Ecological footprint and appropriated carrying capacity : a tool for planning toward sustainability
Bibliographic record
Abstract
There is mounting evidence that the ecosystems of Earth cannot sustain current levels of economic activity, let alone increased levels. Since some consume Earth’s resources at a rate that will leave little for future generations, while others still live in debilitating poverty, the UN’s World Commission on Environment and Economic Development has called for development that is sustainable. The purpose of this thesis is to further develop and test a planning tool that can assist in translating the concern about the sustainability crisis into public action. The research advances the concept of “Ecological Footprint” or “Appropriated Carrying Capacity” (EF/ACC) as a planning tool for conceptualizing and developing sustainability. To meet this purpose, I document the development of the EF/ACC concept, explore its potential use in public decision-making towards sustainability, apply the concept in a real world context, and finally, empirically analyze its usefulness to actors in the public domain. The research shows that the EF/ACC concept can link global social and ecological concerns to individual and institutional decision-making. Though the tool needs further refinement to make it readily applicable to the planning practitioners’ everyday decisions, it has proved useful as a conceptual tool for framing the sustainability challenges. More than 20 EF/ACC applications, by others and by me, range from environmental outdoor education for children to policy and project assessments for municipalities and regions. With these examples, EF/ACC has contributed to translating sustainability into concrete terms and to providing direction for planning toward sustainability.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".