ARTICLE IN PRESS

Energy Policy 37 (2009) 5140–5148

Contents lists available at ScienceDirect

Energy Policy

0301-42

doi:10.1

� Corr

E-m

journal homepage: www.elsevier.com/locate/enpol

Comparative study on efficiency performance of listed coal miningcompanies in China and the US

Hong Fang a, Junjie Wu b,�, Catherine Zeng c

a School of Economics & Management, Beijing University of Aeronautics & Astronautics, Beijing 100083, Chinab Leeds Business School, Leeds Metropolitan University, Leeds, LS1 3ED, United Kingdomc Deloitte Touche Tohmatsu LLP, United Kingdom

a r t i c l e i n f o

Article history:

Received 17 October 2008

Accepted 21 July 2009Available online 12 August 2009

Keywords:

Technical efficiency

DEA

Coal mining industry

15/$ - see front matter Crown Copyright & 20

016/j.enpol.2009.07.027

esponding author. Tel.: +44113 8124792.

ail address: j.wu@leedsmet.ac.uk (J. Wu).

a b s t r a c t

Continually rising energy prices in global markets highlights a serious concern about the need to

improve energy efficiency and the efficiency in energy sector in many countries. China, as one of the

fastest growing countries in the world and the largest coal producer, has high coal consumption but a

low recovery rate of coal utilization. Coal efficiency and the efficiency in coal industry have therefore

attracted a great deal of attention from Chinese policy makers, coal firms and academics. This study

attempts to compare the relative technical efficiency performance of listed coal mining companies in

China and the US using CCR and BCC models in the advanced DEA linear programming. The results show

that the level of relative efficiency in Chinese coal mining enterprises, regardless of total technical

efficiency or decomposed pure technical and scale efficiency, is much lower than in American coal firms.

The study also highlights the input resources that cause the inefficiency of Chinese coal mining

companies. Furthermore, in-depth discussion and analysis of how the institutional environments of the

two countries could cause the differences are provided.

Crown Copyright & 2009 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Along with rapidly rising energy prices in the global marketand increased concerns about development sustainability, moreand more countries have eagerly been looking forward toimproving energy efficiency and the efficiency in energy sector.The improvement of efficiency has therefore been playing a keyrole in the energy strategies of many nations (Ang, 2006; Zhouand Ang, 2008). In order to help improve efficiency performancein the energy industry, efficiency indicators, which monitoreconomy-wide efficiency trends and allow the comparisonof efficiency performance within and outside the sector, haveattracted great interest among researchers in different countries(Zhou and Ang, 2008).

As it has experienced the fastest growth of any economy inhistory during the past two decades, China now faces anunprecedented high energy demand for all kinds of energyincluding coal, China’s main energy source. China is the largestcoal producer in the world. It is reported that in 2005 Chinaproduced 2.19 billion tons of coal which represents 37% of totalcoal production in the world and accounts for 75.9% and 70% ofChina’s total primary energy production and consumption,

09 Published by Elsevier Ltd. All

respectively (Cui, 2007). Coal resource, as a non-renewablemineral resource, is of strategic importance for China. Rationalexploitation and effective utilization of coal resources havebecome two important factors in securing coal energy suppliesin the future. However, the major problem in using coal energy inChina is the serious waste of the scarce resource. Cui (2007)indicates that China has as low as 30% of recovery rate of coalresources (the recovery rate of coal in the US and other westerndeveloped countries is around 80%) and about 28 billion tons ofcoal was wasted between 1980 and 2000.

Data envelopment analysis (DEA), invented by Charnes et al.(1978) and developed by a number of scholars such as Bankeret al. (1984), Fare et al. (1985, 1994), is a well-established non-parametric frontier methodology and has been widely used toevaluate the relative technical efficiency of decision making units(DMUs) which are a set of comparable entities (Ramanathan,2003; Zhou et al., 2008a). DEA has been popular as a main frontiermethod for benchmarking energy sectors in many countries (Zhouet al., 2008a, 2008b), as indicated by the rapid increase in thenumber of studies using DEA, especially in energy and environ-mental analysis (EEA) due to its empirical applicability (Ang andZhang, 2000; Zhou and Ang, 2008).

The application of DEA has been over two decades of history inother countries, in particular in western developed countries.However, the experience of DEA in energy sector performanceevaluation has started only in recent years in China. A few studies

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H. Fang et al. / Energy Policy 37 (2009) 5140–5148 5141

can be found in Chinese literature, among them, examples are Hao(2004), Ran and Hui (2006), Wei and Wang (2005) and Zhang andLi (2007). There are some limitations in these studies, e.g. most ofthem have selected research samples on a multiple industry basis,i.e. not only including coal mining industry but also containinggas, oil, electricity and water sectors. Such a sample frame hardlyrepresents a pure coal mining industry and their findings thusmight distort the true picture when they are used to explain coalefficiency performance in China. In addition, they focus researchon Chinese domestic companies rather than carry out a compar-ison against western efficiency performance benchmarking. To fillthese gaps, the purposes of this study are twofold: (1) comparethe relative technical efficiency performance of pure listed coalmining companies in China and the US using DEA analysis (2)discuss and analyze how the different institutional environmentscould cause the differences in the efficiency performance of thecoal mining industry in the two countries, respectively.

The rest of the paper is organised as follows. The literaturereview will be given in the next section. The third section will be ajustification of the methodology used in the study. The fourthsection will compare the relative technical efficiency performanceof the Chinese and the US’s coal industries through an in-depthdiscussion about the influence of the respective institutions.Conclusion, policy implication and future research will then beaddressed in the final section.

2. Literature review

2.1. China’s energy policy and its impact on energy industry

China’s ambition to become a modern developed country hasdriven its dramatic economic growth by around 8% per year since1980s (Li and Oberheitmann, 2009). Even though this trend hasslowed down in the recent global economic downturn, there hasbeen a sign that China’s economy is on the way of recovery fromthe crisis, faster than had been expected (http://news.bbc.co.uk/1/hi/world/asia-pacific/8106314.stm, accessed 18. 06.09). In orderto ensure the welfare of a 1.3 billion population, the Chinesegovernment aims to quadruple real GDP per capita by 2020against the 2000 figure (Suding, 2005; cited by Li and Oberheit-mann, 2009). Sustainable development is therefore the mostprominent national strategy.

Increasing the 2000 GDP by four times would mean quad-rupling the energy supply unless there is an increase in energyefficiency. According to the International Energy Agency (IEA,2007), China is the second largest energy consumer in the worldwith an annual growth rate in energy consumption of about 10%,an increase which sometimes even surpass the growth in GDP(NBS, 2006; cited by Eichhorst and Bongardt, 2009). It is predictedby IEA (2007) that the consumption for all major kinds of energysuch as coal, electricity, oil and natural gas will continue to rise.The response from the Chinese government to this challenge is tomake energy conservation and energy efficiency the centerof energy policy, in an attempt to solve a number of seriousissues such as perceptions of resource scarcity, high energyprices, security of energy supply and environmental protection(Andrews-Speed, 2009).

The emphasis on energy conservation and energy efficiency wererepeated in the official documents from the Ministry of Energy (1992)and the State Planning Commission (1995) in the 1990s (Andrews-Speed, 2009). The Energy Conservation Law came into force as ofJanuary 1998. A vigorous programme was launched in 2004 aimed atreducing energy intensity by 20% over the period between 2006 and2010. This target was late elaborated and incorporated into the FiveYear Plan for the period 2006–2010 by adding specific objectives and

stage targets (Andrews-Speed, 2009). The Renewable Energy Law tookeffect in January 2006 and is designed to help protect theenvironment, prevent energy shortages and reduce dependence onimported energy. The government aims to increase the renewableenergy consumption rate from only 3% in 2003 to 10% in 2020(National Development and Reform Commission—NDRC, 2006). Aconsultation process on the draft Energy Law closed in 2008 and nowthe draft has been submitted to the National People’s Congress (NPC)for approval and is expected the Energy Law will come into force in2009 (http://finance.sina.com.cn/roll/20090205/01515817350.shtml,accessed 12.06.09). The Energy Law is intended to be the basic lawto guide and co-ordinate other specific energy laws in China’s energysector such as the Renewable Energy Law and Energy ConservationLaw.

It is widely recognized that an effective policy requires itssupportive environment from the whole society includingindustry—the main player of economy. The implement of energypolicy is a mixed measurement of the implements of industrypolicies and other regulations at all levels. As many scholarssuggested energy efficiency should not only be an integral partof most government’s policies but it also requires a changein attitudes and expectations throughout society as a whole(Thollander et al., 2007; Energy Charter Secretariat, 2007;Zografakis et al., 2008; Andrews-Speed, 2009).

The strategy of energy efficiency and energy saving has putheavy pressure on the energy suppliers industry and otherenergy-intensive industries. Effective management of energyproduction and consumption is another important strategy. Inits 2004 report, the Development Research Center of the StateCouncil highlights the priorities of Chinese energy policy. Thosepriorities include ‘‘placing greater emphasis on energy conserva-tion and energy efficiency, especially in industry’’ and ‘‘maintain-ing domestic primary energy resources as the main source ofenergy supply, at the same time striving improving the manage-ment of these resources’’ (Development Research Center,2004; cited by Andrews-Speed, 2009, p. 1338). As coal is thelargest primary and non-renewable energy in China, coal miningindustry is highlighted as in need of urgent the improvement ofproductivity and efficiency.

2.2. Efficiency performance measurement in energy industry and

DEA

There is no generally accepted definition of efficiency in theliterature. For example, Abbott (2006, p. 44) defines efficiency as‘‘resources are being used in an optimal fashion to produceoutputs of a given quantity’’. Tong and Ding (2008, p. 88) indicatethat ‘‘efficiency means getting any given result with the smallestpossible inputs, or getting the maximum possible output fromgiven recourses’’. As there is no single meaningful measure forefficiency performance across all industries, many approachesand performance indicators, which largely depend on differentresearch objectives and the nature of industries, have beenproposed in literature (Ang, 2004, 2006; Zhou and Ang, 2008).

Energy industries such as coal, electricity and gas are marketswith a lack of price and cost competition. This means thatprofitability and rates of return cannot be used to accuratelyevaluate economic performance in energy industry (Abbott,2006). In other words, energy industries, being ‘‘quasi’’-publicsectors, have not been entirely operated on the profit maximiza-tion that normally guides the private sectors (Kulshreshtha andParikh, 2002). In these situations, the levels and changes ofproductivity and efficiency are more appropriate indicators ofperformance for an energy industry (Abbott, 2006).

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The earliest study of efficiency performance measurement canbe traced back to Farrell (1957) who proposes to measure a firm’sefficiency into three components including technical, allocationand scale efficiency. According to Kulshreshtha and Parikh (2002),pure technical efficiency refers to feasible inputs/outputs whereit is technologically impossible to increase any output (or reduceany input) without simultaneously reducing another output(or increasing another input); in other words, technical efficiencyexpresses the relationship between inputs and outputs as that afirm is efficient when it reaches maximum output for the giveninputs or minimum inputs for the given outputs (Hawdon, 2003).Allocation efficiency refers to the ability to combine inputsand outputs in optimal proportions at their popular price forthe DMUs, i.e. allocation efficiency ‘‘reflects a firm’s ability to usethe inputs in optimal proportions’’ (Wang, et al., 2007, p. 610).Kulshreshtha and Parikh (2002) do not provide a definition forscale efficiency; however, it can be simply described as the bestusage of productivity relative to demand.

According to Jamasb et al. (2005), though efficiency perfor-mance indicators can help us to understand complex issues andsystems, they might be constrained. In particular in the useof time-series and cross-countries analysis as many differencessuch as accounting standards, inflation, FX rate conversions andpurchasing power parities exist between those countries. There-fore, the criteria for the selection of efficiency performanceindicators are especially important in a cross-country comparativestudy.

Criteria for selection of efficiency performance indicators arediscussed in a number of researches (examples see Jamasb et al.,2005, 2008; Vaninsky, 2006) and the arguments focus on whetherthe indicators are measurable, comparable and consistent. Jamasbet al. (2005) suggest that a useful indicator, whether it is aphysical, monetary or qualitative, should be clear in definition andeasy to measure. Furthermore, if the indicator is consistent indefinition then it can be measured and compared over time.However, comparability may be problematic in practice, especiallywhen the data is collected from different countries which are indifferent stages of economic development, institutional environ-ment and regulatory system. Furthermore, other factors such asthe reliability of sources, the level of cost and effort also need tobe considered when collecting aggregated data.

DEA has been widely used to study and compare the efficiencyof energy industries in different countries. It is a non-parametricfrontier analysis which was suggested by Charnes et al. (1978)who used it to measure the performance of educational institu-tions but rooted in the idea of Farrell’s in 1957. DEA is one of thetwo most popular techniques of estimating frontiers (the other isstochastic parametric frontier analysis which is based on econo-metric methods). DEA is widely recognized having certainadvantages over parametric techniques. For example, it does notimpose the assumption on the form of the input–output. Thisfeature is especially useful in cases that the relationship is notknown or specified by theory. It also defines a correspondencebetween multiple inputs and multiple outputs and can be used asa multi-factor analysis model without formulating any functionalform on the relationship between variables (Thakur et al., 2006).

DEA, as a useful tool for performance analysis, is used toevaluate the relative efficiencies for DMUs by using some specificlinear programming models. Every evaluated unit is thought as anindependent DMU and then aggressive DMUs form a group beingassessed by the DEA analysis (Tong and Ding, 2008). DEA startswith the purpose of evaluating relative efficiency rather thanchoosing a specific course of action which is the normal way that atraditional decision analysis does (Zhou et al., 2008b). DEAtherefore intends to examine the relationship among multipleinputs and outputs by enveloping the observed data to determine

a so-called ‘‘best practice frontier’’ for production and thenefficiency measures are calculated in relation to the productionpossibility frontier (Kulshreshtha and Parikh, 2002).

Compared to other measures of productivity and efficiency,DEA has a number of advantages. First it does not require anyprior assumptions on the relationships between input and outputdata (therefore it is a non-parametric approach) (Seiford andThrall, 1990; Zhou et al., 2008b). Second, it only requires physicalquantities of inputs and outputs for evaluating technical and scaleefficiency indicators (i.e. only allocation efficiency needs factorprices) and thus the information required for DEA is fewer andless than that in the traditional case (Abbott, 2006). Third, it is amore objective efficiency assessment as the weighting of eachindex is the optimal weighting determined by dimensionless realdata from the DMU (Tong and Ding, 2008). In addition, Tong andDing also point out that the reason for DMU inefficiency can befound by a projection analysis of each DMU and then animprovement can be planned for the future.

Frontier modes such as DEA require the identification of inputsand outputs (Ramos-Real et al., 2009). Zhou et al. (2008b) carriedout a literature survey from 100 journal articles published from1983 to 2006 in the use of DEA techniques and concluded thatsince the wave of deregulation in energy sectors in the late 1980s,DEA has attracted the attention of scholars worldwide and hasbeen a major frontier approach for benchmarking energy sectorsin many countries. Examples of these studies include those suchas Weyman-Jones (1991) who studied the technical efficiency ofthe UK electricity distribution industry; Boyd and Pang (2000)examined the relationship between productivity and efficiency;Hu and Wang (2006) proposed a total-factor energy efficiencyindex in China; Azadeh et al. (2007) developed an integrated DEAapproach to evaluating the efficiency of energy-intensive manu-facturing sectors; Wei et al. (2007) investigated the efficiencychange in Chinese iron and steel sectors by using DEA-basedMalmquist index approach. In using DEA to measure theproductivity and efficiency of the coal mining sector, there areresearches such as Byrnes et al. (1984, 1988) in the US andKulshreshtha and Parikh (2002) in India.

Having reviewed a number of empirical researches onefficiency performance in the energy sector using DEA, aninteresting observation is that there is an unequal amount beenpublish on different energy resources, for example, there areplenty of literature on the electricity industry (see Jamasb andPollitt, 2001; Zhou et al., 2008b) while much less can be found onother industries such as coal, gas and oil. For this reason, asummary including studies on different industries in utility sectoris shown in Table 1.

As can be seen from Table 1, financial indicators, which areunder control of companies, are the main components of inputsand outputs. Moreover, the most widely used inputs are capital-related (e.g. total assets, operating cost) and labour-related (e.g.number of employees) indicators while on the other hand, primeoutputs remain revenue-related (e.g. sales, profit) indicators.

3. Research methodology

A large number of DEA models are available for researchersto study productivity and efficiency issues. For example, the non-parametric Malmquist productivity decomposes productivitychanges into two parts: technological changes and efficiencychanges index and can be used to conduct performance compari-sons of DMUs by investigating DMU productivity changes overtime. However, selecting an appropriate model depends on sectorcharacteristics, research objectives and data availability. As somedata are unavailable to calculate total-factor productivity (TFP) of

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Table 1Summary of inputs and outputs selection.

Studies Data Inputs Outputs Methods

Vaninsky (2006) Electric power generation in US in 1991–2004 Operating expenses Utilization of net capacity DEA

Energy loss

Jamasb et al. (2008) 39 US gas transmission companies in

1996–2004

Total expenses Delivery DEA

Compressor capacity

Network length

Ramos-Real et al.

(2009)

18 Brazilian electricity distribution firms in

1998–2005

Length of electricity grid (km) Sales (GWh) DEA

Number of employees Number of customers

Losses (GWh)

Wang et al. (2007) Hong Kong electricity supply industry in

1978–2003

Capital expenditure Sales of electricity

delivered (M kWh)

Malmquist index

Labor Customer density

(customer/km2)

Kulshreshtha and

Parikh (2002)

Opencast and underground coal mining firms

in India in 1985–1997

Opencast mining: mining machineries Cranes,

dumpers Manshift

Opencast mining:

overburden removal

Malmquist index

Underground mining: mining machineries,

Rope haulage Manshift

Underground mining: coal

Pe0rez-Reyes and

Tovar (2009)

14 Peruvian electricity distribution companies

in 1996–2006

Number of workers Sales (MWh) Malmquist index

Distribution power losses (MWh) Numbers of customers

Network length (km)

Hawdon (2003) International gas industry in 33 countries in

1998 and 1999

Employment Gas consumption DEA

Length of pipelines Numbers of customers

Wolf (2009) 1001 international oil/gas firms in 1987–2006 Oil/gas reserves Revenue Multivariate

regression analysisTotal assets Net profit

Number of employees

Abbott (2006) Australia’s electricity supply industry in

1969–1999

Capital stock Electricity consumed DEA

Energy used (in TJ)

Labor employed

Barros (2008) Portugal hydroelectric energy generating

plants in 2001–2004

Number of workers Production in MWh Malmquist index

Capital

Operational costs Capacity utilization

Investment

Estache et al. (2008) 12 electricity firms from 12 African countries in

1998–2005

Capital Sale Malmquist index

Labor Electricity generation

Customers

Wei and Wang (2005) 10 listed Chinese coal companies in 2003 Total assets per share Earning per share DEA

Net assets per share Operating profit per share

Operating cost per share

Ran and Hui (2006) 16 listed Chinese coal companies in 2005 Total capital Net profit DEA

Number of employees

Operating cost Operating profit

H. Fang et al. / Energy Policy 37 (2009) 5140–5148 5143

the Malmquist productivity index, this study attempts to focus ona comparative assessment of the relative technical efficiencylevels of listed coal mining companies in China and the USobserved during a 5-year periods (2001–2005). This will be doneby applying CCR and BCC models in DEA analysis, the justificationof research methods used is explained below.

3.1. Technical efficiency measurement—the CCR and BCC models

DEA can be carried out with the assumption of constantreturns to scale (CRS) of production technology (so-called the CCRmodel as developed by Charnes et al., 1978), and with theassumption of variable returns to scale (VRS) production techni-que (named the BCC model as developed by Banker et al., 1984).Using the CCR and BCC model, the efficiency score for multipleinputs and outputs can be formulated as:

Efficiency ¼ weighted sum of outputs=weighted sum of inputs:

ð1Þ

There are two orientations in DEA analysis: input orientationor output orientation, depending on the nature of problem(Kashani, 2005). An input orientation is to minimize thecombination of inputs to yield a combination of outputs (in thecase of multiple inputs/outputs) while an output orientation

considers a maximum of combination of outputs from thecombination of inputs.

For a comprehensive understanding of the CCR and BCCmodels, refer to Charnes et al. (1978) and Banker et al. (1984),respectively. The following formulations are defined by Charnesand Cooper (1962) and cited by Kashani (2005):

For n number of DMUs, the model is formulated as a linearprogramming problem with each DMU using varying quantities(and combinations) of inputs xi (i ¼ 1,y, s) to produce varyingquantities (and combinations) of outputs yi (i ¼ 1,y, m).

An input minimization problem in the CCR model can bepresented as

Minimize Z0 ¼ y� e � b � Sþ � e � b � S�

subject to yl� Sþ ¼ y0;

yx0 � xl� S� ¼ 0;

l; Sþ; Z0; ð2Þ

where l is an N�1 vector of constants and y and x are the outputand input vectors, respectively. Ss are the slack variables, b is an(1�N) row of 1s, e is a very small number, and y is a scalar thevalue of which will be the efficiency score of the ith DMU. Theconstant returns to scale model thus identifies the source, andestimates the amount of inefficiencies and yields an objectiveevaluation of overall efficiency (Kashani, 2005).

As the results of the input and output orientations would beonly equivalent in the case of constant returns to scale, the CCR

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Table 2Comparative efficiencies of Chinese and American coal mining companies.

DMU Chinesesamples

Americansamples

TE PTE SE Returns

to scale

1 YITAI 1 1 1 –

2 SHEN HUO 1 1 1 –

3 LANHUA 0.993 1 0.993 irs

4 YANZHOU 0.836 1 0.836 drs

5 JINGYUAN 0.461 1 0.461 irs

6 PINGMEI 0.946 0.967 0.978 drs

7 PANJIANG 0.819 0.932 0.879 irs

8 LUAN 0.870 0.885 0.983 drs

9 HENGYUAN 0.728 0.870 0.837 irs

10 GUOYANG 0.856 0.867 0.987 drs

11 JINNIU 0.808 0.818 0.988 irs

12 XISHAN 0.649 0.731 0.888 drs

13 ZHENGZHOU 0.653 0.729 0.896 irs

14 TAIYUAN 0.680 0.716 0.949 irs

15 SHANGHAI 0.659 0.666 0.989 irs

16 KAILUAN 0.616 0.637 0.967 irs

17 ANTAI 0.555 0.590 0.941 irs

Mean of Chinese companies 0.772 0.848 0.916

18 BTU 0.789 1 0.789 drs

19 ACI 0.903 1 0.903 drs

20 ANR 1 1 1 –

21 ARLP 1 1 1 –

22 CNX 0.913 1 0.913 drs

23 FCL 0.807 0.861 0.938 drs

24 NRP 1 1 1 –

H. Fang et al. / Energy Policy 37 (2009) 5140–51485144

model was extended to BCC model by considering the case ofvariable returns to scale (Banker et al., 1984). The BCC modelincorporates an additional convexity constraint (Resende,2002).

In the BCC model, variable returns to scale are modeled byallowing b �lZ1, thus an input-oriented is defined as

Minimize Z0 ¼ y� e � b � Sþ � e � b � S�

subject to yl� Sþ ¼ y0;

yx0 � xl� S� ¼ 0;

b � lZ1;l; Sþ; S�Z0: ð3Þ

The non-zero slacks and the value of y*r1 indicates thesource and amount of any inefficiency. As a result, the BCCmodel alternatively introduces a scale constraint into themodel. It distinguishes between technical and scale efficien-cies by estimating pure technical efficiency at the given scaleof operation, and identifies whether increasing, decreasing, orconstant returns to scale possibilities are present for furtherevaluation (Kashani, 2005, pp. 917–918). Fare et al. (1994) alsosuggests that efficiency changes can be further decomposed to‘‘pure’’ technical efficiency changes and scale efficiencychanges in order to separately measure the efficiency im-provement either from ‘‘pure’’ changes in input mix or frombetter usage of the production size to the demand (Estache etal., 2008).

25 PVR 1 1 1 –

Mean of American companies 0.927 0.983 0.943 –

Notes: TE ¼ technical efficiencies under CRS (constant returns to scale) assump-

tion; PTE ¼ pure technical efficiencies under VRS (variable returns to scale)

assumption; SE ¼ scale efficiencies; ‘‘drs’’ ¼ diminishing returns to scale (DRS);

‘‘irs’’ ¼ increasing returns to scale (IRS); ‘‘–’’ ¼ constant return to scale.

3.2. Inputs/outputs selection and sampling

Following the general consensus and discussion in Section 2.2,our choice of input variables are operating costs, total assets andnumbers of employees. The justification behind the selected inputindicators is that operating costs are major cost drivers in coalmining firms, which have a direct impact on performanceefficiency, total assets represent the economic scale of a companyand is the most stable physical capital base for its economicefficiency, while staff costs of a firm are significantly related to itscost-effectiveness. The output variables consist of earnings pershare, operating revenue and net profit before tax, which areunder the control of firms and are reliable and consistent withthose in other studies such as the examples in Table 1. Otherfactors considered when selecting inputs and outputs includetheir measurability based on publicly available data and theircomparability between samples in the two countries. The inputand output data of samples are obtained from their annual reportsbetween the years 2001 and 2005.

Some researchers argue that the selection of input and outputindicators is not only decided by data availability but also by thenumber of DMUs (e.g. Dyson et al., 2001; Ramanathan, 2003;Zhou et al., 2008b). They suggest two widely used rules of thumbin empirical application: the number of DMUs should be largerthan the product and should be at least two times larger than thesum of the number of inputs and outputs.

This study aims to compare the relative efficiency performanceof Chinese listed coal mining companies and their counterparts inthe US. We selected the Chinese sample from two databases: GTAResearch Service Center (http://www.gtarsc.com) and Cninf(http://www.cninfo.com.cn). According to the sources, as of March2007, there were 22 listed companies in the Chinese coal industry,18 of which were in the coal mining industry and from these, 17were selected based on inputs/outputs data availability. Theselection of the American sample is in accordance with OSIRIS(global database of the listed companies) (http://osiris.bvdep.com/en/OSIRIS.html). Eight coal mining companies were selected

because of the data completion, of which, BTU, ACI and CNX arerecognized as the world’s top 10 largest coal companies with highefficiency. See Table 2 for the names of sample companies. Boththe CCR model (i.e. under constant returns to scale assumption)and the BCC model (i.e. under variable returns to scaleassumption) are used for DEA analysis.

The popular and widely used free DEA software package—

DEAP, developed by Coelli (1996), is applied to calculate totaltechnical efficiency by using the panel data. We aim to achieve thefollowing objectives: (1) measure the relative technical efficiencyof the sampled coal mining companies in China and the US; (2)identify the causes of inefficiency through a project analysisapproach and (3) discuss the reasons why there are differencesbetween the Chinese and American firms energy efficiencyperformance.

4. Data analysis and discussion

4.1. General efficiency comparison

4.1.1. Total technical efficiency

Table 2 shows performance scores which are based on the CCRand BCC models and computed for a comparison of energyefficiency between listed coal mining companies in China and theUS during 2001–2005. The scores of ‘‘TE’’ represent total technicalefficiency under the constant returns to scale assumption. As canbe seen from this column, there are 4 out of 8 American firms(ANR, ARLP, NRP and PVR) at the best practice frontier (i.e. TE ¼ 1,

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Table 3Projection analysis of inefficient factors.

Company name PE Inefficient DMU

Shortfall rate Redundancy rate

Y1 (%) Y2 (%) Y3 (%) X1 (%) X2 (%) X3 (%)

JINGYUAN 1 – – – – – –

SHENHUO 1 – – – – – –

JINNIU 0.818 592.55 0.00 0.00 18.18 18.18 76.26

TAIYUAN 0.716 1090.63 0.00 101.73 28.38 28.38 74.07

XISHAN 0.731 448.51 0.00 0.00 26.89 26.89 68.13

ZHENGZHOU 0.729 672.65 0.00 11.73 27.09 27.09 84.59

LANHUA 1 – – – – – –

YANZHOU 1 – – – – – –

PANJIANG 0.932 3825.00 0.00 411.75 17.51 6.80 76.75

SHANGHAI 0.666 542.08 0.00 53.48 33.39 33.39 90.49

YITAI 1 – – – – – –

ANTAI 0.590 697.30 0.00 132.43 40.99 40.99 61.02

HENGYUAN 0.870 102.78 0.00 0.00 13.02 13.02 68.24

KAILUAN 0.637 270.24 0.00 35.41 36.31 36.31 83.39

GUOYANG 0.867 1670.20 0.00 44.98 13.28 13.28 90.50

PINGMEI 0.967 1657.34 0.00 0.00 3.34 3.34 94.67

LUAN 0.885 655.62 0.00 9.98 11.51 11.51 89.02

BTU 1 – – – – – –

ACI 1 – – – – – –

ANR 1 – – – – – –

ARLP 1 – – – – – –

CNX 1 – – – – – –

FCL 0.861 0.97 0.00 6.96 13.93 20.07 13.93

NRP 1 – – – – – –

PVR 1 – – – – – –

Notes: Y1, Y2, Y3 stand for three outputs: earnings per share, operating revenue,

net profit before tax; X1, X2, X3 represent three inputs: operating cost, total assets

and number of employees.

H. Fang et al. / Energy Policy 37 (2009) 5140–5148 5145

suggesting 50% of the American sample), 3 out of 8 (ACI, CNXand FCL, representing 37.5%) are close to the frontier (i.e.0.8o ¼ TEo1) and only one firm (BTU) is thought of as inefficient.However by contrast, only 2 out of 17 Chinese firms (YITAI andSHENHUO, meaning 12% of the Chinese sample companies) are atthe best practice frontier, 7 out of 17 (41.2%) are close to thefrontier, and 8 out of 17 (47%) are inefficient companies.Additionally, the mean is 92.7% and 77.2% over the observedperiod for the US and Chinese companies, respectively. The resultscan generate a conclusion that the level of total technicalefficiency in China’s coal mining companies is lower than that oftheir American counterparts.

4.1.2. Pure technical efficiency and scale efficiency

As explained earlier, total technical efficiency can be decom-posed into ‘‘pure’’ technical efficiency and scale efficiency. In Table2, ‘‘PTE’’ scores express ‘‘pure’’ technical efficiency given thevariable returns to scale assumption. As we can see from thiscolumn, it still shows higher scores with US firms and lowerscores for Chinese companies. There are 7 out of 8 American firms(BTU, ACI, ANR, ARLP, CNX, NRP and PVR, standing for 87.5%)obtaining 1 for ‘‘PTE’’ score, while FCL case is close to puretechnical efficiency with a score of 0.861. However only 5 out of 17Chinese companies (YITAI, SHEN HUO, LANHUA, YANZHOU,JINGYUAN, 29.4% of the Chinese sample) are at the frontier ofpure technical efficiency, 6 out of 17 (accounting for 35.3%) areclose to the frontier of pure technical efficiency and the rest areinefficient (see Table 2 for detail). The mean score of puretechnical efficiency for the US sample is 98.3% while the meanscore for the Chinese sample is 84.5%.

The ‘‘SE’’ column, also illustrates that 4 out of 8 Americancompanies (ANR, ARLP, NRP and PVR, accounting for 50%) lie inthe ‘‘frontier’’ while comparatively only 2 out of 17 Chinese firms(YITAI and SHENHUO, accounting for 11.8%) are scoring ‘‘1’’.Furthermore, the mean of ‘‘SE’’ for American and Chinesecompanies is 94.3% and 91.6%, respectively. It is worth mentioningthat 3 US cases (BTU, ACI and CNX) and 3 Chinese firms (LANHUA,YANZHOU and JINGYUAN) have achieved pure technical efficiency(PTE ¼ 1) but not total technical efficiency (i.e. TEo1) becausethey have not fully achieved scale efficiency (i.e. SEo1).

Table 2 concludes that the relative technical energy efficienciesin US listed coal mining companies is closer to the ‘‘frontier’’(i.e. score 1) with the lowest point at 0.789 (BTU case).Comparatively, most Chinese listed coal mining firms are faraway from the relative energy efficiency (i.e. score 1) with thelowest value only 0.461 (JINGYUAN case). In the study, thecompanies from the Chinese sample generally represent relativelyhigh efficiency in the coal mining industry of China because theyare listed companies which are presumed to have better efficiencythan other non-listed firms in the industry. It is then possible toconclude that Chinese coal mining companies have a large need toimprove to meet the level of efficiency performance of theirAmerican counterparts.

4.2. Project analysis of inefficient factors

As mentioned in the literature review, one of the advantages ofthe DEA approach is that it can identify the inefficient factors ofindividual DMU by using project analysis, and then futureimprovements can be planned.

Theoretically, the level of out-of-date technology and ineffi-cient production activity produce a ‘‘redundant proportion ofenergy consumption which needs to be further adjusted’’ (Hu andWang, 2006, p. 3206). In our sample, for those inefficient DMUs inTable 2, appropriate adjustments have been made to make them

‘‘efficient’’ by calculating the slack rate of inputs and outputsusing DEAP software to obtain ideal inputs and outputs.

The formulas used in project analysis are

Redundancy rate ¼ ðActual input� ideal inputÞ=actual input

Shortfall rate ¼ ðIdeal output� actual outputÞ=actual output:

Using 2005 data from DMUs in Table 2, the project analysisresults can be presented in Table 3.

From Table 3, it is observed that among American firms, onlyFCL has not used its input resources effectively. With regards toinefficient factors, it has 13.93% of redundancy rates for operatingcost and staff, respectively, and 20.07% for total assets. The other 7out of 8 (87.5% of the sample) have a full use of input resources toachieve maximum outputs. Comparatively, only 5 out of 17 ofChinese firms (JINGYUAN, SHENHUO, LANHUA, YANZHOU, ac-counting for 29.4% of the Chinese sample) have fully used theirselected resources while the other 12 firms (71.6%) have to adifferent extent wasted those inputs. In detail, there is an averageof 79.76% for staff redundancy rates (the highest is 94.67% forPINGMEI and the lowest is 61.02% for ANTAI), and an average of22.49% for redundancy rates of operating cost ranging from 3.34%(PINGMEI) to 40.99% (ANTAI). There is also an average of 21.59% oftotal assets redundancy rate.

4.3. Discussion

Possible reasons for the observed relative efficiency differencesin China’s listed coal enterprises compared to their US counter-parts can be further analyzed into the following aspects.

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4.3.1. Ownership and scale efficiency

Within the Chinese listed companies, 16 out of 17 (94%) arestate-owned firms with an average of 54.41% state-owned sharesin 2005 ranging from the lowest case (SHEN HUO) of 25% to thehighest case (DATUN) of 68.04%. As a result of a centrally plannedeconomy, state-owned coal mining firms, especially those largeenterprises in our sample, have still borne a number of historicburdens such as redundant personnel and old production facilitiesand conditions which directly cause higher operational cost.Despite raising public funds from the stock exchange marketrecently, which can dilute the share of state-ownership, there areno fundamental changes to the state controlled status. The statedominated ownership naturally rejects potentially effective largeinvestors. In addition, its imperfect corporate governance systemcould lead to the decision making process not according withmarket regulations. Therefore, their advantages of large sizecannot give full play into scale efficiency.

This finding is consistent with many studies that examine therelationship between ownership and performance since thefamous study by Smith (1776). Wolf (2009) illustrated a numberof researches done in the oil industry on this area. For example,based on the 500 largest non-US firms, Boardman and Vining(1989) find that SOEs are significantly underperforming comparedto private companies. Dewenter and Malatesta (2001) separatelyconducted investigations of the ‘Fortune 500’ largest internationalcompanies in 1975, 1985 and 1995 and concluded that privatefirms have significantly higher profitability, lower labor intensity,and lower rates of financial leverage compared to SOEs, regard-less of firm size, location, industry and business-cycle effects.Al-Obaidan and Scully (1991) compared efficiency differencesbetween 44 private and state-owned petroleum firms and foundthat the SOEs as a whole were only 61–65% as technically efficientas private counterparts. Eller et al. (2007) calculated DEA for asample of 80 state-owned and private firms and the resultsindicated that the average technical efficiency scores for SOEs is0.27 while the average score for the five biggest private companiesis 0.73. Victor (2007) explored the relative efficiency of 90 state-owned and private oil companies and found that the biggestprivate oil firms are nearly one-third more efficient in relation toconverting reserves into output.

4.3.2. Development stages and scale efficiency

In theory and also in practice a close relationship existsbetween the development stage and scale efficiency. Despite therebeing a long history of coal exploitation and production in China,the majority of Chinese coal mining companies are still in theinitial or growing stages, by modern energy efficiency criteria,even for the leading firms, e.g. listed companies in our researchsample. SE (Scale efficiency) ¼ 1 means the firm has idealoperating performance from appropriate production scale. Ascan be seen from Table 2, there are 2 Chinese firms (11.8%) and 4US companies (50%) reaching this value. In the column of ‘‘Returnsto scale’’, 10 out of 17 Chinese companies, accounting for 58.8%,fall into the category of ‘‘Increasing returns to scale’’ and another 2belong to ‘‘Constant returns to scale’’ category. Comparativelynone of the American firms are in the category of ‘‘Increasingreturns to scale’’ which might prove that all of the US samples arein the mature stages of the development chain.

4.3.3. Industry concentration and scale efficiency

According to Porter (1998), in a relatively unified market, thetop 4 companies in the same industry should hold more than 40%of market share, which lays out a foundation for an orderlycompetitive environment. Otherwise there will be disorderlyor excessive competition. Scale efficiency has become a serious

constraint in the development of China’s coal mining industry.One of the important factors is that there is quite a lowconcentration of resources in China’s coal mining industry.Statistics indicate that by the end of 2004, the top 8 Chinese coalmining companies only held 19.87% of market share in compar-ison to the 58% market share held by the top 8 companies inAmerica (Zhang and Li, 2007). In the same article, Zhang and Lialso cite that the percentage in Russia and India is even higherwith the top coal firm holding 95% and 77%, respectively.

The low concentration of resources in the industry in Chinaresults in small local coal mines everywhere, many of whichoperate illegally, under scale with poor safety management.However, they exploit the coal resources and leave comparativelyeffective large coal firms in the condition of raw materialsshortage and capacity underutilization. The basic reason behindthis is that allocating resources in China mainly relies onadministrative means rather than on market rule. This will befurther discussed below.

4.3.4. Over-competitiveness and efficiency

It is a historic and political issue in China that the allocation ofnatural and social resources relies mainly on administrativepower rather than on the market. In the coal mining industry,there are three main types of enterprises: large state-owned, localgovernment-owned enterprises and small township mines. Zhangand Xiao (2005) argue that the central and local governments playa game of ‘go’ chess, which makes a low market entry ‘threshold’for the coal mining industry. In addition, imperfect industrypolicies and regulations, and the loose exploitation licenceapproval system leaves many ‘‘holes’’ in which local governmentscan take advantage of to set up local policies that improve localGDP and tax income at the expense of national interest. Andrews-Speed (2009) points out that the performance measurements forlocal governments solely rely on economic growth. This leads tolocal governments trying to increase GDP at the potential expenseof national welfare, resources, energy efficiency and the environ-ment. Local governments use their power to provide key resourcesto local small firms at low cost. Many small township coal mineseven illegally seize precious natural and non-renewable coalresources. At times of rising coal prices, these small mines canmake windfalls and occupy coal markets through unjustifiedmeans, e.g. the free use of coal resources with very high wastingrates. It is reported that the coal recovery rate in these mines isnormally between 15% and 30%, which is far lower than thatof 70% in large companies (Ding, 2000). These large numbers ofsmall coal mines tend to be inefficient coal producers with highlypolluting factories. The large companies become direct victim ofthe internal over-competitiveness that certainly influences theirperformance and efficiency.

4.3.5. High withdrawal threshold and efficiency

There is a strange phenomenon in China’s coal miningindustry. On the one hand, the low threshold for market entryhas caused over-competitiveness within the industry; on theother hand, inefficient enterprises are not easy to withdraw fromthe market and hardly ever go into bankruptcy (partial reasonssee the discussion above). China’s huge population results ineither large state-owned enterprises or local government-ownedfirms which have high staff redundancy rates and labor costs.However, these companies cannot dismiss redundant personneldue to current imperfect social benefit system. The government isconcerned about the stability of society and the negative politicalimage. Moreover, local governments are not willing and areunable to invest in jobseeker allowances and re-employmenttraining. Instead, they encourage inefficient firms to continue

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operating by providing policy and bank loan support, in order toincrease local GDP and tax contributions which are consideredmeasurements of their political performance. By contrast in theUS, the 1990 Atmosphere Purification Law required the reduction ofSO2 by 10 million tons by the year 2000. This requirementresulted in 25 thousand miners being unemployed (accounting for1/6 of mine workforce) and $250 million for re-training paid bythe Federal governments (Wang, 2005). Therefore in China, inthe absence of a fair competitive market environment, effectiveand efficient enterprises do not necessarily succeed, insteadineffective and inefficient firms may take advantage of the poorregulation and exploit resources.

4.3.6. Rules, regulations and efficiency

Due to the short history of economic development in China, thecoal industry policies, rules and regulations are far from thestandard of those in the US. An example can be taken with regardsto the charges of coal resource utilization. Chinese coal miningcompanies need to pay for four items for the utilization of coalresources: (1) mineral resource compensation (so-called ‘‘abso-lute resource rent’’); (2) resource tax; (3) a fee for prospectingmines and (4) a fee for mine exploitation (Pan, 2004). The fees formine prospect and exploitation are charged in terms of mine landsize. In America, coal resource charges include (1) a land-use fee,i.e. land rent; (2) mineral resource compensation, e.g. 12.5% foropencast and 8% for underground coal mining companies basedon their net profit (Liu, 2004). It is obvious that Chinese coalmining enterprises repeatedly pay more for the utilization of coalresources than their counterparts in the US, Pan (2004) argued.

5. Conclusion and policy implication

With coal energy efficiency and the efficiency of the coalenergy industry becoming a growing concern and an importantfactor in securing sustainable development in China, it is mean-ingful to carry out a comparative study on relative efficiency inChinese coal mining enterprises against a western benchmark.The evidence revealed in this research by applying CCR and BCCmodules in the DEA strongly demonstrates that Chinese coalcompanies have a large need for improvement in regards toefficiency performance. However, an institutional environmentalanalysis indicates that the improvement in efficiency of Chinesecoal companies requires a comprehensive approach to beconsidered by policy makers as this gap is caused by historical,political, economic, legislative and cultural factors.

Our conclusions are in line with and are supported by thefindings of Ma and Ortolano (2000), Andrews-Speed (2004) andEconomy (2004), cited in Andrews-Speed (2009). These conclu-sions are that the implementation of an effective solution toimprove energy policy and the efficiency of energy companiesover a period of time requires interaction and commitment fromall parties. This includes support from central and local govern-ments who need to provide a legal framework and transparentadministrative and economic policies. Constraints posed byenergy, natural resources and the environment are the mainfactors which contribute to inefficiency in energy production andconsumption.

Finally, as reviewed in Section 2.1, along with the implementa-tion of recent relevant new energy laws, regulations and policies,there has been evidence that some changes in improvingefficiency performance are occurring in coal mining companiesin China (http://www.reportbus.com/Article/DL/mt/200811/Article_87753.html, accessed 20.06.09). However, to assess whetherthese changes are substantial and sustainable will require moreresearch in the future.

Acknowledgments

Financial support for this study was provided by China’sEducation Ministry (Project no: 07JA00031) and is gratefullyappreciated. The authors would also like to thank anonymousreviewers for their valuable comments which contributed toclarifying and improving the paper.

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