Deriving the Ecological Footprint and Biocapacity of Ontario Cities and Rural Areas
Bibliographic record
Abstract
Background: This original research article presents the first city-scaled accounts of ecological footprint and biocapacity applied to all 577 census subdivisions in Ontario, Canada. These new data relates local consumption of materials and energy, and the production of emissions, to the area of ecosystems used within and outside the same local jurisdictions to support this metabolism. This information can inform many stakeholders understanding of sustainable prosperity in cities and rural areas, which are challenged by complex geographies of supply chains, waste streams, and the jurisdictions in which policies are made or applied. Methods: Local accounts of ecological footprint were generated by integrating census data with provincial-scaled accounts of investment and consumption of goods and services by households and governments, and coefficients relating economic outputs to areas of ecosystems providing economic inputs. Biocapacity was generated by relating land cover and land use data at 15 m of resolution to its potential to support a footprint component. Results: Ecological footprint varies tremendously between and among cities and rural areas in Ontario, from 12 to 0.12 global hectares (gha) per capita (gha/capita). Differences relate to population, household income, commuting durations and modal split, housing types, and average household size. Biocapacity ranged from 10,039 to 0.11 gha/capita within local boundaries. Conclusions: These new and publicly available data can inform opportunities to conserve or enhance biocapacity, and to understand the potential and limitations of local efforts to affect ecological footprints. These are discussed and contextualized within an ecological economics framework of sustainability.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| 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) | 0.003 | 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".