Decisions by Key Office Building Stakeholders to Build or Retrofit Green in Toronto’s Urban Core
Bibliographic record
Abstract
The environmental impact of greenhouse gas emissions from buildings—especially in global cities such as Toronto—is well documented. Green mitigation of new and existing buildings has also been researched. Few studies, however, have focused on the decision to build or retrofit green. Are key stakeholders in Toronto’s office building sector aligning their decisions to achieve sustainable environmental goals? Do they support LEED certification regardless of the impact on market valuation? Are tenants willing to pay higher rents in LEED office buildings? The study first obtained data on 16 LEED and 52 conventional buildings to determine if LEED certification has a significant impact on net asking rent. Pearson correlation and linear regression analysis did not find LEED certification to be statistically significant in explaining the variance in net asking rent (market value). The second stage included interviews with senior executives engaged in Toronto’s office building sector. The expert informtabants were asked to assess if financial drivers are the deciding factor in decisions to pursue LEED certification. They concurred that LEED certification is not the primary driver. It is a combination of numerous factors that overall have an impact on a firm’s financial bottom line.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".