Utilizing nighttime light datasets to uncover the spatial patterns of county-level relative poverty-returning risk in China and its alleviating factors

Abstract

China has launched a series of ambitious poverty alleviation strategies to end extreme poverty, officially announcing the achievement of this goal in 2020. Currently, these counties persist in their efforts to achieve the goal of rural revitalization. Many studies often showcase the spatial patterns of China's remarkable success in counties out of poverty, but often disregard the relative poverty-returning risk (PRR), specifically within China's 832 extreme poverty counties. Nighttime light datasets (NTL) have been extensively employed as a surrogate measure for socioeconomic performance in underserved regions. In this work, we constructed an NTL-based relative PRR index to detect the spatial patterns of PRR among 832 counties. Then, we identified the main influencing factors concerning the PRR mitigation of 14 poverty-stricken clusters based on three dimensions: geophysical, economic, and social. Our results showed that counties in central China are far from returning to extreme poverty with low PRR. However 14 counties (2.41% of the 832) show high PRR, and those counties are mainly clustered in southwest China. This implies that those regions require more poverty-related policy support, financial inflows, and assistance. Regional differences were found in the factors that influence the mitigation of PRR. This is crucial for developing targeted strategies to consolidate poverty eradication. Our findings are essential for China's poverty-related SDGs, ensuring that no one is left behind.