1. Introduction
1.1. Background
As globalization accelerates and the impacts of climate change become increasingly evident, urban food systems are facing severe challenges [1]. In populous countries like China, urban food systems are particularly vulnerable to natural disasters, economic fluctuations, and international political instability [2,3]. Traditional urban food security strategies struggle to address these complex challenges, necessitating new theoretical frameworks to enhance the resilience of urban food systems [4]. Therefore, based on the expansion of research on food system resilience, this study proposes the concept of “urban food resilience”, which aims to enhance the adaptability and recovery capacity of urban food systems under multiple pressures through systematic urban planning and management [5,6]. This paper attempts to integrate urban planning, food systems, and resilience theory to explore the potential pathways and strategies for achieving food system resilience on a metropolitan scale [7,8].
The Zhengzhou Metropolitan Area, as the core of China’s Central Plains Urban Cluster, encompasses Zhengzhou City and its eight surrounding cities, covering approximately 58,800 square kilometers. As of the end of 2021, it had a permanent population of about 46.74 million [9]. The food system in this region displays its complex multi-dimensional characteristics, which include the following aspects:
Agricultural Resource Dependence: Situated at the heart of the Central Plains, the Zhengzhou Metropolitan Area possesses abundant water resources and fertile soil, making it one of China’s key grain production bases. In 2020, the area had about 3.4 million hectares of arable land, accounting for 57.8% of its total area. The annual grain output of the region exceeded 15 million tons, with wheat production accounting for approximately 10% of the national output [10]. This richness in natural resources provides a stable foundation for food production in the area.
Complexity of the Food Supply Chain: Due to the inclusion of multiple cities within the Zhengzhou Metropolitan Area, the food supply chain involves complex stages of production, storage, and consumption. In 2020, the area had over 200 large agricultural product wholesale markets, more than 15,000 supermarkets and convenience stores, and over 30,000 restaurants [11]. From farm to table, each link requires meticulous coordination and management.
Dependence on External Food Sources: Despite favorable local agricultural conditions, the vast population base and diverse consumption demands of the Zhengzhou Metropolitan Area mean that regional food production still cannot fully meet all its food needs. In 2020, the self-sufficiency rate of food in the area was about 85%, with meat self-sufficiency at 75% and fruit at only 60% [12].
Urbanization and Competition for Agricultural Land: The rapid urbanization process in the Zhengzhou Metropolitan Area has intensified the competition between urban land use and agricultural land. From 2010 to 2020, the urbanization rate increased from 47.2% to 62.8%, while the area of agricultural land decreased by about 5.6% [13]. This not only reduces the area available for agricultural production but also poses challenges to the sustainability of food production.
Spatial Analysis: Figure 1 provides a detailed depiction of the spatial location of the Zhengzhou Metropolitan Area within the Central Plains Urban Cluster. This helps us to understand its geographical advantages within the region and its connections with neighboring urban clusters, factors that directly impact the structure and efficacy of the food system. In 2020, the food trade volume between the Zhengzhou Metropolitan Area and surrounding urban clusters reached RMB 50 billion, accounting for about 20% of its total food consumption [14].
1.2. Scientific Significance and Practical Value
The Zhengzhou Metropolitan Area’s geographical and economic characteristics typify China’s central region [15], providing a valuable case study with broad applicative insights for enhancing food resilience in similar urban clusters across China [16]. The area’s rapid urbanization and population growth furnish extensive empirical data for examining urbanization impacts on food systems. Moreover [17,18], its rich agricultural resources and favorable conditions form an optimal setting for researching urban food system self-sufficiency and dependency [2].
This study gains particular importance against the backdrop of escalating natural disasters and crises impacting the food systems. Notably, the Zhengzhou Metropolitan Area, like many global regions, confronts increasing challenges from extreme weather events, such as floods exacerbated by climate change [19]. The devastating July 2021 floods disrupted local food production and distribution, underscoring the critical need for strengthened food resilience [20].
Recent research has focused on the correlation between food resilience, resilient food systems, and their responses to natural disasters and other disruptions. Findings indicate that resilient food systems possess enhanced capabilities to withstand and recover from adversities such as floods, droughts, and economic downturns [4]. For instance, diversified local food production systems have demonstrated a greater resilience to climate-induced disturbances [21]. Additionally, robust food supply chains and effective emergency response mechanisms are vital for maintaining food resilience amid sudden disruptions [22].
By analyzing the food system resilience of the Zhengzhou Metropolitan Area in light of these challenges, this study enriches the broader discourse on how urban food systems can be fortified against various disturbances. It offers practical insights into implementing resilience strategies within the context of rapid urbanization in China, providing valuable guidance for policy and planning in urban areas globally that face similar challenges.
1.3. Urban Food Resilience Theoretical Framework
Urban food resilience refers to the capacity of urban food systems to resist, adapt, recover, and transform in response to various long-term and short-term pressures [4,22]. This concept originates from and integrates multiple foundational theories, including Holling’s ecosystem resilience theory, Folke et al.’s socio-ecological resilience theory, and Meerow et al.’s urban resilience theory [23,24,25]. Compared to these theories, urban food resilience places a greater emphasis on human intervention and specifically focuses on the uniqueness of urban food systems.
Urban food resilience encompasses both broad and narrow scopes. Broadly, it focuses on the long-term sustainability of food systems across ecological, economic, social, and cultural dimensions; narrowly, it pertains to the food system’s emergency response and short-term stability in the face of sudden events [21]. This multi-faceted, multi-dimensional attribute emphasizes the continuity and quality of urban food supply, as well as the system’s adaptability to both the long-term and short-term challenges.
Theoretically, urban food resilience lies at the intersection of urban planning, food systems, and resilience [26]. It innovatively integrates food planning, urban resilience, and food system resilience, forming a new “urban food” system hierarchy(as shown in Figure 2). This conceptual innovation combines macro and micro scales, focusing on long-term sustainability while emphasizing short-term emergency responses. Unlike general food system resilience, it highlights the particularities of urban environments, such as dependency on external supplies and complex distribution networks [5].
The primary innovation of the urban food resilience concept lies in its integration of considerations across multiple scales, highlighting the distinctiveness of urban settings and focusing on practical applications. It provides urban planners and policymakers with an operational framework, facilitating the translation of theory into practice thus enhancing the stability and adaptability of urban food systems in the face of socio-economic and environmental shocks [7,27]. Through this integrated approach, the urban food resilience theory offers a comprehensive perspective for understanding and enhancing the long-term sustainability and short-term response capabilities of urban food systems, guiding future research and practice in urban food security.
2. Methods
2.1. Research Design
This study adopts an innovative dual-level analysis framework aimed at comprehensively assessing the urban food resilience of the Zhengzhou Metropolitan Area. This framework includes the following two complementary research levels: long-term planning impact analysis (2000–2020) and short-term resilience assessment (2018–2022). The long-term analysis focuses on the impact of urban planning on the food system, emphasizing land use changes, infrastructure development, and consumption pattern evolution. The short-term assessment constructs a comprehensive evaluation system to quantify recent levels of food resilience.
Long-Term Planning Impact Analysis (2000–2020): This phase primarily utilizes spatial analysis methods on the key statistical data of the food system, combined with Geographic Information System (GIS) technology, to reveal the shaping effects of long-term planning decisions on the structure and function of the food system [18]. This method captures the impact trajectories of urbanization processes, land use changes, and infrastructure development on the food system [28]. Specifically, we analyzed the following aspects:
1.. Spatial coverage and land use changes:
Changes in the scale and distribution of agricultural land;
Expansion of urban construction land;
Stability of woodland and grassland areas;
Changes in water area, including wetlands.
2.. Spatial distribution of food system infrastructure:
Development and density of road networks, including highways, and railways;
Coverage density and distribution of transportation hubs and stations;
Density and spatial distribution of malls and supermarkets.
3.. Demographic and consumption patterns:
Changes in consumption patterns, including the proportion of food consumption in residents’ expenditures and the increase in imported food consumption
Through these analyses, the study aimed to comprehensively assess how urban planning decisions over two decades have shaped the spatial structure, distribution, and accessibility of the food system in the Zhengzhou Metropolitan Area, thereby impacting its overall resilience.
Short-Term Resilience Assessment (2018–2022): This phase establishes a comprehensive assessment system integrating the quantitative and qualitative methods to evaluate the resilience of the Zhengzhou Metropolitan Area’s food system [4]. The assessment focuses on the following five key components: Food Supply Chain Management, Economic and Affordability, Food Consumption and Demand, Emergency Preparedness and Management, and Social Participation and Governance. Within these components, the study evaluates system links and nodes, including storage capacity, food production to sale time, distribution channels, and emergency response mechanisms. A total of 25 specific indicators across these components were assessed to provide a holistic view of the food system’s resilience.
The methodology employs the Analytic Hierarchy Process (AHP) to determine the weights of various indicators, allowing for a structured approach to ranking the relative importance of different factors affecting food resilience [29]. The fuzzy comprehensive evaluation method is then used to determine the resilience levels and scores for the five-year period (2018–2022), helping to address the inherent uncertainty and complexity in evaluating food system resilience [30]. A worked example of the resilience calculation was conducted for each year, using the formula P = ΣRij × Wij, where P represents the comprehensive evaluation value of metropolitan area food resilience. The results were categorized into the following four resilience levels: very poor, poor, moderate, and good. This comprehensive assessment not only reflects the overall level of food resilience but also identifies the key factors impacting resilience across different components of the food system [22].
2.2. Data Collection Methods
Data collection primarily utilizes the following two types of key data: public government data (2018–2020) and Geographic Information System (GIS) data (2000–2020). Government public data include the statistical yearbooks, agricultural reports, urban development reports, and environmental protection announcements of the Zhengzhou Metropolitan Area, providing key information such as demographic statistics, economic indicators, land use, and agricultural production. GIS data are sourced from the Data Center of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, providing detailed spatial information including land use types, distribution of agricultural production areas, and their dynamic changes [31].
The period of 2018–2022 as the timeframe for assessing urban food resilience in the Zhengzhou Metropolitan Area provides a unique, highly focused observation window. This “intensive” research method helps reveal the following details and dynamic processes of resilience mechanisms, providing an important complement to long-term trend analysis [32]:
Completeness of the Pandemic Cycle: During 2018–2022, China experienced a complete cycle of the pandemic, including the pre-pandemic period (2018–2019), the outbreak and strict control period (2020–2021), and the post-pandemic adaptation period (2021–2022) [16]. This timeframe provides an ideal observation window for studying the resilience performance of urban food systems in the face of sudden public health events. It allows researchers to deeply analyze the food system’s reactions throughout the stages of the pandemic, from initial impact to adaptation and recovery [33].
Completeness of the Resilience Cycle: These four years encompass the major stages described in resilience theory—adaptation and transformation [24]. This timeframe enables the study to observe how urban food systems initially resist challenges, their subsequent recovery processes, long-term adaptation strategies, and systemic transformations. Such a complete observation of the resilience cycle is crucial for comprehensively assessing urban food resilience [4].
Significance of the Policy Cycle: The years 2018–2022 coincide with the conclusion of China’s “13th Five-Year Plan” and the commencement of the “14th Five-Year Plan”, a period during which policy changes profoundly impacted the urban food system [17]. Studying this period captures the shaping effects of policy transitions on food resilience, providing valuable empirical evidence for understanding the relationship between policy interventions and food system resilience.
Concentration Period of Extreme Weather Events: During 2018–2022, several regions in China experienced extreme weather events, including the major rainstorm on 20 July 2021 in the Zhengzhou Metropolitan Area and the severe urban flood disaster it caused. These events provide important cases for assessing the resilience of urban food systems in response to climate change.
2.3. Analytical Techniques
Spatial Analysis: Spatial analysis techniques are used to explore the long-term impacts of urbanization on the food system. This method first involves preprocessing original GIS data using ArcGIS, including projection transformation, cropping, and reclassification [34]. Subsequently, using land use transition matrices and landscape index analysis, the changes in land use during the urbanization process are quantitatively assessed [35]. To identify the aggregation and hotspots of food production spatial patterns, global Moran’s I and local Getis-Ord Gi* statistics are employed for spatial autocorrelation analysis. Additionally, buffer analysis is used to assess the impact of infrastructure (such as roads and markets) on food production and distribution.
Analytic Hierarchy Process (AHP): The Analytic Hierarchy Process (AHP) is used to construct the indicator system for urban food resilience evaluation and to determine indicator weights. In this study, the target layer is the comprehensive evaluation of urban food resilience, the criterion layer includes aspects such as food supply chain management, economic and affordability, food consumption and demand, and the indicator layer contains specific evaluation indicators. Constructing Judgment Matrices: Through expert surveys or the Delphi method, indicators at the same level are compared pairwise to form judgment matrices. Judgment Matrix A = (aij)n×n, where aij represents the importance of indicator i relative to indicator j. Calculating Weight Vectors: The eigenvector method is used to calculate the largest eigenvalue λmax and the corresponding eigenvector W. The normalized W then serves as the weight vector. Consistency Test: The consistency index CI = (λmax − n)/(n − 1) and the consistency ratio CR = CI/RI are calculated, where RI is the random consistency index. When CR < 0.1, the judgment matrix is considered to have satisfactory consistency. Overall Hierarchical Sorting: The combined weights of each layer’s indicators relative to the overall objective from bottom to top are calculated.
Fuzzy Comprehensive Evaluation: The fuzzy comprehensive evaluation method is used to handle the uncertainty and fuzziness in the evaluation process. This method first determines the evaluation factor set U and the evaluation grade set V, and then constructs the membership matrix R, determining the membership degree of each evaluation indicator to each evaluation grade. The weight vector obtained using the AHP method serves as the weight vector for the fuzzy comprehensive evaluation. Through fuzzy synthesis operation B = A · R, the fuzzy evaluation result is obtained. Finally, methods such as the maximum membership degree method or the weighted average method are used to convert the fuzzy evaluation results into clear evaluation grades [36].
3. Results
3.1. Impact of Urban Planning on the Urban Food System of the Zhengzhou Metropolitan Area (2000–2020)
3.1.1. Impact of Urbanization on Food Production
Over the past 20 years, the Zhengzhou Metropolitan Area has undergone rapid urbanization, leading to significant changes in land use patterns, the scale of urban infrastructure, and consumption habits [37]. These changes have directly affected the production, transportation, distribution, and consumption components of the food system, thereby deeply impacting the resilience of the metropolitan area’s food system [18]. Notably, the change in the scale of agricultural land has been significant. As urbanization progressed, a substantial amount of agricultural land was converted to urban construction land (as shown in Figure 3 and Figure 4), potentially reducing the metropolitan area’s food resilience [28]. This land use change not only affected local food production capabilities but also increased the metropolitan area’s dependence on external food supplies thus impacting the overall stability and sustainability of the food system [2].
The scale of urban construction land in the Zhengzhou Metropolitan Area shows a significant upward trend (as illustrated in Figure 5 and Figure 6) [31]. This change reflects not only the expansion of individual urban areas but also the strengthening of connections within the urban cluster [15]. The annually increasing proportion of urban construction land also implies a growing population size and increasing food demand. These changes have profound impacts on the metropolitan area’s food system, especially in the face of disaster challenges, directly affecting the resilience of the food system [4,18].
The woodland and grassland areas of the Zhengzhou Metropolitan Area, as key carriers of biodiversity and sustainable development, have maintained relatively stable scales over the past twenty years (as shown in Figure 7 and Figure 8) [31]. This stability is primarily attributed to the strict ecological protection policies implemented by the Chinese government [38].The maintenance of woodland and grassland has a dual positive impact on the metropolitan area’s food system as follows: firstly, it ensures the diversity and sustainability of ecosystems [39]; secondly, in situations where reduced agricultural land might weaken food resilience, a stable ecological environment provides an important buffer for the food system, thereby offsetting some of the negative impacts of reduced agricultural land [4,40].
A concrete example of this ecological buffering can be seen in the Zhengzhou Yellow River Wetland Park, established in 2015. This park, covering an area of 121 square kilometers, not only serves as a crucial habitat for diverse plant and animal species but also plays a vital role in maintaining the local food system’s resilience [41]. During the severe drought of 2018, when agricultural production in surrounding areas was significantly affected, the wetland park acted as a natural water reservoir, providing irrigation for nearby farmlands. This helped to maintain crop yields and stabilize local food supply, demonstrating the park’s function as an ecological buffer for the food system [42].
Additionally, the Songshan Mountain Forest Park, located in the eastern part of the metropolitan area, exemplifies how maintained woodland can contribute to food system resilience. The forest park, covering over 450 square kilometers, has been instrumental in soil conservation and water regulation. During the heavy rainstorms of July 2021, the forest’s root systems and soil helped to absorb excess water and prevent severe erosion, protecting the downstream agricultural lands from flooding and landslides. This natural buffer significantly reduced crop losses in the affected areas, further illustrating the importance of preserved ecological areas in enhancing food system resilience [43].
These examples demonstrate how the preservation of woodland and grassland areas in the Zhengzhou Metropolitan Area provides tangible benefits to the food system, acting as a buffer against environmental stresses and contributing to overall food resilience.
The water area of the Zhengzhou Metropolitan Area, including wetlands, shows a noticeable increasing trend (as depicted in Figure 9 and Figure 10) [44]. This change is primarily due to the construction of two large water conservancy projects, namely the Xiaolangdi Reservoir and the South-to-North Water Diversion Middle Route Project. Although, theoretically, the increase in water areas might enhance the irrigable land area, a significant impact on the food system has not yet been observed [45]. This is possibly because the main purposes of these water conservancy projects are not directly serving the local food system. Therefore, the impact of this change trend on the metropolitan area’s food resilience—whether positive or negative—still requires further study and assessment [4].
3.1.2. Impact of Urban Planning on Food Transportation and Distribution
Urban planning significantly influences the construction and operation of the food supply chain. The changes in land use, distribution of infrastructure, and patterns of population movement brought about by urbanization have markedly affected the structure and function of the food supply chain [18]. Data show that the density of road networks, the number of transportation hubs and stations, and the density of supermarkets have all shown rapid growth (as illustrated in Figure 11, Figure 12, Figure 13, Figure 14, Figure 15 and Figure 16), reflecting significant improvements in the infrastructure of the metropolitan area’s food supply chain [31,37]. These changes not only enhance the efficiency of daily food distribution but also strengthen the supply chain’s capacity to respond to emergencies, thereby enhancing the overall resilience of the food system [22].
3.1.3. Impact of Urban Planning on Food Consumers
Urban planning also affects food resilience through changes in population dynamics and consumption patterns. Over the past 20 years, the population of the Zhengzhou Metropolitan Area has shown a continuous growth trend, expanding on an already high base and maintaining a relatively young demographic structure. Alongside rapid economic development, the proportion of food consumption in residents’ daily expenditures has continued to decline, while the consumption of imported foods has significantly increased (as shown in Figure 17) [44]. These changes in population and consumption patterns not only pose new demands on the food supply system but also directly impact the metropolitan area’s food resilience [4]. In particular, population growth and shifts in consumption structures may increase the complexity and dependency of the food system thus posing new challenges for maintaining and enhancing food resilience [22].
3.2. Zhengzhou Metropolitan Area Urban Food Resilience Assessment (2018–2022)
3.2.1. Construction of the Indicator System
Principles of Construction
The food resilience assessment system for the Zhengzhou Metropolitan Area is built on several core principles including the following: The principle of hierarchy allows for analysis from the macro to the micro levels [29], ensuring the comprehensiveness and operability of the assessment; the principle of comprehensiveness reflects the multi-faceted nature of food resilience [46], covering the physical supply and storage as well as the social, economic, and political aspects, and emergency response capabilities; the system integrates quantitative and qualitative methods, where some indicators such as food supply time and price volatility are quantifiable [47], while others like regional collaboration mechanisms and NGO participation rely on qualitative assessments; the evaluation system emphasizes the long-term and proactive nature of food resilience [22], such as the proportion of disaster-prepared food storage and emergency logistics network response time; and the system values the role of community and public participation [7], incorporating indicators like the number of community food distribution centers and public participation levels, highlighting the participatory principle in building food resilience.
Explanation of Indicator Selection
The basis for indicator selection includes the following: Based on the theoretical framework of urban food resilience, the selected indicators cover all aspects of the food system and key factors affecting resilience [4]; following the principle of comprehensiveness, the selected indicators cover multiple facets of food resilience, including physical supply, economic affordability, consumption demand, emergency management, and social participation [46]; considering operability, the chosen indicators possess quantifiable or assessable characteristics, facilitating data collection and analysis [47]; tailored to the characteristics of the Zhengzhou Metropolitan Area, the selected indicators reflect local food system features [7]; and consulting relevant field experts to ensure the scientific validity and comprehensiveness of the indicators [22]. The study constructs a criterion layer of indicators centered on food supply chain management, economic and affordability, food consumption and demand, emergency preparedness and management, and social participation and governance, using the Analytic Hierarchy Process to determine the weights of each indicator. Details are provided in Table 1.
Evaluation Criteria
The comprehensive evaluation value of food resilience in the Zhengzhou Metropolitan Area can be categorized into the following four levels: very poor, poor, moderate, and good, as shown in Table 2.
Due to the different scales of the various indicators, they cannot be directly combined. Therefore, when using multi-indicator comprehensive evaluation methods, it is necessary to normalize the indicators to transform the original data, which differ in scale and cannot be directly evaluated, into standardized values mapped onto the [0, 1] range.
Rij is the normalized value, Xij is the original data of the indicator, i is the number of selected indicators, and j is the number of years considered.
Evaluation Method
In the process of using the comprehensive model to evaluate the food resilience of the Zhengzhou Metropolitan Area, individual indicators are evaluated first, and then an overall assessment is made. The higher the values of the influencing factors, the higher the comprehensive evaluation result. The evaluation combines survey data with the actual conditions of the research samples, employing the comprehensive evaluation method. This method is suitable for the single-factor evaluation and multi-factor comprehensive evaluation within the Zhengzhou Metropolitan Area’s food resilience assessment, being relatively straightforward while highlighting the comprehensiveness, hierarchy, objectivity, and comparability of various indicators. This is one of the most commonly used evaluation methods.
The formula (where i = 1, 2, 3, ..., n = 25) represents the comprehensive evaluation value of the metropolitan area’s food resilience, Rij is the standardized value of the i-th indicator in the j-th year, and Wij is the weight of the i-th indicator in the j-th year, with n being the number of evaluation indicators.
3.2.2. Determination of Weights
The weights are determined using the Analytic Hierarchy Process (AHP), which involves several steps including the following: constructing a hierarchical structure to decompose the evaluation problem into the goal layer, criterion layer, and indicator layer; constructing judgment matrices by assessing the indicators at the same level through expert evaluation; calculating weight vectors using the eigenvalue method to find the largest eigenvalue and corresponding eigenvector of the judgment matrices; performing a consistency test to ensure the consistency of the judgment matrices; and finally, performing overall hierarchical sorting to calculate the combined weights of the indicators at the bottom layer relative to the overall goal [48]. The 1–9 scale method is used to construct the judgment matrices, and specific weights are calculated based on this [49]. The study initially selected the most relevant evaluation criteria from numerous factors affecting the food resilience of the Zhengzhou Metropolitan Area, then based on the relationships between these criteria, a multi-level indicator system was established. The hierarchical evaluation indicator system consists of the following three levels: the comprehensive evaluation of the Zhengzhou Metropolitan Area’s food resilience as the goal layer, which is the highest layer of the evaluation system; the food supply chain management, economic and affordability, food consumption and demand, emergency preparedness and management, and social participation and governance as the criterion layer; and including 25 specific indicators as the indicator layer, which is the bottom layer of the entire indicator system.
Constructing Judgment Matrices
The judgment matrix is shown in Table 3.
bij represents the relative importance of bi to bj for factor ak, expressed in the 1 to 9 scale values proposed by TL. Saaty (see Table 4), where bij is either 1, 3, 5, 7, 9, or their reciprocals. The value of the scale bij depends on the statistical values of the opinions of various experts involved in the survey regarding the relative importance of each indicator.Consistency Test
To ensure the rationality of the conclusions drawn using the Analytic Hierarchy Process (AHP), it is necessary to conduct a consistency test of the judgment matrices. The test includes the Consistency Index (CI, ), Random Consistency Index (RI), and the order of the judgment matrix (n). If CI = 0, this indicates that the judgment matrix has perfect consistency. If CI ≠ 0 and the Consistency Ratio (CR = CI/RI) is less than 0.1, the judgment matrix is considered to have satisfactory consistency. If not, the scale values of the judgment matrix should be adjusted.
Determination of Overall Goal Indicator Weights
The determination of the overall goal indicator weights involves calculating the combined weights of each evaluation indicator at the bottom layer relative to the overall goal, based on the results of the individual rankings at each level.
W is the combined weight of an evaluation indicator relative to the overall goal, bi is the weight of element Bi in the criterion layer relative to the goal layer, and ckji is the weight of element Ckji in the indicator layer relative to the criterion layer. Table 5, Table 6, Table 7, Table 8, Table 9 and Table 10 show the judgment matrices and their weights for different layers and indicators.
Finally, the individual and overall goal weights for each indicator are generated as shown in Table 11.
3.2.3. Analysis of Evaluation Results
The evaluation results indicate that the food resilience of the Zhengzhou Metropolitan Area has shown significant dynamic changes and a continuous improvement trend from 2018 to 2022 [4], as depicted in Figure 18. However, the overall level still has significant room for improvement, as shown in Table 12. The evolution over these five years reflects the gradual enhancement of the food system’s adaptability and recovery capabilities in the Zhengzhou Metropolitan Area through continuous policy adjustments and system optimizations [6]. Urban planning plays a decisive role in enhancing food resilience, particularly in land use planning, infrastructure construction, and policy formulation [7]. Despite the evident progress, the “moderate” rating indicates that there is still considerable potential for enhancing the metropolitan area’s food resilience, particularly in light of significant impacts from the COVID-19 pandemic and the “7.20 Rain Flood Disaster” [22].
Interrelationships among Indicators
The management of the food supply chain (B1) is closely related to economic and affordability (B2). For instance, an increase in actual storage capacity (C2) may lead to a decrease in the food price index (C7), thereby improving economic affordability [50]. During 2018–2022, the simultaneous improvement in these two indicators (B1 increasing from 0.1206 to 0.1698, B2 increasing from 0.0389 to 0.1114) reflects this positive correlation.
Emergency preparedness and management (B4) significantly impacts food consumption and demand (B3). As the level of emergency preparedness improves (B4 increasing from 0.0877 to 0.1837), consumer confidence strengthens, reflected in the substantial increase in the food consumption and demand indicator (B3 increasing from 0.0240 to 0.1366) [22].
Social participation and governance (B5) interact with all other indicators. For example, an increase in public participation (C21) may promote the optimization of the food supply chain management and improvements in emergency preparedness [7]. This explains why an improvement in B5 (from 0.0669 to 0.0871) accompanies comprehensive improvements in other indicators.
Contribution to Overall Resilience
Food supply chain management (B1) contributes the most to overall resilience, with a weight of 29.04%. During the study period, the score for B1 increased from 0.1206 to 0.1698, playing a key role in enhancing overall resilience [4].
Emergency preparedness and management (B4) is the second most significant contributing factor, with a weight of 26.20%. The notable improvement in B4 (from 0.0877 to 0.1837) reflects the Zhengzhou Metropolitan Area’s enhanced adaptability in facing COVID-19 and extreme weather events.
Economic and affordability (B2), food consumption and demand (B3), and social participation and governance (B5) have relatively lower weights (15.03%, 14.86%, and 14.87%, respectively), but their synergistic improvement also plays a significant role in boosting overall resilience [32].
Dynamic Changes Analysis
The year 2020 was a turning point, with a significant increase in the score for B4 (emergency preparedness and management) from 0.0851 to 0.1681, likely related to the outbreak of COVID-19, reflecting the rapid response and adaptability of the Zhengzhou Metropolitan Area to crisis management [33].
In 2021, B3 (food consumption and demand) experienced significant growth (from 0.0327 to 0.1047), possibly reflecting the adjustments and recoveries in the consumption patterns, indicating that the food system was beginning to adapt to the “new normal” [51].
By 2022, all indicators reached the highest levels within the study period, suggesting that the food resilience of the Zhengzhou Metropolitan Area is evolving into a more balanced and coordinated system [21].
4. Discussion
4.1. Urban Food Resilience Practices
During the COVID-19 pandemic, the practices related to planning responses for food resilience in the Zhengzhou Metropolitan Area primarily focused on supply chain optimization, efficiency enhancements in food production, community distribution, and policy responses. Through optimizing the food supply chain, urban planning and management departments successfully mitigated the impacts of transport disruptions and mobility restrictions caused by the pandemic [51]. Specific measures included establishing temporary food supply stations, optimizing logistics routes, and leveraging online technologies for purchasing and distribution. Furthermore, to address the pressures on agricultural production caused by the pandemic, the Zhengzhou Metropolitan Area heavily promoted modern agricultural technologies, such as precision farming and smart agriculture, significantly enhancing food production efficiency and disaster resilience [52]. Additionally, to accommodate the residents locked down at home, city managers improved community food distribution mechanisms, organized community volunteers for food delivery, and utilized e-commerce platforms for online purchasing and offline delivery, effectively ensuring residents’ food needs [53]. Lastly, on the governmental level, a series of supportive policies were enacted, including financial aid for food enterprises severely affected by the pandemic and strengthened food safety regulation, further solidifying the foundation for urban food resilience [54,55].
In response to extreme weather disasters, the Zhengzhou Metropolitan Area has also implemented comprehensive measures. Firstly, there has been increased investment in agriculture, such as enhancing investments in farmland water management facilities, improving the efficiency and drought resistance of irrigation systems, and promoting crops with high resilience to enhance the stability of agricultural production [1]. Secondly, considering the impact of extreme weather on the food supply chain, the metropolitan area has focused on improving relevant infrastructure, including upgrading storage facilities to enhance the stability and safety of food storage, as well as improving transportation facilities to ensure the smooth transportation of food [56]. Thirdly, by optimizing the logistics network, such as establishing backup logistics routes and applying smart logistics technologies, the flexibility and responsiveness of the logistics network have increased [50]. Additionally, the Zhengzhou Metropolitan Area has adjusted urban planning concepts, such as improving drainage systems to mitigate the impacts of flood disasters and establishing emergency food storage centers to cope with potential food shortages. On the policy level, the government has implemented a series of food safety and agricultural support policies, such as providing agricultural insurance, supporting post-disaster reconstruction, and establishing a food safety regulatory system to ensure the safety of food supply post-disaster [57,58].
Data trends depicted in Figure 19 and Figure 20 illustrate that during the response to COVID-19 and extreme weather disasters, the Zhengzhou Metropolitan Area experienced an upward trend in logistics infrastructure numbers, the proportion of agricultural automation, and the ratio of community food distribution. These trends reflect positive progress in enhancing urban food resilience within the metropolitan area [37].
4.2. Dynamic Changes and Spatial Distribution Characteristics of Key Indicators
In terms of food supply chain management, the Zhengzhou Metropolitan Area has significantly enhanced its ability to cope with natural disasters and market fluctuations by optimizing the logistics network and improving the quality of storage facilities. These improvements not only increased the efficiency of the supply chain but also strengthened its resilience against external shocks [50].
The enhancement of the economic and affordability indicators reflects the sustained economic growth of the region, which directly boosted the purchasing power of residents, thereby reducing food security risks. This trend correlates positively with the urbanization process and income level improvements [59]. Changes in food consumption and demand reflect a diversification of consumer preferences, with the market supply chain exhibiting greater flexibility and adaptability to these emerging demands, aligning with global urban food system evolution trends [60].
In terms of emergency preparedness and management, continuous investment by government departments and private entities in emergency infrastructure, along with systematic training for relevant personnel, has significantly enhanced the metropolitan area’s response efficiency to sudden events. This enhancement is evident not only in hardware facilities but also in a comprehensive increase in soft power [5]. Improvements in social participation and governance indicators show that increased community involvement and heightened public awareness have fostered the formation of more mature policy feedback mechanisms, which are crucial for building a resilient food system [7].
There are still noticeable spatial disparities within the Zhengzhou Metropolitan Area. Specifically, the central urban areas and the suburbs show significant gaps in food supply chain efficiency and emergency response capabilities, primarily due to uneven infrastructure development and resource distribution. This urban–rural dichotomy in food resilience reflects the common issue of uneven development in rapid urbanization processes [37].
4.3. Key Event Impacts and Policy Effectiveness Assessment
The food resilience of the Zhengzhou Metropolitan Area was tested by multiple significant events between 2018 and 2022, which not only exposed the vulnerabilities of the existing systems but also drove policy adjustments and optimizations. The initial exposure of supply chain vulnerabilities during the pandemic prompted the government and businesses to re-evaluate and strengthen the diversification of supply chains, while also accelerating the application and popularity of e-commerce platforms in food distribution [51]. This digital transformation not only enhanced supply efficiency but also increased the system’s flexibility to respond to emergencies [61]. The “7.20 Rain Flood Disaster” in 2021 highlighted the vulnerability of urban infrastructure under extreme weather conditions and tested the resilience of the food supply system. Post-disaster, the government significantly increased investments in disaster prevention and reduction infrastructure, including improvements to the drainage system and enhancing the disaster resistance of food storage facilities [55]. These measures directly enhanced the metropolitan area’s capacity to cope with future extreme climate events. These policy adjustments have yielded positive results in enhancing the food resilience of the Zhengzhou Metropolitan Area, as reflected in the continuous improvement of food resilience assessment indicators [4].
4.4. Limitations and Recommendations
This study inevitably has limitations. Firstly, there are issues related to data availability. Due to difficulties in accessing certain data, the comprehensiveness and accuracy of the assessments may have been impacted [62]; secondly, the time span considered. While the study primarily focuses on data from 2000 to 2020 and from 2018 to 2022, it may not fully reflect long-term trends; thirdly, geographical limitations [63]. Although the study conducted an in-depth analysis using the Zhengzhou Metropolitan Area as a case study, the results may not be entirely applicable to the other regions [64].
Future research directions include the following: expanding the scope of the study—extending research to other metropolitan areas or urban clusters for comparative studies [65]; long-term tracking—conducting longitudinal studies to better understand the dynamic changes in food resilience [24]; deepening models—further refining and validating urban food resilience planning models to enhance their applicability and predictive power [66]; interdisciplinary research—strengthening collaboration with fields such as climate science and agricultural economics to deepen a multidimensional understanding of food resilience [67]; and policy research—conducting in-depth studies on effective measures to enhance food resilience, providing more actionable recommendations for policymakers [68].
5. Conclusions
This study reveals that the food resilience of the Zhengzhou Metropolitan Area improved annually from 2018 to 2022, although there is still room for overall enhancement. This finding aligns with the view proposed by Tendall et al. (2015) [4] that food system resilience is a dynamic process. However, this research has developed a specific set of assessment indicators that offer a new method for quantitatively measuring food resilience at the metropolitan scale. Given the significant contribution of food supply chain management (B1) to overall resilience, with a weight of 29.04%, it is recommended to increase investment in smart logistics systems to enhance the efficiency of the food system from production to sales [61]. Additionally, a diversified food storage system should be established, including increasing the proportions of modified atmosphere and cold storage grain warehouses, to strengthen capabilities for responding to emergencies [54]. Implementing precision agriculture techniques can effectively reduce loss rates during production and storage processes, further optimizing supply chain management [52]. While there has been improvement in economic and affordability (B2), there is still potential for enhancement. Targeted food subsidy policies should be implemented to ensure food accessibility for vulnerable groups [59]. Supporting local agriculture by increasing the number of agricultural jobs and raising the average annual income of the agricultural population can also enhance overall economic resilience [2].
This study emphasizes the critical role of urban planning in enhancing food resilience, particularly in land use planning, infrastructure construction, and policy formulation. This aligns with the findings of Sonnino et al. (2019) [7]. Moreover, this research explores metropolitan-scale planning practices, offering a new perspective for broader regional food resilience studies. Considering the pivotal role of urban planning in enhancing food resilience, it is recommended to integrate the considerations of the food system into overall urban planning, such as protecting high-quality farmland and planning community food distribution centers [50]. Developing urban agriculture is also an effective way to enhance local food production capacity, particularly in facing global crises, enhancing a city’s self-sufficiency [53].
Considering the regional characteristics of the metropolitan area, it is recommended to strengthen coordination and cooperation mechanisms with neighboring areas (C20) to form a regional food resilience network [32]. Establishing a cross-regional food information sharing platform can improve the efficiency of food supply and demand matching across the entire metropolitan area, further enhancing the overall resilience of the regional food system [52]. Social participation and governance (B5) interact with all other indicators and are crucial for enhancing overall food resilience. It is advised to establish a food policy committee, increase public participation (C21) and NGO involvement (C22), and promote collaboration among the various stakeholders [7]. Enhancing channels for feedback and demands related to food resilience (C23) can facilitate the continuous optimization and adjustment of policies [69]. Additionally, supporting social innovation projects related to food resilience (C24) encourages grassroots innovation, contributing to the formation of a more adaptive food system [62].
Thirdly, this study analyzed the impacts of the COVID-19 pandemic and extreme weather events, such as the “7.20 Rain Flood Disaster”, on the food system, as well as the response measures of the Zhengzhou Metropolitan Area. This resonates with the findings of Béné (2020) [22] regarding the responsiveness of local food systems when faced with shocks. However, through detailed case studies, this research delves deeper into the adaptability and resilience of food systems within the context of China’s metropolitan areas. Considering that emergency preparedness and management (B4) is the second largest contributing factor (with a weight of 26.20%) and exhibited significant improvement during the pandemic in 2020, it is recommended to establish an urban-level emergency food response system and enhance the disaster food storage mechanisms (C15) [22]. Additionally, optimizing the emergency logistics network (C16) is essential to improve food distribution efficiency under extreme conditions [51]. Regularly conducting emergency training and simulation exercises (C18) is also a crucial measure to enhance the emergency response capabilities of relevant personnel [4].
Conceptualization, Y.G. and J.C.; methodology, Y.G.; software, J.S.; validation, Y.G. and J.S.; formal analysis, Y.G. and J.S.; investigation, Y.X. and J.G.; resources, Y.G.; data curation, J.S.; writing—original draft preparation, Y.G.; writing—review and editing, J.C.; visualization, J.S.; supervision, J.C.; project administration, Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.
The data presented in this study are available upon request from the corresponding author.
The authors would like to thank the editor and reviewers for their insightful comments and suggestions.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Spatial location analysis of the Zhengzhou Metropolitan Area (left): Henan Province is located in the People’s Republic of China; (middle): Location of the Central Plains Urban Cluster in central China; (right): Location of the Zhengzhou Metropolitan Area within the Central Plains Urban Cluster).
Enlarge this image.Figure 7. Changes in the scale of woodland and grassland in the metropolitan area.
Enlarge this image.Figure 8. Data on woodland and grassland use and trends in the metropolitan area.
Enlarge this image.Figure 17. Statistics on total population, ageing ratio, consumption proportion, and proportion of imported foods in the metropolitan area.
Enlarge this image.Figure 18. Trend of Comprehensive Evaluation of Food Resilience in the Zhengzhou Metropolitan Area.
Enlarge this image.Figure 19. Statistical Data on Logistics Facilities, Proportion of Agricultural Automation, Ratio of Community Food Distribution, and Policy Responses.
Evaluation Indicators.
| Objective Layer | Criterion Layer | Indicator Layer |
|---|---|---|
| Metropolitan Food Resilience Evaluation System | Food Supply Chain Management B1 | Storage Capacity C1 |
| Actual Storage Amount C2 | ||
| Controlled Atmosphere Grain Storage Capacity C3 | ||
| Low Temperature Near-Low Temperature Grain Storage Capacity C4 | ||
| Time Required from Food Production to Sale (in days) C5 | ||
| Loss Rate: Percentage of food loss or damage during production, storage, transportation, and sale C6 | ||
| Economic and Affordability B2 | Food Price Index C7 | |
| Number of People Employed in Agriculture C8 | ||
| Average Annual Income of Agricultural Population C9 | ||
| Food Consumption and Demand B3 | Annual Per Capita Food Consumption (in kilograms) C10 | |
| Variety of Food Types Consumed C11 | ||
| Daily Caloric Intake Per Capita (in grams or milligrams) C12 | ||
| Proportion of Food Purchases by Channel C13 | ||
| Household Reserves of Specific Foods C14 | ||
| Emergency Preparedness and Management B4 | Proportion of Emergency Grain Reserves C15 | |
| Emergency Logistics Network Response Time: Average transportation time from food reserve locations to demand sites C16 | ||
| Disaster Recovery Time: Time required to restore normal operations when supply chain disruptions or bottlenecks occur C17 | ||
| Number of Emergency Training and Simulation Drills: Number of emergency response trainings and practical simulation drills conducted for relevant personnel C18 | ||
| Number of Community Food Distribution Centers: Number of community centers or other facilities capable of distributing food in emergency situations C19 | ||
| Coordination and Cooperation Mechanisms with Neighboring Areas: Number of coordination mechanisms during food resilience crises with other cities or regions C20 | ||
| Social Participation and Governance B5 | Public Participation Level: Number of public involvement activities in food resilience policymaking or project implementation C21 | |
| Participation Level of Non-Governmental Organizations (NGOs) and Community Organizations C22 | ||
| Establishment Score of Feedback and Complaint Channels: Number of channels available for the public to provide feedback or raise issues regarding food resilience C23 | ||
| Number of Social Governance Innovation Projects: Number of social governance innovation projects related to food resilience implemented C24 | ||
| Number of Media Reports on Food Resilience Issues C25 |
Food Resilience Evaluation Grading.
| Level | Interval | Status | Indicator Characteristics |
|---|---|---|---|
| 1 | [0, 0.30] | Very Poor | Societal activities are stagnant, and the condition of food resilience is extremely poor. |
| 2 | [0.30, 0.60] | Poor | Societal activities are slow, and food resilience is significantly damaged. |
| 3 | [0.60, 0.80] | Moderate | Societal activities are normal, and food resilience is noticeably affected. |
| 4 | [0.80, 1] | Good | Societal activities are active, and food resilience is minimally impacted. |
Pairwise Judgment Matrix.
| ak | B1 | B2 | B3 | … | Bn |
|---|---|---|---|---|---|
| B1 | b11 | b12 | b13 | … | b1n |
| B2 | b21 | b22 | b23 | … | b2n |
| B3 | b31 | b32 | b33 | … | b3n |
| … | … | … | … | … | … |
| Bn | bn1 | bn2 | bn3 | … | bnn |
Scale and Meaning Table for Judgment Matrix Method.
| Scale Value | Meaning |
|---|---|
| 1 | Indicators Bi and Bj are equally important |
| 3 | Indicator Bi is more important than Bj, conversely 1/3 |
| 5 | Indicator Bi is significantly more important than Bj, conversely 1/5 |
| 7 | Indicator Bi is very important compared to Bj, conversely 1/7 |
| 9 | Indicator Bi is extremely important compared to Bj, conversely 1/9 |
| 2, 4, 6, 8 | Intermediate values between the above indicators |
Judgment Matrix and Weights for Goal Layer A.
| A | B1 | B2 | B3 | B4 | B5 | Weight |
|---|---|---|---|---|---|---|
| B1 | 1 | 2 | 1.667 | 1.667 | 1.429 | 29.04% |
| B2 | 0.5 | 1 | 1 | 0.5 | 1.25 | 15.03% |
| B3 | 0.6 | 1 | 1 | 0.5 | 1 | 14.86% |
| B4 | 0.6 | 2 | 2 | 1 | 2 | 26.20% |
| B5 | 0.7 | 0.8 | 1 | 0.5 | 1 | 14.87% |
λmax = 5.078 CI = 0.02 RI = 1.12 CR = 0.017 < 0.1.
Judgment Matrix and Weights for Food Supply Chain Management B1.
| B1 | C1 | C2 | C3 | C4 | C5 | C6 | Weight |
|---|---|---|---|---|---|---|---|
| C1 | 1 | 0.2 | 2 | 2 | 0.2 | 1 | 9.86% |
| C2 | 5 | 1 | 2.857 | 2.857 | 3.03 | 3.333 | 36.07% |
| C3 | 0.5 | 0.35 | 1 | 1 | 0.25 | 0.2 | 6.27% |
| C4 | 0.5 | 0.35 | 1 | 1 | 0.25 | 0.2 | 6.27% |
| C5 | 5 | 0.33 | 4 | 4 | 1 | 1 | 22.38% |
| C6 | 1 | 0.3 | 5 | 5 | 1 | 1 | 19.15% |
λmax = 6.607 CI = 0.121 RI = 1.26 CR = 0.096 < 0.1.
Judgment Matrix and Weights for Economic and Affordability B2.
| B2 | C7 | C8 | C9 | Weight |
|---|---|---|---|---|
| C7 | 1 | 2 | 2 | 49.98% |
| C8 | 0.5 | 1 | 1.111 | 25.89% |
| C9 | 0.5 | 0.9 | 1 | 24.13% |
λmax = 3.001 CI = 0.001 RI = 0.52 CR = 0.001 < 0.1.
Judgment Matrix and Weights for Food Consumption and Demand B3.
| B3 | C10 | C11 | C12 | C13 | C14 | Weight |
|---|---|---|---|---|---|---|
| C10 | 1 | 1.111 | 1.25 | 1.25 | 1.111 | 22.48% |
| C11 | 0.9 | 1 | 1.429 | 1.667 | 2 | 26.37% |
| C12 | 0.8 | 0.7 | 1 | 1.25 | 1 | 18.19% |
| C13 | 0.8 | 0.6 | 0.8 | 1 | 1 | 16.14% |
| C14 | 0.9 | 0.5 | 1 | 1 | 1 | 16.81% |
λmax = 5.039 CI = 0.01 RI = 1.12 CR = 0.009 < 0.1.
Judgment Matrix and Weights for Emergency Preparedness and Management B4.
| B4 | C15 | C16 | C17 | C18 | C19 | C20 | Weight |
|---|---|---|---|---|---|---|---|
| C15 | 1 | 2 | 2.222 | 1.429 | 2.5 | 0.5 | 22.46% |
| C16 | 0.5 | 1 | 0.333 | 1.25 | 1 | 0.667 | 11.35% |
| C17 | 0.45 | 3 | 1 | 1.25 | 2 | 0.667 | 18.55% |
| C18 | 0.7 | 0.8 | 0.8 | 1 | 0.5 | 0.5 | 10.73% |
| C19 | 0.4 | 1 | 0.5 | 2 | 1 | 0.833 | 13.52% |
| C20 | 2 | 1.5 | 1.5 | 2 | 1.2 | 1 | 23.40% |
λmax = 6.387 CI = 0.077 RI = 1.26 CR = 0.061 < 0.1.
Judgment Matrix and Weights for Social Participation and Governance B5.
| B5 | C21 | C22 | C23 | C24 | C25 | Weight |
|---|---|---|---|---|---|---|
| C21 | 1 | 0.909 | 0.769 | 1.429 | 2 | 22.16% |
| C22 | 1.1 | 1 | 0.5 | 1.25 | 1.429 | 19.23% |
| C23 | 1.3 | 2 | 1 | 1.667 | 2 | 29.62% |
| C24 | 0.7 | 0.8 | 0.6 | 1 | 1 | 15.41% |
| C25 | 0.5 | 0.7 | 0.5 | 1 | 1 | 13.57% |
λmax = 5.042 CI = 0.011 RI = 1.12 CR = 0.009 < 0.1.
Comprehensive Evaluation Indicator Weights for the Metropolitan Area’s Food Resilience System.
| Goal Layer | Criterion Layer | Indicator Layer | Weight | Total Weight |
|---|---|---|---|---|
| Metropolitan Area Food Resilience Evaluation System A | B1 Food Supply Chain Management (0.2904) | C1 Storage Capacity | 0.0986 | 0.0286 |
| C2 Actual Storage Quantity | 0.3607 | 0.1048 | ||
| C3 Controlled Atmosphere Grain Storage Capacity | 0.0627 | 0.0182 | ||
| C4 Low and Near-Low Temperature Grain Storage Capacity | 0.0627 | 0.0182 | ||
| C5 Time Required from Food Production to Sale | 0.2238 | 0.0650 | ||
| C6 Loss Rate | 0.1915 | 0.0556 | ||
| B2 Economic and Affordability (0.1503) | C7 Food Price Index | 0.4998 | 0.0751 | |
| C8 Number of Agricultural Employees | 0.2589 | 0.0389 | ||
| C9 Average Annual Income of Agricultural Population | 0.2413 | 0.0363 | ||
| B3 Food Consumption and Demand (0.1486) | C10 Annual Per Capita Food Consumption | 0.2248 | 0.0334 | |
| C11 Number of Food Types Consumed | 0.2637 | 0.0392 | ||
| C12 Daily Caloric Intake Per Capita | 0.1819 | 0.0270 | ||
| C13 Proportion of Food Purchasing Channels | 0.1614 | 0.0240 | ||
| C14 Family Specific Food Reserves | 0.1681 | 0.0250 | ||
| B4 Emergency Preparedness and Management (0.2620) | C15 Proportion of Emergency Grain Reserves | 0.2246 | 0.0588 | |
| C16 Emergency Logistics Network Response Time | 0.1135 | 0.0297 | ||
| C17 Post-Disaster Recovery Time | 0.1855 | 0.0486 | ||
| C18 Number of Emergency Training and Simulation Drills | 0.1073 | 0.0281 | ||
| C19 Number of Community Food Distribution Centers in Built-up Areas | 0.1352 | 0.0355 | ||
| C20 Coordination and Cooperation Mechanisms with Neighboring Areas | 0.2340 | 0.0613 | ||
| B5 Social Participation and Governance (0.1487) | C21 Public Participation Level | 0.2216 | 0.0330 | |
| C22 Participation of NGOs and Community Organizations | 0.1923 | 0.0286 | ||
| C23 Establishment Rating of Feedback and Demand Channels | 0.2962 | 0.0440 | ||
| C24 Number of Social Governance Innovation Projects | 0.1541 | 0.0229 | ||
| C25 Number of Media Reports on Food Resilience Issues | 0.1357 | 0.0202 |
Comprehensive Evaluation Values and Grades of Food Resilience in the Zhengzhou Metropolitan Area by Year.
| Year | Food Supply Chain Management | Economic and Affordability | Food Consumption and Demand | Emergency Preparedness and Management | Social Participation and Governance | Total Weight | Benefit Level |
|---|---|---|---|---|---|---|---|
| 2018 | 0.1206 | 0.0389 | 0.0240 | 0.0877 | 0.0669 | 0.3381 | Poor |
| 2019 | 0.1368 | 0.0866 | 0.0340 | 0.0851 | 0.0787 | 0.4213 | Poor |
| 2020 | 0.1511 | 0.0778 | 0.0327 | 0.1681 | 0.0731 | 0.5028 | Poor |
| 2021 | 0.1516 | 0.0910 | 0.1047 | 0.1640 | 0.0754 | 0.5867 | Poor |
| 2022 | 0.1698 | 0.1114 | 0.1366 | 0.1837 | 0.0871 | Moderate |
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; Sun, Jinyu 1
; Cai, Jianming 2 ; Xie, Yanwen 1
; Guo, Jiahao 1 1 School of Architecture, Zhengzhou University, Zhengzhou 450001, China;
2 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China