Decomposing Carbon Emissions: LMDI with Country-Specific Insights and Panel Data Analysis
Bibliographic record
Abstract
The ongoing planetary crisis has prompted us to conduct this study, which analyzes 18 leading polluters over the period from 1995 to 2023. The countries considered are the United States of America, Mexico, and Canada in North America; India, China, Japan, Russia, and Iran in Asia; Germany, France, the United Kingdom, Poland, Italy, and Ukraine in Europe; and South Africa, Egypt, Algeria, and Nigeria in Africa. The study aims to determine the factors influencing CO2 emissions based on the improvised version of the Kaya identity, focusing on clean energy and fossil fuels. To be precise, population (POP), economic activity factor (EAF), clean energy intensity (CEI), energy transition (ET), and emission per unit of non-renewable energy (EM_NRE) are considered in the present work. Logarithmic Mean Divisa Index (LMDI) has been employed for analyzing individual countries because of the structural differences and varying energy grids. Further, a long-run relationship has been studied using long-run estimators suitable for the four panels. The results suggest that each factor impacts the level of carbon emissions differently. The panel consists of integrated economies with distinct structural differences; hence, historical economic shocks impact the nations in different ways. The result manifests that the countries must increase the share of clean energy in their power grid.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".