Publication

Examining the Influence of Sustainable Development Indicators on Economic Development: A Machine Learning Approach with Evidence from Africa

Osunnaiye, Adetayo V.
; 
Kucukaltan, Berk
Publication Date
2025-11-12
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Keywords
Country performance, Economic development, Machine learning, Random forest algorithm, Sustainable development indicators
Rights
© 2025 The Author(s). This is the Author Accepted Manuscript of the article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0) in accordance with the University of Bradford Rights Retention Policy.
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Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-04-08
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Management and Law Publications
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kucukaltan_et_al_2025.pdf
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Abstract
Purpose Government interventions for economic growth often fail to enhance citizen well-being, highlighting the need for research on effective strategies. Integrating sustainability into economic planning holds potential, yet there is a dearth of research on the interplay between sustainable development indicators (SDIs) and economic development (ED). This study addresses this gap by examining the influence of SDIs on ED through machine learning (ML), specifically the random forest (RF) algorithm, analyzing 408 SDIs across six African countries: Algeria, Egypt, Kenya, Morocco, Nigeria and South Africa. Design/methodology/approach A quantitative approach was adopted, drawing data from two prominent databases: the World Bank’s Sustainable Development Goals database and the United Nations Development Program’s (UNDP) all-composite indices time series dataset. The random forest algorithm was employed to investigate the relationship between SDIs and ED. Findings This study found no negative relationship between the identified SDIs and Human Development Index metrics. The findings reveal that indicators such as gross national income, CO2 emissions, and mortality rates significantly impact ED, while others (e.g. forest area and school enrolment) vary by country. The findings suggest that tailored policies leveraging country-specific resources and capabilities can drive sustainable economic growth and enhance performance management for optimized development outcomes. Originality/value Unlike prior studies on ED, this study shifts the focus from traditional economic variables to non-economic indicators such as health (e.g. under-5 mortality rates), environment (e.g. CO2 emissions) and society (e.g. urban population). The research expands ED metrics, provides country-specific insights and proposes an integrated performance management approach incorporating underexplored variables in the literature.
URI
https://bradscholars.brad.ac.uk/handle/10454/20750
Version
Accepted manuscript
Citation
Osunnaiye AV and Kucukaltan B (2025) Examining the Influence of Sustainable Development Indicators on Economic Development: A Machine Learning Approach with Evidence from Africa. International Journal of Productivity and Performance Management. 74(9): 3131-3152.
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Link to Version of Record
https://doi.org/10.1108/IJPPM-12-2024-0839
Type
Article
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