Linear Regression in Climate Change Analysis Using R: A Complete Guide for Environmental Data Modeling

This article demonstrates how to apply linear regression in R to analyze the relationship between greenhouse gas emissions and global temperature, empowering researchers and policymakers with evidence-based insights into climate change trends.

1. Introduction

Climate change is arguably the greatest scientific, economic, and social challenge of our era. Understanding and addressing its drivers and impacts requires not just good science but also robust, transparent data analysis. At the heart of much of climate science is the task of quantifying how changes in one variable (like atmospheric CO₂) are associated with changes in another (like global temperature). Linear regression—a statistical method that quantifies the relationship between two or more variables—has become foundational in this context.

This article provides a step-by-step guide to applying linear regression to climate change analysis using the R programming language. We will walk through the full analytical workflow, from understanding linear regression in environmental contexts to preparing data, building models, validating results, visualizing outcomes, and discussing limitations and extensions. All code and methods are reproducible and applicable to real-world climate data.