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    碳监测及核算方法研究进展

    Research progress on carbon monitoring and accounting methods

    • 摘要: 综述了碳监测及核算方法的研究进展,系统阐述了直接测量法、模型模拟法和遥感监测法三大碳监测技术体系,以及排放因子法、质量平衡法和过程分析法3类碳核算方法的原理、应用及发展趋势。研究显示,碳监测与核算技术正朝着高精度、广覆盖和强实用性的方向演进,为应对气候变化和实现碳中和目标提供重要支撑。在碳监测方法方面,直接测量法凭借传感器技术(如TDLAS、FTIR)实现排放源端实时监测,其数据合规性强且溯源能力突出,但存在设备成本高、布点代表性不足等局限,通过国产替代和移动监测补充策略可有效缓解。模型模拟法利用WRF-Chem等大气传输模型填补偏远地区监测空白,通过高分辨率模拟和数据同化技术提升精度,但面临输入参数不确定性和计算资源需求高的挑战。遥感监测法则依托卫星(OCO−2)和无人机(GHGSat)构建"星−空−地"一体化网络,实现从全球碳通量评估到工业点源精准监测的多尺度覆盖,但需突破云层干扰和反演算法依赖的技术瓶颈。碳核算方法体系中,排放因子法凭借操作简便性广泛应用于电力、钢铁等行业,但因子偏差问题亟待通过本地化数据库建设解决。质量平衡法基于碳元素流追踪在流程工业(如水泥、化工)中展现高精度优势,需强化在线监测设备部署以提升数据质量。过程分析法通过机理模型(Aspen Plus)实现复杂工业的精细化排放管理,但受限于企业信息化水平和模型开发成本。政策驱动方面,中国"双碳"制度体系推动监测核算标准化进程,2024年发布的12项核心标准强化了监测设备计量认证和数据互认机制。实施中面临跨部门数据孤岛和新兴排放源(如数据中心、氢能产业)核算缺失等挑战,需通过国家碳监测平台建设和专项方法学开发予以突破。研究指出,未来应加强多技术融合(如AI驱动的4D−Var数据同化)、完善"监测−报告−核查"体系,并深度参与全球碳监测计划(GCP)等国际合作,以全面提升对碳中和目标的支撑能力。

       

      Abstract: The research progress of carbon monitoring and accounting methods is reviewed, and the three major carbon monitoring technology systems (direct measurement method, model simulation method and remote sensing monitoring method) as well as the principles, applications and development trends of three carbon accounting methods (emission factor method, mass balance method and process analysis method) are systematically expounded. It is shown that carbon monitoring and accounting technology is evolving in the direction of high precision, wide coverage and strong practicability, providing important support for coping with climate change and achieving carbon neutrality goals. In terms of carbon monitoring methods, the direct measurement method realizes real-time monitoring of the emission source by means of sensor technology ( such as TDLAS and FTIR ). Its data compliance is strong and the traceability ability is outstanding, but there are limitations such as high equipment cost and insufficient representativeness of distribution points. It can be effectively alleviated by domestic substitution and mobile monitoring supplement strategies. The model simulation method uses atmospheric transmission models such as WRF-Chem to fill the monitoring gap in remote areas, and improves the accuracy through high-resolution simulation and data assimilation technology, but it faces the challenges of input parameter uncertainty and high demand for computing resources. The remote sensing monitoring method relies on satellites ( OCO−2 ) and unmanned aerial vehicles (GHGSat) to build a ' satellite-air-ground ' integrated network to achieve multi-scale coverage from global carbon flux assessment to accurate monitoring of industrial point sources, but it needs to break through the technical bottlenecks of cloud interference and inversion algorithm dependence.In the carbon accounting method system, the emission factor method is widely used in power, steel and other industries due to its simplicity of operation, but the problem of factor deviation needs to be solved urgently through the construction of localized database. The mass balance method shows high-precision advantages in process industries ( such as cement and chemical industry ) based on carbon flow tracking, and it is necessary to strengthen the deployment of online monitoring equipment to improve data quality. The process analysis method realizes the refined emission management of complex industries through the mechanism model ( Aspen Plus ), but it is limited by the level of enterprise informatization and the cost of model development. In terms of policy driving, China 's " dual carbon " system promotes the standardization process of monitoring and accounting, and the 12 core standards issued in 2024 strengthen the mechanism of measurement certification and data mutual recognition of monitoring equipment. The implementation faces challenges such as cross-sectoral data islands and lack of accounting for emerging emission sources ( such as data centers and hydrogen energy industries ). It is necessary to break through through the construction of national carbon monitoring platforms and the development of special methodologies. It is pointed out that in the future, multi-technology integration ( such as AI-driven 4D−Var data assimilation ) should be strengthened, the“monitoring-reporting-verification”system should be improved, and international cooperation such as the Global Carbon Monitoring Program ( GCP ) should be deeply involved to comprehensively enhance the support for carb

       

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