energy prices for private households by 15 and 30%, respectively. For industrial sectors and transport, the price increase is
Ž .
3]4% in variant 1 and zero in variant 2. For other sectors services and agriculture prices go up by approximately 15% in variant 1 and 0]3% in variant 2. The
Ž .
additional tax revenue is 3.4 billion guilders per year approx. 0.6% of 1997 GDP in both variants.
Table 3
Effects of increasing energy taxes on energy use in the Netherlands Energy consumption
Level Variant 1 Variant 2
1995 2010 2020 2010 2020
PJ % difference to baseline
Households Fuel 397 y4.4 y4.0 y7.1 y6.5
Electricity 71 y5.1 y5.5 y9.2 y10.5
Ž .
Industry Fuel incl. feedstock 933 y0.6 y0.6 0.0 0.0
Electricity 107 y0.6 y0.6 0.0 0.0
Transport Fuel 421 y0.6 y1.0 0.0 0.0
Other Fuel 415 y2.7 y3.3 y1.1 y1.4
Electricity 84 y3.0 y3.5 0.0 0.0
Conversion Fuel 586 y0.7 y2.0 y1.0 y2.2
Total domestic consumption 3015 y2.2 y2.1 y2.1 y2.0
savings in all sectors of the economy. In variant 2 price changes and therefore additional energy saving are mainly computed for private households.
We also calculated macroeconomic effects of the energy tax increases using a sectoral model of the Netherlands economy. Because of NEMOs top-down nature, its results with respect to energy savings and energy use are less difficult to use as input for such sectoral models than results from bottom-up analyses. The macroeconomic effects appear to be very small, mainly because the additional tax revenues are used to lower other taxes. GDP is 0.1% lower than in the base-line in 2010 and 2020, private consumption is 0.2]0.3% lower, and the effect on total employment is 0.0%.
Because of NEMOs bottom-up roots, the results are more or less compatible with bottom-up models. This enabled bottom-up scientists to develop a ‘subvariant’ in which 15% of the tax revenue is used to subsidize specific technologies. These ‘positive incentives’ would increase the effect of the taxes on energy use from
y2% to approximatelyy3%.
In 1998, the Netherlands government decided to implement a mix of the two
Ž .
variants including ‘positive incentives’ between 1999 and 2001, as part of a major tax reform.
7. Conclusions
future. It is also possible to calculate the effect of policy measures aimed at specific technical options more adequately than by using a top-down model.
We also found that the average pace at which new, energy efficient technology becomes available, is lower between 1990 and 2015 than it was between 1974 and 1995. Therefore, extrapolating historical trends of energy efficiency improvements into the future may not be valid.
However, research on NEMO is not finished. We see three areas for further research. Firstly, we need to look at the demand for energy services. More and more electricity-using techniques are replacing fuel-using techniques. In terms of the model, we need to investigate the substitution between Ze and Zf. Secondly, the development of energy efficiency over time seems to depend on the types of techniques that are available: retrofit andror replacement techniques. As ICARUS was not designed to distinguish between these types of techniques, definition questions as to what is retrofit and what is replacement still need to be addressed. Thirdly, ICARUS does not enable us to estimate all parameters of the model,
Ž .
notably the speed of adjustment of actual ex post to ideal ex post efficiency Box 1 . More empirical research measuring investors’ demand side behavior is required.
Ž .
()
repl. retrof. good-h. repl. retrof. good-h. repl. retrof. repl. retrofit
ybf ydf yuf ybe yde yue yaf ygf yae yge
For more details see Koopmans et al. 1999 .
b
ICARUS is based on the average firm. However, due to heterogeneity of firms, there will always be firms investing in energy efficiency. We impose the
Ž . Ž . Ž . Ž . Ž . Ž
following minima to trends and elasticities: ybf y0.10 ;ydf y0.05 ;yufy0.02; industryy0.01 ;ybe y0.10 ;yde 0.05 ;yue y0.03; industry
. Ž . Ž . Ž . Ž .
y0.01 yaf y0.20 ;ygf y0.10 ;yae y0.20 ;yge y0.10 .
c
We tentatively excluded cogeneration }an energy supply option from the electricity trend. Also, we assumed less assimilation lighting than ICARUS does.
d
Adjusted for new insights by ICARUS makers, not included in ICARUS-version 3.
e
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