Estimation of carbon dioxide emissions in steel industry of Türkiye using statistical methods

Date

2024-12-6

Author

Altuğ, Kerem Can

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Steel production has been a prominent industry causing the recent increase in greenhouse gas emissions and their massive global impact. Given the urgent global need for effective emission control, Türkiye, especially in the Western and Middle Anatolian regions, has recently been a country that has taken initiatives and solutions to reduce the increase in emissions of the last three decades. This study presents a comprehensive statistical approach to estimate and predict carbon dioxide (CO2) emissions associated with crude steel production by blast furnaces in Türkiye. Using yearly crude steel production datasets of Kardemir, Isdemir, Erdemir, collected through various reports, statistical methods including ARIMA, Exponential Smoothing, Prophet, Polynomial Regression and hybrid modeling techniques, were employed to model historical emission trends and forecast future emissions. Through rigorous model evaluation, the study highlights the varying estimation performance of statistical techniques across different temporal scenarios, including short-term, medium-term and long-term forecast scenarios. The performance metrics were used to measure the efficiency of statistical models applied to each dataset (1990-2023). Accordingly, the most successful model types obtained from short-term forecast scenarios of each facility were used to estimate the emission level of the next 5 years after 2023. Research findings proposed that while CO2 emission level from blast furnace crude steel production of Türkiye was approximately 15.1 million tons at the end of 2023, it will increase to 15.7 million (minimum increase of 4%) and 17.7 million (maximum increase of 17.5 %) by the end of 2028, as long as the current production trends without significant intervention continues. These findings underscore the critical importance of adopting emission mitigation strategies tailored to blast furnace operations. By systematically comparing statistical methods under varying conditions, this study provides a foundation for forecasting of sector-specific emissions and actionable insights for policymakers in Türkiye.

Subject Keywords

Carbon dioxide emissions, Steel Industry, Statistical Estimation, Predictive Modeling

URI

https://hdl.handle.net/11511/112997

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Graduate School of Natural and Applied Sciences, Thesis