Evaluation of Rural Landscape Resources Based on Cloud Model and Probabilistic Linguistic Term Set
Weiwen Li,
Yijiang Zhou and
Ge Xun
Additional contact information
Weiwen Li: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
Yijiang Zhou: School of Business Administration, Hunan University, Changsha 410082, China
Ge Xun: School of Economics, Central South University of Forestry and Technology, Changsha 410004, China
Land, 2022, vol. 11, issue 1, 1-17
Abstract:
Rural landscape resources are important ingredients of rural revitalization and modernization in developing countries and regions. Evaluation methods play a crucial role in the development planning, design, transformation, and protection of these resources. However, there has been a lack of research on the evaluation of rural landscape resources, especially from the perspective of rural revitalization. Based on previous evaluation methods and expert consultations on landscape planning and design, we proposed a new approach for evaluating rural landscape resources by establishing a new index system and using the Analytic Hierarchy Process (AHP) and probabilistic linguistic Cloud Model. To demonstrate its applicability and effectiveness, we applied our model to the case of a village (Xiapu) in Guangdong, China, determined the parameter set of its rural landscape resources, and obtained related results showing that the method is practical and can reflect the value of resources objectively and efficiently. Based on this model, further suggestions are provided to improve the design and other utilizations of rural landscape resources.
Keywords: rural revitalization; rural landscape resources; evaluation methods; probabilistic linguistics; Analytic Hierarchy Process (AHP); Cloud Model (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:1:p:60-:d:716236
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