| Summary: | 碩士 === 國立成功大學 === 土木工程學系碩博士班 === 98 === In recent years, construction industry has started working on environmental sustainability, often calculating energy saving and carbon reduction numbers that are converted to money for expressing performance. For example, the reduced amount of cement multiplied by 0.89 is equal to the reduced amount of carbon dioxide; electricity savings multiplied by electricity unit price is equal to cost reduction. These numbers have the advantage of being easy to understand, but the lack of verification, in particular in the sources of data and assumptions, create a further need to enhance the reliability of numbers.
This study assessed the data quality of energy saving and carbon reduction numbers for construction projects, to enhance the reliability of the construction sustainability performance. First, it reviewed relevant literature of environmental sustainability and performance. Theoretical literature has more narrative concepts, and case studies have more examples calculated, in particular, for manufacturing production process in saving energy and reducing waste numbers. Then it proposed the energy saving and carbon reduction assessment system, considering the construction project life cycle, including mining, manufacturing and transportation that are belonging to manufacturing; plan/design, construction, operation, and recovery/removal that are belonging to construction. Follow-up analysis and sustainability measures are proposed for individual stages in construction for considering the environmental impact.
Then it used ten cases from domestic and foreign countries to analyze sustainability effects in energy saving and carbon reduction benefits with quantitative numbers. For example, some amount of cement can be replaced by slag; the cost effectiveness can be compared between the porous asphalt and traditional asphalt. The data quality matrix tool was used to assess the data quality for construction projects. Assessing matrix includes six dimensions: data acquisition method, independence of data supplier, representativeness, temporal correlation, geographical correlation, and technological correlation. Scores are from one to five, five represents the best, one the worst. During the data analysis process some difficulties came up and assumptions were made, giving data quality scores for the six dimensions.
The analysis results show that the most difficult dimensions to achieve full marks (five points) are data acquisition method and independence of data supplier, because the sustainability performance for them is difficult to be directly measured and the third party verification is generally lacking. It was also found that manufacturing stage data has better quality than construction data; the whole life cycle data has the lowest quality. Finally, this research checked the data quality matrix applicability and sorted the applicability degree of the six dimensions by importance and difficulty. It was found that data quality can be improved the most with the data acquisition method. After the data are verified and brought into follow-up analysis and calculations the performance numbers can be more reliable.
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