A circularity accounting network: CO2 measurement along supply chains using machine learning

  1. Jesse, Forrest Fabian 1
  2. Antonini, Carla 2
  3. Luque-Vilchez, Mercedes 3
  1. 1 University of Washington / Beijing Xixuan Laboratory
  2. 2 Universidad Autónoma de Madrid
    info

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

  3. 3 Universidad de Córdoba
    info

    Universidad de Córdoba

    Córdoba, España

    ROR https://ror.org/05yc77b46

Revista:
Revista de contabilidad = Spanish accounting review: [RC-SAR]

ISSN: 1138-4891

Año de publicación: 2023

Volumen: 26

Número: 0

Páginas: 21-33

Tipo: Artículo

DOI: 10.6018/RCSAR.564901 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista de contabilidad = Spanish accounting review: [RC-SAR]

Resumen

Este artículo propone utilizar un tipo de red de aprendizaje automático denominado redes neuronales artificiales para diseñar una red de contabilidad de la circularidad. La red está compuesta por actores humanos y no humanos y contabiliza el impacto de las emisiones y el secuestro de CO2 de los productos a lo largo de las cadenas de suministro mundiales.  La red sirve para conectar a personas y otros actores que comparten un indicador de CO2 y permite a los usuarios visualizar el nivel de (in)circularidad de diferentes productos a través de diagramas específicos calculados por un estimador de CO2 basado en conocimientos de la teoría de las redes de actores. A diferencia de la mayoría de los estudios anteriores sobre contabilidad de la economía circular que desarrollan algún tipo de marco o indicador que representa mediciones a nivel micro, meso o macro, la red de contabilidad de la circularidad no se limita a un nivel concreto de análisis, sino que está diseñada para establecer relaciones entre múltiples usuarios a diferentes niveles (por ejemplo, actores gubernamentales, corporativos o consumidores). El documento presenta el diseño conceptual y una prueba preliminar de la red utilizando datos reales, lo que contribuye a avanzar en el potencial poco explorado de la inteligencia artificial en el ámbito de la contabilidad de la economía circular. La principal aportación de esta red es que los datos proporcionados por el indicador: (i) se derivan de la propia red que aprende de fuentes abiertas; (ii) la red no es estática, sino que sigue fluyendo a medida que se construyen nuevas relaciones dentro de la red, avanzando hacia la autorregulación; (iii) contempla tanto las emisiones como los secuestros a lo largo de las cadenas de suministro.

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