Forecasting CO2 emissions in the Persian Gulf States
Bibliographic record
Abstract
The Persian Gulf States (Bahrain. Iran, Iraq, Qatar, Saudi Arabia, Kuwait and United Arab Emirate) have dominated the oil and gas sector since the discovery of oil in the region. They are the world largest producers of crude oil, producing about 35 and 25 percent of the world natural gas and crude oil respectively. The use of fossil fuels is directly linked to the release of CO2 into the environment. CO2 accounts for 58.8 percent of all greenhouse gases released via human activities, consequently, presenting a malign impact on the environment through climate change, global warming, biodiversity, acid rain and desertification among others. Due to its importance, the data on CO2 emission obtained from US EIA from 1980 – 2010 was regressed using least square techniques and projections were made to the year 2050. Results indicated that each country’s p-value was less than 0.05 which implies that the models can be used for predicting CO2 emissions into the future. The data shows the emission of CO2 by countries from the highest to the lowest in 2016 as: Iran (590.72 Mtonnes; 7.58 tonnes of CO2/person) > Saudi Arabia (471.82 Mtonnes; 18 tonnes of CO2/person) > UAE (218.58 Mtonnes; 41.31 tonnes of CO2/person) > Iraq (114.01 Mtonees; 3.71 tonnes of CO2/person) > Kuwait (92.58 Mtonnes; 36.31 tonnes of CO2/person) > Qatar (68.26 Mtonnes; 37 tonnes of CO2/person) > Bahrain (33.16 Mtonnes; 27.5 tonnes of CO2/person)". The sequence from the country with highest emission (Iran) to the country with lowest emission (Bahrain) will remain the same until 2050. A projection depicting a 7.7 percent yearly increase in CO2 emission in the Persian Gulf States.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".