THE COLLABORATION IN THE ACTION PLAN FOR A BLUE SKY OF CHINESE MUNICIPAL GOVERNMENT BASED ON TEXT DATA MINING AND SOCIAL NETWORK ANALYSIS

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Abstract

Constructing a collaborative mechanism through the self-organizing movement within the system is the main development direction of China’s future mechanism of innova-tion. The reform of the administrative system can give full play to the role of the collaborative mechanism. This article comprehensively uses data mining and social network analysis to analyze the collaboration of China’s municipal departments in air pollution control. Text data includes 101 municipal policies. The paper reveals the collaboration problems in air pollution control and proposes suggestions from the perspective of the departmental responsibility. First, from the perspective of inter-departmental collabo-ration, the lack of responsibility for Department of Finance, Department of Housing and Urban-rural Development, Department of Ecology and Environment, Department of Industry and Information Technology, Department of Transport, Department of Public Security, and Department of Development and Reform is the most obvious, indicating that the comprehensive management departments should be strengthened to assume responsibilities, while the specialized management departments should also be strengthened. Second, from the perspective of intra-departmental responsibility collaboration, the most obvious situation is that the collaborations of responsible for promoting new-energy vehicles, air pollution control in autumn and winter, increasing tax-free incentive policy support, development of inter-modality, adjusting regional industrial layout, optimizing freight structure, and increasing economic policy support are insufficient. The number of departments responsible for supervision should be increased, as well as the number of departments responsible for guidance.

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APA

Yu, W., Wang, S., Wang, X., & Cai, X. (2022). THE COLLABORATION IN THE ACTION PLAN FOR A BLUE SKY OF CHINESE MUNICIPAL GOVERNMENT BASED ON TEXT DATA MINING AND SOCIAL NETWORK ANALYSIS. Revista Internacional de Contaminacion Ambiental, 38, 121–141. https://doi.org/10.20937/RICA.54344

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