ANALYSIS OF THE RELATIONSHIP BETWEEN POVERTY AND FOREST LOSS AT THE DISTRICT LEVEL IN PERU: APPLICATION OF THE SPATIAL AUTOREGRESSIVE MODEL WITH SPATIAL AUTOREGRESSIVE DISTURBANCES (SARAR)

• Autor: Ledesma Goyzueta, Luis
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad Nacional Agraria La Molina
• Repositorio: Revistas - Universidad Nacional Agraria La Molina
• Lenguaje: español
• OAI Identifier: oai:revistas.lamolina.edu.pe:article/2280
• Enlace del recurso: https://revistas.lamolina.edu.pe/index.php/eau/article/view/2280
• Nivel de acceso: acceso abierto
• Materia: deforestation; forest loss; poverty; spatial regression; SAR; SEM; SARAR; deforestación; pérdida de bosques; pobreza; regresión espacial

Deforestation is a problem addressed both nationally and globally, which generates impacts on ecosystems and biodiversity, and these, in turn, can generate social and/or economic costs. The objective of this study was to geospatially analyze the social context that may exert pressure on forest loss or deforestation in Peru at the district level. The dependent variable of interest was the number of hectares of Amazon rainforest loss, recorded on the Geobosques platform, during the last triennial period prior to the COVID-19 pandemic. The method was based on a spatial analysis of discrete variation, where districts are defined as a set of discrete regions with irregular neighborhoods for each district. This paper presents the results of the spatial regression models SAR, SEM and SARAR, in order to show the level of association between forest loss and monetary poverty. According to the results, at the district level, forest loss presents a negative relationship with monetary poverty in the chosen model (SARAR). Therefore, it is important that programs that seek to improve the basic conditions of populations, mainly dedicated to agriculture, are also accompanied by technical assistance in sustainable practices in order to mitigate deforestation or land use change.