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Electricity-consumption data reveals the economic impact and industry recovery during the pandemic.
Wang, Xinlei; Si, Caomingzhe; Gu, Jinjin; Liu, Guolong; Liu, Wenxuan; Qiu, Jing; Zhao, Junhua.
  • Wang X; School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, 2006, Australia.
  • Si C; Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen, China.
  • Gu J; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518116, China.
  • Liu G; School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, 2006, Australia.
  • Liu W; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518116, China.
  • Qiu J; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518116, China.
  • Zhao J; School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, 2006, Australia. jeremy.qiu@sydney.edu.au.
Sci Rep ; 11(1): 19960, 2021 10 07.
Article in English | MEDLINE | ID: covidwho-1462021
ABSTRACT
Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises' production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Industry Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-98259-3

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Industry Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-98259-3