Machine Learning-Based Prediction of U.S. CO2 Emissions: Developing Models for Forecasting and Sustainable Policy Formulation

Abstract

The exponential escalation of carbon dioxide (CO₂) emissions in the U.S. presents a pressing environmental challenge with substantial implications for climate change and public health. The principal objective of this study was to devise robust machine learning algorithms particularly designed for forecasting CO₂ emissions in the United States. This focused exclusively on CO₂ emission data pertinent to America, reflecting the economic, unique environmental, and regulatory context of the nation. The dataset for analysis consisted of a broad-based set of information focused on the main contributors of CO₂ emissions in the United States, ranging from energy consumption and industrial activity to transportation and historical CO₂ emission data. The energy consumption data included facts on electricity generated, fuel consumed, and absolute energy consumption among different sectors of the economy, and industrial activities information provides data on specific outputs from such processes and their emissions. It also included transportation facts on vehicle trends, fuel intensity, and energy-related emissions associated with the sector. These three datasets have been garnered from reliable resources, including the US. These range from detailed EPA emissions inventories and energy reports from the U.S. The analyst deployed credible algorithms such as Random Forest, Logistic Regression, and Support Vector Classifier which had different strengths that can be leveraged based on characteristics of the dataset. According to their accuracy scores, the Random Forest model led the race compared to the other two models, with a higher accuracy rate. With such large integrations of machine learning predictions into climate policy, great opportunities might develop vis-à-vis sustainable development goals in the USA. Advanced analytics will let the policy analyst capture emission and resource trends with greater insight than ever before into the effectiveness of existing regulations; this will let it plug into the SDGs on Climate Action, Sustainable Cities, and Responsible Consumption. For enhancing environmental monitoring systems' efficiency, environmental planning should be incorporated with machine learning models.