Assessment of the Sustainable Utilization Level of Water Resources in the Wuhan Metropolitan Area Based on a Three-Dimensional Water Ecological Footprint Model

Liang, Dongzhe; Lu, Hongwei; Feng, Liyang; Qiu, Lihua; He, Li

doi:10.3390/w13243505

Cited by 10 publications

(8 citation statements)

References 57 publications

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“…Many studies on the water ecological footprint have mainly focused on the evaluation and analysis of regional water resource utilization, without distinguishing between the ow and stock attributes of water resources as natural capital (Liang et al, 2021) as well as analyzing the spatial distribution characteristics of natural capital ow and stock consumption.…”

Section: Research Gapmentioning

confidence: 99%

Spatiotemporal evolution of water ecological footprint based on the emergy-spatial autocorrelation method

Wang

Shi

et al. 2023

Environ Sci Pollut Res

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To quantify and analyze the human demand for water resources and the available supply of water resource systems, this study combined emergy analysis and spatial autocorrelation analysis to establish a quanti cation and analysis system for water ecological footprint (WEF). First, the emergy theory of ecological economics and WEF were combined to propose an emergy quanti cation method for WEF and water ecological carrying capacity (WEC). Based on the spatial autocorrelation method, three-dimensional ecological footprint indicators (footprint size and depth) were introduced to analyze the spatial correlation and spatial aggregation of capital ow occupation and capital stock consumption in the water resource system. Using the Yellow River Basin (YRB) as the study area to verify the applicability of the WEF quanti cation and analysis system based on the emergy-spatial autocorrelation method, the following results were obtained. (1) From 2003 to 2018, the per capita WEF of the YRB generally showed a slow growth trend. The WEC was much lower than the WEF, and the water ecological de cit status remained unchanged. (2) Compared to the upper and lower reaches of the YRB, the middle reaches had a higher WEF, and the WEC of the YRB was generally high in the west and low in the east. (3) Except for Sichuan and Qinghai, the provinces (regions) in the YRB relied on water resource capital stock for social and economic development, and the sustainable development of the region was threatened. (4) Utilization of the water resources capital in the basin was generally unsustainable.

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Section: Research Gapmentioning

confidence: 99%

Spatiotemporal evolution of water ecological footprint based on the emergy-spatial autocorrelation method

Wang

Shi

et al. 2023

Environ Sci Pollut Res

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“…In terms of economic effects, some studies have found that economic effects increase water consumption and wastewater emissions [21,26,46], while others have found a nonlinear U-shaped link between economic scale and water resource sustainability [24]. The amount of water resources utilized and sewage discharged can be reduced through strict water resources management and legislation in undeveloped regions [47,48].…”

Section: Influencing Factor Of the Sdg 6 Composite Index In Less Deve...mentioning

confidence: 99%

“…It is difficult to reverse industrial contamination in regions such as air, water, and soil. The city can guide the transformation of high energy-and water-consuming sectors to green ecological and high-value-added industries, and encourage the growth of clean production and low-water-consuming industries [46,49]. Simultaneously, it can support the growth of the tertiary industry, focus on the development of the ecotourism industry, optimize the industrial structure, and boost economic development.…”

Section: Influencing Factor Of the Sdg 6 Composite Index In Less Deve...mentioning

confidence: 99%

Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data

et al. 2023

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Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding.

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“…The water environment resources are an essential source of natural capital flow. Reducing industrial wastewater discharge in cities is conducive to protecting water environment resources, and the utilization efficiency of water resources will be improved [44]. Land resources are also essential donors of natural capital flow.…”

Section: The Main Factors Affecting Ecological Sustainabilitymentioning

confidence: 99%

Assessment of Urban Agglomeration Ecological Sustainability and Identification of Influencing Factors: Based on the 3DEF Model and the Random Forest

Yang

Zhong

et al. 2022

IJERPH

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The evaluation of ecological sustainability is significant for high-quality urban development and scientific management and regulation. Taking the Chengdu urban agglomeration (CUA) as the research object, this paper combined the three-dimensional ecological footprint model (3DEF) and random forest to evaluate the ecological sustainability of the study area and identify the influencing factors. The study results indicate that: (1) From 2000 to 2019, the ecological sustainability of Chengdu urban agglomeration was divided into four types, and the overall ecological sustainability of this region showed a downward trend. The areas with higher ecological sustainability were mainly distributed in the northern part of the urban agglomeration (Mianyang City) and the southern part (Leshan City and Ya’an City), while the cities in the central region (Chengdu City, Meishan City, and Ziyang City) had lower ecological sustainability. (2) The main factors affecting the ecological sustainability of urban agglomerations are industrial wastewater discharge, industrial smoke (powder) dust discharge, and green coverage of built-up areas, followed by urbanization and population size. Through this study, we have two meaningful findings: (a) Our research method in this paper provides a new way to study the factors affecting the ecological sustainability of urban agglomerations. (b) The results of the identification of influencing factors might be the reference for urban environmental infrastructure construction and urban planning.

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Assessment of the Sustainable Utilization Level of Water Resources in the Wuhan Metropolitan Area Based on a Three-Dimensional Water Ecological Footprint Model

Cited by 10 publications

References 57 publications

Spatiotemporal evolution of water ecological footprint based on the emergy-spatial autocorrelation method

Spatiotemporal evolution of water ecological footprint based on the emergy-spatial autocorrelation method

Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data

Assessment of Urban Agglomeration Ecological Sustainability and Identification of Influencing Factors: Based on the 3DEF Model and the Random Forest

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