Advancing the carbon pricing framework in Indonesia: A systematic review of policies, challenges, and global lessons
Bibliographic record
Abstract
Indonesia, the largest GHG emitter and carbon sink reservoir in Southeast Asia, faces the dual challenge of achieving net zero emissions by 2060 while sustaining economic growth. This research examines Indonesia's carbon pricing framework, focusing on two key mechanisms: a modest carbon tax and a nascent ETS. Using an SLR and bibliometric analysis, it evaluates implementation progress, including the introduction of a carbon tax at 2 USD per tonne of CO 2 and an ETS targeting coal-fired power plants, alongside persistent challenges such as limited sectoral coverage, low price signals, and institutional fragmentation. Drawing insights from both emerging economies (e.g., South Africa, Colombia) and advanced systems (e.g., Sweden, Canada), the analysis underscores the importance of phased implementation, strong governance, and international alignment. The paper proposes actionable pathways, including a graduated carbon tax roadmap, staged ETS expansion, and enhanced regional collaboration within ASEAN. This study offers a novel scientific contribution by conducting a multidimensional mapping of Indonesia's carbon pricing literature, which categorizes 65 studies by policy instruments, implementation phases, and sectoral coverage, and by identifying a critical disconnect between academic recommendations and real-world implementation. These findings provide an evidence-based foundation for designing more effective and internationally aligned policy reforms. By aligning these reforms with global best practices and leveraging domestic resources, Indonesia can strengthen its carbon pricing architecture and offer a replicable model for sustainable development in other emerging economies.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".