精确的高分辨率二氧化碳（CO2）排放数据对于实现全球碳中和具有重要意义。在此，我们首次提出了化石燃料和水泥生产的近实时全球网格化每日二氧化碳排放数据集（GRACED），其全球空间分辨率为 0.1° x 0.1°，时间分辨率为 1 天。网格化化石排放量是根据近实时数据集（碳监测）中的全国二氧化碳日排放量、点源排放数据集全球能源基础设施排放数据库（GID）、全球大气研究排放数据库（EDGAR）的空间模式以及卫星二氧化氮（NO2）检索的时空模式计算出不同行业的排放量。我们对全球二氧化碳排放量的研究响应了对高质量、细粒度、近实时二氧化碳排放量估算日益增长的迫切需求，以支持不同空间尺度的全球排放监测。我们展示了 2019 年 1 月 1 日至 2020 年 12 月 31 日期间电力、工业、居民消费、地面交通、国内和国际航空以及国际航运部门的排放变化空间模式。这有助于深入了解各部门的相对贡献。", "ds_source": "
(1) 自 2019 年 1 月 1 日起，以《碳监测》（Carbon Monitor）为名发布的全球化石 燃料和水泥生产部门二氧化碳日排放量近实时数据集（数据见 https://carbonmonitor.org/） 。\n
(2) 全球碳网格（Global Carbon Grid）发布的基于整合多种数据流框架的 2019 年 0.1°高分辨率全球部门二氧化碳排放年度数据，包括点源、国家级部门活动和排放以及运输排放和分布http://gidmodel.org 。\n
(3) 2019年全球月度网格化排放量，分辨率为0.1°×0.1°，由EDGAR（https://edgar.jrc.ec.europa.eu/overview.php?v=50_GHG）提供\n
(4) 2017年10月发射的哨兵-5 号卫星上的对流层监测仪器（TROPOMI）提供的 2019 年和 2020 年每日二氧化氮热化学气相沉积（TCVD）检索数据。", "ds_process_way": "
我们将碳监测的排放部门与 GID 和 EDGAR 的部门联系起来。我们认为 GID 在排放源定位方面具有最高的准确性，因此我们尽可能依赖该数据库。但是，对于国内航空、国际航空和国际航运部门，GID 并没有区分相关的国内和国际子部门：因此，我们直接使用 EDGAR 的月度空间模式来计算这些部门的空间分布。\n
其次，我们进行了空间网格化处理。我们使用 GID 子行业的全球年度二氧化碳排放空间模式和 EDGAR 子行业的 2019 年全球月度二氧化碳排放空间模式，对 Carbon Monitor 国家级日排放量进行空间降维。我们假定在 GID 和 EDGAR 的最后一年（2019 年）之后，排放的空间模式保持不变。这一假设的有效性取决于国家和调整的时间跨度，而从 2019 年到 2020 年，由于 COVID-19 在不同地区的影响时间和程度存在很大差异，国家以下的排放量可能在一个国家内迅速变化。因此，对于对全球总排放量有重大影响的排放大国，我们使用基于 TROPOMI NO2 检索数据的国家以下代用数据，将国家碳排放总量分配到区域总量中，然后再根据 GID 和 EDGAR 空间模式进行 0.1° 的第二次降尺度。该分析可根据每年最新的高分辨率排放图和其他空间代用数据进行持续更新。", "ds_quality": "
此外，它还提供了最新、最精细的概况，说明化石二氧化碳排放在何时何地因突发事件（如 2019 年冠状病毒疾病 [COVID-19]）和其他人类活动干扰而减少或反弹。随着全球从疫情中恢复并实现能源系统的去碳化，该数据集的定期更新将使政策制定者能够更密切地监测气候和能源政策的有效性，并迅速做出调整。", "ds_acq_start_time": "2019-01-01 00:00:00", "ds_acq_end_time": "2020-05-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 517898, "ds_files_count": 2, "ds_format": "excel", "ds_space_res": "0.1°", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "17f55697-5514-498d-b112-4eb9dc077c45.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 3, "publish_time": "2024-05-22 10:57:58", "last_updated": "2026-01-14 11:00:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6473.2024", "i18n": { "en": { "title": "Near-real-time global gridded daily CO2 emissions", "ds_format": "excel", "ds_source": "
(1) Starting from January 1, 2019, a near real-time dataset of daily carbon dioxide emissions from the global fossil fuel and cement production sectors, published under the name \"Carbon Monitor\" (data available at https://carbonmonitor.org/ ）.\n
(2) The 2019 0.1 ° high-resolution annual global sectoral carbon dioxide emissions data, based on an integrated framework of multiple data streams, released by the Global Carbon Grid, includes point sources, national level sectoral activities and emissions, as well as transportation emissions and distribution http://gidmodel.org .\n
(3) Global monthly gridded emissions in 2019, with a resolution of 0.1 °× 0.1 °, determined by EDGAR（ https://edgar.jrc.ec.europa.eu/overview.php?v=50_GHG ）Provide\n
(4) The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 satellite, launched in October 2017, provides daily retrieval data on thermal chemical vapor deposition (TCVD) of nitrogen dioxide for 2019 and 2020.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Accurate high-resolution carbon dioxide (CO2) emission data is of great significance for achieving global carbon neutrality. Here, we propose for the first time a near real-time global gridded daily carbon dioxide emissions dataset (GRACED) for fossil fuel and cement production, with a global spatial resolution of 0.1 ° x 0.1 ° and a temporal resolution of 1 day. Grid based fossil emissions are calculated for different industries based on the national daily carbon dioxide emissions from near real time datasets (carbon monitoring), spatial models from the Global Energy Infrastructure Emissions Database (GID) and the Global Atmospheric Research Emissions Database (EDGAR), and spatiotemporal models retrieved from satellite nitrogen dioxide (NO2) searches. Our research on global carbon dioxide emissions responds to the growing urgent need for high-quality, fine-grained, near real-time estimates of carbon dioxide emissions to support global emission monitoring at different spatial scales. We presented the spatial patterns of emission changes in the electricity, industrial, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019 to December 31, 2020. This helps to gain a deeper understanding of the relative contributions of each department.
", "ds_time_res": "日", "ds_acq_place": "Global", "ds_space_res": "0.1°", "ds_projection": "", "ds_process_way": "We linked the emission monitoring department with the departments of GID and EDGAR. We believe that GID has the highest accuracy in locating emission sources, so we rely as much as possible on this database. However, for the domestic aviation, international aviation, and international shipping sectors, GID does not differentiate between the relevant domestic and international sub sectors: therefore, we directly use EDGAR's monthly spatial model to calculate the spatial distribution of these sectors.\n
Secondly, we conducted spatial grid processing. We use the global annual carbon dioxide emission spatial model of GID sub industry and the 2019 global monthly carbon dioxide emission spatial model of EDGAR sub industry to perform spatial dimensionality reduction on the national level daily emissions of Carbon Monitor. We assume that the spatial pattern of emissions remains unchanged after the last year of GID and EDGAR (2019). The validity of this assumption depends on the time span of the country and adjustment, and from 2019 to 2020, due to the significant differences in the impact time and degree of COVID-19 in different regions, emissions below the national level may rapidly change within a country. Therefore, for major emitters that have a significant impact on global total emissions, we use sub national proxy data based on TROPOMI NO2 retrieval data to allocate national carbon emissions to regional totals, and then perform a second 0.1 ° downscaling based on GID and EDGAR spatial models. This analysis can be continuously updated based on the latest high-resolution emission maps and other spatial proxy data every year.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "二氧化碳", "排放量", "近实时" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2019 ], "ds_contributors": [ { "true_name": "刘竹", "email": "zhuliu@tsinghua.edu.cn", "work_for": "清华大学地球系统科学系", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘竹", "email": "zhuliu@tsinghua.edu.cn", "work_for": "清华大学地球系统科学系", "country": "中国" } ], "ds_managers": [ { "true_name": "刘竹", "email": "zhuliu@tsinghua.edu.cn", "work_for": "清华大学地球系统科学系", "country": "中国" } ], "category": "气象" }