Reforestation, livelihoods and income equality: Lessons learned from China's Conversion of Cropland to Forest Program
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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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".