{"title":"项目早期温室气体排放关键因素评估的实证方法","authors":"Woosik Jang, S. Na, Harry Lee","doi":"10.3311/ccc2019-094","DOIUrl":null,"url":null,"abstract":"Since the middle of the 18th century, the entire world has been devoted to industrial development. Based on the development of industry, many countries around the world have achieved rapid economic growth [1]. This resulted not only in economic growth but also a large increase in the amount of greenhouse gases (GHG), which is evident as climate change and environmental disruption. To overcome such adverse effects, this study focuses on assessing the key environmental factors at the early stage of a project for decision makers. To support this objective, the authors have collected 210 real road construction cases and investigated 23 dependent factors including design parameters and physical environment of construction projects. Subsequently, statistical analysis as factor selection and regression modeling is conducted to extract the key factors. As a result, a total of five factors are extracted: “Area of Rice field,” “Total Project Cost,” “Number of Bridges,” “Design (maximum) Speed,” and “Length of Earthwork” via regression analysis. Moreover, the proposed regression model accounts for 69% and 67% of the total variance of “Total Environmental Loads” indicated by R and adjusted R, respectively, at 99% of the significance level. Thus, the proposed model is expected to play a positive role in the decision-making process based on mathematical and empirical evidences. © 2019 The Authors. Published by Budapest University of Technology and Economics & Diamond Congress Ltd. Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2019.","PeriodicalId":231420,"journal":{"name":"Proceedings of the Creative Construction Conference 2019","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Empirical Approaches for Assessing Key Factors Pertaining to Greenhouse Gas Emissions in Early Stages of Projects\",\"authors\":\"Woosik Jang, S. Na, Harry Lee\",\"doi\":\"10.3311/ccc2019-094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since the middle of the 18th century, the entire world has been devoted to industrial development. Based on the development of industry, many countries around the world have achieved rapid economic growth [1]. This resulted not only in economic growth but also a large increase in the amount of greenhouse gases (GHG), which is evident as climate change and environmental disruption. To overcome such adverse effects, this study focuses on assessing the key environmental factors at the early stage of a project for decision makers. To support this objective, the authors have collected 210 real road construction cases and investigated 23 dependent factors including design parameters and physical environment of construction projects. Subsequently, statistical analysis as factor selection and regression modeling is conducted to extract the key factors. As a result, a total of five factors are extracted: “Area of Rice field,” “Total Project Cost,” “Number of Bridges,” “Design (maximum) Speed,” and “Length of Earthwork” via regression analysis. Moreover, the proposed regression model accounts for 69% and 67% of the total variance of “Total Environmental Loads” indicated by R and adjusted R, respectively, at 99% of the significance level. Thus, the proposed model is expected to play a positive role in the decision-making process based on mathematical and empirical evidences. © 2019 The Authors. Published by Budapest University of Technology and Economics & Diamond Congress Ltd. Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2019.\",\"PeriodicalId\":231420,\"journal\":{\"name\":\"Proceedings of the Creative Construction Conference 2019\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Creative Construction Conference 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3311/ccc2019-094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Creative Construction Conference 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3311/ccc2019-094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Empirical Approaches for Assessing Key Factors Pertaining to Greenhouse Gas Emissions in Early Stages of Projects
Since the middle of the 18th century, the entire world has been devoted to industrial development. Based on the development of industry, many countries around the world have achieved rapid economic growth [1]. This resulted not only in economic growth but also a large increase in the amount of greenhouse gases (GHG), which is evident as climate change and environmental disruption. To overcome such adverse effects, this study focuses on assessing the key environmental factors at the early stage of a project for decision makers. To support this objective, the authors have collected 210 real road construction cases and investigated 23 dependent factors including design parameters and physical environment of construction projects. Subsequently, statistical analysis as factor selection and regression modeling is conducted to extract the key factors. As a result, a total of five factors are extracted: “Area of Rice field,” “Total Project Cost,” “Number of Bridges,” “Design (maximum) Speed,” and “Length of Earthwork” via regression analysis. Moreover, the proposed regression model accounts for 69% and 67% of the total variance of “Total Environmental Loads” indicated by R and adjusted R, respectively, at 99% of the significance level. Thus, the proposed model is expected to play a positive role in the decision-making process based on mathematical and empirical evidences. © 2019 The Authors. Published by Budapest University of Technology and Economics & Diamond Congress Ltd. Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2019.