Geospatial Analysis in Machine Learning for CO2 Emissions Prediction Analysis in 2100: A Continent-Wise Analysis

Geospatial analysis plays a very crucial role in predicting environmental changes, especially in the context of CO2 emissions and climate forecasting. This study uses continent-wise geospatial analysis to assess the potential patterns and implications of CO2 emissions by 2100, using ML techniques. The objective of this research is to spatially integrate the data with ML models to predict the geographical distribution of CO2 emissions at regional levels, thereby ascertaining which factors are predominant in influencing rates of emissions. For this purpose, the world is stratified into continents so that fine-grained regional differences in trends of emissions may be understood, keeping in mind aspects such as population density, industrialization, energy consumption, and climate policies. These are regression, classification, and neural networks that the machine learning algorithms use on satellite data, demographic information, and economic indicators to predict future emissions. This helps explain those hotspots in the first place, regions of high vulnerability to impacts from emissions, and opportunities for targeted mitigation strategies. Ultimately, this study provides a basis for understanding the geographical dynamics of CO2 emissions and more effective strategies by policymakers and environmental researchers to achieve emission reduction and climate change mitigation in the following century.