The contribution of data-driven poverty alleviation funds in achieving mid-21st-Century multidimensional poverty alleviation planning

Abstract

The first Sustainable Development Goal (SDG) is intended to eradicate multi-dimensional poverty globally. The same multidimensional poverty indices for India and the Middle East/Africa in 2020 indicate that 10–14 years are still required to reach the level of China’s poverty eradication. Using machine learning, spatial statistics, and a scenario analysis, we demonstrate how a Monte Carlo simulation of poverty alleviation funds-guided shared socioeconomic pathways (PAFs-SSPs) in China reveals the necessity to adopt an integrated poverty alleviation strategy. This approach employs multi-dimensional development indicators to reduce wide regional differences. We developed the data-driven model framework of a PAFs-SSPs to analyze the multifaceted and long-term planning needs of poverty alleviation policies, which can be applied to the formulation of poverty alleviation policies in different developing countries. Our findings point to the importance of implementing multidimensional development policies in China to achieve the first SDG worldwide.