En esta monografía, se presenta un análisis del impacto de la pandemia en términos de demanda eléctrica comparándolo con un modelo de un año habitual para cada uno de los países seleccionados. Esto permitió identificar primero cuánta electricidad se dejó de demandar y por ende de cobrar en la pandemia; cuáles fueron las alteraciones a este consumo; y cuál es el camino hacia la recuperación. Este documento establece cuál es el impacto de la pandemia con respeto a un año estándar modelado específico para cada país y empleando la historia de comportamiento de la demanda eléctrica del país hacia el futuro. Los resultados ofrecen información sobre la energía potencialmente no vendida durante la primera mitad de la pandemia, con implicaciones para las empresas que ofrecen servicio eléctrico; cuáles fueron las modificaciones del comportamiento de la ciudadanía, con potenciales implicaciones para la operación del sistema; y por último, cuál es el camino hacia la recuperación de la normalidad con base en estimaciones del FMI y los datos históricos de cada país analizado.
The COVID-19 pandemic increased uncertainty about the financial future of many organizations, and regulators alerted auditors to be increasingly skeptical in assessing an entity’s ability to continue as a going concern. An auditor’s assessment of an entity’s ability to continue as a going concern is a matter of significant judgment. This paper proposes to use machine learning to construct a Decision Tree Automated Tool, based on both quantitative financial indicators (e.g., Z-scores) and qualitative factors (e.g., partners’ judgment and assessment of industry risk given the pandemic). Considering both quantitative and qualitative factors results in a model that provides additional audit evidence for auditors in their going-concern assessment. An auditing firm in Spain used the model as a supplemental guide, and the model’s suggested results were compared to auditors’ reports to evaluate its effectiveness and accuracy. The model’s predictions were significantly similar to the auditors’ assessments, indicating a high level of accuracy, and differences between the model’s proposed outcomes and auditors’ final conclusions were investigated. This paper also provides insights for regulators on both the use of machine-learning predictive models and additional factors to be considered in future going-concern assessment research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.