Ecosystem Services under Climate Change Impact Water Infrastructure in a Highly Forested Basin

Li, Xupu; Zhang, Liwei; O’Connor, Patrick J.; Jun-ping, Yan; Wang, Bin; Liu, De Li; Wang, Pengtao; Wang, Zhuangzhuang; Wan, Luwen; Li, Yingjie

doi:10.3390/w12102825

Cited by 15 publications

(8 citation statements)

References 69 publications

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“…A GWR model, an extension of the traditional regression analysis method [52,62], is an effective way to explore the impacts of urbanization on CEs because it considers spatial heterogeneity and it almost eliminates spatial autocorrelation [63,64]. Specifically, the equation is as follows:…”

Section: Geographically Weighted Regression Modelmentioning

confidence: 99%

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Zhen-wei

Mao

et al. 2022

IJERPH

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Within the context of the “30·60 dual carbon” goal, China’s low-carbon sustainable development is affected by a series of environmental problems caused by rapid urbanization. Revealing the impacts of urbanization on carbon emissions (CEs) is conducive to low-carbon city construction and green transformation, attracting the attention of scholars worldwide. The research is rich concerning the impacts of urbanization on CEs but lacking in studies on their spatial dependence and heterogeneity at multiple different scales, especially in areas with important ecological statuses, such as the Han River Ecological Economic Belt (HREEB) in China. To address these gaps, this study first constructed an urbanization level (UL) measurement method. Then, using a bivariate spatial autocorrelation analysis and geographically weighted regression model, the spatial relationships between UL and CEs from 2000 to 2020 were investigated from a multiscale perspective. The results were shown as follows. The total CEs in the HREEB witnessed an upsurge in the past two decades, which was mainly dispersed in the central urban areas of the HREEB. The ULs in different regions of the HREEB varied evidently, with high levels in the east and low levels in the central and western regions, while the overall UL in 2020 was higher than that in 2000, regardless of the research scale. During the study period, there was a significant, positive spatial autocorrelation between UL and CEs, and similar spatial distribution characteristics of the bivariate spatial autocorrelation between CEs and UL at different times, and different scales were observed. UL impacted CEs positively, but the impacts varied at different grid scales during the study period. The regression coefficients in 2020 were higher than those in 2000, but the spatial distribution was more scattered, and more detailed information was provided at the 5 km grid scale than at the 10 km grid scale. The findings of this research can advance policy enlightenment for low-carbon city construction and green transformation in HREEB and provide a reference for CE reduction in other similar regions of the world.

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Section: Geographically Weighted Regression Modelmentioning

confidence: 99%

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Zhen-wei

Mao

et al. 2022

IJERPH

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“…Some scholars have investigated the ES of the HRB. For example, Li, et al [ 40 ] took the upper Hanjiang River as their study area and examined the changes of water-related ES, such as soil conservation and flood control services, as a result of climate change. Qi et al [ 41 ] explored the role of forest restoration in ES in the HRB and found a positive impact.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Relationship between Land Use Intensity and Ecosystem Services’ Value in the Hanjiang River Basin: A Case Study of the Hubei Section

Yang

Zheng

Wang

et al. 2022

IJERPH

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An increased land use intensity due to rapid urbanization and socio-economic development would alter the structure and function of regional ecosystems and cause prominent environmental problems. Revealing the impact of land use intensity on ecosystem services (ES) would provide guidance for more informed decision making to promote the sustainable development of human and natural systems. In this study, we selected the Hanjiang River Basin (HRB) in Hubei Province (China) as our study area, explored the correlation between land use intensity and ecosystem Services’ Value (ESV), and investigated impacts of natural and socio-economic factors on ESV variations based on the Geographical Detector Model (GDM) and Geographically Weighted Regression (GWR). The results show that (1) from 2000 to 2020, land use intensity in HRB generally showed an upward trend, with a high spatial agglomeration in the southeast and low in the northwest; (2) the total ESV increased from 295.56 billion CNY in 2000 to 296.93 billion CNY in 2010, and then decreased to 295.63 CNY in 2020, exhibiting an inverted U-shaped trend, with regulation services contributing the most to ESV; (3) land use intensity and ESV had a strong negative spatial correlation, with LH (low land use intensity vs. high ESV) aggregations mainly distributed in the northwest, whereas HL (high land use intensity vs. low ESV) aggregations were located in the southeast; (4) natural factors, including annual mean temperature, the percentage of forest land, and slope were positively associated with ESV, while socio-economic factors, including GDP and population density, were negatively associated with ESV. To achieve the coordinated development of the socio-economy and the environment, ES should be incorporated into spatial planning and socio-economic development policies.

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“…Meanwhile, quantitative research on urban ES has been growing, and UGS have become a critical research subject of these studies [10][11][12]. Therefore, the ES provided by UGS have been generating increasing attention from researchers and relevant management agencies recently in China [13], and residents' preference for ES is pyramidally regarded as a key reference factor for future urban planning [14][15][16]. However, the classification of ES provided by UGS is still a challenge to be resolved [17].…”

Section: Introductionmentioning

confidence: 99%

Residents’ Preference for Urban Green Space Types and Their Ecological-Social Services in China

Zhao

Zhang

et al. 2022

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With accelerated urbanization and rapid expansion of the urban population, there is an increasing demand among urban residents for urban green spaces (UGS) and the ecosystem services (ES) they provide. The existing research mostly focuses on the spatial pattern of UGS types and ES provided by different UGS but ignores that residents’ preferences for UGS types and their ecological and social services should also be incorporated into the spatial planning decision-making of UGS. A web-based questionnaire was distributed randomly in urban areas of China and 1050 valid samples were collected in this study. Descriptive statistical analysis methods, structural equation modeling, and Pearson correlation were used to parse the residents’ preference for both UGS types, ecological–social services, and the relevant impact factors. The results showed that: (1) the strongest preference of residents for UGS and their ecological and social services are the attached green space, “beautifying the city”, and “physical and mental relaxation”, respectively; (2) the leading factors for residents’ access to UGS are “age” and “sufficient time”, except for attached green space. The most significant effect on both ecological and social services is the “season” factor. Further, “social gathering” and “exercise” are the services most and least likely to be affected. (3) Future planning of UGS should reinforce construction of attached green space and improve the aesthetics-related ES they provide. Construction of park green space and plaza green space should be enhanced to deal with the aging trend in society. Further, maintaining the existing construction of attached green space and building regional green space are crucial to the sustainability of UGS and its ecological–social services.

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Ecosystem Services under Climate Change Impact Water Infrastructure in a Highly Forested Basin

Cited by 15 publications

References 69 publications

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Quantifying the Relationship between Land Use Intensity and Ecosystem Services’ Value in the Hanjiang River Basin: A Case Study of the Hubei Section

Residents’ Preference for Urban Green Space Types and Their Ecological-Social Services in China

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