The Role of Social Capital in Rural Households’ Perceptions toward the Benefits of Forest Carbon Sequestration Projects: Evidence from a Rural Household Survey in Sichuan and Yunnan Provinces, China
Bibliographic record
Abstract
We examined the associations between social capital and rural households’ perceptions toward social, economic, and environmental benefits of forest carbon sequestration projects by employing the proportional odds model based on data collected from a rural household survey in Sichuan and Yunnan Provinces, China. Results revealed that: (i) households’ perceptions toward environmental benefits are more positive than their perceptions toward economic benefits and social benefits, and their perceptions toward economic benefits are more positive than their perceptions toward social benefits; (ii) households having a good relationship with village officials have higher odds of holding more positive perceptions toward social, economic, and environmental benefits of the projects; (iii) households which are members of local associations are more likely to have positive perceptions toward benefits of the projects; (iv) households whose members are more frequently involved in village-level public events are more likely to have more positive perceptions toward benefits of the projects; (v) households having more educated household heads have higher odds of holding better perceptions toward the benefits of FCS projects; and (vi) households of Yunnan Province are less likely to express positive perceptions toward benefits of the projects. Based on the research results, we concluded that social capital is significantly and positively associated with rural households’ perceptions toward benefits of forest carbon sequestration projects. Some policy implications are provided regarding how to make use of social capital elements to shape farmers’ perceptions toward benefits of the projects for the purpose of achieving a higher level of local acceptability for and sustainability of the projects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".