Analysis of Factors Affecting CO2 Emissions in Türkiye Using Quantile Regression

Abstract

This study aims to show how the impact of factors on carbon dioxide (CO2) emissions differs at the quantile level and to demonstrate the superiority of the quantile regression method over the OLS method by using quantile regression and ordinary least squares (OLS) methods in order to examine the factors affecting CO2 emissions in Türkiye in depth. Covering the period 1990–2021, this study evaluates the relationship between CO2 emissions and GDP per capita growth, population growth, and renewable energy consumption. One of the important findings of the study is that the increase in the population ratio, which is insignificant according to the OLS method, positively affects CO2 emissions at the 0.25 quantile point. According to both OLS and quantile regression methods, GDP growth does not affect CO2 emissions, while renewable energy consumption has a significant and negative effect according to both models. These results demonstrate that economic growth has no discernible impact on CO2 emissions in Türkiye, while investments in renewable energy can significantly lower emissions and open the door for quantile regression to be used more widely in related research. Unlike traditional methods that focus only on the conditional mean, the quantile regression method provides a comprehensive framework for Türkiye’s sustainable development policies by exploring factor effects at different emission levels.