Evaluation of the efficient and sustainable source utilization index for G7 countries
Bibliographic record
Abstract
This study analyzes the performance of G7 countries on efficient and sustainable resource use between 2010 and 2023 using time series analysis. The study uses data sets published by the Green Growth Institute and evaluates the Effective and Sustainable Resource Use Index based on four main components covering energy, water and material and land use. In the light of the data obtained, it is observed that the overall index scores of G7 countries have exhibited a steady upward trend over the years. After 2015, there has been a significant acceleration in the rate of increase in the index, and this is associated with the effects of the Paris Climate Agreement. A country-by-country analysis reveals that Canada stands out with its investments in renewable energy, Germany has taken successful steps to increase resource efficiency in industry, and France has displayed a stable performance thanks to its low-carbon emission energy strategies. Moreover, countries such as the United States, Japan and the United Kingdom continue their efforts to increase resource utilization efficiency in different sectors. The findings of the study reveal that G7 countries are improving their resource management practices in line with sustainable development goals, but there are differences in the pace of development across countries. In this context, it is concluded that policies for sustainable resource utilization should be designed in a flexible and innovative manner according to the unique conditions of countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 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) | 0.001 | 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".