Nexus between environmental vulnerability and agricultural productivity in BRICS: what are the roles of renewable energy, environmental policy stringency, and technology?
Bibliographic record
Abstract
This study aims to examine the effect of carbon dioxide emission and air pollution on agricultural productivity while accounting for the effect of renewable energy use, ICT, technological innovation, environmental policy stringency, and democracy for Brazil, Russia, India, China, and South Africa (BRICS) during the period 1990–2019. Several econometric procedures including mean group estimates are employed. The result suggests that both carbon dioxide emission and air pollution negatively affect the productivity of the agricultural sector. The effects of renewable energy, ICT, technological innovation, and democracy are found to be increasing agricultural productivity. Environmental policy stringency coefficient confirms the porter hypothesis. The result from the causality test suggests that bidirectional causality exists between CO 2 , PM 2.5 , renewable energy, technological innovation, ICT, and agricultural productivity. Finally, the study provides several policy suggestions for the governments of the BRICS economies in order to increase agricultural productivity while tackling the environmental vulnerability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".