Resources Science ›› 2021, Vol. 43 ›› Issue (2): 293-303.doi: 10.18402/resci.2021.02.08

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Ecological poverty and its influencing factors in an alpine area: Case study of the Selinco area

YANG Ding1,2,3(), YANG Zhenshan1,2()   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-01-12 Revised:2020-07-31 Online:2021-02-25 Published:2021-04-25
  • Contact: YANG Zhenshan E-mail:yangding18@126.com;yangzs@igsnrr.ac.cn

Abstract:

Ecological poverty research is of great significance for understanding the mechanism of interaction between the ecological environment and poverty in areas with formidable ecological environments, and for giving support to formulating policy measures to consolidate the achievements of poverty alleviation. However, there exist only few studies on the evaluation of ecological poverty and analysis of its influencing factors in alpine areas with harsh ecological environments. Taking the Selinco area of Tibet as an example, this study constructed an index system of ecological poverty. By using the BP neural network and the Decision Making Trial and Evaluation Laboratory (DEMATEL) method, the ecological poverty level and influencing factors of ecological poverty of 30 township-level administrative units in the Selinco area were evaluated and analyzed. The results show that: (1) The average level of ecological poverty of the townships is 2.97, and most townships are in the third level. Townships with a higher level of ecological poverty are located in mountainous areas with harsh natural conditions, while townships with lower levels are located in areas with better natural conditions near the lakes. (2) There are differences in the impact direction of the factors on ecological poverty. Slope, relief, mean temperature, and elevation have a positive effect on ecological poverty, while drainage density, average precipitation, soil texture structure, and vegetation are the opposite. Therefore, residents should be guided to reduce the interference to highly ecologically fragile areas, strengthen the protection of pastures and water sources, develop characteristic industries such as modern animal husbandry and tourism service industry, and further promote community development to reduce livelihood vulnerability. (3) Altitude, slope, and relief are the key factors that affect ecological poverty and are correlated with other factors such as average temperature and precipitation. Considering the key factors such as altitude and topography, it is recommended to optimize the layout of residential areas and actively respond to ecological poverty. These results not only provide some references for formulating long-term effective poverty reduction strategies from the perspective of the ecological environment but also provide a reference for ecological poverty monitoring in other areas.

Key words: ecological poverty, BP neural network, influencing factors, mean impact value (MIV), decision making trial and evaluation laboratory (DEMATEL), alpine area, Selinco area