Quasi-experimental evidence that the urban tree canopy reduces residential energy consumption
Bibliographic record
Abstract
The relationship between the urban tree canopy (UTC) and residential space-conditioning electricity use has been explored over the past decades, but depending on the methods and assumptions, there is a wide disparity in the estimated energy savings. Using a quasi-experimental research design, we combine aerial imagery of the UTC with high-frequency electricity consumption data for 1,968 houses in Ottawa, Canada, to revisit the causal links between the UTC and residential electricity demand. We estimate that a 10-percentage point increase in the UTC within the 12.5-meter buffer of a house corresponds to a 2.9% reduction in electricity consumption during the period that trees are in leaf. For the average house in our sample, UTC covers 25% of the area within 12.5-metres from the house, and results in 3.0% reduction in annual average electricity consumption. The effect of tree coverage varies by the spatial configuration of trees relative to buildings: UTC closer to the building reduces electricity consumption by a larger amount than UTC farther from the building, and UTC to the west of the house has a larger effect compared to coverage in other directions. To understand how the effects of UTC on electricity consumption are moderated by weather variables we combine a causal quasi-experimental framework with machine learning. We find that UTC has a larger impact on electricity consumption at higher temperatures, lower wind speeds, and lower relative humidity. Savings from UTC reach 15% of residential electricity consumption on hot summer afternoons and the peak residential load is 17.9% lower as a result of the UTC.
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 | 0.000 | 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".