Why the Central government prefers to centralize spatial planning approval authority in China? An explanation based on land risk

Abstract

There are various explanations for the centralization of planning approval authority, but few studies focus on the explanation of selective centralization from a national governance perspective. To address this gap, this paper uses the concept of land risk to identify the reason why central government centralizes the planning approval authority of a prefectural-city in China. An econometric quantitative approach is adopted. The findings suggest that land risk from farmland protection is an important factor affecting centralization. Specifically, the probability of centralization increases by approximately 2% for each unit reduction in per capita farmland area, by 1.6% for every 1% increase in land transfer area and by 1% for each level increase in illegal land use. Furthermore, the cities with high land risk are mainly concentrated in four provinces along eastern coastal China. Additionally, the result is predicted with an overall accuracy of 93%, which proves the validity of the model.

Keywords:

• Land risk
• approval authority
• spatial planning
• state governance
• China

Notes

1 After the amendment of the Land Management Law in 2019, land use planning was upgraded to spatial planning in China, but the land use regulation and allocation of land quotas remained the same. As the research sample in this paper covers the period prior to 2019, the spatial planning proposed in the amendment of the Land Management Law does not affect the research in this paper. For the sake of understanding, the term land use planning is still used in this paper.

2 China’s government hierarchy is divided into five levels: the central level, provincial level, prefecture level, county level and township level. The local governments discussed in this article are those at the provincial level and below.

3 Other cities have to reach an urban population size between 0.5 to 1 million.

4 The results for the three control variables in further estimates and robustness test are basically unchanged. To keep the paper concise, we do not present the specific results in Tables 3 and 4.

5 Of course, it is ideal to make predictions of the probability that cities whose approval authorities have not been transferred to the central government in 2009 will be chosen in the future, and compared that with the actual situation. Unfortunately, there has not been a new round of adjustment of planning approval authority since 2009, which means this attempt cannot be realized. Nevertheless, it can be affirmed that the model performs well overall and can effectively support the theoretical hypothesis.

6 The GDP data are derived from the provincial and national statistical yearbooks from 2007 to 2009.

7 Data on farmland occupied by construction, per capita farmland area and the number of illegal land use cases is collected from the China Land and Resources Yearbook (2007–2009).

Additional information

Funding

This research has received financial support from the Soft Science Foundation of Zhejiang Province, P.R. China through project No. 2021C35066, the Ministry of Education of P.R. China through project No. 20JZD013.