A Study of the Spatiotemporal Evolution Patterns and Coupling Coordination between Ecosystem Service Values and Habitat Quality in Diverse Scenarios: The Case of Chengdu Metropolitan Area, China

Huang, Gaoliu; Feng, Shiming; Hu, Chunguang

doi:10.3390/su16093741

Cited by 3 publications

(1 citation statement)

References 87 publications

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“…Since the reform and opening up, Chinese cities have experienced rapid development. During the continuous expansion of urban areas, many natural ecological resources have been converted into urban construction land, severely damaging the diversity of the ecological environment, as confirmed by numerous studies [11,12]. Additionally, many studies have shown that impervious surfaces in cities easily cause local increases in the Land Surface Temperature (LST), while ecological lands such as forests, grasslands, and water bodies can mitigate temperature rises [13].…”

Section: Introductionmentioning

confidence: 98%

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Xu,

Huang,

Zhang

2024

Atmosphere

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With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors using mathematical models. Taking the Wuhan Urban Agglomeration (WHUA) as a case study, it explores the seasonal characteristics of the LST and employs Principal Component Analysis (PCA) to categorize the driving factors. Additionally, it compares traditional models with machine-learning models to select the optimal model for this investigation. The main conclusions are as follows. (1) The WHUA’s LST exhibits significant differences among seasons and demonstrates distinct spatial-clustering characteristics in different seasons. (2) Compared to traditional geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power in investigating the driving effects of the LST. (3) Human Activity (HA) dominates the influence throughout the year and shows a significant positive correlation with the LST; Physical Geography (PG) exhibits a negative correlation with the LST; Climate and Weather (CW) show a similar variation to the PG, peaking in the transition; and the Landscape Pattern (LP) shows a weak positive correlation with the LST, peaking in winter while being relatively inconspicuous in summer and the transition. Finally, through comparative analysis of multiple driving factors and models, this study constructs a framework for exploring the seasonal features and driving factors of the LST, aiming to provide references and guidance for the development of the WHUA and similar regions.

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Section: Introductionmentioning

confidence: 98%

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Xu,

Huang,

Zhang

2024

Atmosphere

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Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value Based on the Markov–FLUS Model in Ezhou City, China

Zhang,

Chen,

Zhang

et al. 2024

Sustainability

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Changes in land use patterns, types, and intensities significantly impact ecosystem services. This study follows the time series logic from history to the expected future to investigate the spatial and temporal characteristics of land use changes in Ezhou and their potential impacts on the ecosystem services value (ESV). The results show that the Markov–FLUS model has strong applicability in predicting the spatial pattern of land use, with a Kappa coefficient of 0.9433 and a FoM value of 0.1080. Between 2000 and 2020, construction land expanded continuously, while water area remained relatively stable, and other land types experienced varying degrees of contraction. Notably, the area of construction land expanded significantly compared to 2000, and it expanded by 70.99% in 2020. Moreover, the watershed area expanded by 9.30% from 2000 to 2010, but there was very little change in the following 10 years. Under the three scenarios, significant differences in land use changes were observed in Ezhou City, driven by human activities, particularly the strong expansion of construction land. In the inertial development scenario, construction land expanded to 313.39 km2 by 2030, representing a 38.30% increase from 2020. Conversely, under the farmland protection scenario, construction land increased to 237.66 km2, a 4.89% rise from 2020. However, in the ecological priority development scenario, the construction land area expanded to 253.59 km2, a 10.13% increase from 2020. Compared to 2020, the ESV losses in the inertia development and farmland protection scenarios were USD 4497.71 and USD 1072.23, respectively, by 2030. Conversely, the ESV under the ecological protection scenario increased by USD 2749.09, emphasizing the importance of prioritizing ecological protection in Ezhou City’s development. This study may provide new clues for the formulation of regional strategies for sustainable land use and ecosystem restoration.

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Assessing the Relative and Combined Effect of Climate and Land Use on Water-Related Ecosystem Services in the Yangtze River Economic Belt, China

Wu,

Yao,

Tang

et al. 2024

Water

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The ecosystem service (ES) is essential for residents’ health and well-being. The ecosystem service value (ESV) is one of the measures to scientifically quantify the wealth of ESs. However, climate and human activities intensely affect the sustainability of ESs. Therefore, knowing the relative and combined effects of climate and human activities on ESs and ESV can be crucial. This study selects the Yangtze River Economic Belt (YREB) as the study area to detect how climate and human activities affected the ES and ESV changes during 2001–2020, including net primary productivity, water yield, soil retention, water purification, and integrated ESV. The results show that the southern YREB has relatively higher ESs than the northern YREB, except for the NDR-P, which is mainly located in the urban agglomeration area. The general ranking for the ESV of different provinces in the YREB is sequenced from higher to lower as Sichuan, Yunnan, Hunan, Jiangxi, Guizhou, Hubei, Zhejiang, Anhui, Jiangsu, Chongqing, and Shanghai. Specifically, the ESV of Sichuan is the highest at about 972 billion yuan (133.57 billion USD), while the lowest ESV has been discovered in Shanghai at approximately 0.25 billion yuan (0.03 billion USD). It can be noticed that the regions where climate is the major influencing factor for ESs are concentrated upstream of the YREB, and human activities mainly influence ESs in highly urbanized areas. Furthermore, climate and human activities account for the highest proportion (86%) of synergistic effects for ESV in Yunnan. In contrast, Jiangsu, Zhejiang, and Shanghai accounted for the lowest proportions, at 18%, 26%, and 31%, respectively. This study may provide crucial insights into how ESs and ESV in the YREB have changed during the study period to inform policymakers, drawing more attention to the inhibitory and synergistic effects arising from the interaction between climate and human activities, to make more reliable decisions on adapting to climate crises in the future.

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A Study of the Spatiotemporal Evolution Patterns and Coupling Coordination between Ecosystem Service Values and Habitat Quality in Diverse Scenarios: The Case of Chengdu Metropolitan Area, China

Cited by 3 publications

References 87 publications

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Multi-Scenario Simulation of Land Use Change and Ecosystem Service Value Based on the Markov–FLUS Model in Ezhou City, China

Assessing the Relative and Combined Effect of Climate and Land Use on Water-Related Ecosystem Services in the Yangtze River Economic Belt, China

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