- Email: [email protected]
Understanding industrial energy use: structural and energy intensity changes in Ontario industry
Energy Economics 20 Ž1998. 29]41
Understanding industrial energy use: structural and energy intensity changes in Ontario industry Douglas T. Gardner a,U , Mahmoud A.T. Elkhafif b a
b
Algorithmics Inc., 185 Spadina A¨enue, Toronto, Ontario, M5T 2C6, Canada Consumers Gas, Economic Studies Department, 500 Consumers Road, North York, Ontario, MZJ 1P8, Canada1
Abstract A large number of studies have examined the relative importance of structural versus intensity changes on the evolution of industrial energy use. Relatively little research, however, has been done to better understand why these changes have occurred. Using an econometric approach, this paper attempts to provide insight into the changes in industry structure and energy intensity that occurred in Ontario between 1962 and 1992. The estimation successfully identifies and measures cyclical and trend components for both the structural and intensity indices, the cyclical component being due to changes in industrial output, the trend component, likely due to changes in technology and consumer preferences. Q 1998 Elsevier Science B.V. Keywords: Energy intensity; Structural shift; Industrial energy use
1. Introduction Industrial energy use may be viewed as a function of total industrial output, the composition of that output and the means by which that output is produced. A large number of studies have estimated the relative importance of these factors in explaining how industrial energy use has evolved in different localities ŽMarlay, U 1
Corresponding author. Tel: q1 416 217 1400; e-mail: [email protected] e-mail: [email protected]
0140-9883r98r$19.00 Q 1998 Elsevier Science B.V. All rights reserved PII S0140-9883Ž97.00008-X
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
30
1984; Ang, 1987; Boyd et al., 1987; Li et al., 1990; Howarth et al., 1991; Gardner, 1993.. Relatively little work, however, has been done to go beyond these simple descriptions of what happened to examine the question of why. This paper uses an econometric approach in an attempt to better understand observed changes in a Divisia index measure of industry structure and energy intensity over the 1962]1992 period in Ontario, the industrial heart of Canada. Werbos Ž1987., using a different methodology than that used here, analyzed structural shift in the US over the 1958]1983 period, concluding that growth in GNP, real interest rates and time were important factors while the price of energy was not. Boyd and Ross Ž1989. examined the impact of the relative price of materials versus all commodities, GNP and time on an electricity structural index for the US over the 1958]1985 period, finding that only time was statistically significant. Previous econometric studies of Ontario industrial energy use include those of Mountain et al. Ž1989. and Elkhafif Ž1992.. These studies did not explicitly examine the impact of structural change, however. The next section describes the study methodology along with the estimation results. A discussion of these results and a conclusion follows.
2. Methodology and estimation results In year t, the aggregate energy intensity index Ž Yt . is simply the ratio of total industrial energy use Ž Et . to industrial output in real value added terms Ž Q t .: Yt s
Et Qt
Ž1.
In the Divisia index approach, the aggregate energy intensity index is then represented as the product of a structural index Ž St . and an intensity index Ž It .: Yt s S t It
Ž2.
The former measures changes due to shifts in the composition of the industrial sector; the latter reflects changes in manufacturing technology and product mix within individual industries. The Divisia index decomposition of the aggregate intensity index is derived in the Appendix. It is one of several different methods that have been used to decompose changes in industrial energy use into structural and intensity factors. The advantages and disadvantages of these different methods have been discussed by a number of authors ŽBoyd et al., 1988; Howarth et al., 1991; Liu et al., 1992.. Gardner Ž1993. used a Divisia index approach to examine the evolution of industrial energy use in Ontario, based on purchases of refined petroleum products Žoil., natural gas, electricity and coal by 21 different industries from 1962 to 1984. Figs. 1]3 show the intensity, structural and aggregate intensity indices for energy, fossil fuel and electricity over the 1962]1992 period. The indices shown for the
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
31
1984]1992 period are based on an eight industry disaggregation of industrial energy use, since data at the 21 industry disaggregation level is available only for the 1962]1984 period.2 For purposes of discussion, the study period may be divided into three periods: pre-oil-shock Ž1962]1973., oil-shock Ž1973]1985. and post-oil-shock Ž1985]1992.. The aggregate energy intensity declined at a roughly constant rate over the pre-oil-shock and oil-shock periods. The reasons for this decline differ significantly in the two periods, however. In the pre-oil-shock period, the decline was due almost entirely to a decrease in the intensity index, the structural index remaining relatively constant. In the oil-shock period, in contrast, the structural index declined significantly; the intensity index, perhaps surprisingly, declined only slightly. In the post-oil-shock period, the structural index flattened out once again while the intensity index actually rose slightly, leading to an increase in the aggregate intensity index. The fossil fuel indices mirror the same pattern as the energy indices, although the decline in the intensity index during the oil-shock period is somewhat larger. The electricity structural index follows a similar pattern to the fossil fuel structural index, although it has decreased more over the study period. The electricity intensity index, on the other hand, differs significantly from the fossil fuel intensity index, particularly in the oil-shock period during which time it rose dramatically while the fossil fuel intensity index declined. Equations were estimated for the structural and intensity indices of electricity, fossil fuel Žoil, natural gas and coal together. and total energy. A log-linear model specification was used in each equation. The structural index equations are described next, followed by the intensity index equations.
3. Structural index equations Energy intensive industries are generally process industries, creating basic materials from raw materials. Key energy intensive industries in Ontario include mining, primary metals, pulp and paper, chemicals, and non-metallic minerals. Non-energy intensive industries often involve fabrication and assembly activities, using basic materials created by energy intensive industries. Important examples in Ontario include transportation equipment, plastic products and electrical and electronic products. The shift away from materialrenergy intensive industries in most developed nations during the post-oil-shock period has been well documented, although the magnitude of this effect varies considerably. A number of different explanatory factors for these changes exist. One possibility is a growing consumer preference for services and high value-added, low material-intensive products ŽWilliams et al., 1987.. The emergence of new and 2
The lost detail was for industries with low energy use. Hence, the indices for the 1984]1992 period are likely little different from those which would have been calculated had data at the 21 industry disaggregation level been available Žsee the index Eq. Ž11...
32
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Fig. 1. Energy indices.
improved materials, better manufacturing technology and improved product design that have tended to reduce the quantity of materials and energy embodied in finished goods, might also be contributing factors ŽRoss et al., 1987; Gardner and Robinson, 1993.. An inputroutput analysis conducted for the US, for example, estimated that one fifth of the drop in the aggregate energy intensity index over the 1972 to 1985 period was attributable to such ‘indirect’ efficiency improvements ŽUS Congress, 1990.. If such changes in technology and consumer preferences have occurred at a relatively constant rate over time, as is perhaps reasonable, a time trend term may serve to model the impact of these factors. Changing trade patterns Že.g. penetration of basic materials markets by foreign countries. is another possible influence, although there is no reason that such changes should have occurred at a constant rate. The price of energy is another possible factor: changes in the price of energy may be expected to affect the price of energy-intensive products the greatest, leading to lower sales if energy prices increase and higher sales if energy prices decrease. Fig. 4 shows the prices of electricity, fossil fuel and energy in Ontario over the study period. Lastly, energy-intensive industries such as primary metals, chemicals and non-metallic minerals are sensitive to investment in structures and machinery. Since investment is related to growth in economic output Žrather than the level., economic growth is another possible factor to be considered. Fig. 5 shows both the level and change in industrial output in Ontario over the study
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
33
Fig. 2. Fossil fuel indices.
period. The model which was tested is hence as follows: ln S t s b1 q b 2 lnQ t 9 q b 3Tt q b4 ln Pt
Ž3.
where Q t 9 s Q trQty1 and Pt is the own price of energy. Table 1 shows the parameter estimates with t-statistics in brackets, along with summary fit statistics. Autoregressive parameters were fit as necessary to correct for autocorrelation. Growth in economic output and time are significant in all equations at the 5% level, with the sole exception of the time term in the energy equation which is significant at the 10% level. As expected, the sign of the economic growth term is positive in all equations, indicating that energy intensive industries tend to be more sensitive to growth in economic output than non-energy intensive industries. The sign of the time trend term is negative in all equations, lending support to the time trend hypothesis. Price, on the other hand, is not significant in any of the equations, although it is of the expected sign. A first order autoregressive term was significant in each equation. The diagnostic statistics show a good statistical fit and give no indication of problems with the estimated equations. 4. Intensity index equations A number of possible explanatory factors for the energy intensity indices exist. Economic growth impacts on energy intensity in two ways. Firstly, it tends to
34
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Fig. 3. Electricity indices.
increase capacity utilization leading to the more efficient use of manufacturing facilities and hence, lower energy intensity. Secondly, it stimulates investment in new Žpresumably more efficient . technology, thus tending to lower the intensity index once again. Another factor which was considered is the price of energy. For the fossil fuel and electricity intensity equations, both electricity and fossil fuel prices were considered. Lastly, the possibility of a trend in intensity unrelated to these factors was tested by including time as an explanatory variable. The impact of non-price-induced Table 1 Estimation results for the structural index equations Coefficient
b1 b2 b3 b4 ARŽ1. R2 Root M.S.E. D-W statistic
Equation Energy
Fossil fuel
Electricity
0.401 Ž1.12. 0.112 Ž2.79. y0.00290 Žy1.72. y0.0420 Žy1.04. y0.708 Žy4.43. 0.924 0.0149 2.03
0.173 Ž0.89. 0.110 Ž2.61. y0.00358 Žy3.40. y0.0165 Žy0.73. y0.660 Žy3.90. 0.906 0.0153 2.05
0.180 Ž0.28. 0.106 Ž2.53. y0.00538 Žy2.59. y0.0162 Žy0.25. y0.756 Žy5.78. 0.947 0.0162 1.90
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Fig. 4. Industrial energy prices in Ontario.
Fig. 5. Industrial output and growth in Ontario.
35
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D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
changes on energy intensity has been called ‘autonomous energy efficiency improvement’ ŽAEEI } also known as ‘autonomous end-use energy-intensity improvement’.. There is disagreement among analysts as to whether the term should be reserved for the impact of efficiency improvements within individual industries Ži.e. should measure the impact on strictly the intensity index., or whether it should also include the effect of structural change Ži.e. should measure the impact on the aggregate intensity index.. Grubb et al. Ž1993. consider it to measure the impact of at least three factors: technical developments that increase energy productivity, structural change and policy-driven uptake of more efficient technologies.3 For the energy intensity index, it is expected that the overall time trend, if significant, would be negative, reflecting technological improvements which tend to reduce the required inputs of all factors of production. For particular energy forms, however, it is possible that technological change or other factors 4 may have led to an increase in intensity. The intensity index equation for energy is thus of the following form: ln It s c1 q c2 lnQ t 9 q c 3Tt q c 4 ln Pte
Ž4.
where Pte is the price of energy. The intensity index equations for fossil fuel and electricity are ln It s c1 q c2 lnQ t 9 q c 3Tt q c 4 ln Ptl q c5 ln Pt f
Ž5.
where Ptl is the price of electricity and Pt f is the price of fossil fuel. Table 2 gives the parameter estimates for the intensity equations and summary fit statistics. Economic growth is significant in all equations and has the expected sign. Time is also significant in all equations. None of the price terms are significant, with the possible exception of the fossil fuel term in the electricity intensity index which is significant at the 10% level. Autoregressive terms were fit as required to correct for autocorrelation. The summary fit statistics show a reasonable fit to the data and give no indication of any estimation problems. There are a number of possible reasons why the price terms are insignificant in these equations, aside from the conclusion that price is unimportant. First of all, the Divisia index is an imperfect energy price measure since it reflects not only 3 Structural change is the result not just of changing consumer preferences, however, but also the result of such technical developments as improved materials, manufacturing technology and product design Žso-called indirect efficiency improvements.. Grubb et al. Ž1993. also note that, to the extent that AEEI reflects the impact of policy-driven uptake of more efficient technologies, the term is badly named, since such changes are clearly not ‘autonomous’. 4 The extension of natural gas availability to new areas would be one example. 5 From 1962 to 1969, for example, the Divisia fossil fuel price index rose slightly, despite the fact that the price of all the components which make up the index Žoil, natural gas and coal. actually declined Žsubstantially in the case of oil and natural gas.. This reflected the rise in the market share of higher-priced oil and natural gas at the expense of low-priced coal, driven by technological factors, environmental concerns and the extension of natural gas availability.
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
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Table 2 Estimation results for the intensity index equations Coefficient
c1 c2 c3 c4 c5 ARŽ1. ARŽ2. R2 Root M.S.E. D-W statistics
Equation Energy
Fossil fuel
Electricity
y0.0980 Žy0.20. y0.333 Žy3.84. y0.00956 Žy3.94. 0.0151 Ž0.27.
0.611 Ž0.37. y0.329 Žy3.09. y0.0126 Žy2.47. y0.0688 Žy0.39. 0.0142 Ž0.27. y0.652 Žy3.78.
y0.124 Žy0.11. y0.290 Žy6.12. 0.00830 Ž2.12. y0.0787 Žy0.79. 0.101 Ž1.74. y1.284 Žy6.37. 0.437 Ž1.98. 0.968 0.0217 2.07
y0.524 Žy2.98. 0.891 0.0300 1.72
0.930 0.0388 1.63
energy prices but also energy market shares, which may change for reasons unrelated to price.5 Secondly, since the price of electricity has risen steadily over the study period, multicollinearity with the time trend term may be a problem in those equations with an electricity price term. Thirdly, the model may be misspecified. A Koyck lag model which was also tested showed quite similar results, however, none of the price terms being significant with the exception of the fossil fuel term in the electricity index equation it is also possible that responses to increases and decreases in price may not be symmetric. This is a factor which deserves further research, whenever data availability permits.
5. Discussion In order to focus on the statistically significant results, the insignificant explanatory variables were dropped from the models and the equations reestimated.6 Table 3 shows the estimated coefficients. The reported coefficients in the structural index equations were all significant at the 5% level, those in the intensity index equations at the 1% level. The robustness of the results is indicated by the insensitivity of the estimated coefficients to the different model specification. The aggregate intensity index coefficients are obtained by simply summing the respective coefficients from the structural and intensity indices since the aggregate intensity index is the product of these two indices. Although the decline of the structural indices coincides with the period of high energy prices during the oil-shock period Žsee Fig. 4., the estimation results do not
6
The fossil fuel term in the electricity intensity index, although marginally significant, was dropped since the insignificance of the electricity price term suggested that this result was questionable.
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D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Table 3 Re-estimated coefficients
Structural index Intensity index Aggregate intensity index
Output growth Time Output growth Time Output growth Time
Energy
Fossil fuel
Electricity
0.109 y0.00443 y0.332 y0.00899 y0.222 y0.0134
0.108 y0.00409 y0.333 y0.0141 y0.225 y0.0182
0.107 y0.00582 y0.281 0.00906 y0.174 0.0032
support the hypothesis that energy prices played a role in this decline. Given that energy comprises only a small share of total industry costs, this result is perhaps not surprising. Instead the results suggest that the relative decline of the energy intensive industries is part of a long-term trend, masked in the pre-oil-shock and post-oil-shock periods, to some extent, by high economic growth. In periods of high output growth, the structural indices tend to maintain a higher level than is justified by long-term time trends while in recessionary periods, the reverse is true.7 The results also indicate that electricity-intensive industries have declined at a faster rate than fossil-fuel-intensive industries. While the results support the hypothesis that energy-intensive industries tend to be affected more by changes in industrial production than non-energy-intensive industries, the impact is not large } roughly a 9% increase in output is required to increase the structural indices by 1%. A similar explanation for the intensity indices appears to hold as for the structural indices: changes in intensity are the result of long-term trends, modified in the short-term by changes in output. In the case of the energy and fossil fuel intensity indices, the long-term time trend has been down, at a rate of 0.9 and 1.4% per year, respectively. The electricity intensity index, on the other hand, has increased at a rate of 0.9% per year. This increase perhaps reflects the unique properties of electricity ŽSchmidt, 1987. and differences in the economic potential for efficiency improvements compared to other fuels.8 The intensity indices are considerably more sensitive to changes in output than the structural indices. It is interesting to calculate the overall impact of the different factors on the aggregate intensity indices Žsee Table 3.. While output growth tends to increase the structural indices, the overall impact is negative on the aggregate intensity indices due to the effect on the intensity indices. The impact of time on the aggregate
7
The 1990]1992 recession appears to have gone against this tendency, the structural indices having actually risen slightly. 8 The potential for efficiency savings is generally greater in process heat applications than in motive power applications, where electricity use is concentrated. Electricity use is also often widely dispersed throughout a plant, thus making efforts to reduce use more difficult than for fossil fuel, the use of which is often concentrated in a few areas.
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
39
intensity indices is mixed: in the case of fossil fuel and energy, the overall trend is down due to the negative effect on both the structural and intensity indices. In the case of electricity, however, the negative effect on the structural index is more than offset by the positive impact on the intensity index. For energy as a whole, the results indicate autonomous time trends have led to a decrease in the aggregate intensity index at a rate of over 1.3% per year. Almost exactly one third of this amount is due to structural change, the remainder being due to change of the intensity index.
6. Conclusion This paper has endeavored to provide some insight into the changes in industry structure and energy intensity that occurred in Ontario over the 1962 to 1992 period. A number of findings emerged. The results suggest that the relative decline in output of the energy intensive industries was principally due to long-term trends, possibly related to changes in technology, trade patterns and consumer preferences. Time trends were also a major factor in the change in the intensity indices. Energy intensive industries were found to be particularly sensitive to economic growth. Economic growth also tended to improve the efficiency of energy use within individual industries, thus reducing the intensity indices. Better understanding of changes in energy use is critically important to policy makers. If autonomous time trends, due for whatever reasons, may reasonably be expected to reduce energy use in the future significantly, for example, then the cost of reducing greenhouse gas emissions will not be nearly as great as otherwise, nor the need for action today as urgent ŽManne and Richels, 1991; Grubb et al., 1993.. Measuring the magnitude and identifying the sources of such trends in the past provides valuable information for making such judgments. The results from this research suggest that autonomous time trends have been an important factor behind changes in Ontario industrial energy use over the past 30 years. Further empirical studies of this type for different localities would provide a useful comparison to these results.
Appendix 1 The Divisia index decomposition of the aggregate intensity index is derived as follows ŽBoyd et al., 1987.. Let E, Q and Y be the rate of industrial energy use, output and aggregate energy intensity, respectively, all being implicit functions of time. Further, let Ei and Qi be the rate of energy use and output, respectively, in industry i, ei s EirQi the energy intensity, and si s QirQ, the output share. Then Ys
E Q
s
Ei Q i
ÝQ i
i
Q
s
Ý e i si i
Ž6.
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
40
Differentiating with respect to time t and then dividing by Y gives dY 1 dt Y
s
d e i si
Ý i
s
q
dt Y
i
d ei 1
e i si
dt e i
Y
ž / Ýž / Ý i
d ei 1
s
dt e i
i
Ý
d si e i dt Y q
i
wi q
d si 1
e i si
dt si
Y
ž / Ýž / Ý
d si 1
i
dt si
wi
Ž7.
where wi s e i s i
1 Y
s
Ei Q i 1 Qi Q Y
s
Ei
Ž8.
E
is the energy share of industry i. Integrating the right-hand-side expression of Ž7. over some Žyear-long. time interval w t y 1, t x exactly requires explicit expressions for e i , si and w i as functions of time. Since these are unavailable, the integral is commonly approximated as follows:
ln
Yt
ž / Yty1
f
Ý ln i
ž
e it e i ,ty1
/ž
wi ,ty1 q wit 2
/
q
Ý ln i
sit
wi ,ty1 q wi t
si ,ty1
2
ž /ž
/
Ž9.
where, with a slight abuse of notation, all variables are now indexed according to year. Taking the antilog then gives Yt Yty1
s exp
ln
i
s
It
e it
wi ,ty1 q wi t
e i ,ty1
2
½Ý ž /ž
/5
St
? exp
si t
wi ,ty1 q wi t
si ,ty1
2
½Ý ž /ž ln
i
/5 Ž 10.
Ity1 Sty1
where It Ity1 St Sty1
s exp
ln
i
s exp
e it
wi ,ty1 q wi t
e i ,ty1
2
sit
wi ,ty1 q wi t
si ,ty1
2
½Ý ž /ž ½Ý ž /ž ln
i
/5 /5
Ž 11.
Normalizing the base year indices to one Že.g. Y1962 s 1, I1962 s 1, S1962 s 1., the latter expressions allow the intensity and structural indices to be calculated recursively for the entire study period. Furthermore, it follows that Yt s It St .
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
41
References Ang, B.W., 1987. Structural changes and energy-demand forecasting in industry with applications to two newly industrialized countries. Energy 12 Ž2., 101]111. Boyd, G., McDonald, J.F., Ross, M., Hanson, D.A., 1987. Separating the changing composition of US manufacturing production from energy efficiency improvements: a Divisia index approach. The Energy Journal 8 Ž2., 77]96. Boyd, G.A., Hanson, D.A., Sterner, T., 1988. Decomposition of changes in energy intensity: a comparison of the Divisia index and other methods. Energy Economics 10 Ž4., 309]312. Boyd, G.A., Ross, M.H., 1989. The role of sectoral shift in trends in electricity use in United States and Swedish manufacturing and in comparing forecasts. In: Electricity } Efficient End Use and New Generation Technology, and Their Implication for Electric Utility Planning and Policy. Lund University Press. Elkhafif, E., 1992. Estimating disaggregated price elasticities in industrial energy demand. The Energy Journal 13 Ž4., 209]217. Gardner, D.T., 1993. Industrial energy use in Ontario from 1962 to 1984. Energy Economics 15 Ž1., 25]33. Gardner, D.T., Robinson, J.B., 1993. To what end? A conceptual framework for the analysis of energy use. Energy Studies Review 5 Ž1., 1]14. Grubb, M., Edmonds, J., ten Brink, P., Morrison, M., 1993. The costs of limiting fossil-fuel CO 2 emissions: a survey and analysis. Annual Review of Energy and the Environment 18, 397]478. Howarth, R.B., Schipper, L., Duerr, P.A., Strøm, S., 1991. Manufacturing energy use in eight OECD countries. Energy Economics 13 Ž2., 135]142. Li, J., Shrestha, R.M., Foell, W.K., 1990. Structural change and energy use: the case of the manufacturing sector in Taiwan. Energy Economics 12 Ž2., 109]114. Liu, X.Q., Ang, B.W., Ong, H.L., 1992. The application of the Divisia index to the decomposition of changes in industrial energy consumption. The Energy Journal 13 Ž4., 161]177. Manne, A.S., Richels, R.G., 1991. Buying greenhouse insurance. Energy Policy 19 Ž4., 543]552. Marlay, R.C., 1984. Trends in industrial use of energy. Science 226, 1277]1283. Mountain, D.C., Stipdonk, B.P., Warren, C.J., 1989. Technological innovation and a changing energy mix } a parametric and flexible approach to modelling Ontario manufacturing. The Energy Journal 10 Ž4., 137]158. Ross, M., Larson, E.D., Williams, R.H., 1987. Energy demand and material flows in the economy. Energy 12 Ž10r11., 953]967. Schmidt, P.S., 1987. Electricity and industrial productivity: a technical and economic perspective. Energy 12 Ž10r11., 1111]1120. US Congress, Office of Technology Assessment, 1990. Energy Use and the US Economy, OTA-BP-E57. US Government Printing Office, Washington, DC. Werbos, P., 1987. Industrial structural shift: causes and consequences for electricity demand. In: Faruqui, A., Broehl, J. ŽEds.., The Changing Structure of American Industry and Energy Use Patterns, EPRI EM-5075-SR. Williams, R.H., Larson, E.D., Ross, M.H., 1987. Materials, affluence and industrial energy use. Annual Review of Energy 12, 99]144.
Understanding industrial energy use: structural and energy intensity changes in Ontario industry Douglas T. Gardner a,U , Mahmoud A.T. Elkhafif b a
b
Algorithmics Inc., 185 Spadina A¨enue, Toronto, Ontario, M5T 2C6, Canada Consumers Gas, Economic Studies Department, 500 Consumers Road, North York, Ontario, MZJ 1P8, Canada1
Abstract A large number of studies have examined the relative importance of structural versus intensity changes on the evolution of industrial energy use. Relatively little research, however, has been done to better understand why these changes have occurred. Using an econometric approach, this paper attempts to provide insight into the changes in industry structure and energy intensity that occurred in Ontario between 1962 and 1992. The estimation successfully identifies and measures cyclical and trend components for both the structural and intensity indices, the cyclical component being due to changes in industrial output, the trend component, likely due to changes in technology and consumer preferences. Q 1998 Elsevier Science B.V. Keywords: Energy intensity; Structural shift; Industrial energy use
1. Introduction Industrial energy use may be viewed as a function of total industrial output, the composition of that output and the means by which that output is produced. A large number of studies have estimated the relative importance of these factors in explaining how industrial energy use has evolved in different localities ŽMarlay, U 1
Corresponding author. Tel: q1 416 217 1400; e-mail: [email protected] e-mail: [email protected]
0140-9883r98r$19.00 Q 1998 Elsevier Science B.V. All rights reserved PII S0140-9883Ž97.00008-X
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
30
1984; Ang, 1987; Boyd et al., 1987; Li et al., 1990; Howarth et al., 1991; Gardner, 1993.. Relatively little work, however, has been done to go beyond these simple descriptions of what happened to examine the question of why. This paper uses an econometric approach in an attempt to better understand observed changes in a Divisia index measure of industry structure and energy intensity over the 1962]1992 period in Ontario, the industrial heart of Canada. Werbos Ž1987., using a different methodology than that used here, analyzed structural shift in the US over the 1958]1983 period, concluding that growth in GNP, real interest rates and time were important factors while the price of energy was not. Boyd and Ross Ž1989. examined the impact of the relative price of materials versus all commodities, GNP and time on an electricity structural index for the US over the 1958]1985 period, finding that only time was statistically significant. Previous econometric studies of Ontario industrial energy use include those of Mountain et al. Ž1989. and Elkhafif Ž1992.. These studies did not explicitly examine the impact of structural change, however. The next section describes the study methodology along with the estimation results. A discussion of these results and a conclusion follows.
2. Methodology and estimation results In year t, the aggregate energy intensity index Ž Yt . is simply the ratio of total industrial energy use Ž Et . to industrial output in real value added terms Ž Q t .: Yt s
Et Qt
Ž1.
In the Divisia index approach, the aggregate energy intensity index is then represented as the product of a structural index Ž St . and an intensity index Ž It .: Yt s S t It
Ž2.
The former measures changes due to shifts in the composition of the industrial sector; the latter reflects changes in manufacturing technology and product mix within individual industries. The Divisia index decomposition of the aggregate intensity index is derived in the Appendix. It is one of several different methods that have been used to decompose changes in industrial energy use into structural and intensity factors. The advantages and disadvantages of these different methods have been discussed by a number of authors ŽBoyd et al., 1988; Howarth et al., 1991; Liu et al., 1992.. Gardner Ž1993. used a Divisia index approach to examine the evolution of industrial energy use in Ontario, based on purchases of refined petroleum products Žoil., natural gas, electricity and coal by 21 different industries from 1962 to 1984. Figs. 1]3 show the intensity, structural and aggregate intensity indices for energy, fossil fuel and electricity over the 1962]1992 period. The indices shown for the
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
31
1984]1992 period are based on an eight industry disaggregation of industrial energy use, since data at the 21 industry disaggregation level is available only for the 1962]1984 period.2 For purposes of discussion, the study period may be divided into three periods: pre-oil-shock Ž1962]1973., oil-shock Ž1973]1985. and post-oil-shock Ž1985]1992.. The aggregate energy intensity declined at a roughly constant rate over the pre-oil-shock and oil-shock periods. The reasons for this decline differ significantly in the two periods, however. In the pre-oil-shock period, the decline was due almost entirely to a decrease in the intensity index, the structural index remaining relatively constant. In the oil-shock period, in contrast, the structural index declined significantly; the intensity index, perhaps surprisingly, declined only slightly. In the post-oil-shock period, the structural index flattened out once again while the intensity index actually rose slightly, leading to an increase in the aggregate intensity index. The fossil fuel indices mirror the same pattern as the energy indices, although the decline in the intensity index during the oil-shock period is somewhat larger. The electricity structural index follows a similar pattern to the fossil fuel structural index, although it has decreased more over the study period. The electricity intensity index, on the other hand, differs significantly from the fossil fuel intensity index, particularly in the oil-shock period during which time it rose dramatically while the fossil fuel intensity index declined. Equations were estimated for the structural and intensity indices of electricity, fossil fuel Žoil, natural gas and coal together. and total energy. A log-linear model specification was used in each equation. The structural index equations are described next, followed by the intensity index equations.
3. Structural index equations Energy intensive industries are generally process industries, creating basic materials from raw materials. Key energy intensive industries in Ontario include mining, primary metals, pulp and paper, chemicals, and non-metallic minerals. Non-energy intensive industries often involve fabrication and assembly activities, using basic materials created by energy intensive industries. Important examples in Ontario include transportation equipment, plastic products and electrical and electronic products. The shift away from materialrenergy intensive industries in most developed nations during the post-oil-shock period has been well documented, although the magnitude of this effect varies considerably. A number of different explanatory factors for these changes exist. One possibility is a growing consumer preference for services and high value-added, low material-intensive products ŽWilliams et al., 1987.. The emergence of new and 2
The lost detail was for industries with low energy use. Hence, the indices for the 1984]1992 period are likely little different from those which would have been calculated had data at the 21 industry disaggregation level been available Žsee the index Eq. Ž11...
32
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Fig. 1. Energy indices.
improved materials, better manufacturing technology and improved product design that have tended to reduce the quantity of materials and energy embodied in finished goods, might also be contributing factors ŽRoss et al., 1987; Gardner and Robinson, 1993.. An inputroutput analysis conducted for the US, for example, estimated that one fifth of the drop in the aggregate energy intensity index over the 1972 to 1985 period was attributable to such ‘indirect’ efficiency improvements ŽUS Congress, 1990.. If such changes in technology and consumer preferences have occurred at a relatively constant rate over time, as is perhaps reasonable, a time trend term may serve to model the impact of these factors. Changing trade patterns Že.g. penetration of basic materials markets by foreign countries. is another possible influence, although there is no reason that such changes should have occurred at a constant rate. The price of energy is another possible factor: changes in the price of energy may be expected to affect the price of energy-intensive products the greatest, leading to lower sales if energy prices increase and higher sales if energy prices decrease. Fig. 4 shows the prices of electricity, fossil fuel and energy in Ontario over the study period. Lastly, energy-intensive industries such as primary metals, chemicals and non-metallic minerals are sensitive to investment in structures and machinery. Since investment is related to growth in economic output Žrather than the level., economic growth is another possible factor to be considered. Fig. 5 shows both the level and change in industrial output in Ontario over the study
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
33
Fig. 2. Fossil fuel indices.
period. The model which was tested is hence as follows: ln S t s b1 q b 2 lnQ t 9 q b 3Tt q b4 ln Pt
Ž3.
where Q t 9 s Q trQty1 and Pt is the own price of energy. Table 1 shows the parameter estimates with t-statistics in brackets, along with summary fit statistics. Autoregressive parameters were fit as necessary to correct for autocorrelation. Growth in economic output and time are significant in all equations at the 5% level, with the sole exception of the time term in the energy equation which is significant at the 10% level. As expected, the sign of the economic growth term is positive in all equations, indicating that energy intensive industries tend to be more sensitive to growth in economic output than non-energy intensive industries. The sign of the time trend term is negative in all equations, lending support to the time trend hypothesis. Price, on the other hand, is not significant in any of the equations, although it is of the expected sign. A first order autoregressive term was significant in each equation. The diagnostic statistics show a good statistical fit and give no indication of problems with the estimated equations. 4. Intensity index equations A number of possible explanatory factors for the energy intensity indices exist. Economic growth impacts on energy intensity in two ways. Firstly, it tends to
34
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Fig. 3. Electricity indices.
increase capacity utilization leading to the more efficient use of manufacturing facilities and hence, lower energy intensity. Secondly, it stimulates investment in new Žpresumably more efficient . technology, thus tending to lower the intensity index once again. Another factor which was considered is the price of energy. For the fossil fuel and electricity intensity equations, both electricity and fossil fuel prices were considered. Lastly, the possibility of a trend in intensity unrelated to these factors was tested by including time as an explanatory variable. The impact of non-price-induced Table 1 Estimation results for the structural index equations Coefficient
b1 b2 b3 b4 ARŽ1. R2 Root M.S.E. D-W statistic
Equation Energy
Fossil fuel
Electricity
0.401 Ž1.12. 0.112 Ž2.79. y0.00290 Žy1.72. y0.0420 Žy1.04. y0.708 Žy4.43. 0.924 0.0149 2.03
0.173 Ž0.89. 0.110 Ž2.61. y0.00358 Žy3.40. y0.0165 Žy0.73. y0.660 Žy3.90. 0.906 0.0153 2.05
0.180 Ž0.28. 0.106 Ž2.53. y0.00538 Žy2.59. y0.0162 Žy0.25. y0.756 Žy5.78. 0.947 0.0162 1.90
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Fig. 4. Industrial energy prices in Ontario.
Fig. 5. Industrial output and growth in Ontario.
35
36
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
changes on energy intensity has been called ‘autonomous energy efficiency improvement’ ŽAEEI } also known as ‘autonomous end-use energy-intensity improvement’.. There is disagreement among analysts as to whether the term should be reserved for the impact of efficiency improvements within individual industries Ži.e. should measure the impact on strictly the intensity index., or whether it should also include the effect of structural change Ži.e. should measure the impact on the aggregate intensity index.. Grubb et al. Ž1993. consider it to measure the impact of at least three factors: technical developments that increase energy productivity, structural change and policy-driven uptake of more efficient technologies.3 For the energy intensity index, it is expected that the overall time trend, if significant, would be negative, reflecting technological improvements which tend to reduce the required inputs of all factors of production. For particular energy forms, however, it is possible that technological change or other factors 4 may have led to an increase in intensity. The intensity index equation for energy is thus of the following form: ln It s c1 q c2 lnQ t 9 q c 3Tt q c 4 ln Pte
Ž4.
where Pte is the price of energy. The intensity index equations for fossil fuel and electricity are ln It s c1 q c2 lnQ t 9 q c 3Tt q c 4 ln Ptl q c5 ln Pt f
Ž5.
where Ptl is the price of electricity and Pt f is the price of fossil fuel. Table 2 gives the parameter estimates for the intensity equations and summary fit statistics. Economic growth is significant in all equations and has the expected sign. Time is also significant in all equations. None of the price terms are significant, with the possible exception of the fossil fuel term in the electricity intensity index which is significant at the 10% level. Autoregressive terms were fit as required to correct for autocorrelation. The summary fit statistics show a reasonable fit to the data and give no indication of any estimation problems. There are a number of possible reasons why the price terms are insignificant in these equations, aside from the conclusion that price is unimportant. First of all, the Divisia index is an imperfect energy price measure since it reflects not only 3 Structural change is the result not just of changing consumer preferences, however, but also the result of such technical developments as improved materials, manufacturing technology and product design Žso-called indirect efficiency improvements.. Grubb et al. Ž1993. also note that, to the extent that AEEI reflects the impact of policy-driven uptake of more efficient technologies, the term is badly named, since such changes are clearly not ‘autonomous’. 4 The extension of natural gas availability to new areas would be one example. 5 From 1962 to 1969, for example, the Divisia fossil fuel price index rose slightly, despite the fact that the price of all the components which make up the index Žoil, natural gas and coal. actually declined Žsubstantially in the case of oil and natural gas.. This reflected the rise in the market share of higher-priced oil and natural gas at the expense of low-priced coal, driven by technological factors, environmental concerns and the extension of natural gas availability.
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37
Table 2 Estimation results for the intensity index equations Coefficient
c1 c2 c3 c4 c5 ARŽ1. ARŽ2. R2 Root M.S.E. D-W statistics
Equation Energy
Fossil fuel
Electricity
y0.0980 Žy0.20. y0.333 Žy3.84. y0.00956 Žy3.94. 0.0151 Ž0.27.
0.611 Ž0.37. y0.329 Žy3.09. y0.0126 Žy2.47. y0.0688 Žy0.39. 0.0142 Ž0.27. y0.652 Žy3.78.
y0.124 Žy0.11. y0.290 Žy6.12. 0.00830 Ž2.12. y0.0787 Žy0.79. 0.101 Ž1.74. y1.284 Žy6.37. 0.437 Ž1.98. 0.968 0.0217 2.07
y0.524 Žy2.98. 0.891 0.0300 1.72
0.930 0.0388 1.63
energy prices but also energy market shares, which may change for reasons unrelated to price.5 Secondly, since the price of electricity has risen steadily over the study period, multicollinearity with the time trend term may be a problem in those equations with an electricity price term. Thirdly, the model may be misspecified. A Koyck lag model which was also tested showed quite similar results, however, none of the price terms being significant with the exception of the fossil fuel term in the electricity index equation it is also possible that responses to increases and decreases in price may not be symmetric. This is a factor which deserves further research, whenever data availability permits.
5. Discussion In order to focus on the statistically significant results, the insignificant explanatory variables were dropped from the models and the equations reestimated.6 Table 3 shows the estimated coefficients. The reported coefficients in the structural index equations were all significant at the 5% level, those in the intensity index equations at the 1% level. The robustness of the results is indicated by the insensitivity of the estimated coefficients to the different model specification. The aggregate intensity index coefficients are obtained by simply summing the respective coefficients from the structural and intensity indices since the aggregate intensity index is the product of these two indices. Although the decline of the structural indices coincides with the period of high energy prices during the oil-shock period Žsee Fig. 4., the estimation results do not
6
The fossil fuel term in the electricity intensity index, although marginally significant, was dropped since the insignificance of the electricity price term suggested that this result was questionable.
38
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
Table 3 Re-estimated coefficients
Structural index Intensity index Aggregate intensity index
Output growth Time Output growth Time Output growth Time
Energy
Fossil fuel
Electricity
0.109 y0.00443 y0.332 y0.00899 y0.222 y0.0134
0.108 y0.00409 y0.333 y0.0141 y0.225 y0.0182
0.107 y0.00582 y0.281 0.00906 y0.174 0.0032
support the hypothesis that energy prices played a role in this decline. Given that energy comprises only a small share of total industry costs, this result is perhaps not surprising. Instead the results suggest that the relative decline of the energy intensive industries is part of a long-term trend, masked in the pre-oil-shock and post-oil-shock periods, to some extent, by high economic growth. In periods of high output growth, the structural indices tend to maintain a higher level than is justified by long-term time trends while in recessionary periods, the reverse is true.7 The results also indicate that electricity-intensive industries have declined at a faster rate than fossil-fuel-intensive industries. While the results support the hypothesis that energy-intensive industries tend to be affected more by changes in industrial production than non-energy-intensive industries, the impact is not large } roughly a 9% increase in output is required to increase the structural indices by 1%. A similar explanation for the intensity indices appears to hold as for the structural indices: changes in intensity are the result of long-term trends, modified in the short-term by changes in output. In the case of the energy and fossil fuel intensity indices, the long-term time trend has been down, at a rate of 0.9 and 1.4% per year, respectively. The electricity intensity index, on the other hand, has increased at a rate of 0.9% per year. This increase perhaps reflects the unique properties of electricity ŽSchmidt, 1987. and differences in the economic potential for efficiency improvements compared to other fuels.8 The intensity indices are considerably more sensitive to changes in output than the structural indices. It is interesting to calculate the overall impact of the different factors on the aggregate intensity indices Žsee Table 3.. While output growth tends to increase the structural indices, the overall impact is negative on the aggregate intensity indices due to the effect on the intensity indices. The impact of time on the aggregate
7
The 1990]1992 recession appears to have gone against this tendency, the structural indices having actually risen slightly. 8 The potential for efficiency savings is generally greater in process heat applications than in motive power applications, where electricity use is concentrated. Electricity use is also often widely dispersed throughout a plant, thus making efforts to reduce use more difficult than for fossil fuel, the use of which is often concentrated in a few areas.
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39
intensity indices is mixed: in the case of fossil fuel and energy, the overall trend is down due to the negative effect on both the structural and intensity indices. In the case of electricity, however, the negative effect on the structural index is more than offset by the positive impact on the intensity index. For energy as a whole, the results indicate autonomous time trends have led to a decrease in the aggregate intensity index at a rate of over 1.3% per year. Almost exactly one third of this amount is due to structural change, the remainder being due to change of the intensity index.
6. Conclusion This paper has endeavored to provide some insight into the changes in industry structure and energy intensity that occurred in Ontario over the 1962 to 1992 period. A number of findings emerged. The results suggest that the relative decline in output of the energy intensive industries was principally due to long-term trends, possibly related to changes in technology, trade patterns and consumer preferences. Time trends were also a major factor in the change in the intensity indices. Energy intensive industries were found to be particularly sensitive to economic growth. Economic growth also tended to improve the efficiency of energy use within individual industries, thus reducing the intensity indices. Better understanding of changes in energy use is critically important to policy makers. If autonomous time trends, due for whatever reasons, may reasonably be expected to reduce energy use in the future significantly, for example, then the cost of reducing greenhouse gas emissions will not be nearly as great as otherwise, nor the need for action today as urgent ŽManne and Richels, 1991; Grubb et al., 1993.. Measuring the magnitude and identifying the sources of such trends in the past provides valuable information for making such judgments. The results from this research suggest that autonomous time trends have been an important factor behind changes in Ontario industrial energy use over the past 30 years. Further empirical studies of this type for different localities would provide a useful comparison to these results.
Appendix 1 The Divisia index decomposition of the aggregate intensity index is derived as follows ŽBoyd et al., 1987.. Let E, Q and Y be the rate of industrial energy use, output and aggregate energy intensity, respectively, all being implicit functions of time. Further, let Ei and Qi be the rate of energy use and output, respectively, in industry i, ei s EirQi the energy intensity, and si s QirQ, the output share. Then Ys
E Q
s
Ei Q i
ÝQ i
i
Q
s
Ý e i si i
Ž6.
D.T. Gardner and M.A.T. Elkhafif r Energy Economics 20 (1998) 29]41
40
Differentiating with respect to time t and then dividing by Y gives dY 1 dt Y
s
d e i si
Ý i
s
q
dt Y
i
d ei 1
e i si
dt e i
Y
ž / Ýž / Ý i
d ei 1
s
dt e i
i
Ý
d si e i dt Y q
i
wi q
d si 1
e i si
dt si
Y
ž / Ýž / Ý
d si 1
i
dt si
wi
Ž7.
where wi s e i s i
1 Y
s
Ei Q i 1 Qi Q Y
s
Ei
Ž8.
E
is the energy share of industry i. Integrating the right-hand-side expression of Ž7. over some Žyear-long. time interval w t y 1, t x exactly requires explicit expressions for e i , si and w i as functions of time. Since these are unavailable, the integral is commonly approximated as follows:
ln
Yt
ž / Yty1
f
Ý ln i
ž
e it e i ,ty1
/ž
wi ,ty1 q wit 2
/
q
Ý ln i
sit
wi ,ty1 q wi t
si ,ty1
2
ž /ž
/
Ž9.
where, with a slight abuse of notation, all variables are now indexed according to year. Taking the antilog then gives Yt Yty1
s exp
ln
i
s
It
e it
wi ,ty1 q wi t
e i ,ty1
2
½Ý ž /ž
/5
St
? exp
si t
wi ,ty1 q wi t
si ,ty1
2
½Ý ž /ž ln
i
/5 Ž 10.
Ity1 Sty1
where It Ity1 St Sty1
s exp
ln
i
s exp
e it
wi ,ty1 q wi t
e i ,ty1
2
sit
wi ,ty1 q wi t
si ,ty1
2
½Ý ž /ž ½Ý ž /ž ln
i
/5 /5
Ž 11.
Normalizing the base year indices to one Že.g. Y1962 s 1, I1962 s 1, S1962 s 1., the latter expressions allow the intensity and structural indices to be calculated recursively for the entire study period. Furthermore, it follows that Yt s It St .
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41
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