Renewable Energy Sources in Iran: Policy and Regulation
Bibliographic record
Abstract
According to the Fifth Five Year Development Plan, in Iran, renewable resources, under the green horizon scenarios, must provide 5,000 MW of electricity. Among different types of renewable source of energy, there is no shortage of information in Iran, which is located near to zero line (earth’s equator), with about 300 clear sunny days in a year, about setting policies promoting solar energy. Taking into account the availability and benefits of solar energy for Iran, this paper has focused on solar energy.Recent statistics show that, if the current development plans proceed, the capacity of the installed renewable energy systems would reach 2.8GW by 2030. This requires more than 2800 million US dollar investment in 20 years, i.e., 2010 to 2030. Despite the advantages of using solar energy, such as reducing greenhouse gases, it is important to note that solar power is 2.5 to 5 times as expensive as electricity from existing conventional power sources, such as coal and other sources. In order to encourage people to use solar power, there is a need to change our laws and establish an integrated energy regulation, involving tax policy mechanisms to support the deployment of solar energy in Iran. As Iran is dependent upon its fossil fuels, the transition from fossil fuels to renewable, which is a worldwide goal to reduce GHG or CO2 emissions, requires the adoption of a comprehensive policy and integrated regulation nationwide, taking a multidisciplinary approach. This paper exemplifies and considers the 2005 Energy Policy Act and Investment Tax Credit (ICT) for residential energy property, illustrating how solar-energy-regulation could contribute to the sustained development of solar energy. The main purpose is to help the development of sustainable solar energy regulation in Iran.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".