The Drivers of Greenhouse Gas Emissions Intensity Improvements in Major Economies: Analysis of Trends 1995–2009
Bibliographic record
Abstract
This article analyses the trends in greenhouse gas (GHG) emissions intensity over the period 1995–2009 in a mix of developing and developed economies that account for almost two-thirds of global emissions. From the accounting point of view, it distinguishes between the production-based emissions (PBEs) and consumption or demand-based emissions (DBEs). Several studies find that while PBEs in many developed economies during the last decades have stabilised, the DBEs are on the rise. Understanding the relative influence of various factors that have shaped the different patterns of emissions growth can provide us with important policy insights for controlling GHG emissions. The article undertakes a decomposition exercise to understand the variations/fluctuations in both PBEs and DBEs intensities due to changes in technology and changes in economic structure (i.e., composition of aggregate production and final consumption). The main findings of this article are that, over the period 1995–2009, technological change has been the key driver of emissions intensity improvements in both PBEs and DBEs. Emissions intensity improvements in consumption activities have been slower than production, particularly in EU 27. Structural changes or changes in the composition of aggregate production and demand have relatively smaller contribution in overall intensity improvement. Structural shifts in the economy have somewhat negatively contributed to emissions intensity improvements in Canada and China. In India, structural shifts in both production and consumption activities have contributed significantly to emissions intensity improvements. When taking account of trade, changes in the sources of imports have worked against overall emissions intensity improvements, particularly in the developed economies of Canada, European Union (EU 27) and USA, where imports from relatively emissions intensive sources have increased during the period. JEL: D58, Q56, O13
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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 | 0.001 |
| 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".