Integrating Environmental, Social, and Economic Dimensions to Monitor Sustainability in the G20 Countries
Bibliographic record
Abstract
Several regions have struggled to define and implement strategic priorities to ensure resource supply security and environmental, economic, and social sustainability. The circular economy is gaining more and more importance as one of the forms of transition towards a sustainable future that integrates, in a balanced way, economic performance, social inclusion, and environmental resilience, for the benefit of current and future generations. In light of the challenges of solving or avoiding future problems, the G20 bloc created proposals and action plans to support the transition towards a more circular economic model while at the same time fostering discussions on the implementation of the 2030 Agenda for Sustainable Development. Therefore, the main objective of this study is to monitor and compare the performance of 19 countries in the G20 bloc (the 20th member is the European Union) from 2000 to 2020 to assess their progress toward environmental, economic, and social sustainability supported by the CE principles. To achieve this objective, the five sectors sustainability model was used and was supported by goal programming as a multicriteria analysis tool generating a synthetic sustainability indicator to assist decision making. The results showed that the countries with the best overall sustainable performance (environmental, economic, and social) in 2020 were Canada (which also occupied the best position in 2000), Australia, Italy, the United Kingdom, and the United States, while Argentina, South Africa, India, Indonesia, and China showed lower sustainability. The results can serve as a reference for decision making by stakeholders in designing policies and incentives to encourage the adoption of the circular economy and boost economic development without compromising welfare or the environment.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".