An Optimized Method for BMI in Environmental Projects Based on the Value-Oriented AHP

Abstract

Effective rural solid waste management (RSWM) is crucial for sustainable rural development, particularly in developing countries, which face dual challenges from economic growth and environmental protection. To build a more sustainable business model for RSWM, this study employs a value proposition analysis approach to systematically analyze the multi-level requirements of various stakeholders involved in the current models of RSWM. It then proposes a novel optimizing approach for RSWM models from the perspective of business model innovation (BMI) by integrating the value proposition (VP) theory with the algorithm of the Analytic Hierarchy Process (AHP) to fill the research gap. In this study, an AHP-based evaluating algorithm is firstly proposed based on the viewpoints of multiple stakeholders’ value propositions. Using this method, four typical pilot RSWM models across China are assessed and ranked, followed by a comprehensive analysis of the results and the incorporation of hierarchical criteria from multiple value dimensions. Building on the analysis of the results, optimization strategies for a novel RSWM model are proposed by constructing a conceptual framework of the business model. In addition, the analysis also indicates that both phases of sorting and collection and transportation are the main factors for fulfilling the overall satisfaction of the RSWM models. Lastly, this paper concludes by summarizing the relevant theoretical and managerial implementations of the proposed approach, providing a foundation for the scientific development of appropriate RSWM models by providing a new idea for BMI especially for environmental management projects that include multiple stakeholders.

1. Introduction

As urban and rural development continues to achieve significant milestones, economic and living standards in rural areas have gradually improved. However, this progress has been accompanied by the increasingly prominent issue of rural waste management [1,2] The construction of beautiful rural areas, intrinsically linked to the scientific and efficient management of rural waste, serves as the foundational project for building picturesque rural communities. As a global issue, waste management involves multiple stakeholders, and mishandling it can lead to the tragedy of the commons, resulting in the wastage of social resources [3,4]. Considering the characteristics of waste generation, disposal methods, and classification approaches, different governance models have been proposed and successfully applied [5,6,7]. Rural waste management possesses characteristics such as a public nature, specialization, comprehensiveness, and long-term implications. Developed countries like the United States, Germany, and Japan have established multi-stakeholder collaborative governance systems [8,9,10,11].

At present, China has developed various typical forms of rural waste management models, including decentralized and centralized governance models based on classification premises [1], as well as different modes such as village collection, town transportation, and county-level disposal [2]. Through continuous practical exploration, these models have largely overcome the drawbacks associated with government-led approaches, addressing issues related to management practices and cost control [3,12]. Exemplary instances of multi-stakeholder participation in rural governance models have emerged, as observed in different places such as Jinhua in the province of Zhejiang, Shandong’s Changyi, Sichuan’s Longhu, and Guangxi’s Hengxian [1,13].

Currently, rural solid waste management is undergoing a transition from a government-centric management model to a multi-stakeholder collaborative governance model [2]. This shift aims to ensure the public nature of waste management while actively mobilizing market and societal participation. Existing governance models hold significant lessons for other regions. The recycling and transportation of rural waste constitute crucial aspects of waste management systems, directly impacting the rational utilization of resources and the sustainable development of the environment [1,14]. However, there is a current lack of systematic and scientific evaluation methods, leading local governments to adapt measures according to local conditions without critical guidance in the selection and design of waste recycling and transportation models. The absence of an evaluation method makes it challenging to determine best practice. Therefore, a scientifically effective evaluation of rural waste collection and transportation models is of paramount importance for guiding the diverse governance models adopted by the government.

The value proposition canvas is one of the most widely applied tools for business model innovation and design [15]. Introduced by Osterwalder et al., this tool involves a comprehensive analysis of customer needs from multiple perspectives [16,17]. By embedding scientific value propositions, it facilitates the development of excellent products and services, laying the foundation for the creation of new business models. Applying this method, various novel business models, such as intelligent bookstores, have been constructed based on core user value propositions [18]. These models have been successfully employed in the evaluation of business activities and public affairs involving multiple stakeholders [19]. To the best known of the relevant domains, there is seldom an attempt to assess or optimize business models for a complex social–ecological management system such as the RSWM based on the perspective of VPs.

By applying the value proposition method, a comprehensive analysis of various stakeholders within the rural solid waste collection and transportation system is conducted. This involves systematically organizing their core needs and value considerations. Through the use of fuzzy mathematical methods, a multi-level evaluation index system is constructed, forming an evaluation method for rural solid waste collection and transportation models under the framework of multi-stakeholder value dimensions. This research fills a gap in the field by providing a systematic approach. On one hand, the evaluation method scientifically assesses existing rural solid waste collection and transportation models from the perspective of multi-stakeholder value dimensions. This theoretical support aids in decision-making regarding the choice of transportation models. On the other hand, through the assessment of existing models, issues within these models are clarified. By addressing these issues, the further optimization and innovation of rural solid waste collection and transportation business models can be achieved. To be specific, this study contributes to the relevant fields by proposing a new approach for BMI in environmental management projects based on the VP canvas adopted from the domain of new product development. Moreover, a hybrid evaluating method that integrates the VP canvas and the AHP algorithm is presented for weighting and then optimizing the business models of RSWM. The in-depth evaluation and analysis of four typical RSWM models across China also indicated the most influential factors for the overall degree of satisfaction for proposing more ideal models for RSWM and other environmental management projects involving multiple stakeholders.

The rest of this paper is organized as follows: a brief literature review first explains the theoretical foundations of the proposed method and existing relevant studies; the framework of the methodology is explained in detail to address the main sections of the proposed evaluation approach; subsequently, the proposed evaluation approach is applied to assess several typical rural solid waste collection and transportation models and to rank their priorities based on assessing the results; afterwards, a novel rural solid waste collection and transportation model is formulated based on the analysis results. Lastly, limitations and future opportunities are summarized in the discussion section.

2. Literature Review

2.1. Current Rural Solid Waste Management in China

With the rapid increase in population and economic development over recent decades, there has been a trend leading to an unprecedented increase in rural solid waste generation in developing countries worldwide [2]. The proper management of rural solid waste is essential for achieving sustainable development, especially in developing countries like China [20]. Rural solid waste primarily originates from household waste, which includes wet waste (leftovers, foliage, and food residues), recyclable waste (paper, plastic, metal, and cloth), and non-recyclable waste (hazardous materials, glass, and medicine), with kitchen waste constituting the main proportion [21]. The lack of proper rural solid waste management has led to water, soil, and air pollution in rural and downstream areas, posing serious health risks to local residents [22,23,24].

The efforts that continue in the construction of rural solid waste management systems involve three areas: waste collection, waste transportation, and waste disposal/treatment services [25]. However, several studies have indicated that the rural solid waste management service in rural China is insufficient [26], leading to the majority of villages discarding waste randomly, incinerating it temporarily, or dumping it on riverbanks and roadsides without any initial treatment. Previous studies also showed that the uneven distribution of rural solid waste management services across China is associated with the unequal distribution of resources across regions [12]. Due to the low densities of rural households, regional differences, and unbalanced economic development, rural solid waste has complicated characteristics and diversified sources, contributing to the challenge of its management [26].

Evidence from previous studies has shown that the richer a village, the higher the probability it provides waste collection and transportation services [2]. In China, one of the most widely applied frameworks for rural solid waste management in the relatively developed eastern provinces includes Beijing, Zhejiang, Jiangsu, and Guangdong. This model is characterized by household classification, village collection, township transfer, and county treatment [27]. However, applying this model to facilitate rural solid waste management in other less-developed regions is restricted by various factors, such as high transport costs, a lack of manpower, and the budget for supervision at the local authority level [12].

Efforts have been made by different regions with varying environmental and economic conditions to conduct pilot models for the wise management of rural solid waste [21,28]. However, challenges continue to remain over time from the perspective of China’s complicated national conditions [29,30]. Some studies have attempted to identify the challenges to improving rural solid waste management by revealing obstacles in decentralized generation sources, poor infrastructure for collection, and an imperfect legislation system [12,31]. Most studies on rural solid waste management in China have been narrative or descriptive reviews or opinion articles [12,32,33]. Quantitative studies on this aspect have been limited and are far behind urban areas. Without a widely accepted model for rural solid waste management, it is of vital importance to propose a quantitative assessment method for evaluating RSWM models under different conditions. Local authority decision-making on RSWM strategies has shifted from focusing mainly on economic evaluation or higher government policies to more comprehensive evaluations, including environmental and economic perspectives. However, a systematic quantitative method is still lacking in the relevant research domains.

2.2. Value Proposition-Oriented Methods for Environmental Management

Confronted with the dual challenges of treating a rapidly ever-growing amount of waste and simultaneously transitioning to a circular waste management framework, developing countries like China require the involvement of a much larger number of stakeholders [1]. Waste management is a complex social–ecological system that depends heavily on the participation of a broad range of stakeholders and widespread environmental awareness [34]. In-depth investigation is often the premise for identifying interdependence among different stakeholders and improving their interaction performance within the system [13]. Consequently, the stakeholder theory framework is widely used to uncover the realities of waste management, as well as the complex interests among stakeholders [1]. Specifically, these stakeholders include governments at different levels, waste treatment enterprises, the public, and other non-government organizations involved in this comprehensive project that requires multiple mechanisms to function together [35].

Widespread stakeholder cooperation is the main route to achieving successful waste separation and recycling, as different stakeholders can balance their behavior to use or conserve resources and realize long-term benefits for the ecosystem [34,36]. To formulate the circular framework for the waste management ecosystem, the value proposition (VP) originally designed for business model innovation is often applied to analyze multiple stakeholders’ roles and their network, since the VP plays a key role in the co-creation of value among various stakeholders [37].

The initial definition of the VP describes how a company’s offer differs from those of its competitors and explains why customers buy from the company, mainly in the product development and marketing domain [38]. The development of the VP, with the integration of multiple stakeholders, provides an important mechanism for aligning value within a marketing system. More recently, this hybrid framework has been extended to consider the value configuration of various social actors interacting and exchanging across a network [37]. From the ecosystem viewpoint, each process in waste management from waste generation to disposal is interlinked and influenced by the others along the waste chain [34]. To achieve a closed-loop waste management system that requires broad participation by multiple stakeholders in the social–ecological system with various VPs, three main problems need to be addressed: firstly, how to come up with the novel idea of VPs and convince other partners to commit to it; secondly, how to establish the interactive process of value proposition adaptation; thirdly, how to find a way to align different interests from various stakeholders along the way.

Recently, approaches and tools originally designed for constructing business models—defined as the process of how an organization creates, delivers, and captures values—find their wide application in the innovation of environmental projects founded on the cooperation of multiple stakeholder [39,40]. Among these methods, the business model canvas (BMC) is one of the most widely used tools for supporting innovations in sustainability in various fields of application [40,41,42]. With a graphical template, the BMC consists of a set of building blocks aimed at driving any organization’s innovation, whether private, public, or non-profit [43]. Through the BMC, it is possible to describe how the positive effects of products or services are created, represented as the gains created, and how negative effects are diminished, shown as the pain relievers [43]. As an extensively referenced model, the BMC provides a well-organized structure for visualizing the components, consisting of value proposition, generation, and delivery for organizing business models [44]. Due to its distinguished flexibility and application strength, the BMC remains the most widely used approach for business description [39]. Moreover, the BMC and its variants have been applied to understanding future business models in the case of district heating and other sustainable innovations in environmental projects [45,46]. However, there has not been any attempt to apply the BMC or its variants to analyze the interactions among multiple stakeholders in the rural solid waste management system in relevant fields

2.3. Assessment Methods for Rural Solid Waste Management Strategy

The selection of appropriate environmental infrastructure, such as the optimization of waste treatment systems [47,48], often involves very complex problems that require addressing a set of sometimes conflicting requirements simultaneously. These problems must be supported by mathematical algorithms to evaluate and determine the most suitable option [49]. Thus, selecting the right environmental project alternatives is a multi-criteria decision-making (MCDM) process, as the outcome can impact not only the project’s economic feasibility but also its environmental and social influences [48,50]. In a typical MCDM process, alternatives are evaluated based on various qualitative and quantitative criteria, making it expensive to acquire heterogeneous data and challenging to compare these criteria during the decision-making process [48].

To address the diversity of indicators involved in the MCDM, mathematical algorithms and tools have been proposed to streamline the process. These include the analytical hierarchy process (AHP), the elimination and choice translating reality (ELECTRE) method, the technique for order preference by similarity to the ideal solution (TOPSIS), and the preference ranking organization method for enrichment evaluations (PROMETHEE) [51]. Despite their differences, these algorithms share the common goal of solving decision-making problems by considering multiple criteria and helping decision-makers choose between alternatives by evaluating different indicators [52]. Among these algorithms, the AHP is particularly popular due to its simplicity and effectiveness, as it is logically comprehensible and applicable in a wide range of scenarios [48].

The AHP, built on the theory of relative measurement, minimizes the common drawbacks of MCDM and can solve complex MCDM problems by establishing a prioritization and weighting scale based on the judgments of decision-makers [53]. The AHP’s fundamental objective is to consider different criteria simultaneously by establishing a hierarchy of importance among them, primarily based on the judgments of decision-makers. Decision alternatives are placed at the bottom of the proposed hierarchy, with the decision goal at the top. The AHP allows for the consideration of both quantitative and qualitative criteria [54] and solves MCDM problems structured by multiple interconnected and sometimes conflicting criteria by developing priorities among the decision criteria within the context of the overall decision goal [55].

There are numerous studies applying the AHP to support MCDM in environmental projects, demonstrating its feasibility in assessing many decision criteria simultaneously and indicating the most advantageous options from a comprehensive viewpoint, including technical, social, economic, and environmental feasibilities [56]. The AHP enables solutions that satisfy the multiple objectives of high cycle performance, low environmental impact, and reasonable cost [47]. Typically, the AHP is used in conjunction with other methods, such as the sustainability balanced scorecard, the pressure–state–response model, the digital deposit model, and energy–economic–environment analysis, to create hybrid methods that leverage the advantages of each method. In the typical application of hybrid methods, the AHP is mainly used to determine the weight of each criterion, while the other approach establishes a rational indicator system in a hierarchical structure to achieve the decision goal with multiple dimensions. However, to the best of our knowledge in the relevant fields, there has been no previous effort to combine the AHP with the value proposition method for evaluating models to wisely manage rural solid waste.

2.4. Summary of the Literature Review

The rapid pace of economic development and urbanization has fueled the substantial growth of rural household waste that leads to the ever-growing amount of rural solid waste [1,2]. An approach designed for facilitating reasonable business models for rural solid waste management (RSWM) plays an essential role in the achievement of the sustainability development and goals set by the Paris Treaty, which is even more important for developing countries. Therefore, there are many efforts to propose models for RSWM, allowing for the cooperation of various stakeholders by bridging gaps among different and sometime conflicting interests [12].

The problem of rural solid waste management (RSWM) markedly differs from urban household waste management, characterized by the uneven distribution of RSWM services associated with unequal resource distribution across regions [25], scattered sources of rural solid waste generation, poor infrastructure for treatment and disposal, and an imperfect legislation system. Consequently, there is still a significant gap in the prevalence of different processing models in RSWM. Previous studies have revealed that various stakeholders are interconnected throughout the RSWM life cycle, from generation to treatment. However, a feasible approach to evaluate or prioritize the existing models for RSWM from the joint perspectives of multiple stakeholders is still lacking.

Based on the aforementioned review, a research gap remains to be addressed due to the absence of a feasible evaluation approach to assess and optimize the options for RSWM that simultaneously consider environmental and socio-economic conditions. Although several studies have utilized the AHP-oriented assessment for urban waste treatment, a similar evaluation method for RSWM is still non-existent. To bridge this gap, this research proposes a hybrid evaluation approach by integrating the value proposition and AHP to assess, prioritize, and optimize the solutions for RSWM issues. In the proposed approach, the value proposition is applied to construct a hierarchical structure of the criteria based on the analysis of the input from multiple stakeholders involved in the RSWM system. The AHP is then used to quantify the weights of these criteria, enabling a reasonable assessment to prioritize the most effective strategy for wise RSWM management.

3. Methodology

3.1. Framework of the Proposed Method

In this paper, a hybrid approach that integrates the value proposition method with the AHP is proposed to assess the existing models for facilitating reasonable rural solid waste management (RSWM), aiming to address the aforementioned research gap. To achieve this goal, the framework of the proposed approach is illustrated in Figure 1 and consists of three main phases: the first phase involves building a criteria hierarchy, the second phase entails weighting all the criteria in the hierarchy using the AHP, and the third phase calculates all the alternative solutions based on the criteria and the AHP algorithm. Each phase includes several specific steps to achieve the goal of quantitatively measuring and comparing the models proposed for RSWM from the perspective of aligning different value propositions from the multiple stakeholders who play important roles in the life cycle of RSWM.

3.2. Building of the Criteria Hierarchy Based on the VPs of Multi-Stakeholders

It has been revealed by several previous pilot studies that successful models for RSWM lie in the appropriate cooperation of the multi-stakeholders who play joint roles in the life cycle of rural solid waste from generation to final treatment [29,57]. In other words, those effective RSWM models have allowed for the simultaneous consideration of various value positions from multi-stakeholders interacting in the process of rural solid waste (RSW). Therefore, it is appropriate to apply the AHP to quantitate and prioritize the RSWM models based on the value proposition of multi-stakeholders with a hierarchical criteria set, which is the premise of using the AHP.

Step 1.1 Multi-stakeholders’ analysis in RSWM

From the viewpoint of life cycle, RSW is generated mainly from decentralized rural households and treated during disposal or incinerated in the end. All the stakeholders interact with each other in several main activities that make up the whole RSW life cycle process, which is illustrated in Figure 2.

Referring to Figure 2, there are four main stages in the life cycle of RSW based on the findings revealed by former studies [20,58,59], namely, generation, classification, transportation, and treatment. Moreover, several typical stakeholders with their behaviors are also justified in some pilot studies, such as in the widely accepted model i.e., the household classification, village collection, township transfer, and country treatment. Those stakeholders and their main responsibilities in the cooperation of RSWM are explained as follows.

Rural household: This is the main source for RSW. It is also where the first classification of solid waste occurs, according to certain classification standards in most cases, before RSW is dumped in collection points.

Transport organization: this takes the form of individuals or enterprises that are usually recruited and paid by government who are responsible for collecting and transporting RSWM after the necessary classification by following the required standards.

Treatment enterprises: These enterprises operate the RSWM infrastructure to finally process the RSW in different technical ways. Meanwhile, they play significant roles in technological innovations for more sustainable waste treatment methods.

Government: The government plays a significant role in facilitating the overall RSWM by administrating all the stages in the life cycle of RSWM. To be specific, the government supervises the classification and dumping behaviors of rural households and the operation performance of both transportation organizations and waste treatment enterprises.

Step 1.2 Collection of voices from multi-stakeholders

This study has conducted field investigations that mainly take the form of in-depth interviews in six rural villages in the Beijing–Tianjin–Hebei region, China, targeting the main multi-stakeholders, including the households, government officials, employees, or individuals involved in recycling and transportation enterprises and the personnel from waste treatment companies. Field investigations were used to extract the value propositions and main requirements of all the multi-stakeholders involved in the RSWM scenario.

Step 1.3 Classification of VPs based on the VP canvas

A value proposition refers to the benefits that users anticipate from specific products and services. It is a deep exploration of users’ real needs and systematically considers the core value of innovative products or services from the perspective and context of the user. It is the most critical component in building a successful business model. The value proposition canvas is the central design method and tool for a value proposition, enabling a concise and clear understanding and systematic organization of user needs. It clarifies the value creation model to ensure that the design, testing, and delivery of products or services align with the core value concerns of the users.

The value proposition canvas consists of two interrelated parts: customer profile and value map.

The customer profile forms the foundation, primarily through collecting user opinions and observing behaviors and activities, categorizing user needs into three aspects: jobs, pains, and gains.

Customers’ jobs not only include the functional tasks related to interacting with the target product or service, like using a product or participating in an activity for specific operational behaviors, but also encompass related social behaviors and personal/emotional behaviors and reactions, as well as the corresponding work background and scenarios.

Pains include the potential adverse outcomes, failures, and risks encountered in the interaction process with the target product and service, such as unwanted negative effects, obstacles, or detrimental factors that cause loss or reduce efficiency, and the risks of possible errors and losses.

Gains encompass the expected and potential benefits users seek in their interactions with the target product and service, specifically including resource savings, quality exceeding expectations, social value, and the fulfillment of higher-level needs.

Based on the customer profile, the value map on the left side lists the key features of the target product or service that align with different user needs. The value map translates the user’s value proposition into the main value parameters (MVPs) for product service design, guiding subsequent research and development and business operations. Corresponding to the various requirements in the customer profile, the value map also includes three dimensions:

Product or service: the offered key parameters and features that correspond to the expected goals, functions, and requirements listed in the user’s jobs.

Pain relievers: solutions for undesired results, obstacles, and risks proposed by the user.

Gain creators: solutions to create gains to meet users’ expected and potential needs.

The current evolution and development of multi-stakeholder collaborative governance models in rural waste collection and transfer systems have led to the coexistence of various practical models. By employing the Value Proposition Design analysis method to examine the value propositions of stakeholders within the rural waste collection and transfer systems, summarizing the MVPs (Most Valuable Propositions) of these stakeholders is fundamental to proposing evaluation indicators for rural waste collection and transfer models. This approach underscores the importance of understanding diverse stakeholder perspectives to enhance the effectiveness and efficiency of waste management practices in rural settings. The results of the field investigations are summarized and formatted into three categories and shown in

Table 1. VPs of stakeholders in the RSWM classified into three types.

Stakeholder	Job Performance	Pain Points	Expectations
Rural Households	Clean surrounding environment, timely waste transfer, neat village appearance	Recycling point too far, difficult waste sorting methods, lack of reward for extra sorting labor	Door-to-door collection, payment for waste collection, psychological rewards like honorary titles
Rural Government	Village appearance, resident satisfaction, effectiveness of waste classification promotion	High financial input, resident complaints, repetitive labor, investment	Households’ autonomous classification, low-cost transfer, policy subsidies, fund recovery, legal regulations
Transfer Enterprises (Individuals)	Labor intensity during collection, transfer timeliness and efficiency, service profit growth	Poor initial classification, manual sorting, unscientific collection points distribution, health and hygiene risks	Higher recycling value and profit, automated and unmanned sorting, increased bargaining power
Resource Recovery Enterprises	Waste processing capacity, processing efficiency, waste processing cost	Production interruptions due to discontinuous recycled materials, high costs leading to losses	Higher government subsidies, advanced harmless processing technology, cost savings through automation

.

Step 1.4 Formulation of the hierarchy of VPs

The value proposition sequences of stakeholders in RSWM, as derived from interviews in the field investigation, were compiled into a questionnaire. This questionnaire was then distributed as a means of conducting a survey to validate the authenticity and importance of each identified need within the tentative requirement sequences. In this sense, the process of transforming qualitative interview data into a quantifiable survey format is described, which aims to systematically assess and prioritize the needs of stakeholders involved in rural waste management. This approach underscores the importance of empirical validation in understanding the multifaceted needs within complex RSWM.

Based on the information collected from the survey questionnaires, statistical analysis and frequency analysis were conducted. The results of the comprehensive analysis based on the importance and statistical frequency provide the Most Valuable Propositions (MVPs) and core value propositions corresponding to each stakeholder.

More than 600 questionnaires were distributed through online and field channels. Of these, 400 were distributed to rural households, yielding 365 valid responses. In addition, 120 questionnaires were given to industry professionals, with 85 valid responses received. Government officials were handed 60 questionnaires, from which 50 valid responses were obtained, and 40 were distributed to waste management professionals, with 32 valid responses collected. Concurrently, during the questionnaire survey, a five-level Likert scale was implemented to confirm each value proposition, using 1, 3, 5, 7, and 9 to measure the importance of each value proposition. According to the results of the questionnaires’ statistical analysis, the key value propositions and significant needs of each stakeholder were identified as shown in Table 1, and the importance of each value proposition was calculated and depicted in Figure 3.

RSWM involves the value propositions of various stakeholders. Therefore, evaluating its effectiveness is not limited to a single set of criteria but extends to fulfilling the value propositions of multiple stakeholders, making it a typical multi-attribute, multi-objective decision-making problem. The AHP is a multi-attribute decision analysis method that combines qualitative and quantitative approaches. It is particularly suitable for complex target structures where data may be lacking. The AHP employs a hierarchical structure to describe the decision-making problems, reflecting their stepwise control relationships. The top level represents the decision-making goal, with the criteria layers in the middle, which can be broken down into more sub-criteria as needed. By utilizing pairwise comparisons, the AHP combines various complex factors and the personal elements of decision-makers to logically and quantitatively express decision-making. The AHP method is widely used for scientific decision-making in systems or plans involving multiple attributes, such as risk assessment.

Based on the key value propositions and confidence requirements of the stakeholders in the rural waste collection and transfer system as identified in Table 1, the AHP method is used to establish a hierarchical structure for evaluating and decision-making regarding rural waste collection and transfer models. This structure primarily includes three levels: the objective layer, the primary criteria layer, and the secondary criteria layer, as shown in Figure 3.

Referring to Figure 3, the primary criteria layer comprises four specific factors: C1, rural households’ value propositions; C2, the government’s value propositions; C3, the value propositions of enterprises and individuals in the industry, and C4, waste treatment enterprises’ value propositions. Based on the value propositions of various stakeholders as summarized in

Table 2. Means of assessment scores for the criteria of stakeholders’ relative importance.

Based on Means	C1	C2	C3	C4
C1	1	5/11	17/5	29/5
C2	11/5	1	31/5	37/5
C3	5/17	5/31	1	17/5
C4	5/29	5/37	5/17	1

and according to the statistical analysis therein, each primary criterion can be further divided into several secondary criteria as shown in Figure 3.

3.3. Weighting of Criteria with the AHP

Step 2.1 Recruit the group of evaluators

At first, five researchers specializing in the relevant field were recruited to conduct the pairwise comparisons of the significance of the value propositions among various stakeholders within the RSWM.

Step 2.2 Mark the relative importance of stakeholders

Using Saaty’s 1-to-9 scale [60], where 1 means equal importance and 9 means the extreme importance of one element over another, the arithmetic means of these pairwise comparisons marked by the five recruited evaluators are presented, as shown in Table 2.

Step 2.3 Quantitatively asses each criterion using the AHP

The pairwise comparation matrix is firstly constructed using Formula (1) by comparing the pairwise criteria in terms of their impact on an upper level criterion. If criterion A has one of the above values when compared with B, then B has the reciprocal value when compared with A.

$$A=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ a_{n1}& a_{n2}& \cdots & a_{nn}\end{array}\right]\approx \left[\begin{array}{cccc}{\displaystyle \raisebox{1ex}{$w_1$}\!\left/ \!\raisebox{-1ex}{$w_1$}\right.}& {\displaystyle \raisebox{1ex}{$w_1$}\!\left/ \!\raisebox{-1ex}{$w_2$}\right.}& \cdots & {\displaystyle \raisebox{1ex}{$w_1$}\!\left/ \!\raisebox{-1ex}{$w_n$}\right.}\\ {\displaystyle \raisebox{1ex}{$w_2$}\!\left/ \!\raisebox{-1ex}{$w_1$}\right.}& {\displaystyle \raisebox{1ex}{$w_2$}\!\left/ \!\raisebox{-1ex}{$w_2$}\right.}& \cdots & {\displaystyle \raisebox{1ex}{$w_2$}\!\left/ \!\raisebox{-1ex}{$w_n$}\right.}\\ ⋮& ⋮& \ddots & ⋮\\ {\displaystyle \raisebox{1ex}{$w_n$}\!\left/ \!\raisebox{-1ex}{$w_1$}\right.}& {\displaystyle \raisebox{1ex}{$w_n$}\!\left/ \!\raisebox{-1ex}{$w_2$}\right.}& \cdots & {\displaystyle \raisebox{1ex}{$w_n$}\!\left/ \!\raisebox{-1ex}{$w_n$}\right.}\end{array}\right]$$

(1)

The pairwise comparison matrix is then normalized using Formula (2) by dividing each element of a column by the sum of its column.

$$R_{ij}={\displaystyle \frac{a_{ij}}{{\sum }_{k=1}^n{a}_{kj}}}$$

(2)

The average raw value is subsequently normalized using Formula (3), which gives the priority vector (weights) for each criterion/sub-criterion.

$$w_i={\displaystyle \frac{1}{n}}{\sum }_{j=1}^n{R}_{ij}$$

(3)

The relative importance among different stakeholders rated by the results shown in the table is normalized as $W=\left[w_1,w_2,w_3,w_4\right]$ = [0.293, 0.546, 0.111, 0.050].

The consistency of the index is checked by the CI that is calculated through Formula (4), where ${\lambda }_{max}$ is the average of the sum of each column of the initial pairwise comparison matrix times its corresponding weight, and it can be calculated through Formula (5), here, where $A$ is the pairwise comparison matrix and $w$ is the weight vector.

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(4)

$${\lambda }_{max}={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{\displaystyle \frac{{\left(Aw\right)}_i}{w_i}}$$

(5)

Compare the CI with the Random Index (RI), which depends on the order of the matrix, which is calculated through Formula (6).

$$CR={\displaystyle \frac{CI}{RI}}$$

(6)

Calculating the results of the relative importance of stakeholders include ${\lambda }_{max}=4.1209,CI=0.0403,$ and $CR=0.0447<0.1$. The CR is less than 0.1, which is considered acceptable.

Step 2.4 Normalize the overall hierarchical criteria

The comprehensive utilization of the importance scores collected by the questionnaires is employed to gather statistical results on the importance of each secondary indicator. Subsequently, Formulas (1)–(6), along with comparative relationships, are applied to determine the relative weights of each secondary indicator, with the results illustrated in the left part of Figure 4.

3.4. Evaluation of RSWM Models by the Proposed Method

Step 3.1 Choice of the primary analysis of typical RSWM models

In summarizing the governance experience of developed countries globally, the current RSWM models in China have undergone a period of practical implementation and optimization. According to the specific characteristics of these pilot practices [12,57,59,61], they can generally be divided into four separated models:

Model 1: The Changyi model in Shandong province

This model is inspired by the Public–Private Partnership (PPP) model in the United States. It entrusts professional environmental sanitation companies with the unified management of rural domestic waste, supervised by governmental environmental sanitation departments. The waste resource utilization projects are developed and managed through a franchise operation, with the costs shared between the government budget, rural collectives, and ordinary villagers. This model, separating management from operation, enhances waste management efficiency and service quality.

Model 2: The Jinhua model in Zhejiang province

Based on the German system of the government-led collection, transfer, and processing of waste (4), this model has evolved into a typical government-led, villager participation rural waste classification and transfer scheme. It emphasizes the active participation of rural households in waste sorting at the source to reduce the waste volume. The specific transfer process includes a four-part waste classification method that takes place twice and a transfer mode that ensures waste does not touch the ground. The government invests in and subsidizes the waste transfer and processing equipment, ensuring a sustainable waste management mechanism.

Model 3: The Hengzhou model in Guangxi province

This model represents a regional centralized processing approach. Garbage processing centers are established at the village level, and centralized ecological incineration devices are constructed. This practically achieves the disposal of rural waste within the village itself. Additionally, village sanitation workers are recruited to undertake cleaning, transportation, and classification tasks, representing a typical village collective-led waste disposal model.

Model 4: The Danling model in Sichuan province

Developed for China’s western regions, characterized by remote geography and less developed economies, this model is a waste recycling and transfer scheme led and mainly operated by rural households. It involves selecting village sanitation contractors through village-internal tendering, essentially a PPP model. The contractors sign agreements with village committees to undertake village waste transportation and cleaning duties. Introducing market competition, the contractors are responsible for secondary waste classification, collection, and transportation services, significantly reducing waste management costs.

Step 3.2 Interaction analysis of multi-stakeholders

Applying the analysis method proposed in Step 1.1, firstly, the participation activities of various stakeholders in each model are organized. Utilizing the legends and modeling strategies in Figure 2, the relationships between various stakeholders in the different activities of the RSWM are established. This process enables the determination of the corresponding relationships between each model and the secondary evaluation indicators. The analysis results of all the typical models are established as shown in Figure 5.

Step 3.3 Determination of multi-stakeholders

By analyzing the main characteristics of the four aforementioned rural waste recycling and transfer models, particularly through a comparison of their strengths and weaknesses, a team of five recruited researchers conducted an assessment based on the secondary evaluation indicators presented in Figure 3. Utilizing a scale of natural numbers from 1 to 9 to represent the degree of ideality, each model’s specific values under various indicator systems were determined. These values were then averaged to calculate the assessment values, representing the current degree of ideality of each model under the various evaluation indicators. The specific values are illustrated in the right part of Figure 4.

Step 3.4 Determination of multi-stakeholders

Applying the composite weight coefficients of the four categories of stakeholders and utilizing Formula (7), where $w_i$ indicates the relative weight of the stakeholders, $c_{ij}$ indicates the normalized weights of each secondary criteria, and ${\overline{v}}_{ij}$ indicates the average value of the scores, judged by five raters, for alternative RSWM models related to certain secondary criteria, the comprehensive evaluation results for each model are calculated, as shown in Figure 5.

$$V={\sum }_{i=1}^4{w}_i·\left({\sum }_{j=1}^n{c}_{ij}·{\overline{v}}_{ij}\right)$$

(7)

4. Results Analysis and Optimizing the BM of RSWM

4.1. Results of Prioritization with Possible Explanations

Based on the results in Figure 4, the Danleng model in Sichuan province, i.e., the fourth alternative model, ranks the highest in the comprehensive evaluation scores, exhibiting unparalleled levels of idealization across the four dimensions of the value propositions; the Hengzhou model ranks in second place; and the Changyi model is third, with the Jinhua model as the last one.

The computational analysis of the assessment results enables the determination of the extent of idealization for the four RSWM models in relation to the value dimensions of the four stakeholder groups. There are several possible explanations for the prioritization results to reveal a set of significant influencing factors.

According to the relative weight of each criterion on the first level, it is evident that the government’s value proposition, i.e., C2, occupies the predominant share in the overall value dimensions, which supports the comment that the rural government usually has the highest weight in the decision-making of RSWM options indicated by a former study [20]. Amongst these RSWM pilot models, the Dengling model demonstrates the highest value dimension for the rural government; meanwhile, the other three models are relatively minimal with regards to the C2 criterion. The most significant reason may lie in its highest scores from the viewpoint of rural governments’ VPs. Detailed comparisons reveal that the government in the Dengling model can leverage very limited financial investment to mobilize the active participation of a vast number of rural households and achieve higher satisfaction levels at the same time.

The comparison further reveals that the dimension centering on rural households ranks in second place in the determination of idealization for RSWM models based on the weights in Figure 5. Regarding the criteria on the VPs of rural households, the Hengzhou model registers the highest satisfaction with respect to responses to their voices. This can be attributed to the model’s approach of employing responsible village sanitation workers for door-to-door garbage collection, coupled with the relatively low fees charged. Consequently, the Hengzhou model scores better in terms of C11 to C13, indicating a better performance in proximity to RSW instruments and the convenience of RSW sorting and collection services. In contrast, the Jinhua model imposes higher demands for active farmer participation and falls short in proximity and convenience in waste classification. Additionally, it has stringent requirements for incentivizing farmer participation, leading to a significantly lower score in the farmers’ value dimension, which may explain the reason why this model has the lowest score in all the exemplar models.

Since the government and farmers constitute 84% of the total weight in the comprehensive evaluation, a RSWM model that adequately addresses the value propositions of both these stakeholders can achieve a high overall score. Based on the aforementioned analysis and ranking results, the Danling model scores the highest, effectively balancing the core value propositions of both the rural government and farmers, achieving the active participation of the majority of rural households with minimal financial investment from rural government, followed by the Hengzhou model, which scored the highest satisfaction of the VPs of rural households.

4.2. Main Factors and Their Correlations Related to RSWM

In this study, the performance of four typical widely used pilot RSWM models in China are quantified using a hybrid method that jointly applies value proposition theory and the AHP. In-depth explanations are revealed by a correlation analysis of four-dimensional VPs, which is applied to reveal the relationships among the VPs from different stakeholders, which are shown in

Table 3. Correlation analysis of multi-stakeholders.

Indicators’ correlations		C1	C2	C3	C4
C1	Pearson coefficient	1	0.058	0.026	−0.316
C2	Pearson coefficient	-	1	0.776 **	0.198
C3	Pearson coefficient	-	-	1	0.096
C4	Pearson coefficient	-	-	-	1

** indicates statistically significant coefficient at the 0.01 level (two-tailed).

. Referring to Table 3, there is a significant positive correlation between the VPs of the rural government and that of the sanitation workers who take responsibility for providing RSW collection and transportation services, while a certain negative correlation exists between the VPs of rural households and the VPs of waste treatment enterprises.

Moreover, further correlation analysis of the secondary criteria in

Table 4. Correlation analysis results of secondary criteria.

Correlation	C11	C12	C13	C14	C21	C22	C23	C31	C32	C33	C41	C42	C43
C11	1	0.500	0.695	0.041	0.667	−0.287	−0.498	−0.295	−0.042	0.427	−0.609	0.661	0.218
C12	-	1	0.532	−0.622	0.554	−0.810	−0.867	−0.702	−0.766	0.215	−0.475	0.031	0.286
C13	-	-	1	0.018	0.680	−0.313	−0.569	−0.235	−0.076	0.193	−0.657	0.544	0.067
C14	-	-	-	1	−0.104	0.726	0.591	0.667	0.874	−0.118	−0.140	0.298	−0.629
C21	-	-	-	-	1	−0.432	−0.519	−0.517	−0.161	0.411	−0.693	0.558	0.278
C22	-	-	-	-	-	1	0.864	0.870	0.785	−0.190	0.350	0.243	−0.323
C23	-	-	-	-	-	-	1	0.761	0.700	−0.181	0.476	−0.025	−0.344
C31	-	-	-	-	-	-	-	1	0.806	−0.179	0.348	0.150	−0.461
C32	-	-	-	-	-	-	-	-	1	0.005	0.072	0.352	−0.407
C33	-	-	-	-	-	-	-	-	-	1	−0.425	0.437	0.384
C41	-	-	-	-	-	-	-	-	-	-	1	−0.347	0.176
C42	-	-	-	-	-	-	-	-	-	-	-	1	0.195
C43	-	-	-	-	-	-	-	-	-	-	-	-	1

shows that the government can significantly reduce the labor intensity of sanitation workers by motivating the majority of rural households to actively participate in source reduction, waste sorting, and designated disposing. This is achieved through the strategic placement of designated collection points. Consequently, a reduction in the labor intensity of waste sorting, collection and transportation also decreases the expenses the government needs to bear for waste collection and transportation, ultimately reducing government expenditure.

Referring to Table 4, groups of contradictions are observed in the VPs of different stakeholders, revealed by the correlation between the secondary criteria in Table 4. For example, the analysis results in Table 4 indicate that the convenience of classification i.e., C12 for rural households, is significantly negatively correlated with the rural government investment and labor intensity of sanitation workers indicated by the criteria of C22, C23, C31, C32, etc., which is in line with the finding that the distance between the residence and garbage collection facilities is negatively significantly correlated with rural households’ RWS disposal behaviors [10]. Another obvious negative correlation is observed between the VPs of rural households and waste treatment companies. Possible reasons may lie in the fact that technological advancement and the cost control of treatment companies rely on more complex and detailed requirements for waste classification.

Based on a former study [25], there are mainly three phases, including RWS sorting and collection, transportation, and treatment, in the whole RSWM process. The correlation between the secondary criteria and specific RSWM phases are illustrated by Figure 6.

4.3. Optimization of the BM of RSWM Based on the Proposed Approach

Drawing upon the respective strengths of existing models, a new business model (BM) for RSWM has been developed, which combines the advantageous components from different models to achieve a higher satisfaction for the muti-stakeholders overall. This novel BM for RSWM is constructed on the foundational architecture of the Danling model, integrated with the convenience advantages for rural households as seen in the Hengzhou model. Following the method of value design, the optimized results of the rural waste recycling and transfer model are presented in Figure 7.

The model effectively integrates the strengths of the existing four rural waste recycling and transfer models and further optimizes and develops them, creating a new type of rural transfer model, whose principal mechanism is shown in Figure 8. The optimized rural waste recycling and transfer model, oriented towards multi-stakeholder value propositions, possesses the following notable features.

First, to better realize the value proposition of farmers, the model adopts a scheduled door-to-door garbage collection method. Farmers no longer need to dispose of their waste at designated points. Timely door-to-door collection also effectively improves the timeliness of waste collection. Moreover, enterprises profiting from waste processing provide farmers with waste sorting facilities, such as segregated waste bins and garbage bags.

Second, higher government satisfaction is reflected in the government’s responsibility to invest in and construct nearby centralized composting facilities in villages. This allows part of the waste to be processed for resources locally. The government only needs to be responsible for educating farmers about waste segregation, recruiting village sanitation workers, and implementing a fixed remuneration system. This significantly reduces the government’s investment in waste collection and transfer, while effectively improving the farmers’ satisfaction.

Third, village sanitation workers, operating under a fixed remuneration system, will communicate and mutually supervise some aspects of waste collection and sorting with farmers according to their workload and convenience. Due to the community relationship between village sanitation workers and farmers, communication costs are greatly reduced, and the quality of the related work is improved. Village sanitation workers, in collaboration with external waste collection and transfer organizations, can reduce their workload, further enhancing their satisfaction.

Fourth, as enterprises profiting from waste processing, waste treatment companies must ensure the scale of their operations, which depend on the quality of the initial sorting at the farmer’s end. Therefore, it is necessary to provide primary waste collection facilities, such as garbage bins and corresponding recyclable facilities. Building appropriate waste transfer stations and waste transportation logistics organizations ensures the scale and quantity of the waste awaiting processing. Waste treatment companies must continuously enhance their profitability through technological innovation, as stronger profitability can provide more mature capital for source sorting and waste transportation.

5. Discussions

5.1. Main Contributions

The contributions of this study on the relevant domains mainly include three aspects.

With a theoretical lens, the main contribution of this study lies in the proposal of a new hybrid evaluating approach by integrating the AHP algorithm with value proposition canvas models to assess the degree of ideality for RSWM business models. Building on the framework of this approach, on the one hand, strategies can be proposed to optimize the business models for environmental management projects that take the form of an ecological–economic–social system, including the interests of multiple stakeholders. On the other hand, the application of the value proposition canvas, which is usually used for BMI in new products or services, is further extended by being introduced to and then testified to be feasible in the domain of environmental protection projects. In addition to the practice of RSWM, the potential for using the proposed approach can be explored to prioritize the BM of other environmental projects.

With regards to the evidence for improving the RSWM practice, there are main findings on several facets revealed by the results of building the two-level criteria hierarchy and the correlation analysis among those criteria. Firstly, the relative importance of VPs from the four stakeholders interacting in the RSWM are calculated using the proposed method with the results indicated in Figure 4. Among those stakeholders, the VPs from the rural government play the most significant role in the overall performance of the RSWM models, which supports the point raised in former studies that economic evaluation or policies from rural government have the highest weight in the decision-making of RSWM options [14]. Secondly, the VPs from rural households rank second place, which means that the voices of rural households also have an important influence on RSWM, since the active participation and cooperation of rural households are key to the successful proposal of RSWM models [12]. From this lens, successful RSWM models need to take the reasonable considerations of the VPs from both the rural household and rural government simultaneously. Thirdly, referring to Figure 6, the first two phases take the major propositions (86.5%) of the overall RSWM model, which supports the finding that the cost of collection and transportation can account for up to 80% of the total cost of the RSWM system [62]. Therefore, the success of designing models for facilitating effective RSWM chiefly lie in proposing appropriate methods for boosting the cooperation of multi-stakeholders in the RSW sorting, collection, and transportation phases by simultaneously considering their VPs.

With respect to implementation, the findings from the results analysis indicated that the overall performance of the BM for RSWM lie in appropriate answers to the VPs from different dimensions collected from multiple stakeholders. To be specific, those findings are useful for inspiring more reasonable solutions to various implementation problems. From the perspective of the rural government, who take the main responsibility for constructing the BM and strategies for RSWM, it is suggested that prioritizing and improving the satisfaction of rural households is key and fundamental to encouraging their active participation in waste classification and collecting, as well as improving the time and efficiency of the transportation work. In terms of rural households, it is also useful to motivate their active participation in the classification and collection by educating them about the efficient and timely waste transportation and treatment requiring their efforts and cooperation. Given that the demand weight of treatment companies is small within the entire RSWM process, these treatment enterprises require segregation and large-scale waste treatment, since it enhances the efficiency of waste resource recovery and significantly reduces the unit cost. On the other hand, to meet the needs of some large-scale treatment companies, there can be delays in the timeliness of rural waste collection and transfer, requiring a process of waste concentration. This increases the workload related to waste transfer and aggregation, thus lowering farmer satisfaction. Therefore, treatment enterprises are suggested to take an active part in the classification, collection, and transportation phases to refine those practices, with the purpose of significantly reducing the total cost for waste treatment. For policy makers or regulators, as rural waste classification practices keenly depend on the initial sorting by farmers, it is advisable to impose higher requirements on source sorting by rural households, as well as reasonable compensatory measures to compensative for the increase in their learning costs and the labor involved in participating in waste classification.

5.2. Limitations and Opportunities for Future Study

This study utilizes the principles and methods of Value Proposition Design to systematically and scientifically evaluate existing rural waste collection and transportation models and to optimize and develop a new BM for RSWM from the perspective of the key value propositions of multiple stakeholders. However, the research method’s applicability and the complexity of the research subject introduce three significant limitations, inspiring in-depth research in the future.

Firstly, the Value Proposition Design method is typically applied to products and services aimed at a single customer group. Yet, the RSWM system involves multiple stakeholders, among whom value propositions can often conflict. For instance, contradictions between the value propositions of rural households and treatment enterprises are evident in the correlation analysis of the secondary criteria in Table 4. However, due to the limits of this research scope, there is no further discussion about strategies or measures to cope with those contradictions among VPs from different stakeholders. Thus, there is huge potential to raise new capable methods and tools for mediating conflicts among multiple stakeholders to more effectively meet their diverse value propositions.

Secondly, despite the main research purpose of this study being to propose two hierarchy criteria adapted from the comprehensive analysis of the VPs from the four involved stakeholders in RSWM to assess and optimize the BM for RSWM by addressing the balance between the interests of multiple stakeholders, the very limited sample size applied by this study has hindered any in-depth correlation analysis and sensitivity analysis of the criteria as the influencing factors for the overall assessment of RSWM models. These problems can be resolved by conducting a more robust correlation analysis to unveil significant factors for optimizing RSWM practices in the future.

Thirdly, the development and performance of RSWM models in China are profoundly influenced by the country’s economic and socio-environmental conditions. Given China’s vast territory, there are significant economic disparities among different rural areas. Consequently, the current state of existing RSWM models varies across these areas, with multiple models coexisting. Therefore, local adaptations are crucial, especially when deciding on rural RSWM practices based on the volume and type of waste generated. A more scientific measurement model is required to identify the optimal practice model for more effective RSWM.

6. Conclusions

With the purpose of providing a comprehensive prioritization approach for assessing and optimizing the BM of RSWM, this paper has provided a new approach by integrating the method of Value Proposition Design and the AHP algorithms. With the proposed approach, four pilot RSWM models were used as exemplar cases to reveal the significant influencing factors for the appropriate BMI in environmental management projects, taking the form of complex ecological–economic–social systems that involve multiple stakeholders usually accompanied by contradictory interests. An analysis of the prioritizing results revealed that the RSWM model that effectively met the core value propositions of both the government and rural households obtained the highest rated score across the four comprehensive value dimensions of stakeholders. Additionally, the research identified various interrelationships among the value proposition dimensions of different stakeholders. Leveraging these interrelationships and building on existing models, the Value Proposition Design method was utilized to propose an optimized model for rural waste collection and transportation. This paper concluded with a summary of methodological limitations and the applicability of the results in this current study, as well as a forward-looking perspective on future research directions.