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Pricing and energy efficiency decisions by manufacturer under channel coordination
A hierarchic framework for the propagating impacts of the China-U.S. trade war on volume of Chinese containerized exports
1. | School of Transportation Science and Engineering Beihang University, Beijing, 100191, China |
2. | Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China |
3. | Civil Engineering Department Dalian University of Technology, Dalian, 100191, China |
4. | School of Traffic and Transportation Beijing Jiaotong University, Beijing, 100044, China |
5. | Transport Planning and Research Institute, Beijing, 100028, China |
The China-U.S. trade war between the world's two largest economies has received increasing attention. Due to the existing interdependencies within economic sectors, the trade war could bring about ripple effects and cause more damaging impacts than intuitive thoughts. By integrating Inoperability Input-output Model (IIM) and Partial Least Squares Regression (PLSR), we developed a hierarchic IIM-PLSR framework in this study to unravel the ripple effects of the China-U.S. trade war on volume of Chinese containerized exports. The results show that the China-U.S. trade war will affect the operability and output value of not only the tariff-targeted industries but the other interdependent industries. Contrary to expectations, the results show that the China-U.S. Trade War have an insignificant influence on the volume of containerized exports. Even in the worst scenario, the reduction percentage of containerized exports due to China-U.S. trade war is only 0.335%. This study brings fresh insights to stakeholders in the port industry for the implementation of rational port planning policies.
References:
[1] |
S. Aksoy and Y. Durmusoglu,
Improving competitiveness level of Turkish intermodal ports in the Frame of Green Port Concept: A case study, Maritime Policy & Mgmt., 47 (2020), 203-220.
doi: 10.1080/03088839.2019.1688876. |
[2] |
D. Artuso, Y. Borbon-Galvez, J. Ferencz, M. Langeveld, C. Sys, T. Vanelslander and B. Zondag, Study on the Analysis and Evolution of International and EU Shipping, Technical report, Brussels: European Commission, 2015. Available from: https://ec.europa.eu/transport/sites/transport/files/modes/maritime/studies/doc/2015-sept-study-internat-eu-shipping-final.pdf. Google Scholar |
[3] |
S. Athanasatos, S. Michaelides and M. Papadakis,
Identification of weather trends for use as a component of risk management for port operations, Natural Hazards, 72 (2014), 41-61.
doi: 10.1007/s11069-012-0491-z. |
[4] |
BBC News, Trump Steel Tariffs: Trading Partners Threaten Retaliation, 2018. Available from: https://web.archive.org/web/20180302081539/http://www.bbc.com/news/world-us-canada-43251320. Google Scholar |
[5] |
A. Calatayud, J. Mangan and R. Palacin,
Vulnerability of international freight flows to shipping network disruptions: A multiplex network perspective, Transportation Res. Part E: Logistics Transport. Rev., 108 (2017), 195-208.
doi: 10.1016/j.tre.2017.10.015. |
[6] |
L. M. Carrascal, I. Galván and O. Gordo,
Partial least squares regression as an alternative to current regression methods used in ecology, Oikos, 118 (2009), 681-690.
doi: 10.1111/j.1600-0706.2008.16881.x. |
[7] |
C. C. Chou, A mixed fuzzy expert system and regression model for forecasting the volume of international trade containers, Internat. J. Innovative Comput. Infor. Control, 6 (2010), 2449-2458. Google Scholar |
[8] |
M. Darayi, K. Barker and C. D. Nicholson,
A multi-industry economic impact perspective on adaptive capacity planning in a freight transportation network, Internat. J. Prod. Econ., 208 (2019), 356-368.
doi: 10.1016/j.ijpe.2018.12.008. |
[9] |
C. Ducruet,
The polarization of global container flows by interoceanic canals: Geographic coverage and network vulnerability, Maritime Policy & Mgmt., 43 (2016), 242-260.
doi: 10.1080/03088839.2015.1022612. |
[10] |
A. Estache, L. Trujillo and E. Quinet, Forecasting the Demand for Privatized Transport: What Economic Regulators Should Know, and Why, Technical report, The World Bank, 2000.
doi: 10.1596/1813-9450-2446. |
[11] |
Y.-Z. Feng, G. ElMasry, D.-W. Sun, A. G. M. Scannell, D. Walsh and N. Morcy,
Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets, Food Chemistry, 138 (2013), 1829-1836.
doi: 10.1016/J.FOODCHEM.2012.11.040. |
[12] |
Y. Gong, K. X. Li, S.-L. Chen and W. Shi, Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war, Transportation Res. Part E: Logistics Transport. Rev., 136 (2020).
doi: 10.1016/j.tre.2020.101900. |
[13] |
X. Gou and J. S. L. Lam,
Risk analysis of marine cargoes and major port disruptions, Maritime Economics & Logistics, 21 (2019), 497-523.
doi: 10.1057/s41278-018-0110-3. |
[14] |
M. Greenberg, C. Haas, A. Cox Jr., K. Lowrie, K. McComas and W. North,
Ten most important accomplishments in risk analysis, 1980-2010, Risk Analysis, 32 (2012), 771-781.
doi: 10.1111/j.1539-6924.2012.01817.x. |
[15] |
M. S. Haggerty, J. R. Santos and Y. Y. Haimes,
Transportation-based framework for deriving perturbations to the Inoperability Input-Output Model, J. Infrastructure Syst., 14 (2008), 293-304.
doi: 10.1061/(ASCE)1076-0342(2008)14:4(293). |
[16] |
Y. Y. Haimes, B. M. Horowitz, J. H. Lambert, J. R. Santos, C. Lian and K. G. Crowther,
Inoperability Input-Output Model for interdependent infrastructure sectors. I: Theory and methodology, J. Infrastructure Syst., 11 (2005), 67-79.
doi: 10.1061/(ASCE)1076-0342(2005)11:2(67). |
[17] |
C. Izaguirre, I. J. Losada, P. Camus, P. González-Lamuño and V. Stenek,
Seaport climate change impact assessment using a multi-level methodology, Maritime Policy & Mgmt., 47 (2020), 1-14.
doi: 10.1080/03088839.2020.1725673. |
[18] |
Y. Jiang, J.-B. Sheu, Z. Peng and B. Yu,
Hinterland patterns of China Railway (CR) express in China under the Belt and Road Initiative: A preliminary analysis, Transportation Res. Part E: Logistics Transport. Rev., 119 (2018), 189-201.
doi: 10.1016/j.tre.2018.10.002. |
[19] |
A. John, D. Paraskevadakis, A. Bury, Z. Yang, R. Riahi and J. Wang,
An integrated fuzzy risk assessment for seaport operations, Safety Science, 68 (2014), 180-194.
doi: 10.1016/j.ssci.2014.04.001. |
[20] |
J. Jung, J. R. Santos and Y. Y. Haimes,
International trade inoperability input-output model (IT-IIM): Theory and application, Risk Analysis, 29 (2008), 137-154.
doi: 10.1111/j.1539-6924.2008.01126.x. |
[21] |
P. Kaluza, A. Kölzsch, M. T. Gastner and B. Blasius,
The complex network of global cargo ship movements, J. Roy. Soc. Interface, 7 (2010), 1093-1103.
doi: 10.1098/rsif.2009.0495. |
[22] |
H. L. Kee, A. Nicita and M. Olarreaga,
Import demand elasticities and trade distortions, Rev. Econ. Stat., 90 (2008), 666-682.
doi: 10.1162/rest.90.4.666. |
[23] |
W. Leontief, Input-Output Economics, Oxford University Press, 1986.
doi: 10.1038/scientificamerican1051-15.![]() |
[24] |
C. Lian and Y. Y. Haimes,
Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input-output model, Systems Engineering, 9 (2006), 241-258.
doi: 10.1002/sys.20051. |
[25] |
F. Lu,
China-US trade disputes in 2018: An overview, China & World Economy, 26 (2018), 83-103.
doi: 10.1111/cwe.12257. |
[26] |
M. Manuela González and L. Trujillo,
Reforms and infrastructure efficiency in Spain's container ports, Transportation Res. Part A: Policy and Practice, 42 (2008), 243-257.
doi: 10.1016/j.tra.2007.08.006. |
[27] |
H. Martens,
Reliable and relevant modelling of real world data: A personal account of the development of PLS Regression, Chemometrics Intell. Laboratory Syst., 58 (2001), 85-95.
doi: 10.1016/S0169-7439(01)00153-8. |
[28] |
H. Martens and M. Martens,
Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR), Food Quality Pref., 11 (2000), 5-16.
doi: 10.1016/S0950-3293(99)00039-7. |
[29] | A. Mas-Colell, M. D. Whinston and J. R. Green, Microeconomic Theory, Oxford University Press, New York, 1995. Google Scholar |
[30] |
B.-H. Mevik and R. Wehrens,
The PLS package: Principal component and partial least squares regression in R, J. Stat. Software, 18 (2007), 1-23.
doi: 10.18637/jss.v018.i02. |
[31] |
Ministry of Finance of the People's Republic of China, The Customs Tariff Commission of the State Council Issued a Notice on the Tariff Imposed on Some Imported Goods from the United States, 2018. Available from: http://gss.mof.gov.cn/zhengwuxinxi/gongzuodongtai/201804/t20180408{_}2862847.html. Google Scholar |
[32] |
National Bureau of Statistics of China, China Statistical Yearbook, 2019. Available from: http://www.stats.gov.cn/tjsj/ndsj/2019/indexeh.htm. Google Scholar |
[33] |
National Bureau of Statistics of China, National Data, 2019. Available from: http://data.stats.gov.cn/english/easyquery.htm?cn=C01. Google Scholar |
[34] |
S. Nguyen, P. S.-L. Chen, Y. Du and W. Shi,
A quantitative risk analysis model with integrated deliberative Delphi platform for container shipping operational risks, Transportation Res. Part E: Logistics Transport. Rev., 129 (2019), 203-227.
doi: 10.1016/j.tre.2019.08.002. |
[35] |
Office of the United States Trade Representative, Statement By U.S. Trade Representative Robert Lighthizer on Section 301 Action, 2018. Available from: https://ustr.gov/about-us/policy-offices/press-office/press-releases/2018/july/statement-us-trade-representative. Google Scholar |
[36] |
Office of the United States Trade Representative, Under Section 301 Action, USTR Releases Proposed Tariff List on Chinese Products, 2018. Available from: https://ustr.gov/about-us/policy-offices/press-office/press-releases/2018/april/under-section-301-action-ustr. Google Scholar |
[37] |
Office of the United States Trade Representative, USTR Finalizes Tariffs on 200 Billion of Chinese Imports in Response to China's Unfair Trade Practices, 2018. Available from: https://ustr.gov/about-us/policy-offices/press-office/press-releases/2018/september/ustr-finalizes-tariffs-200. Google Scholar |
[38] |
E. Özceylan, C. Çetinkaya, M. Erbaş and M. Kabak,
Logistic performance evaluation of provinces in Turkey: A GIS-based multi-criteria decision analysis, Transportation Res. Part A: Policy and Practice, 94 (2016), 323-337.
doi: 10.1016/j.tra.2016.09.020. |
[39] |
P. Paflioti, T. K. Vitsounis, C. Teye, M. G. H. Bell and I. Tsamourgelis,
Box dynamics: A sectoral approach to analyse containerized port throughput interdependencies, Transportation Res. Part A: Policy and Practice, 106 (2017), 396-413.
doi: 10.1016/j.tra.2017.08.001. |
[40] |
R. Pant, K. Barker, F. Hank Grant and T. L. Landers,
Interdependent impacts of inoperability at multi-modal transportation container terminals, Transportation Res. Part E: Logistics Transport. Rev., 47 (2011), 722-737.
doi: 10.1016/j.tre.2011.02.009. |
[41] |
Y. Rashed, H. Meersman, C. Sys, E. Van de Voorde and T. Vanelslander,
A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports, Transportation Res. Part A: Policy and Practice, 117 (2018), 127-141.
doi: 10.1016/j.tra.2018.08.010. |
[42] |
J. Z. Resurreccion and J. R. Santos,
Uncertainty modeling of hurricane-based disruptions to interdependent economic and infrastructure systems, Natural Hazards, 69 (2013), 1497-1518.
doi: 10.1007/s11069-013-0760-5. |
[43] |
J. R. Santos,
Inoperability input-output modeling of disruptions to interdependent economic systems, Syst. Engineering, 9 (2006), 20-34.
doi: 10.1002/sys.20040. |
[44] |
J. R. Santos and Y. Y. Haimes,
Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures, Risk Analysis, 24 (2004), 1437-1451.
doi: 10.1111/j.0272-4332.2004.00540.x. |
[45] |
M. Z. Shahid, A. S. Maulud and M. A. Bustam,
Non-invasive monitoring of $CO_2$ concentration in aqueous diethanolamine (DEA), methyldiethanolamine (MDEA) and their blends in high $CO_2$ loading region using Raman spectroscopy and partial least square regression (PLSR), Internat. J. Greenhouse Gas Control, 68 (2018), 42-48.
doi: 10.1016/j.ijggc.2017.11.006. |
[46] |
W. Shan, Q. Yan, C. Chen, M. Zhang, B. Yao and X. Fu,
Optimization of competitive facility location for chain stores, Ann. Oper. Res., 273 (2019), 187-205.
doi: 10.1007/s10479-017-2579-z. |
[47] |
The New York Times, Trump to Impose Sweeping Steel and Aluminum Tariffs, 2018. Available from: https://www.nytimes.com/2018/03/01/business/trump-tariffs.html. Google Scholar |
[48] |
The Shipbuilders' Association of Japan, Shipbuilding Statistics, https://www.sajn.or.jp/e/press. Google Scholar |
[49] |
S. A. Thekdi and J. R. Santos,
Supply chain vulnerability analysis using scenario-based input-output modeling: Application to port operations, Risk Analysis, 36 (2015), 1025-1039.
doi: 10.1111/risa.12473. |
[50] |
S. Wold,
Personal memories of the early PLS development, Chemometrics Intell. Laboratory Syst., 58 (2001), 83-84.
doi: 10.1016/S0169-7439(01)00152-6. |
[51] |
S. Wold, M. Sjöström and L. Eriksson,
PLS-regression: A basic tool of chemometrics, Chemometrics Intell. Laboratory Syst., 58 (2001), 109-130.
doi: 10.1016/S0169-7439(01)00155-1. |
[52] |
D. Wu, N. Wang, A. Yu and N. Wu,
Vulnerability analysis of global container shipping liner network based on main channel disruption, Maritime Policy & Mgmt., 46 (2019), 394-409.
doi: 10.1080/03088839.2019.1571643. |
[53] |
G. Xie, N. Zhang and S. Wang,
Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology, Transportation Res. Part E: Logistics Transport. Rev., 108 (2017), 160-178.
doi: 10.1016/j.tre.2017.08.015. |
[54] |
M. Zhang, H. Mu, G. Li and Y. Ning,
Forecasting the transport energy demand based on PLSR method in China, Energy, 34 (2009), 1396-1400.
doi: 10.1016/j.energy.2009.06.032. |
[55] |
B. Zondag, P. Bucci, P. Gützkow and G. de Jong,
Port competition modeling including maritime, port, and hinterland characteristics, Maritime Policy & Mgmt., 37 (2010), 179-194.
doi: 10.1080/03088831003700579. |
show all references
References:
[1] |
S. Aksoy and Y. Durmusoglu,
Improving competitiveness level of Turkish intermodal ports in the Frame of Green Port Concept: A case study, Maritime Policy & Mgmt., 47 (2020), 203-220.
doi: 10.1080/03088839.2019.1688876. |
[2] |
D. Artuso, Y. Borbon-Galvez, J. Ferencz, M. Langeveld, C. Sys, T. Vanelslander and B. Zondag, Study on the Analysis and Evolution of International and EU Shipping, Technical report, Brussels: European Commission, 2015. Available from: https://ec.europa.eu/transport/sites/transport/files/modes/maritime/studies/doc/2015-sept-study-internat-eu-shipping-final.pdf. Google Scholar |
[3] |
S. Athanasatos, S. Michaelides and M. Papadakis,
Identification of weather trends for use as a component of risk management for port operations, Natural Hazards, 72 (2014), 41-61.
doi: 10.1007/s11069-012-0491-z. |
[4] |
BBC News, Trump Steel Tariffs: Trading Partners Threaten Retaliation, 2018. Available from: https://web.archive.org/web/20180302081539/http://www.bbc.com/news/world-us-canada-43251320. Google Scholar |
[5] |
A. Calatayud, J. Mangan and R. Palacin,
Vulnerability of international freight flows to shipping network disruptions: A multiplex network perspective, Transportation Res. Part E: Logistics Transport. Rev., 108 (2017), 195-208.
doi: 10.1016/j.tre.2017.10.015. |
[6] |
L. M. Carrascal, I. Galván and O. Gordo,
Partial least squares regression as an alternative to current regression methods used in ecology, Oikos, 118 (2009), 681-690.
doi: 10.1111/j.1600-0706.2008.16881.x. |
[7] |
C. C. Chou, A mixed fuzzy expert system and regression model for forecasting the volume of international trade containers, Internat. J. Innovative Comput. Infor. Control, 6 (2010), 2449-2458. Google Scholar |
[8] |
M. Darayi, K. Barker and C. D. Nicholson,
A multi-industry economic impact perspective on adaptive capacity planning in a freight transportation network, Internat. J. Prod. Econ., 208 (2019), 356-368.
doi: 10.1016/j.ijpe.2018.12.008. |
[9] |
C. Ducruet,
The polarization of global container flows by interoceanic canals: Geographic coverage and network vulnerability, Maritime Policy & Mgmt., 43 (2016), 242-260.
doi: 10.1080/03088839.2015.1022612. |
[10] |
A. Estache, L. Trujillo and E. Quinet, Forecasting the Demand for Privatized Transport: What Economic Regulators Should Know, and Why, Technical report, The World Bank, 2000.
doi: 10.1596/1813-9450-2446. |
[11] |
Y.-Z. Feng, G. ElMasry, D.-W. Sun, A. G. M. Scannell, D. Walsh and N. Morcy,
Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets, Food Chemistry, 138 (2013), 1829-1836.
doi: 10.1016/J.FOODCHEM.2012.11.040. |
[12] |
Y. Gong, K. X. Li, S.-L. Chen and W. Shi, Contagion risk between the shipping freight and stock markets: Evidence from the recent US-China trade war, Transportation Res. Part E: Logistics Transport. Rev., 136 (2020).
doi: 10.1016/j.tre.2020.101900. |
[13] |
X. Gou and J. S. L. Lam,
Risk analysis of marine cargoes and major port disruptions, Maritime Economics & Logistics, 21 (2019), 497-523.
doi: 10.1057/s41278-018-0110-3. |
[14] |
M. Greenberg, C. Haas, A. Cox Jr., K. Lowrie, K. McComas and W. North,
Ten most important accomplishments in risk analysis, 1980-2010, Risk Analysis, 32 (2012), 771-781.
doi: 10.1111/j.1539-6924.2012.01817.x. |
[15] |
M. S. Haggerty, J. R. Santos and Y. Y. Haimes,
Transportation-based framework for deriving perturbations to the Inoperability Input-Output Model, J. Infrastructure Syst., 14 (2008), 293-304.
doi: 10.1061/(ASCE)1076-0342(2008)14:4(293). |
[16] |
Y. Y. Haimes, B. M. Horowitz, J. H. Lambert, J. R. Santos, C. Lian and K. G. Crowther,
Inoperability Input-Output Model for interdependent infrastructure sectors. I: Theory and methodology, J. Infrastructure Syst., 11 (2005), 67-79.
doi: 10.1061/(ASCE)1076-0342(2005)11:2(67). |
[17] |
C. Izaguirre, I. J. Losada, P. Camus, P. González-Lamuño and V. Stenek,
Seaport climate change impact assessment using a multi-level methodology, Maritime Policy & Mgmt., 47 (2020), 1-14.
doi: 10.1080/03088839.2020.1725673. |
[18] |
Y. Jiang, J.-B. Sheu, Z. Peng and B. Yu,
Hinterland patterns of China Railway (CR) express in China under the Belt and Road Initiative: A preliminary analysis, Transportation Res. Part E: Logistics Transport. Rev., 119 (2018), 189-201.
doi: 10.1016/j.tre.2018.10.002. |
[19] |
A. John, D. Paraskevadakis, A. Bury, Z. Yang, R. Riahi and J. Wang,
An integrated fuzzy risk assessment for seaport operations, Safety Science, 68 (2014), 180-194.
doi: 10.1016/j.ssci.2014.04.001. |
[20] |
J. Jung, J. R. Santos and Y. Y. Haimes,
International trade inoperability input-output model (IT-IIM): Theory and application, Risk Analysis, 29 (2008), 137-154.
doi: 10.1111/j.1539-6924.2008.01126.x. |
[21] |
P. Kaluza, A. Kölzsch, M. T. Gastner and B. Blasius,
The complex network of global cargo ship movements, J. Roy. Soc. Interface, 7 (2010), 1093-1103.
doi: 10.1098/rsif.2009.0495. |
[22] |
H. L. Kee, A. Nicita and M. Olarreaga,
Import demand elasticities and trade distortions, Rev. Econ. Stat., 90 (2008), 666-682.
doi: 10.1162/rest.90.4.666. |
[23] |
W. Leontief, Input-Output Economics, Oxford University Press, 1986.
doi: 10.1038/scientificamerican1051-15.![]() |
[24] |
C. Lian and Y. Y. Haimes,
Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input-output model, Systems Engineering, 9 (2006), 241-258.
doi: 10.1002/sys.20051. |
[25] |
F. Lu,
China-US trade disputes in 2018: An overview, China & World Economy, 26 (2018), 83-103.
doi: 10.1111/cwe.12257. |
[26] |
M. Manuela González and L. Trujillo,
Reforms and infrastructure efficiency in Spain's container ports, Transportation Res. Part A: Policy and Practice, 42 (2008), 243-257.
doi: 10.1016/j.tra.2007.08.006. |
[27] |
H. Martens,
Reliable and relevant modelling of real world data: A personal account of the development of PLS Regression, Chemometrics Intell. Laboratory Syst., 58 (2001), 85-95.
doi: 10.1016/S0169-7439(01)00153-8. |
[28] |
H. Martens and M. Martens,
Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR), Food Quality Pref., 11 (2000), 5-16.
doi: 10.1016/S0950-3293(99)00039-7. |
[29] | A. Mas-Colell, M. D. Whinston and J. R. Green, Microeconomic Theory, Oxford University Press, New York, 1995. Google Scholar |
[30] |
B.-H. Mevik and R. Wehrens,
The PLS package: Principal component and partial least squares regression in R, J. Stat. Software, 18 (2007), 1-23.
doi: 10.18637/jss.v018.i02. |
[31] |
Ministry of Finance of the People's Republic of China, The Customs Tariff Commission of the State Council Issued a Notice on the Tariff Imposed on Some Imported Goods from the United States, 2018. Available from: http://gss.mof.gov.cn/zhengwuxinxi/gongzuodongtai/201804/t20180408{_}2862847.html. Google Scholar |
[32] |
National Bureau of Statistics of China, China Statistical Yearbook, 2019. Available from: http://www.stats.gov.cn/tjsj/ndsj/2019/indexeh.htm. Google Scholar |
[33] |
National Bureau of Statistics of China, National Data, 2019. Available from: http://data.stats.gov.cn/english/easyquery.htm?cn=C01. Google Scholar |
[34] |
S. Nguyen, P. S.-L. Chen, Y. Du and W. Shi,
A quantitative risk analysis model with integrated deliberative Delphi platform for container shipping operational risks, Transportation Res. Part E: Logistics Transport. Rev., 129 (2019), 203-227.
doi: 10.1016/j.tre.2019.08.002. |
[35] |
Office of the United States Trade Representative, Statement By U.S. Trade Representative Robert Lighthizer on Section 301 Action, 2018. Available from: https://ustr.gov/about-us/policy-offices/press-office/press-releases/2018/july/statement-us-trade-representative. Google Scholar |
[36] |
Office of the United States Trade Representative, Under Section 301 Action, USTR Releases Proposed Tariff List on Chinese Products, 2018. Available from: https://ustr.gov/about-us/policy-offices/press-office/press-releases/2018/april/under-section-301-action-ustr. Google Scholar |
[37] |
Office of the United States Trade Representative, USTR Finalizes Tariffs on 200 Billion of Chinese Imports in Response to China's Unfair Trade Practices, 2018. Available from: https://ustr.gov/about-us/policy-offices/press-office/press-releases/2018/september/ustr-finalizes-tariffs-200. Google Scholar |
[38] |
E. Özceylan, C. Çetinkaya, M. Erbaş and M. Kabak,
Logistic performance evaluation of provinces in Turkey: A GIS-based multi-criteria decision analysis, Transportation Res. Part A: Policy and Practice, 94 (2016), 323-337.
doi: 10.1016/j.tra.2016.09.020. |
[39] |
P. Paflioti, T. K. Vitsounis, C. Teye, M. G. H. Bell and I. Tsamourgelis,
Box dynamics: A sectoral approach to analyse containerized port throughput interdependencies, Transportation Res. Part A: Policy and Practice, 106 (2017), 396-413.
doi: 10.1016/j.tra.2017.08.001. |
[40] |
R. Pant, K. Barker, F. Hank Grant and T. L. Landers,
Interdependent impacts of inoperability at multi-modal transportation container terminals, Transportation Res. Part E: Logistics Transport. Rev., 47 (2011), 722-737.
doi: 10.1016/j.tre.2011.02.009. |
[41] |
Y. Rashed, H. Meersman, C. Sys, E. Van de Voorde and T. Vanelslander,
A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports, Transportation Res. Part A: Policy and Practice, 117 (2018), 127-141.
doi: 10.1016/j.tra.2018.08.010. |
[42] |
J. Z. Resurreccion and J. R. Santos,
Uncertainty modeling of hurricane-based disruptions to interdependent economic and infrastructure systems, Natural Hazards, 69 (2013), 1497-1518.
doi: 10.1007/s11069-013-0760-5. |
[43] |
J. R. Santos,
Inoperability input-output modeling of disruptions to interdependent economic systems, Syst. Engineering, 9 (2006), 20-34.
doi: 10.1002/sys.20040. |
[44] |
J. R. Santos and Y. Y. Haimes,
Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures, Risk Analysis, 24 (2004), 1437-1451.
doi: 10.1111/j.0272-4332.2004.00540.x. |
[45] |
M. Z. Shahid, A. S. Maulud and M. A. Bustam,
Non-invasive monitoring of $CO_2$ concentration in aqueous diethanolamine (DEA), methyldiethanolamine (MDEA) and their blends in high $CO_2$ loading region using Raman spectroscopy and partial least square regression (PLSR), Internat. J. Greenhouse Gas Control, 68 (2018), 42-48.
doi: 10.1016/j.ijggc.2017.11.006. |
[46] |
W. Shan, Q. Yan, C. Chen, M. Zhang, B. Yao and X. Fu,
Optimization of competitive facility location for chain stores, Ann. Oper. Res., 273 (2019), 187-205.
doi: 10.1007/s10479-017-2579-z. |
[47] |
The New York Times, Trump to Impose Sweeping Steel and Aluminum Tariffs, 2018. Available from: https://www.nytimes.com/2018/03/01/business/trump-tariffs.html. Google Scholar |
[48] |
The Shipbuilders' Association of Japan, Shipbuilding Statistics, https://www.sajn.or.jp/e/press. Google Scholar |
[49] |
S. A. Thekdi and J. R. Santos,
Supply chain vulnerability analysis using scenario-based input-output modeling: Application to port operations, Risk Analysis, 36 (2015), 1025-1039.
doi: 10.1111/risa.12473. |
[50] |
S. Wold,
Personal memories of the early PLS development, Chemometrics Intell. Laboratory Syst., 58 (2001), 83-84.
doi: 10.1016/S0169-7439(01)00152-6. |
[51] |
S. Wold, M. Sjöström and L. Eriksson,
PLS-regression: A basic tool of chemometrics, Chemometrics Intell. Laboratory Syst., 58 (2001), 109-130.
doi: 10.1016/S0169-7439(01)00155-1. |
[52] |
D. Wu, N. Wang, A. Yu and N. Wu,
Vulnerability analysis of global container shipping liner network based on main channel disruption, Maritime Policy & Mgmt., 46 (2019), 394-409.
doi: 10.1080/03088839.2019.1571643. |
[53] |
G. Xie, N. Zhang and S. Wang,
Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology, Transportation Res. Part E: Logistics Transport. Rev., 108 (2017), 160-178.
doi: 10.1016/j.tre.2017.08.015. |
[54] |
M. Zhang, H. Mu, G. Li and Y. Ning,
Forecasting the transport energy demand based on PLSR method in China, Energy, 34 (2009), 1396-1400.
doi: 10.1016/j.energy.2009.06.032. |
[55] |
B. Zondag, P. Bucci, P. Gützkow and G. de Jong,
Port competition modeling including maritime, port, and hinterland characteristics, Maritime Policy & Mgmt., 37 (2010), 179-194.
doi: 10.1080/03088831003700579. |

Notation | Description |
${a_{ij}}$ | proportion of sector $i$'s input used by sector $j$ with respect to total production of sector $j$ |
${\bf{A}}$ | matrix of technical coefficient |
${{\bf{A}}^*}$ | demand-based interdependency matrix |
${{\bf{b}}_{\bf{0}}}$ | vector of offset coefficients |
${\bf{B}}$ | matrix of regression coefficients |
${c_j}$ | final demand for sector $j$ |
${\bf{C}}$ | vector of final demand |
$c_i^*$ | normalized degraded demand for sector $i$ |
${{\bf{c}}^*}$ | vector of normalized degraded demand |
${\hat c_i}$ | value of nominal final demand of sector $i$ |
$\bf{\hat c}$ | vector of nominal final demand |
${\tilde c_i}$ | value of degraded final demand of sector $i$ |
$ \bf{\tilde c}$ | vector of degraded final demand |
${E_{\rm{i}}}$ | import demand elasticity of goods of sector $i$ |
${\bf{F}}$ | matrix of residuals |
${p_i}$ | total output of sector $i$ |
${\bf{P}}$ | vector of sector output |
${p_{ij}}$ | input from sector $i$ to sector $j$ |
${\hat p_i}$ | the nominal output of sector $i$ |
$\bf{\hat p}$ | vector of nominal output |
${\tilde p_i}$ | the degraded output of sector $i$ |
$\bf{\tilde p}$ | vector of degraded output |
${q_i}$ | normalized output loss of sector $i$ |
${\bf{q}}$ | vector of normalized output loss |
${t_i}$ | tariff rate for goods of sector $i$ |
${\bf{T}}$ | latent variables |
${V_i}$ | value of commodities of sector $i$ |
${\bf{X}}$ | matrix of predictor variables |
${\bf{Y}}$ | matrix of response variables |
${\xi _i}$ | demand change percentage of sector $i$ |
Notation | Description |
${a_{ij}}$ | proportion of sector $i$'s input used by sector $j$ with respect to total production of sector $j$ |
${\bf{A}}$ | matrix of technical coefficient |
${{\bf{A}}^*}$ | demand-based interdependency matrix |
${{\bf{b}}_{\bf{0}}}$ | vector of offset coefficients |
${\bf{B}}$ | matrix of regression coefficients |
${c_j}$ | final demand for sector $j$ |
${\bf{C}}$ | vector of final demand |
$c_i^*$ | normalized degraded demand for sector $i$ |
${{\bf{c}}^*}$ | vector of normalized degraded demand |
${\hat c_i}$ | value of nominal final demand of sector $i$ |
$\bf{\hat c}$ | vector of nominal final demand |
${\tilde c_i}$ | value of degraded final demand of sector $i$ |
$ \bf{\tilde c}$ | vector of degraded final demand |
${E_{\rm{i}}}$ | import demand elasticity of goods of sector $i$ |
${\bf{F}}$ | matrix of residuals |
${p_i}$ | total output of sector $i$ |
${\bf{P}}$ | vector of sector output |
${p_{ij}}$ | input from sector $i$ to sector $j$ |
${\hat p_i}$ | the nominal output of sector $i$ |
$\bf{\hat p}$ | vector of nominal output |
${\tilde p_i}$ | the degraded output of sector $i$ |
$\bf{\tilde p}$ | vector of degraded output |
${q_i}$ | normalized output loss of sector $i$ |
${\bf{q}}$ | vector of normalized output loss |
${t_i}$ | tariff rate for goods of sector $i$ |
${\bf{T}}$ | latent variables |
${V_i}$ | value of commodities of sector $i$ |
${\bf{X}}$ | matrix of predictor variables |
${\bf{Y}}$ | matrix of response variables |
${\xi _i}$ | demand change percentage of sector $i$ |
Index | Sector |
1 | Agriculture, Forestry, Animal Husbandry & Fishery |
2 | Mining |
3 | Manufacture of Foods, Beverage & Tobacco |
4 | Manufacture of Textile, Wearing Apparel & Leather Products |
5 | Coking, Gas and Processing of Petroleum |
6 | Chemical Industry |
7 | Mineral Products |
8 | Manufacture and Processing of Metals and Metal Products |
9 | Manufacture of Machinery and Equipment |
10 | Other Manufacture |
11 | Production and Supply of Electric Power, Heat Power and Water |
12 | Construction |
13 | Transport, Storage, Post, Information |
14 | Wholesale and Retail Trades, Hotels and Catering Services |
15 | Real Estate, Leasing and Business Services |
16 | Financial Intermediation |
17 | Other Services |
Index | Sector |
1 | Agriculture, Forestry, Animal Husbandry & Fishery |
2 | Mining |
3 | Manufacture of Foods, Beverage & Tobacco |
4 | Manufacture of Textile, Wearing Apparel & Leather Products |
5 | Coking, Gas and Processing of Petroleum |
6 | Chemical Industry |
7 | Mineral Products |
8 | Manufacture and Processing of Metals and Metal Products |
9 | Manufacture of Machinery and Equipment |
10 | Other Manufacture |
11 | Production and Supply of Electric Power, Heat Power and Water |
12 | Construction |
13 | Transport, Storage, Post, Information |
14 | Wholesale and Retail Trades, Hotels and Catering Services |
15 | Real Estate, Leasing and Business Services |
16 | Financial Intermediation |
17 | Other Services |
(A) | |||||||||
Year | Output Value ( |
||||||||
2000 | 424.64 | 129.788 | 235.226 | 274.376 | 133.6 | 346.593 | 100.75 | 252.498 | |
2002 | 458.846 | 165.648 | 232.496 | 251.035 | 103.532 | 346.359 | 93.195 | 343.031 | |
2005 | 631.891 | 312.606 | 415.481 | 450.87 | 214.574 | 642.728 | 246.616 | 675.398 | |
2007 | 785.001 | 468.514 | 670.965 | 694.72 | 356.157 | 995.41 | 366.135 | 1265.196 | |
2010 | 1112.963 | 780.927 | 1082.658 | 911.499 | 484.051 | 1497.191 | 643.248 | 1711.171 | |
2012 | 1435.703 | 860.544 | 1412.234 | 1064.191 | 692.57 | 1943.109 | 748.26 | 2285.335 | |
2015 | 1718.842 | 864.351 | 1837.159 | 1340.565 | 710.363 | 2507.955 | 1032.203 | 2491.578 | |
(B) | |||||||||
Year | Output Value (${X_i}$) | ||||||||
${X_9}$ | ${X_{10}}$ | ${X_{11}}$ | ${X_{12}}$ | ${X_{13}}$ | ${X_{14}}$ | ${X_{15}}$ | ${X_{16}}$ | ${X_{17}}$ | |
2000 | 668.386 | 143.308 | 136.844 | 355.742 | 169.719 | 271.888 | 177.297 | 82.929 | 231.551 |
2002 | 713.382 | 223.037 | 136.121 | 451.684 | 234.513 | 390.004 | 278.252 | 117.429 | 493.718 |
2005 | 1459.226 | 350.667 | 311.768 | 683.392 | 562.924 | 547.33 | 330.367 | 164.755 | 777.982 |
2007 | 2339.123 | 585.645 | 524.45 | 1007.028 | 681.737 | 700.79 | 426.421 | 312.777 | 967.112 |
2010 | 3766.195 | 794.671 | 766.34 | 1643.172 | 1061.82 | 1038.7 | 833.196 | 518.377 | 1465.646 |
2012 | 4147.833 | 895.583 | 809.107 | 2225.493 | 1397.658 | 1533.136 | 1225.254 | 947.499 | 2091.395 |
2015 | 5198.017 | 1196.406 | 987.575 | 3241.663 | 1915.727 | 2169.057 | 1902.89 | 1416.748 | 2887.539 |
(A) | |||||||||
Year | Output Value ( |
||||||||
2000 | 424.64 | 129.788 | 235.226 | 274.376 | 133.6 | 346.593 | 100.75 | 252.498 | |
2002 | 458.846 | 165.648 | 232.496 | 251.035 | 103.532 | 346.359 | 93.195 | 343.031 | |
2005 | 631.891 | 312.606 | 415.481 | 450.87 | 214.574 | 642.728 | 246.616 | 675.398 | |
2007 | 785.001 | 468.514 | 670.965 | 694.72 | 356.157 | 995.41 | 366.135 | 1265.196 | |
2010 | 1112.963 | 780.927 | 1082.658 | 911.499 | 484.051 | 1497.191 | 643.248 | 1711.171 | |
2012 | 1435.703 | 860.544 | 1412.234 | 1064.191 | 692.57 | 1943.109 | 748.26 | 2285.335 | |
2015 | 1718.842 | 864.351 | 1837.159 | 1340.565 | 710.363 | 2507.955 | 1032.203 | 2491.578 | |
(B) | |||||||||
Year | Output Value (${X_i}$) | ||||||||
${X_9}$ | ${X_{10}}$ | ${X_{11}}$ | ${X_{12}}$ | ${X_{13}}$ | ${X_{14}}$ | ${X_{15}}$ | ${X_{16}}$ | ${X_{17}}$ | |
2000 | 668.386 | 143.308 | 136.844 | 355.742 | 169.719 | 271.888 | 177.297 | 82.929 | 231.551 |
2002 | 713.382 | 223.037 | 136.121 | 451.684 | 234.513 | 390.004 | 278.252 | 117.429 | 493.718 |
2005 | 1459.226 | 350.667 | 311.768 | 683.392 | 562.924 | 547.33 | 330.367 | 164.755 | 777.982 |
2007 | 2339.123 | 585.645 | 524.45 | 1007.028 | 681.737 | 700.79 | 426.421 | 312.777 | 967.112 |
2010 | 3766.195 | 794.671 | 766.34 | 1643.172 | 1061.82 | 1038.7 | 833.196 | 518.377 | 1465.646 |
2012 | 4147.833 | 895.583 | 809.107 | 2225.493 | 1397.658 | 1533.136 | 1225.254 | 947.499 | 2091.395 |
2015 | 5198.017 | 1196.406 | 987.575 | 3241.663 | 1915.727 | 2169.057 | 1902.89 | 1416.748 | 2887.539 |
Year | Volume (in 1000 TEU) |
2000 | 25500 |
2001 | 28800 |
2002 | 33000 |
2003 | 41700 |
2004 | 51000 |
2005 | 58200 |
2006 | 68400 |
2007 | 79400 |
2008 | 83300 |
2009 | 77000 |
2010 | 91740 |
2011 | 96240 |
2012 | 97700 |
2013 | 98650 |
2014 | 102430 |
2015 | 104960 |
Year | Volume (in 1000 TEU) |
2000 | 25500 |
2001 | 28800 |
2002 | 33000 |
2003 | 41700 |
2004 | 51000 |
2005 | 58200 |
2006 | 68400 |
2007 | 79400 |
2008 | 83300 |
2009 | 77000 |
2010 | 91740 |
2011 | 96240 |
2012 | 97700 |
2013 | 98650 |
2014 | 102430 |
2015 | 104960 |
Tariff rate | Sector | Value of commodities involved in the trade war (billion $) | ||
Scenario 1 | Scenario 2 | Scenario 3 | ||
25% | Manufacture and Processing of Metals and Metal Products | 1 | 5 | 20 |
Manufacture of Machinery and Equipment | 0 | 45 | 180 |
Tariff rate | Sector | Value of commodities involved in the trade war (billion $) | ||
Scenario 1 | Scenario 2 | Scenario 3 | ||
25% | Manufacture and Processing of Metals and Metal Products | 1 | 5 | 20 |
Manufacture of Machinery and Equipment | 0 | 45 | 180 |
Index | Base Scenario | Scenario 1 | Scenario 2 | Scenario 3 | |||
Output | Output | Inoperability | Output | Inoperability | Output | Inoperability | |
1 | 1718.842 | 1718.836 | 0.00035% | 1718.538 | 0.01771% | 1717.624 | 0.07086% |
2 | 864.351 | 864.315 | 0.00413% | 863.576 | 0.08965% | 861.25 | 0.35872% |
3 | 1837.159 | 1837.153 | 0.00034% | 1836.842 | 0.01728% | 1835.889 | 0.06914% |
4 | 1340.565 | 1340.561 | 0.00031% | 1340.339 | 0.01689% | 1339.659 | 0.06755% |
5 | 710.363 | 710.352 | 0.00160% | 710.029 | 0.04696% | 709.028 | 0.18789% |
6 | 2507.955 | 2507.934 | 0.00083% | 2506.706 | 0.04978% | 2502.96 | 0.19915% |
7 | 1032.203 | 1032.197 | 0.00051% | 1031.958 | 0.02366% | 1031.226 | 0.09465% |
8 | 2491.578 | 2491.415 | 0.00654% | 2488.814 | 0.11094% | 2480.517 | 0.44395% |
9 | 5198.017 | 5197.99 | 0.00051% | 5190.058 | 0.15311% | 5166.182 | 0.61243% |
10 | 1196.406 | 1196.394 | 0.00102% | 1195.983 | 0.03535% | 1194.714 | 0.14144% |
11 | 987.575 | 987.553 | 0.00228% | 986.953 | 0.06304% | 985.084 | 0.25222% |
12 | 3241.663 | 3241.662 | 0.00003% | 3241.617 | 0.00142% | 3241.478 | 0.00570% |
13 | 1915.727 | 1915.714 | 0.00071% | 1915.081 | 0.03375% | 1913.141 | 0.13503% |
14 | 2169.057 | 2169.046 | 0.00053% | 2168.329 | 0.03356% | 2166.146 | 0.13423% |
15 | 1902.89 | 1902.88 | 0.00054% | 1902.338 | 0.02901% | 1900.682 | 0.11606% |
16 | 1416.748 | 1416.733 | 0.00104% | 1416.166 | 0.04104% | 1414.421 | 0.16420% |
17 | 2887.539 | 2887.533 | 0.00023% | 2887.18 | 0.01245% | 2886.101 | 0.04982% |
Index | Base Scenario | Scenario 1 | Scenario 2 | Scenario 3 | |||
Output | Output | Inoperability | Output | Inoperability | Output | Inoperability | |
1 | 1718.842 | 1718.836 | 0.00035% | 1718.538 | 0.01771% | 1717.624 | 0.07086% |
2 | 864.351 | 864.315 | 0.00413% | 863.576 | 0.08965% | 861.25 | 0.35872% |
3 | 1837.159 | 1837.153 | 0.00034% | 1836.842 | 0.01728% | 1835.889 | 0.06914% |
4 | 1340.565 | 1340.561 | 0.00031% | 1340.339 | 0.01689% | 1339.659 | 0.06755% |
5 | 710.363 | 710.352 | 0.00160% | 710.029 | 0.04696% | 709.028 | 0.18789% |
6 | 2507.955 | 2507.934 | 0.00083% | 2506.706 | 0.04978% | 2502.96 | 0.19915% |
7 | 1032.203 | 1032.197 | 0.00051% | 1031.958 | 0.02366% | 1031.226 | 0.09465% |
8 | 2491.578 | 2491.415 | 0.00654% | 2488.814 | 0.11094% | 2480.517 | 0.44395% |
9 | 5198.017 | 5197.99 | 0.00051% | 5190.058 | 0.15311% | 5166.182 | 0.61243% |
10 | 1196.406 | 1196.394 | 0.00102% | 1195.983 | 0.03535% | 1194.714 | 0.14144% |
11 | 987.575 | 987.553 | 0.00228% | 986.953 | 0.06304% | 985.084 | 0.25222% |
12 | 3241.663 | 3241.662 | 0.00003% | 3241.617 | 0.00142% | 3241.478 | 0.00570% |
13 | 1915.727 | 1915.714 | 0.00071% | 1915.081 | 0.03375% | 1913.141 | 0.13503% |
14 | 2169.057 | 2169.046 | 0.00053% | 2168.329 | 0.03356% | 2166.146 | 0.13423% |
15 | 1902.89 | 1902.88 | 0.00054% | 1902.338 | 0.02901% | 1900.682 | 0.11606% |
16 | 1416.748 | 1416.733 | 0.00104% | 1416.166 | 0.04104% | 1414.421 | 0.16420% |
17 | 2887.539 | 2887.533 | 0.00023% | 2887.18 | 0.01245% | 2886.101 | 0.04982% |
Validation: RMSEP | |||||
Cross-validated using 6 leave-one-out segments. | |||||
Response: Y | |||||
CV | (Intercept) | 1 component | 2 components | 3 components | 4 components |
1.095 | 0.5619 | 0.3028 | 0.3306 | 0.3297 | |
Training: % variance explained | |||||
1 component | 2 components | 3 components | 4 components | ||
X | 97.24 | 99.44 | 99.67 | 99.92 | |
Y | 86.83 | 97.53 | 99.38 | 99.84 |
Validation: RMSEP | |||||
Cross-validated using 6 leave-one-out segments. | |||||
Response: Y | |||||
CV | (Intercept) | 1 component | 2 components | 3 components | 4 components |
1.095 | 0.5619 | 0.3028 | 0.3306 | 0.3297 | |
Training: % variance explained | |||||
1 component | 2 components | 3 components | 4 components | ||
X | 97.24 | 99.44 | 99.67 | 99.92 | |
Y | 86.83 | 97.53 | 99.38 | 99.84 |
Variable | Coefficient | Std. Error | Df | t value | Pr(> |
X1 | 0.022995 | 0.066024 | 5 | 0.3483 | 0.741817 |
X2 | 0.275605** | 0.094744 | 5 | 2.9089 | 0.033441 |
X3 | 0.032528 | 0.030684 | 5 | 1.0601 | 0.337592 |
X4 | 0.162531 | 0.131415 | 5 | 1.2368 | 0.27109 |
X5 | 0.174269 | 0.120794 | 5 | 1.4427 | 0.208691 |
X6 | 0.053442*** | 0.010296 | 5 | 5.1908 | 0.003494 |
X7 | 0.071065 | 0.057688 | 5 | 1.2319 | 0.272759 |
X8 | 0.183614* | 0.08237 | 5 | 2.2291 | 0.076248 |
X9 | 0.160552** | 0.055204 | 5 | 2.9083 | 0.033464 |
X10 | 0.122573 | 0.113811 | 5 | 1.077 | 0.330685 |
X11 | 0.251876 | 0.140159 | 5 | 1.7971 | 0.132254 |
X12 | -0.068757 | 0.053546 | 5 | -1.2841 | 0.255396 |
X13 | 0.015483 | 0.064637 | 5 | 0.2395 | 0.820203 |
X14 | -0.110264 | 0.057606 | 5 | -1.9141 | 0.113786 |
X15 | -0.176734 | 0.135578 | 5 | -1.3036 | 0.249177 |
X16 | -0.149952 | 0.102439 | 5 | -1.4638 | 0.203121 |
X17 | -0.074279 | 0.040517 | 5 | -1.8333 | 0.126235 |
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1 |
Variable | Coefficient | Std. Error | Df | t value | Pr(> |
X1 | 0.022995 | 0.066024 | 5 | 0.3483 | 0.741817 |
X2 | 0.275605** | 0.094744 | 5 | 2.9089 | 0.033441 |
X3 | 0.032528 | 0.030684 | 5 | 1.0601 | 0.337592 |
X4 | 0.162531 | 0.131415 | 5 | 1.2368 | 0.27109 |
X5 | 0.174269 | 0.120794 | 5 | 1.4427 | 0.208691 |
X6 | 0.053442*** | 0.010296 | 5 | 5.1908 | 0.003494 |
X7 | 0.071065 | 0.057688 | 5 | 1.2319 | 0.272759 |
X8 | 0.183614* | 0.08237 | 5 | 2.2291 | 0.076248 |
X9 | 0.160552** | 0.055204 | 5 | 2.9083 | 0.033464 |
X10 | 0.122573 | 0.113811 | 5 | 1.077 | 0.330685 |
X11 | 0.251876 | 0.140159 | 5 | 1.7971 | 0.132254 |
X12 | -0.068757 | 0.053546 | 5 | -1.2841 | 0.255396 |
X13 | 0.015483 | 0.064637 | 5 | 0.2395 | 0.820203 |
X14 | -0.110264 | 0.057606 | 5 | -1.9141 | 0.113786 |
X15 | -0.176734 | 0.135578 | 5 | -1.3036 | 0.249177 |
X16 | -0.149952 | 0.102439 | 5 | -1.4638 | 0.203121 |
X17 | -0.074279 | 0.040517 | 5 | -1.8333 | 0.126235 |
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1 |
Validation: RMSEP | |||||
Cross-validated using 6 leave-one-out segments. | |||||
Response: Y | |||||
CV | (Intercept) | 1 component | 2 components | 3 components | 4 components |
1.095 | 0.4108 | 0.3903 | 0.5343 | 12.22 | |
Training: % variance explained | |||||
1 component | 2 components | 3 components | 4 components | ||
X | 98.27 | 99.61 | 99.78 | 100 | |
Y | 92.57 | 94.35 | 96.64 | 97.62 |
Validation: RMSEP | |||||
Cross-validated using 6 leave-one-out segments. | |||||
Response: Y | |||||
CV | (Intercept) | 1 component | 2 components | 3 components | 4 components |
1.095 | 0.4108 | 0.3903 | 0.5343 | 12.22 | |
Training: % variance explained | |||||
1 component | 2 components | 3 components | 4 components | ||
X | 98.27 | 99.61 | 99.78 | 100 | |
Y | 92.57 | 94.35 | 96.64 | 97.62 |
Variance | Coefficient | Std. Error | Df | t value | Pr(> |
X2 | 0.245531** | 0.072592 | 5 | 3.3823 | 0.019624 |
X6 | 0.236153*** | 0.038451 | 5 | 6.1416 | 0.001663 |
X8 | 0.244841*** | 0.052856 | 5 | 4.6322 | 0.005672 |
X9 | 0.244020*** | 0.050796 | 5 | 4.8039 | 0.004867 |
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1 |
Variance | Coefficient | Std. Error | Df | t value | Pr(> |
X2 | 0.245531** | 0.072592 | 5 | 3.3823 | 0.019624 |
X6 | 0.236153*** | 0.038451 | 5 | 6.1416 | 0.001663 |
X8 | 0.244841*** | 0.052856 | 5 | 4.6322 | 0.005672 |
X9 | 0.244020*** | 0.050796 | 5 | 4.8039 | 0.004867 |
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1 |
Scenario | Scenario1 | Scenario2 | Scenario3 |
Reduction (in 1000 TEU) | 3 | 88 | 352 |
Reduction percentage | 0.002% | 0.084% | 0.335% |
Scenario | Scenario1 | Scenario2 | Scenario3 |
Reduction (in 1000 TEU) | 3 | 88 | 352 |
Reduction percentage | 0.002% | 0.084% | 0.335% |
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