Analysis of decarbonization path in New York state and forecasting carbon emissions using different machine learning algorithms

Abstract

AbstractThe state of New York admitted 143 million metric tons of carbon emissions from fossil fuels in 2020, prompting the ambitious goal set by the CLCPA to achieve carbon neutrality. The paper focused on analyzing and predicting carbon emissions using four different machine-learning algorithms. It examined emissions from fossil fuel combustion from 1990 to 2020 and validated four different algorithms to choose the most effective one for predicting emissions from 2020 to 2050. The analysis covered various economic sectors including transportation, residential, commercial, industrial, and electric power. By analyzing policies, the paper forecasted emissions for 2030 and 2050, leading to the identification of different pathways to reach carbon neutrality. The research concluded that in order to achieve neutrality, radical measures must be taken by the state of New York. Additionally, the paper compared the most recent data for 2021 with the forecasts, showing that significant measures need to be implemented to achieve the goal of carbon neutrality. Despite some studies assuming a trend of decreased emissions, the research revealed different results. The paper presents three pathways, two of which follow the ambitious plan to reach carbon neutrality. As a result, the emission amount by 2050 for the different pathways was projected to be 31.1, 22.4, and 111.95 of MMt CO2 e, showcasing the need for urgent action to combat climate change.