Transportation Sector in Environmentally-Extended Economic Modelling: Case Study of Alberta, Canada
Bibliographic record
Abstract
The inclusion of environmental metrics is an integral part of a complete representation of the socio - economic system of a region. This research had the aim of advancing an economic input - output model of the Canadian province of Alberta, with extension to environmental accounts. This involved the development of a detailed account of inter - industry transactions and consumption of goods and services by households, government, and capital formation. The provincial input - output model of Alberta - developed by Statistics Canada as part of its 2010 System of National Accounts - was substantially expanded to include a more granular representation of industries, commodities, and agents of final demand. Environmental accounts were developed for energy use, water use, and the production of greenhouse gas emissions. This work produced an expansive model with a variety of potential avenues for retrospective and policy analysis. Transportation contributes approximately one quarter of all such emissions in an industrialized economy. This paper summarizes model results for this sector, specifically the industries with high demands for transportation services and the contributions of personal transportation delineated by income quintile. Results confirm the importance of transportation to the provincial economy and any potential environmental policies. This research involved the development of a novel representation of environmental metrics for use by industry and agents of final demand. This approach is described and potential extensions identified within the transportation – land use modelling field.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".