Motivational Crowding in Payments for Ecosystem Service Schemes: a Global Systematic Review
Bibliographic record
Abstract
We contribute to the growing body of literature on the ecological and socio-psychological impacts of providing payments as rewards for conservation. We conducted a systematic review of 74 payments for ecosystem services (PES) schemes and identified contextual factors that correlate with psychological mechanisms that enhance (”crowd-in”) or erode (”crowd-out”) autonomous motivation. Such indicators of crowding-in were more likely when schemes empowered local participants, provided in-kind non-monetary community benefits, and aimed to foster feelings of autonomy. Schemes that thwarted feelings of autonomy correlated with indicators of motivational crowding-out. Although motivational crowding had no effect on ecological success, indicators of crowding-in positively predicted social success (χ2 = 8.60, n = 48, p = 0.003) and crowding-out negatively predicted social success (χ2 = 9.59, n = 47, p = 0.002). Compared to past studies highlighting the negative impacts of extrinsic rewards on autonomous motivation, our study provides a more nuanced perspective and demonstrates that extrinsic incentives such as payments can promote crowding-in of autonomous motivation if schemes are designed equitably and provide opportunities for autonomous decision-making. Our study demonstrates how the application of psychological theories can contribute to the design of fair and effective PES schemes.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".