Why do electricity utilities cooperate with coal suppliers? A theoretical and empirical analysis from China

Energy Policy 46 (2012) 520–529

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Why do electricity utilities cooperate with coal suppliers? A theoretical and empirical analysis from China Xiaoli Zhao a,d,n, Thomas P. Lyon b, Feng Wang c, Cui Song a,d a

School of Economics and Management, North China Electric Power University, Beijing, China Erb Institute for Global Sustainable Enterprise, University of Michigan, Ann Arbor, USA c School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, China d Institute for Low Carbon Economy and Trade, North China Electric Power University, Beijing, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 September 2011 Accepted 10 April 2012 Available online 27 April 2012

The asymmetry of Chinese coal and electricity pricing reforms leads to serious conflict between coal suppliers and electricity utilities. Electricity utilities experience significant losses as a result of conflict: severe coal price fluctuations, and uncertainty in the quantity and quality of coal supplies. This paper explores whether establishing cooperative relationships between coal suppliers and electricity utilities can resolve conflicts. We begin with a discussion of the history of coal and electricity pricing reforms, and then conduct a theoretical analysis of relational contracting to provide a new perspective on the drivers behind the establishment of cooperative relationships between the two parties. Finally, we empirically investigate the role of cooperative relationships and the establishment of mine-mouth power plants on the performance of electricity utilities. The results show that relational contracting between electricity utilities and coal suppliers improves the market performance of electricity utilities; meanwhile, the transportation cost savings derived from mine-mouth power plants are of importance in improving the performance of electricity utilities. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Electricity and coal firms Cooperation Coal and electricity price

1. Introduction Over the period of 2000–2008, Chinese energy consumption has increased at an average rate of 9.1% per annum (Zhao and Yin, 2011). In the rapid development of Chinese energy industry, the electricity and coal industries occupy important place. Before the mid-1980s, both the electricity and coal industries were regulated strictly by highly centralized administrative planning in China. At that time, the coal used by electricity utilities was supplied according to the administrative plan. However, the highly centralized control system of coal supply and the low, artificially set price of coal caused serious inefficiencies and deficits in the coal industry. The Chinese government had to grant heavy subsidies to the coal industry each year. In 1983, two-tier pricing was implemented in the coal industry; and in 1993, the Chinese government began deregulation reform. Since then, a series of additional marketoriented reforms have been carried out in the coal industry. However, the electricity industry was and still remains centralized and strictly regulated. As a result, the discrepancy between price increases between coal suppliers and electricity utilities caused more n Corresponding author at: School of Economics and Management, North China Electric Power University, Beijing, China. Tel.: þ86 10 51963566, þ 86 13910778294; fax: þ86 10 80796904. E-mail address: [email protected] (X. Zhao).

0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2012.04.020

intense conflict between the two parties. Such conflict was characterized by the following aspects: First, the key issue of conflict was that the price of coal supplied to electricity utilities was much lower than that supplied to other producers; second, the amount of coal supplied to electricity utilities lacked guarantees as coal suppliers often failed deliver coal supplies reliably; and third, the coal quality supplied to electricity utilities was much lower than expected. To mitigate the conflict between electricity utilities and coal suppliers, Chinese decision-makers promulgated the policy of co-movement between electricity and coal prices in 2004. The co-movement policy was implemented three times: May 2005, June 2006 and July 2008; and the electricity sale price increased 0.0252 Yuan/kW h, 0.025 Yuan/kW h and 0.25 Yuan/kW h, respectively. However, in contrast to the growth rate of coal prices in the same period, increases in the level of electricity prices were limited due to government concerns about increases in the Consumer Price Index (CPI). Thus, the co-movement mechanism between coal and electricity prices could not resolve effectively the growing conflict between electricity utilities and coal suppliers. In view of the huge losses from coal price fluctuations and the lack of coal supply guarantees, electricity utilities are inclined to control coal supplies by establishing close relationships (such as through vertical integration or long-term contracts) with coal suppliers. Is cooperation between electricity utilities and coal suppliers effective in resolving existing conflicts? This paper will

X. Zhao et al. / Energy Policy 46 (2012) 520–529

600 Price of within plan coal

500 400 300

Price of market

200 100

1983-1992 Two-tier pricing

1993-1995 Market pricing except for coal to electricity (CE)

20 08

Fig. 2. Ex-mine prices of within-plan coal and market coal (RMB Yuan/tce). Data source: 1990–2004: Taiyuan market information and analysis of economic operation on coal market; 2005–2008: Zhongneng power industry fuel corporation.

Reforms of coal pricing

1950s-1982 Strict and unified planned coal pricing

20 06

20 04

20 02

98 20 00

6

19

19 9

92 19 94

0

90

China has relatively abundant coal resources. The estimated raw coal reserve in China attributes to about 20% of the world’s total reserve and ranks second in the world in size (Wang, 2007). China’s coal deposit per capita is 79% of the world average. However, deposits of oil and natural gas are limited. The oil deposit and natural gas deposit per capita in China only attribute to 6.1% and 6.5% of the world average. With this resource endowment structure, coal use dominates in China. In the early founding days of China (China was founded in 1949), coal consumption accounted for more than 90% of total primary energy consumption. In the 1970s with the development of oil and electricity industries, Chinese coal consumption decreased gradually to 70% of total energy consumption. To promote economic development, China executed energy price policies that set artificially low rates from the 1950s to the 1980s. In 1989, the planned price of raw coal from state-owned important mines (SIM) was RMB Yuan36.44, which was lower than cost. As a result, the Chinese coal industry has experienced deficits for a long time. To maintain the normal operation of coal enterprises, the Chinese government granted heavy subsidies to the coal industry each year. For example, subsidies granted to the coal and oil sectors from 1985 to 1989 had reached more than RMB Yuan10 billion (Wang, 2001). The Chinese government’s burden of heavy financial subsidies was one driver of coal price deregulation in 1993. Another driver was the movement towards market-oriented reforms in China’s broader economy. In 1978, China began the process of economic reforms rooted in principles of market-oriented reform. In the 1980s, some industries, including the coal industry, introduced two-tier pricing, which means that outputs above the quota could be priced higher than planned prices. As a result, the traditional planned track gradually diminished, and coal increasingly was sold out of plan on the free market (Wang, 2007). However, the biggest consumer of coal, the electricity industry, remained under the strict regulation of electricity tariffs, which make market-oriented reforms in coal pricing problematic. Today, the market pricing of coal supplied to

19

2.1. History of Chinese coal price reform

RMB Yuan/tce

2. History of coal and electricity price reforms in China

electricity utilities has not been realized completely. Fig. 1 shows the reform process of coal pricing in China. First stage: Strict and unified planned coal pricing, from the 1950s to 1982. Coal pricing was characterized by a strictly unified planned track with artificially set low prices. Second stage: Two-tier pricing, from 1983 to 1992. A system of a dual track approach or two-tier pricing was introduced in 1983 (In January 1983, the original State Planning Commission, Economic and Trade Commission, and Ministry of Finance jointly issued the document entitled ‘‘Notice on the Price Increase of Coal Overcapacity in Part of Coal Mines, Trial’’) to provide an incentive to coal mines to increase output and to protect low-efficient downstream industries. Output above the quota could be priced higher than the planned price, and the surplus coal could be sold on the free market (Wang, 2007). Third stage: Market pricing except for coal to electricity (CE), from 1993 to 1995. Coal pricing was deregulated in 1993, which means that coal prices were determined by market supply and demand. However, the electricity industry was exempt from deregulation because the electricity industry remained strictly regulated, and the central government tightly controlled the electricity tariff. Thus, the price of coal supplied to the electricity industry remained regulated and determined by planning. This double-track coal pricing only existed for the coal supplied to the electricity industry. The coal sold on the market was referred to as ‘‘market coal,’’ and the coal sold to the electricity industry was referred to as ‘‘within-plan coal.’’ Fourth stage: Government guided pricing (GP) for CE, from 1996 to 2001. Because within-plan coal prices were lower than market coal prices, coal suppliers did not want to sell coal to electricity utilities, which led to the emergence of conflict. In 1996, the National Planning Commission published the document entitled ‘‘Implementation of National Guidance on the Coal Price Notification.’’ It stipulated that the price of coal used for electricity utilities would be determined on the basis of the guided price provided by the central government each year. The shift from ‘‘planned price’’ to ‘‘guided price’’ suggested the

19

begin with a discussion of the history of Chinese coal and electricity price reforms to foster a deeper understanding of the roots of the conflict. Then, we will illustrate the theory of relational contracting and its potential role in alleviating conflicts between the two parties. Next, an empirical analysis of the value created from cooperation between electricity utilities and coal suppliers will be presented. Finally, the impact of mine-mouth power plants on the performance of electricity utilities will be investigated empirically.

521

1996-2002 Government guided pricing (GP) for CE

2003-2005 GP is canceled except for important CE contracts

Fig. 1. shows that Chinese coal pricing reform has experienced six stages.

After 2006 Deregulation of coal pricing for important CE contracts

522

X. Zhao et al. / Energy Policy 46 (2012) 520–529

Table 1 History of electricity pricing reforms in China. Period

On-grid power price

Characteristics

Before 1985

Internal transfer price

1985–1996 1997–2003 2004-present 2005-present

Capital and interest price Operation period price Yardstick power price Co-movement price of coal and electricity

No independent on-grid power price because the entire electricity sector was organized as a vertically integrated state-owned utility. Price was decided by the time of repayment of principal and interest (about 10 years). Price was decided by the operation time of power plants (about 20–30 years). Price was determined by average power generation cost. The electricity generation prices co-move with coal prices.

Chinese government’s intent to deliver more rights of coal pricing to the market, aiming to alleviate conflicts between electricity utilities and coal suppliers (under the mechanism of ‘‘guided price,’’ the central government only published a reference price, and the two parties could negotiate the specific price within a certain range of the reference price). The guided price increased each year, but it remained lower than the market price. In general, the guided price was implemented smoothly from 1996 to 2001 because electricity demand increased relatively slowly, which led to limited increases in demand for coal (coal supplied to the electricity industry accounted for about 60% of total coal use.)1 The gap between the within-plan coal price and market coal price was not large, which could be accepted by most coal suppliers. Fifth stage: GP is canceled except for important CE contracts, from 2002 to 2005. With the sharp increase in electricity demand in 2002, coal demand also increased heavily. As a result, the gap between the price of market coal and within-plan coal widened (Fig. 2). Under this context, coal suppliers increasingly did not want to sell coal to electricity utilities under the guided price (the price of within-plan coal), which was much lower than market price. It was difficult for the two parties to sign a contract. For example, in the Coal Exchange Fair organized by the State Development and Reform Commission (SDRC) in 2003, only 40% of the Important Contracts (some electricity utilities have the right to sign joint coal purchasing contracts – known as ‘‘Important Contracts’’ – with coal suppliers and transportation entities, which guarantees coal transportation by the Ministry of Railways) were signed,2 and the price was not confirmed in most contracts (Zhao, 2005). Even if a contract had to be signed with the intervention of the National Development and Reform Commission (NDRC), the coal suppliers found ways to avoid implementing the contract or to substitute low-quality coal for high-quality coal. Moreover, the influence of the NDRC on mitigating the conflict between coal suppliers and electricity utilities grew weaker. Hence, in 2002, the government announced the cancellation of the guided price of coal except for the coal supplied to electricity utilities under ‘‘Important Contracts,’’ which meant that Chinese coal pricing reform again moved toward market-oriented mechanisms. Sixth stage: Deregulation of coal pricing for important CE contracts, from 2006 to present. Each year, the NDRC utilized significant time and effort to mediate conflicts between coal suppliers and electricity utilities under ‘‘Important Contracts.’’ Moreover, the effectiveness of mediation was limited because the causes of the conflict were rooted in the price gap between within-plan coal and market coal. Thus, the Chinese government decided to implement market-oriented reforms, which intended to allow the market to resolve conflicts between electricity utilities and coal suppliers. As a result, in June 2006, the coal

1 Data source: Economic Research Institute of State Grid Corporation, 2008. Composite Report on Transmission Coal and Power, June. 2 Data source: The improvement in the relationship between coal firms and electricity firms is supported by National Development and Reform Commission. www.dt-gspgc.com/Article_Shown.asp? ArticleID¼ 225, 2005, 1, 4. Last Access: 2009, 8, 8.

price of ‘‘Important Contracts’’ also was deregulated, which meant the complete elimination of the two-tier coal pricing mechanism. Market mechanisms determined all coal prices, and planning prices or guided prices no longer existed. However, because the Chinese electricity market remains under strict regulation, difficulties exist in adjusting the electricity tariff. Most electricity (about 70%) is generated by coal in China; hence the asymmetry of pricing regulation between the coal and electricity markets led the Chinese central government to abandon the original intent of relying purely on market-pricing mechanisms and to intervene in the coal market when necessary. For example, on 19 June 2008, the NDRC announced that to prevent coal and electricity prices from rising (on the same day, the NDRC announced that the electricity retail price would increase RMB Yuan0.025 beginning 1 July 2008), the price of coal supplied to electricity utilities would be regulated temporarily from 20 June 2008 to 31 December 2008. During this period, the factory price of coal supplied to electricity utilities would be kept below the actual settlement price on 19 June 2008. To ease inflation pressures, in December 2010, the NDRC published the document entitled ‘‘Notice on making good cooperation across coal production, transportation and demand in year 2011,’’ which stipulated that the price of coal supplied to electricity utilities under ‘‘Important Contracts’’ in 2011 should keep the same pricing level as that of the previous year. In sum, Chinese coal pricing reform has been moving toward market-oriented mechanisms since year 1983, and for the most part, market-oriented mechanisms have been utilized for coal pricing. However, because of the significant status of coal in the electricity industry and the strict regulation still exerted in electricity pricing, the market pricing of coal cannot be realized perfectly. Under special situations, such as the sharp increase of coal prices, central government intervention is unavoidable. 2.2. History of Chinese electricity pricing reform In contrast to coal pricing reform, Chinese electricity pricing reform has been characterized by highly centralized planned administrative regulation. Similar to the coal industry, before the mid-1980s, artificially low electricity pricing was exerted, which caused serious shortages in electricity supply. To attract more investment in the electricity industry and improve operation efficiencies, on-grid electricity prices experienced three reforms from 1985 to 2004. Moreover, to mitigate the pressures faced by electricity utilities caused by the significant increase of coal prices, in May 2005, the price co-movement policy of coal and electricity was implemented. In price co-movement policies, electricity generation prices co-move with coal prices. Co-movement is not a free market adjustment; the NDRC regulates and periodically implements price co-movement policies to avoid extreme price fluctuations. Actual adjustment of electricity prices only occurs if the fluctuation of coal prices exceeds 5%; otherwise, fluctuations are accumulated to the next period (Ma and He, 2008). The history of electricity pricing is shown in Table 1.

X. Zhao et al. / Energy Policy 46 (2012) 520–529

3. Theory of relational contracting and its potential role in alleviating conflicts between electricity utilities and coal suppliers The definition of ‘‘relational contracting’’ was put forward first by Williamson in 1979 as a suitable concept for progressively increasing the ‘‘duration and complexity’’ of contracts. Relational contracting includes two types of governance structures: bilateral governance (usually referred to as long-run contracts) and unified governance (internal organization or vertical integration). In bilateral structures, although the parties maintain autonomy, the highly idiosyncratic nature of the exchanged asset provides both parties incentive to sustain the relationship rather than to permit it to unravel; however, the advantage of vertical integration is that adaptations can be made in a sequential way without the need to consult on, complete or revise inter-firm agreements (Williamson, 1979). Under relational contracting, the reduction of

150 140 130 120 110 100 90

80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06

80

19

Whether considering the internal transfer price of electricity before 1985 or the Yardstick power price after 2004, highly centralized electricity planning mechanisms have not changed; only the electricity price level has changed. Although electricity prices have increased significantly since 1985, the growth rate of electricity prices was much lower than that of coal prices. Even after 2005 when the co-movement policy of coal and electricity prices was implemented, electricity prices remained under strict administrative regulation, and their growth was limited. For example, after the first co-movement of coal and electricity prices occurred from 1 May 2005 until 30 June 2006, when the second co-movement policy of coal and electricity prices was implemented, two accumulated periods passed. In 2007, no price co-movement policies were utilized. In June 2008, when coal prices had increased about 40%, the third co-movement policy of coal and electricity prices was implemented. However, the increases in electricity prices were determined by the possible acceptable range of the CPI rather than the growth in coal prices. As a result, the limited increase in electricity prices could not offset the skyrocketing increases in coal costs, even under price co-movement policies. Thus, price co-movement policies cannot resolve conflicts between electricity producers and coal suppliers effectively. The history of Chinese electricity pricing reform reveals two issues. First, the persistent electricity tariff under highly strict regulations created mismatches with fluctuating coal prices under free market mechanisms, and this mismatch is the most significant driver behind conflicts between electricity utilities and coal suppliers. Second, the co-movement policy of coal and electricity prices cannot resolve conflicts between these two parties effectively. In China, market-oriented reforms in electricity pricing remain unlikely in the near future. Currently, such reform faces obstacles. For example, electricity transmission and distribution have not been separated. Electricity distribution still operates under a monopoly, and electricity transmission fees have not been settled. The most significant obstacle to market-oriented electricity pricing would be concerns by the central government regarding inflation pressures. As a result, the asymmetry of pricing mechanisms between the coal and electricity industries likely remains in the near future. Hence, seeking other ways to resolve conflicts between coal suppliers and electricity utilities is an arduous task faced by the leaders of the electricity industry. One of the ways to exert control over the coal supply and price would be to make investments in the coal industry or establish cooperative relationships with coal suppliers. In the next few sections, we will focus on cooperation between electricity utilities and coal suppliers by using relational contracting theory and conducting an empirical analysis.

523

Fig. 3. Chain index of ex-mine coal prices in China. ‘‘Ex-mine coal price’’ refers to the coal price produced at the mine, excluding transportation price. Data source: China Statistic Yearbook (2008).

transaction costs through savings from signing contracts, implementing contracts (supervision costs), and dealing with contracts violation could improve governance efficiency (Heide, 1994; Grandori, 1987; Williamson, 1991). Relational contracting is suitable for transactions with physical asset, human-capital or geographical site specificity (Williamson, 1983). We argue that trade between electricity utilities and coal suppliers can adopt this type of relational contracting because the object of transaction – coal – possesses characteristics of specificity. The coal used by electricity utilities seems standard; however, coal is characterized by its special nature for several reasons. First, different types of coal have different calorific powers of combustion and proportions of ash (or sulfur) content. Second, different types of coal match with different types of power generation units (boiler types) to utilize the coal most efficiently. Third, the transport cost of coal is high, and the transport capacity of coal is lacking in China. Hence, the geographical site of the coal mine is of significant importance for electricity utilities and is also one of the specificities of coal. The physical and geographical site specificities of coal provide the basis for establishing relational contracting between electricity utilities and coal suppliers. Additional drivers for establishing relational contracting between electricity utilities and coal suppliers pertain to the instability of the coal supply and severe coal price fluctuations. Fig. 3 shows that since 1985, China’s coal prices have fluctuated greatly. Coal cost accounts for about 70% of the total cost in electricity utilities in China.3 And thus the volatile prices of coal bring about severe unfavorable impact on the operation of electricity utilities. Moreover, the coal used by electricity utilities accounts for 55% of the total coal consumption,4 which means that a significant amount of trade occurs each year between electricity utilities and coal suppliers. Hence, relational contracting between electricity utilities and coal suppliers is important for electricity utilities to reduce the risk of coal price fluctuations and the costs of signing contract, implementing contracts, and dealing with contracts violation (transaction cost). China’s electricity utilities have realized the possible strategic benefits of establishing close relationships with coal suppliers. Table 2 presents the Five Big Power Corporations (FBPC) – Huaneng Group, Datang Group, Huadian Corporation, Guodian Corporation and the Power Investment Corporation – and the Luneng Group Corporation that have established the ambitious

3 Data source: Lai Weixing, Wang Liang. Electricity utilities purchase coal from foreign countries since coal prices increase continually (in Chinese). Guangzhou Daily, 2009, 5, 15. http://gzdaily.dayoo.com/html/2009-05/15/content_568334. htm. Last access: 2012, 2, 22. 4 Data source: Economic Research Institute of State Grid Corporation, 2008. Composite Report on Transmission Coal and Power, June.

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Table 2 Comparison between the current and target amounts of coal to be supplied to electricity utilities under relational contracting (10,000 tce/year). Data source: Zhong neng power industry fuel corporation, 2009. Shareholding ratio of coal mines

Huaneng Group

Datang Group

Guodian Corporation

Huadian Corporation

Power Investment Corporation

Luneng Group

100%

Current amounts Target amounts

1,903 17,960

1000 3600

0 0

0 0

7,500 15,600

0 0

50–100%

Current amounts Target amounts

840 5,940

1000 1000

8,930 14,170

3535 5700

500 500

900 5,900

o50%

Current amounts Target amounts

2,350 7,950

2100 2700

1,600 1,800

2805 7005

0 0

0 0

Others

Current amounts Target amounts

3,300 12,200

2100 8500

4,180 7,350

1580 2920

500 6,685

4,670 19,480

Note: ‘‘Others’’ refers to the amount of coal supplied to electricity utilities under long-term contracts or through the shareholding of coal mines by electricity utilities, but the specific proportion of shares is not clear.

Table 3 Reliability test of other dependent variables and independent variables. Variable

Meaning of variable

Status of variable

RV CV TV V TRC TRL PRC PRR SDR

Relation improvement value Enterprise capacity improvement value Transaction process improvement value Total value (Market performance) Transaction characteristics Trust level Product characteristics Price risk Supply and demand risk

Dependent variable at Dependent variable at Dependent variable at Dependent variable at Independent variable Independent variable Independent variable Independent variable Independent variable

goal of controlling5 more coal resources through unified governance (internal organization or vertical integration) in the future. In the 12th Five Year Planning Strategy, the FBPC and Luneng Group Corporation have taken the establishment of close relationships with coal suppliers as one of their significant strategy goals. For example, Datang Group Corporation set the goal of controlling 100 million tons of coal produced per year to guarantee 50% of its total coal demand in 2015. Power Investment Corporation set the goal of controlling 100 million tons of coal produced per year to guarantee 70% of its total coal demand in 2015. Meanwhile, Luneng Group Corporation identified an integrated development model of coal and electricity production. The inclination of power generation corporations to cooperate with coal suppliers implies that relational contracting would improve the market performance of electricity utilities by ensuring a stable coal supply and reducing the risk of coal price fluctuations. In the next section, an empirical analysis is provided to examine this prospect.

4. Empirical analysis of the value created by cooperation between electricity utilities and coal suppliers 4.1. Methodology and data collection To identify the role of relational contracting between electricity utilities and coal suppliers on the performance improvement of electricity utilities, we develop a regression model with dependent 5

Here, ‘‘controlling’’ involves two meanings: First, the quantity of coal to be supplied to electricity utilities can be ensured; second, little risk of coal price fluctuation exists. The above two aims can be realized by establishing close relationships (such as through vertical integration or long-term contracts) between electricity utilities and coal suppliers.

Coefficient of inter-consistency medium level medium level medium level final level

0.9304 0.7864 0.9041 0.8605  0.2705 0.7213 0.7717 0.7054 0.7471

variables at two levels. The dependent variables at the medium level include: relation improvement value (RV), enterprise capacity improvement value (CV), and transaction process improvement value (TV); the dependent variables at the final level is market performance (V). The independent variables include transaction characteristics (TRC), trust level (TRL), product characteristics (PRC), price risk (PRR), and supply and demand risk (SDR) (Table 3). The selection of these variables is based on studies by Chang (2003); Barret and Konsynski (1982), Mukhopadhyay (1995), Bouchard (1993), and Vidyarthi et al. (2003), and on transaction cost economics theory. We then conduct reliability and validity tests with the results shown in Tables 3 and 4, respectively. As discussed in the previous section, cooperative relationships between electricity utilities and coal suppliers include two components: one is long-run bilateral contracting; the other is internal organization through 100% share holding or a proportion of share holding. However, this study is concerned primarily with electricity utilities that have established internal organization with coal suppliers. Hence, questionnaires were sent only to electricity utilities that had established internal relationships with coal suppliers. Because of the limited number of such electricity utilities, only 47 questionnaires were sent out for this survey, and 34 valid questionnaires were utilized for this analysis. Thus, this part of the empirical investigation is exploratory research (Mahmood and Soon, 1991). To ensure the representativeness of each questionnaire, only one questionnaire was sent to each electricity utility, and a staff person familiar with the cooperative relationship was asked to answer the questionnaire. 4.2. Results and discussions The impacts of relational contracting between electricity utilities and coal suppliers on the performance improvement of electricity utilities are shown as Fig. 4. The first important finding

X. Zhao et al. / Energy Policy 46 (2012) 520–529

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Table 4 Principal component analysis of dependent variables at the medium level. Variables

Sub-variables

Factor loading Factor1

RV: Relation improvement value

CV: Enterprise capacity improvement value

TV: Transaction process improvement value

RV1 Lower of contract signing cost RV2 Lower of contract supervision cost RV3 Lower of dispute settlement cost RV4 Reduce contract dispute RV5 Promote communication RV6 Reduce risk RV7 Lower coal supply cost CV1 increase the management knowledge for coal firms CV2 increase the financial capacity of utilities CV3 increase the negotiation power with transportation firms TV1 lower of coal storage cost TV2 ensure supply in time TV3 ensure coal quality TV4 increase trust degree of two parties TV5 improve the suitability of transaction cost change

Factor 2

Factor 3

0.799 0.864 0.869 0.675 0.594 0.623 0.563 0.831 0.569 0.830 0.570 0.666 0.897 0.684 0.761

Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. A rotation converged in five iterations. Principal component analysis of the dependent variables at the final level and principal component analysis of independent variables are not provided in this paper. These analysescan be obtained from the authors.

-0.274*

0.601**

Trust level Product characteristics

0.401* 0.393*

Enterprise capacity improvement value

Price risk Supply and demand risk

Relation improvement value

0.478** 0.414**

Market performance

-0.455** Transaction process improvement value

0.502**

Transaction frequency 0.147* -0.415**

Fig. 4. Value creation through cooperation between electricity utilities and coal suppliers. **denotes statistic significant at 5%; *denotes statistic significant at 10%.

here is that the relation improvement value, enterprise capacity improvement value, and transaction process improvement value – which pertain to relational contracting between the two parties – improve the market performance of electricity utilities. The second important finding is that trust level and supply and demand risk have statistically significant roles on the relation improvement value (the coefficients are 0.401and 0.455, respectively). The negative coefficient means that with the increase of supply and demand risk, the value creation caused by cooperation between electricity utilities and coal suppliers will suffer negative impacts. The third important finding is that among the independent factors affecting market performance (trust level, price risk, supply and demand risk, and transaction frequency), trust level has the most significant impact on the market performance of electricity utilities with the coefficient of 0.601. This implies that improved performance through cooperation relies significantly on the level of credit between the two parties. The last important finding is that with growth in transaction frequency, the market performance of cooperation improves. Greater transaction frequency leads to more communication and trust between the two parties, and thus better market performance.

The above empirical evidence demonstrates that cooperation between electricity utilities and coal suppliers improves the market performance of electricity utilities. This improved performance has driven increased enthusiasm from electricity utilities for unified governance (where electricity utilities expand investments into coal mines). Among the several types of cooperative relationships that can be established with coal suppliers, electricity utilities have given special attention to cooperative relationships based on the establishment of mine-mouth plants. The most important role of mine-mouth power plants is to ensure a stable coal supply: first, mine-mouth power plants usually establish close relationships with nearby coal mines, which provide a guaranteed coal supply; second, the transportation capacity of coal in China is limited, and the lack of transportation vehicles for coal is one important constraint to supplying coal in a timely manner. A mine-mouth power plant could resolve these persistent issues and thus ensure coal supply security. Joskow (1985) illustrates that building mine-mouth power plants effectively resolved conflict between electricity utilities and coal suppliers in the 1970s in the United States. In the next section, we empirically analyze the impact of mine-mouth plants on the performance of electricity utilities in China.

5. The impact of mine-mouth plants on the performance of electricity utilities 5.1. Methodology 5.1.1. Neo-classical model We use the variable of ‘‘profit’’ to represent the ‘‘performance’’ of electricity utilities. According to conventional neo-classical theory, the Profit Function Model is as follows:

pt ¼ f ðPt ,Q t ,C t Þ

ð1Þ

where, pt, Pt, Qt, Ct represent profit, price, quantity, and cost, respectively. For electricity utilities, production costs mainly include fixed costs (which are indicated by capital stock) and variable costs. Variable costs mainly include three parts: coal cost, coal transportation cost and labor cost. Labor cost consists of only a small proportion of variable costs compared to the other two types of costs; thus, labor cost is not addressed in this paper. Moreover,

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X. Zhao et al. / Energy Policy 46 (2012) 520–529

the linear correlation between coal cost (represented by the ex-mine coal price) and coal transportation cost is highly significant at 97.2%; thus this analysis also excludes coal cost. According to the neo-classical production model, the quantity Qt in Eq. (1) is decided by capital Kt and Labor Lt, i.e., Q t ¼ f ðK t ,Lt Þ

ð2Þ

5.1.3. Test for co-integration Co-integration is used to identify whether there is a long-run equilibrium relationship between multi-economic variables. Error Correction Model (ECM) is used to identify the balance between the long-term statistic relationship and short-term dynamic relationship. The unified form of ECM is:

Dyt ¼ Pyt1 þ G1 Dyt1 þ    þ Gp1 Dytp þ 1 þ mt

ð5Þ

For the same reason previously discussed, the impact of labor Lt on quantity Qt is ignored. On the basis of Eqs. (1) and (2), we derive the following model:

where, for i ¼ 1,. . .,p1, we can get P ¼ ðIk A1     Ap Þ, Ai is a matrix of ðK  KÞ;

AProt ¼ f ðPelect ,CPC t ,GK t Þ

Gi ¼ ðAi þ 1 þ    þAp Þ;

ð3Þ

where AProt is the sales income of the unit installed capacity of electricity utilities (RMB Yuan/kW); Pelect is the index of ex-work power price (on-grid power price); CPCt is unit transaction cost of coal, (RMB Yuan/Ton); and GKt is the capital stock of unit installed capacity of electricity utilities (RMB Yuan/kW). Following Jin and Yu (1996), Shan and Sun (1998), and Yuan et al. (2008) among others, we use the annual average balance of the net value of fixed assets of electricity industrial enterprises as the indicator of capital stock. The subscript ‘‘t’’ denotes the time period. Taking the Ln of the variables Prot , Pelect, CPCt, and Kt in Eq. (3), we have Eq. (4): 





Following Granger (1988), and Engle and Granger (1987), we estimated a VEC model for the Granger causality test for our problem at hand. The VEC representation is as follows: p X

a1,k nk,tp þ

p X

þ

p X

g2 D ln Pelects þ

s¼1 p X

g1 D ln AProts

s¼1

k¼1

ð4Þ

A dot at the top of a variable means that the variable is now in a growth rate form (Time series data is non-stationary in general. Before conducting a co-integration test, the original data should be converted into natural logarithms to reduce variable fluctuations. Moreover, the natural logarithms for variables can make the first differences show their growth rates). The constant parameters a, b, c are the elasticity of profit with respect to electricity price, coal transportation cost, and capital stock. The possible relationships among the above variables in long-run movements can be examined by using tests for multivariate co-integration. To avert fake regressions caused by the analysis of series data, we first study the stationary properties of the variables. If the series is integrated and of the same order, one can proceed with co-integration tests. There are a variety of unit root tests that sometimes yield conflicting results. Therefore, to proceed with co-integration and VEC analysis, one needs to be confident with the order of integration of the series used (Yuan, et al., 2008).

r X

D ln AProt ¼ m1 þ

þ



AProt ¼ aPelect þbCPC t þ cGK t

mt ¼ ðm1t ,. . ., mKt Þ0 is an error term that cannot be observed.

g3 D ln CPC ts

s¼1

g4 D ln GK ts þ g5 Ecmt1 Z1,t

ð6Þ

s¼1

D ln Pelect ¼ m2 þ

r X

a2,k nk,tp þ

p X

p X

j2 D ln Pelects þ

s¼1

þ

p X

j1 D lnAProts

s¼1

k¼1

þ

p X

j3 D ln CPC ts

s¼1

j4 D ln GK ts þ j5 Ecmt1 þ Z2,t

ð7Þ

s¼1

D ln CPC t ¼ m3 þ

r X

a3,k nk,tp þ

k¼1

þ

p X

p X

p X

p X

s2 D ln Pelects þ

s¼1

þ

s1 D ln AProts

s¼1

s3 D ln CPC ts

s¼1

s4 D ln GK ts þ s5 Ecmt1 þ Z3,t

ð8Þ

s¼1

5.1.2. Unit root test To have robust results, we conducted five different unit root tests, namely augmented Dickey–Fuller (ADF), Elliot–Rothenberg– Stock Dickey–Fuller GLS detrended (DF-GLS), Phillips–Perron (PP), Kwiatkowski–Phillips–Schmidt–Shin (KPSS), and Ng-Perron MZa (NP). It also is well known that the unit root tests are sensitive to different lag structures; hence, we use two methods to make lag selections: the Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) to improve reliability. All unit root tests (except the KPSS) employed in this paper have a null hypothesis that the series in question has a unit root against the alternative of stationarity. The null of KPSS states that the variable is stationary. If the test results show that an economic time series of a nonstationarity stochastic trend exists in the model, calculating the difference on the economic time series is necessary. Such an economic time series, Xt, is called the ‘‘difference stationary process.’’ If the test results show that a deterministic trend exists in the model, it is necessary to eliminate the deterministic trend and make the following shift: Xt  bt ¼ a þ mt. In this situation, Xt is called the ‘‘trend stationary process.’’ Most non-stationarity time series can be shifted into stationarity time series by conducting differences procedures once or several times.

D ln GK t ¼ m4 þ

r X k¼1

þ

p X s¼1

þ

p X

a4,k nk,tp þ

p X

r1 D ln AProts

s¼1

r2 D ln Pelects þ

p X

r3 D ln CPC ts

s¼1

r4 D ln GK ts þ r5 Ecmt1 þ Z4,t

ð9Þ

s¼1

where, r is integration rank, and p is lag length and decided according to information criterion and the final prediction error. In general, two lag selection information criteria according to AIC are employed, and we also use the criteria in the original analysis. However, the sample size used in this paper is relatively small (from 1984 to 2007), and it leads to the failure of all the coefficients of variables in passing the t test. Hence, one lag selection information criteria is used in the final VEC model. The parameters nk,tp are the co-integrating vectors, derived from the long-run co-integrating relationships during co-integration tests. ai,k is the adjustment coefficient. The parameters mi ,i ¼ 1,2,. . .,4 are intercepts, and the symbol D denotes the difference of the variable following it.

X. Zhao et al. / Energy Policy 46 (2012) 520–529

527

Table 5 Unit root test results of all variables in the study. Variable

ADF

DF-GLS

PP

KPSS

NP(MZa)

ln APro ln Pelec ln CPC ln GK Ln APro ln Pelec ln CPC ln GK

 1.832(0,SIC,AIC)  1.339(1,SIC,AIC) 0.920(2,SIC)  1.475 (0,SIC,AIC)  1.721(0,SIC,AIC)  1.447(1,SIC,AIC)  3.516(1,SIC,AIC)c  3.320(5,SIC,AIC)c

 1.616 (0,SIC,AIC)c  0.849(1,SIC,AIC) 0.092(1,SIC,AIC)  1.496 (0,SIC,AIC)  2.063(0,SIC,AIC)  3.012(3,SIC,AIC) c  3.683(1,SIC,AIC) b  3.040(4,SIC,AIC)c

 1.824  1.005 1.368  1.776  1.721  0.925  2.149  1.588

0.518b 0.652b 0.707b 0.173 0.139c 0.134c 0.145c 0.085

 3.551(0,SIC,AIC)  2.063(1,SIC,AIC) 0.343(1,SIC,AIC)  3.948(0,SIC)  7.350(0,SIC,AIC)  7.084(0,SIC,AIC) c  27.454(1,SIC,AIC)a  11.054 (1,SIC)

Panel B: first difference Int. ln APro ln Pelec ln CPC ln GK Int. ln APro And ln Pelec Trend ln CPC ln GK

 4.892(0,SIC,AIC)a  2.207(0,SIC,AIC)  3.817(0,SIC,AIC)a  3.602(0,SIC,AIC)b  4.970(0,SIC,AIC)a  2.340(0,SIC,AIC)  4.041(3,SIC) b**  3.647(0,SIC,AIC)b

 5.017(0,SIC,AIC)a  2.227(0,SIC,AIC) b  4.932(1,SIC,AIC)a  3.676(0,SIC,AIC)a  5.224(0,SIC,AIC) a  2.408(0,SIC,AIC)  4.388(3,SIC,AIC)a  3.805 (0,SIC,AIC)a

 4.909a  2.126  4.188a  3.605b  5.065 a  2.283  5.519a  3.647b

0.210 0.178 0.249 0.141 0.124c 0.144c 0.254a 0.095

 10.840(0,SIC,AIC)b  7.084(0,SIC,AIC) c  11.212(0,SIC)b  10.468(0,SIC,AIC)b  10.797(0,SIC,AIC)  7.252(0,SIC)  4.740(3,SIC,AIC)  10.525(0,SIC,AIC)

Panel A: level Int.

Int. And Trend

*Denotes insignificance by SIC but significance by AIC. a

Denotes significance at the 1% critical level. Denotes significance at the 5% critical level. Denotes significance at the 10% critical level. nn Denotes insignificance by AIC but significance by SIC. b c

in general it seems to indicate that all variables are integrated of order 1.

5.2. Data collection and disposal The data on electricity was not published in China’s statistical yearbook until 1984; hence, we take 1984 as the start year of this study. In the meantime, we take 1984 as the base year and the relative data used in this paper is converted to constant prices in accordance with prices in 1984. The sale income of unit install capacity of electricity utilities, AProt , is converted to constant prices by using the GDP index. The annual average balance of net value of unit fixed assets of electricity industrial enterprises GK is converted to constant prices by using the ex-work power price index. The price of coal transportation CPC t is converted to constant prices by using the CPI. The coal transportation cost supplied to electricity utilities Ctr is calculated as follows: Ctr t ¼ Irailt  r t

ð10Þ

r t ¼ Coalt =T t

ð11Þ

where Irail is the revenue of railway transportation; r t is the proportion of the wagon’s coal volume to the total volume of wagon;6 Coalt is the coal volume by railway transportation; T t is the total volume by railway freight transportation. The China Statistic Yearbooks (1985–2008) provided data for sale income of electricity utilities, GDP index, index of power price, annual average balance of net value of fixed assets of electricity industrial enterprises, the revenue of railway transportation, and the coal volume and total wagon volume by railway transportation; the data on power capacity is collected from the China Electricity Statistic Yearbooks (1985–2008). Table 5 shows all null hypotheses expect KPSS are unit root; whereas, in KPSS the null is stationary. The result reveals that the four tests (except GK) indicate that almost all variables are nonstationary in their level data. However, the stationary property is found in the first difference of the variables at the 10%, 5% or 1% critical level. Although some conflicting results on the stationary property in the first difference are found between different tests, 6 Most coal is transported by wagon in China. Hence, in this paper, we only take account of wagon costs.

5.3. Results and discussion 5.3.1. Co-integration test results and discussion The results of testing for the number of co-integrating vectors are reported in Table 6, which presents the maximum eigenvalue (lmax ), the trace statistics and the 1%, 5% critical value, as well as the tested co-integrating normalized LnAPro. Table 4 shows that trace test statistics and l max test indicate existence of one co-integration vector at the 5% and 1% significance level. We provide the co-integration model in Eq. (12) in accordance with the results of the l max test and trace test. As far as the results of the co-integrating vector normalized on the profit of electricity utilities is concerned, the coefficients of electricity price, coal transportation cost, and capital stock of electricity utilities are found to affect the level of profit of electricity utilities. Electricity price and the capital stock of electricity utilities have a positive effect on the performance (sale income) of electricity utilities by 0.357% (statistic significance is at 10%) and 0.732% (statistic significance is at 20%), respectively. The coal transportation cost has a negative effect on the profit of electricity utilities by 0.095% (statistic significance is at 10%). In 2007, according to the data of China Statistical Yearbook (2008) the profit of electricity utilities was 19.20 billion RMB Yuan. Hence, a 1% of reduction in transportation cost will lead to an increase of 182 million RMB Yuan. This shows that the coal transportation cost plays an important role in improving electricity utilities performance. ln APro ¼ 8:680 þ 0:357 ln Pelec0:095 lnCPC þ0:732 lnGK ð4:032Þ

ð1:282Þ

ð4:242Þ

ð12Þ

The result of the test on residual serial error (Table 7) shows that the Null hypothesis is refused, which means the residual is stationary serial. Hence, the long-run relationship (co-integration relationship) exists between the variables of profit of electricity utilities, ex-work electric power price, coal transportation cost and capital stock. It is demonstrated once more that the result in model of Eq. (12) is reliable.

528

X. Zhao et al. / Energy Policy 46 (2012) 520–529

Table 6 Co-integration test results for (lnAPro, lnPelec, lnCPC, lnGK). Trace

lmax

H0

H1

Statistics

1%

5%

H0

H1

Statistics

1%

5%

r ¼0 rr1 rr2

rZ 1 rZ 2 rZ 3

46.061* 17.780 4.452

54.682 35.458 19.937

47.856 29.797 15.495

r ¼0 r r1 r r2

rZ1 rZ2 rZ3

28.280** 13.328 4.441

32.715 25.861 18.520

27.584 21.132 14.265

Normalized cointegrating equation on ln APro lnAPro

lnPelec

lnCPC

lnGK

1.0000

 0.357 (0.089) [  4.032]

0.095 (0.074) [1.282]

 0.732 (0.173) [  4.242]

nn n

Denotes significance at the 1% critical level. Denotes significance at the 5% critical level.

Table 7 Unit root test result for residual serial error.

Accumulated Response of LNAPRO to Cholesky One S.D. Innovations

1.2

ADF

DF-GLS

PP

KPSS

NP(MZa)

 3.737(SIC,AIC)b

 3.771(SIC,AIC)a

 3.743b

0.095

 10.502 (SIC,AIC)b

1.0 0.8

5.3.2. Vector error-correction On the basis of the co-integration test, we set up an error correction model (ECM) to test the short-term relationship between variables. According to Eq. (12), the error correction term is as follows: Ecmt1 ¼ ln AProt1 0:357 lnPelect1 þ 0:095 lnCPC t1 0:732 lnGK t1 8:680

0.4 0.2

ð13Þ

The most significant driver behind the causality between variables is the equilibrium mechanism in the long-run period. However, long-run equilibrium in an economic system presents disequilibrium attributed to the effect of random factors in the short-run period. This means the economic system could not maintain equilibrium between the performance of electricity utilities (represented by profit) and its explanatory variables, which would be affected by both long-run trends and short-run fluctuations. The deviation caused by disequilibrium is denoted by ECM. In view of the fact that this paper focuses on the performance of electricity utilities, we provide the ECM test of Eq. (14) taking D (lnAPr ot ) as an example.

D ln AProt ¼ 0:0640:455Ecmt1 0:177D ln AProt1 þ 1:421D lnPelect1 þ0:020D lnCPC t1 0:019D lnGK t1

0.6

ð14Þ

Eq. (14) suggests that electricity price and capital stock still have a positive impact on the performance of electricity utilities in the long run. However, in contrast to the results of the long-run equilibrium, the coal transportation cost also has a positive impact on performance. The Johansen test shows that the characteristic root converges in the long-run equilibrium, the error in the short term would be attributed to other occasional factors in the short run, and it has little impact on long-run equilibrium. 5.3.3. Generalized impulse response analysis The impulse response functions (IRFs) are based on a moving average representation of the VAR model, and the dynamic responses of one variable to another are evaluated over horizons. To assess how a shock to one explanatory variable affects another explanatory variable and how long the effect lasts, we utilize generalized impulse response (Koop et al., 1996; Pesaran and Shin, 1998). The impulse response results of the explanatory

0.0 -0.2 1

2

3

4 LNPELEC

5

6 LNCPC

7

8

9

10

LNGK

Fig. 5. Impulse response results of explanatory variables on performance of electricity utilities. The horizontal axis represents time trend, and the vertical axis represents impact level.

variables on the performance of electricity utilities are plotted out in Fig. 5. Fig. 5 shows that the shock of electricity price, coal transportation cost, and capital stock to the performance of electricity utilities is found to be larger over horizons, which means that high transaction costs has significant negative impacts on the performance of electricity utilities over time.

6. Conclusion This paper has offered an analysis of the coal and electricity pricing reform history in China, and presents the impact of asymmetry in pricing reforms of the coal and electricity industries on conflict between the two parties. We also provide a theoretical and empirical investigation on the importance of establishing close relationships with coal suppliers for electricity utilities. Since the onset of deregulation reform in the coal market in 1993, China’s coal price has experienced a gradual increasing

X. Zhao et al. / Energy Policy 46 (2012) 520–529

trend; however, the electricity price is still regulated strictly by the central government. Hence, along with the trend of increasing coal prices, the conflict between electricity utilities and coal suppliers also has escalated. The policy of co-movement between coal and electricity prices established in 2004 played little role in settling the conflict. As a result, electricity utilities seek new ways to control coal price fluctuations and guarantee the amount and quality of coal supply by establishing close relationships with coal suppliers. According to relational contracting theory, relational contracting favors forming effective incentives and avoiding unfavorable results attributed to limited rationality; reducing the assignment and implementation costs of contracting by sharing information; and mitigating market risks by decreasing the transaction uncertainty rooted in asset specificity. The empirical investigation demonstrates the positive impact of cooperation between electricity utilities and coal suppliers on the improvement of the market performance of electricity utilities. One of the most important forms of cooperation between electricity utilities and coal suppliers entails the establishment of mine-mouth power plants. Hence, this paper concludes with an empirical study of the role of mine-mouth power plants on the performance of electricity utilities. Using co-integrated analysis method with data from 1984 to 2007, the results show that the transportation cost savings caused by mine-mouth power plants is of importance in improving the performance of electricity utilities.

Acknowledgements This study is funded by the National Natural Science Foundation of China (Project number: 70773040, and 71073053). The authors want to thank Jiahai Yuan and Bin Yan for their valuable suggestions. In addition, the authors thank Sufang Zhang for her help in the grammar correction of the paper. The authors especially appreciate the anonymous reviewers for their valuable comments. These comments and suggestions significantly improve and refine our paper. References Barrett, S., Konsynski, B., 1982. Interorganization information sharing systems. MIS Quarterly 6, 93–105.

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