Macro-Regional Spatial Decision Support for Geo-Distributed Data Center Siting in Europe
Regional Screening and Robustness Under Weight Uncertainty
Bibliographic Data
| ID | 22031679 |
|---|---|
| Authors | Vasile Paul Bresfelean (0000-0002-7240-6187, Babeș-Bolyai University, corresponding author), Calin-Adrian Comes (0000-0003-2013-5589, Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș), Paula Pop-Nistor (0009-0004-9832-2821, Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș) |
| Year | 2026 |
| Volume | 15 |
| Issue | 7 |
| Pages | 294 |
| Publication date | 2026-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi15070294 |
| OpenAlex | W7166830639 |
| Language | EN |
| References cited | 56 |
Digital infrastructure expansion in Europe raises a spatial planning problem: early-stage screening needs to compare regional conditions while also checking whether rankings remain stable when decision priorities change. This study evaluates 24 European Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) regions for geo-distributed data center development. The 2022 decision matrix uses five Eurostat criteria: information and communications technology (ICT) specialists’ share in employment, average hourly labor cost, renewable electricity share, non-household electricity price and population density. Four criteria are national intensive proxies assigned to the selected NUTS-2 regions, while population density is directly observed at the NUTS-2 level. After a log10 transformation of population density and min–max normalization, we compare the weighted sum model (WSM), TOPSIS and VIKOR across four weighting scenarios. We then apply a random-weighting audit based on Stochastic Multicriteria Acceptability Analysis (SMAA) principles, using 10,000 Dirichlet weight draws, followed by a local Dirichlet sensitivity analysis around the Balanced profile. Results show that the most stable high-performing profiles are not limited to the established FLAP-D market reference. Latvija (LV00), Stockholm (SE11), Helsinki-Uusimaa (FI1B), Eesti (EE00) and Área Metropolitana de Lisboa (PT17) form the main high-performing set across stochastic rank metrics, while several mature Western metropolitan regions remain more sensitive to cost and territorial-pressure criteria. The study provides a reproducible spatial decision support framework for macro-regional screening rather than micro-siting
Decision support system · Electricity · Metropolitan area · Multiple-criteria decision analysis · Population · Spatial decision support system · Weighting · Geographic Information Systems Studies · Smart Cities and Technologies · Urban Transport and Accessibility
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| Highly cited | No |
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