forecasting cargo throughput in portuguese ports · the aim of this thesis is to forecast the cargo...

Forecasting cargo throughput in Portuguese ports

Andrea Mainardi

Dissertação para obtenção do Grau de Mestre em

Engenharia e Arquitectura Naval

Orientador: Prof. Tiago Santos

Júri

Presidente: Prof. Carlos Guedes Soares

Orientador: Prof. Tiago Santos

Vogal: Prof.a Regina Salvador

Julho 2016

iii

Acknowledgments

I would like to thank my coordinator, Professor Tiago Santos for the countless hours he dedicated to

me and for the precious help when it looked like there was no chance of solving problems.

I would like to thank also my parents, for the endless support they gave me in all these years of

studying. Thank you Teresa for helping me to maintain a fully functioning mind and body.

And finally thanks to my friends who taught me in these years in Lisbon much more than any

university ever could, you know who you are!

iv

Abstract

Reliable port throughput forecasts are of the utmost importance for ports. Given the high investment

and long time needed to improve the port infrastructure and superstructure, a good balance is

required between port development and expected throughput. An over-dimensioned port will lead to

revenues not covering the capital and operating costs, while an under-dimensioned port will introduce

delays in the process of cargo loading and unloading, discouraging ship-owners to come back to the

port. Adequate port development thus requires reliable cargo throughput forecasts.

The aim of this thesis is to forecast the cargo throughput in Portuguese ports using a mix of Multiple

Linear Regression (MLR) and qualitative considerations. Port throughput data from the last 15 years

is obtained from the various Portuguese port authorities and from Instituto Nacional de Estatìstica

(INE) and is analysed. Economic and industrial indicator are collected from different sources, namely

INE, Banco do Portugal, OECD and PorData, aiming at identifying explanatory variables for observed

cargo throughput in ports. Cargo throughput is split into categories and compared with the economic

and industrial indicators to find similarities. Then a forecast of port throughput over the next 10 years

is presented. Considerations are made about the relation between the various ports of the country

and how they interact, as well as about the interaction between port throughput and economy.

Conclusions are drawn regarding main drivers of cargo throughput increase in Portuguese ports and

forecasts are presented for individual ports, the entire port range and different cargo types.

Keywords: Forecast, cargo throughput, ports, linear regression.

v

Resumo

Dado o elevado investimento a longo prazo necessário para melhorar as infra-estruturas e

superestruturas portuárias, previsões do tráfego confiáveis são de extrema importância para os

portos. É necessário um bom equilíbrio entre o desenvolvimento portuário e o rendimento esperado.

Um porto sobredimensionado conduzirá a receitas que não cobrem os custos operacionais e de

capital, enquanto um porto sob-dimensionado atrasará o processo de carga e descarga,

desencorajando os armadores a voltar ao porto. Um desenvolvimento portuário adequado requer,

assim, previsões do tráfego fiáveis.

O objetivo desta tese é a previsão da movimentação de carga nos portos portugueses, usando uma

mistura de Regressão Linear Múltipla (MLR) e considerações qualitativas. Dados sobre o tráfego dos

últimos 15 anos são obtidos das várias autoridades portuárias Portuguesas e do Instituto Nacional de

Estatística (INE). Esses dados estatísticos são analisados em detalhe. Indicadores económicos e

industriais são recolhidos a partir de diferentes fontes, como o INE, o Banco do Portugal, a OCDE e

Pordata, com o objectivo de identificar as variáveis explicativas para a movimentação de carga

observada nos portos. O trafego é depois dividido em categorias e comparado com os indicadores

industriais e económicos, procurando semelhanças. A seguir, uma previsão do tráfego ao longo dos

próximos 10 anos é apresentada. Considerações são feitas sobre a relação entre portos, assim como

sobre a interação entre a movimentação de carga e a economia. Sao retiradas conclusões sobre as

forças motrizes do aumento da movimentação de carga nos portos, e previsões são apresentadas

para os portos individualmente, para grupos de portos e para os diversos tipos de carga.

Palavras-chave: Previsão, Tráfego portuário, Portos, Regressão linear.

vi

Table of Contents

Acknowledgments ...................................................................................................................................... iii

Abstract ....................................................................................................................................................... iv

Resumo ....................................................................................................................................................... v

List of Figures ............................................................................................................................................... ix

List of Tables ............................................................................................................................................... xii

List of Acronyms ........................................................................................................................................ xiii

1. INTRODUCTION ...................................................................................................................... 1

2. STATE OF THE ART ................................................................................................................. 3

2.1 Techniques .............................................................................................................................................. 3

2.2 Literature Review .................................................................................................................................... 8

2.2.1 Applications of traditional methods ...................................................................................................... 8

2.2.2 Comparison of performances and new methods .................................................................................. 9

2.2.3 In-depth considerations ....................................................................................................................... 10

2.2.4 Summary .............................................................................................................................................. 11

3. GEOGRAPHICAL AND INDUSTRIAL OVERVIEW ..................................................................... 13

3.1 Industrial Overview ............................................................................................................................... 14

3.2 Lisbon .................................................................................................................................................... 15

3.3 Leixões .................................................................................................................................................. 16

3.4 Sines ...................................................................................................................................................... 16

3.5 Setúbal .................................................................................................................................................. 17

3.6 Aveiro .................................................................................................................................................... 18

3.7 Figueira da Foz ...................................................................................................................................... 18

3.8 Viana do Castelo ................................................................................................................................... 19

3.9 Southern Ports ...................................................................................................................................... 19

4. ANALYSIS OF CARGO THROUGHPUT IN PORTUGUESE PORTS ............................................. 21

4.1 Port of Lisbon ........................................................................................................................................ 22

4.2 Port of Leixões ...................................................................................................................................... 24

4.3 Port of Sines .......................................................................................................................................... 27

vii

4.4 Port of Setúbal ...................................................................................................................................... 28

4.5 Port of Aveiro ........................................................................................................................................ 31

4.6 Port of Figueira da Foz .......................................................................................................................... 32

4.7 Port of Viana do Castelo ....................................................................................................................... 33

4.8 Port of Faro ........................................................................................................................................... 35

4.9 Port of Portimão ................................................................................................................................... 36

4.10 Port throughput recap .......................................................................................................................... 36

5. METHODOLOGY FOR CARGO THROUGHPUT FORECASTING ............................................... 44

5.1 Explanatory Variables ........................................................................................................................... 44

5.2 Multiple Linear Regression ................................................................................................................... 47

5.3 Linear Interpolation .............................................................................................................................. 49

6. RESULTS ............................................................................................................................... 50

6.1 Products of Forest and Agriculture ....................................................................................................... 50

6.2 Crude oil and LNG ................................................................................................................................. 51

6.3 Minerals ................................................................................................................................................ 52

6.4 Food Products ....................................................................................................................................... 53

6.5 Wood, cork and paper products ........................................................................................................... 55

6.6 Coal and oil products ............................................................................................................................ 56

6.7 Chemical products ................................................................................................................................ 57

6.8 Non-metallic mineral products ............................................................................................................. 58

6.9 Metallic products .................................................................................................................................. 60

6.10 Transport material ................................................................................................................................ 61

6.11 Secondary raw materials ...................................................................................................................... 62

6.12 Unknown cargo ..................................................................................................................................... 63

6.13 Cruise Passengers ................................................................................................................................. 64

6.14 Ports Overview ..................................................................................................................................... 65

7. CONCLUSIONS ...................................................................................................................... 71

7.1 Past trends in cargo throughput ........................................................................................................... 71

7.2 Forecasts for 2015-2024 ....................................................................................................................... 72

7.3 Recommendations for further research ............................................................................................... 73

REFERENCES ................................................................................................................................ 75

APPENDIX A – NST 2007 CATEGORIZATION ................................................................................ 78

viii

APPENDIX B – DATA .................................................................................................................... 82

ix

List of Figures

FIGURE 1 - EXAMPLES OF FUNCTIONS FITTING DIFFERENT SETS OF POINTS ......................................................... 5

FIGURE 2 - CORRELATION COEFFICIENT OF DIFFERENT SETS OF POINTS ............................................................... 6

FIGURE 3 - TYPICAL MULTILAYER PERCEPTRON STRUCTURE ................................................................................. 7

FIGURE 4 - MAIN PORTS AND INDUSTRIES OF PORTUGAL ................................................................................... 13

FIGURE 5 - MAP OF THE PORT OF LISBON ............................................................................................................ 16

FIGURE 6 - MAP OF THE PORT OF LEIXÕES ........................................................................................................... 16

FIGURE 7 - MAP OF THE PORT OF SINES ............................................................................................................... 17

FIGURE 8 - MAP OF THE PORT OF SETÚBAL ......................................................................................................... 18

FIGURE 9 - MAP OF THE PORT OF AVEIRO ............................................................................................................ 18

FIGURE 10 - MAP OF THE PORT OF FIGUEIRA DA FOZ .......................................................................................... 19

FIGURE 11 - MAP OF THE PORT OF VIANA DO CASTELO ...................................................................................... 19

FIGURE 12 - CRUISE QUAY IN PORTIMÃO ............................................................................................................. 20

FIGURE 13 - PORT OF FARO .................................................................................................................................. 20

FIGURE 14 - DRY BULKS THROUGHPUT IN THE PORT OF LISBON ......................................................................... 22

FIGURE 15 - LIQUID BULKS THROUGHPUT IN THE PORT OF LISBON .................................................................... 22

FIGURE 16 - GENERAL CARGO THROUGHPUT IN THE PORT OF LISBON ............................................................... 23

FIGURE 17 - CONTAINER THROUGHPUT IN THE PORT OF LISBON ....................................................................... 23

FIGURE 18 - CRUISE PASSENGERS THROUGHPUT IN THE PORT OF LISBON ......................................................... 24

FIGURE 19 - DRY BULKS THROUGHPUT IN THE PORT OF LEIXÕES ........................................................................ 24

FIGURE 20 - LIQUID BULKS THROUGHPUT IN THE PORT OF LEIXÕES ................................................................... 25

FIGURE 21 - GENERAL CARGO THROUGHPUT IN THE PORT OF LEIXÕES .............................................................. 25

FIGURE 22 - CONTAINER THROUGHPUT IN THE PORT OF LEIXÕES ...................................................................... 26

FIGURE 23 - CRUISE PASSENGERS THROUGHPUT IN THE PORT OF LEIXÕES ........................................................ 26

FIGURE 24 - DRY BULKS THROUGHPUT IN THE PORT OF SINES ........................................................................... 27

FIGURE 25 - LIQUID BULKS THROUGHPUT IN THE PORT OF SINES ....................................................................... 27

FIGURE 26 - GENERAL CARGO THROUGHPUT IN THE PORT OF THE PORT OF SINES ........................................... 28

FIGURE 27 - CONTAINER THROUGHPUT IN THE PORT OF SINES .......................................................................... 28

FIGURE 28 - DRY BULKS THROUGHPUT IN THE PORT OF SETÚBAL ...................................................................... 29

FIGURE 29 - LIQUID BULKS THROUGHPUT IN THE PORT OF SETÚBAL ................................................................. 29

FIGURE 30 - GENERAL CARGO THROUGHPUT IN THE PORT OF SETÚBAL ............................................................ 30

FIGURE 31 - CONTAINER THROUGHPUT IN THE PORT OF SETÚBAL ..................................................................... 30

FIGURE 32 - RORO THROUGHPUT IN THE PORT OF SETÚBAL .............................................................................. 31

FIGURE 33 - DRY BULK THROUGHPUT IN THE PORT OF AVEIRO .......................................................................... 31

FIGURE 34 - LIQUID BULK THROUGHPUT IN THE PORT OF AVEIRO ..................................................................... 32

FIGURE 35 - GENERAL CARGO THROUGHPUT IN THE PORT OF AVEIRO .............................................................. 32

x

FIGURE 36 - DRY BULKS THROUGHPUT IN THE PORT OF FIGUEIRA DA FOZ......................................................... 33

FIGURE 37 - GENERAL CARGO THROUGHPUT IN THE PORT OF FIGUEIRA DA FOZ............................................... 33

FIGURE 38 - DRY BULK THROUGHPUT IN THE PORT OF VIANA DO CASTELO ....................................................... 34

FIGURE 39 - LIQUID BULK THROUGHPUT IN THE PORT OF VIANA DO CASTELO .................................................. 34

FIGURE 40 - GENERAL CARGO THROUGHPUT IN THE PORT OF VIANA DO CASTELO ........................................... 35

FIGURE 41 - DRY BULK THROUGHPUT IN THE PORT OF FARO .............................................................................. 35

FIGURE 42 - GENERAL CARGO THROUGHPUT IN THE PORT OF FARO .................................................................. 36

FIGURE 43 – CRUISE PASSENGER THROUGHPUT IN THE PORT OF PORTIMÃO .................................................... 36

FIGURE 44 - OVERALL THROUGHPUT OF PORTUGUESE MAINLAND PORTS (TONS) ............................................ 37

FIGURE 45 – CARGO THROUGHPUT IN THE PORT OF SINES, SPLIT BY NST2007 CATEGORIES ............................. 41

FIGURE 46 - CARGO THROUGHPUT IN THE PORT OF LEIXÕES, SPLIT BY NST2007 CATEGORIES .......................... 41

FIGURE 47 - CARGO THROUGHPUT IN THE PORT OF LISBON, SPLIT BY NST2007 CATEGORIES ........................... 41

FIGURE 48 - CARGOS LOADED ON SHIPS IN PORTUGAL, SPLIT BY NST2007 CATEGORIES ................................... 42

FIGURE 49 - CARGO UNLOADED FROM SHIPS IN PORTUGAL, SPLIT BY NST2007 CATEGORIES ........................... 43

FIGURE 50 - HEAVY INDUSTRIES SALES (CONSTANT PRICE 2014) ........................................................................ 45

FIGURE 51 - LIGHT INDUSTRIES SALES (CONSTANT PRICE 2014) .......................................................................... 45

FIGURE 52 - ENERGY CONSUMPTION IN PORTUGAL (TEP)................................................................................... 45

FIGURE 53 - PORTUGUESE GDP AND DOMESTIC CONSUMPTION (CONSTANT PRICE 2014) ............................... 46

FIGURE 54 - GDP OF THE MAIN ECONOMIC PARTNERS OF PORTUGAL (CONSTANT PRICE 2014) ....................... 46

FIGURE 55 - COMPARISON OF TEU THROUGHPUT BETWEEN SINES AND THE MAIN TRANSSHIPMENT PORTS OF

THE WESTERN MEDITERRANEAN. THE NUMBER IN THE LEGEND INDICATES THE THROUGHPUT CAPACITY

OF THE PORT IN TEUS/YEAR. ........................................................................................................................ 47

FIGURE 56 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS - LOADED TONS OF PRODUCTS OF FOREST

AND AGRICULTURE ....................................................................................................................................... 51

FIGURE 57 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS - UNLOADED TONS OF PRODUCTS OF FOREST

AND AGRICULTURE ....................................................................................................................................... 51

FIGURE 58 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS - UNLOADED TONS OF CRUDE OIL AND LNG

...................................................................................................................................................................... 52

FIGURE 59 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF MINERALS ................... 53

FIGURE 60 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF MINERALS .............. 53

FIGURE 61 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF FOOD PRODUCTS ........ 54

FIGURE 62 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF FOOD PRODUCTS .. 54

FIGURE 63 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF WOOD, CORK AND

PAPER PRODUCTS ......................................................................................................................................... 55

FIGURE 64 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF WOOD, CORK AND

PAPER PRODUCTS ......................................................................................................................................... 56

FIGURE 65 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF OIL PRODUCTS ............ 57

xi

FIGURE 66 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF COAL AND OIL

PRODUCTS .................................................................................................................................................... 57

FIGURE 67 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF CHEMICAL PRODUCTS 58

FIGURE 68 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF CHEMICAL PRODUCTS

...................................................................................................................................................................... 58

FIGURE 69 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF CEMENT/GLASS .......... 59

FIGURE 70 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF CEMENT/GLASS ..... 60

FIGURE 71 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF METALLIC PRODUCTS . 61

FIGURE 72 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF METALLIC PRODUCTS

...................................................................................................................................................................... 61

FIGURE 73 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED AND UNLOADED TONS OF CARS 62

FIGURE 74 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED TONS OF SECONDARY RAW

MATERIALS.................................................................................................................................................... 63

FIGURE 75 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – UNLOADED TONS OF SECONDARY RAW

MATERIALS.................................................................................................................................................... 63

FIGURE 76 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS – LOADED AND UNLOADED TONS OF

UNKNOWN CARGO ....................................................................................................................................... 64

FIGURE 77 - FORECAST OF THROUGHPUT IN PORTUGUESE PORTS - CRUISE PASSENGERS IN TRANSIT .............. 65

FIGURE 78 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF LISBON ........................................................ 65

FIGURE 79 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF LEIXÕES ....................................................... 66

FIGURE 80 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF SINES........................................................... 66

FIGURE 81 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF SETÚBAL ..................................................... 67

FIGURE 82 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF AVEIRO ....................................................... 67

FIGURE 83 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF FIGUEIRA DA FOZ ........................................ 68

FIGURE 84 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF VIANA DO CASTELO .................................... 68

FIGURE 85 - FORECAST OF CARGO THROUGHPUT IN THE PORT OF FARO ........................................................... 69

FIGURE 86 – FORECAST OF CARGO THROUGHPUT IN PORTUGUESE PORTS, THE NATIONAL TOTAL .................. 70

xii

List of Tables

TABLE 1 - LIST OF PORTUGUESE POWER PLANTS (SOURCE:WIKIPEDIA) .............................................................. 14

TABLE 2 - THROUGHPUT GROWTH OVERVIEW .................................................................................................... 37

TABLE 3 - MAIN CATEGORIES OF CARGO HANDLED IN PORTUGUESE PORTS IN 2014 ......................................... 39

TABLE 4 - CARGO THROUGHPUT CATEGORY TIME SERIES AND THEIR EXPLANATORY VARIABLE ....................... 47

xiii

List of Acronyms

ANN – Artificial Neural Network

AVR – Aveiro

FdF – Figueira da Foz

IMF – International Monetary Fund

LSB – Lisbon

LXS – Leixões

MLP – Multi-Layer Perceptron

MLR – Multiple Linear Regression

OECD – Organization for Economic Co-operation and Development

PTM – Portimão

SNS – Sines

STB – Setúbal

VdC – Viana do Castelo

1

1. INTRODUCTION

Forecasting the future has always been one of mankind’s dreams, to develop a forecast it is

necessary to simplify reality, discarding some variables to keep only the essential ones. The critical

point then is to strike a balance when removing variables, a model taking into account too few is an

oversimplification of reality, thus probably wrong. A model with too many variables, on the other hand,

is difficult to manage and introduces a great number of errors.

Traffic forecast is of utmost importance for ports. Traffic includes the number of ships coming into the

port but also the amounts of different types of cargo handled in the port (cargo throughput). This

thesis is focused on forecasting cargo throughput for the different cargo types.

Given the high investment needed to improve the port infrastructure and superstructure and the

impedance that the works give to the port traffic, forecasting results are of the utmost importance.

When managing a port a good balance is required regarding development, an over-dimensioned port

will make the revenues not cover the expenses. While an under-dimensioned port will introduce

delays in the process of cargo loading and unloading, discouraging ship-owners to come back in the

port.

Given these circumstances, it is in the best interests of port administrations to always have an idea

about the amount of traffic that their ports will expect, to do so they usually ask to some private

consultancy companies to study their traffic and give a long term (about 20 years) forecast, or either

conduct the study themselves.

Such studies are of special importance in the case of the Portuguese economy, which has a high

degree of openness. Given the high share of commerce passing through ports (about 60%) it can be

concluded that seaports play a decisive role in the nation’s economic growth.

Most of the goods handled in Portuguese ports are from or directed to local industries, and given the

size of Portugal a direct comparison between industry performance and port throughput is possible.

The aim of this thesis is to develop a forecast of cargo throughput for the mainland Portuguese ports

for the next 10 years. Data about the cargo throughput in the past years (2001-2014) is gathered from

different sources, like the Instituto Nacional de Estatistica and the various port authorities, and

analysed. This analysis in done on different levels, first the cargo throughput is analysed port by port,

subdividing the cargo by its means of transportation, this means dry and liquid bulks, containers,

general cargo, Ro-Ro and cruise passengers. Afterwards the cargo throughput is split into categories

based on the goods transported, this subdivision follows the European standard NST2007, and again

analysed, port by port, cargo by cargo. Then data about the Portuguese economy and industry is

gathered, and relations between the different time series are investigated (for example: relation

between the yearly income of cement industries and the yearly throughput of cement through the

ports). Different forecasting methods are investigated to find the one which suits better the data

2

available and the objective of the thesis. The forecast is crafted category by category, afterwards the

data is summed up and an overview of the forecast port by port is made.

The ports analysed are the ones in the Portuguese mainland: Lisbon, Leixões, Sines, Setúbal, Aveiro,

Figueira da Foz, Viana do Castelo, Faro and Portimão. The industries analysed are the main ones

responsible for cargo throughput in ports, this includes: alimentary, cement, glass, paper, wood,

metallurgy, automobile, oil and chemical product industries. The energy production is also analysed,

given its high importance in the demand for goods like coal and crude oil. Economic data include the

GDP and domestic production of Portugal, as well as the GDP of China, USA and Europe.

The structure of this thesis is as follows:

Chapter 2 deals with the state of the art of forecasting, explaining the basic techniques and

the most recent findings about their applications.

Chapter 3 presents the ports and industries of continental Portugal through a small overview

of the various ports and the industries locate in their hinterlands.

Chapter 4 presents the port throughput data gathered

Chapter 5 investigates the correlation between time series, investigates the feasibility of

different methods and explains the chosen one.

Chapter 6 shows the forecast results.

Chapter 7 sums up the findings and gives some conclusions.

3

2. STATE OF THE ART

This chapter covers the different forecasting techniques known and their applicability and reliability

regarding port traffic, as well as the up to date application of the various methods, and a reflection

about which methods are to be chosen for this specific application.

2.1 Techniques

Forecasting methods can be subdivided in 3 big groups: Qualitative methods, Time series and Causal

prediction. An overview of these methods will be given, as well as an explanation of the most used

error measures.

Qualitative methods are, at their basis, simple human opinions, this makes them subjective and prone

to different kinds of cognitive biases. They are used when past data is difficult to gather or

unavailable, and are subject to all the advantages and disadvantages related to human

consciousness. Different methods are available: top-down or bottom-up approach, in the first the

manager or managers make a forecast and take a decision based on that, otherwise a poll can be

done between sales agents or between customers and then the outcome passed to the managers to

take an informed decision.

Still when making a forecast one of the best ways is to take the opinion of experts, a pool of experts

has a deep and highly structured knowledge about the field they’re asked to forecast, thus they are

able, using their skills to make a prediction about the future. The downside to this is of course that

every human being’s perception is biased up to a certain point, to try to overcome this problem people

recur to something like the Delphi method (taking his name from a famous oracle in ancient Greece,

one of the first forecaster of known history).

In the Delphi method a first round of forecast is asked to the experts, afterwards an external agent

compiles an anonymous summary of the opinions and brings them back to the experts, the process is

repeated a certain amount of times until the results are deemed satisfactory by some predefined

criterion, may it be number of rounds or stability of results. This method has the advantage that by

submitting to the experts their own judgment in an anonymous form it helps to overcome biases,

authority issues, encourages free expression and makes it easier to acknowledge one’s own

mistakes. Even if apparently simplistic, the qualitative approach allows to take easily into account

unpredictable events (wild cards), which cannot absolutely be considered by strict mathematical

models.

Time series base the forecast only on the past behaviour of the variable considered, thus greatly

simplifying the task of prediction by reducing to a minimum the amount of data needed to gather.

The main assumption underlining the existence of time series is that things that happened in the past

will continue to happen, thus implying constant growth, or tendency to oscillate around a certain point

of equilibrium, as well as seasonality and other cycles of longer period.

4

When considering time series different approaches can be used.

Naïve method, the most basic, in which the variable is assumed to have in the next portion of time the

same value it had when last measured.

Decomposition method, which writes the variable as a function of time using different components:

trend, seasonality, long term cycles and random component. This decomposition is usually done in 2

different ways: either additive decomposition, when the 4 components are modelled and added to

each other; or multiplicative decomposition, when the components are modelled and multiplied by

each other. The main difference between the two ways is that in the multiplicative model the seasonal

oscillation increases together with the general trend while in the additive model the seasonality is

usually constant.

Exponential smoothing is a technique used to smooth out the data, it’s different from a moving

average in the way that the average is calculated on windows of different dimension.

Moving average model (MA), using an average calculated on a sub-set of the available data, the time

series is then considered as the sum of the moving average with a white noise component. The

moving average itself is also useful as a general data treatment tool, for example if data exhibit

seasonality a moving average done with a period of one year can help to separate seasonality from

the rest of the data.

Another method is autoregressive model (AR) which investigates the relation that the values of the

series have with themselves through autocorrelation. It is most commonly used on series that exhibit

a random behaviour, oscillating around a central value instead of having a monotone nature.

Lastly, the Box & Jenkins or ARIMA model (Auto Regressive Integrated Moving average), this method

is an intersection of the previous two, the data is analysed and a specific model is created as a blend

between auto-regressive and moving average, this model can be used only when the series has no

trend.

Another method, which can be used as a time series forecast, as well as a regression analysis is the

Grey system forecast [1], [2], which needs only a small dataset to produce reliable forecasts, the

study of grey systems involves all those fields in which the accessible information is incomplete, in

reality every situation studied is incomplete to some degree, may it be incompleteness about the

parameters, the structure, the boundaries or the behaviour of the system. Grey systems come in help

by refusing the complete accuracy wanted from traditional mathematical models, instead they accept

the incompleteness as part of every system and thus give more flexibility when making forecasts.

Even if so thoroughly studied, time series are useful mainly when there is no access to other data,

and the need to predict the future based only on the past behaviour of the variable.

Causal models are the more broadly used methods when approaching port throughput forecast, as

well as other economic activities. Here one or more explanatory variables are used to make the

prediction. Usually, when dealing with ports, the predicted variables is cargo throughput, the

dependant variable, while a variety of explanatory variables is used, such as GDP, population of the

hinterland, GDP per capita or energy consumption. It is worth noting that when forecasting different

5

variables the explanatory ones have to be chosen carefully. The most used causal forecasting

techniques are: regression analysis and neural networks.

Regression is a technique that comes from statistics, it permits to establish relationships between

variables, and it is the technique that will be used in this thesis. Regression can take several forms:

Single variable, like for example when port throughput is related to the GDP of the port

hinterland. Also the common linear interpolation is a form of single variable linear regression,

where the explanatory variable is time;

Multi variable, when each one of the forecasted values is a function of more than one other

time series;

Linear, when the explanatory function is linear, 𝑌 = 𝛼 + 𝛽𝑋

Nonlinear, when the function takes a different form: logarithmic, exponential, quadratic.

Usually a general equation is written, then the parameters of the equation are calculated using the

least square method, which minimizes the total difference between the points of the function and the

points of the data set. It should be noted that using this method to make the fitting implies that the

data points are normal distributed around the function points, this is not always true as pointed out in

[3]. Figure 1 shows some examples of linear and nonlinear fitting of functions.

Figure 1 - Examples of functions fitting different sets of points

To evaluate the “goodness” of the fit, the correlation coefficient is calculated:

6

𝜌 =𝑐𝑜𝑣 [𝑋, 𝑌]

𝜎𝑋𝜎𝑌 (1)

Where cov is the covariance of X and Y and sigma is the standard deviation. Note that even when

nonlinear relationship are present the data set can usually easily be linearized and the data then

treated as if linear. Figure 2 shows how different sets of data have a different value of ρ (or r).

Figure 2 - Correlation coefficient of different sets of points

It is really important to note, when doing regression analysis, that correlation does not imply causality,

it may happen for a series of reasons that two variables are correlated but causality should be

supposed only when a logical assumption is available, then check if the correlation coefficient

supports the assumption.

Other widely used methods are the so-called artificial neural networks. Neural network is a broad term

referring to any kind of algorithm that tries to replicate the functioning of a brain by modelling a

network of object with behaviour similar to those of neurons. ANNs bring with them some of the

advantages of human mind without carrying all the biases. For forecasting purposes the neural

network used almost all the times is the “multi-layer perceptron” (the structure of which is shown in

Figure 3). When using multi-layer perceptron the various series of data are organized in vectors, and

each vector contains the time series of a variable. A first layer reads the input vectors in different

“neurons” afterwards a second layer reads data from the first layer and combines the values using

different “weights”, after this the values from the second “hidden” layer are combined again with

different weights and the results are presented.

7

Figure 3 - Typical Multilayer Perceptron structure

Neural networks have some advantages over other prediction methods, first of all they’re self-training,

this means that what happens in the hidden layer is not defined by the operator, and the network

refines himself iteration after iteration. This also brings a flexibility not present in other forecasting

techniques, on every set of data on which the network is used the method tailors itself to the needs of

the time series.

Once a prevision has been made there is the need for a benchmarking tool to attest the quality of the

prevision. This tool is the mean forecasting error. Usually in the development of a new method a test

time series is analysed, the method is then applied to the first 80% of the data, and the remaining

20% is used to assess the goodness of the forecast.

There are different formulas to calculate errors, here is a short overview on the most used ones:

Mean percentage error, MPE, the average of the percentage errors:

𝑴𝑷𝑬 = 𝟏𝟎𝟎

𝒏∑

𝒂𝒊 − 𝒇𝒊

𝒂𝒊

𝒏

𝒊=𝟏

(2)

where n is the sample size, a the actual value and f the forecasted value;

Mean absolute percentage error, MAPE, same as above, just the absolute value of the error

is taken:

𝑴𝑨𝑷𝑬 = 𝟏𝟎𝟎

𝒏∑ |

𝒂𝒊 − 𝒇𝒊

𝒂𝒊| (3)

𝒏

𝒊=𝟏

Mean square error, MSE, the average of the square error

𝑴𝑺𝑬 = 𝟏

𝒏∑(𝒂𝒊 − 𝒇𝒊)

𝟐 (4)

𝒏

𝒊=𝟏

8

Root of the mean square error, RMSE, the square root of the above, one of the most used

𝑹𝑴𝑺𝑬 = 𝟏

𝒏√∑(𝒂𝒊 − 𝒇𝒊)

𝟐

𝒏

𝒊=𝟏

(5)

In 2006 Rob Hyndman [4] proposed a new error calculation that should overcome some conceptual

problems in other error calculations, the Mean Absolute Scaled Error (MASE)

𝑴𝑨𝑺𝑬 = 𝟏

𝒏∑ (

|𝑒𝑡|

1𝑛 − 1

∑ |𝑌𝑖 − 𝑌𝑖−1|𝑛𝑖=2

)

𝒏

𝒊=𝟏

=∑ |𝑒𝑡|𝑛

𝑡=1

𝑛𝑛 − 1

∑ |𝑌𝑖 − 𝑌𝑖−1|𝑛𝑖=2

(6)

Where et is the forecast error, and at the denominator there is the error of the naïve series, if this error

is bigger than 1 then the forecast is less precise than the simple naïve method.

Each error is used in different applications, however one of the most used ones is the RMSE.

2.2 Literature Review

Literature is full of examples of different application of forecasting methods, this section shows the

main applications to port cargo throughput and freight rates forecasting. The technique used for the

forecast in this thesis will then be explained in chapter 5. Such technique will consist of a long time

forecast of ports throughput in Portugal, considering the different classes of cargo (container, general

cargo, ro-ro, dry and liquid bulk), categories (crude oil, cement, metals…) as well as differentiating

export and import (when possible).

This literature review will be structured as follows. Firstly an overview of specific applications of the

methods shown in the previous subchapter (Time series, MLR and ANN). Then the focus will be

moved on papers that apply different methods and study their relative performance, as well as

publications that employ a mixture of methods to reach a result. Afterwards there will be some general

suggestions, insights and in-depth considerations provided by various sources (private publications,

books). Finally a resume is made.

2.2.1 Applications of traditional methods

Regression analysis is one of the most used methods, even so some things can be done to improve

it, for example it is noted in [5] that the relation can change itself during the years, different stages in

the economic development of a country can change the nature of the imports and exports, in their

case the economic development of Taiwan changed the content of the containers throughout the

years, moving from bulky and cheap basic resources to highly refined expensive ones, thus the

amount of TEUs moved by each unity of GDP changes, this nonlinearity can be taken into account to

create a more precise forecast.

Artificial neural networks are broadly used. There are many examples in literature in which some

advice for the work can be found. In [6] it is shown that the performance of neural networks is higher

on longer term forecast, the researchers also suggest that a short term forecast should use a more

9

complicated model with less variables (less input nodes and more hidden nodes) while a long term

forecast should take into account more variables, but analyse them with a simpler network.

References [7], [8] show one of the few application of neural networks to forecast the traffic in ports

while most researches are about freight rates, like for example [9]. Their application is solid, and the

confrontation with linear regression analysis shows how neural networks are more precise when

making long term forecast. ANNs are also used to forecast traffic, like for example in Suez canal [10].

Or to forecast the traffic for a specific terminal, like in [11].

In [12] researchers made a study on the applicability of neural networks, checking literature about

traffic forecast in the past 20 years, it can be seen how neural networks are having a really good

reception even if they are relatively new methods.

Time series forecast is also used, in [13] different methods are applied to the same problem to check

their relative performance, these models are: decomposition model, regression model with seasonal

dummy variable, grey model, hybrid grey model and SARIMA. ARIMA methods are also used in [14]

to forecast freight rates in the dry bulk market.

2.2.2 Comparison of performances and new methods

As shown in [8], ANNs have an advantage over regression analysis when considering container

throughput in Bangkok port, when comparing the actual data with the forecast they get a correlation

coefficient of 0.8620 with linear regression and 0.9509 with ANN. It is worth noting though that linear

regression needs much less data to perform a good forecast, and is also much simpler and faster.

Some authors take another different approach, like in [15], where instead of just focusing on the port

economy the whole macro economy of the hinterland of an extended complex of ports is modelled,

including a model of the logistic layer of the chain. Such a comprehensive approach has some

advantages regarding the overall economy of the hinterland, but it is very scarce regarding the

situation in each and every port of the range.

When using causal methods it is important to choose wisely the variables. Reference [16] presents a

forecast of port traffic in Portugal using GDP to explain most of the cargo throughput, and the

country’s energy consumption to forecast the import of fossil fuels (oil and coal). Some other authors,

like in [17] developed a specific mathematical tool to relate the demand of import and export to

macroeconomic variables.

Many paper use also the so called System Dynamics method, which is a mixture of Linear Regression

with Bayesian networks, examples of these can be found in [18] where it is used to forecast the freight

rates of capsize dry bulk carriers. Another alternative way is the commodity based approach, where

the economics of the hinterland are also considered and modelled, like for example the South African

example in [19]. This approach is similar to the CDE-MPR used in [20] to forecast container

throughput in the ports of the region of the Pearl River delta in China. Another different approach

(error correction model) is used in [21] to forecast the container throughput in Hong Kong.

Considering the incredible amount of variables acting on the shipping system it should be

acknowledged, as in [22], that this is a chaotic system, and thus give up on the idea of using linear

10

models to explain it and instead focus on models that take this chaotic nature into account. Another

way of taking this chaotic behaviour into considerations is by using fuzzy time series like in [23].

2.2.3 In-depth considerations

Some authors focused on the interaction between some parameters of the model, namely the number

of observations used to fit the model and the forecast horizon. Nielsen et al. shown [24] that model fit

and forecast performance cannot be achieved at the same time. Specifically, when trying to make a

model that fits a higher amount of past data the quality of forecast decreases, while a model that

provides a quality forecast comes with an inferior correlation to past data. Thus when forecasting it is

of primary importance to use a well-known and stable method, and blindly increasing the amount of

past data thinking that this will lead to a more precise long-term forecast is counterproductive.

In [3] two interesting ideas are presented, the first is that shipping time series do not follow normal

distribution, when analysing long series of past data it can be seen how the values deviate from the

norm more than 3 standard deviations many more times than expected from a Gaussian distribution.

Statistical data presents “fat tails” which implies the presence of kurtosis and skewness. Also in this

paper short cycle (periodic and non-periodic) variations in freight rate are explained with V-statistic. It

is worth saying that freight rate and port throughput are completely different (even if related) set of

data.

The variables they depend on are different so it is not sure that these conclusions can be applied to

port forecasting.

One of the main problems when forecasting a long-term situation is the inevitable presence of strongly

nonlinear, non-predictable economic shocks (such as the crisis of 1974 and 2008). Many other

variables enter in the picture, such as laws and political decisions. An exhaustive report [25] from

MDS Transmodal about the forecast of traffic in UK ports is available and allows good insights about

what to consider when forecasting long term-situations.

A deep knowledge is needed about the country’s energetic policy, including the amount of oil,

coal and gas used for various purposes (energy generation, refineries), the situation of

national reserves as well as the existence of environmental protective laws and the impact

that they will have in the future. A balance has to be made forecasting the need to import.

Market studies about the situation of the car market, one of the main drivers of Ro-Ro traffic.

The situation of agriculture, to have a better overview on the dry bulk traffic.

The economic agreement with neighbouring countries, to know how much of traffic directed in

other countries can be handled by the country’s ports.

An interesting study has been carried out [26] about the evolution of neighbouring ports in East Africa,

it is shown that in modern times one of the most influencing variables on the growth of one port

instead of another is simply the inland connectivity, ports which are more easy to access via a

different array of transports (road, train, inland waterways) have a much higher development potential.

When forecasting it is important to achieve a balance between the amount of past data used and the

forecasting horizon, a number of papers have been analysed.

11

In a private study on the port of Vancouver , [27], 23 years of past data are used to forecast 35 years

in the future. A scenario-based study about the ports in the Baltic region , [28], uses 12 years of data

to predict 8. In a Vietnamese study using ARIMA, [29], 20 years of past data are used to forecast

traffic for the 6 years to come. Another paper, [5], uses a modified regression analysis, in which 13

years of past data are used to forecast 5 future years.

A couple of studies analysing the throughput of the Hamburg-La Havre range, [15], [30], use a

combination of methods to make previsions, in both cases the time series extend in the past as much

as they do in the future.

Even with these differences, most or the studies analysed forecast the time window analysed is

symmetrical, the number of years ahead is equal to the years abaft.

Two classic books from the 80’s analyse the process of port traffic forecasting and port planning, [31]

and [32], they both insist on the importance of having a deep knowledge of the hinterland of any port

before attempting any forecast, knowing exactly where the goods are coming from and where they are

going is fundamental to attempt a decent forecast. They also note that any port that is trying to

develop a forecast usually uses a combination of basic methods, tailored on the data available and

the characteristics of the hinterland. Given the high sensibility of port throughput to one-of-a-kind,

unpredictable economic events it is wise to prepare some different future scenarios, guessing the

most important socio-politic developments of the future years. These scenarios will then influence the

forecast in different ways.

2.2.4 Summary

Summarizing, the field of publications about forecasting looks wide and fragmented.

Many studies however focus on forecasting freight rates, or other variables which are much more

volatile than port throughput, for example [3], [9], [10], [14], [18], [22]–[24].

Looking at the studies who concentrate on port throughput, most of them are taking into account only

containerized cargo, [5], [7], [8], [13], [27], [29], [19]–[21], [11], [33]–[35]. This focus on containerized

cargo is due to the homogeneity of it. Single container act as unitary cargo, and most authors don’t

even take into consideration the content of each container, the only exception being [5]. This

approach, however simplistic, is effective due to the big variety of cargos shipped via container.

A core group of techniques are widely used and remixed, this includes regression, ARIMA and neural

networks, and each method is applied preferably on series with different characteristics. Most

research papers focus on taking one of these well-known techniques and tailor them to certain

situations (for example [5]), or compare the performance of different methods when applied to the

same problem (for example [8], [13]).

Overall the publications analysed belong mostly to two main groups:

Private firm studies, usually commissioned by port authorities, shipping companies or terminal

managements. These studies are usually not clear about the techniques used to make the

forecast, and focus instead on the surrounding conditions to give a context to the forecast,

this is most likely due to a blend of qualitative and quantitative methods, making the

explanation of the method difficult.

12

Research papers, these studies are very clear about the mathematics behind the forecast.

They focus on modifications of well-known forecasting techniques and their validation and

comparison with different other approaches. Usually these studies don’t give forecasts per-se,

instead the time series analysed are used both to create and to validate the method.

The technique used for this work will be explained in chapter 5, after giving an overview of the

situation of the sector in Portugal.

13

3. GEOGRAPHICAL AND INDUSTRIAL OVERVIEW

The geographical and industrial conformation of Portugal is analysed, to have a deeper understanding

of where the ports are, how they relate to each other and with their hinterlands.

Ports located in the Portuguese west coast have been collectively called “Portuguese Range” [36],

they constitute a multi-port gateway region, situated at the far west end of Europe. They have the

potential to be the gateway for cargo directed towards Western Europe, sitting at the extreme of the

European rail freight corridor n°4. In the past years the throughput of the Portuguese ports grew,

together with the connections between Portugal and Spain.

In Figure 4 a map of Portugal with the analysed ports and industries is shown. The industries

presented on the map are just the main ones of continental Portugal, in each port section a more in-

depth description of the industries present in the hinterland of each port is given.

Figure 4 - Main ports and industries of Portugal

14

It is evident how the range can be divided in 3 groups: Northern ports, Leixões, Viana do Castelo,

Aveiro and Figueira da Foz; Central ports, Lisbon, Setúbal and Sines; Southern ports, Portimão and

Faro.

3.1 Industrial Overview

The main industries present in Portugal are: refineries, cement factories, steel mills, paper factories,

one automobile factory and various power plants.

A list of the power plants running on fossil fuels is shown in Table 1.

Table 1 - List of Portuguese power plants (source:Wikipedia)

Station District Capacity Primary fuel

Lares Power Station Coimbra 826 MW Natural gas Pego I Power Station Santarém 576 MW Coal Pego II Power Station Santarém 837 MW Natural gas Ribatejo Power Station Lisbon 1176 MW Natural gas Sines Power Station Setúbal 1180 MW Coal Tapado do Outeiro II Power Station Porto 990 MW Natural gas Tunes Power Station Faro 165 MW Diesel Barreiro Cogeneration Station Setúbal 64.5 Fuel oil (Cogeneration)

The two refineries of the country are located in Sines and Matosinhos, they are both managed by

Galp. The one in Sines produces: Gasoline; diesel; LPG (liquefied petroleum gas); Fuel oil; naphtha

(used in the petrochemical industry to produce polymers from which plastic, fibres for textiles and

even bubble gum is produced); jet fuel (fuel for airplanes); bitumen (for asphalt and insulate); sulphur

(for pharmaceutical products, farming and pulp whitening). It has a distilling capacity of 10.9 million

tons per year, or 22 thousand barrels per day. The refinery in Matosinhos produces: Fuel oil; base oil;

aromatics; solvents; greases; paraffin; bitumen and sulphur. It has a production capacity of 4.46

million tons per year. (source: galpenergia.com)

Close to the port of Setúbal there is a Volkswagen Autoeuropa factory, the factory was previously

owned by a joint venture between Ford and Volkswagen, eventually in 2008 Ford left the factory and

production declined. Nowadays the factory produces cars almost up to it capacity of 172500 cars per

year. (source:Wikipedia)

An important industrial sector for Portugal is cement. There are various companies producing cement

in the country, the main ones are Secil and Cimpor. Secil has 3 production complexes, one in Setúbal,

producing 2 million tons per year, one in Leiria producing 1.35 million tons per year and one in

Alcobaça producing 380 thousand tons per year. (source:secil.pt) Cimpor has several factories

around the country and abroad, one in Loule producing 350 thousand tons per year, and other three

in Lisbon, Figueira da Foz and Coimbra.

Also paper production plays an important role in Portugal, for the economy in general as well as for

ports. There are 2 big producers of paper in the country: Portucel Soporcel (now called Navigator

Company) and Altri. Portucel Soporcel is one of the biggest paper producer in Europe, it manages 3

15

big factories in Setúbal (510,000 tons/year), Figueira da Foz and Cacia. The total production of the

company is around 1.6 million tons of paper per year (source:thenavigatorcompany.com). Altri has 3

subsidiaries companies that manage paper factories in Portugal: Celbi, Celtejo and Caima. Together

they produce 790 thousand tons of paper and pulp per year. (source:altri.pt)

Steel production in Portugal is done by recycling metal scraps, the procedure is convenient because it

demands much less energy than the operation of a traditional blast furnace. The main steel mills of

the country is managed by Lusosider, it is located in Seixal (between Lisbon and Setúbal), it produces

550 thousand tons of laminated steel per year (source:lusosider.pai.pt).

3.2 Lisbon

Lisbon is the capital of Portugal, the city is located along the northern shore of the estuary of the Tejo

River, and the estuary is very broad, creating a natural bay where the port terminals are distributed. It

is a landlord port, the port authority owns the land on which the terminals are located, but the day-to-

day management of the activities is carried by private companies, every terminal is governed by a

different company. In the northern side there are container, RoRo and general cargo terminals, plus 2

cruise terminals and recreational docks. On the southern side of the river there are several dry and

liquid bulk terminals, as well as some small general cargo ones. Today the expansion of Lisbon’s port

is hindered by the city around it, most terminals are completely surrounded by urban development so

there is no space left where to expand the quays and superstructure. Even so Lisbon is still one of the

main ports of Portugal, handling 14% of the national cargo.

Two of the container terminals of the port have been recently bought by a Turkish company, Yıldırım.

The container throughput in these two terminals have been stagnating for the past 10 year, so a

possible evolution is now in the hands of this company.

16

Figure 5 - Map of the port of Lisbon

Lisbon works as a hub for the many industries located along the two sides of the Tejo River. Most

heavy industries are located on the southern side of the river (steel mills, chemical factories) while on

the northern side and along the course of the river there are paper and fertilizers factories, as well as

several power plants.

3.3 Leixões

Leixões is the main port of northern Portugal, it is situated on the Atlantic coast 4 km north of the

estuary of the Douro River, where the city of Porto is. It is an artificial landlord port, in the bay all the

terminals are distributed: dry and liquid bulks, containers, general cargo, RoRo and cruise. Leixões is

the second national port, handling 24% of the national cargo. Leixões’s container terminal is now

been used at its maximum capacity, expansion work are already being carried out. Leixões is one of

the Portuguese ports that has been growing more in the past years.

Figure 6 - Map of the port of Leixões

The northern region of Portugal is densely populated. In the hinterland of Leixões there are several

industries: a steel mill, a refinery, paper factories, some caves and wood and cork harvesters.

Leixões is at the end of the navigable Douro waterway, thus acts as a hub for all the industries located

along its course, it is important noting that the Douro waterway comprises a good part of Spanish

territory.

3.4 Sines

Sines is a port located in the south of Portugal, along the Atlantic coast. It is an artificial port, it came

into operation only in 1978. Given the natural deep waters surrounding the port it is the port of choice

for the bigger vessels docking in Portugal. The container terminal, opened in 2004, is the most

important in Portugal, its importance as a transshipment hub is rapidly growing. Currently Sines

handles 45% of the national cargo.

17

Sines is the main energetic hub of Portugal, with the only coal power plant of the country located

close by and big refineries and chemical production industries also around the city. In its hinterland

there are several marble and copper caves, as well as paper factories.

Figure 7 - Map of the port of Sines

3.5 Setúbal

Setúbal is a city located 40 kilometres south of Lisbon, it sits at the estuary of the Sado river. The port

area develops between 2 protected natural parks, but even so this port has plenty of space to account

for future development. It is a mixed landlord/private port, with most terminals owned by the port

authority and some smaller ones privately owned. Upstream from the port there is also the Lisnave

shipyard, once the biggest European shipyard, today it is mainly used for reparation. The port area

extends over the northern shore of the Sado, except the SECIL cement terminal which is right at the

estuary of the river. This port has very good intermodal transport capability due to its position along

the north-south and east-west axes. In 2014 the port handled 10% of the national cargo.

Setúbal, given its closeness with the AutoEuropa factory, has the biggest RoRo terminal of the

country, which is able to dock the biggest car carriers existent today. Various cement factories are

present around Setúbal, as well as steel mills, paper factories and copper and marble mines in the

hinterland.

18

Figure 8 - Map of the port of Setúbal

3.6 Aveiro

Aveiro is a city located 70kms south of Porto, the port is located in the inland lagoon of Ria de Aveiro,

it is a protected natural area, and thus port development has to be extremely careful. It is the most

recent port infrastructure of the country, thus it is well organized without congestion issues.

Figure 9 - Map of the port of Aveiro

In the hinterland of the port there are mainly cement factories, chemical refineries, paper and glass

factories.

3.7 Figueira da Foz

Figueira da Foz is a city located in between Porto and Lisbon, the port develops around the estuary of

the Mondego river, it is mostly a local spoke port, dedicated to dry bulks and general cargo.

19

Figure 10 - Map of the port of Figueira da Foz

The main factories in the hinterland are paper, glass and cement producers.

3.8 Viana do Castelo

Viana do Castelo is a small town in the far north of Portugal, situated at the end of the Lima River it is

mostly a local port, handling mostly general cargo and dry bulks, as well as some liquid bulks.

The industries presents in the hinterland of Viana are a paper factory, a mine and a factory that

produces wind turbines.

Figure 11 - Map of the port of Viana do Castelo

3.9 Southern Ports

Algarve is the southernmost region of continental Portugal, there are two small ports along the south

coast, one in Portimão and one in Faro.

20

Portimão has a cruise quay, shown in Figure 12, given the high touristic value of the region it is a

heavily-used terminal.

Faro mostly uses its dry bulk terminal, shown in Figure 13, to export cement produces in the nearby

factory.

Figure 12 - Cruise quay in Portimão

Figure 13 - Port of Faro

21

4. ANALYSIS OF CARGO THROUGHPUT IN PORTUGUESE

PORTS

To perform the forecast a variety of data was gathered from different sources, in this chapter the

sources will be shown and an explanation of the data will be given, port by port, industry by industry.

Data gathered includes:

Tons loaded and unloaded, in the main ports of Portuguese mainland (Lisbon, Leixões, Sines,

Setúbal, Aveiro, Viana do Castelo, Figueira da Foz and Faro), subdivided in categories (dry

bulks, liquid bulks, general cargo, containers and ro-ro), as well as the cruise passengers,

where present;

The main categories of cargo handled in the different ports, to have a deeper understanding

of which are the main drivers of port throughput;

Econometric indicators, related to the Portuguese (and world) economy, like Portuguese

GDP, population, inflation, domestic consumption and finally world GDP;

Industrial indicators, the yearly performances of different sectors of the industry related to the

main goods traded in ports, this includes tourism, alimentary, metallurgic, cement and glass,

petroleum and chemical industry, as well as the production of vehicles and the yearly

production of electricity, subdivided by the different sources of energy.

Data was gathered from different sources:

Instituto Nacional de Estatistica (INE) [37], provides yearly publications called Estatisticas dos

Transportes e Comunicaçoes and Estatisticas da Produçao Industrial. From here it was

retrieved the amount of tons loaded and unloaded, for each port, for each category as well as

the industrial indicators, INE publications go from 2001 to 2014.

Port authorities publish a yearly account of the port throughput, all the available information

from the past years was gathered, given the individuality of each port authority the data is not

homogeneous, for some ports only 5 years of data is available, for some others there is no

distinction between loaded and unloaded cargo. The ports analysed are Lisbon [38], Leixões

[39], Sines [40], Setúbal [41], Figueira da Foz [42], Aveiro [43], Viana do Castelo [44], Faro

and Portimão [40].

Economic indicators were taken from 3 different websites: PorData [45] a Portuguese data

aggregator, OECD data [46] the database of OECD countries and the International Monetary

Fund [47].

In this section an overview of cargo throughput in each port is given. For extended data on the main

cargos within each type of transportation refer to Appendix B. For each port in consideration only the

most significant data is analysed.

22

4.1 Port of Lisbon

Lisbon is mainly a dry and liquid bulk importer, as well as an important container port. From Figure 14

it can be seen how dry bulk throughput has been more or less stable in the past years. The main

imported dry bulks are cereals (corn, soy, wheat and canola) and scrap metal, together accounting for

84% of the imports in 2014. The main exported dry bulks are cement, sand, fertilizers, forage and

malt, worth 91% of export in 2014.

Figure 14 - Dry bulks throughput in the port of Lisbon

Figure 15 shows the liquid bulk throughput in the port of Lisbon. The throughput decreased around

2004 and 2007 but overall it continued stable since 2001. The main imported liquid bulks are

ammonia, diesel and fuel oil, together accounting for 71% of the imports in 2014. The main exported

liquid bulks are fuel oil, biodiesel and vegetable oils, accounting for 94% of exports in 2014.

Figure 15 - Liquid bulks throughput in the port of Lisbon

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23

Figure 16 shows how the general cargo throughput decreased steadily in the past 9 years, this can be

due to the progressive containerization of cargo as well as the change in the transportation method of

cement, which went gradually from general cargo to dry bulk. The main cargos handled are: plastic

scraps, cement, cars and bananas.

Figure 16 - General cargo throughput in the port of Lisbon

Figure 17 shows the container throughput. It should be noted that unlike the other categories of cargo

an analysis of what is shipped is more difficult. Here the most traded items, which are milk and cream

for the unloaded, and paper unloaded, account only for the 6% of the total. It is important also to note

that even if loaded and unloaded tons are substantially different, the TEU throughput is equal between

loaded and unloaded, this means that many of the unloaded containers are actually empty, unloaded

in the port only to be filled with cargos and loaded on a ship once again.

Figure 17 - Container throughput in the port of Lisbon

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Lisbon is also a major port for cruise ships, the graph in Figure 18 shows how the number of

passengers in transit in the port has grown steadily in the past years, and it should be taken into

account also that soon a new cruise terminal will open. To have a better reading of the graph the

number of passengers in transit has been divided by 10.

Figure 18 - Cruise passengers throughput in the port of Lisbon

4.2 Port of Leixões

Leixões is mainly a dry and liquid bulk importer, as well as an important container port. Dry bulk

throughput, shown in Figure 19, has been more or less stable in the past years. The main imported

dry bulks are wood chips, corn, metal scrap, wheat and sugar, together accounting for 87% of the

imports in 2014. The main exported dry bulks are cobblestones and wood chips, worth 89% of export

in 2014, in the past also cement was a worth noting export;

Figure 19 - Dry bulks throughput in the port of Leixões

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The liquid bulk throughput is shown in Figure 20. The main imported liquid bulks are crude oil and oil

products, accounting for 95% of the imports in 2014. The main exported liquid bulks are oil products,

aromatics and lubricants, accounting for 99% of exports in 2014. In the past years imports decreased,

mostly due to shrinking oil products import, while export steadily increased, duplicating in the last 10

years.

Figure 20 - Liquid bulks throughput in the port of Leixões

General cargo throughput, shown in Figure 21, had a shift around 2008-2010. Before it was mainly

driven by imports and after by exports. Imports are now driven by unprocessed iron and steel, which

accounts for 89% in 2014. In past years there was an important share of imports given by wood in

various forms, which disappeared starting from 2007, year when wood started appearing in the dry

bulk importation. Exports are also governed by iron and steel products, which explains 86% of traffic

in 2014.

Figure 21 - General cargo throughput in the port of Leixões

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Figure 22 shows the container throughput in the port of Leixões. Here, like in Lisbon, the difference

between loaded and unloaded tons hints at the import of empty containers. Again, many different

cargos are carried in containers, the main component of export being paper with the 8% of total and

plastic is the main import with 10% of total. The total throughput reached in the past years the

maximum capacity of the terminal, 650000 TEUs, but work for expansion are already underway.

Figure 22 - Container throughput in the port of Leixões

Furthermore, in Leixões there is a cruise terminal, the data about cruise passengers is shown in

Figure 23. For Leixões the number of passengers in transit has been divided by 100. The amount of

passengers in transit has been growing continuously.

Figure 23 - Cruise passengers throughput in the port of Leixões

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4.3 Port of Sines

Sines has the biggest container terminal in the country, but also liquid and dry bulks are very

important for the port. Dry bulk throughput, shown in Figure 24, is dominated by imported coal, which

was responsible of 97% of throughput in 2014, coal is mainly used to power the nearby power plant.

Figure 24 - Dry bulks throughput in the port of Sines

Figure 25 shows the Liquid bulk throughput. The main liquid bulks are: crude oil and liquefied natural

gas (LNG) accounting for 75% of the unloaded tons in 2014. Oil products account for 94% of loaded

tons in 2014. Sines is the only port in Portugal with a LNG terminal, the tons handled in the last years

have been stable. Crude oil trade decreased slightly while oil products increased slightly.

Figure 25 - Liquid bulks throughput in the port of Sines

General cargo throughput, shown in Figure 26, has low impact on the port throughput, but in 2007-

2008 there was a shift from loaded to unloaded, it was not possible to find clear data about which

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goods are being loaded in ships. However, considering the industries present in the hinterland of

Sines, it should most likely be paper from the near paper factories.

Figure 26 - General cargo throughput in the port of the port of Sines

Sines deep water container terminal is the biggest container terminal in Portugal, it was open only in

2004 but the handling is increasing rapidly. No data is available about the content of containers

loaded and unloaded, but Sines is mostly a transshipment port, last year’s transshipped containers

accounted for 85% of the total tons of containers moved.

Figure 27 - Container throughput in the port of Sines

4.4 Port of Setúbal

Setúbal is the main Ro-Ro port in Portugal, but dry bulks and general cargo are also important for the

port. Dry bulk throughput is shown in Figure 28. Import has been stable in the last years, here the

most important items are coal, wood, fertilizers and agricultural products, and together they account

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for 90% of the total in 2014. The export on the other hand have been growing, with cement, clinker,

marble and copper ore accounting for 92% of loaded tons in 2014.

Figure 28 - Dry bulks throughput in the port of Setúbal

Liquid bulks is shown in Figure 29. Imports have been steadily declining in the last years, with acids

and petroleum products accounting for 80% of the total in 2014. This decline is due to the closing of

the nearby thermal power plant, which was importing fuel through the port. Loaded cargo is practically

not existent.

Figure 29 - Liquid bulks throughput in the port of Setúbal

General cargo throughput is shown in Figure 30. Also in Setúbal there has been a shift around

2007/2008 from import to export, imports are mostly unprocessed iron and steel and wood, 98% of

the total, while exports processed iron and steel products and bagged cement, accounting for 97% of

the total.

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Figure 30 - General cargo throughput in the port of Setúbal

Container throughput, shown in Figure 31, has been increasing steadily in the past years, the only

data available about the content of containers is from the past couple of years. Containers are mainly

being loaded on ships, and 37% of the loaded tons in 2014 are paper.

Figure 31 - Container throughput in the port of Setúbal

Setúbal is the main Ro-Ro port in Portugal, the graph in Figure 32 shows the Ro-Ro throughput of

Setúbal. It can be seen how the traffic has been decreasing steadily. Until 2009 two car factories were

active, then the Opel factory closed and the Auto Europa one remained.

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Figure 32 - RoRo throughput in the port of Setúbal

4.5 Port of Aveiro

Aveiro handles mostly dry bulks and general cargo, as well as some liquid bulks. Dry bulk throughput

is shown in Figure 33. The main dry bulks handled are cement, clay and carbonate loaded. Glass

scraps, corn and wood chips unloaded, together accounting for 75% of the total in 2014. The total

amount has been increasing steadily in the past years, with exports having a higher weight.

Figure 33 - Dry bulk throughput in the port of Aveiro

Liquid bulk throughput, shown in Figure 34, has been rising in the last years, with oil and chemical

products being the main cargos, adding up to 75% of the total.

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Figure 34 - Liquid bulk throughput in the port of Aveiro

General cargo throughput is shown in Figure 35. The main components here are wood, chemical

wood paste, cement and metals, accounting for 91% of throughput in 2014.Cement and chemical

wood paste, loaded cargo, account for the inversion between loaded and unloaded cargos that

happened around 2009.

Figure 35 - General cargo throughput in the port of Aveiro

4.6 Port of Figueira da Foz

Figueira da Foz handles mostly dry bulks and general cargo. Dry bulk throughput, shown in Figure 36,

have been increasing in the last years, the data available for the port doesn’t differentiate between

loaded and unloaded cargo, even so the most notable items handled by the port are: glass scraps and

wood chips, accounting for 84% of the total, Clay and salt are exported, while the scraps and wood

chips are imported to be refined in nearby factories.

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Figure 36 - Dry bulks throughput in the port of Figueira da Foz

General cargo throughput is shown in Figure 37. Here the main items are wood, wood paste and

cement, accounting for 98% of the total. Separating imports and exports is straightforward, with wood

unloaded while wood paste and cement are loaded.

Figure 37 - General cargo throughput in the port of Figueira da Foz

4.7 Port of Viana do Castelo

Viana do Castelo is a small port handling cargo on short sea routes. Detailed data is not available, but

anyway some conclusions about the traffic can be made. Dry bulk throughput, shown in Figure 38,

has been continuously decreasing in the past years, minerals and cement are the main cargos

unloaded, they decreased drastically in the past years and currently the port limits itself to importing

cement. Nowadays the exports are minerals from nearby mines.

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Figure 38 - Dry bulk throughput in the port of Viana do Castelo

The liquid bulk throughput is shown in Figure 39, here Oil products account for the total of the

throughput, the switch between loaded and unloaded can be due to an increase in production of the

nearby refinery of Leixões, which allowed the port to stop importing oil products and start exporting

them.

Figure 39 - Liquid bulk throughput in the port of Viana do Castelo

General cargo throughput, shown in Figure 40, is driven mainly by paper from nearby factories as well

as components of wind turbines, produced in the area.

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Figure 40 - General cargo throughput in the port of Viana do Castelo

4.8 Port of Faro

The port in Faro is much smaller than all the other ports analysed. Dry bulk throughput, shown in

Figure 41, is very unstable. Loaded tons are mainly minerals and food products, while in the past

minerals and metals were accounting for most of the unloaded tons.

Figure 41 - Dry bulk throughput in the port of Faro

General cargo, shown in Figure 42, is dominated by the export of the cement, started in 2011.

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Figure 42 - General cargo throughput in the port of Faro

4.9 Port of Portimão

In Portimão, the subject of study is the cruise terminal. Cruise passenger throughput is shown in

Figure 43. The throughput has been very unstable in the years analysed, but the recent opening of a

new terminal will most likely increase the throughput.

Figure 43 – Cruise passenger throughput in the port of Portimão

4.10 Port throughput recap

After examining all the ports some general conclusions are:

Export has been ever-rising in the past 14 years, while import remained stable, the total

throughput of the ports analysed is shown in Figure 44.

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Traffic is slowly moving towards the biggest ports, with the smaller ones handling mostly

national trade routes (hub and spoke model), different ports also specialize in different type

of cargo, the main example of this be Sines with containers and Setúbal with RoRo.

Containerization is driving the general cargo handling down, imports which once were

coming through general cargo ships are now coming in containers, except for cargos which

are handled in bulks which are bigger than containers, like wood, cement, paper and steel

products; this articles are driving the general cargo exportation up.

While the amount of TEUs loaded and unloaded is the approximately equal in all the years

analysed, the amount of tons loaded is consistently 40% higher than the tons unloaded, this

because many empty containers have to be unloaded from ships in order to load them with

goods to be exported.

Liquid bulk unload is also decreasing, due to the increase of alternative sources of energy,

while the export of petroleum products is increasing, same thing happens with dry bulks, but

here cement is driving exports up.

Passenger traffic is ever increasing as Portugal is becoming more and more a well-known

touristic destination.

Figure 44 - Overall throughput of Portuguese mainland ports (tons)

Table 1 shows the difference in traffic between 2001 and 2014, to see which types of cargo grew in

this period.

Table 2 - Throughput growth overview

Growth Overview (Million Tons) 2001 2014 Difference

Lisbon Dry Bulk 4.89 5.23 0.33

Liquid Bulk 1.71 1.47 -0.24

General Cargo 0.49 0.08 -0.41

Containers 3.50 5.07 1.56

Leixões Dry Bulk 2.07 2.32 0.25

Liquid Bulk 7.36 7.83 0.47

General Cargo 0.83 1.02 0.19

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Sines Dry Bulk 4.68 4.89 0.21

Liquid Bulk 14.90 18.08 3.18


Containers 0.00 11.95 11.95

Setúbal Dry Bulk 2.77 3.18 0.41

Liquid Bulk 1.67 0.38 -1.29



RoRo 0.47 0.24 -0.23

Figueira da Foz Dry Bulk 0.22 0.85 0.63


Aveiro Dry Bulk 1.14 2.45 1.31

Liquid Bulk 0.43 1.12 0.70


Viana do Castelo Dry Bulk 0.52 0.16 -0.36

Liquid Bulk 0.05 0.03 -0.02

General Cargo 0.49 0.26 -0.23

Faro Liquid Bulk 0.17 0.01 -0.16


Total 54.95 81.87 26.92

It is clear how container throughput is driving the growth, on the total 27 million tons of growth, 18 are

coming from containerized cargo. The growth in general cargo and dry bulks is mostly driven by

cement and paper. Worth noting is the decrease in liquid bulk traffic in Setúbal, this is due to the

closing of a nearby power plant in the past years (wildcard event).

Table 2 shows the main imported and exported goods in each port, in 2014. The data is taken from

the INE publication about transport, the cargoes are subdivided following the NST2007 European

classification.

This categorization of cargos is not consistent in the time window analysed, two different methods are

used before (NST/R) and after 2007 (NST2007), a detailed confrontation of these 2 methodologies

can be found in Appendix A. While in the following list the categories of NST2007 (the ones used

throughout the graphs and tables present in this thesis) are explained:

1. Products of agriculture and forest, this includes cereals, potatoes, fruits, meat and fish, as

well as wood.

2. Crude oil and LNG.

3. Minerals, more specifically any non-energetic products of the extractive industry.

4. Alimentary products, this includes food, drinks and tobacco.

5. Textiles and leather products, this category is not included in the study, given its small weight

in the total throughput.

6. Wood, cork and paper.

7. Coal and oil products, this category includes both regular coal and coke coal.

8. Chemical products and plastic, this category includes organic and non-organic chemical

products, fertilizers, pharmaceuticals, plastic and rubber.

9. Non-metallic mineral products, this category includes mainly cement and glass.

39

10. Basic metals, this includes processed and non-processed parts of iron and steel (does not

include machines).

11. Machines, this category is also not analysed given the small weight.

12. Transport material, this category includes different kinds of vehicles, it is not directly analysed,

and instead the focus is placed on the RoRo throughput of Setúbal, main inlet and outlet of

vehicles in the country.

13. Furniture, also not analysed.

14. Secondary raw materials, this includes wood, metal and glass scraps.

15. Mail, not analysed.

16. Equipment used in the transport of material, not analysed.

17. 18. 19. 20. Other goods, not analysed.

XX. Unknown cargo, these are the containers transshipped in Sines.

In the table, the percentage presented tells how much of the total import (or export) is given by the

categories shown. On the right column the related industry is presented.

Table 3 - Main categories of cargo handled in Portuguese ports in 2014

Most traded goods NST 2007

Th.Tons Item(s) Industry related

Lisbon Exports 4 1326 Food Products Alimentary

perc. 77.3 9 893 Cement and sand Cement

3 459 Minerals Extractive

8 459 Chemical products Chemical

6 285 Paper Paper

1 266 Prod. of Agricult. and Forests

Alimentary/Paper

Imports 1 3328

Prod. of Agricult. and Forests

Alimentary/Paper

perc. 87.5 7 931 Coke and Oil Products Refineries/Cement

14 779 Scrap metal Metallurgic

4 728 Food Products Alimentary

8 417 Chemicals Chemical

Leixões Exports 7 1992 Products Refineries

perc. 79.0 10 974 Processed steel Metallurgic

9 837 Cement Cement



6 554 Paper Paper

Imports 2 4021 Crude Refineries

perc. 82.2 7 1413 Oil Products Refineries

14 1104 Wood/Glass/Metal scraps Paper/Glass/Metallurgic

1 917 Prod. of Agricult. and Forests

Alimentary/Paper

8 597 Chemicals Refineries


Sines Exports 7 6115 Oil Products Refinery

perc. 92.1 xx 5084 Unknown Transhipment

2 373 Crude Refinery

3 355 Copper/Marble Extractive

8 324 Chemical Products Chemical

Imports 2 8417 Crude + LNG

Refinery + LNG energy prod.

perc. 98.6 7 7626 Coal and Oil Products Refinery + Coal energy

40

prod.

xx 5030 Unknown Transhipment


1 64 Cereals Alimentary

Setúbal Exports 9 3118 Cement Cement

perc. 89.1 10 689 Processed steel Metallurgy

3 472 Copper/Marble Extractive

6 310 Paper Paper

12 148 Transport material Automobile

Imports 10 633 Processed steel Metallurgy

perc. 79.4 8 633 Chemicals Chemical

7 462 Coke/Products Cement/Refineries

1 218 Prod of Agricult. and Forests Alimentary

6 199 Wood Paper

Figueira da Foz Exports 6 836 Paper Paper

perc. 99.8 3 272 Minerals Extractive

14 81 Wood/Glass/Metal scraps Paper/Glass/Metallurgy

1 73 Prod of Agricult. and Forests Alimentary/Paper

9 19 Cement Cement

Imports 1 363 Prod of Agricult. and Forests Alimentary/Paper

perc. 99.9 14 240 Glass scrap Glass


6 67 Wood Paper

10 4 Processed steel Metallurgy

Aveiro Exports 9 1204 Cement Cement

perc. 95.5 6 362 Paper Paper

14 254 Glass scrap Glass


3 131 Carbonate Extractive

Imports 8 591 Chemical wood paste Paper

perc. 90.9 7 525 Coke/Products Refinery/Cement

10 400 Processed steel Metallurgy

1 313 Prod of Agricult. and Forests Alimentary/Paper


Viana do Castelo Exports 6 127 Paper Paper

perc. 99.2 7 71 Coke and Oil Products Refinery/Cement

11 66 Machines Wind turbines


Imports 6 48 Wood Paper

perc. 99.3 9 46 Cement Construction

10 32 Processed steel Construction

3 23 Minerals

Faro Export 9 334 Cement Cement

perc 99.0

To give a better understanding of which categories of cargo drive the growth in which ports some

graphs are provided, the throughput in the port of Sines is shown in Figure 45, Leixões in Figure 46

and Lisbon in Figure 47. It can be seen how Sines specializes on a few different cargos while Leixões

and Lisbon have a much more varied throughput, this is mostly due to the power plant and refinery

close to Sines, as well as the metropolitan areas surrounding Leixões and Lisbon.

41

Figure 45 – Cargo throughput in the port of Sines, split by NST2007 categories

Figure 46 - Cargo throughput in the port of Leixões, split by NST2007 categories

Figure 47 - Cargo throughput in the port of Lisbon, split by NST2007 categories

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Secondary raw Mat. Unknown

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42

This data about NST2007 categories is then summed at national level. In Figure 48 are represented

the loaded tons on ships, which have increased significantly, per categories. The most represented

categories are cement/glass and oil products. It is important to note how, even if category 7 comprises

both coal and oil products, coal is only imported in Portugal. A big part of the products loaded on ships

are traded with other national ports (in 2014 40% of oil products are in national transit). Other

important categories are: transshipped containers, wood/cork/paper, alimentary products and

minerals.

In Figure 49 are represented the unloaded tons from ships, per categories. The most represented

goods here are crude oil, LNG, coal and oil products (categories 2 and 7).

This indicated how most of the throughput of ports is driven by energetic demand, consumable

products have less importance.

Figure 48 - Cargos loaded on ships in Portugal, split by NST2007 categories

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Figure 49 - Cargo unloaded from ships in Portugal, split by NST2007 categories

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44

5. METHODOLOGY FOR CARGO THROUGHPUT FORECASTING

After carrying out the state of the art on forecasting and analysing all the data available a forecast

methodology has been developed.

First, it was tried to use Artificial Neural Networks (ANN), but in spite of having a significant amount of

data available, each time series is quite short (13 years), making ANN impossible to use. Afterwards

the suitability of Auto Regressive Integrated Moving Average (ARIMA) method was investigated, which

also resulted impossible to use. A quick comparison of various papers shows that ARIMA is usually

applied on time series with at least 50 data points, while ANN are applied to series with more than 100

points. These techniques also excel when applied to series with an important fluctuant component.

This is not our case since the data analysed has, mostly, a linear trend behaviour.

Considering all the data available the possible method to use is multiple linear regression, which could

map the performance of the industries and GDP to that of the throughput of ports. Here it is important

to remember that the throughput data is available for each port, while the industrial and GDP data is

available at national scale. So the regression is to be used on the national aggregate of throughput for

each category.

Each cargo throughput time series is analysed together with the industrial and economic time series

which can be related to it.

5.1 Explanatory Variables

To assess industrial performance, sales values have been used. For each year there are publications

available from INE reporting the revenues of different industries divided between internal, European

and world markets. Unfortunately the industrial data is available only as sales in Euros, for the

purpose of this thesis it would be more useful to have industrial sales in tons, to avoid being

influenced by the varying price of products throughout the years analysed. To mitigate this problem

the sales have been corrected for inflation, with constant prices at 2014, however this does not

remove completely the sensitivity to changing prices of goods due to variations in the world market.

The industries analysed are subdivided in two groups. The first one is that of: heavy industries, shown

in Figure 50, encompassing iron and steel (metallurgy), vehicles, oil product refineries, chemical

refineries and cement/glass. It can clearly be seen how the 2008 crisis affected all of them.

Light industries, shown in Figure 51, covering alimentary, wood, paper and tourism (which is more an

activity rather than an industry). Here the influence of the 2008 crisis is less visible than in the heavy

industries, except for tourism the other industries appear more or less stationary.

45

Figure 50 - Heavy industries sales (Constant price 2014)

Figure 51 - Light industries sales (Constant price 2014)

Data about energy production and consumption was also gathered from the PorData website, and is

shown in Figure 52. It can be seen how oil and coal-based energy consumption decreased gradually

in the years, while the importance of renewable energies is continuously increasing.

Figure 52 - Energy consumption in Portugal (TEP)

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Coal Oil LNG Renewable

46

Economic data was gathered from the OECD Data [46], and the PorData websites [45]. In Figure 53

are shown the Portuguese GDP and domestic consumption. Domestic consumption increased slightly

in the observed years, passing from 64% to 68% of the GDP from 2001 to 2014. It is interesting to

note how, when corrected for inflation, the Portuguese GDP appears almost constant. A prolonged

crisis after 2008 brought the GDP down to its 2001-values.

Figure 53 - Portuguese GDP and Domestic consumption (Constant price 2014)

Together with the Portuguese economic indicators, also the GDP of its main economic partners: EU,

China and USA is retrieved. For all the GDP time series gathered the OECD website provides long

term forecasts up to 2060.

In Figure 54 the GDPs of China, USA and European Union are compared, it is evident how the whole

Eurozone has almost the same reduced growth as Portugal, while China exhibits the well-known

significant growth.

Figure 54 - GDP of the main economic partners of Portugal (Constant price 2014)

The variables showed till now will be used when forecasting the various categories of cargo, however

one category is left, transshipped containers.

Transshipped containers cannot be related to any aspect of the Portuguese economy. Instead they

depend on a series of variables such as: port handling speed, terminal capacity, shipping companies’

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47

strategies and location of the port in relation to the main international trade routes. To have a better

understanding of this particular category, data about the main transshipment hubs of the

Mediterranean Sea have been gathered, they are shown in ¡Error! No se encuentra el origen de la

referencia.¡Error! No se encuentra el origen de la referencia.. The behaviour appears the same

for all ports, a constant growth (with different speed) stopped only when the throughput reaches the

terminal capacity, point beyond where the terminal performance usually degrades rapidly. In Sines the

maximum capacity has not been reached yet, even so the port authority is already working on an

expansion of the terminal, and subsequent expansions are already programmed. If the expansion

works will be carried out on time the terminal will never reach its maximum capacity, so it is

considered that the growth will continue undisturbed.

5.2 Multiple Linear Regression

After gathering industrial and economic data, the time series are analysed, looking for correlations that

can be used for the forecast.

Table 4 shows the cargo throughput category series together with their explanatory variables.

Table 4 - Cargo throughput category time series and their explanatory variable

Cargo category Explanatory Variable R2

Products of Agriculture

and Forest

Unloaded: Paper and Alimentary industrial sales

Loaded: None

Un:0.279

Crude oil and LNG Unloaded Crude oil: Refinery sales, Oil energy consumption

Unloaded LNG: LNG energy consumption

Crude:0.836

LNG:0.896

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2

3

4

5

6

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

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00

20

01

20

02

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03

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20

08

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09

20

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20

11

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20

13

20

14

20

15

Mill

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s

Algeciras 5mil Valencia 7.1mil Sines 1.7mil

Tangier Med 4.3mil Gioia Tauro 4.2mil Marsaxlokk 3.5mil

Figure 55 - Comparison of TEU throughput between Sines and the main transshipment ports of the western

Mediterranean. The number in the legend indicates the throughput capacity of the port in TEUs/year.

48

Minerals None

Alimentary Products Unloaded: Domestic consumption

Loaded: Alimentary industry foreign sales

Un:0.211

L:0.847

Wood, Cork and Paper Unloaded: Paper industrial sales

Loaded: Paper and wood industry foreign sales

Un:0.003

L:0.414

Coal and Oil Products Unloaded Coal: Coal energy consumption

Unloaded Products: Oil product industry sales

Loaded Products: Oil product industry sales

Un.C:0.848

Un.P:0.312

L:0.023

Chemical products and

Plastic

Unloaded: Chemical industry sales

Loaded Chemical industry foreign sales

Un: 0.133

L:0.640

Non-metallic mineral

products

Unloaded: None

Loaded: Cement/Glass industry foreign sales

L:0.894

Metallic Products Unloaded: None

Loaded: Metallic industry sales

L:0.262

Transport Material Unloaded: None

Loaded: Vehicle industry foreign sales

L:0.646

Secondary Raw

Materials

Unloaded: Cement/Glass, Metallurgy and Paper industry

sales

Loaded: None

Un:0.888

Unknown Cargo See ¡Error! No se encuentra el origen de la referencia.

Cruise Passengers Tourism income 0.313

Linear regression describes the dependent variable y as a function of n independent variables x as:

𝑦 = 𝑎1𝑥1 + 𝑎2𝑥2 + ⋯ + 𝑎𝑛𝑥𝑛 + 𝑏 (1)

Where an and b are parameters specific to each model. In this application linear regression is carried

out with Microsoft Excel, the function is fitted using the least squares method.

When n equals one the method is called linear regression, when n is greater than one the method is

multiple linear regression.

Correlation between these time series is investigated. Some of them result strongly correlated (R2>0.9)

like unloaded secondary raw materials, containers and loaded non-metallic mineral products. Some

other result not related (R2<0.5) like transport material, wood/cork/paper and loaded alimentary

products.

Regardless of the time series correlating or not, to make a forecast using multiple linear regression,

forecasts of the explanatory variables are needed. The forecasts need to be consistent for the future

time window analysed (10 years). Reliable forecasts for the industrial explanatory variables needed

could not be found, this makes forecasting with this method impossible.

However the forecasts available for the GDP make it possible to forecast the container throughput.

This study has already been carried out, and will be published as [48].

49

5.3 Linear Interpolation

Given the impossibility of using more sophisticated methods, due to lack of detail or of time span of the

existing statistical data, the adopted approach is to simply project linearly the present trend to the

future for each category of cargo.

Even if it is simple, this method permits taking into account some specific behaviour of the time series

that linear regression would have not. For example, the economic crisis of 2008 scrambled the data,

changing considerably the trend for more than one category of cargo. In the time series in which this

happen it will be taken into account using only a sub-set of data to calculate the trend.

The trend will be calculated using the LINEST function of Microsoft Excel.

After making a forecast for each category of cargo it is necessary to split the results between the

various ports. The past trend is analysed, looking for patterns in the national distribution of cargo

throughput in each category. Relationships between ports are investigated, checking how much of the

total cargo goes through each port. Even when the throughput in the various ports is unstable, the

throughput of the two big groups of ports has a smoother variation. This property is taken into account,

keeping the balance between the two groups. These considerations are then used to estimate the

percentage of cargo that will pass through each port in the future.

50

6. RESULTS

After developing a forecast for each category of cargo the result is split between ports according with

its current market share. In the next pages the various forecasts are shown through graphs, while

comprehensive tables can be found in the Appendix B.

6.1 Products of Forest and Agriculture

This category includes cereals, fruits and other natural food products, as well as raw wood for various

purposes. The throughput up to 2014, as well as the forecast up to 2024 is shown in Figure 56

(loaded tons) and Figure 57 (unloaded tons).

The throughput has been increasing slowly in the past years. This is probably due to the increase in

manufacture of food products as well as paper in the past years, this behaviour is supposed to

continue. The unloaded tons do not correlate with paper and alimentary production.

The loaded tons, exported goods, are going mostly through Lisbon, where the throughput has been

constant in the time window analysed, in percentage Lisbon passed from 90% in 2001 to around 50%

in 2014. The increase in throughput in 2009 comes from a sudden spike of cargo throughput in

Aveiro. After 2009 though the throughput decreased linearly, coming to almost 0 in 2014. Other

important outlets of goods are Setúbal (18% of the total in 2014), Leixões and Figueira da Foz. For

the future it is supposed that every port will keep its weight in percentage. The total throughput will

grow linearly.

The unloaded tons, imported goods, are also being handled mostly in Lisbon, where the throughput

has been almost constant in the years analysed, the other important port for this category is Leixões.

Like in many other categories of cargo, in the years analysed the northern ports are growing slightly

faster than the central ones, this trend is supposed to continue, the northern range will pass from

handling 32% of the throughput to 36%. The central range will pass from 68% to 64%. The total

throughput will continue to grow linearly.

51

Figure 56 - Forecast of throughput in Portuguese ports - Loaded tons of products of forest and agriculture

Figure 57 - Forecast of throughput in Portuguese ports - Unloaded tons of products of forest and agriculture

6.2 Crude oil and LNG

In the time window analysed the throughput of this category of cargo has been extremely stable, the

throughput up to 2014, as well as the forecast up to 2024 are shown in Figure 58 (unloaded tons).

This constant throughput is due to the continuous need of these goods for energy production. It is

interesting to note that even if the exportation of oil and chemical products increased the importation

of crude oil stayed constant. The reasons for this regularity are not obvious, it can be due to improved

efficiency in the industrial process or to changes in local patterns of consumption.

Loading of cargo happens only in Sines, and it doesn’t follow any apparent law. Probably it is just

transhipments resulting from occasional requests from other national ports. Given the aleatory

behaviour this cargo throughput will not be forecasted.

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The only two ports unloading considerable quantities of crude oil are Sines and Leixões, reasonable

given the fact that the only two refineries of the county are close to the ports. In the past years the

throughput stayed constant in Sines while it increased slightly in Leixões. This trend is linearized and

projected to the future.

Sines is the only national port handling LNG, the terminal opened in 2003, and after a couple of years

the throughput has been stable around 2 million tons per year. Given the singular behaviour of the

international LNG shipping market, it is safe to assume this throughput to continue uninterrupted,

unless wildcard events will happen in the future.

Figure 58 - Forecast of throughput in Portuguese ports - Unloaded tons of crude oil and LNG

6.3 Minerals

The throughput up to 2014, as well as the forecast up to 2024 are shown in Figure 59 (loaded tons)

and Figure 60 (unloaded tons).

This category includes any non-energetic products of the extractive industry. There is no data about

the extractive industry in the INE publications, but the loaded tons in Portuguese ports are growing

exponentially, probably thanks to the increased openness of Portuguese economy in the last years.

The amount of unloaded cargo in this category declined up to 2009 and since then have recovered

slowly.

The minerals are loaded mainly in Lisbon, Setúbal and Sines, together accounting for almost 70% of

the total in 2014, these cargos are mostly marble and copper coming from mines in the region of

Alentejo. The other important pole for minerals is the region between Aveiro and Figueira da Foz,

accounting for 25% of the throughput in 2014.

Import is very fragmented between the different ports, each one giving a small and irregular

contribution to the total throughput.

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Figure 59 - Forecast of throughput in Portuguese ports – Loaded tons of minerals

Figure 60 - Forecast of throughput in Portuguese ports – Unloaded tons of minerals

6.4 Food Products

This category includes food, drinks and tobacco. The throughput here has been growing steadily,

increasing more than 30% in the past 14 years.

Loaded tons, shown in Figure 61, are the driver of the growth, and considering the increasing tourism

and international fame of Portugal it is easy to agree that the food exports will continue to grow.

Lisbon has been historically the main port in this category, second Leixões and the other ports

following with a much smaller share. It is likely that the northern range of ports will increase its

importance in the future, even so this will not change much the equilibrium between ports. The total

throughput will continue to grow linearly.

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Unloaded tons, shown in Figure 62, have been almost constant in the years analysed, this trend is

supposed to continue in the future, however the northern ports here are becoming more important

with time, this trend is also supposed to continue in the future. The only two ports in the north

unloading food products are Aveiro and Leixões, the growth that will happen in the northern range is

supposed to be only in Leixões, Food products come mostly via container, and are consumed more in

areas with high population density, thus the throughput growth in Leixões. In the Central range, it is

unlikely that Lisbon will lose its importance as a food products port, more likely is that the decrease in

throughput will happen in Setúbal. Throughput in Sines is already very small, most likely it will

continue as it is.

Figure 61 - Forecast of throughput in Portuguese ports – Loaded tons of Food products

Figure 62 - Forecast of throughput in Portuguese ports – Unloaded tons of Food products

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6.5 Wood, cork and paper products

The loaded tons of this category of cargo, shown in Figure 63, were stagnating between 2001 and

2009. Then the exports started increasing rapidly, but this growth is slowing down already. It is

interesting to note how the exports grew considerably while the industrial foreign sales didn’t change

much, this is probably due to a large fluctuation of prices. The loaded tons in this category are likely to

continue this slow growth of the past few years (2011 onwards), due to the coming together and

strengthening of the main industries in the country. The throughput is spread throughout the various

ports, with the northern ports handling 70% of the throughput. No trends are evident here, the share of

throughput for each port is considered to keep constant in the next 10 years.

The unloaded tons, shown in Figure 64, decreased rapidly until 2005, and then started increasing

slowly again, this trend of the last years is considered to continue. Throughput for each port is very

irregular, with northern and central range each handling around 50% of the total, being unable to

forecast any trend, the ports are considered to continue handling the same percentage as 2014.

Figure 63 - Forecast of throughput in Portuguese ports – Loaded tons of Wood, cork and paper products

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Figure 64 - Forecast of throughput in Portuguese ports – Unloaded tons of Wood, cork and paper products

6.6 Coal and oil products

The throughput up to 2014, as well as the forecast up to 2024 for this category is shown in Figure 65


The level of coal imported has been slowly decreasing in the past years. Coal is consumed mainly in

the various power plants of the country. The amount of energy produced from coal has also been

decreasing slowly in the past years. This trend is expected to continue in the future. Coal is only

imported and the only national inlet is the port of Sines.

Regarding oil products, imports decreased slightly while exports grew and compensated, keeping the

total constant. This is probably due to upgrades to the 2 big refineries in Leixões and Sines, allowing

them more production and, probably, also an increase in efficiency. This trend will probably continue

in the future.

About the distribution of throughput in the various ports, products are loaded only in Leixões and

Sines, in the time window analysed the throughput in both ports grew, the contribution to the total

however increased slightly for Leixões, mirroring the slight increase in crude oil import.

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Figure 65 - Forecast of throughput in Portuguese ports – Loaded tons of oil products

Figure 66 - Forecast of throughput in Portuguese ports – Unloaded tons of coal and oil products

6.7 Chemical products



This category includes organic and non-organic chemical products, fertilizers, pharmaceuticals, plastic

and rubber. The throughput, except for a dip in 2008/2009, has been growing constantly. As sales

correlate weakly with throughput, a simple linear extrapolation is used for both loaded and unloaded

tons.

Distribution of throughput throughout ports is quite stable. For loaded cargo the northern and central

range both contribute with 50% of the throughput. For unloaded cargo the throughput is moving slowly

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towards the northern range. All the trends of the 14 years analysed are linearly interpolated and

forecasted to the future.

Figure 67 - Forecast of throughput in Portuguese ports – Loaded tons of chemical products

Figure 68 - Forecast of throughput in Portuguese ports – Unloaded tons of chemical products

6.8 Non-metallic mineral products



This category includes mainly cement and glass. In this industry the national sales have been steadily

decreasing in the past years, while the foreign sales where increasing, the total however is

decreasing. The decrease in national sales can be attributed to the stagnation and crisis of the

Portuguese economy, the development of big national infrastructures as well of private construction

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slowed down considerably, this trend however will not continue indefinitely, in the next years the

national sales will likely stabilize around the present value. Foreign sales as well will not continue to

grow indefinitely, in the past years the cement production reached the limit of international demand.

This can be seen already in preliminary data of 2015, for example in Setúbal the production of cement

overcame the exportation. It is also important to note, when analysing the export, that in the last years

the main driver of the cement industry worldwide was China and its ever-developing construction

sector. However it is known that now the growth of China is slowing down, making the demand of

cement worldwide decrease. The correlated decrease in price will hopefully be exploited by emerging

economies, but anyway the rate of growth of the exports will decrease.

The main loading ports are: Setúbal (43%), Aveiro (25%), Lisbon (12%), Leixões (11%) and Faro

(5%), the trend of throughput throughout the years is smooth, this trend has been projected to the

future. Unloaded tons are incoherent, in the available statistical data, however the northern and

central range behave smoothly, and given the impossibility of forecasting the throughput for each port

the forecast is done only for the two ranges.

Figure 69 - Forecast of throughput in Portuguese ports – Loaded tons of Cement/glass

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Figure 70 - Forecast of throughput in Portuguese ports – Unloaded tons of Cement/glass

6.9 Metallic products



This category includes processed and non-processed items of iron and steel (does not include

machines). As with many other cargo categories, the economic crisis of 2008 marked a shift from

import to export. The loaded tons have been growing steadily in the years analysed, while the

unloaded tons are decreasing, both with very poor correlation with industrial sales.

The throughput of each port is steady, northern and central range both contribute for more or less

50% of the throughput, for both loaded and unloaded tons. These behaviours are supposed to

continue.

For the loaded tons the linear trend is used for the forecast, for the unloaded tons the average value

of the last years is used.

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Figure 71 - Forecast of throughput in Portuguese ports – Loaded tons of Metallic products

Figure 72 - Forecast of throughput in Portuguese ports – Unloaded tons of Metallic products

6.10 Transport material

This category corresponds to different kinds of vehicles loaded or unloaded in Portuguese ports. This

category is not analysed directly, instead the focus is placed on the RoRo throughput of Setúbal, main

inlet and outlet of vehicles in the country.

The throughput have been decreasing until 2009, afterwards continues oscillating around a constant

value. It is supposed for the throughput to continue around this value, with more loaded vehicles than

unloaded, given the continuing low purchase of vehicles in Portugal.

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Figure 73 - Forecast of throughput in Portuguese ports – Loaded and Unloaded tons of cars

6.11 Secondary raw materials

This category includes wood, metal and glass scraps, unloaded, and household waste, loaded. The

throughput up to 2014, as well as the forecast up to 2024 for this category are shown in Figure 74


The loaded tons were almost zero for several years, then they raised rapidly and reached an apparent

equilibrium, this equilibrium is supposed to continue in the future. As for individual ports, the actual

share of throughput is supposed to stay constant.

The unloaded have been rising rapidly in the past years, and all the considerations made above on

the paper, metal and glass industry apply here. The throughput is considered to continue growing

linearly, and each port is supposed to keep its share of the total throughput.

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Figure 74 - Forecast of throughput in Portuguese ports – Loaded tons of Secondary raw materials

Figure 75 - Forecast of throughput in Portuguese ports – Unloaded tons of Secondary raw materials

6.12 Unknown cargo

For the unknown cargo, containers transshipped in Sines, it is observed that after the first years of

operation of the terminal the amount of containers transshipped has been oscillating between 70 and

90% of the total. It is supposed that as the throughput in the terminal increases, the amount of

container transshipped will pass from the 80% of 2014 to 70% in 2024, this is due to the fact that big

container shipping companies, such as MSC, are trying to increase the domestic throughput in Sines,

exploiting also rail transport to Spain [36]. The forecast for container throughput was taken from a

paper about container throughput in Portugal [48], The weight of containers has been taken as the

average in Sines in the years analysed, 10.32 tons/TEU. The total has been divided in 2, half loaded

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and half unloaded, as the same number of transshipped containers enters and exits the port. The

forecast, together with the data up to 2014 is shown in Figure 76.

Figure 76 - Forecast of throughput in Portuguese ports – Loaded and Unloaded tons of Unknown cargo

6.13 Cruise Passengers

Only 3 ports in continental Portugal receive cruise passengers (the ports of Azores and Madeira are

not analyzed in this thesis). Two of them have significant cruise terminals: Lisbon and Leixões.

Portimão possesses only a small cruise terminal building. The past data together with the forecast for

the passengers in transit is shown in Figure 77. For all 3 ports the throughput has been growing

linearly in the past years, this behavior is supposed to continue, also given the fact that both Leixões

and Portimão have new cruise terminals who have been inaugurated in the past years, while in Lisbon

work for a new terminal are already underway.

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Figure 77 - Forecast of throughput in Portuguese ports - Cruise passengers in transit

6.14 Ports Overview

After forecasting individually the main categories of cargo, the data for each port is summed and the

graphs are hereby shown.

The port of Lisbon, shown in Figure 78, will continue growing as an export hub, these exported tons

come mostly from alimentary products, the unloaded tons will remain constant. However, of all the

ports Lisbon is the one more dependent on political decision, decision of the city council and of the

new terminal managers. These decisions could make the port increase or decrease radically its

throughput.

Figure 78 - Forecast of cargo throughput in the port of Lisbon

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In Leixões, shown in Figure 79, the picture is similar to the one in Lisbon. However this port is much

less dependent on political decision, has more space to develop and works are already underway.

Figure 79 - Forecast of cargo throughput in the port of Leixões

The throughput in the port of Sines, shown in Figure 80, will continue to grow with a similar pace.

Driven by the transshipment of containers, the import of coal, crude oil and LNG, as well as the export

of oil products, Sines port throughput is based on goods with a very stable demand.

Figure 80 - Forecast of cargo throughput in the port of Sines

The port of Setúbal, whose throughput forecast is shown in Figure 83, will also continue to grow. The

future of the port is, however, very related to the one of Lisbon, given the great vicinity of the two ports

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many goods can pass through one or the other indifferently. Competition between the two ports is

difficult, a good tactic for the port authorities would be to try and differentiate the ports, making each

one a preferred way for specific goods, this is already happening, with Setúbal being an important port

for cement and RoRo cargo, while Lisbon concentrates on alimentary products.

Setúbal is also very dependent on the cement throughput, responsible today for 40% of the port

throughput, an international crisis of cement could bring a great deal of harm to the port.

Figure 81 - Forecast of cargo throughput in the port of Setúbal

The throughput in Aveiro, shown in Figure 82, has great potential for growth, the whole northern

region of Portugal is growing, and Aveiro, with its modern equipment, is a preferred way for goods in

its hinterland. Competition with Figueira da Foz and Leixões will always be a problem for the port.

Figure 82 - Forecast of cargo throughput in the port of Aveiro

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The throughput in Figueira da Foz, shown in Figure 83, will also grow, even if slower than Aveiro. In

its area Figueira da Foz is important for general cargo.

Figure 83 - Forecast of cargo throughput in the port of Figueira da Foz

The forecast of throughput in Viana do Castelo, shown in Figure 84, shows more than any other the

switch between import and export than happened in Portugal in the past years. The growth in loaded

tons here is driven by the oil products coming from the nearby factory in Leixões, minerals and paper.

Figure 84 - Forecast of cargo throughput in the port of Viana do Castelo

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The throughput in the port of Faro is shown in Figure 85, the graph shows only the loaded tons of

cement, responsible for 99% of the throughput in the past 4 years. Faro, like Setúbal, will be very

sensible to the developments of the international cement situation.

Figure 85 - Forecast of cargo throughput in the port of Faro

Resuming, the total throughput of the ports of continental Portugal are shown in Figure 86, it can be

seen how the loaded tons will most likely continue to grow, eventually reaching the unloaded tons

around 2024. It should be noted that the growth in unloaded tons is due only to the containers

transshipped in Sines, without them the unloaded tons appear about contant in the forecast.

The forecast indicates that in 10 years (2024) the Portuguese ports may be handling almost 107

millions of cargo. This may be assumed to be a conservative estimate as 2015 numbers indicate

already a large increase over 2014. It’s interesting to note how Portuguese GDP and port throughput

are not related, the usual assumption here falls short.

0

100

200

300

400

500

600

Tho

usa

nd

to

ns

70

Figure 86 – Forecast of cargo throughput in Portuguese ports, the national total

0

20

40

60

80

100

120M

illio

n t

on

s


71

7. CONCLUSIONS

This thesis presents a methodology for forecasting cargo throughput in Portuguese ports over a

period of 10 years, from 2015 to 2024. The methodology uses statistical data collected from several

different sources, all of them publicly available. Forecasts are mainly obtained from linear

extrapolations from past trends for major selected groups of cargos.

7.1 Past trends in cargo throughput

Regarding past trends in cargo throughput, it is considered that as years go by, the Portuguese

economy is opening more and more. Even so it comes as a surprise that the throughput of ports grew

so much. In the 14 years analysed, the throughput (net of the transshipped containers in Sines) grew

at an average of 2.1% per year, while the GDP more or less stagnated.

Apart from the alimentary industry, the industrial strengths of Portugal are coming from industries

which are not independent: the fabrication of cement, paper (to some extent), steel, oil products and

chemical products all depend on the importation of some raw materials to be carried out. This makes

Portugal very sensible to unpredictable events that can happen to the global markets (wildcards).

For example, the exportation of cement have been growing constantly in the past 14 years, this

makes it look like a trend which will persist in the future, however global events like the recent slowing

of growth of the Chinese economy and other developing economies will most likely slow down the

growth of every industry worldwide related to the dry bulk market.

In what concerns containerized cargo throughput in all the ports, it should be noted that since 2003

the port of Lisbon, in this market, has in fact been stagnating. Leixões is the opposite case with a

strong and sustained growth. Overall, the containerized cargo represents 2/3 of the growth in

Portuguese ports, implying that they largely outperform the Portuguese economy in this market

segment.

As to the other important contributions for the growth in cargo throughput, it must be first mentioned

the increase in liquid bulk handling in Sines, related to the capacity expansion of the local refinery. Dry

bulk cargo and general cargo have also increased significantly in both Aveiro and Setúbal, mainly due

to the increased exportation of cement and paper products and imports of different raw materials.

Setúbal shows a large decrease in liquid bulks mainly due to the closing of the local power plant

(running on fuel oil). These examples point to another conclusion of this study, which is the close

relationship of non-containerized cargo throughput with the performance of a few specific industries or

activities.

The recent take-over of container terminals in Lisbon, Leixões and Setúbal by the Turkish group

Yildirim constitutes a wildcard variable adding uncertainty to the forecast. No forecast has been done

for throughput in Lisbon because of stagnation in last years, but it is now predictable that throughput

72

might increase again and some capacity increase is also needed in the future. Another option would

be to divert the throughput towards Setúbal, where significant capacity is available.

Even so it looks sure that Portuguese ports will continue to prosper in the future, Sines port still has

the potential to grow as a transshipment port, given good decisions made by the port authority. The

increasing international reputation of Portugal will most likely drive the food exportations up. Portugal

is also home to some of the biggest paper factories in Europe, this will most likely keep the prices

competitive for the future and help the throughput keep its consistency.

Future outlooks for cement are not good, given the Chinese and global situation. Also for the

metallurgical industry the conditions of the Chinese economy will most like have some negative

influences. The recent oil crisis may have an impact on oil refineries and probably slow down the

growth, this effect will however be small, given the fact that most of the output of refineries is

consumed in the country.

It is interesting to note also how, if the containers transshipped in Sines are not considered, the

Northern ports are growing faster than the Southern ones, this is a reflection of the higher

industrialization of northern Portugal, as well as the proximity of higher industrialized regions of Spain.

7.2 Forecasts for 2015-2024

The statistical data used in this thesis is mostly the one publicly available, complemented by some

data obtained on request from the port administrations. However, in some cases the actual data was

not actually disclosed. Studies on port forecasting are usually made by private companies and in a

few cases by Universities, employ a team of people dedicated to the job. The work is generally

commissioned by port authorities or terminal operators, which then disclose all the data necessary,

making it possible to produce better results. However, most reports include very few details on the

methods for forecasting which were used and generally use the simplest ones.

What looks evident from this study is that trying to make forecasts in this field is extremely

complicated. The whole sector, even while being one of the main drivers of the world economy, is

based on hard competition between very few players. This makes the whole system easily subject to

big shocks that can start from problems that arise simply from blindness to change and unneeded

optimism, the example of the Chinese economy is the most explicit.

However, even if maritime shipping is sensible to small perturbation, it is still driven by the world

economy which is a big ensemble of small realities, and thus any shock can alter the balance for a

while but the whole machine in itself is unstoppable.

This instability of the market makes strict mathematical methods inadequate for forecasting. Expert

opinion is more valuable than long time series because of their ability to consider out of the norm

events.

Most papers that employ a strict mathematical method don't usually forecast any value, they limit

themselves to craft a model based on 80% of the data and using the remaining 20% to validate it.

73

When looking at other forecasts, regardless of the methods applied, most times the forecasted values

do not deviate sensibly from simple linear or exponential interpolations. Anyway, these small

differences are not so important for port authorities, which are more concerned on having a general

prospect of future evolution of cargo throughput, to be able to supply sufficient terminal capacity.

Considering the short time window analysed, the 2008 crisis makes the matter even more

complicated, effectively scrambling the data, destroying past trends. It is also evident how, for many

categories of cargo, the 2008 crisis marked a moment in which the economy switched from import-

based to export-based. This change is substantial, and happened so recently that any new trend in

cargo throughput didn’t have much time to manifest.

It should be remembered that the calculations made for this thesis take the INE publications data as a

starting point, there are some discrepancies between these data and the ones supplied by the various

port authorities, so also the forecast should be read while keeping this in mind.

In any case, this thesis allows the conclusion that the total cargo throughput of the Portuguese range

will probably reach 107 million tons by 2024, with unloaded and loaded cargo being almost equal.

Looking at the range port by port it can be seen how the growth is driven by the 2 main ports of the

country, Sines and Leixões. Lisbon’s growth depends on several management decisions, as written

before, so there is nothing much that can be said about it. The port of Aveiro will most likely grow

rapidly until reaching the same throughput level of Setúbal. Smaller ports like Figueira da Foz, Viana

do Castelo and Faro will also grow but much slower than the rest of the range. Cruise passenger

throughput will also continue the linear behaviour seen in the past.

On the side of this thesis, a paper was written forecasting the throughput of containers up to 2024.

The results obtained from these calculations show that the container throughput in the 4 main port of

Portugal (Lisbon, Leixões, Sines and Setúbal) will reach 4.5 million TEUs by the end of 2024.

7.3 Recommendations for further research

The ultimate objective of a master thesis is to accumulate in-depth knowledge, on a subject,

understand more about it, apply methods of analysis, develop independent work on a particular topic

and obtain conclusions in the process. This thesis allows conclusions on cargo throughput in

Portuguese ports, shows the strong interaction with the local and national economy around them, and,

ultimately, shows how even non-strictly quantitative methods can provide plausible forecast.

For future reference and more in-depth research the first option would be to gather information on

future industrial growth in the different industries related with main cargo types and use this

information to improve forecasts for individual cargos.

Another possibility would be to move from a linear extrapolation of past trends to a quantitative

forecast based on neural networks. This would require collecting a greater amount of past data and

with more detail in the information. For example, the data could be four-monthly data from the past

20/25 years. This data should then be separated in cargo categories for each port, making a

distinction between loaded and unloaded cargo. With this amount of data if could be possible to use

74

neural network forecasting. With this approach it will be possible to see hidden relations between the

time series, including through which ports the flow of goods passes. Another advantage of this

method is that it does not require forecasts for the explanatory variables, like linear regression does.

75

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APPENDIX A – NST 2007 CATEGORIZATION

Category Description

1 Products of agriculture, hunting, and forestry; fish and other fishing products

1.1 Cereals

1.2 Potatoes

1.3 Sugar beet

1.4 Other fresh fruit and vegetables

1.5 Products of forestry and logging

1.6 Live plants and flowers

1.7 Other substances of vegetable origin

1.8 Live animals

1.9 Raw milk from bovine cattle, sheep and goats

1.A Other raw materials of animal origin

1.B Fish and other fishing products

2 Coal and lignite; crude petroleum and natural gas

2.1 Coal and lignite

2.2 Crude petroleum

2.3 Natural gas

3 Metal ores and other mining and quarrying products; peat; uranium and thorium

3.1 Iron ores

3.2 Non ferrous metal ores (except uranium and thorium ores)

3.3 Chemical and (natural) fertilizer minerals

3.4 Salt

3.5 Stone, sand, gravel, clay, peat and other mining and quarrying products n.e.c.

3.6 Uranium and thorium ores

4 Food products, beverages and tobacco

4.1 Meat, raw hides and skins and meat products

4.2 Fish and fish products, processed and preserved

4.3 Fruit and vegetables, processed and preserved

4.4 Animal and vegetable oils and fats

4.5 Dairy products and ice cream

4.6 Grain mill products, starches, starch products and prepared animal feeds

4.7 Beverages

4.8 Other food products n.e.c. and tobacco products (except in parcel service or grouped)

4.9 Various food products and tobacco products in parcel service or grouped

79

5 Textiles and textile products; leather and leather products

5.1 Textiles

5.2 Wearing apparel and articles of fur

5.3 Leather and leather products

6 Wood and products of wood and cork (except furniture); articles of straw and plaiting materials; pulp, paper and paper products; printed matter and recorded media

6.1 Products of wood and cork (except furniture)

6.2 Pulp, paper and paper products

6.3 Printed matter and recorded media

7 Coke and refined petroleum products

7.1 Coke oven products; briquettes, ovoids and similar solid fuels

7.2 Liquid refined petroleum products

7.3 Gaseous, liquefied or compressed petroleum products

7.4 Solid or waxy refined petroleum products

8 Chemicals, chemical products, and man-made fibers; rubber and plastic products ; nuclear fuel

8.1 Basic mineral chemical products

8.2 Basic organic chemical products

8.3 Nitrogen compounds and fertilizers (except natural fertilizers)

8.4 Basic plastics and synthetic rubber in primary forms

8.5 Pharmaceuticals and parachemicals including pesticides and other agro-chemical products

8.6 Rubber or plastic products

8.7 Nuclear fuel

9 Other non metallic mineral products

9.1 Glass and glass products, ceramic and porcelain products

9.2 Cement, lime and plaster

9.3 Other construction materials, manufactures

10 Basic metals; fabricated metal products, except machinery and equipment

10.1 Basic iron and steel and ferro-alloys and products of the first processing of iron and steel (except tubes)

10.2 Non ferrous metals and products thereof

10.3 Tubes, pipes, hollow profiles and related fittings

10.4 Structural metal products

10.5 Boilers, hardware, weapons and other fabricated metal products

11 Machinery and equipment n.e.c.; office machinery and computers; electrical machinery and apparatus n.e.c.; radio, television and communication equipment and apparatus; medical, precision and optical instruments; watches and clocks

11.1 Agricultural and forestry machinery

80

11.2 Domestic appliances n.e.c. (White goods)

11.3 Office machinery and computers

11.4 Electric machinery and apparatus n.e.c.

11.5 Electronic components and emission and transmission appliances

11.6 Television and radio receivers; sound or video recording or reproducing apparatus and associated goods (Brown goods)

11.7 Medical, precision and optical instruments, watches and clocks

11.8 Other machines, machine tools and parts

12 Transport equipment

12.1 Automobile industry products

12.2 Other transport equipment

13 Furniture; other manufactured goods n.e.c.

13.1 Furniture

13.2 Other manufactured goods

14 Secondary raw materials; municipal wastes and other wastes

14.1 Household and municipal waste

14.2 Other waste and secondary raw materials

15 Mail, parcels

15.1 Mail

15.2 Parcels, small packages

16 Equipment and material utilized in the transport of goods

16.1 Containers and swap bodies in service, empty

16.2 Pallets and other packaging in service, empty

17 Goods moved in the course of household and office removals; baggage and articles accompanying travellers; motor vehicles being moved for repair; other non market goods n.e.c.

17.1 Household removal

17.2 Baggage and articles accompanying travellers

17.3 Vehicles for repair

17.4 Plant equipment, scaffolding

17.5 Other non market goods n.e.c.

18 Grouped goods: a mixture of types of goods which are transported together

19 Unidentifiable goods: goods which for any reason cannot be identified and therefore cannot be assigned to groups 01-16.

19.1 Unidentifiable goods in containers or swap bodies

19.2 Other unidentifiable goods

20 Other goods n.e.c.

20 Other goods not elsewhere classified

XX Unknown goods

82

APPENDIX B – DATA

LOADED CARGO – PRODUCTS OF FOREST AND AGRICULTURE (TONS)

Total Avr Faro FdF Lxs Lsb Stb Sns VdC

2001 261920 1034 2120 0 23251 229952 5563 0 0

2002 262991 0 2173 0 24538 228618 7662 0 0

2003 319707 0 2575 0 26116 279635 11381 0 0

2004 269675 1758 1996 42 32646 214598 16950 1685 0

2005 301283 0 1096 0 34237 240054 20189 5707 0

2006 313411 0 1100 42 27408 253567 10869 20425 0

2007 391217 0 0 0 34690 280203 22244 54080 0

2008 295529 0 0 0 49323 182726 5609 57871 0

2009 730816 489249 67 0 30463 177731 4857 28449 0

2010 720028 332604 6300 37254 42835 199552 79579 21904 0

2011 658037 233329 0 41129 36744 202467 106861 37507 0

2012 594273 176587 1537 45302 56003 179166 71766 63912 0

2013 517763 78013 0 60134 62289 215019 74070 28238 0

2014 566441 9825 6930 73390 64927 265767 101159 44443 0

2015 693645 0 3468 128324 69365 346823 97110 48555 0

2016 727054 0 3635 134505 72705 363527 101788 50894 0

2017 760463 0 3802 140686 76046 380232 106465 53232 0

2018 793872 0 3969 146866 79387 396936 111142 55571 0

2019 827281 0 4136 153047 82728 413641 115819 57910 0

2020 860690 0 4303 159228 86069 430345 120497 60248 0

2021 894099 0 4470 165408 89410 447050 125174 62587 0

2022 927508 0 4638 171589 92751 463754 129851 64926 0

2023 960917 0 4805 177770 96092 480459 134528 67264 0

2024 994326 0 4972 183950 99433 497163 139206 69603 0

UNLOADED CARGO – PRODUCTS OF FOREST AND AGRICULTURE (TONS)


2001 4666345 549537 0 4584 624864 3264362 207800 0 15198

2002 4895796 578909 0 4260 612792 3529849 169986 0 0

2003 4667699 436984 0 6102 732818 3345085 136740 0 9970

2004 4353077 375827 0 3559 732818 3005865 226237 6845 1926

2005 5235165 590938 1727 18726 834044 3596254 173421 20055 0

2006 4779793 401138 0 39764 899547 3250190 157303 31851 0

2007 5324408 249768 0 0 899311 4009060 94449 71820 0

2008 4999297 0 0 0 976894 3819277 78119 125007 0

2009 4465212 226190 0 0 892891 3220249 68502 57380 0

2010 5812226 739158 0 217565 1050310 3488280 311798 5115 0

2011 5233636 386227 0 200813 1004478 3338042 282653 21423 0

2012 4921409 238336 0 219502 1083236 3161272 186686 32377 0

2013 5350772 529590 0 435932 843576 3240217 268139 33318 0

2014 5309790 419183 0 362810 917485 3327554 218468 64290 0

2015 5397260 431781 0 323836 971507 3346301 269863 53973 0

2016 5450084 436007 0 327005 981015 3379052 272504 54501 0

2017 5502908 440233 0 330174 1045553 3356774 275145 55029 0

2018 5555732 444459 0 333344 1055589 3388997 277787 55557 0

2019 5608556 448684 0 336513 1121711 3365134 280428 56086 0

2020 5661380 452910 0 339683 1132276 3396828 283069 56614 0

2021 5714204 514278 0 342852 1142841 3371380 285710 57142 0

2022 5767028 461362 0 346022 1211076 3402547 288351 57670 0

2023 5819852 523787 0 349191 1222169 3375514 290993 58199 0

2024 5872676 469814 0 352361 1291989 3406152 293634 58727 0

83

LOADED CARGO – CRUDE OIL AND LNG (TONS)


2001 0 0 0 0 0 0 0 0 0

2002 163162 0 0 0 0 0 0 163162 0

2003 0 0 0 0 0 0 0 0 0

2004 443983 0 0 0 0 0 0 443983 0

2005 181532 0 0 0 9980 0 0 171552 0

2006 73664 0 0 0 0 0 0 73664 0

2007 0 0 0 0 0 0 0 0 0

2008 306788 0 0 0 18 185 0 306585 0

2009 44045 0 0 0 190 217 0 43638 0

2010 2545 0 0 0 3 199 411 1932 0

2011 1360 0 0 0 814 446 19 81 0

2012 109654 0 0 0 577 1999 3293 103785 0

2013 361973 0 0 0 175 2310 0 359488 0

2014 391494 0 0 0 19 18577 22 372876 0

UNLOADED CARGO – CRUDE OIL AND LNG (TONS)

Total LNG - Sines Crude - Sines Crude - Leixões

2001 12593971 0 9278307 3315664

2002 11528265 0 8573051 2955214

2003 12692178 171207 9286648 3234323

2004 12673393 1012409 8426661 3234323

2005 13377881 1295209 8579902 3502770

2006 13464864 1586501 8253712 3624651

2007 12362626 2060099 6949139 3353388

2008 14416955 2003482 8354192 4059281

2009 12375781 2031898 7119226 3224657

2010 13289752 2102379 8192221 2995152

2011 12605324 2153243 7030165 3421916

2012 12624709 1627359 7453565 3543785

2013 13431209 2014587 7441812 3974810

2014 12438183 1431718 6985014 4021451

2015 12942137 2000000 6873528 4068609

2016 12877809 2000000 6762042 4115767

2017 12813481 2000000 6650556 4162925

2018 12749153 2000000 6539070 4210083

2019 12684825 2000000 6427584 4257241

2020 12620497 2000000 6316098 4304399

2021 12556169 2000000 6204612 4351557

2022 12491841 2000000 6093126 4398715

2023 12427513 2000000 5981640 4445873

2024 12363185 2000000 5870154 4493031

84

LOADED CARGO – MINERALS (TONS)


2001 774955 34643 0 41758 8596 281891 393724 14343 0

2002 681589 23375 0 45550 23231 214457 367404 7572 0

2003 697273 13993 0 68850 21981 223838 362538 6073 0

2004 745968 22275 0 57049 25408 199785 404886 36565 0

2005 823416 67524 0 56133 24103 159671 425287 90698 0

2006 962696 48321 4402 163004 32667 216562 368126 129456 158

2007 1138490 54642 17532 181059 33484 211081 446430 190699 3563

2008 1041223 0 19947 0 22373 223606 523125 246292 5880

2009 1089187 91039 10127 0 39420 263715 452986 203749 28150

2010 1408009 139974 28599 137578 48870 355538 335907 308369 53174

2011 1460961 90466 6250 214065 52558 403028 336418 318416 39760

2012 1501262 50870 3802 168035 55467 403073 359239 388649 72127

2013 1688252 102136 5840 225497 69152 534981 473538 241029 36079

2014 1874832 202268 14861 271855 61029 459398 471864 354784 38773

2015 1964541 157163 0 255390 78582 491135 530426 392908 58936

2016 2054250 184882 0 267052 82170 513562 534105 410850 61627

2017 2143959 214396 0 278715 85758 514550 578869 407352 64319

2018 2233668 223367 0 290377 89347 580754 558417 402060 89347

2019 2323377 232338 0 325273 92935 580844 557610 441442 92935

2020 2413086 265439 0 337832 96523 579141 603271 434355 96523

2021 2502795 275307 0 375419 100112 650727 525587 475531 100112

2022 2592504 311100 0 388876 103700 674051 544426 440726 129625

2023 2682213 321866 0 402332 107289 670553 616909 429154 134111

2024 2771922 360350 0 443507 110877 720700 498946 498946 138596

UNLOADED CARGO – MINERALS (TONS)


2001 841798 161969 2550 100452 360698 69452 7116 24191 115370

2002 966933 124229 4650 78078 410223 76188 3161 118796 151608

2003 843694 162588 15225 104024 329867 76476 4745 0 150769

2004 841954 195446 15114 121015 329867 53400 1954 3723 121435

2005 488924 202370 14759 127290 62589 44148 4274 8324 25170

2006 520922 158729 8936 180563 79389 76475 2364 13266 1200

2007 522884 182245 12755 176326 72077 40385 2039 35557 1500

2008 329246 0 15770 0 89288 52163 86409 65843 19773

2009 282079 64850 6960 0 82921 25966 75772 19410 6200

2010 430342 147039 0 144695 82356 32159 16488 498 7107

2011 407141 138947 4002 100670 83293 35496 33071 9662 2000

2012 382554 126104 7268 115188 75348 29032 12034 4298 13282

2013 411770 153694 0 114072 70037 29031 3107 14786 27043

2014 502522 165142 0 157927 77395 27837 22114 28663 23444

2015 502522 165142 0 157927 77395 27837 22114 28663 23444

2016 502522 165142 0 157927 77395 27837 22114 28663 23444

2017 502522 165142 0 157927 77395 27837 22114 28663 23444

2018 502522 165142 0 157927 77395 27837 22114 28663 23444

2019 502522 165142 0 157927 77395 27837 22114 28663 23444

2020 502522 165142 0 157927 77395 27837 22114 28663 23444

2021 502522 165142 0 157927 77395 27837 22114 28663 23444

2022 502522 165142 0 157927 77395 27837 22114 28663 23444

2023 502522 165142 0 157927 77395 27837 22114 28663 23444

2024 502522 165142 0 157927 77395 27837 22114 28663 23444

85

LOADED CARGO – ALIMENTARY PRODUCTS (TONS)


2001 1069496 718 0 0 281600 784221 2957 0 0

2002 1258491 28858 0 0 275808 939999 13825 0 1

2003 1467170 109958 0 88 322980 988452 45692 0 0

2004 1435855 92244 0 130 346029 942655 44075 10722 0

2005 1393699 43670 0 22 345277 943762 19783 41185 0

2006 1744030 23430 0 0 425386 1121963 16824 156427 0

2007 1903949 53771 0 0 443442 1170087 26093 210556 0

2008 2074975 0 0 0 535730 1297994 40713 200538 0

2009 1902879 84014 4100 0 543699 1099741 35254 136071 0

2010 1908017 17083 17600 0 553933 1174675 60019 84707 0

2011 2088830 34972 2088 0 593013 1318200 53706 86839 12

2012 2254410 35735 0 0 804474 1206035 15028 193138 0

2013 2308056 45052 0 12 698563 1395520 43822 125087 0

2014 2393833 34 0 0 766465 1326235 146955 154144 0

2015 2489738 49795 0 0 771819 1419151 99590 149384 0

2016 2585643 51713 0 0 827406 1447960 103426 155139 0

2017 2681548 53631 0 0 858095 1474851 134077 160893 0

2018 2777453 55549 0 0 916559 1499825 111098 194422 0

2019 2873358 57467 0 0 948208 1522880 143668 201135 0

2020 2969263 59385 0 0 1009549 1544017 148463 207848 0

2021 3065168 61303 0 0 1042157 1563236 183910 214562 0

2022 3161073 63221 0 0 1106376 1580536 158054 252886 0

2023 3256978 65140 0 0 1172512 1595919 162849 260558 0

2024 3352883 67058 0 0 1207038 1609384 201173 268231 0

UNLOADED CARGO – ALIMENTARY PRODUCTS (TONS)


2001 1834912 61523 0 0 556048 865064 344089 8188 0

2002 1838883 41706 0 885 581904 870467 334176 4238 5507

2003 1737959 23758 0 49 537884 938469 229051 5051 3697

2004 1825528 31359 0 72 537884 955917 275131 22949 2216

2005 1668011 22646 0 0 536036 833882 212124 62858 465

2006 1417927 26721 0 2746 387495 676235 208408 116322 0

2007 1584430 26763 0 0 395850 809346 217459 135012 0

2008 1693304 0 0 0 450287 967441 123256 152320 0

2009 1664092 71759 0 0 479112 920165 108082 84974 0

2010 1619584 140065 0 4195 382607 936839 152783 3095 0

2011 1818715 221956 0 0 455353 952129 165389 23804 84

2012 1714146 220495 4210 2506 499382 811377 121352 54824 0

2013 1733343 260725 0 0 537379 721751 193417 20071 0

2014 1681740 215564 0 0 551401 728365 156175 30235 0

2015 1676740 217976 0 0 545032 737766 142431 33535 0

2016 1671740 234044 0 0 541735 731386 131140 33435 0

2017 1666740 233344 0 0 557616 725032 117414 33335 0

2018 1661740 232644 0 0 573391 718703 103768 33235 0

2019 1656740 231944 0 0 589062 712398 90202 33135 0

2020 1651740 231244 0 0 604627 706119 76716 33035 0

2021 1646740 230544 0 0 620088 699865 63310 32935 0

2022 1641740 229844 0 0 635443 693635 49984 32835 0

2023 1636740 229144 0 0 650694 687431 36737 32735 0

2024 1631740 228444 0 0 665839 681252 23571 32635 0

86

LOADED CARGO – WOOD/CORK/PAPER (TONS)


2001 1000449 225685 0 354122 55169 61864 230828 0 72781

2002 948220 239155 0 313447 48467 47494 232840 0 66817

2003 936960 224571 0 308761 54257 49551 246438 0 53382

2004 906003 226680 0 309246 45747 54272 222195 447 47416

2005 979018 271909 0 292308 46329 39684 256776 8295 63717

2006 1217086 464059 0 292238 49391 40441 242494 41713 86750

2007 1369108 496516 0 360347 93174 42737 218724 59747 97863

2008 1050775 0 0 0 301616 264163 228204 149551 107241

2009 1205492 134613 0 0 386924 297800 197607 120826 67721

2010 2201962 436096 0 588796 344774 322248 270846 147717 91485

2011 2611423 459976 0 678645 417450 337849 360907 227564 129032

2012 2714572 457278 0 750571 559246 317516 195832 288049 146080

2013 2884311 560963 0 814823 533697 355166 240991 247267 131404

2014 2949338 557592 0 835605 553852 285452 310160 279348 127329

2015 3067687 582861 0 858952 552184 337446 306769 276092 153384

2016 3186036 605347 0 892090 573487 350464 318604 286743 159302

2017 3304385 627833 0 925228 594789 363482 330439 297395 165219

2018 3422734 650320 0 958366 616092 376501 342273 308046 171137

2019 3541083 672806 0 991503 637395 389519 354108 318697 177054

2020 3659432 695292 0 1024641 658698 402538 365943 329349 182972

2021 3777781 717778 0 1057779 680001 415556 377778 340000 188889

2022 3896130 740265 0 1090916 701303 428574 389613 350652 194807

2023 4014479 762751 0 1124054 722606 441593 401448 361303 200724

2024 4132828 785237 0 1157192 743909 454611 413283 371955 206641

UNLOADED CARGO – WOOD/CORK/PAPER (TONS)


2001 1507302 24640 0 214190 520970 56175 342481 0 348846

2002 1143208 38406 0 103367 434292 76545 369581 0 121017

2003 739640 32521 0 93985 377324 76018 42731 10269 106792

2004 687326 16109 0 61147 377324 36043 67671 34122 94910

2005 588244 7636 0 53055 306071 37317 10072 45452 128641

2006 575599 4665 0 70483 206298 42215 42119 71078 138741

2007 868426 8271 0 77689 256912 80629 220784 70856 153285

2008 691354 0 3259 0 186576 179445 148882 60331 112861

2009 872847 222616 0 0 234623 149483 130554 22374 113197

2010 1004532 9678 0 82837 288826 127194 284497 4654 206846

2011 1058882 19744 0 70234 271855 114110 440230 27917 114792

2012 794619 8608 0 81995 275374 86210 269830 7707 64895

2013 769265 14610 0 88880 240092 79591 249781 15875 80436

2014 718443 20273 0 67110 267358 90295 199244 26201 47962

2015 885769 13287 0 88577 265731 97435 181583 31002 70862

2016 903095 13546 0 90310 270929 99340 221258 31608 72248

2017 920421 13806 0 92042 276126 101246 234707 32215 73634

2018 937747 14066 0 93775 281324 103152 239126 32821 75020

2019 955073 14326 0 95507 286522 105058 243544 33428 76406

2020 972399 14586 0 97240 291720 106964 247962 34034 77792

2021 989725 14846 0 98973 296918 108870 252380 34640 79178

2022 1007051 15106 0 100705 302115 110776 256798 35247 80564

2023 1024377 15366 0 102438 307313 112681 261216 35853 81950

2024 1041703 15626 0 104170 312511 114587 265634 36460 83336

87

LOADED CARGO – COAL/OIL PRODUCTS (TONS)


2001 5110784 0 0 0 555807 136214 0 4418755 8

2002 4804955 647 0 2 744261 69727 0 3990318 0

2003 5322273 0 0 9 678717 124264 4879 4514404 0

2004 5208022 0 0 14 879836 147673 6922 4173577 0

2005 6112508 0 0 3 1017579 62285 3102 5029539 0

2006 6979302 0 0 0 996316 105647 2211 5875128 0

2007 6663868 0 0 0 1131968 42555 1053 5488292 0

2008 6319814 0 0 0 1560684 103425 3258 4652447 0

2009 5992990 129640 0 0 1168690 61310 2821 4619270 11259

2010 6901426 0 0 0 1053614 87477 18089 5732952 9294

2011 6384586 0 0 0 1487144 87735 49278 4732626 27803

2012 6875260 0 0 0 1506365 117339 15582 5197130 38844

2013 9090734 0 0 0 2102159 147730 36708 6752954 51183

2014 8283302 0 0 0 1991516 104627 1409 6114584 71166

2015 8527787 0 0 0 2118519 85278 0 6226836 85278

2016 8772272 0 0 0 2249433 87723 0 6335176 87723

2017 9016757 0 0 0 2384259 90168 0 6439605 90168

2018 9261242 0 0 0 2522997 92612 0 6540122 92612

2019 9505727 0 0 0 2570590 95057 0 6636727 190115

2020 9750212 0 0 0 2714706 97502 0 6729420 195004

2021 9994697 0 0 0 2862735 99947 0 6818202 199894

2022 10239182 0 0 0 3014675 102392 0 6903072 204784

2023 10483667 0 0 0 3170527 104837 0 6984030 209673

2024 10728152 0 0 0 3330291 107282 0 7061076 214563

UNLOADED CARGO – COAL/OIL PRODUCTS (TONS)

Total Avr Faro FdF Lxs Lsb Stb

Sns - Products

Sns - Coal VdC

2001 12658534 0 169517 4329 3272738 1571539 1831532 845488 4911364 52027

2002 13455084 0 140606 2463 2784343 1609835 2257412 1060906 5558834 40685

2003 12599986 716 126182 0 2968650 1116895 1671454 1308977 5363545 43567

2004 13337944 490 60294 0 2968650 929923 1564445 2495921 5267375 50846

2005 14798083 898 14150 11481 2738514 1343890 2075297 3243795 5311652 58406

2006 14096280 0 14236 20751 2340950 990651 1421887 3461671 5797652 48482

2007 14004173 0 9570 33043 2704082 948709 1192854 4313857 4749359 52699

2008 10805604 0 0 0 2179020 1076387 1330852 1559404 4616943 42998

2009 11965032 86842 0 0 2425221 1349709 1167017 1953327 4953308 29608

2010 9583159 412351 0 3129 2417143 1284253 1045745 1151941 3256278 12319

2011 10351454 449449 0 20317 2125851 1236453 733603 1634179 4151602 0

2012 10674308 560426 0 10564 1637517 1151527 602300 1165759 5546215 0

2013 10695361 530543 0 2391 1373464 1061082 525015 2400629 4773373 28864

2014 11136136 703467 0 0 1413390 931309 462250 2613849 5011871 0

2015 10944555 594455 0 0 1545584 951129 475564 2437267 5000000 0

2016 10764845 576484 0 0 1498860 922375 403539 2421235 5000000 0

2017 10585135 558513 0 0 1396284 893622 390959 2401608 5000000 0

2018 10405425 594597 0 0 1297302 864868 324325 2378387 5000000 0

2019 10225715 522571 0 0 1254172 836114 313543 2351572 5000000 0

2020 10046005 555061 0 0 1160581 807361 252300 2321162 5000000 0

2021 9866295 486629 0 0 1070585 827270 243315 2287159 5000000 0

2022 9686585 421793 0 0 1031049 796719 234329 2249561 5000000 0

2023 9506875 450687 0 0 946444 766169 180275 2208369 5000000 0

2024 9327165 432716 0 0 908705 735618 129815 2163582 5000000 0

88

LOADED CARGO – CHEMICAL PRODUCTS/PLASTIC (TONS)


2001 700113 98491 0 0 276370 291149 34103 0 0

2002 801156 86158 0 0 355151 318390 41457 0 0

2003 936268 114728 0 1 408274 378260 35005 0 0

2004 1007935 116863 5 42 378673 330344 43136 138872 0

2005 1188747 124983 0 2 440164 279278 34525 309795 0

2006 1085424 146516 0 1 413767 223743 22423 278974 0

2007 1294830 121673 0 25 432462 339668 24240 376762 0

2008 1232573 0 0 0 416595 413815 15027 387136 0

2009 1310899 237194 0 0 437701 399980 13012 223012 0

2010 1410377 268530 0 0 389000 399110 45374 308363 0

2011 1937789 306748 0 0 534388 466511 306335 323807 0

2012 2009396 346341 530 240 603539 424679 154593 479474 0

2013 1977553 391431 103 3811 656123 407306 111401 407373 5

2014 1858793 370310 198 109 582168 458561 123117 324084 246

2015 1923277 384655 0 0 576983 480819 96164 384655 0

2016 1987761 397552 0 0 596328 496940 99388 397552 0

2017 2052245 410449 0 0 615674 513061 102612 410449 0

2018 2116729 423346 0 0 635019 529182 105836 423346 0

2019 2181213 436243 0 0 654364 545303 109061 436243 0

2020 2245697 449139 0 0 673709 561424 112285 449139 0

2021 2310181 462036 0 0 693054 577545 115509 462036 0

2022 2374665 474933 0 0 712400 593666 118733 474933 0

2023 2439149 487830 0 0 731745 609787 121957 487830 0

2024 2503633 500727 0 0 751090 625908 125182 500727 0

UNLOADED CARGO – CHEMICAL PRODUCTS/PLASTIC (TONS)


2001 1802277 337342 0 14258 227233 588553 506327 77414 51150

2002 1896203 375568 0 9623 242549 543285 545536 132332 47310

2003 2136510 411566 0 3545 443138 509346 521494 183209 64212

2004 2024255 430080 0 503 443138 435970 513181 171306 30077

2005 2025798 412156 0 0 310388 491812 529944 247834 33664

2006 2070175 414322 0 0 364662 446249 392954 414275 37713

2007 2157520 466792 0 0 406131 377226 408388 472636 26347

2008 1475624 0 0 0 395520 394845 237559 447700 0

2009 1627264 222422 0 0 356390 497760 208314 342378 0

2010 2343292 621467 0 14608 432061 507206 513136 254814 0

2011 2496511 676961 0 3 566692 524259 445664 282932 0

2012 2529322 715026 0 0 471742 423412 479601 439541 0

2013 2441091 707148 0 1004 479314 354867 473608 425150 0

2014 2755184 790801 25 902 597407 417298 632746 316005 0

2015 2907196.9 814015 0 0 668655 436080 697727 290720 0

2016 3008059.9 872337 0 0 721934 451209 661773 300806 0

2017 3108922.9 901588 0 0 746142 435249 715052 310892 0

2018 3209785.9 930838 0 0 770349 449370 738251 320979 0

2019 3310648.9 993195 0 0 827662 430384 728343 331065 0

2020 3411511.9 1023454 0 0 852878 443497 750533 341151 0

2021 3512374.9 1053712 0 0 913217 421485 772722 351237 0

2022 3613237.9 1120104 0 0 939442 433589 758780 361324 0

2023 3714100.9 1151371 0 0 965666 445692 779961 371410 0

2024 3814963.9 1220788 0 0 1030040 419646 762993 381496 0

89

LOADED CARGO – CEMENT/GLASS (TONS)


2001 1508315 4814 0 260 716681 358624 427936 0 0

2002 1876286 9640 0 4302 629075 549042 684227 0 0

2003 2640931 4539 0 83351 653229 565369 1334443 0 0

2004 3318326 40622 0 153186 846317 810635 1388592 78974 0

2005 3617863 218101 0 123350 731231 805628 1492230 247323 0

2006 4112087 231838 0 149376 697383 1180578 1721058 131854 0

2007 4638987 421629 0 55512 783271 1064362 2096970 217243 0

2008 4125156 0 0 0 823309 998032 1987797 316018 0

2009 3386142 206521 0 0 610895 565565 1721280 281833 48

2010 4708389 670361 0 19619 654333 595920 2505604 262437 115

2011 4206727 502836 50087 56075 835424 605505 1974523 182257 20

2012 4871741 882405 246892 43049 832555 482593 2197652 185500 1095

2013 6046277 1310873 286213 29126 779293 917993 2524375 198262 142

2014 7282376 1856428 333807 18977 836800 892883 3117994 225487 0

2015 7612024 1789587 380601 37299 837323 1065683 3273170 228361 0

2016 7941672 1946504 397084 38914 873584 1032417 3414919 238250 0

2017 8271320 2027300 413566 40529 909845 1075272 3556668 248140 0

2018 8600968 2108097 430048 42145 946106 1118126 3698416 258029 0

2019 8930616 2278200 446531 43760 893062 1160980 3840165 267918 0

2020 9260264 2362293 463013 45375 926026 1203834 3981913 277808 0

2021 9589912 2542286 479496 46991 958991 1150789 4123662 287697 0

2022 9919560 2629675 495978 48606 991956 1190347 4265411 297587 0

2023 10249208 2819557 512460 50221 1024921 1127413 4407159 307476 0

2024 10578856 2910243 528943 51836 952097 1269463 4548908 317366 0

UNLOADED CARGO – CEMENT/GLASS (TONS)


2001 2142659 354534 0 70948 59340 498483 824257 0 335097

2002 1288311 421706 0 70429 80362 336618 13378 0 365818

2003 1340566 404991 0 73038 112188 364930 81681 0 303738

2004 1280669 399955 0 87224 112188 294461 151960 2864 232017

2005 1310328 331104 0 120813 264821 140832 172899 12362 267497

2006 1253957 255564 0 119684 247719 76791 251093 87491 215615

2007 1515531 215463 0 179926 233474 92692 521714 49151 223111

2008 602649 0 0 0 197720 86580 65877 100852 151620

2009 618483 168541 0 0 189932 39162 57767 30927 132154

2010 545967 166699 0 0 205386 37871 32380 6614 97017

2011 511163 160451 0 0 198004 44741 23254 13276 71437

2012 309667 50051 4023 3358 148912 23439 4577 15339 59968

2013 243651 54519 0 0 93782 34880 2344 9897 48229

2014 238189 58145 0 0 85796 23311 8299 16996 45642

2015 232727 58182 0 0 93091 23273 4655 6982 46545

2016 227265 56816 0 0 90906 22726 4545 4545 45453

2017 221803 55451 0 0 88721 22180 4436 6654 44361

2018 216341 54085 0 0 86536 21634 4327 6490 43268

2019 210879 52720 0 0 84351 21088 4218 6326 42176

2020 205417 51354 0 0 82167 20542 4108 6163 41083

2021 199955 49989 0 0 79982 19995 3999 5999 39991

2022 194493 48623 0 0 77797 19449 3890 5835 38899

2023 189031 47258 0 0 75612 18903 3781 5671 37806

2024 183569 45892 0 0 73427 18357 3671 5507 36714

90

LOADED CARGO – METALLIC PRODUCTS (TONS)


2001 241445 13277 0 3889 113780 62230 45928 2173 168

2002 211599 17259 0 2236 97007 65360 28813 238 686

2003 277191 16451 0 6650 115276 107641 30061 1112 0

2004 337296 23134 0 7177 137979 94993 65371 8642 0

2005 349794 25167 0 4017 168789 72362 65752 12841 866

2006 431027 41809 0 2710 218162 78069 71514 18763 0

2007 512270 52466 0 355 247043 73990 111845 26296 275

2008 769428 0 0 0 363855 72917 226975 105059 622

2009 877328 273338 0 0 289511 76344 196543 41592 0

2010 889387 71123 0 699 349090 70623 383882 13970 0

2011 1374099 83863 0 0 710535 84873 459877 34951 0

2012 1702199 64466 957 17917 908041 98069 541445 71135 169

2013 1819660 155680 0 4244 862524 96195 633162 67855 0

2014 1975886 129862 0 2235 974393 92448 688789 88098 61

2015 2117892.8 167314 0 2118 1016589 84716 741262 105895 0

2016 2259899.8 178532 0 2260 1084752 90396 790965 112995 0

2017 2401906.8 189751 0 2402 1152915 96076 840667 120095 0

2018 2543913.8 200969 0 2544 1221079 101757 890370 127196 0

2019 2685920.8 212188 0 2686 1289242 107437 940072 134296 0

2020 2827927.8 223406 0 2828 1357405 113117 989775 141396 0

2021 2969934.8 234625 0 2970 1425569 118797 1039477 148497 0

2022 3111941.8 245843 0 3112 1493732 124478 1089180 155597 0

2023 3253948.8 257062 0 3254 1561895 130158 1138882 162697 0

2024 3395955.8 268281 0 3396 1630059 135838 1188585 169798 0

UNLOADED CARGO – METALLIC PRODUCTS (TONS)


2001 2541014 803733 0 37637 319002 300061 982469 22841 75271

2002 2550746 887998 6009 54331 268942 367010 878905 23398 64153

2003 2238810 855592 7573 18261 185731 277178 837678 9749 47048

2004 2534279 984269 4443 24489 185731 287045 994711 25849 27742

2005 2068209 845289 8895 1890 214680 295749 673015 10394 18297

2006 2549765 964311 10860 5990 372250 325473 822462 19280 29139

2007 2419500 762629 11126 3067 539581 255406 776984 38672 32035

2008 1384335 0 9704 0 415989 226328 576760 125282 30272

2009 1211101 261295 0 0 230711 147176 505758 50083 16078

2010 1521844 573151 0 0 278545 175145 470130 11007 13866

2011 1511935 522916 0 75 323413 118857 519887 14924 11863

2012 1145993 476976 0 2623 251845 76785 294921 25953 16890

2013 1393232 511794 0 891 279685 40895 517902 19619 22446

2014 1613664 535447 0 3834 337256 38876 633457 33122 31672

2015 1500000 525000 0 0 345000 30000 540000 30000 30000

2016 1500000 525000 0 0 345000 30000 540000 30000 30000

2017 1500000 525000 0 0 345000 30000 540000 30000 30000

2018 1500000 525000 0 0 345000 30000 540000 30000 30000

2019 1500000 525000 0 0 345000 30000 540000 30000 30000

2020 1500000 525000 0 0 345000 30000 540000 30000 30000

2021 1500000 525000 0 0 345000 30000 540000 30000 30000

2022 1500000 525000 0 0 345000 30000 540000 30000 30000

2023 1500000 525000 0 0 345000 30000 540000 30000 30000

2024 1500000 525000 0 0 345000 30000 540000 30000 30000

91

LOADED CARGO – SECONDARY RAW MATERIALS (TONS)


2001 5685 0 0 0 127 5558 0 0 0

2002 21812 0 0 0 38 21774 0 0 0

2003 1384 0 0 0 352 1032 0 0 0

2004 2204 0 0 0 1146 1017 0 0 41

2005 77024 0 0 0 21567 20008 35449 0 0

2006 31174 0 0 0 11256 15692 4127 99 0

2007 55842 0 0 0 12078 39429 3986 349 0

2008 116010 0 0 0 34189 24282 24955 32584 0

2009 359329 124997 0 0 48193 77320 21609 87210 0

2010 702985 429242 0 0 57175 80149 44261 92158 0

2011 821952 516019 0 11544 59017 53638 47183 134551 0

2012 831463 508168 0 14107 91500 52688 19833 145167 0

2013 879312 529100 25 31932 127717 45445 17024 128069 0

2014 825579 391531 820 81126 147772 37195 11141 155994 0

2015 839577 436580 0 83958 100749 41979 8396 167915 0

2016 839577 436580 0 83958 100749 41979 8396 167915 0

2017 839577 436580 0 83958 100749 41979 8396 167915 0

2018 839577 436580 0 83958 100749 41979 8396 167915 0

2019 839577 436580 0 83958 100749 41979 8396 167915 0

2020 839577 436580 0 83958 100749 41979 8396 167915 0

2021 839577 436580 0 83958 100749 41979 8396 167915 0

2022 839577 436580 0 83958 100749 41979 8396 167915 0

2023 839577 436580 0 83958 100749 41979 8396 167915 0

2024 839577 436580 0 83958 100749 41979 8396 167915 0

UNLOADED CARGO – SECONDARY RAW MATERIALS (TONS)


2001 275520 0 0 0 221185 30818 23517 0 0

2002 410935 13035 0 0 319945 42385 27895 0 7675

2003 794627 8333 0 0 412992 334610 32757 0 5935

2004 941833 8741 0 0 412992 484264 24040 0 11796

2005 1043075 5285 0 0 485356 544208 5520 0 2706

2006 1160280 4538 0 0 501047 654657 0 38 0

2007 1134155 2628 0 0 428939 652141 50108 339 0

2008 1275405 0 0 0 757925 513488 0 3992 0

2009 1475300 137421 0 0 757734 579009 0 1136 0

2010 1754284 10612 0 247945 981186 495219 11486 32 7804

2011 2118723 0 0 253080 1106891 722864 18863 694 16331

2012 1632337 8451 0 274349 727458 525894 58689 20851 16645

2013 1966055 11104 0 286270 906496 661050 99133 2002 0

2014 2248821 19795 0 239676 1103662 778834 102478 4376 0

2015 2292737.7 22927 0 275129 1146369 802458 45855 0 0

2016 2424808 24248 0 290977 1212404 848683 48496 0 0

2017 2556878.3 25569 0 306825 1278439 894907 51138 0 0

2018 2688948.6 26889 0 322674 1344474 941132 53779 0 0

2019 2821019 28210 0 338522 1410509 987357 56420 0 0

2020 2953089.3 29531 0 354371 1476545 1033581 59062 0 0

2021 3085159.6 30852 0 370219 1542580 1079806 61703 0 0

2022 3217229.9 32172 0 386068 1608615 1126030 64345 0 0

2023 3349300.2 33493 0 401916 1674650 1172255 66986 0 0

2024 3481370.5 34814 0 417764 1740685 1218480 69627 0 0

92

LOADED AND UNLOADED CARGO – UNKOWN CARGO – TRANSSHIPED CONTAINERS IN

SINES

TONS

TEUS

Loaded Unloaded Total Loaded Unloaded Total

2001 0 0 0 0 0 0

2002 0 0 0 0 0 0

2003 0 0 0 0 0 0

2004 0 0 0 0 0 0

2005 0 0 0 0 0 0

2006 0 0 0 0 0 0

2007 0 0 0 0 0 0

2008 3426 4903 8329 35356 50598 85955

2009 460640 613092 1073732 4753804 6327109 11080914

2010 1164305 1536734 2701039 12015628 15859095 27874722

2011 1539129 1783716 3322845 15883811 18407949 34291760

2012 1745971 1993612 3739583 18018421 20574076 38592497

2013 4347268 4307033 8654301 44863806 44448581 89312386

2014 5083806 5030003 10113809 52464878 51909631 104374509

2015 4821810 4821810 9643620.1 49761080 49761080 99522159

2016 5735553.8 5735553.8 11471108 59190915 59190915 118381829

2017 6672414.5 6672414.5 13344829 68859317 68859317 137718635

2018 7622142.6 7622142.6 15244285 78660512 78660512 157321024

2019 8591350.7 8591350.7 17182701 88662739 88662739 177325479

2020 9588609.8 9588609.8 19177220 98954453 98954453 197908906

2021 10619146 10619146 21238292 109589586 109589586 219179172

2022 11686690 11686690 23373379 120606638 120606638 241213275

2023 12793810 12793810 25587619 132032115 132032115 264064230

2024 13942385 13942385 27884770 143885414 143885414 287770828

*tons/TEUs = average = 10.32 tons/TEUs

93

LOADED AND UNLOADED CARGO – RORO THROUGHPUT IN SETÚBAL

TONS

UNITS Loaded Unloaded Total Loaded Unloaded Total

2001 257974 210163 468137 137975 169416 307391

2002 177110 231749 408859 152231 116715 268946

2003 139098 226939 366037 147891 87227 235118

2004 169448 209926 379374 136122 107426 243548

2005 176839 195292 372131 128504 117121 245625

2006 201514 175536 377050 130883 112407 243290

2007 140440 184812 325252 82073 111399 193472

2008 142854 182913 325767 83993 112837 196830

2009 102066 99470 201535 67074 65481 132555

2010 122591 132321 254912 80051 93478 173529

2011 175955 89116 265071 112231 62338 174569

2012 156979 57213 214191 97417 38943 136360

2013 130178 69243 199421 77245 47504 124749

2014 148408 87573 235982 88584 60455 149039

2015 148408 87573 235982 93339 57614 150953

2016 148408 87573 235982 93339 57614 150953

2017 148408 87573 235982 93339 57614 150953

2018 148408 87573 235982 93339 57614 150953

2019 148408 87573 235982 93339 57614 150953

2020 148408 87573 235982 93339 57614 150953

2021 148408 87573 235982 93339 57614 150953

2022 148408 87573 235982 93339 57614 150953

2023 148408 87573 235982 93339 57614 150953

2024 148408 87573 235982 93339 57614 150953

94

CRUISE PASSENGERS THROUGHPUT IN CONTINENTAL PORTUGAL

Leixões Lisbon Portimão

Emb- arked

Disemb- arked Transit

Emb- arked


Emb- arked


2001 71 95 11910 21666 23338 103812

8457

2002 133 118 17650 15742 15881 132636

5355

2003 95 104 22565 24317 20675 166987

9721

2004 132 129 21622 17809 17464 206284 2 2 6502

2005 95 117 17504 20882 23211 195431 10 13 36370

2006 74 88 20467 20404 20564 229925 414 1 26645

2007 122 131 15610 16007 15703 273475 182 126 5798

2008 33 50 25382 18862 20054 368588 0 25 11217

2009 602 615 16407 43101 40701 331885 625 625 23588

2010 140 224 27130 26248 26365 395884 374 383 33843

2011 281 195 41353 25273 24091 453280 305 282 44841

2012 240 686 74687 23424 20582 478598 16 242 18506

2013 601 426 45593 24653 26790 507411 58 30 20141

2014 487 381 63572 21315 20121 459997 137 993 13504

2015 400 400 67158 21000 21000 549580 500 500 28107

2016 450 450 70744 21500 21500 581865 560 560 29336

2017 500 500 74330 22000 22000 614150 620 620 30565

2018 550 550 77916 22500 22500 646435 680 680 31794

2019 600 600 81502 23000 23000 678720 740 740 33023

2020 650 650 85088 23500 23500 711005 800 800 34252

2021 700 700 88674 24000 24000 743290 860 860 35481

2022 750 750 92260 24500 24500 775575 920 920 36710

2023 800 800 95846 25000 25000 807860 980 980 37939

2024 850 850 99432 25500 25500 840145 1040 1040 39168

forecasting cargo throughput in portuguese ports · the aim of this thesis is to forecast the cargo...

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