A Review of the Status of Fossil and Renewable Energies in Southeast Asia and Its Implications on the Decarbonization of ASEAN
Bibliographic record
Abstract
The ten nations of Southeast Asia, collectively known as ASEAN, emitted 1.65 Gtpa CO2 in 2020, and are among the most vulnerable nations to climate change, which is partially caused by anthropogenic CO2 emission. This paper analyzes the history of ASEAN energy consumption and CO2 emission from both fossil and renewable energies in the last two decades. The results show that ASEAN’s renewable energies resources range from low to moderate, are unevenly distributed geographically, and contributed to only 20% of total primary energy consumption (TPEC) in 2015. The dominant forms of renewable energies are hydropower, solar photovoltaic, and bioenergy. However, both hydropower and bioenergy have substantial sustainability issues. Fossil energies depend heavily on coal and oil and contribute to 80% of TPEC. More importantly, renewable energies’ contribution to TPEC has been decreasing in the last two decades, despite the increasing installation capacity. This suggests that the current rate of the addition of renewable energy capacity is inadequate to allow ASEAN to reach net-zero by 2050. Therefore, fossil energies will continue to be an important part of ASEAN’s energy mix. More tools, such as carbon capture and storage (CCS) and hydrogen, will be needed for decarbonization. CCS will be needed to decarbonize ASEAN’s fossil power and industrial plants, while blue hydrogen will be needed to decarbonize hard-to-decarbonize industrial plants. Based on recent research into regional CO2 source-sink mapping, this paper proposes six large-scale CCS projects in four countries, which can mitigate up to 300 Mtpa CO2. Furthermore, this paper identifies common pathways for ASEAN decarbonization and their policy implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".