Abstract
The sponge city is a new concept of stormwater management for ecological city construction, which aims to restore water-cycle processes and reduce runoff. Cities in coastal districts are suffering from serious instability due to high population density, urbanization, and land-use changes. However, previous research contains few evaluations of balancing urban ecological indicators of sponge city performance, including geographical, environmental, economic, and social factors, and their effect on resilience at a macro level to develop low-impact development schemes. In this study, we developed an integrated framework using factor analysis, geographical statistics, multi-objective analysis, and remote sensing methods to extract the factors influencing sponge city resilience and to establish spatial pattern schemes. The results indicated that the urbanization degree and plant adaptability had the greatest impact on sponge city performance, with weights of 45 and 27%, respectively. Sponge city spatial pattern schemes performed the best in the combination scenario of 14.8–46.8% green roofs (by area ratio) supported by grooves and rain barrels +10% herbaceous basins divided into units by ecological tree pools +10% permeable pavements and sidewalks. This scenario balanced facilities and cost to optimize the spatial pattern, which improved sponge city adaptability and urban ecological conditions.
HIGHLIGHTS
An integrated evaluation system analyzing urban resilience was proposed.
We first established a link between sponge city and urban ecological resilience.
Factors of urbanization degree and plant adaptability possessed the most impact.
Green roofs were suitable for applying in high urbanized areas with dense buildings.
The framework could provide designers with valid experience in urban management.
INTRODUCTION
Rapid urbanization has caused wetlands and water bodies to disappear, which has disrupted natural water-cycle processes, reduced stormwater storage capacity, decreased groundwater recharge, increased runoff peaks, and led to urban waterlogging (Bah et al. 2023). Traditional urban engineering infrastructures (e.g., drainage pipeline networks) cope mainly with rainwater with a short return period of between one and 10 years, which is not enough to alleviate waterlogging (Jia et al. 2017). As urban stormwater problems become increasingly severe, the use of ‘hard’ projects such as impervious drainage ditches and underground pipeline networks has been found to be insufficient (Putri et al. 2023). At present, stormwater management has become a worldwide focus, and countries have developed concepts and natural facilities (e.g., green roofs, permeable pavements, rain barrels, herbaceous basins, and rain gardens) based on their own situation (Koc et al. 2021). Examples include low-impact development (LID) in the US, sustainable urban drainage systems (SuDS) in the UK, and water-sensitive urban design (WSUD) in Australia (Ferrans et al. 2022). In the same vein, the first ‘sponge city’ initiative in China was proposed in 2011.
About 2.4 billion (∼40%) people in the world live within 100 km of a seacoast and suffer from serious water stress and instability due to high population density, urbanization, and land-use changes (Bredes et al. 2023). Coastal cities tend to suffer more severe damage when disasters occur, and therefore applying improved and optimized sponge city strategies in these locations is a significant step (Pereira et al. 2017). However, the effectiveness of a sponge city strategy can be affected by several factors, such as the factors of geographical environment, budget, soil surface, land use, and socioeconomic conditions (Yuan et al. 2022). For instance, drainage through an urban drainage pipeline will be hindered by high ocean tidal water level, which will decrease the city's overall ability to control stormwater (Li et al. 2018). Other researchers have indicated that annual mean precipitation can reach 900–2,500 mm because of the monsoon system, but that various combinations of sponge city measures appear to offer benefits in this context (e.g., decentralized controls could be a promising strategy) (Jackisch & Weiler 2017). The ability of sponge cities to control stormwater generally depends on infiltration from soil, including permeable pavements that transform impervious surfaces and infiltrate runoff to the soil (Eckart et al. 2017). Storage and infiltration capabilities to reduce runoff could be increased by soil amendments such as compost, lime, or organic materials, which could alter the physical, chemical, and biological characteristics of soils to improve plant growth (Chen et al. 2023). Moreover, sponge cities are always costly to build, and developed countries could become more reluctant to deal with waterlogging and stormwater problems due to worsening socioeconomic conditions such as lack of funding, low environmental awareness, and lack of public acceptance (Ishaq et al. 2023).
Principal component analysis (PCA) is widely used to analyze the influence of a series of factors on a process; factor analysis is an extension of this approach. Previous studies of sponge cities and factor analysis optimization have been mostly limited to providing schemes based on analyzing the impact of each factor and of geographical features at a small scale; few have calculated the comprehensive benefits after implementing an optimal plan. Impact factors could be extracted using factor analysis, after which the benefits of sponge city schemes in mitigating damage to resilience could be quantified using geographical statistics such as Moran's I and Getis-Ord Gi*.
The present study first integrated factor analysis, geographical statistics, multi-objective analysis, and remote sensing into a new framework, which included and balanced both geographical and socioeconomic factors at a macro level and then optimized sponge cities in detail with remote sensing in coastal areas in China, taking Tianjin as an example. In this research, the most significant factors affecting sponge city benefits and their effect weights were obtained by factor analysis. The urban resilience distribution in the study area was then obtained through a ‘resilience damage’ score based on ecological factors in geographical statistics. Finally, the spatial patterns of optimal sponge city schemes and their comprehensive benefits represented by resilience promotion were analyzed. The proposed evaluation system could help to integrate sponge city planning into urban planning systems and reduce the waste of natural and social assets. Moreover, our study can serve as a reference to designers around the world for sponge city optimization and waterlogging risk reduction.
METHODS AND MATERIALS
Study area
Research framework and data
Research framework for the study: (a) framework for formulating optimal strategy in Tianjin and (b) framework for analyzing factors and their effect size.
Research framework for the study: (a) framework for formulating optimal strategy in Tianjin and (b) framework for analyzing factors and their effect size.
Factor analysis
Generally, the operation of a sponge city infrastructure is affected by the geographical environment, the economy, and society. Geographical environment factors include climate, hydrology, topography, soil properties, air quality, and land-use types. Economic factors can be divided into residents’ income, consumption level, and real estate development. Additional social factors can be categorized as population distribution, urban traffic conditions, and the state of scientific research. Macro-indicators with strong correlation can be selected for factor analysis when analyzing impact factors on sponge cities at a national scale. Moreover, in this research, the word ‘indicator’ is used to represent an influence on sponge cities before factor analysis, and the phrase ‘impact factor’ is used to show the indicators in each category (i.e., each factor) after classification.
Figure 2(b) shows the factor analysis framework. Compared with PCA, factor rotation and overall score calculation are added, which makes it more reasonable to classify each indicator into the final factors and to calculate their effect sizes (i.e., their weight values).
With the overall score calculated, the weight of each factor was also determined. The factor results were then applied to the study area, and further detail indicators were selected by analyzing conditions in Tianjin.
Formulas and calculations
PCA and factor rotation
PCA is a multivariate statistical technique that is widely used to reduce high dataset dimensionality and improve interpretability while minimizing information loss. Relevant studies have shown that PCA can comprehensively evaluate the effects on sponge city performance of environmental, economic, and social concerns by analyzing the weight of every factor (Zhang et al. 2021). Nevertheless, the factors extracted by PCA differ little in the loading of each variable, making the results difficult to explain and define in a professional manner. Factor analysis has extended PCA by adding factor rotation to change the projection area of each common factor in the direction of the original variable, so that the factors can be explained and named appropriately (Johnson & Wichern 2014).
Indicator standardization
The dimensions and orders of magnitude associated with the variables are not uniform, and it is often necessary to conduct standardized processing for the collected data, also called normalization.
First, to unify the effect of different indicators on sponge city performance (promoting or hindering), evaluation indicators are often divided into positive, negative, and moderate indicators because different types of indicators require different standardization approaches:
- (1)
Positive indicators should not be processed.
- (2)
- (3)




Eigenvalue calculation and principal component extraction



Generally, the number of principal components can be determined as those with an eigenvalue >1 and a cumulative variance contribution >50%.
Component matrix and factor rotation



Overall score calculation










Geographical statistics
Global Moran's I



Local Moran's I










Getis-Ord Gi*
Global spatial autocorrelations such as global Moran's I and the Getis-Ord general G (a method similar to Global Moran's I that reveals whether high or low clustering tendencies are more significant) measure overall clustering or dispersion. In contrast, Getis-Ord Gi* is a local spatial autocorrelation statistic that is widely used to investigate specific spatial distributions and local clusters and that is more intuitive than the local Moran's I (Ord & Getis 1995).
Multi-objective optimization module








RESULTS
Factor extraction and classification
Impact factor classification and overall score weight calculation. Note: The hexagram shows the 12 indicators selected: (a) number of sponge city studies; (b) shallow soil organic carbon (%); (c) average price of nearby real estate (CNY); (d) soil permeability coefficient (m/d); (e) annual GDP per capita (10,000 CNY); (f) annual mean precipitation (mm); (g) annual average temperature (°C); (h) number of motor vehicles; (i) impervious surface coverage (%); (j) maintenance cost (CNY); (k) construction cost (CNY); and (l) population density (persons/km2). The right-hand side shows the four extracted factors: (A) urbanization degree; (B) financial burden; (C) rainwater characteristics; and (D) plant adaptability. The pie chart shows the overall score weight of each factor.
Impact factor classification and overall score weight calculation. Note: The hexagram shows the 12 indicators selected: (a) number of sponge city studies; (b) shallow soil organic carbon (%); (c) average price of nearby real estate (CNY); (d) soil permeability coefficient (m/d); (e) annual GDP per capita (10,000 CNY); (f) annual mean precipitation (mm); (g) annual average temperature (°C); (h) number of motor vehicles; (i) impervious surface coverage (%); (j) maintenance cost (CNY); (k) construction cost (CNY); and (l) population density (persons/km2). The right-hand side shows the four extracted factors: (A) urbanization degree; (B) financial burden; (C) rainwater characteristics; and (D) plant adaptability. The pie chart shows the overall score weight of each factor.
Refined indicators and resilience damage scores
The initial indicators represent the macro-impact of sponge cities in the whole of China and the four factors used to categorize it. However, more detailed indicators should be refined from the four factors together with actual conditions in Tianjin, so that the effect on sponge city performance can be more clearly seen on the urban level. To this end, four indicators (one per factor) were refined to detail the four factors, and their influence on sponge city benefits in the study area was analyzed (Table 3). Hard surfaces represent the degree of urbanization in a district, which damages the natural environment and tends to have a negative effect on rainwater control. Cost has a negative effect on sponge cities from an economic standpoint because the great expense increases financial burden and causes disadvantages in actual planning and design work. Unlike the hard-surface coefficient, the runoff coefficient mainly depends on regional precipitation and land-use type, which reveals the urban ecological structure in another aspect and is closely related to its factors and to the initial indicators. According to the last factor, green space is the foundation of plant growth, and an insufficiently green condition usually represents a bad ecological environment. This provides detrimental conditions for all natural creatures, including plants and micro-ecosystems in the green infrastructure. Based on the theory of ‘simulating the natural conditions and maintaining or restoring ecological hydrological characteristics before the damage of urban development as far as possible’ of LID (USEPA 2000), a resilience damage score was used to represent the ecological resilience corresponding to the relation between each sponge city and its natural environment before development. The data on detailed indicators represent a degree of damage to resilience in each subdistrict divided by the study area (Table 3). This created a link between impact factors on the sponge city, urban ecological resilience, and stormwater management, where a higher score means severe damage and worsening rainwater problems. Thus, applying sponge city strategies would mitigate the disadvantages based on the urban ecological indicators, not only optimizing sponge city adaptation, but also promoting urban ecological resilience and the ability to cope with urban stormwater.
Detailed indicators with their factors
Factor (Weight) . | Name . | Refined indicator . |
---|---|---|
A (45%) | Urbanization degree | Hard-surface proportion (%) |
B (18%) | Financial burden | Total cost |
C (10%) | Rainwater characteristic | Runoff coefficient |
D (27%) | Plant growth environment | Inverse green space ratio (1/%) |
Factor (Weight) . | Name . | Refined indicator . |
---|---|---|
A (45%) | Urbanization degree | Hard-surface proportion (%) |
B (18%) | Financial burden | Total cost |
C (10%) | Rainwater characteristic | Runoff coefficient |
D (27%) | Plant growth environment | Inverse green space ratio (1/%) |
Note: Indicators that could increase resilience damage are positive, whereas those that reduce damage are negative. The negative indicators were standardized by inversion so that the score variation trend could be consistent.
DISCUSSION
Spatial autocorrelation analysis in ecological resilience damage
The combined resilience damage scores in the 72 subdistricts were calculated from the data of the detailed indicators corresponding to their extracted factors and effect weights. Their spatial autocorrelation was then analyzed using global Moran's I and Getis-Ord general G. The results show that the global Moran's I was 0.37 and the z-score was 6.37, which indicated a positive correlation (I > 0) and a <1% likelihood that this clustered pattern could occur by random chance (z-score >2.58). This, in turn, revealed that the resilience damage distribution is strongly clustered in these subdistricts. Moreover, the Getis-Ord general G test indicated that resilience damage was concentrated in areas of high scores (i.e., existed significantly in the study area), with a G-value of 0.015 > 0 corresponding to a z-score of 5.36 > 2.58, which is consistent with the results of Moran's I.

Urban ecological resilience damage distribution. The number in each subdistrict is its serial number, and a total of 72 subdistricts were classified: (a) distribution results for local Moran's I; (b) distribution results for Getis-Ord Gi*, with hot and cold spot analysis. The colored planes forming the background under the subdistrict boundaries are major buildings.
Urban ecological resilience damage distribution. The number in each subdistrict is its serial number, and a total of 72 subdistricts were classified: (a) distribution results for local Moran's I; (b) distribution results for Getis-Ord Gi*, with hot and cold spot analysis. The colored planes forming the background under the subdistrict boundaries are major buildings.
Adaptability evaluation of LID facilities in the optimization scheme
The infiltration capacity of surfaces in Tianjin is only 0.03–0.05 m/d, and the water level and groundwater salinity are respectively 1–3 m and 1,000–3,000 mg/L (Wang et al. 2020), which are not appropriate for large-scale construction of infiltration facilities. However, it could also be inferred that the central regions of districts (Figure 4(b)) contain mainly existing construction, making ground-based retention and storage facilities like herbaceous basins difficult to implement widely. Therefore, the green roof supported by rain barrels (GR, Green roof-Rain barrel) could serve as the primary LID facility for recovering urban ecological structure by collecting stormwater instead of infiltrating most of it to groundwater. Permeable pavements and herbaceous basins could still be used in sponge cities as a supplementary measure to encourage rainwater absorption. Moreover, the area ratio of hard-surfaced roofs in central districts in the study area is commonly 75–85% as calculated by remote sensing analysis, which also indicates that GR is more suitable for central urban districts to restore natural structure and enhance resilience.
Feasibility of green infrastructure and spatial pattern design of sponge city
Spatial pattern and area ratio increase of LID and resilience promotion after implementation of sponge city schemes: (a) specific ratio increases and layout of GR in each subdistrict and (b) resilience damage distribution after GR implementation.
Spatial pattern and area ratio increase of LID and resilience promotion after implementation of sponge city schemes: (a) specific ratio increases and layout of GR in each subdistrict and (b) resilience damage distribution after GR implementation.
Optimization in LID spatial pattern and detailed design in facilities
In Palla & Gnecco (2015), a combination of permeable pavements, herbaceous basins, and green roofs performed the best among combined LID facilities, but a reduction of at least 5% in hard surfaces through land-use transformation could contribute visible hydrologic benefits such as recovering urban ecological resilience. Moreover, permeable pavements and herbaceous basins were better added at a greater than 10% ratio in a reformed region with much existing construction and were preferred for application to non-automotive pathways and sidewalks with light traffic (Weiss et al. 2019). The unit prices of permeable pavement and herbaceous basins were 300 and 90 CNY/m2, respectively, which are significantly cheaper than the price of a green roof at 440 CNY/m2. Eventually, 10% of the area was intended for herbaceous basins, with analysis of remote images and current conditions of low infiltration and high water level and salinity in this coastal study area with high urbanization. However, the correlations in the results revealed that the balance between maximum resilience and minimum cost would be reached when the area ratio of permeable pavement was as low as possible and trending to zero. Hence, 10% permeable pavements were finally implemented in addition to the standard in Tianjin.
Optimal spatial pattern schemes of green infrastructure and their detailed design: (a) layout and structure of general green roofs; (b) schematic of triangular green roofs; and (c) schematic of a permeable sidewalk and a herbaceous basin formed by ecological tree pools.
Optimal spatial pattern schemes of green infrastructure and their detailed design: (a) layout and structure of general green roofs; (b) schematic of triangular green roofs; and (c) schematic of a permeable sidewalk and a herbaceous basin formed by ecological tree pools.
Figure 6 shows the detailed structures of green roofs, herbaceous basins, and permeable pavements, all designed according to relevant research and engineering standards. Green roofs are still in an early stage in China, but have been widely used in Singapore, America, and various European countries (He 2020), which provides a representative experience for construction in highly urbanized cities. Layers of green roof can be divided into plants and base material, infiltration, drainage, protection, water resistance, drainpipes, and structural roofs (Figure 6(a)). In general, flat roofs can accept green roofs directly, but a triangular roof with two steep slopes needs a long groove at the bottom to collect rainwater and convey it to storage in the rain barrel (Figure 6(b)). Similarly, permeable pavement consists of permeable units, a bedding layer, a permeable base course, drainpipes, a permeable sub-base layer, and a soil subgrade. The pipes can help drain excess rainwater coming from the road surface and alleviate the stress of soil infiltration. Moreover, the permeable pavements were set at about 2 m wide to cover the sidewalk completely (Figure 6(c)). A typical herbaceous basin is composed of a storage layer (plant layer), an improved soil layer, the original soil layer, and an overflow well connected to a set of drainpipes. Ecological tree pools are used to break the long grassy expanse into several rectangular units, solving the problem that the herbaceous basin needs to occupy a long continuous stretch of land for storing, retaining, and infiltrating runoff and would be difficult to build in the central districts of cities. The ecological tree pools are designed in 1.2 × 1.2 m squares and are laid out over the permeable pavements (Tan & Zhu 2013). The pit surrounding each tree connects to a vertical rainwater collection pipe that is then linked to drainpipes in the permeable sidewalks. They can not only increase the green space ratio but also help permeable pavements to absorb stormwater. To further cope with the runoff generated by traffic roads, gutter inlets could be built on the side to connect roads and pools (Figure 6(c)), helping to collect road runoff and share the drainage pressure in old municipal gutters.
Limitations and future research directions
This paper has proposed a novel approach to evaluate the impact factors on sponge cities while also optimizing spatial patterns and dealing with damage to urban ecological resilience. An evaluation system for resilience restoration and sponge city benefits based on factor analysis, geographical statistics, remote sensing, and multi-objective optimization has been developed to evaluate the operating performance of various schemes. Moreover, contributions to urban resilience from the geographical environment as well as social and economic factors and the effect of sponge cities were all analyzed using the resilience damage score method, and detailed optimal sponge city plans were also designed and laid out. However, the practical performance of different optimal schemes for water infiltration, runoff control, and rainfall processing was still not addressed explicitly in this research. The benefits in practical cases should also be revealed to make the spatial pattern optimization and facility design more compelling.
In subsequent research, numerical simulation measures will be used to build a model that simulates actual operating conditions, the water retention process in actual storm events, and the layout of green roofs, herbaceous basins, permeable pavements, and underground pipes.
CONCLUSIONS
This study established a rapid and simplified framework for assessing the effects of sponge city strategies on urban ecological resilience recovery at a macro level. Various impact factors and their contributions to restoring resilience after damage were systematically integrated into the evaluation framework. Using factor analysis, geographical statistics, remote sensing, and multi-objective processing, maps were produced and used to visualize the benefits of sponge city strategies in the aspects of promoting urban resilience and decreasing stormwater trends, and specific LID facilities were designed for coastal areas. A number of conclusions were reached from this study using impact-benefit analysis:
- (1)
Factors A (urbanization degree) and D (plant adaptability) had the greatest impact on sponge city performance, with effect weights of 45 and 27%, respectively. Urban ecological resilience is positively related to distance from the city center because the damage scores of hard-surface ratio, runoff coefficient, and green space ratio, which are the detailed indicators corresponding to factors A, C (rainwater characteristics), and D are higher with more intense urban development.
- (2)
Results indicated that the best-performing sponge city spatial pattern scheme in addressing urban ecological resilience damage was the combination scenario of 14.8–46.8% green roofs (area ratio) +10% herbaceous basins +10% permeable pavements. This was the most appropriate application of LID facilities and achieved a balance between facility construction and cost.
- (3)
General LID practices that mainly depend on infiltration to underground or groundwater, or that need much land to operate, are not suitable for widespread use in prosperous coastal areas with large hard-surface roofs and high salinity and groundwater levels. Green roofs connected to a groove and supported by rain barrels could fit these conditions, including triangular roofs with a steep slope.
- (4)
Specific measures for urban ecological resilience recovery, including green roofs supported by grooves and rain barrels, permeable sidewalks, and herbaceous basins divided into units by ecological tree pools, were adopted in highly urbanized coastal areas. However, further research (such as numerical simulation) should be carried out to determine the operating effect of a sponge city under actual working conditions.
ACKNOWLEDGEMENTS
This study was supported by the National Natural Science Foundation of China (No. 41907149), the Tianjin Graduate Research Innovation Project (No. 2022SKY195) and the China Postdoctoral Science Foundation (No. 2018M631732). We thank International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.