Depopulation and aging–challenges for Croatia's climate resilience
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".