Investigating the Relationship between Energy Consumption and Environmental Degradation with the Moderating Influence of Technological Innovation
Bibliographic record
Abstract
Energy consumption (ECON) in BRICS countries is fueled by fossil fuels, mainly coal. Increased environmental degradation (ED) in BRICS countries is mostly driven by coal consumption. This study utilizes quantile regression for the analysis, enabling the development of targeted energy reorganization and emission reduction policies in BRICS countries. This study uses data spanning from 1990 to 2022 to explore the impact of ECON on ED. Additionally, technological innovation was used to create a moderating role in the nexus between ECON and ED. The model focuses on CO2 emissions and the ecological footprint across ten BRICS countries. Among the nations included in the panel, the results indicate a significant dependence on cross-sectional factors. The study shows that ECON has a detrimental impact on ED across all quantiles. However, technological innovation reduces ED. In terms of a moderating role, technological innovation mitigates the negative influence of ECON on ED. Therefore, it is necessary to implement distinct policies in order to accomplish carbon emission reduction goals in various countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".