Measuring Environmental Efficiency through the Lens of Technology Heterogeneity: A Comparative Study between China and the G20
Bibliographic record
Abstract
Drawing on a perspective of technology heterogeneity, this study advances the analytical framework for evaluation of environmental efficiency (EE) across diverse economies. To improve the continuity and robustness of efficiency estimation, we construct a Hybrid Malmquist–Luenberger index under the meta-frontier (MHML) technique to allow a dynamic evaluation of environmental efficiency and to probe the underlying sources of inefficiency. Decomposition of the MHML index into component factors of efficiency change (EC), Best Practice Change (BPC) and Technological Gap Change (TPC) allows an improved understanding of causality and enhanced guidance for decision-making units (DMUs). Empirical tests based on panel data of the Group 20 countries spanning 2000–2014 reveal an upward improving trend in environmental efficiency but is also characterized by notable evidence of technological heterogeneity. Whereas technical progress was the main cause of environmental efficiency improvements in the G20 countries, for the BRICS (i.e., Brazil, Russia, India, China, South Africa), economic growth rates played a more significant in contrast to the role of technical change and allocation efficiency. The lagging growth rates of environmental efficiency for the G20 countries compared to the BRICS is a reflection of the fact that room for optimization in G20 countries was not as high as it was for BRICS and, China, in particular. China has been catching up with frontier technology whereas developing countries were shifting away from benchmark technology frontier. The developed economies remain the best performers and leaders in environmental technology. However, the BRICS countries, represented by China, remain on an upward trajectory of improvements’ in EE with gains from managerial sufficiency and technological advancement. The MHML index developed here provides a robust quantitative measure for policy interventions to support overall national environmental performance. Context-specific suggestions are proposed to foster efficiency gains and green transition for Chinese development scenarios against best performing economies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".