Open AccessArticle

Identifying Administrative Villages with an Urgent Demand for Rural Domestic Sewage Treatment at the County Level: Decision Making from China Wisdom

Zixuan Wang

^1,2,

Pengyu Li

^1,2,3,

Wenqian Cai

⁴,

Zhining Shi

⁵,

Jianguo Liu

⁶,

Yingnan Cao

^1,6,

Wenkai Li

⁷,

Wenjun Wu

⁸,

Lin Li

^1,2,

Junxin Liu

^1,2 and

Tianlong Zheng

^1,2,3,*

State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China

University of Chinese Academy of Sciences, Beijing 100049, China

National Joint Research Center for Ecological Conservation and High Quality Development of the Yellow River Basin, Beijing 100012, China

⁴

Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China

⁵

Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, South Australia, Adelaide, SA 5095, Australia

⁶

Key Laboratory of Environmental Pollution Control and Remediation at Universities of Inner Mongolia Autonomous Region, College of Resources and Environmental Engineering, Inner Mongolia University of Technology, Hohhot 010051, China

⁷

SCEGC No. 12 Construction Engineering Group Co., Ltd., Ankang National High-Tech Industries Development Zone, Ankang 725000, China

⁸

State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing 100041, China

Author to whom correspondence should be addressed.

Sustainability 2025, 17(2), 800; https://doi.org/10.3390/su17020800

Submission received: 25 November 2024 / Revised: 2 January 2025 / Accepted: 5 January 2025 / Published: 20 January 2025

(This article belongs to the Section Sustainable Water Management)

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Figure 1
Research framework for evaluating the priority strategy of domestic sewage treatment in administrative villages. "> Figure 2
The criterion layer index weightings of (a) village distribution characteristics; (b) basic characteristics of villagers; (c) village economic levels; and (d) sanitation facility conditions for 8 major agricultural regions. "> Figure 3
The sub-criteria weightings for the 8 major agricultural regions, including the geographic locations of each region. "> Figure 4
The ranking of the priority treatment of rural domestic sewage in county-level administrative villages of H County in the middle and lower reaches of the Yangtze River and F County in Yungui Plateau regions, China. (a) H County in the middle and lower reaches of the Yangtze River; (b) F County in Yungui Plateau regions. Note: The blank regions are uninvestigated villages and the county built-up areas. The county built-up areas lack a rural population, so they have been excluded from the study. "> Figure 5
Sobol sensitivity analysis of the AHP-TOPSIS ranking for two counties. The first-order indices (S1) measure the impact of individual input parameters on the output. Total effect indices (ST) assess the influence of individual input parameters and their interactions with each other on the output result. Both indicators range from 0 to 1. (a) Sobol sensitivity analysis results for the rural domestic sewage treatment ranking of administrative villages in H County, the middle and lower reaches of the Yangtze River; (b) Sobol sensitivity analysis results for the rural domestic sewage treatment ranking of administrative villages in F County, Yungui Plateau regions. VT = Village type; VW = villagers’ will; WS = water supply; E = elevation; POTI = proportion of toilet improvement; DONV = dispersion of natural villages; HD = housing dispersion; PORP = proportion of resident population. ">

Versions Notes

Abstract

Rural domestic sewage management is a crucial pathway for achieving Sustainable Development Goal (SDG) 6 targets. Addressing the crucial challenge of prioritizing administrative villages for rural domestic sewage treatment at the county scale requires dedicated planning. However, county-level comprehensive evaluation models designed specifically for this purpose are currently limited. To address this gap, we developed a model based on 13 evaluation indicators encompassing village distribution characteristics, villager demographics, rural economic levels, and sanitation facility conditions. To gauge the varying emphasis on these factors by different groups, a questionnaire survey was conducted among experts, enterprises, and government departments involved in the rural sewage sector in China. Two counties from distinct regions were then chosen to validate these models. The Analytic Hierarchy Process (AHP) coupled with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to rank the importance of the factors and determine the prioritization of rural domestic sewage management in each area. The model results indicated that priority should be given to the county government, township government, ecologically sensitive areas, and administrative villages near tourist attractions in the two selected empirical counties for governance. A sensitivity analysis showed that altitude consistently exhibited high sensitivity in influencing the ranking results across all scenarios (0.4–0.6). In addition, the empirical results obtained were largely consistent with the priorities of local governments. The proposed framework offers a practical application for decision-making systems in rural domestic sewage management at the county level, providing theoretical support and scientific strategies. This holds great significance for achieving SDG 6.

Keywords:

rural sewage treatment; priority planning; AHP-TOPSIS; sensitivity analysis; county level; administrative village

1. Introduction

In 2020, at least 494 million people still practice open defecation, and over 80% of wastewater generated by human activities is discharged into rivers or oceans without treatment; this leads to the death of millions of people annually, mostly children, due to diseases related to insufficient water supply, sanitation, and hygiene [1]. In response, the United Nations has set Sustainable Development Goal (SDG) 6: “ensure availability and sustainable management of water and sanitation for all” [2]. Rural domestic sewage management is directly related to SDG 6.3: “improve water quality, wastewater treatment, and safe reuse”. According to the findings of the Second National Census on Pollution Sources conducted in 2017, the scale of discharge for rural domestic sewage was recorded at 7.59 billion tons. As of 2023, China has approximately 480,000 administrative villages and 2.36 million natural villages, with a low rural domestic sewage treatment rate of only 31% [3]. It is evident that a significant number of administrative and natural villages lack proper facilities for rural domestic sewage treatment. Continuous efforts in rural areas are still required to promote the implementation of the treatment initiatives.

One of China’s objectives for 2025 is to achieve a nationwide rural domestic wastewater treatment rate of 40% [4]. It is essential to implement a stratified, zoned, and phased approach to rural domestic wastewater management in order to meet the objective [5]. Decision makers are prioritizing administrative villages with significant demonstration value or those requiring urgent governance to meet the objective. However, due to variations in geographical locations, socio-economic conditions, and infrastructures of villages across different regions, the urgency of governance may vary among administrative villages. Additionally, stakeholders in different regions have diverse evaluation criteria. Consequently, there is an urgent need to develop a standardized guideline to scientifically select administrative villages that should be prioritized for governance in different regions. Presently, there is a significant scarcity of comprehensive evaluation models at the county scale specifically designed for planning purposes, which hinders the attainment of scientifically sound strategies for improving rural domestic sewage treatment systems.

To effectively prioritize governance decision making, it is essential to engage in an inclusive process that incorporates diverse perspectives, critical analysis, and informed judgment [6,7]. This becomes particularly crucial when considering the varied viewpoints of stakeholders. Environmental management responsibilities are typically a collaborative effort involving multiple stakeholders, including government authorities, environmental experts, and the private sector. Collaboration and active engagement from these stakeholders are essential to effectively address the challenges and find suitable solutions. Multiple-Criteria Decision Making (MCDM) refers to a collection of methods utilized to assist individuals in making decisions based on their preferences when faced with multiple conflicting criteria [8]. The Analytic Hierarchy Process (AHP) is a widely used multi-criteria analysis method that has been applied in numerous studies [9]. AHP serves as a consensus-driven model and is particularly suitable for situations where multiple parties engage in negotiation to resolve conflicts [10]. It facilitates the integration of various expert opinions, enabling a comprehensive comparison that captures all relevant perspectives [11]. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method operates on the principle of selecting the optimal solution, which is determined by choosing the solution furthest away from the least ideal solution and closest to the most ideal solution. It is widely applied across various domains and is often combined with the AHP method to enhance the accuracy and reliability of decision making [12].

Yan et al. developed an evaluation index system and framework for prioritizing urban sewage treatment in China using the AHP method with empirical data, revealing a significant association between the prioritization trend and factors such as population, economic aspects, and the emission intensity of sewage and pollutants [13]. Although prior research has made significant progress in urban sewage treatment prioritization, it is crucial to note that these efforts have been largely concentrated within urban areas. In contrast, the rural domain, characterized by its unique features and complex challenges, has remained relatively understudied in this context. The dearth of analogous comprehensive investigations in rural regions represents an untapped area and underscores the need to adapt and refine existing methodologies. Consequently, the present study endeavors to bridge this research lacuna by specifically focusing on rural domestic sewage management, thereby introducing a novel perspective and methodological approach previously unexplored in the literature, which holds the potential to significantly enhance our understanding and management of rural sewage treatment systems. At present, scholars have extensively employed the AHP-TOPSIS for criteria selection, prioritization, and evaluation in sewage management. Janjua and Hassan employed the fuzzy AHP-TOPSIS theory to rank reservoirs and extended the method to the Indus River reservoirs in Pakistan [14]. Rubio-Aliaga et al. considered energy, economic, and environmental criteria to identify and classify optimal groundwater extraction solutions, conducting a case study in the southeastern region of Spain [15]. In a recent study, Jiang et al. developed a comprehensive set of criteria that included economic costs, life cycle environmental impacts, technological characteristics, and operational management [16]. They established an AHP-TOPSIS model to assess the suitability of various rural domestic sewage treatment models in a county of Hunan Province, China.

Previous research on rural domestic sewage management has shown limited attention to large-scale planning, such as at the national or regional level [16]. However, in practice, conducting extensive preliminary planning is a crucial foundation for policy formulation, feasibility studies, and financial allocation by different levels of government. China is a vast country with significant disparities in various aspects such as economic development, government budget allocation, population distribution, and geographical characteristics, particularly in its rural areas. Formulating a prioritized governance strategy for domestic sewage management in large administrative villages necessitates the integration of various factors such as village distribution, villager characteristics, village economic level, and sanitation facility conditions in each respective region. In general, solving the complex decision-making problem of prioritizing governance in administrative villages can be accomplished through the utilization of MCDM methods and data-driven analysis. This approach considers the regional characteristics of national or provincial planning and is feasible for practical application in rural county areas.

This paper established a methodology for prioritizing administrative villages for rural domestic sewage treatment at the county level. It adopted a comprehensive approach by considering the actual conditions of domestic sewage treatment in rural China and incorporating the perspectives of different stakeholders. This study established an AHP-TOPSIS evaluation framework at the county level, which takes into account village distribution characteristics, basic characteristics of villagers, village economic level, and sanitation facility conditions. Within this framework, the analysis assessed regional differences in the weights of criteria and discussed the prioritized ranking of administrative villages for domestic sewage treatment in different regions. The empirical analysis involved two counties from different regions and utilized sensitivity analysis methods to quantify the contribution of each parameter to the results. The outcomes of the analysis, along with suggested management strategies, not only provides valuable references for policy makers but also offers guidance for the effective allocation of funds for rural domestic sewage treatment. This research could benefit other countries facing similar challenges in implementing rural domestic sewage management strategies.

2. Materials and Methods

2.1. Research Framework

The research framework primarily consists of three components, as illustrated in Figure 1. Initially, a socio-economic indicator system was established, drawing on expert opinions and the existing literature. This system comprises 13 indicators from four dimensions, including the target layer, the dimension layer, and the indicator layer. Furthermore, a questionnaire survey was conducted among three stakeholder groups, namely experts, businesses, and government entities. The AHP method was employed to determine the weights of each dimension in the hierarchy. Following this, the TOPSIS analysis method was applied to rank the order of priority of rural domestic sewage treatment in administrative villages of the two empirically studied counties. Finally, a sensitivity analysis was conducted to evaluate the contribution of each indicator to the results.

2.2. Construction of the Index System

To evaluate the priority strategy of domestic sewage treatment systems in administrative villages, this study considers decisive factors such as the conditions for administrative village sewage treatment and expert experiences and simultaneously incorporates findings from literature surveys. After eliminating factors with low relevance, redundant attributes, and missing data, a set of 13 indicators influencing the prioritization of rural domestic sewage treatment is derived. These indicators include village type (VT), dispersion of natural villages (DONV), housing dispersion (HD), elevation (E), average homestead area per household (AHAPH), proportion of resident population (PORP), proportion of elderly and children in the population (POEC), level of education (LOE), villagers’ Will (VW), collective income of the village (CIOV), household income (HI), proportion of toilet improvement (POTI), and water supply (WS). Finally, an index framework for assessing the prioritization of administrative village sewage treatment systems is established, comprising target, dimension, and indicator layers. As shown in Table 1, the criterion layer includes four dimensions for the 13 indicators: village distribution characteristics, basic characteristics of villagers, village economic level, and sanitation facility conditions. The index layer consists of the 13 indicators corresponding to the four dimensions (the detailed explanations of each index are provided in the Supplementary Information).

2.3. Data Sources

We conducted three separate surveys to obtain prioritized rankings of these factors from stakeholders representing different interest groups. The three groups included staff from government departments responsible for rural domestic sewage management, researchers with experience in these fields, and technical managers within sewage management companies. The data from government departments were collected from 16 provinces in eight major agricultural regions of China; the specific agricultural divisions are listed in Table 2 (excluding the Qinghai–Tibet Plateau region). We obtained the basic data for AHP analysis through a questionnaire survey with three groups: experts, government managers and enterprise practitioners across the country. A total of 428 questionnaires were collected from government managers, with 406 valid responses. These questionnaires were distributed across 16 provinces and cities in 8 major agricultural regions of China, excluding the Qinghai–Tibet Plateau. This part of the data supports the spatial difference analysis of government decision-making weights. In addition, a total of 93 expert questionnaires were collected, and 91 valid responses were collected. A total of 68 questionnaires from technical managers within sewage management companies were collected, and 60 of them were valid. Due to the small number of experts and enterprises, the spatial difference analysis could not be carried out, so only a weight comparison analysis between support and government decision making was performed. The questionnaire was produced and published by Sojump (https://www.wjx.cn/, accessed on 20 May 2023). In addition, two counties were selected as the empirical areas, which were in the Yungui–Sichuan region and the middle and lower reaches of the Yangtze River. The data were collected through on-site investigations and government consultations from 80 administrative villages in F County and 130 administrative villages in H County. Due to village-level data confidentiality, data on AHAPH, LOE, POEC, CIOV, and HI were excluded from the data collection. Based on the preliminary AHP analysis, the weights of the missing indicators are 10.98% and 10.00% in the two regions, respectively. Therefore, we conducted an analysis based on the available data and reasonably assumed that the absence of these low-weight data indicators would not significantly impact on the final ranking results [17].

2.4. Analytical Methods

2.4.1. Analytic Hierarchy Process (AHP)

The significance and extensive utilization of the AHP method as a prominent method within the realm of MCDM methodologies have been widely acknowledged [10]. One of the key advantages of the AHP method is its ability to incorporate a hierarchical structure for criteria, enabling users to allocate weights with specific emphasis on criteria and sub-criteria. The scales utilized in the comparative analysis of the AHP technique facilitate intuitive integration of expert experience and knowledge [18]. In this study, the role of the AHP method was to determine the weights of each criterion, thereby highlighting the perceptions of stakeholder importance regarding the various indicators. The AHP analysis was performed on the SPSSPRO platform (https://www.spsspro.com/, accessed on 5 January 2024).

The AHP method comprises the following steps:

Identification of criteria and sub-criteria to evaluate the suitability of alternatives for achieving the goal.
Pairwise comparison of criteria and sub-criteria using the Saaty scale presented in Table 3 to determine their relative importance.
Computation of the consistency ratio (CR), where CR reflects the likelihood of randomly obtained values in the pairwise comparison matrix. A CR ≤ 0.10 indicates satisfactory consistency, whereas a CR > 0.10 suggests the presence of significant inconsistency. In such cases, meaningful results may not be derived from the AHP methodology [19].

Regarding opinions for government departments, the weights for each index in eight regions were calculated using the AHP method and summarized in Tables S1–S8. The weight analysis outcomes of scientific research experts and enterprise employees are presented in Tables S9 and S10. It is noteworthy that all comparisons in this study exhibit consistency with CR values of less than 0.10, indicating the appropriateness of the derived weights.

2.4.2. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

The TOPSIS method is a multi-criteria decision-making approach designed to rank alternatives based on their relative closeness to an ideal solution [20]. The method was employed in the present study to evaluate and prioritize the two counties according to the given eight indicators (excluding the AHAPH, LOE, POEC, CIOV, and HI) and the weights obtained by AHP. The TOPSIS methodology consists of the following steps [21].

(1) Normalize the decision matrix by transforming the original values into dimensionless ones, ensuring that all criteria are considered at the same comparable scale. The normalized formula is shown in Equation (1).

t_{i j} = \frac{A_{i j}}{\sqrt{\sum_{i = 1, j = 1}^{n} {A_{i j}}^{2}}}

(1)

where t_ij is the row i, column j of the normalized matrix; A_ij is the row i, column j of the decision matrix; i is the order number of alternatives; and j is the number of indicators.

(2) Calculate the weighted normalized decision matrix by multiplying the normalized values by their corresponding criterion weights. The weighted normalized matrix was calculated using Equation (2).

x_{i j} = t_{i j} \times w_{j}

(2)

where x_ij is the weighted normalization matrix and w_j is the weight of the index calculated by AHP.

(3) The most important step in TOPSIS is to identify the ideal (best) and negative ideal (worst) solutions for each criterion. Indices with larger values indicating better performance are referred to as benefit-oriented indices, whereas those with smaller values representing superior performance are called cost-oriented indices. In this study, except for the slope of the terrain, all are benefit-oriented indices. Determinations of the ideal and negative ideal solutions are presented below (Equations (3) and (4)).

V^{-} = M i n (x_{i j}), j \in j^{-}

(3)

V^{+} = M a x (x_{i j}), j \in j^{+}

(4)

where j⁻ is the cost-based indicators and j⁺ is the benefit-based indicators.

(4) Calculate the Euclidean distances between each alternative and both the ideal and negative ideal solutions. The Euclidean distance is represented by the formula delineated in Equations (5) and (6).

{d_{i}}^{-} = \sqrt{\sum_{j = 1}^{m} {(x_{i j} - x_{b j})}^{2}}

(5)

{d_{i}}^{+} = \sqrt{\sum_{j = 1}^{m} {(x_{i j} - x_{b j})}^{2}}

(6)

where d_i⁺ is the distance to the positive ideal solution and d_i⁻ is the distance to the negative ideal solution.

(5) Compute the relative closeness coefficient (C_i) for each alternative by dividing the negative ideal distance by the sum of both ideal and negative ideal distances. The TOPSIS formula for calculating the C_i of an alternative (i) to the ideal solution is shown in Equation (7).

C_{i} = {d_{i}}^{-} / ({d_{i}}^{+} {+ d}_{i}^{-})

(7)

where C_i is the relative closeness coefficient of alternative i.

(6) Rank the alternatives based on their relative closeness coefficients in descending order. The larger the C_i, the higher the priority of domestic sewage treatment in administrative villages, and vice versa.

2.4.3. Sensitivity Analysis

Sensitivity analysis is a crucial process that aims to validate the model and ensure its performance aligns with the predefined objectives [22]. It becomes necessary to conduct a sensitivity analysis as the results obtained from the MCDM approach can sometimes lack precision. This method proves valuable in calculating the fractional modification in the existing weights of factors, resulting in changes in attribute priorities and rankings [23]. In this study, a sensitivity analysis was performed using first-order Sobol indices and total-order Sobol indices. First-order Sobol indices were primarily used to assess the independent effects of each input parameter, while total-order Sobol indices were used to evaluate the overall effects, including interactions with other parameters. The combined use of these indices provides a more comprehensive understanding of the contributions of individual parameters to the uncertainty in model outputs [24]. SALib V1.4.7 is an open-source library written in Python that was used for conducting the sensitivity analysis. It does not directly interact with the model under investigation. Instead, SALib is responsible for generating model inputs using the sample function and calculating sensitivity indices from model outputs using an analysis function. The usage steps are as follows:

(1): Determine the model input and its sample range.
(2): Run the sample function to generate the model input.
(3): Use the generated input analyze model to save the model output.
(4): Run the analyze function on the output to calculate the sensitivity index.

The model development and sensitivity analysis in this study were performed using Python V3.10 (the code is available in the Supplementary Information).

3. Results

3.1. Government Perspective

It can be observed that, in comparison with other regions, the governments of the Loess Plateau, the Sichuan Basin and its surrounding areas, and the Yunnan–Guizhou Plateau appear to assign less attention to the distribution characteristics of villages, with lower weights assigned to VT, DONV, and HD, as shown in Figure 2a and Figure 3. Moreover, a decreasing pattern was observed in the weights of the sub-indicators (VT, DONV, HD, E, and AHAPH) across all regions. Notably, government departments in every region prioritized PORP and VW over POEC (Figure 3). In Figure 2b,c, a distinct emphasis is observed in South China, where government departments prioritize the basic characteristics of villagers and the economic level of villages when selecting administrative villages for rural domestic sewage treatment. This is despite the relatively low weights (<0.1). Conversely, government officials in the Loess Plateau, the Sichuan Basin and its surrounding areas, and the Yunnan–Guizhou Plateau considered sanitation facility conditions as crucial for prioritizing domestic sewage treatment in administrative villages compared with other regions (Figure 2d).

In general, except for the Loess Plateau region, the Sichuan Basin and the surrounding areas, and the Yungui Plateau region, government stakeholders in other regions shared similar views. They believed that the distribution characteristics of villages had the greatest impact on the prioritization of administrative villages for treatment, while the economic level of villages had the least influence. However, government managers in the Loess Plateau region, the Sichuan Basin and the surrounding areas, and the Yungui Plateau region considered the water facility conditions of an administrative village to be more important factors (Figure 2 and Figure 3).

3.2. Divergent in Preferences of Stakeholders

The AHP weightings for different stakeholders considering their respective preferences for prioritized factors are presented in Table 4. Expert groups tend to prioritize sanitation facility conditions as a key factor for rural residents. Practitioners show preferences for both village distribution characteristics and sanitation facility conditions. Government managers, on the other hand, lean towards village distribution characteristics as the primary determinant for prioritizing the treatment of administrative villages.

3.3. Empirical Study

The TOPSIS decision result is expressed as a spatial layout, which intuitively reflects the prioritization of administrative villages by decision makers. In this study, we take the counties of H County in the middle and lower reaches of the Yangtze River and F County in Yungui Plateau regions in China as examples. As depicted in Figure 4 and Figure 5, upon applying the proposed decision-making framework to these two counties, we have produced the zoning map of prioritized rural domestic sewage treatment in county-level administrative villages. It is worth noting that the blank areas on the map represent villages that were not surveyed, as well as county built-up areas. Because the county built-up areas lack rural populations, they are not included in this study.

H County, situated in the middle and lower reaches of the Yangtze River, is intersected by one of its primary tributaries. The river valleys and plains contain numerous ecologically sensitive areas along the riverbanks, which are significantly influenced by natural geographical conditions and human activities. These areas include reservoirs, drinking water source protection zones, and regions highly susceptible to disturbances. This study focused on 130 administrative villages of H County, which collectively govern 969 natural villages across 13 towns. Our research reveals that roughly 62.5% of these natural villages lack basic domestic sewage collection infrastructure. According to the TOPSIS results, the top 50% of administrative villages are predominantly clustered around county and town government offices, reservoirs, and other ecologically sensitive areas and are close to tourist sites (Figure 4b). This aligns with the earlier AHP analysis results for the middle and lower reaches of the Yangtze River region, where the weight of VTs was 0.2232 (Figure 3).

Spanning an area of 1885 km², F County is primarily characterized by mountainous regions at its northern and southern extremities and abundant hills in the middle. Natural villages are scattered around valleys and slopes. Our investigation reveals a persisting issue of incomplete rural domestic sewage collection and treatment facilities in F County. The results show that among the top 50% of the prioritized management administrative villages, some are near the county government headquarters, while others are near the town government (Figure 4a). These findings align with the VT weight (0.1367) derived from the AHP analysis (Figure 3).

Building upon the data we collated and our comprehensive on-site investigations of both H County and F County, the decision reached closely mirrors the real-world circumstances.

3.4. Performance Assessment of AHP-TOPSIS

This study conducted 1024 sampling iterations in the Sobol sensitivity analysis to assess the impact of eight input parameters on the model output. We calculated the individual contributions of each parameter as well as their combined impact through all possible combinations. Figure 3 presents the first-order Sobol indices and total-order Sobol indices for the eight indicators in two real-life scenarios: H County and F County.

In H County, the most influential indicator was elevation (S1 = 0.49, ST = 0.60), with minimal contributions from the remaining indicators. This conclusion implies a substantial variation in elevation among the administrative villages in H County. Decision makers are advised to focus more on these high-sensitivity indicators during the decision-making process. Additionally, closer monitoring and management of these highly sensitive indicators are recommended to improve the rationality of decision making.

In F County, both the first-order and total-order Sobol indices for the proportion of toilet improvement and elevation reach approximately 0.4, indicating a high degree of sensitivity. This underscores that the primary distinctions among administrative villages in F County lie in proportion of toilet improvement and elevation, which aligns with the actual situation.

4. Discussion

This paper develops discussions from four perspectives: focus of the government, the appraisal framework itself, future applications of the appraisal framework, and policy support.

4.1. Focus of the Government

Based on past practical experiences, decision makers can initially assess the necessity for sewerage treatment of an administrative village based on its village type. For example, if a village is located near the township government and its surrounding areas, one might consider extending the sewage treatment network from the township to that administrative village [25]. Urgency arises when a village is situated in a sensitive area such as a drinking water source protection zone, prompting immediate treatment of its domestic sewage [26]. Villages that are scheduled for relocation or that are expected to naturally become uninhabited or abandoned within three years are excluded from consideration. Factors such as DONV, HD, E, and AHAPH are crucial in determining treatment methods and the difficulty of pipeline installation [27]. Based on this analysis, the lower weights are assigned to the village distribution characteristics in the Loess Plateau and Yungui Plateau regions. This is due to their shared features of steep slopes and widely scattered villages and houses, which makes the difference in village distribution characteristics less pronounced. Additionally, due to the underdeveloped state of regional water infrastructure development, government authorities place greater emphasis on water infrastructure considerations (Figure 2d).

Figure 2b,c reveal that, compared with other regions, government departments in South China prioritize the basic characteristics of villagers and the collective income of the village when selecting administrative villages for rural domestic sewage treatment. Due to the advanced progress in rural domestic sewage treatment in this region, domestic sewage from selected exemplary villages has already been effectively managed and treated, with the regional government department placing some emphasis on the basic characteristics of villagers [28,29]. This could potentially contribute to the observed regional disparities. In addition, the weight of village economic level in all regions is relatively low (<0.1), which reflects the current stage where most rural domestic sewage treatment facilities are funded by government and local financial subsidies. Therefore, government departments do not prioritize the economic levels of villages in rural domestic sewage treatment.

4.2. Future Directions

The indices within the evaluation framework require further refinement due to significant uncertainty in some datasets, such as DONV and HD (Figure 5). The current calculation method employed involves simply dividing the number of natural villages or population by the village area. While this method is convenient and utilizes readily available data, the rationale of this approach needs further improvement. Chen et al. introduced the theoretical measure of rural population residential dispersion (Dr), which is considered a landmark model [30]. However, applying this model in this study is challenging due to difficulties in obtaining the required data. This suggests that further research and exploration are needed to improve and advance the theoretical framework, which could lead to identifying ways to maintain methodological integrity while improving data availability.

Furthermore, the TOPSIS method adopted in this research included only eight indices, with the exclusion of five others due to their confidentiality and difficulty in accessing data. Despite their smaller weighting after the AHP analysis, the excluded indicators might still carry significant importance in practical applications. As shown in Figure 5, even though the weighting assigned to altitude is not high, it exhibits sensitivity in the results. Currently, the process of data acquisition, especially for confidential and hard-to-access factors, proves challenging and could potentially limit the practical utility of the method. The focus of future work will involve adjusting the existing indicators to those readily available within a reasonable range [31].

The experts and practitioners involved in this AHP analysis assigned weights based on their cognitive understanding, which may not align with the assessments conducted by governmental management personnel considering their local conditions. Their perspectives may not encompass all scenarios, leading to some bias between these two decision-making groups. To address this issue, a certain number of experts can be selected in each agricultural area for scoring, though this strategy may require additional human resources. In the initial phase of this study, we did not take into account the experience and familiarity of experts with the subject matter when extending the sensitivity analysis. To further test the robustness of the weight assignments, future efforts will incorporate the quantification of the experience and subject matter familiarity of experts. This will be achieved by collecting data on the affiliations of the surveyed experts with non-profit or for-profit entities, their educational credentials, years of experience in the field of wastewater management, and their level of familiarity with the research subjects in this survey [32]. After obtaining more accurate weights, our work will be more detailed, and the research focus will be further narrowed down to a natural village. The prioritization of domestic sewage treatment in natural villages will guide the actual work more scientifically and effectively [33,34].

4.3. Future Application

Artificial Intelligence has the potential to process large datasets for rapid calculation and model development, while Geographic Information Systems provide precise geographical and environmental information. The integration of the AHP-TOPSIS approach with Artificial Intelligence algorithms and Geographic Information Systems presents a significant opportunity to reduce computational overhead and enhance usability for decision makers [20].

This research, centered on its specific focus area and data derived from China, highlights a crucial consideration regarding the potential applicability of this methodology to different global scenarios, particularly in developing countries or those at similar stages of development. This observation emphasizes a critical question regarding the need for ongoing optimization and improvement to ensure the adaptability of this method across a wide range of development circumstances contexts [35,36]. It implies that future applications of the AHP-TOPSIS methodology may require recalibration to suit diverse socio-economic contexts.

4.4. Policy Support

Following the analysis and discussion of the research findings, the following policy implications can be deduced: (1) Administrative villages with higher integrated suitability rankings indicate a stronger urgency or demand for rural domestic sewage management. When formulating objectives for domestic sewage management, such villages should be prioritized to improve the quality of the water environment in rural areas; (2) For administratively ranked villages at the lower end of the scale, internal assessments could be carried out within constituent natural villages, identifying those with higher suitability for trial implementations of sewage management schemes. These lower-ranked administrative villages should be incorporated into long-term strategic planning to ensure progressive development of rural domestic sewage management; (3) It is recommended that policy makers strictly oversee the expansion of management endeavors within their local capacities to mitigate the risks of reckless development; and (4) For prioritized administrative villages, regional variations should be recognized and accommodated in the formulation of rural domestic sewage management measures. Customization according to specific circumstances is critical to encourage widespread application.

5. Conclusions

This research is based on an index system that covers four dimensions: village distribution characteristics, basic characteristics of villagers, the economic levels of villages, and the conditions of sanitation facilities. Using the Analytic Hierarchy Process, the study determined the weights for these four dimensions and thirteen specific indices. The weights were comparatively analyzed across eight agricultural zones. The priority for domestic sewage management was ranked among administrative villages in two empirical counties. Additionally, a sensitivity analysis was employed to identify sensitive indices for these two counties. The main conclusions are as follows:

(1): There are differences in preferences among government administrators in the Loess Plateau region, the Sichuan Basin and its surrounding regions, and the Yungui Plateau region compared with administrators in other regions.
(2): Stakeholders manifest unique preferences for various factors.
(3): In F County, the administrative villages that are among the top 50% in terms of adaptability and that are prioritized for sewerage management are primarily those situated around the county government headquarters and township governments.
(4): Administrative villages ranking in the top 50% for adaptability in H County are predominantly located near the county and township government facilities, reservoirs, ecologically sensitive areas, and tourist attractions.
(5): In two empirical scenarios, altitude consistently shows a high degree of sensitivity in influencing the ranking outcomes.

By establishing a suitability evaluation framework, this study addresses the pre-processing issue of prioritizing administrative villages for rural domestic sewage management. This paper highlights the priorities of different stakeholders in managing administrative villages, thereby aiding the refinement of government policies. By coupling multi-objective decision-making methods with sensitivity analysis, decision makers are assisted in understanding and mitigating the impact of uncertainty on the decision-making process. This evaluative framework serves as a robust tool for guiding project planning and creating feasibility study reports, which can benefit developing nations, including China. Government agencies, water service companies, design firms, and other stakeholders can use this framework for effective guidance and recommendations, thereby facilitating more scientifically sound and rational decision-making processes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17020800/s1, Table S1: AHP results of Northern arid and semi-arid region; Table S2: AHP results of Northeast Plain Area; Table S3: AHP results of Yunnan-Guizhou plateau; Table S4: AHP results of south China region; Table S5: AHP results of Sichuan Basin and its surrounding areas; Table S6: AHP results of the middle and lower reaches of the yangtze river; Table S7: AHP results of Loess Plateau Region; Table S8: AHP results of Huang-Huai-Hai plain; Table S9: AHP results of scientific research experts; Table S10: AHP results of enterprise employees; Table S11: TOPSIS results of FG County; Table S12: TOPSIS results of HA County. Refs. [37,38] are cited in the Supplementary File.

Author Contributions

Z.W.: Writing—original draft, software, validation, visualization. P.L.: conceptualization, writing—review and editing, supervision. W.C.: conceptualization, Investigation. Z.S.: writing—review and editing, supervision. J.L. (Jianguo Liu): funding acquisition, project administration. Y.C.: supervision, project administration. W.L.: conceptualization. W.W.: conceptualization, methodology. L.L.: funding acquisition, project administration. J.L. (Junxin Liu): funding acquisition, project administration. T.Z.: conceptualization, writing—review and editing, supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin (2022-YRUC-01-050202-02), the Qin Chuang Yuan Cited High-level Innovation and Entrepreneurship Talents Project (QCYRCXM-2022-73), the Na-tional Key R&D Program of China (grant number: 2020YFD1100500), the National Natural Science Foundation of China (grant number: 51838013), the project of Inner Mongolia “Prairie Talents” En-gineering Innovation Entrepreneurship Talent Team, and the Innovation Team of the Inner Mon-golia Academy of Science and Technology (CXTD2023-01-016). And The APC was funded by the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin (2022-YRUC-01-050202-02).

Institutional Review Board Statement

Not applicable. This study is an anonymous questionnaire survey, which does not involve sensitive personal information or commercial interests, and does not cause harm to the human body. According to Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Beings, the use of anonymized information data to conduct research can be exempted from ethical review. Therefore, this study did not conduct ethical review and met the requirements of relevant laws and regulations in China.

Informed Consent Statement

Informed consent for participation was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interests in the subject matter or materials discussed in this manuscript.

Nomenclature

AHP	Analytic Hierarchy Process	MCDM	Multiple-Criteria Decision Making
AHAPH	Average homestead area per household	POEC	Proportion of resident population
CIOV	Collective income of the village	PORP	Proportion of elderly and children in the population
CR	Consistency ratio	POTI	Proportion of toilet improvement
DONV	Dispersion of natural villages	TOPSIS	Technique for Order Preference by Similarity to Ideal Solution
E	Elevation	VT	Village type
HD	Housing dispersion	VW	Villagers’ will
HI	Household income	WS	Water supply
LOE	Level of education

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Figure 1. Research framework for evaluating the priority strategy of domestic sewage treatment in administrative villages.

Figure 2. The criterion layer index weightings of (a) village distribution characteristics; (b) basic characteristics of villagers; (c) village economic levels; and (d) sanitation facility conditions for 8 major agricultural regions.

Figure 3. The sub-criteria weightings for the 8 major agricultural regions, including the geographic locations of each region.

Figure 4. The ranking of the priority treatment of rural domestic sewage in county-level administrative villages of H County in the middle and lower reaches of the Yangtze River and F County in Yungui Plateau regions, China. (a) H County in the middle and lower reaches of the Yangtze River; (b) F County in Yungui Plateau regions. Note: The blank regions are uninvestigated villages and the county built-up areas. The county built-up areas lack a rural population, so they have been excluded from the study.

Figure 5. Sobol sensitivity analysis of the AHP-TOPSIS ranking for two counties. The first-order indices (S1) measure the impact of individual input parameters on the output. Total effect indices (ST) assess the influence of individual input parameters and their interactions with each other on the output result. Both indicators range from 0 to 1. (a) Sobol sensitivity analysis results for the rural domestic sewage treatment ranking of administrative villages in H County, the middle and lower reaches of the Yangtze River; (b) Sobol sensitivity analysis results for the rural domestic sewage treatment ranking of administrative villages in F County, Yungui Plateau regions. VT = Village type; VW = villagers’ will; WS = water supply; E = elevation; POTI = proportion of toilet improvement; DONV = dispersion of natural villages; HD = housing dispersion; PORP = proportion of resident population.

Table 1. Indicators for evaluating the priority of domestic sewage treatment for each administrative village.

Criterion Layer	Index Layer	Description of Indicators
Village distribution characteristics	Village type	It includes villages in sensitive areas such as villages with management conditions; tourist villages; drinking water source protection areas; villages that have completed water and toilet improvement and environmental problems caused by sewage; ‘community-based’ villages in and around township government sites, including villages that have received honorary titles such as beautiful villages and demonstration villages in the process of improving the per capita environment and building infrastructure; and villages that have been demolished or lost naturally within three years.
	Dispersion of natural villages	Number of natural villages/Area of administrative villages
	Housing dispersion	Number of houses/Area of administrative villages
	Elevation	DEM
	Average homestead area per household	Residential area/Number of registered households
Basic characteristics of villagers	Proportion of Resident Population	Permanent population/Household population
	Proportion of elderly and children in the population	Population under 14 years old and over 65 years old/Resident population
	Level of education	Years of education per capita in the administrative village
	Villagers’ Will	The urgency of villagers’ willingness to carry out rural sewage treatment is divided into urgent, general, and not urgent.
Village economic level	Collective income of the village	Income from various production and service activities of village collective economic organizations within the annual scope
Village economic level	Household income	The annual income of each household in the village
Sanitation facility conditions	Proportion of toilet improvement	Number of water toilets/Number of toilets for permanent residents in villages
Sanitation facility conditions	Water supply	Number of farmers using tap water supply/Number of households

Table 2. Agricultural regional division in China.

Number	Region	Province
1	Qinghai–Tibet Plateau	Tibet Autonomous Region, Qinghai
2	Northeast Plain Area	Heilongjiang, Jilin, Liaoning
3	Northern arid and semi-arid region	Xinjiang Uighur Autonomous Region, Gansu, Ningxia Hui Autonomous Region, Inner Mongolia Autonomous Region
4	Loess Plateau Region	Shanxi, Shaanxi
5	Huang-Huai-Hai plain	Henan, Shandong, Hebei, Tianjin, Beijing
6	Middle and Lower Reaches of Yangtze River	Jiangsu, Anhui, Hubei, Zhejiang, Hunan, Jiangxi, Shanghai
7	South China region	Fujian, Guangdong, Hainan, Taiwan
8	Sichuan Basin and its surrounding areas	Sichuan, Chongqing
9	Yunnan–Guizhou plateau	Yunnan, Guizhou, Guangxi Zhuang Autonomous Region

Table 3. Saaty scale.

Numerical Scale	Explanation
1	If m and n carry equal importance
3	If m carries slightly more importance than n
5	If m carries more importance than n
7	If m is strongly more important than n
9	If m is extremely more important than n
2, 4, 6, 8	Intermediate values between adjacent scale values

Table 4. Comparisons of AHP weight results of different stakeholders.

Criterion Layer	Index Layer	Experts		Businesses		Government Entities		Comprehensive Advice
Village distribution characteristics	Village type	0.2751	0.1379	0.4637	0.2694	0.5173	0.23476	0.4187	0.214
	Dispersion of natural villages		0.0532		0.0779		0.1267		0.0859
	Housing dispersion		0.0495		0.0641		0.08809		0.0672
	Elevation		0.0242		0.0309		0.04809		0.0344
	Average homestead area per household		0.0102		0.0215		0.01963		0.0171
Basic characteristics of villagers	Proportion of resident population	0.0654	0.0355	0.0741	0.0487	0.1558	0.08577	0.0984	0.0567
	Proportion of elderly and children in the population		0.0036		0.0044		0.01163		0.0065
	Level of education		0.0036		0.0058		0.00946		0.0063
	Villagers’ will		0.0227		0.0152		0.049		0.029
Village economic level	Collective income of the village	0.0576	0.0304	0.0446	0.0226	0.0535	0.02455	0.0519	0.0259
Village economic level	Household income	0.0576	0.0271	0.0446	0.022	0.0535	0.02897	0.0519	0.026
Sanitation facility conditions	Proportion of toilet improvement	0.602	0.3076	0.4176	0.2244	0.2734	0.12851	0.431	0.2202
Sanitation facility conditions	Water supply	0.602	0.2944	0.4176	0.1931	0.2734	0.14489	0.431	0.2108

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MDPI and ACS Style

Wang, Z.; Li, P.; Cai, W.; Shi, Z.; Liu, J.; Cao, Y.; Li, W.; Wu, W.; Li, L.; Liu, J.; et al. Identifying Administrative Villages with an Urgent Demand for Rural Domestic Sewage Treatment at the County Level: Decision Making from China Wisdom. Sustainability 2025, 17, 800. https://doi.org/10.3390/su17020800

AMA Style

Wang Z, Li P, Cai W, Shi Z, Liu J, Cao Y, Li W, Wu W, Li L, Liu J, et al. Identifying Administrative Villages with an Urgent Demand for Rural Domestic Sewage Treatment at the County Level: Decision Making from China Wisdom. Sustainability. 2025; 17(2):800. https://doi.org/10.3390/su17020800

Chicago/Turabian Style

Wang, Zixuan, Pengyu Li, Wenqian Cai, Zhining Shi, Jianguo Liu, Yingnan Cao, Wenkai Li, Wenjun Wu, Lin Li, Junxin Liu, and et al. 2025. "Identifying Administrative Villages with an Urgent Demand for Rural Domestic Sewage Treatment at the County Level: Decision Making from China Wisdom" Sustainability 17, no. 2: 800. https://doi.org/10.3390/su17020800

APA Style

Wang, Z., Li, P., Cai, W., Shi, Z., Liu, J., Cao, Y., Li, W., Wu, W., Li, L., Liu, J., & Zheng, T. (2025). Identifying Administrative Villages with an Urgent Demand for Rural Domestic Sewage Treatment at the County Level: Decision Making from China Wisdom. Sustainability, 17(2), 800. https://doi.org/10.3390/su17020800

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