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Authors:
TaeHyung Kwon
Sunghwan Kim
Juyeong Maeng
Keywords: Life cycle assessment; Carbon opportunity cost; Renewable Energy; Environmental impact; Python-based modeling.
Abstract:
This study introduces an Advanced Comparative Life Cycle Assessment (LCA) model that refines the accuracy of environmental impact evaluations for renewable energy by integrating external factors like sea depth, solar irradiance, and wind speed. Traditional LCA approaches, which often overlook these critical variables, result in imprecise carbon footprint estimations. Developed with Python and incorporating libraries such as SciPy, Windrose, and pvlib, this model provides a nuanced analysis tailored to specific environmental conditions, particularly focusing on Korea. It aims to correct the shortcomings of existing LCA methods by factoring in regional variability, thereby offering more accurate assessments of carbon emissions and environmental impacts of renewable energy projects. This research facilitates informed decision-making in the renewable energy sector by improving the understanding of carbon footprints and supporting sustainable development policies. The study underscores the importance of considering local environmental conditions in the deployment of renewable energy technologies to achieve more sustainable and informed energy solutions.
Pages: 10 to 11
Copyright: Copyright (c) IARIA, 2024
Publication date: April 14, 2024
Published in: conference
ISSN: 2308-3727
ISBN: 978-1-68558-151-0
Location: Venice, Italy
Dates: from April 14, 2024 to April 18, 2024