杨静,汪峰,赵婧,等.基于大数据的“邻避”设施环境社会风险评估方法研究[J].中国环境管理,2023,15(3):118-125.
YANG Jing,WANG Feng,ZHAO Jing,et al.Environmental and Social Risk Assessment Method of NIMBY Facilities Based on Big Data[J].Chinese Journal of Environmental Management,2023,15(3):118-125.
基于大数据的“邻避”设施环境社会风险评估方法研究
Environmental and Social Risk Assessment Method of NIMBY Facilities Based on Big Data
DOI:10.16868/j.cnki.1674-6252.2023.03.118
中文关键词:  环境社会风险  评估  邻避设施  大数据
英文关键词:environmental and social risk  assessment  NIMBY facility  big data
基金项目:国家自然科学基金青年科学基金项目“基于大数据的‘邻避’设施环境社会风险的识别、预测和防控策略研究”(72204274)。
作者单位E-mail
杨静 生态环境部环境发展中心, 北京 100029  
汪峰 南京信息工程大学商学院, 江苏南京 210044  
赵婧 生态环境部环境发展中心, 北京 100029  
刘海东 生态环境部环境发展中心, 北京 100029  
赵芳 生态环境部环境发展中心, 北京 100029 zhaofang@edcmep.org.cn 
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中文摘要:
      着力防范化解环境社会风险是社会和谐稳定的重要前提。本文基于国内全网2019—2020年关于“垃圾焚烧”的网络舆情数据,通过情感词典、TF-IDF关键词提取和人工识别方法进行“邻避”冲突的原因分析,在此基础上构建本土化的“邻避”设施环境社会风险指标体系和评估方法,结合环境、人口和社会等多源大数据,基于自然断点法对风险等级进行分类,以全国生活垃圾焚烧发电设施的重点区域为案例开展环境社会风险的分析评估。结果表明,担忧破坏生态环境、反对选址位置、担忧排放物有毒是贯穿2019—2020年两年的主要反对原因,占据反对原因的50%以上。之后,本文对各项反对因素进行归纳汇总,构建了12项指标的风险评价指标体系,其中环境因素、人口因素和社会因素为一级指标,其权重分别达到0.33、0.49和0.18,人口因素是影响程度最大的因素;对案例风险评估结果表明全国范围和六大重点区域的大多数设施拟建设于重大风险或特大风险地区,需着重关注这些地区的生活垃圾焚烧发电项目规划布局和风险防控问题。
英文摘要:
      Preventing and resolving environmental and social risks is an important prerequisite for social harmony and stability. Based on the records of public opinion data from 2019 to 2020 related to "garbage incineration" collected from internet, this paper analyzed the causes of NIMBY conflicts through the emotional dictionary, TF-IDF keyword extraction and artificial identification methods, then established a localized environmental and social risk index system and assessment tools for NIMBY facilities. Combined with the environment, population, social and all multi-source data, environmental and social risk levels were categorized and classified using the natural break point method. Taking the key regions of domestic waste incineration power generation facilities in China as cases, the environmental and social risk were analyzed and evaluated. The results showed that environmental damage, toxic emissions, and objections to the proposed plant location were the main reasons for opposition throughout the two years, accounting for more than 50% of the reasons for opposition from 2019 to 2020. After summarizing all the opposing factors, a risk evaluation index system of environmental factor, population factor, and social factor with 12 first-level indicators was constructed. The weight of the environmental factor, population factor, and social factor was 0.33, 0.49, and 0.18 respectively, and the population factor was the most influential. The risk assessment results of the case showed that most of the facilities in the whole country and specifically in six key regions are planned to be built in major risk areas or very large risk areas. Therefore, it is necessary to pay more attention to the planning layout and risk prevention and control of domestic waste incineration power generation facilities in these areas.
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