1. Understanding the Corrective Effect of the Urban Growth Boundary Policy on Land Finance Dependence of Local Governments in China.
- Author
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Niu W, Nie T, Chen X, Wang T, Shi J, Xu Z, and Zhang H
- Subjects
- China, Cities, Conservation of Natural Resources, Local Government, Policy, Ecosystem, Urbanization
- Abstract
The preference for land urbanization of local governments promotes urban sprawl, which leads to the dilemma of land finance dependence (LFD) of local governments and the negative constraints on the ecosystem of urban areas in China. However, how the urban growth boundary (UGB) policy corrects local governments' reliance on land finance has not been discussed in depth. In July 2014, the UGB policy began to be piloted in fourteen cities in China, providing a setting to further reveal the effectiveness of the UGB policy. By constructing an evolutionary game simulation model to clarify the behavioral strategies that local governments tend to adopt in the context of the UGB policy implementation, this study proves that the effective implementation of the UGB policy, by controlling the urban land capacity, can help solve local governments' LFD dilemma in China. The UGB policy consists of a set of technical means and policy tools that controls urban sprawl. It breaks the "unlimited land capacity" situation faced by local governments in China by limiting the urban land capacity within a given period of time, and has become a new solution to the dilemma of LFD. The implementation of the UGB policy highlighted the shortage of urban land, which has led to the increasing cost of land finance for local governments and constraints on local governments' LFD behavior. The shortage has also forced local governments to adjust and optimize their fiscal revenue structure. The UGB policy induced ongoing evolution in the benefit distribution among relevant entities in land finance, motivated local governments and other entities to adjust their primary strategies, and made it possible to address the dilemma of LFD in China.
- Published
- 2022
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