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Title	Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment By Artificial Intelligence Driven Carbon Footprint Forecasting
ID_Doc	53162
Authors	Ayasrah F.T.; Alsharafa N.S.; Sivaprakash S.; Srinivasa Rao B.; Sudhakar Sengan; Kumaran N.
Year	2024
Published	Journal of Machine and Computing, 4, 4
DOI	http://dx.doi.org/10.53759/7669/jmc202404105
Abstract	Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact. ©2024 The Authors. Published by AnaPub Publications.
Author Keywords	Carbon Emissions; Low-Carbon Urban Planning; LSTM; Machine Learning; Vector Autoregression

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