Carbon emissions assessment of wooden building materials manufactured from a digital and intelligent production line
LI Pei-xian1,2, SONG Guang-han1, YANG Xiao-lu1, SONG Xiang-nan3, LU Yu-jie1,2
1. College of Civil Engineering, Tongji University, Shanghai 200092, China; 2. Key Laboratory of Performance Evolution and Control for Engineering Structures of Ministry of Education, Tongji University, Shanghai 200092, China; 3. School of Management, Guangzhou University, Guangzhou 510006, China
Abstract：In order to understand carbon emissions from wooden building materials produced in digital and intelligent factories and to provide ground references and benchmarks for carbon reduction strategies, a life-cycle assessment (LCA) was conducted in this study to quantify the carbon footprints of main wooden building materials. The results show that the carbon footprints (per unit mass:kg CO2e/kg) for a wood floor tile, a wood door, and a wood door frame are 1.21, 1.26, and 0.47, respectively. The carbon footprints of the floor tile and the door are greater than their domestic counterparts in the literature. The raw materials and production processes are the main sources of carbon emissions for wooden building materials, accounting for 54.74%~77.47% and 18.57%~38.94% of the total carbon emissions, respectively, while the carbon emissions during transportation and waste gas treatment process account for less than 9% of the total carbon emissions. The Monte Carlo simulations show a relatively low uncertainty of the calculated carbon emissions in this study. Digital and intelligent factories could potentially reduce carbon emissions in the future by procuring low-carbon raw materials, optimizing the product designs, improving the energy efficiency of equipment, and using clean energy like solar and wind.
李佩娴, 宋广翰, 杨晓露, 宋向南, 卢昱杰. 基于数智化生产线的木质建材生产碳排放评估[J]. 中国环境科学, 2022, 42(8): 3922-3930.
LI Pei-xian, SONG Guang-han, YANG Xiao-lu, SONG Xiang-nan, LU Yu-jie. Carbon emissions assessment of wooden building materials manufactured from a digital and intelligent production line. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(8): 3922-3930.
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