Full Disclosure: The Effects of Energy Benchmarking & Reporting Programs on Promoting Efficient Buildings
Bibliographic record
Abstract
Buildings and their operations are responsible for roughly half of all greenhouse gas emissions from Canadian urban centres. Despite this large number, building owners have failed to adopt the well-developed and cost-saving green building technologies which offer an ideal solution to this problem. This is due to market failures created by the tendency for energy efficiency information to be hidden in real estate transactions: uncertainty about the market performance of energy efficient buildings, lack of consumer awareness of energy efficiency, and the misallocation of incentives. \nEnergy disclosure policies that require commercial building owners to publicly disclose their annual energy use and offer the ability to make energy use transparent in the marketplace. Disclosure allows decision-makers such as owners, prospective buyers and lenders to incorporate energy information into investment and consumption decisions, therefore establishing a market signal that increases consumer demand for efficient buildings. \nThis study analyzes whether empirical evidence validates this theory by comparing the financial performance of buildings between a market where disclosure policies have been in place for nine years (New York City) to one where it hasn’t (Toronto). LEED certification is used as a proxy for energy efficiency in this study, and the sample size includes: 100 buildings in the Toronto market and 224 buildings in the New York City Market. Hedonic models are applied to each market to determine the correlation of certification is with the following financial metrics: rental rates, occupancy levels, and assessed building value. \nThe results show that in Toronto, LEED certification is correlated with 18% lower rental rates, 20% higher assessed building values, and an 8% decrease in occupancy rates. In New York City, LEED certification is correlated with a 28% premium in rental rates, a 0.8% increase in building value, and a 40% increase in occupancy levels. The results show that green buildings do perform measurably better relative to conventional buildings in New York City in terms of these financial metrics. However, the findings were not statistically significant. This study points to the need for more research on the effects of this nascent but promising, cost-effective, and city-driven public policy tool.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".