Based on the work presented in this dissertation, it is clear that there is a measurable and predictable take-back effect of any residential insulation project. In this chapter I shall discuss the policy implications of that take-back effect. Using the theory developed in Chapter 3, I shall first show a simple technique for developing similar estimates of the takeback effect in other energy projects, and second show how the work can be applied to any change in "production technologies" including those which involve commercial and industrial end-uses of commodities other than energy. Finally, I will suggest future research to further the understanding of the response of demand to changes in technology beyond just the takeback effect. I. Application of the Results to Residential Heating Projects The takeback effect will result in lower energy savings due to a home insulation project (or an improved building insulation standard) than would have been estimated using engineering estimates. That difference might (or might not) be enough to make a project which looked cost effective (based on the engineering estimates) prove not to be cost effective The econometric results presented in Chapter 5 (as modified to account for the effects of measurement error in the variable LOSS) permit one to estimate the change in temperature settings of a typical household which would result from changes in the level of heat loss due to changes in the level of insulation. The change in heat loss can be calculated using equation (4.6). An estimate of the takeback effect can be developed with a little work, although the results will depend upon regional considerations. Consider an energy conservation project which converts a home from poorly insulated (having an r-value of 10) to the highest level of insulation (an r-value of 40). The heat loss would then be decreased from x to x/4. For poorly insulated homes, the median value of the variables which occur in the key equations are: SURFACE: 35.4 DHOUR: 30.9 EDUCATN2: 4.0 PEOPLE: 3.0 HUMIDD: 0.0 FULLTIME: 1.0 RESET: 0.0 Therefore, by equation (4.3), prior to the change in insulation levels are: (6.1) LOSS = 34.5 *30.9/ 10 = 109.39 and after the change in insulation they become: (6.2) LOSS = 34.5 *30.9/ 40 = 27.35 Based on the regressions for temperature changes in Chapter 5: · Night temperature would go from 19.92oC to 20.23oC (up 0.32oC), · Daytime temperature would go from 20.46oC to 20.62oC (up 0.16oC), · Evening Temperature would go from 20.27oC to 20.50oC (up 0.23oC), and · Average temperature would go from 20.19oC to 20.43oC (up 0.25oC) These numbers are consistent with the estimates reported in Schwarz and Taylor (1995) and others which are discussed in Chapter 2. Heat loss through the walls is, combining equations (4.5) and (4.6): (6.3) LOSS = SURFACE * DHOUR/RVALUE Therefore, if an energy project increases the r-value of the home by a factor of four, then heat loss would be reduced to one-fourth of the pre-project heat loss, if the temperature of the home is not increased from the pre-project level. Or, equivalently, the engineering estimate of energy savings would be 3/4 of the energy used for heating prior to the implementation of the project. (Note that, at this point, we are only discussing energy to replace heat lost through the walls, which is actually about a third of the total energy used in heating, the other two-thirds of the energy is used to replace heat lost through air infiltration and through windows.) The actual effect of an increase in household temperature on energy consumption would depend on the numbers of hours that the house was then being heated (if outside temperature exceeds the indoor temperature setting, then no heating will take place). If we assume, as a crude estimate, that a house in Manitoba will be heated roughly three quarters of the time , then an average increase in temperature setting of 0.25 oC, predicted by the model, would be an increase in degree hours (Fahrenheit ) of (0.75 *0.25*9/5 ) = 0.3375 degree hours, or (0.3375/30.9) = 1.0922 percent. However, since the walls are now four times as energy efficient, this would only decrease the energy savings by 1/4 * 1.09 percent from the original engineering estimate of savings. However, that is only the loss through the walls. As I indicated in Chapter 4, only one-third of the total heat loss for the house is through the walls, the other two thirds are through air infiltration and loss through windows. (However, Generally speaking, improving the insulation of a home will only decrease heat loss through the walls, so improving the R-Value of the walls will not result in an energy saving for these other two sources of heat loss. Heat loss through these two sources would also be proportional to the increase in degree hours of heating. Therefore, if heating increases by 1.09 percent, the additional heat loss through the windows and through air infiltration would be two times the original heat loss through the walls, times 1.09 percent. Combining heat loss from the walls and increased heat loss through the windows and air infiltration, the total increased heat loss would be ((2 + 1/4) * .010922) = 0.024575 (2.5 percent) times the initial (pre-project) heat loss through the walls. Since the engineering estimate of savings was 3/4 of the pre-project heat loss through the walls, the takeback effect is: (6.4) Takeback = [2.25*LOSS(pre-project)*1.0922%]/[0.75*LOSS(pre-project)] = 3 * 1.0922% = 3.2766% Therefore, when a conservation project is evaluated based on an engineering assessment of the energy savings, the benefits are exaggerated, and projects which are not actually cost beneficial may appear to be so when the takeback effect is ignored. A 3.3 percent takeback effect may or may not be sufficient to change the results of a cost benefit analysis of a project from showing a cost beneficial result to showing a non-cost beneficial result, depending upon the peculiarities of the individual project. However, it is clear that the 3.3 percent takeback should be considered in evaluating the project. A sample benefit/cost analysis (using the Minnesota Department of Public Service’s Ben/Cost Model) for an insulation is presented in the appendix. The first model run shows a benefit cost ratio (for the Societal Test, which is the criterion that the Minnesota Department of Public Service uses) of 1.02, based on an engineering estimate of savings, indicating that the project is cost beneficial. However, when the energy savings are reduced from the engineering estimate of 18 percent to (.18*(1-.032766)) 17.41 percent (see the second run), the benefit to cost ratio drops to 0.98, which indicates that the project is not cost beneficial. II. Generalizing Takeback Estimates for Other uses. It would be comparatively straightforward to develop more accurate estimates of the effectiveness of energy conservation measures before undertaking an energy conservation program by generalizing the discussion at the end of Chapter 3. In the long run, it would not be necessary to develop estimates of the level of energy services demanded for each household for each end-use (heating, cooling, T.V.s, water heating etc.), in order to apply the results of this study to other end-uses than heating. The work can be generalized from easily available data, as discussed below. There is a simple relationship between the price effect on the demand for energy (which may be determined by more commonly available data) and the price effect on the demand for energy services. A sketch of the relationship is presented below: The relationship between the amount of energy services the consumer receives (H) and the energy used for providing the energy service (E) is defined by the transformation ratio (K) between the two: (6.4) H = E * K The price of obtaining a unit of energy service is: (6.5) PH= (PE / K) Since T/H =1, where T is the total level of energy services ; and H is any increment to energy services. T/PH is (by substitution): (6.6) (T/ PH) = [(E*K)] / [(Pe/K)] |K constant Since K is a constant (this analysis assumes that the only changes in demand are behavioral and not technological, i.e. that we are dealing with a "short term" not a "long term" elasticity response), we can take it outside of the derivatives, and simplify (6.6) to (6.7) (T/PT) = K2 * (E/Pe) Therefore, given that we know (E/Pe), we can compute T/PT. Since (6.8) PH = Pe/K If, at T=T1, K changes from K1 to K2 then PT changes from Pe/K1 to Pe/K2, and therefore T would change from T1 to (6.9) T2 = T1 + (T/PT) * (Pe/K2 - Pe/K1) In terms of the original question, this means that E would change by (or that the price induced takeback effect would be): (6.10) T1/K1 - T2/K2 Using that relationship, one could examine the short term price response of demand for electricity for several end-uses, and the results could be used for preliminary studies of the takeback effect in studies involving other fuels, or, with minor modifications, in any study of the effect of exogenously introduced technological change on the demand for a factor input. III. Expansion to Other Take-back Effects Although the proceeding analysis was based on the takeback effect in residential heating, the basic theory can be extended to cover the entire gamut of takeback effects, including commercial and industrial processes, although it may not be as easy to define the objective function being maximized, and the equations to be solved in a corporate world may involve complex simultaneous supply and demand modeling. However, there is no fundamental difference between producers and consumers. (Indeed, this is the insight which leads to the Becker style household production function.) For consumers, the utility to be maximized is an elusive theoretical construct based on several arguments. For a producer, the utility is more concrete and based on a single argument - profits. For both, energy is an input to the production function, not an argument to the utility function itself. For both, an increase in the efficiency of use of an input to the production function results in a decrease in the total cost of production. Similarly, once the form of the production function and the consumer's reaction to a change in price have been determined, it is not necessary to know the actual form of the utility function. Rather one can go directly from the price sensitivity of the demand function for energy for a given end-use to the sensitivity to improvements in technology, as described above. The only practical problem which must be solved, is determining the price sensitivity of demand for a given end-use. I offer some simple suggestions in the following section. IV. Suggestions for Future Research The work done here is directly applicable in a wide variety of areas of technology assessment. Two suggestions for further development come immediately to mind. The first of these is developing estimates of the takeback effect in various end uses with other derived demands. The second area is a straightforward extension of the concept of the take-back effect to look at the related "income" or cross-elasticity effects, which would further diminish the energy (or other) savings from an improvement in production technology. This second area can be expanded into the realm of "cross-technological elasticity." by looking at the effect that a change in the efficiency of use of one input has on the demand for other inputs into the production function. A. Estimating Other Takeback effects As shown above, the take-back effect is relatively simple to compute given an estimate of the price elasticity of demand for a given energy service, or indeed of any primary demand. If the underlying production technology does not change, however, then the short term price response in the consumption of energy (or any other derived demand) for a particular end-use will be the same as the short term price response for the energy (or other) service. For instance, the Parti and Parti approach estimated the change in electricity consumed for various end-uses based on changes in price and income. In the short run that is also the change in the demand for energy services based on changes in price or income, since, by definition of “short run,” consumers have not changed the technology they use to produce those services with. Using the transformation methods outlined above, one could therefore estimate technological elasticity from such a study without any other econometric gyrations. B. Inputs Used in Other Aspects of Production Electricity is a good example of an input which is used in more than one aspect of production. In a household, it is used independently in the production of heat, of air conditioning, of refrigeration, of hot water, of cooking, etc. A cost savings in one end-use (say heating), may well lead to an increase in its use in another end-use due to cross price elasticities. A similar result could occur in an industrial process, where electricity might be used at a number of steps along an assembly line. Increasing the energy-efficiency of one use of electricity along the line might lead to lower prices for the product, and hence through the takeback effect, increased uses at other points in the line. Or it might increase the demand for complimentary goods, which are also produced using electricity. In such a case, the difference between engineering estimates of energy savings and actual savings will be greater than the take-back effect, in and of itself, would explain. An extension of this work to look at cross-price elasticities for services would be a logical step, which could be approached using the framework presented here. V. Conclusions The essential insight of this work is that changes to responses in technology are simply responses to changes in price. Once that is established, and the production function is adequately defined, traditional tools are fully capable of estimating changes in demand due to technological changes. This is true whether we are talking about "own" technological elasticity which was examined in this work, or "cross" technological elasticity, which is a comparatively simple extension of the work done here. The techniques presented in this work have the second advantage of permitting a better estimate of energy savings before a project is put into place, rather than afterwards. In addition, these techniques permit the effect of a project to be estimated using the entire population of a service area (or beyond) rather than based on a very limited sample size. This will permit more accurate estimates of the effects of energy conservation projects. The size of the take-back effect will vary depending upon the price elasticity of demand for the primary argument in the utility function. In the case of heat, the price elasticity is not very large, but might be large enough to affect the results of a cost/benefit analysis of an energy conservation program.