A machine learning approach to carbon emissions prediction of the top eleven emitters by 2030 and their prospects for meeting Paris agreement targets
Bibliographic record
Abstract
The continued rise in global carbon dioxide ([Formula: see text]) emissions challenges international climate policy, particularly the goals of the Paris Agreement. This study forecasts [Formula: see text] emissions through 2030 for the eleven highest-emitting nations-China, the United States, India, Russia, Japan, Iran, Indonesia, Saudi Arabia, Canada, South Korea, and Germany-while assessing their progress toward Nationally Determined Contributions (NDCs). Using data from 1990 to 2023, we apply a robust data pipeline comprised of six machine learning models and sequential squeeze feature selection incorporating eleven economic, industrial, and energy consumption variables. We have modelled the scenario with an average prediction accuracy of 96.21%. Results indicate that Russia is on track to exceed its reduction targets, while Germany and the United States will fall slightly short. China, India, Japan, Canada, South Korea, and Indonesia are projected to miss their commitments by significant margins. At the same time, Iran and Saudi Arabia are expected to increase emissions rather than reduce them. These findings highlight the need for strengthened energy efficiency policies, expanded renewable energy adoption, enhanced carbon pricing mechanisms, and stricter regulatory enforcement. Emerging economies require international collaboration and investment to support low-carbon transitions. This study provides a data-driven assessment of emission trajectories, emphasizing the urgency of coordinated global action, technological innovation, and adaptive policy measures to align emissions with the 1.5[Formula: see text] warming threshold. This work represents a novel integration of multivariate machine learning modelling, data-driven feature selection, and policy-oriented emission forecasts, establishing new methodological and empirical benchmarks in climate analytics.
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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 | 0.001 | 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".