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doi: 10.3934/jimo.2020151
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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

* Corresponding authors: Bin Yu and Lianjie Jin

Received  January 2020 Revised  July 2020 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.

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 & Management Optimization, 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.  Google Scholar

[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. AthanasatosS. 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.  Google Scholar

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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

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A. CalatayudJ. 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.  Google Scholar

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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

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M. DarayiK. 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.  Google Scholar

[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.  Google Scholar

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[12]

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C. IzaguirreI. J. LosadaP. CamusP. 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.  Google Scholar

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A. JohnD. ParaskevadakisA. BuryZ. YangR. 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.  Google Scholar

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[22]

H. L. KeeA. Nicita and M. Olarreaga, Import demand elasticities and trade distortions, Rev. Econ. Stat., 90 (2008), 666-682.  doi: 10.1162/rest.90.4.666.  Google Scholar

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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.  Google Scholar

[25]

F. Lu, China-US trade disputes in 2018: An overview, China & World Economy, 26 (2018), 83-103.  doi: 10.1111/cwe.12257.  Google Scholar

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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.  Google Scholar

[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.  Google Scholar

[28]

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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.  Google Scholar

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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

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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

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E. ÖzceylanC. ÇetinkayaM. 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.  Google Scholar

[39]

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

Note: See Table 2 for Sector index

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

Note: See Table 2 for Sector index

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 $i$
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
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
(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
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
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%
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
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| $)
X1 0.022995 0.066024 5 0.3483 0.741817
X2 0.275605** 0.094744 5 2.9089 0.033441
X3 0.032528 0.030684 5 1.0601 0.337592
X4 0.162531 0.131415 5 1.2368 0.27109
X5 0.174269 0.120794 5 1.4427 0.208691
X6 0.053442*** 0.010296 5 5.1908 0.003494
X7 0.071065 0.057688 5 1.2319 0.272759
X8 0.183614* 0.08237 5 2.2291 0.076248
X9 0.160552** 0.055204 5 2.9083 0.033464
X10 0.122573 0.113811 5 1.077 0.330685
X11 0.251876 0.140159 5 1.7971 0.132254
X12 -0.068757 0.053546 5 -1.2841 0.255396
X13 0.015483 0.064637 5 0.2395 0.820203
X14 -0.110264 0.057606 5 -1.9141 0.113786
X15 -0.176734 0.135578 5 -1.3036 0.249177
X16 -0.149952 0.102439 5 -1.4638 0.203121
X17 -0.074279 0.040517 5 -1.8333 0.126235
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1
Variable Coefficient Std. Error Df t value Pr(>$ \left| t \right| $)
X1 0.022995 0.066024 5 0.3483 0.741817
X2 0.275605** 0.094744 5 2.9089 0.033441
X3 0.032528 0.030684 5 1.0601 0.337592
X4 0.162531 0.131415 5 1.2368 0.27109
X5 0.174269 0.120794 5 1.4427 0.208691
X6 0.053442*** 0.010296 5 5.1908 0.003494
X7 0.071065 0.057688 5 1.2319 0.272759
X8 0.183614* 0.08237 5 2.2291 0.076248
X9 0.160552** 0.055204 5 2.9083 0.033464
X10 0.122573 0.113811 5 1.077 0.330685
X11 0.251876 0.140159 5 1.7971 0.132254
X12 -0.068757 0.053546 5 -1.2841 0.255396
X13 0.015483 0.064637 5 0.2395 0.820203
X14 -0.110264 0.057606 5 -1.9141 0.113786
X15 -0.176734 0.135578 5 -1.3036 0.249177
X16 -0.149952 0.102439 5 -1.4638 0.203121
X17 -0.074279 0.040517 5 -1.8333 0.126235
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1
Table 9.  Preliminary fitting results in PLS model
Validation: RMSEP
Cross-validated using 6 leave-one-out segments.
Response: Y
CV (Intercept) 1 component 2 components 3 components 4 components
1.095 0.4108 0.3903 0.5343 12.22
Training: % variance explained
1 component 2 components 3 components 4 components
X 98.27 99.61 99.78 100
Y 92.57 94.35 96.64 97.62
Validation: RMSEP
Cross-validated using 6 leave-one-out segments.
Response: Y
CV (Intercept) 1 component 2 components 3 components 4 components
1.095 0.4108 0.3903 0.5343 12.22
Training: % variance explained
1 component 2 components 3 components 4 components
X 98.27 99.61 99.78 100
Y 92.57 94.35 96.64 97.62
Table 10.  Jack-knifed test results when number of components is 1
Variance Coefficient Std. Error Df t value Pr(>$ \left| t \right| $)
X2 0.245531** 0.072592 5 3.3823 0.019624
X6 0.236153*** 0.038451 5 6.1416 0.001663
X8 0.244841*** 0.052856 5 4.6322 0.005672
X9 0.244020*** 0.050796 5 4.8039 0.004867
Signif. Codes: 0 '****' 0.001 '***' 0.01 '**' 0.05 '*' 0.1 ' ' 1
Variance Coefficient Std. Error Df t value Pr(>$ \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%
Scenario Scenario1 Scenario2 Scenario3
Reduction (in 1000 TEU) 3 88 352
Reduction percentage 0.002% 0.084% 0.335%
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