Analysis of Inter-Temporal Change in the Energy and CO2 Emissions Efficiency of Economies: A Two Divisional Network DEA Approach
Bibliographic record
Abstract
Measuring changes in energy consumption and carbon dioxide emissions of various large economies is fundamental for analyzing the impact and effectiveness of various policies in this direction. This study analyzes intertemporal changes in energy and CO2 emissions efficiency of economies by applying a network data envelopment analysis approach that takes into consideration the internal structure of the analysis units. We have applied two divisional network data envelopment analysis models for analysis of the economic and distributive efficiency of economies from 2001 to 2011. The results are very useful in analyzing the situation; we found that none of the economies was efficient in both aspects in the sample period, implying that none of the countries in the analysis was efficient in the production and distribution of economic outputs simultaneously. Brazil, Canada, China and Germany showed improvement in economic efficiency but the distribution efficiency of the most of the economies is low because of the increase in population and high-income class. Most of the countries had an increase in the high-income class but China performed better in the second division because it has managed to improve its middle-income class in the recent past by moving more people from low-income class to middle income class. It is suggested that countries should emphasize on economic restructuring and expansion of the middle-income class to improve their performance in the production and distribution of economic outputs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".