Machine Learning Models for Prediction and Forecasting of CO2 Emission with Exploratory Data Analysis

Abstract

CO2 emissions play a major role in global warming, leading to serious consequences such as extreme weather events, rising sea levels, and ecological imbalances. To address this pressing issue, it is crucial that we fully understand the factors influencing CO2 emissions to develop effective strategies for reduction and sustainability. The growing concern over climate change and its harmful effects on our environment has motivated researchers and policymakers to seek innovative solutions for curbing greenhouse gas emissions, especially CO2 emissions. However, traditional statistical methods have their limitations when it comes to handling large and complex datasets. This is where machine learning steps in as a powerful tool, offering the ability to analyze vast amounts of data and make accurate predictions. This presents a promising avenue for forecasting CO2 emissions and creating sustainable policies. Machine learning allows us to identify hidden patterns and relationships within the data, enabling us to make more precise predictions and reliable forecasts. Therefore, this work focuses on exploring various machine learning models for predicting and forecasting CO2 emissions. Additionally, we plan to incorporate exploratory data analysis (EDA) techniques, which will help us visualize and interpret the data effectively. Through EDA, we can identify crucial features, understand data distributions, and pinpoint outliers that might influence model performance. The significance of our study lies in the valuable insights it can provide to policymakers and environmentalists. By making accurate predictions about CO2 emissions, we can help design effective policies that control and reduce emissions, optimize resource allocation, and promote the shift towards renewable energy sources. Furthermore, precise forecasts can assist in planning adaptation measures to mitigate the impact of climate change.