Revisit Economic Growth and CO2 Emissions Nexus in G7 Countries: Mixed-Frequency VAR Model
Bibliographic record
Abstract
Abstract Applying a mixed frequency vector autoregressive (MF-VAR) approach, we examine relationships betweenCO2 emissions and economic growth from 1970Q1 to 2019Q4 among G7 countries. We incorporate primary energy consumption as a control variable, to avoid any bias from an omitted variable. Our empirical results, using forecast error variance decomposition and a Granger causality check, suggest MF-VAR exhibits better explanatory ability over the more commonly used VAR model employing single frequency data. Results from LF-VAR exhibit a feedback loop connecting economic expansion and emissions of CO2 with one-way Granger causality from primary energy consumption to growth of the economies studied. MF-VAR model results also indicate, in G7 countries, economic expansion exhibits a one-way causal link to CO2 emissions in Canada, UK, and US cases. Interestingly, MF-VAR shows feedback between economic expansion and primary energy use in Germany, while LF-VAR quantifies the link from economic growth to CO2 emissions in Canada and from CO2 to economic growth in the UK. Results raise implications for the G7 samples’ policy makers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".