Advancing a New Generation of Sustainability-Based Assessments for Electrical Energy Systems: Ontario as an Illustrative Application—A Review
Bibliographic record
Abstract
Negative social and ecological trends are putting essential life-support systems at risk. Necessary responses include sustainability transformations in diverse sectors to enhance the planetary capacity to deliver more positive effects to all. Sustainability-based assessment frameworks are tools to guide the evaluation of initiatives in different human sectors and promote decisions that enhance overall social and ecological well-being. However, advancing sustainability remains difficult, in part because it must be pursued in a world of complex interactions and must respect the specifics of each case and context. This paper reports the process of building a sustainability-based assessment framework for electrical energy systems carried out by Aguilar. This work further specified the framework for electrical energy systems for application in the case and context of the electrical energy system in the Canadian province of Ontario. The illustrative application revealed that Ontario’s electrical energy system has made some progress towards contributions to sustainability but requires improved efforts to be on a path to adequate transformation. The research found that the sustainability-based assessment framework for electrical energy systems is promising and well-suited for further application to particular electricity-related initiatives. However, more applications are needed to further test the utility of the framework and refine the proposed criteria.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".