Evaluation System Creation and Application of “Zero-Pollution Village” Based on Combined FAHP-TOPSIS Method: A Case Study of Zhejiang Province

Abstract

With the implementation of China’s rural revitalization strategy, the construction of low-pollution villages has become an urgent demand for rural residents to achieve an ecologically livable environment. This paper aims to construct a new evaluation system for a “zero-pollution village” inspired by the principle of CASBEE. A village is regarded as a relatively independent ecosystem, and the evaluation system is designed to improve the “internal environmental quality Q” of a village while reducing the “external environmental load L”. Thus, the evaluation system included two target strata (Q and L) and 3 layers comprising 6 criteria layers, 21 sub-criteria layers, and 76 indicators in the indicator layers. A new combined weight method based on FAHP-TOPSIS was presented to determine the weights of each layer of indicators. After verifying the evaluation system by applying it to 3 representative villages, 26 villages in Zhejiang Province were evaluated. These results indicate that the evaluation system was effective for evaluating the construction of a zero-pollution village. The current village environment in Zhejiang Province has been significantly improved; however, the vast majority of villages have not yet reached the zero-pollution standard.

1. Introduction

1.1. Research Background

The concept of the “zero-pollution village” comes from the United Nations Environment Programme’s Towards a Zero-Pollution Planet initiative, which means to build a sustainable village where pollutant emissions do not exceed the self-cleaning capacity of the environment, where multiple parties collaborate when building, where resources are recycled and used efficiently, and where the ecology is clean and healthy. “Zero pollution” aims to achieve the ultimate goal of minimal pollution, adequate resource utilization and safe disposal in villages, and a dynamic balance between pollution and purification [1].

Statistics from the World Health Organization (WHO) estimate that more than 13 million deaths around the world each year are due to avoidable environmental causes. In low- and middle-income countries, some 829,000 people die as a result of inadequate water, sanitation, and hygiene each year [2]. Furthermore, environmental pollution also reduces human wellbeing and social and economic development. From the United Nations Declaration on the Human Environment 1972 to the “Stockholm+50” meeting in 2022, the international community has undergone half a century of environmental protection and has gradually reached a consensus on addressing environmental and climate issues. The theme of the third United Nations Environment Assembly held in Kenya was “Building a Zero-Pollution Planet”. Erik Solheim, Executive Director of the United Nations Environment Programme, presented a report on the theme “Towards a Zero-Pollution Planet” to the conference, explaining the challenges posed by global environmental pollution and degradation and proposing a comprehensive framework for action and solutions to combat pollution on this basis [3].

With its rapid economic development, China is also facing serious challenges in pollution control. In 2017, China proposed the strategic plan of “rural revitalization”, which is “prosperous industry, ecological livability, civilized countryside, effective governance, and rich living”. Among them, “ecological livability” is ranked second in the “rural revitalization” strategic plan, which indicates China’s determination to actively promote green development in rural areas, advocate for green and low-carbon lifestyles, and implement the rural revitalization strategy.

Zhejiang Province is located in the Yangtze River Delta region of China, with a developed rural economy and a large rural population base. After the reform and opening-up in China, with the rapid development of Zhejiang’s rural economy, environmental pollution and ecological damage have become increasingly serious. In order to change this situation, Zhejiang Province has undertaken the Thousand Villages Demonstration and Ten Thousand Villages Improvement project in rural areas in the past twenty years, which achieved obvious results and won the UN Champions of the Earth Award in 2018. It has set a benchmark for the improvement in rural habitats in China and also provided a model for global ecological restoration to achieve environmental protection and economic development. Combined with the waste-free city pilot project just launched by the state, the construction of zero-pollution villages is a new path to effectively promote rural ecological revitalization and build beautiful villages with high quality development [4]. From 2019, Yuantou Village in Zhejiang Province began the creation of a zero-pollution village, marking the beginning of the construction of zero-pollution villages in Zhejiang Province [4,5]. In this context, it is important to establish a scientific evaluation index system to assess the completion of zero-pollution villages.

1.2. Literature Review

1.2.1. Experiences of Rural Community Construction

There are many successful experiences in rural revitalization around the world, such as Village Improvement in the United States, the Village Creation Movement in Japan, the New Village Movement in South Korea, Village Renewal in Germany, the Agricultural Land Organization in the Netherlands, etc. [6,7]. These successful experiences provide a reference for the construction of rural communities in China [8].

At present, there is no concept of a zero-pollution village outside China; the similar concept of an eco-village is different from that in China, as its association with rural areas and agriculture is relatively weak. Whether in urban or rural areas, residential clusters based on low energy consumption and green ecological concepts can be referred to as “eco-villages” [9]. This concept was first formally introduced by G. Robert in 1991, who systematically described the challenges and development problems faced by eco-villages [10]. According to the Global Ecovillage Network, a representative organization of the International Ecovillage Movement was established in 1995, and as of now, there have been more than 1000 registered eco-villages worldwide [11], e.g., Earthaven Eco-Village in the United States, Crystal Waters Eco-Village in Australia, Gaia Eco-Village in Argentina, and Munkesoegaard Eco-Village in Denmark [12]. These cases indicate that eco-villages are a new type of ecological community that advocates for sustainable development in ecology, economy, culture, and society, with a core concept of a low environmental impact [9].

After years of exploration, China has formed development models that are suited to the local characteristics of rural revitalization, such as the Anji Model, Yongjia Model, Gaochun Model, Jiangning Model, and so on. The construction of a beautiful countryside is flourishing and has provided a large practical sample volume for the construction of an evaluation system.

1.2.2. Evaluation Tools and Approaches of Rural Communities

Since the 1990s, many countries and regions around the world have successively introduced community ecological evaluation tools [13]. In 2009, BREEAM Communities, an evaluation tool for green ecological communities, was launched in the UK to evaluate urban areas of community size and above. In the same year, the US also developed LEED ND, an eco-evaluation system for communities and neighborhoods that are smaller than communities, which can be used to evaluate larger areas (urban areas). In Japan, the CASBEE consists of different environmental performance assessment tools that are tailored to specific purposes [14], such as CASBEE for the urban development of a building block assessment and CASBEE-City for environmental assessments at the city scale [15]. Germany, on the other hand, introduced the internationally applicable ecological evaluation system DGNB UD for urban and business areas in 2016. However, there has not been as much in the literature in terms of comprehensive evaluations on the quality of rural communities abroad, probably owing to the small urban–rural differences in developed countries and the fact that environmental governance does not specifically distinguish between urban and rural areas [16].

In addition, scholars have performed relevant studies on rural ecological community construction and evaluations. Beeton et al. in Australia considered rural community sustainability based on human–nature interdependence, multiple forms of capital, and conversion properties, including environmental improvement management, community development models, and proposed community criteria for sustainable and rural ecological development [17]. Franco et al. presented a method to assess the ecological suitability of land-use change in southeast Portugal [18]. Uwasu et al. discussed an evaluation method to rebuild declining rural communities and improve the wellbeing of rural community residents in Japan [19].

China is a largely agricultural country, and as early as the 1980s, some scholars began to research the evaluation of rural ecological environments [20]. In recent years, there has been increasing attention to environmental pollution problems in rural areas. Zhao et al. provided an analysis framework to obtain ecological strategies for the sustainability of rural communities based on the ecological evaluation method [21]. Some scholars have evaluated the current development status of rural revitalization. For example, Zhang et al. studied the paths of rural revitalization in alpine pastoral areas of the Qinghai–Tibet Plateau using settlement patch data obtained by visual interpretation of high-definition remote sensing images [22]. Kong et al. developed a multi-level rural ecological assessment index for assessing rural ecology using the Delphi-AHP method [23]. Geng et al. explored the current states of rural revitalization of 30 regions in China by constructing a 5E framework [24]. In addition, with the development of rural economy, the contradiction between ecological environment and rural economic development has become a new issue faced by China’s rural areas. Thus, Ren proposed a comprehensive evaluation framework of rural financial ecological environment [25]. At present, China has promulgated its evaluation standards for green ecological urban areas [26]; however, there is no evaluation standard for the construction of zero-pollution villages.

1.3. Existing Problems and Aims of the Research

In recent years, through the development of beautiful countryside constructions in China, the quality of the rural environment has been greatly improved. However, the following problems still exist, as identified by the above literature review and field survey.

First, the construction of zero-pollution villages has only been undertaken for a relatively short period of time in China, and there has been a lack of experience. Earlier research found that most of these index systems focused on “green” and “low-carbon” but without an evaluation of zero-pollution villages.

Second, the existing evaluation system or research is mostly feasible for villages that have agriculture as their leading industry. Nevertheless, with the deepening of globalization, many villages in China have changed from traditional agriculture to industry or tourism [27].

Third, through the improvement of village appearance, the environmental quality of rural public space has been greatly improved. However, the surrounding environment of village settlements has been ignored, which is also closely related to villagers’ lives. The reason for this is that the original evaluation system did not clearly limit the scope of the village area or take into account the possible load on the external environment while the internal environment of the village is improved.

Based on the above problems, the purpose of this study is to take Zhejiang province as an example to construct a scientific and easy-to-operate zero-pollution village evaluation index system through field research, the literature review, and expert consultation; this could be used to evaluate the construction degree of zero-pollution villages in Zhejiang rural areas and provide a reference for the construction of zero-pollution villages further.

2. Research Methods

The major process of this research was divided into four steps as shown in Figure 1. Step one: According to the principle of CASBEE, the preliminary evaluation index system was established through a field survey, review of the literature, and expert interviews. This step was the foundation of the whole study; therefore, multiple rounds of expert consultation were carried out before determining the final evaluation index system. Step two: The combined weights and integrated weights at all levels were calculated by a combined FAHP-TOPSIS method, considering the influence of subjective and objective factors. Step three: In order to verify the accuracy of the evaluation system, three typical villages were chosen and evaluated thoroughly via a field survey, literature review, and questionnaire survey. Then, the evaluated results were compared with the existing construction guidelines for pollution-free villages. Step four: A total of 26 villages volunteered to participate in the evaluation at the construction level of their zero-pollution villages with field measurements and the questionnaire survey. In order to make the survey results more accurate, multiple telephone inquiries were conducted for some field investigations.

2.1. Evaluation Principles and Methods of Evaluation System

Unlike the evaluation tools BREEAM (Wolford, UK), LEED (Washington, DC, USA), and DGNB (Stuttgart, Germany), which only categorize and list the evaluation terms in a hierarchical manner [28], under the Japanese CASBEE, the community and its surrounding environment are considered a whole ecosystem and are divided into two spaces by a virtual enclosed space boundary. The negative aspects of environmental impacts beyond the boundary of the community to the outside and improvements to the living amenities for residents are all taken into consideration [29]. As a well-known tool to evaluate environmental performance, CASBEE has been widely adopted [14].

Further, the principle of CASBEE is also suitable for the evaluation system of zero-pollution villages, which can be used to comprehensively evaluate the environmental performance of zero-pollution villages. When evaluating a zero-pollution village, a hypothetical boundary was set to enclose the village in three dimensions, as shown in Figure 2a. A zero-pollution village is a village that continuously improves the internal environmental quality of the rural community while minimizing the external environmental load on the rural community to achieve a “harmonious coexistence with the environment”. The behavior of the villagers in discharging pollutants outside the imaginary boundary is called the external environmental load L (referred to as “Load” or “L”), and the behavior to maintain the survival and development of the village is called the internal environmental quality Q (referred to as “Quality” or “Q”).

In order to evaluate the efficiency of the environmental impact, a single-value evaluation index and an assessment of the village environment efficiency (VEE) was introduced, whose value was the ratio of Q to L. A higher Q value and lower L value represent a higher internal environmental quality and lower environmental load and can lead to a higher VEE value, which means that a lower impact on the external environment can be achieved. On the contrary, a lower VEE value indicates greater damage to the environment, which is more detrimental to the sustainable development of rural communities.

$$VEE=\frac{Q}{L}.$$

(1)

Furthermore, the Q values, L values, and VEE values of rural communities can be visually reflected in the two-dimensional zero-pollution village environmental evaluation map (Figure 2b). The zero-pollution village environmental assessment map not only evaluates the current pollution level and livability of rural communities but also compares the evaluation results between the present and the future with different policy measures [15]. Meanwhile, the graph area can be divided into five zones: Zone A is zero pollution, Zone B is low pollution, Zone C is general pollution, Zone D is heavier pollution, and Zone E is severe pollution. These five ranks, with a score of 1–5, are listed in

Table 1. Village environmental assessment scale.

Grade	Pollution Level	VEE Value
A	Zero pollution	3.0–5.0
B	Low pollution	1.5–3.0
C	General pollution	1.0–1.5
D	Heavier pollution	0.5–1.0
E	Severe pollution	0–0.5

.

2.2. Methods of Determining Indicator Weights

In order to handle multi-criteria decision-making problems, many methods have been used to determine the indicator weights, which can be divided into two categories. One category includes the subjective evaluation methods, such as the expert consultation method (Delphi method), the analytic hierarchy process method (AHP method), and the fuzzy analytical hierarchy process method (fuzzy AHP method or FAHP method). The other category includes the objective weight methods, such as the entropy weight method, coefficient of variation method, criteria importance though intercriteria correlation method (CRITIC method), and technique for order preference by similarity to an ideal solution method (TOPSIS method). However, both of them have limitations. Subjective weight methods are heavily influenced by the decision makers’ subjective intentions and are often constrained by their knowledge and experience, leading to a significant degree of subjectivity [30]. On the other hand, objective evaluation methods rely on the interdependencies between data but are susceptible to the impact of extreme values. Considering such a dilemma, scholars often use a combination of subjective and objective methods. Zhu et al. [31] proposed a decision-making framework by integrating the FAHP and TOPSIS methods for design concept evaluation under uncertain environments. Sadeghi et al. [32] presented a new framework for the assessment tools of green buildings using K-means clustering and fuzzy AHP. Kılıc et al. [33] determined the most suitable rural settlement area using the GIS-APH method. Yang et al. [34] integrated the CRITIC method and FAHP method to determine the weights for renewable energy heating systems.

The evaluation of zero-pollution villages often involves subjective indicators and objective indicators: a hybrid approach of subjective and objective methods, namely, the FAHP-TOPSIS method, was adopted to determine the weights in this study. Initially, the FAHP method, based on the classic AHP proposed by T.L. Saaty [35] which addressed certain limitations of the traditional AHP, was used to calculate the subjective weights. Subsequently, the entropy-TOPSIS method was employed to adjust the weights based on the decision maker’s familiarity with different knowledge domains, aiming to enhance the scientific rigor of the evaluation. This iterative process resulted in a final set of weights. On the basis of the evaluation framework constructed as shown in Figure 3, the specific steps to further determine the weights of indicators at all levels are as follows:

2.2.1. Fuzzy Analytical Hierarchy Process Method (FAHP Method)

In order to ensure the authority of this evaluation, expert judgments are often introduced for subjective indicators, which cannot be properly measured because of an expert’s subjectivism [34]. The classic AHP method excels among subjective analysis and evaluation methods, allowing effective judgment and decision making for qualitative problems that are otherwise challenging to quantify [36]. However, it suffers from complexities in the calculation process, particularly in the consistency testing of the judgment matrix, and the scientific basis of the consistency of judgment rules has been subject to debate. Additionally, the AHP often overlooks the fuzziness of human judgment when constructing pairwise comparison matrices. To overcome these limitations, scholars have often combined the principles of the AHP with the fuzzy mathematics theory, namely, the development of the FAHP method [32], which has been widely adopted by scholars. For example, Li et al. utilized the FAHP method in an actual PPP expressway project from China [37]. Zarghami et al. used the FAHP method to customize a sustainability assessment tool for Iran [38].

In 1983, the Dutch scholars P.J.M. Van Laarhoven and W. Pedrycz employed fuzzy triangular functions for scoring [39]. However, its scientific validity has been questioned by researchers. Other scholars have also proposed many improvement methods. The most commonly used method among these has involved incorporating fuzzy consistency matrices into AHP analysis, effectively mitigating the challenges associated with consistency testing in AHP.

(1) Create a survey questionnaire and invite experts to score.

The FAHP analysis method assesses the relative importance of indicator elements through pairwise comparisons of various factors, resulting in the formation of a fuzzy complementary judgment matrix. A survey questionnaire was created using the 0.1 to 0.9 scale method. Experts were invited to score the pairwise comparisons of factors according to the scoring criteria illustrated in

Table 2. Meaning of the FAHP 0.1–0.9 scale.

Scale	Implications
0.1	$i$ is far less important than $j$ compared to the two elements
0.3	$i$ is slightly less important than $j$ compared to the two elements
0.5	$i,j$ are equally important compared to each other
0.7	$i$ is obviously more important than $j$ compared to the two elements
0.9	$i$ is extremely more important compared to $j$
0.2, 0.4, 0.6, 0.8	The median value of the above neighbor judgment of $i,j$

. Because the zero-pollution village evaluation system involves multiple disciplines and fields, experts from these related disciplines and fields were invited. Finally, 40 experts from universities, research institutes, design institutes, township governments, and other departments that were majoring in architecture, urban and rural planning, ecology, and agriculture were invited to score. The basic information of the experts including the age, years of employment, positional title, education, and professional familiarity are listed in

Table 3. Basic information of the experts.

Project	Classification	Number	Proportion
Age/years	30 or less	2	5%
	30~39	14	35%
	40~49	18	45%
	50~59	4	10%
	60 or more	2	5%
Years of employment/years	Less than 10 years	8	20%
	10~19	20	50%
	20~29	8	20%
	30 above	4	10%
Positional title	Junior	2	5%
	Intermediate	23	57.5%
	Senior	15	37.5%
Education	Bachelor’s degree or less	1	2.5%
	Undergraduate	5	12.5%
	Master’s degree	21	52.5%
	Doctorate	13	32.5%

. It can be seen that the invited experts were mainly older, with rich work experience, higher education, and professional titles.

(2) Construct an FAHP scoring matrix

The 0.1 to 0.9 scale method mentioned above was employed to perform pairwise comparisons of the indicators at each level, resulting in the generation of the FAHP scoring matrix, denoted as matrix A. The judgment matrix was expressed as follows:

$$\mathrm{A}=\left[\begin{array}{cc}\begin{array}{cc}{\mathrm{a}}_{11}& {\mathrm{a}}_{12}\\ {\mathrm{a}}_{21}& {\mathrm{a}}_{22}\end{array}& \begin{array}{cc}\dots & {\mathrm{a}}_{1\mathrm{n}}\\ \dots & {\mathrm{a}}_{2\mathrm{n}}\end{array}\\ \begin{array}{cc}\mathrm{︙}& \mathrm{︙}\\ {\mathrm{a}}_{\mathrm{n}1}& {\mathrm{a}}_{\mathrm{n}2}\end{array}& \begin{array}{cc}\mathrm{︙}& \mathrm{︙}\\ \dots & {\mathrm{a}}_{\mathrm{n}\mathrm{n}}\end{array}\end{array}\right]$$

(2)

where $a_{ij}$ represents the relative importance of pairwise comparisons between indicator factors $i$ and $j,$ as listed in Table 2. The matrix A should satisfy the requirements of a fuzzy complementary matrix: ${0<a}_{ij}<1$; $a_{ij}+a_{ji}=1$; $a_{ii}=0.5$; ($i,j=1,2,\dots ,n$).

(3) Sum up the rows of matrix A, and calculate the weight $w_i$:

$$a_i=\sum _{k=1}^n{a}_{ik},\left(i,k=\mathrm{1,2}\cdots n\right)$$

(3)

$$w_i=\frac{1}{n}-\frac{1}{n-1}+\frac{{2a}_i}{n(n-1)}$$

(4)

Then, we constructed the single layer index weight vector $\mathrm{W}\mathrm{I}$.

$$\mathrm{W}\mathrm{I}={\left[w_1 w_2\dots w_n\right]}^{\mathrm{T}}$$

(5)

(4) Check consistency

We constructed the fuzzy consistency matrix W and performed a consistency test. Due to the subjective nature of expert scoring in the FAHP, inconsistencies can often arise in the scoring matrix. In this study, the Particle Swarm Optimization (PSO) algorithm was used to adjust the expert scoring matrix.

$$w_{ij}=\frac{n-1}{2}\left(w_i-w_j\right)+0.5$$

(6)

$$\mathrm{W}=\left[\begin{array}{cc}\begin{array}{cc}{w}_{11}& w_{12}\\ w_{21}& {\mathrm{w}}_{22}\end{array}& \begin{array}{cc}\dots & w_{1n}\\ \dots & w_{2n}\end{array}\\ \begin{array}{cc}\mathrm{︙}& \mathrm{︙}\\ w_{n1}& w_{n2}\end{array}& \begin{array}{cc}\mathrm{︙}& \mathrm{︙}\\ \dots & w_{nn}\end{array}\end{array}\right]$$

(7)

$$\mathrm{C}\mathrm{I}\left(\mathrm{A},\mathrm{W}\right)=\frac{{\sum }_{\mathrm{i}=1}^n{\sum }_{\mathrm{j}=1}^n\left|w_{ij}-a_{ij}\right|}{n^2}$$

(8)

2.2.2. Entropy-TOPSIS Method

It is an objective fact that due to differences in work experience, education level, and familiarity with different majors of the invited experts and the ambiguity of the assessment information, a critical gap could be identified between the evaluation value and the actual value [31]. Therefore, the credibility of expert scoring was corrected with the entropy-TOPSIS method, an objective method, in which the determination of weight does not involve any subjective preference but totally relies on the objective data set [40]. Firstly, a matrix was constructed through a questionnaire survey with age, education, length of service, professional title, and professional familiarity as evaluation indicators. Then, the relative closeness of each expert was calculated, which represented the feasibility of expert scoring. The specific steps are as follows:

(1) The judgment matrix was constructed according to the age, working age, education, and professional familiarity of the experts. Considering the differences in the attributes between the units and orders of magnitude, the matrix was normalized [41]. The structure of the normalized matrix could be expressed as follows:

$$Z=\begin{array}{c}{A}_1\\ A_2\\ ⋮\\ A_n\end{array}\stackrel{\begin{array}{cccc}{Z}_1& Z_2& \cdots & Z_m\end{array}}{\left[\begin{array}{cccc}{z}_{11}& z_{12}& \cdots & z_{1m}\\ z_{21}& z_{22}& \cdots & z_{2m}\\ ⋮& ⋮& \ddots & ⋮\\ z_{n1}& z_{n2}& \cdots & z_{nm}\end{array}\right]}$$

(9)

where $A_1,A_2,\dots ,A_n$ denote the experts; $Z_1,Z_2,\dots ,Z_m$ represent the evaluation indexes of the experts; $Z_{ij}$ indicates the performance value of the $j$th index of the $i$th experts; $i=1,2,\dots ,n$ and $j=1,2,\dots ,m$.

(2) The entropy weight ($w_i$) was calculated. The process of determining the weight for the entropy-TOPSIS method involved establishing a normalized judgment matrix for each evaluation index and calculating the weight of each index based on the entropy value ($E_i$), which could be expressed using the following equation:

$$E_i=-\frac{1}{\ln n}\sum _{i=1}^n{p}_{ij}\mathit{ln}{p}_{ij}$$

(10)

where $n$ is the number of experts; $p_{i\cdot j}$ is the contribution rate of different factors that can be calculated according to the following equation:

$$p_{i\cdot j}=\frac{z_{ij}}{{\sum }_{i=1}^n{z}_{ij}}$$

(11)

Finally, the quantitative values could be placed into the above decision matrix.

$$x_{ij}=w_j{z}_{ij}$$

(12)

where $w_j$ represents the weight of the $j$th index.

(3) The positive ($x^{+}$) and negative ($x^{-}$) scores were determined using a weighted normalized decision matrix.

$$x^{+}=\left\{x_1^{+},x_2^{+},\dots ,x_m^{+}\right\}=\left\{{max}_{ij}\right\}$$

(13)

$$x^{-}=\left\{x_1^{-},x_2^{-},\dots ,x_m^{-}\right\}=\left\{{min}_{ij}\right\}$$

(14)

(4) The Euclidean distances of every export to the positive ideal score ($D_i^{+})$ and the negative ideal score ($D_i^{-}$) could be calculated as follows.

$$D_i^{+}=\sqrt{\sum _{j=1}^m{\left(x_{ij}-x_j^{+}\right)}^2}$$

(15)

$$D_i^{-}=\sqrt{\sum _{j=1}^m{\left(x_{ij}-x_j^{-}\right)}^2}$$

(16)

(5) The relative closeness ($C_i$) of each expert was calculated:

$$C_i=\frac{D_i^{-}}{D_i^{-}+D_i^{+}}$$

(17)

where 0 $\le C_i\le 1$. An ideal situation would be where the evaluation is fair and objective, meaning that $C_i$ would be equal to one. However, due to the subjectivity of the experts’ scoring, it was impossible for $C_i$ to reach the ideal value. Therefore,${ C}_i$ could be used to represent the credibility of experts. A larger $C_i$ indicates that the expert’s estimation was more credible, whereas a smaller $C_i$ indicates that the expert’s credibility was lower.

2.2.3. Combined Weights and Integrated Weights

The combined weight is based on the indicator weight obtained by the FAHP method and corrected by the credibility of experts. The integrated weight is the weight calculated layer by layer from the top to the bottom of the evaluation system. The steps are as follows:

(1) Calculate the credibility of the experts

The credibility of each expert was calculated according to Section 2.2.2. Among them, professional familiarity was divided into six aspects according to the criterion layer, including economic development, living environment, culture, effective governance, waste emissions and disposal, and the resource utilization of rural areas.

(2) Calculate the combined weights

First, the average combined weight of all experts in a row was calculated:

$$W_i^{\prime }=\frac{{\sum }_{k=1}^m{w}_{i·k}\times C_k}{m}$$

(18)

where $w_{i·k}$ represents the indicator weight of the $k$th expert calculated by FAHP; $C_k$ is the credibility of the $k$th expert. $m$ is the number of experts invited. $k=1,2,\dots ,m$.

Then, the contribution rate of the corresponding indicator layer was calculated, which combined the weight of each layer’s indicators:

$$W_i=\frac{W_i^{\prime }}{{\sum }_{j=1}^n{W}_i^{\prime }}$$

(19)

where ${0<W}_i<1$,${\sum }_{j=1}^n{W}_i$ = 1.

(3) Calculate the integrated weights

The integrated weight refers to the weights of all indicators in the same layer of the evaluation index system, which can intuitively reflect the importance of each indicator. The integrated weights could be obtained by multiplying the combined weights of the upper and lower indicators, as shown in Figure 3. The calculation formula for this was as follows:

$$S_{ij}=W_i\times W_{ij}$$

(20)

where ${0<S}_{ij}<1$,${\sum }_{j=1}^n{S}_{ij}$ = 1.

3. Results and Analysis

3.1. Construction of Evaluation Index System

Based on the successful experiences of ecological villages at home and abroad, and the interviews with experts, a preliminary indicator evaluation system was constructed based on the principle of CASBEE with two target layers: Q and L. Multiple rounds of expert consultation were conducted after the initial construction of the evaluation system through field research, theoretical analysis, and the literature review until all experts reached a consensus. Finally, a synthesized evaluation model of a zero-pollution village was established with four levels, consisting of 2 target layers, 6 criterion layers, 21 sub-criterion layers, and 76 indicator layers. Figure 4 shows the framework of the evaluation index system for the zero-pollution village.

3.2. Calculation Results of Experts’ Credibility

The age, education, length of service, positional title, and professional familiarity of the experts were the influencing factors of subjective judgment. In the evaluation system, L could be related to the field of waste disposal and resource utilization, and Q was involved in economic development, the living environment, culture, and rural governance. Therefore, six judgment matrices were constructed and normalized. The entropy weights of the five influencing factors in both the criterion and sub-criterion layers were calculated by Equations (9)–(11) and listed in

Table 4. Entropy weights of the five factors in L and Q.

Layer	Age	Education	Length of Service	Positional Title	Professional Familiarity
L	0.137	0.070	0.194	0.371	0.229
Q	0.146	0.074	0.208	0.397	0.174
L1	0.142	0.077	0.217	0.436	0.128
L2	0.128	0.070	0.196	0.395	0.211
Q1	0.118	0.064	0.180	0.362	0.276
Q2	0.137	0.075	0.209	0.421	0.158
Q3	0.145	0.079	0.221	0.445	0.110
Q4	0.105	0.057	0.161	0.324	0.352

. The results show that the entropy weight of the positional title was the largest, which was 0.371 and 0.397 in the criterion layer. The smallest was education, with 0.070 and 0.074 in the criterion layer.

Then, the Euclidean distances of the ideal point to the positive ideal value and the negative ideal value were all calculated according to Equations (12)–(17). Finally, the relative closeness ($C_i$), namely, the credibility of the experts in L and Q, was obtained and illustrated in Figure 5. As shown in the figure, the credibility values of the experts differed depending on their age, work experience, education level, and familiarity with their profession. Most experts’ credibility of L and Q were very close, but there were also some experts whose credibility values had significant differences between these two parts. The reason for this was due to individuals’ different familiarity with different fields.

3.3. Calculation Results of Weights

3.3.1. Subjective Weights Based on FAHP

The fuzzy pairwise comparison matrices were created by the survey questionnaires of experts, and the calculation process is shown in Figure 3. After consistency testing, the objective weights of L and Q were generated by Equation (4). The criteria and sub-criteria weights are listed in

Table 5. The subjective weights of the criteria and sub-criteria layers in L.

Criteria Layer	Subjective Weights	Sub-Criteria Layer	Subjective Weights
Waste L1	0.512	Domestic waste L11	0.245
		Agricultural pollutants L12	0.245
		Industrial pollutants L13	0.263
		Operating pollutants L14	0.248
Resources L2	0.488	Land resources L21	0.269
		Water resources L22	0.257
		Material resources L23	0.237
		Energy resources L24	0.237

and

Table 6. The subjective weights of the criteria and sub-criteria layers in Q.

Criteria Layer	Subjective Weights	Sub-Criteria Layer	Subjective Weights
Economic development Q1	0.2439	Economic strength Q11	0.3388
		Industrial development Q12	0.3511
		Population structure Q13	0.3102
Living environment Q2	0.2705	Environmental protection Q21	0.2534
		Village appearance Q22	0.2668
		Infrastructure Q23	0.2696
		Green building Q24	0.2102
Green humanities Q3	0.2344	Ecological civilization publicity Q31	0.3347
		Civilization training Q32	0.3238
		Villager awareness Q33	0.3415
Effective governance Q4	0.2512	Organization Q41	0.3281
		Community management Q42	0.3433
		Villager participation Q43	0.3286

.

As illustrated in Table 5, the discharge and disposal of waste had a higher weight than that of resource consumption, which were 0.512 and 0.488, respectively. It indicates that the discharge and unreasonable disposal of waste can bring a higher load to the rural environment. Industrial pollutants (L13) were ranked as the priority sub-criteria due to the greater impact of harmful substances when discharged on the environment; the second was operating pollutants, and then domestic waste and agricultural pollutants. In terms of resource consumption, land resources had the highest weight of 0.269, followed by water resources (0.257), material resources (0.237), and energy resources (0.237).

Regarding the internal quality of rural communities (Q), the living environment (Q2) was obviously higher than the others, followed by effective governance, economic development, and green humanities. In the aspect of the living environment, village appearance (Q22) and infrastructure (Q23) were considered to be the most important aspects, with weights of 0.2668 and 0.2696, respectively. However, green building (Q24) was regarded to be the least important at present due to the fact that the development of rural buildings still lagged behind that of cities. It is clear that environmental pollution accompanies human civilization, and the higher the level of human civilization, the more serious the environmental pollution. The development of ecological technology and the improvement of infrastructure could provide a technical guarantee for human beings to reduce environmental pollution while continuously improving their quality of life [42,43]. From the effective governance and green humanities aspects, community management (Q42) and villager awareness (Q33) play the most important roles, respectively. Changing old habits and establishing environmental protection concepts and awareness through education and propaganda could guarantee that environmental protection continues. Industrial development (Q12) was the highest-ranked aspect of the economic development of rural areas, which is also very important for rural revitalization.

3.3.2. Combined Weights Adjusted by Experts’ Credibility

As set out in Section 2.2.3, the combined weights were calculated based on the subjective weights and experts’ credibility (Table 4) in different layers; therefore, it was scientific and reasonable to combine these two aspects. The combined weights and subjective weights of the criteria and sub-criteria layers are all presented in Figure 6. A comparison of the combined weights and subjective weights shows how the combined weights changed after they were adjusted by experts’ credibility. In the external environmental load (L), the weight of the waste increased; however, the weight of resources decreased conversely. In the sub-criteria layer, the weight of industrial pollutant emissions increased obviously, and the weight reduction in agricultural pollutant emissions was the most significant, followed by energy consumption. For the internal environmental quality (Q), the combined weight of economic development increased by 2.42%; the weight of green humanities was reduced by 2.52%. Other indicators in the sub-criterion layer also experienced different changes.

3.3.3. Integrated Weights

After obtaining the combined weights of different layers, the integrated weights were calculated with Equation (20), and this process is depicted in Figure 3. The combined weights and integrated weights of different layers are listed in Appendix A and Appendix B. As listed in the Appendix A and Appendix B, the importance of the indicators could be obtained and ranked by the integrated weights. In the criteria layer of L, the weight of industrial pollutant emissions was found to be the most important, with a weight of 0.0879. Environmental protection and economic development are mutually constrained and mutually promoted. Economic development increases the probability of environmental pollution, but a high-quality environment cannot be created without economic and industrial development. Therefore, there is a need to find a balance between economic development and environmental protection [44,45,46]. Meanwhile, industrial development was the highest-ranked aspect in Q.

4. Empirical Analysis Based on Three Typical Villages

In response to the initiative of the United Nations Environment Programme (UNEP) towards to a zero-pollution Earth, China’s first construction guide of zero-pollution villages was promoted in 2018 in Zhejiang Province, which stipulates the requirements for the construction of production, life, and ecology in a zero-pollution village [46]. Figure 7 is the comparison between the construction guide and the evaluation system established in Section 3. In order to verify the correctness and feasibility of the evaluation system, three typical villages were selected as shown in Figure 8: Figure 8b shows Yuantou Village in Wenzhou City, the first zero-pollution demonstration village in Zhejiang Province, which is famous for its modern agriculture and rural tourism; Figure 8c shows Zhangluwan Village in Huzhou City, a modern village with modern agriculture and handicraft industry; and Figure 8d shows Yang’ao Village in Taizhou City, an ordinary village in Zhejiang Province, with traditional farming and a high proportion of young adults working outside. The location map of the three villages is shown in Figure 8a.

By comparing the Q and L of the three villages in Figure 9, it can be seen that the environmental quality levels of Yuantou and Zhangluwan Villages were higher than that of Yang’ao Village in all aspects. In terms of environmental load L, Yuantou and Zhangluwan Villages had less domestic waste emissions and agricultural pollutant emissions but greater water resources and energy consumption, while Yang’ao Village had higher domestic waste emissions and agricultural pollutant emissions and less industrial and operational pollutant emissions and building energy consumption. This was mainly caused by the industrial economy of the villages and the different governance measures for villages.

Yuantou Village actively promotes the circular agriculture model of comprehensive waste utilization; adopted a water reuse system; and established an anaerobic technology sewage treatment project. In addition, the village vigorously promotes waste classification and resource utilization, introducing the “Internet + intelligent waste classification and recycling machine” and a microbial fermentation treatment system for domestic waste, making organic fertilizer from kitchen waste, straw, and rice straw for use in the fields. Furthermore, Yuantou Village encourages the participation of all people to create zero-pollution cell projects, such as zero-pollution families, zero-pollution lodges, zero-pollution shops, etc., and advocates for the creation of zero-pollution cell projects. Pollution stores advocate for a green low-carbon life with villagers playing the main roles, guiding them to form good living habits and improve environmental awareness.

In order to reduce the direct use of traditional fuels, Zhangluwan village actively promotes the use of clean energy such as electricity and solar energy. In addition, the village dismantled turtle farming sheds and broiler farming sheds, reclaiming them as fields for scientific farming. With wood veneer processing and piano parts manufacturing as the leading industries, the village has adopted the recycling of wood veneer waste, collected for papermaking or burned into charcoal, so that industrial pollution sources meet the standard emission rate of 100% compliance and the industrial solid waste disposal utilization rate of more than 95%. New rural residential houses with local characteristics were built according to the requirements of the energy saving standards. The village beautification and lighting, landscape greening, and other measures have improved the internal environment of the village.

The traditional planting industry is the main industry in Yang’ao Village, there is a lack of special industries, and the proportion of villagers working outside the village is high. With the development of new rural construction, the village has established a centralized domestic sewage treatment system and domestic waste recycling system, but the waste classification system has not yet been formulated and implemented. Agricultural pollutants are mainly pesticide and fertilizer wastes casually discarded by some farmers in the process of planting. In addition, because of the single industrial structure, the village has no industry and trade services, and industrial pollutant emissions and business pollution emissions are low. In addition, although the village government and planning are open for consultation, the villagers’ participation is low because of the serious aging of the inhabitants, and the lack of motivation to participate makes it difficult to establish a long-term mechanism for the construction of a zero-pollution village, resulting in the waste of limited funds and the distortion of public resource allocation.

According to the scoring method of the zero-pollution village evaluation index system and the village environmental efficiency (VEE) calculation method, the Q, L, and VEE values of the three villages were calculated and plotted on the two-dimensional environmental evaluation map as noted in Figure 10a. The weighted Q and L values of Yuantou, Zhangluwan, and Yang’ao Villages were 4.27 and 1.34, 3.90 and 1.69, and 2.76 and 2.37, respectively, and the VEE scores were 3.18, 2.31, and 1.16, which were located in Zone A, Zone B, and Zone C, respectively. This indicated that Yuantou Village had the highest pollution-free degree, followed by Zhangluwan Village, and the worst one was Yang’ao Village. The scores obtained from the construction guide were 92.5, 78.5, and 49 for Yuantou, Zhangluwan, and Yang’ao Villages, respectively (Appendix C). There were three villages with environmental quality Q and an environmental load. As shown in Figure 10a, S1 is Yuantou Village, S2 is Zhangluwan Village, and S3 is Yang’ao Village. As a result, the evaluation results of the evaluation system established based on CASBEE are basically consistent with the results of the construction guidelines and can be used for the evaluation of zero-pollution villages.

5. Construction Status of the Zero-Pollution Villages in Zhejiang Province

In order to understand the construction status of zero-pollution villages in Zhejiang Province, with the help of county governments, 50 villages were randomly invited to participate in the survey. Finally, 26 villages agreed to participate in the survey. These surveys were carried by field survey, telephone communication, and by commissioning village committees to conduct questionnaires (e.g., villagers’ satisfaction, etc.). The results are listed in Figure 10b. There were 3 villages in the C zone (general pollution zone), accounting for 11.5%, and 23 villages were in the B zone (low-pollution zone), taking up 88.5%. Although there is no detailed data on the construction level of zero-pollution villages in Zhejiang Province at present, the official statistical data of the Zhejiang Provincial Bureau of Statistics show that by 2022, the classification coverage of rural domestic waste reached 100%, the harmless treatment rate reached 100%, the recycling rate of domestic waste reached 60%, and the resource utilization rate reached 100%, and 14 beautiful countryside demonstration counties and 8989 beautiful countryside demonstration villages were created, with a 93% coverage of beautiful villages in the whole province [47].

It can be seen that through the Ten Million Project and the construction of beautiful villages in Zhejiang Province, the village environment has been greatly improved, but it has still not reached the pollution-free standard [12].

The analysis found that the higher the awareness of environmental protection among villagers, the higher the VEE value of villages that can consciously maintain the results of environmental improvement. For example, in Li Zu Village, Yiwu City, through the use of green points to measure the approval of the home base, the initiative has greatly improved the effect of environmental management. Such initiatives can effectively enhance the villagers’ motivation to protect the environment and develop environmental awareness and habits [48].

In addition, the participation of social organizations and enterprises has driven the zero-pollution village construction level. For example, Yuantou Village signed a co-construction support agreement with the Wenzhou Environmental Protection Volunteer Association and Du Bai New Material Co. The source village provided free food waste to the enterprise for the production of fertilizer, which was mutually beneficial. At the same time, the evaluation also reflected the shortcomings of building a zero-pollution village in Zhejiang Province, such as poor building thermal performance and high energy consumption in rural residences in general. Paying attention to and solving these problems by continuing the Ten Million Project and other initiatives to raise villagers’ awareness will help Zhejiang villages move toward zero pollution.

6. Conclusions

This paper first established a scientific and effective evaluation system to assess the construction of zero-pollution villages based on CASBEE. The weights of these indicators were determined through the FAHP-TOPSIS method. Then, this evaluation system was validated with the guidelines for the construction of pollution-free villages in three villages in Zhejiang Province. Finally, the construction status of zero-pollution villages in rural areas was assessed. The main conclusions of this study are as follows:

(1) The indicator system consisted of 2 target layers, 6 criteria layers, 21 sub-criteria layers, and 76 indicator layers, comprehensively considering economic development, living environment, management, and environmental pollution.

(2) An improved FAHP method was employed to determine the combined weights and integrated weights of indicators. Considering the knowledge level and professional limitations of these experts, the indicator weights were adjusted with the TOPSIS method. The entropy weight of the positional title was the largest, while that of education was the smallest.

(3) In order to present the assessment results simply and clearly, a single value evaluation index VEE and two-dimensional assessment diagram have been introduced.

(4) The evaluation results of another 26 villages indicated that 11.5% of the villages surveyed were in Zone B (low-pollution zone), while 88.5% were in Zone C (general pollution zone). It can be seen that by constructing the villages in Zhejiang Province, their environment was greatly improved but still cannot reach the status of zero-pollution villages at present.

(5) Balancing the contradiction between rural industrial development and environmental pollution, coordinating environmental comprehensive management with multiple stakeholders and technical measures, and enhancing villagers’ environmental awareness and public participation are important measures for building a zero-pollution village.

However, this study also has some limitations. Firstly, this evaluation system does not encompass all types of villages. Given the uneven development of rural areas in Zhejiang Province, it was necessary to consider the variations in geographical climate, resource environment, economic development, and industrial structures across different regions when determining the indicators and weights for the evaluation system. Secondly, there is a need to further enhance the accuracy of the weights at each level. Although adjustments have been made to the indicator weights based on the expert’s level of knowledge, professional familiarity, and work experience, it is crucial to note that these adjustments represent relative values among the surveyed experts rather than absolute values. Thirdly, the survey results of 26 villages cannot fully reflect the construction level of zero-pollution villages in Zhejiang Province, and further expansion of the research scope is needed. Therefore, future research should be carried out in these aspects.