The environmental and social-economic sustainability of water use relies heavily on the appropriate treatment of wastewater. Despite the scale of urban wastewater treatment reaching 200 million cubic meters (m3
) per day by 2021 [1
], the issue of wastewater reduction remains a big challenge to China. In 2017, China directly discharged 69.97 billion m3
of wastewater in its intermediate production and final consumption [2
]. Rapidly growing quantities of sewage sludge emerged as another challenge related to wastewater [3
]. The increase in wastewater and sewage sludge discharge contaminates freshwater, increases the need for greywater to dilute polluted water, exacerbates water scarcity and ecosystem degradation [4
] and, even more seriously, affects human health. In recent years, wastewater treatment has attracted much attention for sustainable water development. The Chinese government has drawn up focused proposals to improve the quality and efficiency of wastewater treatment, such as the ’Three-year Action Plan’ for specific secondary industries, tertiary industries, and water-related sectors [5
] as well as the ’Guiding Opinions on Promoting the Utilization of Wastewater Resources’ for sewage recycling [6
It is necessary to analyze the mechanisms of wastewater propagation among sectors in China in order to explore the theoretical foundations for implementing the above policies and promoting sustainable wastewater management. Additionally, it will facilitate the understanding of the wastewater propagation mechanisms by identifying key wastewater propagation chains at a national scale. Since each economic sector generates direct wastewater discharge in the production process due to the final demand of the sector, the sector has indirect wastewater discharge from the upstream production process to the downstream production process up to consumers [7
]. Thus, the wastewater propagation chain is comprised of linked relationships that causes fluctuation in wastewater discharges from directly related sectors when there is a change in the production processes or demand in a specific sector. The changes in wastewater discharges from these related sectors will further lead to changes in wastewater discharges from other sectors; it extends the production chains in input-output analysis, which characterizes the economic distances among sectors [8
]. Moreover, the linkages in wastewater propagation chains measure the wastewater propagation distance from one sector to another through production connections.
The economic sectors, as the primary sources of wastewater discharge, bear the responsibility to reduce it. With the adjustment of the industrial structure, the connections between sectors are evolving [12
]. Scientific measurement of wastewater discharge in each sector and rational analysis of sectoral wastewater discharge relationship can provide scientific theoretical basis and practical policy support for reducing wastewater discharge in China. In addition, such measurements have the potential to enable simulation of wastewater reduction scenarios at the national level. Furthermore, it is necessary to identify certain sectors in the chains, which are densely linked with other sectors in terms of wastewater discharge. Correctly identifying sectors around dense linkages would help retain more linkages and thus enhance the effectiveness of implementing wastewater reduction measures in recognized chains. Therefore, an in-depth study of wastewater propagation chains that retain more useful wastewater discharge linkages is necessary.
Researchers commonly apply input-output analysis to identify key sectors and paths in resource and environment studies. Wang et al. [13
] developed a new hypothesis containing an extraction method to explore key sectors and paths in the Chinese energy sectors and then applied it to the same problem of SO2
emissions across economic sectors in Shandong Province [14
]. Zhao et al. [15
] used a modified hypothesis extraction method to analyze CO2
linkages in South Africa and China [16
]. Guo et al. [17
] applied an elasticity approach to define China’s key energy and CO2
emissions sectors. However, these key sector and path identification methods heavily rely on quantifying the impact of a specific sector on other sectors or the impact of other sectors on a particular sector. We refer to the effect of a sector affecting other sectors as external impacts and the impact of other sectors on a given sector as internal impact. They do not reveal the integrated effects of external and internal impacts in identifying key sectors and paths.
In economics, the average propagation lengths (APL), a method firstly proposed by Dietzenbacher et al. [10
], is a popular method to quantify linkages among sectors [8
]. An APL indicates how close two sectors are to each other. It is usually an integer. An APL value of 1 indicates that 2 sectors are closely linked. If there is a change in production or consumption in one sector, the other closely related sectors will be the first to see the most affects. In other words, APL is the average number of steps in which one sector directly or indirectly affects the production of another sector. For example, production in the agriculture sector may directly affect the output in the service sector. It could also affect the production of poultry and livestock sector first and then affect the service sector’s output through poultry and livestock production. Then APL is the average lengths of all these possible affecting paths. It was later extended to environmental issues, especially the analysis of waste propagation chains, since waste, such as wastewater and sludge, is also generated in the production process, with one sector affecting the output of another sector. Tu [7
] used the traditional APL method to analyze wastewater propagation chains in the Beijing-Tianjin-Hebei region. However, this classic model has a drawback in that it tends to select links with lower APL values but larger wastewater discharge intensities. Moreover, many links with intensities smaller than a preset threshold are thus overlooked.
The graphical theory is often used to model the dependence among random variables in statistics. For example, covariance models the correlations among random variables. The sectors in the input-output table can be seen as random variables in a covariance graph. The relationships of external and internal impacts correspond to out-edges and in-edges, respectively. Tan et al. [22
] proposed the hub penalty function into the problem of covariance matrix estimation. Moreover, the estimated covariance matrix structure specifies the correlation patterns, as previously Chaudhuri et al. [23
] and Xue et al. [24
] developed new methods to estimate the covariance matrix with zeros and the sparse large covariance matrix, respectively.
This research aimed to find a method to identify key wastewater propagation chains at a national scale in order to provide a scientific theoretical basis and practical policy support for effectively reducing wastewater discharge. Inspired by graphical theory, this study utilized the idea of a covariance graph model to overcome the shortcomings of the traditional Tu’s model [7
] and identify more effective intersectoral wastewater propagation chains. The APL-HCG model can address the shortcoming of the traditional Tu’s model [7
] shortcoming that ignores many linkages when identifying wastewater propagation chains. It captures linkage correlation by considering the closeness of sectoral linkages, not only external impact but also internal impact relationships. Hub wastewater propagation chains are identified by retaining linkages whose correlation values in the estimated APL covariance matrix are greater than a preset threshold. The case studies show that the identified hub wastewater propagation chains have better wastewater reduction effects. The contributions of our works are as follows: (1) we proposed a new coupled model to identify wastewater propagation chains; (2) we identified hub wastewater propagation chains that are more effective in reducing wastewater in China in 2002, 2007, 2012, and 2017.
The following contents of this paper are presented as follows. The second section introduces the data and the average propagation lengths coupled hub covariance graph (APL-HCG) model used to identify hub wastewater discharge propagation chains. The third section gives the results of the hub wastewater propagation chains obtained by applying APL-HCG to the national input-occupancy-output (IOO) tables and compares them with the results of the traditional Tu’s model [7
]. The third section also presents simulations of wastewater reduction scenario on the hub wastewater propagation chains. The fourth section discusses the limitations of the APL-HCG model, its extension to other pollutant indicators, and the support of current research for the APL-HCG results. The final section summarizes the advantages of the proposed model, the results compared with the traditional model and provides some policy implications.
Currently, there are few studies on wastewater reduction paths. This paper proposed APL-HCG, which is markedly different from the traditional Tu’s model [7
], to explore hub wastewater propagation chains. The APL-HCG relies on a preset threshold to identify HWPCs. However, the results of a smaller threshold cover the results of a larger threshold. In other words, the HWPCs with a larger threshold are a subset of the HWPCs with a smaller threshold. Without a threshold, a 10% reduction in final demand of the HWPCs sector has a wastewater reduction effect of 26.78, 33.16, 35.33, and 33.54 in unit
for the whole economic system, representing 6.09%, 5.96%, 5.16%, and 4.79% of the total wastewater discharge in 2002, 2007, 2012, and 2017. Zheng et al. [26
] noted that primary manufacturing of foods, clothing, wood, and paper dominated the wastewater discharge from 2002–2015 in Guangdong Province. At the national level, sector 10 and sector 11 are densely surrounded by many linkages and have a considerable degree of closeness to their linking sectors. Xiao et al. [3
] calculated the wastewater and sludge footprints of the Chinese city of Xiamen, which discharged
of wastewater in 2012. They found that ’Education services’, ’Grain mill products’ and food-related sectors all had significant direct wastewater discharge and their wastewater footprints were also very considerable. Tu [7
] analyzed inter-industry linkages of wastewater discharge in the Beijing-Tianjin-Hebei region in China, based on the traditional model. ’Petroleum, coking products and nuclear fuel processed products’, ’Hotel and catering’, ’Electricity and heat production and supply’, ’Textile’, ’Papermaking, printing and cultural, educational and sporting goods’ and ’Food manufacturing and tobacco processing’ were anchored as key sectors for wastewater discharge. Zheng et al. [27
] indicated that China’s Food & Tobacco sector still needs to significantly improve resource utilization efficiency through technological innovations and a green food industry chain. Furthermore, the results of this study showed that sector 10, named Food Manufacturing and Tobacco Processing, was a recognized significant sector in the HWPCs in 2007, 2012 and 2017. The results of these existing studies did not provide a linkage relationship among the sectors. However, the key sectors identified by the results of current studies are in reasonably comparable sector categories with the sectors on the HWPCs of this study, and therefore they can support the conclusions that it has reached.
In a subsequent study, APL-HCG can identify hub propagation chains for pollutant indicators such as COD, sewage sludge, and wastewater treatment sub-processes. The data for these indicators are first collected and compiled into occupancy by sector under the input matrix of the input-output table. Then, the direct consumption factors of each sector are calculated based on the occupancy of each sector for pollutant indicators. Finally, we substitute the direct consumption factors into the backward or forward APL formula introduced in the Methodology section. Equations 1–9 are the main steps in calculating the HWPCs for these indicators. There are three tuning parameters in Formula (7), and the results will vary as these three parameters vary. In this study, some sectors that densely surround many linkages and have a considerable degree of closeness with the sectors they link to always stay the same, except for appropriate changes in the parameters. Therefore, we choose the tuning parameters to make the number of these sectors both reasonable and interpretable. In addition, we set the linkages selection thresholds so that the number of sectors included in the HWPCs reached around 15, which facilitates the interpretation and comparison of the results. These empirical heuristics avoid the flexibility associated with tuning parameters. In addition to the flexibility of tuning parameters, the APL-HCG model has a wide range of applications and also has the value of extension.
Aiming to find the most interpretable and effective paths for wastewater reduction, we take advantage of the input-output analysis and covariance graphical model to explore and pinpoint the important sectors and propagation chains of wastewater discharge. We proposed a new general model APL-HCG (average propagation lengths coupled hub covariance graph [22
]), where the APL covariance matrix carries the closeness of the sectoral linkages. We also simulated the wastewater reduction effect of identified wastewater propagation chains. This approach differs from previous studies.
The proposed APL-HCG model estimates the closeness of sectoral linkages, represented by the estimated APL covariance among sectors. The APL-HCG identifies effective hub wastewater propagation chains at the national level in 2002, 2007, 2012, and 2017. The scenario analysis shows that the HWPCs outperform the WPCs of the traditional Tu’s model [7
] by 0.14%, 1.61%, 0.47%, and 0.10% for wastewater reduction in 2002, 2007, 2012, and 2017. In addition, the estimated covariances show information on the grouping of sectors, which the closeness of sector linkages within each group is substantial. Additionally, the changes in sectoral groupings reflect the dynamic process of evolving trends in sectoral linkages. The APL-HCG model is also applicable to other pollutant indicators.
Policymakers may conduct wastewater reduction policies based on HWPCs to reduce direct and implied wastewater discharge throughout the economic system. The focus of reducing wastewater discharge could capture the sectors in the hub wastewater propagation chains screened in this study for which the collaborative treatment is effective. In the collaborative treatment of the sectors in the wastewater propagation chains, we can deploy a combination of centralized and decentralized wastewater treatment and appropriately increase the fines imposed for unit wastewater discharged. Liu et al. [28
] indicated that it is optimal to use different wastewater treatment modes in each sector. Therefore, a combined wastewater management mode can be implemented for the sectors on the identified HWPCs. The sectors of secondary industries such as sector 20, sector 21, sector 46, and sector 47 can adopt a centralized wastewater treatment mode. Conversely, the tertiary industry sectors such as sector 36 on the identified HWPCs are best implemented with a decentralized wastewater treatment mode. Furthermore, the results presented in this paper imply specific sectors where fines per unit of wastewater discharge should be increased; Liu et al. [28
] and Yu et al. [29
] suggest that this is a possible intervention to limit wastewater discharge in China. In addition, encouraging sectors on HWPCs to improve water-use efficiency and enhance the application of advanced treatment of wastewater technologies (e.g., advanced treatment [30
], coupled microalgal-bacterial biofilms [31
], chemically enhanced primary treatment combined with side stream partial nitritation/anammox (PN/A) [32
], nanomaterial enhanced treatment techniques [33
]) throughout the production and wastewater treatment process may reduce future wastewater discharge. As Lin [30
] summarized, it is inevitable to replace simple treatment with advanced treatment to improve the water quality of wastewater treatment when considering the trade-off between wastewater treatment and environmental impacts. Akao et al. [31
] stated that it efficiently reduces wastewater pollutants and saves energy costs by incorporating a coupled microalgal-bacterial biofilm treatment in wastewater ponds. Paulu et al. [32
] noted that the implementation of mainstream PN/A could reduce the overall environmental impact and thus replace the activated sludge process completely.