Carbon Footprint Monitoring System Using Machine Learning and Deep Learning Techniques

India is one of the world’s largest producers of greenhouse gases (GHGs), and its emissions are projected to continue to grow in the forthcoming years. The country is susceptible to climate change’s effects, which include rising sea levels, more frequent and severe extreme weather events, and food and water scarcity. By following the carbon footprint, India can take action to lower its GHG emissions and lessen the effects of climate change by paying attention to its carbon footprint. In comparison to 2005 levels, the nation wants to reduce its carbon GHG emission (the amount of CO2e per unit of GDP) to 33-35% before 2030. This goal is part of India’s commitment to the Paris Agreement, which seeks to keep the rise of temperature worldwide to less than 20 C above preindustrial levels. To overcome this problem, the emission of greenhouse gases is calculated and analyzed using Machine Learning Models. To monitor the real-time environment, we profound an IoT device to capture the real-time data in the nearby locality and predict the GHG emission using the Birch and LSTM model. The model shows the emission of greenhouse gases in the current scenario and also predicts the future environment.