{"title":"阿联酋不同类型建筑的运行碳统计分析","authors":"Fatma Hosny","doi":"10.14525/jjce.v18i2.15","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":51814,"journal":{"name":"Jordan Journal of Civil Engineering","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Statistical Analysis of Operational Carbon for Different Building Types in the UAE\",\"authors\":\"Fatma Hosny\",\"doi\":\"10.14525/jjce.v18i2.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":51814,\"journal\":{\"name\":\"Jordan Journal of Civil Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jordan Journal of Civil Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14525/jjce.v18i2.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jordan Journal of Civil Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14525/jjce.v18i2.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
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.
期刊介绍:
I am very pleased and honored to be appointed as an Editor-in-Chief of the Jordan Journal of Civil Engineering which enjoys an excellent reputation, both locally and internationally. Since development is the essence of life, I hope to continue developing this distinguished Journal, building on the effort of all the Editors-in-Chief and Editorial Board Members as well as Advisory Boards of the Journal since its establishment about a decade ago. I will do my best to focus on publishing high quality diverse articles and move forward in the indexing issue of the Journal.