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January  2022, 18(1): 219-238. doi: 10.3934/jimo.2020151

A hierarchic framework for the propagating impacts of the China-U.S. trade war on volume of Chinese containerized exports

Bin Yu 1,2,3, , Mengyan Hao 1, , Yonglei Jiang 4, and  Lianjie Jin 5,,

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

* Corresponding authors: Bin Yu and Lianjie Jin

Received  January 2020 Revised  July 2020 Published  January 2022 Early access  October 2020

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.

Keywords: Trade war, container, export, inoperability input-output model, PLSR.
Mathematics Subject Classification: 91B60, 62H12.
Citation: Bin Yu, Mengyan Hao, Yonglei Jiang, Lianjie Jin. A hierarchic framework for the propagating impacts of the China-U.S. trade war on volume of Chinese containerized exports. Journal of Industrial and Management Optimization, 2022, 18 (1) : 219-238. doi: 10.3934/jimo.2020151
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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.

[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.

[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. 

[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. 
[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.

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[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.

[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.

[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.

[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.

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Figure 1.  Structure of the proposed hierarchic framework
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Figure 2.  Economic Loss and Inoperability due to China-U.S. Trade War in Scenario 2

Note: See Table 2 for Sector index

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Figure 3.  Comparison of with/without considering the interdependencies (InterD) in Scenario 3

Note: See Table 2 for Sector index

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Table 1.  Overview of notations used in the IIM-PLSR framework
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 $i$
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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 $i$
Table 2.  All 17 sectors in China
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
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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
Table 3.  Output value of each sector in China (in billion dollars)
(A)
Year Output Value ($ {X_i} $)
$ {X_1} $ $ {X_2} $ $ {X_3} $ $ {X_4} $ $ {X_5} $ $ {X_6} $ $ {X_7} $ $ {X_8} $
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)
YearOutput 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
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(A)
Year Output Value ($ {X_i} $)
$ {X_1} $ $ {X_2} $ $ {X_3} $ $ {X_4} $ $ {X_5} $ $ {X_6} $ $ {X_7} $ $ {X_8} $
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)
YearOutput 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
Table 4.  Annual volume of containerized exports in China
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
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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
Table 5.  Scenarios for the China-U.S. trade war
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
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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
Table 6.  Output value (in billion dollars) and inoperability prediction in each scenario
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%
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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%
Table 7.  Preliminary fitting results in PLS model
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
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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
Table 8.  Jack-knifed test results when number of components is 2
Variable Coefficient Std. Error Df t value Pr(>$ \left| t \right| $)
X1 0.022995 0.066024 5 0.3483 0.741817
X2 0.275605** 0.094744 5 2.9089 0.033441
X3 0.032528 0.030684 5 1.0601 0.337592
X4 0.162531 0.131415 5 1.2368 0.27109
X5 0.174269 0.120794 5 1.4427 0.208691
X6 0.053442*** 0.010296 5 5.1908 0.003494
X7 0.071065 0.057688 5 1.2319 0.272759
X8 0.183614* 0.08237 5 2.2291 0.076248
X9 0.160552** 0.055204 5 2.9083 0.033464
X10 0.122573 0.113811 5 1.077 0.330685
X11 0.251876 0.140159 5 1.7971 0.132254
X12 -0.068757 0.053546 5 -1.2841 0.255396
X13 0.015483 0.064637 5 0.2395 0.820203
X14 -0.110264 0.057606 5 -1.9141 0.113786
X15 -0.176734 0.135578 5 -1.3036 0.249177
X16 -0.149952 0.102439 5 -1.4638 0.203121
X17 -0.074279 0.040517 5 -1.8333 0.126235
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1
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Variable Coefficient Std. Error Df t value Pr(>$ \left| t \right| $)
X1 0.022995 0.066024 5 0.3483 0.741817
X2 0.275605** 0.094744 5 2.9089 0.033441
X3 0.032528 0.030684 5 1.0601 0.337592
X4 0.162531 0.131415 5 1.2368 0.27109
X5 0.174269 0.120794 5 1.4427 0.208691
X6 0.053442*** 0.010296 5 5.1908 0.003494
X7 0.071065 0.057688 5 1.2319 0.272759
X8 0.183614* 0.08237 5 2.2291 0.076248
X9 0.160552** 0.055204 5 2.9083 0.033464
X10 0.122573 0.113811 5 1.077 0.330685
X11 0.251876 0.140159 5 1.7971 0.132254
X12 -0.068757 0.053546 5 -1.2841 0.255396
X13 0.015483 0.064637 5 0.2395 0.820203
X14 -0.110264 0.057606 5 -1.9141 0.113786
X15 -0.176734 0.135578 5 -1.3036 0.249177
X16 -0.149952 0.102439 5 -1.4638 0.203121
X17 -0.074279 0.040517 5 -1.8333 0.126235
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1
Table 9.  Preliminary fitting results in PLS model
Validation: RMSEP
Cross-validated using 6 leave-one-out segments.
Response: Y
CV (Intercept) 1 component 2 components 3 components 4 components
1.095 0.4108 0.3903 0.5343 12.22
Training: % variance explained
1 component 2 components 3 components 4 components
X 98.27 99.61 99.78 100
Y 92.57 94.35 96.64 97.62
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Validation: RMSEP
Cross-validated using 6 leave-one-out segments.
Response: Y
CV (Intercept) 1 component 2 components 3 components 4 components
1.095 0.4108 0.3903 0.5343 12.22
Training: % variance explained
1 component 2 components 3 components 4 components
X 98.27 99.61 99.78 100
Y 92.57 94.35 96.64 97.62
Table 10.  Jack-knifed test results when number of components is 1
Variance Coefficient Std. Error Df t value Pr(>$ \left| t \right| $)
X2 0.245531** 0.072592 5 3.3823 0.019624
X6 0.236153*** 0.038451 5 6.1416 0.001663
X8 0.244841*** 0.052856 5 4.6322 0.005672
X9 0.244020*** 0.050796 5 4.8039 0.004867
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1
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Variance Coefficient Std. Error Df t value Pr(>$ \left| t \right| $)
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
Table 11.  Annual reduction of containerized exports due to China-U.S. trade war
Scenario Scenario1 Scenario2 Scenario3
Reduction (in 1000 TEU) 3 88 352
Reduction percentage 0.002% 0.084% 0.335%
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Scenario Scenario1 Scenario2 Scenario3
Reduction (in 1000 TEU) 3 88 352
Reduction percentage 0.002% 0.084% 0.335%
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