Pragmatic investigation of the effect of green and low-carbon economies on food safety in Africa
Bibliographic record
Abstract
This research examines the relationship between a green economy—defined as an economy that promotes sustainable development through low-carbon, resource-efficient, and socially inclusive practices—and food safety across 37 African countries from 2005 to 2020. Drawing on data from the Food and Agricultural Organization (FAO), the Country Policy and Institutional Assessment (CPIA), and World Development Indicators, this study employs the generalized method of moments (GMM) approach to address endogeneity issues inherent in economic analyses. The findings indicate that a shift toward a greener economy significantly enhances food safety, with each one-point improvement in green economic indicators associated with a 0.24% increase in food safety levels. This underscores that as African economies reduce carbon footprints and adopt sustainable agricultural practices, they experience fewer food safety challenges, largely due to improved environmental health and reduced biodiversity loss. The study concludes that prioritizing green economic growth is essential for environmental sustainability and the agricultural sector’s stability. These insights emphasize the need for policymakers and stakeholders to implement green economy strategies that enhance both ecological resilience and food security, ultimately improving health and livelihood outcomes in African communities. This study stands apart from existing literature by uniquely focusing on the relationship between the green economy and food safety within the African context, which remains underexplored despite the continent’s pressing environmental and food security challenges. Utilizing a dynamic panel Generalized Method of Moments (GMM) model, the research rigorously addresses endogeneity concerns to provide robust insights into how environmental management and other green economy policies influence food safety outcomes across 37 African nations. This methodological approach enables more accurate capture of temporal dynamics and causal relationships, offering policymakers context-specific, evidence-based recommendations tailored to Africa's socio-economic and ecological realities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".