What Drives and Stops Deforestation, Reforestation, and Forest Degradation? An Updated Meta-analysis
Bibliographic record
Abstract
This article updates our previous comprehensive meta-analysis of what drives and stops deforestation (Busch and Ferretti-Gallon 2017). By including six additional years of research, this article more than doubles the evidence base to 320 spatially explicit econometric studies published in peer-reviewed academic journals from 1996 to 2019. We find that deforestation is consistently associated with greater accessibility (as influenced by natural features such as slope and elevation and built infrastructure such as roads, cities, and cleared areas) and with higher economic returns (from agriculture, livestock, and timber). Some demographic variables are consistently associated with less deforestation (e.g., Indigenous people, poverty, and age) or more deforestation (e.g., population), and others are not associated with the level of deforestation (e.g., education and gender). Policies that directly influence allowable land-use activities are associated with less deforestation (e.g., protected areas, enforcement of forest laws, payments for ecosystem services, community forest management, and certification of sustainable commodities). But policies and institutions that primarily seek other ends are not consistently associated with more or less deforestation (e.g., democracy, general governance, conflict abatement, and land-tenure security). We introduce reforestation and forest degradation as new dependent variables alongside deforestation. Greater population is consistently associated with more forest degradation, whereas steeper slope, greater distance from cities, and lower population are consistently associated with more reforestation.
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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 | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.042 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".