Short- and long-run cross-border European sustainability interdependences
Bibliographic record
Abstract
Abstract The increasing interest in climate change risks, environmental degradation, corporate social responsibility, and environmental, social, governance principles has motivated the recent soaring focus of policymakers, market practitioners, and academics on sustainable investments. In this vein, we investigate the cross-country interconnectedness among sustainability equity indices. Using a bivariate Dynamic Conditional Correlations-Mixed Data Sampling (DCC-MIDAS) specification, we study the short- and long-run time-varying dependence dynamics between European and five international (Australia, Brazil, Japan, US, and Canada) sustainability benchmarks. Our cross-country dynamic correlation analysis identifies the interdependence types and hedging characteristics in the short- and long-run across the business cycle. The significant macro- and crisis-sensitivity of the sustainability correlation pattern unveils strong countercyclical cross-country sustainability interlinkages for most index pairs and crisis periods. We further reveal the high- and low-frequency contagion transmitters or interdependence drivers in the macro environment during the 2008 global financial turmoil, the European sovereign debt crisis, and the recent pandemic-induced crash. Finally, we demonstrate that climate change risks and policy considerations are potent catalysts for both countercyclical and procyclical cross-border sustainability spillovers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".