高时空分辨率FFCO2排放清单的构建方法及研究展望

doi:10.11821/dlxb202203011

地理学报 ›› 2022, Vol. 77 ›› Issue (3): 650-664.doi: 10.11821/dlxb202203011

• 碳收支与生态系统服务 • 上一篇下一篇

高时空分辨率FFCO₂排放清单的构建方法及研究展望

马立¹(), 王璟煦¹, 张迪迪¹, 王明珠¹, 宋玉彪¹, 曾辉²()

1.河北工程大学地球科学与工程学院,邯郸 056038
2.北京大学深圳研究生院,深圳 518071

收稿日期:2021-06-15 修回日期:2021-12-02 出版日期:2022-03-25 发布日期:2022-05-25
通讯作者: 曾辉(1964-), 男, 辽宁凤城人, 教授, 博士生导师, 研究方向为景观与区域生态风险评估。E-mail: zengh@pkusz.edu.cn
作者简介:马立(1982-), 男, 河北邯郸人, 博士, 讲师, 研究方向为空间分析与建模。E-mail: mali@hebeu.edu.cn
基金资助:
深圳市稳定支持计划项目(GXWD20201231165807007-20200812142216001);深圳市基础研究自由探索项目(JCYJ20180302150417674);国家自然科学基金项目(41871191)

Developing FFCO₂ emission inventory with high spatio-temporal resolution: Methodology and prospects

MA Li¹(), WANG Jingxu¹, ZHANG Didi¹, WANG Mingzhu¹, SONG Yubiao¹, ZENG Hui²()

1. School of Earth Science and Engineering Hebei University of Engineering, Handan 056038, Hebei, China
2. Shenzhen Graduate School, Peking University, Shenzhen 518071, Guangdong, China

Received:2021-06-15 Revised:2021-12-02 Published:2022-03-25 Online:2022-05-25
Supported by:
Shenzhen steady support project(GXWD20201231165807007-20200812142216001);Shenzhen Basic Research Free-exploration Project(JCYJ20180302150417674);National Natural Science Foundation of China(41871191)

摘要/Abstract

摘要：

化石能源（FF）CO₂排放是全球人为温室气体排放的主体,作为衔接国家排放清单和大气反演验证途径的关键环节,2019年联合国政府间气候变化专门委员会（IPCC）对《国家温室气体清单指南》进行修订,势必将推动高分辨率FFCO₂排放清单的进一步规范发展。本文结合修订版指南中对于高分辨率排放清单的具体要求,从全球尺度、国家及以下尺度两个层面对高时空分辨率FFCO₂排放清单的构建方法进行梳理和归纳,并对其研究趋势进行展望。① IPCC方法学的进一步修订与完善,将有助于进一步提高FFCO₂排放清单的时空分辨率和精度;而构建包含间接排放的高分辨率FFCO₂排放清单正在兴起。② 作为大气反演模型的先验数据,采用自下而上的部门方法,直接获取排放统计数据,是编制高分辨率FFCO₂排放清单的首要途径;而通过替代变量及建模途径进行排放总量的时空分配,也是编制高分辨率FFCO₂排放清单的必要手段。③ 清单的不确定性分析中,需要考虑时空分配所带来的不确定性信息;基于大气观测的反演验证途径将作为独立于排放清单的一种客观核算手段,将在清单的质量保证/质量控制与验证中发挥重要作用。

关键词: 排放清单, 高分辨率, 碳排放, 反演, 时空格局

Abstract:

According to the statistics of the United Nations Environment Program (UNEP), fossil fuel (FF) CO₂ emission comprises a major proportion of global anthropogenic greenhouse gas emissions. The inverse modeling approach was proposed to verify the results of the traditional bottom-up inventory based on atmospheric concentration (remote sensing and ground-based measurement) in the refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories on the 49th IPCC Plenary Session in 2019. This approach would further promote the development of high-resolution FFCO₂ emission inventory which serves as the spatially and temporally distributed form of emission inventory and the prior input data for the inverse model. This paper hereby summarized the spatial distribution and temporal disaggregation approaches of high-resolution FFCO₂ emission inventory from global and national scales to a city-wide scale, and put forward the future research prospects based on the specific requirements for high-resolution emission inventories in this 2019 refinement to the IPCC Guidelines: (1) the refined IPCC methodology will help to further improve the spatial and temporal resolution and the accuracy of FFCO₂ emission inventories; and the high-resolution FFCO₂ emission inventory concerning indirect emissions is emerging. (2) It is a good practice to compile a high-resolution FFCO₂ emission inventory with the detailed spatial and temporal information from facility-level emission data, the remote sensing images and the intelligent transportation big data, while the use of geospatial surrogate data or proxy variables, and modeling such as regression, assimilation and artificial neural network, is also necessary to improve the accuracy of the estimates both spatially and temporally when the directly applicable data are unavailable. (3) The uncertainty information of the spatial distribution and temporal disaggregation should be qualitatively or quantitatively analyzed. The atmospheric inversion verification approach, as an independent and objective accounting method, will play an important role in QA/QC and verification of the high-resolution FFCO₂emission inventory.

Key words: emission inventory, high-resolution, carbon emissions, inversion, spatio-temporal pattern

马立, 王璟煦, 张迪迪, 王明珠, 宋玉彪, 曾辉. 高时空分辨率FFCO₂排放清单的构建方法及研究展望[J]. 地理学报, 2022, 77(3): 650-664.

MA Li, WANG Jingxu, ZHANG Didi, WANG Mingzhu, SONG Yubiao, ZENG Hui. Developing FFCO₂ emission inventory with high spatio-temporal resolution: Methodology and prospects[J]. Acta Geographica Sinica, 2022, 77(3): 650-664.

图/表 2

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清单数据库	构建方法	气体种类	统计年份	空间分辨率	时间分辨率
EDGAR^[25]	部门方法	CO₂、大气污染物	1970—2018年	0.1°×0.1°	年、月
GCP-GridFED^[19]	部门方法	CO₂、O₂	1959—2019年	0.1°×0.1°	年、月
CEDS^[26]	部门方法	CO₂、大气污染物	1750—2014年	0.5°×0.5°	年、月
PKU-FUEL^[27]	部门方法	CO₂、大气污染物	1960—2014年	0.1°×0.1°	年、月
ODIAC^[28]	降尺度	CO₂	2000—2019年	1 km×1 km	年、月
CDIAC^[29]	降尺度	CO₂	1751—2016年	1°×1°	年、月
FFDAS^[16]	降尺度	CO₂	1997—2010年	0.1°×0.1°	年

清单数据库	排放尺度	气体种类	构建方法	空间分辨率	时间分辨率	统计年份
PKU-FUEL^[27]	全球尺度	CO₂、大气污染物	部门方法	10 km×10 km	年、月	1960—2014
MEIC^[63]	国家尺度	CO₂、大气污染物	部门方法	0.25°×0.25°	年、月	2008、2010—2017
CHRED^[64]	国家尺度	CO₂	部门方法	1 km×1 km、 10 km×10 km	年	2007、2012

高时空分辨率FFCO₂排放清单的构建方法及研究展望

Developing FFCO₂ emission inventory with high spatio-temporal resolution: Methodology and prospects

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