Investigating the Future Dynamics of Multi-Port Systems: The Case of Poland and the Rhine–Scheldt Delta Region

Abstract

The objective of this paper is to investigate the future evolution of port systems considering the development of major and minor ports, inter-port competition, and feasible cargo shifts resulting from improved capacity or congestion faced by ports. The literature review on port system dynamics indicates that the relationships that emerge between major and minor ports located in the range stem from competition and cooperation. However, we argue that there are essential ports that play a predominant role in shaping these relationships, while inter-port relations in the system are based on competition. With the use of transshipment forecasts, existing and emerging interdependencies among major and minor ports in the system, and capacity development and/or changes in the level of capacity utilisation, the ex-ante dynamics of the port system are evaluated. The subject of research is two port systems, namely, the Polish port system and the Rhine–Scheldt Delta port system. We investigate the future dynamics in each port system and find that the evolution pattern has different features if the minor ports improve capacity or challenge the major ports by offering free capacity. This paper contributes to research on the evolution of multi-port formations and provides new insights to the peripheral port challenge phenomenon.

1. Introduction

Capacity planning is a vital and complex aspect in the process of seaport development. If the capacity of the port is significantly greater than the demand for port services, this can result in inefficient use of the port infrastructure and superstructure and thus more expensive port services. On the other hand, when the demand for port services is greater than the port capacity, then the time of ship and cargo handling is longer, and congestion in the port is inevitable. In turn, this increases the delivery costs and losses of cargo receivers. The point is that port services cannot be stored. Therefore, the supply of port services is determined by the port transshipment capacity. This is why an increase in their volume is incremental and requires investments that are cost and time-consuming.

Moreover, investment in port infrastructure requires considerable time to be accomplished and has an extremely long economic life and a long payback period. Therefore, to avoid the consequences of the poor adjustment of port supply and demand and to create a basis for determining the amount of supply, it is necessary to predict the demand for port services [1]. Before making any decisions about increasing port capacity, the long-term demand for port facilities and services must be determined. It is necessary to prepare port cargo transshipment forecasts.

Determining the level of long-term demand for port facilities and services is required for port master/development plans, and/or investment project evaluations, and/or the evaluation of investment alternatives. While planning investment, the challenge is optimising capacity whereby port facilities and services are provided at the appropriate service time and cost level [2].

In the port industry, throughput is a product, and the creation of capacity is an investment. Port investment results in a potential throughput increase, which is achieved where there is demand for it.

As elaborated in one study [3], an investment in a port infrastructure or superstructure, regardless of who the port authority or the port operators are, normally affects an increase in throughput both in total and per unit of time, as well as an improvement in the level of service in terms of quantity and quality. This reduces the operating and time costs of the port service (i.e., generalised cost) and causes throughput to increase by a growth rate that is directly related to the degree of competition that exists on the market of the port services. Port investors (port authority and/or port operators) increase profits due to cost reduction and increased volume of transshipments. Cutting the generalised cost in ports causes a decrease in the generalised cost in the whole transport chain, both for carriers using the port and its representatives.

This triggers within the transport industry the same kinds of effects: lower costs, higher volumes, and higher profits. Furthermore, such a mechanism is also seen for shippers (and possibly for intermediate operators, such as logistics operators, forwarders, etc.), Lower generalised costs cause both volumes and profit to rise. The final beneficiaries of the lowered prices for transported goods are eventually the final consumers [3].

Port capacity is a measure of the maximum throughput in tonnes, TEU, or the number of vessels that a port and its terminals can service over a given period, while the port throughput reflects the actual amount of cargo or the number of vessels handled over time. Capacity utilisation is the ratio between the actual throughput and the designed capacity and is expressed as a percentage. While the (theoretical) design capacity is defined as the maximum technically possible utilisation rate that can be achieved with existing port resources and facilities, e.g., infrastructure, equipment, labour, technology, etc., then effective (commercial) capacity indicates maximum capacity that can be reached at the quality of service acceptable for most customers, for instance, by incorporating a general acceptable level of congestion. The maximum capacity that each port or terminal can achieve under these arrangements in terms of the faster turnaround time of a ship is described as the commercial capacity [2].

One of the key drivers behind utilisation is, therefore, throughput and, as such, rising freight volumes carried on larger vessels. Terminal performance is affected not only by a disrupted arrival pattern of ships, but also by how quickly cargo is removed from a terminal. Increasing dwell times put pressure on spatial yard capacity and land-side delivery, and collection peaks create disruptions in the workload planning at terminals [4]. As the capacity utilisation rate is above a certain level (in container handling, according to the rule of thumb, the container terminal starts to become congested when its capacity utilisation exceeds 70%), maintaining the performance and quality of transshipment operations, and keeping the turn-around time of the ships at an acceptable level, encounters problems [5]. Port operations are provided at higher costs, and the service time lasts longer. This is attributed to port congestion, and it is especially relevant to containers, whereas in such conditions, the dynamics of container flows slow down.

Regarding the terminology for classifying seaports and port systems, it is worth highlighting that there is no universally accepted terminology, both in theory and in practice. For example, smaller ports are often described in the literature as small and medium-sized, secondary, minor, peripheral, local, regional, or feeder ports, while larger ports are often named major, large, primary, or hub ports, as well as loading centers or gateways [6]. For the purposes of this article, smaller ports are referred to as minor or small and medium-sized ports (SMPs) and are defined as ports that are not as large in size, throughput volumes, or capacity as major seaports. A port system can be defined as a system of two or more ports found in proximity within a given area; however, port system delimitation and delineation are quite complex and the subject of advances in research [7]. Nevertheless, we adopt the view that the port system consists of major (gateway) and minor (smaller) ports competing for the same hinterland.

The scale and scope of port activity are a main property that makes key differences between minor and major ports. What is specific for major ports is that they accommodate large oceangoing vessels and are able to obtain high cargo throughput, while serving a vast hinterland that is mostly international. In turn, small seaports handle smaller vessels, usually in short- and medium-range shipping, have smaller and typically more diversified cargo throughput, and serve their regional hinterland. The major ports face major problems manifested in increased transport congestion and insufficient land resources for expansion. However, in smaller ports, transport congestion is less critical and land resources are typically more considerable [8]. In general, problems in smaller ports’ functioning and development are rarely investigated, although studies on their role in circular and in biomass supply chains [9,10], as well as studies concerning efficiency of container operations [11], positioning in the network [12], their competitiveness and connectivity [13], and their economic importance for regional economy [14] are advancing.

Numerous studies on port system development exist and, according to a recent review [7], research dimensions can be classified as follows:

• Container traffic volumes and related market shares within the given port systems; measurement of container concentration or inequality with the use of the Gini coefficient (including Lorenz curves and Gini decomposition analysis) and the Herfindahl–Hirschman Index (HHI);
• Analysis of complementarity vs. substitutability between ports. Here, the focus is on assessing whether nodes of the same port system act as substitutes to each other (implying competition) or complements (pointing to a high level of interdependence among the nodes);
• Traffic-forecasting studies which deploy time series analysis and more advanced forecasting methods to develop scenarios and prognoses for the future traffic volumes and related market shares of nodes in a port system.

According to Hayuth [15], the port system dynamics feature concentration and, when it eventually reaches its limits, they invert into the process of de-concentration. As a port system develops, diseconomies of scale in some large loading centres appear in the form of insufficient space for expansion and port congestion. This encourages smaller ports or even new ports to attract cargo. The phenomenon is referred to as the peripheral port challenge and refers to the last fifth phase of the Hayuth model [16].

In the research [17], the concentration and de-concentration processes on the examples of two-port systems found in the Rhine–Scheldt Delta port cluster and the port system found in the West Mediterranean port range were investigated. Both port systems consist of several small and medium-sized ports, as well as major ports. It is argued that de-concentration within a port system occurs when some of the cargo is shifted from major ports to smaller and new ports or when the big loading centres only absorb a small part of the container traffic growth in the whole port system.

Seaports are becoming increasingly interrelated with other ports and inland ports. The question arises about finding the right balance between competition and cooperation to achieve a sustainable competitive advantage for both the individual load centres in a port system and the system.

Feng and Notteboom [18] examined the empirical case of Yingkou port in the logistics system of the Bohai Sea of China, which places Yingkou port into a more competitive position in contrast to the dominant ports in that area. Academics conclude that small and medium-sized ports (SMPs) often look for cost advantage in specific niche markets. They might also secure growth by serving the dominant ports in a multi-port gateway region. Such a strategy demands close cooperation between ports.

In another work [19], the role of SMPs in the multi-gateway region of northeast China was studied in the context of five variables: (a) the handled cargo volume and market share; (b) the international connectivity; (c) a cluster’s relative position; (d) the port city and hinterland connections; and (e) the logistics and distribution function. They introduced the Herfindahl–Hirschman Index (HHI index) to measure the market concentration of the multi-port region and elaborate with historical data their position changes in the relevant port system. In conclusion, they say that SMPs develop independently, which requires ports to find their specific competitive advantage or cooperate when they seek cooperation with larger neighbouring ports.

In research on the phenomenon of the dynamics of the port system and evolution patterns, minor ports were found to be instrumental to the peripheral port challenge and thus to the de-concentration of the port system.

In another research [20], the port system located in the Pearl River Delta (PRD) with hub and peripheral ports were studied to reveal system dynamics. Scholars analysed 25 container ports in the region and used historical data from between 1970 and 2007 on container throughputs and capacity and introduced the Gini coefficient to assess the changes in concentration level of container traffic in the port system. With the use of an econometric model, they estimated container traffic and compared it with past traffic development. As claimed, since 1997, the development of the container port system in the PRD has stepped into the phase of the peripheral challenge, and the challenge is mainly between the Hong Kong and Shenzhen ports.

In further studies, e.g., [21,22,23], deconcentrating patterns in port systems are investigated under the assumption that the peripheral port challenge is an inherent ingredient of the development of contemporary port systems. It is argued that de-concentration within a port system occurs when some of the cargo is shifted from large to smaller ports or when the large load centres only absorb a small portion of the container traffic growth in the whole port system.

The above studies concentrate on ex-post port system evolution and thereafter the tendencies revealed in the past are evaluated in view of concentration or de-concentration of the multi-port formations; some recommendations for port policy are also contemplated.

Yet, studies focused on the ex-ante evolution of port systems remain under-researched. Its assessment requires prediction of freight flows in the port system, which is a challenging task. It becomes more complex if we consider interactions between ports and the future cargo flow shifts among ports, while these issues remain poorly recognised in academic work.

The objective of the paper is to investigate the future evolution of port systems considering the development of major and minor ports, inter-port competition, and feasible cargo shifts resulting from improved capacity or congestion faced by ports. The literature review on port system dynamics indicates that the relationships that emerge between major and minor ports located in each range stem from competition and cooperation. However, we argue that there are essential ports that play a predominant role in shaping these relationships, while inter-port relations in the system are based on competition.

With the use of transshipment forecasts, existing and emerging interdependencies among major and minor ports in the system, and capacity development and/or changes in the level of capacity utilisation, the ex-ante dynamics of the port system are evaluated. The subject of investigation is two port systems, namely, the Polish port system and the Rhine–Scheldt Delta port system. We investigate the future dynamics in each port system and find that the evolution pattern has different features if the minor ports improve capacity or challenge the major ports by offering free capacity.

This paper thus contributes to studies on multi-port formations through the ex-ante evolution of the port system and provides new insights on the peripheral port challenge phenomenon.

The remainder of this paper is organised as follows. Section 2 provides a comprehensive review of research on long-term demand forecasting in ports. Section 3 and Section 4 include a quantitative analysis of demand prediction, considering major and minor ports, developments of capacity, and feasible inter-port shifts of cargo flows. The investigation concerns the Polish port system and the Rhine–Scheldt Delta port system. In Section 5, we discuss lessons to be learnt, while conclusions are made in Section 6.

2. Review of Selected Port Transshipment Prognostic Studies

From the perspective of the public investor (port authorities), the main aspects of port planning and development are as follows [24]:

• Demand forecasting for port facilities and services defines the long-term direction of port development, the future port size (land and water areas), and the portfolio of commodities to be served;
• To optimise/rationalise investment decisions, it is necessary that port traffic demand exercises consider its competitive environment and changes thereof;
• Authorities create the infrastructural preconditions for enabling private concessionaires (port operators) to realise their own projects, thus the future demand development should stimulate and guide port operators in their own business planning, also trying to harmonise such investment programmes in terms of timing, sizing, and technological levels;
• Traffic forecasts have become fundamental for port planning and development, as well as to support associated investment decisions, to manage processes in public–private partnerships, and to renew concessions or land leases.

Therefore, a good assessment of future demand and the timing of port capacity increases are crucial to position the port for sustained growth.

A review of research works on the subject matter of port transshipment methods devoted to forecasting [24,25] revealed that: (1) the subject has not received sufficient attention and, most likely, no common accepted and universal guide on port throughput forecasting exists; (2) in practice, predictions are usually based on casual relationships between port transshipments and other exogenous variables, such as demographic, economic or industrial growth; (3) most articles dealing with the subject do not relate to casual models applied in practice, but instead refer to methods that are based on mere trend extrapolation from historical data and trend-based models. However, these methods are less suitable for long-term predictions of port throughput.

In a pragmatic approach, prognostic works apply the causal relationships between port transshipments and socio-economic variables, while the latter are selected on theories, knowledge, experience, and best practice of the researcher. Cargo transshipment is dependent on imports and exports, which are a component of GDP that is considered from an expenditure approach. Therefore, GDP is a proper factor that can be considered as an explanatory variable in cargo transshipment models. In one study [25], a mix of quantitative and qualitative methods are applied to (very) long-term port throughput forecasts in the Le Havre–Hamburg range, including, in their framework system, dynamic modelling, judgement, and causal relations. Their combined method consists of three steps. First, they propose a probabilistic forecast of the working-age population. Second, expert judgment is applied to define assumptions concerning the development of several variables (i.e., employment, annual working hours, and GDP per hour) to derive the GDP forecast for the sample geographic area. Third, the authors calculate the port throughput forecast by scrutinising the causal relation between GDP and port throughput.

In another study [26], the authors proposed an approach for predicting cargo throughput that applied a cargo transport model to the Hamburg–Le Havre range. In that model, two main components of the study were used: expert judgment and commodity-specific research. Needless to say, a disaggregated forecasting approach is needed in terms of commodity types, as each coherent and homogeneous commodity group has different throughput drivers and terminal requirements.

The relationships between GDP and industrial growth and port transshipments have been applied to Antwerp port to forecast container transshipments and liquid bulk cargo [27]. Other empirical evidence [28] proved that the correlation coefficient between GDP and container handling was remarkably high and amounted to 0.97. It confirms a strong and positive correlation between variables of interest. The two-dimensional linear regression equation showed the relationship between ports and the regional economy. In one paper [5], the empirical analysis is based on an annual time series (1995–2017) for total container throughput for the main ports within the Hamburg–Le Havre range and economic indices based on GDP components. The study shows that there is a long-term relationship between the EU-19 trade indices and total container throughput. Therefore, there is a lot of empirical evidence showing very strong and positive correlations between transshipments in ports and the value of goods and services produced in the economy, measured by GDP or gross value added.

In another paper [29], forecasts of the import container transshipments of Taiwan were developed with the use of a modified regression model. There are attempts, e.g., [30], to use relevant indexes of containerisation that refer to the macroeconomic descriptors of the country of cargo origin and the country of cargo destination, such as foreign trade, population, and gross domestic product.

So far, port cargo turnover predictions have focused mainly on major ports and container turnover. With a few exceptions, e.g., [31], the issue of port development throughput prediction in minor seaports is limited in scientific works. Cargo throughput forecasts in smaller seaports cover many types of cargo (besides containers) and are mostly decided by the relationships that occur between major and minor seaports that are in the given port system.

3. The Polish Port System

The system of Polish seaports includes the major ports of Gdańsk, Gdynia, Świnoujście, and the minor port of Szczecin, which are in the Baltic Sea within a range of 500 km. Gdańsk is a deep-water port that is able to service the largest vessels in the Baltic Sea. It notes the largest transshipments of oil and oil products and dry bulk goods, as well as being a container gateway with dynamically increasing container freight; in 2020, recorded 20 million tonnes (around two million TEU). Gdynia specialises in containers provided by feeder services and short sea shipping (around 0.9 million TEU in 2020), roll-on–roll-off traffic, and cereal, while Świnoujście focuses on roll-on–roll-off ferry traffic, dry bulk, and transshipment of liquified natural gas in the modern LNG terminal. Szczecin is a minor and universal port with a total annual throughput limited to ten million tonnes.

In the period observed in the study, 2007–2020, the port in Gdańsk recorded the highest growth rate of cargo throughput. Transshipments in the port of Gdańsk more than doubled in that time and increased on average by 8.39% annually. In the same period, the port of Świnoujście saw cargo throughput increase by 32.8%, that is, by an average of 6.7% annually, while in the port of Gdynia, it grew by 41.1%, that is, by an average of 2.91% annually. The port in Szczecin was the only port that did not record an increase in cargo throughput. For example, in 2007, the cargo handled by the port of Szczecin amounted to 9487 thousand tonnes, and, in 2020, the number of tonnes transshipped was almost the same, amounting to 9285 thousand tonnes, which decreased the cargo throughput by 2.29% during the analysed period (Figure 1). As a result, the role of the port in Szczecin as a load center decreased compared to other Polish seaports (

Table 1. Transshipments in the Polish port system by type of cargo and ports (selected years only).

Year	Coal	Ore	Other Bulk Cargo	Cereal	General Cargo	Containerised Cargo	Crude Oil and Oil Products	Total
	(In Thous. Tonnes)
	Gdańsk
2007	1893	0	3234	770	1498	840	11,591	19,826
2010	3181	0	2687	781	1185	4947	14,401	27,182
2015	4488	0	3446	1455	1108	10,706	14,710	35,914
2020	5700	0	5100	1500	2100	20,000	13,700	48,100
	Gdynia
2007	670	0	3241	1456	4205	6144	1273	16,989
2010	1684	4	2264	1664	3307	4853	916	14,692
2015	1386	0	1356	3711	4431	6848	402	18,134
2020	1685	0	1564	5430	5158	9051	1772	24,660
	Świnoujście
2007	2308	561	203	65	5386	0	716	9238
2010	4700	160	217	34	6133	0	843	12,087
2015	1588	1459	177	646	8701	0	1378	13,948
2020	1548	1289	267	676	13,486	0	4243	21,508
	Szczecin
2007	2015	525	2761	1452	1830	601	302	9487
2010	2595	311	2009	1309	1753	581	198	8756
2015	1532	393	3288	1098	1878	675	361	9226
2020	1009	396	2873	1200	2409	700	699	9285

Source: Port authorities.

).

The total transshipments in the Polish port system are presented in Figure 1.

The port of Szczecin lost its share in the market. The share of Szczecin port in total freight handling in major ports in the Polish port system decreased from 17.1% in 2007 to 9.0% in 2020 (a decrease of 8.1 percentage points).

It is worth highlighting that the port of Szczecin is located 65 km south of the Baltic Sea and is connected to the sea by a waterway. The quality of the waterway determines the access to the port from the sea. The current depth of the fairway, which is 10.5 m, allows safe navigation and seagoing service ships with a draught of 9.15 m and a carrying capacity of up to 20,000 deadweight tonnes. At the current stage of development of sea trade and shipping, accessibility to Szczecin port from the sea is considered low. The navigational conditions of all other significant and major ports located on the southern coast of the Baltic Sea allow them to serve larger sea vessels compared to the port of Szczecin.

In summary, regarding the port of Szczecin, the limited access to the seaport is a key factor of the adverse trends in the growth rate of cargo throughput. The port in Szczecin plays the role of a minor port in the port system and is marginalised in the logistics grid because the competing and neighbouring major ports in Gdańsk, Gdynia, and Świnoujście have deeper canals, fairways, and basins, as well as deep-water quays; thus, they are able to serve larger vessels and increase their cost advantage and position in handling trade.

However, large-scale investments are being accomplished recently in the port of Szczecin. As a consequence, the nautical access to the port will be improved. Right now, the Świnoujście–Szczecin waterway is deeper by up to 12.5 m, and the port authority is adjusting some seasides to that depth in the Kaszubski Basin and the Dębicki Canal. Due to such an improvement, larger vessels of up to 40,000 DWT will be served in the port. Thus, it will also significantly increase cargo handling capacity.

In the work on forecasts for the Polish port system, the GDP forecasts expressed in current prices were used. Then, on the basis of the regression models, cargo transshipments were forecasted for the main types of cargo. Consequently, based on the cargo forecasts, annual growth chain indexes were calculated, and then, after investment in the port in Szczecin was finished, such indexes were used to forecast the freight transsipment for the port of Szczecin under new circumstances, that is, an improved access to the sea. However, the Polish GDP forecast did not consider the economic impact of the COVID-19 pandemic.

To forecast cargo transshipments in the major Polish seaports, regression models of transshipments by cargo type in relation to GDP in Poland were used with the following general formula:

$$C_{tj}={\alpha }_{1j}·GD{P}_t+{\alpha }_{0j}+{\xi }_{tj}$$

(1)

where:

$C_{tj}$—the cargo throughput of the j-th group over time t;

GDP_t—the observations of GDP in Poland over time t;

${\alpha }_{1j},{\alpha }_{0j}$—the structural parameters of the regression model for the j-th cargo group; and

${\xi }_{tj}$—the random component of the model over time t for the j-th cargo group.

To estimate the parameters of the regression models for each main cargo type, the ordinary least squares estimator (OLSE) was used based on the data set from 2007–2020. Cargo transshipment models with estimates and regression statistics by type of cargo are presented below.

• Bulk goods (coal + ore + other bulk)

$$\begin{gathered} {\widehat{C}}_{t1}=\underset{\left(1.32\right)}{5.17·}GD{P}_t+\underset{\left(2,337,892\right)}{5,229,160}; R^2=0.560 \\ t\text{-}Stat: 3.909 2.237 \\ p\text{-}value: 0.002·0.045 \end{gathered}$$

(2)

2.

Cereal

$$\begin{gathered} {\widehat{C}}_{t2}=\underset{\left(0.86\right)}{3.22·}GD{P}_t-\underset{\left(1,526,802\right)}{1,563,098}; R^2=0.537 \\ t\text{-}Stat: 3.730-1.024 \\ p\text{-}value: 0.003·0.326 \end{gathered}$$

(3)

3.

General cargo

$$\begin{gathered} {\widehat{C}}_{t3}=\underset{\left(0.80\right)}{7.51·}GD{P}_t-\underset{\left(1,410,461\right)}{6,533,897}; R^2=0.881 \\ t\text{-}Stat: 9.409-4.632 \\ p\text{-}value: 0.000·0.001 \end{gathered}$$

(4)

4.

Containers

$$\begin{gathered} {\widehat{C}}_{t4}=\underset{\left(0.73\right)}{13.77·}GD{P}_t-\underset{\left(1,281,923\right)}{11,175,750}; R^2=0.968 \\ t\text{-}Stat: 18.974-8.718 \\ p\text{-}value: 0.000·0.000 \end{gathered}$$

(5)

5.

Crude oil and oil products

$$\begin{gathered} {\widehat{C}}_{t5}=\underset{\left(0.73\right)}{13.77·}GD{P}_t-\underset{\left(2,164,490\right)}{12,687,123}; R^2=0.922 \\ t\text{-}Stat: 11.939-5.861 \\ p\text{-}value: 0.000·0.000 \end{gathered}$$

(6)

The models presented above are accompanied by some important regression statistics that show the goodness of fit of the models used and the statistical significance of the parameter estimates. The coefficient of determination R² provides a measure of how well observed outcomes are replicated by the model. Models (4)–(6) are characterised by a very high degree of explanation of the variance of the endogenous variable (transshipments). The degree of matching of the models to empirical data was, respectively, 88.1%, 96.8%, and 92.2%. In the case of models (2) and (3), this level was moderate and amounted to 56.0% and 53.7%. Furthermore, the standard errors of the estimation are given in the brackets below each parameter estimate.

The next statistic presented above is a Student’s t-test (t-Stat), which is a comparison of the estimate obtained with the standard error of the estimation. It is used in significance tests of parameter estimates. The absolute value of the t-Stat should be greater than the critical value, which is 2.179 for a significance level of α = 0.05 and degrees of freedom of df = 12. The latter depends on the number of observations (n = 14) and the number of parameters to be estimated (k = 2). This implies requirements for an empirical significance level denoted as a p-value less than 0.05.

In summary, the fit of the models for general cargo, containers, and crude oil and products turned out to be remarkably high, while for bulk cargo, such as coal, ore, cereal, and other bulk cargo, it was medium. Importantly, from the point of view of the forecasts, all estimates of the slope parameters of the models were statistically significant.

Applying the extrapolation of the cargo turnover models, forecasts of all types of cargo transshipment in the major Polish seaports were produced (

Table 2. Forecasted throughput in major Polish seaports by type of cargo and ports (selected years only).

Year	Bulk Goods (Coal + Ore + Other Bulk)	Cereal	General Cargo	Containerised Cargo	Crude Oil and Oil Products	Total
(In thous. tonnes)
			Gdańsk
2022	11,461	1286	1246	16,080	16,679	46,752
2025	12,288	1448	1439	18,486	19,259	52,920
2030	13,784	1740	1789	22,842	23,930	64,084
2035	15,335	2042	2151	27,354	28,768	75,650
2040	16,929	2354	2523	31,995	33,746	87,547
2043	17,944	2552	2760	34,949	36,913	95,118
Gdynia
2022	3448	4656	3060	7277	2157	20,599
2025	3697	5240	3534	8366	2491	23,328
2030	4147	6298	4393	10,337	3095	28,270
2035	4613	7393	5282	12,379	3721	33,388
2040	5093	8520	6197	14,480	4365	38,654
2043	5398	9237	6779	15,817	4774	42,005
Świnoujście
2022	3294	579	8002	0	5165	17,040
2025	3531	652	9242	0	5964	19,389
2030	3962	784	11,486	0	7411	23,642
2035	4407	920	13,811	0	8909	28,047
2040	4865	1060	16,202	0	10,451	32,578
2043	5157	1149	17,724	0	11,432	35,462

Source: Own calculations.

).

With reference to all major Polish ports, the highest growth rate in the forecast period refers to oil and oil products (+180.6%), containerised cargo (+136.0%), and conventional general cargo (+128.4%). The smallest increases relate to cereal (+88.0%) and dry bulk cargo (coal, iron ore, and other bulk) (+77.4%).

The admissibility of forecasts was assessed by ex-ante errors such as variance of prediction error, standard error of prediction, and relative prediction error. Furthermore, 95% confidence intervals were stated in the forecast period. Such intervals create the area that shows the most probable range of variability for forecasts (

Table 3. Summary of ex-ante prediction errors and 95% Neyman confidence intervals for transshipment forecasts in major ports (Gdańsk–Gdynia–Świnoujście) by type of cargo (selected years only).

Year	Variance of Prediction Error	Standard Error of Prediction	Relative Prediction Error	The Lower (95%) Neyman Confidence Curve for the Prediction	The Upper (95%) Neyman Confidence Curve for the Prediction
	[Thous. Tonnes]2	Thous. Tonnes	%	Thous. Tonnes	Thous. Tonnes
Bulk goods (coal + ore + other bulk)
2022	4,064,847	2016	11.2%	13,518	22,341
2025	4,824,963	2197	11.4%	14,446	24,052
2030	6,598,568	2569	12.0%	15,837	27,060
2035	9,005,603	3001	12.7%	17,097	30,200
2040	12,046,067	3471	13.4%	18,275	33,421
2043	14,174,393	3765	13.9%	18,955	35,381
Cereal
2022	1,733,646	1317	20.7%	3470	9232
2025	2,057,833	1435	20.0%	4037	10,310
2030	2,814,271	1678	19.6%	4879	12,209
2035	3,840,864	1960	19.8%	5637	14,193
2040	5,137,614	2267	20.1%	6340	16,231
2043	6,045,339	2459	20.3%	6744	17,472
General cargo
2022	1,451,718	1205	10.1%	9281	14,540
2025	1,728,382	1315	9.5%	10,959	16,696
2030	2,373,932	1541	9.1%	13,662	20,383
2035	3,250,035	1803	8.9%	16,286	24,148
2040	4,356,693	2087	8.9%	18,861	27,962
2043	5,131,353	2265	8.9%	20,390	30,266
Containers
2022	1,222,134	1106	4.9%	20,210	25,048
2025	1,450,670	1204	4.6%	23,509	28,776
2030	1,983,921	1409	4.4%	28,920	35,074
2035	2,707,618	1645	4.3%	34,260	41,445
2040	3,621,763	1903	4.4%	39,555	47,860
2043	4,261,663	2064	4.4%	42,717	51,724
Crude oil and oil products
2022	3,484,225	1867	8.0%	19,143	27,312
2025	4,135,767	2034	7.5%	22,512	31,406
2030	5,656,030	2378	7.2%	27,984	38,375
2035	7,719,244	2778	7.1%	33,335	45,465
2040	10,325,409	3213	7.0%	38,609	52,632
2043	12,149,725	3486	7.1%	41,749	56,957

Source: Own calculations.

and Figure 2).

The matrix of indices of changes in demand for cargo group transshipments, established for major Polish ports, was used to predict the demand for transshipment services in the port of Szczecin. Applying the average transshipments for the period 2017–2020 in the port of Szczecin and the matrix of chain indices derived from the forecasts of throughput in the major ports, the recursive equation was formulated to produce the forecasts of cargo transshipment in the port of Szczecin, as follows:

$$C_{t,j}^{*}=C_{t-1,j}^{*}·i_{t/t-1,j}+{\overline{T}}_{t-k-1,j}$$

(7)

where:

$C_{t,j}^{*}$—forecasts for the j-th cargo group in time t;

$C_{t-1,j}^{*}$—forecasts for the j-th cargo group in time t − 1;

$i_{t/t-1,j}$—the annual chain indices of cargo throughput growth in the j-th cargo group; and

${\overline{T}}_{t-k-1,j}$—the average level of transit in the j-th cargo group determined from k time periods.

The predicted cargo throughput volumes were limited by the handling capacity of the port of Szczecin. The outcome of this procedure is verified and calibrated demand forecasts in the minor port of Szczecin (

Table 4. Throughput forecasts by cargo group in the port of Szczecin (selected years only).

Year	Bulk Goods (Coal + Ore + Other Bulk)	Cereal	General Cargo	Containerised Cargo	Crude Oil and Oil Products	Total
(In thous. tonnes)
2022	4402	1155	2754	706	339	9357
2025	4681	1287	2996	812	391	10,168
2030	5187	1526	3400	1003	484	11,600
2035	5710	1773	3803	1202	581	13,069
2040	6249	2028	4206	1406	680	14,568
2043	6591	2189	4448	1535	743	15,508

Source: Own study.

).

The predicted increase in transshipments is 6.1 million tonnes, from 9.4 million tonnes in 2022, to 15.5 million tonnes in 2043.

4. The Rhine–Scheldt Delta Port System

The Rhine–Scheldt Delta port system includes such ports as the port of Rotterdam (NL), the port of Antwerp (BE), respectively, the largest and third largest container ports in Europe, as well as Europe’s fourth largest port of Amsterdam (NL), followed by the two medium-sized ports of Ghent and Zeebrugge (BE). The port of Rotterdam is a downstream port; Antwerp, Amsterdam, and Ghent are upstream ports; while Zeebrugge is a deep-water coastal port [32]. Rotterdam, Antwerp, and Amsterdam (named ‘ARA’ ports) are the major ports, while Zeebrugge and Ghent play the role of minor ports in the Rhine–Scheldt Delta system.

Table 5. Transshipments in the Rhine–Scheldt Delta port system by port and type of freight (selected years only).

Year	Liquid Bulk Goods	Dry Bulk Goods	Large Containers	Ro–Ro Mobile Self-Propelled Units	Ro–Ro Mobile Non-Self-Propelled Units	Other Cargo Not Elsewhere Specified	Total
	(In thous. tonnes)
	Rotterdam
2000	145,255	87,455	51,336	9110	1238	8152	302,545
2005	167,870	87,695	70,998	9600	1381	8276	345,819
2010	209,503	81,448	85,929	8136	0	10,746	395,763
2015	216,571	82,693	105,281	3821	7905	20,659	436,931
2020	187,680	57,581	128,734	3606	7589	24,047	409,236
2021	199,734	71,655	133,753	5352	10,113	23,824	444,431
	Antwerp
2000	33,252	25,268	32,984	477	1382	22,625	115,988
2005	36,841	26,685	59,529	1894	3484	17,384	145,817
2010	40,467	19,438	83,644	2591	2890	10,983	160,012
2015	66,123	13,910	95,387	1937	2796	9955	190,107
2020	67,574	11,509	115,680	1045	3809	6701	206,319
2021	70,798	13,391	114,574	1073	4461	11,553	215,852
	Amsterdam
2000	13,678	41,553	585	428	29	5035	61,309
2005	19,314	43,774	714	489	14	4999	69,304
2010	37,444	46,121	593	604	6	5131	89,899
2015	43,861	42,716	271	500	153	11,274	98,776
2020	45,340	37,166	625	356	83	5904	89,474
2021	40,947	39,386	1162	464	152	5999	88,111
	Zeebrugge
2000	4479	2385	5531	1287	18,082	897	32,660
2005	4163	1719	5514	2426	13,580	1040	28,442
2010	5872	1534	12,397	2164	10,833	1078	33,878
2015	4791	1286	2663	2539	11,368	1168	23,815
2020	12,154	1710	4430	2014	10,784	631	31,723
2021	11,162	1738	6720	4043	15,790	677	40,130
	Gent (Ghent)
2000	2930	16,218	52	104	1204	4209	24,717
2005	3340	13,055	31	76	1008	4619	22,127
2010	4240	18,159	140	221	1604	3207	27,572
2015	3692	16,814	4	0	2069	3564	26,143
2020	4515	19,846	9	0	1963	3094	29,427
2021	5422	20,590	10	0	2490	3180	31,691

Source: Eurostat data browser for gross weight of goods transported to/from main ports; variables denoted as [mar_go_am_be], [mar_go_am_nl].

shows the development of traffic by port and freight category and the analysis is based on Eurostat data and its nomenclature (Eurostat aggregates cargo to six types: liquid bulk goods (liquefied gas, crude oil, oil products, and other liquid bulk goods); dry bulk goods (ores, coal, agriculture, and other dry bulk goods); large containers (20-foot freight units, 40-foot freight units, freight units over 20-feet and under 40-feet in length, and freight units over 40-feet long); other general cargo (including small containers) and forestry products, iron, and steel products. Self-propelled mobile roll-on–roll-off and non-self-propelled mobile roll-on–roll-off units are self-clear).

The total transshipments in the Rhine–Scheldt Delta port system are presented in Figure 3.

Three major ports in Amsterdam, Rotterdam, and Antwerp specialise in the processing and trading of oil and oil products. While Rotterdam and Antwerp are important in every throughput category, Amsterdam focuses on oil products. Rotterdam features many oil terminals and oil refineries, but fewer petrochemical plants. The port of Antwerp has fewer oil terminals and oil refineries, but more and larger petrochemical plants. Amsterdam is clearly a logistics oil centre focused on the storage, blending, and distribution of oil products [33]. The port of Zeebrugge focuses on the transshipment of liquefied natural gas with the use of a modern and recently expanded in capacity LNG terminal. Considering the highly specified technology interconnected with oil refining and processing and the long-term established cooperation with the refineries and petrochemical plants in the vicinity, as well as most of these industries receiving oil and oil products by pipelines and/or river, the possible shifts of oil flows between ports are very limited, while, in the case of liquefied natural gas processed at the Zeebrugge LNG terminal, there is no alternative for transshipment at all.

Dry bulk is the important cargo group handled in the port system. In the ports of Rotterdam, Antwerp, and Amsterdam, coal, iron ore, and scrap predominate. A substantial part of the dry bulk is processed in the ports before it is transshipped into Europe. As most bulk commodity users are in seaports or control seaport terminals, bulk flows do not often switch between ports. The growing supply of green energy and the reduced demand for coal and ores from the steel sector explain the decline in dry bulk transshipment in the ARA ports, while in Ghent, a moderate growing trend in transshipment results from the more diversified cargo group in transshipment, including ores and agricultural dry commodities.

In the roll-on–roll-off traffic sector, there is a clear declining trend in ro–ro mobile self-propelled units and increasing traffic of ro–ro mobile non-self-propelled units. Zeebrugge is a leading port for roll-on–roll-off traffic, including ferry traffic, and it is the hub port for the automotive industry for new vehicles.

The ports of Rotterdam and Antwerp are, respectively, the largest and third largest container ports in Europe, and the fiercest competition is between these ports. In the late 1990s, Antwerp decided to build container capacity along the Scheldt River in front of the locks, thereby allowing considerable savings in the port turnaround time of container vessels [34]. The last expansion in 2005 concerned the new Deurganck dock, with an annual capacity of 9.8 million TEU. Rotterdam has deep-water access and made a large investment in a new port area called Maasvlakte II. From 2013, a capacity of 5.2 million TEU was operational out of the total container capacity of Maasvlakte II, estimated at 12.0 million TEU [5]. As a result of expansion in container capacity, both ports recorded substantial increases in container transshipments; however, dynamics of transshipment in both ports and thereafter their share in the market were subject to periodical fluctuation. Zeebrugge is an important centre for containerised cargo, accommodating ocean container ships. It recorded a very dynamic growth in container transshipment at more than 12 million tonnes in 2010 and thereafter exhibited a similar dynamic decrease in transshipment, down to 2.3 million tonnes in 2015. Since 2019, transshipment volumes in Zeebrugge have been increasing again, and, in 2021, Zeebrugge recorded 6.7 million tonnes of handled containerised cargo, which translates to +52.3% yearly growth. Recapitulating, container flows are sensitive to port congestion and they are very susceptible to the cargo shifts between ports.

To determine the transshipment forecasts in the Rhine–Scheldt Delta port system, we applied regression models in relation to the GDP of Belgium, the Netherlands, France, and Germany, but again the GDP forecast did not consider the economic impact of the COVID-19 pandemic.

To estimate the parameters of regression models for each main cargo type, as before for the Polish port system, the ordinary least squares estimator (OLSE) was used based on the data set for 2000–2021. Basic regression statistics and models are summarised below.

• Liquid bulk goods

$$\begin{gathered} {\widehat{C}}_{t1}=\underset{\left(0.0047\right)}{0.0543·}GD{P}_t-\underset{\left(24,608\right)}{12,873}; R^2=0.872 \\ t\text{-}Stat: 11.657-0.523 \\ p\text{-}value: 0.000·0.607 \end{gathered}$$

(8)

2.

Dry bulk goods

$$\begin{gathered} {\widehat{C}}_{t2}=\underset{\left(0.0030\right)}{-0.0122·}GD{P}_t+\underset{\left(15,648\right)}{206,374}; R^2=0.460 \\ t\text{-}Stat:-4.124 13.189 \\ p\text{-}value: 0.001·0.000 \end{gathered}$$

(9)

3.

Large containers

$$\begin{gathered} {\widehat{C}}_{t3}=\underset{\left(0.0019\right)}{0.0608·}GD{P}_t-\underset{\left(10,045\right)}{145,986}; R^2=0.981 \\ t\text{-}Stat: 31.984-14.632 \\ p\text{-}value: 0.000·0.000 \end{gathered}$$

(10)

4.

Ro–Ro mobile self-propelled units

$$\begin{gathered} {\widehat{C}}_{t4}=\underset{\left(0.0004\right)}{-0.0023·}GD{P}_t+\underset{\left(2,306\right)}{20,788}; R^2=0.583 \\ t\text{-}Stat:-5.285 9.718 \\ p\text{-}value: 0.000·0.000 \end{gathered}$$

(11)

5.

Ro–Ro mobile non-self-propelled units

$$\begin{gathered} {\widehat{C}}_{t5}=\underset{\left(0.0004\right)}{0.0047·}GD{P}_t-\underset{\left(2,235\right)}{17,185}; R^2=0.861 \\ t\text{-}Stat: 11.150-7.689 \\ p\text{-}value: 0.000·0.000 \end{gathered}$$

(12)

6.

Other cargo not elsewhere specified

$$\begin{gathered} {\widehat{C}}_{t6}=\underset{\left(0.0009\right)}{0.0034·}GD{P}_t+\underset{\left(4,874\right)}{16,252}; R^2=0.410 \\ t\text{-}Stat: 3.727 3.335 \\ p\text{-}value: 0.001·0.003 \end{gathered}$$

(13)

Models (8), (10), and (12) are characterised by a very high degree of explanation of the variance of the endogenous variable (transshipments). The degree of matching of the models to empirical data was, respectively, 87.2%, 98.1%, and 86.1%. In the case of models (9), (11), and (13), this level was moderate and amounted to 46.0%, 58.3%, and 41.0%.

All estimates of parameters turned out to be statistically significant, which means that the absolute values of the t-Stat were greater than the critical value (2.086) calculated for α = 0.05 and degrees of freedom df = 20. The only exception is the intercept parameter in model (8), but it does not constitute an obstacle in forecasting, as it does not multiply the forecast error in the extrapolation process.

In summary, the fit of the models for liquid bulk goods, large containers, and ro–ro mobile non-self-propelled units turned out to be remarkably high, while for dry bulk goods, ro–ro mobile self-propelled units, and other cargo not elsewhere specified, it was medium. Importantly, from the point of view of forecasts, all slope parameters of the models were statistically significant, as before for the Polish port system.

Applying the extrapolation of the cargo turnover models, forecasts of all types of cargo transshipment were produced in the major ports of the Rhine–Scheldt Delta, namely, Rotterdam, Antwerp, and Amsterdam (

Table 6. Forecasted volumes of transshipments in the major ports of the Rhine–Scheldt Delta port system (selected years only).

Year	Liquid Bulk Goods	Dry Bulk Goods	Large Containers	Ro–Ro Mobile Self-Propelled Units	Ro–Ro Mobile Non-Self-Propelled Units	Other Cargo Not Elsewhere Specified	Total
	(In thous. tonnes)
Rotterdam
2022	225,527	71,615	140,651	3893	9948	22,652	474,285
2025	239,128	68,867	153,387	3435	11,214	23,425	499,457
2030	261,797	64,288	174,615	2829	13,323	24,714	541,566
2035	284,466	59,708	195,842	2367	15,432	26,003	583,820
2040	307,136	55,129	217,070	2007	17,541	27,292	626,175
2043	320,737	52,381	229,807	1828	18,806	28,066	651,625
Antwerp
2022	79,941	13,384	120,483	780	4388	10,984	229,960
2025	84,762	12,870	131,393	689	4947	11,359	246,019
2030	92,797	12,014	149,577	567	5877	11,985	272,817
2035	100,832	11,158	167,760	475	6807	12,610	299,643
2040	108,868	10,303	185,944	402	7738	13,235	326,489
2043	113,689	9789	196,854	367	8296	13,610	342,604
Amsterdam
2022	46,235	39,364	1222	337	150	5704	93,011
2025	49,023	37,854	1333	298	169	5899	94,574
2030	53,670	35,337	1517	245	200	6223	97,193
2035	58,318	32,819	1701	205	232	6548	99,824
2040	62,965	30,302	1886	174	264	6872	102,463
2043	65,754	28,792	1996	159	283	7067	104,050

Source: Own calculations.

). One of the most important premises of forecast theory known as the ‘dynamic status quo’ was adopted, which means that statistical regularities observed in the past continue during the forecast period.

As before, the admissibility of forecasts was examined by ex-ante errors such as variance of prediction error, standard error of prediction, relative prediction error, and 95% confidence intervals in the forecast period (

Table 7. Summary of ex-ante prediction errors and 95% Neyman confidence intervals for transshipment forecasts in the major ports of the Rhine–Scheldt Delta port system by type of cargo (selected years only).

Year	Variance of Prediction Error	Standard Error of Prediction	Relative Prediction Error	The Lower (95%) Neyman Confidence Curve for the Prediction	The Upper (95%) Neyman Confidence Curve for the Prediction
	[Thous. Tonnes]2	Thous. Tonnes	%	Thous. Tonnes	Thous. Tonnes
Liquid bulk goods
2022	394,929,541	19,873	5.7%	310,169	393,235
2025	423,625,525	20,582	5.5%	329,903	415,923
2030	486,168,053	22,049	5.4%	362,200	454,330
2035	567,105,442	23,814	5.4%	393,876	493,358
2040	666,437,693	25,815	5.4%	425,057	532,879
2043	734,866,577	27,108	5.4%	443,574	556,784
Dry bulk goods
2022	159,680,506	12,636	10.2%	97,953	150,772
2025	171,283,055	13,088	10.9%	92,243	146,940
2030	196,570,661	14,020	12.6%	82,347	140,930
2035	229,295,798	15,143	14.6%	72,058	135,315
2040	269,458,466	16,415	17.1%	61,454	130,014
2043	297,126,081	17,237	18.9%	54,969	126,956
Large containers
2022	65,810,291	8112	3.1%	245,401	279,310
2025	70,592,134	8402	2.9%	268,556	303,670
2030	81,014,099	9001	2.8%	306,904	344,513
2035	94,501,348	9721	2.7%	344,999	385,609
2040	111,053,881	10,538	2.6%	382,893	426,907
2043	122,456,737	11,066	2.6%	405,550	451,764
Ro–Ro mobile self-propelled units
2022	0.0396 *	0.1990 *	2.3%	3245	7736
2025	0.0447 *	0.2114 *	2.5%	2787	7013
2030	0.0555 *	0.2356 *	2.9%	2178	6089
2035	0.0686 *	0.2620 *	3.3%	1721	5396
2040	0.0835 *	0.2889 *	3.7%	1376	4851
2043	0.0930 *	0.3050 *	3.9%	1210	4576
Ro–Ro mobile non-self-propelled units
2022	3,257,515	1805	12.5%	10,714	18,258
2025	3,494,209	1869	11.4%	12,423	20,235
2030	4,010,081	2003	10.3%	15,216	23,584
2035	4,677,681	2163	9.6%	17,954	26,989
2040	5,497,007	2345	9.2%	20,646	30,438
2043	6,061,432	2462	9.0%	22,244	32,526
Other cargo not elsewhere specified
2022	15,490,609	3936	10.0%	31,114	47,565
2025	16,616,173	4076	10.0%	32,165	49,201
2030	19,069,324	4367	10.2%	33,799	52,045
2035	22,243,990	4716	10.4%	35,309	55,012
2040	26,140,171	5113	10.8%	36,722	58,076
2043	28,824,207	5369	11.0%	37,532	59,953

* For natural logarithms. Source: Own calculations.

and Figure 4).

Since in the port of Ghent the transshipment of dry bulk concerns a different segment to the major ports and the potential for dry bulk shifts in-between ports in the system is low because there is also no evidence for Ghent’s structural limits in developments, the transshipment forecasts for Ghent were obtained with the use of predicting models, as for major ports (

Table 8. Forecasted transshipments in the port of Ghent (selected years only).

Year	Liquid Bulk Goods	Dry Bulk Goods	Large Containers	Ro–Ro Mobile Self-Propelled Units	Ro–Ro Mobile Non-Self-Propelled Units	Other Cargo Not Elsewhere Specified	Total
	(In thous. tonnes)
2022	5371	18,930	0	0	2292	3329	29,922
2025	5739	19,311	0	0	2436	3329	30,816
2030	6353	19,947	0	0	2677	3329	32,306
2035	6968	20,582	0	0	2918	3329	33,796
2040	7582	21,217	0	0	3159	3329	35,286
2043	7950	21,598	0	0	3303	3329	36,180

Source: Own calculations.

).

In the port of Zeebrugge, for forecasting liquid and dry bulk goods, as well as for roll-on–roll-off traffic and the category of other cargo, we used the same assumptions as for other ports in the system, but in prognostic works for container traffic, the procedure differs.

In the following, with reference to [35], there are estimates of the present container-handling capacities:

• Rotterdam: Maasvlakte II (+5.2 million TEU, from 2013 onwards), totalling 14.5 million TEU (145 million tonnes),
• Antwerp: Deurganckdock (+9.8 million TEU, from 2005 onwards), totalling 12.5 million TEU (125 million tonnes).

Regarding Zeebrugge, the present container capacity is estimated at 1.0 million TEU (CSP Zeebrugge Terminal), while from 2015 onwards, the port decreased capacity by 1.1 million TEU and moved container cranes from Zeebrugge to Antwerp.

Matching the incremental growth of capacity with ever-fluctuating demand is a challenge resulting in periodical capacity surpluses or shortages and enhancement or deterioration of the shipping lines service in Antwerp and/or Rotterdam. As the capacity utilisation rate (calculated as the ratio of the actual total port throughput divided by the designed capacity) is above a certain level, maintaining the performance and quality of the container operations, and keeping the turn-around time of container ships at an acceptable level, encounters problems. The dynamics of container flows slow down, which is attributed to port congestion. As a function of the capacity utilisation rate level, set at a level of more than 70%, the following congestion periods in containers were found [5]:

3.

The port of Antwerp faced congestion problems during 2003–2004; the utilisation rate had a stable average of 70% during 2010–2014, which slightly increased to 75%;

4.

The port of Rotterdam faced congestion problems during the whole period of 2003–2012, except for a few years: 2006 and 2009.

In this period, the utilisation rate was above the given level and the large ports of Antwerp and Rotterdam faced reduced growth of container transshipment, while the port of Zeebrugge recorded dynamic growth in container transshipment. However, from 2010 onwards, as a function of growing container capacities in major ports, the utilisation rate declined, thus making service conditions for shipping lines competitive, which resulted in decreasing transshipment in the port of Zeebrugge and increasing container growth in the ports of Antwerp and Rotterdam. Since 2019 onwards, transshipment volumes in Zeebrugge have been increasing again due to enormous problems at the deep-sea terminals of Antwerp and Rotterdam caused by the COVID-19 pandemic and disruptions in the supply chains, which upset or delayed sailing schedules of container shipping lines, causing problems with access to free containers, empty box logistics included. Within three years (2019–2021), the volumes of containerised cargo in Zeebrugge more than doubled.

To capture the future effects of the shifts in container flows in the system, we used previously elaborated container transshipment forecasts for Antwerp and Rotterdam and matched the predicted demand with the capacity utilisation rate in the major ports, set at a level of 80%. When reaching this default utilisation rate, both ports face congestion, which is counterbalanced by the added capacity in these ports. In terms of the port of Antwerp, more container-handling capacity comes from planned investments in the ’Saeftinghe Development Area’. The project is planned in phases to supply, in total, an 11 million TEU capacity. With forecasting demand and capacity utilisation rate controlled at a level of 80%, the first phase with a capacity of 5.1 million TEU is needed to be operational from 2022, while the second phase with a capacity of 5.9 million TEU should be operational as from 2034.

For Rotterdam, at Massvlakte II, there is still room for another 6.8 million TEU. Following the level of capacity utilisation around 80%, this added capacity should be operational by 2022. The simulation of container traffic flows in the port system is depicted in

Table 9. Forecasts of containerised cargo transshipment in Rotterdam, Antwerp, and Zeebrugge and the effect of shifts between ports.

Year	Rotterdam				Antwerp				Zeebrugge
	Forecasts	Capacity Cap	Capacity Utilisation 80% *	Shifts to Zeebrugge	Forecasts	Capacity Cap	Capacity Utilisation 80% *	Shifts to Zeebrugge	No Shifts	Total
2022	140,651	213,000	170,400		120,483	176,000	140,800	0	4965	4965
2023	144,896	213,000	170,400		124,119	176,000	140,800	0	4965	4965
2024	149,142	213,000	170,400		127,756	176,000	140,800	0	4965	4965
2025	153,387	213,000	170,400		131,393	176,000	140,800	0	4965	4965
2026	157,633	213,000	170,400		135,030	176,000	140,800	0	4965	4965
2027	161,878	213,000	170,400		138,666	176,000	140,800	0	4965	4965
2028	166,124	213,000	170,400		142,303	176,000	140,800	909	4965	5874
2029	170,369	213,000	170,400	1061	145,940	176,000	140,800	1818	4965	7845
2030	174,615	213,000	170,400	2123	149,577	176,000	140,800	2728	4965	9816
2031	178,860	213,000	170,400	3184	153,213	176,000	140,800	3637	4965	11,786
2032	183,106	213,000	170,400	4246	156,850	176,000	140,800	2728	4965	11,938
2033	187,351	213,000	170,400	4246	160,487	176,000	140,800	1818	4965	11,029
2034	191,597	213,000	170,400	4246	164,124	235,000	188,000	0	4965	9211
2035	195,842	213,000	170,400	4246	167,760	235,000	188,000	0	4965	9211
2036	200.088	213,000	170,400	4246	171,397	235,000	188,000	0	4965	9211
2037	204,333	213,000	170,400	4246	175,034	235,000	188,000	0	4965	9211
2038	208,579	213,000	170,400	4246	178,671	235,000	188,000	0	4965	9211
2039	212,824	213,000	170,400	4246	182,307	235,000	188,000	0	4965	9211
2040	217,070	213,000 *	170,400	4246	185,944	235,000	188,000	0	4965	9211
2041	221,316	213,000 *	170,400	4246	189,581	235,000	188,000	909	4965	10,120
2042	225,561	213,000 *	170,400	4246	193,218	235,000	188,000	1818	4965	11,029
2043	229,807	213,000 *	170,400	4246	196,854	235,000	188,000	2728	4965	11,938

* An improvement in the capacity in Rotterdam is needed. The ports of Antwerp and Rotterdam are faced with congestion when the capacity utilisation rate exceeds 80%. Source: Own calculations.

.

In the case of Antwerp port, for each year in the periods 2028–2033 and 2041–2043, from 0.9 to 3.64 million tonnes of containerised cargo should shift to Zeebrugge for transshipment.

In the case of Rotterdam, for each year in the period 2029–2043, there is up to 4.25 million tonnes of containerised cargo likely to be transferred for transshipment in Zeebrugge.

Forecasted container transshipment can be calculated in the minor port according to Equation (14):

$$C_{tm}^{*}=\{\begin{array}{cc}\hfill C_{tm}^b,& if \underset{j=1,2,\dots ,N}{\forall } C_{tj}^{*}<\delta \ast Ca{p}_j\hfill \\ \hfill C_{tm}^b+{\displaystyle {\displaystyle \sum }_{j=1}^N}\Delta C_{\frac{t}{t-1}j}^{*},& for \underset{j=1,2,\dots ,N}{\forall }where C_{tj}^{*}\ge \delta \ast Ca{p}_j\hfill \end{array}$$

(14)

where:

$C_{tm}^{*}$—the forecasts for container transshipment forecasts in the minor port in time t;

$C_{tm}^b$—the baseline forecasts of container transshipments in the minor port without cargo shifts between ports;

$C_{tj}^{*}$—the forecasts for container transshipments in the j-th major port in time t;

$\delta$—the threshold value for the occurrence of the congestion risk (the capacity utilisation rate, default value $\delta =80\%$);

$Ca{p}_j$—the maximal capacity in the j-th major port;

$\Delta C_{\frac{t}{t-1}j}^{*}$—the nominal increase in the transshipments of containers in the j-th major port in time t compared to time t − 1 (previous period);

N—the number of major ports in the port system.

Incorporating the estimated shifts of cargo flows from Antwerp and Rotterdam into the forecast base volume of containerised cargo transshipment in Zeebrugge (estimated at 4.965 million tonnes), the future containerised cargo volumes to be handled in Zeebrugge have been determined and are presented in Figure 5.

Then, the overall transshipment forecast for the port of Zeebrugge, supplemented with predicted container freight, presents as in

Table 10. Forecasts of transshipments in Zeebrugge (selected years only).

Year	Liquid Bulk Goods	Dry Bulk Goods	Large Containers	Ro–Ro Mobile Self-Propelled Units	Ro–Ro Mobile Non-Self-Propelled Units	Other Cargo not Elsewhere Specified	Total
	(In thous. Tonnes)
2022	11,045	1451	4965	2451	10,947	1008	31,869
2025	11,138	1407	4965	2451	10,450	1008	31,420
2030	11,292	1332	9816	2451	9622	1008	35,521
2035	11,446	1258	9211	2451	8793	1008	34,168
2040	11,600	1184	9211	2451	7965	1008	33,420
2043	11,693	1140	11,938	2451	7468	1008	35,698

Source: Own calculations.

.

5. Lessons to Be Learnt

In this paper, an attempt was made to elaborate on the complex problem of the future dynamics of port systems. Considering the relationships between minor and major ports, forecasted transshipment, and inter-port cargo shifts, the evolution was investigated using the example of the multi-port systems in Poland and in the Rhine–Scheldt Delta. The analysis of the dynamics of both port systems was performed with the use of competition relations, more specifically, in the function of the capacity extension or capacity utilisation. Therefore, the other research dimensions of the port system dynamics such as transitions in the maritime and hinterland networks were not considered. However, as claimed in [7], such an approach can still provide additional insights into the evolution of multi-port formations and the vulnerability of the port peripheral challenge.

Although the analysis considers the relationship between major and minor ports in the system and it is performed from the perspective of minor ports potential for development, it differs in evolution pattern for each port system.

In the Polish port system, the minor port (Szczecin) suffers from structural constraints related to the obsolete accessibility of the waterside and, consequently, loses the possibility of further development. If throughput forecast is produced with direct reference to the trends and internal conditions of that minor port, it is burdened with structural limitations to the port’s growth and ignores relations with the major ports. Therefore, in this case, the method of demand forecasting for the minor port is relative, as it refers in the first step to the estimated demand for transshipments in major ports, while in the second step, it uses the obtained indices of transshipment dynamics in major ports to develop forecasts of the cargo throughput in the minor port. In the next stage, the forecast is verified against the existing and planned capacity of the minor port and scrutinised with issues such as market niches, competition, and trade patterns. Thus, this approach of investigation of the future dynamics of port systems includes relations between major and minor ports in the system and multi-staged validation of minor port development potentials. It is difficult to determine to what extent the forecast throughput volumes in minor ports result from their cooperation with large ports in handling growing demand, and to what extent it comes from the improvement of the competitive position of the minor port and the seizure of emerging market niches. In this approach, these relations are not distinguished, but we assumed that the total impact of the above relations is reflected in the throughput dynamic indices previously established for major ports and then treated as a reference to produce throughput forecasts for minor ports. This procedure assumes that improved port accessibility (infrastructural investments and facilities improvements) enhances minor port competitiveness, thus increases the volume of transshipments comparable to the pace of development in large seaports.

This assumption has been verified and confirmed in recent studies concerning the port of Szczecin, where the effects of improvement of port nautical accessibility on the shippers’ decisions [36], as well as on the maritime component of supply chains, have been identified and quantified [37].

In the Rhine–Scheldt Delta port system, both major and minor ports feature progress, although their dynamics are different regarding cargo groups and temporal trends. It allows for elaborating forecasts with the use of trends and internal and external conditions for each port and freight categories. When analysing the relationships among major and minor ports, we investigated the vulnerability potential of the main cargo group. The propensity for shifts for liquid bulk (oil and oil products, chemicals, and liquid natural gas) is very low; for dry bulk and roll-on–roll-off traffic, it is low to moderate; while for containers, it is very high. Analysing ex-post relations between major ports (Antwerp and Rotterdam) and the minor port (Zeebrugge), we found that if the major port utilisation rate of container capacity is high and/or trade and transport markets are disrupted, the smaller port experiences an increase in container turnover. Thereafter, when the major ports build up capacity and/or markets calm down, container throughput increases in large ports, while container-handling volumes in the minor port decrease.

This indicates that there are large ports that affect the small port’s volumes of container transshipment and that the container flows in the system are bi-directional, from large ports to the smaller port and from the smaller port to large ports. Inter-port relations are based on competition; small ports challenge the major ports offering free capacity, while major ports reduce outflow of containers to smaller ports, counteracted by increasing capacity. Under these conditions, the projected volume of container handling in a small port fluctuates (like in the port of Zeebrugge), and the development of the port system is subject to change, from concentration to de-concentration and vice versa.

When the further extension of capacity in major ports encounters barriers, then the phase of inter-port collaboration comes into play. Since April 2022, the ports of Antwerp and Zeebrugge have been merged and operate under one name: Port of Antwerp–Bruges. Although there are no tangible results of these institutional transitions in the short term, it is noticeably clear that this collaboration is aimed at securing strategic assets (land and container capacities), thus reducing the main constraints in the long-term development of major port.

6. Conclusions

As for the major Polish seaports (Gdańsk, Gdynia, and Świnoujście), the highest growth rate in the planning horizon (2020–2043) refers to oil and oil products (+180.6%), but this is somehow misleading because this cargo category also includes liquid natural gas, which exbibits the highest growth rate in this cargo group. The high growth rate refers to containerised cargo (totalling +136.0%), and, in case of Gdańsk, it relates to the expected development of ocean container shipping, which creates ground for reinforcing the position of Gdańsk port as the gateway within the Baltic. The predicted growth of containers in the port of Gdynia is linked with the envisaged development of European trade and container short-sea and feeder shipping. These forecasted upward trends are already considered when planning the expansion of container-handling capacity in both Polish ports.

The general cargo growth in Polish ports in 2020–2043, estimated at +128.4%, is correlated with expected trade developments of steel products and other break bulk cargo. Minor growth relates to cereals (+88.0%) and dry bulk (coal, iron ore, and other bulk) (+77.4%). In the former cargo group, the projections are subject to high uncertainty due to the high volatility in the volume and trade directions (export vs. import), as well as the strategic importance of grain and trade susceptibility to shocks. The expected low developments in dry bulk cargo, mainly coal and iron ore, are the result of the decrease in energy and material consumption and the tendency to withdraw from the use of fossil fuels. Regarding the minor port of Szczecin, throughput forecasts show that it will retain its universal character in the future, and a moderate increase in the transshipment (+6.15 million tonnes in years 2022–2043) confirms that it will serve as a complementary port to the major ports in the range.

For Rotterdam and Antwerp, the overall forecasted traffic development within the planning horizon amounts to + 177.3 million tonnes (+37.4%) and +112.6 million tonnes (+49.0%), respectively. Both ports will encounter reduced throughput volumes in dry bulk, i.e., in iron ore, coal, and scrap, but the decline dynamics are foreseen higher in Antwerp than in Rotterdam. In oil and oil products, predicted transshipment volumes exhibit a higher growth rate in Rotterdam than in Antwerp. This may be related to the differences in navigational accessibility of both ports. While the port of Rotterdam can handle vessels up to 300.000 DWT, Antwerp can receive vessels with tonnage up to 150.000 DWT. While calling to Rotterdam, both shipping carriers and shippers can enjoy considerable cost savings resulting from economies of scale [38], which is eventually reflected in the diversified dynamics of transshipments in these two ports. Compared to Antwerp, Rotterdam port features a slower decline in dry bulk and a higher growth in oil and oil products. However, the decrease in volumes of dry bulk cargo comes from the reduced use of fossil fuels and the increased efficiency of the power and steel industries in continental Europe.

In Rotterdam, container traffic growth is estimated at +89.2 million tonnes, which is 50.3% of the overall cargo increase predicted. In Antwerp, container traffic growth is accounted for up to +76.4 million tonnes, which is 67.8% of the estimated total transshipment growth. However, these volumes and their magnitude in port turnover are highly dependent on the timing of the planned capacity extension aimed at securing container growth in both ports. In case of delays in capacity expansion and subsequent increase in congestion, container flows will look for services in the neighbouring port of Zeebrugge.

After unsuccessful attempts with container service, the port of Amsterdam is developing into a processing and distribution centre for petroleum products, and because of technology and interconnected pipeline systems, its turnover to some degree depends on volumes of oil and oil products transhipped in Rotterdam. In Amsterdam, for the total predicted increase in transshipment, amounted to +11.0 million tonnes, the share of petroleum products is +5.8 million and, for the most part, these are highly specialised products. The port of Amsterdam maximises not so much throughput volumes, but the added value of cargo processing and distribution, thus ensuring an economically viable future.

The forecasted transshipments in the port of Ghent show that: (i) the industry has reached a good level of maturity, thus traffic developments feature moderate growth rates (in the 22 years planning horizon, totalling +6.3 million tonnes, that is, by +20.9%); (ii) the port is advancing by capturing market niches (non-iron ores and agricultural dry and wet commodities); (iii) it is largely indifferent to competition from other ports in the region. The predicted volume and structure of throughput confirm that small and medium-sized ports, Ghent included, tend to develop universally.

Unlike the other nodes in the system, Zeebrugge is a port that specialises in roll-on–roll-off traffic, LNG, and container handling. Although forecast volumes of ro–ro and LNG traffic feature rather moderate fluctuations, containers are subject to dynamic and trend-increasing transshipments, reflecting bidirectional cargo flow shifts induced by the capacity utilisation level in the ports of Antwerp and Rotterdam. Problems with the efficient service of ships and containers in Antwerp and Rotterdam ports can result from capacity shortages, and/or market disruptions, and/or shocks. Whatever the reason, major ports face congestion and shipping lines are looking for alternative nodes to ensure a shorter turnaround service time for their mega-vessels. Zeebrugge is a deep-water port offering service at lower costs, hence accommodating container flows from neighbouring major ports unless these ports ensure additional capacity is operational. If the ports of Antwerp and/or Rotterdam extend their capacity, container handling stabilises or even decreases in the port of Zeebrugge, and this situation lasts until the ports of Antwerp and Rotterdam experience further problems with efficient service of the growing demand for container reloading. Therefore, the projected transshipment volumes of containers at Zeebrugge varies over time in the range from about 5.0 to 9.0 million tonnes to 12.0 million tonnes in 2043.

Finally, the limitations of this study are the result of its assumptions and the prognostic data used. When predicting port transshipment, a historical relationship between port transshipment and GDP was used. However, in the future, these relationships may change with unknown magnitude and direction. Furthermore, the projected port cargo forecasts may be affected by future unpredictable structural, social, political, and economic changes and shocks. Furthermore, as [39] underlined, factors such as bounded rationality, inertia, and opportunistic behaviour can lead to a deviation from the predicted development of minor ports.

Further research should focus on the problems of complementarity and competition in multi-port systems. It is also essential to continue research on the geographical and functional definition of relevant port systems and to identify the relationships of competition and cooperation between ports in the system.