q Work sponsored by the University of Chicago/Argonne National Laboratory Collaborative Grant Program and the US Environmental Protection Agency

* O $ ce of Atmospheric Programs under contract No. W-31-109-ENG-38.

* Corresponding author. Tel.: 001-630-252-5393; fax: 001-630-252- 4498.

**E-mail address:** gboyd @ anl.gov (G.A. Boyd) Energy Policy 28 (2000) 289

} 296 

# Estimating the linkage between energy e $ ciency and productivityq 

## Gale A. Boyd ! , * , Joseph X. Pang "

! **Policy and Economic Analysis Group, Argonne National Laboratory, DIS-900 Rm, 1-14, 9700 C. Cass A** v **enue, Argonne IL, 60439-4815, USA** " **Economics Department, Uni** v **ersity of Chicago, IL 60637, USA** Received 18 August 1999

Abstract

Many analyses have referred to industrial productivity bene " ts associated with energy e $ ciency that are at least as great or larger than the energy bene " ts. If plants with higher energy intensity also tend to have lower productivity, then energy policy needs to consider this. This study examines this issue for two segments of the glass industry, using plant level data from the Census Bureau. Productivity is de " ned by the di ! erence in & best practice ' production e $ ciency, as measured by data envelopment analysis (DEA). This study uses regression analysis to estimate how di ! erences in plant level electricity and fossil fuel intensity, i.e. energy use per unit of production, are attributable to di ! erences in plant level productivity and other economic variables, like energy prices and cumulative production. In every case, productivity di ! erences between plants are statistically signi" cant in explaining di ! erences in plant energy intensity. The coe $ cient that links productivity to energy e$ ciency yields a less than proportional impact for only one industry and fuel type. For others the relationship is implies that a 1% increase in productivity increase energy e$ ciency by more than 1%. This e ! ect is statistically signi " cant for # at glass, but not container glass. The estimates of the price and learning-by-doing coe $ cients seem reasonable, but without imposing the assumption that all plants in the industry are equally productive. This lends further credence to a signi " cant relationship between productivity and energy e$ ciency. ( 2000 Elsevier Science Ltd. All rights reserved.
**JEL classi** x **cation:** L61; O30; Q20 **Keywords:** Productivity; Energy e $ ciency; Frontier production function; Learning by doing

1. Introduction

Advocates of energy e $ ciency have described productivity bene " ts associated with energy e $ ciency projects that are as least as great or larger than the energy bene " ts (Elliott **et al**., 1997; Porter and van der Linde, 1995). This typically is interpreted to mean that outputper unit of non-energy input(s) increase because of a project identi " ed to increase output per unit of energy. The **association** of non-energy bene " ts from any project maydepend upon the original intent of the project. Improvements in the overall productivity of a process are likely to reduce energy use, hence productivity is associated with energy e $ ciency. On the other hand, the search for energy e $ ciency improvements may identify a project that improves the productivity of other input factors as well.

Porter and van der Linde (1995) maintains that a search for energy e $ ciency that leads to a productivity improvement is a common occurrence. Other analysts have agreed that productivity improvements can reduceenergy and other inputs, but have questioned the anecdotal nature of case studies of energy e$ ciency and associated bene " ts as to their widespread validity (Palmer **et al**., 1995). This paper o ! ers a test of howwidespread this occurrence may be, by providing a rigorous measure of inter-plant productivity di ! erences andestimating the statistical linkage between that productivity measure and energy intensity. If plants that have higher energy intensities are signi" cantly related to 0301-4215/00/$ - see front matter ( 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 1 - 4 2 1 5 ( 0 0 ) 0 0 0 1 6 - 1plants with low productivity, then there is support for the notion that energy (in)e $ ciency may be a guidepost for productivity improvements. If higher energy intensities are signi" cantly related to plants with low productivity, the implications are that policies that are focused on energy e$ ciency, e.g. greenhouse gas policy, may not cost as much once the non-energy bene " ts are included. In addition, energy technology R&D policy might pro " tably focus onbroader productivity issues, rather than narrowly focusing on improving the energy use of speci" c types of equipment. Given the di$ culty in determining a causal linkage, the empirical validation of how di ! erences in energy e $ ciency **are correlated with** di ! erences in productivity is still important. This empirical validation needs to be asgeneral as possible, since there are important policy implications. However, it is reasonable to expect that any link between energy e $ ciency and productivity will be as industry-speci " c as is the energy use in those sectors. This paper proposes an empirical approach to the question and implements it for two segments of the glass industry, # at and container. The glass industry is an energy-intensive manufacturing sector, which has undergone signi"

- cant technological changes in recent years. The Oxy-fuel furnace has replaced the traditional furnace capacity inmany plants. While the empirical results for these segments of the glass industry may not generalize to otherindustries, the approach can be extended to other industries that produce a homogeneous output.

The link between energy e $ ciency and productivity is estimated by relating inter-plant energy and production e $ ciency, holding other economic variables constant. The industry segments for empirical study are chosen to be rather narrow, so that the underlying assumption that technical opportunities are similar can be best supported. We do not examine speci " c energy e $ ciency projects, but instead compare di ! erences in energy intensity acrossplant, within each of the two industry segments. A regression analysis is used to estimate how plant level energyintensity (energy use per unit of production) is attributable to plant level productivity and other & traditional ' economic variables, like energy prices. This study uses plant level data from the Census Bureau. Without plant level data these relationship could not be meaningfully estimated.

This paper uses a rigorous production functionapproach to measuring inter-plant productivity. Productivity is measured against the & best practice ' production function, or production frontier, via Shephard ' s distance function (1970). The empirical implementation of the distance function is a solution to a linear programming (LP) problem. This approach, which is also called data envelopment analysis (DEA), assigns an index to eachplant, which represents the amount by which all production inputs could be reduced, without changing output levels. A plant that has an index of 1.0 is & best practice '

. The study computes the DEA-based production e$ ciency for the two glass industry sectors and uses it to estimate the linkage between energy e$ ciency and productivity.

The paper is organized as follows. First, we present the measurement of the & best practice ' production and e $

- ciency. Then, we discuss the con " dential data used in the analysis and the constraints placed on the study by usingthat data. Finally, we present the results from the regression model used to estimate the relationship between in energy e $ ciency and inter-plant productivity and discuss the implications.
2. Frontier production function approach Measuring energy ` e $ ciencya is typically handled bycomparing measures of energy intensity, the energy/output ratio. Although the best measure of energy e$ ciency has been the subject of some discussion (Freeman **et al**., 1997) it is much simpler than measuring productivity.This paper uses the frontier production function to provide a measure of productivity, analogous to comparing energy intensities across plants.

The production function is de " ned as, y " f(X) where y is the maximum level of output that that can be produced, given a vector, X , of inputs into the production process. The input vector, X , can be as richly described as the data allow. This functional relationship allows us to represent the technical ability to substitute one factor of production holding the level of output constant.

When combined with the **beha** v **ioral assumption** that economic agents have complete knowledge of the prices of inputs and can use the complete knowledge of this function, f(X), to minimize costs for a given level ofproduction many powerful economic concepts and results are obtained. However, these are strong assumptions and they should not, or need not, be imposed on an empirical analysis. In this paper, we take an approach that does not impose assumptions about optimal behavior.

The minimal assumption of optimal behavior has two implications for this analysis. The " rst is that maximal output of the production function is not assumed to hold for all observations, i.e. a plant may be ine$ cient relative to some other, better performing plant. This allows us to de " ne a productivity index that is part of the study. The second is that when cost minimization is not assumed, **optimal factor demand equations do not exist**. This does not mean that prices have no impact in economic decisions, but that the theory of duality does not provide the speci " cation of factor demands, including energy. For this reason, a simple regression model for the in # uence of productivity and other economic variables on the energy

} output ratio is used. 290 **G.A. Boyd** / **Energy Policy 28 (2000) 289**

} **296**Fig. 1. DEA Representation of & Best practice ' production set,  ̧ (y).
Our measure of relative productivity, or production e $ ciency, is based on non-parametric techniques (see Fa

K re **et al**., 1985) The input set  ̧ (y) is de " ned as  ̧ (y) " M X : y * f(X) N . Our e $ ciency measure is based on the distance function, D (y , X) " min Mj : x j 3 ̧ (y) N (Shephard, 1970). The set  ̧ (y) can be de " ned by linearprogramming using observed data. These linear programming (LP) methods for implementing the distance function, also called data envelopment analysis (DEA), use k observed input and output combinations as thebasis of the production technology by solving the following LP:

D (y 0 , x 0 ) " min j S 6"+%#5 50 ,

z > * y 0 , zX ) j x 0 , z 3 Rk ` , j , y 3 R ` , X 3 RN ` . To understand intuitively how this approach works,consider Fig. 1. For, discussion, each point on Fig. 1 represents a combination of inputs that all produce the same output level. Plants represented by points B and C are technically e $ cient, i.e. since they are on the isoquant they cannot reduce inputs and maintain the given output level y . However, plants A and D is ine $ cient (less productive). These plants could reduce inputs by the amount indicated by the arrow and still remain in the input set  ̧ (y). We now ask the question, & Is energy any di ! erent from other inputs? ' . The answer from the perspective of the proposed productivity measurement is & No. ' All inputsare treated the same. Our proposed measure of productivity computes the amount that energy, and all other inputs, could be reduced without reducing output levels. In the case of plant A, and the other plants represented by the solid dots, it appears that the low productivity is related to very high-energy use. However, plant D and those others represented by the clear dots have lower energy intensity and lower amounts of production (in)e $ ciency as well. If the data are such that plants that are highly ine $ cient tend to lie in the region of production space that has higher energy input, then there is support for the hypothesis that energy e$ ciency and productivity are statistical related. If ine$ cient plants were more randomly distributed in production space, then we would not " nd a statistical link between productivity and energy e $ ciency.

To compute the amount of production e$ ciency for input into the empirical analysis, the LP speci" ed above, using the KLEFM input speci" cation, was run for each observation in the data set. A plant is e$ cient, i.e. on the isoquant, if the LP assigns it an index of j " 1.0. When j ( 1.0, the plant is not e $ cient. The average level of production e $ ciency was 76 and 83% for # at and container glass, respectively.

3. Plant level data

We derive our data from the Longitudinal Research Database (LRD). The LRD is the plant level data which forms the basis of much empirical economic analysis of manufacturing. The LRD was developed by the Center for Economic Studies (CES) at the Bureau of the Census. CES has constructed a panel of plant level data from the Annual Survey of Manufactures and the Census of Manufacturers. The LRD includes economic activity, **G.A. Boyd** / **Energy Policy 28 (2000) 289**

} **296** 2911 Our linear programming approach would automatically treat a plant as e $ cient if there were no other plants with su$ ciently similar production technologies, as represented by the input vector. Table 1 Summary statistics for 1987

} 1995 balanced panel data for # at glass
Variable Number of observations Mean Standard deviation Electric-output ratio 126 0.83 0.37 Fuel-output ratio 126 0.023 0.011 Cumulative output (Million $) 126 0.541 0.462 Capital-output ratio 126 1.78 1.03 Electric price ( $ /kwh) 126 0.046 0.016 Natural gas price ( $ /Mbtu) 126 2.84 1.34 LP e $ ciency measure 126 0.77 0.17

2 A single year for container glass was used since we construct a dummy variable for plants that include electricity based melting furnaces in their production process. The data on furnace design did not include the year the electric furnace was installed, only that it was present in 1995. This restricted our ability to construct a panel for this segment. e.g. total shipment of output, labor cost, energy, plant and equipment, materials, for a sample of plants during the survey years and complete coverage during the years of the economic census. Under Title 13 of the US Code, this data is con " dential. However, academic and government researchers may access this micro-data under the CES research associate program. Con " dentiality restrictions prevent the disclosure of any information which would allow for the identi " cation of a speci " c plant or " rm ' s activities. Sample statistics or statistical coe$

- cients, which do not reveal the identity of individual establishments and " rms, can be released after review at CES. The ability to access the plant level data, rather than aggregate data signi" cantly enhances the information that can be obtained about microeconomic performance, particularly when examining the microeconomic issue of e $ ciency. This paper analyzes a set of manufacturing plants inthe glass industry. The paper examines two major industry sub-segments, container glass and # at glass. These sub-segments were chosen because they meet several criteria: homogeneous production technology, energyintensity, and data availability. Homogeneous production technology is very important in order for the production function approach to provide reasonable comparison among plants. 1 Economic variables in our data set include:

f >

* output (value of shipments), f K

* capital stock, f  ̧

* labor (total man-hours with adjustment for nonproduction workers), f E

* consumption of electricity in kWh, f F

* cost of combustible fuels, and f M

* cost of materials (less energy).

All costs were de # ated using industry speci " c price de # ators. The data for # at glass was a panel data from 1987

} 1995. For container glass a single year, 1995, was used. 2 Plant level electric prices are also derived from the LRD. Since the plant level fossil fuel prices could not be similarly derived, state level natural gas prices were matched to the LRD based on plant location.

4. Results Linear regression is used to estimate the in # uence of the computed DEA production e$ ciency and other variables on energy intensities (energy/output ratios). Electricity (kWh) and fossil fuel (de # ated expenditures) were examined separately. The explanatory variables included electricity and natural gas prices, the log of cumulative output, capacity utilization, time trend (for # at glass) and a dummy variable for electric furnaces (for container glass). Summary statistics are shown in Tables 1 and 2 for the # at and container glass industries, respectively. The # at glass segment includes 14 plants over nine years. The container glass segment is a much larger component of the glass industry, making the panel data less of an issue. Even though a theoretical system of optimal factor demands do not exist under conditions of ine$ ciency, energy prices are still expected to send signals to decisionmakers. The plant level price of electricity could be computed from the LRD, but not the fuel price. The statelevel industrial price for natural gas was used, since nearly all fossil fuel used in glass plants is natural gas. The log of cumulative output of the plant is included to represent plant speci " c learning by doing (LBD) regarding energy e $ ciency. For process industries, the level of utilization appears to strongly e ! ect energy intensity (Boyd **et al**., 1992, 1991). Typically, when output decreases energy use decreases less than proportionally, 292 **G.A. Boyd** / **Energy Policy 28 (2000) 289**

} **296**Table 2 Summary statistics for 1995 data for container glass
Variable Number of observations Mean Standard deviation Electric-output ratio 45 1.08 0.25 Fuel-output ratio 45 0.018 0.0088 Cumulative output (Million $) 45 0.561 0.221 Capital-output ratio 45 5.31 1.88 Electric price ( $ /kwh) 45 0.041 0.015 Natural gas price ( $ /Mbtu) 45 2.62 0.67 LP e $ ciency measure 45 0.86 0.11

Table 3 Regression results for # at glass ( t -ratios shown under the estimates) Intercept Year Log cumulative production Capacity utilization Electric price Natural gas price Productivity Adjusted R 2

Electric 4.07 0.08

! 0.22

! 0.18

! 11.24 0.02

* 0.30 **6.91 4.72**

! **5.03**

! **5.13**

! **4.69 0.78**
4.58 0.07

! 0.21

! 0.08

! 11.50 0.02

! 0.97 0.42 **8.40 4.29**

! **5.30**

! **2.14**

! **5.27 1.14**

! **5.08**
Fuels 0.094 0.002

! 0.005

! 0.004 0.042

! 0.004 0.32 **5.536 3.539**

! **3.904**

! **3.588 0.604**

! **5.566**
0.116 0.001

! 0.005 0.001 0.031

! 0.004

! 0.042 0.58 **8.581 3.032**

! **4.609 0.707 0.566**

! **6.615**

! **8.800**

Table 4 Regression results for container glass ( t -ratios shown under the estimates) Intercept Electric furnace dummy Log cumulative production Capacity utilization Electric price Natural gas price Productivity Adjusted R 2

Electric 3.44 0.83

! 0.18

! 0.01

! 2.80 0.03 0.35 **2.33 4.97**

! **1.69**

! **0.44**

! **1.21 0.59**
4.08 0.85

! 0.18 0.00

! 4.33 0.05

! 0.70 0.41 **2.84 5.35**

! **1.84 0.25**

! **1.88 0.99**

! **2.25**
Fules 0.009

! 0.008 0.003

! 0.001 0.040

! 0.010 0.60 **0.218**

! **1.866 1.081**

! **2.560 0.637**

! **6.869**
0.029

! 0.008 0.003

! 0.001

! 0.009

! 0.009

! 0.022 0.66 **0.766**

! **1.850 1.085**

! **1.800**

! **0.150**

! **6.853**

! **2.728**
hence intensities rise. To capture industry wide e ! ects of technology, a time trend is included in the # at glass sector. The regressions were run " rst without the measure of technical e $ ciency and then with. Table 3 shows the results for the # at glass industry. Including production e $ ciency in the regression improves the # at glass results. When production e$ ciency is included the magnitude of the utilization variable drops, becoming insigni " cant in the fossil fuel equation. Cumulative production, capacity utilization, energy prices, and productivity all have the expected signs. Only the cross-equation energy prices are not signi" cant.

Table 4 show that including productivity in the energy intensity equation improves container glass regressionresults in a similar fashion. The capacity utilization variable declines in size and become insigni" cant for the electric equation. The & own price ' coe $ cient on electricity increases in size and is & marginally ' signi " cant. As before, all coe $ cients have the expected sign, except for the coe $ cient on log cumulative production in the fuels **G.A. Boyd** / **Energy Policy 28 (2000) 289**

} **296** 293Table 6 Price elasticities and learning ratio (evaluation at data mean)
Fuel Electric Flat glass Electric price 0.06 !

! 0.63 " N. gas price

! 0.43 " 0.08 ! Learning ratio 86% " 82% " Container glass Electric price

! 0.02 !

! 0.19 " N. gas price

! 1.11 " 0.13 ! Learning ratio 110% # 87% "

! not signi " cantly di ! erent from 0. " signi " cantly di ! erent from 0 at 0.01 con " dence level.

# not signi " cantly di ! erent from 1.0. Table 5 Average and frontier energy intensity and productivity elasticity
Fuel Electric Flat glass Average intensity 0.023 0.830 Frontier intensity 0.014 0.601 Elasticity

! 3.07 !

! 1.61 ! Container glass Average intensity 0.020 0.945 Frontier intensity 0.016 0.825 Elasticity

! 1.39 "

! 0.85 "

! signi " cantly di ! erent from 1.0 at 0.01 con " dence level. " not signi " cantly di ! erent from 1.0. equation. All coe $ cients are signi " cant, except for the cross price terms and log cumulative production in the fuels equation. Since the productivity coe$ cients are signi " cant, there is support for a link between intensity and productivity. While the signs and signi " cance of the coe $ cients are easily interpreted, the magnitudes are not. One way to understand the magnitude of the productivity coe$ cient is to compute the di ! erence between the sample mean energy intensity and the energy intensity for an e$ cient plant. Since, a linear regression predicts the sample mean intensity when evaluated at the mean inputs, we subtractone minus the sample mean productivity for each industry, times the appropriate productivity coe$ cient, from the sample mean intensity. This is shown in Table 5. The energy intensity of & best practice ' plants are substantially lower than the average. The best practice energy intensity ranges from 13 to 42% lower than the average.

This established that production e$ ciency, as observed empirically, is not so randomly distributed in production input space as to be statistically unrelated to the level of energy intensity. The coe$ cients are signi "

- cantly di ! erent from zero, but we also wish to see whether they depart from the proportional construction of the productivity measure. Since energy is & just another input ' in the production process, it is true by construction of the productivity measure that less productive plants should be able to use less energy. Since the productivity measure is proportional in all inputs, we would like toknow if the statistical link between energy and productivity is more or less than proportional. To see this we recomputed the productivity coe$

- cients, interpreted as elasticities. The productivity coe$

- cients are evaluated at the frontier value of energy intensity, since we wish to know how energy e$ ciency and productivity are related relative to best practice plants. These elasticities are also shown in Table 5. Three of the four elasticities are greater than one. However, the container glass elasticities are not signi" cantly di ! erent from unity, while elasticities for # at glass are. The fuel elasticity for # at glass is quite large, suggesting a 3% increase in fuel intensity associated with each 1% decline in productivity. This is particularly important, since fossil fuel use is the primary energy used in # at glass, withelectricity being used for ancillary manufacturing functions. In both of the glass making sectors, the elasticity of fuel use with respect to productivity is larger than the elasticity of electricity use with respect to productivity. Even though economic optimality is not imposed on the entire estimation, the model estimates signi" cant impacts of energy prices and learning by doing. Table 6 presents the price coe$ cients interpreted as elasticities. The price variables are evaluated at the sample means rather than the frontier, since we would like to see 294 **G.A. Boyd** / **Energy Policy 28 (2000) 289**

} **296**3 Future study might include an equation for equipment choice, but our available data would not support such an addition. what types of signals the energy prices provide to the glass plants in general. The own price elasticities for # at glass seem quite reasonable. The cross price elasticities are not signi " cant, indicating no substantial substitution. The own price elasticities for container glass show a wide range of variation; near zero for electric and above unity for natural gas. These results need not be viewed as unreasonable. The dummy variable for electric furnaces may make it di$ cult to estimate a price response in a single equation. It may be that given the choice ofelectric furnace (or not) there is little (additional) response to electric prices, but substantial # exibility in gas use. 3

The model also investigates whether there are plant speci " c LBD that e !ects energy intensities. This is summarized by the learning ratio (see Table 6). The learning ratio is de " ned as the percent of unit cost remaining when cumulative production doubles. In this case, it is the percent of energy consumption per unit of output. LBD has a signi " cant e ! ect on electric intensities and on fuel intensities for # at glass. The learning ratio is not signi"

- cantly di ! erent from 100% for fuel intensity in container glass. The learning ratios are quite reasonable, albeit higher than the ` typical a 80% learning ratio found in the review paper by (Dutton and Thomas, 1984). In a more recent study, Bahk and Gort (1993) " nd higher learning ratios, implying less LBD.

5. Conclusion

This paper proposes an approach to examine the link between productivity and energy e$ ciency, and tests thislink empirically using plant level micro data. Productivity is measuring using a frontier production function approach to de " ne a benchmark of best practice. Linear programming is used to compute this inter-plantmeasure of productivity. Two energy intensive sub-sectors of the glass industry were chosen to provide speci" c examples. The choice of a narrowly de " ned industrysector allows one to assume that the production processes and opportunities are comparable between plants in that sector. Since the assumption of production e$

- ciency or cost minimization is not imposed, optimal factor demand systems do not exist. However, energy prices are still expected to provide information about energy intensity decisions, so a simple regression model is used to hold energy prices, as well as capacity utilization and LBD constant.

The regression models con " rm that di !erences between plants in productivity in # uence energy intensity. All coe $ cients that links productivity to energy e$ ciency are signi " cant. Since the productivity measure is constructed to be proportional in all inputs we want to know if the elasticity of energy to productivity is greater than unity. If it is then there is support for a stronger link between energy intensity and productivity. A more that proportional impact is estimated for both fuels in the # at glass sector and for fossil fuel in container glass. In other words, a 1% di ! erence in overall productivity results in more than a 1% di ! erence in energy e $ ciency, except for electricity use in container glass. The results for # at glass are signi " cantly greater than unity, but the results for container glass are not. The results demonstrate that productivity di ! erences are important determinants of energy e $ ciency, in some cases, substantially so. It also appears that the e ! ects are industry speci" c.Other economic variables, like energy prices and learning by doing also are found to be important and consistent with basic theory. Estimated price elasticities range from

! 0.2 to

! 1.1, depending on the energy type and sector. Learning ratios that were signi" cantly di ! erent from unity range from 82 to 87%. The coe$ cient on capacity utilization is not signi" cant when productivity isentered in the model, suggesting that utilization is subsumed within the productivity measure.

This paper presents a method to empirically assess the claims of widespread linkage between energy e$ ciency and productivity. The results show that best practice plants are systematically more energy e$ cient, holding prices and learning constant. In every case, productivity di !erences between plants account for at least a proportional di ! erence in energy intensity. In the # at glass sector, the paper supports the notion that higher energyintensity may well be a indicator of lower plant productivity, since the estimated elasticities are signi" cantly greater than unity.

Whether these results are at odds with the claims that production bene " ts are **greater** than energy bene " ts depends on the interpretation of bene " ts. There is supportthat energy intensity and productivity have at least a proportional link. Since energy costs are a small component of total costs, any productivity bene " t that is associated with an energy intensity improvement is likely to have a large non-energy cost component. Therefore, a search for energy e $ ciency that reveals a productivity improvement will have much larger & non-energy ' bene " ts. The results lend statistical support to the notion that there is a linkage, in one case a very strong one, between energy intensity and productivity. The notion that there are non-energy bene " ts, which reduce the overall costs of policies to improve energy intensity, is also supported by this work. For example, the most e ! ective greenhouse gas reduction policies would be those that best exploit thislinkage. R & D Policy that promotes productivity enhancing technology in the energy using sectors is consistent with these empirical results. The widespread policy **G.A. Boyd** / **Energy Policy 28 (2000) 289**

} **296** 295implications for these speci " c empirical results depend upon whether they can be generalized to other industry sectors, or replicated.

Acknowledgements I would like to thank George Tolley of the University of Chicago Economics Department for his suggestionsand technical support in this collaborative research project. I would also like to thank Joe Roop, Skip Laitner, Stephan DeCanio, Richard Howard and an anonymous referee for their comments on an earlier draft of this paper. The usual disclaimer applies.

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