Assessing ASEAN’s Liberalized Electricity Markets: The Case of Singapore and the Philippines
Bibliographic record
Abstract
The efforts towards the liberalization of electricity markets have sped up recently in some countries within the Association of Southeast Asian Nations (ASEAN) region. This step of opening up the electricity markets is aimed at establishing competitive and efficient electricity markets that not only reduce electricity prices, but also support a sustainable future by reducing carbon dioxide (CO2) emissions from electricity generation and promoting the wider adoption of renewable energy (RE)-based electricity generation. This paper assesses the effects of the electricity market liberalization process in Singapore and the Philippines on these expected outcomes during the period 2015–2020. The regression analysis results suggest that in the specified period, the liberalization of the electricity market in Singapore has delivered both household and industry electricity price reductions and improvement in the RE share. However, there is no significant effect of the electricity market liberalization process on the electricity generated CO2 emissions. For the same period, the results imply that with the electricity market liberalization process in the Philippines, the electricity prices for household consumers and electricity-generated CO2 emissions have increased. Additionally, the liberalization process has no significant impact on both the RE share and industry electricity prices in the Philippines. To overcome the obstacles and strike a balance between the expected outcomes, policy recommendations are given for ASEAN economies following the pathway of liberalized electricity markets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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, 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".