Published: Jan. 1, 2024

In response to the escalating challenges posed by climate change and industrial inefficiency, this thesis presents a comprehensive investigation aimed at advancing predictive modeling of global CO2 emissions enhancing operational efficiency in steel manufacturing through Electric Arc Furnace (EAF) temperature optimization. Leveraging rich dataset sourced from World Development Indicators database alongside meticulously curated specific EAF operations, our study applies an innovative blend econometric machine learning techniques, including Pooled Ordinary Least Squares (Pooled OLS), Random Effects (RE), Fixed (FE), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. The objective is twofold: refine emission forecasts establish reliable model for predicting flat bath production, critical determinant energy product quality. Our analysis elucidates complex dynamics governing emissions, identifying key factors such as renewable consumption, GDP per unit use, total greenhouse gas significant determinants. These insights not only contribute academic discourse on environmental sustainability but also provide solid foundation policymakers devise more effective strategies reduction. Concurrently, realm manufacturing, breaks new ground harnessing data predict unprecedented accuracy. This advancement holds implications conservation optimization, addressing urgent need practices. bridges gap between theoretical research practical application sets benchmark utilization data-driven approaches science engineering. By offering detailed comparison techniques their prowess, it guides future directions underscores potential sophisticated analytical methods tackling some most pressing challenges. Ultimately, role achieving sustainable future, providing valuable that can inform both policy process

Language: Английский