Greening Vancouver through Energy Benchmarking : a brief overview of the City’s Benchmarking initiative
Bibliographic record
Abstract
This study uses multi-unit residential buildings’ (MURBs) utility data collected from False Creek South residents to evaluate the initial steps of the City of Vancouver’s energy benchmarking initiative. The aim of this report is to highlight some of the benefits of benchmarking, initiate an example of preliminary utility analysis on MURBs and provide feedback on the data collection process outlined by the City’s draft benchmarking guide. Aggregated electricity-use and water consumption data from 18 buildings was obtained through BC Hydro and City of Vancouver. Through benchmarking, we were able to visualize and calculate total electricity and water consumption for entire buildings and building complexes, as well as identify yearly and seasonal trends in electricity and water use respectively. Only a four-month natural gas dataset (Fortis BC) was available to us for a single building. This was used to propose an estimate of greenhouse gas emissions during summer/fall months and compare the emissions of two buildings with different heating systems. In addition, we were able to provide a preliminary analysis of heat loss in MURBs using thermal imaging. This could be incorporated in the City’s benchmarking initiative as a visual reference to understanding heat loss, as well as encourage building retrofits for improved efficiency and reduced greenhouse gas emissions. Eventually, all of the preliminary analysis of energy, water and gas consumption along with heat loss information would be beneficial as a stepping stone in updating energy policies and creating new energy efficient incentives in the City’s target of reducing energy use and greenhouse gas emissions in existing buildings by 20% over 2007 levels. For improving the benchmarking data collection and process, we recommend: ● Clarifying utility terminology ● Re-organizing the data collection steps ● Modifying data request forms ● Advising utility companies on the City’s benchmarking initiative to facilitate the data collection process for building managers/owners ● Offering an Energy Star Portfolio Manager tutorial ● Incorporating thermal imaging to observe and keep track of building performances through heat loss
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".