359 SAJEMS NS Vol 4 (2001) No 2 Some Recommendations towards Reducing Electricity Consumption in the South African Manufacturing Sector IN Blignaut and T de Wet I Department of Economics, University of Pretoria ABSTRACT This paper investigates the means of reducing electricity consumption in the South African manufacturing sector. It concludes that neither the price of electricity, nor taxes, subsidies or legislation are likely to bring about the required change. A change in the production structure using relatively more labour and less capital is also unlikely in the immediate future, given the socio- economic and legislative milieu currently prevailing in South Africa. The only feasible solution that seems likely is a change in technology, which includes the more efficient use of electricity. Given the possible international agreement regarding global climate change commitments and procedures, clean development mechanisms may therefore yet provide the answer. JELN67 1 INTRODUCTION AND PROBLEM STATEMENT Few would deny the importance of electricity or of a strong manufacturing sector in the economic development of a country. Within the context of global climate change and the negative environmental impact of the externalities associated with the generation of electricity, it is increasingly important to reduce the levels of electricity consumption without jeopardising the manufacturing base of the economy. It is therefore valid to ask what measures are necessary to reduce electricity consumption by the manufacturing sector. In an attempt to answer the question, this paper starts by giving an overview of the consumption and production of electricity in South Africa, followed by a discussion of the research method used. Then the price elasticity of electricity demand is investigated. This, in turn, is followed by a discussion of the production structure of manufacturing, and the correlation between the use of capital and electricity. Finally, some concluding remarks are made. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2 360 2 CONSUMPTION AND PRODUCTION OF ELECTRICITY IN SOUTH AFRICA: AN OVERVIEW 2.1 Demand for Electricity Electricity is an essential input in any economy, particularly in a developing country, since without it little development is possible. It is therefore not surprising that the Reconstruction and Development Programme (RDP) stresses the importance of an increased provision of electricity in the following terms (ANC, 1994: 108): "The benefits of cheap electricity presently enjoyed by large corporations must be extended to all parts of the economy." From this statement it follows that, from the outset, it was the aim of the ANC to continue to supply cheap electricity and to extend its consumption to as many people as possible. There is no question that this objective has enjoyed high priority and that considerable progress has been made in pursuit of it (NER Annual Report, 1998). This is supported by the fact that the average annual growth in electricity consumption is highest in the residential sector, namely 4 per cent (see Table 1). From Table 1 it is also clear that manufacturing is the largest single consumer of electricity (comprising approximately 41 per cent of total demand). Furthermore, the rate at which electricity consumption is growing in this sector is faster than the national average of 1,8 per cent a year over the period 1989- 1995, namely 2,1 per cent. Since manufacturing is such a major consumer of electricity, this paper focuses on this sector only. Table 1 Electricity consumption in South Africa: 1989-1995 (GWb) Consumer 1989 1)/1) of 1992 %of 1995 I % of Annual total total total growth rate Mining 34963 ~33359 23.49 33612 • 21.59 -0.7% Transnet Ltd. 4915 3 4347 3.06 I 4036 2.59 -3.2% . Domestic use 211 5.07 23834 16.78 26663 I 17.13 4.0% Manufacturing 561 0.07 54474 38.35 63801 ! 40.99 2.1% Commerce, con- 14282 10.19 15715 11.06 17513 11.25 3.5% struction, and other. business i Other purpose 8721 6.22 1 7.25 10034 i 6.45 2.4% Total 140169 100 142031 100 155661 ! 100 1.8% Source: Stats SA, Census of Electricity, Gas and Steam 1995. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 361 SAJEMS NS Vol 4 (2001) No 2 2.2 Supply of Electricity ESKOM is by far the largest single producer of electricity in South Africa, and is currently the fifth largest electricity utility in the world in tenus of both sales and capacity (ESKOM Annual Report, 1999). Not only is it such a large producer of electricity, its share in domestic production has also increased considerably since 1960. As is indicated in Table 2, ESKOM produced 62.3 per cent of the total electricity in 1960, and 98.3 per cent in 1999, mainly through its coal-based fire stations. In 1960, all of the electricity produced was coal-based, but due to other technologies such as nuclear power, coal's contribution has declined slightly to approximately 93 per cent in 1999. Consequently, ESKOM is the largest single consumer of coal in South Africa, absorbing approximately 40 per cent of the total coal production in 1999. Its consumption of coal increased more than seven fold over the period under consideration from 12.5 million tonne to 88.5 million tonne. For comparative purposes, this con- sumption of coal should be seen in the light of the fact that total coal exports comprised 29.5 per cent of the market in the same year. (South Africa is the world's second largest exporter of coal with Australia being first and America third). Table 2 Selected energy aDd electricity statistics in South Africa: 1960- 1999 Coal Coal con- I Coal con- Total ESKOM's Electricity production sumption sumption by electricity contribution supply: coal for elect. ESKOM generated to local fired (million generation (million (GWh) electricity ESKOM tODnes) (million tonnes) supply pwr pint tonnes) I (GWh) (GWht 1960 I 38.1 16.4 i 12.5 25840 16094 17306 1965 47.6 22.1 16.7 34490 23143 24583 1970 I 53.1 29.5 I 21.6 50791 34890 37321 1975 69.1 39.1 34.2 74894 I 57869 60400 1980 113.1 55.0 46.8 98951 87539 82342 1985 I 172.0 67.4 59.5 141384 112305 113941 1990 184.1 76.2 70.9 165516 I 144440 134744 1992 176.1 i 78.0 75.9 166260 146392 i 136830 1995 212.5 I 87.6 79.4 . 174571 161848 i 151730 1997 224.5 93.6 90.2 190700 181372 J 170464 1999 220.74 I 93.4 88.5 191734 177934 i 165665 Sources: DME, South African Energy Statistics No.2, 1995. DME, South Africa's Mineral Industry 1998/99, 1999. ESKOM, Annual Report, R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2 362 various issues. NER, Annual Reports, various issues. Stats SA, Census o/Electricity, Gas and Steam 1995, 1995. Chamber of Mines, Mining Statistics in Brie/1999, 2000. 2.3 Electricity Prices ESKOM has committed itself to be one of the producers of electricity at the lowest cost in the world (ESKOM Annual Report, 1999). ESKOM's tariffs are much lower than those of many developed nations with electricity utilities of comparable size, for example Japan, Germany, the United Kingdom and the United States (Van Horen, 1996: 8). According to Doppegieter (1999: 52) South Africa has the second cheapest electricity in the world, beaten only by New Zealand. In the national context, electricity prices have declined in real terms since ESKOM announced its price compact in 1991. In announcing and motivating the price compact, ESKOM was convinced that cheap electricity is essential for rapid economic growth (Van Horen, 1996: 9). In terms of this price compact, ESKOM undertook to decrease the real price of electricity substantially over the period 1992-2000. The achieved price reductions over the period 1970-1999, are shown in Table 3. From this table it emerges that the real price of electricity (1995 100) for all economic sectors declined by 7.8 per cent, and that for the manufacturing sector by 21.1 per cent over the whole period. Since 1990, the price of electricity for all sectors declined by 29.5 per cent and that for manufacturing by 35.6 per cent. Table 3 Real electricity prices in South Africa: 1970-1999 (c/kWb) -4.71 -5.57 -3.95 2.84 -1.03 17.30 -7.84 33.12 -7.81 4.76 ! -6.97 1979 14.46 -6.16 I 1980 , 13.54. -6.36 i R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 363 SAJEMS NS Vol 4 (2001) No 2 Table 3 continued Ave: all sectors ManuCacturin2 Ave: all sectors ManuCacturin2 Real % Real % Real % ReaJ % price chan~e price i change price change price change 1981 13.24 -2.22 13.25 -2.21 1996 10.53 -5.56 9.41 -9.52 1982 14.19 7.18 14.30 I 7.92 1997 10.17 -3.42 9.25 -1.70 1983 i 15.17 6.91 • 15.24 i 6.57 1998 9.86 -3.05 8.84 -4.43 1984 14.49 -4.48 14.53 -4.66 1999 I 9.49 -3.75 8.05 -8.94 . Over -7.77 Over -21.08 I i period· period Source: DME, South African National Energy Prices. 200~0: 70. The low and declining price of electricity encouraged the consumption of electricity to grow at the average annual rate of 5.4 per cent from 1960 to 1999. From 1990 to 1999, the average annual growth rate of electricity consumption was 1.9 per cent, which is marginally higher than the increase in the real Gross Domestic Product of 1.6 per cent over the same period. However, according to Doppegieter (1998: 3-59) such figures are misleading. Electricity is consumed wastefully in South Africa and much electricity can be saved. The inefficient use of electricity is attributed to a number of reasons, amongst others the low coal and electricity prices and large coal reserves. One of the major factors contributing to the low electricity price is the relative cheap coal that ESKOM buys. In 1998, ESKOM paid an average price of R41.31/tonne, compared to the R54.55/tonne Sasol paid, and the R156.36/tonne the metallurgic industries paid. These price differences can be attributed to different contract arrangements, entitlements and differences in the quality of coal (DME, 1999). 2.4 The Social and Environmental Cost of Electricity Generation The increase in the consumption of both coal and electricity contributes not just to economic growth, but also to the increase in negative social and environmental externalities. From Table 4 is it clear that ESKOM is a dominant polluter of greenhouse gases in South Africa, contributing to more that 45 per cent of domestic emissions. These emissions are mainly derived from the coal- based plants ofESKOM. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (200 I) No 2 364 Table 4 Carbon dioxide, Sulphur dioxide and Nitrogen emissions in South Africa: 1990, 1994 and 1995 CO2 (Million tonnes) , S02 (Thousand tonnes) ! ESKOM SA ESKOM % of SA % of SA 45.2 1088.32j 1760.0 61.8 45.0 1166.80 48.1 1198.17 - 345. -. NOx {Thousand tonnes ES- KOM 913.8 961.2 SA ,ESKOMi ! % of SA 2268· 40.3 Sources: Doppegieter, 1998: 3-69 & 3-80. Van der Merwe & Scholes, 1999. UNFCCC, 2000. Viewed from an international perspective, South Africa was the 15th highest emitter of carbon dioxide in 1995 and 1997 in absolute terms (Doppegieter, 1998: 3-69 & UNFCCC, 2000). South Africa was hence the 10th highest non- Annex I (cf developing country) polluter of carbon dioxide per capita in both 1995 and 1997, or 28th overall (including the developed economies) (Doppegieter, 1998: 3-69 & UNFCCC, 2000). These rankings do not reflect the country's economic strength and are clearly disproportionate. Chronic exposure to emissions of this magnitude has serious mortality and morbidity implications, such as chronic bronchitis and other respiratory diseases. Acid deposition in water bodies also severely impacts on the quality of surface and even ground water. This health hazard is further magnified by the fact tliat there is sufficient evidence to believe that global climate change is mainly due to anthropocentric (economic) activities (Houghton et al., 1996). In the light of these environmental and social costs due to the externality associated with coal-based electricity generation (see also Van Horen, 1996 & 1997), there is a need to reduce the generation of electricity from this source. This can be achieved in one of two ways, or a combination of both. ESKOM can change its production technology, that is divert from coal to alternative energy sources, or there should be a decline in the consumption of coal-based electricity. A possible change in the production technology of ESKOM and its associated cost will, however, not be discussed here. The remainder of this paper will focus on the demand for electricity by the largest single consumer of electricity, the manufacturing sector, and an appropriate policy required to bring about a reduction in the consumption of electricity. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 365 SAJEMS NS Vol 4 (2001) No 2 3 METHOD AND DATA In the attempt to find an appropriate policy to reduce the consumption of electricity in the manufacturing sector, this study investigates the effect of a change in the price of electricity on the consumption of energy by calculating the price elasticity of electricity demand. Because of data limitations, a static approach had to be used and elasticity was calculated on the ceteris paribus assumption, that all other factors that might influence electricity demand remain constant. This exercise produced some surprising results, which caused the authors to investigate the production structure in manufacturing by means of a Cobb-Douglas production function, estimated for each year in the sample period .. These estimates indicate that the production structure in South African manufacturing has changed rather drastically and that this change has socio- economic as well as environmental consequences. Based on these results, the effect of the change in production structure on electricity consumption in South Africa was tested and some policy proposals for the reduction of electricity consumption in manufacturing were made. The data used in the empirical tests was obtained from Statistics South Africa's census of manufacturing and the Department of Mineral and Energy Affairs' yearbooks. The sample years included in the study are 1972, 1976, 1979, 1982, 1985, 1988, 1991, 1993 and 1996. The manufacturing sector has been disaggregated into 27 subsectors. This implies a cross-sectional data set for nine years across 27 subsectors. The data for estimating the price elasticities are the price of electricity measured in RIMWh and the total consumption of electricity measured in MWh for the 27 sub sectors. The data used for estimating production functions for the manufacturing industry, and in calculating the effect of structural change in production on electricity use, are net production (i.e. turnover less consumption of fixed capital), the value of fixed capital stock and the cost of electricity. These aggregates are expressed in constant 1995 prices. Each of these three variables is then expressed as a ratio of the number of labourers employed. All variables are published in nominal terms only, and the GDP deflator was used to deflate fixed capital stock whilst the CPI was used to deflate the other variables. 4 PRICE ELASTICITY OF ELECTRICITY DEMAND IN THE SOUTH AFRICAN MANUFACTURING SECTOR There exists, at least theoretically, a negative correlation between the volume consumed of any commodity and its price, ceteris paribus. One of the easiest, and generally accepted, methods to measure the quantity to which electricity R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2 366 consumption will change with a change in price is, by calculating the price elasticity of electricity demand2• The calculated price elasticity of demand for electricity is reported in Table 5: Table 5 Price elasticity of demand for electricity in 27 manufactnring subsectors in the South African economy: 1976-1996 1197611979 1982 1985 . 1988 1991 1993 11996 Ave. over period Food 0.743 0.074 0.659 ~0.281 0.300 -2.179 4.406 -0.674 0.156 Beveraj!;e industries : 1.203 0.231 . 0.385 ~0.031 0.654 -2.877 2.763 -0.503 . 0.105 Tabacco products • 1.007 0.405 . 0.187 0.243 • 0.882 -0.406 1.220 1.367 0.720 Textiles 0.540 ,,0.052 -0.013 0.023 f.4.486 5.043 -1.550 0.709 -0.047 Wearing apparel, 0.707 0.019 0.384 0.040 i 0.673 . -1.816 8.752 -0.143 0.451 except footware Leather and leather : 0.712 ~0.318 0.181 rO.142 .0.860 -2.3361 3.305 : 1.082 0.172 products Footwear 0.536 0.160 0.345 0.040 10.615 -0.775 2.584 i -0.669 0.232 Wood and wood and 0.745 0.255 : 0.884 0.019 0.806 1 -2.194 1 5.467 ! 0.287 0.389 cork products, except i furniture Furniture and fixtures, 0.615 0.056 0.986 0.260 1.080 I -2.367 9.345 • -2.130 0.316 except primarily of metal Paper and paper 0.620 0.182 ! -0.522 1.524 1 0.319 -2.157! -1.490 1.313 0.255 products ! Printing, publishing 1.214 1 0.641 0.495 0.295 0.778 i -2.217 1 5.403: -0.643 0.299 and alfied industries Industrial chemicals 1.60 0.512 0.439 0.222 1.085 -1.018 6.227 5 0.831 ~!:~~~emiCal 0.248 i 0.170 ! 0.251 1.883 0.658 -1.180 1-17.946 : 1.692 -0.250 Rubber products 2.498 ~0.208 0.090 0.313 0.146 -1.040 1 4.186. 0.330 0.760 Plastic products, not 1.122 ! 0.202 1.020 0.342 0.661 : -1.236 3.811 1.557 0.793 elsewhere classified Pottery, china and 0.552 10.150 ! 0.781 0.058 0.414 : -1.173 3.746 1.046 0.302 earthenware Glass and glass 0.564 LO.504 0.527 0.099 0.716 -2.715 i 1.546 0.274 -0.099 products i Other non-metallic OA17 1..0.250 OA85 0.722. 1.841 . -1.635 -1.077 • 0.494 -0.306 products I Iron and steel basic 0.597 I 0.666 ! 0.119 0.252 • 1.224 -1.974 3.460 0.805 0.571 industries Non-ferrous metal 0.580 I 0.402 ! 0.137 0.567 1.085 ! -2.509 6.172 • 2.921 0.708 basic industries . R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 367 SAJEMS NS Vol 4 (2001) No 2 Table 5 continued 1976 1979 1982 11985 19881 1991 1993 11996 Ave. over period Fabricated metal 10.825 0.020· 0.790 0.507 0.352 I -1.298 . 2.409· 0.103 I 0.292 products, except i I • I machinery and I I I ! equipment l Machinery, except 10.678 0.109. 0.846 0.286 • 0.203 I -1.882 3.242 -1.994 0.Q30 i ! • electrical machinery I Electrical machinery, I 1.1161 0.242 0.262 0.161 0.741 -1.653 6.329 -0.607. 0.485 apparatus, appliances I I and supplies i Motor vehicles, parts 1 0.547 0.054. 0.852 l 0.278 0.450 -1.567 6.720 0.167 i 0.428 land accessories· . Transport equipment, 0.564 : 0.106 0.950 0.652 0.39 -2.500 -7.425 5.264 0.209 except motor vehicles, I parts and accessories I Professional and .0.134 1°.214 i -0.220 1.586 0.171 -2.355 7.066 -2.825 i -0.111 scientific, measuring i and contrOlling ! ! i equipment, I photographic and [optical goods i Other manufacturing 0.502 0.115 0.869 0.236 0.258 -1.182 2.243 3.042 0.517 industries Source: Own analYSIS. Two things are evident from Table 5. Firstly, in most subsectors an increase in the price of electricity has not resulted in a decrease in the use of (i.e. quantity demanded of) electricity. One might at least have expected that in the majority of cases price elasticity of demand would have a negative sign, but this is not the case. Secondly, the price elasticities are small and lie between 1 and -1, indicating that electricity consumption is price inelastic. One may therefore conclude that the manufacturing sector is not sensitive to the price of electricity in its decision-making. This outcome is in line with earlier results (Van Horen, 1997: 66). As mentioned above, electricity is cheap in South Africa and its price declining. This has resulted in the cost of electricity as a percentage of total cost for almost all subsectors and in alI the years being less than 10 per cent, with an average of 4.5 per cent over all the years and subsectors. This can be seen in Table 6. These results indicate that there should be other, non-price, factors causing electricity consumption to change in the South African manufacturing sector. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol4 (2001) No 2 368 These results reiterate that the price of electricity is a very weak instrument to bring about a change in electricity consumption in South African manufacturing. The following section examines the change in production technology in the South African manufacturing sector since 1972, to determine what does in fact cause electricity consumption to change. Table 6 Electricity cost as percentage of total cost in 27 manufacturing subsectors in South Africa: 1972-1996 Industry: 1972 1976 1979 1982 1985 1988 1991 1993 1996 Ave. Description Food 6.624 8.687 10.189 11.632 9.138 9.136 3.632 4.481 1.043 3.082 Beverage industries 4.649 11.765 10.581 10.547 9.905 8.045 2.301 2.574 0.658 2.209 Tabacco products 1.635 3.303 4.074 8.981 7.631 4.203 3.440 3.267 1.051 2.108 Textiles 5.272 7.453 9.563 8.445 8.111 0.921 4.167 4.515 1.773 3.389 Wearing apparel, 1.J90 1.724 2.448 2.217 2.062 2.884 J.l85 1.794 0.631 1.195 except footware Leather and leather 3.390 4.515 5.249 5.253 5.349 6.366 2.353 2.714 0.687 1.751 products Footwear 1.467 1.848 2.182 1.931 2.061 2.419 1.328 1.611 0.598 1.205 Wood and wood and 5.660 7.189 7.935 9.207 8.493 10.678 4.365 6.530 1.796 4.004 cork products, except furniture Furniture and 2.069 3.263 1 4.465 4.414 3.893 5.309 1.858 3.868 0.931 2.214 fixtures, except lJJrimarilv of metal Paper and paper 9.254 9.196 19.376 11.675 15.361 16.988 6.451 5.579 1.916 4.985 [products Printing, publishing 1.326 2.527 2.440 2.427 2.369 3.090 1.260 1.750 0.607 1.312 and allied industries Industrial chemicals 8.968 14.685 19.051 18.620 16.282 33.086 14.148 11.038 3.941 7.806 Other chemical 6.575 5.539 7.129 7.146 7.636 9.871 5.215 1.947 0.827 3.482 ~oducts Rubber products 1.542 7.960 8.882 6.850 7.307 6.490 3.731 4.784 1.932 3.660 Plastic products, not 3.181 4.892 6.248. 7.393 6.960 7.277 3.645 4.262 1.779 3.144 elsewhere classified Pottery, china and 10.531 14.093 18.643 16.216 16.882 13.876 7.493 10.498 2.844 6.282 earthenware Glass and glass 16.910 23.100 23.531 20.112 23.829 22.790 7.138 9.796 3.421 8.689 Iproducts Other non-metallic 16.995 20.295 23.892 26.052 32.903 16.515 8.115 8.115 3.313 9.415 'products Iron and steel basic 13.349 13.913 24.010 23.719 28.248 49.209 2J.l44 29.991 9.028 17.876 industries Non-ferrous metal 25.672 31.471 41.959 44.116 40.261 60.832 19.289 34.814 I J.l76 19.404 basic industries R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 369 SAJEMS NS Vol 4 (2001) No 2 Table 6 continued Industry: . 1972 1976 1979 1982 1985 1 1988 1991 11993 1996 Ave. Description Fabricated metal 3.147 4.262 5.505 5.736 5.211 6.322 3.256 4.004 1.2791 2.825 products, except machinery and lequipment ~achtnery,except electrical machinery Electrical machi- nery, apparatus, appliances and supplies 2.649 3.121 i 4.324 3.955 4.428 5.202, 2.013 2.902 0.658 1.994 1 ' 2.106! 3.100 4.85114387i4.l79i 4.960 2.339 3.451 0.9l9 2.131 ~otor vehicles, parts i 2.848. 3.185 4.423 4.847 3.496 4.507 1.945 2.915 0.560! 1.372 and accessories ' Transport equip- 11.8251. 2.4231 3.413 1 4.270 3.789 3.007 1.406 1.136 1 1.450 1.993 ment, except motor I ,. i i vehicles, parts and. 1 II accessories ; Professional and 2.992 3.351 3.998 2.515 3.330 3.363 1.563 1.741 0.372 1.229 scientific, measu- ring and controlling equipment, photo- graphic and optical goods • I i Other manufacturing industries 2.092 2.8181 3.093 4.125 3.808 4.38611.7581 2.326 1 0.889 1.567 Source: Own analysIs. 5 PRODUCTION FUNCTIONS FOR THE SOUTH AFRICAN MANUFACTURING SECTOR To calculate the change in production technology in the South African manufacturing sector since 1972, Cobb-Douglas production functions have been estimated for each of the sample years over the 27 subsectors. The function estimated for each year is given by: Qi = eLi a C/, Ii, with Qi = Production in subsector i Lj = Number of labourers employed in subsector i Cj ;: Capital employed in subsector i ej = Error tenn a. Elasticity of labour !) ;: Elasticity of capital R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2 370 The results of this estimation are reported in Table 7. Table 7 Results of estimation of Cobb-Douglas production functions for the South African manufacturing sector: 1972-1996 Date a ! B a+B C 1972 0.669556 0.276535 0.946091 3.619703 (7.813961) (4.155798) (5.133022) 1976 0.562570 0.366546 0.929116 3.514579 (7.189039) (6.210758) (6.298416) 1979 0.515375 0.425608 0.940983 3.173307 (7.179832) (7.818833) - (5.776759) 1982 0.562756 0.398406 0.961162 3.153122 (9.790596) (9.570515) (7.280743) 1985 0.500285 0.382278 0.882563 4.037432 (6.153849) (7.093315) (6.752536) 1988 0.458135 0.423226 0.881361 3.963550 (6.727693) (9.085144) (7.676274) 1991 0.415977 0.480410 0.896387 3.624855 (5.576698) (9.034326) (6.771560) 1993 0.492383 0.392499 0.884882 4.076646 (6.696717) (7.811565) (6.712788) 1996 0.462675 0.451166 0.913841 6.592393 (4.804403) (6.854599) (8.855874) Source: Own analysIs. From Table 7 one may conclude that the South African manufacturing sector is producing at constant returns to scale. This is indicated by the sum of the two elasticities (a + 13) which remained approximately 0.9 from 1972 to 1996, and is not statistically significantly different from 1. The most interesting result that emerges from these estimations, however, is that the elasticity of labour decreased significantly from 1972 to 1996. In 1972 the elasticity of labour was 0.66 and it decreased by approximately 30 per cent to 0.46 in 1996. In contrast to this, the elasticity of capital increased by approximately 67 per cent from 0.27 in 1972 to 0.45 in 1996. This indicates that capital's share as input in the production process increased considerably while that of labour decreased. It also implies that labour has been substituted by capital. If the increase in the use of capital results in an increase in the use of electricity, the substitution of capital for labour has an effect on the natural environment through an increase in emissions. This relationship between capital R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 371 SAJEMS NS Vol 4 (2001) No 2 as input in the manufacturing sector and the use of electricity in manufacturing will be investigated next. 6 CAPITAL AND ELECTRICITY USE IN THE SOUTH AFRICAN MANUFACTURING SECTOR Firstly, the correlation coefficients between capital and electricity use from 1972 to 1996, within each subsector, were calculated to test whether a relationship exists between the use of capital in the manufacturing sector and electricity. The results from this estimation are reported in Table 8. Table 8 Correlation coefficients between capital input and energy input in 27 subsectors in the manufacturing sector of South Africa: 1972-1996 Industry: Description i Correlation coefficient I between capital input and electricity input Food -0.100 Beverage industries -0.133 Tabacco products I -0.770 Textiles ! 0.289 Wearing apparel, except footware 0.629 Leather and leather products -0.257 Footwear -0.207 Wood and wood and cork products, except furniture 0.030 Furniture and fixtures, except primarily of metal 0.428 Paper and paper products 0.688 Printing, publishing and allied industries -0.007 ~;. Industrial chemicals 0.833 Other chemical products 0.707 Rubber p.Ioducts 0.101 Plastic products, not elsewhere classified I 0.817 Pottery, china and earthenware i 0.230 Glass and glass products 0.217 Other non-metallic products 0.774 Iron and steel basic industries -0.564 Non-ferrous metal basic industries 0.774 Fabricated metal products, except machinery and 0.761 equipment R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2- 372 Table 8 continued Industry: Description Correlation coefficient between capital input and electricltv inDut Machinery, except electrical machinery 0.738 Electrical machinery, apparatus, appliances and supplies 0.630 Motor vehicles, parts and accessories. -0.031 Transport equipment, except motor vehicles, parts and 0.315 accessories Professional and scientific, measuring and controlling 0.580 equipment, photographic and optical goods Other manufacturing industries - 0.360 Source: Own analysis. These results indicate a positive correlation between capital and electricity input in 19 of the 27 sub sectors. This positive correlation indicates that an increase in capital will result in an increase in electricity input and vice versa. Using another form of analysis, the results from the correlation coefficient calculation are confirmed by cross-sectional ordinary least squares estimation of the simple function, E, = a, + rK, + E: • with Ej Ki ei o'i =: Electricity input for subsector i Capital input for subsector i Error term Constant This has been done for all the sample years. The estimated coefficients are reported in Table 9, and confirm that an increase in capital does indeed result in an increase in energy input for each of the sample years. In each cross-sectional estimation the coefficient on capital is highly significant as indicated by the t- values. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 373 SAJEMS NS Vol4 (2001) No 2 Table 9 Results from cross-sectional ordinary least squares estimation of capital on energy input Constant I Coefficient (t-value Adj. RZ (t-value in parenthesis) . in parenthesis) 92168.75 0.587700 0.848850 1972 (0.633830) (11.89110) 458800.3 0.407422 0.850220 1976 (2.569611 ) (12.18968) i -6209.79 0.662099 0.917549 1979 (-.242557) (16.70959) 645645.7 0.407459 0.591870 1982 (1.449727) (6.103691) 932277.2 0.342793 0.410409 1985 (1.600755) 4.370171 620388.1 0.713901 0.385599 1988 (0.701271) (4.161447) -5677.44 0.514078 0.601022 1991 (-.1 048) (6.337707) 581618.6 0.457981 0.444243 1993 (0.969181 ) (4.667230) 303616.9 0.615572 0.518659 1996 (0.443322) (5.386630) Source: Own analYSIS. The increase in the use of capital over time and across the 27 subsectors thus resulted in an increase in electricity consumption by manufacturing. This conclusion has been reached from the calculation of the correlation coefficients between capital and energy within each subsector over the time period and the estimation of the simple regression of energy on capital across all the subsectors. 7 REDUCTION OF ELECTRICITY CONSUMPTION IN THE SOUTH AFRICAN MANUFACTURING SECTOR: SOME POLICY OPTIONS It has been established that the low electricity prices (based on low coal prices) contribute to the high and increasing, but often inefficient, consumption of electricity. This consumption of electricity contributes significantly to the emission of greenhouse gases and other negative social and environmental externalities. Since 1970 the production structure in the manufacturing sector, the single most important consumer of electricity with the highest energy R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2· 374 intensity3 of all economic sectors (Doppegieter, 1999: 67), has changed dramatically in favour of capital at the expense of labour. The increased use of capital has also contributed to the increased use of electricity as a productive input. Furthermore, the price of electricity is a weak policy instrument to manage the consumption of electricity. Diesendorf (1996) concludes also that correct pricing of electricity is not sufficient for efficient electricity consumption. Since electricity consumption is not sensitive with respect to price, it is likely that a tax on electricity consumption would have little, if any, impact as well. The probable outcome of such a tax is that it would be viewed as an additional cost item and passed on to the fmal consumer, depending on lhe price elasticity of the final product. In such a scenario, there will be high social welfare loss. O'Connor (1999: 96) supports this view, arguing that a tax on electricity is only effective if price elasticity is high and substitutes cause less pollution. Whalley (1999: 123) is concerned that environmental tax policy may become so complex, that it would be of little use given its difficulty of implementation. Another policy option is to prescribe maximum levels of electricity intensity per industrial subsector by legislation. This would mean a cap on electricity consumption per unit of production. Such legislation however implies high transaction costs on account of the policing and implementation of such a system, and is open to abuse. The required institutions to implement and manage such environmental legislation in developing countries tend to be weak and ineffective. This further adds to the high transaction cost of this policy option {see also Da Motta, Huber & Ruitenbeek, 1999: 184). In principle, a subsidy is an inefficient policy instrument since it causes social welfare losses (Rosen, 1998: 295-97). Under some circumstances, one could convincingly argue that a subsidy is a temporary measure to facilitate the change between two different policy and operational regimes. Such regimes might include a change in technology to bring about a reduction in pollution. In this regard, however, Da Motta, Huber and Ruitenbeek (1999: 186) view subsidies for abatement investments as having a limited impact, stating: Subsidies for abatement investments have, however, been of limited impact since environmental enforcement has not been effective enough to increase firms' demand for these expenditures. Moreover frrms are using these incentives inadequately because of the lack of proper follow-up procedures, in fiscal and environmental terms to monitor their investments. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 375 SAJEMS NS Vol 4 (2001) No 2 From the above discussion it follows that conventional policy measures to bring about a change in electricity consumption behaviour of firms are likely to have a negligible effect. This is so, because these policy measures address the symptom of the problem, the electricity consumption and the ensuing emissions, and not the cause, which is an inappropriate production method and high capital intensity of the production structure. Murthy, Panda and Parikh (1997) and Pimentel et al. (1994) are convinced that electricity conservation is possible only through a change in technology and the more efficient use of electricity, brought about by a change in the production structure (i.e. capital and labour input) and method. Which technological changes. are then required to bring about the desired reduction in electricity consumption? Table 10 highlights the main procedures to reduce CO2 emissions in manufacturing. These measures are in addition to those offered by Doppegieter (1998: 4-66 - 4-74), Van der Merwe and Scholes (1999), Halnaes, Callaway and Meyer (1999) and the World Bank (1998). Table 10 Selected mechanisms for reducing CO2 emissions in manufacturing CO2 reduction I Application . Reduction I Need for new mechanisms i I potential . technolo2)' Energy intensity i Housekeeping (mainte- I Low I Low reduction I nance) Conservation High Medium I Fundamental process High High changes Energy source • Coal/oil to natural gas High Low switching Fossil fuels to electricity High High Co-generation High Medium Fossil fuels to biomass Low I Medium Flow changes Materials recycling High I High I Materials substitution I High I High Process integration High High Source: InternatIOnal Energy Agency, 1994: 132. From Table 10 it may be concluded that technology options for reducing electricity consumption are in fact available. One major objection to the introduction of these technologies would be the additional cost burden that it would imply. This is, however, not necessarily true. South African industry may greatly benefit from international trade using the flexible mechanisms (cj clean development mechanisms (CDM» considered under the banner of global climate change policies within the Kyoto protocol framework (see Zhang, 2000). R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2. 376 South Africa acceded to the Kyoto Protocol on 13 June 200 I. Many studies have indicated that developing countries, such as South Africa, have a lot to gain from CDM projects (World Bank, 2000; OECD, 1999 & KPMG, 2000). Pending international agreement on the CDM process and mechanisms, a way does exist of gaining superior, clean, technology without having to pay the full bilL 8 CONCLUSION Electricity intensity in the South African manufacturing sector is particularly high. This in itself may not be a problem, but it contributes greatly to the generation of electricity, which in turn significantly contributes to the emission of greenhouse gases and other pollutants. These negative externalities have very high social and environmental costs. It is therefore important to reduce the electricity consumption of the manufacturing sector. Electricity consumption in the manufacturing industry is, however, not price sensitive. To use price as a policy tool to reduce the consumption of electricity will therefore not contribute much. Therefore, to reduce consumption one of three options is available. Apply conventional policy mechanisms, use electricity more efficiently through a change in technology or substitute labour for capital. From this study it seems that none of the conventional policy mechanisms, that is, taxes, legislation or subsidies, would achieve the objective of a cost-effective permanent reduction in electricity consumption. The substitution of labour for capital in South Africa, however laudable, is likely to be restricted by numerous socio-political factors. The global climate change debate, in terms of the Kyoto protocol, and especially the flexible instruments such as clean development mechanisms (CDM) proposed by the protocol, has brought a unique opportunity for South African firms. South African firms can exchange their old technology for a better one, gain in foreign direct investment and contribute to a cleaner environment. This seems an effort well worth investigating. ENDNOTES The authors would like to thank all those who have contributed to this paper, but are solely responsible for all remaining errors and the views expressed here are those of the authors and do not reflect those of the R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 377 SAJEMS NS Vol 4 (2001) No 2 authors and do not reflect those of any institution that they may be involved with. 2 The price elasticity of electricity demand is calculated as the percentage change in the demand for electricity divided by the percentage change in the price for electricity. This implies that the change in demand is solely due to a change in price. 3 Calculated by dividing the real GDP at factor costs by final energy use. REFERENCES ANC (1994) The Reconstruction and Development Programme. johannesburg: Umanyano Publications. 2 CHAMBER OF MINES (2000) Mining Statistics in Brief 1999. Johannesburg: Chamber of Mines. 3 DA MOTTA, R.S., HUBER, R.M. & RUITENBEEK, R.J. (1999) "Market Based Instruments for Environmental Policymaking in Latin America and the Caribbean: Lessons from Eleven Countries", Environment and Development Economics, 4: 177-201. 4 DEPARTMENT OF MINERALS AND ENERGY (DME) (1995) South African Energy Statistics No.2. Pretoria: Department of Minerals and Energy. 5 DEPARTMENT OF MINERALS AND ENERGY (DME) (1999) South Africa's Mineral1ndustry 1998199, Pretoria: Department of Minerals and Energy. 6 DEPARTMENT OF MINERALS AND ENERGY (DME) (2000) South African National Energy Prices, June 2000, Pretoria: Department of Minerals and Energy. 7 DlESENDORF, M. (1996) "How Can a "Competitive" Market for Electricity Be Made Compatible with the Reduction of Greenhouse Gas Emissions?" Ecological Economics, 17: 33-48. 8 DOPPEGIETER, J.1., DU TOIT, J. & LIEBENBERG, J. (1998) Energy Futures 1998199, Stellenbosch: Institute for Futures Research. 9 DOPPEGIETER, J.J., DU TOIT, 1. & LIEBENBERG, J. (1999) Energy Indicators 1999/2000, Stellenbosch: Institute for Futures Research. 10 ESKOM Annual Report, Various issues, Johannesburg. 11 HALNAES, K., CALLAWAY, lM. & MEYER, H.L. (1999) Economics of Greenhouse Gas Limitations, Riso, Denmark: UNEP. 12 HOUGHTON, J.J., MEIRO FILHO, L.G., CALLANDER, B.A., HARRIS, N., KA TTENBERG, A.. & MASKELL, K. (1996) Climate Change 1995, "The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report (SAR) of the Intergovernmental R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . SAJEMS NS Vol 4 (2001) No 2- 378 Panel on Climate Change (IPCC), Cambridge: Cambridge University Press. 13 INTERNATIONAL ENERGY AGENCY (1994) Energy and Environmental Technologies to Respond. to Global Climate Change Concerns, Paris: IENOECD. 14 KPMG (2000) South African National Strategy Study on CDM. Unpublished report to the National Climate Change Committee, Cape Town:KPMG. 15 MURTHY, N.S., PANDA, M. & PARIKH, J. (1997) "Economic Growth, Energy Demand and Carbon Dioxide Emissions in India: 1990-2020, Environment and Development Economics, 2: 173-93. 16 NATIONAL ELECTRICITY REGULATOR (NER) (t998) Lighting Up South Africa: 199711998, Johannesburg: NER. 17 NATIONAL ELECTRICITY REGULATOR (NER) Various issues. Electricity Supply Statistics, Johannesburg: NER 18 O'CONNOR, D. (1999) "Applying Economic Instruments in Developing Countries: from Theory to Implementation, Environment and Development Economics, 4: 91-110. 19 ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT (OECD) (1999) Implementing Domestic Tradable Permits for Environmental Protection, Paris: OECD. 20 PIMENTEL, D., HERDENDORF, M., EISENFELD, S., OLANDER, L., CARROQUINO, M., CORSON, C., MCDADE, J., CHUNG, Y., CANNON, W., ROBERTS, J., BLUMAN, L. & GREGG, J. (1994) "Achieving a Secure Energy Future: Environmental and Economic -Issues", Ecological Economics, 9: 201-19. 21 ROSEN, H.S. (1998) Public Finance, New York: McGraw-Hill. 22 STATISTICS SOUTH AFRICA (Stats SA) (1995) Census of Electricity, Gas and Steam 1995, Pretoria: Statistics South Africa. 23 STATISTICS SOUTH AFRICA (Stats SA) Various issues, Census of Manufacturing, Pretoria: Statistics South Africa. 24 UNITED NATIONS FRAMEWORK CONVENTION ON CLIMATE CHANGE (UNFCCC) (2000) The Hague 2000: Sixth session of the conference of parties UN Framework Convention on Climate Change. Press Kit, http://cop6.unfccc.intlpdf/presskc6e2.pdf. 25 VAN DER MERWE, M.R & SCHOLES, R.I. (eds.) (1999) SA Greenhouse Gas Inventory, Department of Environment Affairs and Tourism: Unpublished report. 26 V AN HOREN, C. (1996) Counting the Social Costs: Electricity and Externalities in South Africa. Cape Town: Elan Press and UCT Press. 27 VAN HOREN, c. (1997) "Cheap Energy - At v,,'hat Cost?" In Bethle- hem, L. and Goldblatt, M. The Bottom Line, Cape Town: University of Cape Town Press. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) . 379 SAJEMS NS Vol 4 (2001) No 2 28 WHALLEY, J. (1999) "Environmental Considerations in Tax Policy Design", Environment and Development Economics, 4: 111-24. 29 WORLD BANK. (1998) Pollution Prevention and Abatement Handbook 1998, Washington, D.C.: World Bank. 30 WORLD BANK (2000) Taxes and Tradable Permits as Instruments for Controlling Pollution: Theory and Practice, Report no. WP/001l3. Washington, D.C.: World Bank. 31 ZHANG, Z.x. (2000) "Estimating the Size of the Potential Market for the Kyoto Flexibility Mechanisms, Weltwirtschaftliches Archiv - Review of World Economics, 136(3); 491-521. R ep ro du ce d by S ab in et G at ew ay u nd er li ce nc e gr an te d by th e P ub lis he r (d at ed 2 00 9) .