The effects of COVID-19 on the resilience of urban life in China

The study examines how the COVID-19 pandemic affected urban life and resilience in mainland China, with a focus on differences across city tiers (first-, second-, and third-tier cities) and on individual citizens' experiences. Framed by urban resilience theory and the BRIC/DROP models, the research addresses the growing need to understand and strengthen urban systems amid increasing disasters and rapid urbanization. The authors aim to quantify and interpret pandemic impacts on social, economic, institutional, and infrastructural dimensions of urban life, identify management tools perceived as effective, and draw lessons for enhancing resilience. The research questions are: (1) What are the impacts of pandemics on urban functions and life in different cities from the perspective of citizens? (2) Which management tools are perceived to be most effective and well-accepted by citizens? (3) What lessons can be drawn for building a more resilient response system? The study underscores the importance of moving beyond analyses centered on large, popular cities to include second- and third-tier cities and focuses on individual-level resilience within urban systems.

The paper reviews the evolution of resilience concepts from ecological and engineering resilience to social-ecological and economic resilience, culminating in integrated definitions of urban resilience as the capacity of socio-ecological-technological systems to maintain, adapt, and transform under stress. It highlights challenges in operationalizing resilience due to its multifaceted nature and critiques of overgeneralization. Several frameworks and indices are discussed, notably BRIC (Baseline Resilience Indicators for Communities), the City Resilience Index (CRI), and the Climate Disaster Resilience Index (CDRI). The authors adopt the BRIC framework, derived from the DROP model, to conceptualize and measure resilience across social, economic, institutional, and infrastructural dimensions. The literature points out biases in case studies toward first-tier and coastal megacities and the emphasis on healthcare system or psychological resilience, with limited attention to individual-level urban life and to second- and third-tier cities. The study aims to fill these gaps by using first-hand data and by extending the concept of networked resilience across cities.

Design and framework: The study applies the BRIC/DROP resilience framework to individual citizens, focusing on social, economic, institutional, and infrastructural dimensions, excluding environmental aspects less salient during COVID-19 for urban residents. Research questions target impacts on urban life, perceived effectiveness of management tools, and implications for resilience-building, including networked resilience. Data collection: An online survey was conducted in 2020 via Sojump (https://www.wjx.cn/index.aspx), the largest Chinese survey platform. The target population was adult Chinese urban residents living in cities (county-level and above) during the COVID-19 outbreak. Sampling was random within the platform’s urban audience, without quotas on age, ethnicity, or region. A total of 501 responses were collected; after screening for invalid or missing data, 420 valid responses remained. The sample distribution was proportional to the urban population of respondents’ cities and largely represents working-age urban citizens (18–54) without regional or occupational biases or special engagement with government or healthcare services. Ethics approval was obtained from the Human Ethics Committee, Fenner School of Environment and Society, Australian National University. City tier classification: Cities were grouped into three tiers based on 2018 national urban population statistics and political/socioeconomic status. First-tier cities: Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin, Chongqing (populations >10 million; high political/economic status). Second-tier: provincial capitals (excluding the four municipalities and Guangzhou) and key coastal cities with similar profiles (e.g., Suzhou, Dongguan), generally with >1 million urban population. Some high-population cities (e.g., Wuhan, Xi’an, Nanjing, Chengdu) were treated as second-tier due to comparable status. Third-tier: remaining prefectural cities and counties (urban population typically <1 million; lower political importance); fourth-tier towns were excluded. Questionnaire: 21 items—8 demographics; 10 scale questions on COVID-19 impacts across social (e.g., entertainment, communication), economic (occupation, finance, family income change), and infrastructural (transport, healthcare, hospitals, food) dimensions; 1 multi-choice question on preferred COVID-19 regulations; and 2 open-ended questions on institutional impacts and strategies to enhance urban resilience. Variables followed the BRIC categories, adapted for individual-level measurement. Family income decline was computed as post- minus pre-COVID income categories. Measurement validity and reliability: Principal component analysis (with KMO=0.739; Bartlett’s test χ2≈735.53, df=45, p<0.001) supported construct validity for scale items; transportation loaded across categories but was assigned to infrastructure. Cronbach’s alpha for scale items was 0.72. Quantitative analysis: A binary logit regression modeled whether life satisfaction declined during COVID-19 (dependent variable computed from 5-point scales of life satisfaction before vs after). Independent variables included demographics (age, gender, household size), city tier, social impacts (entertainment, communication), infrastructural impacts (transport, healthcare, hospital access, food access), economic impacts (occupation, finance), and a binary indicator of family income decline. Parameters were estimated via MLE in R. Qualitative analysis: Thematic and text analyses (NVivo 12) of open-ended responses identified citizen perspectives on resilience-building. Word frequency guided initial coding; themes were organized under BRIC dimensions (social, economic, institutional, infrastructural) and sub-themes. For the multi-choice regulation question, a weighted comprehensive score ranked public support: score = (Σ frequency × option weight) / number of respondents; higher scores indicate stronger support. Supplementary: Data are available from the corresponding author upon request; R code available on request.

- Pandemic impacts: COVID-19 constrained social and economic activities and reduced access to critical infrastructure (food, transport, healthcare). Most respondents experienced reduced income, employment disruptions, and social anxiety. - Life satisfaction determinants (logit regression): Significant positive associations with decline in life satisfaction were found for reduced entertainment access (Estimate 0.503, p=0.003), impaired communication with others (0.416, p=0.030), financial impact (0.609, p=0.003), and family income decline (0.933, p<0.001). Relative to first-tier cities, residents in second-tier (City2: 0.743, p=0.004) and third-tier (City3: 0.593, p=0.039) cities had higher odds of reporting declines in life satisfaction. Age, gender, household size, and specific access variables (transport, healthcare, hospitals, food) and occupation impact were not significant in the regression. - Resilience by city tier: Second-tier cities exhibited the least resilient urban life during COVID-19. Only 16.6% of second-tier respondents reported no financial impacts versus 32.0% in first-tier and 28.8% in third-tier cities. Only 21.5% of second-tier respondents reported no occupational impact versus 32.7% (first-tier) and 40.4% (third-tier). In infrastructure access, second-tier cities had higher shares reporting medium/heavy impacts than first- and third-tier cities. Third-tier cities were more resilient than second-tier cities economically; 40% of third-tier respondents reported no occupational impact—about double second-tier and 7% higher than first-tier. - Mechanisms: First-tier cities’ more complex, diversified economies, flexible restrictions enabling services (e.g., food delivery supplied ~20% of food resources), and stronger national support underpinned higher resilience. Third-tier cities had fewer cases (with transfers to provincial capitals), lower living costs, and higher job security due to a larger share of state-owned employees (example shares: Shenzhen 3.00%, Dongguan 6.54%, Zhanjiang 27.99%). Second-tier cities absorbed regional patient loads and population flows, faced resource strains, and received external support that did not match expenditures, lowering resilience. - Community and governance: 24% of respondents engaged in community-level voluntary activities supporting vulnerable groups and local management. Short-term success in containment increased trust in national government and acceptance of strong measures; city-level isolation emerged as the most preferred strategy. - Public preferences for mitigation (comprehensive scores): City-level isolation (6) ranked highest, followed by social distancing (5), rapid testing (4), accurate and transparent information (4), and enhancements of medical facilities, travel restrictions, self-isolation, and governmental collaboration (each 3). - Thematic priorities for resilience-building (share of comments): Medical facilities enhancement (8.89%), effective emergency response and planning (8.19%), economic recovery and stability (6.32%), governmental actions and leadership (6.16%), employment and income (5.88%), basic infrastructure (5.11%), financial support by government (4.90%), among others.

Findings indicate marked inter-city heterogeneity in citizen-level resilience to the pandemic. Despite stronger city-level infrastructure and socioeconomic status, second-tier cities—confronting denser populations, higher caseloads, and resource burdens—had citizens with lower economic and infrastructural resilience and greater declines in life satisfaction than first- and third-tier cities. First-tier cities leveraged diversified economies, logistics (e.g., food delivery), and national support to buffer shocks, while third-tier cities benefited from fewer cases, lower costs, and higher shares of secure state-owned employment. These results address the research questions by: (1) demonstrating how pandemics disrupt social, economic, and infrastructural aspects of urban life, with severity varying by city tier; (2) identifying city-level isolation and social distancing as the most supported management tools, alongside testing and transparent information; and (3) motivating a shift from isolated, city-by-city resilience-building toward networked resilience that coordinates inter-city resource flows and support. Community engagement and short-term increases in trust in government facilitated acceptance and implementation of stringent measures, though trust can have mixed effects and differs between national and local levels, underscoring the need for careful governance design. Overall, the study emphasizes focusing on “resilience of whom and to what,” centering citizen resilience and recognizing uneven capacities across city tiers.

The study contributes first-hand, individual-level evidence on how COVID-19 affected urban life across Chinese city tiers, revealing that second-tier cities’ citizens were least resilient, first-tier citizens most resilient, and third-tier citizens more resilient than expected. It highlights that city-level advantages do not necessarily translate to higher citizen-level resilience under pandemic stress and that inter-city inequalities in resilience are substantial. Policy-wise, the authors propose combining baseline resilience—investments in critical infrastructure, essential financial protections, effective emergency plans, and social support—with a networked resilience framework that enables inter-city flows of materials, workforce, knowledge, and institutional support. Such combinations can improve resource efficiency, bolster less-developed cities, and enhance both short- and long-term preparedness for pandemics and other prolonged hazards. Future research should extend beyond metropolises to second- and third-tier cities and pursue interdisciplinary approaches linking public management and urban studies to design robust measures and implementations for urban resilience.

The study is limited to mainland China; impacts and management strategies may differ in other administrative and cultural contexts. Some disadvantaged and vulnerable groups (e.g., older adults) are underrepresented. The sample size, while adequate overall (n=420), can be sparse at the individual-city level; however, the analysis targets tiers rather than specific cities. Funding and sensitivity constraints in 2020 limited broader sampling.