Industrial Coal Demand in China: a Provincial Analysis

Transcription

1 Unversy of Mlan - Bcocca - Statstcs From the SelectedWorks of Matteo Manera 2008 Industral Coal Demand n Chna: a Provncal Analyss Crstna Cattaneo Matteo Manera, Unversy of Mlano-Bcocca Elsa Scarpa Avalable at:

2 Industral Coal Demand n Chna: A Provncal Analyss Crstna Cattaneo, Matteo Manera and Elsa Scarpa NOTA DI LAVORO FEBRUARY 2008 IEM Internatonal Energy Markets Crstna Cattaneo, Fondazone En Enrco Matte, Mlan and Unversy of Sussex Matteo Manera, Department of Statstcs, Unversy of Mlan-Bcocca and Fondazone En Enrco Matte Elsa Scarpa, Edson Tradng Ths paper can be downloaded whout charge at: The Fondazone En Enrco Matte Note d Lavoro Seres Index: Socal Scence Research Network Electronc Paper Collecton: The opnons expressed n ths paper do not necessarly reflect the poson of Fondazone En Enrco Matte Corso Magenta, 63, Mlano (I), web se: e-mal: workng.papers@feem.

3 Industral Coal Demand n Chna: A Provncal Analyss Summary In recent years, concerns regardng the envronmental mplcatons of the rsng coal demand have nduced consderable efforts to generate long-term forecasts of Chna s energy requrements. Nevertheless, none of the prevous emprcal studes on energy demand for Chna has tackled the ssue of modellng coal demand n Chna at provncal level. The am of ths paper s to fll ths gap. In partcular, we model and forecast the Chnese demand for coal usng tme seres data dsaggregated by provnces. Moreover, not only does our analyss account for heterogeney among provnces, but also, gven the nature of the data, captures the presence of spatal autocorrelaton among provnces usng a spatal econometrc model. A fxed effects spatal lag model and a fxed effects spatal error model are estmated to descrbe and forecast ndustral coal demand. Our emprcal results show that the fxed effect spatal lag model better captures the exstng nterdependence between provnces. Ths model forecasts an average annual ncrease n coal demand to 2010 of 4 percent. Keywords: Energy demand, Coal demand, Chna, Spatal econometrcs, Panel data, Forecastng JEL Classfcaton: C23, E6, Q31, Q41 Ths paper has been presented at the 1st Internatonal Workshop on Emprcal Methods n Energy Economcs, organzed by ETH Zurch, Swzerland, August The authors would lke to thank Marzo Galeott, Alessandro Lanza, Massmo Flppn and Anl Markandya for helpful comments and suggestons. Address for correspondence: Matteo Manera Department of Statstcs Unversy of Mlano-Bcocca Va Bcocca degl Arcmbold, 28 Buldng U7 - Offce Mlano Italy Phone: Fax: E-mal: matteo.manera@gmal.com

4 1. Introducton The exceptonal economc development of Chna has been tradonally the central ssue for theoretcal as well as appled economsts, who are nterested n fndng and explanng the cause of such a explosve economc growth. The key ngredents of the Chnese economc performance have been deeply analysed, n order to understand Chna s potental and s future drectons. Only recently however, the nterest shfts toward the analyss of the mplcatons of such spectacular development, n partcular concernng the mpact on the world energy economy. Chna snce the onset of economc reforms has shown sustaned economc growth at an average annual rate of 9.5 percent: ths enabled the country to ncrease by more than 10 tmes both total GDP and per capa GDP durng ths perod (WDI, 2006). Ths surge emerged after the central government set n 1978 the bass for shftng the system toward a socalst modernzaton and toward a revalzed domestc economy, open up to outsde word. Ths expanson however, along wh a massve ndustralzaton, urbanzaton and motorzaton, nduced a substantal ncrease n energy consumpton. In 2004 for example, the rate of growth of energy consumpton reached 18.3 percent (APERC, 2007) and the actual levels make Chna the largest energy user n Asa and the second largest n the world, after only USA. Chna s energy structure has been tradonally domnated by coal, gven the abundance of coal mnes located n the country. For example n the half of 20 th century coal was nearly the sole fuel for energy producton n Chna, whle currently other fuels enter the energy mx. Nevertheless, the share of coal remans extremely hgh, largely domnatng the energy scenaro. A bg share n demand s absorbed by the heavy ndustry, whch accounts for more than 80 percent of total fnal energy consumpton. Chna n fact s characterzed by an exceptonally hgh share of valued added n ndustry and a low share n servces. A man challenge for Chna therefore, remans the rebalance of the economy, through a relatve shft from ndustry-led growth toward more servces-led growth as well as a more labour ntensve urban growth. Ths change wll allow not only to balance the path of development, algnng Chna to the economc structure of the ndustralzed countres, but also to save energy and resources and to protect the envronment. In fact the contnued ndustry led expanson requres more energy and puts heavy burden on the envronment, nducng a non-sustanable growth over the longer horzon. Although the effort s placed to forge a sustanable energy path, whch eventually substutes more effcent fuels for coal, ths fuel wll reman the greatest energy nput. In recent years, concerns regardng the envronmental mplcatons of the rsng energy demand, nduces consderable efforts to generate long term forecastng of Chna s coal requrements. The 2

5 economc analyss, whch are all conducted at the country level, are almost unanmous n projectng ncreasng trend n coal demand. To our knowledge, however, no pror study models the coal demand n Chna at provncal level and produces future forecastng. The avalably of a data set at provncal level allows us to fll n the current gap n research. The am of ths paper s to analyse the Chnese ndustral coal demand usng a panel dsaggregated by provnces: ths allows us to account for the heterogeney between provnces. Moreover, gven the nature of the data, the spatal autocorrelaton between provnces wll be captured, usng a spatal econometrc model. The remander of the paper s organzed as follows. Secton 2 presents a bref revew of the lerature on energy consumpton n Chna. Secton 3 outlnes the methodology adopted. Secton 4 descrbes the data set used and analyses the overall energy background n Chna. Secton 5 dentfes potental sources of spatal nteracton among provnces. Secton 6 presents the econometrc results whle Secton 7 dsplays forecastng. Fnally, Secton 8 contans summary and conclusons. 2. Lerature revew The emprcal lerature whch attempts to modellng the energy demand n Chna has shown two dstnct goals. One s the estmaton of long run ncome elastcy of energy demand, generally based on tme seres technques such as contegraton and vector error-correcton, as well as the provson of energy demand forecasts. A second one s the detaled analyss of the factors responsble for the energy ntensy declne experenced by Chna durng the ongong extraordnary economc development. Ths latter goal s acheved through econometrc model whch decomposes the contrbutons of varables such as sectoral shft or subsector productvy change to total energy use. Among the frst group can be ced Mash and Mash (1996), who employ the dynamc OLS procedure developed by Stock and Watson usng annual data from 1953 to The advantage of ths methodology s that allows to account for both the endogenous nature of the regressors, as well as for the non-statonary of the seres used n modellng demand functons n a tme seres context. Controllng for the smultaney bas between coal demand and s determnants, the authors report absolute long run elastces, for both prce and ncome, close to uny for Chna. Demand for coal modellng and forecast s provded as well by Chan and Lee (1997), who use data from 1953 to Applyng the Engle-Granger s error correcton model, whch nclude prce, ncome as well as a structural shft as regressors, the authors report long-run elastces slghtly lower than unary, suggestng a less than proportonate reacton of coal demand to ncome and prce changes. They fnally predct a rse for coal consumpton from around 1.2 bllon tons n 1994 to a level of

6 bllon tons n 2000 as a consequence of a ten per cent average ncrease n natonal ncome, together wh a four per cent annual ncrease n real coal prce and a one per cent drop n share of heavy ndustry s output n natonal ncome. A less than unary GDP elastcy for electrcy demand s reported n Ln (2003) through a vector error correcton model for the perod It should be noted that, extendng the sample perod to the pror PRC s reforms phase, the elastcy becomes larger but stll lower than one. Moreover, accordng to the author, the electrcy demand rates are projected to grow by 5.8 percent on average between 2002 and Ths predcton ndcates a remarkable declne n the consumpton growth rate f compared wh s hstorcal trend, whch averaged 9 percent n the perod Crompton and Wu (2005) propose an alternatve methodology to forecast energy consumpton n Chna, applyng a Bayesan vector autoregresson (BVAR). Ths methodology avods the problem of overparameterzaton, typcal of VAR models wh large number of lags. Wh a sample perod rangng from 1956 to 2003, they forecast over the perod an average 3.8 per cent rse n total energy consumpton and a 3.3 per cent ncrease n coal demand. Adams and Shachmurove (2007) apply an energy balance framework to model energy functons n Chna. Ths methodology s tradonally used as an accountng system to nvestgate energy supply and utlzaton, but the authors employ to provde energy demand prospects for 2010 and Econometrc estmaton of the lnkage parameters suggests an ncome elastcy for fuel consumpton varyng between 0.5 and 0.6. The sze of the parameter, whch s sensbly lower than one, reflects eventually some ongong mprovements n the use of energy or alternatvely a shft toward products whch are less energy-ntensve. Moreover, the authors conclude that there must be a substuton of more effcent fuels for coal gven that they predct a decrease n coal demand of sx percent, despe the ncrease n fnal energy consumpton of 2.6 percent. In a cross secton context, Tang and La Crox (1993) analyse the mpact of economc actvy on total energy consumpton n Chna, usng pooled cross secton data at provnce level for the perod They report ncome elastcy for total energy around one, whch s lower than the coeffcent estmated wh a smlar methodology for developng countres. In fact, Zlberfarb and Adams (1981) and Rester (1987), applyng pooled cross secton data for developng countres, fnd that the ncome elastcy s sgnfcantly greater than one and s stable over tme. Tang and La Crox suggest that a unary elastcy mples a constant energy ntensy for Chnese provnces along the process of economc development. A dfferent belefs, regardng the trend n energy ntensy n Chna, s shared by a strand of emprcal lerature, whch notes a declne n energy consumpton per un of GDP, despe the ncrease n total energy consumpton. The objectve of these papers, whch nclude among others 4

7 Kambara (1992), Garbacco et al. (1999), Chu at al. (2006), s to nvestgate the explanaton for such declne n energy ntensy. The authors conclude that techncal and structural changes are responsble for the mproved effcency n Chna. Moreover, applyng dsaggregated frm-level data, Fsher-Vanden et al. (2004) state that nearly half of the declne n energy ntensy s due to gans n effcency at frm level, whch may nvolve the development of new materals. 3. Methodologcal approach The man feature of spatal models s the ntroducton of some forms of nteractons whn agents, who are nfluenced n ther choces by other agents rather than behavng n solaton. In partcular, gven the geographcal component of these models, the concepts of locaton and spatal externales are wdely used. The exstence of such nteractons generate the so called spatal dependence : n general, ths term refers to the presence of a functonal relatonshp between what happens at one pont n space and what happens elsewhere (Anseln, 1988). In the modern economc theory, a spatal approach can be found n the new economc geography, as emboded, among others, n the works of Krugman (1991, 1996). These models try to explan the concentraton of producton by means of ncreasng returns, path dependence and mperfect competon, whch determne spatal externales and spllovers. Gven the exstence of such spatal nteractons, an emprcal analyss of these models requres a spatal econometrc approach. A classcal economc framework, where total costs are mnmzed subject to a producton functon, generates the followng ndustral coal demand: C = R( Z ) (1) where C s the coal demand n provnce at tme t and Z contans all standard factors that nfluence demand, such as nput prces and output. However, may the case that the demand functon depends also on a strategc-nteracton term, represented by the coal demand n other provnces. If hstorcally, the coal-fred power statons were bult close to mnes to reduce the transportaton costs, subsequent frm may have located n the same area. Ths choce could be made to benef from pecunary externales, generated by the exstence of coal-specfc nfrastructure, such as nstalled grds for power transfer. Ths mples that the coal-based energy sector concentrates where other energy producng frms are located, whch reflects the areas wh good 5

8 access to the fuel nput, rather than the area most ndustralzed. If ths s the case, standard demand determnants cannot fully explan the ndustral coal demand, but spatal nteracton term should enter the analyss. The dffcult task n spatal model s to correctly ncorporate the form of spatal dependence: there are two man sources of complexy. Frst of all, contrary to the tme seres analyss, where the tme structure unvocally determnes the correlaton between observatons, n spatal analyss the defnon of the dependence s farly arbrary. For example, the geographcal space may not be consdered n s narrow physcal dmenson but can be extended to nclude concepts of socal space and socal capal; moreover, the dstance metrcs employed does not lm to the standard Eucldean, but socal dstance or economc dstance can be reasonably ntroduced. Therefore, the estmaton of spatal functons requres the dentfcaton of whch locatons are neghbours applyng a spatal weghtng matrx: however, gven the arbrarness explaned above, the wegh matrx can be modelled accordng to multple crera. The second complexy depends on the way the spatal dependence s specfed n the regresson model. In fact the spatal correlaton can take the form of spatal lag or spatal error (Anseln, 1988): n the frst case, the ncluson of a spatal dependent varable s requred. Applyng panel data, the model becomes: C = ρwc + Z β + μ + ε (2) ' where ρ s the spatal autoregressve parameter, and W s the spatal weght matrx, whch defnes the structure of the dependence among the local uns of observatons, such as the provnces. In the second case, a spatal process for the dsturbance terms should be specfed: C ε = Z β + μ + ε (3) ' = λwε + v where λ s the spatal autoregressve coeffcent. A b of manpulaton n equaton (2) leads to: C -1 ' -1 = ( I - ρ W ) Z β + ( I - ρw ) ( μ + ε ) (4) The matrx nverse ( I ρw ) 1 s a full matrx whch nduces error terms n all locatons (Anseln and Bera, 1998). It follows that ths smultaney should be accounted for applyng a maxmum 6

9 lkelhood estmaton or an nstrumental varable framework. Gven the presence of smultaney, n fact, the OLS estmaton would be based and nconsstent. In the case of the spatal error, the arrangement of equaton (3) produces: C -1 = Z β + μ + ( I - λw ) v (5) ' Ths regresson fals the standard condon of sphercal error terms, as the error covarance matrx contans non-constant dagonal elements. Therefore, the exstence of spatal dependence n the dsturbances nduces hetheroskedastcy n ε rrespectve of the covarance structure of ν. The nusance related to the spatal error dependence ntroduces a problem of neffcency n the estmated parameters 1 and agan maxmum lkelhood estmaton s called for. 4. Data descrpton The dataset employed for ths analyss s taken from the Chna Energy Databook (2004), publshed by the Chna Energy Group of the Lawrence Berkeley Natonal Laboratory. The dataset collects mportant statstcal nformaton, drawn from natonal as well as provncal energy balances. Among other, the databook reports nformaton on energy producton, energy consumpton, dsaggregated for energy types, sectors and uses. Moreover, offers macroeconomc ndcators, such as Gross Domestc Product and Gross Output Value, and populaton ndcators, such as populaton countng. Of partcular relevance for ths analyss s the total ndustral coal demand, whch embodes the ndustral energy consumpton, the ndustral non energy use as well as the consumpton lost durng the process of energy converson. For Chna, the demand for coal n the ndustral sector absorbs a consderable share of the aggregate volume and ths share has slghtly ncreased over tme, as reported n the Chna Energy Databook (2004). In fact, the ndustral sector s responsble for 85 per cent of total demand n 1995 and more than 90 per cent n Therefore ndustry s the prmary drver for coal demand. A second mportant feature s the relevance of coal as a source of energy. Ths fuel n fact s extremely abundant n the country and ths makes Chna the larger producer and consumer of coal n the world. The Chna Energy Databook (2004) reports the composon of the prmary energy demand, by fuel source: coal represents the key component of energy demand, accountng for over 1 Anslen and Bera (1998) nterpret the spatal error dependence as a nusance nduced by the spatal autocorrelaton n measurement errors or n varables that are not crucal to the model, such as gnored spllovers across the uns of observaton. 7

10 60% of the prmary energy consumpton mx, followed by ol, hydro-power and natural gas. The mportance of coal slghtly decreases durng tme but ths fuel stll remans the undsgused drver of prmary energy. Regardng the composon of output value, Chna shows great unbalances, as far as s characterzed by an exceptonally hgh share of value added n ndustry and an exceptonally low share n servces. Ths feature holds true not only comparng Chna to Uned States and Latn Amerca, but also to the levels predcted by s basc characterstcs. For example, ndustry s sometmes fve tmes as large as the second bgger sector, whch s agrculture (Chna Energy Databook, 2004). Ths prelmnary overlook of the Chnese economc and energy suaton makes use of aggregated ndcators provded by the data set. It should be noted, however, that the same nformaton s as well avalable at provncal level: ths feature allows us to study the Chnese energy demand through usng a panel dmenson, n order to account for the heterogeney between provnces. Concernng the admnstratve structure, Chna s composed by 22 provnces, fve autonomous regons, four muncpales, and two specal admnstratve regons. Unfortunately, for some of the terrores, the data set offered ncomplete nformaton: to form a balanced structure, therefore, only 27 admnstratve uns have been selected. Moreover, the provncal energy balances are accessble from 1995 to 2002 on yearly frequency. Ths makes a panel of 216 observatons. 5. Source of provncal nteractons The structure of the generatng capacy n Chna vares enormously from regon to regon and s nfluenced by the locaton of the major sources of energy: hydropower plans, for example, are manly located n the central and south Chna, whereas the thermal-power plans can be found n the north and north-east regons where coal s abundant (IEA, 2006). FIGURE 1 HERE Chna owns substantal reserves of conventonal energy resources, but coal largely domnates the other energy nputs: n fact, Chna possess extensve coal reserves, whch make the country the world's largest coal producer. Moreover, the coal reserves are hghly geographcally concentred as the two-thrds of the reserves are located n the northern provnces. The black crcles n Fgure 1 ndcate the se of the top major coal mnes, whch supply more than 10 mllon tonnes per annum: the map shows that these mnes are concentrated n eght provnces, all placed n the north-east and 8

11 north regons. Table 1 reports the exact producton of the mnes n 1996: among others, Daton n Shanx produces more than 30 mllon tonnes alone, whle Kaluan n Hebe and Pngdngshan n Henan supply more than 17 mllon tonnes. TABLE 1 HERE Regardng the power generaton, should be noted that the ten major energy producng provnces, whch use coal as an energy source are those at close dstance from the major coal mnes zones, descrbed above. As publshed n the Chna Energy Databook (2004), these are: Shanx, Shandong, Inner Mongola, Henan, Hebe, Anhu, Helongjang, Shaanx, Schuan and Laonng. Ths ndcates that the power generaton ndustry s concentrated n few nland provnces: coal-fred power statons are bult at close dstance from the mnes, despe the largest centres for energy demand are located n the ndustralzed coastal areas. Ths correspondence s eventually due to transportaton constrants, as the Chnese nland coal transport nfrastructure, through whch more than one half of total coal supply s moved, are neffcent and hghly congested. On the contrary, there has been a great effort n ncreasng the energy transfer capacy from the country s resource rch areas to the hgh energy demandng regons: n fact, about 40 percent of total nvestment n the power sector are represented by transmsson nvestment. Fnally, Table 2 reports the major ten provnces demandng ndustral coal and agan they reflect the poson of the coal mnes. Ths analyss suggests that the locaton of the energy source eventually plays an mportant role n explanng the ndustral coal demand and therefore, nterdependence between close by provnces can arse 2. In partcular, ths may justfy a form of nterdependence, where the coal demand of provnce s a functon of coal demand n provnce j. TABLE 2 HERE It s mportant to note that, although the demand for ndustral coal ncludes both energy and nonenergy use of coal, the latter holds only a margnal role. In fact, the fracton of coal employed for the producton of energy over total ndustral use s stable around 99 percent durng the sample perod. Ths allows us to analyse the ndustral coal demand followng the descrbed spatal structure, whch s largely based on the lnk between coal mnes, power generaton and demand for 2 Despe the extensve coal reserves avalable, Chna s ncreasng the volume of mported coal, n partcular to meet the energy demand n the coastal provnces. The exstence of a large fracton of coal mports n the Coastal area could weaken the hypothess of pecunary externales avalable n the coal reserve zone. However, the provncal energy balance shows the n the perod the relevance of mports was mnmal, and some of the provnces demandng mported coal were not located n the coastal regon. 9

12 coal. As a frst nstance, to nvestgate the spatal autocorrelaton n the ndustral coal varable, a smple pcture showng the quntle dstrbuton of the ndustral coal demand can be reported. As s shown n Fgure 2, a cluster of hgh values can be dentfed n the North-East regons, n proxmy to the coal mnes zone, whereas a cluster of low demand s located n the Western area. 3 In fact, the fve provnces demandng on average more than 70 mllon tonnes of coal are dsplayed n dark black and are concentrated n a bounded zone. Conversely, the fve provnces demandng less than 20 mllon tonnes look slghtly more dspersed, though three of them are neghbours. These provnces can be dentfed by the whe colour. FIGURE 2 HERE The presence of spatal correlaton between the uns of observatons can be further detected through a test whch captures the extent to whch values smlary matches wh locatons smlary. In partcular, the coal demand can show posve spatal correlaton f lkewse values tend to cluster n space, or can dsplay negatve correlaton whenever the locatons are surrounded by neghbour wh dssmlar values. Fnally, a zero spatal correlaton mples that s not possble to dentfy a specfc spatal pattern of coal values. There are a number of wdely used statstcs whch detect autocorrelaton whn the sample as a whole: for ths reason they are defned global measures. These statstcs represent a sort of covarance n connectng locatons relatve to the varance across locatons. Among the global measures are the Moran s I statstcs: I = N S 0 j w j z z j (6) / z 2 and the Geary s c statstcs: 2 2 c = ( N 1) w ( x x ) / 2( S ) z (7) j j j 0 wh z x μ, and where w are the sngle elements of the weght matrx W. As = S 0 = j w j suggested, the choce of the weghtng matrx can be determned accordng to alternatve crera. In 3 Gven the panel dmenson of the observatons, the varable employed s a 8 years average of the coal demand. 10

13 ths analyss three dfferent rules are appled. In the frst matrx (W1) the neghbourhood set s defned accordng to common borders: { } W ω j =, where ω = 1 f provnce and j have common border and zero otherwse. It should be noted that to faclate nterpretaton, the weght matrx s often standardzed, lettng the row elements sum one. Therefore j w j = J ω j=1 j ω j. The second matrx 2 (W2) s based on a dstance decay functon, where ω j = 1/ Dstj, where Dst j equals the Eucldean dstance between provnce and j, f Dst j s less than 600 Km and 0 otherwse. In the thrd case (W3), the standardzed matrx s computed settng ωj = 1f provnces and j share a common major coal mne and 0 otherwse. We defne a major coal mne, a mne that produces more than 10 mllon tonnes of coal and we dentfy the common neghbourhood as the crcle space confned whn an area of 300 km from the capal of the provnces. A spatal clusterng of hgh/hgh or low/low values results from I>-1/(n-1) and c<1, whereas a negatve spatal autocorrelaton mples that I<-1/(n-1) or c>1. TABLE 3 HERE An alternatve way to measure the value smlary between one locaton and s neghbourhood set s offered by the local statstcs. These local ndcators offer assocaton coeffcents for each partcular locaton: therefore, they produce an ndex of autocorrelaton for each sngle locaton. A wdely used local measure s the local Moran, whch dffers from the global counterpart n that s based on a sngle summaton rather than a double sum. In fact dsaggregates Moran's I nto contrbutons for each locaton: I = z w z (8) j j j Table 3 and 4 report the value of the global measures applyng the full set of matrxes along wh the test statstcs and s sgnfcance value 4 : there s no clear evdence of partcular global spatal autocorrelaton. In fact, the Moran s I and the Geary statstcs cannot depct an unform pcture regardng the structure of the global autocorrelaton. In fact, the Geary s c statstc detects a statstcally sgnfcant correlaton n one case only, whereas the Moran s I rejects the null 4 The test assumes that the dstrbuton of the statstc s approxmately normal 11

14 hypothess of no spatal correlaton n all cases. The sgn of the statstcs ndcates the tendency toward a posve autocorrelaton, but overall ths result s que fral. TABLE 4 HERE Turnng to a local analyss, Table 5 reports the local Moran statstc, computed applyng the same set of weght matrxes. Four provnces - Hebe, Shanx, Shandong and Henan - reveal posve and sgnfcant spatal correlaton and the result holds rrespectve of the matrx appled. The nterestng feature s that the locatons whch match the hgh values of ther neghbours, are located n the north, whch covers part of the coal mne area. A posve statstc, n fact, ndcates that the four provnces not only own hgh coal demand but they are surrounded by locatons wh hgh coal demand. Ths fndng suggests that the spatal autocorrelaton exsts, but s not a global phenomenon. In fact, the presence of spatal correlaton essentally turns up n solated clusters. TABLE 5 HERE 6. Emprcal results and tests To account for possble spatal autocorrelaton, equatons (2) and (3) are estmated applyng Maxmum Lkelhood Estmaton (MLE). The dependent varable s the provncal level ndustral coal demand (C), whch ncludes ndustral energy demand, ndustral non energy demand and the consumpton lost durng the process of energy converson. Accordng to the standard economc theory, the determnants of nput factors are the level of producton, proxed by real gross value added (RGVA) and nput prces. Unfortunately, the latter varable s not avalable for Chna at provncal level, and notwhstandng we are aware of the bg lmaton, the econometrc analyss has been conducted whout ths mportant control. It should be noted, however, that the prce system n Chna has been hghly regulated, and despe the ongong deregulaton, the strength of the mpact of prces should be lmed and less sgnfcant as n a market economy (Chu et al. 2006). For example, untl 2002 the prce of coal for energy generaton, was set below market values as was establshed by the central government authores at the Annual Coal Procurement Conference. Moreover, n the power sector, the chance to swch to alternatve fuels s lmed gven that the coal-fred energy generaton s extremely large, accountng for nearly 80 percent of total energy producton. Ths would lm the effect of the prce of substute n the coal ndustry demand. A prelmnary analyss of the aggregate coal seres reveals that the demand has not always followed 12

15 a regular trend, and n partcular sgnfcant breaks occurred n dfferent ponts n tme. In fact, whle the ndustral coal demand grew steadly between 1990 and 1996, declned n the perod and after ths pont dsplayed a new rsng trend. Ths suggests that the demand dd not grow predctably wh ndustral producton. In order to fully capture the evoluton of the coal demand, an addve tme trend s ntroduced as well as an nteracton between the trend and the gross value added varable. We beleve that ths specfcaton can profcently capture potental structural changes occurrng as a consequence of the rapd economc transformaton. To account for the heterogeney across provnces, fxed effects provncal dummes are added. Fnally the varable populaton (POP) s ncluded to control for provncal sze dfferences. To estmate the fxed effects spatal lag model and the fxed effects spatal error model 5, the three alternatve spatal weght matrxes, descrbed n Secton 5 are used. It should be noted that the structure of the spatal dependence s mantaned nvarant wh respect to tme. Therefore, for estmaton purposes, the weght matrx becomes: Ψ = I T W (9) where IT s the T by T denty matrx, W s one of the N by N standardzed weght matrx and s the Kronecker product. Ths s because the way dependence s modeled here depends on geographcal locaton, whch does not vary over tme. In matrx W3, on the contrary the structure depends not only on locaton but also on coal mnes dmenson. However, gven the abundance of coal n Chna, s plausble that the supply of fuel by the major coal mnes dd not experence sgnfcant drops over tme. Table 6 reports the estmated parameters. The estmates have the expected sgns: the provnce sze, expressed by the populaton varable, exerts a posve and hghly sgnfcant mpact on fuel demand. The coeffcent of the varable RGVA alone s not statstcally sgnfcant, whereas the nteracton between the trend and varable RGVA exerts a posve and sgnfcant mpact on coal. 6 Ths ndcates that the margnal effect of output s not constant along the tme. It should be noted that n the spatal lag model, the margnal effect of RGVA s gven by the product of two terms, whch are the estmated coeffcent β and the spatal term ( I - ρw ) -1, as ndcated by the reduced 5 Anseln (2003), however, notes that the fxed effects model, whch ncorporates spatal dependence, s affected by ncdental parameter problem. 6 The possble endogeney of the output varable s tested n a non-spatal context, usng the Wu-Hausman test. A one year lag of the RGVA varable s used as an nstrument: ths varable has the advantage of beng hghly correlated wh the potental endogenous varable and orthogonal to the error process n the structural equaton. The test cannot reject the hypothess of exogeney. 13

16 form n equaton (4). 7 The computed elastces of RGVA on coal have a posve value only n year 2002 and are reported n Table 7. TABLE 6 HERE The tme trend has not an ndependent effect on coal demand. Ths may suggest that n the perod consdered no relevant technologcal mprovement occurred n Chna. The autocorrelaton parameters are posve and always hghly sgnfcant. Wh respect to the lag model, ths ndcates that the provncal ndustral coal demand s nfluenced not only by standard economc factors, such as producton and provnce sze, but also by the exstence of synerges among provnces n the energy producton. Gven that transportaton costs are lkely to represent a sgnfcant component of fnal prce, n partcular under a regulated prce system, may be the case that the spatally lagged dependent varable captures part of ths prce effect. In fact, provnces located at close proxmy wh coal mnes, clearly bear lower transportaton costs, and ths nduces, ceters parbus, a hgher demand for coal. Therefore, can also be the case that the ncluson of the spatally lagged varable partly compensates for the absence of the nput prces. Fnally, the F-test on the fxed effect rejects the hypothess that the provncal dummy coeffcents are jontly zero. Dfferent tests have been developed to detect a model msspecfcaton due to the exstence of spatal dependence, n the form of spatally lagged dependent varable or spatal error autocorrelaton. The Moran s I test conducted on the regresson resduals s labeled undrectonal test, n the sense that under the alternatve hypothess, any form of spatal autocorrelaton among the resduals s mpled, as the test does not pont a specfc alternatve. Tables 8 presents the results of the dagnostc test: a clear rejecton of a standard model, whch does not ncorporate spatal dependence s ndcated. In fact, regardless the weght matrx used, the Moran s I test suggests the exstence of spatal dependence, notwhstandng cannot favor a specfc spatal process model. To speculate further the form of spatal correlaton, Lagrange Multpler (LM) test can be performed: n s plane form, s a test aganst a sngle form of spatal correlaton, eher spatal lag or spatal error, as assumes that the alternatve spatal dependence s not present. For example, LM-lag s a test on ρ = 0 assumng that λ = 0. The lmaton of ths test s that s not robust to 7-1 Gven that the spatal weght matrxes are row-standardzed, the product -, where s a vector of ones, gves a constant vector. However n both matrx W2 and W3, the rows relatve to some provnces contan only zero terms. Applyng these matrxes, the above product returns a vector whch contans two dstnct values. 14 ( I ρw ) * * β rgva

17 msspecfcaton of the assumptons: n the prevous example, for nstance, the test s not vald f λ 0. Accordng to the Table reported, the LM tests reject the hypothess of no spatal correlatons n the resduals. However, gven that both LM-lag and LM-error reject the null hypothess, a msspecfcaton of the assumpton s lkely to arse, nvaldatng the concluson of the tests. TABLE 7 HERE To overcome the prevous lmaton and followng the classcal specfcaton search approach (Florax et al., 2003), when both LM-lag and LM-err are sgnfcant, robust LM test should be employed. In fact, ths robust test has the advantage of beng robust to local msspecfcaton. For example, the LM-lag n s robust verson, tests that ρ = 0 n the presence of local msspecfcaton n terms of spatal dependent error process. The nterestng feature s that only through usng the coal mne weght matrx, namely matrx W3, provncal autocorrelaton s fully captured. In fact, whle usng matrx W2 both robust tests are nconclusve, applyng matrx W3 the tests clearly favour the spatal lag model. Ths fndng on the one hand provdes support to the use of the W3 weght matrx, whch takes nto account the spatal structure of the coal mne zone, whle on the other explcly dscrmnates aganst the spatal error specfcaton. Fnally the log-lkelhood of the estmatons ponts n a smlar drecton, as far as the spatal lag model, whch uses the spatal weght matrx W3, shows the best f. TABLE 8 HERE 7. Forecastng A further objectve of ths paper s to provde an accurate forecastng for the ndustral coal demand n Chna at provncal level. We beleve that both the use of a dsaggregated background, whch fully captures the heterogeney between provnces, as well as the ncluson of a spatal structure, wll mprove the predctons. Baltag and L (2004) derve the best lnear unbased predctor (BLUP) for the h locaton at a future perod T+S n the context of spatal autocorrelaton. 8 The BLUP for the fxed effects spatal error model s computed as: 8 The authors provde dervaton only n the context of the autoregressve error dependence model 15

18 Ĉ βˆ + ',T S = Z,T S μˆ (10) + + T ' where ˆ μ ˆ = C. Z. β, C. = C / T and Z. = Z / T. t = 1 T t=1 Ths looks exactly lke the BLUP for fxed effects whout spatal correlaton except that the parameters are estmated by MLE, assumng λ 0. The BLUP for the fxed effects spatal lag model s calculated as: Ĉ βˆ + ',T S = ρˆ WĈ,T S + Z,T S μˆ (11) where μˆ s defned as before and the parameters are estmated by MLE, assumng ρ 0. 9 To perform out-of-sample forecast tll 2010, the exogenous drvers need to be computed at provncal level for the eght years ahead: the drvers are the ndustral value added and populaton. Dfferent alternatve methodologes are appled to provde the better match between the actual computaton and the nformaton avalable from publshed economc outlook (IMF, 2006; ADB, 2007; UN, 2006). In partcular, the provncal level forecasts of the covarates are frst aggregated and then compared wh the avalable country level nformaton. An autoregressve and a tme trend approach as well as four exponental smoothng methods are computed: among these are sngle smoothng, double smoothng, Holt-Wnters wh addve varaton and Holt-Wnters whout seasonal component. Fnally, for the ndustral value added a fxed growth rate s appled. In the perod between 2002 and 2007 the WDI (2006) and IFS (2007) report an average annual ncome growth of 10 percent. At the same tme, the economc outlooks predct that a prodgous ncome growth wll contnue n the country: n fact, growth n Chna s pushed by ncreases n total factor productvy and capal accumulaton, and these factors guarantee a sustanable growth over the long term. Moreover, the hgh productvy growth rates are mantaned by further mprovements n polcs and n the nstutonal framework. It should be noted that Chna adopts the so called Fve-Year-Plan, whch provdes the gude rules for s economc development and guarantees a form of control upon the economc suaton. The man target for the ongong 11 th Fve-Year-Plan s a sustaned growth rate of the natonal economy. For overall Chna, an economc growth around nne per cent s predcted between 2007 and Among the sx methodologes descrbed, the forecast based on autoregressve seres s the one that 9 In the Appendx a detaled dervaton of the formula for the FE model wh spatally lagged dependent varable s provded. 16

19 performs better, as far as, aggregatng the data from provncal to country level, produces a measure of the ndustral output n lne wh the expected growth ndcator. A second check conducted on the computed predcton pertans the sze of the provncal shares to total country output. The contrbutons of the sngle provnces remaned almost constant between 1995 and 2002 and no varaton n ths structure s expected to arse for the future perods. Moreover Azz and Duenwald (2001), for example, report large performance dscrepances among provnces and uneven regonal development. However, they also state that, despe the consderaton of the growth lerature, the convergence n ncome whn Chna s not lkely to happen n the comng future. Therefore the preferred methodologes are those whch mantan a stable provncal share over the consdered horzon: these are the autoregressve seres, the Holt-Wnters whout seasonal varaton, and the double smoothng. Smlar consderatons are adopted to dentfy the preferred populaton seres to apply n the forecastng. UN (2006) provdes the baselne ndcator for the populaton trend at country level: ths data set predcts a steady populaton growth at around one per cent for Chna. Moreover, the 11 th Fve-Year-Plan clearly states that the rate of ncrease n Chna s populaton should be mantaned whn one percent. Both the tme trend approach and the double smoothng are n lne wh ths predcton. The selected seres for populaton and gross value added are used to formulate forecast of the ndustral coal demand. The estmated parameters from the prevous econometrc analyss are appled, assumng no structural change n the overall economc and technologcal context. We are aware ths s a strong assumpton, n partcular as far as Chna s pursung a target for ensurng an effcent, relable but also envronmentally tolerant power ndustry (IEA, 2006), whch wll requre a set of reforms n the energy sector. It s also true, nevertheless, that the technologcal paradgm s lkely to be mantaned durng the forecast perod, as the tme length consdered for predcton s farly short. The forecast s performed applyng the econometrc models dscussed above: the FE-model wh spatal error and the FE-model wh spatal lag dependence. To ncorporate the spatal structure, only matrx W3 s consdered, gven that seems to be the matrx whch better captures the spatal autocorrelaton. The outcome s reported n Table 9. Dependng on the specfcaton and on the models used, the predcted average annual ncrease n ndustral coal demand vares between 2.5 and four percent. Nevertheless, f we consder the spatal lag model only, the estmated annual growth vares whn 3.2 and four percent. In partcular, specfcaton A, whch uses the AR method for RGVA and the double smoothng for populaton predcts growth rate of four percent, whch s almost n lne wh the forecast suggested by the Internatonal Energy Agency, whch ndcates an 17

20 average annual ndustral coal ncrease of 4.5 percent over the perod A slghtly lower forecast s produced by Crompton and Wu (2005), who report an annual growth rate of 3.3 per cent for coal, over the perod Fnally Dong (2000), applyng an uncertan dynamc system modellng approach, predcts over the perod an average annual ncrease of coal of fve percent. Fgure 3 dsplays the ascendng trend of the forecasts produced by the two models applyng drvers specfcaton A: as already dscussed, the spatal lag model seems to better capture the growth scenaro antcpated by the avalable energy outlooks. Fnally the ndustral coal predctons for 2010, dsaggregated by provnces, are reported n Table 10. The leadng poson of the provnces located at close dstance from the coal mnes seem to be mantaned throughout the tme. In fact, the ten major demandng provnces hghlghted n the nal analyss 10 are those whch are more lkely to demand the hgher level of coal n the future. 8. Conclusons In recent years, concerns regardng the envronmental mplcatons of the rsng energy demand, nduces consderable efforts to generate long term forecastng of Chna s coal requrements. The economc analyss, whch are all conducted at the country level, are almost unanmous n projectng ncreasng trend n coal demand. To our knowledge, however, no pror study models the coal demand n Chna at provncal level and produces future forecastng. The avalably of a data set at provncal level allows us to fll n the current gap n research. In ths paper we have analysed the Chnese ndustral coal demand usng a panel dsaggregated by provnces. Moreover, gven the nature of the data, the spatal autocorrelaton between provnces has been captured, usng a spatal econometrc model. The dependent varable of our model s the provncal level ndustral coal demand, whch ncludes ndustral energy demand, ndustral non energy demand and the consumpton lost durng the process of energy converson. A prelmnary analyss of the aggregate coal seres has revealed that the demand has not always followed a regular trend, and n partcular sgnfcant breaks occurred n dfferent ponts n tme. In fact, whle the ndustral coal demand grew steadly between 1990 and 1996, declned n the perod and after ths pont dsplayed a new rsng trend. Ths behavour suggests that the demand dd not grow predctably wh ndustral producton. In order to fully capture the evoluton of the coal demand, an addve tme trend has been ntroduced, as well as an nteracton between the trend and the gross value added varable. To account for the 10 See Table 2. 18

21 heterogeney across provnces, fxed effects provncal dummes have been added. Fnally, populaton s ncluded to control for provncal sze dfferences. The estmates have shown the expected sgns: the provnce sze, expressed by the populaton varable, exerts a posve and hghly sgnfcant mpact on fuel demand. The coeffcent of real gross value added alone s not statstcally sgnfcant, whereas the nteracton between the trend and real gross value added exerts a posve and sgnfcant mpact on coal. Ths ndcates that the margnal effect of output s not constant along the tme. The tme trend has not an ndependent effect on coal demand. Ths may suggest that n the perod consdered no relevant technologcal mprovement occurred n Chna. The autocorrelaton parameters are posve and always hghly sgnfcant. Wh respect to the lag model, ths ndcates that the provncal ndustral coal demand s nfluenced not only by standard economc factors, such as producton and provnce sze, but also by the exstence of synerges among provnces n the energy producton. Dfferent tests have been developed to detect a model msspecfcaton due to the exstence of spatal dependence, n the form of spatally lagged dependent varable or spatal error autocorrelaton. A clear rejecton of a standard model, whch does not ncorporate spatal dependence s ndcated. A further objectve of ths paper was to provde an accurate forecastng for the ndustral coal demand n Chna at provncal level. We have shown that both the use of a dsaggregated background, whch fully captures the heterogeney between provnces, as well as the ncluson of a spatal structure, mproves the predctons. To perform out-of-sample forecast tll 2010, the exogenous drvers need to be computed at provncal level for the eght years ahead, the drvers beng the ndustral value added and populaton. Dfferent alternatve methodologes have been appled to provde the better match between the actual computaton and the nformaton avalable from publshed economc outlooks. Among the alternatve methodologes adopted, the forecast based on autoregressve seres s the one that performs better, as far as, aggregatng the data from provncal to country level, produces a measure of the ndustral output n lne wh the expected growth ndcator. Smlar consderatons are adopted to dentfy the preferred populaton seres to apply n the forecastng. The forecast s performed applyng the econometrc models dscussed above: the fxed effects model wh spatal error and the fxed effects model wh spatal lag dependence. Dependng on the specfcaton and on the models used, the predcted average annual ncrease n ndustral coal demand vares between 2.5 and four percent. Nevertheless, f we consder the spatal lag model only, the estmated annual growth vares whn 3.2 and four percent. Fnally the ndustral coal predctons for 2010, dsaggregated by provnces, have been computed. The leadng poson of the 19

22 provnces located at close dstance from the coal mnes seem to be mantaned throughout the tme. In fact, the ten major demandng provnces hghlghted n the nal analyss are those whch are more lkely to demand the hgher level of coal n the future. Appendx Consder the followng spatal panel data model: C = ρwc + Z β ' + u u = μ + ε for =1,, N; t=1,, T. Rearrangng the model we obtan: C = B Z β + B μ + B ε ' where B = ( I ρw ). Goldberger (1962) demonstrates that the best lnear unbased predctor (BLUP) contans a correcton term whch depends upon the varance-covarance matrx of the error term ( Ω ) and upon the covarance matrx between future and current dsturbances (ω ). To obtan the BLUP of, T S therefore, we need to compute Ω = E(εε ') and ω = E ( ε, T + Sε ). C + The varance-covarance matrx s gven by: Ω = E( εε ') = ( I ( B' B) 2 1 σ ε T ) It should be noted that n a fxed effect modelω = E ( ε, + ε ) = 0, snce theε are not serally T S correlated over tme. Therefore, the correcton term becomes: ω' Ω 1 ε cancels out and the BLUP of C, T + S ˆ T S Bˆ -1 ' Z ˆ T S β Bˆ -1, + =, + + C ˆ μ 20

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26 Fgure 1. Locaton of the major coal mnes (mnes whch produce more than 10 mllon tonnes) Source. EPA, 1996 Fgure 2. Industral coal demand clusters: 8 years average (mllon tonnes, Mt) 24