Suitability Assessment and Implementation Methodologies for Rural Waste Management of Selected Districts of Beijing

Abstract

At present, with the increasing global awareness of sustainable development and environmental protection, significant attention has been directed toward the ecological living environment in rural areas. Selecting appropriate rural waste treatment methods is crucial for promoting the sustainable development of the rural ecological environment. Existing research reveals that, in certain regions—ecological conservation zones—there is a lack of targeted evaluation systems for rural waste treatment methods. Moreover, how regional characteristics can be integrated with quantitative assessment outcomes to inform specific treatment solutions remains relatively less explored. This study took Beigou Village in Huairou District, Beizhuang Town in Miyun District, Wangping Town in Mentougou District, and Dakezhuang Township in Yanqing District—all located within Beijing’s ecological conservation areas—as research subjects. It develops a suitability evaluation framework for rural waste treatment, encompassing five dimensions: economic investment, technological factors, environmental pollution, social benefits, and carbon emissions. This study combined the Analytic Hierarchy Process (AHP) and the entropy weight method to determine indicator weights. The fuzzy comprehensive evaluation method was then employed to calculate comprehensive scores and conduct a graded assessment. The evaluation results effectively differentiated the sample grades (e.g., Beigou Village received a comprehensive score of 2.373, rated as “Good”). Based on the evaluation results and field investigations, tailored solutions—including physical, thermal, recycling, and integrated treatment approaches—were proposed for each village and town. This study investigated the precise “evaluation–solution” matching for rural waste treatment in ecological conservation areas, demonstrating distinct novelty compared to previous research. It provides direct guidance for waste management in the four villages and towns within Beijing’s ecological conservation areas, thereby enhancing the efficiency of resource utilization in rural regions.

1. Introduction

1.1. Research Background

Since the implementation of China’s Rural Revitalization Strategy, which was launched in 2017, rural development has rapidly become a key focus for society. Protecting and improving rural living environments is a crucial component of this revitalization work. According to the “Five-Year Action Plan for the Improvement of Rural Living Environments (2021–2025)” issued by the General Offices of the Communist Party of China Central Committee and the State Council [1], the standard for the harmless disposal of rural household waste must be significantly raised, and long-term mechanisms for management and maintenance must be fully established by 2025. In 2024, China’s National Development and Reform Commission (NDRC) and relevant ministries issued the Guiding Opinions on Vigorously Implementing Renewable Energy Replacement Initiatives [2]. The policy mandates coordinated management of rural organic waste (also termed biomass waste, referring to waste derived from organisms that contains organic matter and is biodegradable by microorganisms) [3] and similar materials, including rational deployment of biomass power generation projects. Therefore, implementing suitable rural waste management methods not only improves the living environment for rural residents but also aligns with national development objectives. This approach supports rural revitalization while promoting sustainable environmental development in the countryside.

In 2022, Beijing processed approximately 7.4057 million tons. Incineration facilities handled approximately 70% of this waste, serving as the primary method for safe disposal [4]. However, the incineration process may cause environmental pollution. The Beijing Municipal Regulations on Domestic Waste Management emphasize support for technological innovation, advocating for the construction and operation of waste treatment facilities to high standards. This includes utilizing advanced technologies to integrate methods such as incineration, biological treatment, and sanitary landfill for municipal solid waste (MSW) [5] disposal, thereby supporting local ecological conservation and development [6].

In 2022, the General Offices of the Beijing Municipal Committee of the Communist Party of China and the Beijing Municipal People’s Government promulgated the “Beijing Action Plan for Improving Rural Living Environments and Building Beautiful Countryside During the ‘14th Five-Year Plan’ Period” [7]. The plan emphasizes the need to prioritize the sustainable development of rural living environments in Beijing, enhance the standard of rural waste management, and improve the collection, transportation, and disposal system for rural household waste. Beijing’s ecological conservation zones include Mentougou District, Pinggu District, Huairou District, Miyun District, Yanqing District, Changping District, as well as the mountainous areas of Fangshan District. To systematically conduct this research, Beigou Village in Huairou District, Beizhuang Town in Miyun District, Wangping Town in Mentougou District, and Dakezhuang Township in Yanqing District were selected as the study subjects. These four sample villages and towns exhibit geographically representative distribution characteristics within the ecological conservation zones, covering diverse geographical locations such as the north, northeast, and west. Furthermore, these areas demonstrate distinct variations in population size, economic structure, waste generation characteristics, and existing waste treatment infrastructure, which facilitates the construction of a more universally applicable evaluation system. Additionally, as policy pilot demonstration zones, the experiences and challenges encountered in these regions hold significant reference value for waste management practices across ecological conservation areas.

Growing global awareness of sustainable development and environmental protection has solidified the transition to green energy as an irreversible trend. Governments worldwide increasingly focus on addressing rural waste management [8]. Driven by the triple forces of accelerated urbanization, carbon emission reduction pressures, and the demands of a circular economy, countries are adopting context-specific waste treatment methods through technological innovation and institutional design to mitigate environmental pollution caused by waste disposal [9]. As demonstrated by Berlin’s municipal sanitation company, BSR, which focuses on efficient sorting and resource recovery, waste management is transformed into a closed-loop system for resource circulation and energy generation [10]. Through in-depth collaboration with the San Francisco municipal government, Recology, a waste management company based in San Francisco, has facilitated the implementation of an innovative classification system and economic incentive mechanisms while enhancing processing technologies. These efforts have made San Francisco the first city in the United States to achieve an 80% garbage recycling rate [11].

Municipal solid waste management in Beijing’s ecological conservation zones [12] should be tailored to local conditions while assessing successful and novel approaches globally. Appropriate waste treatment methods contribute to enhanced resource efficiency and reduced carbon emissions in rural areas.

1.2. Comparative and Integration of Background Information with Literature Review

Research on the applicability of rural solid waste management in China primarily examines three dimensions: analytical perspectives, treatment methodologies, and research approaches. From multi-level perspectives—regional, governmental, and community—researchers contribute recommendations for optimizing current waste treatment systems. Drawing on the case of Y Town in Jining City, Shu Jingran [13] proposes strategies to enhance multi-stakeholder collaboration in rural household waste management. These strategies focus on five key areas: enhancing village-level self-governance, promoting active market participation, cultivating villager engagement, and facilitating multi-party collaborative governance. This study adopts a region-specific perspective, focusing on Beijing’s ecological conservation areas—regions characterized by their dual attributes of ecological protection and rural development. The research selected four villages and towns as case studies: Beigou Village in Huairou District, Beizhuang Town in Miyun District, Wangping Town in Mentougou District, and Dakezhuang Township in Yanqing District. These locations exhibit representative diversity in geographical distribution, economic structures, waste composition, and existing waste treatment infrastructure, ensuring comprehensive coverage and strong representativeness.

Discussions regarding the suitability of waste treatment methods have featured a variety of research approaches by scholars. Chen et al. [14] evaluated three rural solid waste landfill models in Qingdao: on-site pit burial, centralized landfill with leachate treatment, and centralized landfill with both leachate treatment and methane recovery. By establishing a scientific suitability evaluation framework for rural waste treatment, this study proposes tailored waste management recommendations based on the specific characteristics of each village and town, thereby enhancing resource efficiency.

Researchers also employ diverse advanced methodologies to investigate the suitability of waste disposal methods. For instance, Luo et al. [15] employed the Fuzzy-AHP method to compare and assess recycling models for discarded furniture, establishing a standardized evaluation framework for recycling systems of waste wooden furniture. Sun et al. [16] assessed the performance of 21 MSW treatment demonstration projects in Guangxi using the DEA model [17], and analyzed significant differences in their overall technical efficiency using the Kruskal–Wallis test. This study employs an integrated application of the AHP-entropy weight method and fuzzy comprehensive evaluation method, determining suitable waste treatment approaches for each village and town by analyzing the impact of various indicators, thereby providing a scientific basis for the decision-making process.

Research by scholars on evaluating the suitability of rural waste treatment predominantly focuses on the coordinated optimization of technical efficiency, environmental benefits, and socioeconomic feasibility. Lima Priscila de Morias et al. [18] conducted a study applying Life Cycle Assessment (LCA) methodology to rural waste management in Brazil. The research examined the current predominant practices of incineration and landfilling, while evaluating proposed alternatives including source separation and household composting. Aryampa et al. [19] documented and analyzed the current state of waste management at urban waste disposal sites in East Africa, examining the drivers of waste generation, collection, and disposal, as well as their impact on the sustainability of Kampala City. Omotayo et al. [20] based on descriptive statistics, employed Tobit and Probit regression models for composite solid waste to explore factors influencing households’ willingness to pay for solid waste management through principal component analysis. The study demonstrated that households’ socioeconomic characteristics contribute to solid waste generation and disposal costs. Makarichi [21], K.E. [22], Amaral et al. [23] focused on the reuse of organic waste and waste carbon materials, enhancing the efficiency of waste treatment and recycling processes.

Laura F. et al. [24] developed a decision-support framework (DSF) through systematic evaluation of methodologies and case studies in Latin America to guide the selection of sustainable solutions for sewage sludge treatment in the region. Kühn, V.O. et al. [25] conducted an in-depth analysis of contaminant concentrations in soil and groundwater across 70 non-sanitary landfills from 59 cities documented in 50 selected articles. Their research concluded that pollution levels show stronger correlation with geological and geotechnical conditions as well as waste management practices than other factors. Ferronato, N. et al. [26] analyzed urban-level actions implemented in seven developing countries to address plastic pollution and achieve three Sustainable Development Goal, including recycling strategies, segregated waste collection, and public education initiatives. The study ultimately proposed three recommendations for scaling up these practices.

In summary, existing research indicates that scholars both domestically and internationally have conducted considerable studies on rural domestic waste treatment. Through systematic planning and management, categorized waste is processed via different methods to achieve harmless treatment, volume reduction, and resource recovery. A comprehensive review of relevant literature reveals that while prior studies have yielded certain theoretical and practical outcomes, there remains a notable gap in research exploring how to utilize data models to identify more suitable waste treatment patterns.

This study draws upon the extensive body of research established by numerous scholars, combined with field investigations in four of Beijing’s ecological conservation areas, to explore locally adapted waste treatment solutions. Employing Analytic Hierarchy Process (AHP), entropy weight method, and fuzzy comprehensive evaluation, we develop tailored waste management strategies for specific village contexts, thereby contributing both methodological innovation and practical policy recommendations to the field.

1.3. Broad Research Aims and Significance of Study

Current assessments reveal substantial waste generation in rural China, with significant regional disparities in treatment capacity. Many rural areas face critical deficiencies in both economic investment and technical capabilities for waste management. For instance, many facilities remain idle due to a lack of operation and maintenance funding after construction. In a recently poverty-alleviated county in western China, only 11 out of 27 waste treatment centers are operating normally [27]. Additionally, the costs of rural waste treatment are significantly higher than in urban areas, primarily due to decentralized sources, small volumes, and long transport distances [28]. These phenomena have led to measurable environmental degradation, with consequent impacts on residents’ physical and mental wellbeing.

As a key area designated to promote the integrated development of urban and rural areas in the Beijing–Tianjin–Hebei region [29], the primary mission of Beijing’s ecological conservation zone [30] remains ensuring the ecological and environmental security of the capital. This study investigated the applicability and optimization approaches of current rural waste management methods, aiming to facilitate green development in Beijing’s Ecological Conservation Zone and other global regions. The findings provide actionable insights for enhancing waste treatment efficiency in rural areas. Guided by the ecological civilization vision and the dual carbon goals, a more advanced and comprehensive target system should strive to establish a resource-efficient and climate-friendly integrated waste management paradigm. This paradigm encompasses two progressive core objectives: firstly, to maximize resource efficiency; and secondly, to drive the evolution of waste management systems toward low-carbon and ultimately zero-carbon operations.

This study is grounded in the Rural Revitalization Strategy and related policies aimed at improving rural living environments. It focuses on the specific environmental, economic, and technological conditions of villages and towns within Beijing’s ecological conservation zones. The analysis process of this study is shown in Figure 1. The proposed rural waste treatment approach is expected to address existing challenges while also offering valuable insights for achieving sustainable development goals in rural areas globally. For instance, the aim of SDG 11 (Sustainable Cities and Communities) is to reduce environmental pollution caused by improper waste disposal and contribute to the development of sustainable rural communities; SDG 12 (Responsible Consumption and Production) seeks to promote waste classification and the practice of circular economy models in rural areas; and SDG 13 (Climate Action) aims to curb greenhouse gas emissions and mitigate the impacts of climate change, thereby providing insights for improving rural living environments.

Figure 1. Research Workflow Diagram for Rural Waste Suitability Assessment. Figure Source: Created by the authors in June 2025.

2. Methodology

2.1. Research Subjects

The research team conducted field investigations in four selected villages and towns within the ecological conservation zones—Beigou Village in Huairou District, Dazhuangke Township in Yanqing District, Beizhuang Town in Miyun District, and Wangping Town in Mentougou District (Figure 2)—during March and April 2025. After distributing questionnaires and conducting initial exchanges with relevant personnel to understand the basic situation of domestic waste management in each village and town, the research team held focused discussions with responsible officials from the respective local governments. These meetings aimed to gain comprehensive insights into the organizational frameworks of the selected towns and villages, collection and transportation systems, disposal, funding allocation, infrastructure development, and implementation outcomes related to rural domestic waste management, as well as to identify existing challenges.

Figure 2. Schematic Diagram of Research Site Selection. Figure Source: Created by the authors in June 2025.

2.2. Development of the Evaluation Framework

Current global assessments reveal inadequate waste management services in many rural regions, characterized by immature treatment technologies and insufficient supporting infrastructure [31]. Of particular concern is the direct discharge of untreated waste into the environment. Developing context-appropriate waste treatment methods and establishing scientific evaluation frameworks can address diverse rural needs while enhancing community acceptance of waste management solutions—a critical step toward achieving ecological and societal sustainability [32].

Effective waste management in Beijing’s Ecological Conservation Zone plays a pivotal role in safeguarding the capital’s ecological security [12]. Establishing a comprehensive evaluation framework incorporating economic, technological, environmental, social and carbon emission indicators enables holistic assessment of rural waste treatment suitability [33]. The construction of the evaluation index system requires benchmarking against international standards while selecting readily measurable and operationally meaningful factors. For economic indicators, we adopted the EU’s circular economy assessment framework, incorporating capital investment, operational expenses, and maintenance costs [34]. Technical indicators were derived from the Basel Convention, encompassing five dimensions: site selection complexity, technological difficulty, system unreliability, operational hazards, and waste separation requirements. For environmental impact assessment, three key indicators were selected in accordance with Beijing Municipal Domestic Waste Management Regulations [35] soil contamination, air pollution, and water pollution. Social benefit evaluation adopted the criteria from China’s 14th Five-Year Plan for Municipal Solid waste separation and Treatment Facilities Development (issued by NDRC and MOHURD) [36], focusing on waste harmlessization rate, waste reduction efficiency, and resource recovery performance. Carbon emission assessment adopted two indicators “direct and indirect emissions” as specified in China’s Guidelines for Fully Implementing the New Development Philosophy to Achieve Carbon Peak and Neutrality [37]. Notably, while higher scores indicate greater reliability and safety for corresponding indicators, they signify worse performance for other metrics. Therefore, to standardize the evaluation criteria, the unreliability and unsafety of rural waste treatment technologies are adopted as evaluation indicators in the assessment system of this study.

To scientifically evaluate the applicability of current rural waste treatment methods, it was essential to establish a reasonable evaluation indicator system (Table 1). This paper constructed an evaluation model based on the Analytic Hierarchy Process (AHP), entropy weight method, and fuzzy comprehensive evaluation method. The integration of these three approaches enabled the quantification of difficult-to-measure indicators through membership degrees in the fuzzy comprehensive evaluation, achieving an organic combination of quantitative and qualitative indicators, as well as integrating subjective judgment with digital processing.

Table 1. Rural Garbage Treatment Applicability Evaluation System.

Objective Layer	Criteria Layer	Indicators Layer	Indicator Description
Rural Garbage Treatment Applicability I	Economic Investment I1	Initial Investment for Facilities and Equipment I11 [38]	The entire initial capital investment required for purchasing and installing infrastructure and equipment in the early stages of garbage treatment system construction.
		Operating Cost I12 [39]	The ongoing costs required for the daily operation of the garbage treatment system.
		Maintenance Cost I13 [40]	The costs for repair, maintenance, and technical support required to ensure the long-term normal operation of garbage treatment facilities and equipment.
	Technical Factors I2	Difficulty in Selecting Treatment Sites I21 [41]	The comprehensive challenges faced in selecting suitable sites for constructing garbage treatment facilities in rural areas, including natural conditions, social acceptance, policies, and regulations.
		Garbage Treatment Technology Difficulty I22 [42]	The requirements for professional knowledge, equipment complexity, and personnel skills during the installation and operation of garbage treatment technologies.
		Unreliability of Treatment Technology I23 [43]	The probability of technical failures, efficiency fluctuations, or inability to adapt to changes in garbage composition during actual operation.
		Unsafe performance indicators of waste treatment technologiesI24 [44]	The potential threats to personnel health, ecological environment, and community safety during the application of the technology.
		Difficulty in Adapting Treatment Technology to Garbage Classification I25 [45]	The degree of dependence of the effective operation of the technology on the precision of front-end garbage classification.
	Environmental Pollution I3	Soil Pollution I31 [46]	The risk of soil structure damage, fertility decline, or ecological function degradation caused by harmful substances (such as heavy metals, organic pollutants, leachate, etc.) entering the soil through infiltration, leaching, or direct contact during garbage treatment.
		Air Pollution I32 [47]	The impact of harmful gases, particulate matter, or odors released during garbage treatment on air quality and human health.
		Water Pollution I33 [48]	The deterioration of water quality caused by pollutants from leachate, wastewater, or rainwater runoff generated during garbage treatment entering surface water or groundwater.
	Social Benefits I4	Degree of Harmless Treatment of Garbage I41 [49]	The effect of removing or stabilizing harmful substances during the treatment process.
		Degree of Garbage Reduction I42 [50]	The proportion of reduction in the volume or mass of garbage from generation to final disposal.
		Degree of Garbage Resource Utilization I43 [51]	The proportion and efficiency of converting garbage into reusable resources (such as fertilizers, energy, raw materials, etc.) through technical means.
	Carbon Emissions I5	Direct Carbon Emissions I51 [52]	The greenhouse gases directly released during garbage treatment.
		Indirect Carbon Emissions I52 [53]	The indirect greenhouse gas emissions caused by energy consumption or resource substitution during garbage treatment.

Table Source: Compiled by the authors in June 2025.

2.3. Calculation of Indicator Weights

2.3.1. Procedure for Assigning Weights Using AHP

The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making analysis method combining qualitative and quantitative factors, proposed by American operations researcher Thomas L. Saaty in the 1970s. Its core principle involves decomposing a complex multi-objective decision problem into a hierarchical structure comprising objectives, criteria, and alternatives. Through pairwise comparisons, it determines the relative importance (i.e., weights) of factors within each hierarchy level and utilizes mathematical methods to prioritize them, ultimately providing a decision-making basis for selecting the optimal solution.

This study employed a combined Analytic Hierarchy Process (AHP) and entropy weight method to establish a comprehensive weighting scheme for the evaluation system indicators. Building upon this foundation, the fuzzy comprehensive evaluation method is applied to calculate composite scores for four selected typical villages and towns within ecological conservation areas. Subsequently, appropriate waste treatment methods are proposed for these four cases to enhance the applicability of rural waste management approaches.

The application of AHP for evaluating rural waste treatment applicability possesses solid scientific justification, as numerous experts and scholars have utilized this method to investigate suitable waste management models [54,55]. Experts and scholars stated that its core strength lies in systematically deconstructing complex decision-making problems into hierarchical structures comprising objectives, criteria, and indicators. This approach perfectly aligns with the inherent requirement of balancing multiple dimensions—including economic, technological, environmental, social, and carbon emission factors—in waste treatment technology selection. The consistency check mechanism integrated into AHP ensures logical rigor by filtering out subjective contradictions. The extensive application of this model in environmental management has validated its effectiveness and reliability in addressing multi-criteria complex problems, thereby establishing a robust methodological foundation for constructing a scientific and transparent evaluation framework in this study.

The Analytic Hierarchy Process (AHP) [56] was employed to determine indicator weights. First, expert evaluations established the relative importance among indicators. A standardized 1–9 scale quantified relative importance: 1 (equally important), 3 (slightly more important), 5 (significantly more important), 7 (strongly more important), and 9 (extremely more important). Intermediate values (2, 4, 6, 8) represented gradations between these levels. Finally, expert ratings were compiled into a judgment matrix for subsequent weight calculation and importance ranking.

Based on the judgment matrix, the maximum eigenvalue and eigenvector are determined, and the steps for solving the weights of each indicator are as follows:

(1) Normalize the elements of the judgment matrix by columns using the formula by the authors in June 2025:

$${\mathrm{B}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{A}}_{\mathrm{i}\mathrm{j}}}{\sum {\mathrm{A}}_{\mathrm{i}\mathrm{j}}}}$$

(1)

A_ij—the score given by the i-th expert to the weight of the j-th indicator.

B_ij—the elements of the judgment matrix after column-wise normalization.

(2) Sum the normalized matrix by rows using the formula:

$$W_j = {\displaystyle \frac{B_j}{\sum B_j}}$$

(2)

W_i—the weighting vector of the j-th indicator.

(3) Normalize the resulting vector to obtain the final weight vector W using the formula by the authors in June 2025:

$${\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}={\displaystyle \frac{{\mathrm{A}}_{\mathrm{i}\mathrm{j}}}{\sum {\mathrm{A}}_{\mathrm{i}\mathrm{j}}}}$$

(3)

λ_max—the largest eigenvalue of the judgment matrix, which is used for the subsequent consistency check.

(4) Calculate the consistency:

The consistency index of the matrix is calculated using the formula:

$$\mathrm{C}.\mathrm{I}.={\displaystyle \frac{\lambda -\mathrm{n}}{\mathrm{n}-1}}$$

(4)

The random consistency ratio is calculated as:

$$\mathrm{C}.\mathrm{R}.={\displaystyle \frac{\mathrm{C}.\mathrm{I}.}{\mathrm{R}.\mathrm{I}.}}$$

(5)

n—the order of the matrix (number of indicators).

C.I.—Consistency Index, used to measure the logical consistency of the judgment matrix.

R.I.—random consistency index.

C.R.—random consistency ratio.

2.3.2. Procedure for Assigning Weights Using Entropy Method

Entropy was originally a concept in thermodynamics, with its physical meaning being a measure of the degree of disorder within a system, which can be employed to describe phenomena of disorganization in a system. Information entropy can be used to represent the degree of dispersion of an indicator: the larger the entropy value, the smaller the degree of dispersion of the indicator, and consequently, the smaller the weight assigned to that indicator. Based on this principle, information entropy can be utilized to calculate the weight values of various indicators, thereby providing a basis for decision-making. A higher degree of variability in an indicator’s data, which indicates a greater amount of information provided, corresponds to a lower entropy measure. As a result, a larger weight should be assigned to this indicator, granting it a more substantial role in the comprehensive evaluation. Conversely, influencing factors with a small amount of information, as they provide limited knowledge and contribute less to reducing uncertainty, are considered relatively minor in the valuation [57].

(1) To determine the relative importance of indicators, ten experts were invited to evaluate all indicators. The scores were normalized across the various indicators in the evaluation system using Equation (6) by the authors in June 2025 to establish each expert’s relative weighting, resulting in evaluation matrix P. The raw data matrix P was then standardized.

$$\mathrm{p}=\left[\begin{array}{cccc}{a}_{11}& a_{11}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ \dots & \dots & \dots & \dots \\ a_{m1}& a_{m2}& \cdots & a_{mn}\end{array}\right]$$

(6)

The raw data matrix P obtained above was subjected to standardized processing, with reverse indicators uniformly converted to forward orientation to ensure data directionality aligns with evaluation logic. This study assumes that extreme values in the expert scoring data used are representative and can effectively define the evaluation intervals.

(2) Equation (7) was applied by the authors in June 2025 to calculate indicator vectors.

$${\mathrm{p}}_{\mathrm{i}\mathrm{j}}=({\mathrm{a}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}\{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}\left\}\right)/\mathrm{m}\mathrm{a}\mathrm{x}\left\{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}\right\}-\mathrm{m}\mathrm{i}\mathrm{n}\left\{{\mathrm{a}}_{\mathrm{i}\mathrm{j}}\right\}$$

(7)

a_ij—the raw score value of the i-th sample on the j-th indicator in the raw data matrix.

p_ij—the proportion of the score value of the i-th sample on the j-th indicator to the total score of that indicator, i.e., the normalized value.

(3) Finally, indicator weights were computed using Equation (8).

$$E_i=-{\displaystyle \frac{1}{\ln m}}{\displaystyle \sum _{i=1}^m{p}_{ij}}\cdot \ln p_{ij}$$

(8)

E_i—the information entropy of the i-th indicator. A smaller entropy value indicates a greater degree of variation in the data for that indicator, and the more information it provides.

2.3.3. Combined Weighting Method

In calculating the weights of individual indicators, the sole use of either the Analytic Hierarchy Process (AHP) or the Entropy Weight Method has certain limitations. Relying solely on the Analytic Hierarchy Process (AHP) introduces excessive subjectivity, while using the entropy weight method alone may result in weights that are divorced from practical contexts. A linear weighting scheme assigning 50% to each method represents a commonly adopted and robust combination strategy. This approach effectively integrates the strengths of both methods, neutralizes their limitations, and thereby enhances the reliability and credibility of the evaluation outcomes. Therefore, this paper integrates the indicator weights derived from both AHP and the Entropy Weight Method using a combined weighting approach [58]. Let α represent the weight calculated by AHP and β represent the weight calculated by the Entropy Weight Method. The final indicator weight is determined by taking an equal contribution (50%) from each method, expressed by the authors in June 2025 as:

ω = 0.5α + (1 − 0.5) × β

(9)

α—the weights derived from the AHP.

β—the weights obtained through the entropy weight method.

2.4. Specific Steps of the Fuzzy Comprehensive Evaluation Method

Fuzzy Comprehensive Evaluation (FCE) is a multi-factor decision analysis method based on fuzzy mathematics. It applies the principle of fuzzy relation composition to quantify factors with ambiguous boundaries or non-quantifiable characteristics, thereby conducting a comprehensive assessment of the membership grade status of the evaluated subject from multiple factors as adopted from numerous previous researchers.

FCE is widely applied in environmental management research. For instance, Luo [15] et al. employed the Fuzzy-AHP method to evaluate recycling models for discarded furniture, while Sun [16] et al. integrated Data Envelopment Analysis (DEA) with fuzzy evaluation in their study. The applicability assessment of rural waste treatment involves multiple qualitative indicators, such as “treatment technology safety risks” and “social benefits,” which are difficult to accurately capture using traditional quantitative methods. Therefore, the adoption of fuzzy comprehensive evaluation demonstrates strong logical rationality and practical operability. The specific procedural steps are as follows:

(1)

Establish Evaluation Grades: Within the evaluation system for village garbage disposal applicability, assign m distinct evaluation grades to each indicator, corresponding to defined scoring criterion ranges. Thus, the evaluation grades for each indicator are divided into m levels, and the value domain of these evaluation grades is denoted as S = (S₁, S₂, …, S_m).

(2)

Define Applicability Levels: The evaluation system involves multiple criterion-layer factors, and the comprehensive score reflects different levels of village garbage disposal applicability. Classify the level of village garbage disposal applicability into n grades. The value domain for garbage disposal applicability is denoted as R = (R₁, R₂,…, R_n), where each value in R represents a distinct level of applicability.

(3)

Construct Fuzzy Evaluation Matrix and Compute Comprehensive Score: Synthesize the value domains S and R to construct the fuzzy comprehensive evaluation matrix U. This matrix U is then multiplied by the weights of the criterion-layer indicators. The results are aggregated to obtain the applicability level of village garbage disposal for each study object as adopted from numerous previous researchers.

$$\mathrm{U}=\left[\begin{array}{cccc}{\mathrm{r}}_{11}& {\mathrm{r}}_{12}& \dots & {\mathrm{r}}_{1\mathrm{m}}\\ {\mathrm{r}}_{21}& {\mathrm{r}}_{22}& \dots & {\mathrm{r}}_{2\mathrm{m}}\\ ⋮& ⋮& \ddots & ⋮\\ {\mathrm{r}}_{\mathrm{n}1}& {\mathrm{r}}_{\mathrm{n}2}& \dots & {\mathrm{r}}_{\mathrm{n}\mathrm{m}}\end{array}\right]$$

(10)

(4)

Calculate Fuzzy Comprehensive Evaluation Result Y.

$${\mathrm{Y}}_{\mathrm{k}}=\mathrm{m}\mathrm{i}\mathrm{n}\{1,\sum _{\mathrm{j}=1}^{\mathrm{m}}\mathrm{m}\mathrm{i}\mathrm{n}({\mathrm{a}}_{\mathrm{j}},{\mathrm{r}}_{\mathrm{j}\mathrm{k}}\left)\right\},\mathrm{k}=\mathrm{1,2},\cdots ,\mathrm{n}$$

(11)

a_j—the weight of the j-th indicator.

r_jk—the membership degree of the j-th indicator to the k-th evaluation grade.

2.5. Field Investigation Method

In terms of collecting villagers’ perceptual data, this study conducted questionnaire surveys based on the principle of random sampling in each sample village and town. Specifically, a multi-stage random sampling method was adopted: first, three representative zones were randomly selected in each township; then, using the residential household registration list as the sampling frame, systematic random or simple random sampling was employed to select households; finally, within the selected households, the “last birthday method” was applied to randomly choose one adult resident as the respondent. The effective sample size for each village ultimately ranged from 15 to 30 individuals, with a total valid sample size of 103 individuals in this study.

This rigorous random sampling procedure effectively minimized subjective selection bias, enabling the sample to effectively infer the characteristics of the overall village or town population. It thereby established an authentic and reliable data foundation for the subsequent fuzzy comprehensive evaluation. Thus, taking these four villages and towns as case studies and drawing on mature waste management models from various global regions, this paper conducts an in-depth analysis of the rural domestic waste management practices in the sample areas.

3. Research Procedure and Analysis

3.1. Indicator Weight Calculation via Analytic Hierarchy Process (AHP)

To minimize the influence of individual subjective factors while balancing both specialized expertise and governmental perspectives, this study ultimately established a 10-member expert panel through purposive sampling. The composition of the expert group integrated both professional expertise and governmental perspectives, comprising seven specialists from the fields of environmental engineering, agricultural resource management, municipal engineering, carbon emissions, and public health, who provided theoretical and technical support. Additionally, three government representatives—including officials from the local Ecological Environment Bureau, Agriculture and Rural Affairs Bureau, and administrative cadres from the sample townships—were involved to ensure the evaluation system aligned with policy compliance, managerial feasibility, and grassroots practical considerations. This multidisciplinary expert structure effectively ensured that the indicator weights reflected both cutting-edge academic insights and the practical needs of local governance. To determine the relative importance of indicators across all levels, the scoring results were compiled to establish judgment matrices for each level, taking the first-level indicators as an example (Table 2).

Table 2. Tier-1 Indicator Comparison Matrix.

I	I1	I1	I1	I1	I1
I1	1	2	1	3	5
I2	1/2	1	1/2	2	5
I3	1	2	1	3	5
I4	1/3	1/2	1/3	1	2
I5	1/5	1/5	1/5	1/2	1

Table Source: Compiled by the authors in June 2025.

Taking the first-level indicators as an example, the indicator weights were calculated. Using the judgment matrix, data were imported into the spss24.0 statistical software, the maximum eigenvalue (λ_max = 5.0502) and its eigenvector w = [0.6308; 0.3885; 0.6308; 0.2045; 0.1067] were derived. Normalizing w yielded the weight vector α = [0.3216; 0.1981; 0.3216; 0.1043; 0.0544].

To validate the effectiveness of the computed results, researchers must perform a consistency check: C.I. = 0.01255.

Since deviations in consistency may arise from random factors, when assessing whether the judgment matrix exhibits satisfactory consistency, it is necessary to compare the Consistency Index (C.I.) with the Random Consistency Index (R.I.). The R.I., established through numerous repeated experiments, provided standard values corresponding to different matrix orders (n) and has been widely accepted and applied in academic research (Table 3). With a matrix order of 5, the R.I. is determined as 1.12. The calculated C.R. yields 0.0112, which is less than 0.1. This confirms that the calculated eigenvector is acceptable, thereby validating the obtained indicator weights.

Table 3. Standard Reference Table for Commonly Used R.I. Values.

Matrix Order (n)	1	2	3	4	5
R.I.	0	0	0.52	0.89	1.12

Table Source: Compiled by the authors in June 2025.

3.2. Indicator Weight Calculation via Entropy Weight Method

Ten experts scored Tier-1 indicators on a 1–9 scale (Table 4).

Table 4. Expert Scores for Tier-1 Indicators.

Tier-1 Indicators	P1	P2	P3	P4	P5
I1	9	8	6	8	7
I2	6	7	8	6	6
I3	6	8	9	8	8
I4	5	5	6	5	6
I5	4	5	5	5	5

Table Source: Compiled by the authors in June 2025.

After normalizing the scores to matrix P, the entropy vector was computed as E = [0.9943; 0.9956; 0.9949; 0.9975; 0.9977]. The resultant weight vector β = [0.2844; 0.2211; 0.2556; 0.1263; 0.1126]. Tier-2 weights were derived similarly.

3.3. Comprehensive Weight Calculation

Final weights for Tier-1 indicators combined AHP and entropy weights equally:

ω₁ = [0.3030;0.2096;0.2886; 0.1153;0.0835]

Tier-2 weights followed the same procedure.

ω₂₁ = [0.2370;0.5177;0.2453]

ω₂₂ = [0.1825;0.2239;0.2512;0.1600;0.1825]

ω₂₃ = [0.3414;0.3949;0.2637]

ω₂₄ = [0.2376;0.3812;0.3812]

ω₂₅ = [0.4659;0.5341]

3.4. Fuzzy Comprehensive Evaluation

Based on the actual conditions of rural waste treatment applicability in the four sample villages and towns within Beijing’s ecological conservation areas, the experts evaluated the performance of indicators including economic investment, technological factors, environmental pollution, social benefits, and carbon emissions. A scoring method was adopted to standardize the quantitative criteria for the first-level indicators across the sample villages and towns, with the scoring for these indicators divided into four levels. Integrating the scoring standards with the actual conditions of the sample areas, the evaluation results were categorized into four grades: “Severe” “Moderate” “Minor” and “Negligible” assigned scores of 1 to 4, respectively. Through calculation via spss24.0 statistical software, the fuzzy evaluation vectors for the first-level indicator factors were derived, yielding the membership degrees of the first-level indicators (Table 5).

Table 5. Membership Degrees of Tier-1 Indicators.

Evaluation Target	Indicator	Severe	Moderate	Minor	Negligible
Beigou Village, Huairou District	I1	0.133	0.303	0.272	0.291
	I2	0.201	0.194	0.302	0.302
	I3	0.148	0.259	0.259	0.335
	I4	0.176	0.247	0.225	0.352
	I5	0.15	0.2	0.3	0.35
Beizhuang Town, Miyun District	I1	0.349	0.277	0.295	0.079
	I2	0.287	0.272	0.256	0.184
	I3	0.362	0.217	0.276	0.145
	I4	0.374	0.276	0.201	0.15
	I5	0.3	0.35	0.3	0.05
Wangping Town, Mentougou District	I1	0.358	0.277	0.284	0.081
	I2	0.318	0.302	0.284	0.095
	I3	0.28	0.293	0.243	0.183
	I4	0.32	0.328	0.235	0.117
	I5	0.35	0.25	0.3	0.1
Dakezhuang Township, Yanqing District	I1	0.462	0.35	0.188	0
	I2	0.408	0.387	0.204	0
	I3	0.423	0.296	0.281	0
	I4	0.526	0.368	0.105	0
	I5	0.526	0.316	0.158	0

Table Source: Compiled by the authors in June 2025.

4. Results Outcomes Analysis

4.1. Analysis of Calculated Results for Evaluation Indicator Weights

Combined AHP-entropy weights for criterion and indicator layers are summarized in Table 6.

Table 6. Summary of Indicator Weights.

Objective Layer	Criteria Layer	Weight	Indicator Layer	IW (Indicator Weight)	CW (Combined Weight)	Ranking
Evaluation of the applicability of rural waste disposal	Economic Investment	0.3030	Initial Facility Investment	0.2370	0.0718	6
			Operational Costs	0.5177	0.1569	1
			Maintenance Costs	0.2453	0.0743	5
	Technical Factors	0.2096	Site Selection Difficulty	0.1825	0.0383	13
			Technical Complexity	0.2239	0.0469	8
			Degree of Technical Unreliability	0.2512	0.0527	7
			Degree of Technical Unsafety	0.1600	0.0335	15
			Waste Sorting Compatibility	0.1825	0.0383	13
	Environmental Pollution	0.2886	Soil Contamination	0.3414	0.0985	3
			Air Pollution	0.3949	0.1140	2
			Water Pollution	0.2637	0.0761	4
	Social Benefit	0.1153	Harmlessness Degree of Waste	0.2376	0.0274	16
			Waste Reduction Efficiency	0.3812	0.0440	10
			Waste Resource Recovery Rate	0.3812	0.0440	10
	Carbon Emissions	0.0835	Direct Emissions	0.4659	0.0397	12
			Indirect Emissions	0.5341	0.0456	9

Table Source: Compiled by the authors in June 2025.

Economic Investment (0.3030), Environmental Pollution (0.2886), and Technical Factors (0.2096) were the top-weighted criteria. The dominance of Economic Investment reflects its critical role in rural waste management, where heavy reliance on government funding (with limited private capital) strains budgets and compromises operational efficacy.

Environmental Pollution risks—including non-segregation and delayed processing—may cause atmospheric contamination from volatile organic compounds and soil/water pollution from leachate, leading to “village encircled by waste”. Despite progress, Technical Factors require breakthroughs to advance system efficiency.

To validate the scientific validity and rationality of this evaluation system, the evaluation criteria within the system were benchmarked against successful cases from other countries and regions worldwide.

In terms of economic investment, with the goal of becoming one of the cleanest countries in Africa, the Ghanaian government has allocated 42,992,636 Ghanaian cedis for sanitation management, representing a 21% increase compared to the budget allocation in 2017. This funding largely involves improving the current waste collection system [59].

In terms of treatment technology, waste management technologies in some countries have reached a relatively mature stage. For instance, Finland’s ZenRobotics has developed an intelligent waste sorting system that utilizes visual sensors to identify the surface structure, shape, and material composition of items, thereby determining their categories. The items are then automatically sorted and classified by the system. This technology has achieved a high level of reliability and safety.

In terms of environmental pollution, global municipal solid waste (commonly known as “domestic waste,” referring to solid waste generated in daily urban life or activities serving urban daily life, as well as waste regarded as municipal solid waste under laws and administrative regulations) is projected to increase from 2.01 billion metric tons in 2016 to approximately 3.4 billion metric tons by 2050 [60]. In Brazil, the number of cities with selective waste collection programs accounts for about 32% of all Brazilian municipalities, involving 39,100 formal waste collectors and public administrative bodies. By 2022, the collected municipal solid waste represented 1.47% of the total, effectively reducing environmental pollution caused by waste [61].

In terms of social benefits, achieving waste reduction, recycling, and harmless treatment has garnered widespread global attention. At a waste treatment plant in Bangkok, Thailand, the complex composition of waste led the facility to adopt mechanical biological treatment technology for the harmless processing of municipal solid waste. This process converts the waste into refuse-derived fuel and biogas, ultimately transforming it into reusable electrical energy.

In terms of carbon emissions, policy instruments addressing climate change primarily fall into two categories: The first comprises direct emission reduction measures, such as carbon taxes, tradable emission permits, and incentives for utilizing non-fossil fuels or energy conservation; the second focuses on technological policies aimed at reducing the costs associated with carbon emission reduction [62]. This study can draw on advanced waste management approaches from various countries and regions to explore and adapt waste treatment models for the sample villages and towns, thereby enhancing overall benefits.

4.2. Comprehensive Evaluation and Analysis of Villages and Towns

4.2.1. Scores and Rating Levels of the Evaluation Results for Each Village and Town

Based on the calculated membership degrees, the scores for the first-level indicators and the comprehensive scores for each village and town in the ecological conservation areas were derived. The scoring is a comprehensive index that utilizes fuzzy mathematics to transform subjective and ambiguous expert evaluations into quantifiable numerical values ranging between 1 and 4. The values in Table 7 represent a dimensionless composite index calculated based on a predefined evaluation scale (1–4 points). A higher score (closer to 4) indicates a lower degree of adverse impact in waste treatment under that criterion, reflecting better applicability. Conversely, a lower score (closer to 1) signifies a higher degree of adverse impact and poorer applicability. The evaluation grades were categorized into “Good,” “Fairly Good,” and “Average,” with the villages and towns assigned corresponding ratings based on their score ranges (Table 8). A schematic diagram is shown in Figure 3. In the aforementioned evaluation indicator system, a higher indicator score implies more adverse implications, meaning that villages and towns with higher overall scores correspond to lower evaluation rating levels.

Table 7. Evaluation score results.

Object	Economic Investment	Technical Factors	Environmental Pollution	Social Benefit	Carbon Emission	Overall Score	Ranking	Evaluation Results
Huairou District Beigou Village	2.278	2.294	2.220	2.247	2.150	2.373	4	Good
Miyun District Beizhuang Town	2.896	2.662	2.796	2.874	2.900	2.674	3	Relatively Good
Mentougou District Wangping Town	2.912	2.843	2.670	2.851	2.850	2.694	2	Relatively Good
Yanqing District Dakezhuang Township	3.274	3.204	3.142	3.421	3.368	3.048	1	Fair

Table Source: Compiled by the authors in June 2025.

Table 8. The specific value ranges corresponding to each grade level.

Evaluation Grade	Value Range (Score)	Explanation
Fair	Above 3.0	Most unfavorable, with poor treatment applicability
Relatively Good	2.4–3.0	Moderate applicability
Good	Below 2.4	Most favorable, with good treatment applicability

Table Source: Compiled by the authors in June 2025.

Figure 3. Scoring results diagram. Figure Source: Created by the authors in June 2025.

To test the robustness of the weight settings in the evaluation system, this study conducted a sensitivity analysis on the combined weights. By increasing and decreasing the weights of key indicators by 10%, changes in the comprehensive scores of the sample villages and towns were observed to assess the model’s sensitivity to weight adjustments. The results showed that when the weight of operational cost (I₁₂) was increased by 10%, the comprehensive scores of Beigou Village, Beizhuang Town, Wangping Town, and Dakezhuang Township rose from 2.373, 2.674, 2.694, and 3.048 to 2.381, 2.679, 2.701, and 3.052, respectively. Conversely, when the weight was decreased by 10%, the comprehensive scores correspondingly declined, though the ranking order among the villages and towns remained unchanged. Further analysis repeated the same process for key indicators such as soil pollution (I₃₁) and air pollution (I₃₂), revealing that the maximum fluctuation in comprehensive scores remained below a 5% threshold, and none of the evaluation grades for the samples changed. These findings indicate that the evaluation system developed in this study is insensitive to weight variations, demonstrating strong robustness in the evaluation outcomes.

4.2.2. Analysis of the Evaluation Results for Each Village and Town

Based on the fuzzy comprehensive evaluation results, among the four evaluated units, Dakezhuang Township in Yanqing District received a “fair” rating, Wangping Town in Mentougou District and Beizhuang Town in Miyun District were rated as “relatively good”, while Beigou Village in Huairou District achieved an “good” evaluation.

This indicates that, among the four surveyed villages and towns in Beijing’s ecological conservation area, Huairou’s Beigou Village has established a relatively sound rural waste management system with treatment methods well-suited to local conditions. In contrast, Yanqing’s Dakezhuang Township demonstrates a lack of maturity in rural waste management compared to the other three locations. Further research is therefore needed to explore and customized waste treatment solutions for this area.

Dak ezhuang Township in Yanqing District achieved the highest scores across all five criterion layers. However, in the “economic investment” layer (score: 3.274), its performance was constrained by the Ecological Conservation Redline [63]—a mandatory protection boundary designated by the state to safeguard ecological security and enhance ecosystem services. This restriction limited the development of various industries, resulting in heavy reliance on higher-level government subsidies for fiscal revenue and consequently restricted overall economic development. This paradox highlights how ecological protection policies may inadvertently perpetuate fiscal dependencies in conservation zones. While Dakezhuang Township achieved relatively high scores in environmental pollution (3.421) and social benefits (3.368), it faces challenges in the quality of human settlements. Specifically, haphazard waste disposal persists in certain villages and alleys. To address rural waste management and sewage treatment, evidence-based regulatory policies require urgent formulation and effective implementation. These issues are also related to its inherent conditions. Dazhuangke Township is located deep within the Yan Mountains and is predominantly characterized by mountainous terrain. This complex topography leads to a dispersed distribution of villages, making infrastructure construction difficult and costly. Limited transportation access has slowed the pace of improvements in the township’s governance capabilities. Additionally, the climatic conditions in the mountainous area restrict the large-scale development of traditional agriculture. Dakezhuang Township’s underdeveloped industrial structure and regional backwardness have limited its overall management capacity. Consequently, solid waste treatment requires significant enhancement, particularly in improving sanitization, reducing volume, and increasing resource recovery rates.

Based on the evaluation results across all criterion layers, both Mentougou District’s Wangping Town and Miyun District’s Beizhuang Town demonstrate moderate performance levels. Specifically, Wangping Town scored moderately in economic investment (2.912) and social benefits (2.851), indicating a need for increased investment in waste treatment infrastructure. The Ecological and Environmental Protection Center is responsible for implementing waste sorting, reduction, and resource recovery initiatives. Nevertheless, enhancing support mechanisms for these tasks remains essential. Beizhuang Town in Miyun District demonstrates relatively strong performance regarding carbon emissions (2.900). To sustain this, selecting appropriate waste treatment methods is critical to minimize both the direct and indirect emissions generated during processing.

Among the evaluated villages, Beigou Village in Huairou District received the lowest scores across all criteria layers in the applicability assessment system. This indicates that Beigou Village performed best among the selected villages at every level assessed. Located near the Mutianyu Great Wall scenic area, the village boasts an abundance of ecological resources. Beigou Village employs a “community-wide participation + property-style management” model for household waste management. This approach, implemented through villager self-provisioning, enhances the social benefits of the waste management system by establishing effective operational practices. Beigou Village achieved a carbon emission score of 2.150, the highest among all indicators assessed. Situated within Huairou District, Beijing’s designated model district for rural household waste sorting, the village benefits from the district’s advanced waste treatment techniques. This foundation has also enabled marked progress in reducing carbon emissions.

5. Discussion

5.1. Analysis of Existing Waste Management Modes in Villages and Towns

In recent years, the overall waste management efficiency in Beijing has continued to improve. By 2023, the municipal household waste recycling rate had reached over 38.5% [64]. Among the four selected sample villages and towns, Beigou Village in Huairou District, as a tourist village, is significantly influenced by homestays and agricultural activities. Homestay tourists and the chestnut industry are the main waste producers in Beigou Village, resulting in the highest per capita daily waste generation of approximately 5.1 kg. In the other areas, local residents are the primary waste producers. Beizhuang Town in Miyun District had the lowest per capita daily waste generation, at about 1.4 kg. With increased economic investment in the waste treatment industry by district governments, waste management efficiency in villages and towns has improved significantly. For example, in Dazhuangke Township, Yanqing District, the average daily volume of household kitchen waste sent to treatment plants increased from 6.25 tons to 40.3 tons, and the separation rate rose from 1.41% to 20.43%. Meanwhile, the average daily volume of other waste sent to treatment plants decreased from 437.9 tons to 156.8 tons, a reduction of 64.19%. Disposal processes have also been optimized. Dazhuangke Township introduced and put into operation on-site treatment facilities, cumulatively processing 19,000 metric tons of kitchen waste [65].

The waste composition varies among villages and towns. For instance, Beigou Village primarily produces agricultural waste, while Dazhuangke Township is dominated by ash and soil. The existing primary waste treatment technologies and management models also differ across these villages and towns (Table 9). According to available data, in terms of current waste treatment rates, Beigou Village achieved a household waste treatment rate of over 90% in 2023 [66], marking a significant indicator of environmental management success and demonstrating that waste is being disposed of in a standardized and safe manner. In Beizhuang Town, through the use of waste sorting equipment, the daily household waste generated across the town is sorted, resulting in a 65% reduction at the source. This reduction is largely due to the sorting and recycling of recyclables such as scrap iron, glass bottles, and plastic bottles, establishing a solid foundation for waste classification and reduction. To achieve higher standards in waste treatment and environmental protection, chemical looping combustion (CLC) [67]—a clean combustion technology that ingeniously integrates carbon capture with the combustion process through an “oxygen carrier” cycling mechanism— offers a feasible pathway that aligns with environmental requirements and high-standard development goals.

Table 9. Existing Rural Waste Management Models in the Sampled Villages and Towns.

Sample Village and Town	Primary Waste Composition	Corresponding Population and Context	Applicable Conditions and Technology
Huairou District Beigou Village	Residual Waste: Approximately 1200 kg daily, accounting for 75%. Kitchen Waste: Approximately 400 kg daily, accounting for 25%. Agricultural Waste: Chestnut burs, with an annual output of approximately 500,000 kg.	Local villagers and tourist homestay guests (The village is a cultural tourism destination with multiple homestays, generating additional waste from tourists).	Management Model: “Household Classification, Village Collection, Town Treatment”. Technology: Kitchen and residual waste are treated uniformly by the town; agricultural waste like chestnut burs is crushed and composted into organic fertilizer for field application.
Miyun District Beizhuang Town	Recyclables (e.g., scrap iron, glass bottles, plastic bottles); Construction waste; Organic soil-like material; Screen oversize material; Combustible waste.	Local residents	Management Model: “Household Classification, Village Collection, Town Treatment”, utilizing municipal solid waste sorting processors. Technology: Sorting processes including spray disinfection, manual sorting, magnetic separation, gravity separation, screening, and air classification achieve 65% source reduction.
Mentougou District Wangping Town	Kitchen Waste: Approximately 1 ton cleared and transported daily, locally converted into organic fertilizer. Implements a five-category classification: Soil and Ash, Kitchen, Recyclable, Hazardous, and Combustible waste.	Local residents	Management Model: “Household Classification, Village Collection, Town Transfer”, emphasizing source separation. Technology: Kitchen waste undergoes aerobic composting via crushing, dehydration, degreasing, and mixing with sawdust and fermenting agents; soil and ash waste is used for pit filling and land reclamation.
Yanqing District Dakezhuang Township	Soil and Ash: Accounts for 44.75%. Kitchen Waste: Accounts for 36.56%. Recyclables (Paper, Plastic, Textiles, Glass, Metal, etc.): Collectively account for approximately 17.11%.	Local residents	Management Model: “Household Classification, Village Collection, Town Transfer”, emphasizing source separation. Technology: Kitchen waste is processed into organic fertilizer via crushing, dehydration, degreasing, and mixing with sawdust and fermenting agents for aerobic composting; soil and ash waste is used for pit filling and land reclamation.

Table Source: Compiled by the authors in June 2025.

In terms of waste recycling rates, Wangping Town in Mentougou District maintained a 40% recycling rate for renewable resources in 2023. The town has developed distinctive practices in the on-site recycling of kitchen waste, achieving a 95% on-site reduction through a specific process that converts kitchen waste into organic ferilizer [68]. Recyclable waste accounts for a relatively high proportion in the town. The Yanqing District government has established special subsidy funds for waste classification to purchase waste treatment services and is constructing a township-level household waste transfer station in Dazhuangke Township to advance the development of temporary household waste transfer facilities.

Waste management in these villages and towns is not a short-term initiative but rather the result of sustained efforts over many years. For instance, Wangping Town began implementing waste segregation as early as 2007, while the exchange program in Dazhuangke Township has been maintained for over six years. At the same time, these localities continuously explore more effective methods, such as the introduction of automated sorting equipment in Beizhuang Town and the use of voucher incentives in Wangping Town, reflecting their commitment to ongoing improvement. Judging by the continuously implemented and refined governance measures, the significant results achieved, and the broad participation of villagers, the rural waste management efforts in Beigou Village (Huairou District), Beizhuang Town (Miyun District), Wangping Town (Mentougou District), and Dazhuangke Township (Yanqing District) have generally maintained a positive developmental trajectory. Based on the current waste management situations in these villages and towns, this paper analyzes suitable waste management methods tailored to each locality in the concluded study (Table 9) [64].

5.2. Study on Suitable Rural Waste Management Methods for Each Village and Town

Based on an in-depth investigation of the current waste management situations in the sample towns and villages—Beigou Village in Huairou District, Dazhuangke Township in Yanqing District, Beizhuang Town in Miyun District, and Wangping Town in Mentougou District—appropriate waste management models were selected for analysis (Table 10).

Table 10. Methods of rural waste disposal in towns and villages.

Processing Method	Treatment Characteristics	Corresponding Towns and Villages	Application Condition
Physical Treatment	Carrying out physical treatment operations such as waste sorting, compaction, and landfilling.	Huairou District Beigou Village	Possessing a well-established system for sorted collection, transport, and categorized treatment of domestic waste. Having a less diverse waste composition makes treatment relatively straightforward. These factors contributed to higher operational efficiency and lower processing costs in these locations.
Thermal Treatment	Implementing centralized treatment methods, primarily incineration, for energy recovery	Miyun District Beizhuang Town	Possessing a well-established system for sorted collection, transport, and categorized treatment of domestic waste. Having relatively well-developed incineration plants and power generation systems. Benefiting from government policy backing.
Recycling and Reuse	By recycling and reusing materials, thereby mitigating environmental pollution and reducing raw material consumption.	Mentougou District Wangping Town	Possessing a domestic waste management system featuring sorted collection, transport, and categorized treatment. Higher recyclable content within the waste stream. Robust institutional backing through government policies.
Integrated Treatment	Employing advanced and integrated treatment methods can effectively reduce carbon emissions.	Yanqing District Dakezhuang Township	Commitment from government and relevant departments to rural waste governance, supported by substantial human and financial resources. Diverse waste composition resulting in greater processing challenges.

Table Source: Compiled by the authors in June 2025.

5.2.1. Beigou Village, Huairou District: Physical Treatment with Multi-Agent Collaboration

Huairou District’s Beigou Village established a multi-agent collaborative governance model centered on physical treatment. This framework forms an integrated waste management chain encompassing “household-level sorting, village collection, district-level treatment, comprehensive awareness campaigns, and township government support”. Regarding environmental impact, Beigou Village achieved a pollution assessment score of 2.220, indicating good performance. This outcome stems from the village’s waste management system generating minimal hazardous substances during the processing process. Therefore, a physical treatment model primarily based on sorted landfilling can be adopted. In contrast to sanitary landfill, which focuses on “controlling” pollution from mixed waste through engineering measures, sorted landfilling emphasizes “reduction and resource recovery” at the front end to minimize the amount of waste requiring landfill disposal. This approach downgrades landfilling to a supplementary role in final disposal. It is not an isolated disposal unit but rather a systematic strategy that integrates front-end sorting, resource recovery, and end-of-pipe disposal, resulting in minimal pollution to soil and air during the treatment process. In waste management operations, relevant authorities distributed standardized sorting bins to households.

In the field of waste management, relevant authorities uniformly distribute standardized waste sorting bins and employ scientific methods for systematic site selection and layout. Specifically, GIS spatial analysis technology is utilized to assess population density and waste generation distribution, combined with network analysis to calculate residents’ walking accessibility. Additionally, collection vehicle routes are simulated to ensure operational feasibility, followed by field surveys and public consultations to optimize the plan. This data-driven methodological framework not only ensures convenience for resident disposal but also meets the requirements of mechanized waste collection. It reflects the refined layout philosophy of front-end sorting in physical treatment and establishes a solid foundation for enhancing subsequent waste processing efficiency.

In Beigou Village, various departments perform their respective duties in waste management with high operational efficiency. The village has implemented refined front-end waste sorting to ensure standardized disposal of household waste. It has innovatively adopted a resource utilization model where agricultural waste is crushed into fertilizer and returned directly to the fields. Meanwhile, behavioral norms are fostered among villagers through a combination of village regulations and awareness campaigns, cultivating habitual waste sorting practices. Local government provides comprehensive support by allocating sanitation facilities, offering financial assistance, and promoting initiatives like the “Clean Your Plate Campaign.” This responsibility-partitioned waste governance model, with specialized entities performing distinct operational roles, has been validated through global adoption. For instance, in the United States, urban residents place household waste at designated locations on scheduled collection days. Municipal sanitation departments or contracted companies then deploy waste transport vehicles to convey this waste to transfer stations for subsequent sorting. Recyclable materials are reprocessed into secondary resources, while non-recoverable waste is transported to designated engineered landfills, optimizing overall treatment efficiency. To further enhance the outcomes, Beigou Village should explore high-value utilization of waste and digital management. Local governments could promote regional collaborative treatment and market-oriented mechanisms to collectively deepen waste management effectiveness, creating a replicable model for rural practices.

5.2.2. Beizhuang Township, Miyun District: Thermal Treatment with Robust Funding

Amidst Beijing’s comprehensive implementation of mandatory household waste sorting policies, Beigou Township in Miyun District deployed automated sorting facilities in September 2022 alongside source reduction strategies. This initiative alleviated processing burdens at municipal treatment centers while yielding annual savings of approximately ¥60,000 in waste transportation costs from the township to district landfill sites. Waste-to-energy technology serves as a renewable energy solution by converting thermal energy—generated through the combustion of municipal solid waste—into mechanical work, which is subsequently transformed into electrical power. This process achieves sustainable energy recovery within a circular resource framework. Regarding waste landfilling and incineration which are prone to generating substantial pollutants, modern waste treatment facilities equipped with multi-level control measures have evolved into highly engineered systems. At landfill sites, pollution control primarily focuses on managing leachate and landfill gas. By installing composite liner systems consisting of high-density polyethylene (HDPE) membranes and clay layers, combined with leachate collection networks and biochemical treatment processes, these facilities effectively intercept and treat high-concentration wastewater that could potentially contaminate soil and groundwater. For waste incinerators, pollution control centers on flue gas purification, while solid residues from incineration undergo strict management: fly ash is stabilized and securely landfilled as hazardous waste, whereas relatively inert bottom ash is maximally repurposed for resource recovery.

Globally, numerous regions are actively developing waste-to-energy industries [69]. Geographically, these facilities predominantly concentrate in countries and territories experiencing rapid urbanization and facing significant waste disposal demands [70]. Currently, approximately 15% of global municipal solid waste undergoes energy recovery through incineration, with the deployment concentrated predominantly in the Global North, particularly in Japan, the United States, and European nations [71]. In recent years, substantial growth in China’s waste incineration capacity has propelled the Asia-Pacific region to become the largest segment of the global waste-to-energy project market [72]. Beigou Township achieved a relatively high economic factor score of 2.896 among the sampled villages. This financial capacity enables sustained operation of the incineration facility, maintenance of power generation systems, and implementation of waste-to-energy technology for domestic waste processing. Furthermore, post-sorting combustible waste is transported to the Miyun District landfill. This operational strategy enhances the efficiency of waste-to-energy conversion at district facilities, achieving the threefold objective of waste reduction, resource recovery, and environmentally safe treatment.

5.2.3. Wangping Town, Mentougou District: Material Recovery with Centralized Coordination

In Wangping Town, Mentougou District, waste collection, transport, and processing follow a partitioned operational hierarchy: “township collection, district transfer, and regional centralized treatment.” This model achieves on-site resolution of food waste within township boundaries while ensuring standardized processing for all waste streams. Wangping Town achieved favorable scores for environmental impact (2.670) and carbon emissions (2.850), indicating relatively low pollution levels and carbon output within acceptable parameters. These conditions enable prioritized implementation of resource recovery methods for waste processing. Germany’s Extended Producer Responsibility (EPR) system [73] is a benchmark for the global circular economy. Its core principle is to legally obligate producers to take responsibility for the entire life cycle of their products, particularly the recycling, treatment, and reuse of waste. This system operates via specialized recycling agencies jointly financed by manufacturers, packagers, distributors, and waste management sectors, establishing an integrated nationwide collection infrastructure.

Additionally, these agencies coordinate licensed collectors on optimized routes to retrieve post-consumer packaging. Sorted materials are routed to designated processors—either reprocessed into secondary materials or returned directly to manufacturers for closed-loop integration. Wangping Town similarly implements a responsibility-specific governance model for waste management. The township operates an environmental center housing three specialized facilities: an enclosed waste transfer station, a resource recovery center, and an on-site food waste treatment facility. Following scheduled, location-based sorted collection, the local resource recovery company provides on-demand collection services for recyclables (waste generated in daily life that can be recycled and reused after being cleaned and processed) [74]. For hazardous waste (waste generated in daily life that poses direct or potential hazards to human health or the natural environment requires special safety treatment to prevent its toxic and hazardous substances from contaminating the environment or endangering health) [75], relevant authorities coordinate periodic pickups, which are subsequently processed by municipal-level units through centralized, environmentally safe treatment. This systematic approach minimizes ecological hazards while optimizing material recovery.

5.2.4. Dakezhuang Township, Yanqing District: Integrated Treatment with Environmental Remediation Objectives

Dakezhuang Township in Yanqing District demonstrated the lowest scores across all assessment dimensions. Consequently, an integrated treatment approach is recommended for managing rural waste in this locality. Globally, integrated waste treatment approaches are widely adopted across numerous countries and regions. For instance, each district in Tokyo operates dedicated waste processing facilities that achieve dual outcomes: resource recovery and energy generation through waste incineration—providing both electricity and district heating services. Tokyo’s integrated model now demonstrates high operational maturity. The operation of waste treatment plants enhances urban energy conservation and environmental protection, which necessitates meticulous waste segregation and recycling at the source. In Japan, rural areas and small cities implement exceptionally detailed waste separation systems. For instance, Hekinan City in Aichi Prefecture categorizes waste into approximately 26 types, while Minamata City in Kumamoto Prefecture adopts a 24-category system.

The Japanese government asserts that resolving waste recovery challenges necessitates a fundamental transition away from the “mass-production, mass-consumption, and mass-disposal” economic model toward establishing a circular society with significantly reduced environmental footprints. Implementing integrated waste treatment necessitates committed governmental engagement with rural waste management challenges. In Dakezhuang Township, the government-led program promotes waste segregation and resource recovery through economic incentives, such as plastic-for-goods exchanges. This approach achieves cost-effective household waste separation-demonstrating a viable model for economically constrained mountainous regions. Following the segregation of plastic waste, residents self-manage inert debris and food waste, while village sanitation workers conduct secondary sorting of residual collected waste. This decentralized approach substantially reduces landfill volumes at district facilities. Concurrently, increased investment in district-level processing plants ensures secure and efficient treatment of terminal waste. For resource-constrained regions, this model provides a practical alternative for enhancing material recovery through household waste segregation, reducing operational expenditures, and promoting local socioeconomic development.

6. Conclusions

This study establishes a framework for assessing the suitability of rural waste management systems, informed by China’s current rural context and global comparative models. The framework comprises five criterion layers—economic investment, technological feasibility, environmental impact, social benefits, and carbon emissions—supported by sixteen measurable indicators. Through multi-expert consultation, we applied an entropy-weighted AHP method to determine composite indicator weights. Analysis of these weight distributions revealed priority issues that require urgent intervention in current rural waste management systems. Subsequently, this study selected four towns and villages within Beijing’s ecological conservation area—Beigou Village (Huairou), Beizhuang Township (Miyun), Wangping Town (Mentougou), and Dakezhuang Township (Yanqing)—as empirical cases. Using a fuzzy comprehensive evaluation, we calculated membership degrees across criterion layers. These values were then synthesized to compute dimensional scores and comprehensive indices, enabling systematic assessment of rural waste management performance across all sample locations.

Based on an in-depth analysis of four sample villages and towns, this paper proposes differentiated waste management solutions: Beigou Village in Huairou District should develop a tourism-oriented circular economy model by designing standardized sorting systems for homestays and creating high-value utilization pathways for chestnut burs; Beizhuang Town in Miyun District could establish a technology-driven precision treatment system incorporating advanced technologies like CLC [67] and building a big data platform for waste classification in Beizhuang Town; Wangping Town in Mentougou District needs to implement a resource management system based on circular economy principles, developing specialized organic fertilizer formulations and introducing extended producer responsibility mechanisms; Dakezhuang Township in Yanqing District should innovate low-cost governance models by optimizing “plastic waste buy-back” programs and promoting in situ utilization techniques for inert waste. The study further recommends establishing a waste management alliance for ecological conservation areas to achieve facility sharing and data interoperability. Through such differentiated approaches and regional coordination, waste governance efficiency can be comprehensively enhanced, providing replicable practical models for similar regions. The findings provide actionable insights into Beijing’s ecological conservation zone, offering transferable frameworks for the formulation of rural waste management strategies.

It should be noted that, in the FCE, subjective membership degree ratings may influence the results to a certain extent. In this study, the weights and scores of each criterion layer are based on expert assessments and questionnaire data, which inherently involve a degree of subjectivity. This subjective evaluation may introduce bias into the results: on one hand, it may overestimate certain social benefit indicators that are difficult to quantify; on the other hand, it may underestimate the actual constraints imposed by objective conditions such as topography and climate on waste management. To some extent, this affects the distribution of the final evaluation results. To enhance the objectivity of evaluation results, future research could introduce interval fuzzy numbers in the model formulation to quantify evaluation uncertainties. Through sensitivity analysis and validation with field operational data, a more realistic evaluation system can thereby be established.