Abstract
To support China’s national poverty alleviation strategies, it is urgent to develop a scientific method for identifying the poverty-stricken villages and the contributing factors. Based on the anti-poverty plan of “Entire-Village Advancement” of China and the human-environment interaction perspective, the paper proposes a participatory poverty identification model that utilizes geographic information system to quantify and integrate various contributing factors for poverty at the village level. First, a set of poverty identification factors are determined from the human-environment interaction perspective. Secondly, the game theory is used to combine the participatory subjective weight method and the objective entropy method to weight the factors, and a participatory poverty identification with minimum variance model is developed to identify the poverty-stricken villages and their contributing factors. Finally, the model is applied to Qianjiang District in Chongqing, and the case study demonstrates the effectiveness of the model. The model not only identifies the poverty-stricken villages systematically but also helps guide policies for effective poverty interventions.
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Supported by Natural Science Foundation of China (No. 41371375), as well as by Twelve-Five science and technology support program of China (No. 2012BAH33B03).
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Wang, Y., Qian, L. A PPI-MVM Model for Identifying Poverty-Stricken Villages: A Case Study from Qianjiang District in Chongqing, China. Soc Indic Res 130, 497–522 (2017). https://doi.org/10.1007/s11205-015-1190-4
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DOI: https://doi.org/10.1007/s11205-015-1190-4