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Detecting influences of factors on GDP density differentiation of rural poverty changes

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  • Wang, Guangjie
  • Peng, Wenfu

Abstract

We use geographical detectors to quantify the interactive influence of impact factors on gross domestic product (GDP) density changes on rural poverty, for identifying the differentiation mechanism in rural poverty. The optimal characteristics of the main factors that benefit GDP density growth were determined. We proposed poverty alleviation policies and measures for different types of poverty-stricken regions. Distance to main roads, the normalised differential vegetation index (NDVI), land use, average annual temperature, and elevation can satisfactorily account for GDP density changes in rural poverty. Impact factors have an interactive influence on GDP density. The synergistic effect of impact factors manifests itself as mutual enhancement and nonlinear enhancement, and the interaction of two impact factors strengthens the influence of each individual factor. We revealed the poverty differentiation mechanisms of economic development in poverty villages,and put forward targeted poverty alleviation measures with stratified guidance and key breakthroughs.

Suggested Citation

  • Wang, Guangjie & Peng, Wenfu, 2021. "Detecting influences of factors on GDP density differentiation of rural poverty changes," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 141-151.
    • Handle: RePEc:eee:streco:v:56:y:2021:i:c:p:141-151
    DOI: 10.1016/j.strueco.2020.10.004
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    References listed on IDEAS

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    1. Zhou, Yang & Guo, Liying & Liu, Yansui, 2019. "Land consolidation boosting poverty alleviation in China: Theory and practice," Land Use Policy, Elsevier, vol. 82(C), pages 339-348.
    2. Liao, Chuan & Fei, Ding, 2019. "Poverty reduction through photovoltaic-based development intervention in China: Potentials and constraints," World Development, Elsevier, vol. 122(C), pages 1-10.
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    Cited by:

    1. Tian, Zhihua & Hu, An & Yang, Zhen & Lin, Yongran, 2024. "Highway networks and regional poverty: Evidence from Chinese counties," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 224-231.
    2. Yonghua Li & Song Yao & Hezhou Jiang & Huarong Wang & Qinchuan Ran & Xinyun Gao & Xinyi Ding & Dandong Ge, 2022. "Spatial-Temporal Evolution and Prediction of Carbon Storage: An Integrated Framework Based on the MOP–PLUS–InVEST Model and an Applied Case Study in Hangzhou, East China," Land, MDPI, vol. 11(12), pages 1-22, December.

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