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Exploring the Nonlinear Relationship of Environmental Sustainability Factors and Economic Growth in West Africa: Novel Machine Learning Evidence

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  • Seth Acquah Boateng
  • Jiancheng Xi
  • Michael Provide Fumey
  • Jeff Kwaku Kumi

Abstract

This study uses advanced machine learning techniques to explore the nonlinear relationship between environmental sustainability factors and economic growth in West Africa from 1991 to 2023. Applying linear regression, Random Forest, Gradient Boosting, and Support Vector Regression, the analysis identifies critical thresholds and regime‐dependent dynamics often overlooked by traditional models. The results reveal that CO2 emissions and forest rent significantly influence economic growth, with CO2 exhibiting a U‐shaped relationship, supporting the environmental Kuznets curve hypothesis. Renewable energy adoption shows diminishing returns beyond a threshold, highlighting complex trade‐offs between sustainability and development. The study also uncovers critical tipping points and regime shifts, providing actionable insights for policy interventions. This research contributes to a deeper understanding of sustainable development pathways in West Africa by quantifying the dynamic interactions among key environmental variables. The findings support evidence‐based policymaking that balances economic advancement with environmental protection in emerging economies.

Suggested Citation

  • Seth Acquah Boateng & Jiancheng Xi & Michael Provide Fumey & Jeff Kwaku Kumi, 2026. "Exploring the Nonlinear Relationship of Environmental Sustainability Factors and Economic Growth in West Africa: Novel Machine Learning Evidence," Sustainable Development, John Wiley & Sons, Ltd., vol. 34(S1), pages 1197-1220, January.
    • Handle: RePEc:wly:sustdv:v:34:y:2026:i:s1:p:1197-1220
    DOI: 10.1002/sd.70216
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