A circular economy (CE) promotes the reuse, reincorporation and valuation of waste and by-products under the framework of sustainable development through models and indicators that evaluate scenarios of second use and reduction in non-incorporated outputs to reduce negative externalities and pressures on the dimensions of development. A CE model applied to the transformation process of RFF in agro-industries is developed, which consists in the identification of the residue coefficients of EFB (22.48% ± 0.8), fiber (15.58% ± 0.49), husk (6.03% ± 0.66) and ash (0.55% ± 1.67). Subsequently, the valuation trends of potential second use were verified through a systematic review, which allowed the construction of the scenario of avoided costs of USD 678,721.5, a product of the total use of the outputs under bioenergy and nutrient source approaches. Finally, the RRSFM indicator was constructed, which can reach the level of 72% and a degree of improvement of 26% by 2026. In parallel, the HCRRS indicator revealed a reduction of 57.1%, 59.6% and 82.8% in emissions of t CO2-eq product in the comparison of scenarios for the use of residues and by-products of palm oil from agro-industries in the Casanare Department.
El objetivo de este trabajo fue realizar una revisión sobre el uso de las técnicas de inteligencia artificial (IA) aplicadas a la formulación de políticas públicas que contribuyan a la vocación agrícola de las regiones, para lo cual se usó una metodología descriptiva con enfoque mixto. El diseño metodológico utilizado fue el PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Las publicaciones analizadas fueron tomadas de la base de datos de Scopus. Para el análisis cuantitativo se utilizaron las herramientas informáticas VosViewer y la librería Bibliometrix del lenguaje R. Como resultado se encontró que las técnicas de IA se han aplicado para identificar zonas con vocación agrícola o para encontrar mejores prácticas agrícolas que promuevan el desarrollo sostenible. Se concluyó que esta área de investigación es incipiente y que es necesario generar nuevos modelos que sean más robustos e incluyan variables demográficas, sociales, ambientales, económicas y políticas.
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