Depopulation and aging–challenges for Croatia's climate resilience
Notice bibliographique
Résumé
Introduction: Climate change and the implementation of mitigation and adaptation policies have significant socioeconomic implications. Conversely, socioeconomic developments shape the capacity to design and enforce climate policies, creating a feedback loop. This paper localizes the impact of Climate change in Croatia and explores the feedback between socio-economic development and climate change. The starting hypothesis is that limitations in human capital critically hinder climate-resilient development in Croatia. Methods: The study uses climate data and literature to contextualize Croatia's climate vulnerability. A sector-specific analysis is conducted to identify key sectors for climate-resilient development based on their potential for emissions reduction, climate vulnerability, and current and potential economic importance. The current economic importance is evaluated based on contribution to GDP and employment, and key opportunities and obstacle for future growth are identified. Results: Climate data and literature indicate that Croatia, a high-income EU member, is experiencing warming faster than the global average and ranks among the least climate-resilient high-income countries. A sector-specific analysis identifies the most critical sectors for Croatian climate-resilient development based on their emission reduction potential, climate vulnerability, and growth opportunities. Among these are sectors that contribute the most to GDP and employment-such as tourism, construction, and healthcare -which already suffer from significant labor shortages. The results indicate that a shrinking workforce is the key constraint for implementation of climate-resilient development. Discussion: Significant improvements in labor productivity, higher participation rates, integration of foreign workers in the labor market, and efforts to address skills shortages are necessary. This presents a challenge, given increasing damages from extreme climate events, ongoing depopulation, a limited supply of a highly educated workforce, and low participation in lifelong learning. For climate-resilient development, it is essential to design policies that adequately address aging and depopulation -both of which limit economic growth and reduce capacity for climate adaptation. Conclusions: Findings of the Croatian case offer insights for Mediterranean and island countries reliant on climate-sensitive sectors like tourism (e.g., Greece, Thailand). The high-income yet low-resilience paradox is relevant for regions such as Southern Europe, Australia, and California. EU membership highlights institutional misalignments between supranational climate agendas (e.g., the European Green Deal) and subnational demographic realities. These dynamics are relevant to aging societies (Japan, Germany) and post-industrial economies (Poland, Canada) navigating green transitions, emphasizing the need to integrate demographic strategies into climate governance.
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».