The Temporal Spatial Dynamic of Land Policy in China: Evidence from Policy Analysis Based on Machine Learning

• Published in: Mathematical problems in engineering Vol. 2022; pp. 1 - 13
• Main Authors: Li, Xiao, Yao, Yuting, Zhu, Meihong
• Format: Journal Article
• Language: English
• Published: New York Hindawi 23.11.2022; John Wiley & Sons, Inc
• Subjects: Communication; Dirichlet problem; Documents; Economic conditions; Electric vehicles; Government; Government subsidies; Industrial development; Land acquisition; Land development; Land management; Land use; Machine learning; Policy analysis; Political parties; Text analysis; Texts; Urbanization; China
• DOI: 10.1155/2022/8500272
• ISSN: 1024-123X

Extracting useful information from a large number of policy texts is a challenging and insufficiently discussed topic. Utilizing large sample policy texts and a method of machine learning, this study contributes to the research gap by systematically analyzing the temporal evolution and spatial differentiation of China’s land policy from 1998 to 2018. A framework comprising six major themes of land policy, namely, “land development, land acquisition and demolition, cultivated land protection, land planning, land consolidation and utilization, and land confirmation and transfer” is first established, according to the theoretical and institutional background of land management. Based on this framework, the Latent Dirichlet Allocation analysis of more than 20,000 policy documents at different levels of government shows that, (1) temporally, the priority of land policy evolves with the spirit of the central document and the macropolitical and economic conditions and, (2) spatially, there are significant differences in land policies among provinces. Overall, the analysis of land policy documents shows the tradeoff between cultivated land protection and land development and also the emphasize on other topics, with the changes in land policy priorities in different periods and regions.