Global warming is posing serious problems for the world, and greenhouse gas emissions are a key contributor. In the year 2017, energy providers contributed 46% of worldwide Emissions of CO2, showing great opportunity for a decrease. Greenhouse gases, particularly carbon dioxide (CO2) production, are one of the primary causes of global warming, making it one of the world's biggest environmental challenges. For interim planning for the national aim to reduce Carbon footprints, more accuracy in short-term forecasts is essential. To make rational choices, rising climate change conventions require accurate predictions of participating countries' future emission growth paths. The most frequent way of forecasting is trend analysis, which is the act of formulating forecasts based on previous and present data. Forecasting models are becoming increasingly crucial in showing complicated correlations between large amounts of inaccurate information and unpredictable variables. The proposed work uses the Auto-Regressive Integrated Moving Average (ARIMA) model and Holt-Winters Exponential Smoothing (HWES) forecast model to predict carbon footprints in countries on an annual basis. This work intends to estimate time series data on emissions of CO2 in countries throughout the world using quantitative tools. The researcher’s grasp of CO2 emissions projections will be boosted by this work. Furthermore, the findings of this study can be used by government bodies to establish strategic plans.

CO2 Emission, Global Warming, Forecasting, Time Series