地理学报 ›› 2020, Vol. 75 ›› Issue (9): 1934-1947.doi: 10.11821/dlxb202009009

• 气候与生态环境 • 上一篇    下一篇

京津冀地区县域环境胁迫时空格局及驱动因素

周侃1(), 李会2, 申玉铭2()   

  1. 1. 中国科学院地理科学与资源研究所 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
    2. 首都师范大学资源环境与旅游学院,北京 100048
  • 收稿日期:2019-09-06 修回日期:2020-03-23 出版日期:2020-09-25 发布日期:2020-11-25
  • 作者简介:周侃(1986-), 男, 云南丽江人, 博士, 副研究员, 硕士生导师, 研究方向为资源环境承载力与区域可持续发展。E-mail: zhoukan2008@126.com
  • 基金资助:
    国家自然科学基金项目(41971164);中国科学院战略性先导科技专项(XDA23020101)

Spatiotemporal patterns and driving factors of environmental stress in Beijing-Tianjin-Hebei region: A county-level analysis

ZHOU Kan1(), LI Hui2, SHEN Yuming2()   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
  • Received:2019-09-06 Revised:2020-03-23 Published:2020-09-25 Online:2020-11-25
  • Supported by:
    National Natural Science Foundation of China(41971164);Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23020101)

摘要:

环境胁迫反映人类生产生活过程中污染物输出对区域环境系统产生的综合压力。本文基于县域污染物排放和人口社会经济数据库,运用熵权法综合测度县域环境胁迫指数(ESI),解析2012—2016年京津冀地区环境胁迫的时空格局及各类主体功能区环境胁迫特征,并针对县域环境胁迫的空间效应,在STIRPAT模型框架基础上,运用地理加权回归方法定量估计县域环境胁迫的社会经济驱动力。结果表明:① 京津冀地区面临的环境胁迫态势显著缓解,2012年以来ESI降幅达到54.68%。其中,以北京、唐山、天津、石家庄等中心城区以及滨海新区下降最为显著,县域环境胁迫程度由中心城区向外围呈梯度递减,到2016年环境胁迫高等级县域已经消除;② 5年间京津冀地区县域环境胁迫的空间溢出效应趋强,并在津唐地区呈现空间锁定和路径依赖;③ 优化开发和重点开发区域两类城市化地区是京津冀水气环境的主要承压区,其环境胁迫程度占京津冀地区全域的65.98%,仍然是环境污染防治与管控的重点区域;④ 人口规模和经济发展水平是县域环境胁迫的控制性因素,此外还受环境处理技术水平、农业生产投入强度、国土开发强度以及城镇化等因素影响;⑤ 各驱动因素对县域环境胁迫的驱动作用存在空间异质性,需针对驱动力作用强度及其空间差异,采取差别化环境治理与源头减排对策,精准提升京津冀地区环境协同管制效果。

关键词: 环境胁迫, 时空格局, 主体功能区, 驱动因素, 京津冀地区

Abstract:

Environmental stress is used to indicate the integrated pressure on regional environmental system caused by various pollutant outputs during human life and production activities. Based on the pollutant emission and socio-economic database of the counties in Beijing-Tianjin-Hebei (BTH) region, this paper comprehensively calculates the environmental stress index (ESI) by entropy weight method at the county scale, and analyzes the spatio-temporal pattern and the differences among four types of Major Functional Zones (MFZ) in this region from 2012 to 2016. In addition, the socio-economic driving forces of environmental stress is quantitatively estimated by means of geographical weighted regression (GWR) method based on the STIRPAT model framework. The results show that: (1) The situation of environmental stress in the BTH region had been significantly alleviated, with an ESI decline of 54.68% since 2012. The decline was most significant in central urban areas of Beijing, Tangshan, Tianjin and Shijiazhuang, and Binhai New District. The degree of environmental stress in counties decreased gradually from the central urban areas to the suburban areas, and the high-level stress counties were eliminated in 2016. (2) The spatial spillover effect of environmental stress had been further enhanced on the county scale since 2012, and the spatial locking and path dependence emerged in cities of Tangshan and Tianjin. (3) Urbanized zones (development-optimized and development-prioritized zones) are the major bearing areas of environmental pollutants in the BTH region, with ESI accounting for 65.98% of the whole region, which should be focused on prevention and control of environmental pollution. (4) The controlling factors of environmental stress in counties include population size and economic development level. In addition, technical capacity of environmental disposal, agricultural production input intensity, territorial development intensity and urbanization had a certain degree of influence. (5) There was spatial heterogeneity in the driving effects of various driving factors on the environmental stress. Therefore, it is necessary to adopt differentiated environmental governance and reduction countermeasures from emission sources, according to the intensity and spatial difference of driving forces, so as to improve the accuracy and adaptability of environmental collaborative control in the BTH region.

Key words: environmental stress, spatio-temporal pattern, Major Functional Zones (MFZ), driving factors, Beijing-Tianjin-Hebei region