Spatially explicit estimates of forest carbon emissions, mitigation costs and REDD+ opportunities in Indonesia
Bibliographic record
Abstract
Carbon emissions from the conversion and degradation of tropical forests contribute to anthropogenic climate change. Implementing programs to reduce emissions from tropical forest loss in Southeast Asia are perceived to be expensive due to high opportunity costs of avoided deforestation. However, these costs are not representative of all REDD+ opportunities as they are typically based on average costs across large land areas and are primarily for reducing deforestation from oil palm or pulp concessions. As mitigation costs and carbon benefits can vary according to site characteristics, spatially-explicit information should be used to assess cost-effectiveness and to guide the allocation of scarce REDD+ resources. We analyzed the cost-effectiveness of the following REDD+ strategies in Indonesia, one of the world's largest sources of carbon emissions from deforestation: halting additional deforestation in protected areas, timber and oil palm concessions, reforesting degraded land and employing reduced-impact logging techniques in logging concessions. We discover that when spatial variation in costs and benefits is considered, low-cost options emerged even for the two most expensive strategies: protecting forests from conversion to oil palm and timber plantations. To achieve a low emissions reduction target of 25%, we suggest funding should target deforestation in protected areas, and oil palm and timber concessions to maximize emissions reductions at the lowest cumulative cost. Low-cost opportunities for reducing emissions from oil palm are where concessions have been granted on deep peat deposits or unproductive land. To achieve a high emissions reduction target of 75%, funding is allocated across all strategies, emphasizing that no single strategy can reduce emissions cost-effectively across all of Indonesia. These findings demonstrate that by using a spatially-targeted approach to identify high priority locations for reducing emissions from deforestation and forest degradation, REDD+ resources can be allocated cost-effectively across Indonesia.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".