Sustainable Climate Change Predictions: Linear Regression Modeling of Temperature Trends

Among the most urgent problems confronting humanity today is climate change. Climate change is a pressing global issue, with the Intergovernmental Panel on Climate Change (IPCC) estimating that human activity is responsible for over half of the observed rise in global temperature between 1951 and 2010. By the end of the 21st century, the IPCC predicts a 1.5 to 4.5 degree Celsius rise in global surface temperature, depending on greenhouse gas emissions. Time and temperature are the relevant variables in this scenario. Future temperature changes can be predicted by fitting a linear regression model to historical temperature data. To forecast future climate change, a number of studies have used linear regression modeling. Linear regression analysis is a method for forecasting future climate change, involving finding relationships between variables such as time and temperature. Studies have shown that even with significant reductions in greenhouse gas emissions, the average global temperature is predicted to rise by 2.2 degrees Celsius by the end of the 21st century. If emissions are not reduced, the average global temperature could rise by up to 6 degrees Celsius by the end of the 21st century. However, linear regression modeling’s accuracy depends on factors such as past data accessibility, assumptions about temperature-time relationships, and the model’s ability to consider additional variables like shifting ocean currents and land use. Despite these limitations, linear regression modeling remains a useful tool for projecting future temperatures and understanding the potential consequences of climate change.