| Title | A circularity accounting network: CO2 measurement along supply chains using machine learning |
|---|---|
| ID_Doc | 20421 |
| Authors | Jesse, FF; Antonini, C; Luque-Vilchez, M |
| Title | A circularity accounting network: CO2 measurement along supply chains using machine learning |
| Year | 2023 |
| Published | |
| Abstract | This paper proposes to use a type of machine learning network called artificial neural networks to design a circularity accounting network. The network is composed of human and non-human actors and accounts for the impact of products CO2 emissions and sequestration along global supply chains. The network serves to connect people and other actors that share a CO2 indicator and allows users to visualize the level of (un-) circularity of different products through specific diagrams calculated by a CO2 estimator drawing on insights from actor-network theory. Unlike most previous circular economy accounting studies that develop some type of framework or indicator that represent measurements at micro, meso or macro levels, the circularity accounting network is not confined to a particular level of analysis but is designed to build relationships between multiple users at different levels (e.g., government, corporate or consumer actors). The paper presents the conceptual design and a preliminary test of the network using real data, helping to advance the underexplored potential of artificial intelligence in the field of circular economy accounting. The main contribution of this network is that data provided by the indicator: (i) is derived from the network itself learning from open sources, the network (ii) is not static but keeps flowing as new relationships are built within the network, moving toward self-regulating, (iii) contemplates both emissions and sequestrations along supply chains.(c) 2023 ASEPUC. Published by EDITUM -Universidad de Murcia. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| https://revistas.um.es/rcsar/article/download/564901/348321 |
No similar articles found.