乔兰, 邓乃夫, 李庆文, 王秀格, 张庆龙, 王雅文. 公路工程建设阶段全生命周期碳排放智能估算方法[J]. 工程科学学报, 2023, 45(12): 2173-2186. DOI: 10.13374/j.issn2095-9389.2022.10.21.001
引用本文: 乔兰, 邓乃夫, 李庆文, 王秀格, 张庆龙, 王雅文. 公路工程建设阶段全生命周期碳排放智能估算方法[J]. 工程科学学报, 2023, 45(12): 2173-2186. DOI: 10.13374/j.issn2095-9389.2022.10.21.001
QIAO Lan, DENG Naifu, LI Qingwen, WANG Xiuge, ZHANG Qinglong, WANG Yawen. Intelligent assessment method of life-cycle carbon emission during the highway construction phase[J]. Chinese Journal of Engineering, 2023, 45(12): 2173-2186. DOI: 10.13374/j.issn2095-9389.2022.10.21.001
Citation: QIAO Lan, DENG Naifu, LI Qingwen, WANG Xiuge, ZHANG Qinglong, WANG Yawen. Intelligent assessment method of life-cycle carbon emission during the highway construction phase[J]. Chinese Journal of Engineering, 2023, 45(12): 2173-2186. DOI: 10.13374/j.issn2095-9389.2022.10.21.001

公路工程建设阶段全生命周期碳排放智能估算方法

Intelligent assessment method of life-cycle carbon emission during the highway construction phase

  • 摘要: 深度分析了国内外交通行业碳排放清单及碳排放因子目录,结合中国交通行业特点,基于现有的公路工程估算指标及中国传统交通行业施工机械设备台班单价计算表,总结归纳符合中国交通行业特点的碳排放因子数据. 基于归因全生命周期评价方法(ALCA),采用“自下而上”的公路工程碳排放计量思路,将公路工程项目建设期内工程活动分为分项工程–分部工程–单位工程,建立了公路工程建设期碳排放数据库及碳排放测算模型. 在此基础上,利用MATLAB搭建了公路工程碳排放智能估算软件并对国内两段支线高速公路的路面工程碳排放进行了估算分析,实现了仅通过公路里程量、工程方量和公路等级等信息即可快速估算并统计碳排放量,同时能够智能溯源各阶段碳排放要素并定位碳排放大户,为交通行业节能减排提供数据和理论基础. 分析显示,在路面工程中,稳定土基层和沥青路面面层的碳排放主导整体碳排放量,占据了99.6%以上,其中,以C32.5水泥为主的材料占据了75%左右的碳排放.

     

    Abstract: The Chinese government has announced its “carbon peak and carbon neutrality goals” for 2030 and 2060, respectively. All industry sectors are working toward developing carbon policies to support the national dual carbon goals. The transportation industry, which is one of the three major sources of CO2 emission in China, has indirectly contributed to the greenhouse effect and global warming and serves as a threat to human living spaces. Therefore, highway construction projects are the key targets for emission reduction in China. This paper presents a detailed analysis of the carbon emission inventories and carbon emission factor catalogs in the domestic and foreign transportation industries. The carbon emission factor data of China’s transportation industry are compiled and summarized based on the existing estimation indices for highway projects and the unit price calculation sheet of construction machinery and equipment in China’s traditional transportation industry. A “bottom-up” approach to measuring carbon emissions of highway projects, based on the attribution life-cycle assessment (ALCA) method, is adopted. In this method, the project activities are divided into subprojects, divisional projects, and unit projects during the construction period of the highway project, thereby establishing a carbon emission database and a carbon emission assessment model. Accordingly, intelligent assessment software for the carbon emission of highway projects was developed using MATLAB, and the carbon emissions of pavement projects for a certain mileage of two domestic highway feeders were estimated and analyzed. In general, our method enables the rapid assessment and statistical analysis of carbon emissions using information about the highway mileage, highway grade, and engineering volume. Moreover, it can intelligently track the carbon emission elements at each stage and identify the major carbon emitters during the construction of highway projects. Based on the assessment of two case studies of pavement projects, the software shows that the major carbon emitters that accounted for 75% of overall emissions were C32.5 cement, gravel, and modified asphalt in the different concrete layers. This implies that the construction phase is the dominant contributor to the overall carbon emissions. Among the machinery and equipment, the asphalt mixture mixing equipment is the major carbon emitter, with emissions of 380 t·h−1, accounting for roughly 30% of the overall carbon emissions from the machinery and equipment. In asphalt pavement engineering, the main source of carbon emissions is the stable soil base and the asphalt pavement layer, accounting for more than 99.6% of the total carbon emissions, while the emissions from the pavement cushion and sporadic engineering are almost negligible. Therefore, our intelligent assessment method can provide robust data and a theoretical basis for energy conservation and the reduction of emissions in the transportation industry.

     

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