Carbon dioxide emissions prediction of five Middle Eastern countries using artificial neural networks
Bibliographic record
Abstract
Greenhouse gas (GHG) emissions contribute considerably to global warming and climate change. Since energy systems notably influence GHG emissions, such emissions can be modeled at the national level based on the energy sources utilized by a country. Economic activity also affects GHG emissions. In this work, an Artificial Neural Network (ANN) approach, Group Method of Data Handling (GMDH), is used for determining emissions of carbon dioxide, the most significant GHG, on the basis of shares of various energy sources used as primary energy supply and GDP as an indicator of economic activity. Five countries are considered as case studies: Iran, Kuwait, Qatar, Saudi Arabia, and United Arab Emirates (UAE). Comparing the results achieved by the developed model and actual quantities shows that the ANN model has acceptable accuracy for predicting CO2 emissions. The average absolute relative error and the R-squared values of the GMDH model are 2.3% and 0.9998, respectively. These values demonstrate the precision of the model in forecasting emissions of CO2.
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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 | 0.001 |
| 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) | 0.000 | 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 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".