FACTORS LEADING TO INCREASED CARBON DIOXIDE EMISSIONS OF THE APEC COUNTRIES: THE LMDI DECOMPOSITION ANALYSIS
Bibliographic record
Abstract
This paper explores the factors that lead to increased carbon dioxide emissions in the 18 countries of the APEC. We apply the LMDI multiplicative decomposing method to 18 countries between 1971 and 2012. We summarize these factors that are as follows: (1) population increase and economic growth play a key role in increased carbon dioxide emissions. (2) All the 18 countries of the APEC have improved their energy efficiency as manifested in the change of energy intensity ([Formula: see text]), which is less than 1 in the 42 years; (3) In terms of energy substitution effect ([Formula: see text]) and fuel coefficient effect ([Formula: see text]), the decomposition results point out that Hong Kong, Indonesia, and Malaysia witnessed growth in [Formula: see text] and [Formula: see text], indicating the only factor to reduce the emissions for these three countries is intensity effect, which gives rise to relatively higher emission for these three countries during the period. In the case of Peru, the Philippines, Singapore, Thailand, and Vietnam, we witnessed increases in [Formula: see text], but decreases in [Formula: see text]; In the case of Australia, Canada, Chile, China, Japan, South Korea, Mexico, New Zealand, Taiwan, and the US, there seem to decrease in [Formula: see text], but increases in [Formula: see text] during the 42-year period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".