Evaluation of intensive urban land use based on an artificial neural network model: A case study of Nanjing City, China

Qiao, Weifeng; Gao, Junfeng; Liu, Yansui; Qin, Yueheng; Cheng, Lian; Ji, Qingqing

doi:10.1007/s11769-017-0905-7

Cited by 20 publications

(9 citation statements)

References 31 publications

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“…The second is the evaluation of urban land use intensity based on different scale units and land types. The scale of evaluation includes national, provincial, municipal, and even specific land types, such as industrial land, residential land, and commercial land [12][13][14]. Third, quantitative analysis of influencing factors of the intensive use of urban land using various model methods.…”

Section: Introductionmentioning

confidence: 99%

How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China

et al. 2018

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Urban land intensive use is an important part of regional planning and plays an important role in the sustainable development of cities. However, the research on how to measure urban land use intensity is still controversial. This paper gives a perspective of multi-objective decision to explore the criterion of urban land use intensity in the process of urbanization. The theory of factor substitution was applied to analyze the mechanism of urban land intensive use in different urbanization stage. Additionally, the matching degree model (MDM), curve estimation model, and multi-objective programming model were employed in the case study of Wuhan urban agglomeration to give a multi-objective decision on urban land intensive use. The results show that the level of urban land intensive use lagging behind the level of urbanization development in more than 60% of the counties. When the level of urbanization is less than 30%, 30–70% and greater than 70%, the optimal intensity of urban land use characterized by fixed investments of unit land is 2.6252, 4.6132, and 6.7648 million RMB respectively. Based on optimal intensity, the counties in Wuhan urban agglomeration were divided into key development zone and optimized development zone. Finally, different management strategies were designed for different zones for urban land intensive use.

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Section: Introductionmentioning

confidence: 99%

How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China

et al. 2018

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“…e backpropagation (BP) neural network is currently the most widely used ANN [48,49]. It has been used increasingly in geographical and ecological sciences because of its ability to model both linear and nonlinear systems without the need to make any assumptions.…”

Section: Methodsmentioning

confidence: 99%

Risk Assessment of Maize Drought in China Based on Physical Vulnerability

Chen

Jia

Pan

2019

Journal of Food Quality

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Applying disaster system theory and with reference to the mechanisms that underlie agricultural drought risk, in this study, crop yield loss levels were determined on the basis of hazards and environmental and hazard-affected entities (crops). Thus, by applying agricultural drought risk assessment methodologies, the spatiotemporal distribution of maize drought risk was assessed at the national scale. The results of this analysis revealed that the overall maize drought risk decreases gradually along a northwest-to-southeast transect within maize planting areas, a function of the climatic change from arid to humid, and that the highest yield loss levels are located at values between 0.35 and 0.45. This translates to drought risks of once in every 10 and 20 years within 47.17% and 43.31% of the total maize-producing areas of China, respectively. Irrespective of the risk level, however, the highest maize yield loss rates are seen in northwestern China. The outcomes of this study provide the scientific basis for the future prevention and mitigation of agricultural droughts as well as the rationalization of related insurance.

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“…Based on the existing research results (Qiao et al, 2017;Shao et al, 2020) and combined with the actual situation in the provinces (municipalities and autonomous regions) and the availability of data, the intensive land use evaluation system (Table 1) was constructed. It contains 13 indicators in three criteria levels, including land use degree, land input level, and land output benefit.…”

Section: Construction Of the Evaluation Index Systemmentioning

confidence: 99%

Does intensive land use decoupled from carbon emissions? an empirical study from the three grand economic zones of China

Zhang

Liang

et al. 2022

Front. Environ. Sci.

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In the context of the increasingly severe global carbon emission crisis, smoothly reducing emissions without affecting economic development and the continuous improvement of people’s living standards has become an important problem facing China, the country with the largest carbon emissions in the world. From the perspective of intensive land use (ILU), this paper uses standard deviational ellipse, decoupling theory, and a logarithmic mean Divisia index (LMDI) model to analyze the level of ILU and carbon emissions in 30 provinces, municipalities, and autonomous regions in China and explores ways to reduce emissions. The results show that: 1) The level of ILU in China’s 30 provinces (municipalities and autonomous regions) has shown steady progress and the overall carbon emissions from land use have shown a slow growth trend, but the carbon emissions of some provinces have shown a downward trend; 2) The ILU and the carbon emissions standard deviational ellipse have good spatial consistency, both of which have a north by east to south by west distribution pattern. The center of the carbon emission standard deviational ellipse moves to the northwest as a whole; 3) 63.33% of provinces are in the ideal decoupling stage of ILU and carbon emissions; 4) According to their contribution value, the influencing factors of China’s carbon emissions are, in descending order, energy intensity, economic scale, population scale, land use structure, energy carbon emission intensity, and land scale. Important directions for future efforts include actively adjusting the industrial structure and economic development mode, increasing the proportion of clean energy and energy utilization rate, controlling the speed of construction and land expansion, and promoting low-carbon emissions.

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Evaluation of intensive urban land use based on an artificial neural network model: A case study of Nanjing City, China

Cited by 20 publications

References 31 publications

How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China

How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China

Risk Assessment of Maize Drought in China Based on Physical Vulnerability

Does intensive land use decoupled from carbon emissions? an empirical study from the three grand economic zones of China

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