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
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".