Insurance, Environment, and Growth: A Panel Study Across European Countries
Bibliographic record
Abstract
This study examines the impact of insurance market development on Carbon dioxide (CO2) emissions and economic growth in the European Union (EU-15) and Central and Eastern European (CEE-11) countries over the period 1996–2022. Long-run relationships are analysed using panel cointegration tests and Mean Group (MG), Pooled Mean Group (PMG), and Dynamic Fixed Effects (DFE) estimators. At the same time, causal links are assessed through the Granger non-causality test. Results show that in EU-15 countries, insurance development positively affects both environmental quality via reduced CO2 emissions (elasticities between 0.2078 and 0.2860), and economic growth (0.109–0.829). In CEE-11 countries, a positive effect on growth (0.102–0.205) is confirmed, but no significant environmental impact is observed. The findings highlight the need for policies that support green insurance initiatives and investments in low-carbon transition projects, especially in the CEE-11 region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".