A data envelopment analysis of the efficiency of China’s thermal power generation

Utilities Policy 10 (2001) 75–83 www.elsevier.com/locate/utilpol

A data envelopment analysis of the efficiency of China’s thermal power generation Pun-Lee Lam a,∗, Alice Shiu a a

Department of Business Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China Received 11 January 2002; received in revised form 2 September 2002; accepted 8 September 2002

Abstract This study applies the data envelopment analysis (DEA) approach to measure the technical efficiency of China’s thermal power generation based on cross-sectional data for 1995 and 1996. Our results show that municipalities and provinces along the eastern coast of China and those with rich supplies of coal achieved the highest levels of technical efficiency. There is no clear evidence of excess capacity. However, the presence of labor slack in many regions indicates that labor redundancy was a serious problem. In our second stage regression analysis, we find that fuel efficiency and the capacity factor significantly affect technical efficiency. Provinces and autonomous regions that were not under the control of the State Power Corporation (SPC) achieved higher levels of efficiency. The presence of foreign investment, however, did not have a significant effect on efficiency.  2002 Elsevier Science Ltd. All rights reserved. JEL classification: L5; Q4 Keywords: DEA approach; technical efficiency; China’s thermal power generation

1. Introduction The electric power industry in China has been under state ownership and control since 1949. At present, the state sector owns all of the transmission lines and nearly all of the distribution networks. It also accounts for most of the power generation. Since the 1980s and the adoption of an open-door economic policy, structural reforms, market incentives, and decentralization policies were introduced to attract investment into the power sector. Local governments and power enterprises co-operated with foreign investors to form independent power producers (IPPs). From that time, generating capacity in China has expanded rapidly. Today, China is the world’s second largest producer of electric power, both in terms of installed capacity and actual generation. Despite its growing importance, empirical studies on the productivity of China’s power

∗

Corresponding author. Tel.: 852-2766-7123; fax: 852-2765-0611. E-mail address: [email protected] (P.-L. Lam).

sector are lacking, mainly due to insufficient data about the factor inputs that are used in the power sector. In recent years, the Chinese authorities have begun to publish more information about the production and performance of the power sector over time, and across different regions. With more information available, the way is opened for efficiency analyses of China’s power sector. China relies heavily on thermal power generation, which accounts for three-quarters of the total generating capacity. In this study, we apply data envelopment analysis (DEA) to measure the cross-sectional efficiency of China’s thermal power generation. We also conduct a second stage regression analysis to identify the important factors that affect the performance of thermal power generation in China. Our empirical results shed light on the environmental factors that affect generation efficiency, which will provide useful information for the restructuring of the generation market in China. This paper is organized as follows. In Section 2 we outline the current structure of the electric power industry in China. In Section 3 we review the results of the efficiency measurement of power industries in various

0957-1787/02/$ - see front matter  2002 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 7 - 1 7 8 7 ( 0 2 ) 0 0 0 3 6 - X

76

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

countries. Section 4 describes our methodology, and we report our results in Section 5. The last section provides conclusions and discusses the implications of our empirical results for China’s thermal power generation.

2. China’s power industry China’s electric power industry came into existence in 1882, with the establishment of the country’s first generation plant in Shanghai (Ministry of Energy, 1989; Contemporary China Press, 1994; Economic Management Press, 1997). In the early years of the industry’s development, electric power systems expanded very slowly. Most power generating units were of small capacity with low thermal efficiency, and were concentrated in a few large cities in the coastal regions. Power supply to the interior provinces was very limited (Ministry of Energy, 1989). Since 1949, electric power in China has been under state ownership and control. The state sector presently owns all transmission networks. From the 1950s to 1970s, the transmission networks expanded from the coastal regions to the interior areas, and from the cities to the extensive countryside. In the 1980s and 1990s, after China adopted an open-door economic policy, structural reforms, market incentives, and decentralization policies were introduced to attract investment into the power sector. Since then, the country’s installed generating capacity and total annual generation have increased rapidly. During the past decade, the Chinese government has allowed foreign companies to participate in building up the country’s generating capacity. Despite the remarkable growth in electricity generation that has been experienced over the last two decades, the per capita consumption of electricity in China is still low when compared to consumption in developed countries. Furthermore, distribution is uneven; while many areas of the country continue to suffer from power shortages, a few areas are oversupplied. To date, there are seven regional power networks and five independent provincial power grids in China. The generation, transmission, and distribution of electricity in different areas are vertically integrated and under the management of a public monopoly. Regional and provincial state-owned power enterprises are responsible for the construction, management, and operation of electric power systems. The system of state-owned enterprises (SOEs) was established after the Communist Party took power in 1949 (Chow, 1997). Since then, the state sector has dominated the economy and suffered chronic problems of mounting losses, huge borrowing, and low productivity. SOEs have had to shoulder the social responsibility of creating employment, and providing employees with various kinds of fringe benefits and service facilities

such as housing, education, and medical care (Zhu, 1999). As a result of this social burden, SOEs in China’s electric power industry are grossly unproductive. At present, China’s power sector employs more than 2 million workers, and the problem of redundancy is very serious. In recent years, the power industry’s SOEs have been corporatized and converted into “independent holding companies”, which are responsible for the economic management and planning of their respective regions. At the provincial level, power corporations are separated from the power bureaus. Since restructuring, all power corporations, be they at the regional or provincial level, operate under commercial principles and no longer receive large government subsidies. To obtain new capital for expansion, some power companies have issued shares to private shareholders, and publicly traded shares are available to foreign investors. It is hoped that corporatization and listing on the stock market will create greater pressure on SOEs in the power sector to improve efficiency. In January 1997, the State Council formed the State Power Corporation of China (SPC), which is the largest power corporation in China. The functions of running state-owned assets and enterprise management that were formerly undertaken by the Ministry of Electric Power (MEP) were transferred to the SPC upon its formation. The SPC is now responsible for the construction of power networks nationwide, as well as the operation and management of inter-regional and inter-provincial power grids and large-scale power plants. Most of the regional and provincial power corporations are under the control of the SPC, except those in Guangdong, Hainan, Inner Mongolia, and Xizang (Tibet), which are controlled by their provincial governments. Thermal power and hydropower have long been China’s dominant generating capacities. In 1999, they accounted for 75.7% and 23.5% of the total capacity respectively (China Electric Power Information Center, 2000). The share of nuclear power in total capacity was only 0.8%. Although China has a large potential for hydropower, less than 20% of it is exploited. Of the thermal generating capacity, approximately 90% is coal-fired, and the combined share of oil and gas fired capacity is only 10%. In the 1980s and the early 1990s, the local governments in China constructed a lot of small power plants with a capacity less than 50 MW, which did not require approval from the central government (Wirtshafter, 1990; Yang and Yu, 1996). This policy of decentralization provided incentives and flexibility for local governments to expand generating capacity quickly. However, the wide use of low quality fuel for power generation and the proliferation of small generating units reduced the thermal efficiency of China’s energy sector. The average thermal efficiency of electricity generation in

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

fossil fuel plants ranges between 27% and 29%, compared to around 38% in Organization for Economic Cooperation and Development (OECD) countries (Economic Management Press, 1997; Financial Times Energy, 1999). The development of hydropower and the improvement in the energy efficiency of coal-fired plants will help to improve the economic and energy efficiency of China.

77

Since the early 1960s, there has been a growing interest in measuring the productivity and efficiency of the power sector. Different methods have been used to measure technological change and economies of scale. In the early period, the technological change of the power industry was measured by studying shifts in production and cost functions (e.g. Komiya (1962); Nerlove (1963); Barzel (1963, 1964); Dhrymes and Kurz (1964); McFadden (1964); Galatin (1968); Courville (1974); Cowing (1974); Boyes (1976); Christensen and Greene (1976).1 In the 1970s, empirical studies of productivity tended to focus on the effect of ownership structure on efficiency. De Alessi (1974) explored the economic consequences of government ownership and regulation in the electric power industry of the U.S. Meyer (1975) investigated the comparative efficiency of private and public electric utilities in the U.S. He used a dummy approach to pool both private and public firms and measure the cost difference due to ownership structure, without taking factors such as input prices and technology into account. Pescatrice and Trapani (1980) also used a dummy variable to measure the productivity difference between private and public electric utilities in the U.S. Unlike Meyer’s study, however, they used a translog function that controlled the effects due to output, input prices, and technology. Their results suggested that the unit cost of public firms was 24–33% lower than that of private firms. The source of inefficiency was due to the rate-ofreturn constraints that were imposed on privately-owned electric utilities. The study that was conducted by Dilorenzo and Robinson (1982) indicated similar results, while that of Atkinson and Halvosen (1986) indicated that price inefficiency existed in both public and private electric utilities. More recently, frontier methods such as data envelopment analysis (DEA) and stochastic frontier analysis (SFA) have been used to measure efficiency and productivity. The DEA approach is non-parametric, whereas

the SFA is a parametric approach. The DEA approach does not require the arbitrary assumptions regarding the functional form or the distributional form of the error terms that the SFA approach uses,2 and it is more suitable for handling the multiple inputs and outputs of the power industry. Both approaches are susceptible to data measurement errors, but the SFA approach has additional problems of specification errors and omitted variables. Since the early 1980s, economists have applied the DEA approach to measure the productivity performance of electric utilities under different ownership structures. Instead of using cost and production functions, they specify and construct a piecewise linear technology from the observed input and output data. Productivity measurement is used to evaluate the performance of the power industry under different ownership structures and regulatory regimes. The use of the DEA approach not only allows us to compare individual firms to best practice firms, but also to identify sources of inefficiency. Such benefits allow regulators to formulate policies on deregulation and privatization, and to determine the appropriate productivity factor when imposing price-cap regulation or yardstick competition on electric utilities. The DEA-like linear programming methods have been applied to measure the productivity of the power sector in the U.S. and in other countries. Unlike parametric analyses, which focus on generation, both generation and transmission and distribution have been covered in studies using the DEA approach. Fare et al. (1985) were the first to use the DEA approach to compare the efficiency of public and private electric utilities. They used the data from Atkinson and Halvorsen’s study, and found that public utilities were more efficient than private utilities, and that inefficiency was largely due to the lack of allocative efficiency. Cote (1989) applied the stochastic frontier cost function to estimate the technical efficiency of electric utilities under different ownership structures. His results suggested that co-operatives were the most efficient type of ownership structure, while small private and public electric utilities had similar levels of technical inefficiency. Fare et al. (1990) used the Malmquist productivity index to study the productivity growth of 19 coal-fired generating plants in Illinois from 1975 to 1981. They found that the average rates of productivity growth were relatively stable, except during a productivity slowdown from 1976 to 1977. Pollitt (1995) found no significant differences in efficiency between different types of ownership or economic organization in a study of generation utilities in OECD countries. Coelli (1997) applied both

1 Cowing and Smith (1978) survey econometric analyses of steamelectric generation based on production and cost functions.

2 Refer to Lovell (1996) for a review of the ability of the SFA and DEA approaches to measure efficiency and productivity change.

3. Previous productivity studies of power generation

78

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

the DEA and SFA approaches to measure the total factor productivity (TFP) change of 13 base-load, coal-fired plants in Australia from 1981–1982 to 1990–1991. The empirical results suggested a TFP growth over the 10year period of up to 16%. Olatubi and Dismukes (2000) used the DEA approach to measure cost efficiency opportunities for coal-fired generation facilities in the U.S., and found that allocative inefficiency was the most important source of inefficiency in 1996. The DEA approach has also been applied to measure efficiency in electricity transmission and distribution (e.g. Weyman-Jones (1991); Miliotis (1992); Hjalmarsson and Veiderpass (1992a, 1992b); Bagdadioglu et al. (1996); Kumbhakar and Hjalmarsson (1998)).

4. Methodology In this study, our focus is on technical efficiency. The non-parametric DEA approach is used to compute the technical efficiency of China’s thermal power generation. The use of the DEA does not require any specification of the functional form of the production relationship. Given the inputs that are used and the output that is produced, the prior weighting of the relative importance of outputs and inputs is not required. DEA analyzes each decision-making unit (DMU) separately and identifies those which exhibit best practice. A frontier of these units is then constructed, and the efficiency measure of each DMU is determined relative to this best practice frontier. Those DMUs that lie on the frontier are regarded as relatively efficient and have scores of 1, whereas those that lie inside the frontier are regarded as relatively inefficient and have scores of less than 1 but greater than 0. The DEA model can be applied in an input or output orientation. In this study, we use an output-orientation for measuring the provincial efficiency for electricity generation. Suppose we have k = 1,…, K number of firms which produce M outputs, i.e. ym, kt, m = 1,…, M, using N inputs, xn, kt, n = 1,…, N, at each time period t, where t = 1,…, T. zkt represents the intensity levels, which make the activity of each observation expand or contract to construct a piecewise linear technology. Ft0(xkt,ykt) ⫽ maxd represents the output-oriented Farrell efficiency measure that indicates the maximum possible expansion of yt for firm k at period t.

The above model imposes constant returns to scale

冘 K

(CRS). If the condition of

zkt ⫽ 1 is added, then

k⫽1

variable returns to scale (VRS) are imposed (see Fig. 1). In Fig. 1, DMUs A, C, and D are considered relatively efficient under VRS, whereas only DMU C is considered relatively efficient under CRS. They all have technical efficiency scores of 1, which implies that they are operating on the best practice frontier. DMU F is considered as relatively inefficient under both kinds of returns to scale. Under CRS, the output-oriented technical efficiency score for DMU F is given by TEo,crs = ZF/ZK, whereas under VRS, the output-oriented technical efficiency score for DMU F is given by TEo,vrs = ZF/ZS. In our study, we assume VRS in power generation. Consider the VRS frontier in Fig. 1. One section runs parallel to the horizontal axis (i.e. DE), and this feature could lead to the presence of input slack, which is the extent to which inputs could be further reduced even when a DMU is projected onto the production frontier. Consider inefficient DMU G. Even when G is projected onto the frontier, one can further reduce the amount of input (i.e. DH) and produce the same level of output. This amount of DH is known as input slack.3

5. Data and results Detailed information about the power generation of thermal power plants in China, like total output, generating capacity, and fuel consumption can be obtained from

Ft0(xkt,ykt) ⫽ maxd,

冘 K

st dym,ktⱕ

冘

zktym,kt m ⫽ 1,…,M,

k⫽1

K

zktxn,ktⱕxn,kt n ⫽ 1,…,N,

k⫽1

zktⱖ0,k ⫽ 1,…,K

Fig. 1. Graphical illustration of the DEA approach and the presence of input slacks.

(1) 3

We used the DEAP software to compute the DEA models and the labor slack. The slack was calculated using the multi-stage method that is discussed by Coelli et al. (1998).

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

79

Table 1 Summary statistics for 30 DMUs in China’s generation Electricity generation

Electricity generated (GWh)b

Generating capacity (MW)b

Total fuel used (TJ)b

Labor (Number of persons)a

1995 Mean S. D. Minimum Maximum 1996

26911.47 21231.48 165.00 73676.00

5431.35 4291.36 47.10 16260.40

456012.21 335372.61 79.55 1153665.52

25676.67 18897.22 315.00 71280.00

Mean S. D. Minimum Maximum

29270.03 22798.49 133.00 79223.00

5962.12 4702.97 55.70 19723.30

487508.78 353979.89 293.08 1239205.20

23691.30 19471.14 198.00 75330.00

a b

Labor refers to the number of workers engaged in thermal power generation in the power sector. GWh refers to gigawatt hours, MW refers to megawatt and TJ refers to terajoule.

the Annual Report of Electricity Industry that is published by the China Electricity Press. However, the report only provides the total labor input for the power industry, and does not provide separate figures of the labor input for thermal power generation. Fortunately, we were able to locate the figures for 1995 and 1996 in China’s Industrial Markets Yearbook (Li and Tse, 1997 and 1999), and our study covers those two years. Our cross-sectional study covers 30 provinces, autonomous regions, and municipalities, each of which is considered as a Decision Making Unit (DMU). The data that is available allows us to measure the technical efficiency scores of the different DMUs in each of the two years. Electricity that is generated from thermal power plants in each DMU is used as the output variable, while capital, fuel, and labor are the three inputs. Capital is measured in terms of installed thermal generating capacity in megawatt (MW). Fuel consumption is measured in terajoules (TJs). The different fuels (such as coal, oil, and gas) that are used for power generation are converted into TJ equivalents, and then aggregated to obtain the amount of fuel input. Labor input is the number of workers in thermal power generation. Table 1 shows the summary statistics for the output and inputs that were used to construct the DEA models. The construction of the models allows the investigation of relative efficiency scores and the presence of slack in each year for power generation. The results for each cross-sectional DEA model for 1995 and 1996 are shown in Table 2. The average efficiency score increased from 0.888 in 1995 to 0.903 in 1996. The technical efficiency scores show great variations among DMUs. Eight provinces (Beijing, Hebei, Shanghai, Jiangsu, Shandong, Guangdong, Xizang, and Ningxia) operated along the production frontier in both years. Municipalities and provinces along the eastern coast achieved higher levels

of technical efficiency. Provinces with rich supplies of coal, such as Shanxi, Shandong, Hebei, and Henan, also achieved higher levels of technical efficiency. The technical efficiency for Yunnan was the lowest among all of the DMUs. The input slack results are presented in Table 3. The presence of slack reveals the scope for further non-radial reduction in inputs once an inefficient DMU is projected onto the production frontier. The excessive use of inputs accounts for about 7.85–8.54% of total fuel and 11.10– 26.13% of total labor used in those DMUs with slacks. Capital slack is insignificant, which implies that excess capacity is not a serious problem in China’s power sector. Instead, the problem of power shortage continues to exist in many provinces. The amount of labor slack increased substantially from 1995 to 1996. As compared to the efficient production frontier, some DMUs faced serious problems of labor redundancy. In 1996, the labor input for each DMU with slack could have been reduced by an average of 6,312 without affecting output. The sharp increase in labor slack might have been due to the restructuring of the power sector in 1996 and the subsequent formation of the SPC in 1997. In the face of restructuring, stateowned power enterprises in some provinces were quick to shed labor to improve their efficiency. As a result, labor productivity became more diverse among DMUs. However, it should be emphasized that the problem of labor redundancy would become much more serious if we were to compare China’s power sector to the best performance that was achieved by power companies in other developed countries. Once we computed the cross-sectional efficiencies, we performed the second stage (Tobit) regression analysis to identify factors that might influence technical efficiency. In our regression model, the estimated cross-

80

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

sectional efficiency score (EFF-SCORE) for 1996 is regressed on a number of potentially influencing exogenous factors. The specific vector of variables that are used include capacity factor (CAPACITY), fuel use per kWh of electricity (FUEL), the average size of generating units (SIZE), the average age of generating units (AGE), and two dummy variables. The two dummy variables are used to indicate whether the region is under the control of the SPC (0 = not under SPC control and 1 = under SPC control) and the presence of foreign investment in the power sector (FOREIGN) (0 = without foreign investment and 1 = with foreign investment). Because of the lack of information about the load factors (i.e. average load divided by peak load) of all provinces, we used capacity factor (i.e. average load divided by installed capacity) to measure the impact of load characteristics on generating efficiency. If the installed capacity is planned to meet the peak load, then the capacity factor will be highly correlated with the load

Table 3 Summary statistics of slack variables Input

Capacity 1995 1996 Fuel 1995 1996 Labor 1995 1996

No. of DMUs with slacks

1 0

Mean (amount of slack per DMU)

21.9 0

Northern Region Beijing Tianjin Hebei Shanxi Inner Mongolia North Eastern Region Liaoning Jilin Heilongjia Eastern Region Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Southern Region Henan Hubei Hunan Guangdong Guangxi Hainan South Western Region Sichuan Guizhou Yunnan North Western Region Xizang (Tibet) Shaanxi Gansu Qinghai Ningxia Xinjiang Average

2.17 0

8 3

41017 39000

7.85 8.54

9 17

2923 6312

11.10 26.13

Table 2 Cross-sectional variable returns to scale (VRS) DEA efficiency scores DMU

% of slack in total input

Efficiency scores for 1995

Efficiency scores for 1996

1.000 0.890 1.000 0.971 0.825

1.000 0.845 1.000 0.973 0.883

0.910 0.832 0.852

0.895 0.950 0.838

1.000 1.000 0.856 1.000 0.940 0.743 1.000

1.000 1.000 0.891 0.969 0.956 0.758 1.000

0.970 0.836 0.822 1.000 0.685 0.719

0.968 0.749 0.801 1.000 0.713 1.000

0.848 0.834 0.661

0.841 0.896 0.647

1.000 0.936 0.991 0.694 1.000 0.813 0.888

1.000 0.940 0.989 0.767 1.000 0.825 0.903

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

factor of a region. It is expected that a higher capacity factor (CAPACITY) will better utilize the existing generating capacity, and this will increase the efficiency score. The quality of fuel that is used for power generation in different power plants will definitely affect the efficiency of thermal power generation. Although information about the heat content of fuel (e.g. in terms of joules per kilogram of coal) is not available, the power enterprises in different provinces have published data on fuel use (in terms of standard coal in grams) per kWh of electricity. If the fuel that is used for power generation is of lower quality (in terms of heat content), then the fuel use per kWh of electricity will be higher. It is expected that the relationship between the efficiency score and the fuel use (FUEL) is negative. Higher fuel use implies a lower quality of fuel, which will lower the efficiency score. As mentioned earlier, the thermal efficiency of power plants in China is low because of the proliferation of small generating units constructed in the 1980s and the early 1990s. Power enterprises in China only provide information about the operating characteristics of generating units with a capacity of 100 MW or above, information about smaller units is not available. Therefore, our data on the average size (SIZE) and age (AGE) of generating units are based on information from those units with a capacity of 100 MW or above, which accounted for almost 60% of the total capacity. Data for three DMUs (Hainan, Qinghai and Xizang) are not available, hence we only have 27 observations when the variables SIZE and AGE are included. In the light of this data limitation, the estimated coefficients for these two variables should be interpreted with caution. To measure

81

the effect of small units on efficiency scores, we add a variable SMALL, which is defined as the proportion of generating units smaller than 100 MW, in our second stage (Tobit) regression analysis. The results of our second stage regression are shown in Table 4. As expected, the coefficients for CAPACITY and FUEL have significant effects on the technical efficiency of DMUs. A higher capacity factor means a higher utilization rate of generating capacity, which could save capital investment and help to achieve a higher level of technical efficiency. The use of high quality fuel (which implies a smaller amount of fuel use) has also raised the technical efficiency. Our results show that DMUs that were independent of the SPC achieved higher levels of technical efficiency, while the presence of foreign investment (FOREIGN) did not have any significant effect on technical efficiency. Based on the sample data of generating units with a capacity of 100 MW or above, the size and age of generating units are not significant factors in the technical efficiency of thermal power generation. A surprising result is that the variable SMALL is positive and significant, which implies that a wider use of small generating units in a province has increased efficiency. These small units are less efficient than large units in terms of size and fuel use, but since they are not subject to tight control by provincial and central governments, their flexibility in operations and labor recruitment might have increased their overall efficiency. 6. Conclusions and implications This paper examines the technical efficiency of China’s thermal power generation industry. We apply the

Table 4 Regression analysis of technical efficiency Variable CONSTANT CAPACITYa FUELb SPCc FOREIGNd SIZEe AGEf SMALLg R2 No. of observations

Coefficient 1.5626∗∗∗ (0.3009)h 0.3646∗ (0.2069)h ⫺0.0017∗∗∗ (0.0006)h ⫺0.2015∗∗∗ (0.0722)h 0.0138 (0.0416)

0.5186 30

Coefficient 1.4843∗∗∗ (0.3085)h 0.5554∗∗∗ (0.1946)h ⫺0.0022∗∗∗ (0.0006)h ⫺0.0621 (0.0657) 0.0119 (0.0347) 0.0001 (0.0004)

0.5561 27

Coefficient 1.0307∗∗∗ (0.3927)h 0.5457∗∗∗ (0.1822)h ⫺0.0013∗ (0.0008)h ⫺0.0787 (0.0636) 0.0142 (0.0332) 0.0004 (0.0003) 0.0107 (0.0065) 0.5942 27

CAPACITY = capacity factor (average load divided by installed capacity). FUEL = fuel use (in terms of standard coal) per unit of electricity generated. c SPC = 1 for provinces under SPC control, it is equal to 0 otherwise. d FOREIGN = 1 for provinces with foreign investment, it is equal to 0 otherwise. e SIZE = average size of thermal generating units with capacity 100 MW or above. f AGE = average age of thermal generating units with capacity 100 MW or above. g SMALL = proportion of generating units smaller than 100 MW. h ∗∗∗, ∗∗, ∗ Significant at 1%, 5% and 10%, respectively; standard errors are in parentheses. a

b

Coefficient 1.6591∗∗∗ (0.3066)h 0.6090∗∗ (0.2656)h ⫺0.0027∗∗∗ 0.0008h ⫺0.1632∗∗ (0.0732)h ⫺0.0147 (0.0444) 0.2624∗ (0.1421)h 0.5193 30

82

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

DEA approach to measure the technical efficiency of different regions in China. Our study involves 60 observations from 30 provinces, autonomous regions, and municipalities in 1995 and 1996. Apart from comparing the efficiency among different regions, our study examines the presence of input slack and the possible factors that affect efficiency. Our results show that there was a slight increase in the average efficiency scores in the two years under study. In general, municipalities and provinces along the eastern coast and those with rich supplies of coal achieved higher levels of technical efficiency. Consistent with previous studies, our results indicate that environmental factors affect efficiency scores, and any benchmarking method should take these into consideration. There is no clear evidence of excess capacity. However, the presence of labor slack in many regions indicates that labor redundancy is a serious problem. In our second stage (Tobit) regression analysis, we find that capacity factor and fuel efficiency are significant factors affecting technical efficiency. Government policies such as peak load pricing and demand-side management would help to improve the load curve and the utilization rate of generating capacity. These policies would help increase the technical efficiency of thermal power generation. The policy of mandated closures of inefficient diesel generating units would help to improve the fuel efficiency of power generation. The presence of foreign investment did not have any significant effect on the efficiency scores. Hence, the inflow of foreign capital into China’s power market seems to help overcome capital constraints rather than to transfer new technologies and improve technical efficiencies. The Chinese government has recently announced the restructuring of the SPC, which will be divided into separate regional transmission companies and required to divest its generating facilities. The generating assets of the SPC will be divested to four or five IPPs, with the aim of reducing the dominant power of the SPC. Our results indicate that provincial power enterprises that are independent of the SPC have achieved higher levels of efficiency. Provinces with relatively more small generating units have also achieved higher levels of efficiency. Most of these small units were constructed in the 1980s and the early 1990s, without the approval of the central government. Our results provide some support for divestiture of the generating assets of the SPC and relaxing the control of the central government on the power sector. The policies of decentralization and divestiture would help improve the performance of power sector in different regions of China. Acknowledgements The authors would like to thank Guangdong Electric Power for arranging a study visit in early 1999. We

would also like to thank Harry Wu at the Hong Kong Polytechnic University, Rolf Fare at Oregon State University, Ziping Wu at Queen’s University, and participants at the international workshop on the Chinese economy for their valuable comments on earlier drafts. Thanks are also due to Pat Lam and Alice Ng for their assistance throughout the study. The work that is described in this paper was supported by research grants from the Hong Kong Polytechnic University (Project A/C Code: G-T196) and the Research Grants Council of Hong Kong (Project A/C PolyU 5213/01H). References Atkinson, S.E., Halvorsen, R., 1986. The relative efficiency of public and private firms in a regulated environment: The case of U.S. electric utilities. Journal of Public Economics 29 (3), 281–294. Bagdadioglu, N., Price, C.M.W., Weyman-Jones, T.G., 1996. Efficiency and ownership in electricity distribution: A non-parametric model of the Turkish experience. Energy Economics 18 (12), 1–23. Barzel, Y., 1963. Productivity in the electric power industry, 19291955. The Review of Economics and Statistics 45 ((Nov.)), 395– 408. Barzel, Y., 1964. The production function and technical change in the steam power industry. Journal of Political Economy 72 (2), 133– 150. Boyes, W.J., 1976. An empirical examination of the Averch-Johnson effect. Economic Inquiry 14 (1), 25–35. China Electric Power Information Center 2000. Electric Power Industry in China. China Electric Power Information Center, Beijing. China Electricity Press, 1995 and 1996–1997. Annual Report of Electricity Industry. China Electricity Press, Beijing. Chow, D.C.K., 1997. An analysis of the political economy of China’s enterprise conglomerates: A study of the reform of the electric power industry in China. Law and Policy in International Business 28 (2), 383–433. Christensen, L.R., Greene, W.H., 1976. Economies of scale in U.S. electric power generation. Journal of Political Economy 84 (4), 655–676. Coelli, T., 1997. Total factor productivity growth in Australian coalfired electricity generation: A Malmquist index approach. Paper presented at the international conference on public sector efficiency, UNSW, Sydney, 27–18 November, 1997. Coelli, T., Prasada Rao, D.S., Battese, G.E., 1998. An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Boston. Contemporary China Press 1994. Electric Power Industry in Contemporary China. Contemporary China Press, Beijing. Cote, D.O., 1989. Firm efficiency and ownership structure - The case of U.S. electric utilities using panel data. Annals of Public and Cooperative Economics 60 (4), 431–450. Courville, L., 1974. Regulation and efficiency in the electric utility industry. The Bell Journal of Economics and Management Science 5 (1), 53–74. Cowing, T.G., 1974. Technical change and scale economies in an engineering production function: The case of steam electric power. Journal of Industrial Economics 33 (1), 135–152. Cowing, T.G., Smith, V.K., 1978. The estimation of a production technology: A survey of econometric analyses of steam electric generation. Land Economics 54 (2), 156–185. De Alessi, L., 1974. An economic analysis of government ownership and regulation: Theory and the evidence from the electric power industry. Public Choice 19 (Fall), 1–42.

P.-L. Lam, A. Shiu / Utilities Policy 10 (2001) 75–83

Dhrymes, P.J., Kurz, M., 1964. Technology and scale in electricity generation. Econometrica 32 (3), 287–315. Dilorenzo, T.J., Robinson, R., 1982. Managerial objectives subject to political market constraints: Electric utilities in the U.S. Quarterly Review of Business and Economics 22 (2), 113–125. Economic Management Press 1997. China’s Energy Development Report. Economic Management Press, Beijing. Fare, R., Grosskopf, S., Logan, J., 1985. The relative performance of publicly-owned and privately-owned electric utilities. Journal of Public Economics 26 (1), 89–106. Fare, R., Grosskopf, S., Yaisawarng, S., Li, S.K., Wang, Z., 1990. Productivity growth in Illinois electric utilities. Resources and Energy 12, 383–398. Financial Times Energy, 1999. Power in Asia: The Asian electricity market. Financial Times Energy, London, March 8, 1999, issue No. 272. Galatin, M., 1968. Economies of Scale and Technological Change in Thermal Power Generation. North Holland Publishing Company, Amsterdam. Hjalmarsson, L., Veiderpass, A., 1992a. Productivity in Swedish electricity retail distribution. Scandinavian Journal of Economics 94 (suppl.), 193–205. Hjalmarsson, L., Veiderpass, A., 1992b. Efficiency and ownership in Swedish electricity retail distribution. The Journal of Productivity Analysis 3 (1-2), 7–23. Komiya, R., 1962. Technological progress and the production function in the United States steam power industry. Review of Economics and Statistics 44 (2), 156–166. Kumbhakar, S.C., Hjalmarsson, L., 1998. Relative performance of public and private ownership under yardstick competition: Electricity retail distribution. European Economic Review 42 (1), 97–122. Li, S., Tse, D.K., 1997/1999. China’s Industrial Markets Yearbook. City University of Hong Kong Press, Hong Kong. Lovell, C.A.K., 1996. Applying efficiency measurement techniques to the measurement of productivity change. Journal of Productivity Analysis 7 (2-3), 329–340.

83

McFadden, D., 1964. Notes on the estimation of the elasticity of substitution, Working paper no. 57. Institute of Business and Economic Research, University of California, Berkeley. Meyer, R.A., 1975. Publicly owned versus privately owned utilities: A policy choice. The Review of Economics and Statistics 62 (4), 391–399. Miliotis, P.A., 1992. Data envelopment analysis applied to electricity distribution districts. Journal of Operational Research Society 43 (5), 549–555. Ministry of Energy 1989. China’s Construction in Four Decades (19491989): Electric Power Industry in China. Coastal International Investment Consultant Co. Ltd., Hong Kong. Nerlove, M., 1963. Returns to scale in electricity supply. In: Zellner, A. (Ed.), Readings in Economic Statistics and Econometrics. Little Brown Co., Boston, pp. 409–439. Olatubi, W.O., Dismukes, D.E., 2000. A data envelopment analysis of the levels and determinants of coal-fired electric power generation performance. Utilities Policy 9 (2), 47–59. Pescatrice, D.R., Trapani III, J.M., 1980. The performance and objectives of public and private utilities operating in the United States. Journal of Public Economics 13 (2), 259–276. Pollitt, M.G., 1995. Ownership and Performance in Electric Utilities. Oxford University Press/Oxford Institute for Energy Studies, Oxford. Weyman-Jones, T.G., 1991. Productivity efficiency in a regulated industry - The area electricity boards of England and Wales. Energy Economics 13 (2), 116–122. Wirtshafter, R.M., 1990. Decentralization of China’s electricity sector: Is small beautiful? World Development 18 (4), 505–512. Yang, M., Yu, X., 1996. China’s power management. Energy Policy 24 (8), 735–757. Zhu, T., 1999. China’s corporatization drive: An evaluation and policy implications. Contemporary Economic Policy 17 (4), 530–539.