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Leveraging Entrepreneurship for Environmental Sustainability: A Machine Learning Approach to SDG Achievement

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  • Mehmet Ali Köseoglu

Abstract

In the face of escalating environmental challenges, the United Nations' Sustainable Development Goals (SDGs) have become crucial benchmarks for sustainability, with SDG 7 (Affordable and Clean Energy), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action) addressing key issues like energy efficiency, resource management, and climate change. This study explores the impact of the entrepreneurship ecosystem (EE) on these environmental goals, using data from the Global Entrepreneurship Monitor (GEM) and applying advanced machine learning techniques such as bagging, random forest, boosting, Shapley Additive Explanations (SHAP), and Partial Dependence Plots (PDPs). Grounded in sustainability, innovation, Resource‐Based View (RBV), and institutional theories, the study reveals the critical role of the business services sector, entrepreneurial intentions, and entrepreneurial education in driving progress toward environmental sustainability. The findings offer actionable insights for policymakers and contribute to the academic understanding of how entrepreneurship can be leveraged to achieve SDGs, emphasizing the need for a robust and integrated entrepreneurial ecosystem to foster sustainable practices.

Suggested Citation

  • Mehmet Ali Köseoglu, 2025. "Leveraging Entrepreneurship for Environmental Sustainability: A Machine Learning Approach to SDG Achievement," Business Strategy and the Environment, Wiley Blackwell, vol. 34(7), pages 7980-8000, November.
    • Handle: RePEc:bla:bstrat:v:34:y:2025:i:7:p:7980-8000
    DOI: 10.1002/bse.70011
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