Estimation of greenhouse gas emissions using linear and logarithmic models: A scenario-based approach for Turkiye's 2030 vision

Abstract

Turkiye pledged to considerably reduce its greenhouse gas (GHG) emissions by the year 2030 as a part of its commitment under the Paris Agreement. The problem with analyzing mitigation of greenhouse gas emissions requires generation of accurate, reliable and consistent emission forecasts. This study aimed to accurately forecast Turkiye's total CO₂ emissions per capita and per capita CO₂ emissions from energy industries, industrial processes and agricultural sectors till 2030 using linear and logarithmic models based on increasing and decreasing scenarios. Turkiye's total CO₂ emissions per capita in 2030 could reach to 7.6 and 7.7 tons of CO₂, with total emissions of about 635 and 643 million tons (Mt) based on linear and logarithmic models, respectively. Linear modeling results showed that per capita CO₂ emissions from Turkiye's energy industries, industrial processes and agricultural sectors could reach to 5.3, 0.9 and 0.9 tons in 2030, respectively, while logarithmic modeling results yielded that per capita CO₂ emissions from Turkiye's energy industries, industrial processes and agricultural sectors could be 5.5, 1.1 and 0.9 tons in 2030, respectively. The accuracy of fit for linear and logarithmic models was assessed by calculating root mean square error (RMSE < 0.2036) and mean absolute percentage error (MAPE < 12.3347) values which showed that the models fitted well with the timeline data. In conclusion, Turkiye's greenhouse gas emissions can be reduced if utilization of renewable energy sources in the country's energy portfolio is increased, and the shares of energy intensive processes in the industrial and agricultural sectors are reduced.