The Drivers of GHG Emissions Intensity Improvements in Major Economies Analysis of Trends 1995-2009
Bibliographic record
Abstract
This paper analyzes the trends in GHG emissions intensity over the period 1995-2009, in a mix of developing and developed economies that account for constitute almost 2/3rd of global emissions. In doing so it distinguishes between the demand-based emissions (DBEs) and production-based emissions (PBEs). Several studies find that while PBEs in the developed economies during the period have been stabilized, the DBEs are on the rise. Understanding the relative influence of various factors that has shaped the differential patterns of emissions growth will provide us with important policy insights for controlling GHG emissions. It undertakes a decomposition exercise to understand the changes in both PBEs and DBEs intensities due to changes in technological change and structural shifts in production and final consumption. Main findings of the paper are that technology change has been the key driver of emissions intensity improvements in production and consumption. Intensity improvements in production activities has been more than those in consumption. Structural changes in composition of production and demand has relatively smaller contribution in overall intensity improvement. Structural shifts have somewhat negatively contributed to emissions intensity improvements in Canada and China. In India structural shift in both production and consumption activities have contributed significantly in emissions intensity improvements. Changes in regional composition of final consumptions have worked against emissions intensity improvements particularly in developed economies of Canada, EU 27 and United States.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".