(2010) as they are not done as thoroughly and frequently as for other resources and assessment methods and definitions vary between countries. This causes upward and downward corrections and jumps in the estimates that should however not be interpreted as signs of increasing scarcity. In fact we do not see any reason why the market should be affected by a reserve constraint in the mid-term until 2030. The level of worldwide reserves has been constant at high levels for the last 25 years.61 We also have to keep in mind that the definition of reserves is dynamic and that coal is underexplored in several world regions which increases the likelihood of substantial reserve additions in the next decades (see Minchener, 2009).
4.5.5 Model evaluation and criticism
The “COALMOD-World” model uses 1671 single data inputs and is expressed by 6599 single equations that calculate values for 6599 single variables. It is programmed in GAMS using the mixed complementarity (MCP) format and the solver PATH (Ferris and Munson, 2000). The model solves in less than 10 seconds using a standard desktop computer (Pentium®Dual-core with 2.50 GHz CPU and 2.91 GB RAM). This rapidity allows for a very flexible use of the model for fast data update and test or for scenario or sensitivity analysis. The main weakness is the important amount of data input needed and the difficulty to find the appropriate data. Better access to experts and proprietary data could remedy these data issues.
4.6 Conclusions
In this chapter, we present a tool for analysis of the future global steam coal market, the “COALMOD-World” model. From a starting point in 2006, we are able to give insights into how production and trade flows will develop until 2030, using two different scenarios for demand projection: one with a continuously increasing demand and one with a stabilizing demand. We are able to give a differentiated answer to the question about
“the end of cheap coal?”. Based on our model analysis, we find that it is not geological reserve depletion but capacity constraints as well as slow expansions of production and export capacities that could make coal more expensive in the future.
The resulting scarcity that starts appearing after 2015 can be measured in the model as a demand gap expressed by the percentage of the reference demand that is not satisfied.
In the increasing demand scenario the gap represents 6.2% of the demand in 2025 and 9.3% in 2030. In the stabilizing demand scenario these values are 3% and 4.7% for 2025 and 2030 respectively. We also calculated that in the increasing demand scenario 18.6%
of the assumed reserves are depleted until 2030 and only 16.3% of the reserves in the stabilizing demand scenario.62
61Source: data compiled by the World Energy Council: http://www.worldenergy.org/
62In both scenarios three mining basins are completely depleted: Vietnam, Mongolia and the Chinese
This makes “peak coal” a less imminent question than the issue of the investments in production and export capacity needed to satisfy future demand. In the increasing demand scenario we calculate that through 2025, 124.6 billion USD in production ca-pacity investments are needed and, due to the high amount of imports, 6.6 billion USD of investments are needed in export port capacities. In the stabilizing demand scenario 84.8 billion USD in production capacity investements are required and only 2.4 billion USD for port capacity expansions.
In the mid-term the more pressing issue concerning the use of coal are the environ-mental externalities, especially the carbon dioxide emissions affecting climate change.
Coal is considered by many to be the number one climate enemy. If more restrictions on carbon dioxide emissions or a higher carbon price were imposed, there would be a direct effect on electricity generation from coal. However, in the mid-term this is more likely to happen in the main importing developed countries like the EU or Japan than in India or China where most of the future increase in demand and imports will come from. Therefore in the following Chapter 5 we use the COALMOD-World model to see the interactions between different climate policies and the global steam coal market.
Henan province. Additionally, Venezuela hits its reserve constraint in the increasing demand scenario.
4.A. Appendix
4.A Appendix
4.A.1 Mathematical Formulation of the Model
The profit maximization problem described in Sections 4.3.2 to 4.3.4 has the following Karush-Kuhn-Tucker conditions (KKTs) of optimality that are obtained after deriving the Lagrangian function of each player type with respect to their decision variables and dual variables of constraints.
• Producers KKTs:
0≤ 1
1 +rf a
·h
−pac
+ ∂CafP
∂xaf c+trans_caf c·κaf
i
+αPaf ·κaf +5
2 ·αfRes·κaf+αT cap_caf c ·κaf ⊥xaf c≥0 (4.25)
0≤ 1
1 +rf a
·h
−pae
+ ∂CafP
∂yaf e +trans_eaf e·κafi +αPaf·κaf+5
2 ·αResf ·κaf +αT cap_eaf e ·κaf ⊥yaf e≥0 (4.26)
0≤ 1
1 +rf a
·CP invaf
5 −X
a0>a
αPaf+αP invaf ⊥P invaf ≥0 (4.27)
0≤ 1
1 +rf a
·CT inv_caf c
5 −X
a0>a
αT cap_caf c +αT inv_caf c ⊥T inv_caf c≥0 (4.28)
0≤ 1
1 +rf a
·CT inv_eaf e
5 − X
a0>a
αT cap_eaf e +αT inv_eaf e ⊥T inv_eaf e≥0 (4.29)
0≤P capf −X
a0<a
h X
c
xaf c·κaf+X
e
yaf e·κaf
!
·mc_int_varfi
+X
a0<a
P invaf− X
c
xaf c·κaf +X
e
yaf e·κaf
!
⊥αPaf ≥0 (4.30)
0≤P maxinvaf −P invaf ⊥αP invaf ≥0 (4.31)
0≤Resf −X
a∈A
hX
c
xaf c·κaf +X
e
yaf e·κaf
+X
c
x(a−1)f c·κ(a−1)f +X
e
y(a−1)f e·κ(a−1)f
∗ 5 2
i⊥αResf ≥0 (4.32)
0≤T cap_cf c+ X
a0<a
T inv_caf c−xaf c·κaf ⊥αT cap_caf c ≥0 (4.33)
0≤T cap_ef e+ X
a0<a
T inv_eef c−yaf e·κaf ⊥αT cap_eaf e ≥0 (4.34)
0≤T maxinv_caf c−T inv_caf c⊥αT inv_caf c ≥0 (4.35)
0≤T maxinv_eaf e−T inv_eaf e⊥αT inv_eaf e ≥0 (4.36)
0≤mc_intaf =mc_int(a−1)f +mc_slp(a−1)f· X
c
x(a−1)f c·κ(a−1)f +X
e
y(a−1)f e·κ(a−1)f
!
·mc_int_varf
, mc_intaf (free) (4.37)
0≤mc_slpaf =mc_slp_startf +mc_slp_varf·X
a0<a
P inva0f,
mc_slpaf (free) (4.38)
• Exporters KKTs:
0≤ 1
1 +re a
·h
−pac
+pae+Cportae·κae+searateaec·κae
i
+µEae·κae⊥zaec ≥0 (4.39)
0≤ 1
1 +re
a
·CEinvae
5 −X
a0>a
µEae+µEinvae +µEmaxe ⊥Einvae≥0 (4.40)
4.A. Appendix
0≤Ecape+X
a0<a
Einvae−X
c
zaec·κae⊥µEae≥0 (4.41)
0≤Emaxinvae−Einvae⊥µEinvae ≥0 (4.42)
0≤maxcape−Ecape−X
a
Einvae⊥µEmaxe ≥0 (4.43)
• Producers Quality Factor:
κaf =κf +δf·X
a0≤a
X
c
xaf c+X
e
yaf e
!
, κaf (free) (4.44)
• Final Demand Equation:
pac−pac
X
f
xaf c,X
e
zaec
= 0 , pac (free) (4.45)
• Market Clearing Condition:
0 =yaf e−X
c
zaec , pae (free) (4.46)
• Chinese Export Restriction:
0≤China_lica ECHN − X
N oChina(c)
zaec·κae⊥πa ECHN ≥0 (4.47)
The KKT (Karush Kuhn Tucker) optimality conditions of each model player and the additional final demand, market clearing and quality equations form a mathematical equilibrium problem in the MCP format. This model is programmed in GAMS and it is solved using the PATH solver (Ferris and Munson, 2000).
4.A.2 Nodes of COALMOD-World
Table4.4:NodesoftheCOALMOD-WorldModel
CountryProducersExportersConsumersPortCanadaC_CANOntarioNoU.S.P_USA_PRBPowderRiverBasinE_CANVancouverC_USA_RockyNoP_USA_APPAppalachianE_USA_EastHamptonRoadsC_USA_EastBostonP_USA_RockyRockyMountainsC_USA_CentralNoP_USA_ILLIllinoisBasinC_USA_SouthNoC_USA_GulfMobileColombiaP_COLE_COLPuertoBolivarVenezuelaP_VENE_VENMaracaiboMoroccoC_MARMohammediaPortugalC_PRTSinesSpainC_ESPGijonUKC_GBRImminghamNL_F_BELC_NFBNetherlands,France,BelgiumRotterdamGermanyC_DEURotterdamDenmarkC_DNKAabenraaFinlandC_FINKotkaItalyC_ITATarantoPolandP_POLE_POLGdanskC_POLNoTurkeyC_TURMersin/SamsunIsraelC_ISRAshdodEurasiaP_RUSKemerovo/KuznetsE_RUS_WestBaltic/RigaC_RUS_SibiriaNoE_RUS_EastVostochnyC_RUS_CentralNoE_Black_SeaMariupolP_UKRUkrainian/RussianDonetsC_UKRNoP_KAZKazakhstan/EkibastuzC_KAZNoSouthAfricaP_ZAFE_ZAFRichardsBayC_ZAFNoMozambiqueP_MOZE_MOZMaputoIndiaP_IND_NorthChhattisgarh,Jharkhand,MadhyaC_IND_EastBihar,Jharkhand,WestBengal,Orissa,NoPradesh,UttarPradesh,WestBengalChhattisgarhP_IND_OrissaOrissaC_IND_NorthDelhi,Punjab,Rajasthan,UttarPradeshNoP_IND_WestMaharashtraC_IND_WestGujarat,Maharashtra,MadhyaPradeshMundraP_IND_SouthAndhraPradeshC_IND_SouthAndhraPradesh,TamilNadu,KarnatakaChennaiThailandC_THABangkokMalaysiaC_MYSLumutVietnamP_VNME_VNMCamphaC_VNMNo
IndonesiaP_IDNE_IDNC_IDNNoChinaP_CHN_SISShanxi,Shaanxi,InnerMongolia,HebeiE_CHNQinhuangdaoC_CHN_SISShanxi,Shaaxi,InnerMongoliaNoP_CHN_NortheastLiaoning,Jilin,HeilongjiangC_CHN_NortheastHeilongjiang,Jilin,LiaoningNoP_CHN_HSAHenan,Shandong,Jiangxi,Fujian,JiangsuC_CHN_MainBeijing,Tianjin,Hebei,Henan,ShandongNoP_CHN_YGGuizhou,Hunan,Chongqing,SichuanC_CHN_EasternJiangsu,Hubei,Chongqing,Shanghai,Shanghai/NingboZhejiangC_CHN_SouthJiangxi,Guizhou,Sichuan,Guangdong,GuangzhouFujian,GuangxiandHunanMongoliaP_MNGC_MNGNoKoreaC_KORUlsan
JapanC_JPNYokohamaTaiwanC_TWNKaohsiungPhilippinesC_PHLPagbilaoAustraliaP_AUS_QLDE_AUS_QLDDalrympleBayP_AUS_NSWE_AUS_NSWNewcastle
4.A. Appendix
4.A.3 Data of COALMOD-World
Table 4.5: Data and assumptions for the endogenous cost mechanism Mining Intercept
Country Model Producers Basin Type Increase mc_slp_var_f mc_int_varf
U.S. P_USA_PRB 2 slow −1·10−6 0.02
P_USA_Rocky 2 moderate −1·10−6 0.04
P_USA_ILL 3 moderate 0 0.04
P_USA_APP 3 high 0 0.06
Colombia P_COL 2 slow −1·10−5 0.05
Venezuela P_VEN 1 high 1·10−2 0.2
Poland P_POL 3 slow 0 0.05
Ukraine P_UKR 3 moderate 0 0.2
Kazakhstan P_KAZ 2 moderate −1·10−4 0.1
Russia P_RUS 2 slow −2·10−5 0.05
South Africa P_ZAF 3 moderate 0 0.1
India P_IND_North 2 moderate 0 0.07
P_IND_Orissa 3 high 0 0.25
P_IND_West 3 moderate 0 0.15
P_IND_South 3 moderate 0 0.15
Vietnam P_VNM 4 high 1·10−2 0.3
Indonesia P_IDN 2 slow −5·10−6 0.05
China P_CHN_SIS 2 moderate −2·10−8 0.08
P_CHN_Northeast 3 moderate 0 0.1
P_CHN_HSA 3 moderate 0 0.18
P_CHN_YG 3 moderate 0 0.14
Australia P_AUS_QLD 2 slow −2·10−3 0.05
P_AUS_NSW 2 slow −2·10−3 0.05
Mongolia P_MNG 1 high 1·10−3 0.2
Mozambique P_MOZ 1 high 1·10−2 0.4
Table 4.6: Data assumptions for the per 5-years capacity expansion limitations in Mtpa Production capacity limitation Export capacity limitation
U.S. 276 20
Colombia 22 20
Venezuela 10 10
Poland 14 5
Ukraine 7 10
Russia 51 40
South Africa 47 15
India 63
Indonesia 51 10
China 292 10
Australia 44 30
4.A.4 Results of COALMOD-World
Table 4.7: Results of COALMOD-World: Domestic trade flows in Mtpa for the increasing demand and the stabilizing demand scenarios
Scenario Increasing demand Stabilizing demand
From To 2006 2010 2015 2020 2025 2030 2020 2030
P_USA_PRB C_USA_Rocky 63 104 104 104 104 98 104 102
P_USA_PRB C_USA_Central 358 359 373 367 362 356 348 312
P_USA_PRB C_USA_Gulf 70 166 166 167 167 167 163 178
P_USA_Rocky C_USA_Rocky 40 9 12 10 10 14 7
P_USA_ILL C_USA_South 43 23 19 10 6 3 30 32
P_USA_ILL C_USA_Gulf 70 85 85 90 90 90 70 62
P_USA_APP C_USA_South 62 78 85 92 94 96 70 55
P_USA_APP C_USA_East 58 57 59 58 57 57 56 48
P_USA_APP C_USA_Gulf 79 0 13 11
P_POL C_POL 67 65 59 55 51 47 43 32
P_UKR C_UKR 32 34 31 28 31 30 27 25
P_KAZ C_KAZ 42 43 40 38 41 43 35 34
P_RUS C_RUS_Siberia 70 96 94 88 92 96 79 78
P_RUS C_RUS_Central 23 26 24 25 12 10 16 16
P_ZAF C_ZAF 91 102 102 114 126 136 97 92
P_IND_North C_IND_East 110 115 116 97 110 97 107 116 P_IND_North C_IND_North 142 157 174 208 208 234 198 214
P_IND_Orissa C_IND_East 0 9 26 84 41 86 60 55
P_IND_Orissa C_IND_South 60 26 13
P_IND_West C_IND_West 60 58 57 56 55 54 40 33
P_IND_South C_IND_South 21 37 18 38 29 36 27 30
P_VNM C_VNM 3 2 1
P_IDN C_IDN 23 29 36 42 48 53 38 41
P_CHN_SIS C_CHN_Northeast 13 24 35 4
P_CHN_SIS C_CHN_Main 339 429 518 553 593 626 492 505 P_CHN_SIS C_CHN_Eastern 110 131 200 200 200 200 111 250 P_CHN_SIS C_CHN_SIS 228 287 360 386 405 415 349 351 P_CHN_Northeast C_CHN_Northeast 148 190 233 232 240 244 222 235
P_CHN_HSA C_CHN_Eastern 100 157 157 193 193 193 252
P_CHN_YG C_CHN_South 248 297 273 267 257 252 267 247 P_MNG C_MNG 0.15 0.13 0.13 0.20 0.25 0.28 0.20 0.34
4.A. Appendix
Table 4.8: Results of COALMOD-World: International trade flows in Mtpa for the increasing demand and the stabilizing demand scenarios (part 1/2)
Scenario Increasing demand Stabilizing demand
From To 2006 2010 2015 2020 2025 2030 2020 2030
P_USA_APP C_CAN 16 16 17 16 16 16 15 13
P_COL C_USA_Gulf 21 19 27 15 31
P_COL C_MAR 1 5 6 7 5 5
P_COL C_PRT 5 5 4 4 3
P_COL C_ESP 7 17 15 14 12
P_COL C_GBR 21 29 24 20 2 3
P_COL C_DEU 24 23 17 12
P_COL C_ITA 6 12 5 12 2
P_COL C_TUR 3 6
P_COL C_CHN_Eastern
P_COL C_KOR 11 54 10
P_COL C_JPN 32 87 79 8 54
P_COL C_TWN 10
P_VEN C_ESP 10
P_VEN C_NFB 9
P_VEN C_ITA 10 4 8
P_VEN C_TUR 4 9 3
P_VEN C_ISR 1 7
P_VEN C_IND_West 8 14
P_VEN C_CHN_Eastern 2
P_POL C_GBR 23 21 22 23 24 35 28 24
P_POL C_NFB
P_POL C_DNK 4
P_KAZ C_RUS_Siberia 5
P_KAZ C_RUS_Central 30 30 30 25 43 48 30 30
P_RUS C_MAR 7
P_RUS C_PRT 3 2
P_RUS C_ESP 12 9
P_RUS C_GBR 13 0 2
P_RUS C_NFB 14 22 21 19 17 16 15 11
P_RUS C_DEU 11 10 13 16 26 23 22 16
P_RUS C_DNK 8 8 7 7 6 6 5 0
P_RUS C_FIN 10 10 9 8 7 7 6 5
P_RUS C_ITA 13 7
P_RUS C_TUR 15 15 17 12 16 14
P_RUS C_ISR 5 21 27 33 15 15
P_RUS C_UKR 1
P_RUS C_IND_West 5
P_RUS C_CHN_Eastern 12 60
P_RUS C_KOR 4 18 25 45 48 25 25
P_RUS C_JPN 15
P_ZAF C_MAR 5 4
P_ZAF C_PRT 5
P_ZAF C_ESP 19
P_ZAF C_GBR 7
Table 4.9: Results of COALMOD-World: International trade flows in Mtpa for the increasing demand and the stabilizing demand scenarios (part 2/2)
Scenario Increasing demand Stabilizing demand
From To 2006 2010 2015 2020 2025 2030 2020 2030
P_ZAF C_ITA 19 19
P_ZAF C_TUR 4 4
P_ZAF C_ISR 13 16 11
P_ZAF C_IND_West 21 28 48 56 58 51 64
P_ZAF C_IND_South 24 47 56 76 81 57 67
P_ZAF C_THA
P_ZAF C_MYS 16 13 8 9 17
P_VNM C_CHN_South 20 20 P_IDN C_IND_West 16 P_IDN C_IND_South 15
P_IDN C_THA 7 8 10 11 12 13 10 11
P_IDN C_MYS 13 16 3 24 11 8
P_IDN C_CHN_Eastern 11
P_IDN C_CHN_South 22 154 202 193 206 156 199
P_IDN C_KOR 50
P_IDN C_JPN 20 7
P_IDN C_TWN 58 73 40 41 6
P_IDN C_PHL 8 9 12 8 12 13
P_CHN_SIS C_KOR 60 26
P_AUS_QLD C_CHN_Eastern 9
P_AUS_QLD C_CHN_South 0
P_AUS_QLD C_KOR 3
P_AUS_QLD C_JPN 57 60 31 30
P_AUS_QLD C_TWN 42 86 97 100 44 81
P_AUS_NSW C_CHN_Eastern 20 85
P_AUS_NSW C_CHN_South 57 86
P_AUS_NSW C_KOR 12 17 30
P_AUS_NSW C_JPN 13 35 73 63 47
P_AUS_NSW C_TWN 1 2
P_AUS_NSW C_PHL 4 13 14
P_MNG C_RUS_Siberia 2
P_MNG C_CHN_Main 5 22 30 33 33 35 36
P_MNG C_CHN_SIS 5 5 5 10 15 5 9
P_MOZ C_MAR
P_MOZ C_PRT
P_MOZ C_ESP 0.11
P_MOZ C_GBR 1
P_MOZ C_ITA 0.05
P_MOZ C_TUR
P_MOZ C_ISR 0
P_MOZ C_IND_West 0.03 1 6
P_MOZ C_IND_South 0 3 7 8 13 6 8
P_MOZ C_THA
P_MOZ C_MYS 3 5 1 2
Chapter 5
Climate Policies and the Global Steam Coal Market: Interactions until 2030
5.1 Introduction
This chapter presents an analysis of the different possible interactions between climate policies and the global steam coal market.63 Worldwide steam coal consumption is con-tinuously growing, drawn by economic growth and increasing electricity demand in Asia.
The use of coal is also a major contributor to global carbon dioxide emissions that have a potentially high adverse effect on the future climate. Hence, the relationship between the coal market and climate policy is of prime importance for the effectiveness of these policies and needs to be investigated further.
In the set of possible interactions we show that a shift of production towards the present due to strategic behavior of coal resource owners as a reaction to future climate policies because they would be able to sell less coal in the future is not to be expected.
We therefore concentrate our analysis on pure supply and demand market effects using the COALMOD-World model described in Chapter 4. This multi-period model of the global steam coal market calculates yearly future market equilibria. We implement three different climate policy shocks in different climate policy and market environments al-ready in place. The scenarios are: a unilateral European climate policy, an Indonesian export-limiting supply-side policy and a carbon capture and storage (CCS) fast roll-out policy.
Our main findings are that coal market adjustments effects can have a potentially adverse effect on climate policy effectiveness in the case of an unilateral European climate policy. However, this effect never overcompensates saved emissions; even a unilateral European climate effort will always be beneficial to the global climate. In the case of the
63This chapter is an updated and modified version of Haftendorn et al. (2011) and a modified version of the forthcoming article Haftendorn et al. (2012b).
Indonesian supply-side scenario and the CCS fast-roll out policy we see positive market adjustment effects that speak for the effectiveness of these less conventional measures.
After having evaluated the different modeling results, we draw conclusions and policy recommendations for the climate policy of the European Union (EU).