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  • 1.
    Gawusu, Sidique
    et al.
    Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA.
    Jamatutu, Seidu Abdulai
    School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China.
    Zhang, Xiaobing
    School of Energy and Power and Engineering, Nanjing University of Science and Technology, Nanjing, China.
    Moomin, Solahudeen Tando
    Global Development Institute, School of Environment, Education and Development, University of Manchester, Manchester, UK.
    Ahmed, Abubakari
    Department of Urban Design and Infrastructure Studies, Faculty of Planning and Land Management, SD Dombo University of Business and Integrated Development Studies, Bamahu-Wa, Ghana.
    Mensah, Rhoda Afriyie
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.
    Das, Oisik
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.
    Ackah, Ishmael
    Department of Economics, School of Liberal Arts and Social Science, Ghana Institute of Management and Public Administration, Accra, Ghana.
    Spatial analysis and predictive modeling of energy poverty: insights for policy implementation2024In: Environment, Development and Sustainability, ISSN 1387-585X, E-ISSN 1573-2975Article in journal (Refereed)
    Abstract [en]

    Understanding and alleviating energy poverty is critical for sustainable development. This study harnesses a suite of Machine Learning (ML) algorithms to predict Multidimensional Energy Poverty Index (MEPI) and to highlight the spatial distribution of energy poverty. We assess the predictive accuracy of Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Multiple Linear Regression (MLR), and XGBoost models. The RF model outperforms others, achieving an R2 value of 0.92 and a Pearson Correlation Coefficient (PCC) of 0.97 on the testing dataset, indicative of a highly accurate prediction capability. XGBoost also demonstrates strong predictive power with corresponding values of 0.88 and 0.94, respectively. Our spatial analysis, revealing significant clustering of energy poverty with a Global Moran’s I value of 150.39, indicates that energy poverty is not only geographically concentrated but also intricately linked to socio-economic factors such as income levels, access to education, and nutritional status. These insights underscore the necessity of region-specific and socio-economically informed policy interventions. The results inform targeted interventions, particularly highlighting the critical roles of education and nutrition in mitigating energy poverty. The RF model’s accuracy rate of 92% on the testing set suggests that improvements in these sectors could significantly influence MEPI scores. The integration of ML and spatial analysis offers a nuanced and actionable understanding of energy poverty, paving the way for targeted, evidence-based policy formulation aimed at achieving SDG7: ensuring access to affordable, reliable, sustainable, and modern energy for all.

  • 2.
    Gawusu, Sidique
    et al.
    Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.
    Tando, Moomin Solahudeen
    Global Development Institute, School of Environment, Education and Development, The University of Manchester, United Kingdom.
    Ahmed, Abubakari
    Department of Urban Design and Infrastructure Studies, Faculty of Planning and Land Management, SD Dombo, University of Business and Integrated Development Studies, Bamahu-Wa, Ghana.
    Jamatutu, Seidu Abdulai
    School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China.
    Mensah, Rhoda Afriyie
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.
    Das, Oisik
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.
    Mohammed, Abdul-Latif
    Department of Management, Newcastle Business School, The University of Newcastle, Australia.
    Yakubu, Ibrahim Nandom
    Department of Business and Education, School of Business, University for Development Studies, Tamale, Ghana.
    Ackah, Ishmael
    Department of Economics, School of Liberal Arts and Social Science, Ghana Institute of Management and Public Administration, Accra, Ghana.
    Decentralized energy systems and blockchain technology: Implications for alleviating energy poverty2024In: Sustainable Energy Technologies and Assessments, ISSN 2213-1388, E-ISSN 2213-1396, Vol. 65, article id 103795Article, review/survey (Refereed)
1 - 2 of 2
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