AI for Solving SDGs: A Trend Analysis of Public Interest in Sustainable Development Goals and Artificial Intelligence in Africa
Abstract This work explores the intersection of artificial intelligence (AI) and the sustainable development goals (SDGs) in Africa, focusing on public interest in AI and SDGs in addressing the continent’s unique developmental challenges. Using Google Trends Explore (GTE) data, we analyse AI and SDGs search interest across African countries over a nine-year period from September 2015 to August 2024. We investigate relative AI adoption indices for solving the SDGs, the current adoption readiness rankings, and the differences in regional trends across Africa. We employ Croston’s intermittent time series trend forecast estimation method, agglomerative hierarchical clustering on principal components, and visual analytics to extract empirical insights from monthly AI- and SDGs-related relative GTE search interest across African regions. The study highlights regional disparities in search trends, with the Southern, Western, Eastern, Northern, and Central African regions ranking first through fifth on their current adoption readiness rankings for solving SDGs with AI. The current differences in regional trends across the African regions are multi-dimensional. They can be better simplified by supplementing the GTE search data with additional indicators and economic drivers such as GDP per capita, digital literacy rates, internet penetration, and national policy frameworks on AI and sustainable development in future analyses.
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Ade-Ibijola, A., & Okonkwo, C. (2023). Artificial intelligence in Africa: Emerging challenges. In Responsible AI in Africa: Challenges and Opportunities (pp. 101-117). Springer International Publishing Cham. https://doi.org/10.1007/978-3-031-08215-3_5
African Union. (2020). The Digital Transformation Strategy For Africa (2020-2030). A. Union. Retrieved from: https://tinyurl.com/yc8b38jb
African Union. (2024a). Continental Artificial Intelligence Strategy: Harnessing AI for Africa’s Development and Prosperity. A. Union. Retrieved from: https://tinyurl.com/s3tmk9w4
African Union. (2024b). Member States. Retrieved September 05, 2024 from https://au.int/en/member_states/countryprofiles2
Ajaj, R., Buheji, M., & Hassoun, A. (2024). Optimizing the readiness for Industry 4.0 in fulfilling the Sustainable Development Goal 1: Focus on poverty elimination in Africa. Frontiers in Sustainable Food Systems, 8, 1393935. https://doi.org/10.3389/fsufs.2024.1393935
Arfanuzzaman, M. D. (2021). Harnessing artificial intelligence and big data for SDGs and prosperous urban future in South Asia. Environmental and sustainability indicators, 11, 100127. https://doi.org/10.1016/j.indic.2021.100127
Arthur, K. K., Asongu, S. A., Darko, P., Ansah, M. O., Adom, S., & Hlortu, O. (2024). Financial crimes in Africa and economic growth: Implications for achieving sustainable development goals (SDGs). Journal of Economic Surveys. https://doi.org/10.1111/joes.12652
Astobiza, A. M., Toboso, M., Aparicio, M., & López, D. (2021). AI ethics for sustainable development goals. IEEE Technology and Society Magazine, 40(2), 66-71. https://doi.org/10.1109/MTS.2021.3056294
Banga, K., & te Velde, D. W. (2018). Digitalisation and the Future of Manufacturing in Africa. ODI London. https://tinyurl.com/jy7h6v3n
Bjola, C. (2022). AI for development: Implications for theory and practice. Oxford Development Studies, 50(1), 78-90. https://doi.org/10.1080/13600818.2021.1960960
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic record, 88, 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute, 2, 267. https://tinyurl.com/3dcwyaa5
Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. C., Wang, C. B., & Bernardini, S. (2020). The COVID-19 pandemic. Critical Reviews in Clinical Laboratory Sciences, 57(6), 365–388. https://doi.org/10.1080/10408363.2020.1783198
Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Journal of the Operational Research Society, 23(3), 289-303. https://doi.org/10.1057/jors.1972.50
Deloitte. (2025). AI trends 2025: Adoption barriers and updated predictions. https://www.deloitte.com/us/en/services/consulting/blogs/ai-adoption-challenges-ai-trends.html
Fazal, A., Ahmed, A., & Abbas, S. (2024). Importance of artificial intelligence in achieving sustainable development goals through financial inclusion. Qualitative Research in Financial Markets. https://doi.org/10.1108/QRFM-04-2023-0098
Franzén, A. (2023). Big data, big problems: Why scientists should refrain from using Google Trends. Acta Sociologica, 66(3), 343-347. https://doi.org/10.1177/00016993221151118
Google Cloud. (2025). AI Business Trends 2025. https://cloud.google.com/resources/ai-trends-report
Google Research Brief. (2024). AI in Action: Accelerating Progress Towards the Sustainable Development Goals. https://static.googleusercontent.com/media/publicpolicy.google/en//resources/research-brief-ai-and-SDG.pdf
Google Trends. (2024). Google Trends Explore Data. Retrieved August 05, 2024 from https://www.google.com/trends
Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. https://doi.org/10.1016/j.ijme.2019.100330
Grekov, A. N., Vyshkvarkova, E. V., & Mavrin, A. S. (2024). Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms. Forecasting, 6(2), 343. https://doi.org/10.3390/forecast6020019
HamidiMotlagh, R., Babaee, A., Maleki, A., & Taghi Isaai, M. (2020). Innovation policy, scientific research and economic performance: The case of Iran. Development Policy Review, 38(3), 387-407. https://doi.org/10.1111/dpr.12423
Hoang, T. G., Nguyen, G. N. T., & Le, D. A. (2022). Developments in financial technologies for achieving the Sustainable Development Goals (SDGs): FinTech and SDGs. In Disruptive technologies and eco-innovation for sustainable development (pp. 1-19). IGI Global. https://doi.org/10.4018/978-1-7998-8900-7.ch001
Holzinger, A., Weippl, E., Tjoa, A. M., & Kieseberg, P. (2021). Digital transformation for sustainable development goals (sdgs)-a security, safety and privacy perspective on ai International cross-domain conference for machine learning and knowledge extraction. https://doi.org/10.1007/978-3-030-84060-0_1
Hoosain, M. S., Paul, B. S., & Ramakrishna, S. (2020). The impact of 4ir digital technologies and circular thinking on the united nations sustainable development goals [Article]. Sustainability (Switzerland), 12(23), 1-16, Article 10143. https://doi.org/10.3390/su122310143
Husson, F., Lê, S., & Pagès, J. (2010). Exploratory multivariate analysis by example using R [Book]. https://doi.org/10.1201/b10345
Jedwab, R., Christiaensen, L., & Gindelsky, M. (2017). Demography, urbanization and development: Rural push, urban pull and… urban push? Journal of Urban Economics, 98, 6-16.
Jun, S.-P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130, 69-87. https://doi.org/10.1016/j.techfore.2017.11.009
Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82. https://doi.org/10.1056/NEJMp2005835
Kourentzes, N. (2014). On intermittent demand model optimisation and selection. International Journal of Production Economics, 156, 180-190. https://doi.org/10.1016/j.ijpe.2014.06.007
Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R package for multivariate analysis [Article]. Journal of Statistical Software, 25(1), 1-18. https://doi.org/10.18637/jss.v025.i01
Mavragani, A., Ochoa, G., & Tsagarakis, K. P. (2018). Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. Journal of Medical Internet Research, 20(11), e270.
Meitei, A. J., Rai, P., & Rajkishan, S. S. (2023). Application of AI/ML techniques in achieving SDGs: a bibliometric study. Environment, Development and Sustainability, 1-37. https://doi.org/10.1007/s10668-023-03935-1
Mhlanga, D. (2020). Artificial Intelligence (AI) and poverty reduction in the Fourth Industrial Revolution (4IR). https://doi.org/10.20944/preprints202009.0362.v1
Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. https://doi.org/10.3390/su13115788
Mukonza, S. S., & Chiang, J. L. (2023). Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring [Review]. Environments - MDPI, 10(10), Article 170. https://doi.org/10.3390/environments10100170
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Prestwich, S. D., Tarim, S. A., & Rossi, R. (2021). Intermittency and obsolescence: A Croston method with linear decay. International Journal of Forecasting, 37(2), 708-715. https://doi.org/10.1016/j.ijforecast.2020.08.010
Raman, R., Sreenivasan, A., Ma, S., Patwardhan, A., & Nedungadi, P. (2023). Green Supply Chain Management Research Trends and Linkages to UN Sustainable Development Goals. Sustainability, 15(22), 15848. https://doi.org/10.3390/su152215848
Ramezani, M., Takian, A., Bakhtiari, A., Rabiee, H. R., & Sazgarnejad, S. (2023). Bibliometric Analysis of Artificial Intelligence Revolutions in Health-related Sustainable Development Goals [Article]. Health Technology Assessment in Action, 7(4). https://doi.org/10.18502/htaa.v7i4.14654
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., & Beer, L. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence, 3(3), 199-217. https://doi.org/10.1038/s42256-021-00307-0
Roy, A., Basu, A., Su, Y., Li, Y., & Dong, X. (2022). Understanding recent trends in global sustainable development goal 6 research: scientometric, text mining and an improved framework for future research. Sustainability, 14(4), 2208. https://doi.org/10.3390/su14042208
Salvia, A. L., Leal Filho, W., Brandli, L. L., & Griebeler, J. S. (2019). Assessing research trends related to Sustainable Development Goals: Local and global issues. Journal of Cleaner Production, 208, 841-849. https://doi.org/10.1016/j.jclepro.2018.09.242
Sampene, A. K., Agyeman, F. O., Robert, B., & Wiredu, J. (2022). Artificial intelligence as a path way to Africa’s transformations. Artificial Intelligence, 9(1).
Schultz, C. R. (1987). Forecasting and Inventory Control for Sporadic Demand Under Periodic Review. Journal of the Operational Research Society, 38(5), 453-458. https://doi.org/10.1057/jors.1987.74
Shafi, I., Sohail, A., Ahmad, J., Espinosa, J. C. M., López, L. A. D., Thompson, E. B., & Ashraf, I. (2023). Spare parts forecasting and lumpiness classification using neural network model and its impact on aviation safety. Applied Sciences, 13(9), 5475. https://doi.org/10.3390/app13095475
Sinde, R., Diwani, S., Leo, J., Kondo, T., Elisa, N., & Matogoro, J. (2023). AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector [Article]. Frontiers in Artificial Intelligence, 6, Article 1133677. https://doi.org/10.3389/frai.2023.1133677
Singh, A., Kanaujia, A., Singh, V. K., & Vinuesa, R. (2024). Artificial intelligence for Sustainable Development Goals: Bibliometric patterns and concept evolution trajectories. Sustainable Development, 32(1), 724-754. https://doi.org/10.1002/sd.2706
Singh, S., & Verma, S. B. (2024). Resolving Covid-19 with Blockchain and AI: A Systematic Review. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31454. https://doi.org/10.14201/adcaij.31454
Snyder, R. (2002). Forecasting sales of slow and fast moving inventories. European Journal of Operational Research, 140(3), 684-699. https://doi.org/10.1016/S0377-2217(01)00231-4
Steingard, D., Balduccini, M., & Sinha, A. (2023). Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs). AI & SOCIETY, 38(2), 613-629. https://doi.org/10.1007/s00146-022-01459-2
Svetunkov, I., & Boylan, J. E. (2023). iETS: State space model for intermittent demand forecasting. International Journal of Production Economics, 265, 109013. https://doi.org/10.1016/j.ijpe.2023.109013
Teunter, R. H., Syntetos, A. A., & Zied Babai, M. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606-615. https://doi.org/10.1016/j.ejor.2011.05.018
Truby, J. (2020). Governing artificial intelligence to benefit the UN sustainable development goals. Sustainable Development, 28(4), 946-959. https://doi.org/10.1002/sd.2048
UNESCO. (2022). AI policies in South Africa. UNESCO. Retrieved 1/11/2024 from: https://oecd.ai/en/dashboards/countries/SouthAfrica
UNESCO. (2024). Shaping Kenya’s AI Future: UNESCO Contributes to National AI Strategy Formulation. UNESCO. Retrieved 11/11/2024 from: https://www.unesco.org/en/articles/shaping-kenyas-ai-future-unesco-contributes-national-ai-strategy-formulation
United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. United Nations. Retrieved 11/11/2024 from https://sustainabledevelopment.un.org/post2015/transformingourworld
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals [Review]. Nature Communications, 11(1), Article 233. https://doi.org/10.1038/s41467-019-14108-y
Wakunuma, K., Ogoh, G., Eke, D. O., & Akintoye, S. (2022). Responsible AI, SDGS, and AI governance in Africa 2022. IST-Africa Conference (IST-Africa). https://doi.org/10.23919/IST-Africa56635.2022.9845598
African Union. (2020). The Digital Transformation Strategy For Africa (2020-2030). A. Union. Retrieved from: https://tinyurl.com/yc8b38jb
African Union. (2024a). Continental Artificial Intelligence Strategy: Harnessing AI for Africa’s Development and Prosperity. A. Union. Retrieved from: https://tinyurl.com/s3tmk9w4
African Union. (2024b). Member States. Retrieved September 05, 2024 from https://au.int/en/member_states/countryprofiles2
Ajaj, R., Buheji, M., & Hassoun, A. (2024). Optimizing the readiness for Industry 4.0 in fulfilling the Sustainable Development Goal 1: Focus on poverty elimination in Africa. Frontiers in Sustainable Food Systems, 8, 1393935. https://doi.org/10.3389/fsufs.2024.1393935
Arfanuzzaman, M. D. (2021). Harnessing artificial intelligence and big data for SDGs and prosperous urban future in South Asia. Environmental and sustainability indicators, 11, 100127. https://doi.org/10.1016/j.indic.2021.100127
Arthur, K. K., Asongu, S. A., Darko, P., Ansah, M. O., Adom, S., & Hlortu, O. (2024). Financial crimes in Africa and economic growth: Implications for achieving sustainable development goals (SDGs). Journal of Economic Surveys. https://doi.org/10.1111/joes.12652
Astobiza, A. M., Toboso, M., Aparicio, M., & López, D. (2021). AI ethics for sustainable development goals. IEEE Technology and Society Magazine, 40(2), 66-71. https://doi.org/10.1109/MTS.2021.3056294
Banga, K., & te Velde, D. W. (2018). Digitalisation and the Future of Manufacturing in Africa. ODI London. https://tinyurl.com/jy7h6v3n
Bjola, C. (2022). AI for development: Implications for theory and practice. Oxford Development Studies, 50(1), 78-90. https://doi.org/10.1080/13600818.2021.1960960
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic record, 88, 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute, 2, 267. https://tinyurl.com/3dcwyaa5
Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. C., Wang, C. B., & Bernardini, S. (2020). The COVID-19 pandemic. Critical Reviews in Clinical Laboratory Sciences, 57(6), 365–388. https://doi.org/10.1080/10408363.2020.1783198
Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Journal of the Operational Research Society, 23(3), 289-303. https://doi.org/10.1057/jors.1972.50
Deloitte. (2025). AI trends 2025: Adoption barriers and updated predictions. https://www.deloitte.com/us/en/services/consulting/blogs/ai-adoption-challenges-ai-trends.html
Fazal, A., Ahmed, A., & Abbas, S. (2024). Importance of artificial intelligence in achieving sustainable development goals through financial inclusion. Qualitative Research in Financial Markets. https://doi.org/10.1108/QRFM-04-2023-0098
Franzén, A. (2023). Big data, big problems: Why scientists should refrain from using Google Trends. Acta Sociologica, 66(3), 343-347. https://doi.org/10.1177/00016993221151118
Google Cloud. (2025). AI Business Trends 2025. https://cloud.google.com/resources/ai-trends-report
Google Research Brief. (2024). AI in Action: Accelerating Progress Towards the Sustainable Development Goals. https://static.googleusercontent.com/media/publicpolicy.google/en//resources/research-brief-ai-and-SDG.pdf
Google Trends. (2024). Google Trends Explore Data. Retrieved August 05, 2024 from https://www.google.com/trends
Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. https://doi.org/10.1016/j.ijme.2019.100330
Grekov, A. N., Vyshkvarkova, E. V., & Mavrin, A. S. (2024). Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms. Forecasting, 6(2), 343. https://doi.org/10.3390/forecast6020019
HamidiMotlagh, R., Babaee, A., Maleki, A., & Taghi Isaai, M. (2020). Innovation policy, scientific research and economic performance: The case of Iran. Development Policy Review, 38(3), 387-407. https://doi.org/10.1111/dpr.12423
Hoang, T. G., Nguyen, G. N. T., & Le, D. A. (2022). Developments in financial technologies for achieving the Sustainable Development Goals (SDGs): FinTech and SDGs. In Disruptive technologies and eco-innovation for sustainable development (pp. 1-19). IGI Global. https://doi.org/10.4018/978-1-7998-8900-7.ch001
Holzinger, A., Weippl, E., Tjoa, A. M., & Kieseberg, P. (2021). Digital transformation for sustainable development goals (sdgs)-a security, safety and privacy perspective on ai International cross-domain conference for machine learning and knowledge extraction. https://doi.org/10.1007/978-3-030-84060-0_1
Hoosain, M. S., Paul, B. S., & Ramakrishna, S. (2020). The impact of 4ir digital technologies and circular thinking on the united nations sustainable development goals [Article]. Sustainability (Switzerland), 12(23), 1-16, Article 10143. https://doi.org/10.3390/su122310143
Husson, F., Lê, S., & Pagès, J. (2010). Exploratory multivariate analysis by example using R [Book]. https://doi.org/10.1201/b10345
Jedwab, R., Christiaensen, L., & Gindelsky, M. (2017). Demography, urbanization and development: Rural push, urban pull and… urban push? Journal of Urban Economics, 98, 6-16.
Jun, S.-P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130, 69-87. https://doi.org/10.1016/j.techfore.2017.11.009
Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82. https://doi.org/10.1056/NEJMp2005835
Kourentzes, N. (2014). On intermittent demand model optimisation and selection. International Journal of Production Economics, 156, 180-190. https://doi.org/10.1016/j.ijpe.2014.06.007
Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R package for multivariate analysis [Article]. Journal of Statistical Software, 25(1), 1-18. https://doi.org/10.18637/jss.v025.i01
Mavragani, A., Ochoa, G., & Tsagarakis, K. P. (2018). Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. Journal of Medical Internet Research, 20(11), e270.
Meitei, A. J., Rai, P., & Rajkishan, S. S. (2023). Application of AI/ML techniques in achieving SDGs: a bibliometric study. Environment, Development and Sustainability, 1-37. https://doi.org/10.1007/s10668-023-03935-1
Mhlanga, D. (2020). Artificial Intelligence (AI) and poverty reduction in the Fourth Industrial Revolution (4IR). https://doi.org/10.20944/preprints202009.0362.v1
Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability, 13(11), 5788. https://doi.org/10.3390/su13115788
Mukonza, S. S., & Chiang, J. L. (2023). Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring [Review]. Environments - MDPI, 10(10), Article 170. https://doi.org/10.3390/environments10100170
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Prestwich, S. D., Tarim, S. A., & Rossi, R. (2021). Intermittency and obsolescence: A Croston method with linear decay. International Journal of Forecasting, 37(2), 708-715. https://doi.org/10.1016/j.ijforecast.2020.08.010
Raman, R., Sreenivasan, A., Ma, S., Patwardhan, A., & Nedungadi, P. (2023). Green Supply Chain Management Research Trends and Linkages to UN Sustainable Development Goals. Sustainability, 15(22), 15848. https://doi.org/10.3390/su152215848
Ramezani, M., Takian, A., Bakhtiari, A., Rabiee, H. R., & Sazgarnejad, S. (2023). Bibliometric Analysis of Artificial Intelligence Revolutions in Health-related Sustainable Development Goals [Article]. Health Technology Assessment in Action, 7(4). https://doi.org/10.18502/htaa.v7i4.14654
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., & Beer, L. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence, 3(3), 199-217. https://doi.org/10.1038/s42256-021-00307-0
Roy, A., Basu, A., Su, Y., Li, Y., & Dong, X. (2022). Understanding recent trends in global sustainable development goal 6 research: scientometric, text mining and an improved framework for future research. Sustainability, 14(4), 2208. https://doi.org/10.3390/su14042208
Salvia, A. L., Leal Filho, W., Brandli, L. L., & Griebeler, J. S. (2019). Assessing research trends related to Sustainable Development Goals: Local and global issues. Journal of Cleaner Production, 208, 841-849. https://doi.org/10.1016/j.jclepro.2018.09.242
Sampene, A. K., Agyeman, F. O., Robert, B., & Wiredu, J. (2022). Artificial intelligence as a path way to Africa’s transformations. Artificial Intelligence, 9(1).
Schultz, C. R. (1987). Forecasting and Inventory Control for Sporadic Demand Under Periodic Review. Journal of the Operational Research Society, 38(5), 453-458. https://doi.org/10.1057/jors.1987.74
Shafi, I., Sohail, A., Ahmad, J., Espinosa, J. C. M., López, L. A. D., Thompson, E. B., & Ashraf, I. (2023). Spare parts forecasting and lumpiness classification using neural network model and its impact on aviation safety. Applied Sciences, 13(9), 5475. https://doi.org/10.3390/app13095475
Sinde, R., Diwani, S., Leo, J., Kondo, T., Elisa, N., & Matogoro, J. (2023). AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector [Article]. Frontiers in Artificial Intelligence, 6, Article 1133677. https://doi.org/10.3389/frai.2023.1133677
Singh, A., Kanaujia, A., Singh, V. K., & Vinuesa, R. (2024). Artificial intelligence for Sustainable Development Goals: Bibliometric patterns and concept evolution trajectories. Sustainable Development, 32(1), 724-754. https://doi.org/10.1002/sd.2706
Singh, S., & Verma, S. B. (2024). Resolving Covid-19 with Blockchain and AI: A Systematic Review. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31454. https://doi.org/10.14201/adcaij.31454
Snyder, R. (2002). Forecasting sales of slow and fast moving inventories. European Journal of Operational Research, 140(3), 684-699. https://doi.org/10.1016/S0377-2217(01)00231-4
Steingard, D., Balduccini, M., & Sinha, A. (2023). Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs). AI & SOCIETY, 38(2), 613-629. https://doi.org/10.1007/s00146-022-01459-2
Svetunkov, I., & Boylan, J. E. (2023). iETS: State space model for intermittent demand forecasting. International Journal of Production Economics, 265, 109013. https://doi.org/10.1016/j.ijpe.2023.109013
Teunter, R. H., Syntetos, A. A., & Zied Babai, M. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606-615. https://doi.org/10.1016/j.ejor.2011.05.018
Truby, J. (2020). Governing artificial intelligence to benefit the UN sustainable development goals. Sustainable Development, 28(4), 946-959. https://doi.org/10.1002/sd.2048
UNESCO. (2022). AI policies in South Africa. UNESCO. Retrieved 1/11/2024 from: https://oecd.ai/en/dashboards/countries/SouthAfrica
UNESCO. (2024). Shaping Kenya’s AI Future: UNESCO Contributes to National AI Strategy Formulation. UNESCO. Retrieved 11/11/2024 from: https://www.unesco.org/en/articles/shaping-kenyas-ai-future-unesco-contributes-national-ai-strategy-formulation
United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. United Nations. Retrieved 11/11/2024 from https://sustainabledevelopment.un.org/post2015/transformingourworld
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals [Review]. Nature Communications, 11(1), Article 233. https://doi.org/10.1038/s41467-019-14108-y
Wakunuma, K., Ogoh, G., Eke, D. O., & Akintoye, S. (2022). Responsible AI, SDGS, and AI governance in Africa 2022. IST-Africa Conference (IST-Africa). https://doi.org/10.23919/IST-Africa56635.2022.9845598
Olorede, K. O., & Ade-Ibijola, A. (2026). AI for Solving SDGs: A Trend Analysis of Public Interest in Sustainable Development Goals and Artificial Intelligence in Africa. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e32890. https://doi.org/10.14201/adcaij.32890
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