您当前的位置:首页 > 论文详情

江苏先进制造业集群政策文本量化研究

请选择邀稿期刊:

Towards Sustainable Prosperity? Policy Evaluation of Jiangsu Advanced Manufacturing Clusters

摘要: 在构建国家竞争优势的过程中,中国政府引导、激励先进制造业集群成为集聚效应的孕育者和创新战略的承载者,通过发挥要素禀赋建立比较优势。文章从“有为政府”视角梳理了先进制造业集群政策的目标和逻辑,突出了研究政策文本的重要性。江苏是中国制造业重要基地,文章以先进制造业集群为切入点,收集了江苏省政府和省级各单位的政策文本,建立了量化研究框架,从主题、工具、强度等方面探索政策导向和实践路线,以PMC模型评估样本政策。结果显示政策文本关键词契合高质量发展的要求,描述了江苏先进制造业集群的发展方向和对政策主客体的定位,体现了长期利益价值导向和生态体系规范。江苏先进制造业集群政策文本样本体现了较高的内部一致性,显示出合理的效果水平。由此,文章提出对区域产业政策的规划和实施要基于“有为政府”制度特色发挥比较优势,通过内部一致、科学高效的政策手段科学利用先进制造业集群,在发展具有竞争力的区域产业的基础上改善民生、发展经济。

Abstract: The government of China, to hone manufacturing’s competitive edge, has adopted a series of regional industrial policies to stimulate advanced manufacturing clusters as facilitators of agglomeration effects, utilisers of factor endowments, and implementers of innovation solutions, by playing the role of facilitating state. Focusing on advanced manufacturing cluster policies in East China’s Jiangsu Province, the research employs a mixed data analysis methodology on a sample of 52 documents collected from the Jiangsu government and affiliated units. The policy orientations and priorities are examined under the triple analytical framework in terms of themes, instruments, and controls, applying textual mining and the PMC index model. The results reveal the alignment of policy themes with high-quality development strategy, the government preferences for the supply dimension in policy instruments, the emphasis on planning directive in policy controls, together with the relatively high internal consistency of the AMC policy toolkit by Jiangsu. Therefore, the research spotlights the importance of leveraging comparative strengths based upon the facilitating state institution, and of deploying internally coherent, scientific, and efficient initiatives that exploit more potential of advanced manufacturing clusters to foster sustainable prosperity from the perspective of New Structural Economics.

版本历史

[V1] 2024-07-20 05:59:04 PSSXiv:202407.00986V1 下载全文
点击下载全文
在线阅读
许可声明
metrics指标
  •  点击量612
  •  下载量155
  • 评论量 0
评论
分享
收藏