Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set Approach
The electricity supply in South Africa is characterized by load-shedding. This study analyzed the determinants of the multidimensional energy poverty index (MEPI) in South Africa. The data, which were taken from the 2019–2021 General Household Survey (GHS), were analyzed using Tobit regression. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/5/2089 |
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| author | Abayomi Samuel Oyekale Thonaeng Charity Molelekoa |
| author_facet | Abayomi Samuel Oyekale Thonaeng Charity Molelekoa |
| author_sort | Abayomi Samuel Oyekale |
| collection | DOAJ |
| description | The electricity supply in South Africa is characterized by load-shedding. This study analyzed the determinants of the multidimensional energy poverty index (MEPI) in South Africa. The data, which were taken from the 2019–2021 General Household Survey (GHS), were analyzed using Tobit regression. The results showed that between 2019 and 2021, the use of clean energy for cooking declined from 85.97% to 85.68%, respectively, whereas the use of clean energy for water heating declined from 87.24% in 2020 to 86.55% in 2021. Space heating with clean energy declined from 53.57% in 2019 to 50.35% in 2021. The average fuzzy MEPI was 0.143 and Western Cape and KwaZulu-Natal provinces had the highest average values with 0.180 and 0.176, respectively. In the combined dataset, the Tobit regression results showed that, compared to Western Cape, the fuzzy MEPI significantly decreased (<i>p</i> < 0.01) by −0.038, 0.028, 0.045, 0.023, 0.029, 0.038, 0.037, and 0.042 for residents in Eastern Cape, Northern Cape, Free State, Kwazulu-Natal, North West, Gauteng, Mpumalanga, and Limpopo provinces, respectively. In addition, the fuzzy MEPI for the Black, Coloured, Asian, and White respondents decreased by 0.042, 0.062, and 0.084, respectively. The fuzzy MEPI for male-headed households and the number of social grants increased, whereas the fuzzy MEPI significantly decreased (<i>p</i> < 0.01) for the monthly salary and age of household heads. It was concluded that energy poverty in South Africa manifests through unclean energy utilization for space heating. The promotion of clean energy utilization should focus on deprived provinces, farms, and tribal areas. |
| first_indexed | 2024-03-11T07:26:19Z |
| format | Article |
| id | doaj.art-3349446ad2684cfe9f7c29954c0728a8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T07:26:19Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3349446ad2684cfe9f7c29954c0728a82023-11-17T07:34:00ZengMDPI AGEnergies1996-10732023-02-01165208910.3390/en16052089Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set ApproachAbayomi Samuel Oyekale0Thonaeng Charity Molelekoa1Department of Agricultural Economics and Extension, North-West University Mafikeng Campus, Mmabatho 2735, South AfricaDepartment of Agricultural Economics and Extension, North-West University Mafikeng Campus, Mmabatho 2735, South AfricaThe electricity supply in South Africa is characterized by load-shedding. This study analyzed the determinants of the multidimensional energy poverty index (MEPI) in South Africa. The data, which were taken from the 2019–2021 General Household Survey (GHS), were analyzed using Tobit regression. The results showed that between 2019 and 2021, the use of clean energy for cooking declined from 85.97% to 85.68%, respectively, whereas the use of clean energy for water heating declined from 87.24% in 2020 to 86.55% in 2021. Space heating with clean energy declined from 53.57% in 2019 to 50.35% in 2021. The average fuzzy MEPI was 0.143 and Western Cape and KwaZulu-Natal provinces had the highest average values with 0.180 and 0.176, respectively. In the combined dataset, the Tobit regression results showed that, compared to Western Cape, the fuzzy MEPI significantly decreased (<i>p</i> < 0.01) by −0.038, 0.028, 0.045, 0.023, 0.029, 0.038, 0.037, and 0.042 for residents in Eastern Cape, Northern Cape, Free State, Kwazulu-Natal, North West, Gauteng, Mpumalanga, and Limpopo provinces, respectively. In addition, the fuzzy MEPI for the Black, Coloured, Asian, and White respondents decreased by 0.042, 0.062, and 0.084, respectively. The fuzzy MEPI for male-headed households and the number of social grants increased, whereas the fuzzy MEPI significantly decreased (<i>p</i> < 0.01) for the monthly salary and age of household heads. It was concluded that energy poverty in South Africa manifests through unclean energy utilization for space heating. The promotion of clean energy utilization should focus on deprived provinces, farms, and tribal areas.https://www.mdpi.com/1996-1073/16/5/2089clean energypovertymultidimensional energy poverty indexMEPIfuzzy setSouth Africa |
| spellingShingle | Abayomi Samuel Oyekale Thonaeng Charity Molelekoa Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set Approach Energies clean energy poverty multidimensional energy poverty index MEPI fuzzy set South Africa |
| title | Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set Approach |
| title_full | Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set Approach |
| title_fullStr | Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set Approach |
| title_full_unstemmed | Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set Approach |
| title_short | Multidimensional Indicator of Energy Poverty in South Africa Using the Fuzzy Set Approach |
| title_sort | multidimensional indicator of energy poverty in south africa using the fuzzy set approach |
| topic | clean energy poverty multidimensional energy poverty index MEPI fuzzy set South Africa |
| url | https://www.mdpi.com/1996-1073/16/5/2089 |
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