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How will innovation-driven development policy affect sustainable urban land use: Evidence from 230 Chinese cities

Ronghui Tan, Tianqi Zhang, Dayong Liu, Hengzhou Xu

2021Sustainable Cities and Society39 citationsDOIOpen Access PDF

Abstract

Government policy has a significant effect on rural–urban land conversion, ultimately affecting sustainable urban land use. Unlike most studies that have centred on the effects of local land-use policies, such as regulations and taxes, the present study investigates the effect of the innovation-driven development policy initialised recently by China on urban land growth at the national level. Although the geography of innovation, high-tech agglomeration and sectoral restructuring are place-based activities, existing evidence shows several links between urban innovation and urban land growth. However, only a few empirical studies have examined their relationships to date. Thus, we aim to determine the effect of urban innovation on the size and the spatial distribution of urban land using a statistical dataset from 2007 to 2015 and a spatial data source at two-time points for 230 Chinese cities. We employ three fixed effects models to control for the possible correlation between independent variables and unobservable city-level effects. Our results indicate that urban innovation has indeed driven urban land growth but does not make the urban landscape more scattered than before. Further examinations suggest that the urban innovation effect varies across the whole country, and the innovation-urban land growth connection relies on the geographic location rather than the economy size.

Topics & Concepts

Economic geographyUrban sprawlUrban densityUrban planningRestructuringUrban climateChinaLand useUrbanizationUrban agglomerationGeographySustainable developmentUrban spatial structureEconomic growthRegional scienceBusinessEconomicsPolitical scienceBiologyCivil engineeringFinanceEngineeringLawArchaeologyEcologyLand Use and Ecosystem ServicesRegional Economics and Spatial AnalysisSpatial and Panel Data Analysis