1 Introduction

The COVID-19 outbroke in 2020 has had a profound global impact, with its rapid transmission, extensive repercussions, and formidable containment challenges. By December 2022, the number of confirmed cases worldwide had exceeded 600 million. This crisis not only poses an immediate threat to human life and well-being but also has far-reaching effects on global trade, industrial supply chains, international travel, social cohesion, and various other domains. As a result, it presents a significant obstacle to the resilience and sustainability of cities around the world.

The concept of resilience originated in mechanical physics, describing a material's ability to recover from deformation. In the 1970s, Holling introduced resilience in ecology, initially focusing on recovery (Holling 1973). Its definition later expanded to include resistance and buffering against external pressures, encompassing ecosystem equilibrium and adaptation (Wan and Liang 2022). Over time, resilience extended to entire systems and their coordination and adaptation processes. In the 1990s, resilience expanded beyond natural ecosystems to human systems, particularly in urban contexts (Shao and Xu 2015). With increasing urbanization and the close relationship between humans and cities, attention naturally shifted towards disaster prevention, mitigation, and risk resilience in urban environment (Longyu et al. 2022). This led to the emergence of urban resilience, emphasizing the capacity of urban systems to maintain functionality despite external shocks. Resilience concepts have evolved from engineering and ecological resilience to include evolutionary resilience, highlighting dynamic adaptation, continuous learning, and the development of self-adjustment capabilities.

Numerous studies have predominantly concentrated on urban resilience, such as adapting to global climate change and mitigating natural disaster risks. Meerow & Glaeser examine the relationship between cities and resilience, highlighting the economic dimensions of resilience. They discuss how cities have historically demonstrated resilience by adapting and recovering from various shocks, and emphasize the role of entrepreneurship and innovation in promoting urban resilience (Meerow et al. 2019; Glaeser and Gottlieb 2018). Stone, Hess, and Frumkin investigate the relationship between urban form and vulnerability to extreme heat events, specifically comparing sprawling cities to compact cities. They find that sprawling cities tend to be more vulnerable to the impacts of climate change, due to factors such as increased heat island effects and limited access to green spaces (Stone et al. 2018). Leichenko and O'Brien explore the intersection of climate change and society, emphasizing the need for transformative approaches to address the challenges posed by climate change. They highlight the importance of understanding the social, economic, and political dimensions of resilience in order to effectively respond to climate-related risks and vulnerabilities (Leichenko and O'Brien 2019). Shi et al. present a roadmap towards justice in urban climate adaptation research. They argue that resilience strategies and policies should prioritize equity and social justice, particularly in vulnerable and marginalized communities. Their study emphasizes the need to address social inequalities and ensure that resilience efforts benefit all members of society (Shi et al. 2016).

Mysiak et al. studied the Sendai Framework for Disaster Risk Reduction and evaluated its success or warning sign within the Paris Climate Agreement. This study emphasized the importance of disaster risk reduction frameworks in promoting urban resilience and reducing disaster risks (Mysiak et al. 2016). Fekete et al. assessed the vulnerability and adaptive capacity of Budapest using an indicator-based approach to evaluate urban resilience. The research revealed the potential vulnerabilities and adaptive potential of the city in the face of natural disasters (Villagra et al. 2017). Zhang et al. evaluated the resilience of Xiamen, China, to floods and droughts through a case study. This research provided important insights into the measures and strategies adopted by the city in response to climate change risks (Tumini et al. 2017). Pelling and Dill studied disaster politics and explored critical moments for societal and political regimes to adapt to climate change. This study highlighted the significance of political factors in urban resilience and reducing disaster risks (Pelling and Dill 2010).

In 2021, the concept of resilience gained international attention in light of the COVID-19 pandemic, measuring the recovery ability of urban functions based on factors like infections, deaths, vaccination status, economic production differentials, logistics, traffic volume, and quality of education and culture (Bloomberg 2020). Scholars organized assessment indicators into four dimensions in the context of the pandemic: infrastructure, layout, technological, and economic resilience. The construction of urban infrastructure, with a focus on transportation facility connectivity in foreign countries and innovative applications in China, highlights differences in approaches. Similarly, the relationship between urban construction distribution and population development is noted globally, impacting vulnerability and resistance (Zhiya 2021; Nengzi 2021).Some researchers emphasize the necessity of a comprehensive urban emergency response system, encompassing pre-prediction and early warning, emergency response, and post-reconstruction initiatives. The UNISDR stresses the importance of integrating risk knowledge, monitoring, alerting services, distribution, communication, and emergency response capabilities in building an efficient urban emergency system. Emergency logistics, a crucial element for ensuring supply during emergencies, is identified as a key link to all emergency efforts (Mingke 2003; Liming 2021). Other research also confirms the graded adverse impact of the urban pandemic on family welfare and underscores the need for attention to urban food security and necessities support under the pandemic (Bukari et al. 2022; Wang and Fu 2023).

In summary, these studies contribute to our understanding of urban resilience by examining its multidimensional nature, economic dimensions, relationship with urban form, intersection with climate change and society, and the importance of social justice in resilience efforts. They provide valuable insights for researchers, policymakers, and practitioners working towards enhancing urban resilience. Building on these insights, this research focuses on the supply guarantee system during the city-wide lockdown of Shanghai from March to May 2022. The research aims to assess the ability to guarantee the basic livelihood of residents in the face of major emergencies from the perspective of urban resilience and provide suggestions for the future.

2 Methodology

2.1 Research objects

This study focuses on Shanghai, the largest city in China, which consists of 16 districts covering a total area of 6340.5 square kilometers. As of October 2023, the city had a permanent population of 24.8745 million. The COVID-19 outbreak in Shanghai occurred in March 2022, leading to different stages and degrees of lockdown measures from March to June. The city's response to the pandemic can be divided into four phases: the Grid Control Period (March 1st—March 27th), the Static Period (March 28th—April 10th), the Partition Control Period (April 11th–April 21st), and the Normalized Period (After May 16th).

During the period from March 28th to May 16th, public venues, including commercial, catering, and entertainment establishments, were closed throughout the city. Although citizens could make purchases through certain e-commerce apps, logistical challenges and inventory shortages made it difficult, and quota supply modes were implemented. Some residential communities organized group purchases directly from manufacturers. However, many individuals under lockdown faced inconvenience in accessing food, clothing, daily necessities, and medical supplies. To address this, the Shanghai municipal government established "Supply Points" in each district to provide basic supplies to residents.

This research aims to evaluate the effectiveness of these Supply Points by analyzing their distribution in relation to the population and conducting a public questionnaire survey. The findings of this study are expected to provide recommendations for improving the urban supply guarantee system and enhancing the city's resilience in the future.

2.2 Data acquisition

2.2.1 Density of supply points

Based on the press-released list of Supply Points, this research identified a total of 548 Supply Points in the city. These points include superstores, agricultural trade and e-commerce establishments, which offer a wide range of supplies including fresh fruits and vegetables, snacks, rice, flour and oil, dairy products and bakery items, daily necessities, mother and baby products, and pet supplies. Using the POI data from AutoNavi's Map, the corresponding Supply Points were analyzed using kernel density analysis, with a service radius of '3 km' for the facilities (refer to Fig. 1). The density of Supply Points (‘S’) in the surrounding residential districts was measured to evaluate the immediate and direct radiation effect of each facility and the level of coverage it provides.

Fig. 1
figure 1

Density of supply points (‘S’)

2.2.2 Population agglomeration magnitude (‘P’)

In this study, we generated a thermal map of the population distribution in Shanghai using location data collected from billions of cell phone users accessing Baidu Apps at 8:00 PM on April 15th. This map accurately reflects the population distribution during the lockdown period when people were required to stay at home. We conducted density analysis to determine the population density in each region and visualized the results on the map. Although there may be slight discrepancies in the exact population figures, the relative levels of population aggregation accurately depict trends and real-time distribution. Darker colors on the map indicate higher population concentrations, while lighter colors indicate lower concentrations. Based on the population thermal data, we analyzed the population distribution in Shanghai during the lockdown period. We calculated the local population density 'd' in each district and the degree of population agglomeration 'C' within a 3 km radius of each supply point. The magnitude of population agglomeration 'P' served by each supply point was obtained by dividing 'C' and 'd'. The calculation formula is as follows.

$$P_{i} = \frac{{C_{i} }}{{d_{i} }}$$

The calculation results are as follows in Fig. 2.

Fig. 2
figure 2

Population agglomeration magnitude (‘P’)

2.2.3 Public survey

To examine the role of Supply Points in ensuring residents' basic livelihoods in each district, an online public survey was conducted. The questionnaire was distributed between April 20th and April 30th, covering all 16 districts of Shanghai. A total of 1104 valid responses were collected. The survey results provide valuable insights into the impact of the epidemic lockdown on residents' lives. Among the respondents, 26.72% expressed concerns regarding the guarantee of basic necessities.

The survey identified the top three categories of items with the highest demand frequency, which are crucial for daily life: fresh vegetables, snacks and beverages, and fruits. Convenience foods, fresh meat, eggs, and fish closely followed. By calculating the composite score of demand frequency and ranking, items were categorized as high or low demand. High demand items include vegetables, fruits, fresh food, snacks, rice, flour, grain, and oil, while low demand items consist of daily consumables, dairy products, pet supplies, mother and child products, and medicine. Notably, basic foodstuffs such as rice, flour, and oil were not ranked at the top, indicating their sufficient supply.

2.3 Analysis methods

2.3.1 Weight of supply points (‘W’)

According to the public survey, this research tried to quantify the feelings on supply points by the view of local population. Through ‘scoring scale’ method, this research calculates the degree of information mastery (1–4) and the degree of livelihood guarantee (1–5) of the local population with respect to the Supply Points in the district. The weight ‘W’ of Supply Points is calculated for each district in Shanghai.

The calculation formula is as follows:

$$W_{i} = \overline{{\left( {{\text{Information mastery}}\cdot{\text{Life security level}}} \right)_{i} }}$$

2.3.2 Supply capacity (‘K’)

By superimposing the population distribution during the lockdown period in Shanghai with the actual supply capacity, and calculating the ‘supply and demand’ ratio, the supply capacity ‘K’ is obtained. Based on this, whether the supply capacity of the Supply Points in each district is suitable for the demand is analyzed. The larger the value of ‘K’, the higher the matching degree between the service capacity of the Supply Points in this district and people.

The calculation formula is as follows:

$$K_{i} = \frac{{S_{i} \cdot W_{i} }}{{P_{i} }} = \frac{{S_{i} \cdot W_{i} }}{{C_{i} /d_{i} }}$$

where ‘S’ stands for the density of supply points, ‘W’ represents the regional supply weight, and ‘P’ indicates the population agglomeration magnitude of each supply point in the district.

2.4 Superimposed intensity (‘N’)

Taking the service radius of 3km around each point as the supply coverage, it can be concluded that when the distance between two Supply Points is less than 6km, the supply coverage will overlap and produce superimposed effects. Accordingly, the supply capacity will be strengthened with different weights according to the superimposed range size and distance. In other words, the closer the two supply points are, the greater the superimposed intensity will be, and vice versa. Therefore, the kernel density of 6km aperture is calculated for the Supply Points in each district, and the superimposed intensity ‘N’ is obtained.

2.5 Supply resilience (‘R’)

By multiplying the values of the above two influencing variables, we obtain the regional range of supply resilience ‘R’ where the facility is located, which is calculated as follows.

$$R_{i} = K_{i} \cdot N_{i} = \frac{{S_{i} \cdot W_{i} }}{{P_{i} }} \cdot N_{i}$$

2.6 Research roadmap

3 Results

Table 1 displays the calculation results of the Weight of Supply Points ('W'). Based on the figure, Jiading District, Putuo District, Hongkou District, and Minhang District have higher supply guarantee weights. This indicates that Supply Points in these districts play a more significant role in meeting the basic needs of residents. On the other hand, Pudong New District, Baoshan District, Jinshan District, Yangpu District, and Chongming District have lower supply guarantee weights. This suggests that the actual utility of the Supply Points in these districts may be lower. Alternatively, it is possible that residents in these districts rely less on the Supply Points as a primary source of material security.

Table 1 Weight of supply points ('W') of each district

Figures 3, 4 illustrates the calculation results of Supply Capacity ('K'). The central area of Shanghai exhibits high per capita supply capacity, while Songjiang District, Fengxian District, and Baoshan District, situated outside the outer ring road, also show some scattered dot-shaped spaces, indicating favorable per capita supply capacity. On the other hand, Chongming District, Pudong New District, Jinshan District, and Qingpu District generally have weak per capita supply capacity across most spatial districts.

Fig. 3
figure 3

Research roadmap

Fig. 4
figure 4

Supply capacity (‘K’)

Figure 5 illustrates the distribution of the superimposed intensity ('N') of the Supply Points, which aligns with the urban development circle structure. The central city serves as the center of high intensity, with the intensity gradually decreasing outward in an orderly manner. Notably, Songjiang District and Fengxian District each have a superimposed sub-center within their territories.

Fig. 5
figure 5

Superimposed intensity (‘N’)

As show in Fig. 6, the resilience of supply points in Shanghai during the lockdown varies significantly, indicating uneven and insufficient development of urban emergency supply guarantee and regional supply guarantee effectiveness.

Fig. 6
figure 6

Supply resilience (‘R’)

The specific horizontal differences can be characterized as follows:

Low-level Supply Resilience districts: Chongming, Jinshan, Qingpu, and Pudong New District. These districts have relatively few supply points, especially Chongming, Jinshan, and Qingpu, with only 7, 11, and 7 supply points, respectively. Compared to the central city, these districts have limited supply points and lower resilience. However, it's important to note that these districts have a large rural area where the flexibility of supply of basic living materials is higher than in urban areas.

Moderate-level Supply Resilience districts: Songjiang, Fengxian, Baoshan, Jiading, and Minhang districts, located on the edge of the central city. These districts have a considerable number of supply points but are loosely distributed, making it difficult to form a strong clustering effect. However, most of these districts have high guarantee weight, indicating a better evaluated guarantee effect. This is particularly evident in Minhang and Jiading districts, ranking first and second in guarantee weight, respectively.

High-level Supply Resilience districts: Huangpu, Jing'an, Hongkou, Putuo, Yangpu, Changning, Xuhui, and other downtown areas. These districts have supply points with high service radiation and supply guarantee overlay intensity. The high resilience of supply guarantee in these districts is attributed to their compatibility with the high-density demand characteristics of supply guarantee in the central city's high-density population areas.

According to Fig. 6, the resilience of supply points in Shanghai during the lockdown varies significantly, highlighting the uneven and insufficient development of urban emergency supply guarantee and regional supply guarantee effectiveness. The districts can be categorized into low, moderate, and high-level Supply Resilience based on the number and distribution of supply points. The low-level districts, such as Chongming, Jinshan, Qingpu, and Pudong New District, have limited supply points and a large rural area that provides flexibility in the supply of basic living materials. The moderate-level districts, including Songjiang, Fengxian, Baoshan, Jiading, and Minhang, have a considerable number of supply points but lack a strong clustering effect. On the other hand, the high-level districts, such as Huangpu, Jing'an, Hongkou, Putuo, Yangpu, Changning, and Xuhui, have supply points with high service radiation and supply guarantee overlay intensity, catering to the high-density demand characteristics of the central city's population.

4 Discussion

The spatial distribution of Supply Resilience levels in Shanghai is correlated with the urban development structure, particularly the 'central circle' structure. The central city has a high overall resilience level of supply guarantee, while the suburban areas have varying levels of resilience, with some in the medium level and some in the low level.

The remote suburban areas generally have a low resilience level. The reasons for this distribution can be analyzed from the perspectives of supply and demand. On the supply side, the opening of supply points plays a role, while on the demand side, the structure of demand for supply points is important. Remote suburban districts such as Chongming, Jinshan, Qingpu, and the coastal part of Pudong New District are not considered key districts for emergency management due to their remote location, sparse population distribution, and large number of rural areas. The rural areas have a relatively low dependence on external supplies, making them more resilient than urban areas. Therefore, the low scores of these suburban areas in the analysis of supply points only indicate a small effect. The central city, with its large population concentration, is the key area for urban emergency supply guarantee. When districts are locked down, residents mainly rely on supply points to obtain essential supplies. According to the research, most supply points in the central city have played a significant role and can meet the demands of residents. In contrast, the situation in near suburban areas is more complicated. Areas close to the center of Shanghai, such as Jiading, Baoshan, Minhang, and parts of Pudong New District, have a larger demand for supply points due to population concentration. Most of these districts serve as basic supply points, but some areas have not been fully utilized, leading to a lack of guaranteed supplies. The more peripheral areas, represented by Songjiang, Jiading, Baoshan, and parts of Fengxian, have a moderate population size and scattered distribution. The pressure of supply guarantee demand is relatively less severe, and the resilience level is good.

Based on comprehensive analysis and questionnaire research, the study concludes that community group purchases, neighborhood exchanges, and government/unit distribution of supplies also contribute to access to supplies. The questionnaire data reveals that nearly 30% of the city's local population lacks basic guarantees. Therefore, alternative methods should be encouraged in addition to relying solely on urban supply points.

The resilience level in Fengxian and Songjiang districts is higher than in other suburban areas. These districts are important agricultural output areas in Shanghai and played a crucial role in the supply guarantee chain during the epidemic lockdown. Agriculture provides local residents with the means to rely on their own agricultural planting for livelihood, ensuring basic self-sufficiency. The resilience level of supply guarantee in Shanghai does not show a continuous graded change. Adjacent districts can have contrasting resilience levels, indicating a fragmented and separated state with limited resource interoperability. The distribution of supply guarantee facilities is fragmented, and there is a lack of synergy and overall coordination between districts at the city level. Even during lockdowns, the logistics of supply guarantee materials should not come to a complete standstill. Inter-regional material circulation, exchange, and collaboration are necessary to relieve the pressure of the epidemic. Building a positive inter-regional relationship based on synergy, mutual assistance, and needs-based deployment is crucial.

5 Suggestions

5.1 A more scientific supply system

Firstly, the layout of supply points should be based on population distribution trends. In densely populated areas, supply points should be appropriately scaled and densely distributed. In sparsely populated areas, the number and layout of supply points can be reduced and loosely distributed. Then, should pay attention to the needs of the elderly, children, and patients during lockdown periods. These groups are more likely to face difficulties in accessing basic necessities, and the supply system should prioritize their needs. Furthermore, instead of relying solely on government channels, encourage diversified supply channels such as commerce, charity assistance, and barter. This will create a more inclusive and stable supply system.

5.2 A self adaptation mechanism

To enhance the self-organizational power of regions, it is important to involve multiple organizational subjects, including the government, enterprises, institutions, NGOs, media, and communities. This will bring diverse perspectives and solutions to the supply guarantee system. Cities should strive for self-sufficiency in terms of material demand to reduce dependence on external supply chains. This will increase responsiveness and initiative during public crises. Urban agriculture, with its integration of spatial and demand characteristics, can contribute to self-organized supply chains. Having a self-production and self-consumption system in place ensures regional survival during emergencies. The simultaneous balance of independence and synergy is crucial for urban resilience. Independence allows each unit to maintain minimal operation, while synergy enables coordination and mutual assistance among regional units.

5.3 A comprehensive risk assessment system

Use a redundancy mindset and a normal-extraordinary approach to create a backup system for supply guarantee. Normalized facilities form the basis of resistance, while extraordinary facilities assist during severe shocks to ensure the functioning of emergency guarantee functions. Develop a comprehensive risk governance process that includes early warning, implementation, and feedback. This process should enable independent planning, open supply channels, material allocation and delivery, and continuous optimization based on practice and self-learning. In risk governance, prioritize small-scale spatial units, such as communities, to increase the capacity for collective action and regional autonomy.

6 Conclusion

Through the analysis conducted in this research, significant regional variations in the level of urban supply guarantee during the epidemic lockdown in Shanghai have been observed. The central urban districts, including Huangpu, Jing’an, Hongkou, Putuo, Yangpu, Changning, and Xuhui, are characterized as high Supply Resilience districts. Songjiang, Jiading, Baoshan, Fengxian, and Minhang districts mostly exhibit medium Supply Resilience, while Chongming, Jinshan, Qingpu, and Pudong New Districts are mainly classified as low Supply Resilience areas.

Considering the characteristics of population distribution, economic development, industrial positioning, and other development levels, we further discuss the supply and demand aspects. Key features and opportunities related to supply guarantee in Shanghai include the notion that urban Supply Points are not the sole means for residents to obtain essential goods. Rural areas can contribute positively to regional Supply Resilience, and there is no spatial continuity in the Supply Resilience level within urban areas.

Based on the identified challenges and opportunities from the aforementioned analysis, this study proposes strategies to enhance the resilience level of supply guarantee in the three major cities. It is crucial to establish a comprehensive and scientifically designed supply guarantee system that addresses distribution, target focus, and supply channels. Additionally, enhancing urban self-organizational adaptability through multiple stakeholders, urban agriculture, and an independent-synergistic balance is essential. Furthermore, a robust, reflexive, and credible comprehensive supply guarantee risk management and evaluation mechanism should be developed.

Considering the global impact and influence of the COVID-19 pandemic on human social life since its outbreak in 2020, it is anticipated that such epidemics will persist and occur episodically. Therefore, the post-epidemic normalization outlook emphasizes the urgent need to bring urban support guarantee infrastructure and operational systems to a state of 'always ready' normalization. In the face of major public emergencies like this, the concept of 'Supply Resilience' should be incorporated into the normalization of the post-epidemic era and become one of the key indicators for urban resilience planning and evaluation.