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Record W3179328460

Reforming Electricity Market Based Upon Renewable Energy Resources in United States, European Union, China, Brazil, India and Indonesia: A Lesson Learned from Finance and CGE Modeling

2017· article· en· W3179328460 on OpenAlexaboutno aff
Yayan Satyakti

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsComputable general equilibriumEconomicsEnergy subsidiesInvestment (military)Renewable energyEuropean unionSubsidyRestructuringBusinessEnergy policyInternational economicsMarket economyFinanceMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

In the past two and half decades, developing countries has been struggle to reformed both market and institutional of their electricity sectors. Empirically, the evidence of electricity reform determined by proper sequence of combination of vertical and horizontal restructuring, privatization and effective regulation, securing Foreign Direct Investment (FDI), cross subsidy and pricing reform. Since developing countries recognizing the challenge of climate change, investment in renewables energy technology has increased significantly. China has had strong growth in wind energy sectors especially for wind energy sectors surpassing the US as a global market leader. Technological improvement and cost reduction have promote the renewables become more competitive due to technology development, deployment and economic of scale. Nowadays, the private investment become major players in renewable energy project. On the other hands, although the progress has been shown strong growth, mobilizing private investment is intricate. Increasing of investment cost altered by level of risk of different policies for investors. In financial context investors compare investment opportunities between conventional and renewables by assessing those risk and return. The preference between policies vs risk-return consideration on renewable energy investment is debatable both by policy makers and academia. In this paper I investigates the linkages between financial aspect and macro economic performance by reconciling Real Option Modelling on Bottom-Up Modeling into Top-Down Computable General Equilibrium (CGE) modeling to evaluate the implication of cost and price in electricity both conventional and renewables and market regime both developed countries (United States, Canada, European Union) and developing countries (China, Brazil, India, Indonesia, South Korea, and South Africa). The novelty of my approach is associating financial model into bottom up energy sector as iterative adjustment proposed by Boehringer-Rutherford (2009) and feeding into Top Down Economic Equilibrium (CGE) Model. To my best knowledge this approach has not been conducted by previous studies. The CGE were calibrated with multi-years Global Trade Analysis Project (GTAP) Database Version 9 (2004, 2007 and 2011). The bottom up modeling were conducting from GTAP Power 9 Database and Energy Database from International Energy Agency Database (EIA). The real option estimated from representative major firm of energy sectors in those countries as well as oil prices with daily frequency data since 2004. First, The Real Option and Bottom Up – Top Down CGE Modeling were calibrated and projected towards 2020, the calibrated results shows developing countries require evolving regulation to ensure volatility risk given uncertainty price and technical risk which some renewables unable to perform competing alternative energy technologies. Second, I performed scenario where improving risk in renewables as performing increasing oil price volatility and return volatility in the model hampered on cost in bottom up model and unsecure in energy supply and decreasing of welfare especially in developing countries. Third, I conduct liberalizing scenario by removing electric subsidy in several developing countries with different system of electric market across developing countries and shows that welfare impact higher in developed countries whereas developing countries lowered welfare, shows that liberalised electricity market more efficient in developed country rather than in developing country. Electricity reform benefited more for developed economies rather than developing countries. Developing countries should evolving technological improvement to anticipated risk in the future. Adopting electric market reform require institutional and political commitment to ensure that market and price are certain for investors.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2017
Admission routes1
Has abstractyes

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