The prospects of innovative agri-environmental contracts in the European policy context: Results from a Delphi study
Bibliographic record
Abstract
Innovative agri-environmental contracts are increasingly studied in the literature, but their adoption has been relatively slow and geographically scattered. Action-based agri-environmental measures remain the predominant policy mechanism across Europe. A three-round Policy Delphi study was conducted with policy makers, scientific experts, farmers’ representatives, and NGOs from across 15 different European countries, to investigate how and under which circumstances novel contractual solutions could be implemented more widely. The expert panel perceived result-based and collective contractual elements as the most promising. Although considered beneficial from several aspects, value chain contracts were perceived less relevant to the policy environment. The Common Agricultural Policy (CAP) Pillar 2 measures were highlighted by the experts as the key policy area to implement novel contracts by national or regional authorities, but Pillar 1 eco-schemes, being launched in the CAP 2023–2027, were also considered as a potentially suitable framework for testing and implementation. The Delphi panel envisaged innovative contracts should be adopted by governments in iterative steps and not as a complete substitute for current payment schemes, but rather as an additional incentive to them. Such an incremental approach allows contractual innovations to capitalise on existing best practices. But it also implies the risk that innovative contracts could remain marginal and fail to substantially change farmers’ behaviour, resulting in a failure to improve environmental conditions.
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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 | 0.132 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".