Statistical Analysis of Operational Carbon for Different Building Types in the UAE

The building sector contributes significantly to the greenhouse effect, generating significant carbon dioxide (CO2) emissions throughout the life cycle of buildings. Traditional methods for assessing emissions, such as software evaluation and site inspection, are time-consuming and do not adequately account for variability and uncertainty in emission data. This research aims to investigate and analyze the statistical characteristics of the operational carbon produced from different types of buildings in the context of the United Arab Emirates (UAE). The investigation focused on residential, commercial and educational buildings and their heating, ventilation, air conditioning (HVAC) systems, walls and window systems. All scenarios were statistically evaluated through linear-regression analysis, correlation analysis, Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs). The results of linear-regression analysis revealed an average accuracy (R2 ) of 0.958. The results of correlation analysis indicated that upgrading the HVAC system in residential and commercial buildings reduced the operational carbon, while in educational buildings, upgrading the window systems reduced the operational carbon. Finally, the PMF and CDF analyses indicated that upgrading the HVAC system in residential and commercial buildings was the optimal option, which reduced the carbon percentage by 28.56% and 28.48%, respectively. However, upgrading the window system was the optimal option for educational buildings, reducing the carbon percentage by 75.80%. Keywords: Operational carbon, Linear regression, Correlation analysis, Probability mass functions, Cumulative distribution functions.