Urban sustainability through emerging technologies
Bibliographic record
Abstract
Cities consume high energy per unit area as a result of rising concentration of population and therefore can play an increasingly crucial role in driving global sustainability. Though integrating energy efficiency and sustainability can prove challenging, the fusion of both objectives is a critical step towards a green future. This paper focuses on building and transportation sectors which have the highest energy consumption and greenhouse gas (GHG) emission in Ontario. The electricity sector is also considered because of its convergence with transportation and building sectors. Emerging technologies discussed in the paper utilize renewable sources and demonstrate higher efficiency resulting in lower GHG levels. The paper also highlights best practices from leading jurisdictions in building and transportation, intended to serve as an example for urban areas to follow. Use cases and economic considerations of key “game changers” in the electricity sector such as energy storage, micro-grids and direct current transmission and distribution are outlined. Electric utilities are increasingly gravitating to new/emerging technologies as well as non-wires solutions for greater efficiency and system needs. As buildings, transportation and electricity sectors converge, electricity system planners would need to take a broader approach of energy planning, considering other forms of energy from generation to delivery to utilization.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".