2019年冠状病毒病(COVID-19)
汽油
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
2019-20冠状病毒爆发
经济
计算机科学
汽车工程
环境科学
病毒学
工程类
医学
废物管理
传染病(医学专业)
爆发
病理
疾病
作者
Shiqi Ou,Xin He,Weiqi Ji,Wei Chen,Lang Sui,Yu Gan,Zifeng Lü,Zhenhong Lin,Sili Deng,Steven Przesmitzki,Jessey Bouchard
出处
期刊:Nature Energy
[Springer Nature]
日期:2020-07-17
卷期号:5 (9): 666-673
被引量:63
标识
DOI:10.1038/s41560-020-0662-1
摘要
Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people's resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.
科研通智能强力驱动
Strongly Powered by AbleSci AI