韦有周,崔晴,刘一寒,林香红.海上风电场区位分布的动态演进和影响因素研究[J].海洋通报,2024,(3):
海上风电场区位分布的动态演进和影响因素研究
Study on dynamic evolution and influencing factors of location distribution of offshore wind farms
投稿时间:2023-10-20  修订日期:2023-11-23
DOI:10.11840/j.issn.1001-6392.2024.03.011
中文关键词:  海上风电  区位分布  时空异质性  影响因素  地理探测器模型  fsQCA
英文关键词:offshore wind power  spatiotemporal heterogeneity  location distribution  influencing factors  Geographic Detector model  fsQCA
基金项目:上海市哲学社会科学规划课题项目(2020BJB025)
作者单位E-mail
韦有周 上海海洋大学 经济管理学院上海 201306 yzwei@shou.edu.cn 
崔晴 上海海洋大学 经济管理学院上海 201306 cuiqing96@163.com 
刘一寒 上海海洋大学 经济管理学院上海 201306 liuyihan0318@163.com 
林香红 国家海洋信息中心天津 300171 tianshilezhuzhu@163.com 
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中文摘要:
      为应对能源安全和气候变化,培育新兴产业,我国大力发展海上风电产业。经过十余年发展,海上风电累计装机总量已跃居全球首位,但在这一过程中,风电场的区位分布表现出显著的时空异质性,即随着时间推移,其经历一个由江苏到福建、广东、山东等地动态演化的过程,研究不同阶段影响区位分布的主导因素及其变化,对政府制定有效政策以促进产业发展具有重要意义。根据国家发布的政策文件以及中国风能协会等公布的数据,本文划分了海上风电发展阶段,采用动态偏离-份额拓展模型来测度产业转移以揭示区位分布的演进;进一步将模糊集定性比较分析法与地理探测器模型结合,从动态的角度分析了产业发展各阶段区位分布的主导因素及其变化,并剖析了增长突出省区市的发展路径及其差异化。研究表明,发展初期,海上风电场区位分布受政府扶持力度和自然因素的影响较多,随着产业发展及技术进步,创新环境与专业人才逐渐成为关键因素,典型的发展路径有政策驱动型、水域-政策驱动型、风能驱动型、创新-知识型、知识-政策驱动型等。进一步加快人才培养、完善产业链条、优化扶持方式应成为推动产业健康持续发展的努力方向。
英文摘要:
      In order to cope with energy security and climate change, and cultivate emerging industries, China has vigorously developed the offshore wind power industry. After more than ten years of development, the total installed capacity of offshore wind power has leapt to the first place in the world. But in this process, the location selection of wind farms shows significant spatiotemporal heterogeneity. that is, as time goes by, it experiences a dynamic evolution process from Jiangsu to Fujian, Zhejiang, Guangdong, Shandong. It is of great significance for the government to formulate effective policies to promote the industrial development on the basic of studying the dominant factors affecting the location distribution in different stages. According to the policy documents issued by the state and the data published by China Wind Energy Association, the development stages of offshore wind power was divided. And the evolution of location distribution was measured from a new research perspective by using extended model of the dynamic offset-share model. Then, by the combination of the fuzzy set qualitative comparative analysis (fsQCA) and the geographic detector model, the change of dominant factors of location distribution was analyzed from a dynamic perspective, and the development paths of provinces and cities with prominent growth rate were analyzed. The study shows that in the early stage of development, the location selection is largely affected by government support and natural factors. With industrial development and technological progress, innovation environment and professional talents gradually become the key factors. Typical development paths include policy oriented, water condition-policy oriented, wind oriented, innovation-talent oriented, talent-policy oriented, etc. Accelerating the cultivating of talents, improving the industrial chain, and optimizing the way of support should become the direction of efforts to promote the healthy and sustainable development of the offshore wind power industry.
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