An open-source regionalization approach for subnational EEMRIO insights and proof-of-concept application to EU steel circularity

Journal Article (2026)
Author(s)

Anniek J. Kortleve (Universiteit Leiden)

Nicolas Navarre (Universiteit Leiden)

Paul Behrens (University of Oxford, Universiteit Leiden)

Hale Cetinay (Universiteit Leiden, Stedin)

Franco Donati (Universiteit Leiden)

Benjamin Sprecher (TU Delft - Industrial Design Engineering, TU Delft - Industrial Design Engineering)

Arnold Tukker (TNO, Universiteit Leiden)

José M. Mogollón (Universiteit Leiden)

Research Group
Design for Sustainability
DOI related publication
https://doi.org/10.1007/s44498-026-00024-0 Final published version
More Info
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Publication Year
2026
Language
English
Research Group
Design for Sustainability
Journal title
Journal of Industrial Ecology
Issue number
1
Volume number
30
Pages (from-to)
347-361
Downloads counter
23
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Abstract

Environmentally extended multi-regional input–output (EEMRIO) analysis provides a robust methodology for assessing economic, social, and environmental footprints across nations and regions. Increasing its geographical resolution is essential for addressing local environmental issues and informing targeted policy decisions. While subnational EEMRIOs ideally rely on survey data, such data are often unavailable or resource-intensive to process. As a result, partitioners resort to proxies and algorithms. Yet, the transparency of these algorithms and the underlying data are often suboptimal. Here, we present a novel, open-source, top-down regionalization approach applicable to any EEMRIO database. Our method builds on location quotients (LQ), extending their application to a multi-regional framework. This extension ensures calculations remain traceable, eliminates the need for supplemental balancing procedures, and requires minimal, readily available additional proxy data, making it highly accessible for practitioners. Using European steel trade as a proof-of-concept, we demonstrate how this approach assesses local impacts, highlights local–global trade interactions, and identifies opportunities that national IO data often obscure.