Physical versus behavioural emissions reductions: Quantifying and comparing emissions reduced by behaviour and emissions reduced by technology of net-zero communities
Bibliographic record
Abstract
Modern climate change research calls for more diverse and creative solutions past simply improving technology; there is not one solution to climate change. A multidisciplinary field like planning can affect both physical changes in a city or behaviour changes in people to reduce greenhouse gas emissions. Despite these considerations, behavioural emissions reductions remain an underexplored topic of literature, especially in terms of emission quantification. Without this information, planners cannot make the most informed and resource efficient policy decisions to combat climate change. This thesis fills this literature gap by quantifying behavioural emissions reductions and comparing them to the best-case scenario for physical emissions, net-zero communities, in the context of Ontario’s first, recently completed, net-zero community located in London, Ontario. This thesis also begins to explore relationships and patterns between sources of behavioural emissions reductions and how they can compound into greater reductions. Within the study area, net-zero homes produced 6.89 fewer tonnes of CO2e/year compared to the average home, 67.5% of which were physical emissions reductions and 32.5% of were behavioural. Residents here generally improved few behaviours to a large magnitude rather than improving many/all behaviours to a small magnitude. While no specific behaviour patterns were identified, the there was evidence in favour of patterns existing, which could by identified with a larger sample. Overall, while behavioural emissions reductions were less effective than physical, they can be implemented both concurrently and instead of physical when necessary. There is also potential for behavioural emissions reductions to be more effective than physical given the right context and if behavioural patterns are used to their fullest.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".