Reducing Greenhouse Gas emissions is not the only solution
Bibliographic record
Abstract
With the increasing strength and frequency of climate change events, the urgency to mitigate climate change impact is ever so important. Canada reported his Nationally Determined Contribution (NDC) and submitted its ambition to reduce his Greenhouse Gas (GHG) emissions by 40-45% by 2030 compared to 2005 and reach net-zero emissions by 2050. Interest for energy system modelling has been increasing for planning future energy systems, as a result of growing concern for sustainable development and transition towards renewable energies. Nevertheless, planning a decarbonization of energy system can potentially lead to environmental burden shifting, and can increase impacts on other environmental impacts such as ecosystem quality or human health. Thus, this project aims towards integrating Life Cycle Analysis (LCA) and more specifically Life Cycle Impact Assessment (LCIA) within Energy Systems Modelling, in order to minimize not only climate change impacts, but human health and ecosystem quality. In addition, a dual spatial resolution was integrated in the model in order to increase data precision and computational efficiency. The results show that a low-carbon energy system, based on the optimization of GHG emissions allows to greatly reduce all the environmental impacts under analysis, compared to an all economic- based energy system, but a multi-objective optimization allows to simultaneously reduce impacts on ecosystem quality and human health, while reducing GHG emissions and keeping good economical and technical performances. The results also show that it is theoretically possible for Canada to keep on track with their emission reduction ambition by 2030, by deploying more renewable energies. This project does not allow assessing Canada’s potential to reach net-zero emissions, as environmental impacts from carbon capture technologies are still to be characterized and integrated in the model.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".