Fossil fuel efficiency as a pathway to decarbonisation and the role of international trade: A perspective of the LCC hypothesis
Bibliographic record
Abstract
Abstract The continued global dependency on fossil fuel sources in the face of increasing international trade may hamper the goal of decarbonisation. To condense climate risk and attain net zero, efficiency measures are proposed. This study unambiguously investigates the environmental quality implications of fossil fuel efficiency in the face of the international trade based on the Load Capacity Curve (LCC) hypothesis for Canada from 1965 to 2022. The application of a dynamic ARDL simulations method uncovers the following findings: (i) fossil fuel efficiency engenders load capacity factor ( $$lnLCF$$ lnLCF ) in the short run (SR) and long run (LR). This suggests a dampening trend of environmental externalities as fossil fuel efficiency increases. (ii) the connection between income expansion and $$lnLCF$$ lnLCF follows an inverted U-type pattern, thus invalidating the LCC hypothesis in Canada. (iii) the effect of international trade is positive but only significant in the SR. (iv) urban development is positively linked to $$lnLCF$$ lnLCF but its effect is perhaps significant only in the LR. These findings corroborate the responses of the $$lnLCF$$ lnLCF to a 1% positive and negative counterfactual shocks in fossil fuel efficiency, income expansion, international trade, and urban development. Moreover, the findings are robust to the results of the nonparametric multivariate quantile-on-quantile regression model. Therefore, the policy take of these results is that to achieve net-zero emissions targets by 2050, environmental policies ought to promote not only fossil fuel efficiency but also the growth of income expansion, trade, and urbanization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".