Understanding Global Warming: An In-depth Analysis and Future Predictions

This research conducts a comprehensive analysis of global warming trends by integrating Earth surface temperature, CO2 emissions, and world population data. Applying sophisticated machine learning models (SARIMAX, ARIMAX, VAR, VECM), the study uncovers complex interplays between these variables and provides accurate future trend predictions. The data collection and preprocessing ensure reliability, while the analysis of Earth surface temperature identifies the patterns. The investigation into the relationship between CO
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emissions and climate dynamics reveals the impact of human activities. Exploring the correlation between world population growth and environmental repercussions, Machine learning models scrutinize population trends. Predictive modeling using SARIMAX, ARIMAX, VAR, and VECM forecasts future trends, validated for accuracy. Conclusions emphasize the unique contributions of each model, discussing implications for climate change mitigation. Future research should focus on recommendations that aim to refine predictions, contributing valuable insights to the ongoing dialogue on global warming for evidence-based decision-making toward a sustainable future.