4. What does the project Seek to achieve ? The objective of the project is to extend our previous study on Short Sea Shipping as suggested in its original proposal, which was funded by APEC Transportation Working Group in 2007 New Phase study aims Develop Intermodal transportation network model Enhance more seamless, efficient and effective interconnectivity among various modes Reducing congestion, pollution, noise and other externalities Addressing the issue of the green house gas(GHG) emission From transportation sources to respond to upcoming regulation on, international shipping by the IMO surface transportation modes by the Kyoto protocol 1 2 Using this model, total transportation/ logistics cost in a given economy can be minimized and various policy options and practices are test and analyzed for the optimal policy formulation

5. Intermodal Transportation Network in Korea Network in Korea

6. Incheon Seoul Intermodal Transportation Network port Busan port Uiwaing ICD Quing dao port Shanghai port Yokohama port Osaka port Long Beach port Seattle port South Korea Japan China United States (Sample Network of Export) 7

7. Ports / Cities / ICDs The oversea ports (34) Countries Cities Western Europe Eastern Europe North America United States South America Central Asia Africa Japan Hong Kong China South East Asia Amsterdam Hamburg Vancouver Houston, Detroit Long Beach, New York Savannah, Seattle Santos Korean Regions (11) Seoul Busan Incheon Gyunggi Gangwon Chungbuk Jeonbuk Gyunnam Chungnam Jeonnam Gyungbuk Jeddah Singapore, Klang, Other seaports Durban Osaka, Yamaguchi, Yokohama, Nagoya, Hakata, Tokyo Other seaports Keelung, Kaosiung, Other seaports Ningbo, Shanghai, Qingdao, Xingang, Weihai, Yantai, Other seaports Korean seaports (5) Busan Gwangyang Incheon Ulsan Pyeoungtak ICDs (2) Uiwang Yangsan 8

8. Model Formulation

9. Validation External cost Carbon Tax Emission Trading Scheme 9

10. Validation Model

11. Validation Model (Notations) s et of externalities s et of seaports in Korea s et of overseas ports s et of ICDs s et of transport mode s : {1, 2, 3, 4} where 1 denote truck, 2 train, 3 barge, and 4 liner ship s et of regions Sets internalization ratio of the external cost logistics c ost of transport mode m from node i to j [US $/TEU] where TEU is a twenty foot equivalent unit i mport or export amount from origin i to destination j [TEU] external cost of externality e of transport mode m [US $/TEU-km] CO 2 emission limit [ton] CO 2 emissions from transport mode m [ton/TEU-km] f reight rate per TEU of transport mode m from node i to j [US $/TEU] distance from node i to j [km] price of CO 2 emission permits in the CO 2 trading market [US $/ton] t ransit time of transport mode m from node i to j [day] tax on CO 2 emission s [US $/ton] daily time cost of a container [US $/TEU] index for Uiwang ICD index for Yangsan ICD CO 2 emission s from all inland transport modes higher than the emission limit [ton] CO 2 emission s from all inland transport modes lower than the emission limit [ton] total container movements by transport mode m [TEU-km] container volume transported by transport mode m from node i to j among trad ing volumes originating from a destined to b [TEU] Coefficients Decision Variables 10

12. Validation Model (Objective Function) Minimize the sum of the freight rate and time cost of all containers transported via all transport modes and routes [P1] Minimize subject to (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (1) 11

13. External Cost Model

14. Model Structure Pollution Accident Noise Congestion Willingness to Pay Method Internalizing External Cost Analyzing the impact on Modal Shift 12

15. We assume that the government imposes a tax on the external cost (as the internalization ratio of the external cost) to carriers and carriers will pass the tax subsequently onto shippers in the form of surcharge Internalization ratio of the external cost External cost of externality e of transport mode m [US$/TEU-km] Distance from node i to j [km] 13

16. Carbon Tax Model

17. The Carbon Tax can be incorporated into the model by including the tax into the logistics cost with the same assumption made in the external cost model Tax on CO2 emissions [US$/ton] CO2 emissions from transport mode m [ton/TEU-km] Distance from node i to j [km] 14

18. Emission Trading Scheme Model

19. If the total amount of the emissions is more than the limit, the higher emitter should purchase extra permit from lower emitters who have the surplus CO2 emission permits in a trading market The purchased amount of CO2 emissions if TKm is the total container movement by mode m (12) (13) (14) 15

20. The purchased amount of CO2 emissions Extra incurring cost from purchasing CO2 emission permit Extra incurring revenue from selling CO2 emission permit 16

21. Allocated cost and revenue are proportionally allocated to each container by considering its travel distance as follows and ** transport mode contrary to mode m in terms of CO2 emission Finally, the logistics cost per TEU in the emission trading scheme model is as follows 17

22. Data Collection

23. 3 transportation modes (truck, train, barge) for inland transportation, one mode (mainline ship) for sea shipping 2 ICDs 34 foreign seaports 5 domestic seaports 11 domestic region Year 2009 import and export container cargo data Data structure 18

24. Import amount/TEU Origin DestinationSeoulBusanIncheonGGGWCBCNJBJNGBGN USA Houston756342101443101001503935299435 Detroit00000000000 Long Beach395731545265572448848656758183495254931364919565 New York7449304911404599951047155575681426913870 Savannah12676491520028828166177329691187126044216270 Seattle164524507369810356156280634524854574840485614 West Europe Amsterdam307961145751871825635247506311547662731010014471 East Europe Hamburg435551685174992630952562548684581265041482621281 Africa Durban703428931147424589957138465069425393660 Central Asia Jeddah865929831700532797131017351622187326383748 SE. Asia Singapore165715606390610818196214530672058230149717471 Klang179944826618613608219175728371273138243246329 others952472873228992694231184994315825641268532556036334 N. America Vancouver2266689613562133652713369461532553656788011344 S. America Santos24520107983657146963293163478012221125945113694 China Dalian228265988681119966296252651211641174656417681 Ningbo1605345315425121792021502251075376840415728 Shanghai63813178242055550156807637311310364737891615023212 Qingdao56611120741891355309745597213760391941961194515302 Xingang936388149562121281858691341492 Yantai134971495669114337173873246536237616031628 Weihai21336167811756227582671112329445048818771580 others151122283796147014224619221287328494762281572738734631 Japan Osaka147176081260791331931852279986785153357744 Yamaguchi4742213763627756363194226625218692715 Yokohama143275677283591101861746266384182849927352 Nagoya8330310618945475107964149547346427414148 Hakata28971262421167737390573219 11091742 Tokyo2179347982183724192307249293322420 others289911170350431761436638685626264328011029515408 HongKong Honkong212985424785516838266187832221004105748606567 Taiwan Keelung53811690159138896855789330231214982112 Kaohsiung95963200245065391181142170888497028324033 others2038472568135819326398598712427577 Source: Statistical Yearbook of International Trade and Logistics published by Korea Customs Trade and Development Institute (KCTDI, 2010) and publically available web database of Korea Ministry of Land, Transport and Marine Affairs (http://www.spidc.go.kr:10443/). 19

25. Export amount/TEU Origin DestinationSeoulBusanIncheonGGGWCBCNJBJNGBGN USA Houston8740227051549112984162 Detroit31120020135 Long Beach247729695584518840113446661478810058503472220139532 New York51332320127041122769112908667196249399455 Savannah44081991108835292367832500587177042418115 Seattle3455155785227631856141961477148533206344 West Europe Amsterdam2056281774999157979603810121397359359281849433359 East Europe Hamburg3486413637893526939160963342044811856574153080055566 Africa Durban2272572438980184879003315120954249182241624529340 Central Asia Jeddah53641207181544342260247292523043214144647034611884316 SE. Asia Singapore131605041360110183598230874673921185541133821681 Klang1181932584847925340916335929259312126760917243 others8745924207378967017530831146844033127785170455156121652 N. America Vancouver161217094394412781840289892383000115691530428909 S. America Santos4060416237120323247819476838225437563303293539966058 China Dalian91891788410972832481044589213535211431711236 Ningbo1046416754150681921712664317335017855484825116 Shanghai386028552155202903311114963217039145433032106361221 Qingdao28883373314953231855822850185305154226521004725899 Xingang11272471665392817245046024666984735 Yantai878679064967740147578346333084821583095 Weihai12110541925810743149663556037959318701886 others67206110993242553584160272244242611496489242801271323 Japan Osaka81103380230064754051363444410753428726714492 Yamaguchi159272439412778628290119252215362948 Yokohama38001422125830431736002020571211831066196 Nagoya45341652153336042016982359617218336167702 Hakata2311102457018391214131318387140422004174 Tokyo92413386301972744111441481713494971743416312 others16831713845191331185328639222217567121532931796 Honkong 29920677011924225918713980169138219401901676444773 Taiwan Keelung5754186516614199222951306819819921440010886 Kaohsiung5986226816384639269106534572027987151459257 others39407921032263487800256842572400825773250 Source: Statistical Yearbook of International Trade and Logistics published by Korea Customs Trade and Development Institute (KCTDI, 2010) and publically available web database of Korea Ministry of Land, Transport and Marine Affairs (http://www.spidc.go.kr:10443/). 20

26. Distance / km (a) Between ICD/Port and Region (b) Between ICD/Port and Port SeoulBusanIncheonGGGWCBCNJBJNGBGN ICD Uiwang 34.7312.093.3114.7160.0132.0112.0162.7265.3272.0241.3 Yangsan 286.725.3394.7360.0393.3245.3357.3266.7297.3108.0 Port Busan411.220.0418.4364.0412.0277.6271.2268.8328.8114.480.8 Gwangyang361.6176.0360.8356.8504.8260.0203.2120.8171.2191.2120.8 Incheon46.4412.020.847.2175.2177.6187.2245.6286.4283.2356.8 Ulsan420.892.0427.2405.6423.2320.0284.0339.2401.6160.0120.8 Pyeongtaek336.8360.092.080.8187.2112.8120.0177.6248.0262.4336.8 Port BusanGwangyangIncheonUlsanPyeongtaek Yangsan ICD57.6 --- Port Busan-224.0752.0-728.0 Gwangyang224.0-665.0-641.0 Incheon752.0665.0--70.0 Ulsan----- Pyeongtaek728.0641.070.0-- Source: website of Sea Rates (http://www.searates.com/) Source: website of Naver (http://map.naver.com/) 21

27. Transit Time and Freight Rate for Inland Mode The transit time between inland nodes was calculated using the distance divided by the speed of corresponding transport modes, i.e., Transit time : truck: 80 km/h, train: 50 km/h, and barge: 23.5 km/h loading/unloading time : truck: 3 h, train: 9 h, and barge: 6 h Since train is not connected between all nodes, the connectivity between nodes for train is given in the below table Lim (2004) Region Port SeoulBusanIncheonGGGWCBCNJBJNGBGN Busan - * ---OOOOOOO Gwangyang - O ** ---OOOO-- Incheon -O--------- Ulsan -O--OOO--OO Pyeongtaek -O--------- * no direct connection * * directly connected 22

28. Transit Time(day) and Freight Rate (US$/TEU) for Inland Mode PortBusanGwangyangIncheonUlsanPyeongtaek USA Houston18.73(2393) * 18.89(2449)- ** -- Detroit17.70(2537)17.81(2593)--- Long Beach15.80(1657)15.98(1780)--- New York18.45(2657)18.54(2667)--- Savannah19.51(1732)19.63(1788)--- Seattle13.77(1657)13.93(1780)--- West Europe Amsterdam30.00(1042)31.00(1078)--- East Europe Hamburg30.00(1257)31.00(1380)31.00(1447)-- Africa Durban25.00(1557)26.00(1680)--- Central Asia Jeddah26.00(1857)25.00(1880)31.00(1947)-- Southeast Asia Singapore5.00(707)9.00(730)10.00(747)7.00(740)- Klang9.00(851)10.00(811)9.00(899)10.00(815)- others7.70(779)7.70(739)7.70(826)7.90(742)- North America Vancouver10.00(1657)11.00(1780)--- South America Santos30.00(2357)31.00(2480)--- China Dalian1.28(348)1.14(471)0.75(438)1.28(435)0.76(448) Ningbo1.49(364)1.32(367)1.58(400)1.58(380)- Shanghai1.35(348)1.16(471)1.37(438)1.43(435)1.28(448) Qingdao1.30(348)1.11(471)0.95(438)1.34(435)0.90(448) Xingang1.83(348)-1.73(438)-1.65(448) Yantai1.20(452)-0.75(521)1.22(480)0.72(511) Weihai1.11(428)0.95(403)0.65(492)1.13(405)0.62(488) others1.70(385)1.60(376)1.20(449)1.80(380)1.20(496) Japan Osaka0.96(548)1.16(571)1.40(588)0.91(535)- Yamaguchi0.40(609)0.50(632)1.40(648)0.40(595)- Yokohama1.58(548)1.78(571)1.95(588)1.52(535)- Nagoya1.16(548)1.36(571)1.57(588)1.11(535)1.58(468) Hakata0.34(448)---- Tokyo1.59(548)1.80(571)1.95(588)1.54(535)- others1.20(548)1.40(543)2.20(645)1.20(542)2.20(475) Honkong 3.32(548)3.15(571)3.38(588)3.41(665)3.30(598) Taiwan Keelung2.15(557)2.01(580)2.37(597)2.23(544)- Kaohsiung2.67(557)2.53(580)2.88(597)2.76(544)- others18.73(2393)18.89(2449)18.61(2537)18.64(2459)- Source: website of Schedule Bank (http:// www.schedulebank.com/) 24

29. External Cost (US$/TEU-km) TruckTrainBarge Pollution0.7890.2180.425 Congestion0.9140.000 Accidents0.4060.1050.000 Noise0.2880.1340.000 Wear and tear0.0880.000 The external cost was calculated by multiplying 36.14 (ton/TEU), 1.2 (US $/EURO), and the external cost (EURO/ton-km) in Beuthe et al. (2002). 25

30. Test results and Implications and Implications

31. Validation ModelReal (year 2009)Model-Real Truck89.8%90.1%-0.3% Train8.4%7.5%0.9% Barge1.8%2.4%-0.6% Comparison Between Validation Model’s Result and Real Modal Splits The model generates a similar transport modal split to the real modal split in Korea in 2009 obtained from the website of Korea Maritime Institute (http://www.kmi.re.kr) Therefore, the validation model is deemed to suffice to reflect the real situation in Korea and hence the extended models of respective external cost, carbon tax, and emission trading scheme can be further tested 26

32. Effect of Taxation of External Cost Internalization ratio Modal splitTotal external cost TruckTrainBargeUS $Reduction ratio 0.089.8%8.4%1.8%1,979,457,974.5 a 0.0% 0.180.4%17.8%1.7%1,528,970,569.9 b -22.8% 0.276.6%18.4%4.9%1,365,868,713.7 b -31.0% 0.364.4%31.2%4.4%1,111,425,072.6 b -43.9% 0.462.9%33.0%4.1%1,079,725,639.7 b -45.5% 0.562.0%34.1%3.9%1,062,882,179.8 b -46.3% 0.642.1%54.2%3.8%783,090,973.9 b -60.4% 0.739.9%56.3%3.8%740,176,901.0 b -62.6% 0.839.9%58.2%1.8%708,121,474.6 b -64.2% 0.939.8%58.4%1.8%705,295,432.0 b -64.4% 1.038.5%60.4%1.1%673,451,621.7 b -66.0% ( a / b ) / a * 100% 27

33. Effect of Carbon Tax Regulation Carbon tax (US $/ton) Modal splitTotal CO 2 emissions TruckTrainBargetonReduction ratio * 089.8%8.4%1.8%1,648,733.5 a 0.0% 10082.6%16.8%0.6%1,355,374.4 b -17.8% 20080.2%19.2%0.6%1,299,523.2 b -21.2% 30079.2%20.2%0.6%1,271,809.2 b -22.9% 40078.1%21.3%0.6%1,232,194.4 b -25.3% 50067.1%32.9%0.0%1,099,448.6 b -33.3% 60064.8%35.2%0.0%1,022,488.2 b -38.0% 70063.1%36.9%0.0%1,000,540.7 b -39.3% 80062.6%37.4%0.0%996,039.1 b -39.6% ( a / b ) / a * 100% 28

34. CO2 price (US$/ton) Emission Limit factor Modal ShiftTotal CO2 Emission TruckTrainBarg e tonRegulation ratio 089.8%8.4%1.8%1,648,733.5 a 0.0% Effect of Emission Trading Scheme Regulation (a) Actual CO2 price in recent years 10 0.989.8%8.4%1.8%1,648,733.5 b 0.0% 0.789.8%8.4%1.8%1,648,733.5 b 0.0% 0.589.8%8.4%1.8%1,648,733.5 b 0.0% 20 0.989.8%8.4%1.8%1,648,733.5 b 0.0% 0.789.8%8.4%1.8%1,648,733.5 b 0.0% 0.590.9%8.5%0.6%1,609,064.6 b -2.4% 30 0.989.8%8.4%1.8%1,648,733.5 b 0.0% 0.789.8%8.4%1.8%1,648,733.5 b 0.0% 0.589.6%9.8%0.6%1,568,108.9 b -4.9% ( a / b ) / a * 100% 29

35. CO2 price (US$/ton) Emission Limit factor Modal ShiftTotal CO2 Emission TruckTrainBarg e tonRegulation ratio 089.8%8.4%1.8%1,648,733.5 a 0.0% Effect of Emission Trading Scheme Regulation (b) High CO2 price 200 0.989.8%8.4%1.8%1,648,733.5 b 0.0% 0.789.0%10.4%0.6%1,546,708.5 b -6.2% 0.588.8%10.5%0.6%1,525,551.4 b -7.5% 500 0.984.4%13.8%1.8%1,499,263.1 b -9.1% 0.788.8%10.5%0.6%1,529,772.1 b -7.2% 0.588.4%11.0%0.6%1,399,639.6 b -15.1% 800 0.984.2%13.6%2.2%1,516,653.8 b -8.0% 0.786.2%13.2%0.6%1,445,549.3 b -12.3% 0.584.1%15.2%0.6%1,284,681.6 b -22.1% ( a / b ) / a * 100% 30

36. Result of External Cost Model The train’s share sharply increases absorbing the share of truck and even barge. The barge increases in the share are not remarkable even though the share increases from current 1.8% to 4.9% when the internalization ratio is 0.2 then begins to decline gradually to 1.1% when the tax is levied to reflect the whole external costs This may have been caused by the double size pollution effect of barges compared with train although the barge incurs no congestion, accidents, noise, and wear and tear 31

37. The external costs incurred by all inland transport modes decrease sharply along with the internalization ratio’s increase. The initial reduction of external costs is remarkable, for instance with 10% internalization, the external costs are reduced by 23%. The reduction of the external costs increases gradually until 50% of internalization ratio then it appears to reach saturation point when internalization ratio is beyond 60%. Note that we did not change all possible links of trains and barge in the test in order to show the results under the current transportation network system. However, if all possible links of trains and barge have been connected, the total external costs would have been much more reduced than the result. Result of External Cost Model 32

38. Result of the Carbon Tax and ETS Models We examined which regulation between a carbon tax and emission trading scheme is more effective to reduce CO2 emission Carbon Tax Model Emission Trading Scheme Model (Under the scenario of different tax) (Using the heuristic procedure under the scenario of different CO2 prices and emission limit factors) 33

39. In case of the carbon tax model, the share of train increases significantly absorbing most of shares of truck and barge and the total CO2 emissions from all inland transport modes decline along with the increase of the carbon tax On the other hand, the change in the modal split and the total CO2 emissions under the emission trading scheme model with actual CO2 market prices in recent years is not significant even though the share of train increases slightly as the CO2 price increases and emission limit factor decreases Result of the Carbon Tax and ETS Models 34

40. This result implies that shippers’ modal and route choice may not be significantly affected from an emission trading scheme regulation although the Korean government enforces the regulation and moreover some revenue gained from selling CO2 emission permits is allocated to shippers in the form of freight rate reduction if current CO2 market prices are maintained Comparing the results of the carbon taxation and ETS, the emission trading scheme appears to be a less effective instrument than the carbon tax to reduce CO2 emissions in the transportation sector Result of the Carbon Tax and ETS Models 35

41. Result of the Carbon Tax and ETS Models (under the condition of carbon prices is as high as the carbon tax) This is somewhat surprising result in contrast with those views supporting ETS(Council of the European Union, 2008) The result may have been caused by too low CO2 prices even though the price ranges are based on actual CO2 market prices in recent years There will be an argument that if the carbon prices were as high as the carbon tax, the results of the ETS would have been much different, so we test this argument using much higher carbon prices in the ETS model The result show that total CO2 emissions from all inland transportation modes declines significantly along with the increase of the CO2 price, while the modal shift is not significant 36

42. EU ETS carbon spot price (2008-2013) Source: Thompson Reuters Point Carbon

43. The transport policy should be directed toward the inclusion of the external costs into carriers’ pricing to reduce the externalities. Just a mere initial low percentage of taxation of the external costs would result in significant reductions of the externalities. The excessive taxation on the external cost, however, may not be a good policy instrument for more use of barge and advisable modal shift. Therefore, an optimal amount of tax should be explored looking into not only the reduction of external costs, but also the balance in modal split. Furthermore, the changes in modal split and external costs will be affected by extended linkage among the nodes and expansion of transportation structure along with the enhanced efficiency of the modes. Policy Implication 37

44. International organization and governments should devise some policy instruments to increase the CO2 price which is stagnant around several ten US$/ton in the current market The Korean government should develop a scheme of sharing the burden and benefit of the extra cost and revenue between transport modes arising from the ETS to balance the modal split Policy Implication 38

45. Limitation of study The limitation of this study is using parameters of the external costs from other studies Future study should conduct our own estimation of the external cost function 39

46. It is the end of the slides