Reforestation, livelihoods and income equality: Lessons learned from China's Conversion of Cropland to Forest Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Despite global momentum in restoration activities, their socio‐economic implications are little studied. Thus far, the limited evidence available tends to overlook equity and equality outcomes. In this work, we aimed at investigating fairness within the Chinese Conversion of Cropland to Forest Program (CCFP), given the relevance of local people's support for the long‐term success of land restoration and for the inherent belief that equity should be pursued also by environmental policies. Additionally, we propose a methodology to investigate equity and equality, from a quantitative perspective. Our results suggested a shift in the overall households' economic structure, with the main changes being a decrease in farming activities (−44 pp) and a sharp increase in out‐migration (+44 pp), with the most significant variation within the lowest income groups (−57 pp and + 75 pp, respectively). We also observed that both equality (the Gini coefficient decreased by 23%) and equity (higher income increase for low‐income groups) improved, and the best enhancement happened in the regions where the CCFP has been implemented for a longer time. Moreover, data showed that the main driver of inequality was households' income deriving from remittances, both before and after the Program implementation (with concentration coefficient equal to 1.1 and 1.0, respectively) but its effect decreased over time suggesting an increase in out‐migration opportunities for lower‐income households. Finally, we found that the level of participation in the Program holds a quite strong explanatory power for both on‐farm and off‐farm income (explaining 19% and 18% of their respective variability).
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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) | 0.000 | 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 it