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Title	Optimizing Hyderabad'S Carbon Footprint: A Machine Learning Approach For Smart City Sustainability
ID_Doc	40821
Authors	Kaur A.; Gill K.S.; Malhotra S.; Devliyal S.
Year	2024
Published	2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024
DOI	http://dx.doi.org/10.1109/AIC61668.2024.10731094
Abstract	The purpose of this study is to evaluate Hyderabad City's progress towards Sustainable Development Goal 11, which aims to make urban areas more welcoming, secure, resilient, and environmentally friendly for all people. More than half of humanity now resides in urban areas. Roughly seven out of ten individuals will call an urban region home by the year 2050. With its potential to become India's IT and financial capital and home to the country's defence and missile research centre, Hyderabad is already a large city. However, in the past two years, the city's reputation as an ideal location for startups and investors has spread around the globe. This is due to its robust ecosystem and logical support, and predictions indicate that it will become a megacity and global leader in IT by 2030, with further growth expected until 2050. So, in order to ensure the city's sustainable development, In this paper, we will take a look at the city's carbon emissions from the past and look forward to what the future holds. We'll also make some predictions about sustainable development, like how the city's roads and transport will be improved to make it more resilient and eco-friendlier. The study used data from the city of Hyderabad from 1993 to 2019 to calculate emissions and create a carbon estimate dataset for the transport sector from 2002 to 2019. It also forecasted vehicle growth and carbon estimates up to 2030. © 2024 IEEE.
Author Keywords	Artificial Intelligence; Classification; Classification Analysis; EF (Emission factors); Emissions; Machine Learning; Model Training; SDG11

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