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基于集对分析的中国海洋战略性新兴产业支撑条件评价
引用本文:韩增林,夏雪,林晓,赵林.基于集对分析的中国海洋战略性新兴产业支撑条件评价[J].地理科学进展,2014,33(9):1167-1176.
作者姓名:韩增林  夏雪  林晓  赵林
作者单位:1. 辽宁师范大学 海洋经济与可持续发展研究中心,辽宁 大连 116029
2. 北京师范大学 地理学与遥感科学学院,北京 100875
基金项目:国家自然科学基金项目(4107109441101114)
摘    要:21世纪以来,在海洋科技和海洋新兴产业发展的助推作用下,海洋战略性新兴产业逐渐成为全球海洋经济一个新的增长点。本文从基础、经济、科技、人才、政府和环境支撑6个方面提出建立海洋战略性新兴产业支撑条件评价指标体系,运用AHP-EVM赋权法,结合集对分析,对中国大陆沿海11个省、市、自治区支撑条件进行定量评价,并运用模糊C均值聚类分析法对评价结果进行分类,结果表明:①沿海省市间的支撑条件差距较明显,广东、山东和上海处于第一层次,在空间分布上呈现出“三足鼎立”的格局,各子支撑条件全面均衡发展;第二层次的江苏、辽宁综合支撑条件较好;第三层次的福建、浙江、天津综合支撑条件一般,缺乏对综合支撑条件贡献较大的优势子支撑条件;第四层次的河北、广西、海南综合支撑条件差,子支撑条件劣势突出。②通过沿海省市6大子支撑条件的对比分析,得出了各省市支撑条件的优劣势,为沿海各省市制定海洋战略性新兴产业发展规划和布局提供了科学依据。

关 键 词:海洋战略性新兴产业  支撑条件  定量评价  集对分析  

Quantitative evaluation of supporting conditions of emerging strategic marine industries in China based on set pair analysis
Zenglin HAN,Xue XIA,Xiao LIN,Lin ZHAO.Quantitative evaluation of supporting conditions of emerging strategic marine industries in China based on set pair analysis[J].Progress in Geography,2014,33(9):1167-1176.
Authors:Zenglin HAN  Xue XIA  Xiao LIN  Lin ZHAO
Institution:1. Research Center for Marine Economy and Sustainable Development,Liaoninag Normal University, Dalian 116029, Liaoning, China
2. School of Geography, Beijing Normal University, Beijing 100875, China
Abstract:The twenty-first century is an era that humanity challenges the ocean, competition for marine resources among countries in the world becomes increasingly more intense. The ocean becomes a global forefront of a new round of competition, and the development of emerging strategic marine industries becomes national competitions in seizing the strategic high ground. Since the beginning of the century, with the development of marine technology, emerging strategic marine industries gradually became a new growth point of the global economy. Based on the factors affecting industrial development and taking into consideration existing research results and the characteristics of the emerging strategic marine industries, this study selected 22 indicators from six areas, including basic support, economic support, technology support, human resources support, government support, and environmental support to build an evaluation index system of supporting conditions for emerging strategic marine industries. Using this supporting condition evaluation index system, a combination of factor weight derivation methods, set pair analysis, and fuzzy c-means clustering analysis, this study analyzes and compares the supporting conditions of emerging strategic marine industries in 11 coastal provinces in mainland China. The results show that: (1) Overall, there are clear differences among these provinces in supporting conditions. The coastal provinces can be divided into four groups with varied supporting conditions. The first group includes Guangdong, Shandong, and Shanghai Provinces, where comprehensive support is excellent. These provinces are the "three pillars" of emerging strategic marine industry development in China, with a balanced support of each of the six conditions. The second group includes Jiangsu and Liaoning Provinces, where the comprehensive support is relatively good. The third group contains Tianjin, Fujian, and Zhejiang Provinces. In this group, comprehensive support is average, and there is a general lack of single strong supporting condition that contributes to the comprehensive support. The fourth group includes Hebei, Hainan, and Guangxi Provinces. In these provinces, comprehensive support is weak and positive single supporting condition is missing. (2) Comparative advantages and disadvantages of various provinces are analyzed, which provides important support for development planning of emerging strategic marine industries in the coastal provinces and municipalities. In this study, only the current status of China's emerging strategic marine industry supporting conditions have been evaluated. This article proposes that a dynamic trend analysis should be considered in future research.
Keywords:emerging strategic marine industry  supporting condition  quantitative evaluation  set pair analysis  
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