Carbon dioxide emissions, energy consumption and economic growth: The historical decomposition evidence from G-7 countries
Bibliographic record
Abstract
This paper investigates the relationship between carbon dioxide emissions, energy consumption and economic growth in the G-7 countries from a historical perspective. To this end, taking time varying interaction and business cycle into account, we use the historical decomposition method for the first time in the literature. Our results provide evidence that Canada, Italy, Japan and partly the United States need to sacrifice economic growth if they aim to reduce CO2 emissions by decreasing the fossil-based energy use. This situation is not valid since the early 1990s for France, throughout the analysis period for Germany and a few exceptions in all periods for the UK. Furthermore, empirical results provide evidence contrary to the EKC hypothesis for Canada, Germany, Japan, the UK and the US. We found BC-shaped and N-shaped curve for France and Italy, respectively. Although the EKC hypothesis is not valid for Germany and the UK, economic growth has no damaging effect on environmental quality. Also, this effect seems to be cyclical for the US. While the energy conservation theory is fully supported for Canada, it is strongly supported for France, Italy, Japan and the US with the exception of some periods. In addition to these findings, we find strong evidence to support the growth theory for all the G-7 countries.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".