GLOBAL CLIMATE CHANGE TEMPERATURE ANOMALY TREND ANALYSIS AND CO2 EMISSION CORRELATION USING PYTHON

Abstract

Global climate change is increasingly evident through rising temperature anomalies and their strong association with anthropogenic greenhouse gas emissions. This study presents a data-driven analysis of global surface temperature anomaly trends and their correlation with atmospheric carbon dioxide (CO₂) emissions using Python-based analytical tools. Historical datasets from reputable climate databases were processed and visualized to identify long-term warming patterns and variability. Time-series analysis reveals a consistent upward trend in global temperature anomalies, particularly since the mid-20th century, coinciding with rapid industrialization and increased fossil fuel consumption. Statistical methods, including correlation and regression analysis, were employed to quantify the relationship between CO₂ emissions and temperature anomalies. Results indicate a strong positive correlation, suggesting that rising CO₂ levels are a significant driver of global warming. Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn were utilized for data cleaning, analysis, and visualization, enabling reproducible and scalable research workflows. The findings reinforce the scientific consensus on climate change and highlight the importance of reducing greenhouse gas emissions to mitigate future temperature rise. This study demonstrates the effectiveness of computational approaches in climate data analysis and supports policy-making efforts aimed at environmental sustainability.