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Exploring the Role of Robots and Artificial Intelligence in Advancing Renewable Energy Consumption.

Authors :
Badareu, Gabriela
Doran, Marius Dalian
Firu, Mihai Alexandru
Croitoru, IonuČ› Marius
Doran, Nicoleta Mihaela
Source :
Energies (19961073); Sep2024, Vol. 17 Issue 17, p4474, 17p
Publication Year :
2024

Abstract

This study investigates the relationship between artificial intelligence (AI), industrial robots, and renewable energy consumption, driven by the rapid technological advancements and widespread adoption of AI tools in various industries. This research aims to evaluate the environmental implications of these technologies, specifically their impact on renewable energy usage. Employing a comprehensive analytical framework, this study utilizes advanced methodologies, including regularization factors, to accurately estimate the effects of these variables. Through a thorough data analysis, the research quantifies how AI and industrial robots influence the shift towards renewable energy sources. The findings reveal that investments in AI significantly enhance renewable energy consumption, as demonstrated by both conventional estimation techniques and those that integrate regularization factors. Conversely, the use of industrial robots is found to have a detrimental effect on renewable energy consumption. These results have important implications for policymakers, industry leaders, and sustainability researchers. This study encourages policymakers and investors to prioritize funding for AI solutions that promote renewable energy adoption, while it advises industry managers to strategically modify their use of industrial robots to reduce their environmental impact. Ultimately, this research lays a critical foundation for future inquiries and policy initiatives aimed at aligning technological advancements with sustainable energy practices. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19961073
Volume :
17
Issue :
17
Database :
Complementary Index
Journal :
Energies (19961073)
Publication Type :
Academic Journal
Accession number :
179645191
Full Text :
https://doi.org/10.3390/en17174474