Spatiotemporal evolution characteristics of urbanization and its coupling coordination degree in Russia — perspectives from the population, economy, society, and eco-environment

Chu, Nanchen; Zhang, Pingyu; Xiang-li, WU

doi:10.1007/s11356-022-20215-z

Cited by 40 publications

(19 citation statements)

References 6 publications

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“…The weighting of the indicators is determined by the Entropy‐weighting method, which can reduce subjective arbitrariness and errors, and is more scientific and accurate. There are large differences in magnitude and scale between the raw data of different indicators, and there is a lack of comparability between the data, so the raw data need to be standardized for extreme differences (Chu et al, 2022):

u_{italicij} = (x_{italicij} - \min (x_{j})) / (\max (x_{j}) - \min (x_{j})), (u_{italicij} is an positive indicator),

u_{italicij} = (\max (x_{j}) - x_{italicij}) / (\max (x_{j}) - \min (x_{j})), (u_{italicij} is an inverse indicator),

where

x_{italicij}

is the raw data of the j indicator in year i ,

u_{italicij}

is the standardized data of the j indicator in year i , max( x j ), min( x j ) are the maximum and minimum values of the raw data of the j indicator respectively. A larger positive indicator is beneficial to the development of the system, whereas the inverse indicator is the opposite.…”

Section: Methodsmentioning

confidence: 99%

“…The weighting of the indicators is determined by the Entropy-weighting method, which can reduce subjective arbitrariness and errors, and is more scientific and accurate. There are large differences in magnitude and scale between the raw data of different indicators, and there is a lack of comparability between the data, so the raw data need to be standardized for extreme differences (Chu et al, 2022):…”

Section: Coupling Coordination Modelmentioning

confidence: 99%

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Characterization of the evolution of runoff‐sediment relationship in Min River based on coupling coordination theory

Wang

et al. 2023

River Research & Apps

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Global climate change and human activities have profoundly affected the hydrological processes in the basin. The study of the characteristics of runoff and sediment variations in the basin and their driving factors is of great significance in promoting the efficient use of water resources and high-quality development in the basin. The determination of the abrupt change point of the runoff-sediment relationship is the key to delineate the base period, and is important to assess the contribution of climate change and human activities to the change of the runoff-sediment relationship. Therefore, this study constructs a binary coupled coordination model of runoff and sediment load to describe the coupled runoff-sediment coordination relationship, and uses the Pettitt method and the double cumulative curve method to test for mutations. The effects of climate change and human activities on changes in runoff and sediment load were assessed using the Budyko framework-based elasticity factor method and the cumulative slope rate of change method. The results show that the

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u_{italicij} = (x_{italicij} - \min (x_{j})) / (\max (x_{j}) - \min (x_{j})), (u_{italicij} is an positive indicator),

u_{italicij} = (\max (x_{j}) - x_{italicij}) / (\max (x_{j}) - \min (x_{j})), (u_{italicij} is an inverse indicator),

where

x_{italicij}

is the raw data of the j indicator in year i ,

u_{italicij}

Section: Methodsmentioning

confidence: 99%

Section: Coupling Coordination Modelmentioning

confidence: 99%

Characterization of the evolution of runoff‐sediment relationship in Min River based on coupling coordination theory

Wang

et al. 2023

River Research & Apps

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“…The advantage of the coupling coordination degree model is that it not only reflects the coupling coordination status among subsystems, but it can also quantify the development level of each subsystem [40]. Determining the indicator weights among systems is important for coupled coordination models, and the main methods are principal component regression [41], geographic and time-weighted regression (GTWR) [42], and the entropy weighting method [43,44]. Principal component regression is mainly applicable to models with a large number of indicators; GTWR considers the spatial geographical location of a study area; and the entropy weighting method is based on the weights obtained from the calculation of the indicator values.…”

Section: Coupled Coordination Modelmentioning

confidence: 99%

“…Principal component regression is mainly applicable to models with a large number of indicators; GTWR considers the spatial geographical location of a study area; and the entropy weighting method is based on the weights obtained from the calculation of the indicator values. The coupling coordination degree model has been widely used in the fields of ecological environment and urbanisation [43,45,46], upstream and downstream enterprises of industrial chains [47,48], and land use and transportation development [49,50]. At the same time, scholars have mainly used coupled coordination models to study the interrelationship between traffic accessibility and economic development [51,52].…”

Section: Coupled Coordination Modelmentioning

confidence: 99%

Analysis of Coupling Coordination Relationship between the Accessibility and Economic Linkage of a High-Speed Railway Network Case Study in Hunan, China

Zou

Xiong

2022

Sustainability

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The construction of a high-speed railway is important to the transportation network and economic development of a region. To further understand the interaction between accessibility and urban economic linkage in the context of high-speed railway networks, this study investigated the spatial and temporal changes in the coupling coordination between accessibility and economic linkage after a high-speed railway was opened in Hunan Province, China, using a coupling coordination model. The aim of this research is to explore the role that high-speed rail construction plays in regional development. Results indicate that (1) after the high-speed railway was opened, the accessibility of cities in Hunan Province has improved significantly, although the overall pattern has not changed by much. This is because it still shows a radiation pattern, with the Changsha–Zhuzhou–Xiangtan urban agglomeration at its core, which has subsequently spread to surrounding areas; (2) the improvement in urban accessibility has strengthened the economic linkages between cities, and the overall spatial pattern has gradually evolved from a point-axis pattern to a network pattern. The bipolar trend of total regional economic linkage has become more pronounced than what it was before; (3) the overall coordination degree of accessibility and economic linkage coupling in Hunan Province is primary coupling, and the phenomenon of regional polarization is prominent, thus showing the overall spatial pattern of ‘strong in the east and weak in the west.’ Further strengthening the construction of the high-speed railway in the northern part of Hunan, promoting the economic construction in the western and southern parts of Hunan, and building a spatial pattern of synergistic integration for Hunan’s continued transportation and economic development are suggested.

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“…One of the most important factors in determining the urbanization level using the comprehensive indicator method is the selection of indicators. In terms of the construction of the indicator system, the study of building the urbanization indicator system from the four aspects of space, population, economy, and society has a significant impact [ 25 ]. Urbanized systems and natural ecosystems interact in a complex way.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of the Coupling Relationship between Habitat Quality and Urbanization in the Lower Yellow River

Sun

Han

Kong

et al. 2023

IJERPH

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Natural habitats are damaged by human interference to varying degrees during the urbanization process, which can impede a region’s high-quality development. In this study, we examined the spatial–temporal evolution characteristics of habitat quality and urbanization in the Lower Yellow River from 2000 to 2020 using the integrated valuation of ecosystem services and tradeoffs (InVEST) model and the comprehensive indicator method. We also evaluated the coupling relationship between the habitat quality and urbanization using the coupling coordination degree model. The findings indicate the following aspects: (1) Between 2000 and 2020, the Lower Yellow River’s habitat quality was typically mediocre, with a steady declining trend. The majority of cities displayed a trend toward declining habitat quality. (2) Both the urbanization subsystem and the urbanization level in 34 cities have demonstrated a consistent growth tendency. The urbanization level is most affected by economic urbanization among the subsystems. (3) The coupling coordination degree have revealed an ongoing trend of growth. In most cities, the relationship between habitat quality and urbanization has been evolving toward coordination. The results of this study have some reference value for ameliorating the habitat quality of the Lower Yellow River and solving the coupling coordination relationship between habitat quality and urbanization.

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Spatiotemporal evolution characteristics of urbanization and its coupling coordination degree in Russia — perspectives from the population, economy, society, and eco-environment

Cited by 40 publications

References 6 publications

Characterization of the evolution of runoff‐sediment relationship in Min River based on coupling coordination theory

Characterization of the evolution of runoff‐sediment relationship in Min River based on coupling coordination theory

Analysis of Coupling Coordination Relationship between the Accessibility and Economic Linkage of a High-Speed Railway Network Case Study in Hunan, China

Spatiotemporal Analysis of the Coupling Relationship between Habitat Quality and Urbanization in the Lower Yellow River

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