Analysis of Inter-Temporal Change in the Energy and CO2 Emissions Efficiency of Economies: A Two Divisional Network DEA Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it