State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
Sci Total Environ. 2021 Jan 1;750:141688. doi: 10.1016/j.scitotenv.2020.141688. Epub 2020 Aug 13.
The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2-3-year lag. However, a timely assessment of COVID-19's impact on provincial CO emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of -22.3% in freight transport and -59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers.
2019 年冠状病毒病（COVID-19）的爆发给中国乃至全球的人类生命和经济造成了巨大损失。它显著降低了国内生产总值（GDP）、发电量、工业活动和交通量；因此，它减少了中国与化石燃料和水泥相关的二氧化碳（CO）排放。由于获取活动数据的时间延迟，传统的排放量清单通常存在 2-3 年的滞后。然而，及时评估 COVID-19 对省级 CO 减排的影响对于准确了解减排及其对缓解措施的意义至关重要；此外，这些信息可以为模型研究提供限制。在这里，我们使用国家和省级 GDP 数据以及中国排放账户和数据集（CEADs）清单来估算 2020 年第一季度（Q1）的排放量减少情况。我们发现，与 2019 年第一季度相比，CO 排放量减少了 2577 万吨（11.0%）。第二产业对总减排量的贡献最大，为 1868 万吨 CO（72.5%），主要是由于煤炭消耗和水泥产量下降。在省级层面，由于第二产业下降了 48.2%，湖北省对减排的贡献最大（4060 万吨）。此外，交通排放量显著增加（651 万吨），与 2019 年第一季度相比，货运量减少了 22.3%，客运量减少了 59.1%。我们使用点、线和面积源（PLAS）方法对 GDP 方法进行了测试，结果非常接近（减排 10.6%）。一个政策启示是改变人们的工作方式和沟通方式，通过在家工作和召开电话会议来减少交通排放。此外，在缺乏详细能源活动数据的情况下，GDP 被发现具有估算排放变化的潜力。我们提供了省级数据，可以作为模型研究的空间分解限制，进一步为碳循环社区和政策制定者提供支持。
