Environmentally Sound Technologies for Climate Change Mitigation in BRICS Countries: A Comparative Policy and Legal Perspective
Bibliographic record
Abstract
The adoption of environmentally sound technologies (ESTs), with potential for significantly improving environmental performance relative to other technologies, provide one of the effective steps for achieving the Sustainable Development Goals (SDGs). The grouping of emerging economies of Brazil, Russia, India, China and South Africa (BRICS) significantly contributes to greenhouse gas (GHG) emissions and climate change. Hence the BRICS countries have a great potential in mitigating GHG emissions. They can play a key role in the global climate change negotiations. Therefore, adoption of ESTs in these countries play a crucial role in mitigating climate change. In this context, this paper analyses the national laws and plans in BRICS countries pertinent to ESTs that can contribute in attaining the “stabilization of GHG concentrations in the atmosphere” (Article 2) objective of the United Nations Framework Convention on Climate Change (UNFCCC). The legal provisions for development, dissemination, and technology transfer commitments concerning ESTs in general and within the BRICS countries in particular are analyzed to understand the current position and future directions toward climate change mitigation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".