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Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data.

ABSTRACT: 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 (CO₂) 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 CO₂ 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. CO₂ (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO₂ (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.

SUBMITTER: Han P

PROVIDER: S-EPMC7425766 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data.

Han Pengfei P Cai Qixiang Q Oda Tomohiro T Zeng Ning N Shan Yuli Y Lin Xiaohui X Liu Di D

The Science of the total environment 20200813

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<sub>2</sub>) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2-3-year lag. However, a timely ass ...[more]

PMID: 32835964

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Project description:Among studies of the factors that influence carbon emissions and related regulations, economic aggregates, industrial structures, energy structures, population levels, and energy prices have been extensively explored, whereas studies from the perspective of fiscal leverage, particularly of local government investment (LGI), are rare. Of the limited number of studies on the effect of LGI on carbon emissions, most focus on its direct effect. Few studies consider regulatory effects, and there is a lack of emphasis on local areas. Using a cointegration test, a panel data model and clustering analysis based on Chinese data between 2000 and 2013, this study measures the direct role of LGI in carbon dioxide (CO2) emissions reduction. First, overall, within the sample time period, a 1% increase in LGI inhibits carbon emissions by 0.8906% and 0.5851% through its influence on the industrial structure and energy efficiency, respectively, with the industrial structure path playing a greater role than the efficiency path. Second, carbon emissions to some extent exhibit inertia. The previous year's carbon emissions impact the following year's carbon emissions by 0.5375%. Thus, if a reduction in carbon emissions in the previous year has a positive effect, then the carbon emissions reduction effect generated by LGI in the following year will be magnified. Third, LGI can effectively reduce carbon emissions, but there are significant regional differences in its impact. For example, in some provinces, such as Sichuan and Anhui, economic growth has not been decoupled from carbon emissions. Fourth, the carbon emissions reduction effect in the 30 provinces and municipalities sampled in this study can be classified into five categories-strong, relatively strong, medium, relatively weak and weak-based on the degree of local governments' regulation of carbon emissions. The carbon emissions reduction effect of LGI is significant in the western and central regions of China but not in the eastern and northeast regions. This study helps overcome the limitations of previous studies on the regulatory effects of LGI on carbon emissions, and the constructed model may more closely reflect actual economic conditions. Moreover, the current study can benefit countries similar to China that aim to objectively identify the impacts of their LGI on carbon emissions, and such countries can use it as a reference in the formulation of investment policies based on their economic and industrial characteristics.

Dataset Information

Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data.

Publications

Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data.

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