Market Transition and Regional Adjustments in the Polish Coal Industry. Wojciech Suwala and Walter C. Labys RESEARCH PAPER 2001

Transcription

1 Market Transition an Regional Ajustments in the Polish Coal Inustry By Wojciech Suwala an Walter C. Labys RESEARCH PAPER 2001 WOJCIECH SUWALA Research Director Mineral an Energy Economy Research Institute, Polish Acaemy of Sciences, PO box 49, Krakow 65, Polan WALTER C. LABYS Professor of Resource Economics Faculty Research Associate, Regional Research Institute Natural Resource Economics Program, West Virginia University Morgantown, WV This research has been supporte by the U.S.-Polish Maria Skloowska-Curie Joint Fun, grant PAN/DOE Classification JEL: C61, Q41, P23, R12, R15 Key Wors: Market Transition, Coal Moels, Spatial Equilibrium Moels, Polish Coal Inustry Abstract: The Polish coal inustry has been unergoing restructuring to provie for its transition to a competitive environment. The coal inustry has respone by attempting to ownsize its capacity, to increase exports, to reuce employment an to increase prouctivity. In facing the challenges of the impacts of stronger environmental regulations, the inustry has also been force to consier regional ajustments in ifferent istricts of Polan. These kins of transition ifficulties warrant the formulation of a moeling approach that woul analyze regional restructuring policies. This paper presents the results of a spatial equilibrium optimization approach which has been esigne to meet this nee. The moeling framework consists of two-couple programming moels. The first moel explains coal prouction an investment an analyzes ecisions relate to the closures of unprofitable mines, the construction of new mines in ifferent locations, an the installation of beneficiation equipment an emissions reuction equipment. The secon moel evaluates coal eman regional istribution patterns base on a spatial equilibrium optimization proceure. The two moels are operate sequentially to analyze several scenarios that escribe the evolution of the Polish coal market through its recent an future transitions.

2 MARKET TRANSITION AND REGIONAL ADJUSTMENTS IN THE POLISH COAL INDUSTRY by Wojciech Suwala an Walter C. Labys Introuction Coal is the major energy source for the Polish economy; har coal constitutes some 63% an brown coal 13% of the total primary energy supply. This ominance is likely to continue, though its relative position in total energy supply will ecrease. Present coal mining conitions erive from an era of planne management. The previous centrally planne economy continue to 1990 an prouce a high uncertainty in mining prouction costs, utilization rates, prices, profits, etc. While coal price levels were initially regulate, present regulation is restricte to price rate increases. The profitability of mines continues to eteriorate, an even to be negative. Base on a government plan to restructure this inustry, all iniviual mines were consoliate into six coal companies an one holing company, except for three iniviual mines an mines awaiting closure (Morawski, 1994)(Karbownik, Stachowicz, 1994). The establishment of coal companies was intene to be a first step towars creating prouction units that can function viably in competitive markets. Such restructuring efforts, however o not seem to have ha an impact on the inustry sufficient for it to become profitable. As a consequence, a new program has been launche which is esigne to facilitate the require ecrease in current excess employment an to relieve ebts (Reforma..., 1998). This woul result in shifts in regional employment patterns. Receive stuies which evaluate coal mining restructuring emphasize that coal mining coul be viable in the long term, but eman strong aherence to plans (Raetzki, 1997)(Mhyera, Lorentz, 1997)(Suwala, 1995). Polan has always fixe coal prices accoring to ifferent regulatory schemes. These schemes have unergone a transformation from that of a comman system with regulate prices where consumers pai the same price no matter where they were locate, to that of a system where price setting is more regionally competitive. Coal prices have risen at a rate corresponing to overall costs increases, though they are below average prouction costs. Coal pricing thus remains problematic. It oes not appear that this problem can be solve until coal mine costs can approach competitive costs an, an even then until consumers accept the resulting higher prices. 2

3 The Coal Moelling Framework The moel framework consists of two inepenent but couple components: a coal prouction or supply moel, an a coal istribution or eman moel. Each of the moels is base on a spatial programming methoology. The nature of the moels an their linkage is roughly escribe in Figure 1. The supply moel is operate as a mixe integer program to etermine the cost-quantity structure of mines, particularly those efficient enough to compete with imports on a cost basis. These results are then employe to generate supply curves to provie linkage to the eman moel (Suwala, Labys, 1998). The parameters of the supply curves provie the basis for etermining coal shipments in the eman moel, epening on major coal emans an coal prices. The eman moel is operate initially as a transport program an subsequently as a spatial equilibrium or quaratic program. The first operation enables moel solution to simulate a controlle market case, corresponing to the comman or planne inustry perio an the transition perio. In this case fixe eman curves intersect the linear supply curves to etermine prices an sales. The secon operation employing spatial equilibrium enables competitive market solutions which reflect the transition to a market economy. In aition to impacts on profit an costs, the objective function base on net social payoff is employe to etermine the influence of increasing environmental stanars on the coal inustry. The coal supply moel The regional coal supply moel etermines coal prouction on an iniviual mine basis an esignates coal shipments accoring to coal types; at the same time profits of the iniviual mining companies are compute. The prouction units are efine such that each mine is consiere as a separate unit, either existing or projecte. The possibilities of operating the supply moel on an iniviual mine basis has been consiere in the past. For example Hibbar et al (1979, 1980) evelope this approach for the US copper an aluminum inustries, an Jaskowski (1975), Fieorenko(1981) an Goitzman an Korneev(1984) applie it to centrally planne inustries. Mines can be consiere uner construction, open or close. In aition each mine can be accompanie by a projecte coal beneficiation plant. Note that all of the mines are represente by binary variables. Since the moel also solves over time, all variables are time esignate. The selection of which of these mines to be constructe, operate close or augmente as well as their total number is etermine through the temporal an spatial optimization of the objective function. This function iscounts the costs of coal mining, incluing operational an capital costs, export revenues, costs of importe coal, costs of mines closures, operational an investment costs of treating salinate waters an soli wastes, costs of emission reuction in power plants, costs of emissions charges, an costs of coal transport. There are also several groups of constraints to limit the coal activities unerlying the objective function. They limit the possibilities of interconnections among the binary variables, an capacities relate to emission reuction technologies. Aitional equations were use to balance the ischarge of saline waters, the balance of emissions at power stations, an the balance of employment in mines. Next the profits of the coal mining companies are constraine so as to avoi subsiies. The moel is complete by specifying the prouctive activities which relate coal prouction an consumption. Coal supply curves are base on the assumption that a company will operate as long as price 3

4 excees their long run marginal costs. The average costs of coal companies an other market agents are employe to estimate the long term marginal costs of coal prouction. The coal eman moel The coal eman or istribution moel etermines regional coal allocation among the major coal consumers an computes price levels for coal on the omestic market. Coal emans originate from particular suppliers (inex s ), two of which are omestic sources an two import sources. The major coal consumers are esignate accoring to their location (thirteen regions - inex r ) an to their en-use ( inex u ): (1) power stations, (2) inustry, (3) coke ovens, (4) househols an other users. The etermination of coal prices epens on the relation of these various coal emans to the ifferent coal supplies. In the case of the comman or controlle market, emans are fixe an prices are etermine by intersection with supply curves. Given fixe eman an supply prices an transport costs, allocation follows from a regional transport programming algorithm. In the case of the transition to a market economy, eman an supply quantities an prices are solve simultaneously using a spatial equilibrium algorithm, Takayama an Juge, Aitionally net social payoff is compute as a welfare measure, reflecting proucers an consumers surplus. The structure of the coal eman moel, as shown in Figure 2, follows the above sequence from supply an eman to price etermination. The quantity supplie from any single source is limite by its capacity (escription of the variables in the moel appears in the Appenix): s, τ s, τ -1 ( X s,r, u, τ+ EX s, τ+ ) XM s, τ τ T s S D u U τ ZP - TT ZP The market version has an aitional constraint, which relates amounts of coal shipments from a particular source to its supply curve via the total supply XP s,τ: s, τ s, τ -1 ( X s,r, u, τ + EX s, τ + ) XP s, τ τ T s S D u U τ The eman for coal is a erive eman base on en-uses in the househol, inustrial, power generation an cokery sectors. Coal supplie to one of these consumer groups in a region shoul satisfy its requirements, assuming substitutability between coal types. The substitution rate is equal to 1 for all of the coal types. The controlle market version of the moel assumes fixe emans: ZP - TT ZP 4

5 τ T u U s S D u X s,r, u, τ ZXU Du r, u, τ The eman equation in the competitive market version of the moel requires flexibility on the right sie of equation, e.g. eman (XU D r,u,τ): τ T u U s S D u This equation coul be also expresse in units other than tons, such as energy content, which is the usual measure for coal elivere to power stations: X s,r,u, τ XU D u r, u, τ τ T u U s S Du X Du s,r,u, τ* s, τ,e r, u, τ α ZXE Here the substitution rate also equals 1 in relation to the energy content of the elivere coal. Since one of the purposes of the moel is to evaluate the impact of rising environmental stanars, present an future environment regulations must enter the moel as a constraint on coal burning. These regulations influence not only the emans of power stations but also coal consumption patterns. Primary emissions from coal burning at power stations are compute from: u U E τ T s S D * * = τ Xs, r, u, τ α,f u,f,f EM s, τ µ f, r, u, These emissions can be reuce given the presence of emission reuction installations; the net value shoul be lower than the allowe limits of emissions for particular regions: τ T u U E f F - Q xu EM f MAX EM f,r,u, τ j,r,u, τ j f, r, τ j T L * This relation allows one also to inclue factors for the "cross" reuction of emissions, e.g. a ecrease in particulates emissions cause by flue gas esulphurization(fgd). In this case the xu factor for FGD equals 1 for SO2 an equals 0.07 for particulates. Capacities relate to emission reuction technologies must satisfy: T j L τ T Qr,j, τ =Qr,j, τ -1+ Qr,j, τ - Qr,j, τ -ω j 5

6 This equation balances capacity in perio ϑ, which is relate to capacity in previous perios (ϑ - 1), future capacity increases in ϑ perios, an obsolete capacity commissione Τ perios before (Τ is the lifetime of technology j). Both the supply an eman components enter into price etermination. However government price regulations also can influence prices. Coal price formation is base not only on prouction costs but also on the parity prices for exporte an importe coal. These regulatory effects naturally iminish as one procees from a controlle market to a competitive market version of the moel. In the former case some competition still exists, mainly between importe an omestic coal, but also between coal for omestic consumption an exports. Assuming that no aministrative regulation (uties, import quotas, etc.) influence coal exports an imports, the lower an the upper limit to the omestic coal price are set on the basis of the export an import parity prices respectively. The export an import parity form reunant constraints for prices, when the respective coal export revenues an import costs are inclue in the objective function. An aitional upper limit conition can also be set for coal prices base on the concept of netback value. This concept assumes that a consumer is willing to pay a price which coul be erive from its revenues less other costs than fuel. In both versions of the moel, coal prices are etermine by the interaction of the eman (fixe or flexible) an supply curves: s S D τ T p s, τ = a 3,s +a 4,s x* ( EX s, τ + u U Finally the objective function must be formulate, first for the controlle market version an then for the competitive market version. In the former case the objective function is compose of the costs of elivere coal (costs of coal purchase an costs of transport), costs of emissions reuction, emissions charges, an revenues from coal exports: X s,r,u, τ + ZP s, τ - TT ZP τ s, τ-1 ) { [ τ T D s S u U X s,r,u, τ * ( p s, τ + ct s,r, τ )+ c j* Q j T L j,r,u, τ + e j T L j, τ * Q j,r,u, τ ] + ( EM f F u U f,r,u, τ - Q j T L j,r,u, τ * xu f j )* h f,u -EX s, τ * p X, τ }* Γ τ min In the case of the competitive market version, flexible supply an eman equations are introuce an these must further satisfy the integrability conition (Takayma, Juge, 1971). The objective function now is efine as quasi-welfare function to satisfy spatial market equilibrium conition. 1 1 { [a * XUD - a * (XUD ) 2 ] - [a * XP + a * (XP ) 2 1,r,u r, u, u 2,r,u r, u, u τ τ s, s, ] 2 3, s τ 2 4, s τ τ T u U s S D - [ X s,r, u, τ s S u U D * cts,r, τ + c j j T L * Q j,r,u, τ + e j, τ j T L * Q j,r,u, τ ] - (EMf,r,u, τ- Q j,r,u, τ T f F u U j L * xuf j ) * 6 hf,u + EX s, τ * px, τ }* Γτ max

7 While the operation of the supply moel generate the neee linear supply curves, sufficient ata were not available to estimate the equivalent linear eman curves. Instea we employe an approximation proceure in which available ata were employe at least to fin the range of the eman curve. The slope of the curves was then estimate within this range by aopting surrogates for the price elasticities along the curves. These equivalent elasticities appear from ifferent countries with similar eman conitions, as reporte in Dahl (1992) an Toman et al (1994), e.g. elasticity values of for power generation, -1.0 for inustry, for coke proucers, an -2.5 for househols an tertiary sector consumers. As more eman ata become available for the Polish coal sector, we hope to replace these surrogates with more accurate estimates. Evaluations of the accuracy an performance of the eman moel also were mae, an resulte in our making appropriate moel ajustments an revisions. This fine-tuning permitte that the policy simulation analyses were as realistic as possible. Policy simulation analysis The above moel framework has been simulate to test policies of importance for restructuring the Polish coal inustry. A series of scenarios have been selecte to investigate the major policy issues to analyze the response of the coal inustry as it is avance through three major transition perios. The Pre-transition scenario (roughly the perio ) assumes no environmental controls, a comman mechanism for coal pricing, no clean coal technologies, no mine closures, an both fixe coal emans an supplies. The Transition scenario (roughly the perio ) emboies the 1990 environmental regulations, a pricing mechanism base on import parity with price ifferentiation at the eman points, limite application of clean coal technologies base on governmental support, fixe emans but supply now varying accoring to the generate coal supply curves, an restricte mine closures. The Market or post transition scenario ( after 2000) avances environmental regulations to the mi-term year 2000 goals, allows prices to ajust competitively, expans the application of clean-coal technology ( some of this is privately fune ), closes mines on a profitability or cost basis, an ajusts coal emans an supplies accoring to competitive market conitions. Our approach to policy simulation analysis requires that we generate a variety of scenarios; these require certain ajustments in the moel parameters an switche equation specifications. These scenarios inclue the following (1) Changes in Environmental Stanars: these emboy ajustments in emissions limits an emissions charges; (2) Changes in the Coal Pricing System: the price specification will change as each system is successively applie; (3) Changes in the application of Clean Coal Technologies: these changes will be observe as the costs of installing an running the capacity increase; (4) Changes in Coal Demans: these emans are initially fixe an then begin to vary to reflect competitive market forces; an (5) Changes in the Number of Coal Mines: these changes reflect mainly closures or a move from a perio of politically etermine closures to that of economically or cost-etermine closures. For each of the major scenarios or changes inicate, the moel specifications also change. Accoring to a change in parameter values or a switch in the unerlying equation, the impacts of these changes are then measure base on changes in the objective function an the selecte performance inicator. 7

8 One aitional scenario has been ae to the first three. The fourth, which is calle Real, merges two others: Transition for the years an Market for the remaining perio. It also reflects the expecte course of coal market evelopment in Polan. The scenarios are marke P, T, M, an R accoring to the first letter of their full names. Two variants are further performe as each of these scenarios accoring to whether a high H or a low L economic growth rate is use to rive energy emans. Moel ajustments To provie simulation of the various scenarios we followe the above metho of systematically changing the values of variable inputs an equation parameters. Certain moel equation ajustments were require as well. The principal nee was to ajust moel specification to reflect the comman or planne inustry configuration from the pre-transition perio as compare to the competitive inustry configuration in the later perio. These various simulations epene on the ajustment of the coal istribution moel as follows: Scenario P : Pre-transition scenario utilizes classic transportation moel equivalent, Scenario T: Transition scenario employs an intertemporal ajustment reflecting a spatial equilibrium moel with fixe emans, Scenario M: Market scenario utilizes an intertemporal an spatial equilibrium moel with flexible emans an, Scenario R: Real scenario represents a mixe case of a spatial an intertemporal equilibrium moel with emans fixe in some perios. The coal istribution moel was ajuste to the scenarios an the principal changes require involve the price etermination mechanism: 1. For the Pre-transition scenario, prices were fixe at 1990 regulate price levels, 2. For the Transition scenario, prices were compute given fixe emans by allowing movement along the supply curves, 3. For the Market scenario prices were compute using flexible supply an eman curves, an now the ajustments were simultaneous, reflecting market equilibrium, 4. For the Real scenario, prices were controlle to reflect actual an expecte policy changes: they are fixe at import parity for the perio but then were allowe to ajust competitively for the perio (this coul be interprete as application of Transition scenario for the years an then the Market scenario for the remaining perio). To evaluate the outcomes concerning the various scenarios, three forms of performance inicators are compute: (1) costs incluing those of mining, of transport an closure, an external; (2) market equilibrium supplies an emans; an (3) coal prices. Note that for the coal istribution moel, the costs of mining are the prouct of quantity of coal supply an prices resulting from the coal supply curves. This prouct represents the actual cost of coal supply, while prices fixe at the list price or import parity level reflect a eviation from the actual cost situation. Values of the objective function are not reporte, since comparisons of their gross values cannot be easily interprete. Moel Simulations The coal supply moel simulations provie us with the supply curves for the eman moel simulations. At the same time, the solutions of the supply moel are optimal an thus provie us 8

9 with some information on require mine closures an coal costs variations across the scenarios. The istribution moel simulations employing the above supply curves prouce the more formal or complete evaluation of the various scenarios. As shown in Table 1, the given cost measures are lowest for the Market an Real scenarios. The unit cost of coal supplie is lowest for the Real scenario. As a measure of overall result the consumers an proucers surplus has been calculate. It oes not present substantial ifferences among scenarios, an the highest values occur for the market scenarios. Costs Table 1 RESULTING COST IMPACTS FOR THE COAL DISTRIBUTION MODEL (billion PLN Compute using compoun present values at 8% iscount rate) Scenario LP LT LM LR HP HT HM HR Prouction an import costs Export revenues Transport costs Total coal supply costs Emission fines Emission reuction costs Total external costs Gran total Unit cost Proucers an consumers surplus Table 2 suggests that the eman for coal is heavily influence by coal prices, in case of scenarios, which allow market ajustments (M an R). The eman of consumers of high elasticities (inustry, househols) ecreases substantially, in some cases to zero. Even for the year 1995 eman is much lower than historical figures which are comparable to those of P an T scenarios. Simultaneously the historical prices were much lower than those compute in our moel, the former were on average 54 PLN/t in 995, while the moel inicates 75 PLN/t (see table 3). Real coal market remains thus in isequilibrium because of unprofitable mines. Deman moel results present market in equilibrium an prices cover economic costs. The prices are therefore higher than historical, an foster the consumers to lower their eman. However, the consumption of low elasticity consumers remains at the same level, an even excees those for the LP an LT scenarios. 9

10 Consumers Table 2 RESULTING IMPACTS ON CONSUMER DEMANDS (mmt) Scenario LP LT LM LR HP HT HM HR Power generation Inustry Househols Cokeries Total eman Power generation Inustry Househols Cokeries Total eman Power generation Inustry Househols Cokeries Total eman We have estimate a variety of price impacts for each coal consuming group, for the year 1995, 2000, an 2010, as shown in Table 3. The ifferences across scenarios are substantial, an vary epening on the consumer grouping. The highest prices occur for Pre-transition scenario an househols, which use the best quality an most expensive coal. Other prices are not so iverse, an there is no regularity in price relations among scenarios. Figure 3 presents prices for the L scenarios, which correspon to the evelopment of omestic coal consumption. We can observe the course of prices evelopment an their relation to actual prices. Prices for the M an R scenarios ten to increase, generally because of the increasing share of expensive importe coal (see table 4). For the R scenario initially ecreasing prices have the minimum in 2000, when the inustry switches to market rules. Then the prices increase at the rate same as for the M scenario, but until 2010 o not excee the price level of the M scenario. The coal mining restructuring for scenario R occurs in 2000, an requires closing more mines in shorter time than for the M scenario. Resulting mining costs an prices are lower for the late years of the analysis, but we o not argue that elay of the restructuring has positive results (here in prices) as the social costs woul be much higher than in the case of early an smooth restructuring. 10

11 Consumers Table 3 RESULTING IMPACTS ON COAL PRICES (PLN/mt) Scenario LP LT LM LR HP HT HM HR 1995 Power generation Inustry Househols Cokeries Average Consumer Power generation Inustry Househols Cokeries Average Consumer Power generation Inustry Househols Cokeries Average Consumer Figure 3 Average coal prices lp lt Coal price 000PLZ/t lm lr hp ht hm hr Actual Year Note: Actual price of 1998 is assigne to the year

12 At high coal prices omestic coal may become less competitive with importe coal. Coal imports are restricte only for pre-transition scenarios, for the others imports constitute substantial share of cal supply. Also the high omestic prices (an costs) make the export inefficient, it tens to be nearly zero, while Polan is now one of the largest coal exporters. Variables Table 4 RESULTING IMPACTS ON MARKET EQUILIBRIUM: (mmt) Scenario LP LT LM LR HP HT HM HR 1995 Domestic prouction Import Stock change Total supply Domestic consumption Export Stock change Total eman Domestic prouction Import Stock change Total supply Domestic consumption Export Stock change Total eman Domestic prouction Import Stock change Total supply Domestic consumption Export Stock change Total eman The ifferences among scenarios for low an high rates of economic growth are as expecte. It is worth noting that higher growth oes not incur higher consumption in case of Market an Real scenarios. For inustry an especially for househols, economic growth creates prosperity, which might possibly be irecte to the switching towars more convenient an clean fuels such as oil an gas for heating. 12

13 Conclusions The goal of this stuy has been to employ a commoity moeling approach to analyze important policy issues as the Polish coal inustry unergoes restructuring in its transition from a comman to a competitive inustry, i.e. see Labys et al. (1989). These issues inclue mainly the impacts of new environmental policies, movements from fixe to competitive pricing policies, an extene mine closure reform policies. The commoity moeling framework employe is regional in character an consists of twocouple programming moels, whose specification has been ajuste to reflect the transition from a comman to a competitive inustry. The first moel analyses coal prouction an investment as well as ecisions relate to the closure of unprofitable mines, the construction of new mines, an the installation of beneficiation equipment. The secon moel inclues the evaluation of coal eman istribution an pricing patterns in aition to the supply optimization proceure. Coal eman eterminants inclue regional eman requirements as well as regional prices an regional environmental stanars. The two moels are operate recursively so that the supply curves generate from the first moel become integrate with the eman curves in the secon moel, proucing market clearing prices an quantities, an concurrently the optimization of social welfare. Moel valiation techniques suggest that the moel coul be appropriately tune an was reasonably accurate for the policy simulation analyses performe. Two rawbacks were encountere in the moeling process, which can be improve in the future. First, greater ata availability will permit better estimation of coal eman price elasticities, an secon, the moels must be integrate an operate simultaneously rather than recursively. The policy simulation analysis resulte in the economic evaluation of the above policy scenarios uring the transition perios that vary between high or low economic growth rates; these reflect: the Comman or Pre-transition scenario, the Transition scenario, the Market or post comman scenario, an the Real or continue moe. The supply moel was operate across the scenarios to generate coal supply curves. The eman moel was then similarly operate to estimate the economic impacts of the various coal policy issues. The resulting impacts were measure in the form of inustry costs, regional mine closures an employment, en-use emans, an market equilibrium quantities an coal prices. The empirical values achieve were in accorance with what might be expecte in reality. Finally, the following conclusions can be rawn concerning the performance of the Polish coal inustry in transition. First, the historically high levels of coal costs an prices foun in the Pretransition perio can not be maintaine in the face of low price coal import opportunities. Only by becoming more competitive an efficient can the Polish coal inustry lower their prices to an internationally competitive level. Secon, inustry restructuring principally in the form of closures of high cost inefficient mines provies an avenue for improving inustry competitiveness. However, regional shifts in employment will occur as mines are close in regions whose coals are less clean. Coal imports will continue until these prices are lowere; coal exports coul expan because the marginal costs of proucing high-quality coal will lower. Thir, the impacts of rising environmental stanars have not necessarily force the closure of mines proucing low quality coal. Our present intention is to further expan an refine the moel framework. This shoul result 13

14 not only in improvements in moel accuracy but also in our ability to analyze scenarios which capture recent an future policies an ajustments in the coal inustry. Bibliography Dahl C., A Survey of Energy eman Elasticities in Support of the Development of NEMS, Colorao School of Mines, Dept. of Mineral Economics, Golen, Colorao, 1993 Fieorenko N.P., Economic optimization, PWE, Warsaw, /in Polish/ Goitzman E.I., Korneev V.G., 1984, Optimization Of Coal Mining Plans, Nera, Moscow, /in Russian/, 1981 Hibbar W.R. et al., An Engineering Econometric Moel of the U.S. Aluminum Inustry, Proceeings of the AIME, New York, 1979 Jaskowski A., Metho of Optimization of Primary Fuels Supply, ZN AGH, Mining, Univ. Of Mining an Metallurgy, Cracow (in Polish), 1975 Karbownik A., Stachowicz J. Social aspects of restructuring har coal mining in Polan, Resources Policy, 20: , 1994 Labys, W.C., Takayama, T., an N. Uri, Quantitative Methos for Market-Oriente Economic Analysis over Space an Time, Lonon: Gower Publishing Company, 1989 Mhyera A., Lorentz J., Polish Har Coal, Mining Magazine, August: Morawski E. 1994, Restructuring the har coal mining inustry in Polan, Resources Policy, 20: , 1977 Raetzki M., Polish Har Coal - the Roa to economic Health, SNS Energy Institute of Sween, 1997 Reforma gornictwa wegla kamienengo w Polsce w latach , (Reform of the Coal Mining in Polan in Years), Governmental Program, 1998, Warsaw Scherer F.M., Inustrial Market Structure an Economic Performance, Ran McNally, Chicago, 1970 Suwala W., Baania moelowe perspektyw górnictwa i rynku wegla kamienego w Polsce, (Moeling the future of coal mining an coal market in Polan), Stuies, Reports an Monographs, No 38, Polish Acaemy of Sciences, Cracow, /in Polish/, 1995 Suwala W. an Labys W.C., 1998, Moeling Transition in the Polish Coal Inustry, Polish Acaemy of Sciences, Mineral an Energy Economy Research Institute Takayama, T. an Juge G.G., Spatial an Temporal Price an Allocation Moels, Amsteram: North Hollan Publishing Company Toman M., Cofala J., Bates R., Alternative Policies for Air Pollution Control in Polan, Environmental an Resource Economics, 4,

15 Appenix COAL MODEL VARIABLES AND PARAMETERS Variable Definition Variables Q j,r,u,ϑ = increase of capacity of technology j, in region r, for consumers group u, in perio ϑ (with superscript g refers to amount assigne to agent), EMf,r,u, ϑ = emission of f pollutant in r region, for u consumers, EX s, ϑ = export of coal type from supplier s in perio ϑ, Qj,r,u, ϑ = capacity of technology j, in region r, for consumers group u, in perio ϑ (with superscript g refers to amount assigne to agent), X s,r,u, ϑ = shipments of coal, supplie by supplier s for consumers group u locate in region r in perio ϑ, XP s, ϑ = supply of coal in perio ϑ from supplier s, XU D r,u, ϑ = eman for coals D by consumers u locate in region r in perio ϑ, ZP s, ϑ = inventory of supplier s of coal type at the en of perio ϑ, P s, ϑ = supply price of coal from supplier s in perio ϑ, Variable Sets D = coal types set, Du = set of coal types allowe for consumer u, F L T R S T U U E = set of pollutants, = set of technologies of emission reuction incluing salinate waters utilization, = set of consumers regions, = set of suppliers, = set of time perios, = set of consumers groups, = set of power stations, Variable Inexes ϑ f f' j = time perio inexes, = coal type inex, = inex of pollutant, = inex of coal component which causes emission of f pollutant ( e.g. sulfur for SO2), = inex of technology, 15

16 r = inex of consumer region, Variable Inexes, cont s = inex of supplier, u = inex of consumers group, Parameters s, ϑ,f = content of substance causing emission (for example ash, sulfur), in coal supplie by supplier s, in subperio ϑ, s,ϑ,e = energy content (e.g.. lower heat value) of coal supplie by s supplier to r region, = intercept of inverse eman function ( a1,r,u a1,r,u >0), = slope of inverse eman function (a2,r,u>0), a2,r,u a 3,s = intercept of inverse supply function ( a3,s >0), a 4,s = slope of inverse supply function (a4,s>0), cj = unit operational costs (per unit of capacity) of running technology j, cts,r,ϑ Cts,U, ϑ = unit cost of transporting coal (per ton an km) from supplier s to region r for the ϑ subperio (market with bar esignates average cost), = average unit transport cost (per ton) of coal from supplier s to omestic consumers for the ϑ subperio, = unit investment costs of technology in perio ej,ϑ ϑ, expresse per unit of capacity, EM MAX f,r, = allowe emission of f pollutant for region r in perio ϑ ϑ, hf,u = unit emission charge pai by user u for pollutant f or measure of environmental loss, p X, = export price (at mine mouth) of coal type, ϑ XM s, = capacity of supplier s for coal type in perio ϑ ϑ, xu f j ZXE D r,u,ϑ = unit consumption (emission reuction per unit of capacity) of pollutant f by technology j, = total energy eman for D types of coal in r region for u group of consumers, ZXU D = total (fixe) eman for D types of coal in r region for u group of r,u,ϑ consumers. ϑ = iscount factor for perio ϑ, :u,f,f= = multiplier for calculation of user u emission of polluter f from content - of component f, = lifetime of technology j Τj TT ϑ = number of years in perio ϑ. 16

17 Figure 1 COAL MODEL FRAMEWORK AND LINKAGES SUPPLY MODEL DEMAND MODEL PRODUCTION (Iniviual Proucers) ALLOCATION CONSUMPTION (Major Consumers) WATER DISCHARGE LIMITS MINE 1 (Spatial an temporal allocation of supplies an emans accoring to prices P, Ps an transport costs TC) HOUSE- HOLDS SALINE WATER UTILIZATION MINE 1 + CLEAN COAL PLANT P INDUSTRY SOLID WASTE UTILIZATION TC Ps COKERIES MINE n Supply region j Deman region i EMISSIONS LIMITS IMPORT LIMITS IMPORT (Allocation between two supply regions, two import regions, thirteen eman regions an ten coal types) POWER SECTOR EXPORT LIMITS EMISSIONS REDUCTION EXPORT 17

18 Figure 2 STRUCTURE OF THE COAL DEMAND MODEL TRANSPORT COSTS SUPPLY CURVES COSTS OF DOMESTIC SUPPLY DEMAND CURVES TOTAL COSTS OF SUPPLY or NET SOCIAL PAYOFF CAPACITY LIMITS DOMESTIC SHIPMENTS STOCK LIMIT LEGEND IMPORT PRICE CAPACITY BALANCE STOCK SUPPLY & DEMAND BALANCES DEMAND IN REGIONS DATA IMPORT COSTS IMPORT EXPORT UNIT EMISSION CHARGES SUPPLY FOR POWER SECTOR SUPPLY FOR HOUSEHOLDS SCENARIOS CALCULATIONS EQUATIONS (CONSTRAINS) EXPORT REVENUES EXPORT PRICE IMPORT LIMIT EXPORT LIMIT TOTAL EMISSION CHARGES EMISSION LIMITS IN REGIONS EMISSIONS COAL QUALITY SUPPLY FOR COKERIES SUPPLY FOR INDUSTRY VARIABLES CONNECTIONS COSTS OF EMISSION REDUCTION EMISSION REDUCTION ONE-WAY TWO-WAY 18