Carbon Credit Risk Mitigation of Deforestation: A Study on the Performance of P2H Products and Services in Indonesia
Bibliographic record
Abstract
This study aims to analyze the performance of the loan products and services offered by Indonesian government’s Forest Development Financing Center (BLU-P2H Center) that target the prevention and repair of forest damage. The methodology used is a systematic literature review that identifies, assesses, and interprets this chosen research topic's findings. The study attempts to answer three formulated research questions— (i) How can P2H products and services policies that align with the carbon credit risk mitigation of deforestation be mapped?; (ii) How should P2H products and services that contribute to carbon credit risk mitigation be investigated?; and (iii) How can the impact of P2H products and services in preventing deforestation be measured? The systematic literature review findings highlight that the Indonesian Government has adopted some important provisions and institutions, namely P2H products and services, and carried out loan disbursements to prevent forest destruction, specifically, the forestry business and environmental investment financing. The findings also indicate that while the government most extensively disbursed certain loans, such as community forest enterprises (HR), there was a low level of loan disbursement for community forest-based loans (HKm) because of the constraints faced by farmers in arable land that produces seasonal crops. Therefore, the study implicates that the forest destruction, particularly in Indonesia, resulting from illegal logging by local communities, needs to be further prevented by increasing the public knowledge on the sustainability impact of deforestation and also by increasing the public’s access to and opportunities for public welfare, alternative livelihoods, and micro-business activities. The study findings also make an original contribution to the literature on the carbon credit risk mitigation of forest damage as they illustrate a government-sponsored, innovative P2H scheme in the form of loan disbursement aiming to reduce and prevent forest destruction.
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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 | 0.000 |
| 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 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".