A Path to Carbon Neutrality: Do Environmental Tax, Green Technologies and Natural Resources Extraction Matter for Environmental Sustainability?
Bibliographic record
Abstract
BRICS countries (Brazil, Russia, China and South Africa) and G-7 countries (Japan, USA, France, UK, Canada, Italy and Germany) have experienced high economic growth while polluting their environment. This study explores how environmental sustainability of these countries can be achieved via environmental technology and renewable energy use. It also shows the uncertainty of environmental tax and natural resources extraction in reducing carbon emissions (i.e. CO2). Unlike previous studies using conventional econometric analysis, we employ novel Bayesian panel regression to analyze data spanning from 1996 to 2021 for twelve countries within the G-7 and BRICS cohorts. The results of Bayesian regression are further verified by using the method of moment quantile regression (MM_QR). The results show that extraction of natural resources (i.e. forest rents, natural gas rents and fossil fuels) and economic growth, contribute to an increase in carbon emissions across these countries. We also observed that implementation of green environmental technology and uptake of renewable energy reduce carbon emissions within these countries. The results further show the uncertainty of environmental tax and other natural resource extraction (i.e. coal rents, oil rents, mineral rents) on minimizing carbon emissions. The study addresses two policy interventions: first, to promote renewable energy and technological innovation in the environmental sector to achieve carbon neutrality. Second, to reduce the effect of natural resource extraction, such as forest and natural gas rents, on increasing carbon emissions. The study offers actionable insights for policymakers in other developing countries to balance economic growth and natural resources extraction with environmental sustainability.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".