Twin Transition Attitudes and Regional Left‐Behindness: Unpacking the Drivers of Interregional Migration Intentions

This article investigates how individual attitudes toward the green and digital transitions, collectively referred to as the twin transition, and expressions of regional left‐behindness shape EU interregional migration intentions across different life domains. The study disaggregates migration intentions by motive, work, education, quality of life, and retirement, to better understand the interplay between personal values and regional structural conditions. The findings reveal that while traditional socio‐demographic characteristics remain strong predictors of work‐related migration intentions, green and digital attitudes significantly influence relocation intentions for education and quality‐of‐life reasons. Notably, individuals with strong green values are more likely to express intentions to move for quality‐of‐life reasons. Rural areas, in this case, are aligned with ecological lifestyles, whereas digital attitudes correlate with urban settlement preferences and, for retirees, confidence in relocating to digitally connected rural regions. Expressions of regional left‐behindness, such as economic decline, demographic ageing, and weak connectivity, act as structural push factors that differentially affect migration intentions. The analysis suggests that twin transition values function as behavioural primers which, combined with structural push or pull factors, guide individual intentions and reveal how unequal capacities to adapt to digital and green transitions shape migration intentions.

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Publication Details

Journal
Social Inclusion
Published
2026-02-25
DOI
https://doi.org/10.17645/si.11543
Primary Topic
Climate Change, Adaptation, Migration
Type
article
Field-Weighted Citation Impact
0.00

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article

Twin Transition Attitudes and Regional Left‐Behindness: Unpacking the Drivers of Interregional Migration Intentions

Olle Järv, Anastasia Panori, Milad S. Malekzadeh, Tuomas Väisänen et al.
Social Inclusion
Climate Change, Adaptation, Migration
article

Twin Transition Attitudes and Regional Left‐Behindness: Unpacking the Drivers of Interregional Migration Intentions

Olle Järv, Anastasia Panori, Milad S. Malekzadeh, Tuomas Väisänen, Elli Papastergiou
article en

Abstract

This article investigates how individual attitudes toward the green and digital transitions, collectively referred to as the twin transition, and expressions of regional left‐behindness shape EU interregional migration intentions across different life domains. The study disaggregates migration intentions by motive, work, education, quality of life, and retirement, to better understand the interplay between personal values and regional structural conditions. The findings reveal that while traditional socio‐demographic characteristics remain strong predictors of work‐related migration intentions, green and digital attitudes significantly influence relocation intentions for education and quality‐of‐life reasons. Notably, individuals with strong green values are more likely to express intentions to move for quality‐of‐life reasons. Rural areas, in this case, are aligned with ecological lifestyles, whereas digital attitudes correlate with urban settlement preferences and, for retirees, confidence in relocating to digitally connected rural regions. Expressions of regional left‐behindness, such as economic decline, demographic ageing, and weak connectivity, act as structural push factors that differentially affect migration intentions. The analysis suggests that twin transition values function as behavioural primers which, combined with structural push or pull factors, guide individual intentions and reveal how unequal capacities to adapt to digital and green transitions shape migration intentions.

Social InclusionVol. 14
University of Helsinki (FI), Aristotle University of Thessaloniki (GR), Helsinki Institute of Physics (FI), General Electric (Finland) (FI)
European Commission, Research Executive Agency
Reduced inequalities
Openalex Percentile: Top 50%
Climate Change, Adaptation, Migration
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