Dr. Boyan Kavalov (p), Dr. Andrius Kučas, Dr. Mert Kompil, Dr. Paola Proietti, Dr. Patrizia Sulis, Dr. Antigoni Maistrali

In line with two flagship initiatives of the EU – “Comprehensive Strategy with Africa” and “Global Gateway” – since 2020 two departments of the European Commission – the Directorate-General for international Partnerships (INTPA) and the Joint Research Centre (JRC) have been jointly performing studies on “Corridors and Urban Systems in Africa” (CUSA).
This presentation summarises the consolidated comparative assessment of eleven EU Global Gateway-supported transport corridors in Africa, performed in the framework of the CUSA studies.
The assessment concludes that all these eleven corridors are attractive for investments.
The largest benefits are expected from interventions in transport infrastructure and accessibility.
The investments in reducing carbon footprint and preserving biodiversity also appear quite promising.
The most challenging area for intervention seems to be digitalisation. Synergies with investments in transport and accessibility could be exploited, to reduce the cost of digitalisation interventions.
The challenges in boosting productivity appear as the most diverse ones and need to be further assessed by corridors, areas and sectors. A potential high-productivity cluster is identified in Western Africa.
Large urban agglomerations and major transport and logistics infrastructure entities often demonstrate different intervention profiles from the remaining wide corridor area. Additional in-depth studies are needed to better understand their specific challenges, opportunities and trade-offs.
The provision of specific recommendations or the impact assessment of particular ongoing projects or potential future interventions is outside the scope of this study.

Prof. Donghyun Kim, Mr Jinhyeong Lee (p)

In Southeast Korea (the Busan–Ulsan–Gyeongnam region), rapid diffusion of AI, cloud services, and
broader digital transformation is expected to increase demand for data-center capacity. At the same
time, site selection is constrained by power availability, land supply, disaster risks, and regulatory
restrictions, which can reinforce both nationwide concentration in the Seoul Capital Area and
intraregional spatial bias. This study aims to identify optimal data-center locations from a demand
fulfillment perspective and to propose a practical framework for screening feasible candidate sites by
incorporating multidimensional location conditions, including environmental, infrastructural, and
socio-economic factors. Methodologically, the study applies a 1 km grid-based Maximal Covering
Location Problem (MCLP). Demand weights are constructed using proxies for data-center usage
intensity, including the concentration of firms and employment (e.g., establishments and workers),
public-sector and urban-core demand, and related activity hubs. Candidate sites are pre-screened by
excluding areas with existing data-center locations, land-use restrictions (zoning), designated hazard
zones, and flood-risk areas. To reflect real-world feasibility beyond pure coverage maximization, we
additionally examine an extended model that introduces a “soft-penalty” index capturing slope,
climate conditions, accessibility to power and communications infrastructure, transportation
accessibility (operations and workforce considerations), and employment-related socio-economic
conditions (e.g., education and quality-of-life proxies). Because the true service area of large-scale
data centers is uncertain, the coverage radius (S) and the number of new facilities (P) are evaluated
through scenario-based sensitivity analyses. We expect high demand weights to emerge along major
industrial and business corridors, while dense residential cores with elevated disaster exposure and
stronger regulatory constraints are largely excluded from candidate sets, yielding optimal solutions
that converge toward low-risk, regulation-light zones near (but typically outside) urban cores and
around industrial hubs. The study provides empirical evidence for a regional dispersion strategy by
reframing data-center planning as an integrated optimization problem combining demand coverage,
regulation, and risk. Limitations include restricted access to direct measures of traffic and grid
headroom, motivating the use of public-statistics-based proxies and future validation with more
granular power and telecom data.

Dr. Andrius Kučas (p), Ms. Katalin Tóth, Dr. Patrizia Sulis, Dr. Mert Kompil, Dr. Paola Proietti, Dr. Boyan Kavalov

This study develops a policy-driven, multi-scale Decision Support Framework (DSF) for the identification, characterisation, and prioritisation of strategic transport and urban development corridors. The methodology operationalises a “policy-first” approach in which policy and strategic objectives are formalised into explicit technical requirements prior to data selection and modelling. This ensures traceability between policy goals, spatial indicators, analytical processes, and ranking outputs. The framework is structured through a domain model documented in Unified Modelling Language (UML) and aligned with ISO and INSPIRE interoperability standards. It integrates three layers: (1) a motivational layer defining policy objectives and scenarios; (2) a business layer describing analytical workflows and use cases; and (3) an application layer implementing spatial data structures and computational procedures. The methodological workflow comprises five sequential phases. First, corridor identification translates policy-defined thresholds – such as population coverage and connectivity requirements into spatially explicit corridor envelopes using network modelling, least-cost path analysis, and travel-time accessibility techniques. Second, spatial characterisation applies a structured indicator system derived strictly from policy objectives. Indicators span socio-economic performance, accessibility and logistics, environmental sustainability, demographic development, and security dimensions. Data harmonisation and geoprocessing procedures ensure cross-corridor comparability. Third, corridors are evaluated using multi-criteria decision analysis (MCDA). Each indicator is associated with a policy-based utility function, and weights are determined either through stakeholder elicitation or analytical weighting schemes. Two complementary ranking techniques are applied: Simple Additive Weighting (SAW) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Cross-validation between methods enhances the robustness and transparency of results across multiple policy scenarios. Fourth, stakeholder consultation is integrated to validate quantitative outcomes and refine weighting structures. Fifth, the framework applies a multi-scale refinement process, subdividing selected corridors into grid-based spatial units at a 5×5 km resolution to identify localised intervention priorities and development constraints. This recursive disaggregation is a defining feature of the framework's architecture, enabling targeted analysis below the corridor level. The methodology is demonstrated through a continental-scale application across Africa, identifying 55 candidate corridors and ranking them under multiple policy scenarios, yielding a prioritised set of 12 strategic investment corridors. Consistent with its UML-based domain model and adherence to ISO and INSPIRE standards, the proposed framework provides an interoperable, transparent, and transferable analytical architecture that bridges policy formulation and spatial modelling, enabling evidence-based corridor prioritisation across scales.

Dr Mafalda Vale, Prof. Marta Ferreira Dias (p), PhD Jorge Bandeira

Demand-responsive transport (DRT) systems constitute an increasingly relevant instrument for addressing structural mobility deficits in low-density territories, where fixed-route public transport frequently exhibits low load factors and high marginal operating costs. This study develops a rigorous Social Return on Investment (SROI) assessment of SIT Flexi, an on-demand transport service deployed across 18 municipalities in Portugal’s Coimbra Region. The analysis operationalises a five metric SROI framework combining: (i) direct user cost differentials relative to market rate taxi fares, (ii) avoided missed medical appointments, (iii) avoided caregiver time costs, (iv) monetised social inclusion effects, and (v) environmental externalities derived from COPERT-based emission modelling. Social outcomes are monetised using financial proxies from international health economic and wellbeing valuation literature, subject to standard SROI corrections for deadweight, attribution and conservative parameterisation.
The results indicate an SROI ratio of 1:1.08, demonstrating that the service generates net positive social value even under stringent assumptions and restricted metrics. Emission modelling reveals that SIT Flexi reduces CO₂ emissions per passenger-trip by approximately 58% relative to the counterfactual travel pattern. Sensitivity tests confirm the robustness of the ratio to variations in proxy selection and attribution levels, while also illustrating that inclusion of psychological and long-term wellbeing effects—systematically omitted here to preserve methodological conservativeness—would increase the estimated social return. The findings substantiate the role of flexible mobility solutions as targeted policy instruments for enhancing territorial cohesion, mitigating social isolation and improving resource efficiency within rural and ageing regional systems.