A sky-high challenge: The carbon footprint of aviation in British Columbia, Canada, and measures to mitigate it.
Bibliographic record
Abstract
Greenhouse gas (GHG) emissions from civil aviation contribute to anthropogenic climate change and are expected to increase significantly in the future. GHG emission inventories exist for civil aviation at the global scale but not subnational scale. In this thesis, I present what seems to be the first detailed analysis of the carbon footprint (CF) of civil aviation at a subnational level together with an assessment of what key stakeholders are doing to mitigate their CF. I calculated the CF of civil aviation in British Columbia (BC), Canada, determined what efforts airlines and airports in BC are engaging in to mitigate it, and make recommendations on how to further decrease future GHG emissions. The annual CF of civil aviation in BC that is subject to the BC Carbon Tax is approximately 524,000 tonnes of CO2. Passenger flights account for 197,000 tonnes (38%), airport operations for 148,000 tonnes (28%), and passenger travel to and from airports for 179,000 tonnes (34%). Large airlines and airports, as well as small airlines in southern BC, are generally proactive in reducing their CF, while small airlines in northern BC and small airports are generally not. To further reduce the CF of civil aviation in BC, I recommend a major effort to reduce emissions from passenger travel to/from airports, improved stakeholder cooperation including better technology dissemination, enhanced passenger and employee education and awareness programs, high quality and more transparent offset programs, and incentives by the provincial government for airlines and airports to reduce their CF while remaining economically competitive. --P. ii.
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".