Evaluation of Rural Tourism Landscape Resources in Terms of Carbon Neutrality and Rural Revitalization

Abstract

Rural tourism landscape resources are important ingredients of rural revitalization and modernization in developing countries and regions. Evaluation methods play a crucial role in the planning, design, transformation, development, and protection of these resources. However, there has been a lack of research on the evaluation of rural tourism landscape resources, especially from the perspective of rural revitalization and carbon neutrality. From the perspective of carbon neutrality and rural revitalization, this article establishes an indicator system to evaluate rural tourism landscape resources based on previous evaluation methods and expert consultations on landscape planning. An evaluation model based on the intuitionistic fuzzy VIKOR method structure matching is also suggested. Some practical suggestions are put forward to promote the values of rural tourism landscape resources through empirical analyses of three regions in Changsha, Hunan, China. Our study shows that the evaluation results could objectively reflect the values and existing problems of rural tourism landscape resources, which could provide practical tools for local government departments to make decisions, and landscape architects to plan and design. Based on this model, further suggestions are provided to improve rural tourism landscape resources.

1. Introduction

Carbon neutrality refers to offsetting generated carbon dioxide (CO₂) through carbon capture, storage, and conversion, within a certain period of time, so as to achieve “zero emission” of greenhouse gases [1,2]. Rural areas are not only an important source of carbon emissions, but also have great potential for carbon reduction and carbon-neutral capacity. According to the Food and Agriculture Organization of the United Nations (UNFAO), agricultural land releases more than 30% of total global anthropogenic greenhouse gas emissions, while agricultural ecosystems can offset 80% of agricultural global greenhouse gas emissions [3]. In September 2020, the Chinese government proposed adopting more effective policies and measures to reach peak CO₂ emissions by 2030 and be carbon neutral by 2060 [4,5].

Carbon neutrality poses challenges to the economic development of many countries [6,7,8,9]. For developing countries, the issue of how to achieve rural revitalization under the condition of carbon neutrality needs urgent study. Our previous research [10] has studied rural landscape resources from the perspective of rural revitalization, and established an evaluation index system to evaluate rural landscape resources and give suggestions to promote rural revitalization. Considering the factors of carbon neutrality, the evaluation system of rural landscape resources needs to be restudied. In particular, rural tourism can bring economic benefits and contribute to rural revitalization, but the correlation with carbon neutrality is complicated. Based on previous research [10], this article continues to study landscape resources of rural tourism from the perspective of rural revitalization with carbon neutrality as the limiting condition.

Rural revitalization is a systematic and comprehensive strategy to strengthen the rural economy, and promotes sustainable rural development. The government presents a series of policies and offers some financial support, businesses use the policy support to promote rural development, and rural residents actively engage in production activities to improve the employment structure [11]. Different countries have formulated rural revitalization strategies according to their national conditions. In China, the rural revitalization strategy includes five revitalization directions, namely, industrial revitalization, talent revitalization, cultural revitalization, ecological revitalization and organizational revitalization [12]. Low-carbon transformation can help rural areas achieve energy poverty alleviation [13]. Thus, rural revitalization requires the promotion of rural industry, preservation of rural culture, and improvements in the capacity of carbon neutrality in rural regions. The planning, design, development, transformation, and protection of the rural landscape should follow the guidance of the rural revitalization strategy and the condition of carbon neutrality, and could also refer to the evaluation results of rural landscape resources.

Rural tourism, according to the definition of United Nations World Tourism Organization (UNWTO), is “a type of tourism activity in which the visitor’s experience is related to a wide range of products generally linked to nature-based activities, agriculture, rural lifestyle/culture, angling, and sightseeing”. Rural tourism activities take place in rural areas with landscapes and land-uses dominated by agriculture and forestry, and traditional social structures and lifestyles [14]. Rural tourism contributes to GDP growth and job creation, and especially influences rural development represented by employment, education, income and consumption significantly [15]. Therefore, rural tourism has great potential to stimulate local economic growth and social change.

With the rapid development of the economy, developing countries and regions have paid more and more attention to the effective use and protection of resources and the environment, including carbon neutrality. Rural resources are a type of ‘capital asset’ of rural tourism, which is increasingly influenced by pressures arising from an ever-wider range of economy, society, politics and environment [16]. In the rural tourism industry, landscape resources provide both assets for, and constraints on, tourism development. The intertwined relationship between rural tourism and landscape comes with a series of costs and benefits [17]. Therefore, the rational utilization of rural tourism landscape resources is conducive to guiding the construction of the countryside and the development of rural tourism.

The evaluation of landscape resources includes their type, quality, structure, distribution, development, and utilization, etc. This not only enables us to determine the value and importance of landscape resources, but also facilitates our understanding about their advantages and disadvantages. In addition to the comprehensive evaluation of landscape resources [10,18,19,20,21,22,23], scholars have evaluated rural landscape resources from different perspectives, including aesthetic quality [24], aesthetic value [25], visual resources [26], leisure agriculture [27,28], agricultural specialty towns [29], ecology [30], ecological space pattern [21], eco-tourism [31,32], sustainable tourism [33], tourism project [34], tourism planning satisfaction degree [35], residents’ perceptions of tourism [36], cultural landscapes [37,38], cultural value [39], landscape classification [28], landscape conservative value [40], landscape experience [41], evaluation models [19], and rural revitalization [10,22], etc. Various evaluation methods for rural landscape resources have been developed in the last few decades. These include analytic hierarchy process (AHP) method [19,21,29,31], fuzzy comprehensive evaluation (FCE) method [35], AHP and FCE methods [18,27], probability linguistic term sets (PLTSs) and unbalanced trapezoidal cloud (UTC) model [10,22], AHP and probabilistic linguistic cloud model methods [10,22], scenic beauty estimation (SBE) method [23], semantic differential (SD) method [41], contingent valuation method (CVM) [40], Delphi technique [34], Rasch model [25], and visual resources management (VRM) system [26,28], etc. Most of the methods have their own application domains, which are related to the characteristics of the indicator properties and indicator values involved. To our knowledge, none of these methods have considered the condition of carbon neutrality.

As for the relationship between carbon neutrality and landscape, Selman believes that carbon neutrality will result in changes in the traditional landscape, so people can learn to see beauty and attractiveness in emerging landscapes of carbon neutrality [42]. Climate risk affects carbon emissions performance [43]. In order to achieve carbon neutrality, regional and industrial factors should be taken into account [44,45,46]. Guo et al. selected Ordos in Inner Mongolia, China, for example, and constructed a landscape spatial structure optimization scheme called ecological function, connectivity and topology (EFCT) model, from the perspective of increasing carbon sinks to achieve carbon neutrality in a landscape of desertification and mining areas [4]. Environmental factors such as air quality can drive changes in carbon prices and affect carbon emissions [45,47]. Hundertmark et al. focused on biogenic carbon fluxes in plants and soils of urban ecosystems across three highly urban campuses owned by Boston University in the U.S., and quantified the role of urban land cover in local carbon budgets, offering insights on landscaping management strategies [48]. Wang et al. considered Huangpu District in Shanghai, China, and studied the ways and spatial characteristics of urban green landscapes affecting carbon neutrality [49]. Sun et al. put forward the connotation of the low-carbon garden, analyzed the current situation of Chinese landscape design from the perspective of carbon neutrality, and gave suggestions [50]. Wang et al. studied the Jiaoshan scenic spot in Zhenjiang, Jiangsu, China, as an example, evaluated the net carbon emissions of carbon-neutral tourist spots and gave suggestions on the construction of low-carbon scenic spots [51]. Tao et al. gave suggestions on plant landscape design from a carbon-neutral perspective [52].

Carbon neutrality and rural revitalization sometimes conflict with each other. For example, the construction of new hotels, stations and factories in rural areas for economic development usually increases carbon emissions, which is not conducive to the implementation of carbon neutrality. The spatial effect also causes economic growth to have a negative effect on carbon neutralization [53]. Nevertheless, they can promote each other in some situations. For example, planting economic forests, and developing the forestry and nursery industries not only increases the income of rural residents, but also performs the function of carbon sequestration for carbon neutrality. Shen proposed that the government first needs to strengthen the top-level design and build a roadmap for reaching the rural carbon peak and carbon neutrality as soon as possible, then the corresponding policies and regulations should follow in a timely manner, and the relevant assessment and incentive mechanism should also be established. In addition, the use of energy resources should be well-structured and efficient [3]. Cao et al. studied the main policies of and paths to promoting carbon neutrality in Germany, and proposed its enlightenment to China, that is, the government needs to design systematic laws, regulations and policies related to carbon neutrality, establish a systematic supervision and management mechanism, promote energy transformation, unify the carbon emission trading system, attach importance to scientific and technological research and development (R&D) investment, and strengthen the overall system design for rural revitalization, climate protection and biodiversity protection [54]. From the perspective of policy making, the above-mentioned researchers suggested that the government should implement carbon neutrality under the rural revitalization strategy.

In summary, there is a lack of research on the evaluation of the rural tourism landscape resources from the perspective of carbon neutrality and rural revitalization. The existing studies usually take AHP and fuzzy analysis as the main methods to evaluate rural landscape resources. Compared with the traditional qualitative and quantitative evaluation methods, such as AHP and fuzzy analysis method, the Višekriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method adopted in this article has a great advantage in obtaining the optimal compromise solution. The VIKOR method was developed to solve multi-criteria decision-making (MCDM) problems with conflicting and non-commensurable (different units) criteria. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria [55]. In the process of complex problem decision making, decision makers have hesitant behavior, and they believe that a single linguistic scale can approximate the ideal evaluator. Therefore, scholars have proposed a hesitating fuzzy linguistic set [56,57,58]. At present, the intuitionistic fuzzy VIKOR method has been used to solve the multi-criteria decision-making problem, and has been extended to the interval-valued environment [59] and the hesitant fuzzy environment [60].

The intuitionistic fuzzy VIKOR method can solve the problem of the index value being difficult to accurately quantify, so that it can cover more uncertain information through reducing the error caused by subjectivity, which makes the evaluation result more reliable and stable. At the same time, multiple types of information can be processed, and the information contained in the original data can be fully utilized to produce evaluation results which are more objective and reasonable [59]. For this reason, this article intends to build a model based on the theory of the intuitionistic fuzzy VIKOR method and consider three rural regions in Changsha, Hunan Province, China, as examples, to evaluate their tourism landscape resources.

The contribution of this article is reflected in two aspects. First, it evaluates the rural tourism landscape resources from the perspective of carbon neutrality and rural revitalization, which provides a new perspective for the evaluation of rural tourism landscape resources. Second, it proposes an evaluation method based on the intuitionistic fuzzy VIKOR method, which provides a reference method for the evaluation of rural tourism landscape resources. On the theoretical level, the research results could enrich the evaluation system of rural tourism landscape resources. On the practical level, they could provide reference suggestions for the development, planning, design, transformation, and protection of the rural tourism landscape in the development of specific regions.

The rest of this article is arranged as follows. Section 2 selects the evaluation object and index. Section 3 builds the evaluation model based on the relevant theories. Section 4 quantitatively analyzes and evaluates the tourism landscape resources in three rural regions of Changsha, Hunan Province, China. Section 5 is the conclusion, explaining the contributions and shortcomings of this evaluation and identifying some future directions.

2. Selection of Evaluation Objects and Indicators

In order to study the evaluation problem of rural tourism landscape resources under the constraints of carbon neutrality, our index set was drawn from rural tourism to promote rural revitalization; therefore, on this basis, the first aspect in rural tourism landscape resources evaluation index was to consider the background of carbon neutrality, modify most of the indicators, and ensure that they met the requirements of carbon neutrality. In this way, the final indicators will not only meet the purpose of using rural tourism landscape resources to promote rural re-vitalization, but also meet the requirements of carbon neutrality.

Through consulting a large number of literature [3,4,13,18,21,23,25,28,29,30,31,34,35,61], we constructed a preliminary evaluation index system by extracting indexes from the literature. For these initial indicators, we asked a number of experts from August to October 2021 for their opinion on the necessity of each indicator. Using these findings and in-depth interviews with multiple experts, inappropriate indicators were added and deleted. These experts were three professors and three on-the-job doctoral students at the university. Among them, two were from the Central South University of Forestry and Technology, two were from Sichuan Agricultural University, and two were from Hunan University (six in total). The three part-time PhD students were from the Garden Bureau, the Environmental Protection Bureau, and a landscape architecture firm, respectively. They all had rich management and practical experience.

Based on expert consultation, the evaluation system of tourism landscape resources is finally established in this paper (see

Table 1. Index system of tourism landscape resources evaluation.

Target Layer	Index Layer	Index Interpretation
Industrial value A1	Existing industrial economic benefits B1	The economic benefits of existing industries
	Perfect degree of industrial chain for circular economy B2	The degree of the perfection of the industrial chain formed through the circulation process of “raw materials-products-waste-raw materials”
	Carbon neutrality degree of existing industries B3	The degree of carbon neutrality in existing industries
Natural resources value A2	Carbon neutrality value of existing natural resources B4	In terms of carbon neutrality, the value of existing natural resources, such as the ability to absorb and store CO2
	Ecosystem diversity B5	Degree of ecological diversity
	Carbon neutrality creation potential of existing resources B6	The potential of carbon neutrality of existing resources, such as carbon sinks
	Carbon neutrality degree of the utilization and maintenance of natural resources B7	The degree of carbon neutrality when people use and maintain natural resources
Cultural resources value A3	Historical and cultural heritage values B8	The value of tangible and intangible cultural heritage, such as ancient buildings and folklore
	Carbon neutrality awareness of residents B9	The depth and breadth of residents’ understanding of carbon neutrality knowledge
	Carbon neutrality degree of the utilization and maintenance of cultural resources B10	The degree of carbon neutrality when people use and maintain cultural resources
	Attribution and identity values B11	People’s sense of belonging and identification with the countryside
Tourism value A4	Abundance of landscape resources B12	The number and richness of the types of landscape resources
	Carbon neutrality tourism value B13	The degree of carbon neutrality of tourism resources and tourism development, such as low-carbon energy conservation
	Landscape resources uniqueness B14	The uniqueness and rarity of landscape resources
	Landscape resources aesthetic value B15	The aesthetic value of landscape resources
Livability A5	Residential area livability B16	The livability of the residential area
	Infrastructure adequacy B17	The adequate degree of public material engineering facilities that serve the production and life of people
	Infrastructure carbon neutrality degree B18	The degree of carbon neutrality in the transport activities of people

).

As for the design of the indexes of layer A, industrial value (A1) is considered from the perspective of rural industries promoting rural revitalization, while natural resources value (A2) is considered from the perspective of carbon neutrality. Cultural resources value (A3) is considered from the perspective of rural culture promoting rural revitalization, while tourism value (A4) is considered from the perspective of rural tourism landscape resources. Livability (A5) is considered from the perspective of improving the rural living environment to promote rural revitalization and rural tourism. Most of the grade B indicators take carbon neutrality into account.

For the indexes of layer B, the existing industrial economic benefits (B1) and the perfect degree of industrial chain for circular economy (B2) in industrial value (A1) are considered from the perspective of rural industries and economic development promoting rural revitalization. The historical and cultural heritage values (B8) and attribution and identity values (B11) in the cultural resources value (A3) are designed from the perspective of protecting and inheriting rural culture and promoting rural revitalization. The abundance of landscape resources (B12), the landscape resources uniqueness (B14) and the landscape resources aesthetic value (B15) in the tourism value (A4) are constructed from the perspective of rural tourism landscape resources. The residential area livability (B16) and the infrastructure adequacy (B17) in the livability (A5) are designed from the perspective of improving rural living environment to promote rural revitalization and rural tourism. The rest of indexes, including the carbon neutrality degree of existing industries (B3), the carbon neutrality value of existing natural resources (B4), the ecosystem diversity (B5), the carbon neutrality creation potential of existing resources (B6), the carbon neutrality degree of the utilization and maintenance of natural resources (B7), the carbon neutrality awareness of residents (B9), the carbon neutrality degree of the utilization and maintenance of cultural resources (B10), the carbon neutrality tourism value (B13) and the infrastructure carbon neutrality degree (B18) are both based on carbon neutrality, considering rural industry economy, culture, landscape resources and tourism.

3. Construction of Evaluation Model

This article is based on the intuitionistic fuzzy sets [62] and VIKOR method [63]. The evaluation information is always expressed by intuitionistic fuzzy numbers, which avoids the problem of group decision information loss in the evaluation process. Based on the intuitionistic fuzzy entropy theory, the determination of the comprehensive weight of the index is improved, the maximum group utility and the minimum personal regret are fully utilized, and the alternatives are sorted by the idea of the closest ideal solution.

3.1. Intuitionistic Fuzzy Transformation of Evaluation Information

Qualitative evaluation indicators are often used to describe preference information in vague linguistic variables such as very good and average. In this article, seven linguistic evaluation granularities are used to describe the evaluation information of linguistic variables, and the corresponding intuitionistic fuzzy numbers are shown in

Table 2. The transformation relationship between linguistic variables and intuitionistic fuzzy numbers.

Linguistic Variables	Intuitionistic Fuzzy Number
Poorest	(0.15, 0.80)
Very Poor	(0.25, 0.65)
Poor	(0.35, 0.55)
Average	(0.50, 0.40)
Good	(0.65, 0.25)
Very Good	(0.75, 0.15)
Best	(0.85, 0.10)

.

3.2. Weight Determination Based on Intuitionistic Fuzzy Evaluation Matrix

3.2.1. Determination of Index Weight

The intuitionistic fuzzy entropy is used to reflect the fuzzy uncertainty of experts on the evaluation information of indicators. Equation (1) is the intuitionistic fuzzy entropy of experts on index evaluation, and Equation (2) is the index weight determined by using the intuitionistic fuzzy evaluation matrix.

$${\mathrm{E}}_{\mathrm{j}}^{\mathrm{k}}=\frac{1}{\mathrm{m}}{\sum }_{\mathrm{i}=1}^{\mathrm{m}}\cos \frac{\mathsf{\pi }\left({\mathsf{\mu }}_{\mathrm{ij}}^{\mathrm{k}}-{\mathrm{v}}_{\mathrm{ij}}^{\mathrm{k}}\right)\left(1-{\mathsf{\pi }}_{\mathrm{ij}}^{\mathrm{k}}\right)}{2}$$

(1)

$${\mathrm{w}}_{\mathrm{j}}^{\mathrm{k}}=\frac{1-{\mathrm{E}}_{\mathrm{j}}^{\mathrm{k}}}{\mathrm{n}-{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{E}}_{\mathrm{j}}^{\mathrm{k}}}$$

(2)

The comprehensive weights of the index can be determined by establishing the minimum deviation optimization model, and the final reasonable weight range can be determined through repeated tests. Finally, an optimization model is built to determine the comprehensive weights of indicators where ${\mathrm{w}}_{\mathrm{j}}^{*}$ represents a composite weight for indicator j. Among them, the weight index ${\mathrm{w}}_{\mathrm{j}}^{\mathrm{k}}$ in the optimization model only includes the weights after meeting the consistency test.

$$\begin{gathered} \min {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\sum }_{\mathrm{k}=1}^{{\mathrm{K}}_1}\left({\mathrm{w}}_{\mathrm{j}}^{\mathrm{k}}-{\mathrm{w}}_{\mathrm{j}}^{*}\right){\left({\mathrm{w}}_{\mathrm{j}}^{\mathrm{k}}-{\mathrm{w}}_{\mathrm{j}}^{*}\right)}^2, \\ \mathrm{s}.\mathrm{t}.\{\begin{array}{c}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{j}}^{*}=1\\ {\mathrm{w}}_{\mathrm{js}}^{\mathrm{k}}\le {\mathrm{w}}_{\mathrm{j}}^{*}\le {\mathrm{w}}_{\mathrm{jt}}^{\mathrm{k}}\end{array} \end{gathered}$$

(3)

3.2.2. Determination of Expert Weights

Regarding the previous single weighting method, the determination of expert weights is not easy due to the complexity of different village evaluation scenarios. This article is a group decision-making information that takes the indicators into account comprehensively and objectively. It is believed that the importance of experts should depend on the overall reliability of their judgment information.

Due to the differences in the scope and cognitive level of each expert, it is necessary to reasonably allocate the weights of experts. According to the theory of intuitionistic fuzzy entropy, the weights of experts are determined, where ${\mathrm{G}}_{\mathrm{k}}$ is the weighted intuitionistic fuzzy entropy, which represents the fuzzy degree of evaluation information given by experts.

$${\mathrm{G}}^{\mathrm{k}}={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{j}}^{\mathrm{k}}{\mathrm{E}}_{\mathrm{j}}^{\mathrm{k}}$$

(4)

$${\mathsf{\lambda }}_{\mathrm{k}}=\frac{1-{\mathrm{G}}_{\mathrm{k}}}{\mathrm{K}-{\sum }_{\mathrm{k}=1}^{\mathrm{K}}{\mathrm{G}}_{\mathrm{k}}}$$

(5)

3.3. Sorting Based on Intuitionistic Fuzzy VIKOR

In the evaluation of cultural and tourism landscape resource value, the ranking based on the intuitionistic fuzzy VIKOR can objectively and intuitively show the status quo of cultural and tourism landscape resource value in each region. At the same time, the opinions of different experts are fully considered to determine the best compromise result of the evaluation sequence. The specific steps are as follows.

Firstly, the expert evaluation information is uniformly transformed into intuitionistic fuzzy form, and the weighted average operator is used to determine the group decision matrix as follows:

$${\mathrm{f}}_{\mathrm{ij}}={\mathsf{\lambda }}_1\widetilde{{\mathrm{h}}_{\mathrm{ij}}^1}\oplus {\mathsf{\lambda }}_2\widetilde{{\mathrm{h}}_{\mathrm{ij}}^2}\oplus \cdots \oplus {\mathsf{\lambda }}_{\mathrm{k}}\widetilde{{\mathrm{h}}_{\mathrm{ij}}^{\mathrm{k}}}=\left(1-{\prod }_{\mathrm{k}=1}^{\mathrm{K}}{\left(1-{\mathsf{\mu }}_{\mathrm{ij}}^{\mathrm{k}}\right)}^{{\mathsf{\lambda }}_{\mathrm{k}}},{\prod }_{\mathrm{k}=1}^{\mathrm{K}}{\left({\mathrm{v}}_{\mathrm{ij}}^{\mathrm{k}}\right)}^{{\mathsf{\lambda }}_{\mathrm{k}}}\right)$$

(6)

Secondly, according to the assembled decision matrix, positive ideal solutions and negative ideal solutions are determined as follows, respectively:

$$\{\begin{array}{c}{\mathrm{f}}_{\mathrm{j}}^{+}=\underset{1\le \mathrm{i}\le \mathrm{m}}{\max }\left({\mathrm{f}}_{\mathrm{ij}}\right)=\left(\left[\underset{\mathrm{i}}{\max } {\mathsf{\mu }}_{\mathrm{ij}},\underset{\mathrm{i}}{\min } {\mathrm{v}}_{\mathrm{ij}}\right]\right)\\ {\mathrm{f}}_{\mathrm{j}}^{-}=\underset{1\le \mathrm{i}\le \mathrm{m}}{\min }\left({\mathrm{f}}_{\mathrm{ij}}\right)=\left(\left[\underset{\mathrm{i}}{\min } {\mathsf{\mu }}_{\mathrm{ij}},\underset{\mathrm{i}}{\max } {\mathrm{v}}_{\mathrm{ij}}\right]\right)\end{array}$$

(7)

Thirdly, the group utility value $\mathrm{S}\left({\mathrm{A}}_{\mathrm{i}}\right)$, personal regret value $\mathrm{R}\left({\mathrm{A}}_{\mathrm{i}}\right)$, and compromise evaluation value $\mathrm{Q}\left({\mathrm{A}}_{\mathrm{i}}\right)$ are calculated.

$$\begin{gathered} \mathrm{S}\left({\mathrm{A}}_{\mathrm{i}}\right)={\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{j}}^{*}\frac{\mathrm{d}\left({\mathrm{f}}_{\mathrm{j}}^{+},{\mathrm{f}}_{\mathrm{ij}}\right)}{\mathrm{d}\left({\mathrm{f}}_{\mathrm{j}}^{+},{\mathrm{f}}_{\mathrm{j}}^{-}\right)} \\ \mathrm{R}\left({\mathrm{A}}_{\mathrm{i}}\right)=\underset{\mathrm{j}}{\max }\left[{\mathrm{w}}_{\mathrm{j}}^{*}\frac{\mathrm{d}\left({\mathrm{f}}_{\mathrm{j}}^{+},{\mathrm{f}}_{\mathrm{ij}}\right)}{\mathrm{d}\left({\mathrm{f}}_{\mathrm{j}}^{+},{\mathrm{f}}_{\mathrm{j}}^{-}\right)}\right] \\ \mathrm{Q}\left({\mathrm{A}}_{\mathrm{i}}\right)=\mathrm{v}\frac{\mathrm{S}\left({\mathrm{A}}_{\mathrm{i}}\right)-{\mathrm{S}}^{-}}{{\mathrm{S}}^{+}-{\mathrm{S}}^{-}}+\left(1-\mathrm{v}\right)\frac{\mathrm{R}\left({\mathrm{A}}_{\mathrm{i}}\right)-{\mathrm{R}}^{-}}{{\mathrm{R}}^{+}-{\mathrm{R}}^{-}} \end{gathered}$$

(8)

In the above equation, ${\mathrm{S}}^{+}=\max \left[\mathrm{S}\left({\mathrm{A}}_{\mathrm{i}}\right)\right]$, ${\mathrm{S}}^{-}=\min \left[\mathrm{S}\left({\mathrm{A}}_{\mathrm{i}}\right)\right]$, ${\mathrm{R}}^{+}=\max \left[\mathrm{R}\left({\mathrm{A}}_{\mathrm{i}}\right)\right]$, ${\mathrm{R}}^{-}=\min \left[\mathrm{R}\left({\mathrm{A}}_{\mathrm{i}}\right)\right]$, and $\mathrm{v}\in \left[0,1\right]$ is the decision preference coefficient.

Finally, according to the values of group utility value, personal regret value and compromise evaluation value, we can obtain the rank of the advantages and disadvantages of tourism landscape resources evaluation in each region and determine the best compromise result of the evaluation sequence. If the compromise evaluation value of region A is the minimum value and meets the following two judgment conditions, A is regarded as the optimal compromise solution with the highest risk priority, that is, it is the optimal region for tourism landscape resources evaluation. Condition one: ${\mathrm{Q}}_2-{\mathrm{Q}}_1\ge 1/\left(\mathrm{m}-1\right)$; and condition two: when ranking according to group utility value and personal regret value, region A is still in the first place, then it meets the stability requirement. The above, ${\mathrm{Q}}_1$ and ${\mathrm{Q}}_2$ in condition one correspond to the smallest value and the second smallest value in the ranking of compromise evaluation values, respectively.

If the above conditions cannot be satisfied at the same time, a compromise solution is obtained. If only condition one is satisfied, it means that all bidders are close to the ideal solution. If only condition two is satisfied, it means that the bidders corresponding to ${\mathrm{Q}}_1$ and ${\mathrm{Q}}_2$ are compromise solutions.

4. Evaluation Results and Analysis

Three regions, P₁, P₂, and P₃ in Changsha, Hunan Province, China, were selected as the evaluation objects (Figure 1).

P₁ is Jinjing Town in Changsha County. The main industries of the village are casting, tea, automobile electrical appliances, and agricultural and sideline products processing. There are many tea farms and tea gardens, as well as modern farms developed by environmental protection and ecological technology. Most of the tourist reception facilities are in the form of country hotels, and bed and breakfast inns (B&Bs). Some are staffed with English translators to receive foreign tourists [64].

P₂ is Guangming Village in Changsha County. The natural environment of the village is beautiful, with distinctive features of “mountains, rivers, and fields”. By introducing agricultural science and technology, the village implements modern enterprise management (including the integration of hotel management, agricultural and sideline product development, the integration of scenic spot management, the integration of network system development, the integration of culture and education, and the integration of experiential rural tourism resources), which drives the development of rural tourism [65].

P₃ is Xunlonghe Village in Wangcheng District of Changsha. In recent years, the village has promoted the comprehensive reform of rural land and developed the tourism industry by building modern farms, a cherry valley, food streets and other tourist attractions, in addition to supporting facilities such as specialty B&Bs, a wooden hotel and an earth warehouse hotel, and successfully held large-scale tourism activities such as a music festival [66]. Through rural tourism, the village has promoted the integrated development of industries, improved the ecological environment, and enhanced the living quality (Figure 2).

Based on the actual situation of rural tourism landscape resources in three regions of Changsha, Hunan Province, China, this article used the intuitionistic fuzzy VIKOR to perform a quantitative analysis on the evaluation of the actual cultural and tourism landscape resources in rural regions. Firstly, questionnaires were prepared. In order to obtain the initial evaluation decision-making information, we designed the questionnaire (see Appendix A), three experts were invited to provide fuzzy evaluation information of 18 evaluation indices based on the situation of tourism landscape resources in three regions. They were respectively affiliated to Hunan University, Central South University of Forest and Technology, and Sichuan Agricultural University. The standardized evaluation decision information is shown in

Table 3. Initial evaluation information of each region after specification.

Index Layer	Experts	P1	P2	P3
A1	C1	(0.65, 0.25)	(0.75,0.15)	(0.65, 0.25)
	C2	(0.65, 0.25)	(0.75,0.15)	(0.50, 0.40)
	C3	(0.75, 0.15)	(0.75,0.15)	(0.65, 0.25)
B1	C1	(0.65, 0.25)	(0.75, 0.15)	(0.50, 0.40)
	C2	(0.50, 0.40)	(0.75, 0.15)	(0.50, 0.40)
	C3	(0.50, 0.40)	(0.85, 0.10)	(0.50, 0.40)
B2	C1	(0.75, 0.15)	(0.85, 0.10)	(0.75, 0.15)
	C2	(0.85, 0.10)	(0.75, 0.15)	(0.65, 0.25)
	C3	(0.85, 0.10)	(0.75, 0.15)	(0.75, 0.15)
B3	C1	(0.75, 0.15)	(0.65, 0.25)	(0.75, 0.15)
	C2	(0.75, 0.15)	(0.75, 0.15)	(0.50, 0.40)
	C3	(0.85, 0.10)	(0.65, 0.25)	(0.65, 0.25)
A2	C1	(0.65, 0.25)	(0.75, 0.15)	(0.75, 0.15)
	C2	(0.65, 0.25)	(0.75, 0.15)	(0.65, 0.25)
	C3	(0.75, 0.15)	(0.75, 0.15)	(0.65, 0.25)
B4	C1	(0.75, 0.15)	(0.75, 0.15)	(0.75, 0.15)
	C2	(0.65, 0.25)	(0.85, 0.10)	(0.65, 0.25)
	C3	(0.75, 0.15)	(0.75, 0.15)	(0.75, 0.15)
B5	C1	(0.50, 0.40)	(0.65, 0.25)	(0.50, 0.40)
	C2	(0.50, 0.40)	(0.65, 0.25)	(0.50, 0.40)
	C3	(0.50, 0.40)	(0.65, 0.25)	(0.50, 0.40)
B6	C1	(0.85, 0.10)	(0.85, 0.10)	(0.75, 0.15)
	C2	(0.75, 0.15)	(0.85, 0.10)	(0.75, 0.15)
	C3	(0.85, 0.10)	(0.85, 0.10)	(0.85, 0.10)
B7	C1	(0.50, 0.40)	(0.75, 0.15)	(0.65, 0.25)
	C2	(0.65, 0.25)	(0.65, 0.25)	(0.65, 0.25)
	C3	(0.65, 0.25)	(0.75, 0.15)	(0.65, 0.25)
A3	C1	(0.65, 0.25)	(0.65, 0.25)	(0.65, 0.25)
	C2	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
	C3	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
B8	C1	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
	C2	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
	C3	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
B9	C1	(0.50, 0.40)	(0.75, 0.15)	(0.50, 0.40)
	C2	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
	C3	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
B10	C1	(0.65, 0.25)	(0.65, 0.25)	(0.65, 0.25)
	C2	(0.50, 0.40)	(0.65, 0.25)	(0.65, 0.25)
	C3	(0.65, 0.25)	(0.65, 0.25)	(0.65, 0.25)
B11	C1	(0.50, 0.40)	(0.50, 0.40)	(0.50, 0.40)
	C2	(0.50, 0.40)	(0.50, 0.40)	(0.50, 0.40)
	C3	(0.65, 0.25)	(0.65, 0.25)	(0.50, 0.40)
A4	C1	(0.75, 0.15)	(0.85, 0.10)	(0.75, 0.15)
	C2	(0.75, 0.15)	(0.85, 0.10)	(0.65, 0.25)
	C3	(0.85, 0.10)	(0.85, 0.10)	(0.65, 0.25)
B12	C1	(0.85, 0.10)	(0.85, 0.10)	(0.85, 0.10)
	C2	(0.75, 0.15)	(0.85, 0.10)	(0.75, 0.15)
	C3	(0.85, 0.10)	(0.85, 0.10)	(0.85, 0.10)
B13	C1	(0.85, 0.10)	(0.75, 0.15)	(0.65, 0.25)
	C2	(0.75, 0.15)	(0.75, 0.15)	(0.75, 0.15)
	C3	(0.75, 0.15)	(0.85, 0.10)	(0.75, 0.15)
B14	C1	(0.65, 0.25)	(0.65, 0.25)	(0.75, 0.15)
	C2	(0.65, 0.25)	(0.75, 0.15)	(0.50, 0.40)
	C3	(0.75, 0.15)	(0.75, 0.15)	(0.50, 0.40)
B15	C1	(0.65, 0.25)	(0.85, 0.10)	(0.65, 0.25)
	C2	(0.75, 0.15)	(0.75, 0.15)	(0.65, 0.25)
	C3	(0.75, 0.15)	(0.85, 0.10)	(0.50, 0.40)
A5	C1	(0.65, 0.25)	(0.65, 0.25)	(0.65, 0.25)
	C2	(0.65, 0.25)	(0.75, 0.15)	(0.50, 0.40)
	C3	(0.65, 0.25)	(0.75, 0.15)	(0.65, 0.25)
B16	C1	(0.50, 0.40)	(0.75, 0.15)	(0.65, 0.25)
	C2	(0.65, 0.25)	(0.75, 0.15)	(0.50, 0.40)
	C3	(0.75, 0.15)	(0.65, 0.25)	(0.50, 0.40)
B17	C1	(0.50, 0.40)	(0.85, 0.10)	(0.65, 0.25)
	C2	(0.50, 0.40)	(0.85, 0.10)	(0.50, 0.40)
	C3	(0.50, 0.40)	(0.85, 0.10)	(0.65, 0.25)
B18	C1	(0.75, 0.15)	(0.75, 0.15)	(0.65, 0.25)
	C2	(0.75, 0.15)	(0.65, 0.25)	(0.65, 0.25)
	C3	(0.75, 0.15)	(0.75, 0.15)	(0.65, 0.25)

.

We first calculated the index weights and comprehensive weights assigned by different experts and then calculated the index weight assigned by expert C1 from Equations (1)–(3), i.e.,

$${\omega }_A{}^1=\left(0.177 0.227 0.127 0.341 0.127\right)$$

$${\omega }_B{}^1=\left(\begin{array}{c}0.038 0.094 0.063 0.077 0.013 0.111 0.038 0.024 0.027\\ 0.035 0.002 0.128 0.080 0.049 0.066 0.038 0.055 0.063\end{array}\right)$$

In the same way, the index weights were assigned by experts C2 and C3 and the comprehensive weights of evaluation indexes were obtained.

$${\omega }_A{}^2=\left(0.137 0.177 0.088 0.291 0.137\right)$$

$${\omega }_B{}^2=\left(\begin{array}{c}0.032 0.094 0.061 0.078 0.015 0.111 0.041 0.029 0.029\\ 0.029 0.002 0.130 0.090 0.045 0.074 0.032 0.052 0.058\end{array}\right)$$

$${\omega }_B{}^3=\left(\begin{array}{c}0.041 0.088 0.062 0.072 0.012 0.120 0.046 0.023 0.023\\ 0.033 0.012 0.120 0.049 0.065 0.073 0.036 0.052 0.059\end{array}\right)$$

$${\omega }_B{}^{*}=\left(\begin{array}{c}0.037 0.092 0.062 0.075 0.014 0.113 0.042 0.025 0.026\\ 0.032 0.006 0.126 0.086 0.047 0.068 0.035 0.053 0.060\end{array}\right)$$

Equations (4) and (5) were used to calculate the weight of each expert, in turn, i.e., ${\mathsf{\lambda }}_1=0.343, {\mathsf{\lambda }}_2=0.297, {\mathsf{\lambda }}_3=0.361$.

Equation (6) was used to gather the expert evaluation matrix into a group evaluation matrix, see

Table 4. Group evaluation matrix.

The Evaluation Index	P1	P2	P3
B1	(0.558, 0.341)	(0.792, 0.130)	(0.500, 0.400)
B2	(0.821, 0.115)	(0.790, 0.131)	(0.724, 0.175)
B3	(0.792, 0.130)	(0.683, 0.215)	(0.653, 0.241)
B4	(0.724, 0.175)	(0.785, 0.133)	(0.724, 0.175)
B5	(0.500, 0.400)	(0.650, 0.250)	(0.500, 0.400)
B6	(0.825, 0.113)	(0.850, 0.100)	(0.792, 0.130)
B7	(0.605, 0.294)	(0.724, 0.175)	(0.650, 0.250)
B8	(0.650, 0.250)	(0.650, 0.250)	(0.500, 0.400)
B9	(0.605, 0.294)	(0.688, 0.210)	(0.500, 0.400)
B10	(0.611, 0.287)	(0.650, 0.250)	(0.650, 0.250)
B11	(0.560, 0.338)	(0.500, 0.400)	(0.500, 0.400)
B12	(0.850, 0.100)	(0.850, 0.100)	(0.825, 0.113)
B13	(0.790, 0.131)	(0.792, 0.130)	(0.719, 0.179)
B14	(0.690, 0.208)	(0.719, 0.179)	(0.606, 0.286)
B15	(0.719, 0.179)	(0.825, 0.113)	(0.602, 0.296)
B16	(0.611, 0.281)	(0.718, 0.180)	(0.558, 0.341)
B17	(0.500, 0.400)	(0.850, 0.100)	(0.611, 0.287)
B18	(0.750, 0.150)	(0.724, 0.175)	(0.650, 0.250)

.

In Table 3, the experts evaluated the performance of the B1–B18 indicators in the three regions of P₁, P₂ and P₃. Respectively, the results of these evaluation values are summarized in Table 4.

According to the group evaluation matrix F, the positive ideal solution and the negative ideal solution under each index were determined by Equation (7).

$$\begin{array}{cc}{f}^{+}=& \left(0.850,0.10\right),\left(0.85,0.10\right),\left(0.821,0.115\right),\left(0.792,0.130\right),\left(0.785,0.133\right),\left(0.825,0.113\right),\hfill \\ & \left(0.792,0.13\right),\left(0.750,0.15\right),\left(0.850,0.10\right),\left(0.719,0.179\right),\left(0.724,0.175\right),\left(0.792,0.13\right),\hfill \\ & \left(0.718,0.18\right),\left(0.65,0.25\right),\left(0.688,0.21\right),\left(0.65,0.25\right),\left(0.65,0.25\right),\left(0.56,0.338\right).\hfill \end{array}$$

$$\begin{array}{cc}{f}^{-}=& \left(0.825,0.113\right),\left(0.792,0.13\right),\left(0.724,0.175\right),\left(0.719,0.179\right),\left(0.724,0.175\right),\left(0.602,0.296\right),\hfill \\ & \left(0.653,0.241\right),\left(0.650,0.25\right),\left(0.50,0.40\right),(0.606,0.286),\left(0.605,0.294\right),\left(0.500,0.40\right),\hfill \\ & \left(0.558,0.341\right),\left(0.611,0.287\right),\left(0.50,0.40\right),\left(0.500,0.40\right),\left(0.500,0.40\right),\left(0.500,0.40\right)\hfill \end{array}$$

According to the distance measurement formula of intuitionistic fuzzy Euclidean and Equation (8), the group utility value, personal regret value, and compromise evaluation value of each indicator were calculated. The ranking results are shown in

Table 5. Ranking of three different values in different regions.

Region	$\mathbf{S}\left({\mathbf{P}}_{\mathbf{i}}\right)$	The Sorting	$\mathbf{R}\left({\mathbf{P}}_{\mathbf{i}}\right)$	The Sorting	$\mathbf{Q}\left({\mathbf{P}}_{\mathbf{i}}\right)$	The Sorting
P1	0.296	2	0.075	2	0.356	2
P2	0.055	1	0.037	1	0.000	1
P3	0.912	3	0.126	3	1.000	3

Group utility value, personal regret value, and compromise evaluation value in different regions.

, when v = 0.5.

As can be seen from Table 5, the sorting result of pros and cons according to the size of the trade-off value is P₂ > P₁ > P₃, where the difference between the trade-off value of P₁ and P₂ is 0.356, which is bigger than 0.25, which satisfies condition one. At the same time, P₂ has the minimum value in group utility value and personal regret value, which satisfies the second condition. As a result, P₂ is the optimal region for tourism landscape resources evaluation.

To investigate the robustness of the evaluation results, a total of 11 times are taken in v $\in$ [0,1] with 0.1 as the step size for sensitivity analysis. The influence of different trade-off coefficient v on the decision-making results was calculated, as shown in Figure 3.

It can be seen from Figure 1 that the trade-off values Q(P₂) of P₂ and P₃ did not change. The ranking result of P₂ was always the best. The ranking result of P₃ was always the worst and the tradeoff value of P₁ varied with the trade-off coefficient. There was a rising change with the change of v. At the same time, any value of P₂ in v $\in$ [0,1] satisfied conditions one and two. Therefore, P₂ was the most stable and optimal region in the evaluation of tourism landscape resources, of all three regions.

Based on sensitivity analysis, to further demonstrate the rationality of the proposed method, the classical entropy-technique for order of preference by similarity to ideal solution entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to deal with the above problems, which calculates the ${\mathrm{D}}_{\mathrm{i}}^{+}$ (i.e., the distance between intuitionistic fuzzy evaluation value and positive ideal solution) and ${\mathrm{D}}_{\mathrm{i}}^{-}$ (i.e., the distance between intuitionistic fuzzy evaluation value and negative ideal solution) in each region. The relative closeness ${\mathrm{T}}_{\mathrm{i}}$ is used to represent the pros and cons of cultural and tourism landscape resources in each region. The expression is given by:

$${\mathrm{T}}_{\mathrm{i}}=\frac{{\mathrm{D}}_{\mathrm{i}}^{-}}{{\mathrm{D}}_{\mathrm{i}}^{-}+{\mathrm{D}}_{\mathrm{i}}^{+}}$$

(9)

Applying the classical entropy–TOPSIS method, we obtained the calculation results, which are shown in

Table 6. ${\mathrm{D}}_{\mathrm{i}}^{+}, {\mathrm{D}}_{\mathrm{i}}^{-}$ and ${\mathrm{T}}_{\mathrm{i}}$ values in various regions.

Region	${\mathbf{D}}_{\mathbf{i}}^{+}$	${\mathbf{D}}_{\mathbf{i}}^{-}$	${\mathbf{T}}_{\mathbf{i}}$	The Sorting
P1	0.112	0.051	0.312	2
P2	0.007	0.190	0.963	1
P3	0.168	0.008	0.045	3

.

As it can be seen from Table 6, the ranking result of landscape resources of cultural tourism in each region was P₂ > P₁ > P₃. The optimal region obtained by this method (entropy–TOPSIS method) and the intuitionistic fuzzy VIKOR method was P₂, and both methods could achieve the purpose of meritocracy for bidders. In contrast, the entropy–TOPSIS method had a subjective and arbitrary evaluation value of qualitative indicators, ignoring the uncertainty of the expert cognitive process and results, while the intuitionistic fuzzy VIKOR method considered the balance of the three indicators and could effectively address the uncertainty in the expert evaluation process in addition to the results. In summary, the intuitionistic fuzzy VIKOR method could further evaluate the best region objectively and effectively overcame the defects of a single type of information considered in the previous cultural tourism landscape resource evaluation methods.

5. Discussions

In layer A, the comprehensive weight size order was A4 > A2 > A1 > A5 > A3. In layer B, the comprehensive weight size order was B12 > B6> B2 > B13 > B4> B15 > B18 > B17 > B14 > B7 > B1> B16 > B10 > B9 > B8 > B5 > B11. The tourism value (A4) and landscape resource abundance (B12) were the most important value in layers A and B, respectively.

To check whether our model produced reliable results, we investigated the regions P₁, P₂, and P₃. The overall evaluation of P₁, P_2, and P₃ in the three regions were average and above. The abundance of landscape resources (B12) in the three regions were best because all the regions are relatively rich in rural landscape resources including hills, valleys, rivers, lakes, plains, forests, grasslands, farmlands, animals and so on. However, the attribution and identity values (B11) were average. The number and type of material cultural heritage in the three regions were rarely. Besides of dwellings in the three regions, ancestral halls, a temple and a famous well were only located in P₁. Due to the lack of appropriate maintenance, some traditional buildings were in disrepair and had lost their original function and style. Only some of the buildings had been restored, and the restored appearance was quite different from the original style, which resulted in the loss of the material and cultural heritage value. Public activities in these regions were mainly concentrated in the square or the small space in front of the ancestral hall. The square pile debris phenomenon seriously occupied the activity space. The small number of cultural activity rooms were inconvenient for villagers to carry out social and cultural activities during festivals, which was not only inconducive to displaying rural culture, protecting and inheriting intangible cultural heritage, but also bad for attracting and accommodating tourists. The traditional folk culture in the three villages had substantially disappeared. The construction of villages was mostly dominated by the government, and the villagers’ awareness of taking the initiative to participate in the construction of their hometown was weak. The rural scene, as held in the memory of the older generation of villagers, no longer exists. Therefore, the historical and cultural heritage values (B8) were good or average, and the attribution and identity values (B11) were average.

P₂ was the optimal region in the evaluation of the three regions. B6, B12, B17 were best; B1, B2, B4, B13, B15 were very good; B1, B3, B5, B7, B8, B9, B10, B14, B16, B18 were good; and B11 was average. It still has room for improvement. P₂ village had large quantities of rebuilt residential buildings. Many tourist attractions and public facilities had been developed, such as antique commercial streets, creative and cultural bases, resort hotels, conference centers, etc., with many tourists and large carbon emissions. Thus, the infrastructure adequacy (B17) was best, and the residential area livability (B16), and the infrastructure carbon neutrality degree (B18) were good. The village had used the terrain conditions to create outdoor parent–child parks, built many artificial facilities, and imported large children’s amusement equipment from Europe. The transportation and installation process had large carbon emissions, which caused damage to the local original natural resources. Thus, the existing industrial economic benefits (B1), the perfect degree of industrial chain for circular economy (B2), and the carbon neutrality tourism value (B13) were very good. The carbon neutrality degree of existing industries (B3) and the carbon neutrality degree of the utilization and maintenance of cultural resources (B10) were good. The original village was rich in forest resources, but the tree species were of relatively poor variety. Some soil and biodiversity loss had occurred in the village, so the carbon neutrality creation potential of existing resources (B6) was best, the carbon neutrality value of existing natural resources (B4) was very good, and the ecosystem diversity (B5) was good. Thus, the carbon neutrality degree of the utilization and maintenance of natural resources (B7) was good. Residents had a certain understanding of the concept of carbon neutrality but this was mostly limited to basic concepts, therefore, the carbon neutrality awareness of residents (B9) was good. The natural landscape resources of the village were mountains, waters, fields, and natural scenery; however, most of the existing buildings were newly built with almost exactly the same structure; few buildings were of unique style and there was a lack of landmark landscape. Thus, the landscape resources uniqueness (B14) was good, and the landscape resources aesthetic value (B15) was very good.

P₁ was the sub-optimal region. B12 was best; B2, B3, B6, B13, B18 were very good; B4, B8, B14, B15 were good; and B1, B5, B7, B9, B10, B11, B16, B17 were average. The industries in P₁ village were tea industry, tourism, casting, automobile electrical appliances, and agricultural and sideline products processing. The tea industry was the mainstay and had formed an industrial chain with tourism, and agricultural and sideline products, therefore, the perfect degree of industrial chain for circular economy (B2), the carbon neutrality degree of existing industries (B3), the carbon neutrality creation potential of existing resources (B6), and the carbon neutrality tourism value (B13) were very good, and the carbon neutrality value of existing natural resources (B4) was good. However, only in recent years had the village begun to try to use tea fields as a specialty landscape resource to develop tourism, which started late, so the existing industrial economic benefits (B1) was average. At the same time, the infrastructure supporting tourism was not complete, and tourism activities occasionally interfered with agricultural production and residential life, but some new infrastructure took into account the use of new energy, such as car charging piles and solar street lamps, therefore, the residential area livability (B16) and the infrastructure adequacy (B17) were relatively low, but the infrastructure carbon neutrality degree (B18) was good. The original vegetation in P₁ village was mostly removed due to the messy appearance and lack of aesthetic value. The original vegetation of hills was mostly destroyed, and after artificial afforestation, trees were of a single species and lacked deciduous tree species. Therefore, the ecosystem diversity (B5) was relatively low. The landscape buildings in the tea garden were relatively dense, which caused some damage to the original tea garden landscape resources and local environment, and the landscape buildings were all similar. Therefore, the landscape resources uniqueness (B14) and the landscape resource aesthetic value (B15) were average. The original cultural landscape was only one temple, and the temple had been transformed many times in recent years, causing carbon emissions. This transformation had destroyed the original exterior, therefore the carbon neutrality degree of the utilization and maintenance of cultural resources (B10) was average. In some areas, vegetation was sparse, soil was bare, and carbon sequestration capacity was weak. Most of the existing vegetation was transported from foreign nurseries for planting and some of the plants had a complex shape. During transportation, planting and horticulture, carbon emissions were generated. Therefore, the carbon neutrality degree of the utilization and maintenance of natural resources (B7) was relatively low. Residents did not know much about the concept of carbon neutrality and the carbon neutrality awareness of residents (B9) was average.

P₃ was the lowest grade region. B12 was best; B6 was very good; B2, B3, B4, B7, B10, B13, B18 were good; and B1, B5, B8, B9, B11, B14, B15, B16, B17 were average. The original vegetation of P₃ village was good preserved, so the carbon neutrality creation potential of existing resources (B6) was very good, the carbon neutrality value of existing natural resources (B4) was good, but plant species lacked variety, so the ecosystem diversity (B5) was average. To promote rural tourism, many new hotels and B&Bs had been built in P₃ village. As there were too many B&Bs, the business was not very good. Thus, the perfect degree of industrial chain for circular economy (B2) and carbon neutrality degree of existing industries (B3) was good, but the existing industrial economic benefits (B1) were average. Formerly idle buildings or rented dwellings were rarely remodeled. To facilitate tourists’ parking, the new parking lot used traditional impermeable asphalt paving materials; with poor water permeability, rainwater accumulated on rainy days, which was not conducive to soil air permeability. Therefore, the carbon neutrality degree of the utilization and maintenance of natural resources (B7), the carbon neutrality tourism value (B13), and the infrastructure carbon neutrality degree (B18) were good. When the village built a modern residential area, many original buildings were demolished and destroyed. Modern facilities could not be added to the base of protection, and some buildings with historical and cultural heritage value were relocated and rebuilt, and failed to be protected in situ. Carbon emissions were generated in the process of demolition, relocation and reconstruction, thus the carbon neutrality awareness of resident (B9) was average, the carbon neutrality degree of the utilization and maintenance of cultural resources (B10) was good, and the residential area livability (B16) was average. Villagers mainly traveled by cars, with few bicycles. There were no bicycle parking points in tourist facilities and no bicycle lanes on tourist roads. Residents did not know much about the concept of carbon neutrality, so the infrastructure adequacy (B17) was relatively low. The new buildings looked almost the same. Thus, the landscape resources uniqueness (B14) and the landscape resource aesthetic value (B15) were average.

6. Conclusions and Suggestions

Based on the construction of the evaluation index system of tourism landscape resources from the perspective of rural revitalization strategy and carbon neutrality, this article proposed an evaluation method under intuitionistic fuzzy environment combined with the VIKOR theory. The evaluation information of the whole evaluation process was always expressed by intuitionistic fuzzy number to avoid the loss of group decision information in the evaluation process. The index weight and expert weight were determined according to the theory of intuitionistic fuzzy entropy, and the comprehensive weight of the index was reasonably determined. The opinions of various experts were considered comprehensively, and the evaluation results were more objective.

(1)

Suggestions for improving the industrial value (A1):

For villages rich in plant resources, the local governments could make use of the existing plant resources to develop the nursery industry. The plants planted in the nursery could be used for the greening of new landscape projects in villages or sold to neighboring villages to reduce the carbon emissions generated in the process of external transportation, which could increase the existing industrial economic benefits (B1). Landscape architects could take carbon neutrality as the theme and highlight of rural tourism, set up exhibition halls to show low-carbon creative design, low-carbon new products, carbon neutral tourism themes, and carbon neutral popular science exhibition, etc. The local government could work with businesses, carry out low-carbon research courses to publicize carbon neutrality knowledge, develop low-carbon cultural creative products and low-carbon life experience. This will compel the existing accommodation industry, catering industry, agriculture, handicraft industry, and other industries to form the industrial chain, allow an increase in people’s understanding of carbon-neutral entertainment and leisure and an increase in their understanding of rural culture, promote the sale of agricultural products, promote agricultural production, and create tourism and agricultural production. The carbon-neutral tourism circular economy industrial chain can promote rural revitalization, increase the perfect degree of industrial chain for circular economy (B2), the carbon neutrality awareness of residents (B9), and the carbon neutrality tourism value (B13). Rural industries and projects should adopt green technology and materials as far as possible to increase the carbon neutrality degree of existing industries (B3) and the carbon neutrality creation potential of existing resources (B6). Chrastina et al. suggested ensuring the active economic involvement of the local community in the development of ecotourism in the form of offering local services and products, such as the production of traditional souvenirs, preparation of local cuisine, preparation of traditional cultural and social events, and the provision of guide services [32]. We reached conclusions similar to the above.

(2)

Suggestions for improving the natural resources value (A2):

Local governments and landscape architects could plant native tree species and other plants with strong carbon sequestration ability, high forestry economic value, and strong ecological protection function in bare land where the original vegetation was destroyed. It is of benefit to restore the local natural landscape resources, protect the rural ecosystem, increase the carbon neutrality value of existing natural resources (B4) and ecosystem diversity (B5). In the case of meeting the function, landscape architects could take full advantage of local materials and abandoned building materials, reducing the use of cement, metal and other materials with large carbon emissions in the production process. Landscape architects could improve the ecological environment through ecological design, improving the carbon neutrality creation potential of existing resources (B6) and the carbon neutrality degree of the utilization and maintenance of natural resources (B7). Landscape architects should not set up too many tourist facilities in the original natural landscape resources. It is necessary to protect the landscape resources and ecological environment to ensure that original production and life are not disturbed. For villages where the original plants are not abundant enough or were destroyed, landscape architects could further enrich the vegetation types and levels of villages to make the countryside more livable, increasing the ecosystem diversity (B5) and the residential area livability (B16). Artificial modeling production and maintenance cost is high, and the production and maintenance will produce carbon emissions. In order to increase the carbon neutrality value of existing natural resources (B4) and the carbon neutrality degree of the utilization and maintenance of natural resources (B7), there should not be too many modeling and manicured plants. Chrastina et al. proposed that in order to preserve the ecological stability of the local landscape, it is necessary to support the biodiversity (as one of the elements of ecotourism) by increasing and restoring the local natural landscape [32]. We came to much the same conclusions and recommendations.

(3)

Suggestions for improving the cultural resources value (A3):

Local governments and the villagers could focus on the protection and reconstruction of ancestral halls, Buddhist temples, churches, and other religious and folk beliefs architecture. Landscape architects could transform the original building into a cultural exhibition center, theaters, museums, etc., to show the local traditional culture and cultural heritage, such as art, music, folklore, and cuisine. Meanwhile, it is convenient for visitors to carry out the folk activities in public places. Landscape architects could turn individual buildings into landmark landscapes of villages, increase residents’ pride in local culture, and make visitors feel the traditional and innovative rural culture, so as to improve the historical and cultural heritage values (B8), the abundance of landscape resources (B12), the landscape resources uniqueness (B14), the landscape resources aesthetic value (B15), and the attribution and identity values (B11). Landscape architects could use local materials, the waste generated by demolition and restoration of original buildings could also be reused, improving the carbon neutrality degree of the utilization and maintenance of cultural resources (B10). Public facilities are designed to disseminate carbon-neutral knowledge and rural culture in rural parks and other public places, to enhance the awareness of villagers and tourists, and to improve carbon neutrality concept, improve the carbon neutrality awareness of resident (B9). In the study of approximate themes, Čurović et al. put forward that for rural revitalization, efforts should be made to ensure the landscape recognizability, which is taken as a basis of the existing quality and characteristics of the space and the relationship of cultural and natural heritage, and to preserve the architectural identity with the planned development and renewal of settlements [67]. Chrastina et al. proposed ensuring the social and cultural diversity of the local landscape through the restoration and maintenance of the traditional way of life of the original inhabitants of the village [32]. We came to much the same conclusions and recommendations as stated above.

(4)

Suggestions for improving the tourism value (A4) and livability(A5):

Landscape architects could use clean energy treatment devices such as solar energy, wind energy, water energy, and biomass energy in villages as part of the rural landscape and scenic spots of carbon neutral tourism, to create carbon neutral demonstration scenic spots, low-carbon tourism scenic spots, carbon neutral parks, and other scenic spots in order to increase the abundance of landscape resources (B12) and the landscape resources uniqueness (B14). Landscape architects should improve the public infrastructure of the village, use low-carbon materials to build infrastructure such as bicycle paths and green parking lots, and to establish specially designed low-carbon tourist bicycle and hiking routes. Landscape architects should improve the waste disposal system and reduce the frequency of littering by visitors and residents through the proper establishment of trash bins, which could increase the carbon neutrality tourism value (B13), the landscape resources uniqueness (B14), the landscape resources aesthetic value (B15), the residential area livability (B16), the infrastructure adequacy (B17), and the infrastructure carbon neutrality degree (B18). Landscapes could be built to meet the different needs of residents and tourists, such as public green spaces, small parks, public activity spaces, and landscape buildings, to not only increase the variety of landscape but also the diversity of tourism activities [10]. Čurović et al. suggested that a modern infrastructure standard should be provided throughout the entire rural region, and tourism should be introduced and adjusted not only to the needs of people in modern society, but also to the rural environment. This would maintain the growth of the economy [67]. We came to much the same conclusions and recommendations as stated above.

In summary, the results of this study are not only helpful to explore the mechanism of continuous optimization of rural tourism landscape resources, but also creatively, to create characteristic sustainability as the path for future development by combining the rural revitalization with carbon neutrality. Our model also has certain limitations. For example, the indicators we selected could be more suitable for the needs of rural development and carbon neutrality law in developing countries and regions. Since different countries and regions have different development strategies and different needs for the value of rural tourism landscape resources, the specific contents for evaluation must be adjusted accordingly.

However, the evaluation index system we established in this article can be generalized to evaluate rural landscape projects in other situations. The evaluation results and suggestions could be important references for the local governments at all levels, land-use planners, landscape architects and other professionals. They also benefit third-party evaluation institutions who evaluate the management performance of local governments in the planning, design, transformation, development, and protection of rural landscape resources.