Farmers’ Willingness to Accept Compensation to Maintain the Benefits of Urban Forests
Bibliographic record
Abstract
The Returning Farmland to Forest Program (RFFP) was implemented in China in 1999 with the goal of supporting environmental restoration by returning significant areas of cultivated land to forest. While afforestation supports long-term ecosystem services like carbon sequestration and the reduction of soil and water loss, it also reduces the amount of available arable land, putting financial pressure on those who depend on it for their livelihoods. In an effort to balance both ecological and economic benefits, regional governments offer financial compensation to farmers to offset these pressures in the form of a dollar amount per hectare of reforested land. The current study explores participants’ willingness to accept pay (WTA), along with the difference between the offered per hectare compensation and the amount deemed acceptable by RFFP participants in the region. To this end, 92 households from the representative afforestation area were surveyed in Huining County, Gansu Province, China - an area of strategic ecological importance in the Loess Plateau. The results showed 12.0% of the surveyed respondents to be satisfied with the current compensation policy, while 88.0% of respondents were not. The respondents’ lower and upper WTA limits were $221/ha/year and $1331/ha/year, respectively, with an average WTA of $777/ha/year. The compensation that respondents would be most willing to accept was distributed in the $444–888/ha/year and the $889–1331/ha/year ranges, accounting for 37.0% and 31.5% of the total responses, respectively. Gender, age, and education were found to be the main factors influencing a respondents’ WTA. Results of the survey suggest that the actual compensation amount ($355/ha/year) is much lower than respondents’ WTA, and that compensation measures and policies should be improved to guarantee a basic income.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".