Assessing the environmental efficiency of OECD countries through the lens of ecological footprint indices
Bibliographic record
Abstract
Environmental efficiency plays a crucial role in achieving sustainable economic development. This study aims to enhance the current understanding of dynamic environmental efficiency by using Data Envelopment Analysis (DEA) in conjunction with the ecological footprint index. This study evaluates 27 OECD countries' environmental performance from 2000 to 2017, employing net capital stock, labor force, and energy consumption as inputs, ecological footprint as undesirable output, and GDP as desirable output. We utilize 16 window Slack-Based Measurement DEA (SBM-DEA) models, each representing consecutive years within the observation period. Additionally, we adopt the Global Malmquist-Luenberger Index (GMLI) techniques to facilitate a simultaneous evaluation of the efficiency levels for each country. Our findings reveal that the United Kingdom and Lithuania were the most and least ecologically efficient countries among the 27 OECD countries, respectively. Over the 18-year observation period, all countries showed both progress and setbacks in environmental efficiency, with a modest overall improvement. Poland, Denmark, Slovakia, and Lithuania were the most improved countries in environmental performance, while Canada and Japan showed the most significant regressions in environmental efficiency. We highlight the need for policymakers to prioritize sustainable economic growth and consider ecological footprints when making economic decisions to enhance environmental efficiency in OECD countries. Our findings have can guide policymakers in designing effective policies and strategies to enhance environmental efficiency and promote sustainable economic development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".