Spatio-Temporal Nonstationary Effects of Impact Factors on Industrial Land Price in Industrializing Cities of China

Shengfu Yang,Chuanrong Zhang,Weidong Li,Dongdong Song,Shougeng Hu

doi:10.3390/su12072792

Abstract

Industrialization has brought about great differences in industrial development and land use demand among different regions and cities, especially in rapidly industrializing countries with a vast territory. In those areas, implementing local-specific policies on industrial land price is of great significance to improve industrial land use efficiency and facilitate the sustainable development of regional economy. Based on the land pricing monition files of 105 industrializing cities, geographically weighted regression (GWR) was applied to detect the spatial variation of the industrial land price and its main impact factors (for example, tax, leased land, population, and location quotient index) in China in 2009, 2011 and 2014. The results show that the relationships were generally spatio-temporally nonstationary. More specifically, while the effect of tax on industrial land price was significantly positive and spatially stable all over China in 2009, the effect varied spatially in the two later studied years, weakened in North and East China and strengthened in South China. The effect of leased land on industrial land price was generally negative; it was very weak in 2009 and 2011 but became negatively strong in most studied cities in 2014, except for a few cities in Middle China. Population had a significant positive effect on industrial land price in the cities of East and Northeast China. For the three studied years, the location quotient index always had negative effect in Bohai Economic Rim and positive effect in Yangtze River Delta Economic Zone, and the negative effect strengthened with time. Meanwhile, the underlying reasons behind the relationships were further analyzed, showing that the spatio-temporal changes of industrial land price are closely correlated with the population mobility, industrial agglomeration, government intervention and economic situation.

Highlights

With the rapid development of industrialization, numerous industrial districts were established in cities and even in some towns, which is quite common in developing countries like China [1,2,3,4]
The purpose of this paper is to detect the spatio-temporal connections of industrial land price and its main impact factors with the geographically weighted regression (GWR) model, so that we can better characterize the spatio-temporal variation of industrial land price in the main cities of China, analyze related local impact factors and their influencing mechanisms, optimize the local specific policies of industrial land use, and boost the development of the regional economy
To improve industrial land use efficiency and facilitate the sustainable development of regional economy, the spatio-temporal nonstationary effects of impact factors on industrial land price in industrializing cities have been studied, which is expected to set a reference for the government to make local specific policies

Summary

Introduction

With the rapid development of industrialization, numerous industrial districts were established in cities and even in some towns, which is quite common in developing countries like China [1,2,3,4]. With a purpose to optimize the resource allocation, in 2006, National Bottom Price Standard for Industrial Land Transfer in China was released, which stipulated that industrial land must be leased by bidding, auction or listing, and, the transaction price should not be lower than the minimum price formulated by various local implementation rules in different cities. Under this system, market forces, which play an increasingly important role in industrial land leasing, motivated the formation of the land prices gradient in some economically developed areas [20]. Supply-side structural reform was firstly proposed by the Chinese government in 2015, hoping to make the supply system more suitable to the change in the demand structure by improving supply efficiency

Objectives

Methods

Results

Conclusion