Kartal Mustafa Tevfik, Ulussever Talat, Pata Ugur Korkut, Depren Serpil Kılıç
Department of Banking and Finance, European University of Lefke, Lefke, Northern Cyprus, Türkiye.
Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon.
Sci Rep. 2024 Feb 14;14(1):3698. doi: 10.1038/s41598-024-54245-z.
The studies have focused on changes in CO emissions over different periods, including the COVID-19 pandemic. Even if CO emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO emissions range between 69 MtCO and 116 MtCO, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO emissions, demonstrating the significance of the energy transition towards clean sources around the world.
这些研究聚焦于不同时期（包括新冠疫情期间）一氧化碳排放量的变化。即便根据年度数据，疫情期间一氧化碳排放量暂时有所减少，但这可能会产生误导。考虑年度数据对于理解总体趋势很重要，但使用频率更高的数据（例如每日数据）更适合用于研究动态关系和外部影响。因此，本研究采用新颖的小波局部多重相关性（WLMC）方法，对2020年1月1日至2023年3月31日的每日数据进行分析，全面探讨了全球一氧化碳排放与细分的发电（EG）来源之间的关联。结果表明：（1）基于主要统计数据，每日一氧化碳排放量在69百万吨一氧化碳至116百万吨一氧化碳之间，这表明在所分析的时间段内存在波动，但没有急剧变化。（2）基于使用动态普通最小二乘法（DOLS）的基线回归，构建的估计模型对一氧化碳排放具有较高的预测能力，达到约94%；（3）在采用WLMC方法的进一步分析中，发电资源之间存在显著的外部性，这会影响一氧化碳排放。研究结果展现了关于时变和频变效应以及对发电对一氧化碳排放影响的细分分析的新颖见解，证明了全球向清洁能源转型的重要性。
