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Nuclear and fossil fuel steam generation of electricity: differences and similarities.

I. Introduction

Nuclear power, produced in about 112 plants, accounts for roughly one-fifth of U. S. electricity. Because of cost overruns, technical problems, loss of public and political confidence and backing, falling fuel prices, and slower than anticipated growth in energy demand, this figure is significantly below what many expected [17, 269]. In fact, no U.S. utility has ordered a nuclear plant in over 15 years. Recent environmental legislation, however, may improve nuclear power's outlook. While nuclear electric power is clearly an important alternative, the basic production technology and costs, and the efficiency and equity of the nuclear rate structure, have received little rigorous statistical investigation.

The costs and efficiency of electric utilities with nuclear plants have not been studied because of perceived differences in the underlying production function or measurement difficulties. A need to preserve technological homogeneity is often suggested as a reason to not include nuclear power. However, most studies have pooled fossil-fuel plants of different vintages, fuel types, capacities, regions, and load characteristics, with little more than a disclaimer concerning aggregation difficulties.(1) It is not clear that nuclear steam electric generation is significantly different from conventional steam production methods, and pooling to measure total firm steam generation costs is a more serious aggregation problem.

Several recent cost studies deal with the uniqueness of nuclear power, but arrive at different conclusions. Naughton [22] rejected the hypothesis of separability of nuclear and fossil-fuel costs for a 1980 cross-section of 78 privately owned electric utilities, where 10% of the steam generation was nuclear and the remainder was fossil-fuel. Naughton's emphasis, however, was on the efficiency of the rate structure, not on generation characteristics. We compare Naughton's results with a study of rate structure efficiency, using our sample, in Thompson, Kamerschen, and Danielsen, [29] and section IV of this paper.

Krautmann and Solow [18] evaluated the cost effectiveness of nuclear power and estimated economies of scale, using a pooled cross-section of 43 plants over the period 1976-78. By using a short-run variable-cost model, they found long-run average-cost curves were concave, contrary to the evidence on fossil-fuel generation found in Nerlove [24] and Christensen and Greene [9]. The Krautmann and Solow [18] study however, does not provide strong evidence about scale economies. Whether nuclear power should be separated from conventional fossil-fuel methods and what will be its implications for electric generation cost estimation and efficiency need additional comprehensive analysis.

Using data from a 1985 cross-section of 40 privately owned electric utilities of which approximately 36% of steam generation was nuclear,(2) we estimate the cost functions of nuclear and fossil-fuel generation at the firm-level. Statistical tests and other relevant measures are used to determine whether nuclear power should be treated as a sample separate from conventional steam generation.

Section II of our paper discusses the development of the statistical model. Section III contains the estimation results and the results of the comparison tests on fossil-fuel and nuclear steam cost functions. Section IV discusses the efficiency of the rate structure for nuclear utilities. Section V contains a summary and conclusion.

II. The Statistical Model

The general form of the generation cost equation used for both nuclear and fossil-fuel steam generation does not vary greatly from the traditional neoclassical cost model according to Cowing and Smith [10].

Arguably, the labor and capital input demand equations for nuclear power contain variables related to the unique technical and regulatory characteristics of this type of generation. Plant age, for example, may be more critical for nuclear plant costs than for other technologies for several reasons. First, the supposed benefits of nuclear power are its low operating and fuel costs. As a plant ages, for example, its downtime increases and more expensive "replacement power" must be provided to meet the demand. The firm has an economic incentive to spend more on maintenance (mostly labor) to avoid this cost. Second, nuclear power is still a relatively new and complex technology. As a plant ages, operation and maintenance workers gain experience. For example, operators can anticipate downtime and can coordinate it with fuel replacement to reduce maintenance and operations costs. As a result of these two conflicting forces, increased costs from deterioration, and decreased costs from the "experience curve,"(3) the effects of plant age could be important. The net effect, however, cannot be hypothesized, a priori.

The Nuclear Regulatory Commission (NRC) regulates every aspect of a nuclear plant's operation. NRC penalties and fines, largely for safety violations, are a small cost relative to operations costs (usually less than 1%). However, fines may indicate a rising trend in current capital and labor costs necessary to resolve or avoid regulatory violations and the adverse publicity they can cause in the future. It is hypothesized that the greater the value of regulatory-imposed fines, the higher the costs, ceteris paribus.

These special characteristics, and other technical constraints of nuclear power, such as limited input substitution, could affect the content and form of the cost function and are, therefore, subject to statistical inquiry. This study utilizes the long-run version of the translog cost function for both fossil-fuel and nuclear power, which assumes the firm is in long-run equilibrium. It is implicit in the long-run cost function that all inputs have adjusted fully to their equilibrium value, given current market prices.

Researchers differ, however, about how capital in the electric utility industry should be treated. Generally, it comes down to the level of aggregation that is employed. At the plant level, a reasonable argument can be made that the majority of capital is quasi-fixed and, therefore, may not have adjusted to its long-run equilibrium value at any given time. However, at the firm level, considerable discretion is generally available over a variety of plants of different ages and technologies. This issue is discussed in Nelson 1231. Nuclear power may represent a special case.

Several studies, including Naughton [221 and Krautmann and Solow [18], have estimated nuclear power costs with a short-run, variable-cost function, presumably on the assumption that nuclear power represents a quasi-fixed investment. Naughton used firm-level data, since his emphasis was on the efficiency of the tariff structure. Krautmann and Solow used nuclear reactor and plant data in their study of scale economies in nuclear plants.

This study, emphasizing firm behavior, implicitly assumes fossil-fuel and nuclear power are similar in their response to capital prices. The multi-firm ownership of many nuclear plants makes distributing the capital expense a complex issue. Even for one plant, the assumption of a fixed capital expense may not be entirely valid. Recent expert testimony on depreciation expenses for a nuclear power plant in this sample explored 4800 separate capital systems that comprise the plant, each of which depreciates, is retired, or is replaced at a different rate. New technology and capital costs apparently play an important role in this process.(4)

For fossil-fuel and nuclear steam production, the following general translog cost function was estimated, based on the model developed by Christensen, Jorgenson, and Lau [7], and Christensen, Jorgenson, and Lau [8], and originally applied to electric utility costs by Christensen and Greene [9]:

[Mathematical Expression Omitted]


C = Total cost of generation,

Q = Output of electricity,

Pi = Price of the ith input.

The data are scaled by sample mean values. Plant vintage and NRC penalties are added to the nuclear equation to account for the embodied technological effects discussed previously. Cost share equations derived from the cost function are used in the Zellner seemingly unrelated regression estimation procedure. The cost function is restricted to be homogeneous of degree one in prices, along with the homogeneity and symmetry restrictions, which are required for any well-behaved cost function according to Diewert [12], or Christensen and Greene [9].(5) Cross-equation parameter restrictions are applied to further enhance the efficiency of the estimates. Several restricted forms of the translog cost equation are estimated to determine the underlying production function. (For a complete discussion of the data sources and variable construction, see the appendix.)

III. Estimation Results

The estimated parameters and t-ratios (in parentheses) for the unrestricted cost functions for fossil-fuel production, nuclear-steam production, and total (pooled) steam production are shown in Table I. The coefficients of nuclear plant age and NRC penalties, estimated separately and together in several restricted forms of the nuclear cost function, were insignificant, and dropped from the table. Apparently the "experience curve" effect is completely offset by rising operating and maintenance costs as a plant ages.(6)

Differences in the Underlying Production Function

Cost Shares. Our estimates show some expected differences in the underlying technologies of fossil and nuclear-steam generation of electricity. Figures 1 and 2 show the estimated input shares of the total cost, derived from the cost share equations, for fossil and nuclear-steam, respectively. As expected, even in the long run, fuel remains the major expense for fossil steam production, and capital is the major expense for nuclear steam production. Labor costs represent about the same percentage in both technologies. The relative size of the capital price and the fuel price coefficients across equations similarly reflect their importance in affecting cost.

Functional Form. The results of likelihood ratio tests on several restricted forms of the cost functions indicate a homothetic production relationship, one where output-price interaction terms are restricted to be zero, is statistically acceptable for both of the generation methods in this sample.(7) It appears that although costs are responsive to the level of output, relative input demand functions are not. However, if a homothetic cost function is considered as the general model, a homogeneous (constant returns-to-scale) function - one with the output-squared term restricted to be zero - could not be rejected for fossil-fuel generation. Fossil-fuel steam production data for an additional 30 firms are analyzed in a similar study. In this sample, the homogeneous form could not be rejected, and the output-squared term was statistically insignificant in all forms of the model.(8)

Separability Tests. Two direct tests on separability led to conflicting results. Nuclear and fossil steam cost functions were pooled (see Table I) and similar to Naughton [22]; a variable

[TABULAR DATA OMITTED] measuring the percent output generated by fossil fuel was included.(9) Using a likelihood ratio test, we were unable to reject the hypothesis these technologies could be combined, as was Naughton.(10) However, when the nuclear and fossil-fuel samples were "stacked" and a Chow test was performed for structural differences in the two technologies, the hypothesis that no differences exist was rejected.(11)

The results in Table I suggest although nuclear power and fossil-fuel generation are more alike than many researchers believe, there are also important differences. Most notably, it appears the cost of nuclear power is more flexible in response to changing levels of output than are fossil-fuel costs.


More elaborate methods could explore the issue of separability. One method would be to construct Divisia indexes for the inputs, and include them along with separate output for fossil-fuel and nuclear generation in a single cost function. Another method would be to stack the samples with adjacent observations from the same firm. Testing for the degree of resulting auto-correlation would then provide evidence for the need to separate generation types. However, neither method provides additional insight into the properties or underlying differences in these two technologies. We feel more useful information is gained by analyzing each sample separately and comparing the results, as we do in the remaining sections of the paper.

Economies of Scale. Additional direct evidence of differences in production technologies can be derived from measuring economies of scale. From the translog cost function we can define scale economies as

[Mathematical Expressions Omitted]

Positive values indicate economies of scale and negative values indicate diseconomies of scale. Following Christensen and Greene [9], Table II presents five groups of firms by output level and scale economies for fossil-fuel and nuclear power for the median firm in each group. Values for the "typical firm" and t-ratios for both generation technologies are also presented in Table II. The typical firm is derived by applying estimated parameters to sample mean values. As expected, scale economies diminish as firm size increases. However, as indicated by the number of firms in each grouping, nuclear generation is more concentrated at smaller output levels with more noticeable economies of scale. The industry average firm for each technology demonstrates this trend more clearly. The average fossil-fueled firm statistically exhibits no scale economies, whereas the typical nuclear firm experiences economies of scale.


These differences can be highlighted by deriving the average cost curve for the typical fossil-fueled and nuclear power firm. Evaluating the translog average-cost function over the sample range of output, with input prices set at their sample means, produces the cost curves in Figure 3. The upper graph shows fossil-fuel output is concentrated in the relatively flat portion of the curve, with a noticeable trend toward higher average costs at higher output levels. On the lower curve, the average cost of nuclear power shows a concentration of output in the declining area of the curve. This supports our previous result that the scale of operation affects cost in a more varied and flexible manner in nuclear power than in fossil-fueled plants. However, these results may conflict with the findings of Krautmann and Solow [18], who show large dual-reactor plants to have lower long-run costs. A direct comparison of our results with Krautmann and Solow's is difficult, since our sample has on the high output end of the scale, firms with multiple dual-reactor plants, which appear to be more inefficient.

Long-run Costs. Insight into another aspect of nuclear power is gained by expanding our analysis slightly. Surprisingly, Figure 3 shows the costs of fossil fuel are noticeably higher than costs for nuclear power on the average. At sample mean output levels, the average costs of fossil-fuel and nuclear generation are 3.86 [cts.] /kWh and 3.00 [cts.] /kWh, respectively. Also, from equation (1) we have

[Mathematical Expressions Omitted]

Equation (3) is the cost of producing the last kWh of electricity for each plant type. The mean long-run marginal cost of nuclear power plants (2.47 [cts.] /kWh) is roughly 1/3 less than for fossil-fuel plants (3.85 [cts.] /kWh). This result is probably due to several factors. First, nuclear power plants are base-load technologies exclusively, whereas fossil-fuel steam plants are partially composed of higher cost, intermediate and peaking units. Similarly, fossil-fuel costs at utilities with nuclear plants are relatively high, because of a greater reliance on non-coal fuels, such as oil and natural gas." (12) Second, by 1985 the "experience curve" for nuclear power likely has resulted in increased efficiency and reduced costs over time, with some savings in capital equipment. Nuclear plants in 1985 had the highest utilization rate of their brief history. Third, managerial and financial responses to the adverse publicity and regulatory pressures facing the nuclear industry undoubtedly account for some savings. Finally, significant long-run costs are undoubtedly associated with nuclear plant cancellations and high-level nuclear waste disposal, which are not included in the generation estimates.

The average marginal cost of fossil-fuel generation is equal to its average cost, but nuclear-power marginal cost is about 15% below its average cost. This indicates nuclear power has not yet reached minimum long-run average costs. Our combined estimated cost (3.54 [cts.] /kWh) is slightly higher than the electric utility industry-wide average costs of approximately 3.33 [cts.] /kWh in 1985 shown in Energy Information Administration [13,14].(13)

Input Substitution. Further examination of our results also supports the common belief nuclear technology allows fewer possibilities for input substitution. Christensen and Greene [9, 660] developed the formula for the Allen partial elasticities of substitution from their translog cost function. The formula for each technology is

[Mathematical Expressions Omitted]

For the ith input, the own-price elasticity of demand is

[Mathematical Expressions Omitted]

where Si is the estimated cost share for the ith input, which is

[Mathematical Expressions Omitted]

The results, reported in Table III, indicate input substitution in nuclear power technology is relatively restricted. The mean values of the estimated elasticities of substitution, measured across firms, are several times larger for fossil steam production than for nuclear steam. The most notable difference in input substitution of these technologies is between capital and labor, lending support to the concept that more fixed complementary relationships exist in a nuclear plant's operation.


The estimated own-price elasticities further support the notion of technical "rigidity" to economic changes in nuclear power. In the nuclear equation, capital is about one-tenth as responsive to price changes as in the fossil-fuel equation. This is expected since nuclear is a more rigidly regulated sector of the industry. The responsiveness of fuel to price changes is about the same for both technologies.

IV. Efficiency and Equity Comparisons

Naughton [22] focuses on the efficiency and equity of utilities' pricing of their retail customers. Briefly, he finds in his 90% fossil-fuel and 10% nuclear sample that first-best efficiency (price equals marginal cost) did not exist, but second-best efficiency pricing (Ramsey or price elasticity-weighted relative price mark-up over marginal cost) cannot be rejected. He finds the commercial class is discriminated against and the industrial class is favored over the commercial but not the residential class.

Results on efficiency and equity using our firm-level data are quite different. Utilizing a sample of 40 nuclear utilities, Thompson, Kamerschen, and Danielsen [29] find the rate structure is first-best and second-best inefficient, and inequitable according to the simple relative markup of price over marginal cost test. In general, both the residential and commercial classes are favored over the industrial class, and the commercial class is favored over the residential class.

In a related study, we analyze similarly a 1985 sample of 30 firms with no existing nuclear facilities. For this non-nuclear sample, the price structure is not first-best efficient, but it is second-best quasi-efficient and virtually subsidy-free. The slight favoritism that does exist is towards the industrial class, as was true in Naughton's study.(14)

Table IV contains t-test statistics for the differences in the mean values of the relative first-best efficiency price-marginal cost differentials, second-best efficiency (Ramsey) numbers, and simple equity measures of price discrimination for the same customer classes in the 40 firm nuclear and the 30 firm non-nuclear samples.(15) We find virtually no significant differences exist in the regulatory treatment of residential users by nuclear utilities relative to non-nuclear utilities. Commercial-class customers have been more favorably treated by all measures in the nuclear sample. The industrial-class users have clearly been the least favorably treated in the nuclear sample by all generally accepted measures.


One possible explanation of this result is that regulators of utilities with nuclear power plants are subject to different personal or political pressures than when they regulate non-nuclear utilities. DeLorme, Kamerschen, and Thompson [11] show private interests outweigh the public interest in regulatory rate decisions to a greater extent when nuclear power is present.

V. Summary and Conclusions

This paper provides an econometric analysis of the production technology and generating costs of nuclear power, as well as the efficiency and equity of the nuclear rate structure. Given growing concern regarding the "greenhouse effect", we are likely to see increasing pressure on utilities to seek alternatives to fossil fuels. The findings of this paper will help to evaluate properly the nuclear alternative.

Our study finds some important differences between the fossil-fuel and nuclear production methods. Technological dissimilarities are demonstrated with tests on the appropriate form of the cost function, and even more so with the estimates of scale economies and elasticities of substitution. Although both technologies can be analyzed using a neoclassical cost model, our research indicates they should be treated as separate samples. We also find that nuclear and non-nuclear rate structures differ significantly in efficiency and at least one measure of equity. These results have important implications for public policy toward nuclear power.

We have demonstrated that nuclear power, minus some of the regulatory and politically determined costs, such as licensing delays, retrofitting, and the handling of plant disallowances and cancellations, can be very competitive with fossil-fuel generation costs in this sample. Current federally-mandated clean air compliance costs with possible future extensions into the areas greenhouse gases and toxic metals, are likely to make capacity additions with fossil-fueled plants unattractive relative to nuclear power.

Public acceptance of nuclear power as a viable option may prove to be the greatest obstacle. As DeLorme, Kamerschen, and Thompson [11, 385-396] have shown, public pressure on regulators of nuclear utilities can result in inefficient rate structures and further economic distortions, aside from just higher costs. Perhaps only years of experience with a safe nuclear industry, and solutions to the issues of plant design and spent fuel disposal, can overcome public concern.


Total Generation Cost. Generation cost variables consist of multi-plant, firm-level U.S. data for fossil-fuel and nuclear plants in a sample of 40 privately owned utilities with some nuclear power generated in 1985. The proportion of fuel type used in the overall steam generation of electricity was 58% coal, 19% nuclear, 16% gas, and 7% oil, as determined by percent total MBTU'S produced by each fuel source. Approximately 70% of fossil-fuel generation was by base-load coal plants in this sample. The intermediate and peak units largely consist of fuel oil and gas turbines. About 8% was generated using fuel oil, and 22% by natural gas. Most of the data are reported by the firms on FERC Form 1, and published in Financial Statistics for Selected Electric Utilities 1985 [13].

The labor cost component of total cost is derived in a manner similar to that of the seminal work of Nerlove [24] and Christensen and Greene [9], except for the additional weighing factor for nuclear power production. All labor cost figures are available in Financial Statistics [13]. Nuclear and fossil-fuel costs are reported directly in Financial Statistics [13].

Our measure of capital costs, however, improved on previous research in several ways. By taking net electric operating income, equal to electric operating revenue after deductions are made for operations and maintenance, depreciation, taxes (including credits), and capital gains and losses, and then adding back in depreciation expenses, we arrive at a pool of funds available for allocation to either interest expense, or the return to equity capital. regardless of the depreciation method used. Further, the measure benefits from using values directly reported by firms on Form 1, and specific to the electric plant. Specifically, the figure used is "Net Electric Utility Operating Income," weighted by the ratio of either Fossil Plant or Nuclear Plant to Total Electric Utility Plant in Service. To each of these figures is added the depreciation expenses reported for each of these plant types. All data were in Financial Statistics [31].

Price of Inputs

Wage rates, a firm-level measure, are calculated similar to that of Christensen and Greene [9], with data from Financial Statistics [13]. Prices in $/MBTU for fossil fuels are calculated from available sources. Steam Electric Plant Factors [21] had the most convenient reporting format. Some missing data are available in Historical Plant Cost and Annual Production Expenses [14]. Nuclear fuel prices are available in Historical Plant Costs and Annual Production Expenses [14]. Many firms reported prices in $/MBTU, but because of different measures of fuel heat content, some did not. However, all firms did report nuclear fuel prices in $/KWH, average BTU's/KWH, and the number of grams of uranium used and either price per gram or expenditure on fuel consumed. The missing values of $/MBTU are then calculated in a manner similar to that used for fossil fuels.

The price of capital is based roughly on the widely used discounted cash flow model, for determining the cost of capital. The choice of this approach was based partly on the need to find a price measure appropriate for the capital cost measure used, but also to include some reflection of the risk element in raising financial capital, frequently mentioned as important in the nuclear utility industry. The cost of equity is taken directly from Salomon Brothers' Electric Utilities Stock monthly report [28], where total return to equity measures the current yield on common stock plus some measure of the market's expectations for future dividend growth. This measure is weighted by the share of common stock in the capital structure. The remaining capital prices (for long-term debt and preferred stock) are taken directly from Financial Statistics [13] by dividing the total payments by the total value outstanding in 1985. These measures reflect the value of the embedded capital and are weighted by their respective share in the capital structure and summed with equity's share.

When the equity return is reported by Salomon Brothers for the operating utilities' holding company, it is used. Therefore, several companies in this sample had the same value, if they were part of a holding company operation. Several companies, largely because of the financial difficulties associated with their nuclear power plants, did not pay dividends in 1985. so no total return figures were available. However, in most cases, these companies had recent rate hearings, and an allowed return on equity figure was reported in P.U.R. Analysis [27]. In other cases, the Salomon Brothers' rating group average was used.

Output of Electricity

In the estimate of the cost of generating electricity, the output measure is the total (annual) generation by fuel type. To determine the allocation of marginal cost at the generation stage. total generation is weighted by the percent sold to each customer class. For transmission and distribution, a similar process is used, except total transmitted power is substituted for generated power. All output data are found in Financial Statistics [13].

Plant Vintage

Vintage is considered the (weighted average) date a firm's nuclear plant(s) first entered commercial operation. Data are from Historical Plant Costs and Annual Production Expenses [14].

NRC Penalties

Nuclear Regulatory Commission penalties are the only variable at the utility level reflecting the additional expenditures nuclear utilities incur for safety reasons [20]. Cumulative values are taken from the Energy Information Administration [15] for years up to 1984. To these are added 1985 values from The 1985 NRC Annual Report [26].


[1.] Aikman, James H. Untitled testimony at Settlement Conference on System Energy Resources, Inc. (SERI) 1991. [2.] Anderson, Richard G. and Jerry G. Thursby, "Confidence Intervals for Elasticity Estimators in Translog Models." Review of Economic's and Statistics, November 1986, 647-56. [3.] Atkinson, Scott E. and Robert F. Halvorsen, "Interfuel Substitution in Conventional Steam-Electric Power Generation." Journal of Political Economy, October 1976, 959-78. [4.] Baldwin, William L. Market Power, Competition, and Antitrust Policy. Homewood, Ill.: Irwin, 1987. [5.] Blackorby, Charles and R. Robert Russell, "Will the Real Elasticity of Substitution Please Stand Up?" (A Comparison of the Allen/Uzawa and Morishima Elasticities). American Economic Review, September 1989, 882-88. [6.] Bopp, Anthony E. and David Costello, "The Economics of Fuel Choice at U. S. Electric Utilities." Energy Economics, April 1990, 82-88. [7.] Christensen, Laurits R., Dale W. Jorgenson, and Lawrence J. Lau, "Conjugate Duality and the Transcendental Logarithmic Production Function," (abstract). Econometrica, July 1971, 255-56. [8.] _____ , _____ , and _____ , "Transcendental Logarithmic Production Frontiers." Review of Economics and Statistics, February 1973, 28-45. [9.] _____ and William H. Greene, "Economies of Scale in U.S. Electric Power Generation." Journal of Political Economy, August 1976, 655-76. [10.] Cowing, Thomas G. and Vernon K. Smith, "The Estimation of a Production Technology: A Survey of Econometric Analysis of Steam Electric Generation." Land Economics, May 1978, 156-86. [11.] Delorme, Jr., Charles D., David R. Kamerschen, and Herbert G. Thompson, Jr., "Pricing in the Nuclear Power Industry: Public or Private Interest?" Public Choice, 1992, 385-96. [12.] Diewert, W. Erwin, "Applications of Duality Theory," in Frontiers of Quantitative Economics, edited by Michael D. Intriligator and David A. Kendrick. Amsterdam: North-Holland, 1974, pp. 106-20. [13.] Energy Information Administration. Financial Statistics of Selected Electric Utilities 1985, (DOE/EIA - 0437 (85)). Washington, D.C.: Department of Energy, 1987. [14.] _____. Historical Plant Cost and Annual Production Expenses for Selected Electric Plants 1985 (DOE/EIA - 0455 (85)). Washington, D.C.: Department of Energy, 1987. [15.] _____. An Analysis of Nuclear Power Plant Operation Cost (DOE/EIA - 0511 (88)). Washington, D.C.: Department of Energy. 1988. [16.] Hayashi, Paul M., Melanie Sevier, and John M. Trapani, "Pricing Efficiency Under Rate-of-Return Regulation: Some Empirical Evidence for the Electric Utility Industry." Southern Economic Journal, January 1985, 776-92. [17.] Hyman. Leonard S. America's Electric Utilities. Past, Present and Future. Arlington, Va.: Public Utilities Reports, 1988. [18.] Krautmann. Anthony C. and John L. Solow, "Economies of Scate in Nuclear Power Generation." Southern Economic Journal, 1988, 70-85. [19.] Mount, Timothy D., Duane Chapman, and T. J. Tyrrell. Electricity Demand in the limited States: An Econometric Analysis, (ORNL-NSF-49). Oak Ridge, Tenn.: Oak Ridge National Laboratory, 1973. [20.] National Association of Regulatory Utility Commissioners. Annual Report on Utilitv and Carrier Regulation. Washington, D.C.: NARUC, 1985. [21.] National Coal Association Staff, Steam Electric Plant Factors-1986 Edition. Washington, D.C.: National Coal Association, 1987. [22.] Naughton, Michael C., "The Efficiency and Equity Consequences of Two-part Tariffs in Electricity Pricing." Review of Economics and Statistics, August 1986, 406-14. [23.] Nelson, Randy A., "Regulation, Capital Vintage, and Technical Change in the Electric Utility Industry." Review of Economics and Statistics, February 1984, 59-69. [24.] Nerlove, Marc. "Returns to Scale in Electricity Supply," in Measurements in Economics, edited by Carl Christ. Stanford, California: Stanford University Press, 1963, pp. 167-98. [25.] Nuclear Energy Policy Group. Nuclear Power Issues and Choices. Cambridge, Mass.: Ballinger Publishing Co., 1977. [26.] Nuclear Regulatory Commission. The 1985 NRC Annual Report, (NUCREG-1145). Washington, D.C.: U.S. Nuclear Regulatory Commission, 1986. [27.] Public Utilities Reports. P.U.R., Analysis of Investor-owned Electric and Gas Utilities, Arlington. V:A.: Public Utilities Reports, Inc. 1986. [28.] Salomon Brothers Staff. Electric Utility Monthly. New York: Salomon Brothers, Inc., 1986. [29.] Thompson, Jr., Herbert G., David R. Kamerschen, and Albert L. Danielsen, "Efficiency in Nuclear Power Pricing." Review of Industrial Organization, 1990, 13-27; "Reply," Fall, 1991, 297-99. [30.] _____ and _____. "Rate Structures in Nuclear and Non-Nuclear Pricing." Working Paper, 1990. (1.) Several recent studies have focused on substitution of fossil fuels at the finn-level with some distinction between base-load and peak-load generating units. Atkinson and Halvorsen [3, 959-978] is generally considered the seminal study in this area. Bopp and Costello [6] provide a survey of fuel substitution research, and present their own analysis on a regional basis. (2.) Fossil-fuel plants produce about 64% of steam-generated electricity. Of the electricity actually supplied by this sample, nuclear steam produced about 30%, fossil-fuel steam about 50%, and the remainder is purchased power. See the appendix for additional sample information. (3.) The experience curve is a broader version of the learning curve. The experience curve includes all costs, such as capital, marketing. purchasing, administrative, and research, as well as the direct labor and production supervisor costs on which the older learning curve is focused. See Baldwin [4, 336 40]. (4.) This testimony was presented by Aikman [1] concerning the Grand Gulf 1 nuclear power plant. (5.) The data may not meet these conditions in the strictest sense since they incorporate, ex post, the effects of rate-of-return regulation, which may affect the minimum cost choice of inputs. The results of previous empirical studies are not consistent about the size, direction, or cost impact of this distortion. However, a study by Hayashi, Sevier, and Trapani [16] shows consistent estimates can be derived from the actual cost data, and the distortion is small. Blackorby and Russell [5] argue under rate-of-return regulation, partial elasticities may not accurately measure input substitution. However, since the firms in both samples are the same, and rate-of-return regulation is not differentially applied to nuclear power, our measures are useful for comparison purposes. We have not explicitly accounted for the effects of rate-of-return regulation. (6.) An Energy Department study [14] found although operation and maintenance costs had been accelerating in nuclear power plants in the decade 1974-1984, costs decreased significantly as nuclear plants aged. (7.) The likelihood test ratio is distributed as [chi.sup.2] with degrees of freedom equal to the number of independent restrictions imposed. [.chi.2] is equal to - 21n[lambda], where [lambda] is equal to the unrestricted likelihood function divided by the restricted likelihood function. (8.) This test was performed on this sample (collected for related research) based on the appearance of the likelihood function values and the estimated parameters of the restricted models, which are not included in the tables. (9.) Data for nuclear and fossil-fuel generation were originally separate samples, with each observation from a separate firm. Since the variables were measured using similar definitions and units, pooling the samples was relatively straight-forward procedure of properly weighing and summing input values. (10.) In this test the [[chi].sup.2] critical value is 20.09 at the 1% level, and the likelihood-ratio-test statistic is 3.933. (11.) The Chow test statistic is an F-ratio equal to 4.0347. The critical F (10, 56) value is 2.03 at the 5% level of significance. (12.) In our 1990 study, an identical analysis was performed on a non-nuclear sample of utilities. The mean price of fossil fuel for this sample ($1.88/MBTU) was about 25% less than the mean price of fossil fuel in this study ($2.50/MBTU). (13.) One early projection by the Nuclear Energy Policy Group [25] of the differences in costs for nuclear power and coal generation for 1985 had coal costs 20% above nuclear power costs. However, storage and disposal costs were included for nuclear power. (14.) In these recent studies, the authors used multiple-output translog cost functions for each generation type and for customer class, to estimate marginal generation costs. Transmission and distribution costs were estimated with a separate long-run multiple output cost model, and the results were added to the generation cost estimates. The marginal cost of purchased power was also calculated and averaged into the estimation. Own-price elasticities of demand use the parameters estimated by Mount. Chapman, and Tyrrell [19] in their variable price model, and our sample price data. Prices were average revenues reported by the firms. Additional information on these studies are available on request. (15.) These results are from Thompson and Kamerschen [30].
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Author:Thompson, Herbert G. Jr.
Publication:Southern Economic Journal
Date:Jul 1, 1993
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