Spatial Differentiation and Influencing Factors of Water Pollution-Intensive Industries in the Yellow River Basin, China

Du, Haibo; Ji, Xuepeng; Chuai, Xiaowei

doi:10.3390/ijerph19010497

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…Many studies have found that economic development, environmental awareness, and spatial variation characteristics are the main drivers of industrial wastewater, , which directly affects water quality . More factors affect the investment in industrial wastewater pollution treatment .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Predominant Driving Factors Impacting Urban Industrial Wastewater Discharge in the Yellow River Basin

Xia

Dai

et al. 2023

ACS EST Water

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Environmental pollution control has become an important task of ecological protection, which is one of the major strategies for high-quality development of the Yellow River basin (YRB) in China. In this paper, a machine learning model is constructed to explore the driving factors that affect industrial wastewater discharge (IWD) in prefecture-level cities in the YRB. On the basis of statistical data from 2003 to 2018, the relationship between IWD and gross regional product in the YRB obeyed the Environmental Kuznets Curve (EKC) and reached an inflection point in 2010, but not all cities fit the EKC. Therefore, three machine learning algorithms, including weighted k-nearest neighbor (knn), random forest, and support vector machine, are used to construct a regression model of IWD. The knn achieved the best fitting, with determination coefficients (R 2 ) of 0.98 and 0.91 in the training and testing sets, respectively. Variable importance and partial dependence plots explain the machine learning model well. This work provides ideas for the management of IWD in prefecture-level cities in the YRB and references for environmental pollution in other cities.

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Section: Resultsmentioning

confidence: 99%

Machine Learning-Based Predominant Driving Factors Impacting Urban Industrial Wastewater Discharge in the Yellow River Basin

Xia

Dai

et al. 2023

ACS EST Water

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“…𝑑 (11) where 𝛤(𝐴) greater than 0 indicates that the spatial distribution of industry 𝐴 is agglomerated in the region; otherwise, it is considered random or dispersed. A larger 𝛤(𝐴) signifies a more significant agglomeration of industry 𝐴 in the region.…”

Section: 𝛤(𝐴) = ∑ 𝛤(𝐴 𝑑)mentioning

confidence: 99%

“…A suitable industrial agglomeration pattern, which considers both the agglomeration degree and the cluster location, can facilitate firms in benefiting from the industrial agglomeration effect and acquiring clear advantages in terms of cost, productivity, and innovation environment [5][6][7][8][9][10]. It serves as a crucial force for the high-quality and sustainable development of regions and cities [11][12][13][14][15]. A comprehensive analysis of industrial agglomeration patterns helps evaluate the performance of existing policies and formulate new ones, thereby promoting the development of a more suitable industrial agglomeration pattern [10,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Duranton and Overman Index and Local Duranton and Overman Index Framework for Industrial Spatial Agglomeration Pattern Analysis

Huang,

Zhuo,

Cao

2024

IJGI

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Accurately measuring industrial spatial agglomeration patterns is crucial for promoting regional economic development. However, few studies have considered both agglomeration degrees and cluster locations of industries. Moreover, the traditional multi-scale cluster location mining (MCLM) method still has limitations in terms of accuracy, parameter setting, calculation efficiency, etc. This study proposes a new framework for analyzing industrial spatial agglomeration patterns, which uses the Duranton and Overman (DO) index for estimating agglomeration degrees and a newly developed local DO (LDO) index for mining cluster locations. The MCLM-LDO method was proposed by incorporating the LDO index into the MCLM method, and it was validated via comparisons with three baseline methods based on two synthetic datasets. The results proved that the MCLM-LDO method can achieve accuracies of 0.945 and 1 with computational times of 0.15 s and 0.11 s on two datasets, which are superior to existing MCLM methods. The proposed framework was further applied to analyze the spatial agglomeration patterns of the industry of computer, communication, and other electronic equipment manufacturing in Guangdong Province, China. The results showed that the framework gives a more holistic perspective of spatial agglomeration patterns, which can serve as more meaningful references for industrial sustainable development.

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“…Therefore, it is necessary to strengthen water management in the middle and upper reaches of the provinces (such as QH and NMG), change their unsustainable water use, and improve industrial water efficiency. Meanwhile, the distribution of water pollution-intensive industries in downstream provinces should be reduced to improve the overall decoupling level (Du et al, 2022). Compared with the agricultural sector, the decoupling status difference between industrial water use and economic development level among provinces is mainly caused by the imbalance of economic development among provinces (Qiao et al, 2021).…”

Section: Driving Factors Of Water Use In Different Sectorsmentioning

confidence: 99%

The driving factors of water use and its decoupling relationship with economic development: A multi-sectoral perspective

Wang

Jian

Wang

et al. 2022

Preprint

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The water resource situation in China is severe, and conflicts between the supply and demand of water resources are prominent. Competition for water from key sectors, such as agriculture, industry, and domestic use, is widespread. The Yellow River, as one of the longest rivers in the world, is an important economic belt and an ecological barrier in China. This study considered the nine provinces along the Yellow River as the study area and the three major water-use sectors: agriculture, industry, and domestic as the research objects. The drivers of water consumption in each sector in the nine provinces along the Yellow River were analyzed using the Logarithmic Mean Divisia Index method. Based on this, a decoupling model was used to explore the relationship between water use in each sector and the corresponding level of economic development. It was found that water use intensity and economic development level were the largest negative and positive influencing factors on water use in each sector, respectively, and the opposite effects of the two may cause the Jevons paradox in water use. The overall agricultural water-saving level in the basin is high and has a large water-saving potential. The negative driving effect of the industrial structure was more significant in provinces with higher development levels. The positive driving effect of residents' consumption levels on domestic water use in rural areas was more obvious than in urban areas. The degree of decoupling between per capita and domestic water consumption in urban areas was the worst in the decoupling of water use in the agricultural, industrial, and domestic sectors and their corresponding levels of economic development. Therefore, focusing on areas with weak agricultural water conservation, promoting industrial structure upgrading, strengthening water conservation education in rural areas, and guiding the water-saving consumption habits of residents can promote the sustainable development of water resources in the provinces along the Yellow River. The research results provide insights into water conservation management in the Yellow River Basin.

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Spatial Differentiation and Influencing Factors of Water Pollution-Intensive Industries in the Yellow River Basin, China

Cited by 15 publications

References 28 publications

Machine Learning-Based Predominant Driving Factors Impacting Urban Industrial Wastewater Discharge in the Yellow River Basin

Machine Learning-Based Predominant Driving Factors Impacting Urban Industrial Wastewater Discharge in the Yellow River Basin

An Integrated Duranton and Overman Index and Local Duranton and Overman Index Framework for Industrial Spatial Agglomeration Pattern Analysis

The driving factors of water use and its decoupling relationship with economic development: A multi-sectoral perspective

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