Chapter 4 Energy security and potential supply disruption
4.4 Results
For the establishments showing the representative fuel pattern in the iron and steel industry, we estimated the amount of reduction in CO2 emissions associated with the carbon tax without exemptions for coal and coke. In the iron and steel industry, coal is used to produce steel and coke, so these fuels are exempted from the carbon tax; of the chosen electricity and fuels, only electricity is actually subject to the carbon tax. We considered a scenario in which tax is added not only to electricity but also to the currently exempted coal and coke. In this way, we estimate the effect of removing the exemptions from the carbon tax.
Tables 3.6 and 3.7 show the estimated amounts of the reduction in CO2 emissions and energy consumption from the FY2012 level associated with the carbon tax by type of fuel pattern.
Only the energy consumption reduction in the fuel pattern of electricity and town gas in the pulp, paper and paperboard industry is a positive value, but it also shows a very small ratio. We understand the positive value giving meaning to a very small reduction, because a fuel price increase results in demand restraint.
61
In the case of the iron and steel industry in Table 3.6, the estimated amounts of reduction in CO2 emissions and energy consumption from the FY2012 level associated with the carbon tax are 1,404×104 t-CO2 and 149,314×106 MJ, respectively. The reduction rates are 9.8%.
In the case of the pulp, paper and paperboard industry in Table 3.7, the estimated amounts of reduction in CO2 emissions and energy consumption from the FY2012 level associated with the carbon tax are 16×104 t-CO2 and 1,711×106 MJ, respectively. The reduction rates are 0.87% and 0.76%, respectively.
Table 3.6 Reduction in CO2 emissions and energy consumption driven by the carbon tax in the iron and steel industry
Fuel pattern
CO2 emissions in FY2012 (×104t-CO2)
Reduction in CO2
emissions (×104t-CO2)
Energy consumption in
FY2012 (×106 MJ)
Reduction in energy consumption
(×106 MJ) Coke
Coal
Electricity 14,269 -1,404 1,519,387 -149,314
Reduction ratio from
the FY2012 level -9.8% -9.8%
Table 3.7 Reduction in CO2 emissions and energy consumption driven by the carbon tax in the pulp, paper and paperboard industry
Fuel pattern
CO2 emissions in FY2012 (×104t-CO2)
Reduction in CO2
emissions (×104t-CO2)
Energy consumption in
FY2012 (×106 MJ)
Reduction in energy consumption
(×106 MJ) Oil
Coal
Electricity 1,443 -13 158,949 -1,313
Oil Town gas
Electricity 130 -2 15,718 -207
Oil
Electricity 102 -1 18,031 -198
Town gas
Electricity 141 0 33,125 7
Total 1,817 -16 225,824 -1,711
Total reduction ratio
from the FY2012 level -0.87% -0.76%
The counterfactual reduction ratio of CO2 emissions in the iron and steel industry is much larger than that in the pulp, paper and paperboard industry. In addition, the reduction ratio
62
of primary energy is much larger than that of the pulp, paper and paperboard industry. These findings imply that a carbon tax that does not contain an exemption could have a significant effect in reducing energy consumption in the production process and could impose a major burden on the iron and steel industry.
63
As shown in Figure 3.1, some of the energy-intensive industries have reduced their emissions by more than 10% during the 12 years from FY1990 to FY2012 prior to the introduction of Japan’s carbon tax system (Group A industries). Moreover, the reduction rate of 2012 stemming from those reduction measures with negative marginal abatement cost taken by the Chemical Industry Association is 0.6%, which is the lowest rate among the energy-intensive industries in Group A, and if we assume that this rate per year is sustainable during the period of commitment, a 6.5% goal may be achieved even if we remove the current carbon tax system. However, we also need to take into account that firms tend to take reduction measures with relative more negative abatement cost and high reduction rate in the initial stage of reduction effort. Tax incentive is needed and would be effective, especially after firms take most of the emissions reduction measures with negative marginal abatement costs. The sets of reduction measures with negative marginal abatement costs vary across firms. Hence, a tax framework that takes firms’ reduction capacities into account and allows firms to reduce emissions in accordance with their capacity would lead to be optimal.
The results indicate the importance of the emission reduction incentives provided by the availability of measures with negative marginal abatement cost, as these supported Japanese industries’ voluntary reduction efforts. However, not all emissions reduction measures have negative marginal abatement costs. Moreover, for some industries with emissions below 10 million t-CO2 per year, we observe little evidence of emissions reduction resulting from the voluntary approach (See Figure 3.1). In this case, an incentive from the carbon tax might be necessary if businesses are to take an action to reduce CO2 emissions. By asking industries about their expected capacity for reducing emissions and observing actual reduction amounts and variations in marginal abatement costs, the government can classify industries into those that can voluntarily reduce emissions and those that need some incentives to reduce CO2 emissions.
This chapter used industry-level data of Japanese industry to estimate the effect of the voluntary approach taken by the member industries of Keidanren during the period between FY2008 and FY2012. In addition, we have evaluated the possible impact of removing tax exemption on energy consumption under the current carbon tax system, which was introduced in late 2012.
8 The polluter-pays principle means that the polluter should bear the “cost of pollution prevention and control measures” (OECD, 1992).
65
The estimation results suggest that Japanese manufacturing, particularly some of the energy-intensive industries, had achieved a significant share of CO2 emissions reduction through voluntary reduction measures prior to the introduction of the carbon tax. The abated emissions reduction by the measures with negative marginal abatement costs accounted for 50-85% of the total abated emissions reduction by these industries. This implies that a cost saving incentive existed for the industries to adopt emission reduction measures without additional tax incentives.
We estimated the effect of possibly removing exemption for the iron and steel industry by calculating the counterfactual energy consumption of the industry and found that removing the tax exemption would have led to a disproportional decrease in energy consumption. The counterfactual reduction ratio of primarily energy in the iron and steel industry of 9.8% is much larger than that of 0.76% in the pulp, paper and paperboard industry.
66 Appendix 1
Table 3.8.1 Investments for reducing CO2 emissions/energy and their expected effects by four electrical and electronics associations
Investment (nominal value) [×106 ¥] Expected amount of reduction in CO2 emissions [104t-CO2/year]
Measures for reducing CO2 emissions FY2008 FY2009 FY2010 FY2011 FY2012 FY2008 FY2009 FY2010 FY2011 FY2012
Renewable energy 1,309 414 3,106 1,802 1,640 0.1 0.1 0.1 0.2 0.2
Co-generation and heat storage 322 305 99 286 256 0.9 4.0 0.2 0.1 0.9
Introduction of high-efficiency equipment 23,022 16,968 9,433 15,966 9,797 8.7 4.4 4.0 3.9 5.4
Management enhancement 4,197 1,091 973 722 764 21.4 18.0 8.0 11.9 7.1
Production-process improvement 4,438 2,426 1,650 2,144 209 8.3 7.4 8.1 6.7 5.4
Control enhancement 1,260 2,487 1,051 961 540 2.1 2.7 1.8 2.3 1.4
Waste-heat recovery 137 212 252 94 165 0.6 0.4 0.9 0.4 0.5
Energy-loss prevention 550 260 469 719 616 0.7 0.9 0.7 0.6 0.4
Fuel substitution 1,410 2,244 894 293 434 2.8 9.6 0.9 0.1 0.3
Others 670 873 604 1,044 585 6.7 2.6 3.7 3.9 3.6
Data source: Results of the Fiscal 2008-2012 Follow-up to the Voluntary Action Plan on the Environment —Section on Global Warming Measures— (Keidanren, 2009-2013).
67
Table 3.8.2 Investments for reducing CO2 emissions/energy and their expected effects by the Japan Paper Association
Investment (nominal value) [×106 ¥] Expected amount of saved energy [TJ/year]
Measures for reducing CO2 emissions FY2008 FY2009 FY2010 FY2011 FY2012 FY2008 FY2009 FY2010 FY2011 FY2012
Introduction of high-efficiency equipment 1,941 1,433 1,231 948 1,108 752 560 779 643 348
Production-process improvement 1,014 1,027 959 639 446 1401 1,411 1,108 574 460
Waste-heat recovery 621 228 314 312 318 457 464 435 404 290
Thermal-efficiency improvement 635 135 458 220 178 296 146 350 162 433
Management enhancement 107 91 104 119 145 257 155 140 194 131
Others 350 320 188 128 141 268 561 253 134 1,764
Fuel substitution 191 224 324 307 32 942 100 109 228 42
Introduction of high-efficiency equipment (L) 1,972 2,642 2,507 1,492 500 168 251 127 427 197
Production-process improvement (L) 210 0 250 239 300 38 0 238 106 10
Waste-heat recovery (L) 0 540 0 832 0 0 71 0 51 0
Thermal-efficiency improvement (L) 478 0 613 0 0 228 0 159 0 0
Management enhancement (L) 0 0 209 0 0 0 0 0 0 0
Others (L) 0 0 0 0 0 0 0 0 0 0
Fuel substitution (L) 44,496 15,238 0 3,343 2,000 8071 1,566 0 243 277
Data source: Results of the Fiscal 2008-2012 Follow-up to the Voluntary Action Plan on the Environment —Section on Global Warming Measures— (Japan Paper Association, 2009-2013).
68
Table 3.8.3 Investments for reducing CO2 emissions/energy and their expected effects by the Japan Chemical Industry Association
Investment (nominal value) [×106 ¥] Expected amount of saved energy [kl (in a crude oil equivalent)/year]
Measures for reducing CO2 emissions FY2008 FY2009 FY2010 FY2011 FY2012 FY2008 FY2009 FY2010 FY2011 FY2012 Change of conditions such as pressure, temperature, return flow ratio, etc. 21 226 402 1,117 980 10,401 22,671 27,214 22,386 25,793
Reduction of number of operating units 31 168 51 431 91 1,203 8,048 1,233 7,017 4,219
Improvement of production plan 0 55 120 361 225 350 2,408 64 2,383 1,492
Long-term continuous operation and lifetime extension 45 45 45 45 615 179 175 0 1,300
Time savings 3 104 846 107 272 1,890 2,996 806 3,686 8,513
Improvement of control method 75 132 613 848 139 3,770 6,680 9,633 10,455 3,186
Reuse or recycle 260 280 415 168 23 12,066 4,833 2,376 2,531 3,639
Recovery and Usage of waste heat 4,101 1,374 11,311 1,158 887 29,608 15,930 48,307 18,706 12,942
Usage of waste as fuel 430 6,141 0 49 55 6,473 64,774 2,117 11,689 2,128
Thermal energy storage technique 123 565 18 224 0 487 3,090 560 2,045 0
Production-process rationalization 3,808 1,351 17,771 702 286 9,555 21,168 42,874 20,938 4,161
Change of production method 715 250 503 0 0 7,960 283 1,602 0 0
Change of catalyst 198 245 940 983 203 725 1,025 3,697 6,824 3,762
Application of Pinch Technology 16 382 56 37 200 96 6,766 1,350 15,328 1,450
Improvement of equipment performance 702 3,142 339 779 694 6,351 5,722 5,486 4,410 1,734
Efficiency improvement by renewal of equipment or material 1,999 1,897 1,713 2,179 2,973 6,910 15,094 12,392 25,414 17,481
Co-generation 2,337 0 3,500 1,030 1,300 14,919 7,726 8,700 5,901 13,200
Introduction of high-efficiency equipment 17,465 7,103 21,193 19,809 3,364 64,987 73,714 33,093 27,374 10,212
Efficiency improvement of lighting apparatus and motors, etc. 582 273 208 810 599 2,838 19,457 3,405 2,144 6,077
Change of products 2,603 5,186 394 1,464 1,354 58,945 51,997 6,393 19,536 39,196
Data source: Results of the Fiscal 2008-2012 Follow-up to the Voluntary Action Plan on the Environment —Section on Global Warming Measures— (Keidanren, 2009-2013).
69 Appendix 2
Table 3.9 Parameter estimates in equation (3.13) for the iron and steel industry Fuel pattern : coke(K), coal (C), electricity (E)
(1) Pooled model (2) FE model
Parameter coef. s.e. coef. s.e.
αK -2.1870 *** (0.1766) 0.0335 (0.2983)
γKK 0.2753 (0.1682) 0.1436 *** (0.0234)
γKC -0.2790 (0.2083) -0.1061 *** (0.0287)
γKE 0.0037 (0.0488) -0.0375 *** (0.0072)
βK 0.1579 *** (0.0099) -0.0012 (0.0182)
αC 1.4036 *** (0.2084) -1.3540 *** (0.2963)
γCK -0.2790 (0.2083) -0.1061 *** (0.0287)
γCC 0.3134 (0.263) 0.1160 *** (0.0362)
γCE -0.0344 (0.0638) -0.0100 (0.0089)
βC -0.0628 *** (0.0117) 0.1378 *** (0.0181)
αE 1.7835 *** (0.114) 2.3205 *** (0.2098)
γEK 0.0037 (0.0488) -0.0375 *** (0.0072)
γEC -0.0344 (0.0638) -0.0100 (0.0089)
γEE 0.0307 (0.0237) 0.0475 *** (0.0039)
βE -0.0951 *** (0.0064) -0.1366 *** (0.0128)
Obs 244 244
R-sq
production gas 0.5112 0.9875
coal 0.1127 0.9840
LL 171 1063
AIC -329 -2020
BIC -304 -1835
df 7 53
Notes: *, ** and *** indicate significance levels of 10%, 5%, and 1%, respectively; The dummy variables in the fixed effects model are not reported.
Table 3.10.1 Parameter estimates in equation (3.13) for the pulp, paper and paperboard Industry Fuel pattern : heavy fuel oil B・C(O), coal (C), electricity (E)
(1) Pooled model (2) FE model
Parameter coef. s.e. coef. s.e.
αO 0.3636 ** (0.1855) -0.4478 (0.331)
γOO 0.1263 (0.0897) 0.1816 *** (0.0439)
γOC -0.1629 * (0.0886) -0.2328 *** (0.0426)
γOE 0.0366 * (0.0221) 0.0511 *** (0.0114)
βO -0.0120 (0.0122) 0.0398 * (0.0226)
αC 0.2235 (0.2178) 0.8834 ** (0.3759)
γCO -0.1629 * (0.0886) -0.2328 *** (0.0426)
γCC 0.2437 *** (0.0947) 0.3403 *** (0.0448)
γCE -0.0808 *** (0.0235) -0.1076 *** (0.0117)
βC 0.0341 ** (0.0143) -0.0071 (0.0256)
αE 0.4128 (0.0674) 0.5644 *** (0.1281)
γEO 0.0366 * (0.0221) 0.0511 *** (0.0114)
γEC -0.0808 *** (0.0235) -0.1076 *** (0.0117)
γEE 0.0442 *** (0.0095) 0.0564 *** (0.0053)
βE -0.0221 *** (0.0044) -0.0326 *** (0.0087)
Obs 320 320
R-sq
oil 0.0231 0.7963
coal 0.0560 0.8148
LL 449 933
AIC -885 -1721
BIC -858 -1446
df 7 73
Notes: *, ** and *** indicate significance levels of 10%, 5%, and 1%, respectively; The dummy variables in the fixed effects model are not reported.
70
Table 3.10.2 Parameter estimates in equation (3.13) for the pulp, paper and paperboard industry Fuel pattern : heavy fuel oil B・C(O), town gas (T), electricity (E)
(1) Pooled model (2) FE model
Parameter coef. s.e. coef. s.e.
αO -1.7944 *** (0.2272) -1.4621 *** (0.5239)
γOO -0.0329 (0.0708) -0.0429 (0.0382)
γOT 0.1353 (0.0955) 0.0708 * (0.0422)
γOE -0.1024 (0.0651) -0.0279 (0.0271)
βO 0.1664 *** (0.0156) 0.1574 *** (0.0370)
αT 1.4617 *** (0.2202) -0.0988 (0.4932)
γTO 0.1353 (0.0955) 0.0708 * (0.0422)
γTT -0.3441 (0.2301) 0.0768 (0.1029)
γTE 0.2088 (0.1562) -0.1476 ** (0.0742)
βT -0.0909 *** (0.0150) 0.0280 (0.0355)
αE 1.3327 *** (0.1893) 2.5609 *** (0.3423)
γEO -0.1024 (0.0651) -0.0279 (0.0271)
γET 0.2088 (0.1562) -0.1476 ** (0.0742)
γEE -0.1064 (0.1158) 0.1755 *** (0.0571)
βE -0.0756 *** (0.0129) -0.1853 *** (0.0248)
Obs 105 105
R-sq
oil 0.5244 0.9028
town gas 0.2641 0.8727
LL 81 283
AIC -148 -459
BIC -130 -318
df 7 53
Notes: *, ** and *** indicate significance levels of 10%, 5%, and 1%, respectively; The dummy variables in the fixed effects model are not reported.
Table 3.10.3 Parameter estimates in equation (3.13) for the pulp, paper and paperboard industry Fuel pattern : heavy fuel oil type B・C (O) and electricity (E)
(1) RE model (2) FE model
Parameter coef. s.e. coef. s.e.
αO -0.3858 *** (0.0756) -0.7798 *** (0.1084)
εOO 0.1103 *** (0.0080) 0.1187 *** (0.0081)
εOE -0.1103 *** (0.0080) -0.1187 *** (0.0081)
βO 0.0890 *** (0.0059) 0.1203 *** (0.0085)
obs 778 778
R-sq
within 0.3124 0.3199
between 0.4139 0.4072
overall 0.2632 0.2555
Wald χ2(2)
=351.48*** F(2,671)=157.84***
Hausman statistics
χ2(2) =22.51***(p=0.0000) Note: *, ** and *** indicate significance levels of 10%, 5%, and 1%, respectively.
71
Table 3.10.4 Parameter estimates in equation (3.13) for the pulp, paper and paperboard industry Fuel pattern : town gas (T) and electricity (E)
(1) RE model (2) FE model
Parameter coef. s.e. coef. s.e.
αT -1.1497 *** (0.1364) -1.2309 *** (0.1589)
εTT 0.1977 *** (0.0291) 0.2119 *** (0.029)
εTE -0.1977 *** (0.0291) -0.2119 *** (0.029)
βT 0.1482 *** (0.0106) 0.1565 *** (0.0124)
Obs 559 559
R-sq
within 0.3453 0.3453
between 0.2834 0.2825
overall 0.3859 0.3856
Wald
χ2(2) =277.70*** F(2,486)=128.18***
Hausman statistics
χ2(2) =25.28***(p=0.000) Note: *,**,and *** indicate significance levels of 10%, 5%, and 1%, respectively.
72
Table 3.11 Parameter estimates in equation (3.15) for energy aggregate demand elasticity
Industry Fuel pattern Parameter coef. s.e.
Iron and steel Coke (K) L.lnE 0.9470 *** 0.02136
Coal (C) lnP* -0.0686 * 0.03336
Electricity (E) _cons 0.9320 ** 0.38632
obs 220
F(2,23) 1022.20 ***
AR(1) -3.12 *** (p = 0.002)
AR(2) -1.05 (p = 0.293)
Hansen Test χ2(19)=21.80 (p = 0.294)
Pulp, paper and paperboard Oil (O) L.lnE 0.3678 0.22390
Town gas (T) lnP* -0.6678 ** 0.30747
Electricity (E) _cons 9.2617 *** 3.34868
obs 81
Wald χ2(2) 5.74 *
AR(1) -0.02 (p = 0.988)
AR(2) -1.32 (p = 0.188)
Hansen Test χ2(10)=10.79 (p = 0.374)
Oil (O) L.lnE 0.7660 *** 0.02539
Coal (C) lnP* -0.1440 ** 0.05464
Electricity (E) _cons 3.6225 *** 0.38014
obs 285
F(2,31) 556.72 ***
AR(1) -0.85 (p = 0.396)
AR(2) -1.05 (p = 0.292)
Hansen Test χ2(21)=26.59 (p = 0.185)
Oil (O) L.lnE 0.8687 *** 0.04640
Electricity (E) lnP* -0.3473 *** 0.10758
_cons 1.8371 *** 0.66783
obs 673
F(2,98) 917.51 ***
AR(1) -2.90 *** (p = 0.004)
AR(2) -0.85 (p = 0.397)
Hansen Test χ2(64)=66.95. (p = 0.376)
Town gas (T) L.lnE 0.9048 *** 0.03744
Electricity (E) lnP* -0.2283 *** 0.06502
_cons 1.4119 *** 0.51397
obs 488
F(2,63) 1192.28 ***
AR(1) -2.15 ** (p = 0.031)
AR(2) -1.53 (p = 0.126)
Hansen Test χ2(19)=22.77 (p = 0.248)
Notes: *, ** and *** indicate significance levels of 10%, 5%, and 1%, respectively; Arellano-Bond test AR(2) and Hansen test results indicate that there is no further serial correlation and the over identifying restrictions are not rejected.
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to the price and availability of energy (Hamilton, 1996). Europe experienced a severe disruption of natural gas imports from Russia via Ukraine in 2009. The Russia-Ukraine gas crisis in 2006 and the Russia-Belarus oil crisis in 2007 were recognized as wake-up calls to improve energy security (Belkin, 2009). While energy security is a vital question, it is not deterministically defined except by its summed indexing of a country. Multidisciplinary studies in the field of energy security is now common concerning geopolitical, economical, policy related, and technological problems (Kiriyama and Kajikawa, 2014).
Much literature assesses energy security indicators and those indicators can be divided into two types: disaggregated indicators and an aggregated indicator. The literature assesses either or both of the indicator types. For example, Gupta (2008) evaluated the relative oil vulnerability of oil-importing countries on the basis of various indicators: the ratio of value of oil imports to gross domestic product (GDP), oil consumption per unit of GDP, GDP per capita, oil share in total energy supply, ratio of domestic reserves to oil consumption, exposure to geopolitical oil market concentration risks, diversification of supply sources, political risk in oil-supplying countries, and market liquidity. The composite oil vulnerability indicator was computed based on those individual indicators as well. The study showed that there were considerable differences in the values of individual indicators of oil vulnerability and overall oil vulnerability indicator among the countries. Sovacool et al. (2011) developed an index for evaluating national energy security policies and performance among the U.S, the EU, Australia, New Zealand, China, India, Japan, Korea, and the ten countries comprising the Association of Southeast Asian Nations. Their study showed the changes in energy security for eighteen countries from 1990 to 2010. As a result of the evaluation, the U.S. and Japan have improved their performance, while the EU has slightly impaired its performance. Martchamadol and Kumar (2014) applied the Aggregate Energy Security Performance Indicator to assess Thailand’s past and future energy security performance.
Le Coq and Paltseva (2009) introduced an index designed to evaluate the short-term risks associated with the external supply of energy to the EU. The index combined measures of energy
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import diversification, political risks of supplying country, risk associated with energy transit, and economic impact of a supply disruption. The study also constructed separate indexes for three primary energy types, oil, gas and coal. The result implied that aggregate approach used in other studies could be misleading, at least for discussions of the short-term response to risks. Kruyt et al. (2009) classified indicators for energy security according to the availability, accessibility, affordability and acceptability, and pointed out that there is no one ideal indicator because the notion of energy security is highly context dependent. In addition, Jansen and Seebregts (2010) suggest that an integrated approach is needed to evaluate energy security to start out to analyze how to meet the requirements of end-use energy services. They also suggest that the concept of energy services security is proposed with a demand-side focus, while conventional approaches tend to focus on the supply side of primary energy resources. Thus, energy security indicator covers many aspects of energy use and some of the studies focus on the demand side of energy resources.
As the literature reviewed above, there are several components discussion of energy security need to take care of. For example, the Asia Pacific Energy Research Center (2007) classifies energy security into four categories: energy resource availability, accessibility barriers, environmental acceptability, investment cost, and affordability. Sovacool et al. (2011) argue that energy security better be composed of five dimensions: availability, affordability, technology development, sustainability, and regulation. Ang et al. (2015) identify seven major energy security dimensions by investigating 104 studies: energy availability, infrastructure, energy prices, societal effects, environment, governance, and energy efficiency. Ren and Sovacool (2014) determine the most meaningful dimensions of energy security and conclude that availability and affordability are more salient than the dimensions of acceptability and accessibility. While many studies have investigated various aspects of energy security, one of the most important concerns is energy availability. Our study provides an analysis of energy security considering future and past import disruption.
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A variety of models exist to evaluate the energy system. The MARKAL model is commonly used to calculate a specific energy system for a long-term period at the national or regional level (Loulou et al., 2004). Tatematsu (2013) analyzed Japan’s long-term demand and supply by applying the MARKAL model. The study showed the feasibility of avoiding the oligopolization of an energy source and suppressing the steep increase of the average power generation cost using NPG with a fixed scale until 2050. Lochner (2011) studied the European natural gas market during the 2009 Russian-Ukrainian gas conflict using the linear optimization technique. Richter and Holtz (2015) studied scenarios involving disruption of the Russian natural gas supply to Europe by using the Global Gas Model. The calculation was conducted in five-year steps from 2010 to 2040.
In this analysis, we use the entropy model to simulate a limited energy resource allocation for short-term scenarios. Månsson et al. (2014) investigated commonly used methodologies for energy security and pointed out that short-term threads and problems related to interregional trade were disguised in the granularity and iteration steps of energy system models.
While energy security needs to be addressed in a comprehensive manner and from a long range point of view, preparation for sudden energy resource supply disruption is also mandatory.
Therefore, the analysis based on short-term supply disruption scenarios is useful to work out countermeasures. In the case of limited-resource disruption, we expect that domestic suppliers attempt to avoid limited-resource maldistribution among consumers. While the linear optimization technique is unavailable to simulate such a situation, the entropy model can do simulate the distribution in a way that demand is not disproportionate across consumers. It is therefore useful to solve the limited resource allocation problem. For example, the entropy model was used in the study of the gasoline shortage in the Tohoku region after the Great East Japan Earthquake (Akamatsu et al., 2013). The study revealed that the amount of gasoline supplied in the Tohoku region during the first two weeks after the earthquake was only one third of the normal demand. The entropy model was constructed to estimate the amount of gasoline supplied from oil
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terminals to each municipality, because shipping planners seem to have allocated gasoline with the aim of reducing imbalances in the demand-supply gap between the municipalities rather than simply minimizing costs during the post-earthquake period. Entropy is interpreted as the degree of disorder. We reinterpret maximizing entropy as realizing unbiased allocation. In this study, we also apply the linear optimization technique to simulate the distribution of electric power sources by electric utilities.
The world energy trade may have two distinctive features: high energy dependency on the Middle East and high occupancy in a type of energy-resource import by one country for some importers. Oil from the Middle East accounted for 26% of the total oil import in the U.S., 21% in France, 82% in Japan, 86% in Korea in 2013 (IEA, 2014), and 52% in China in 2014 (EIA, 2015).
In addition, LNG from the Middle East accounted for 30% of the total LNG import in Japan and 53% in Korea in 2014 (BP, 2015). Furthermore, there are large energy-resource exporters for an import country. Europe imports natural gas by pipeline and LNG carrier. Natural gas from the Russian Federation accounted for 36% of the total natural gas import in Europe in 2014 (BP, 2015). LNG from Qatar accounted for 35% of the total LNG import in Korea in 2014. Thus, the supply disruption of some countries in the Middle East and that of the large energy-resource exporter should be taken into consideration, when energy availability is considered.
In this study, we consider two kinds of potential supply-disruption scenarios: the supply disruption of some countries in the Middle East and that of the largest volume from the exporting country in each type of energy resource. This study intends to show how to manage a variety of supply disruption events through the results of the analysis. This scenario-based analysis can be applied to the countries corresponding to the aforementioned situations such as European countries and Northeast Asian countries.
Energy security is primary concern for large energy importers. Ang et al. (2015) show that majority of the energy security studies deal with Europe, the U.S., and China. While the combination and dependency of foreign energy resources vary by country, the self-sufficiency
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ratio in the primary energy supply indicates the degree of dependency on foreign countries. Japan has one of the lowest self-sufficiency ratios for primary energy supply (i.e., 6%) (METI, 2015).
Therefore, we choose Japan as the subject of this study and conduct the analysis based on the two kinds of scenarios mentioned above.
This chapter is structured as follows. Section 4.2 explains the method for evaluating the impact of supply disruption. Section 4.3 provides a description of the data. Section 4.4 presents the evaluation results. Section 4.5 provides a discussion based on the results of Section 4.4.
Section 4.6 presents the conclusions.
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period of time. We evaluate the consequence 3 months after the event occurrence. As IEA requests that the member countries stockpile oil for at least 90 days (IEA, 2012), we use the point of time to evaluate the energy security vulnerability. We consider various assumption scenarios2 under the following conditions to realize the adverse effects in the respective sectors: the supply disruption starts in April of the first year; we expect no fuel substitutions except in power generation and no domestic supply prioritization; the export of oil products to foreign countries from Japan is ceased after the supply disruption arises; the government and firms release oil stockpiled immediately after the supply disruption arises.
4.2.2 Scenarios
4.2.2.1 One-country supply disruption (OCSD) condition
We assume supply disruption of the largest export country to Japan in each type of energy resource.
The three countries with the largest oil export to Japan are Saudi Arabia, the United Arab Emirates and Qatar in fiscal year 2014. Here, we assume the scenario of supply disruption in Saudi Arabia.
The three countries with the largest LNG export are Australia, Qatar and Malaysia. The top three coal exporting countries are Australia, Indonesia and Russia. As the largest volume for both LNG and coal exports comes from Australia, we assume that the supply from Australia is disrupted. By setting the top export country, the evaluation can cover the consequences of a supply disruption (reduction) of a country whose export flow is less than that of the top export country and in which the country risk is higher. We also examine OCSR of the largest export country to Japan.
4.2.2.2 Multiple countries supply disruption (MCSD) condition
Many of oil exporting countries are characterized by high degree of political instability and the chock points are susceptible to shipping accidents and terrorist attacks in their narrow channels (Gupta, 2008). Japan heavily depends on oil and LNG from the Middle East. Oil and LNG from
2 The main calculation assumptions are provided in Appendix.