Analyzing CO2 Emission Intensity: A Comprehensive Study of Clean and Unclean Energy Sources using ML Techniques

dc.contributor.advisorEkhtiari, Ali
dc.contributor.authorMathew, Alen
dc.date.accessioned2024-03-28T09:56:47Z
dc.date.available2024-03-28T09:56:47Z
dc.date.issued2024
dc.description.abstractThis thesis presents a comprehensive analysis of CO2 emission intensity, examining the intricate interplay of energy sources and their contributions to greenhouse gas emissions. The study evaluates the impact of various energy sources, categorizing them into two main groups: clean energy (wind, solar, hydro, bioenergy, and nuclear) and unclean energy (coal, gas, and other fossil fuels). By utilizing Generalized Linear Models (GLM), this research offers a robust prediction of CO2 emission intensity, providing insights into the relative contributions of different energy sources on a regional and global scale. This analysis, which includes the percentage of energy usage from each source, allows for a more accurate quantification of CO2 intensity, simplifying th e process of rebalancing energy dependence to reduce environmental impact. Furthermore, this research employs Time Series Forecasting Techniques, specifically the AutoRegressive Integrated Moving Average (ARIMA) model, to forecast the trends in CO2 intensity across various regions. These forecasting methods facilitate a deeper understanding of how CO2 emissions are expected to evolve over time and allow for the identification of critical points for intervention and mitigation strategies. Findings reveal significant variations in CO2 emission intensity across energy sources and regions, shedding light on the key players in our environmental challenges. The study's data -driven analysis, incorporating energy usage percentages, offers insights into the relative contributions of different energy sources to CO2 emission intensity and underscores the critical importance of transitioning toward cleaner, more sustainable energy alternatives. This research serves as a valuable resource for policymakers, energy industry stakeholders, and environmental advocates, providing empirical guidance for mitigating the environmental impact of energy production and offering a quantifiable basis for rebalancing energy dependence.
dc.identifier.citationMathew, A. (2024). Analyzing CO2 Emission Intensity: A Comprehensive Study of Clean and Unclean Energy Sources using ML Techniques. Masters Thesis, Dublin Business School.
dc.identifier.urihttps://hdl.handle.net/10788/4491
dc.language.isoen
dc.publisherDublin Business School
dc.rights.holderCopyright: The author
dc.rights.urihttp://esource.dbs.ie/copyright
dc.titleAnalyzing CO2 Emission Intensity: A Comprehensive Study of Clean and Unclean Energy Sources using ML Techniques
dc.typeThesis
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