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

Tan, Ronghui; Zhang, Tianqi; Liu, Dayong; Xu, Honggang

doi:10.1016/j.scs.2021.103021

Cited by 20 publications

(13 citation statements)

References 67 publications

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“…Owing to its diverse composition, the support system may exhibit a significant impact strength difference. However, transportation infrastructure has a stronger influence as a physical support system, and the level of scientific and technological innovation, as a soft factor, has a weak influence, which is in line with the actual situation of the current development of the Shandong's industrialization stage (Tan et al, 2021).…”

Section: Empirical Analysis Resultsmentioning

confidence: 68%

Assessment and Contributing Factors of the Spatial Misallocation of Construction Land: A Case Study of Shandong, China

Cao

Wang

et al. 2022

Front. Environ. Sci.

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Spatial misallocation is an essential reason for the low utilization efficiency of construction land. Optimizing the spatial pattern of construction land allocation can improve the efficiency of economic operations and resilience to food crisis and climate change challenges. This study constructs a quantitative measurement method for the spatial misallocation of construction land (SMCL), proposes a new government market society support (GMSS) analytical framework for the contributing factors with interlocked relationships, and conducts empirical research in Shandong, China, which is a typical area with a rapid development of construction land and significant regional disparity. It is concluded that the SMCL ensues through the interaction and coevolution of the GMSS system, which plays a key role in adjusting the construction land use sequence, structure, and efficiency under the regulation of the upper government. Effectively using the estimation method based on the equal marginal output principle, the SMCL in Shandong is established as a downward trend, with evident temporal and spatial differentiation characteristics and spatial morphological mode changes, although most sub-regions are significantly approaching the adaptation interval with fluctuation. Furthermore, the empirical results of the regression model indicate that there are different effects and intensities on the SMCL among the contributing factors under the GMSS framework, wherein the local government force has an aggravating and the greatest effect, the market forces have a dual and second-ranking effect, the social forces play a positive but still weak role, and the support system has a differentiated improvement effect. However, the impacts of various dimensional factors on the SMCL also have heterogeneity in the development stages and different regions. Generally, in the low-level development stage and underdeveloped areas, the effect of local government intervention is stronger, the market forces’ importance is lower, and the social forces and support systems remain insufficiently robust.

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Section: Empirical Analysis Resultsmentioning

confidence: 68%

Assessment and Contributing Factors of the Spatial Misallocation of Construction Land: A Case Study of Shandong, China

Cao

Wang

et al. 2022

Front. Environ. Sci.

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“…Entrepreneurship Index [2] to measure urban innovation capacity. The urban innovation and entrepreneurship index per capita (inno1) and the urban innovation and entrepreneurship index per unit area [ 14] (inno2) are also used as two other proxy variables for urban innovation capacity to examine the effects of population size and land area.…”

Section: Variable Selectionmentioning

confidence: 99%

“…Exploring the in uence and transmission path of compact city form on urban innovation capacity is important to further verify the feasibility of compact city development strategy in some cities and provide new development ideas for the improvement of innovation capacity in other cities (see Power, 2001) [ 1] ; On the other hand, urban sprawl typically leads to a dilution of agglomeration economy. It negatively affects the accessibility of urban facilities and regional connectivity, resulting in a mismatch between skilled labor and job opportunities(see Tan, R., 2021) [ 2] . Moreover, small and medium-sized cities that experience sprawl often lack the advantages of agglomeration and economies of scale.…”

Section: Introductionmentioning

confidence: 99%

“…It also has a diluting effect on urban spatial agglomeration by suppressing knowledge spillover effects (see Chen, Z., 2022) [ 4] . (2) Systematic studies on compact urban form have demonstrated that it can enhance innovation capacity primarily through spatial proximity, convenient public transportation, poverty eradication, and ethnic segregation (see [ 5] . Spatial proximity is a core driver of innovation capacity by promoting agglomeration and knowledge spillover, and spatial proximity is also a distinctive feature of compact cities, which indirectly illustrates the relationship between urban compactness and urban innovation capacity (see Hamidi et al, 2019) [ 6] .…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the driving effect of urban compactness on urban innovation capacity

Li¹,

Zhao

Yang³

2023

Preprint

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Theoretically, we analyzed the mechanism of urban compactness affecting urban innovation capacity, and empirically analyzed the relationship between urban compactness and urban innovation capacity and its driving effects based on the measurement of compactness in 31 large and medium-sized cities. The findings of the study demonstrate that urban compactness plays a crucial role in promoting urban innovation capacity. Specifically, there are notable variations in the impact based on regional and city size heterogeneity. In terms of regional heterogeneity, the compact development of central and western cities leads to a significant enhancement of urban innovation compared to their eastern counterparts. Moreover, regarding city size heterogeneity, increased compactness in larger cities exhibits a positive influence on urban innovation capacity, while the compact development mode of megacities does not significantly impact innovation development. Furthermore, the influence mechanism of urban compactness on urban innovation capacity operates through multiple pathways, including the direct effect, knowledge spillover effect of human capital, agglomeration economy effect of industrial diversity, and the time-space compression effect of transportation access.

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“…Menzori et al [ 30 ] argued that management capacity affects the geographic location of urban growth and, in turn, the moderation of urban sprawl. Tan et al [ 31 ] concluded that innovation policies are not driving urban sprawl.…”

Section: Introductionmentioning

confidence: 99%

Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China

Zhang

Guan

et al. 2022

IJERPH

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Urban sprawl has become the main pattern of spatial expansion in many large cities in China, and its ecological and environmental effects profoundly impact Chinese urban development. In this paper, nighttime light data and statistical yearbook data are adopted as basic data sources to simulate the evolution trend of the urban sprawl in the upper Yangtze River (UYR), China. First, the urban sprawl index (USI) is employed to assess the level of urban sprawl and to determine the characteristics of urban sprawl under different scales. Second, the spatial autocorrelation model is applied to reveal the spatial pattern change characteristics of urban sprawl from 1992 to 2015. Third, a scenario analysis model of urban sprawl is constructed to simulate the evolution trend of the urban sprawl under different scenarios. Finally, based on the Geodetector, the influence of factors and factor interactions influencing urban sprawl in different time periods is analyzed. The results yield the following main conclusions: (1) The urban sprawl in the UYR first intensifies and then stabilizes over time. The number of cities with high USI in Sichuan province, medium cities, and Chengdu-Chongqing urban agglomeration increases over time, indicating that urban sprawl is intensifying in these areas. (2) The urban sprawl hot spots experience a pattern transformation process of point-like expansion-point-ring expansion-point-axis expansion-axis radiation. (3) Under the scenarios with different scales, the urban land sprawl in large cities is the highest, accounting for more than 47% of the UYR. Urban land sprawl extent in the Chengdu-Chongqing urban agglomeration is the highest, accounting for more than 51% of the UYR. The cities exhibiting the highest sprawl are Chongqing, Lijiang, and Kunming, accounting for 25.84%, 7.37%, and 5.11%, respectively, of the UYR. (4) In the different time scenario simulations, the urban land in large cities exhibits the highest sprawl, accounting for approximately 48.16% of the UYR. The urban land in the Chengdu-Chongqing urban agglomeration demonstrates the highest sprawl, accounting for 50.92% of the UYR. (5) From 1996 to 2002, the driver with the highest influence on urban sprawl was secondary industry share of GDP, with a q-statistic of 0.616. From 2009 to 2015, the driver with the highest influence on urban sprawl was green space per capita with a q-statistic of 0.396.

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

Cited by 20 publications

References 67 publications

Assessment and Contributing Factors of the Spatial Misallocation of Construction Land: A Case Study of Shandong, China

Assessment and Contributing Factors of the Spatial Misallocation of Construction Land: A Case Study of Shandong, China

Analysis of the driving effect of urban compactness on urban innovation capacity

Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China

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