Market-mediated responses confound policies to limit deforestation from oil palm expansion in Malaysia and Indonesia
Bibliographic record
Abstract
The global demand for palm oil has grown rapidly over the past several decades. Much of the output expansion has occurred in carbon- and biodiversity-rich forest lands of Malaysia and Indonesia (M&I), contributing to record levels of terrestrial carbon emissions and biodiversity loss. This has led to a variety of voluntary and mandatory regulatory actions, as well as calls for limits on palm oil imports from M&I. This paper offers a comprehensive, global assessment of the economic and environmental consequences of alternative policies aimed at limiting deforestation from oil palm expansion in M&I. It highlights the challenges of limiting forest and biodiversity loss in the presence of market-mediated spillovers into related oilseed and agricultural commodity and factor markets, both in M&I and overseas. Indeed, limiting palm oil production or consumption is unlikely to halt deforestation in M&I in the absence of active forest conservation incentives. Policies aimed at restricting palm oil production in M&I also have broader consequences for the economy, including significant impacts on consumer prices, real wages, and welfare, that vary among different global regions. A crucial distinction is whether the initiative is undertaken domestically, in which case the M&I region could benefit, or by major palm oil importers, in which case M&I loses income. Nonetheless, all policies considered here pass the social welfare test of global carbon dioxide mitigation benefits exceeding their costs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".