Bibliographic record
Abstract
Eco-efficiency is an important ecological indicator for tracking the progress of how countries’ environmental-adjusted economic activity changes over time. The objective of this research is to calculate country-level eco-efficiency for a group of 18 major countries (G18) that are part of the G20. First, the data envelope analysis (DEA) method is used to calculate eco-efficiency scores. Second, the Malmquist productivity index (MPI) is used to examine how eco-efficiency changes over time. Eco-efficiency is forecast to the year 2040 using automated forecasting methods under a business-as-usual (BAU) scenario. Over the period 1997 to 2040, eco-efficiency varies widely between these countries with some countries reporting positive growth in eco-efficiency and other countries reporting negative growth. Eco-efficiency leaders over the period 1997 to 2019 and 2019 to 2040 include Australia, Brazil, France, Germany, Great Britain, Italy, Japan, Russia, and the United States. Laggards include Canada, China, India, and Indonesia. These laggard countries recorded negative growth rates in eco-efficiency over the period 1997 to 2019 and 2019 to 2040. Negative eco-efficiency growth points to a worsening of environmental sustainability. Large variations in eco-efficiency between countries make it more difficult to negotiate international agreements on energy efficiency and climate change. For the G18 countries, the average annual change in MPI over the period 1997 to 2019 was 0.5%, while the forecasted average annual change over the period 2019 to 2040 was a 0.1% decrease. For the G18 countries, there has been little change in eco-efficiency. The G18 are an important group of developed and developing countries that need to show leadership when it comes to increasing eco-efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".