Effective Poverty Reduction Strategies Using Fragmented Data for Low-Income Economies
DOI:
https://doi.org/10.53469/jgebf.2024.06(12).07Keywords:
data driven innovation, poverty reduction, data analysis, climate change, AI in policymakingAbstract
OECD states, "Data - driven innovation is a key pillar in the growth of the economy… data sets are becoming an important asset in the economy, helping new industries, processes, and products and creating significant competitive advantages. " In the global pursuit of economic growth and societal progress, data - driven innovation has become a foundational pillar in the 21st century. This paper explores the intersection of data - driven innovation and poverty reduction, highlighting how comprehensive data analysis—from surveys and censuses to administrative records - provides critical insights into the complex landscape of poverty. The paper talks about traditional and modern data collection and analysis and the impact of real - world analytics on poverty alleviation. The paper also sheds light on how climate change and poverty are interlinked and how data can help mitigate climate change. It also touches upon the rise of AI and how it can transform policymaking as we know it.
References
Hossin, Md., Du, Jie., Mu, Lei., & Asante, Isaac. (2023). Big Data - Driven Public Policy Decisions: Transformation Toward Smart Governance. Sage Open, 13 (4). https: //doi. org/10.1177/21582440231215123
UN Global Pulse. (2014). Feasibility Study: Supporting Forest and Peat Fire Management Using Social Media. http: //www.unglobalpulse. org/sites/default/files/UNGP_ProjectSeries_Peat_H aze_2014_0. pdf
Mengtong, J., Kefan, L., Zili, H., & Kun, G. (2020). Application of Power Big Data in Targeted Poverty Alleviation—Taking Poverty Counties in Jiangxi Province as an Example. In Communications in computer and information science, 88–98. https: //doi. org/10.1007/978 - 981 - 15 - 2810 - 1_10
Yao, Y., Zhang, J., Hong, Y., Liang, H., & He, J. (2018). Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data. Transactions in GIS, 22 (2), 561–581. https: //doi. org/10.1111/tgis.12330
Patidar, R., Pingale, S. M., & Khare, D. (2021). An integration of geospatial and machine learning techniques for mapping groundwater potential: a case study of the Shipra river basin, India. Arabian Journal of Geosciences, 14 (16). https: //doi. org/10.1007/s12517 - 021 - 07871 - 0
Majeed, B., Peng, J., Li, A., Lin, Y., & Delgado, R. I. (2020). Forecasting the demand of mobile clinic services at vulnerable communities based on integrated multi - source data. IISE Transactions on Healthcare Systems Engineering, 11 (2), 113–127. https: //doi. org/10.1080/24725579.2020.1859305
Akter, S., Hossain, M. A., Hani, U., Vrontis, D., Thrassou, A., & Arslan, A. (2023). Addressing the grand challenges of poverty with data‐driven creative service offerings. Journal of Product Innovation Management, 41 (2), 236–266. https: //doi. org/10.1111/jpim.12679
Ernst, J., & R. Soerakoesoemah. (2021). "Enhancing poverty measurement through big data." https: //www.unescap. org/blog/enhancing - poverty - measurement - through - big - data
World Bank Group. (2021) World Development Report: Data for a better world. https:
//www.worldbank. org/en/news/press - release/2021/03/24/stronger - data - systems - needed - to - fight - poverty
Raj, M. (2023, October 27). Bridging the Gap: Centering Extreme Poverty in Data - Driven Climate Change Solutions. AIF. https: //aif. org/bridging - the - gap - centering - extreme - poverty - in - data - driven - climate - change - solutions
Nazir, U., Ejaz, A., Muhammad, T. Q., Uppal, M., & Khalid, S. (2023, December 16). Towards a spatio - temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia. Climate Change AI. https: //www.climatechange. ai/papers/neurips2023/66
AI could help detect unregulated sources of air pollution in South. (2023, June 15). https: //www.ox. ac. uk/news/2023 - 06 - 15 - ai - could - help - detect - unregulated - sources - air - pollution - south - asia - new - project - shows
Seclea. (December 15, 2022). The Role of AI in Poverty Alleviation. https: //seclea. com/resources/seclea - blogs/the_role_of_ai_in_poverty_alleviation/
Development Initiatives. (February 2014). Using data to get better results on poverty eradication. https: //devinit. org/wp - content/uploads/2014/02/Using - data - to - get - better - results - on - poverty - eradication11. pdf
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Copyright (c) 2024 Megha Manglani
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