Analyzing the Drivers of Ecological Footprint Toward Sustainability in BRICS+
Bibliographic record
Abstract
This study investigates the impact of key macroeconomic and structural variables—economic growth, renewable energy consumption, urbanization, industrialization, globalization, agricultural productivity, and forest area—on the ecological footprint of BRICS+ nations (Brazil, Russia, India, China, South Africa, and their extended partners) from 1990 to 2021. Employing panel-corrected standard errors (PCSEs) and seemingly unrelated regression (SUR) models, the research accounts for cross-sectional dependence and heteroskedasticity to ensure robust empirical estimation. The findings reveal that economic growth, urbanization, and globalization significantly exacerbate ecological degradation. Conversely, renewable energy consumption and agricultural productivity are associated with reductions in ecological footprint, indicating their potential as effective mitigation tools. Industrialization exhibits a negative impact, likely due to structural and reporting anomalies in developing contexts, while forest areas remain statistically insignificant, suggesting that conservation policies alone may be insufficient without systemic support. The study underscores the urgency for integrated policy interventions that promote green growth, urban sustainability, renewable energy deployment, and sustainable land use to achieve long-term environmental resilience in BRICS+ economies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".