Container Port Performance Index 2022.pdf

Transport Global Practice
The Container Port
Performance Index 2022
A Comparable Assessment of Performance
based on Vessel Time in Port
Public
Disclosure
Authorized
Public
Disclosure
Authorized
Public
Disclosure
Authorized
Public
Disclosure
Authorized

© 2023 International Bank for Reconstruction and Development /
International Development Association or The World Bank
1818 H Street NW
Washington, DC 20433
Telephone: 202-473-1000
Internet: www.worldbank.org
This work is a product of the staff of The World Bank, together with external contributions from S&P Global
Market Intelligence. The findings, interpretations, and conclusions expressed in this work do not necessarily
reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent.
The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors,
denominations, and other information shown on any map in this work do not imply any judgment on the
part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of
such boundaries.
Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and
immunities of The World Bank, all of which are specifically reserved.
Rights and Permissions
This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO)
http://creativecommons.org/licenses/by/3.0/igo.+ Under the Creative Commons Attribution license,
you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes,
under the following conditions:
Attribution—Please cite the work as follows: The World Bank, 2023. “The Container Port Performance
Index 2022: A Comparable Assessment of Performance based on Vessel Time in Port (Fine).” World Bank,
Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO.
Translations—If you create a translation of this work, please add the following disclaimer along with the
attribution: This translation was not created by The World Bank and should not be considered an official
World Bank translation. The World Bank shall not be liable for any content or error in this translation.
Adaptations—If you create an adaptation of this work, please add the following disclaimer along with the
attribution: This is an adaptation of an original work by The World Bank. Views and opinions expressed in the
adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by
The World Bank.
Third-party content—The World Bank does not necessarily own each component of the content contained
within the work. The World Bank, therefore, does not warrant that the use of any third-party-owned individual
component or part contained in the work will not infringe on the rights of those third parties. The risk of claims
resulting from such infringement rests solely with you. If you wish to reuse a component of the work, it is your
responsibility to determine whether permission is needed for that reuse and to obtain permission from the
copyright owner. Examples of components can include, but are not limited to, tables, figures, or images.
All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group,
1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org.

I | Table of Contents
Table of contents
Acknowledgements......................................................................................................................................................................iii
Abbreviations and Acronyms..........................................................................................................................��......................iv
Glossary..........................................................................................................................................................��..............v
Foreword.........................................................................................................................................................................................vi
Executive summary........................................................................................................................................................................1
1. Introduction ..........................................................................................................................................�� .................8
2. The Approach and Methodology ......................................................................................................��................12
Introduction..............................................................................................................................................��.....................................................12
The Port Performance Program................................................................................................�..................................................................13
The Automatic Identification System and Port Zoning ..........................................................................��...............13
The Anatomy of a Port Call ..........................................................................��...............14
Overall Port Time Distribution.......................................................................................................................�� ..................16
The Significance of Call Size.......................................................................................................................��..................22
Construction of the CPPI.......................................................................................................................��..................27
3. The Container Port Performance Index 2022..................................................................................................................34
Introduction........................................................................................................................................�� ...........................34
The CPPI 2022.............................................................................................................................�� ..................................34
Ranking by Region.......................................................................................................................��.............................37
Ranking by Throughput ................................................................................................�� ..............................44
4. Conclusions and Next Steps................................................................................................................................................50
Appendix A: The CPPI 2022......................................................................................................................................................51
Appendix B: Constructing the CPPI ..............................................�� ..........................................74
The Structure of the Data..............................................................................................................................��........................................74
Imputation of Missing Values .........................................................................................................��...................................75
Why Is Matrix Factorization Useful?................................................................................................��.................................76
The Statistical Methodology.........................................................................................................��.....................................76
The Administrative Approach.........................................................................................................��.....................................77
Aggregating all Ship Size Groups.........................................................................................................��.....................................79

Table of Contents | II
Tables
Table E.1. • The CPPI 2022: Global Ranking of Container Ports..............................................��.......................................3
Table 2.1 • Average Arrival Time Development per Region and Ship Size, 2021-2022............................................��.18
Table 2.2 • Top 20 Ports that Most Reduced Average Arrival Times, 2021-2022 ....................��................ 19
Table 2.3 • Top 20 Ports that Increased Average Port Arrival Times, 2021-2022 .................��.................................. 20
Table 2.4 • Average Arrival Time Performance per Ship Size Range per Region ..................�� ..................................... 21
Table 2.5 • Smaller Vessel Average Arrival Times ..............................................�� .............................................................. 21
Table 2.6 • Port Calls Distribution ..............................................��........................................................................................ 28
Table 2.7 • Ship Size Group Definitions ................................................................��.............................................................. 28
Table 2.8 • Call Size Sensitivity .....................................................................��..................................................................... 29
Table 2.9 • Quantity of Ports Included per Ship Size Group ..........................��................................................................ 30
Table 2.10 • Example of Imputing Missing Values .......................................��..................................................................... 31
Table 2.11 • An Example of Aggregated Rankings for Four Ports with Randomly Generated Administrative and
Statistical Index Values ........................................................�� .............................................................................................. 32
Table 3.1 • The CPPI 2022 ..........................................................................�� ........................................................................... 35
Table 3.2 • The CPPI by Region: North America ......................................��.......................................................................... 37
Table 3.3 • The CPPI by Region: Central America, South America, and the Caribbean Region .............��............... 38
Table 3.4 • The CPPI by Region: West, Central, and South Asia (Saudi Arabia to Bangladesh) ..................��...... 39
Table 3.5 • The CPPI by Region: East Asia (Myanmar to Japan) .........................................��.......................................... 40
Table 3.6 • The CPPI by Region: Oceania (Australia, New Zealand, and the Pacific Islands) ..................�� ................ 41
Table 3.7 • The CPPI by Region: Sub-Saharan Africa ...........................................................................�� ...................... 42
Table 3.8 • The CPPI by Region: Europe and North Africa ......................................................�� .................................. 43
Table 3.9 • The CPPI by Throughput: Large Ports (More than 4 million TEUs per Year) ............................�� .......... 44
Table 3.10 • The CPPI by Throughput: Medium Ports (between 0.5 million and 4 million TEUs per Year)........�� ... 45
Table 3.11 • The CPPI by Throughput: Small Ports (Less than 0.5 million TEUs per Year) ........................��........... 48
Figures
Figure 2.1 • The Anatomy of a Port Call ...........................................................................��....................................... 15
Figure 2.2 • In-Port Time Consumption ......................................................................................��............................... 16
Figure 2.3 • Global Average Arrival Time Development ............................................................................��................. 17
Figure 2.4 • The Aggregated Correlation between Ship and Call Size .........................................................�� ............. 23
Figure 2.5 • Container Moves Performed per gross Crane Hour across Various Ship Sizes ...........................�� .... 24
Figure 2.6 • Gross Crane Productivity by Call Size ...................................................................................�� ................... 25
Figure 2.7 • Crane Productivity by Crane Intensity ......................................................................................�� ........... 25
Figure 2.8 • Call Size versus Crane Intensity ............................................................................................��.................. 26
Figure 2.9 • Average Moves per Crane ..........................................................................................................��.................. 26
Figure 2.10 • The Structure of the CPPI .................................................................................................��.................... 27
Figure 2.11 • Percentage of Port Calls per Ship Size Group - 2022 ..........................................................��................ 29

III | Acknowledgements
Acknowledgements
This technical report was prepared jointly by the teams from the Transport Global Practice of the
Infrastructure Vice-Presidency at the World Bank and the Maritime, Trade and Supply Chain division of
SP Global Market Intelligence.
The World Bank team was led by Richard Martin Humphreys (Global Lead for Connectivity and Logistics
and Lead Transport Economist, ITRGK), Grace Naa Merley Ashley (Transport Specialist, ITRGK), and
Dominique Guillot (Associate Professor, University of Delaware), under the guidance of Binyam Reja
(Global Practice Manager Transport, ITRGK) and Nicolas Peltier-Thiberge (Global Practice Director
Transport, ITRGK).
The SP Global Market Intelligence team was led by Turloch Mooney (Director, Global Intelligence
Analytics), Andy Lane (Partner, CTI Consultancy), and Michelle Wong (Senior Research Analyst, GTI
Product Management), under the guidance of Jenny Paurys (Head of Global Intelligence Analytics) and
Guy Sear (Managing Director, Global Risk Maritime Global Intelligence Analytics).
The joint team would like to extend special thanks to the following experts for their comments on the
draft of the technical report: Jan Hoffmann (Chief, Trade Logistics Branch, Division on Technology and
Logistics, United Nations Conference on Trade and Development, Geneva), Gylfi Palsson (Lead Transport
Specialist, ILTC1), and Ninan Biju Oommen (Senior Transport Specialist, IEAT1).

Abbreviations and Acronyms | IV
Abbreviations and Acronyms
AIS Automatic Identification System
CI Crane Intensity
COVID-19 Coronavirus Disease 2019
CPPI Container Port Performance Index
EEZ Exclusive Economic Zone
FA Factor Analysis
GCI Global Competitiveness Index
GCMPH Moves per Gross Crane Hour
GDP Gross Domestic Product
GRT Gross Registered Tonnage
ITU International Telecommunication Union
LLDC Landlocked Developing Country
LPI Logistics Performance Index
SIDS Small Island Developing States
TEU Twenty-foot Equivalent Unit
UNCTAD United Nations Conference on Trade and Development

All fast: The point when the vessel is fully secured at
berth and all mooring lines are fast
Arrival time/hours: The total elapsed time between
the vessel’s automatic identification system (AIS)
recorded arrival at the actual port limit or anchorage
(whichever recorded time is the earlier) and its all lines
fast at the berth
Berth hours: The time between all lines fast and all
lines released
Berth idle: The time spent on berth without ongoing
cargo operations. The accumulated time between all
fast to first move plus last move to all lines released
Call size: The number of container moves per call,
inclusive of discharge, load, and restowage
Cargo operations: When cargo is being exchanged,
the time between first and last container moves
Crane intensity (CI): The quantity of cranes deployed
to a ship’s berth call. Calculated as total accumulated
gross crane hours divided by operating (first to last
move) hours
Factor analysis (FA): A statistical method used
to describe variability among observed, correlated
variables in terms of a potentially lower number of
unobserved variables called factors
Finish: Total elapsed time between last container
move and all lines released
Gross crane hours: Aggregated total working time
for all cranes deployed to a vessel call without any
deductions. Time includes breakdowns, inclement
weather, vessel inspired delays, un/lashing, gantry,
boom down/up plus hatch cover and gear-box handling.
Gross crane productivity (GCMPH): Call size or total
moves divided by total gross crane hours.
Hub port: A port which is called at by deep-sea
mainline container ships and serves as a transshipment
point for smaller outlying, or feeder, ports within its
geographical region. Typically, more than 35 percent of
its total throughput would be hub and spoke or relay
transshipment container activity.
Moves: Total container moves. Discharge + restowage
moves + load. Excluding hatch covers, gearboxes, and
other non-container related crane work. Breakbulk
cargo lifts are excluded, however empty platform
(tweendeck or flat-rack) handling moves are included.
Moves per crane: Total Moves for a call divided by the
crane intensity.
Port call: A call to a container port/terminal by a
container vessel where at least one container was
discharged or loaded.
Port hours: The number of hours a ship spends at/
in port, from arrival at the port limits to sailing from
the berth.
Port limits: Either an anchorage zone or the location
where pilot embarkation or disembarkation occurs and
recorded as whichever activity is the earliest.
Port to berth hours: The time from when a ship first
arrived at the port limits or anchorage zone (whichever
activity occurs first) until it is all fast alongside
the berth.
Relay transhipment: Containers transhipped between
ocean going container ships.
Ship size: Nominal capacity in twenty foot equivalent
units (“TEU’s”).
Start: The time elapsed from berthing (all lines fast) to
first container move.
Steam in time: The time required to steam-in from
the port limits and until all fast alongside the berth.
Twenty foot equivalent unit or TEU: A standard
metric for container throughput, and the physical
capacity of a container terminal. A 20-foot container
is equal to 1 TEU, and a 40-foot or 45-foot container is
equal to 2 TEUs. Regardless of container size (10 feet,
15 feet, 20 feet, 30 feet, 40 feet, or 45 feet), each is
recorded as one move when being loaded or discharged
from the vessel.
Vessel capacity: Nominal capacity in twenty foot
equivalent Units (“TEU’s”).
Waiting time: Total elapsed time from when vessel
enters anchorage zone to when vessel departs
anchorage zone (vessel speed must have dropped
below 0.5 knots for at least 15 mins within the zone).
V | Glossary
Glossary

Foreword | VI
Foreword
The challenges caused by the COVID-19 pandemic and its aftermath on the sector eased in 2022, an
easing that has continued into early 2023. This has resulted in an improvement in both port congestion
and a reduction in logistical disruption. The improvement in 2022 has had a positive impact on the
performance and ranking of some ports; where the problem was systemic, as opposed to location
specific, the inherent inefficiency remains. One of the ‘silver linings’ of the pandemic was greater
awareness and focus on the resilience and efficiency of the maritime gateways, where any friction will
result in tangible impacts on consumer choice, price and ultimately economic development.
However, one of the major challenges to stimulating improvement in the efficiency of ports has been the
lack of a reliable, consistent, and comparable basis on which to compare operational performance across
different ports. While modern ports collect data for performance purposes, the Quality, consistency, and
availability of data, the definitions employed, and the capacity and willingness of the organizations to
collect and transmit data to a collating body have all precluded the development of a robust comparable
measure(s) to assess performance across ports and time.
The introduction of new technologies, increased digitalization, and the willingness on the part of industry
stakeholders to work collectively toward systemwide improvements have now provided the opportunity
to measure and compare container port performance in a robust and reliable manner. A partnership
has resulted in this technical report, which is the third iteration of the Container Port Performance Index
(CPPI), produced by the Transport Global Practice of the World Bank in collaboration with the Global
Intelligence Analytics division of SP Global Market Intelligence.
The CPPI is intended, as in its earlier iterations, to serve as a reference point for improvement for key
stakeholders in the global economy, including national governments, port authorities and operators,
development agencies, supranational organizations, various maritime interests, and other public and
private stakeholders in trade, logistics, and supply chain services. The performance of a port may be
assessed based on a myriad of measurements, such as: terminal capacity or space utilization, cost,
landside connectivity services, or ship to shore interchange. The CPPI is based on available empirical
objective data pertaining exclusively to time expended in a vessel stay in a port and should be interpreted
as an indicative measure of container port performance, but not a definitive one.
Nicolas Peltier-Thiberge
Global Practice Director
Transport
The World Bank
Jenny Paurys
Head of Global Intelligence
Analytics
SP Global Market
Intelligence

1 | Executive Summary
Executive Summary
Maritime transport forms the foundation of global trade and the manufacturing supply chain. The maritime
industry provides the most cost-effective, energy-efficient, and dependable mode of transportation for long
distances. More than 80 percent of global merchandise trade (by volume) is transported via sea routes.
A considerable and increasing proportion of this volume, accounting for about 35 percent of total volumes
and over 60 percent of commercial value, is carried in containers.
The emergence of containerization brought about significant changes in how and where goods are
manufactured and processed, a trend that is likely to continue with digitalization. Container ports are
critical nodes in global supply chains and essential to the growth strategies of many emerging economies.
In numerous cases, the development of high-quality container port infrastructure operating efficiently has
been a prerequisite for successful export-led growth strategies. Countries that follow such a strategy will
have higher levels of economic growth than those that do not. Efficient, high quality port infrastructure can
facilitate investment in production and distribution systems, engender expansion of manufacturing and
logistics, create employment opportunities, and raise income levels.
However, ports and terminals, especially container terminals, can cause shipment delays, disruptions in
supply chain, additional expenses, and reduced competitiveness. The negative effect of poor performance in
a port can extend beyond the that port’s hinterland to others as container shipping services follow a fixed
schedule with specific berth windows at each port of call on the route. Therefore, poor performance at one
port could disrupt the entire schedule. This, in turn, increases the cost of imports and exports, reduces the
competitiveness of the country and its hinterland, and hinders economic growth and poverty reduction.
The consequences are particularly significant for landlocked developing countries (LLDCs) and small island
developing states (SIDS).
Comparing operational performance across ports has been a major challenge for improving global value
chains due to the lack of a reliable, consistent, and comparable basis. Despite the data collected by modern
ports for performance purposes, the quality, consistency, and availability of data, as well as the definitions
used and the capacity and willingness of organizations to transmit data to a collating body, have hindered
the development of a comparable measure(s) for assessing performance across ports and time. However,
new technologies, increased digitalization, and industry interests’ willingness to work collectively toward
systemwide improvements now provide an opportunity to measure and compare container port performance
in a robust and reliable manner. The World Bank’s Transport Global Practice and the Global Intelligence
Analytics division of SP Global Market Intelligence have collaborated to produce the third edition of the
Container Port Performance Index (CPPI), presented in this technical paper.
The aim of the CPPI is to pinpoint areas for enhancement that can ultimately benefit all parties involved,
ranging from shipping lines to national governments and consumers. It is designed to act as a point of
reference for important stakeholders in the global economy, including port authorities and operators, national
governments, supranational organizations, development agencies, various maritime interests, and other public
and private stakeholders in trade, logistics, and supply chain services. The development of the CPPI rests on
total container ship in port time in the manner explained in subsequent sections of the report, and as in earlier

Executive Summary | 2
iterations of the CPPI. This third iteration utilizes data for the full calendar year of 2022. One slight change in
this iteration is that the CPPI 2022 only includes ports that had a minimum of 24 valid port calls within the
12-month period of the study, compared to 20 in earlier iterations. The number of ports included in the CPPI
2022 is 348.
In earlier iterations of the CPPI, the calculation of the ranking of the index employed two different
methodological approaches, an administrative, or technical, approach, a pragmatic methodology reflecting
expert knowledge and judgment; and a statistical approach, using factor analysis (FA). The rationale for using
two approaches was to try and ensure that the ranking of container port performance reflects as closely
as possible actual port performance, whilst also being statistically robust. And there has been a marked
improvement in consistency between the rankings that result from the two approaches since the inaugural
CPPI 2020, but some minor inconsistencies remained.
Accordingly, for CPPI 2022, the same methodological approaches are used and then a rank aggregation
method applied to combine the results from the two different approaches and return one aggregate ranking.
The aggregation methodology and the resulting ranking is detailed in the report, while the statistical and
administrative approaches and their respective rankings are detailed in Appendix A. Table E.1 presents the
resulting CPPI 2022.
The two top-ranked container ports in the CPPI 2022 are Yangshan Port (China) in first place, followed by the
Port of Salalah (Oman) in second place. These two ports occupy the same positions in the rankings generated
by both approaches. Port of Salalah was ranked second in both approaches in CPPI 2021. Yangshan Port
ranked third and fourth in the statistical and administrative approaches, respectively, for CPPI 2021. Three
ports in the Middle East are ranked in the top ten (Salalah, Kahlifa, Hamad), as are three of the large Chinese
gateways (Yangshan, Ningbo and Guangzhou).
Of the top 10 ranked ports, 9 have either maintained or improved their position since CPPI 2021. The exception
is Hamad Port, which moved down 5 and 3 places in the administrative and statistical rankings, respectively.
Yokohama fell from 10th and 12th in CPPI 2021 to 15th place in CPPI 2022, and Jeddah fell from 8th place in
CPPI 2021 to 29th place in CPPI 2022.

Port Name
Overall
Ranking
Yangshan 1
Salalah 2
Khalifa Port 3
Tanger-Mediterranean 4
Cartagena (Colombia) 5
Tanjung Pelepas 6
Ningbo 7
Hamad Port 8
Guangzhou 9
Port Said 10
Hong Kong 11
Cai Mep 12
Shekou 13
Mawan 14
Yokohama 15
Algeciras 16
King Abdullah Port 17
Singapore 18
Posorja 19
Tianjin 20
Buenaventura 21
Busan 22
Yeosu 23
Chiwan 24
Kaohsiung 25
Djibouti 26
Laem Chabang 27
Colombo 28
Jeddah 29
Pipavav 30
Dammam 31
Coronel 32
Xiamen 33
Barcelona 34
Callao 35
Port Klang 36
Incheon 37
Jebel Ali 38
Port Name
Overall
Ranking
Fuzhou 39
Marsaxlokk 40
Yarimca 41
Dalian 42
Lazaro Cardenas 43
Wilmington (USA-N Carolina) 44
Kobe 45
Nagoya 46
Shimizu 47
Mundra 48
Sohar 49
Rio Grande (Brazil) 50
Piraeus 51
Port Of Virginia 52
Yantian 53
Tokyo 54
Altamira 55
Haifa 56
Ambarli 57
Jubail 58
Aqaba 59
Bremerhaven 60
Itapoa 61
Zeebrugge 62
Da Chan Bay Terminal One 63
Krishnapatnam 64
Zhoushan 65
Antwerp 66
Rio De Janeiro 67
Savona-Vado 68
Boston (USA) 69
Keelung 70
Santa Cruz De Tenerife 71
Paranagua 72
Khalifa Bin Salman 73
Siam Seaport 74
Diliskelesi 75
Balboa 76
Table E.1 • The CPPI 2022: Global Ranking of Container Ports

Port Name
Overall
Ranking
Shantou 77
Kattupalli 78
Kamarajar 79
Osaka 80
Colon 81
Jacksonville 82
Lianyungang 83
Karachi 84
Hazira 85
Jawaharlal Nehru Port 86
Puerto Limon 87
Cochin 88
Port Everglades 89
Muhammad Bin Qasim 90
Johor 91
Penang 92
Aarhus 93
Puerto Cortes 94
Fort-De-France 95
Pointe-A-Pitre 96
Tanjung Perak 97
Philadelphia 98
Veracruz 99
Nemrut Bay 100
Paita 101
Yokkaichi 102
Limassol 103
Naha 104
Ensenada 105
Malaga 106
Cat Lai 107
Imbituba 108
Hakata 109
Chennai 110
Gemlik 111
Mersin 112
New Orleans 113
Santos 114
Visakhapatnam 115
Pecem 116
Port Name
Overall
Ranking
Danang 117
Wilhelmshaven 118
Puerto Barrios 119
Salvador 120
Shuaiba 121
Gothenburg 122
Gioia Tauro 123
Saigon 124
Taichung 125
Port Akdeniz 126
Sharjah 127
Noumea 128
Puerto Quetzal 129
San Juan 130
Santa Marta 131
Tanjung Emas 132
Omaezaki 133
Gijon 134
Batangas 135
Moji 136
Izmir 137
Vigo 138
Papeete 139
Haiphong 140
Lirquen 141
Shuwaikh 142
Cebu 143
Berbera 144
Port Tampa Bay 145
Quy Nhon 146
Puerto Bolivar (Ecuador) 147
Caucedo 148
Fredericia 149
Odessa 150
Helsingborg 151
Cadiz 152
Wellington 153
Nantes-St Nazaire 154
Chu Lai 155
Cagayan De Oro 156

Port Name
Overall
Ranking
Ancona 157
Rio Haina 158
Casablanca 159
Bar 160
Ravenna 161
Puerto Progreso 162
Salerno 163
Barranquilla 164
Umm Qasr 165
Oslo 166
Gustavia 167
Borusan 168
Philipsburg 169
Vitoria 170
Qingdao 171
El Dekheila 172
Damietta 173
Buenos Aires 174
Leixoes 175
Brest 176
Latakia 177
Suape 178
Larvik 179
Burgas 180
Norrkoping 181
Sepetiba 182
Muuga-Port Of Tallinn 183
Bari 184
Civitavecchia 185
Sines 186
Copenhagen 187
Valparaiso 188
Conakry 189
Vila Do Conde 190
Bluff 191
Bell Bay 192
Subic Bay 193
Novorossiysk 194
Klaipeda 195
Dakar 196
Port Name
Overall
Ranking
Matadi 197
Catania 198
Palermo 199
Rauma 200
Heraklion 201
Kristiansand 202
Apra Harbor 203
Nelson 204
Tema 205
Bilbao 206
Trapani 207
Tomakomai 208
Mariel 209
Rades 210
Caldera (Costa Rica) 211
La Guaira 212
Bordeaux 213
Belawan 214
Shanghai 215
Lisbon 216
Miami 217
Marseille 218
Tripoli (Lebanon) 219
Helsinki 220
Mogadiscio 221
Kotka 222
Beira 223
Alicante 224
Gdynia 225
Freetown 226
Toamasina 227
Panjang 228
Nassau 229
Batumi 230
Riga 231
Point Lisas Ports 232
Saint John 233
Teesport 234
Southampton 235
Manaus 236

Port Name
Overall
Ranking
Arica 237
Mobile 238
Port Of Spain 239
Itajai 240
Varna 241
Hueneme 242
Bangkok 243
St Petersburg 244
Takoradi 245
Venice 246
Gavle 247
Maputo 248
Port Victoria 249
Timaru 250
Davao 251
Agadir 252
San Antonio 253
Durres 254
Puerto Cabello 255
Bejaia 256
San Vicente 257
Dublin 258
Corinto 259
Lagos (Nigeria) 260
London 261
Aden 262
Santo Tomas De Castilla 263
Felixstowe 264
Rotterdam 265
Kingston (Jamaica) 266
Mayotte 267
Alexandria (Egypt) 268
Sokhna 269
Naples 270
Monrovia 271
Mejillones 272
Melbourne 273
Lae 274
Owendo 275
Otago Harbour 276
Port Name
Overall
Ranking
Adelaide 277
Halifax 278
Seattle 279
Iskenderun 280
Tanjung Priok 281
Manzanillo (Mexico) 282
Guayaquil 283
Iquique 284
Tarragona 285
Antofagasta 286
Brisbane 287
Acajutla 288
Gdansk 289
Poti 290
Port Elizabeth 291
Montreal 292
Walvis Bay 293
Constantza 294
Douala 295
San Pedro (Cote D'ivoire) 296
Ashdod 297
Port Reunion 298
Port Botany 299
Baltimore (USA) 300
Valencia 301
Onne 302
Qasr Ahmed 303
Montevideo 304
Cristobal 305
New York New Jersey 306
Chattogram 307
Tin Can Island 308
Livorno 309
Fremantle 310
Dunkirk 311
Dar Es Salaam 312
Lyttelton 313
Tacoma 314
Pointe-Noire 315
Genoa 316

Port Name
Overall
Ranking
Freeport (Bahamas) 317
Lome 318
Le Havre 319
Beirut 320
Thessaloniki 321
Napier 322
Auckland 323
Kribi Deep Sea Port 324
Tauranga 325
Mombasa 326
Port Louis 327
Hamburg 328
Manila 329
Cotonou 330
Nouakchott 331
La Spezia 332
Source: Original table produced for this publication, based on CPPI 2022 data.
There are 14 new entrants to the CPPI 2022, and several significant movers since the CPPI 2021. Over one
hundred and ten ports improved their rankings in CPPI 2022 compared to CPPI 2021, with some of the
largest improvers increasing their ranking by more than 200 positions.
Port Name
Overall
Ranking
Abidjan 333
Rijeka 334
Houston 335
Los Angeles 336
Luanda 337
Ngqura 338
Trieste 339
Charleston 340
Durban 341
Prince Rupert 342
Oakland 343
Cape Town 344
Koper 345
Long Beach 346
Vancouver (Canada) 347
Savannah 348

1.
Introduction
Since the start of maritime trade, ports have played a central role in the economic and social development
of countries. The innovation of containerization by Malcom McLean in 1958 changed the course of the
shipping industry and engendered significant changes to where and how goods are manufactured. Container
ports remain vital nodes in global supply chains and are crucial to the growth strategies of many emerging
economies. The development of high-quality port infrastructure, operated efficiently, has often been a
prerequisite for successful growth strategies, particularly those driven by exports. When done correctly,
it can attract investment in production and distribution systems and eventually, support the growth of
manufacturing and logistics, create employment, and increase income levels.
In contrast, a poorly functioning or inefficient port can hinder trade growth, with a profound impact on LLDCs
and SIDS. The port, along with the access infrastructure (inland waterways, railways, roads) to the hinterland,
is a vital link to the global marketplace and needs to operate efficiently. Efficient performance encompasses
several factors, such as the port’s efficiency itself, the availability of sufficient draught, quay, and dock facilities,
the quality of road and rail connections, the competitiveness of these services, and the effectiveness of the
procedures utilized by public agencies for container clearance. Any inefficiencies or non-tariff barriers among
these actors will result in higher costs, reduced competitiveness, and lower trade volumes (Kathuria 2018).
More specifically, the efficiency of port infrastructure has been identified as a key contributor to the overall
port competitiveness and international trade costs. Micco et al. (2003) identified a link between port efficiency
and the cost of international trade. Clark, Dollar, and Micco (2004) found a reduction in country inefficiency,
specifically transport cost, from the 25th to 75th percentile, resulting in an increase in bilateral trade of
around 25 percent. Wilmsmeier, Hoffmann, and Sanchez (2006) confirmed the impact of port performance
1
Introduction | 8

9 | Conclusions and Next Steps
on international trade costs, finding that doubling port efficiency in a pair of ports had the same impact
on trade costs as halving the physical distance between the ports. Hoffmann, Saeed, and Sødal (2020)
analyzed the short- and long-term impacts of liner shipping bilateral connectivity on South Africa’s trade
flows, and showed that gross domestic product (GDP), the number of common direct connections, and
the level of competition have a positive and significant effect on trade flows.
However, ports and terminals, particularly for containers, can often be the main sources of shipment
delays, supply chain disruptions, additional costs, and reduced competitiveness. Poorly performing ports
are characterized by limited spatial and operating efficiency, maritime and landside access, oversight,
and coordination among the public agencies involved, which lower predictability and reliability. The
result is that instead of facilitating trade, the port increases the cost of imports and exports, reduces
competitiveness, and inhibits economic growth and poverty reduction. The effect on national and regional
economies can be severe [see inter alia World Bank (2013)] and has driven numerous efforts to improve
performance to strengthen competitiveness.
Port performance is also a key consideration for container shipping lines that operate liner services on
fixed schedules, based on agreed pro-forma berth windows. Delays at any of the scheduled ports of call
on the route served by the vessel would have to be made good before the vessel arrives at the next port
of call, to avoid an adverse impact on the efficient operations of the service. As such, port efficiency and
port turnaround time at all the ports of call are important subjects for operators, and monitoring port
performance has become an increasingly important undertaking in the competitive landscape.
One of the major challenges to improving efficiency has been the lack of reliable measures to compare
operational performance across different ports. The old management idiom, ‘you cannot manage what
you cannot measure,’ is reflective of the historical challenge of both managing and overseeing the sector.
While modern ports collect data for performance purposes, it is difficult to benchmark the outcomes
against leading ports or ports with similar profiles due to the lack of comparative data.
Unsurprisingly, there is a long history of attempts to identify a comparative set of indicators to measure
port or terminal performance. A brief review of the literature was provided in The Container Port
Performance Index 2020: A Comparable Assessment of Container Port Performance (World Bank 2021),
CPPI 2020, which illustrated the broad approaches identified and commented on the merits and
demerits of each. The measures fell into three broad categories: Firstly, measures of operational and
financial performance; secondly, measures of economic efficiency; and thirdly, measures that rely,
predominately, on data from sources exogenous to the port. This review is not replicated in CPPI 2021,
and interested readers are directed to CPPI 2020 (World Bank 2021), or the extant literature. One of the
general challenges of nearly all the approaches has been the quality, consistency, and availability of data;
the standardization of definitions employed; and the capacity and willingness of organizations to collect
and transmit the data to a collating body.
At a slightly higher level, there are several aggregate indicators that provide an indication of the
comparative quality and performance of maritime gateways. The World Bank Logistics Performance
Index (LPI) (Arvis et al. 2018) and the World Economic Forum’s Global Competitiveness Index (GCI)
4.0 both report on the perceived efficiency of seaport services and border clearance processes and
indicate the extent to which inefficiencies at a nation’s sea borders can impact international trade
competitiveness. But the aggregate nature of the indicators, and the fact that they are perception
based, means that they offer at best an indication of comparative performance and offer little to guide

Conclusions and Next Steps | 10
spatial or operating performance improvements at the level of the individual port. The United Nations
Conference on Trade and Development’s (UNCTAD’s) Liner Shipping Connectivity Index (LSCI) provides
an indicator of a port’s position within the liner shipping network, which is partly a result of the port’s
performance, but does not directly measure it. Like the CPPI, the LSCI is limited to container ports.
Digitalization offers an opportunity to measure and compare container port performance in a robust and
reliable manner. New technologies, increased digitalization and digitization, and growing willingness on
the part of industry stakeholders to work collectively toward system-wide improvements have created
the capacity and opportunity to measure and compare container port performance. The data used to
compile the CPPI 2022 is from SP’s Global Port Performance Program, which commenced in 2009 to
drive efficiency improvements in container port operations and supporting programs to optimize port
calls. It includes 10 of the world’s largest liner shipping companies, which collectively operate close to
80 percent of the global container ship fleet capacity.
The liner shipping companies provide the program with a series of operational time stamps for each
individual port call. The data are provided monthly and cover the full global networks of each liner
shipping company and their subsidiaries. In 2022, performance time stamp data and other information
for the 348 ports comprising the main index were captured for 156,813 port calls involving 243.9 million
container moves. The nature, source, and scope of the data are discussed in the subsequent chapter.
The aim of CPPI was to utilize the existing empirical data to establish an unbiased metric for comparing
container port performance among different ports, over time. The performance of container ports is
most relevant in terms of customer experience, specifically the speed and efficiency with which customer
assets are handled. In this third edition of CPPI, the focus remains exclusively on quayside performance,
which reflects the experience of a container ship operator - the port’s primary customer - and its
fundamental value stream. The operational efficiency of how ports receive, and handle container ships is
critically important in a carrier’s decision to choose a port over other options.
This year, we have streamlined the computation of the CPPI using an additional method that aggregates
the two methodologies used in the former editions. This will catalyze and stimulate improvements as the
ranking is now more reliable, consistent, and comparable across different ports. The three methodologies
employed in this study, and the justification for their use, are presented in the subsequent chapters. The
results are presented in chapter 3, with further details provided in appendixes A and B.
The purpose of the CPPI is to help identify opportunities to improve a terminal or a port that will
ultimately benefit all public and private stakeholders. The CPPI is intended to serve as a benchmark for
important stakeholders in the global economy, including national governments, port authorities and
operators, development agencies, supranational organizations, various maritime interests, and other
public and private stakeholders engaged in trade, logistics, and supply chain services. The joint team
from the World Bank and SP Global Market Intelligence intends to enhance the methodology, scope,
and data in future annual iterations, reflecting refinement, stakeholder feedback, and improvements in
data scope and quality.

References
•
Arvis, Jean-François, Lauri Ojala, Christina Wiederer, Ben Shepherd, Anasuya Raj, Karlygash
Dairabayeva, and Tuomas Kiiski. 2018. Connecting to Compete 2018: Trade Logistics in the
Global Economy. Washington DC: World Bank. https://openknowledge.worldbank.org/bitstream/
handle/10986/29971/LPI2018.pdf.
•
Clark, Ximena, David Dollar, and Alejandro Micco. 2004. “Port Efficiency, Maritime Transport Costs,
and Bilateral Trade.” Journal of Development Economics 75 (2): 417–450. https://doi.org/10.1016/j.
jdeveco.2004.06.005.
•
Hoffmann, Jan, Naima Saeed, and Sigbjørn Sødal. 2020. “Liner Shipping Bilateral Connectivity and Its
Impact on South Africa’s Bilateral Trade Flows.” Maritime Economics Logistics 2020, 22 (3): 473–499.
DOI: 10.1057/s41278-019-00124-8.
•
Kathuria, Sanjay. 2018. A Glass Half Full: The Promise of Regional Trade in South Asia. Washington DC:
World Bank. https://openknowledge.worldbank.org/handle/10986/30246.
•
Levinson, Marc. 2006. The Box: How the Shipping Container Made the World Smaller and the World
Economy Bigger. Princeton, New Jersey, United States: Princeton University Press.
•
Micco, Alejandro, Ricardo J. Sanchez, Georgina Pizzolitto, Jan Hoffmann, Gordon Wilmsmeier, and
Martin Sgut. 2003. “Port Efficiency and International Trade: Port Efficiency as a Determinant of
Maritime Transport Costs.” Maritime Economics Logistics, 5 (2): 199–218. DOI:10.1057/palgrave.
mel.9100073.
•
UNCTAD (United Nations Conference on Trade and Development). 2021. Review of Maritime Transport
2021. Geneva: UNCTAD. https://unctad.org/webflyer/review-maritime-transport-2021.

•
Wilmsmeier, Gordon, Jan Hoffmann, and Ricardo J. Sanchez. 2006. “The Impact of Port Characteristics
on International Maritime Trade Costs.” Research in Transportation Economics, 16 (1): 117–140.
DOI:10.1016/S0739-8859(06)16006-0.
•
World Bank. 2013. “Opening the Gates: How the Port of Dar es Salaam Can Transform Tanzania.”
Tanzania Economic Update 3, May 21, 2013. https://www.worldbank.org/en/country/tanzania/
publication/opening-the-gates-how-the-port-of-dar-es-salaam-can-transform-tanzania-
backup#:~:text=US%241%2C759%20million%20%E2%80%93%20the%20total,port%20of%20Dar%20
es%20Salaam.
•
World Bank. 2022. The Container Port Performance Index 2021: A Comparable Assessment of
Container Port Performance. Washington, DC: World Bank.

2. The Approach and Methodology
Introduction
Container (liner) shipping services are generally highly structured service rotations. They are typically set up with
weekly departure frequencies, a fixed sequence of port calls, and standard pro forma day and time-specific berthing
windows. Once a service has been defined or adjusted, it will usually remain intact for many months, or even years.
The berthing windows are pre-agreed with the terminal and port operators, usually based on a slightly higher than
expected average quantity of container exchange moves, and ideally modest buffers in the sea legs between ports.
The clear advantages of this model are that shippers can make long-term supply decisions and ports and terminals
schedule and balance their resources to meet expected demand. With a well-planned and well-executed pro forma
schedule, they can achieve higher levels of reliability and predictability. This, in turn, can lead to more effective supply
chain operations and planning as container ships spend around 15 percent to 20 percent of their total full rotation
time in ports, with the balance being spent at sea. Reduced port time can allow ship operators to reduce vessel
speed between port calls, thereby conserving fuel, reducing emissions, and lowering costs in the process.
Conversely, for every unplanned additional hour in port or at anchorage, the ships need to increase speed to
maintain the schedule, resulting in increased fuel consumption, costs, and emissions. In extreme cases, ships
that fall many hours behind their pro forma schedule will start to arrive at ports outside of their agreed windows,
causing berth availability challenges for ports and terminals, particularly those with high berth utilization
rates. This, in turn, causes delay to shipments and disruption to supply chains. A service recovery can involve
significantly higher sailing speeds, and therefore, higher fuel consumption, emissions, and costs, or the omission
of a port or ports from the service rotation.
2
The Approach and Methodology | 12

Time is valuable for stakeholders, and so it is logical to measure port performance based on the total
amount of time ships are required to spend in port. The CPPI 2022 has been developed based on the total
port time in the manner explained in subsequent sections. This iteration has utilized data from the full
calendar year of 2022 and has employed the same two approaches as the earlier editions, an administrative
approach and a statistical approach. The resulting ranking of container port performance reflects as closely
as possible actual port performance, while being statistically robust. The approaches are discussed in this
chapter, with further details on the statistical methodology provided in Appendix B. The results are presented
in chapter 3, and in more details in Appendix A.
The Port Performance Program
The data used to compile the CPPI is from SP Global’s Port Performance Program. The program was
started in 2009 with the goal of supporting efficiency improvements in container port operations and
to support projects to optimize container port calls. The program includes 10 of the world’s largest liner
shipping companies that collectively operate close to 80 percent of global fleet capacity.
The liner shipping companies provide the program with a series of data points comprising operational time
stamps and other bits of information such as move counts for each individual port call undertaken globally.
The data are provided monthly and cover the full global networks of each liner shipping company and their
subsidiaries. In 2022, performance time stamp data were captured for 157,704 port calls involving 243.9
million container moves at 765 container terminals in 434 ports worldwide.
Following receipt from the shipping lines, the port call data undergo several validation and quality checks
before mapping to historical AIS vessel movement data, which enables tracking and verification of the
shipping line data. The geo-fencing of port and terminal zones within the AIS system supports the creation
of several of the performance metrics tracked in the program. Most of the port performance metrics are
constructed from the combined AIS and liner shipping data. The combination of empirical shipping line
data and AIS movement data enables the construction of more accurate and granular metrics to measure
container port performance. Many of the metrics consist of a time component cross-referenced with
workload achieved in that time, either in the form of move counts or a specific task within the container
port call process. Time stamps, definitions, and methods to calculate metrics are fully standardized in
collaboration with the shipping line partners in the program.
The Automatic Identification System and Port Zoning
AIS technology is used to track and monitor vessels in near real time. It sends information on a vessel’s
movement, speed, direction, and other particulars via satellite and terrestrial stations. The system’s
function as a localized service, and indeed global tracking, was initially considered secondary. The AIS
primarily functions as a navigational safety aid, to ensure the safety and efficiency of navigation, safety
of life at sea, and maritime environmental protection.1
AIS was designed for the avoidance of vessel
collision, as outlined in the Safety of Life at Sea (SOLAS) Convention.2
All ships of net tonnage of at least 300 gross register tonnage (GRT) performing international voyages, all
cargo ships of at least 500 GRT not performing international voyages, and all passenger ships, regardless
of size, should be equipped with AIS. This allows vessels to automatically transfer data and a plethora
of navigational and identification information to other nearby ships and relevant port authorities in the
form of structured messages.3
The technical requirements for AIS are specified by the International
Telecommunication Union (ITU) Recommendation ITU-R M.1371-5(02/2014).4
13 |
The Approach and Methodology

For maritime domain awareness and safety purposes, the use of continuous 24/7, near-real-time
online AIS data makes it possible to monitor areas, vessels, and routes; generate shore-based alerts;
and provide useful positional and navigational information in general (IALA 2005). Satellite-based AIS
receivers offer coverage outside the land-based antennas’ range by covering the whole globe from pole to
pole. Satellite AIS coverage can extend to the entire exclusive economic zone (EEZ) or globally, including
remote coastal areas (IALA 2016).
In the case of ports5
, the usage of ‘zones’ helps in recording a vessel’s navigational status and positioning.
AIS zones offer different indicators activated automatically by the vessel’s signal reporting its position.
Every port has at least one zone created in a way that captures the arrivals and sailings of vessels at
cargo-handling facilities but avoids spurious reports being recorded from passing traffic. Where a subject
port is geographically spread out with terminals located remotely, it is likely that there will be more than
one zone, with all zones linked by a standard port identification number.
Ports that straddle a river or another similar body of water will often have zones along opposing
shorelines with a track separating them, thus avoiding the capture of AIS reports from traffic navigating
through a fairway or channel. Once again, the individual zones will be linked to their common port using
the port’s unique identification number.
Zones also cover anchorages to record vessels arriving at a port but awaiting authority to enter, or
vessels laid up awaiting orders. Additional zones cover the arrival of vessels at repair yards or those
navigating locks. Anchorage zones may be created on an ad hoc basis. Not all ports have anchorage
areas and among those that do, not all are shown in nautical charts. Whenever possible, SP Global uses
its own tracking and observation tools to determine where vessels anchor and create zones accordingly.
Each anchorage zone is linked to the relevant port using the subject port’s unique identification number.
AIS is generally reliable, but it also has limitations that can impact the transmission and quality of the
data captured. Some factors that may affect the signal could be the AIS transponder being turned off
deliberately, problematic reception, high traffic density areas, weather conditions, or anomalous positions.
The Anatomy of a Port Call
Every container ship port call can be broken down into six distinct steps. These individual steps are
illustrated in Figure 2.1. ‘Total port hours’ is defined as the total time elapsed between when a ship
reaches a port (either port limits, pilot station, or anchorage zone, whichever event occurs first)
to when it departs from the berth after having completed its cargo exchange.
The time spent from berth departure (All Lines Up) to the departure from the port limits is excluded. This
is because any port performance loss that pertains to departure delays, such as pilot or tug availability,
readiness of the mooring gang, channel access and water depths, forecasting completion time,
communication, and ship readiness will be incurred while the ship is still alongside the berth. Additional
time resulting from these causes will, therefore, be captured during the period between 4. Last Lift and 5.
All Lines Up (“berth departure).

Figure 2.1 • The Anatomy of a Port Call
Source: Original figure produced for this publication
Ships may spend extra time in a port after the departure from a berth, but the time associated with
these additional activities is excluded from the CPPI, as they are not influenced by the operational
performance of the terminal or port. Ships may dwell within a port’s limits for bunkering, repairs, or
simply waiting in a safe area if they are unable to berth on arrival at their next port. Apart from bunkering
being performed simultaneously with cargo operations, these causes of additional port time are not
necessarily reflective of poor performance and hence, are excluded from the CPPI.
Although none of these factors necessarily indicate port inefficiency, they can contribute to additional
time spent in the port. For instance, clearance authorities’ delays can result in delays in the first lift and
idle time after cargo operations have concluded. However, the data available do not provide enough detail
to identify the root causes of such delays. It is assumed that only a small percentage of ships idle at the
berth after cargo operations due to factors unrelated to port performance, and their inclusion does not
significantly affect the CPPI rankings.
The other four components of the port call can logically be grouped into two distinct blocks of time. The
first comprises elapsed time between Arrival Port Limits and All Lines Fast (steps 1 and 2 in Figure 2.1);
the second comprises time elapsed between All Lines Fast and All Lines Up (steps 2 to 5, also commonly
referred to as ‘berth time’ or ‘berth hours’). The logic behind this division is that while there will always
need to be time consumed between steps 2 and 5, the bulk of time between steps 1 and 2, excluding
actual sailing in time, is waiting time, which can be eliminated.
15 |
1 2
POINTS OF
ACTIVITY
6 5
3
4
1 2
3
1
1
2
2
4
5
6
3
3
4
4
5
5
6
6
Arrival Port
Limits
All Lines
Fast
First Lift
Last Lift
All Lines
Up
Exit Port
Limits Steam out
Arrival At Anchorage and Waiting Time at Anchorage
(Berth, Channel, Pilot etc.)
Steam in Time Port Limit to All Lines Fast
Gangway down, authority clearence, abour available,
position crenes, unlash, load approval, etc
All cargo operations, driven by Crane Intesity
and Gross Crane Performance
Lashing and checks, authority clearence, crew onboard,
engine ready, repairs completed, bunkers, channel clear,
tugs pilot

Overall Port Time Distribution
The time stamps in the source data allow us to break down and summarize total port time into three
categories: Arrival Time, Berth Idle, and Cargo Operations. Expressed as a percentage of total port hours
recorded, the distribution of port time per ship size range and globally aggregated is shown in Figure 2.2.
Figure 2.2 • In-Port Time Consumption
Source: Original figure produced for this publication, based on CPPI 2022 data
As there is naturally some correlation between ship size and call size, a higher percentage of time is
required for cargo operations for the larger ships, and this will be explored in detail later in this report.
What is interesting, and surprising at the same time is that only 60 percent of the total port time is
attributable to cargo operations, meaning there is potentially a lot of ‘wastage’ in terms of excess time
in the system.
The average duration of a port call in 2022 was 36.8 hours, which was a slight increase over the
global average of 36.3 hours in 2021. About 10.8 percent (or 3.96 hours) was consumed at the berth
immediately before and after cargo operations. Also known as the ‘Start-Up’ and ‘Finish’ sub-processes
of a port call, each activity does not necessarily need to take more than 30 minutes to complete safely.
There are 33,787 examples of Start-Up recorded as 30 minutes or less and a further 29,367 actual cases
of the Finish consuming 30 minutes or less. There were 12,784 port calls in 2022 where both the Start-Up
and Finish took 30 minutes or less. There is, therefore, an opportunity to eliminate almost three hours
per call of port time globally simply through better planning, preparation, communication, and process
streamlining. This time saved equates to more hours at sea, leading to slower sailing speeds, lower GHG
emissions, and cost savings for the ship operator, which would be significant for each port call.
36.8
32.3 31.5
26.1
20.9
29.7
11.5
10.9 10.6
9.6
7.7
10.7
51.7
56.8 57.9
64.3
71.4
59.6
0%
20%
40%
60%
80%
100%
1,50
Cargo Operations Berth Idle Arrival Time
Ship Size Range (nominal TEU)
1,501-5,000 5,001-8,500 8,501-13,500 13,500 Overall

In the second half of 2020, there was a rebound in the global sales of durable goods, most prominently
in the US, and a sharp increase in the overall container volume demand. This coincided with continued
COVID-19 restrictions and resulted in the emergence of severe port congestion. In 2021, this port
congestion was still manifesting itself, reaching a peak in the third quarter of 2021 and the average arrival
time per port call globally remained above 11 hours until the third quarter of 2022. The fourth quarter of
2022 saw reducing volumes and many ports were able to clear backlogs and reduce average arrival times
to close to 10 hours per port call. The expectation is that the average port arrival time globally in 2023
will continue to decline to levels prior to the start of 2021. (see Figure 2.3)
Figure 2.3 • Global Average Arrival Time Development
Average
Arrival
Hours
9.36
9.99
11.48
11.23
11.33
11.07
11.35
10.1
9
9.5
10
10.5
11
11.5
12
2021 Q1 2021 Q2 2021 Q 2021 Q4 2022 Q1 2022 Q 2022 Q 2022 Q4
At a regional level and broken down by ship size groups, the change in average arrival time per region
and per ship size group over the 2021-2022 period is illustrated in Table 2.1. The column ‘All’ shows the
aggregate change in quantity of hours from arrival at port limits or start of anchorage time, to berthing
for cargo operations to commence for each region, across all ship size groups.
17 |

Table 2.1 • Average Arrival Time Development per Region and Ship Size, 2021-2022
Change (Hr) Ship Size Range
Region 1 1,500 2 1,501-5,000 3 5,001-8,500 4 8,501-13,500 5 13,500 All
AFR 5.0 (10.4) (3.7) (7.0) (8.8) (8.0)
LAM 0.1 1.2 1.3 0.8 3.9 1.0
MED 0.8 1.3 1.5 1.4 5.2 1.5
MEI 8.1 (1.0) 0.3 1.4 2.6 0.6
NAM 3.1 (0.8) 11.2 6.5 10.8 6.0
NEA (1.4) (0.9) 0.7 (1.2) (0.9) (0.7)
NEU 0.2 (0.7) 3.7 5.4 2.5 1.7
OCE 8.9 1.2 2.9 (2.3) 1.8
SEA 0.4 (1.5) (3.6) (2.5) 0.3 (1.5)
Global 1.1 (0.7) 2.4 0.5 1.4 0.4
At a global level, on average each port arrival increased by 0.4 hours, as illustrated in Figure 2.3 where
there were two peak quarters in 2021 compared with three in 2022. The largest increase in average
arrival time was witnessed in North America (USA and Canada) with an average increase in time of 6.0
hours over all vessel sizes. By contrast, performance improved in Africa (Sub-Sahara) with an average
8.0-hour reduction in arrival time across all vessel sizes. Improvements in East Asia and Southeast Asia
were also recorded.
At the ship size level, ships within the 1,501 TEU-5,000 TEU range consumed less time entering ports in
2022 compared to 2021, but the opposite was true for ships in the 5,001 TEU-8,500 TEU range where an
average additional time per call of 2.4 hours was recorded.
At a port level, the top 20 most improving or deteriorating average arrival time developments are
reflected in the following tables. The numbers per port and ship size range are the actual average arrival
hours recorded in 2022. The comparison with 2021 is made for the average arrival hours for all ship
sizes combined.

Table 2.2 • Top 20 Ports that Most Reduced Average Arrival Times, 2021-2022
Ship Size Range
Port 1 1,500 2 1,501-5,000 3 5,001-8,500 4 8,501-13,500 5 13,500 All Calls 2021FY Ch Hrs Ch %
Dar Es Salaam 42.3 104.7 104.3 151 239.6 (135.30) -56.5%
Los Angeles 2.8 20.2 22.6 26.2 36.5 24.7 634 119.3 (94.55) -79.3%
Long Beach 21.3 117.2 13.3 17.7 18.1 27.0 282 119.3 (92.30) -77.4%
Aden 15.2 13.1 13.8 26 60.6 (46.79) -77.2%
Monrovia 6.7 7.2 7.0 26 53.4 (46.40) -87.0%
Douala 35.6 38.2 37.9 189 77.1 (39.19) -50.9%
Pointe-Noire 22.1 24.0 31.2 16.0 24.1 388 51.8 (27.70) -53.4%
Tema 13.3 9.3 7.1 12.0 19.1 9.3 587 30.2 (20.91) -69.3%
Luanda 18.5 29.3 44.8 71.9 32.9 291 49.8 (16.97) -34.0%
Lome 28.0 46.2 30.9 175 43.7 (12.85) -29.4%
Lagos (Nigeria) 3.2 4.7 7.0 4.7 192 16.9 (12.17) -72.0%
Port Victoria 8.9 8.9 45 21.0 (12.08) -57.6%
Yantian 38.5 10.1 11.5 14.4 10.3 11.8 2,954 21.5 (9.71) -45.2%
Dakar 31.7 17.8 8.9 16.7 398 26.3 (9.64) -36.6%
LAE 9.5 12.8 11.0 28 20.5 (9.44) -46.2%
Chattogram 36.0 52.3 49.4 212 58.4 (8.95) -15.3%
Shanghai 24.4 23.9 23.8 24.4 23.9 2,371 31.3 (7.46) -23.8%
Haifa 9.6 6.4 5.3 2.8 12.0 7.7 734 14.7 (7.01) -47.6%
Ngqura 37.2 21.7 18.4 12.9 2.9 18.2 213 25.0 (6.83) -27.3%
Beirut 9.5 6.9 4.1 3.6 2.8 7.2 382 13.7 (6.53) -47.5%
19 |

Table 2.3 • Top 20 Ports that Most Increased Average Arrival Times, 2021-2022
Ship Size Range
Port 1 1,500 2 1,501-5,000 3 5,001-8,500 4 8,501-13,500 5 13,500 All Calls 2021FY Ch Hrs Ch %
Prince Rupert 124.6 95.0 8.3 2.1 65.6 90 13.4 52.17 389.1%
Savannah 25.3 96.1 105.2 165.5 206.3 130.4 1,115 45.11 85.03 52.9%
Houston 4.0 20.5 44.7 93.0 39.3 800 2.8 36.58 1327.3%
Charleston 5.6 21.9 35.3 54.2 58.1 37.3 1,161 6.9 30.38 437.3%
Manila 76.8 58.2 62.3 59.0 612 30.1 28.98 96.4%
Vancouver (Canada) 35.3 66.1 64.2 124.2 60.7 318 41.6 19.10 45.9%
New York New Jersey 12.1 31.2 26.6 40.8 18.2 30.3 1,382 12.5 17.72 141.3%
Poti 26.9 27.5 26.9 69 9.6 17.30 179.3%
Cape Town 60.0 111.7 48.3 74.9 185 57.7 17.16 29.7%
La Spezia 16.4 31.9 15.7 14.9 44.8 31.1 159 14.6 16.53 113.4%
San Pedro (Cote D'ivoire) 43.1 43.1 54 27.4 15.69 57.2%
Abidjan 117.9 78.5 44.7 69.0 292 53.4 15.59 29.2%
Mersin 31.8 28.1 7.6 16.2 8.4 25.6 885 10.5 15.15 144.3%
Mombasa 24.8 19.2 18.2 19.8 254 4.6 15.13 325.9%
Qingdao 33.3 27.5 29.0 33.0 18.6 27.4 2,705 12.8 14.64 114.5%
Trieste 16.8 18.6 32.2 22.8 37.4 22.9 353 8.6 14.31 166.6%
Napier 61.9 26.5 35.4 31.1 144 17.1 14.07 82.4%
Hamburg 12.0 16.7 26.1 27.2 35.7 22.8 1,670 10.5 12.32 117.0%
Koper 15.5 18.1 77.0 20.1 39.0 21.1 462 8.8 12.30 139.6%
Acajutla 53.7 18.3 19.1 43 8.2 10.92 133.7%
Both Los Angeles and Long Beach dramatically reduced their average arrival times. This might have been
at the expense of six of the seven ports with the highest quantity of additional hours incurred and could
potentially be the result of cargo and ship re-routings.
The overall improvements and reductions in average arrival hours in African ports has been driven
by Dar Es Salaam, Monrovia, Douala, Pointe-Noire, Tema, Luanda, Lomé, Lagos, Port Victoria, Dakar,
and Ngqura. The increase is slightly offset by increased average arrival time in Cape Town, San Pedro,
Abidjan, and Mombasa. In East Asia, improvements were seen in Yantian and Yangshan but countered
by increased time in Manila and Qingdao. There are no European ports in the top 20 improvers. Poti, La
Spezia, Mersin, Trieste, Hamburg, and Koper all experienced longer average arrival times.
Waiting time, defined as the period between ‘Arrival Port Limits’ or when the ship enters an anchorage
zone, and ‘All Lines Fast’ can generally be regarded as wasted time. As such, in the construction of the
CPPI, one possibility was to apply a penalty to waiting time. The decision was taken not to do so, as the
introduction of a penalty of this type would be a normative judgement inconsistent with the overall aim
of the study to create bean objective quantitative index.

There was consideration as to whether to apply a discount to waiting time for the smallest segment
of ships. Smaller ships generally suffer less priority than larger ones, and in some hub ports might be
purposely idled at anchorage waiting to load cargo which is arriving from off-schedule ocean going ships.
However, after reviewing average arrival time for the various ship size segments on a regional basis, the
data did not support applying a discount to waiting time for the smallest segment of ships. (see Table 2.4).
Table 2.4 • Average Arrival Time Performance per Ship Size Range per Region
2022 Ship Size Range
Region 1,500 1,501-5,000 5,001-8,500 8,501-13,500 13,500 Average
AFR 27.8 27.6 32.9 20.0 13.7 27.8
LAM 8.0 7.3 8.7 7.7 10.5 7.7
MED 9.7 8.3 7.1 7.3 11.1 8.7
MEI 13.6 7.4 5.7 6.7 7.2 7.2
NAM 9.2 17.3 31.7 43.7 54.2 30.8
NEA 6.3 8.2 8.4 7.1 6.1 7.6
NEU 8.8 8.0 13.5 15.0 16.9 11.7
OCE 17.4 14.3 14.2 8.6 13.9
SEA 10.2 10.2 6.5 6.2 4.3 8.7
Average 10.1 10.2 12.9 11.6 10.6 10.9
Regions that host major hub ports, and where smaller sized ships expended more time to arrive than the
average of all ships, are the Mediterranean, the Middle East, India, and Southeast Asia. Further study
reveals that the following hub ports in these regions did record significantly higher average arrival times for
smaller ships versus the average for all vessel sizes.
Table 2.5 • Smaller Vessel Average Arrival Times
Port
Arrival Hours
(ships 1,500
TEU)
Arrival Hours
(ships 1,500
TEU
Additional Arrival
Hours (as a
percentage)
Original
Overall Rank
Overall
Rank after
Simulation
Jeddah 40.2 8.8 + 357.6% 28 27
King Abdullah 7.8 3.9 + 101.4% 16 16
Khalifa Port 9.3 5.5 + 68.6% 3 4
Singapore 10.4 6.3 + 63.9% 19 20
Marsaxlokk 15.5 9.6 + 61.3% 42 43
Tanger-Mediterranean 9.7 6.3 + 54.4% 5 6
21 |

To test the significance of purposely delayed smaller feeder vessels on the overall ranking, we conducted
a simulation within the overall CPPI model. For all ports (not only the focus ports), we reduced the
quantity of arrival hours by 50 percent for all ship calls where the capacity of the ship is 1,500 TEU or
less in size. The quantity of berth hours for all ships was maintained at 100 percent, as was the average
arrival hours for all other ship size groups.
Table 2.5 displays the original overall rank without any adjustment to feeder ship arrival hours. The last
column presents what the overall rank would have been with 50 percent of arrival hours for ships of
1,500 TEU or less capacity eliminated. The conclusion from the simulation is that such an adjustment
does not materially alter the overall CPPI 2022 rankings, and four of the six focus ports dropped in
rankings during the simulation (Khalifa Port, Singapore, Marsaxlokk, and Tanger-Mediterranean),
although only by one place.
Since it is not possible to see from the data whether waiting time is voluntary or forced, it is difficult
to find a suitable level at which to discount waiting time in this scenario. The port calls of ships with
less than 1,500 TEUs of capacity comprise just 10 percent of the total calls in the CPPI. Therefore,
the disparity in waiting times between ships with less than 1,500 TEUs of nominal capacity and other
segments, as simulated, has only a small impact to the overall CPPI. To keep the data pure and avoid
normative judgment that is inconsistent with an objective quantitative index, the rankings published in
this iteration are not influenced by adjustments made to empirically recorded port hours.
The Significance of Call Size
As illustrated in Figure 2.2, over 60 percent of a port call is consumed through cargo operations, for
the handling of containers. In this aspect of the call, call size is of great significance. Call size is far less
significant when it comes to arrival time, which is more likely to be influenced by ship size.
There have been several earlier studies, in which ships are grouped into size segments (ranges) based
upon their size or capacity and port calls are ranked based on the time elapsed in port or on the berth.
While these studies provide an indication, the optimum outcome requires the workload for each call to be
taken into consideration. In this index, workload is represented by ‘Call Size,’ defined as the total quantity
of containers (regardless of size), which were physically discharged, loaded, or restowed during a port call.

Figure 2.4 • The Aggregated Correlation between Ship and Call Size
Average
Call
Size
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
1,500 1,501-5,000 5,001-8,500 8,501-13,500 13,500
Call Size Ship Size
Ship Size Range (nominal TEU)
Average
Ship
Size
(TEU
capacity)
Although there will be some level of correlation between the ship and call size, it is not a perfect
correlation. For example, an 18,000 TEU capacity ship calling at a port in Thailand or southern Vietnam
might exchange 1,000-2,000 containers per call, but that same ship in Yangshan or Singapore might
exchange more than 4,000 containers. Similarly, in the Thai or southern Vietnamese ports, a 3,000
TEU (‘feeder’ ship) might exchange more than 3,000 containers, potentially twice that of an 18,000 TEU
mainline ship at the same port.
The 60 percent of a port call, during which containers are exchanged, is influenced by two sub-factors:
1.
The quantity of cranes deployed
2.
The speed at which the cranes, especially the long crane (the crane with the highest workload in terms
of cycles), operate
23 |

Gross
Cranne
Moves
per
hr
per
Ship
Size
range
21.3
23.0
23.6
24.3
23.7
19.5
20.5
21.5
22.5
23.5
24.5
1,500 1,501-5,000 5,001-8,500 8,501-13,500 13,500
Ship Size Range
Figure 2.5 • Container Moves Performed per gross Crane Hour across Various Ship Sizes
The variation in containers handled per gross crane hour across all ship sizes is statistically minor.
The global average for all ships is 23.5 moves per hour, so the smallest ships are 9.4 percent less efficient
than the average, whereas ships in the 8,501 TEU-13,500 TEU range are 3.6 percent more efficient than
the average. It is often implied that larger ships are more difficult to work, but the data says otherwise.
On the larger ships, the crane operator has higher hoists and longer trolley distances, which increases
cycle time, but this is offset by more moves per bay and hatch, resulting in more containers handled per
gantry or hatch-cover move. The smaller ships can often encounter list or trim issues, making it harder
for the operator to hit the cell-guides and the hatch-cover and lashing systems.

Groos
Crane
Productivity
23.3
22.4
22.2
22.9
23.5
23.9
24.4
24.3
24.2
23.2
21.0
21.5
22.0
22.5
23.0
23.5
24.0
24.5
25.0
Call Size

2
5
0
2
5
1
-
5
0
0
5
0
1
-
1
,
0
0
0
1
,
0
0
1
-
1
,
5
0
0
1
,
5
0
1
-
2
,
0
0
0
2
,
0
0
1
-
2
,
5
0
0
2
,
5
0
1
-
3
,
0
0
0
3
,
0
0
1
-
4
,
0
0
0
4
,
0
0
1
-
6
,
0
0
0

6
,
0
0
0
Figure 2.6 • Gross Crane Productivity by Call Size
Figure 2.7 • Crane Productivity by Crane Intensity
Moves
per
Gross
Crane
Hour
30.7
22.5 22.6
23.1
23.7
24.0
23.2
21.8
18.0
17.0
19.0
21.0
23.0
25.0
27.0
29.0
31.0
1 2 3 4 5 6 7 8 9
Rounded Crane Intensity
25 |

A review of gross crane productivity versus call size and crane intensity reveals no strong increases
or decreases through the ranges. Assessed on call size ranges, there is a -5.2 percent to 3.8 percent
variation to the average. Meanwhile, an assessment of crane intensity reveals that the first and last
segments have extremely high and low performances, respectively, but in the mid-range, there is little
difference in crane productivity across the seven ranges. This implies that crane speed (productivity)
does not gradually increase (or decrease) as ship size, call size, or crane intensity increases. It is therefore
statistically not a key determinant of operating hours. The far more significant influencer of operating
time is the quantity of cranes deployed (crane intensity).
Figure 2.8 • Call Size versus Crane Intensity
Figure 2.9 • Average Moves per Crane
Crane
Intensity
Call Size Range
1.5
1.8
2.3
2.9
3.3
3.7
4
4.3
5
4.7
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5

2
5
0
2
5
1
-
5
0
0
5
0
1
-
1
,
0
0
0
1
,
0
0
1
-
1
,
5
0
0
1
,
5
0
1
-
2
,
0
0
0
2
,
0
0
1
-
2
,
5
0
0
2
,
5
0
1
-
3
,
0
0
0
3
,
0
0
1
-
4
,
0
0
0
4
,
0
0
1
-
6
,
0
0
0

6
,
0
0
0
Container
Moves
per
Quay
Crane
123

2
5
0
2
5
1
-
5
0
0
5
0
1
-
1
,
0
0
0
1
,
0
0
1
-
1
,
5
0
0
1
,
5
0
1
-
2
,
0
0
0
2
,
0
0
1
-
2
,
5
0
0
2
,
5
0
1
-
3
,
0
0
0
3
,
0
0
1
-
4
,
0
0
0
4
,
0
0
1
-
6
,
0
0
0

6
,
0
0
0
A
v
e
r
a
g
e
211
321
430
524
607
689
791
960
1,691
451
0
200
400
600
800
1000
1200
1400
1600
1800
Call Size Range

As might be expected, the more container moves are to be handled, the more cranes must be deployed.
However, crane intensity lags call size growth, which means that as the call size grows, each crane is
required to handle more containers. Theoretically, if a call with 1,000 moves was assigned 2 cranes, then
one with 5,000 moves would require 10 cranes for a status quo, and that does not happen often, if at
all. Since the exchange rate per crane does not increase progressively with ship size, call size, or crane
intensity growth, the overall operating time increases. This makes call size differentiation the critical
factor to consider when attempting port performance benchmarking and ranking.
Construction of the CPPI
Moving on to the construction of the CPPI, for a port to qualify for inclusion in the CPPI it must have
registered at least 24 valid port calls where port hours can be calculated within the full calendar year. Of
the 434 ports for which SP Global received port call information, 348 are included in the main index of
CPPI 2022. There were 156,813 distinct port calls recorded in the data over the period at those 348 main
ports. A further 86 ports registered less than 24 calls each, accumulatively accounting for 891 port calls
(0.6 percent of the total), these ports are excluded from the CPPI 2022.
The CPPI is based solely on the average port hours per port call, with port hours being the total time
elapsed from when a ship first entered a port to when it departed from the berth. Due to the large
volume of data, it was possible and prudent to break it down into ship size and call size groups or ranges.
However, too much fragmentation would have diluted the data to the extent that more assumptions than
actual empirical data would be present in the index. Therefore, the data was grouped into five distinct
ship sizes, and then within each ship size group by call size group, as reflected in Figure 2.10 below.
Source: Original figure produced for this publication
Figure 2.10 • The Structure of the CPPI
250 moves
251-500
moves
1,001-1,500
moves
1,501-2,000
moves
2,001-2,500
moves
4,001-6,000
moves
6,000
moves
2,501-3,000
moves
501-1,000
moves
3,001-4,000
moves
1,500 TEU
1,501-5,000
TEU
8,501-
13,500 TEU
13,500
TEU
5,001-8,500
TEU
Container Port
Performance index
X
Ship size
Groups
Call size
Groups
27 |

The number of ship size groups was limited to five, and the number of call size groups to 10. That results
in a 50 (5 x 10) matrix for the qualifying ports for the main index of CPPI 2022. However, there were
insufficient port calls in the larger five call size groups for the less than 1,500 TEU ship size group and
similarly for the two larger call size groups for the 1,501 TEU-5,000 TEU ship size group. In total, the data
was distributed into 43 ship-call size groups.
Table 2.6 • Port Calls Distribution
Call Size Group
Ship Size Group 250
251-
500
501-
1000
1001-
1500
1501-
2000
2001-
2500
2501-
3000
3001-
4000
4001-
6000
6000
1 1,500 20.5% 37.2% 36.9% 5.0% 0.2% 0.1% 0.0% 0.0% 0.0% 0.0%
2 1,501-5,000 6.1% 20.1% 36.0% 20.1% 9.9% 4.7% 1.8% 1.1% 0.2% 0.0%
3 5,001-8,500 1.3% 6.5% 20.9% 23.1% 18.6% 12.0% 7.2% 6.8% 2.9% 0.6%
4 8,501-13,500 0.8% 4.0% 13.8% 16.7% 15.0% 13.7% 10.9% 13.4% 8.4% 3.3%
5 13,500 0.2% 0.9% 4.6% 7.1% 8.7% 9.6% 9.5% 18.8% 26.5% 14.1%
The five ship size groups were based on where they might be deployed and the similarities of ships within
each group. Although a sixth group for ships more than 18,000 TEU or 24,000 TEU could have been
added, it would have highly diluted the data in the two larger ship size groups.
Table 2.7 • Ship Size Group Definitions
Nominal TEU
Capacity Range
Description
Less than 1,500
Almost exclusively feeder vessels, often connecting small outlying ports with regional hub ports. Some
intra-regional services will also have ships in this size range.
1,500 to 5,000
A vast quantity of these classic Panamax ships are deployed on intra-regional trades. They are found on
North-South trades to and from Africa, Latin America, and Oceania, as well as Transatlantic services.
5,000 to 8,500
Vessels within this size group are mainly deployed on the North-South trade lanes. Vessel cascading and
improving port capabilities has seen them start to emerge as stock vessels for Africa, Latin America, and
Oceania trades. There is some presence on Transatlantic and Asia–Middle East trades as well.
8,500 to 13,500
These Neo-Panamax vessels are largely deployed on East-West trades, particularly Trans-Pacific, both to
North America’s west coast as well as via either the Panama or Suez Canals to North America’s east coast.
They also feature on Asia–Middle East trades, with some deployed on Asia–Mediterranean rotations.
Greater than
13,500
These ultra-large container ships (ULCS) are mainly deployed on Asia–Europe (serving both North Europe
and the Mediterranean) and Asia–United States trades, especially on Trans-Pacific services calling at North
America’s west coast ports.
The application of ship size groups is less important than call size groups, particularly since the call
data is already split into 10 call size groups. However, the objective of the CPPI is to highlight through
comparison the performance gaps and opportunities to save fuel and reduce emissions. The analysis
should, therefore, consider that the larger the ship, the more fuel it consumes, and the higher the
potential to save fuel and reduce emissions.

Figure 2.11 • Percentage of Port Calls per Ship Size Group - 2022
Almost 50 percent of all ship port calls in 2022 were from the Panamax (1,501-5,000 TEU) size of ships.
With just 10 percent of port calls made by ships more than 13,500 TEU, it was decided not to disaggregate
these further. As the main participants of the Port Performance Program are primarily deep-sea operators,
there was a relatively small number of calls in the feeder segment (less than 1,500 TEU capacity).
An attempt has been made to make the 10 call size groups as narrow as possible by grouping together
calls in instances where they are most likely to have received similar crane intensity provisions. The
analysis then compares all qualifying ports on how close (or far) the individual call size is to the average
call size within each call size group.
Table 2.8 • Call Size Sensitivity
Call Size Sensitivity
Call Size Group
250
251-
500
501-
1000
1001-
1500
1501-
2000
2001-
2500
2501-
3000
3001-
4000
4001-
6000
6000
Average 179 381 736 1,234 1,732 2,228 2,735 3,445 4,785 8,061
Median 188 386 730 1,226 1,725 2,222 2,727 3,420 4,638 7,065
Lower Range 160 328 620 1,042 1,466 1,888 2,318 2,907 3,942 6,005
Upper Range 216 443 839 1,410 1,984 2,555 3,136 3,933 5,334 8,125
Total Ports 290 338 339 289 244 211 183 163 116 65
Within Range 220 318 304 280 244 211 183 162 114 52
Percentage in Range 75.9% 94.1% 89.7% 96.9% 100.0% 100.0% 100.0% 99.4% 98.3% 80.0%
Source: Original table produced for this publication, based on CPPI 2022 data
10%
46%
17%
17%
10%
1,500 1,501-5,000 5,001-8,500 8,501-13,500 13,500
29 |

To assess the sensitivity within each call size group across all 348 qualifying ports, the median call size
between all ports within a call size group was taken and a tolerance range of 15 percent above and below
the median created (see Table 2. 8). In the six call size groups from the 1,001–1,500 to 4,001–6,000
moves groups, more than 96.9 percent of ports have an average call size well within this tolerance range.
Beyond the threshold of 6,000 moves per call, the call size has a much lower impact on crane intensity.
This is because the number of cranes that can be deployed is limited by the overall number of cranes
available or stowage splits. The quantity of ports with an average call size within the tolerance range
in the three smallest call size groups is not as high as the quantity in the six call size groups from the
1,001–1,500 to 4,001–6,000 moves groups. However, for ports with an average call size above the
tolerance range, it would be possible to increase crane intensity to match the slightly higher call sizes,
and, therefore, the conclusion is that objective comparisons can be made within all 10 call size groups.
Imputing Missing Values: the Administrative Approach
The handicap of missing values can be addressed in two different ways in the administrative approach
and the statistical approach. The former involves assigning values to empty categories based on data
that are available when a port has registered a data point within a specific ship size range.
Table 2.9 • Quantity of Ports Included per Ship Size Group
Ship Size Range Quantity of Ports Included Base Call Size
Less than 1,500 TEUs 276 251–500
1,500–5,000 TEUs 330 501–1,000
5,000–8,500 TEUs 220 1,001–1,500
8,500–13,500 TEUs 178 1,501–2,000
More than 13,500 TEUs 105 3,001–4,000
For each ship size group, the call size group that has the largest quantity of data representation is
selected (see Table 2.9) as the Base Call Size group. Ideally, this is a mid-range call size group because the
lowest and highest groups can demonstrate some uniqueness. In cases where there is no actual data for
the base call size group, the next highest group is examined to find an actual data set. If none is found,
then the approach involves looking at the immediately lower call size band. At the end of this exercise,
every port has a value assigned for the base call size group.
Imputing vessel arrival values. Where a call size group does not have an arrival hours value, it is
populated using the overall average arrival time for all vessels registered at that port across all call size
groups within each specific ship size group. This is logical as call size is a less important determinant of
waiting time than ship size.
Imputing berth hours. From the base call size group, moving left toward the lowest group and right
toward the highest group, in groups where no value exists, a value is determined on a pro rata basis given
the adjacent call size group value, actual data or imputed. The rationale is that if within one call size
group a port has either higher or lower berth hours than the average, the adjacent call size group too is
likely to show similar trends.

Table 2.10 provides an example. In this case, port A had a higher quantity of hours in the base call size
group than the group average. It is assumed that would also have been the case had the port registered
actual calls in the 501–1,000 and 1,501–2,000 call size groups. The opposite is true for port B, which
achieved a lower quantity of hours in the base call size group. The calculation for port A in the 501–1,000
call size group is actual hours within the group 1,001–1,500 (12.0) multiplied by the group average factor
(0.9) for a prorated quantity of average berth hours of (10.8).
Table 2.10 • Example of Imputing Missing Values
Port
Call Size Group
501–1,000 1,001–1,500 1,501–2,000
Port A 10.8 12.0 14.4
Port B 7.2 8.0 9.6
Group Average 9.0 10.0 12.0
Factor Multiplier 0.9 Base 1.2
Note: The numbers in the green highlighted cells have been imputed by multiplying the base cells by the factor multiplier determined
by the overall group average.
The inherent risk with this approach is that poor or good performance within just one group will cascade
across all call size groups. It also assumes that a port can add cranes to larger call size groups, which
might not be true in all cases. On the other hand, it would be illogical to assume that any port would
simply achieve the average of the entire group or that a port performing below average in one call size
group would perform much better than average in others where it did not record any actual calls.
Imputing Missing Values: the Statistical Approach
A more rigorous approach is used for the statistical approach through the use of a likelihood-based
method to impute those missing values. With respect of the current data set, the expectation-
maximization (EM) algorithm can be utilized to provide a maximum-likelihood estimator for each
missing value. This approach relies on two critical assumptions: The first one is that the missing values
are random, that is, it is not due to some bias in the sample selection; and the second one is that
the variables under consideration are all normally distributed. These assumptions are not considered
unrealistic in the context of the data set. EM then computes the maximum likelihood estimator for the
mean and variance of the normal distribution given the observed data. Knowing the distribution that
generates the missing data, one can then sample from it to impute the missing values.6
Constructing the CPPI 2022 Index Using a Ranking Aggregation Method
The CPPI has in previous iterations utilized two distinct methodologies: the administrative, or technical
approach that employs expert knowledge and judgment to produce a practical methodology, and a
statistical approach that utilizes factor analysis (FA). CPPI 2022 goes a step further to aggregate the
two rankings to produce one index that to present the performance of ports via both methodologies.
31 |

Borda-type approach for index aggregation
Rank aggregation, that is the process of combining multiple rankings into a single ranking, is an
important problem arising in many areas (Langville and Meyer 2012). For example, in a ranked voting
system, citizens rank candidates in their order of preference and a single winner needs to be determined.
Similarly, recommender systems and search engines can produce many different rankings of items that
are likely to be of interest to a given user. Such rankings can naturally be aggregated to produce a more
robust list of items (Pappa et al. 2020).
Many strategies were proposed in the literature to combine several rankings into one that is as consistent
as possible with the individual rankings (Langville and Meyer 2012, Fagin et al. 2003, Dwork et al. 2001,
Dwork et al. 2012, Oliveira et al. 2020) and references therein. The Borda count (Langville and Meyer 2012,
Chapter 14) provides a simple and effective approach for aggregating rankings, wherein each item to rank is
given points according to the number of items it outranks in its segment. These points are added and then
used to produce a new ranking. Our approach to combine the administrative and the statistical rankings is
inspired by the Borda count, but also considers the index values for attributing the number of points.
The process is as follows: First, each index is scaled to take values into the interval [0,1]. This is
accomplished by applying the following linear transformation:
where m is the minimum value of the index and M the maximum value. Observe that the port with the
smallest index is always given a scaled value of 0 and the port with largest index a scaled value of 1. The
other ports get a scaled value between 0 and 1. Once the indices are scaled, they are added to produce
a combined index. Finally, a ranking is obtained by sorting the ports according to the combined index
in decreasing order. Thus, the port with the largest combined index is ranked first and the port with the
smallest combined index is ranked last.
Table 2.11 • An Example of Aggregated Rankings for Four Ports with Randomly
Generated Administrative and Statistical Index Values
Ports
Administrative
Index
Statistical
Index
Scaled
Administrative
Index
Scaled
Statistical
Index
Combined
Index
Final Ranking
Port 1 1.45 1.97 1.000 1.000 2.000 1
Port 2 1.26 1.21 0.678 0.392 1.070 3
Port 3 1.23 1.31 0.627 0.472 1.099 2
Port 4 0.86 0.72 0.000 0.000 0.000 4
For example, the scaled administrative index value of Port 2 (x = 1.26) is computed as follows: the minimum
and maximum values of the administrative index are m = 0.86 and M = 1.45. Thus, the scaled value is

References
•
Langville, Amy N., and Carl D. Meyer. Who’s# 1?: the Science of Rating and Ranking. Princeton
University Press, 2012.
•
Fagin, Ronald, Ravi Kumar, and Dakshinamurthi Sivakumar. Comparing Top k lists. SIAM Journal on
Discrete Mathematics 17, no. 1 (2003): 134-160.
•
Dwork, Cynthia, Ravi Kumar, Moni Naor, and D. Sivakumar. Rank Aggregation Revisited. (2001):
613-622.
•
Dwork, Cynthia, Ravi Kumar, Moni Naor, and Dandapani Sivakumar. Rank Aggregation Methods for the
Web. In Proceedings of the 10th International Conference on World Wide Web, pp. 613-622. 2001.
•
Ali, Alnur, and Marina Meilă. Experiments with Kemeny Ranking: What Works When? Mathematical
Social Sciences 64, no. 1 (2012): 28-40.
•
Oliveira, Samuel EL, Victor Diniz, Anisio Lacerda, Luiz Merschmanm, and Gisele L. Pappa. Is Rank
Aggregation Effective in Recommender Systems? An Experimental Analysis. ACM Transactions on
Intelligent Systems and Technology (TIST) 11, no. 2 (2020): 1-26.
33 |

3
3.
The Container Port
Performance Index 2022
Introduction
The rankings of container port performance, based on the ranking aggregation approach, are presented in this
chapter. The following section presents the rankings for the top 100 best performing container ports, with the
full rankings of all ports by both approaches presented in Appendix A. The subsequent sections present a
summary by region and port throughput (large, medium, small), so ports in the same region, or with the same
throughput within broad categories, can be easily compared.
The CPPI 2022
Table 3.1 presents the rankings of container port performance in the CPPI 2022. It reflects the aggregation
of the scores from the results from the administrative approach and the statistical approach in the manner
described in the previous section.
In the aggregate index, the two top-ranked container ports in the CPPI 2022 are Yangshan Port (China) in
first place, followed by the Port of Salalah (Oman) in second place. These two ports occupy the same positions
in the rankings generated by the constituent approaches. The Port of Salalah was ranked second in both
approaches in CPPI 2021, while the Yangshan Port ranked third and fourth in the statistical and administrative
approaches, respectively, in CPPI 2021.
The Container Port Performance Index 2022 | 34

Port Name
Overall
Ranking
Jeddah 29
Pipavav 30
Dammam 31
Coronel 32
Xiamen 33
Barcelona 34
Callao 35
Port Klang 36
Incheon 37
Jebel Ali 38
Fuzhou 39
Marsaxlokk 40
Yarimca 41
Dalian 42
Lazaro Cardenas 43
Kobe 45
Nagoya 46
Shimizu 47
Mundra 48
Sohar 49
Piraeus 51
Port Of Virginia 52
Yantian 53
Tokyo 54
Altamira 55
Haifa 56
Port Name
Overall
Ranking
Yangshan 1
Salalah 2
Khalifa Port 3
Tanjung Pelepas 6
Ningbo 7
Hamad Port 8
Guangzhou 9
Port Said 10
Hong Kong 11
Cai Mep 12
Shekou 13
Mawan 14
Yokohama 15
Algeciras 16
Singapore 18
Posorja 19
Tianjin 20
Buenaventura 21
Busan 22
Yeosu 23
Chiwan 24
Kaohsiung 25
Djibouti 26
Laem Chabang 27
Colombo 28
Table 3.1 • The CPPI 2022
35 | The Container Port Performance Index 2022
Three ports in the Middle East have secured positions among the top 10 spots. Three of the large
Chinese gateways–Shanghai (Yangshan), Ningbo, and the southern port of Guangzhou–maintained
places in the top 10. Of the top 10 ranked ports, nine have either maintained or improved their position
since CPPI 2021. The exception is Hamad Port, which moved down five and three places (provide the
rankings, sincethey’re specified for Yokohama and Jeddah) in the administrative and statistical rankings,
respectively. Yokohama fell from the 10th and 12th ranks in CPPI 2021 to the 15th in CPPI 2022, and
Jeddah fell from the 8th (provide both administrative and statistical rankings) to 29th.
There are 14 new entrants to the CPPI 2022, and several significant gainers in terms of ranking. Over 110
ports improved their rankings in CPPI 2022 over CPPI 2021, with some of the largest gainers moving
up more than 200 positions. In contrast, 200 ports fell in the CPPI 2022 rankings, some falling nearly
260 positions, which is 40 positions fewer than the biggest fall in the previous CPPI edition.

Port Name
Overall
Ranking
Ambarli 57
Jubail 58
Aqaba 59
Bremerhaven 60
Itapoa 61
Zeebrugge 62
Krishnapatnam 64
Zhoushan 65
Antwerp 66
Rio De Janeiro 67
Savona-Vado 68
Boston (USA) 69
Keelung 70
Paranagua 72
Siam Seaport 74
Diliskelesi 75
Balboa 76
Shantou 77
Kattupalli 78
Port Name
Overall
Ranking
Kamarajar 79
Osaka 80
Colon 81
Jacksonville 82
Lianyungang 83
Karachi 84
Hazira 85
Puerto Limon 87
Cochin 88
Port Everglades 89
Johor 91
Penang 92
Aarhus 93
Puerto Cortes 94
Fort-De-France 95
Pointe-A-Pitre 96
Tanjung Perak 97
Philadelphia 98
Veracruz 99
Nemrut Bay 100
The CPPI 2022 shows reduced discrepancies between the two approaches compared to its previous
edition. In CPPI 2022, 40 percent of all ports (140 ports) are ranked within three places or less from
themselves in the dual rankings (a 2 percent improvement). In CPPI 2021, 38 percent of all ports
(139 ports) are ranked within three places or less from themselves in the dual rankings (a 20 percent
improvement). In CPPI 2020, just under 18 percent of all ports (61 ports) were ranked within three places
or less from themselves in the dual rankings. The reduction in discrepancies contributes significantly to
having a well-balanced aggregated index.

Ranking by Region
This section presents an overview of the outcomes from the CPPI 2022 report. The first edition of CPPI
was modified based on requests for the presentation of results and rankings by region and throughput
for an improved comparison of ports within the same region and those with similar throughput. The
subsequent sections include a concise tabulation of the results and ranking (from Table 3.2) for the
designated regions.
• North America (United States and Canada)
• Central America, South America, and the Caribbean Region
• West, Central, and South Asia (Saudi Arabia to Bangladesh)
• East Asia (Myanmar to Japan)
• Oceania (Australia, New Zealand, and the Pacific Islands)
• Sub-Saharan Africa
• Europe and North Africa
Table 3.2 • The CPPI by Region: North America
Port Name Region Overall Ranking
Wilmington (USA-N Carolina) NAM 44
Port Of Virginia NAM 52
Boston (USA) NAM 69
Jacksonville NAM 82
Port Everglades NAM 89
Philadelphia NAM 98
New Orleans NAM 113
Port Tampa Bay NAM 145
Apra Harbor NAM 203
Miami NAM 217
Saint John NAM 233
Mobile NAM 238
Hueneme NAM 242
Halifax NAM 278
Seattle NAM 279
Montreal NAM 292
Baltimore (USA) NAM 300
New York New Jersey NAM 306

Tacoma NAM 314
Houston NAM 335
Los Angeles NAM 336
Charleston NAM 340
Prince Rupert NAM 342
Oakland NAM 343
Long Beach NAM 346
Vancouver (Canada) NAM 347
Savannah NAM 348
Table 3.3 • The CPPI by Region: Central America, South America, and the
Caribbean Region
Port Name Region
Overall
Ranking
Cartagena (Colombia) Lac 5
Posorja Lac 19
Buenaventura Lac 21
Coronel Lac 32
Callao Lac 35
Lazaro Cardenas Lac 43
Rio Grande (Brazil) Lac 50
Altamira Lac 55
Itapoa Lac 61
Rio De Janeiro Lac 67
Paranagua Lac 72
Balboa Lac 76
Colon Lac 81
Puerto Limon Lac 87
Puerto Cortes Lac 94
Fort-De-France Lac 95
Pointe-A-Pitre Lac 96
Veracruz Lac 99
Paita Lac 101
Ensenada Lac 105
Imbituba Lac 108
Santos Lac 114
Pecem Lac 116
Puerto Barrios Lac 119
Port Name Region
Overall
Ranking
Salvador Lac 120
Puerto Quetzal Lac 129
San Juan Lac 130
Santa Marta Lac 131
Lirquen Lac 141
Puerto Bolivar (Ecuador) Lac 147
Caucedo Lac 148
Rio Haina Lac 158
Puerto Progreso Lac 162
Barranquilla Lac 164
Gustavia Lac 167
Philipsburg Lac 169
Vitoria Lac 170
Buenos Aires Lac 174
Suape Lac 178
Sepetiba Lac 182
Valparaiso Lac 188
Vila Do Conde Lac 190
Mariel Lac 209
Caldera (Costa Rica) Lac 211
La Guaira Lac 212
Nassau Lac 229
Point Lisas Ports Lac 232
Manaus Lac 236

Port Name Region
Overall
Ranking
Arica Lac 237
Port Of Spain Lac 239
Itajai Lac 240
San Antonio Lac 253
Puerto Cabello Lac 255
San Vicente Lac 257
Corinto Lac 259
Santo Tomas De Castilla Lac 263
Kingston (Jamaica) Lac 266
Port Name Region
Overall
Ranking
Mejillones Lac 272
Manzanillo (Mexico) Lac 282
Guayaquil Lac 283
Iquique Lac 284
Antofagasta Lac 286
Acajutla Lac 288
Montevideo Lac 304
Cristobal Lac 305
Freeport (Bahamas) Lac 317
Table 3.4 • The CPPI by Region: West, Central, and South Asia
(Saudi Arabia to Bangladesh)
Salalah WCSA 2
Khalifa Port WCSA 3
Hamad Port WCSA 8
King Abdullah Port WCSA 17
Colombo WCSA 28
Jeddah WCSA 29
Pipavav WCSA 30
Dammam WCSA 31
Jebel Ali WCSA 38
Mundra WCSA 48
Sohar WCSA 49
Jubail WCSA 58
Aqaba WCSA 59
Krishnapatnam WCSA 64
Khalifa Bin Salman WCSA 73
Kattupalli WCSA 78
Kamarajar WCSA 79
Karachi WCSA 84
Hazira WCSA 85
Jawaharlal Nehru Port WCSA 86
Cochin WCSA 88
Muhammad Bin Qasim WCSA 90
Chennai WCSA 110

Visakhapatnam WCSA 115
Shuaiba WCSA 121
Sharjah WCSA 127
Shuwaikh WCSA 142
Umm Qasr WCSA 165
Aden WCSA 262
Chattogram WCSA 307
Port Name Region
Overall
Ranking
Yangshan EAS 1
Tanjung Pelepas EAS 6
Ningbo EAS 7
Guangzhou EAS 9
Hong Kong EAS 11
Cai Mep EAS 12
Shekou EAS 13
Mawan EAS 14
Yokohama EAS 15
Singapore EAS 18
Tianjin EAS 20
Busan EAS 22
Yeosu EAS 23
Chiwan EAS 24
Kaohsiung EAS 25
Laem Chabang EAS 27
Xiamen EAS 33
Port Klang EAS 36
Incheon EAS 37
Fuzhou EAS 39
Dalian EAS 42
Kobe EAS 45
Nagoya EAS 46
Shimizu EAS 47
Yantian EAS 53
Tokyo EAS 54
Da Chan Bay Terminal One EAS 63
Table 3.5 • The CPPI by Region: East Asia (Myanmar to Japan)
Port Name Region
Overall
Ranking
Zhoushan EAS 65
Keelung EAS 70
Siam Seaport EAS 74
Shantou EAS 77
Osaka EAS 80
Lianyungang EAS 83
Johor EAS 91
Penang EAS 92
Tanjung Perak EAS 97
Yokkaichi EAS 102
Naha EAS 104
Cat Lai EAS 107
Hakata EAS 109
Danang EAS 117
Saigon EAS 124
Taichung EAS 125
Tanjung Emas EAS 132
Omaezaki EAS 133
Batangas EAS 135
Moji EAS 136
Haiphong EAS 140
Cebu EAS 143
Quy Nhon EAS 146
Chu Lai EAS 155
Cagayan De Oro EAS 156
Qingdao EAS 171
Subic Bay EAS 193

Port Name Region
Overall
Ranking
Tomakomai EAS 208
Belawan EAS 214
Shanghai EAS 215
Panjang EAS 228
Table 3.6 • The CPPI by Region: Oceania (Australia, New Zealand, and the Pacific Islands)
Noumea OCE 128
Papeete OCE 139
Wellington OCE 153
Bluff OCE 191
Bell Bay OCE 192
Nelson OCE 204
Timaru OCE 250
Melbourne OCE 273
Lae OCE 274
Otago Harbour OCE 276
Adelaide OCE 277
Brisbane OCE 287
Port Botany OCE 299
Fremantle OCE 310
Lyttelton OCE 313
Napier OCE 322
Auckland OCE 323
Tauranga OCE 325
Port Name Region
Overall
Ranking
Bangkok EAS 243
Davao EAS 251
Tanjung Priok EAS 281
Manila EAS 329

Table 3.7 • The CPPI by Region: Sub-Saharan Africa
Djibouti SSA 26
Berbera SSA 144
Conakry SSA 189
Dakar SSA 196
Matadi SSA 197
Tema SSA 205
Mogadiscio SSA 221
Beira SSA 223
Freetown SSA 226
Toamasina SSA 227
Takoradi SSA 245
Maputo SSA 248
Port Victoria SSA 249
Lagos (Nigeria) SSA 260
Mayotte SSA 267
Monrovia SSA 271
Owendo SSA 275
Port Elizabeth SSA 291
Walvis Bay SSA 293
Douala SSA 295
San Pedro (Cote D'ivoire) SSA 296
Port Reunion SSA 298
Onne SSA 302
Tin Can Island SSA 308
Dar Es Salaam SSA 312
Pointe-Noire SSA 315
Lome SSA 318
Kribi Deep Sea Port SSA 324
Mombasa SSA 326
Port Louis SSA 327
Cotonou SSA 330
Nouakchott SSA 331
Abidjan SSA 333
Luanda SSA 337
Ngqura SSA 338
Durban SSA 341
Cape Town SSA 344

Port Name Region
Overall
Ranking
Tanger-Mediterranean ENA 4
Port Said ENA 10
Algeciras ENA 16
Barcelona ENA 34
Marsaxlokk ENA 40
Yarimca ENA 41
Piraeus ENA 51
Haifa ENA 56
Ambarli ENA 57
Bremerhaven ENA 60
Zeebrugge ENA 62
Antwerp ENA 66
Savona-Vado ENA 68
Santa Cruz De Tenerife ENA 71
Diliskelesi ENA 75
Aarhus ENA 93
Nemrut Bay ENA 100
Limassol ENA 103
Malaga ENA 106
Gemlik ENA 111
Mersin ENA 112
Wilhelmshaven ENA 118
Gothenburg ENA 122
Gioia Tauro ENA 123
Port Akdeniz ENA 126
Gijon ENA 134
Izmir ENA 137
Vigo ENA 138
Fredericia ENA 149
Odessa ENA 150
Helsingborg ENA 151
Cadiz ENA 152
Nantes-St Nazaire ENA 154
Ancona ENA 157
Casablanca ENA 159
Bar ENA 160
Ravenna ENA 161
Salerno ENA 163
Oslo ENA 166
Table 3.8 • The CPPI by Region: Europe and North Africa
Port Name Region
Overall
Ranking
Borusan ENA 168
El Dekheila ENA 172
Damietta ENA 173
Leixoes ENA 175
Brest ENA 176
Latakia ENA 177
Larvik ENA 179
Burgas ENA 180
Norrkoping ENA 181
Muuga-Port Of Tallinn ENA 183
Bari ENA 184
Civitavecchia ENA 185
Sines ENA 186
Copenhagen ENA 187
Novorossiysk ENA 194
Klaipeda ENA 195
Catania ENA 198
Palermo ENA 199
Rauma ENA 200
Heraklion ENA 201
Kristiansand ENA 202
Bilbao ENA 206
Trapani ENA 207
Rades ENA 210
Bordeaux ENA 213
Lisbon ENA 216
Marseille ENA 218
Tripoli (Lebanon) ENA 219
Helsinki ENA 220
Kotka ENA 222
Alicante ENA 224
Gdynia ENA 225
Batumi ENA 230
Riga ENA 231
Teesport ENA 234
Southampton ENA 235
Varna ENA 241
St Petersburg ENA 244
Venice ENA 246

Port Name Region
Overall
Ranking
Gavle ENA 247
Agadir ENA 252
Durres ENA 254
Bejaia ENA 256
Dublin ENA 258
London ENA 261
Felixstowe ENA 264
Rotterdam ENA 265
Alexandria (Egypt) ENA 268
Sokhna ENA 269
Naples ENA 270
Iskenderun ENA 280
Tarragona ENA 285
Gdansk ENA 289
Poti ENA 290
Port Name Region
Overall
Ranking
Constantza ENA 294
Ashdod ENA 297
Valencia ENA 301
Qasr Ahmed ENA 303
Livorno ENA 309
Dunkirk ENA 311
Genoa ENA 316
Le Havre ENA 319
Beirut ENA 320
Thessaloniki ENA 321
Hamburg ENA 328
La Spezia ENA 332
Rijeka ENA 334
Trieste ENA 339
Koper ENA 345
Ranking by Throughput
This section presents the CPPI 2022 by throughput. It offers a summary tabulation (from Table 3.9) by
throughput using the following defined ranges:
• Large: more than 4 million TEUs per year
• Medium: between 0.5 million and 4 million TEUs per year
• Small: less than 0.5 million TEUs per year
Table 3.9 • The CPPI by Throughput: Large Ports (More than 4 million TEUs per Year)
Port Name Region
Overall
Ranking
Yangshan Large 1
Salalah Large 2
Khalifa Port Large 3
Tanger-Mediterranean Large 4
Tanjung Pelepas Large 6
Ningbo Large 7
Guangzhou Large 9
Hong Kong Large 11
Port Name Region
Overall
Ranking
Cai Mep Large 12
Shekou Large 13
Algeciras Large 16
Singapore Large 18
Tianjin Large 20
Busan Large 22
Chiwan Large 24
Kaohsiung Large 25

Port Name Region
Overall
Ranking
Laem Chabang Large 27
Colombo Large 28
Jeddah Large 29
Xiamen Large 33
Port Klang Large 36
Jebel Ali Large 38
Dalian Large 42
Mundra Large 48
Piraeus Large 51
Yantian Large 53
Tokyo Large 54
Bremerhaven Large 60
Zhoushan Large 65
Antwerp Large 66
Colon Large 81
Lianyungang Large 83
Jawaharlal Nehru Port Large 86
Port Name Region
Overall
Ranking
Tanjung Perak Large 97
Cat Lai Large 107
Santos Large 114
Saigon Large 124
Qingdao Large 171
Shanghai Large 215
Rotterdam Large 265
Kingston (Jamaica) Large 266
Tanjung Priok Large 281
Valencia Large 301
New York New Jersey Large 306
Hamburg Large 328
Manila Large 329
Los Angeles Large 336
Long Beach Large 346
Savannah Large 348
Table 3.10 • The CPPI by Throughput: Medium Ports (between 0.5 million and
4 million TEUs per Year)
Port Name Region
Overall
Ranking
Cartagena (Colombia) Medium 5
Hamad Port Medium 8
Port Said Medium 10
Mawan Medium 14
Yokohama Medium 15
King Abdullah Port Medium 17
Posorja Medium 19
Buenaventura Medium 21
Yeosu Medium 23
Djibouti Medium 26
Pipavav Medium 30
Dammam Medium 31
Barcelona Medium 34
Callao Medium 35
Incheon Medium 37
Fuzhou Medium 39
Port Name Region
Overall
Ranking
Marsaxlokk Medium 40
Lazaro Cardenas Medium 43
Wilmington (USA-N Carolina) Medium 44
Kobe Medium 45
Nagoya Medium 46
Shimizu Medium 47
Sohar Medium 49
Rio Grande (Brazil) Medium 50
Port Of Virginia Medium 52
Altamira Medium 55
Haifa Medium 56
Ambarli Medium 57
Jubail Medium 58
Aqaba Medium 59
Zeebrugge Medium 62
Da Chan Bay Terminal One Medium 63

Port Name Region
Overall
Ranking
Krishnapatnam Medium 64
Rio De Janeiro Medium 67
Savona-Vado Medium 68
Boston (USA) Medium 69
Keelung Medium 70
Paranagua Medium 72
Siam Seaport Medium 74
Diliskelesi Medium 75
Balboa Medium 76
Shantou Medium 77
Kattupalli Medium 78
Osaka Medium 80
Jacksonville Medium 82
Karachi Medium 84
Hazira Medium 85
Cochin Medium 88
Port Everglades Medium 89
Muhammad Bin Qasim Medium 90
Johor Medium 91
Penang Medium 92
Aarhus Medium 93
Veracruz Medium 99
Limassol Medium 103
Naha Medium 104
Hakata Medium 109
Chennai Medium 110
Gemlik Medium 111
Mersin Medium 112
New Orleans Medium 113
Danang Medium 117
Wilhelmshaven Medium 118
Gothenburg Medium 122
Gioia Tauro Medium 123
Taichung Medium 125
Sharjah Medium 127
Santa Marta Medium 131
Tanjung Emas Medium 132
Izmir Medium 137
Vigo Medium 138
Port Name Region
Overall
Ranking
Papeete Medium 139
Haiphong Medium 140
Shuwaikh Medium 142
Cebu Medium 143
Berbera Medium 144
Puerto Bolivar (Ecuador) Medium 147
Caucedo Medium 148
Odessa Medium 150
Wellington Medium 153
Ancona Medium 157
Casablanca Medium 159
Umm Qasr Medium 165
Oslo Medium 166
El Dekheila Medium 172
Damietta Medium 173
Buenos Aires Medium 174
Leixoes Medium 175
Latakia Medium 177
Civitavecchia Medium 185
Sines Medium 186
Valparaiso Medium 188
Conakry Medium 189
Subic Bay Medium 193
Novorossiysk Medium 194
Klaipeda Medium 195
Dakar Medium 196
Catania Medium 198
Palermo Medium 199
Apra Harbor Medium 203
Tema Medium 205
Bilbao Medium 206
Rades Medium 210
La Guaira Medium 212
Belawan Medium 214
Miami Medium 217
Marseille Medium 218
Helsinki Medium 220
Mogadiscio Medium 221
Kotka Medium 222

Port Name Region
Overall
Ranking
Gdynia Medium 225
Freetown Medium 226
Toamasina Medium 227
Panjang Medium 228
Batumi Medium 230
Teesport Medium 234
Southampton Medium 235
Manaus Medium 236
Mobile Medium 238
Port Of Spain Medium 239
Itajai Medium 240
Varna Medium 241
Bangkok Medium 243
St Petersburg Medium 244
Takoradi Medium 245
Venice Medium 246
Gavle Medium 247
Timaru Medium 250
Davao Medium 251
Agadir Medium 252
San Antonio Medium 253
Durres Medium 254
Puerto Cabello Medium 255
Bejaia Medium 256
Dublin Medium 258
Lagos (Nigeria) Medium 260
London Medium 261
Aden Medium 262
Santo Tomas De Castilla Medium 263
Felixstowe Medium 264
Alexandria (Egypt) Medium 268
Sokhna Medium 269
Naples Medium 270
Monrovia Medium 271
Melbourne Medium 273
Owendo Medium 275
Otago Harbour Medium 276
Adelaide Medium 277
Halifax Medium 278
Port Name Region
Overall
Ranking
Seattle Medium 279
Iskenderun Medium 280
Manzanillo (Mexico) Medium 282
Guayaquil Medium 283
Iquique Medium 284
Brisbane Medium 287
Acajutla Medium 288
Gdansk Medium 289
Poti Medium 290
Port Elizabeth Medium 291
Montreal Medium 292
Constantza Medium 294
Douala Medium 295
San Pedro (Cote D'ivoire) Medium 296
Ashdod Medium 297
Port Reunion Medium 298
Port Botany Medium 299
Baltimore (USA) Medium 300
Onne Medium 302
Qasr Ahmed Medium 303
Montevideo Medium 304
Cristobal Medium 305
Chattogram Medium 307
Tin Can Island Medium 308
Livorno Medium 309
Fremantle Medium 310
Dar Es Salaam Medium 312
Lyttelton Medium 313
Tacoma Medium 314
Pointe-Noire Medium 315
Genoa Medium 316
Freeport (Bahamas) Medium 317
Lome Medium 318
Le Havre Medium 319
Beirut Medium 320
Napier Medium 322
Auckland Medium 323
Tauranga Medium 325
Mombasa Medium 326

Port Name Region
Overall
Ranking
Port Louis Medium 327
Cotonou Medium 330
La Spezia Medium 332
Abidjan Medium 333
Houston Medium 335
Luanda Medium 337
Ngqura Medium 338
Trieste Medium 339
Table 3.11 • The CPPI by Throughput: Small Ports (Less than 0.5 million TEUs per Year)
Port Name Region
Overall
Ranking
Coronel Small 32
Yarimca Small 41
Itapoa Small 61
Santa Cruz De Tenerife Small 71
Khalifa Bin Salman Small 73
Kamarajar Small 79
Puerto Limon Small 87
Puerto Cortes Small 94
Fort-De-France Small 95
Pointe-A-Pitre Small 96
Philadelphia Small 98
Nemrut Bay Small 100
Paita Small 101
Yokkaichi Small 102
Ensenada Small 105
Malaga Small 106
Imbituba Small 108
Visakhapatnam Small 115
Pecem Small 116
Puerto Barrios Small 119
Salvador Small 120
Shuaiba Small 121
Port Akdeniz Small 126
Noumea Small 128
Puerto Quetzal Small 129
San Juan Small 130
Port Name Region
Overall
Ranking
Omaezaki Small 133
Gijon Small 134
Batangas Small 135
Moji Small 136
Lirquen Small 141
Port Tampa Bay Small 145
Quy Nhon Small 146
Fredericia Small 149
Helsingborg Small 151
Cadiz Small 152
Nantes-St Nazaire Small 154
Chu Lai Small 155
Cagayan De Oro Small 156
Rio Haina Small 158
Bar Small 160
Ravenna Small 161
Puerto Progreso Small 162
Salerno Small 163
Barranquilla Small 164
Gustavia Small 167
Borusan Small 168
Philipsburg Small 169
Vitoria Small 170
Brest Small 176
Suape Small 178
Larvik Small 179
Port Name Region
Overall
Ranking
Charleston Medium 340
Durban Medium 341
Prince Rupert Medium 342
Oakland Medium 343
Cape Town Medium 344
Koper Medium 345
Vancouver (Canada) Medium 347

Port Name Region
Overall
Ranking
Burgas Small 180
Norrkoping Small 181
Sepetiba Small 182
Muuga-Port Of Tallinn Small 183
Bari Small 184
Copenhagen Small 187
Vila Do Conde Small 190
Bluff Small 191
Bell Bay Small 192
Matadi Small 197
Rauma Small 200
Heraklion Small 201
Kristiansand Small 202
Nelson Small 204
Trapani Small 207
Tomakomai Small 208
Mariel Small 209
Caldera (Costa Rica) Small 211
Bordeaux Small 213
Lisbon Small 216
Tripoli (Lebanon) Small 219
Beira Small 223
Port Name Region
Overall
Ranking
Alicante Small 224
Nassau Small 229
Riga Small 231
Point Lisas Ports Small 232
Saint John Small 233
Arica Small 237
Hueneme Small 242
Maputo Small 248
Port Victoria Small 249
San Vicente Small 257
Corinto Small 259
Mayotte Small 267
Mejillones Small 272
Lae Small 274
Tarragona Small 285
Antofagasta Small 286
Walvis Bay Small 293
Dunkirk Small 311
Thessaloniki Small 321
Kribi Deep Sea Port Small 324
Nouakchott Small 331
Rijeka Small 334

4
4. Conclusions and Next Steps
The primary objective of developing the CPPI by utilizing existing empirical data was to create an impartial
benchmark to assess and compare container port performance across different ports, over time. This was
done to facilitate the identification of gaps and opportunities for improvement in a standardized manner,
which could ultimately benefit all stakeholders, including shipping lines, national governments, and consumers.
The CPPI was intended to serve as a crucial point of reference for various stakeholders in the global economy,
such as port authorities and operators, national governments, development agencies, supranational
organizations, and other public and private entities involved in trade, logistics, and supply chain services.
In the future, the CPPI is expected to undergo further refinement in subsequent editions, incorporating
stakeholder feedback, advancements in data scope and quality, and additional trend analysis. The World
Bank-SP Global Market Intelligence team will continue to improve the methodologies, expand the scope
by potentially including more ports, and enhance the data. The next version, CPPI 2023, will be comparable
to the current edition, facilitating trend analysis of container port performance across the aggregate index.
Specifically, subsequent releases will also contain indices aggregated from the statistical and administrative
approaches. CPPI 2022 considers the dissimilarities between the two approaches while simultaneously gaining
a deeper understanding of the vital factors that affect container port performance. The goal remains to
identify opportunities for improvement to benefit all stakeholders, including ports, shipping lines, governments,
line agencies, businesses, and consumers.

Appendix A: The CPPI 2022
Table A.1. • Aggregated Rankings Using Borda-type Approach
Port Name
Overall
Ranking
Yangshan 1
Salalah 2
Khalifa Port 3
Tanjung Pelepas 6
Ningbo 7
Hamad Port 8
Guangzhou 9
Port Said 10
Hong Kong 11
Cai Mep 12
Shekou 13
Mawan 14
Yokohama 15
Algeciras 16
Port Name
Overall
Ranking
Singapore 18
Posorja 19
Tianjin 20
Buenaventura 21
Busan 22
Yeosu 23
Chiwan 24
Kaohsiung 25
Djibouti 26
Laem Chabang 27
Colombo 28
Jeddah 29
Pipavav 30
Dammam 31
Coronel 32

Port Name
Overall
Ranking
Xiamen 33
Barcelona 34
Callao 35
Port Klang 36
Incheon 37
Jebel Ali 38
Fuzhou 39
Marsaxlokk 40
Yarimca 41
Dalian 42
Lazaro Cardenas 43
Kobe 45
Nagoya 46
Shimizu 47
Mundra 48
Sohar 49
Piraeus 51
Port Of Virginia 52
Yantian 53
Tokyo 54
Altamira 55
Haifa 56
Ambarli 57
Jubail 58
Aqaba 59
Bremerhaven 60
Itapoa 61
Zeebrugge 62
Krishnapatnam 64
Zhoushan 65
Antwerp 66
Rio De Janeiro 67
Savona-Vado 68
Boston (USA) 69
Keelung 70
Paranagua 72
Port Name
Overall
Ranking
Siam Seaport 74
Diliskelesi 75
Balboa 76
Shantou 77
Kattupalli 78
Kamarajar 79
Osaka 80
Colon 81
Jacksonville 82
Lianyungang 83
Karachi 84
Hazira 85
Puerto Limon 87
Cochin 88
Port Everglades 89
Johor 91
Penang 92
Aarhus 93
Puerto Cortes 94
Fort-De-France 95
Pointe-A-Pitre 96
Tanjung Perak 97
Philadelphia 98
Veracruz 99
Nemrut Bay 100
Paita 101
Yokkaichi 102
Limassol 103
Naha 104
Ensenada 105
Malaga 106
Cat Lai 107
Imbituba 108
Hakata 109
Chennai 110
Gemlik 111
Mersin 112

Port Name
Overall
Ranking
New Orleans 113
Santos 114
Visakhapatnam 115
Pecem 116
Danang 117
Wilhelmshaven 118
Puerto Barrios 119
Salvador 120
Shuaiba 121
Gothenburg 122
Gioia Tauro 123
Saigon 124
Taichung 125
Port Akdeniz 126
Sharjah 127
Noumea 128
Puerto Quetzal 129
San Juan 130
Santa Marta 131
Tanjung Emas 132
Omaezaki 133
Gijon 134
Batangas 135
Moji 136
Izmir 137
Vigo 138
Papeete 139
Haiphong 140
Lirquen 141
Shuwaikh 142
Cebu 143
Berbera 144
Port Tampa Bay 145
Quy Nhon 146
Puerto Bolivar (Ecuador) 147
Caucedo 148
Fredericia 149
Odessa 150
Helsingborg 151
Cadiz 152
Port Name
Overall
Ranking
Wellington 153
Nantes-St Nazaire 154
Chu Lai 155
Cagayan De Oro 156
Ancona 157
Rio Haina 158
Casablanca 159
Bar 160
Ravenna 161
Puerto Progreso 162
Salerno 163
Barranquilla 164
Umm Qasr 165
Oslo 166
Gustavia 167
Borusan 168
Philipsburg 169
Vitoria 170
Qingdao 171
El Dekheila 172
Damietta 173
Buenos Aires 174
Leixoes 175
Brest 176
Latakia 177
Suape 178
Larvik 179
Burgas 180
Norrkoping 181
Sepetiba 182
Muuga-Port Of Tallinn 183
Bari 184
Civitavecchia 185
Sines 186
Copenhagen 187
Valparaiso 188
Conakry 189
Vila Do Conde 190
Bluff 191
Bell Bay 192

Port Name
Overall
Ranking
Subic Bay 193
Novorossiysk 194
Klaipeda 195
Dakar 196
Matadi 197
Catania 198
Palermo 199
Rauma 200
Heraklion 201
Kristiansand 202
Apra Harbor 203
Nelson 204
Tema 205
Bilbao 206
Trapani 207
Tomakomai 208
Mariel 209
Rades 210
Caldera (Costa Rica) 211
La Guaira 212
Bordeaux 213
Belawan 214
Shanghai 215
Lisbon 216
Miami 217
Marseille 218
Tripoli (Lebanon) 219
Helsinki 220
Mogadiscio 221
Kotka 222
Beira 223
Alicante 224
Gdynia 225
Freetown 226
Toamasina 227
Panjang 228
Nassau 229
Batumi 230
Riga 231
Point Lisas Ports 232
Port Name
Overall
Ranking
Saint John 233
Teesport 234
Southampton 235
Manaus 236
Arica 237
Mobile 238
Port Of Spain 239
Itajai 240
Varna 241
Hueneme 242
Bangkok 243
St Petersburg 244
Takoradi 245
Venice 246
Gavle 247
Maputo 248
Port Victoria 249
Timaru 250
Davao 251
Agadir 252
San Antonio 253
Durres 254
Puerto Cabello 255
Bejaia 256
San Vicente 257
Dublin 258
Corinto 259
Lagos (Nigeria) 260
London 261
Aden 262
Santo Tomas De Castilla 263
Felixstowe 264
Rotterdam 265
Kingston (Jamaica) 266
Mayotte 267
Alexandria (Egypt) 268
Sokhna 269
Naples 270
Monrovia 271
Mejillones 272

Port Name
Overall
Ranking
Melbourne 273
Lae 274
Owendo 275
Otago Harbour 276
Adelaide 277
Halifax 278
Seattle 279
Iskenderun 280
Tanjung Priok 281
Manzanillo (Mexico) 282
Guayaquil 283
Iquique 284
Tarragona 285
Antofagasta 286
Brisbane 287
Acajutla 288
Gdansk 289
Poti 290
Port Elizabeth 291
Montreal 292
Walvis Bay 293
Constantza 294
Douala 295
San Pedro (Cote D'ivoire) 296
Ashdod 297
Port Reunion 298
Port Botany 299
Baltimore (USA) 300
Valencia 301
Onne 302
Qasr Ahmed 303
Montevideo 304
Cristobal 305
New York New Jersey 306
Chattogram 307
Tin Can Island 308
Livorno 309
Fremantle 310
Port Name
Overall
Ranking
Dunkirk 311
Dar Es Salaam 312
Lyttelton 313
Tacoma 314
Pointe-Noire 315
Genoa 316
Freeport (Bahamas) 317
Lome 318
Le Havre 319
Beirut 320
Thessaloniki 321
Napier 322
Auckland 323
Kribi Deep Sea Port 324
Tauranga 325
Mombasa 326
Port Louis 327
Hamburg 328
Manila 329
Cotonou 330
Nouakchott 331
La Spezia 332
Abidjan 333
Rijeka 334
Houston 335
Los Angeles 336
Luanda 337
Ngqura 338
Trieste 339
Charleston 340
Durban 341
Prince Rupert 342
Oakland 343
Cape Town 344
Koper 345
Long Beach 346
Vancouver (Canada) 347
Savannah 348

Table A.2. • The CPPI 2022 (the Administrative Approach)
RANK PER SHIP SIZE RANGE
Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Yangshan 1 215.01 3,664 11 5 3 4 4 4 3
Salalah 2 212.30 1,397 12 4 1 1 2 0
Khalifa Port 3 199.54 896 86 47 10 7 5 5 2
Cartagena (Colombia) 4 197.50 1,274 23 16 18 5 8 12 8
Tanger-Mediterranean 5 193.48 3,097 163 50 11 12 2 6 1
Tanjung Pelepas 6 188.19 3,935 142 104 26 9 3 18 12
Ningbo 7 184.53 4,274 39 27 17 11 13 7 0
Hamad Port 8 182.55 257 1 14 16 11 3 -5
Guangzhou 9 181.18 1,577 89 20 15 14 15 9 0
Hong Kong 10 178.10 3,743 99 58 29 25 7 50 40
Port Said 11 177.29 1,106 52 48 23 15 14 15 4
Yokohama 12 171.48 1,217 33 2 44 8 24 10 -2
Cai Mep 13 170.77 939 19 51 5 53 10 13 0
Shekou 14 169.53 852 125 64 35 19 12 16 2
Mawan 15 166.32 295 62 23 31 18 18 44 29
King Abdullah Port 16 165.14 164 83 6 158 2 6 1 -15
Posorja 17 163.88 203 7 22 2 27 30 66 49
Algeciras 18 162.03 2,078 71 59 30 22 17 11 -7
Singapore 19 157.54 6,370 192 88 59 32 9 31 12
Buenaventura 20 149.84 430 13 43 23 25 20 0
Yeosu 21 149.64 576 43 37 33 30 26 33 12
Busan 22 148.62 4,783 72 65 32 44 19 25 3
Chiwan 23 147.58 879 79 76 42 31 22 17 -6
Djibouti 24 145.91 248 40 39 22 42 32 19 -5
Tianjin 25 145.84 1,035 143 80 57 13 31 27 2
Kaohsiung 26 142.03 2,426 85 103 40 28 29 21 -5
Laem Chabang 27 139.95 1,098 101 81 37 35 27 57 30
Jeddah 28 132.06 1,292 265 172 21 10 23 8 -20
Colombo 29 130.76 1,677 181 69 61 57 21 24 -5
Coronel 30 124.69 160 98 25 47 36 39 9
Pipavav 31 119.04 250 4 1 3 26 -5
Xiamen 32 118.65 2,201 205 179 96 34 20 45 13
Dammam 33 116.21 290 6 83 80 37 41 14 -19
Incheon 34 114.10 185 27 24 8 6 52 18
Barcelona 35 110.00 1,546 139 62 51 48 47 22 -13
Port Klang 36 107.29 2,536 172 116 81 52 38 69 33

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Lazaro Cardenas 37 107.00 725 165 87 63 58 40 92 55
Fuzhou 38 105.90 172 169 162 130 43 28 133 95
Yarimca 39 105.77 491 160 109 45 82 35 28 -11
Jebel Ali 40 102.66 1,931 201 129 67 39 43 38 -2
Wilmington (USA-N Carolina) 41 101.51 170 60 7 46 56 49 8
Marsaxlokk 42 99.25 1,322 212 146 84 50 39 74 32
Callao 43 98.02 833 255 158 99 38 34 258 215
Dalian 44 96.68 660 209 168 124 89 16 83 39
Sohar 45 94.66 148 28 70 54 54 54 47 2
Shimizu 46 94.31 340 15 11 69 17 41 -5
Kobe 47 91.38 1,058 42 15 24 41 40 -7
Nagoya 48 86.15 1,092 9 40 34 51 53 5
Port Of Virginia 49 83.17 1,313 64 63 73 64 55 23 -26
Mundra 50 82.64 690 233 36 48 21 48 -2
Yantian 51 81.91 2,954 259 149 103 73 33 266 215
Rio Grande (Brazil) 52 80.51 296 8 49 71 45 97 45
Piraeus 53 79.30 1,296 194 210 98 76 37 101 48
Tokyo 54 75.68 924 25 54 49 61 56 2
Altamira 55 74.93 576 153 110 82 20 85 30
Ambarli 56 73.76 800 69 135 115 66 51 43 -13
Aqaba 57 72.61 187 14 32 55 72 65 35 -22
Haifa 58 69.75 734 183 189 136 63 44 196 138
Savona-Vado 59 69.01 176 65 73 39 70 94 35
Bremerhaven 60 67.68 1,122 96 112 120 87 50 59 -1
Da Chan Bay Terminal One 61 67.65 227 97 56 78 59 142 81
Balboa 62 67.04 1,412 84 95 60 59 60 -2
Boston (USA) 63 66.36 86 86 56 55 117 54
Shantou 64 64.80 94 198 115 66 49 165 101
Jubail 65 64.54 173 66 60 80 60 249 184
Rio De Janeiro 66 64.12 533 67 85 64 69 93 27
Keelung 67 63.64 659 73 184 87 33 67 0
Zeebrugge 68 63.62 316 193 82 106 79 53 278 210
Itapoa 69 62.78 473 38 62 71 72 3
Paranagua 70 62.15 663 3 119 83 65 198 128
Krishnapatnam 71 61.72 60 2 28 13 95 24
Siam Seaport 72 61.42 346 4 26 36 103 31
Khalifa Bin Salman 73 60.52 120 12 21 40 62 -11
Diliskelesi 74 60.47 161 22 29 72 86 77 3

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Santa Cruz De Tenerife 75 60.32 168 18 44 6 71 -4
Antwerp 76 60.31 3,121 131 145 133 75 52 96 20
Lianyungang 77 59.47 118 178 101 24 78 1
Zhoushan 78 57.11 382 230 206 147 68 42 136 58
Osaka 79 55.31 462 10 14 41 36 -43
Kamarajar 80 54.59 102 8 16 84 4
Penang 81 54.22 135 209 105 26 111 30
Kattupalli 82 53.95 142 21 31 28 106 24
Jacksonville 83 53.54 133 41 86 78 100 17
Cochin 84 53.12 33 10 19 99 15
Karachi 85 52.69 286 148 92 56 90 5
Hazira 86 50.37 97 7 36 68 -18
Puerto Limon 87 48.38 380 46 20 86 -1
Muhammad Bin Qasim 88 45.12 548 129 118 89 81 81 -7
Port Everglades 89 44.97 400 61 78 94 90 116 27
Johor 90 44.53 137 56 25 76 80 -10
Jawaharlal Nehru Port 91 42.74 963 224 91 102 74 54 -37
Puerto Cortes 92 42.33 193 100 97 52 144 52
Philadelphia 93 41.34 466 268 68 77 62 55 -38
Fort-De-France 94 41.17 82 94 139 38 122 28
Colon 95 41.03 1,238 80 138 74 100 64 73 -22
Aarhus 96 39.61 189 48 105 88 62 82 -14
Pointe-A-Pitre 97 39.31 177 92 137 50 109 12
Yokkaichi 98 39.24 213 18 79 91 -7
Tanjung Perak 99 38.97 273 78 96 70 107 8
Limassol 100 36.89 105 70 198 27 147 47
Naha 101 36.33 33 9 120 19
Paita 102 36.19 231 107 58 75 -27
Nemrut Bay 103 35.50 840 128 127 119 101 58 224 121
Veracruz 104 35.40 444 161 102 68 104 0
Pecem 105 33.11 109 99 109 91 121 16
Imbituba 106 32.19 57 140 91 95 51 -55
Chennai 107 30.88 79 94 85 79 -28
Hakata 108 28.34 214 26 3 128 20
Ensenada 109 28.26 166 35 97 115 102 -7
Cat Lai 110 28.04 626 5 9 145 35
Malaga 111 26.48 177 90 95 134 93 68 137 26
Visakhapatnam 112 26.25 59 30 121 98 -14

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Gothenburg 113 25.96 235 202 187 188 29 57 118 5
Santos 114 24.91 1,193 93 202 132 85 66 188 74
Salvador 115 23.99 253 173 128 88 112 -3
Danang 116 23.31 127 16 43 161 45
Puerto Barrios 117 22.77 144 30 42 170 53
Puerto Quetzal 118 21.86 296 79 75 128 110 -8
Shuaiba 119 21.26 163 17 67 185 66
Sharjah 120 20.56 62 84 45 155 35
Saigon 121 19.94 270 24 74 140 19
Noumea 122 19.70 86 76 53 88 -34
Gijon 123 19.48 72 32 77 236 113
Lirquen 124 19.39 57 13 143 108 119 126 2
Taichung 125 19.35 372 46 72 125 0
Omaezaki 126 18.93 45 17 127 1
Santa Marta 127 18.75 231 19 143 16
Batangas 128 18.56 41 109 57 #N/A #N/A
Port Tampa Bay 129 18.46 129 103 61 143 106 64 -65
Gemlik 130 18.27 709 51 52 53 144 113 -17
Port Akdeniz 131 18.04 95 20 101 152 21
Mersin 132 17.99 885 253 295 114 103 45 34 -98
Gioia Tauro 133 17.98 56 145 116 96 65 -68
San Juan 134 17.91 153 82 75 157 23
Moji 135 17.30 24 34 132 -3
Tanjung Emas 136 17.28 124 53 89 153 17
New Orleans 137 16.61 340 90 111 118 115 -22
Haiphong 138 15.79 557 141 123 93 126 63 -75
Quy Nhon 139 15.55 50 108 93 154 15
Vigo 140 15.51 284 37 114 149 9
Papeete 141 15.24 62 98 100 167 26
Puerto Bolivar (Ecuador) 142 15.12 97 55 156 14
Cebu 143 14.86 61 50 117 164 21
El Dekheila 144 14.78 182 112 165 135 139 -5
Wilhelmshaven 145 14.63 315 88 163 125 67 80 233 88
Berbera 146 14.62 47 44 124 184 38
Nantes-St Nazaire 147 14.36 154 114 220 112 105 -42
Wellington 148 14.27 82 180 118 151 3
Izmir 149 14.22 224 107 166 137 253 104
Helsingborg 150 13.89 72 81 120 168 18

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Bar 151 13.52 101 59 130 183 32
Shuwaikh 152 13.33 185 68 132 189 37
Fredericia 153 13.31 57 63 134 176 23
Damietta 154 13.27 550 152 248 129 97 61 58 -96
Casablanca 155 12.98 262 226 270 47 262 107
Salerno 156 12.94 156 110 122 192 36
Puerto Progreso 157 12.91 34 35 152 200 43
Caucedo 158 12.77 559 171 199 140 92 114 -44
Rio Haina 159 12.70 76 134 111 158 -1
Oslo 160 12.61 53 38 153 146 -14
Cadiz 161 12.20 24 120 126 221 60
Philipsburg 162 12.02 51 157 108 177 15
Chu Lai 163 11.95 76 55 151 220 57
Odessa 164 11.67 35 29 71 121 209 45
Cagayan De Oro 165 11.52 42 66 155 208 43
Ancona 166 11.33 130 104 144 179 13
Ravenna 167 11.09 228 105 147 187 20
Buenos Aires 168 10.92 269 127 113 126 84 76 141 -27
Barranquilla 169 10.75 37 77 160 159 -10
Umm Qasr 170 9.65 141 224 113 150 -20
Gustavia 171 9.62 64 1 197 26
Leixoes 172 9.52 143 133 150 205 33
Borusan 173 9.42 88 121 148 -25
Burgas 174 9.41 92 54 175 195 21
Vitoria 175 8.64 62 45 185 217 42
Suape 176 8.50 189 201 122 109 280 104
Brest 177 8.28 24 147 159 #N/A #N/A
Matadi 178 6.68 88 213 125 171 -7
Bari 179 6.61 51 121 183 193 14
Latakia 180 6.60 75 123 182 174 -6
Novorossiysk 181 6.49 140 136 186 102 172 -9
Norrkoping 182 6.48 42 157 182 0
Larvik 183 6.14 34 31 210 27
Dakar 184 5.23 398 238 222 107 303 119
Muuga-Port Of Tallinn 185 4.93 51 132 192 175 -10
Copenhagen 186 4.57 39 74 206 20
Civitavecchia 187 4.57 25 75 162 -25
Apra Harbor 188 4.41 29 176 199 11

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Valparaiso 189 4.29 252 212 90 127 108 -81
Bluff 190 4.07 31 106 207 241 51
Klaipeda 191 3.51 82 111 186 -5
Bell Bay 192 3.45 28 113 218 26
Catania 193 3.23 58 116 191 -2
Palermo 194 3.21 24 117 204 10
Heraklion 195 3.14 34 119 216 21
Conakry 196 2.93 146 126 213 242 46
Sepetiba 197 2.93 59 154 46 147 123 -74
Subic Bay 198 2.92 71 214 170 181 -17
Vila Do Conde 199 2.86 90 34 228 244 45
Kristiansand 200 2.81 30 130 223 23
Rauma 201 2.60 73 138 208 201 0
Sines 202 2.50 28 150 142 46 30 -172
Trapani 203 2.21 29 140 213 10
Nelson 204 1.60 77 148 218 194 -10
Tripoli (Lebanon) 205 1.11 91 60 33 135 87 -118
Bilbao 206 1.02 108 87 229 202 -4
Miami 207 0.99 348 36 308 127 83 29 -178
Mariel 208 0.36 30 177 222 14
Rades 209 0.06 71 182 237 28
Bordeaux 212 (0.08) 28 187 228 16
Caldera (Costa Rica) 213 (0.44) 36 219 260 47
Qingdao 214 (0.45) 2,705 249 243 154 130 48 42 -172
La Guaira 215 (0.48) 86 173 221 265 50
Tomakomai 216 (1.08) 33 170 225 239 23
Belawan 217 (2.12) 87 219 204 250 33
Shanghai 218 (2.45) 2,371 215 217 139 112 316 98
Tema 219 (2.70) 587 240 174 131 99 70 354 135
Lisbon 220 (2.88) 39 155 232 215 -5
Freetown 221 (2.90) 123 122 246 268 47
Southampton 222 (3.51) 430 128 104 113 75 346 124
Helsinki 223 (3.62) 42 164 235 180 -43
Nassau 224 (3.63) 108 47 259 212 -12
Mogadiscio 225 (3.79) 74 230 259 34
Kotka 226 (4.34) 65 166 241 243 17
Alicante 227 (4.52) 66 146 247 229 2
Marseille 228 (4.77) 473 158 167 145 107 71 315 87

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Beira 229 (5.02) 93 184 239 270 41
Panjang 230 (5.44) 43 242 246 16
Toamasina 231 (5.93) 141 124 260 279 48
Arica 232 (6.58) 125 159 164 170 297 65
Saint John 233 (7.37) 81 220 236 240 7
Manaus 234 (7.82) 101 253 263 29
Gdynia 235 (8.03) 266 156 194 117 111 73 255 20
Batumi 236 (8.05) 68 186 254 245 9
Varna 237 (8.18) 47 180 257 225 -12
Itajai 238 (8.91) 462 191 211 142 122 207 -31
Takoradi 239 (9.07) 27 196 256 281 42
Teesport 240 (9.37) 34 206 252 257 17
St Petersburg 241 (9.62) 95 137 272 256 15
Port Of Spain 242 (10.01) 103 176 266 254 12
Hueneme 243 (10.08) 44 264 269 26
Point Lisas Ports 244 (10.56) 45 254 295 51
Mobile 245 (10.75) 339 57 169 149 133 163 -82
Bangkok 246 (11.11) 198 207 262 299 53
Timaru 247 (12.23) 47 273 310 63
Riga 248 (12.70) 56 248 233 214 -34
Gavle 249 (13.00) 52 229 263 252 3
Santo Tomas De Castilla 250 (14.25) 48 251 237 273 23
Port Victoria 251 (14.34) 45 282 289 38
Maputo 252 (14.77) 66 284 321 69
Davao 253 (15.09) 124 199 255 153 274 21
Venice 254 (15.37) 139 154 292 235 -19
Durres 255 (15.46) 72 151 293 309 54
Agadir 256 (15.69) 79 218 276 261 5
Corinto 257 (15.74) 25 286 286 29
Dublin 258 (17.01) 24 208 285 300 42
Bejaia 259 (17.33) 38 231 277 285 26
San Vicente 260 (18.03) 81 136 65 162 166 -94
Puerto Cabello 261 (18.24) 36 41 303 287 26
Felixstowe 262 (18.38) 540 245 200 165 105 67 334 72
Lagos (Nigeria) 263 (18.46) 192 135 250 169 358 95
Manzanillo (Mexico) 264 (19.80) 938 256 133 110 94 83 89 -175
San Antonio 265 (20.44) 319 115 188 144 116 74 320 55
Aden 266 (22.28) 26 211 299 305 39

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Rotterdam 267 (22.58) 2,096 236 216 159 129 63 291 24
Kingston (Jamaica) 268 (24.42) 828 223 275 138 125 131 -137
Mayotte 269 (25.10) 30 304 294 25
Alexandria (Egypt) 270 (27.34) 215 237 191 180 277 7
Monrovia 271 (28.29) 26 246 300 #N/A #N/A
Lae 272 (28.70) 28 210 307 301 29
Mejillones 273 (28.78) 98 177 171 134 231 -42
Naples 274 (29.42) 139 49 203 162 139 283 9
Owendo 275 (30.89) 126 234 306 302 27
Melbourne 276 (31.45) 748 144 193 177 132 308 32
Sokhna 277 (34.32) 138 174 197 151 120 77 353 76
Otago Harbour 278 (34.79) 110 245 186 298 20
Adelaide 279 (35.40) 224 195 146 149 264 -15
Guayaquil 280 (36.20) 498 196 148 123 79 296 16
Iquique 281 (37.83) 136 175 238 100 161 319 38
Tanjung Priok 282 (39.19) 834 118 205 123 170 49 124 -158
Antofagasta 283 (39.65) 30 161 159 234 -49
Acajutla 284 (39.72) 43 270 294 271 -13
Tarragona 285 (40.44) 82 203 141 172 146 160 -125
Halifax 286 (42.58) 239 167 142 163 124 82 46 -240
Poti 287 (45.01) 69 243 315 226 -61
Brisbane 288 (45.42) 660 222 215 161 148 288 0
London 289 (45.86) 1,181 149 106 156 108 86 347 58
Iskenderun 290 (46.11) 204 227 131 205 77 70 -220
Port Elizabeth 291 (46.91) 75 281 190 312 21
Gdansk 292 (49.28) 318 235 171 12 92 203 -89
Seattle 293 (50.45) 152 214 160 153 69 336 43
Walvis Bay 294 (50.72) 104 297 183 117 328 34
Montreal 295 (52.54) 190 290 191 311 16
San Pedro (Cote D'ivoire) 296 (55.86) 54 322 318 22
Douala 297 (57.40) 189 261 316 340 43
Qasr Ahmed 298 (59.98) 56 225 323 282 -16
Constantza 299 (60.31) 262 195 231 164 272 -27
Port Reunion 300 (63.27) 242 179 269 174 150 333 33
Baltimore (USA) 301 (65.01) 358 91 156 173 131 84 76 -225
Valencia 302 (65.27) 810 239 267 179 110 78 135 -167
Port Botany 303 (66.12) 770 200 234 176 157 324 21
Onne 304 (70.19) 66 271 202 342 38

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Montevideo 305 (71.47) 531 58 249 152 104 89 284 -21
Cristobal 306 (72.38) 718 252 223 182 155 134 -172
Ashdod 307 (80.92) 469 190 288 155 145 81 329 22
Dunkirk 308 (81.08) 212 102 92 157 94 350 42
New York New Jersey 309 (82.87) 1,382 204 279 164 156 72 251 -58
Chattogram 310 (83.90) 212 273 325 341 31
Livorno 311 (87.26) 286 168 244 168 165 338 27
Tin Can Island 312 (92.78) 94 197 278 209 339 27
Fremantle 313 (95.23) 247 226 197 158 335 22
Lyttelton 314 (97.18) 216 266 302 200 314 0
Pointe-Noire 315 (101.50) 388 250 301 196 143 362 47
Dar Es Salaam 316 (103.26) 151 264 328 361 45
Freeport (Bahamas) 317 (105.05) 139 310 194 152 352 35
Beirut 318 (106.59) 382 162 181 178 114 93 357 39
Lome 319 (109.51) 175 298 215 349 30
Thessaloniki 320 (111.05) 177 257 287 212 331 11
Genoa 321 (111.41) 730 178 258 175 137 88 337 16
Napier 322 (114.79) 144 274 312 201 290 -32
Auckland 323 (115.66) 153 271 311 203 351 28
Tauranga 324 (118.93) 489 269 296 211 98 325 1
Mombasa 325 (119.08) 254 241 313 210 293 -32
Kribi Deep Sea Port 326 (120.92) 159 262 321 198 355 29
Tacoma 327 (122.69) 103 190 184 163 85 345 18
Hamburg 328 (126.65) 1,670 185 227 181 141 91 232 -96
Le Havre 329 (128.31) 853 188 251 187 136 90 292 -37
Port Louis 330 (142.19) 370 232 261 167 138 95 323 -7
Nouakchott 331 (142.77) 62 276 327 356 25
Cotonou 332 (146.42) 359 272 317 206 348 16
Manila 333 (150.75) 612 267 320 207 327 -6
La Spezia 334 (190.11) 159 228 309 166 140 97 313 -21
Abidjan 335 (200.23) 292 275 324 213 360 25
Rijeka 336 (218.88) 245 217 289 141 174 87 190 -146
Los Angeles 337 (252.55) 634 95 283 195 171 96 370 33
Houston 338 (253.08) 800 189 240 185 177 119 -219
Luanda 339 (268.05) 291 244 305 208 175 366 27
Ngqura 340 (272.15) 213 263 291 192 154 101 363 23
Charleston 341 (278.09) 1,161 150 274 193 167 100 130 -211
Trieste 342 (284.33) 353 247 268 199 166 99 326 -16

Port
Name
Rank
Index
Points
Total
Calls
1,500
1,501-
5,000
5,001-
8,500
8,501-
13,500
13,500
2021
Change
Durban 343 (316.33) 389 260 318 216 173 364 21
Prince Rupert 344 (353.43) 90 330 218 151 344 0
Oakland 345 (365.00) 377 216 280 189 172 102 359 14
Koper 346 (371.36) 462 221 265 219 168 98 129 -217
Cape Town 347 (427.36) 185 319 220 176 365 18
Long Beach 348 (498.13) 282 242 329 204 160 103 369 21
Vancouver (Canada) 349 (593.37) 318 314 214 169 104 368 19
Savannah 350 (941.80) 1,115 258 326 217 178 105 367 17
Table A.3. • The CPPI 2022 (the Statistical Approach)
Port Name 2022 Rank Index Points 2021 Rank Change
Yangshan 1 93.891159 3 2
Salalah 2 91.866199 2 0
Khalifa Port 3 88.783069 5 2
Tanger-Mediterranean 4 84.345303 6 2
Tanjung Pelepas 5 81.404458 16 11
Cartagena (Colombia) 6 80.7642 15 9
Hamad Port 7 80.579775 4 -3
Ningbo 8 80.349495 7 -1
Guangzhou 9 79.285176 9 0
Port Said 10 78.23557 13 3
Hong Kong 11 77.648186 38 27
Cai Mep 12 73.411131 11 -1
Algeciras 13 73.089835 10 -3
Mawan 14 72.78143 110 96
Shekou 15 72.422481 17 2
Tianjin 16 70.245532 26 10
Yokohama 17 70.033099 12 -5
Singapore 18 69.546328 31 13
King Abdullah Port 19 67.653064 1 -18
Posorja 20 65.563411 65 45
Buenaventura 21 65.480099 23 2
Busan 22 65.271824 25 3
Kaohsiung 23 62.883753 21 -2

Chiwan 24 61.748811 20 -4
Yeosu 25 61.424749 29 4
Djibouti 26 61.219598 24 -2
Colombo 27 58.021452 22 -5
Laem Chabang 28 57.88501 48 20
Callao 29 52.181198 186 157
Jeddah 30 51.426625 8 -22
Pipavav 31 50.992508 34 3
Dammam 32 50.942405 14 -18
Barcelona 33 50.575247 19 -14
Xiamen 34 48.913154 40 6
Port Klang 35 46.897259 59 24
Coronel 36 46.714878 49 13
Jebel Ali 37 46.129933 42 5
Fuzhou 38 44.013292 96 58
Incheon 39 43.875315 53 14
Marsaxlokk 40 43.083907 74 34
Kobe 41 40.89719 37 -4
Dalian 42 40.540729 81 39
Yarimca 43 39.982902 30 -13
Nagoya 44 37.529667 43 -1
Wilmington (USA-N Carolina) 45 36.774971 #N/A #N/A
Mundra 46 36.409572 46 0
Lazaro Cardenas 47 35.023765 109 62
Rio Grande (Brazil) 48 34.72914 91 43
Piraeus 49 34.516997 82 33
Shimizu 50 33.684835 45 -5
Haifa 51 33.68135 247 196
Jubail 52 33.535813 191 139
Tokyo 53 33.192474 51 -2
Altamira 54 32.481599 76 22
Port Of Virginia 55 32.125287 27 -28
Yantian 56 31.993295 270 214
Ambarli 57 31.934733 36 -21
Itapoa 58 31.914238 60 2
Zeebrugge 59 31.055198 291 232
Zhoushan 60 30.754019 228 168
Bremerhaven 61 29.912465 54 -7
Antwerp 62 29.273182 78 16
Da Chan Bay Terminal One 63 29.044824 122 59
Krishnapatnam 64 29.038774 87 23

Sohar 65 28.832647 47 -18
Colon 66 28.737558 64 -2
Aqaba 67 28.660159 32 -35
Rio De Janeiro 68 26.736563 83 15
Kattupalli 69 25.480878 95 26
Boston (USA) 70 25.129463 115 45
Jawaharlal Nehru Port 71 24.52839 50 -21
Santa Cruz De Tenerife 72 24.384752 88 16
Keelung 73 24.140515 77 4
Savona-Vado 74 24.02082 111 37
Kamarajar 75 23.869578 71 -4
Khalifa Bin Salman 76 23.665701 61 -15
Paranagua 77 23.565968 166 89
Diliskelesi 78 23.313358 70 -8
Siam Seaport 79 23.124017 100 21
Osaka 80 22.970532 44 -36
Hazira 81 22.680683 69 -12
Jacksonville 82 22.603662 94 12
Puerto Limon 83 22.050489 80 -3
Karachi 84 21.867136 90 6
Port Everglades 85 21.459083 102 17
Shantou 86 21.28065 151 65
Muhammad Bin Qasim 87 20.424853 75 -12
Balboa 88 20.392186 55 -33
Johor 89 19.896225 79 -10
Cochin 90 19.844555 98 8
Aarhus 91 19.570453 67 -24
Lianyungang 92 19.231782 73 -19
Puerto Cortes 93 17.732475 141 48
Tanjung Perak 94 16.469817 103 9
Pointe-A-Pitre 95 16.4253 #N/A #N/A
Fort-De-France 96 16.161755 127 31
Gemlik 97 16.092142 105 8
Veracruz 98 15.990696 85 -13
Nemrut Bay 99 15.746314 242 143
Ensenada 100 15.680764 86 -14
Paita 101 15.435808 84 -17
Malaga 102 15.414967 140 38
Penang 103 15.412607 116 13
New Orleans 104 14.640183 108 4
Philadelphia 105 14.521186 58 -47

Mersin 106 14.482174 28 -78
Yokkaichi 107 14.088427 101 -6
Cat Lai 108 13.752034 132 24
Hakata 109 13.302854 117 8
Wilhelmshaven 110 13.23886 204 94
Limassol 111 13.039884 172 61
Naha 112 12.952755 126 14
Imbituba 113 11.977288 106 -7
Chennai 114 11.54741 92 -22
Gioia Tauro 115 10.935035 112 -3
Santos 116 10.133248 147 31
Danang 117 10.036422 156 39
Shuaiba 118 9.9966817 177 59
Saigon 119 9.9070327 125 6
Port Akdeniz 120 9.6068699 139 19
Puerto Barrios 121 9.4983004 196 75
Visakhapatnam 122 9.4912918 97 -25
Taichung 123 9.3591441 135 12
Salvador 124 8.8773066 130 6
San Juan 125 8.6750957 143 18
Noumea 126 8.5331801 93 -33
Izmir 127 8.4696178 246 119
Tanjung Emas 128 8.3960203 144 16
Qingdao 129 8.321523 33 -96
Sharjah 130 8.2674761 148 18
Santa Marta 131 7.9974024 137 6
Gothenburg 132 7.8368006 152 20
Shuwaikh 133 7.6920939 179 46
Omaezaki 134 7.6414656 120 -14
Vigo 135 7.4942362 138 3
Papeete 136 7.3557644 158 22
Moji 137 7.2633952 121 -16
Gijon 138 7.2546795 224 86
Batangas 139 7.1806229 #N/A #N/A
Haiphong 140 7.0953581 63 -77
Puerto Quetzal 141 7.0730406 134 -7
Cebu 142 7.0085755 171 29
Berbera 143 6.9834428 165 22
Pecem 144 6.5441976 129 -15
Puerto Bolivar (Ecuador) 145 6.4609276 153 8
Quy Nhon 146 6.4548038 146 0

Odessa 147 6.2193002 164 17
Caucedo 148 5.9831249 99 -49
Cadiz 149 5.7500438 217 68
Ancona 150 5.7374415 168 18
Cagayan De Oro 151 5.6934303 226 75
Fredericia 152 5.6819122 183 31
Chu Lai 153 5.6630756 216 63
Lirquen 154 5.4025422 118 -36
Ravenna 155 5.3971409 188 33
Port Tampa Bay 156 5.2737755 66 -90
Helsingborg 157 5.1685519 167 10
Rio Haina 158 4.9524185 155 -3
Casablanca 159 4.8162144 262 103
Umm Qasr 160 4.7689205 136 -24
Wellington 161 4.7374077 192 31
Nantes-St Nazaire 162 4.6900003 #N/A #N/A
Borusan 163 4.6430391 149 -14
Vitoria 164 4.5940646 #N/A #N/A
Gustavia 165 4.5843749 190 25
Barranquilla 166 4.5062808 163 -3
Bar 167 4.4550329 182 15
Puerto Progreso 168 4.2702304 205 37
Salerno 169 3.6117234 195 26
Sepetiba 170 3.434285 119 -51
Oslo 171 3.4049691 142 -29
Philipsburg 172 3.3186682 174 2
Leixoes 173 3.0744614 #N/A #N/A
Latakia 174 3.0369427 173 -1
Larvik 175 2.8861643 206 31
Sines 176 2.879663 35 -141
Buenos Aires 177 2.5092931 124 -53
Muuga-Port Of Tallinn 178 2.391828 160 -18
Brest 179 2.3599453 #N/A #N/A
Norrkoping 180 2.2969698 184 4
Conakry 181 2.2887926 239 58
Tema 182 2.2611246 353 171
Vila Do Conde 183 2.258435 245 62
London 184 2.170447 355 171
Suape 185 2.1573978 287 102
Civitavecchia 186 2.1010276 161 -25
Subic Bay 187 2.0266372 178 -9

Valparaiso 188 1.934846 113 -75
Copenhagen 189 1.8552597 227 38
Bell Bay 190 1.8267218 211 21
Bluff 191 1.7454775 234 43
Rauma 192 1.6600785 210 18
Klaipeda 193 1.6402489 170 -23
Damietta 194 1.5382355 56 -138
Catania 195 1.4882068 193 -2
Burgas 196 1.4835229 197 1
Palermo 197 1.4631996 198 1
El Dekheila 198 1.4627075 133 -65
Bari 199 1.2712493 194 -5
Heraklion 200 1.2697978 212 12
Kristiansand 201 1.1905867 221 20
Nelson 202 1.0345817 189 -13
Tomakomai 203 1.0019976 237 34
Dakar 204 0.8774949 308 104
Apra Harbor 205 0.4193741 203 -2
Novorossiysk 206 0.3849053 159 -47
Rades 207 0.3390818 232 25
Mariel 208 0.3043624 219 11
Bilbao 209 0.2530552 201 -8
Matadi 210 0.2148891 176 -34
Caldera (Costa Rica) 211 0.1884466 264 53
La Guaira 212 0.1403443 263 51
Bordeaux 213 -0.168196 223 10
Trapani 214 -0.296926 209 -5
Shanghai 215 -0.402068 318 103
Belawan 216 -0.424559 238 22
Gdynia 217 -0.518666 225 8
Riga 218 -0.527474 207 -11
Lisbon 219 -0.59083 220 1
Marseille 220 -0.610633 297 77
Beira 221 -1.370338 268 47
Helsinki 222 -1.466602 169 -53
Point Lisas Ports 223 -1.478159 301 78
Kotka 224 -1.504547 231 7
Mogadiscio 225 -1.521115 254 29
Alicante 226 -1.684575 222 -4
Toamasina 227 -1.8707 280 53
Panjang 228 -2.111492 236 8

Batumi 229 -2.145147 233 4
Miami 230 -2.662386 39 -191
Freetown 231 -2.765453 272 41
Nassau 232 -3.359467 208 -24
Tripoli (Lebanon) 233 -3.438159 89 -144
Teesport 234 -3.451123 250 16
Mobile 235 -3.60963 150 -85
Saint John 236 -3.8961 235 -1
Port Of Spain 237 -3.999666 252 15
Manaus 238 -4.190544 259 21
Hueneme 239 -4.777436 274 35
Itajai 240 -4.860375 #N/A #N/A
Arica 241 -5.04979 295 54
Venice 242 -5.249944 230 -12
Bangkok 243 -5.307291 304 61
Varna 244 -5.32636 248 4
Maputo 245 -5.893511 323 78
San Antonio 246 -5.981112 320 74
Southampton 247 -6.017641 348 101
St Petersburg 248 -6.166687 #N/A #N/A
Takoradi 249 -6.414522 290 41
Port Victoria 250 -6.470642 298 48
Gavle 251 -6.593461 256 5
Puerto Cabello 252 -7.384221 284 32
Agadir 253 -7.390557 260 7
Davao 254 -7.499438 279 25
Timaru 255 -7.522139 314 59
San Vicente 256 -7.880376 162 -94
Bejaia 257 -7.947162 289 32
Sokhna 258 -8.319628 352 94
Durres 259 -8.355378 319 60
Dublin 260 -8.49749 299 39
Lagos (Nigeria) 261 -8.503313 358 97
Aden 262 -8.547208 285 23
Corinto 263 -9.068531 283 20
Rotterdam 264 -9.558479 300 36
Kingston (Jamaica) 265 -10.03605 128 -137
Alexandria (Egypt) 266 -10.59644 266 0
Mayotte 267 -10.67549 293 26
Felixstowe 268 -10.97785 336 68
Seattle 269 -11.17249 322 53

Naples 270 -12.15288 267 -3
Santo Tomas De Castilla 271 -12.40153 275 4
Iskenderun 272 -13.25392 72 -200
Melbourne 273 -13.25584 294 21
Mejillones 274 -13.26753 241 -33
Monrovia 275 -13.3903 #N/A #N/A
Halifax 276 -13.76381 18 -258
Lae 277 -14.92325 306 29
Owendo 278 -15.07203 303 25
Otago Harbour 279 -15.55807 292 13
Adelaide 280 -16.17577 257 -23
Tanjung Priok 281 -16.48438 114 -167
Gdansk 282 -17.48768 199 -83
Brisbane 283 -17.58514 281 -2
Iquique 284 -17.89507 311 27
Ashdod 285 -18.07028 342 57
Guayaquil 286 -18.19975 302 16
Tarragona 287 -18.67975 157 -130
Antofagasta 288 -19.69405 273 -15
Montreal 289 -19.98314 313 24
Acajutla 290 -20.63371 269 -21
Port Elizabeth 291 -21.10723 317 26
Walvis Bay 292 -21.48845 332 40
Poti 293 -21.6105 213 -80
Constantza 294 -22.6387 261 -33
Port Botany 295 -24.63636 321 26
Manzanillo (Mexico) 296 -25.09643 52 -244
Port Reunion 297 -25.71414 #N/A #N/A
Douala 298 -27.11337 340 42
Onne 299 -27.60095 343 44
San Pedro (Cote D'ivoire) 300 -27.883 315 15
Baltimore (USA) 301 -28.31127 #N/A #N/A
Montevideo 302 -28.65839 265 -37
Valencia 303 -29.62095 180 -123
New York New Jersey 304 -30.6021 #N/A #N/A
Tin Can Island 305 -31.18211 334 29
Chattogram 306 -32.49516 347 41
Qasr Ahmed 307 -32.62911 288 -19
Cristobal 308 -34.88104 185 -123
Tacoma 309 -36.76471 341 32
Fremantle 310 -38.76647 328 18

Livorno 311 -39.35032 333 22
Dar Es Salaam 312 -42.56314 361 49
Genoa 313 -43.08853 335 22
Le Havre 314 -45.59272 286 -28
Lyttelton 315 -45.61401 312 -3
Lome 316 -45.74376 #N/A #N/A
Pointe-Noire 317 -46.03578 362 45
Freeport (Bahamas) 318 -47.5856 351 33
Port Louis 319 -48.51084 329 10
Dunkirk 320 -51.82757 345 25
Thessaloniki 321 -53.02881 327 6
Napier 322 -54.03063 282 -40
Beirut 323 -54.84456 356 33
Kribi Deep Sea Port 324 -55.07653 357 33
Hamburg 325 -55.9153 258 -67
Auckland 326 -56.08951 350 24
Tauranga 327 -56.9159 330 3
Mombasa 328 -56.93792 296 -32
Manila 329 -64.58673 324 -5
Cotonou 330 -68.15778 346 16
Nouakchott 331 -70.48896 354 23
Abidjan 332 -84.62539 359 27
La Spezia 333 -88.35868 309 -24
Houston 334 -91.60496 123 -211
Rijeka 335 -95.62783 200 -135
Los Angeles 336 -98.4873 369 33
Luanda 337 -107.5817 366 29
Ngqura 338 -118.686 365 27
Durban 339 -123.8653 363 24
Trieste 340 -130.9884 338 -2
Charleston 341 -138.3375 187 -154
Prince Rupert 342 -145.9609 339 -3
Oakland 343 -154.6855 360 17
Cape Town 344 -164.2052 364 20
Koper 345 -196.1089 218 -127
Long Beach 346 -209.2063 370 24
Vancouver (Canada) 347 -224.4264 368 21
Savannah 348 -396.8871 367 19

Appendix B: Constructing the CPPI | 74
Appendix B: Constructing the CPPI
The administrative and statistical approaches are explained in detail in this section.
The Structure of the Data
Before discussing the methodology employed in constructing the CPPI with matrix factorization, it is helpful
to first summarize the structure of available data. The data set is segmented by the following five categories
of ship sizes:
• Feeders: 1,500 TEUs
• Intra-regional: 1,500 TEUs –5,000 TEUs
• Intermediate: 5,000 TEUs –8,500 TEUs
• Neo-Panamax: 8,500 TEUs –13,500 TEUs
• Ultra-large container carriers: 13,500 TEUs
For each category, there are 10 different bands for call size. The port productivity is captured by average
idle hour, which consists of two parts: port-to-berth (PB) and on-berth (B). In the previous CPPI iteration,
total variables used = 5 x 10 x 2. Of course, many of them have missing values. The objective is to build
a model to summarize these variables and then construct a port productivity index for all ports under
consideration. The average waiting time and average berth time is calculated for each call size. The resulting
data is a table/matrix whose rows represent ports and whose columns contain the average waiting and
berth times of each call size.

Table B.1 • Sample Port Productivity Data Structure by Ship Size
SHIP
SIZE (K)
CALL SIZE BAND (NUMBER OF MOVES)
250 251–500 …... 6,000
Ports
Port-to-
Berth
Berth
Total Port
Hours
Port-to-
Berth
Berth
Total Port
Hours
Port-to-
Berth
Berth
Total Port
Hours
1
2
3
. . .
Source: Original table produced for this publication
75 | Appendix B: Constructing the CPPI
Imputation of Missing Values
A major practical problem is that most idle hour variables have a significant number of missing values.
For instance, in the port performance data set, the two smaller ship sizes contain little data for the
larger call sizes. Consequently, as in the administrative approach, the call size groups with more than
2,000 moves were removed from the 1,500 TEU ship category, and the call size groups with more
than 4,000 moves were removed from the 1,501 TEU–5,000 TEU ship category.
A more sophisticated approach is to use likelihood-based methods to impute those missing values.
For the current data set, expectation–maximization (EM) algorithm can be utilized to provide a
maximum-likelihood estimator for each missing value. It relies on two critical assumptions. The first
assumption is that gaps are random, or more specifically, the gaps are not caused by sample selection
bias. The second assumption is that all variables under consideration follow a normal distribution.
Given the data set, these two assumptions are plausible. EM computes the maximum likelihood
estimator for the mean and variance of the normal distribution given the observed data. Knowing the
distribution that generates the missing data, we can then sample from it to impute the missing values.
Matrix factorization can then be performed on the resulting data set, instead of the original one filled
with missing values.
Missing values in the resulting table/matrix are reconstructed using the EM algorithm (Dempster, Laird,
and Rubin 1977). A non-negativity constraint is added to make sure the reconstructed times are non-
negative. Assuming the data has a multivariate Gaussian distribution with mean vector µ and covariance
matrix ∑, the EM algorithm provides an estimate of the two parameters µ and ∑ via maximum likelihood.
Missing values are imputed using their conditional expectation. In this approach, given a row with
available values x_a and missing values x_m, the missing values are imputed by their conditional
expectation E(x_m 1_(x_m )≥|x_a ) given the available data, where the expected value is computed only
over the non-negative values of x_m to ensure the imputed values are non-negative.
In this iteration, arrival and berth hours are aggregated into total port hours, just like in the administrative
approach. The data structure after this aggregation for a particular category k (k = 1, 2, 3, 4, 5) can be
summarized as shown in Table B.1.

Why Is Matrix Factorization Useful?
Essentially, for each port, quite a few variables contain information about its efficiency. These include
average time cost under various categories: (1) different call size bands, and (2) berth/port-to-berth.
The reason matrix factorization can be helpful is that these variables are in fact determined by
a small number of unobserved factors, which might include quality of infrastructure, expertise of staff,
and so on. Depending on the data, very few of such factors can summarize almost all useful information.
The challenge lies in the inability to observe those latent factors; however, a simple example could be
helpful: Imagine three ports, each with four different types of time cost, as shown in table B.2.
PORT COST 1 COST 2 COST 3 COST 4
A 1 2 3 4
B 2 4 6 8
C 3 6 9 12
Table B.2 • Sample Illustration of Latent Factors
Source: Original table produced for this publication
As one can observe, costs 2 to 4 are just some multiples of cost 1. Although we have four variables,
to rank the efficiency of these three ports, just one variable is enough (ABC). This is an extreme
case, but the idea can be generalized if these variables are somehow correlated, but to a less extreme
extent. In that case, the factors are computed as some linear combination of costs 1 to 4. Of course,
if costs 1 to 4 are completely independent of each other, then this method makes no sense. Fortunately,
this is not the case for our data set. Thus, for each port, we can compute its score on all factors and then
combine those scores together to reach a final efficiency score.
Note that in the statistical approach using matrix factorization, the scores are not calculated for each
call size range. On the contrary, the whole data set, including the smaller ports, is used simultaneously to
obtain latent factors. This is in sharp contrast to the administrative approach. The statistical approach
factors in all the correlations among hours for various call size bands, which purely from a statistical
perspective is more efficient.
There is no right or wrong methodology, but the two different approaches that are considered
complementary. Hence, the decision in this iteration of the CPPI to maintain both approaches, to try and
ensure that the resulting ranking(s) of container port performance reflects as closely as possible actual
port performance, whilst also being statistically robust.
The Statistical Methodology
The data are scaled and weighted as in the administrative approach.
• Let p_ij denote average port time of port i in call size j.
• Let p_(avg,j ) denote the average of the average port time of all ports in the given call size.
• Let w_j denote the ratio of port calls that are in the call size group j
• The data are scaled by replacing p_ij by.

A positive value of x_(ij ) means the port is doing better than average, whereas a negative value means it
is doing worse than average.
Let X = (x_(ij )) denote the resulting matrix of scaled port time. Assume X has n rows (n ports) and p
columns (p call size bands). Instead of using factor analysis as in the previous iteration, the matrix X
is decomposed as X ≈ WH where W is a n×k matrix and H is an entrywise non-negative k ×p matrix.
The integer k (the number of columns of W) is chosen to be a small number to compress the data.
The matrix W represents factors and the matrix H factor loadings that are used to explain the data X.
A number of k = 3 factors was found to be adequate to approximate the data matrix X.
Note: In the previous iteration, a factor analysis (FA) approach was used. The FA produces a matrix
factorization X ≈ WH as above, except that the matrix H does not need to be non-negative. This is a
problem since a large positive factor does not necessarily represent a small port time if the corresponding
loading is negative. The new approach fixes that problem by enforcing non-negativity in the loadings
matrix H. This approach produces results that are consistent with the administrative approach.
The CPPI for each ship size is obtained by adding the three columns of W.
The CPPI index is a weighted sum of these indices: Let CPPIi denote the CPPI index for ship size i (i = 1, . . . ,5).
where (α1
, α2
, α3
, α4
, α5
) = (0.46, 1.00, 1.54, 1.97, 2.57)
The Administrative Approach
Aggregating arrival and berth hours into total port hours. This report indicated earlier that a case
could be made for penalizing waiting time which is regarded as pure waste. However, as expressed earlier,
this would be a normative judgment, accordingly both arrival and berth hours are weighted as 1.0 and the
two time segments are summed to form total port hours in CPPI 2022.
Appraising port hours performance. Average port hours are naturally higher in the larger than smaller
call size groups. This can magnify the difference in hours between a subject port and the average port
hours of the overall group. So, appraising on the difference between a port’s average hours and average
hours of the group may skew the scoring unduly toward the larger call size calls. There are also far fewer
calls within the larger than smaller call size groups, and this also needs to be reflected in the construction
of the CPPI to retain maximum objectivity.
The method applied to each call size group individually is that the port’s average port hours is compared
with the group’s average port hours as a negative or positive quantity of hours. The result of that
comparison is weighted by the ratio of port calls in each call size group for the entire group of ports
Table B.3 provides an illustration as to how it is done.

Table B.3 • Port Hours Performance Appraisal
PORT PORT HOURS HOURS DIFFERENCE CALL SIZE GROUP WEIGHT RESULT
Example Port 22.56 12.09 0.160 1.9344
Group Average 34.65
In this illustrative example, the subject port used 12.09 fewer hours than the average of the entire
group (22.56 versus 34.65). Since 16.0 percent of all port calls in this ship size group were in the
subject call size group, the difference in hours (12.09) is multiplied by ratio 0.160 for an overall index
points result of 1.9344. Where a port uses more port time than the average for all ports, the index points
become negative.
Aggregation to a score and rank per ship size group. The “results” achieved per port within each of the
10 call size groups are then summed together to calculate a score within the overall ship size group (it is
five and eight groups rather than 10 groups in the case of the two smaller ship size groups, respectively).
Based upon these scores, there is a sub-ranking performed within each ship size group that can be
reviewed in the final CPPI rankings.
Aggregating all Ship Size Groups
No allowance was made for ports that did not handle ships within specific ship size groups during the
period under consideration. The primary reason is many of the smaller ports are not capable of handling
some of the larger ship sizes and so would in effect be awarded positive (or negative) results for scenarios
that are physically impossible. The omission of scores within some ship size groups would only be an issue
if an attempt was made to compare the performance of major mainline ports with those of far smaller
ports. But this is a comparison that is neither fair nor valuable.
For the comparison between similarly sized ports, this factor will not contribute, or at least not
significantly. In aggregating the scores from the various ship size groups into the overall CPPI in the
administrative approach, a factor was built in to differentiate the importance and significance of
better performance of larger ships over smaller ones. This was constructed based on the relative fuel
consumption (and, therefore, emissions and cost) of different ship sizes in the form of an index (see table
B.4). For each ship size group, a typical mid-range example ship was selected. Based upon the expected
deployment of such ships, a range of sea legs were defined (and weighted), at a typical pro forma service
speed, and the impact on fuel consumption that one hour longer (or shorter) in port would be likely to yield.

NOMINAL TEU
CAPACITY RANGE
EXPECTED DEPLOYMENT SEA LEG
WEIGHT
(PERCENT)
INDEX
WEIGHT
Less than 1,500 TEUs
Feeders
Intra-regional
Singapore–Surabaya
Rotterdam–Dublin
Kingston–Port-au-Prince
Busan–Qingdao
25
25
25
25
0.46
1,500 to 5,000 TEUs
Intra-regional
Africa
Latin America
Oceania
Transatlantic
Shanghai¬–Manila
Rotterdam–Genoa
Algeciras–Tema
Charleston–Santos
Xiamen–Brisbane
Felixstowe–New York
30
30
10
10
10
10
1.00
5,000 to 8,500 TEUs
Africa
Latin America
Oceania
Transatlantic
Asia–Middle East
Hong Kong–Tema
Charleston–Santos
Xiamen–Brisbane
Felixstowe–New York
Shanghai–Dubai
20
20
20
20
20
1.54
8,500 to 13,500 TEUs
Transpacific
Asia–Middle East
Asia–Mediterranean
Busan–Charleston (via Panama)
Hong Kong–Los Angeles
Shanghai–Dubai
Singapore–Piraeus
25
25
25
25
1.97
Greater than 13,500
TEUs
Asia–Mediterranean
Asia–North Europe
Transpacific
Singapore–Piraeus
Singapore–Rotterdam
Hong Kong–Los Angeles
40
40
20
2.57
Table B.4 • Assumptions to Determine a Fuel Consumption Index
The index weight then suggests that it is 2.57 times more costly to recover an additional hour of port
time at sea for a ship with a capacity of more than 13,500 TEUs than it would be for a ship in the
1,500 TEU–5,000 TEU capacity range. The total aggregated index points per port within each ship size
group are then weighted by this cost/environmental factor. The sum of the weighted index points for
each port across all five ship size groups are then summed and the final CPPI ranking is based on those
weighted values.
The primary focus was micro-delays and it was assumed that these would be recovered on long-haul
ocean legs, and not between coastal ports, which would be more costly. Through simulation, if the index
values are tweaked up or down by up to 10 percent, the overall ranking is unaffected. If they are adjusted
so that larger ship size groups have lower indices than smaller ones, it results in radical changes to the
overall ranking. The resulting index for main and secondary ports using the administrative approach is
presented in chapter 3 and appendix A.

Notes
1
International Maritime Organization (IMO) Resolution MSC.74(69) Annex 3.
2
See the International Maritime Organization’s website on “International Convention for the Safety of Life
at Sea (SOLAS), 1974,” (accessed March 2022), at https://www.imo.org/en/About/Conventions/ Pages/
International-Convention-for-the-Safety-of-Life-at-Sea-(SOLAS),-1974.aspx.
3
International Convention for the Safety of Life at Sea (SOLAS), under the revised SOLAS 1974 Chapter V
(as amended)—Safety of Navigation, section 19.2.415, carriage requirements for shipborne navigational
systems and equipment.
4
See ITU’s website on “Technical Characteristics for an Automatic Identification System Using Time
Division Multiple Access in the VHF Maritime Mobile Frequency Band,” (accessed November 2021), at
https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.1371-5-201402-I!!PDF-E.pdf.
5
It may be a conventional land-based port or a stretch of water designated as an area for transferring
cargo or passengers from ship to ship.
6
The precise approach to produce a robust data set is detailed in appendix B.
7
The actual equation is: (Group Average Port Hours/Example Port Hours) x Call Size Group Weight.
References
•
Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum Likelihood from Incomplete Data via the
EM Algorithm.” Journal of the Royal Statistical Society: Series B (Methodological), 39 (1): 1–22.
https://doi.org/10.1111/j.2517-6161.1977.tb01600.x.
•
IALA (International Association of Marine Aids to Navigation and Lighthouse Authorities). 2005.
IALA Guideline 1050: The Management and Monitoring of AIS information. Edition 1.0. Saint Germain:
IALA. https://www.iala-aism.org/product/management-and-monitoring-of-ais-information-
1050/?download=true.
•
IALA (International Association of Marine Aids to Navigation and Lighthouse Authorities). 2016.
IALA Guideline 1082: An Overview of AIS. Edition 2.0. Saint Germain: IALA. 19. https://www.iala-aism.
org/product/an-overview-of-ais-1082/?download=true.
Notes and References | 80

Container Port Performance Index 2022.pdf

Container Port Performance Index 2022.pdf

Recommended

More Related Content

What's hot (20)

Similar to Container Port Performance Index 2022.pdf (7)

More from El Estrecho Digital (20)

Recently uploaded (20)

Container Port Performance Index 2022.pdf