Open AccessApplication of a multigranular approach based on the 2-tuple fuzzy linguistic model for the evaluation of forestry policy indicators
Author(s) -
José Luis Romo Lozano,
Rosa M. Rodríguez,
Roberto Rendón Medel,
Álvaro Labella
Publication year - 2020
Publication title -
revista chapingo serie ciencias forestales y del ambiente (en línea)/revista chapingo serie ciencias forestales y del ambiente
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.201
H-Index - 10
eISSN - 2007-4018
pISSN - 2007-3828
DOI - 10.5154/r.rchscfa.2020.06.043
Subject(s) - computer science , fuzzy logic , clarity , readability , quality (philosophy) , tuple , relevance (law) , linguistics , artificial intelligence , mathematics , political science , biochemistry , chemistry , philosophy , epistemology , discrete mathematics , law , programming language
The need for quality indicators is well recognized by users and proponents of public policy evaluation. Indicators recurrently include qualitative attributes for which there are few studies assessing the level of compliance. Objective: To apply a multigranular approach, based on the 2-tuple fuzzy linguistic model, to evaluate 13 indicators of the National Forestry Program, established in the system of social policy indicators derived from the National Development Plan 2012-2018 of Mexico. Materials and methods: The method uses the 2-tuple fuzzy linguistic representation model and an extension called extended linguistic hierarchies, designed to solve problems with multigranular linguistic information. The indicators’ level of compliance was evaluated based on four criteria: clarity, relevance, monitoring, and adequacy. Results and discussion: The structure defined in evaluating social policy indicators corresponds appropriately to that used with the 2-tuple fuzzy linguistic model. The evaluation resulted in a sorted list in which the indicator “Rate of change of timber forest production” had the best rating with a “very high” level of compliance; 10 other indicators had the “high” level of compliance, and the remaining two indicators were rated with “moderate” compliance. Conclusions: The 2-tuple fuzzy linguistic model allowed the appropriate evaluation of the level of compliance with the desirable attributes of indicators