Abstract
The construction industry accounts for approximately 40% of global greenhouse gas emissions, making it a critical sector for addressing carbon emissions. Urban areas are particularly significant contributors. However, tackling this issue has been challenging due to a lack of comprehensive data and methodologies for assessing the building sector’s impact on such a large scale. This study aims to develop a methodology leading to a software named EcoSphere for collecting data by harnessing the power of machine learning and artificial intelligence and simulating embodied carbon emissions across the entire lifecycle of buildings at a granular urban scale. This software reveals the effects of various scenarios on embodied carbon emissions, including changes in building lifespans, renovation and replacement strategies, area per building, and new construction volumes. Using a bottom-up archetype approach, the study models cities and evaluates the impact of six mitigation strategies on urban-scale carbon emissions. As a result, this research offers standalone software to simulate, assess, and predict the embodied carbon emission in these scenarios and their economic impacts at the national level in the United States. As pilot studies, this approach was applied to Chicago, Indianapolis, Houston, and South Bend demonstrating potential reductions in embodied carbon emissions up to 65 to 80 percent through strategic interventions. The findings underscore the profound influence of urban planning decisions on city decarbonization and offer valuable software for policymakers and researchers aiming to evaluate and implement effective carbon reduction strategies across U.S. cities.
Details
Presentation Type
Theme
KEYWORDS
Urban Decarbonization, Sustainable Development, Sustainable Cities, Carbon Mitigation Strategies
