资源科学 ›› 2021, Vol. 43 ›› Issue (2): 293-303.doi: 10.18402/resci.2021.02.08

• 资源经济 • 上一篇    下一篇

高寒地区生态贫困评价及影响因素分析——以色林错地区为例

杨定1,2,3(), 杨振山1,2()   

  1. 1.中国科学院地理科学与资源研究所,北京 100101
    2.中国科学院区域可持续发展分析与模拟实验室,北京 100101
    3.中国科学院大学,北京 100049
  • 收稿日期:2020-01-12 修回日期:2020-07-31 出版日期:2021-02-25 发布日期:2021-04-25
  • 通讯作者: 杨振山
  • 作者简介:杨定,男,陕西商洛人,硕士研究生,研究方向为城市与区域可持续发展。E-mail: yangding18@126.com
  • 基金资助:
    中国科学院战略性先导科技专项(A类)(XDA20020302);中国科学院青年创新促进研究会优秀会员项目(Y201815)

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

摘要:

生态贫困研究对于理解生态环境恶劣地区生态环境与贫困作用机制、支撑巩固减贫成果的政策制定具有重要意义,然而对高寒地区的生态贫困评价和影响因素探索较少。本文以藏北深度贫困区色林错地区为例,构建生态贫困评价体系,将BP神经网络模型和DEMATEL方法相结合,对该地区生态贫困水平及其影响因素进行分析。研究表明:①色林错地区各乡镇生态贫困指数平均值为2.97,多数乡镇生态贫困等级集中在三级(最贫困为五级),且生态贫困等级较高的乡镇处于地理环境恶劣的山区,生态贫困等级较低的乡镇处于湖盆附近自然条件较好的区域;②各因素对生态贫困的影响方向存在差异,地面坡度、地形起伏度、平均气温与平均海拔对生态贫困有正向作用,河网密度、平均降水、土壤质地结构与植被指数对生态贫困有负向作用;应引导居民尽量减少在生态系统抗干扰能力弱的区域活动,加强优良草场和水源地保护,发展现代畜牧业和旅游服务业等特色产业,推动社区发展以降低生计脆弱性;③平均海拔、地形起伏度和地面坡度是影响生态贫困的关键因素,并与平均气温和降水等因素相关联;应以海拔、地形等为主要考虑因素,优化居民点布局,积极应对生态贫困。研究结果不仅可为从生态环境角度出发制定长期有效的减贫策略提供参考,还可为其他地区生态贫困监测提供借鉴。

关键词: 生态贫困, BP神经网络, 影响因素, MIV值, DEMATEL方法, 高寒地区, 色林错地区

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