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孙涛,孙然好,陈利顶.京津冀城市群地区影响城镇化的关键要素识别及其交互作用.生态学报,2018,38(12):4145~4154 本文二维码信息
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京津冀城市群地区影响城镇化的关键要素识别及其交互作用
Identification of key factors and mutual effects of urbanization and Eco-environment in the Beijing-Tianjin-Hebei urban agglomeration
投稿时间:2018-01-29  修订日期:2018-05-07
DOI: 10.5846/stxb201801290230
关键词城镇化  关键要素  交互作用  灰色关联分析  方差分析  京津冀
Key Wordsurbanization  key factors  interactive effects  grey relationship analysis (GRA)  ANOVA  Beijing-Tianjin-Hebei Province
基金项目国家自然科学基金重大项目(41590841)
作者单位E-mail
孙涛 中国科学院生态环境研究中心 城市与区域生态国家重点实验室, 北京 100085  
孙然好 中国科学院生态环境研究中心 城市与区域生态国家重点实验室, 北京 100085  
陈利顶 中国科学院生态环境研究中心 城市与区域生态国家重点实验室, 北京 100085
中国科学院大学, 北京 100049 
liding@rcees.ac.cn 
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摘要:
中国的城镇化率随经济快速发展而迅速提高,由此导致的生态环境问题不断涌现,城镇化对区域生态环境的胁迫作用日益增加,城市可持续发展受到严峻挑战。目前城镇化和生态环境交互关系的关键影响因子仍不明确,而基于关键影响因子描述城镇化与生态环境要素交互胁迫作用的研究也较少。以京津冀城市群为研究区,使用城镇化率描述城镇化发展过程,使用相关分析初步总结了生态环境要素与城镇化过程的相关性,基于灰色关联分析方法求取了生态环境要素与不同城市城镇化率的关联度,使用方差分析法得到影响城镇化与生态环境交互胁迫的关键要素,最后明确了城市和生态环境要素交互胁迫作用的显著性。结果表明,水资源要素和生态要素与城镇化率相关性较弱;居民用电与城镇化过程的关联度最大、生活用水关联度最小;方差稳定性排名显示建成区面积关联度在各城市间最稳定,常住人口最不稳定;同类别要素间关联度差异不明显,可以将10要素合并为6要素;关联度的稳定性对识别关键要素有重要作用;城市和生态环境要素同时对要素关联度分布具有影响,二者存在显著的交互作用。本研究对明确生态环境对城镇化过程的影响模式,分析生态环境对城市群发展的限制或支撑作用提供参考。
Abstract:
The Chinese urbanization ratio has increased as the economy has developed over the past 30 years. The rapid urbanization has caused increasingly unprecedented environmental problems, such as air pollution, water pollution, water resource shortages, encroachment of cultivated fields, and erratic regional climates. All these factors suggest that there are deeper interactions between the regional eco-environment and urbanization. Currently, most studies have focused on the total relationship between urbanization and the eco-environment through synthetic assessments. Although strong interactions have been determined, the key factors affecting urbanization and their interactions have not been identified and explored. Based on Beijing-Tianjin-Hebei urban agglomeration, which is a large-scale urban agglomeration and important economic zone in China, this study identified key factors and assessed their interactions affecting urbanization and the eco-environment. We used the urbanization ratio to describe urban development; and used land use, water resources, and environmental, ecological, and energy parameters to describe the eco-environment. This study assessed the impacts of eco-environmental factors on urbanization through a (1) correlation analysis to show the preliminary relationships and grey relation analysis (GRA) to rank the relations of each factor affected by urbanization, (2) significance analysis to merge homogeneous factors and determine the effect of interactions between cities and factors using a one-way ANOVA method. The results revealed that (1) total and household water consumption showed the lowest R2; socio-economic parameters showed the best correlations; land use parameters showed a lower R2 than socio-economic parameters; energy parameters showed the same level of accuracy as land use counterparts, and both were considerably more correlated than NDVI, which showed a higher R2 than water consumption parameters; (2) GRA results were heterogeneous among both the 13 cities and factors of the Beijing-Tianjin-Hebei urban agglomeration. Furthermore, the GRA results were different for the same factors in different categories, such as for land use and socio-economic parameters. Together, the max GRA results showed the citizen electricity usage ranked the highest, and household water consumption ranked the lowest. The variance of GRA results showed that built-up area was the most stable and population was the least stable factor; (3) pairwise GRA value comparisons among the factors were conducted to determine homogeneous factors. The fisher-LSD results showed significance to be weak within a category and strong among categories. Based on the significant properties, the total factors were compressed from 10 to 6, which represented the original selected categories whilst maintaining the assessment accuracy; (4) an interaction assessment module was used in a one-way ANOVA to evaluate the interaction effect of factors. We found that the factors significantly represented the different aspects of the ecological-environment, and the cities also significantly represented the differences between each city caused by various industrial structures and different natural environments. The interactive effect between cities and factors was significant, which provided solid evidence to further explore the interactive mode and environmental effect of the factors with increasing urbanization. This work provides a foundation to determine the supportive and suppressive effects between the eco-environment and urbanization.
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