The greenlight for government buildings: strategies for a low-carbon building portfolio
Bibliographic record
Abstract
There is a global focus by governments on retrofitting buildings, as well as incorporating energy efficiency into new construction, as a means to address climate change. Initiatives to reduce energy use, source renewable electricity, and use low-carbon materials are aimed at leading by example, where governments attempt to showcase innovation through green building strategies. Greening government initiatives are promoted to reduce operating costs, improve energy system resilience, grow the “green” economy, support clean energy development, and encourage sustainable building practices. Here, we outline the benefits of greening government initiatives by examining Canada's Greening Government Strategy as a case study approach for transitioning to a low-carbon building portfolio. We focus our review on initiatives that outline how public institutions can transition buildings to reduce their carbon footprint by (1) pairing greening government mandates with adequate support structures for public agencies, (2) using an integrated energy management process for the planning and development of carbon-neutral portfolios, and (3) overcoming barriers to low-carbon project implementation with procurement standards, financial instruments, and staff training. These approaches are defined to offer leadership in the green building industry, strategically identify carbon reduction projects, and reduce barriers to a low-carbon building portfolio.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".