Human Health and\nEcosystem Impacts of Deep Decarbonization\nof the Energy System
Bibliographic record
Abstract
Global warming mitigation strategies are likely to affect\nhuman\nhealth and biodiversity through diverse cause-effect mechanisms. To\nanalyze these effects, we implement a methodology to link TIMES energy\nmodels with life cycle assessment using open-source software. The\nproposed method uses a cutoff to identify the most relevant processes.\nThese processes have their efficiencies, fuel mixes, and emission\nfactors updated to be consistent with the TIMES model. The use of\na cutoff criterion reduces exponentially the number of connection\npoints between models, facilitating the analysis of scenarios with\na large number of technologies involved. The method is used to assess\nthe potential effects of deploying low-carbon technologies to reduce\ncombustion emissions in the province of Quebec (Canada). In the case\nof Quebec, the reduction of combustion emissions is largely achieved\nthrough electrification of energy services. Global warming mitigation\nefforts reduce the impact on human health and ecosystem quality, mainly\nbecause of lower global warming, water scarcity, and metal contamination\nimpacts. The TIMES model alone underestimated the reduction of CO<sub>2eq</sub> by 21% with respect to a full account of emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".