The effect of participation in ecological public welfare positions on farmers' household income composition and the internal mechanism

Journal of Cleaner Production(2023)

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摘要
The ecological public welfare positions policy, which involves low-income people in ecological conservation work, is an essential practical innovation for China to achieve mutual benefits in ecological protection and poverty alleviation. This study explored the effect of participation in ecological public welfare positions (PEPWP) on farmers' household income composition and clarified the internal mechanism by propensity score matching (PSM) and conditional process analysis, based on the field data from 508 formerly registered impoverished households in Jiangxi Province and Hubei Province, China. Results showed that (1) PEPWP was characterized by “self-selection”, which significantly increased farmers' wage level, planting income in Jiangxi Province, and husbandry income in Hubei Province after the elimination of selectivity bias. However, the effect on other sub-incomes was insignificant. (2) There was a moderated mediating model between PEPWP and agricultural income, which demonstrated that farmer's development motivation (FDM) played a partially mediating effect between PEPWP and FDM, and the frequency of skill training (FST) moderated the first part path of this model. (3) EPWP policy steadily increased farmers' income at the vulnerable livelihood level and greatly improved the regional environment. At the same time, it also played an active role in stimulating FDM and rural governance. Conclusions indicated that it was significant to diversify the channels for promoting growth in rural incomes, and pay attention to skill training and the multi-functional role of ecological custodians, in order to activate FDM and assist farmers in eradicating poverty sustainably.
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关键词
Ecological public welfare positions,Farmers' household income,Payment for environmental services,Ecological poverty alleviation,Propensity score matching,Conditional process analysis
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