皖江城市带绿色发展水平测度及空间分异研究
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摘要
基于皖江城市带9市2017年的统计数据,选取相关的绿色发展指标,利用熵值法、改进的TOPSIS法和障碍度模型对皖江城市带的绿色发展水平进行实证分析。研究结果表明:合肥、马鞍山为高水平区,芜湖、铜陵为较高水平区,滁州、宣城、池州为中水平区,六安、安庆为低水平区,绿色发展水平还有待提高。障碍度模型分析得出废水设施处理能力、人均水资源量、森林覆盖率和每万人口中从事R&D活动人员是影响绿色发展水平频次较高的指标。根据以上研究结果,对不同的市提出相应的发展途径与对策,有利于皖江城市带整体绿色发展水平提高。
Based on the statistical data of 9 cities in the cities along the Yangtze River in Anhui Province in 2017, this paper selects relevant green development indicators, and makes an empirical analysis on the green development level in the cities along the Yangtze River in Anhui Province by using entropy method,modified TOPSIS and obstacle degree model. The results show that Hefei and Ma 'anshan are high-level areas, Wuhu and Tongling are high-level areas, Chuzhou, Xuancheng and Chizhou are medium-level areas, Lu 'an and Anqing are low-level areas, and the level of green development needs to be improved. Obstacle model analysis shows that wastewater treatment capacity, water resources per capita, forest coverage and persons engaged in R&D activities per 10,000 population are the indicators that affect the green development level more frequently.According to the above research results, it is beneficial to improve the overall green development level of the cities along the Yangtze River in Anhui Province by proposing corresponding development approaches and countermeasures for different cities.
Based on the statistical data of 9 cities in the cities along the Yangtze River in Anhui Province in 2017, this paper selects relevant green development indicators, and makes an empirical analysis on the green development level in the cities along the Yangtze River in Anhui Province by using entropy method,modified TOPSIS and obstacle degree model. The results show that Hefei and Ma 'anshan are high-level areas, Wuhu and Tongling are high-level areas, Chuzhou, Xuancheng and Chizhou are medium-level areas, Lu 'an and Anqing are low-level areas, and the level of green development needs to be improved. Obstacle model analysis shows that wastewater treatment capacity, water resources per capita, forest coverage and persons engaged in R&D activities per 10,000 population are the indicators that affect the green development level more frequently.According to the above research results, it is beneficial to improve the overall green development level of the cities along the Yangtze River in Anhui Province by proposing corresponding development approaches and countermeasures for different cities.
引文
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[14]程云鹤,万李红,王燊.城镇化与生态环境耦合协调度时空差异动态分析———基于2005-2015年安徽16地市的面板数据[J].安徽理工大学学报(社会科学版),2018,20(5):24-34.
[2]杨平.绿色发展的新内涵[J].辽宁行政学院学报,2016(7):87-90.
[3]王玲玲,张艳国.“绿色发展”内涵探微[J].社会主义研究,2012(5):143-146.
[4]中国省域绿色发展水平测量与空间演化[J].华南师范大学学报(社会科学版),2017(3):37-44.
[5]任亚文,张宇,曹卫东.安徽省绿色发展能力测度与时空分异研究[J].云南地理环境研究,2018(6):9-18.
[6]杨瑞,张然.生态福利绩效视角下的绿色发展评价研究———以青岛市为例[J].生态经济,2018,34(12):58-63.
[7]田泽,魏翔宇,丁绪辉.中国区域产业绿色发展指数评价及影响因素分析[J].生态经济,2018,34(11):103-108.
[8]马骏,李夏.中国绿色发展水平空间分异与影响因素研究[J].河南科学,2017,35(10):1688-1695.
[9]吴旭晓.河南绿色发展水平测度及提升对策[J].区域经济评论,2017(4):132-139.
[10]魏振香,周晗.基于DPSIR模型的东营市绿色经济发展评价[J].中国石油大学学报(社会科学版),2018,34(6):48-53.
[11]周灵.绿色矿业经济评价指标体系构建与实证[J].统计与决策,2018,34(20):68-70.
[12]侯娜.基于绿色经济发展的内蒙古综合评价研究[J].内蒙古统计,2018(4):1-5.
[13]韩磊.基于熵值法和因子分析的湖南省绿色经济发展评价研究[J].中州大学学报,2017,34(4):43-47.
[14]程云鹤,万李红,王燊.城镇化与生态环境耦合协调度时空差异动态分析———基于2005-2015年安徽16地市的面板数据[J].安徽理工大学学报(社会科学版),2018,20(5):24-34.