Adopting Indirect Carbon Pricing Strategies for Indonesia: Insights from Global Practices Using a Bibliometrics and Systematic Literature Review
Bibliographic record
Abstract
Indonesia faces significant challenges in reducing greenhouse gas (GHG) emissions while maintaining economic growth. Carbon pricing, encompassing carbon taxes and Emission Trading Systems (ETS), has emerged as a vital tool in achieving global decarbonization goals. This study aims to assess the adaptation of indirect carbon pricing strategies in Indonesia by synthesizing insights from 27 countries using a Systematic Literature Review (SLR) of 315 scholarly articles. The research identifies Indonesia's unique economic, social, and regulatory challenges, including dependency on fossil fuels, limited renewable energy infrastructure, and governance gaps. The study highlights the effectiveness of indirect carbon pricing mechanisms, such as renewable energy subsidies, energy efficiency programs, and public awareness initiatives, in addressing dispersed emissions from transportation, agriculture, and residential energy use. Additionally, the integration of carbon pricing with complementary policies, including sector-specific benchmarks and international carbon trading, enhances the potential for successful implementation. By adapting best practices from countries like Canada, Sweden, and Germany, Indonesia can establish hybrid carbon pricing models tailored to its context. These strategies can accelerate renewable energy investments, promote economic diversification, and support the country's goal of reducing GHG emissions by 29-41% by 2030 and achieving net-zero emissions by 2060. This research provides actionable recommendations for policymakers and stakeholders to ensure sustainable energy transitions and global climate commitments.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.080 | 0.095 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".