Renewable Energies and Their Advantages for the Sustainability of Companies in the Health Sector

Manotas, Evaristo Navarro; Redondo, Remedios Pitre; Contreras, Janeka López; Cardenas, Meredith Jiménez; Palma, Hugo Hernández

doi:10.32479/ijeep.9975

Cited by 1 publication

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“…Within this revitalized sustainability context, the energy industry is recognized as one of the world's foremost sources of pollution and consumers of non-renewable resources, exemplified by SDG 7, "Affordable and clean energy." This goal outlines strategies and targets for accessible, clean energy generation and transmission for humanity (Li et al, 2023;Manotas et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review

Hernandez-Palma,

Alvarado,

Guiliany

et al. 2024

IJEEP

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The global energy industry fundamentally transformed towards renewable energy sources, driven by the sustainability paradigm. This shift was crucial in addressing the challenges of climate change and resource scarcity. Machine Learning (ML) played a pivotal role in enhancing the efficiency and reliability of renewable energy systems. This study conducted a comprehensive analysis of scientific production at the intersection of ML and renewable energy generation, focusing on Latin America. Employing a methodology based on documentary research and bibliometric processes, utilizing the Scopus database with the support of R and VOSviewer software, our research revealed a significant increase in interest and investment in research related to ML and renewable energies since 2020. This exponential growth scenario in this knowledge area had significant implications for Latin America and the world, fostering technological advancements and the adoption of renewable energies. Countries such as China, India, the United States, South Korea, and Saudi Arabia represented 61% of the global scientific production in this field, underscoring its global relevance. This growth indicated a growing interest and investment in ML applications in renewable energies, aligning with the 2030 Agenda for Sustainable Development. This research aligns with the Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure). It contributed to progress toward a more sustainable future, benefiting society through more efficient and sustainable energy systems, the energy industry through increased innovation and the adoption of clean technologies, and Latin America, which could leverage these findings to sustainably drive its economic and environmental development.

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Section: Introductionmentioning

confidence: 99%