Modeling the Integrated Influence of Social, Ecological, and Economic Components on Achieving Sustainable Development Goals: A Cross-Country Analysis
Bibliographic record
Abstract
This study analyzes the impact of social, ecological, and economic components on achieving Sustainable Development Goals (SDGs) in seven selected countries for the period 2000–2022 (Australia, Canada, Germany, the Netherlands, Switzerland, the United Kingdom, the United States). Using data from the Sustainable Development Reports 2017, 2019, and 2023, a correlation and regression analysis was conducted to assess the relationships between the components and the SDG Index. The results demonstrate a strong positive relationship between social, ecological, and economic factors and progress towards achieving the SDGs, with variations between countries. The study revealed the limitations of aggregated data analysis that negatively affect the implementation of the planning function. The research highlighted the importance of a country-by-country approach in assessing sustainable development progress. The results underscore the importance of developing tailored strategies for achieving the SDGs, which are sensitive to each country’s specific conditions, strengths, and weaknesses in different aspects of sustainability. These conclusions are important for the shaping of policies and strategic planning for achieving the SDGs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".