Bibliographic record
Abstract
ABSTRACT This study analyzes the uncertainty in the amount of electricity supply in solar power generation because actual sunshine duration is unknown in advance. In particular, the study considers how risk transfer systems such as insurance and derivatives affect the prevalence of solar power generation. Furthermore, we investigate how the electricity price in the feed-in tariff (FIT) scheme introduced in Japan in July 2012 relates to the prevalence of solar power generation. If additional revenue is larger than additional cost due to the application of a risk transfer system, we derive the following results from our economic model analysis. First, an increase in electricity price in the FIT scheme and in the expected amount of electricity supply and a decrease in the cost of solar panels increases the availability of a risk transfer system. Second, promoting the availability of a risk transfer system leads to increased solar power generation. JEL Classifications: G22, Q21, Q28 Keywords: solar power generation; risk transfer; feed-in tariff (FIT); economic model I. INTRODUCTION After the Great East Japan Earthquake in March 2011, energy policy in Japan was changed drastically because of concerns about the safety of nuclear power plants. Subsequently, after September 2013, as of June 2015, all nuclear power plants in Japan were shut down. This situation has increased the attention towards renewable energy such as solar power, wind power, and geothermal power as sources of electric power. This is because electric power generation through renewable energy sources emits neither carbon dioxide nor radioactivity. However, according to the summary of press conference comments by chairman of the Federation of Electric Power Companies of Japan (May 23, 2014), the share of electric power generation in renewable energy except for hydroelectric power was only 2.2 percent in fiscal 2013. (1) In order to increase this share, the Japanese government started the feed-in tariff (FIT) scheme for renewable energy in July 2012. Under this scheme, Japanese electric power companies have to purchase electricity produced by renewable energy at a price predetermined by the government. According to the handout that used in the committee in the Agency for Natural Resources and Energy (p.53), solar power generation in Japan has constituted the major share (more than 97 percent) of the increase in power generation from renewable sources. (2) Thus, solar power is the main source of renewable energy in Japan. There are many studies on solar power generation that focus on the FIT scheme in Japan. For example, Ayoub and Naka (2012) developed a simulation analysis for investigating the FIT scheme for renewable energies in Japan. Kosugi (2013) investigated financial support including the FIT scheme for increasing solar power generation in Japan. Since some of the issues related to solar power generation are not specific to Japan, several relevant studies examine them in many other countries. These include Rigter and Vidican (2010) (China), Topkaya (2012) (Turkey), Jacobs et al. (2013) (Latin America and Caribbean region), Tveten et al. (2013) (Germany), Martin and Rice (2013) (Australia), Tongsopit and Greacen (2013) (Thailand), and Moosavian et al. (2013), who discussed energy policies, including FIT schemes, in Australia, Canada, China, Japan, France, Germany, and U.S.A. The FIT scheme can remove the uncertainty in electricity price, because the price of electricity is fixed in the scheme. However, the amount of electricity generated by solar power is still uncertain because the actual duration of sunshine is unknown in advance. Therefore, despite the FIT scheme addressing the issue of uncertainty in electricity price, the uncertainty related to the amount of electricity supply persists. A possible method to cope with this uncertainty is to apply a risk transfer system such as insurance and derivatives. …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".