Ecological footprint and appropriated carrying capacity : a tool for planning toward sustainability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it