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