Human Health andEcosystem Impacts of Deep Decarbonizationof the Energy System
Bibliographic record
Abstract
Global warming mitigation strategies are likely to affect human health and biodiversity through diverse cause-effect mechanisms. To analyze these effects, we implement a methodology to link TIMES energy models with life cycle assessment using open-source software. The proposed method uses a cutoff to identify the most relevant processes. These processes have their efficiencies, fuel mixes, and emission factors updated to be consistent with the TIMES model. The use of a cutoff criterion reduces exponentially the number of connection points between models, facilitating the analysis of scenarios with a large number of technologies involved. The method is used to assess the potential effects of deploying low-carbon technologies to reduce combustion emissions in the province of Quebec (Canada). In the case of Quebec, the reduction of combustion emissions is largely achieved through electrification of energy services. Global warming mitigation efforts reduce the impact on human health and ecosystem quality, mainly because of lower global warming, water scarcity, and metal contamination impacts. The TIMES model alone underestimated the reduction of CO2eq by 21% with respect to a full account of emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.003 |
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".