Drivers of an ecological deficit. Analysis of the OECD countries’ ecological deficit
Bibliographic record
Abstract
The aim of this paper is to research the ecological deficit of 34 OECD countries from 1964 to 2013. Ecological deficit measures the environmental sustainability of human consumption. Ecological Footprint, which has been studied more extensively, measures human impact on the environment. Real GDP, population density, education, the dependency ratio and urbanization are found to have significant effects on ecological deficit. The same factors affect Ecological Footprint, with the exception of population density. When one analyses the relationship between human activity and the environment, focusing solely on the Ecological Footprint may cause some determinants of environmental sustainability to be overlooked. Ecological deficit needs to be considered as well. Perhaps the most remarkable results found are the positive effects on the environment of education and of an increased dependency ratio. Education may stimulate awareness of environmental issues and encourage an intention to improve things. This indicates that education is important not only to human well-being but also to the environment. As the dependency ratio of a nation increases, relatively more inhabitants are children or senior citizens; they consume less than the average adult which decreases demand on the environment. This suggests that the ageing population of developed nations in recent years might be good for the environment. A country comparison of the OECD nations reveals that Canada, Australia and Belgium are the most environmentally sustainable countries when influential factors are held constant. France seems to be the most environmentally friendly of the OECD’s largest economies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".