CO2 Emission Forecasting for Living Standards in Smart Cities

The field of Internet of Things (IoT) is an emerging technology that attempts to use data and sensing networks to develop intelligent solutions to increase living standards. CO2 (carbon monoxide) emissions are a critical challenge, revealing health and environmental complications in the past decade. Machine Learning (ML) is another field that has shown emergence with computation power and community adaptability to build the learning capacity of computers. In this study, we take leverage the junction of these two fields to forecast CO2 emissions over a period to highlight the severity. We implement various machine learning models and a range of metrics to determine the optimal technique(s) for forecasting. A proof of concept will be discussed and data set from the Mauna Loa Observatory (MLO) will be used for forecasting analysis with the python darts library. This model will be used to rise awareness for the CO2 emissions in the smart city for living standards.