%0 Journal Article
%T 新冠疫情对中国碳排放影响的实证研究<br>The Empirical Study on the Impact of COVID-19 on Carbon Emissions in China
%A 施可以
%A 梁进
%J Operations Research and Fuzziology
%P 43-60
%@ 2163-1530
%D 2024
%I Hans Publishing
%R 10.12677/ORF.2024.141005
%X 新冠疫情对我国的生产生活方式产生了不小的冲击，从而影响了各部门的碳排放量。研究疫情对碳排放的影响可以为后续的减排政策提供参考和方向。本文将以月为单位对2019~2021三年的数据进行分析，采用STIRPAT方程，将经济水平、产业结构、固定资产投资和清洁能源使用率作为经济变量，将确诊人数和隔离政策作为疫情变量加入模型，对统计频率较低的数据进行频率转换。通过逐年回归和Chow检验得出以下结论：疫情对2020年的碳排放模型有显著影响，其中确诊人数存在长期作用且影响程度超过了其他经济变量，而隔离政策强度的影响程度较弱并只存在短期作用；疫情后的复工复产和高碳项目的投资比例增加，导致固定投资对碳排放的影响程度加深以及碳排放量的反弹；清洁能源使用率是对碳减排贡献最大且最稳定的变量；经济水平对碳排放的影响逐渐下降，反映了我国低碳经济发展策略的成效。最后，本文使用回归模型和时序模型对2022~2023年我国的碳排放量进行了预测。<br />
The COVID-19 pandemic has drastically altered people’s production and lifestyle, thus impacting carbon emissions in various sectors. Investigating the pandemic’s effect on carbon emissions can provide reference and guidance for subsequent carbon reduction policies. This paper will analyze the panel data of three years (2019~2021) at the monthly level. The STIRPAT equation is used to design four economic factors: economic level, industrial structure, fixed asset invest-ment, and clean energy utilization. Additionally, two factors: new-confirmed cases and lockdown intensity are added to the model, which reflect the impact of COVID-19. For data with low statistical frequency, this paper compares various conversion methods to transform the data from low frequency to high frequency. Through yearly OLS regression and Chow tests, the following conclusions are drawn: COVID-19 has a significant effect on the 2020 carbon emission model, in which the number of new-confirmed cases has a long-term influence on carbon emissions, and the impact is greater than other economic factors, while the impact of lockdown intensity is weak and only has a short-term effect. The post-pandemic work resumption, coupled with an increased proportion of financial investment in high-carbon projects, has deepened the impact of fixed asset investment on carbon emissions and caused a rebound in carbon emissions. Clean energy utilization has the greatest and most consistent contribution to China’s carbon emission reduction. The impact of economic level is gradually decreasing, which may reflect the effectiveness of China’s low-carbon economic development strategy. Finally, this paper uses regression models and time series models to forecast China’s carbon emissions in the years of 2022 and 2023.
%K 碳排放，COVID-19，STIRPAT方程，频率转换方法，回归分析<br>Carbon Emissions
%K COVID-19
%K STIRPAT Equation
%K Frequency-Converting Methods
%K Linear Regression Analysis
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=80758