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Open AccessArticle

Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China

1
Institute of Regional Economics, Shandong University of Finance and Economics, Jinan 250014, China
2
School of Public Administration, Xi’an University of Architecture and Technology, Xi’an 710055, China
3
Department of Geography, University of Wisconsin–Madison, Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Received: 26 November 2019 / Revised: 24 December 2019 / Accepted: 30 December 2019 / Published: 1 January 2020
More and more studies on land transfer prices have been carried out over time. However, the influencing factors of the industrial land transfer price from the perspective of spatial attributes have rarely been explored. Selecting 25 towns as the basic research unit, based on industrial land transfer data, this paper analyzes the influencing factors of the price distribution of industrial land in Dingzhou City, a rural land system reform pilot in China, by using a geographically weighted regression (GWR) model. Eight evaluation factors were selected from five aspects: economy, population, topography, landform, and resource endowment. The results showed that: (1) Compared with the traditional ordinary least squares (OLS) model, the GWR model revealed the spatial differentiation characteristics of the industrial land transfer price in depth. (2) Factors that have a negative correlation with the industrial land transfer price include the proportion of cultivated land area and distance to the city. Factors that have a positive correlation with the industrial land transfer price include the population growth rate, economic growth rate, population density, and number of hospitals per unit area. (3) The results of GWR model analysis showed that the impact of different factors on the various towns of different models had significant spatial differentiation characteristics. This paper will provide a reference for the sustainable use of industrial land in developing countries. View Full-Text
Keywords: industrial land; price; geographically weighted regression model; driving factors; rural land system reform pilot industrial land; price; geographically weighted regression model; driving factors; rural land system reform pilot
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MDPI and ACS Style

Yang, Z.; Li, C.; Fang, Y. Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China. Land 2020, 9, 7.

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