Benchmarking Efficiency in Mediterranean Ports:

A DEA-Based Analysis of Connectivity and Operational Performance

Chariton Tsakalidis

, Eirini Liani, George Tsakalidis

, Kostas Vergidis

and Michael Madas

Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

Keywords: Port Performance, Mediterranean Ports, Data Envelopment Analysis, Compare Port Efficiency,

Benchmarking Performance, Port KPIs.

Abstract: This study investigates the operational performance of major Mediterranean ports through a tailored Data

Envelopment Analysis (DEA) framework. Recognizing the underrepresentation of these ports in existing

benchmarking studies, this research emphasizes both connectivity and efficiency. Utilizing advanced DEA

methodologies—Constant Returns to Scale (CCR), Variable Returns to Scale (BCC) and Window Analysis—

the study evaluates efficiency trends over time, providing actionable insights for enhancement. Key input

variables such as terminal size, berth length and equipment count are analyzed alongside output metrics like

annual container throughput to ensure a comprehensive assessment of port performance. The findings reveal

significant efficiency disparities among Mediterranean ports, with transshipment hubs like Tanger Med and

Piraeus achieving optimal efficiency scores due to strategic investments and infrastructure upgrades.

Conversely, many ports operate below optimal levels, indicating opportunities for technical and managerial

improvements. This research contributes substantially to the field by introducing a novel benchmarking

framework tailored to the unique geopolitical dynamics of the Mediterranean region. It highlights the critical

role of connectivity, infrastructure and technology in driving efficiency while offering a valuable foundation

for policymakers and port authorities to implement targeted strategies that enhance competitiveness and foster

sustainable growth.

1 INTRODUCTION

Maritime trade has long served as a fundamental

pillar of global commerce and the establishment of

supply chains worldwide, facilitating the

transportation of vast quantities of goods across

various regions. The combined advantages of cost-

efficiency and reliability have positioned shipping as

a primary driver of growth in the era of globalization,

particularly in the Mediterranean area—a region of

geopolitical gravity where shipping has expanded its

market share relative to other European regions, as

noted by the European Commission's Internal Market

report. The Mediterranean container market

expanded to reach 55 million TEUs in 2014, driven

by the surge in world trade and higher

containerization rates. Containerized cargo thrives in

https://orcid.org/0009-0001-1572-439X

https://orcid.org/0000-0002-0889-7946

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https://orcid.org/0000-0002-9809-8485

transfer hubs across the Mediterranean, while roll-

on/roll-off (RoRo) services also play an important

role, especially in Short Sea Shipping (SSS)

(Beizhen, 2021). The region acts as a vital link,

connecting South European ports with Africa, the

Americas, Northern Europe and Asia.

Despite a substantial imbalance in cargo volume

between the northern and southern Mediterranean due

to differing economic development levels,

Mediterranean ports have maintained their relevance

on the global stage (Colombo & Soler Lecha, 2020).

They have consistently accounted for approximately

9% of global container traffic over the past two

decades. Enhanced connectivity reinforces these

ports' competitive advantage by facilitating proximity

to major shipping routes (Martinez-Moya et al.,

2024), such as the Suez Canal and the Strait of

Tsakalidis, C., Liani, E., Tsakalidis, G., Vergidis, K. and Madas, M.

Benchmarking Efﬁciency in Mediterranean Ports: A DEA-Based Analysis of Connectivity and Operational Performance.

DOI: 10.5220/0013284600003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 837-844

ISBN: 978-989-758-749-8; ISSN: 2184-4992

837

Gibraltar, while also allowing for value-added

services like warehousing and repackaging.

Ports at the Mediterranean’s edge tend to perform

better in global trade than in local contexts, with

notable examples like Tanger Med, Port Said and

Algeciras leveraging strategic locations. These ports

capitalize on their proximity to important maritime

corridors to support regional and global economies

effectively.

The Mediterranean region, historically a vital

channel for goods transportation, has gained

increasing prominence in global trade, particularly

with the strategic importance of the Suez Canal and

the Strait of Gibraltar (Arvis et al., 2018). Despite its

consistent share of approximately 9% in global

container traffic over the last two decades, the region

faces substantial imbalances in cargo volumes

between its northern and southern ports, largely due

to differing levels of economic development. This

disparity underscores the need for comprehensive

port performance assessments.

While Northern Mediterranean ports have

benefited from economic and political stability, many

southern Mediterranean ports remain less integrated

into global trade networks. Furthermore, competition

among ports has intensified as modern technological

advancements and intermodal systems have reduced

reliance on immediate hinterland cargo, creating new

performance pressures (Pinto et al., 2017).

Previous research on port performance has often

focused on Northern European ports, leaving

Mediterranean ports underrepresented in

benchmarking studies. The growing shipping

volumes in the region and the emergence of

transshipment hubs and gateways demand a rigorous,

data-driven approach to assess the operational

efficiency and competitiveness of Mediterranean

ports. Addressing these gaps is essential for fostering

balanced development and ensuring that

Mediterranean ports remain integral to global supply

chains.

Based on this research gap, this paper aims to

benchmark the operational efficiency of major

Mediterranean ports using a Data Envelopment

Analysis (DEA) framework. This study seeks to

provide a comprehensive assessment of port

connectivity and performance, addressing critical

gaps in regional benchmarking data. The primary

objectives of the paper are: (1) To analyse

connectivity patterns in the Mediterranean Sea,

emphasizing the geopolitical and port-specific

characteristics that shape port performance. (2) To

address the underrepresentation of Mediterranean

ports in performance benchmarking studies by

employing a dynamic efficiency measurement

approach based on panel data. (3) To develop and

implement a tailored DEA model to evaluate the

operational efficiency of major Mediterranean ports,

including the selection of input-output variables and

comparative analysis across model variations. (4) To

investigate the relationship between key

determinants, such as port infrastructure and location,

and the efficiency scores obtained from the DEA

model, providing actionable insights for port

performance improvement.

This paper contributes to the field by applying a

tailored DEA framework to benchmark the efficiency

of major Mediterranean ports. It offers insights into

port connectivity and operational performance while

addressing the region's underrepresentation in

existing benchmarking studies. By analysing key

determinants of efficiency, the paper provides

actionable findings that can guide policy and strategic

improvements for port authorities and stakeholders in

the Mediterranean.

2 LITERATURE REVIEW

2.1 Port Performance and

Benchmarking

The performance of ports is assessed through various

metrics and methodologies. Key performance

indicators (KPIs) such as throughput in TEUs, berth

utilization rates and service times are used widely to

capture aspects of operational efficiency and

productivity (Cullinane & Wang, 2010). Frontier

analysis techniques, particularly DEA and Stochastic

Frontier Analysis (SFA), have become prominent for

benchmarking performance across ports (Cullinane &

Wang, 2010). DEA, a non-parametric method, is

popular for determining efficiency by comparing

Decision-Making Units (DMUs) under the

assumption of exact input-output relationships. It is

commonly used to identify underperformance by

establishing a best-practice frontier based on

observed data.

Comparative studies on ports frequently

categorize them by various attributes such as location,

annual throughput and terminal characteristics. For

example, studies using DEA models have segmented

ports based on TEU volumes or distinguished

between transshipment and gateway ports to

understand performance differentials and the impact

of specific port attributes. These benchmarking

analyses often highlight important efficiency

disparities, even among geographically proximate

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

838

ports, due to differences in infrastructure, equipment

and management practices.

In recent years, multi-step DEA approaches have

been implemented, combining DEA with methods

such as SFA to account for noise in data and enhance

accuracy. For instance, studies comparing ports in

developing countries with their counterparts in

developed regions utilize hybrid models to isolate

technical efficiency from environmental effects. The

DEA-Malmquist index is also employed to track

changes in productivity over time, addressing both

technical and scale efficiencies.

Benchmarking port performance enables

authorities to adopt best practices and focus on

continuous improvement across multiple

performance dimensions, including operational,

financial, environmental and customer satisfaction

metrics. These methodologies allow for the

comparison of port operations in a structured manner,

driving a competitive and systematic approach to

efficiency improvements.

2.2 Key Performance Indicators (KPIs)

KPIs are established as quantifiable metrics used to

evaluate port efficiency across various dimensions,

including operational, financial and environmental

aspects (Duru et al., 2020). Initially, port KPIs

focused on operational aspects such as crane

movements per hour and container throughput,

providing a basis for comparative performance

analysis among terminals. Over time, however, the

scope of KPIs has expanded to include indicators that

account for logistical, customer-oriented, and

sustainability-related factors to reflect the evolving

needs of global port stakeholders (Woo et al., 2011).

The framework for categorizing KPIs often

considers both internal and external performance

dimensions, as different stakeholders—such as port

authorities, customers and environmental agencies—

prioritize various aspects of port efficiency.

Categories may include operational KPIs, like berth

occupancy and average container dwell time, as well

as financial KPIs, such as cost per TEU and revenue

per ton managed.

Modern approaches to KPI development

incorporate complex modelling techniques. For

instance, tools like the fuzzy-Delphi method (Wang

et al., 2014) and the Analytical Hierarchy Process

(AHP) (Ha et al., 2019) are utilized to weigh and

prioritize KPIs based on stakeholder importance,

ensuring that the metrics align with strategic

objectives across the operational landscape.

2.3 Benchmarking Methods

Benchmarking methods for port performance can be

broadly classified into index methods, frontier

analysis and process approaches (Bichou, 2013).

Index methods often involve financial ratios,

snapshot indicators and Total Factor Productivity

(TFP), offering straightforward metrics, but limited in

addressing comprehensive operational contexts.

Frontier analysis, encompassing DEA and SFA,

remains one of the most widely applied techniques

(De Borger B. et al., 2002). DEA employs linear

programming to construct a non-parametric frontier,

enabling the evaluation of multiple inputs and outputs

without assuming specific functional relationships.

Its variants, such as DEA-CCR for constant returns to

scale and DEA-BCC for variable returns, allow for

flexibility in capturing technical and scale efficiency.

Meanwhile, SFA provides a parametric approach that

incorporates stochastic factors to account for

environmental influences and data variability (Chang

& Tovar, 2014).

Advanced applications, like the Malmquist

Productivity Index, integrate DEA to analyze

productivity changes over time, distinguishing

between technical efficiency improvements and

technological advancements (Suárez-Alemán et al.,

2015). Additionally, hybrid approaches combining

DEA with methods like SFA or regression models

enhance robustness by addressing the limitations of

individual techniques.

Process approaches, including Total Quality

Management (TQM) and perception surveys,

contribute qualitative insights by incorporating

stakeholder feedback and expert judgment. These

approaches complement quantitative methods,

ensuring a holistic assessment of port performance.

The selection of a suitable benchmarking

methodology depends on the specific objectives, data

availability and contextual constraints of the analysis.

By employing these methods, researchers and

practitioners can derive actionable insights to drive

port efficiency and competitiveness (Feng et al., 2012).

3 METHODOLOGY

3.1 Benchmarking Framework

To systematically evaluate port performance, this

study employs a comprehensive benchmarking

framework, with Data Envelopment Analysis (DEA)

as the primary methodology. DEA is a linear

programming technique used to assess the efficiency

Benchmarking Efﬁciency in Mediterranean Ports: A DEA-Based Analysis of Connectivity and Operational Performance

839

of Decision-Making Units (DMUs) (Cooper et al.,

2007). It constructs a non-parametric efficiency

frontier based on multiple input and output variables,

allowing for comparative benchmarking without

requiring a predefined functional relationship

(Mustafa et al., 2021). Two primary DEA models—

Constant Returns to Scale (CCR) and Variable

Returns to Scale (BCC)—are implemented. The CCR

model assumes a fixed input-output ratio across all

DMUs, while the BCC model introduces flexibility

for scale efficiencies (Benicio & De Mello, 2019).

This framework integrates methodologies such as the

Malmquist Productivity Index to measure

productivity changes over time and combines DEA

with techniques like Stochastic Frontier Analysis

(SFA) to enhance robustness against environmental

factors and data variability. Key input variables include

berth length, terminal area and equipment quantity,

while output variables focus on container throughput

and other operational metrics. The selection of

variables is guided by expert screening and prior

studies to ensure relevance in the benchmarking

analysis. By leveraging this framework, the study aims

to identify efficiency drivers and provide actionable

insights for port performance optimization. This

approach allows for examining both technical and

scale efficiencies while accommodating diverse

operational contexts. This revision maintains all

critical references and information while making the

text more concise and focused.

3.2 Dataset

The dataset used in this study includes detailed

operational data from various Mediterranean ports,

sourced from publicly accessible databases, port

authorities’ records and commercial maritime reports.

Primary input variables encompass terminal

dimensions, berth length and equipment count, while

output variables include annual container throughput,

expressed in TEUs. Figure 1 illustrates the

relationship between input variables (e.g., terminal

size, berth length, and equipment count) and output

variables (e.g., annual throughput in TEUs) as

utilized in the DEA model.

Figure 1: DEA Graph with Inputs and Outputs.

Supplementary information, such as the year of

data capture and handling capacity, is used to ensure

uniform benchmarking across diverse ports. These

inputs were selected based on industry standards and

prior benchmarking studies to allow accurate

efficiency assessment across comparable contexts.

Table 1 presents the key input-output variables for the

DEA-CCR and DEA-BCC models, detailing the

dimensions of the dataset across multiple ports.

Table 1: Input data for DEA-CCR & BCC.

Sample-Ports (I) Size (I) Berths

(I) Quay

Length

(I)

Int. Tr.

Dest/tions

(O)

An. Teus

(2019)

Alexandria 200 4 732 47 851

Algeciras 306 9 4034 56 5125

Ambarli 95 6 2602 27 3104

Barcelona 1065 11 3000 56 3324

Casablanca 257 12 1500 42 6040

Genoa 700 6 1433 37 2621

Gioia Tauro 440 8 3391 8 2523

Haifa 158 4 1360 46 1470

Izmit (Evyap) 65 4 656 24 1715

La Spezia 150 8 1400 15 1409

Livorno 112 3 1858 27 789

Marsaxlokk 77 5 2801 40 2722

Marseilles 316 17 2798 41 1454

Mersin 112 9 1020 49 1854

Piraeus 220 9 2774 63 5648

Port Said 130 8 947 33 3816

Sines 151 6 1040 10 1420

Tanger Med 335 6 1200 55 4801

Valencia 456 11 3600 68 5439

3.3 Evaluation Metrics

Performance evaluation metrics focus on efficiency

scores derived from DEA using both the CCR and

BCC models. The study also employs technical

efficiency and scale efficiency scores to distinguish

managerial efficiency from size-driven advantages.

Additionally, dynamic metrics like Window Analysis

enable the comparison of port efficiency trends over

time by considering each port as a distinct Decision-

Making Unit (DMU) at different time intervals.

3.4 Procedures

The analysis follows a systematic application of

DEA, beginning with the standard DEA-CCR and

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DEA-BCC models to assess ports under constant and

variable returns to scale. The Malmquist Productivity

Index supplement this process to evaluate efficiency

changes over time. For each port, DEA models are

calibrated based on operational inputs and container

throughput, while Window Analysis is applied for

longitudinal efficiency comparison. The DEA Solver

software facilitates the computation, allowing for

consistent application of both CCR and BCC models

across the dataset (Cooper et al., 2007).

4 EXPERIMENTAL SETUP AND

BASELINE MODELS

4.1 Model Specifications

The analysis employs DEA models, specifically the

output-oriented CCR (constant returns to scale) and

BCC (variable returns to scale) approaches, to

evaluate the relative efficiency of Mediterranean

ports. The DEA framework incorporates inputs such

as terminal size, quay length and the number of

container berths, while the primary output is annual

container throughput in TEUs. The study also

includes time-dependent DEA through Window

Analysis, treating each port as a different DMU

across time periods. This enhances the discrimination

power of the model, identifying trends in port

performance over the observation period.

Key control variables are included to account for

external factors influencing port efficiency. These

variables include: (1) Port connectivity: Measured

through the number of intermodal destinations

served. (2) Economic indicators: Such as regional

GDP and trade openness. (3) Port size: Categories

based on terminal area to examine scale effects. These

variables ensure that the DEA results accurately

reflect operational efficiencies, minimizing biases

caused by external and contextual differences among

ports.

Baseline models include both standard DEA and

its advanced variations for robust comparison:

 Standard DEA Models: CCR and BCC

models to evaluate technical and scale

efficiency.

 Window DEA Analysis: To capture dynamic

performance trends over time.

 Two-Stage DEA: Integrating regression

analysis in the second stage to investigate the

impact of exogenous factors, such as port

governance and hinterland connectivity.

Table 2 provides descriptive statistics for the

input and output variables used in the DEA analysis,

summarizing averages, minimums, and maximums

for terminal dimensions and throughput.

Table 2: Descriptive Statistics of Variables.

Variable Avg. Min Max Std. Dev.

Terminal Area

(hectares)

281.31 65.00 1065.00 248.74

No of berths

(container)

7.68 3.00 17.00 3.44

Quay Length

(meters)

2078.55 656.00 4034.00 1044.85

No of

transshipment

destinations

39.15 8.00 68.00 17.33

Annual

Throughput

(TEUs, 2019)

2953.94 789.00 6040.00 1722.09

5 RESULTS AND ANALYSIS

The results from the DEA analysis show average

efficiency scores of 0.744 and 0.835 for the CCR and

BCC models, respectively. This implies potential

output increases of 62.64% for the CCR model and

36.87% for the BCC model, without requiring

additional inputs. Table 3 summarizes the DEA

results, highlighting that a substantial number of ports

are operating below optimal efficiency levels.

For example, the BCC model identifies ten ports

as efficient, while the CCR model identifies 7,

indicating room for performance improvements

among most ports. The analysis by port type and size

shows that while transshipment ports tend to achieve

higher efficiency, the statistical weight is not robust.

An ANOVA test reveals that the efficiency

differences based on TEUs are statistically important

(p = 0.005), while those based on port type are not (p

= 0.064). The three-group size comparison (small,

medium, large) yields no statistically important

differences, indicating that port size alone does not

determine efficiency outcomes.

For temporal analysis, the Window Analysis

method reveals stability in efficiency scores across

most ports, with the notable exception of ports like

Piraeus, which shows a consistent upward trend due

to strategic improvements and investment. This

method allows for nuanced insights into efficiency

trends over time by treating each period as a different

observation for each port. Table 4 presents the

efficiency scores and ranks for ports under the BCC

model, highlighting performance variations driven by

variable returns to scale. In the reference set column

Benchmarking Efﬁciency in Mediterranean Ports: A DEA-Based Analysis of Connectivity and Operational Performance

841

in Tables 3 and 4, the ports that are used as reference

points are used. These ports are being used as a scale

for the inefficient ports to evaluate their efficiency.

When the ports reach maximum efficiency, i.e. 1,

their reference set is only themselves. The ports of

Casablanca and Tanger Med are used more often than

the others as reference ports, i.e., 8 times.

Table 3: DEA statistics, CCR-focused.

No DMU

CCR-

Score

CCR-

Rank

Reference

Set

Sum of

Lambdas

BCC-

Score

BCC-

rank

CCR

1 Ambarli 1 1 {1} 1 1 1 Constant

2 Casablanca 1 1 {2} 1 1 1 Constant

3 Gioia Tauro 1 1 {3} 1 1 1 Constant

4 Marsaxlokk 1 1 {4} 1 1 1 Constant

5 Piraeus 1 1 {5} 1 1 1 Constant

6 Port Said 1 1 {6} 1 1 1 Constant

7 Tanger Med 1 1 {7} 1 1 1 Constant

8 Algeciras 0.8994 8 {2,7,5} 1,065 0.9254 11 DRS

9 Izmit (Evyap) 0.8836 9 {5,4} 0,529 1 1 IRS

10 Sines 0.8161 10 {2,3} 0,416 1 1 IRS

11 Valencia 0.768 11 {2,7} 1,351 0.9204 12 DRS

12 Genoa 0.6793 12 {2,7} 1,91 0.7384 13 DRS

13 La Spezia 0.5967 13 {2,3} 0,478 0.697 15 IRS

14 Mersin 0.5568 14 {4,6} 0,899 0.5653 16 IRS

15 Haifa 0.5174 15 {5,7} 0,531 0.7314 14 IRS

16 Barcelona 0.5151 16 {2,7} 1,174 0.5625 17 DRS

17 Livorno 0.3766 17 {5,7} 0,401 1 1 IRS

18 Alexandria 0.2903 18 {2,7} 0,592 0.4872 18 IRS

19 Marseilles 0.2374 19 {2,3} 1,512 0.2449 19 DRS

No of efficient DMUs 7 10

Average efficiency 0.744 0.8354

Table 4: DEA statistics, BCC-focused.

No DMU

BCC-

Score

BCC-

Rank

Reference

Set

Scale Efficiency BCC

1 Ambarli 1 1 {1} 1 Constant

2 Casablanca 1 1

{

}

1 Constant

3 Gioia Tauro 1 1

{

}

1 Constant

4 Izmit

(

1 1

{

}

0.8836 IRS

5 Livorno 1 1 {5} 0.3766 IRS

6 Marsaxlok

1 1 {6} 1 Constant

7 Piraeus 1 1 {7} 1 Constant

8 Port Sai

1 1

{

}

1 Constant

9 Sines 1 1

{

}

0.8161 IRS

10 Tan

er Me

1 1

{

}

1 Constant

11 Algeciras 0.9254 11 {2,7,10} 0.9719 Constant

12 Valencia 0.9204 12 {10} 0.8344 Constant

13 Genoa 0.7384 13

{

1,2,4,10

}

0.9199 IRS

14 Haifa 0.7314 14

{

4,5,10

}

0.7074 IRS

15 La S

ezia 0.697 15

{

1,2,9

}

0.8546 IRS

16 Mersin 0.5653 16 {4,6,10} 0.9849 IRS

17 Barcelona 0.5625 17 {2,7} 0.9158 Constant

18 Alexandria 0.4872 18 {4,5,10} 0.5958 IRS

19 Marseilles 0.2449 19

{

2,3

}

0.9693 Constant

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842

Table 5: Summary of DEA results.

Division CCR BCC

Scale

Efficiency

DMU

Efficient 7 (37%) 10 (53%) CRS

Inefficient 12 (63%) 9 (47%) DRS

Total 19 (100%) 19 (100%) IRS

Average Efficiency 0.744 0.835 0.885805

The results underscore the sensitivity of DEA to

data variations and missing values, particularly with

respect to underperforming ports. For example, data

errors or omissions in terminal area and berth length

measurements may affect efficiency scores. To

address these limitations, methods such as SFA and

super-efficient DEA could be integrated to adjust for

data inaccuracies and enhance the robustness of

findings. Future research could explore interval

models and regional comparisons to refine

benchmarks and expand insights.

6 DISCUSSION

The findings of this study highlight important

disparities in the operational efficiency of

Mediterranean ports, emphasizing the value of

benchmarking practices in driving improvements.

Ports such as Tanger Med and Piraeus demonstrate

how targeted investments in infrastructure and

technology can yield substantial efficiency gains,

aligning operations with the demands of global

supply chains. Their strategic use of location and

optimized input-output relationships sets benchmarks

for the region.

Conversely, many Mediterranean ports operate

below potential efficiency levels, indicating the need

for managerial and technical improvements. Focusing

on pure technical efficiency—maximizing outputs

from given inputs—is more critical than merely

scaling operations. This includes adopting resource

optimization through automation, smart terminal

operations, and intermodal connectivity to mitigate

bottlenecks, reduce idle time, and enhance throughput

capacity. Ports that integrate such technologies

perform better overall.

Intermodal connectivity also plays a pivotal role.

Ports with robust links to rail, road, and inland

waterways exhibit higher efficiency, showcasing the

importance of seamless logistics. For southern

Mediterranean ports, developmental and competitive

challenges compared to northern counterparts could

be addressed through enhanced hinterland

connectivity. This would better integrate these ports

into global trade networks and leverage their

geographic advantages.

These findings underscore the necessity for port

managers to optimize terminal layouts, invest in

advanced equipment, and adopt data-driven decision-

making. Collaborative efforts among ports can

generate synergies, sharing best practices and

infrastructure. Policymakers must support these

advancements through financial incentives and

favourable regulations, enabling operational

improvements. For instance, encouraging sustainable

practices, such as energy-efficient technologies,

addresses both efficiency and environmental

concerns.

While the study provides valuable insights, its

reliance on DEA methodology introduces limitations,

particularly its sensitivity to data quality. Future

research could address this by integrating stochastic

methods like SFA, which account for random

variations and external influences. Additionally,

leveraging advanced data collection tools, such as IoT

sensors, could enrich datasets with real-time

performance metrics. Addressing these aspects would

yield a more comprehensive understanding of port

operations.

Overall, this study emphasizes the importance of

strategic investments, technological innovation, and

regional collaboration in driving port efficiency. Ports

adopting dynamic benchmarking approaches and

prioritizing continuous improvement are better

positioned to remain competitive in the evolving

global trade landscape.

7 CONCLUSION

This study provides a comprehensive analysis of

Mediterranean port performance using DEA models,

highlighting critical insights into efficiency drivers

and benchmarking practices. Key findings include:

(a) Efficiency Scores, the average efficiency across

the ports analysed is 74.4% (CCR) and 83.5% (BCC),

indicating significant room for improvement. Ports

such as Tanger Med, Piraeus, and Marsaxlokk

consistently achieve efficiency frontier status. (b)

Role of Port Type and Scale, transshipment ports

demonstrate higher efficiency levels compared to

gateway ports, leveraging economies of scale and

strategic location advantages. However, the size of

the port was found to have an unimportant impact on

Benchmarking Efﬁciency in Mediterranean Ports: A DEA-Based Analysis of Connectivity and Operational Performance

843

efficiency. (c) Temporal Trends, Window Analysis

revealed stable efficiency scores over time, with

notable improvements in ports undergoing strategic

investments. These results emphasize the importance

of managerial practices and technological adoption in

achieving and sustaining efficiency. The implications

of this research extend beyond individual ports,

providing actionable insights for regional and global

port management.

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