MétaCan
Menu
Back to cohort
Record W4376117466 · doi:10.1142/9789811270581_0002

Assessing the Effects of ASEAN Liberalised Electricity Markets: The Case of Singapore and the Philippines

2023· book-chapter· en· W4376117466 on OpenAlexaff
Hassan Ali, Han Phoumin, Beni Suryadi, Aitazaz A. Farooque, Raziq Yaqoob

Bibliographic record

VenueWORLD SCIENTIFIC eBooks · 2023
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsInternational economicsElectricityBusinessInternational tradeEconomicsEconomic geographyDevelopment economicsEngineering

Abstract

fetched live from OpenAlex

The Association of Southeast Asian Nations (ASEAN) electricity market is being liberalised in some regional countries. This step of opening electricity markets is aimed at establishing a competitive and efficient environment that not only reduces electricity prices and CO2 emissions from electricity generation but also promotes the wider use of renewable energy resources. This paper examines the effects of liberalisation on these expected outcomes in the electricity markets of Singapore and the Philippines during 2015–2020. The regression analysis results show that, in the specified period, liberalisation of the electricity market in Singapore has delivered price reductions and improvement in renewable energy share. However, there is no significant effect of liberalisation on the reduction of CO2 emissions from the generation of electricity. The results also imply that, with the electricity market liberalisation in the Philippines, prices for household consumers and CO2 emissions have increased. Also, the liberalisation has no significant impact on renewable energy share and industry electricity prices in the Philippines. To avoid the mixed results and strike a balance between expected outcomes, policy recommendations are given for ASEAN economies following the pathway of liberalised electricity markets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.356
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.230
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueWORLD SCIENTIFIC eBooksSame topicGlobal trade and economicsFrench-language works237,207