COMPARATIVE ANALYSIS OF EMISSION REDUCTION TARGETS TOWARD INDC IMPLEMENTATION IN MALAYSIA, INDONESIA AND THAILAND BY 2050
Bibliographic record
Abstract
Global warming is becoming increasingly evident as greenhouse gas emissions increase worldwide and affect the environment, health and economy. Many Southeast Asian countries face this reality and hence they are concerned about setting and achieving an effective emission reduction strategy. As such, this study analyzes and compares emission reduction targets on selected Southeast Asian countries, including Malaysia, Indonesia and Thailand, by using a long-run Regional Dynamic Integrated Model of the Climate and Economy (RdICME). This study considers the comparative outcomes of BAU (Business as Usual: base case) and INDC (Intended Nationally Determined Contributions) scenarios for the 40-year period from 2010 to 2050. According to BAU scenario, carbon emissions are projected to gradually increase in all countries; however, if Malaysia, Indonesia and Thailand apply their INDC targets as agreed upon in the 2015 Paris Agreement, all three countries will experience significant emissions reductions after 2030. Specifically, by 2050, total emissions will be reduced by 33.88%, 42.50% and 41.68% in Malaysia, Indonesia and Thailand, respectively, if the countries implement their INDCs. According to the INDC targets, all three countries will experience a net reduction of per capita emission intensity by 2030 and onwards; however, Malaysia is projected to face lower marginal damage costs whereas Indonesia and Thailand will face higher marginal damage costs for 2010–2050. This study also finds that the amount of planned investment for INDC emissions reduction is currently insufficient to achieve planned targets. The findings from this study would help country-specific policymakers to oversee the likely gaps to be fulfilled within 2030–2050.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".