Over the past two years, China has wrested domestic control of the COVID-19 pandemic through non-pharmaceutical interventions. However, the extent to which the pandemic has changed people’s travel behavior in the new normal under the zero-COVID policy is not yet clear. This study investigates the profound effects of the zero-COVID strategy on human mobility in 365 Chinese cities over time. Our results suggest the following: (1) Even between city pairs with no local cases, intercity mobility decreased by an average of 16%, whereas intra-city mobility increased by 9% compared with the pre-pandemic baseline. Long-distance travel decreased substantially, while trips below 100 km increased slightly. These new trends indicate a localized pattern which is presumably caused by the wide adoption of teleworking and virtual classes, as well as concerns about the safety and availability of public transportation. (2) Containment measures caused a considerably acute decline in intercity short-distance trips but exerted a markedly lasting effect on long-distance trips. (3) Cities with lower levels of urbanization, smaller population sizes, less labor force, and lower GDP and GDP per capita experienced greater reductions in mobility, which may be due to their insufficient management capabilities. (4) The Chinese government has adopted adaptive measures to contain outbreaks. Stricter and more timely responses led to faster recoveries in the second half of 2021 compared with the first half. Inter- and intra-city mobility decreased by 41% and 26%, respectively, within the first 2 weeks of an outbreak, and it took 6-7 weeks to return to pre-outbreak levels.
AkninLBAndrettiBGoldszmidtR, et al. (2022) Policy stringency and mental health during the COVID-19 pandemic: a longitudinal analysis of data from 15 countries. The Lancet Public Health7(5): e417–e426.
2.
AlexanderDKargerE (2021) Do stay-at-home orders cause people to stay at home? Effects of stay-at-home orders on consumer behavior. The Review of Economics and Statistics1–25. Epub ahead of print 22 September 2021. doi: 10.1162/rest_a_01108.
3.
BarreroJMBloomNDavisSJ (2021) Why Working from home will stick (Working Paper No. 28731). National Bureau of Economic Research: Cambridge, MA, USA. Available at: https://www.nber.org/papers/w28731 (accessed 22 February 2023).
4.
ChangSPiersonEKohPW, et al. (2021) Mobility network models of COVID-19 explain inequities and inform reopening. Nature589(7840): 82–87.
ChinazziMDavisJTAjelliM, et al. (2020) The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science368(6489): 395–400.
7.
ChristidisPChristodoulouANavajas-CawoodE, et al. (2021) The post-pandemic recovery of transport activity: Emerging mobility patterns and repercussions on future evolution. Sustainability13(11): 6359.
8.
ElldérE (2020) Telework and daily travel: New evidence from Sweden. Journal of Transport Geography86: 102777.
9.
FezziCFanghellaV (2021) Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe. European Economic Review139: 103907.
10.
GaleazziACinelliMBonaccorsiG, et al. (2021) Human mobility in response to COVID-19 in France, Italy and UK. Scientific Reports11: 13141.
GoolsbeeASyversonC (2021) Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. Journal of Public Economics193: 104311.
14.
GuanDWangDHallegatteS, et al. (2020) Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour4(6): 577–587.
15.
Guangdong Provincial Bureau of Statistics (2021) Guangdong Statistical Yearbook 2021. Beijing, China: China Statistics Press.
16.
HanXXuYFanL, et al. (2021) Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries. Proceedings of the National Academy of Sciences of the United States of America118(31): e2100201118.
17.
HeSYHuL (2015) Telecommuting, income, and out-of-home activities. Travel Behaviour and Society2(3): 131–147.
JayJBorJNsoesieEO, et al. (2020) Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States. Nature Human Behaviour4(12): 1294–1302.
20.
KraemerMUGYangC-HGutierrezBWuCHKleinBPigottDM, Open COVID-19 Data Working Groupdu PlessisLFariaNRLiRHanageWPBrownsteinJSLayanMVespignaniATianHDyeCPybusOGScarpinoSV (2020) The effect of human mobility and control measures on the COVID-19 epidemic in China. Science368(6490): 493–497.
21.
LaiSRuktanonchaiNWZhouL, et al. (2020) Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature585(7825): 410–413.
22.
LiLZhangSWangJ, et al (2021b) Governing public health emergencies during the coronavirus disease outbreak: Lessons from four Chinese cities in the first wave. Urban Studies: 1-21. Epub ahead of print 26 October 2021. DOI: 10.1177/00420980211049350.
23.
LiAZhaoPHaitaoH, et al. (2021a) How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics. Computers, Environment and Urban Systems90: 101703.
MaLJ (2005) Urban administrative restructuring, changing scale relations and local economic development in China. Political Geography24(4): 477–497.
26.
McKenzieGAdamsB (2020) A country comparison of place-based activity response to COVID-19 policies. Applied Geography125: 102363.
27.
MuXYehAG-OZhangX (2020) The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year. Environment and Planning B: Urban Analytics and City Science48(7): 1955–1971.
28.
National Bureau of Statistics of China (2022) Tabulation on 2020 China Population Census by County. Beijing, China: China Statistics Press.
29.
NiuXYueYZhouX, et al. (2020) How urban factors affect the spatiotemporal distribution of infectious diseases in addition to intercity population movement in China. ISPRS International Journal of Geo-Information9(11): 615.
30.
OnoHMoriT (2021) COVID-19 and telework: An international comparison. Journal of Quantitative Description: Digital Media1: 1–35.
31.
PepeEBajardiPGauvinL, et al. (2020) COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown. MedRxiv. DOI: 10.1101/2020.03.22.20039933.
32.
PullanoGValdanoEScarpaN, et al. (2020) Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study. The Lancet Digital Health2: e638–e649.
33.
SchlosserFMaierBFJackO, et al. (2020) COVID-19 lockdown induces disease-mitigating structural changes in mobility networks. Proceedings of the National Academy of Sciences117(52): 32883–32890.
ShepherdHERAtherdenFSChanHMT, et al. (2021) Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: An analysis of facebook data. International Journal of Health Geographics20(1): 46.
36.
SheridanAAndersenALHansenET, et al. (2020) Social distancing laws cause only small losses of economic activity during the COVID-19 pandemic in Scandinavia. Proceedings of the National Academy of Sciences117(34): 20468–20473.
37.
TianHLiuYLiY, et al. (2020) An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science368(6491): 638–642.
38.
TønnessenM (2021) Movers from the city in the first year of Covid. Nordic Journal of Urban Studies1: 131–147.
39.
Van BavelJJCichockaACapraroV, et al. (2022) National identity predicts public health support during a global pandemic. Nature Communications13(1): 517.
40.
VilhelmsonB (1999) Daily mobility and the use of time for different activities. The case of Sweden. GeoJournal48(3): 177–185.
41.
XiJLiuXWangJ, et al. (2022) A Systematic Review of COVID-19 Geographical Research: Machine Learning and Bibliometric Approach. Annals of the American Association of Geographers: 1–18. Epub ahead of print 26 October 2022. DOI: 10.1080/24694452.2022.2130143.
42.
XiongCHuSYangM, et al. (2020) Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections. Proceedings of the National Academy of Sciences117(44): 27087–27089.
43.
XuY (2021) Causal inference with time-series cross-sectional data: a reflection. SSRN Electronic Journal. Available at SSRN: DOI: 10.2139/ssrn.3979613 (accessed 10 July 2022).
44.
ZhaHZhangYZhouH, et al (2022) Chinese provincial government responses to COVID-19. Working Paper Version 2. Blavatnik School of Government. Available at:www.bsg.ox.ac.uk/covidtracker(accessed 10 July 2022).
45.
ZhaopinZhilian (2022) Remote Work during the Pandemic: Reality and Outlook. National School of Development, Peking University. Available at: http://nsd.pku.edu.cn/docs/20220509205116111743.pdf (accessed 22 February 2023).
46.
ZhuP (2012) Are telecommuting and personal travel complements or substitutes?The Annals of Regional Science48(2): 619–639.
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