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Neutrosophic Sets and Systems
Article . 2019
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PESTEL analysis with neutrosophic cognitive maps to determine the factors that affect rural sustainability. Case Study of the South-Eastern plain of the province of Pinar del Río

Authors: C. Barrionuevo de la Rosa; B. Cárdenas Bolaños; H. Cárdenas Echeverría; R. Cabezas Padilla; G. A. Sandoval Ruilova;

PESTEL analysis with neutrosophic cognitive maps to determine the factors that affect rural sustainability. Case Study of the South-Eastern plain of the province of Pinar del Río

Abstract

Neutrosophic cognitive maps and their application in decision-making have become an important subject for researchers and practitioners. Especially, PESTEL analysis based on neutrosophic cognitive maps is a useful method, which permits to analyse specific topics statically. In the present paper strategies for the external factors that contribute to the identification of agricultural contexts in the South-Eastern plain of the province of Pinar del Río, Cuba are studied based on PESTEL analysis and neutrosophic cognitive maps. Here, PEST analysis incorporates Ecological and Legal factors and their characteristics. This study aims to determine which factors affect the agricultural sustainability of the South - Eastern plain of the province of Pinar del Río. The main contribution of the present paper is that it was identified quantitatively the factors that affect the agricultural sustainability, they are, the technological, political and economic ones.

Keywords

Electronic computers. Computer science, QA1-939, QA75.5-76.95, Mathematics

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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