Overview based on latent dirichlet allocation: the case of study climate change research efforts in Latin America.

Abstract

This study analyzes climate change research efforts in Latin America using Latent Dirichlet Allocation (LDA) for cluster topic modeling. Latin America, characterized by its rich biodiversity and dependence on natural resources, confronts urgent challenges posed by climate change. The region's vulnerabilities are further aggravated by extreme weather events, deforestation, and shifts in precipitation patterns, directly threatening ecosystems, public health, and local economies. The LDA model identifies critical themes, including environmental risks, health impacts, biodiversity loss, and socio-economic responses. This research highlights the necessity of localized adaptation strategies, contrasting sharply with global research that overly focuses on large-scale policy and technological innovations. In Latin America, it is imperative to prioritize practical solutions that address immediate risks, such as involving local communities in disaster risk reduction efforts and effective early warning systems for extreme weather. Moreover, the study underscores the disproportionate impact of climate change on vulnerable populations, particularly indigenous groups and rural communities. By identifying thematic trends and regional disparities, this research provides essential insights into the scientific landscape that will drive future climate change adaptation efforts in Latin America. Tailored policies that consider regional contexts are critical for effectively confronting the specific challenges faced by the region.