What drives changes in carbon emissions? An index decomposition approach for 40 countries
Bibliographic record
Abstract
This study analyzes carbon emission trends and drivers in 40 major economies using the WIOD database, a harmonized and consistent dataset of input-output table time series accompanied by environmental satellite data. We use logarithmic mean Divisia index decomposition to (1) study trends in global carbon emissions between 1995 and 2009, (2) attribute changes in carbon emissions to either influences of economic activity, changes in technology, changes in the structure of the economy, alterations of the fuel mix, or changes in carbon intensities of specific fuel types, and (3) highlight sectoral and regional differences. We first find that heterogeneity in each country is higher than heterogeneity in sectors. This finding might lead to the conclusion that, in order to abate CO2, structural conditions in sectors prevail over regional circumstances. Regarding our results of the decomposition analysis, the drivers of changes in carbon emissions are very heterogeneous. Among the world's top ten emitters, in only three countries - China, Germany and Canada - the main driver of an improved emissions performance was technological change. Conversely, in Japan and Australia structural change of the economy contributed to less severe increases of emissions. The deployment of cleaner energy sources had a positive in some, mainly developed, economies. Moreover, our results for the global level suggest a general move towards more efficient means of production.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".