Greenhouse Gas Emissions Reduction Potentials in Iranian Electricity Generation Sector and Comparison With Canada
Bibliographic record
Abstract
In recent years, greenhouse gas (GHG) emissions and their potential effects on the global climate change have been a worldwide concern. Based on International Energy Agency (IEA), power generation contributes half of the increase in global GHG emissions in 2030. In the Middle East, Power generation is expected to make the largest contribution to the growth in carbon-dioxide emissions. The share of the power sector in the region’s total CO2 emissions will increase from 34% in 2003 to 36% in 2030. Therefore, it is very important to reduce GHG emissions in this industry. The purpose of this paper is to examine greenhouse gas emissions reduction potentials in the Iranian electricity generation sector through fuel switching and adoption of advanced power generation systems and to compare these potentials with Canadian electricity generation sector. These two countries are selected because of raw data availability and their unique characteristics in electricity generation sector. To achieve this purpose two different scenarios have been introduced: Scenario #1: Switching existing power stations fuel to natural gas. Scenario #2: Replacing existing power plants by natural gas combined-cycle (NGCC) power stations (The efficiency of NGCC is considered to be 49%). The results shows that the GHG reduction potential for Iranian steam power plants, gas turbines and combined cycle power plants in first scenario are 9.9%, 5.6%, and 2.6%, respectively with the average of 7.6%. For the second scenario the overall reduction of 31.9%, is expected. The average reduction potential for Canadian power plants for scenario number 1 and 2 are 33% and 59%, respectively. As it can be seen, in Canada there are much higher potentials to reduce GHG emissions. The reason is that in Canada majority of power plants use coal as the primary fuel. In fact almost 73% of electricity in thermal power stations is generated by coal. Whereas in Iran almost all power plants (with some exceptions) are dual fuels and 77% of energy consumed in Iran’s thermal power plants come from natural gas. Also, 21% of total electricity generated in Iran is produced by combined-cycle power plants.
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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".