Modeling willingness to continue participation in payments for ecosystem services programs: A case of China's second phase of the grain for green program in indigenous communities
Bibliographic record
Abstract
Literature on Payments for Ecosystem Services Programs in developing countries is focused on the underlying assumption of a rational economic agent, and useful concepts from social-psychological models are ignored. The existing literature also lacks studies on indigenous communities and the differences in poor and non-poor people's participation. We proposed a Random Utility Model that integrates some concepts of the Expectation Confirmation Theory to examine the factors influencing Yi (indigenous) people's willingness to maintain their reforested land after the end of financial incentives of China's Second Phase of the Grain for Green Program. We compared the willingness and the impacts of influencing factors for poor and non-poor participants. We also analyzed preferences for financial incentive options of participants unwilling to maintain their reforested land. Findings of this study revealed that: (i) similar proportions, about 60 %, of poor as well as non-poor participants are willing to maintain their reforested land; (ii) inertia to change land use and ecological awareness are top two influencing factors for both groups and expectation is the next key factor for poor people; (iii) the signs and magnitudes of influences vary between poor and non-poor groups; (iv) 61 % of unwilling households prefer short-term and 31 % prefer long-term financial incentive options; and (v) participants who have inertia to change land use and have planted ecologically important species are more likely to choose the long-term payment option. Policy recommendations to enhance ecological awareness and inertia to change land use and reduce dependence on farm income were made.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".