Reflecting on REDD+: Challenges Towards Indonesia’s Carbon Pricing
Bibliographic record
Abstract
Abstract Indonesia, as one of many countries committed to implementing climate action frameworks, sees the need to develop solid carbon pricing regulations and expand the scope using its REDD+ (Reducing Emission from Deforestation and Forest Degradation Plus) experience. As a result-based payment for carbon trading, REDD+ can be a beneficial learning lesson for it. Prior to the United Nations Climate Change Conference 26 (COP), Indonesia demonstrated its political resolve to advance its climate policy by issuing a regulation on carbon pricing through Presidential Regulation No. 98/2021. Indonesia’s involvement in REDD+ has resulted in strong foundation accomplishments for carbon pricing implementation in terms of institutionalization, technicalities, and socioeconomic outcomes. However, not all REDD+ projects in Indonesia achieve the desired results. This paper aims to reflect on REDD+ as a lesson learned to identify the challenges towards Indonesia’s carbon pricing. A literature review and comparative study of carbon pricing implementation in Brazil, Canada, China, Colombia, and New Zealand were used to support this paper. This paper highlights several potential challenges to carbon pricing implementation in Indonesia such as determining the right instruments, strengthening government’s political will, transparency and public involvement, distribution of carbon price choices for subnational capacities, and the multi-nature of carbon pricing.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".