Do homebuyers prioritize sustainability? Examining the GHG emission impact of housing choices
Bibliographic record
Abstract
• Survey used real listings with no sustainability cues to assess true preferences. • Location, price, and size ranked above energy efficiency in open-ended responses. • Buyers in all income groups overlooked low-emission homes in their selections. • Chosen homes emitted four times more than the greenest available options, on average. • Standardized GHG labels could support low-carbon residential decision-making. Housing is a major source of global greenhouse gas (GHG) emissions, yet environmental performance is often assumed to play a limited role in homebuying decisions. This study investigates this claim using empirical data and explores how sustainability factors into real-world housing preferences using a choice experiment conducted in Ottawa, Canada. Participants selected from real housing listings without explicit environmental cues, while total annual GHG emissions – from both building energy use and commuting combined – were calculated for each option. Despite having low-emission listings in their choice set, respondents across all income groups overwhelmingly selected higher-emission homes. On average, the chosen homes emitted 6.93 tonnes CO 2 e annually, which is over four times the emissions of the most sustainable available alternatives. Regression analyses revealed systematic deviations from optimal choices, especially among higher-income households. Open-ended responses confirmed that location, price, and size were prioritized over energy efficiency. These findings highlight a persistent disconnect between sustainability potential and actual homebuyer behaviour, underscoring the need for investigating income-sensitive policy tools, emissions labeling, and better decision-support mechanisms to reduce residential carbon footprints. They also point to the opportunity for targeting energy efficiency measures at homes with the highest GHG emissions that are nonetheless most likely to be purchased due to other attractive characteristics.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".