Global environmental sustainability trends: A temporal comparison using a new interval-based composite indicator
Bibliographic record
Abstract
Assessing progress on the pursuit of the Sustainable Development Goals is crucial for evaluating the sustainability of a Country, although this is not easy, considering the interdependencies or interconnections of individual goals with others, and the fact that there are several indicators for each goal. The aims of this research are: (1) to propose a novel interval-based environmental sustainable composite index (ESI) suitable to monitor the worldwide environmental SDGs' implementation at national scale, (2) to solve the problem of missing data in large databases and the subjectivity in computing a composite index (CI), (3) to group and compare statistically countries according to the ESI, and (4) to represent spatially the results to identify areas of the world more or less environmentally sustainable than others. Clustering and Sankey diagrams have supported the temporal and spatial analysis of ESI trends, showing that Canada, Brazil, New Zealand, and several European countries have been the most sustainable in 2019. The novelty of this indicator is that each country presents an ESI central value, the most probable value of the composite indicator, and a range, which represents the uncertainty given by the lower and upper bounds. In this sense, it is possible to better interpret the results of the composite indicator, while simultaneously obtaining a measure of the uncertainty of the results. The composite indicator can be used to monitor countries’ vulnerability towards the unsustainability risk, as well as countries that are not able to escape from a sort of “unsustainability trap”. • The most sustainable are the European countries with Canada, New Zealand, and Brazil. • The least sustainable are Africa, India, Afghanistan, and Persian Gulf countries. • The Environmental Composite Indicators are presented through interval parameters. • Cluster analysis of mean values of center variables identified four different clusters. • Cluster 3 groups all the countries with a positive trends towards 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".