Investigating the Spatiotemporal Disparity and Influencing Factors of Urban Construction Land Utilization Efficiency: Empirical Evidence from Panel Data of China

This paper corrected the long-term misunderstanding of the land utilization efficiency concept. The Undesirable-Window-DEA model, Dagum Gini coefficient, and spatial panel autoregressive model with fixed effect were used to explore the spatial heterogeneity and influencing factors of urban construction land utilization efficiency in China from 2004 to 2016. The results show the following: (1) China’s overall utilization of urban construction land is still at a low level. It decreased first and then rose, with a “flat V-shaped” evolution pattern. (2) During the study period, the Gini coefficient of urban construction land utilization efficiency for all provinces decreased first, then rose, and decreased again. The utilization efficiency Gini coefficients within provincial grouping were eastern region (0.063), central region (0.101), and western region (0.128). The Gini coefficients within provincial grouping were central versus western (0.121), eastern versus western (0.161), and eastern versus central (0.168). For the contribution to overall inequality of land utilization efficiency, the inequality within groups contributes the most (57.57%), and the inequality between groups accounts for about 25.62%. The overall efficiency is improved, with an evolution pattern of “difference narrowed–differences expanded–difference narrowed.” (3) “economic development,” “industrial structure,” “research development investment,” and “land urbanization level” have significantly positive effect on urban construction land utilization efficiency, while other factors have a negative effect, including “urbanization level of population,” “urban population density,” “cultivated field resources level,” “government influence level,” “land urbanization level,” and “financial dependence level.” This study could provide theoretical support for the implementation of cross-provincial/regional urban construction land quotes reallocation and differential construction land management policies.

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Spatial Pattern Characteristics and Influencing Factors of Green Use Efficiency of Urban Construction Land in Jilin Province

Complexity ◽

10.1155/2020/5637530 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Huisheng Yu ◽

Ge Song ◽

Tong Li ◽

Yanjun Liu

Keyword(s):

Influencing Factors ◽

Jilin Province ◽

Utilization Efficiency ◽

Scale Efficiency ◽

Eastern Region ◽

Construction Land ◽

The Sustainable Development ◽

Urban Construction ◽

Temporal And Spatial Characteristics ◽

The Impact

How to explore the allocation and green utilization level of urban construction land resources has an important role in the sustainable development of the city. Taking 47 counties and cities in Jilin Province as an example, this paper evaluates the green utilization efficiency of urban construction land (GUEUCL) in 2011 and 2015 by using the unexpected output super-SBM model and explores the spatial-temporal differentiation characteristics and influencing factors of GUEUCL by using GIS and machine learning methods. The results show that (1) the GUEUCL in Jilin Province is low, mainly distributed in small- and medium-sized areas, with significant positive spatial correlation. The L-L concentration area is mainly distributed in the eastern region, but the degree of spatial concentration is small, the spatial structure characteristics of the two periods are different, and the spatial heterogeneity is large; (2) the internal factor decomposition shows the impact of pure technical efficiency on the comprehensive efficiency and the restriction ability is stronger than the scale efficiency, that is to say, the factors such as management and technology have a greater impact on the comprehensive efficiency; (3) the relative importance of external factors has always been ranked as socioeconomic factors, urban development factors, and natural science and technology factors. This paper focuses on the temporal and spatial characteristics of each county and city and the influencing factors, which provides a certain value reference for the pilot of ecological construction and the development of ecoenvironmental benefit economic system in Jilin Province.

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Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China

Land ◽

10.3390/land8110158 ◽

2019 ◽

Vol 8 (11) ◽

pp. 158 ◽

Cited By ~ 2

Author(s):

Qianru Chen ◽

Hualin Xie

Keyword(s):

Influencing Factors ◽

Carbon Emissions ◽

Spatial Differentiation ◽

Utilization Efficiency ◽

Added Value ◽

Scale Model ◽

Eastern Region ◽

Cultivated Land ◽

Land Utilization ◽

Extension System

Cultivated land is closely related to national food security, rural economic development and social stability. The cultivated land pollution and carbon emissions caused by chemical fertilizers, pesticides, film residues, etc., in the process of cultivated land utilization pose a serious threat to the cultivated land ecosystem in China. The comprehensive analysis on the cultivated land green utilization efficiency (GUECL), its influencing factors, and optimization direction provides a valuable basis for the green utilization of cultivated land. Based on a panel data of 30 provinces (cities or districts) in China from 2001 to 2016, the GUECL in China under the constraints of pollution and carbon emissions was measured by using a super-efficient SBM-VRS (slack based model-variable return to scale) model. The influencing factors and optimization directions of the GUECL were analyzed through the Tobit model and slack values, respectively. The results show that the GUECL in China rose with fluctuations from 2001 to 2016. Since 2014, the eastern region has surpassed the western region and has become the region with the highest mean GUECL value. The room for resource conservation and pollution reduction varies in different regions of China. Farmers’ dependence on cultivated land and agricultural added value are positively related to the GUECL in China. Farmers’ occupational differentiation, agricultural machinery density, and agricultural disaster rate have had negative effects on the GUECL in China. The loss of the GUECL in China is mainly due to the redundancies of land input, pollution emission, and mechanical input. By analyzing these influencing factors and optimization directions, it is concluded that improving rural land transfer market and agricultural infrastructure construction, establishing a new agricultural technology extension system, and vigorously cultivating new professional farmers are the targeted measures to improve the GUECL.

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The correlation analysis between urbanization phenomena and landuse change in Jakarta Special Province

E3S Web of Conferences ◽

10.1051/e3sconf/202020005003 ◽

2020 ◽

Vol 200 ◽

pp. 05003

Author(s):

Ratih Fitria Putri ◽

Aji Wijaya Abadi ◽

Utia Kafafa

Keyword(s):

Urban Area ◽

Open Space ◽

Satellite Image ◽

Remote Sensing Data ◽

Industrial Area ◽

Topographic Map ◽

Land Utilization ◽

Urbanization Level ◽

Landuse Change ◽

Land Urbanization

The population of Jakarta Special Province continues to grow, most significantly after the 1960s, causing an increase of population density. The expanding population has been stimulating the development of the province, reflecting the rising demand of the population including land. Eventually, it influences the urbanization process and affects the dynamic of land utilization in Jakarta. This research is aimed to understand the correlation between urbanization phenomena and landuse change in Jakarta Special Province. The research implements the utilization of statistics, topographic map, and remote sensing data as well. The satellite image (Sentinel-2 Imagery) was used to determine urban area and non urban area which then describes the land urbanization level. During 3 years, Jakarta has experienced landuse change especially in residential area, green open space, and industrial area. In 2017, the urban area covered 76.89 percent of Jakarta’s land and the coverage increased into 78.35 percent in 2019. Land urbanization level raised from 77.16 percent into 78.03 percent which means that roughly 77 percent to 78 percent of land in Jakarta has been urbanized.

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