Driving mechanisms of urbanization: Evidence from geographical, climatic, social-economic and nighttime light data

Huang, Siyi; Yu, Lijun; Cai, Danlu; Zhu, Jianfeng; Liu, Ze; Zhang, Zongke; Nie, Yueping; Fraedrich, Klaus

doi:10.1016/j.ecolind.2023.110046

Cited by 19 publications

(8 citation statements)

References 86 publications

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“…Geodetector is a new statistical method to detect spatial heterogeneity and explain the driving factors behind it. It was initially applied to the study of endemic disease risk and related geographical influencing factors, and then widely used in social and economic fields such as urbanization and economic growth [43][44][45]. In this paper, the Geodetector model is used to detect the determination power of various factors on the evolution of tourism environmental system resilience.…”

Section: The Geodetector Methodsmentioning

confidence: 99%

Research on Spatio-Temporal Evolution Characteristics and Driving Factors of Tourism Destination Environmental System Resilience in Yangtze River Economic Belt

Yang,

Xie,

Cheng

2024

Pol. J. Environ. Stud.

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Resilience serves as a crucial metric for gauging the sustainable development capacity of tourist destinations and has emerged as a novel focal point within the realm of "tourism man-land relationship." This study endeavors to formulate an evaluative index framework for the resilience of tourism destination environmental systems grounded in the "social-economic-ecological-facilities" dimension. Subsequently, employing the entropy weight model, spatial autocorrelation model, and Geodetector model, the research seeks to conduct a quantitative analysis of the spatio-temporal evolution characteristics and driving factors influencing the resilience of the tourism environmental system within the Yangtze River Economic Belt. The overarching objective is to offer a case-based reference that contributes to the interdisciplinary exploration of resilience theory and tourism environmental systems, enabling a scientific assessment of the sustainable development capacity of tourism destinations, and facilitating the optimization of the development habitat of the regional tourism industry. The results show that: (1) The resilience of the tourism environmental system demonstrates an overall ascending trajectory, albeit with a discernible fragility. The growth rate of resilience within each subsystem follows the sequence of "economic environment>social environment>facility environment >ecological environment." Moreover, the emergence of two resilience convergence clusters, namely "society-economy" and "facility-ecology," is observed. (2) The distribution of resilience levels within the tourism environmental systems of provinces and municipalities in the Yangtze River Economic Belt exhibits a noteworthy spatio-temporal transition. The resilience indices of the upstream, midstream, and downstream areas exhibit an upward trend, delineating a pattern of "upstream

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Section: The Geodetector Methodsmentioning

confidence: 99%

Research on Spatio-Temporal Evolution Characteristics and Driving Factors of Tourism Destination Environmental System Resilience in Yangtze River Economic Belt

Yang,

Xie,

Cheng

2024

Pol. J. Environ. Stud.

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“…The symbols σ 2 and σ 2 h denote the variance in Y in the whole area and in each stratum (h), respectively. The value of q x ∈ [0, 1] indicates how much Y is affected by X, which means the larger the value of q x , the stronger the spatial association between X and Y [48,49].…”

Section: Geo-detector Modelmentioning

confidence: 99%

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Pan,

Yang,

Han

et al. 2024

Sustainability

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Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R2BTH = 0.58, R2SP = 0.66, and R2CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

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“…Applying cluster analysis to the classification of standards can overcome the subjective uncertainty of manually setting thresholds. Compared with the natural breaks method [48][49][50][51][52] and the numerical equalization method [53], it is also more scientific and reasonable. Both the k-means cluster and hierarchical cluster can be applied to numerical variables, and the latter has been proven to have a potential advantage [54] and is therefore used in this study.…”

Section: Indicator Selection and Standard Classificationmentioning

confidence: 99%

Spatial Distribution of Water Risk Based on Atlas Compilation in the Shaanxi Section of the Qinling Mountains, China

Wang

Yu³

et al. 2023

Sustainability

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Global climate change and rapid socio-economic development have increased the uncertainty in water resource systems and the complexity of water risk issues. Analyzing water risk and its spatial distribution is integral to the attainment of Sustainable Development Goal (SDG) 6, as this contributes to effective water resource partition management. In this paper, a compiling method of risk atlas with multiple layers is proposed, and the water risk system is divided into five sub-systems including the risk of resource, management, engineering, quality, and disaster. The information used for the risk atlas is calculated by a risk evaluation model based on a Pressure–State–Response (PSR) framework, hierarchical cluster, and set pair analysis (SPA). Risks in the Qinling Mountains of Shaanxi (as a case study) are evaluated and visualized. The results show that grades IV and V of engineering, disaster, and resource risk exceed 40%, indicating that they require prior control. The quality and management risks are not major, but there is still room for improvement. Overall, the risk atlas can effectively and objectively reflect the spatial distribution of water risk and provide a basis for the layout of water risk control measures.

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Driving mechanisms of urbanization: Evidence from geographical, climatic, social-economic and nighttime light data

Cited by 19 publications

References 86 publications

Research on Spatio-Temporal Evolution Characteristics and Driving Factors of Tourism Destination Environmental System Resilience in Yangtze River Economic Belt

Research on Spatio-Temporal Evolution Characteristics and Driving Factors of Tourism Destination Environmental System Resilience in Yangtze River Economic Belt

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Spatial Distribution of Water Risk Based on Atlas Compilation in the Shaanxi Section of the Qinling Mountains, China

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