Coordination between flood prevention capacity and urbanization under climate change in the Yangtze River Economic Belt, China

Urbanization has proven to be an important contributor to economic growth (He et al. 2024). However, the rapid increase in urbanization rate has led to changes in population density and underlying surface conditions have changed. These changes have significant impacts on the local climate and ecology (Zhang et al. 2018). Moreover, improper urban planning can disrupt the local water–heat cycle, resulting in more frequent and severe rainfall (Wu et al. 2024a). Under increasingly complex climate change, this may lead to urban floods, posing a major threat to both human life and property (Chen et al. 2015; Zhang & Li 2020; Li & Zhao 2022), for example, the 7.21 Zhengzhou rainstorm, the 7.31 Beijing rainstorm (Duan et al. 2024), flood events in Armenia (Galstyan et al. 2020) and the extreme flood events in Morocco in 2014 (Saidi et al. 2020). It is well known that areas with good flood prevention capacity (FPC) are likely to have low flood damage and impacts. FPC is defined as the comprehensive ability of a region to effectively mitigate or avoid disaster losses during floods. It includes the level of hydraulic infrastructure, local financial resources and inputs, and even a measure of mitigation. Therefore, analyzing the correlation between urbanization and flooding is crucial for developing effective flood prevention strategies and promoting sustainable urban development.

Previous research indicates that there may be a potential correlation between flooding and urbanization (Zhang & Li 2020; Liu et al. 2022). On the one hand, urbanization can have a positive impact on flood prevention (Li et al. 2021) by providing infrastructure and technical support, such as the construction of integrated underground pipeline corridors. On the other hand, urbanization has improved urban transport and living conditions, providing convenience for work and recreation. Nevertheless, it can lead to overuse of land resources and disrupt the urban ecosystems (Jonkman 2005; Poussin et al. 2015; Waghwala & Agnihotri 2019). For example, the expansion of urban road networks and buildings has increased the impermeable area (Guan et al. 2018) and also reduced the permeability of the ground surface. The excessive development of urban lakes reduces the watershed area and, in turn, weakens the water storage capacity and flood regulation of rivers (Kundzewicz et al. 2019). For instance, rapid urbanization without rational land-use planning has increased flood vulnerability in cities along the Yangtze River (Zope et al. 2015; Bae & Chang 2019). However, flooding is inevitable in the process of urbanization, and it is crucial to balance the coordinated development of FPC and urbanization. Therefore, it is necessary to effectively evaluate the relationship between FPC and urbanization. This evaluation can help to improve urban flood resilience and promote ecological sustainability.

The coupling and decoupling methods has been widely used as a quantitative analytical tool to assess the coordination of multiple systems (Tapio 2005; Vehmas et al. 2007). For example, they have been applied in the following fields: environmental impacts (Sanyé-Mengual et al. 2019), energy (Schandl et al. 2016; Tenaw & Hawitibo 2021; Leitão et al. 2022), socioeconomic (Krausmann et al. 2018; Martinico-Perez et al. 2018) and natural hazards (Han & Zhang 2017; Li et al. 2021). Among them, the coupling coordination between natural hazard prevention and urbanization is crucial for promoting sustainable environmental development (Zhang & Li 2020). Some previous studies have shown that the coordination process between flood prevention and urbanization is dynamic (Tonne et al. 2021; Liu et al. 2022). The incoherence between FPC and urbanization has increased in some flood-prone areas (Gu et al. 2022). This is evidenced by the destruction of existing urban facilities and the increased frequency of flooding. In addition, the coordination between flood prevention (loss-based indicators) and urbanization in China has not yet reached a stage of coordinated development (Liu et al. 2022). Therefore, some scholars have suggested that multi-scale flood prevention coordination in the process of urbanization should be improved. This may enhance flood pressure-bearing capacity and prevention capacity (Lu et al. 2023), and further promote the high-quality development of urbanization (Deng & Xu 2018).

In summary, the related research has made some progress. However, previous studies have mostly focused on quantitative analysis of urbanization and flood risk or measuring the coupling coordination between FPC and urbanization from a single type of indicator (flood losses). The impact of important factors such as flood prevention facility inputs and human inputs on the flood subsystem has been ignored. Few studies have analyzed or isolated decoupling and coupling from the decoupling perspective. However, the decoupling and coupling methods are not independent. Both perspectives are more helpful in deeply analyzing the coordination of FPC and urbanization. Furthermore, previous studies have mostly analyzed regional differences in coupling coordination degree through spatiotemporal analysis, with few further exploring their transfer patterns and dynamic differences.

Therefore, taking the Yangtze River Economic Belt (YREB) as the basic research unit, the FPC and the urbanization indicator system were constructed. The composite index of the two subsystems from 2000 to 2022 was measured using the entropy-improved Criteria Importance Through Intercriteria Correlation (CRITIC) method. Second, the coupling coordination model and the Tapio decoupling model were used to analyze the coordination relationship and its evolutionary trend. The spatial Markov chain and Dagum Gini coefficient were used to analyze the transfer patterns and spatial differences of coupling coordination levels. The barrier degree model was used to clarify the key indicators that limit their coordinated development. This study can provide some reference for effective flood prevention and sustainable urbanization under climate-change scenarios.

The main innovations of this study: (1) Integration of indicators such as FPC, flood pressure-bearing capacity and human input into the traditional indicator system. The aim is to systematically reflect the coordination relationship between flood prevention effect and urbanization. (2) Weights are assigned to indicators using the improved CRITIC method, which is highly objective. (3) The coordination relationship between FPC and urbanization is analyzed from the perspectives of coupling and decoupling.

The following section introduces the study area. Then, this study describes the research methodology in detail, including the evaluation indicator system, the improved CRITIC method for weight and the coupling coordination model for assessment. Next, evaluation results of the coordination between FPC and urbanization in the YREB are presented. Finally, the discussion including propositions and policy strategies is given.

Weighting is an important part of comprehensive evaluation and subsequent analysis. The CRITIC method is a relatively effective objective weighting method, which can effectively consider the correlation and contrast between indicators. However, the dispersion degree of the indicators is not considered. The entropy method can better compensate for this disadvantage. The combination of the two methods can more comprehensively reflect objective attributes, such as dispersion degree and comparison intensity (Mishra et al. 2023).

• (1) CRITIC method

  

  (1)

  

  (2)

  where is the raw (standardized) data for the jth indicator in the year i; σ_j is the standard deviation of the jth indicator; y_j is the conflictiveness of the jth indicator with other indicators and r_ij is the correlation coefficient between indicators i and j.
• (2) Entropy method

  

  (3)

  

  (4)

  where is the entropy value of the jth indicator, and is the entropy method weight of the jth indicator.

• (3) Improved CRITIC method

A comparison of the composite index helps to understand the current level of the urbanization subsystem and the FPC subsystem (Wu et al. 2024b). This forms the basis for the subsequent analysis of the coordination and decoupling status of the two subsystems.

The overall Gini coefficient is decomposed into three components: G_w is the contribution of intra-regional differences, G_nb is the contribution of net inter-regional differences and G_t is the contribution of hypervariance density. G_jj (G_jh) is the intra-regional (inter-regional) Gini coefficient; y_ji (y_hr) is the coupling coordination degree of a certain province within the region j(h); μ is the average value in the YREB. N_j(n_h) is the number of provinces in the region j(h). D_jh is the relative influence of the coupling coordination degree between two regions; d_jh is the difference in the coupling coordination degree between regions; p_jh is the hypervariable first-order distance.

The spatial Markov chain is an extension of the traditional Markov chain that incorporates the spatial lag factor. This method helps to analyze the dynamic evolution of coupling coordination. For the detailed steps, refer to Yan & Zhang (2022).

Analyzing the coordination relationship between urbanization and FPC may help to achieve sustainable urbanization and implement efficient flood management (Rehman et al. 2019). In accordance with the coupling coordination theory and previous research (Maaskant et al. 2009; Schumacher & Strobl 2011; Ogie et al. 2018; Liu et al. 2022), two indicator systems were constructed to analyze the interaction between the urbanization subsystem and the FPC subsystem (Tables 2 and 3). The establishment of the indicator system provides a reliable quantitative basis for assessing the dynamic balance and mutual influence of two subsystems.

Based on the previous studies of urbanization and its related characteristics (Liu et al. 2022; Salvati 2024; Santillan & Heipke 2024), four dimensions of urbanization were analyzed: economic urbanization (A1), population urbanization (A2), social urbanization (A3) and urban construction (A4). First, the literature database was searched for papers based on keywords and titles. Second, the indicators in the previous studies were analyzed in terms of word frequency to obtain the indicator system. Finally, based on the latest government policies and accessibility of data, the urbanization indicator system containing 16 indicators was constructed, as shown in Table 2. For the attributes of the indicators, refer to previous studies (Han & Zhang 2017; Liu et al. 2023).

Combining the principles of applicability and data availability (Zhou et al. 2023), the indicator system (Table 3) was constructed from the dimensions of flood loss and disaster prevention (Schumacher & Strobl 2011; Zhang & Li 2020; Liu et al. 2022). Specifically, the effectiveness of flood prevention is reflected by the damage and impacts caused by floods, as measured by human casualties (Hammond et al. 2015; Zhang & Li 2020), economic losses (Han & Zhang 2017) and agricultural losses (Maaskant et al. 2009; Schumacher & Strobl 2011; Li & Zhao 2022). Furthermore, a large number of hydraulic facilities, such as sluice gates and pumping stations, also affect the flood levels (Ogie et al. 2018). Some scholars have shown that many areas alleviate urban floods by artificially controlling the water level (Lin et al. 2019); therefore, the indicators B21, B22 and B23 were chosen to measure the losses of water conservancy projects (Liu et al. 2022). For flood prevention, China's flood control engineering measures mainly include reservoirs, embankments and floodplains (Mateo et al. 2014; Ogie et al. 2018; Ding et al. 2022), so this paper selected B41 and B52 to characterize the flood resistance and pressure-bearing capacity. In addition, human and financial inputs are reflected by indicators such as B62 and B71 (Liu et al. 2022).

The data were obtained from the China Meteorological Disaster Yearbook, the China Drought and Water Hazard Prevention Bulletin, the China Water Conservancy Statistical Yearbook, the Urban Construction Statistical Yearbook and the Statistical Yearbooks of regions in the YREB for the years 2001–2023. Partial missing values are filled in using the interpolation method.

The FPC is generally affected by urbanization, but not with complete consistency (Han & Zhang 2017; Liu et al. 2022). Fluctuating turning points typically correspond to specific heavy rainfall or flood events. For example, the sudden decrease in prevention capacity in Anhui in 2003 corresponded to the mega-flood of the Huaihe River (a famous river in China) that occurred in the same year. Similarly, the fluctuation of FPC in Hubei in 2016 corresponded to the 6.1 Wuhan rainstorm.

Overall, the Gini coefficient of the YREB shows a fluctuating upward and then a downward development trend (Table 4). This feature indicates that the regional differences in the coupling coordination degree first increase and then decrease. In terms of intra-regional variation, the downstream area shows the greatest variation, followed by the upstream and midstream. Extreme values appeared in 2005 and 2007. In terms of inter-regional variation, the downstream–midstream and downstream–upstream regions show a gradual increasing trend. The difference observed between the downstream–upstream regions is the largest, with a mean value of 0.19. In terms of contribution, the average contribution of the hypervariable density is 2.92%. This indicates that the crossover problem of samples has little impact on the coupling coordination differences between FPC and urbanization. The contribution of inter-regional differences is the largest and remains consistently above 66.84%. Therefore, the spatial differences in the coupling coordination degree between these two subsystems are mainly influenced by the inter-regional differences.

The decoupling state is dominated by three types: strong decoupling, strong negative decoupling and expansion negative decoupling. Table 5 shows that strong decoupling has occurred 50 times. This suggests that the growth in FPC is no longer entirely dependent on increased levels of urbanization. The decoupling state shows differentiated characteristics for each province. The state with the highest frequency of the decoupling state in Anhui is expansion negative decoupling. This indicates that the growth rate of urbanization development is stronger than the FPC in Anhui. The decoupling index of Jiangxi and Guizhou is unstable, and the decoupling state is mainly strong negative decoupling. In particular, Sichuan has a decoupling state of recessive decoupling and recession connection. Possible reasons are that multiple floods in Sichuan have caused significant damage in recent years. Urbanization structure has not been substantially optimized, exacerbating the unfavorable decoupling in the short term. Overall, there is a negative interaction between urbanization and FPC in the YREB. However, the decoupling state has not significantly improved.

The coupling coordination of urbanization and FPC in the YREB has spatial spillover effects, with the coordination degree in neighboring regions affecting the coordination status of the local area. If it is adjacent to areas with a low coordination degree, the probability of this area transferring to a lower coordination degree will increase (spatial Markov matrix as shown in Table 6). For example, as the coupling coordination degree of surrounding areas increases, the probabilities of low-level provinces moving up are 0.13, 0.20 and 0, respectively. For medium–high-level provinces, the probability of transferring to the next level is 0.67, 0.29, 0.29 and 0.19, respectively. It is clear that high-level provinces have a significant spatial influence on neighboring areas.

For areas with a low level of coupling coordination, the probability of transferring to the next level is 0.2 if the neighboring area is at a low level. While the adjacent area is at a medium–high or high level, the probability of upward transfer ranges from 0.38 to 0.67. For regions with a medium–high level, the probability of transferring to the next level is 0.67 and 0.29 when the neighboring region is at low level. However, the probability of transferring to the next level is 0.13 when the neighboring region is at a high level. In summary, the negative effect of the coupling coordination evolution of the two systems in the YREB is greater than the positive effect.

Due to space limitations in this study, only the barrier degrees in 2000, 2010 and 2020 were analyzed. Barrier factors with a significant impact (M_j ≥ 6.00%) (Tian et al. 2023) and ranked in the top three (Shi & Zhang 2024) were filtered out (Table 7). During the study period, 71.11% of the barrier factors were related to the FPC system. The order of barrier factors by dimension is B2 > B3 > B1, with B21 having the highest barrier degree of 54.33%. In terms of inter-provincial comparisons, there are differences in the main barrier factors among the ten regions. Specifically, all barrier factors in Jiangsu are indicators of the FPC subsystem, with the highest barrier factor ranging from B32 (in 2000) to B61 (in 2010). In addition, the barrier factors in Chongqing and Guizhou are mainly from the urbanization subsystem. Among them, A23 is the main barrier factor affecting the coupling coordination in Chongqing.

Before large-scale urbanization, areas with strong surface permeability and high vegetation coverage generally had a lower probability of flood risk (Liu et al. 2022). However, the development of urbanization has led to a series of unfavorable factors (Ogie et al. 2018; Rehman et al. 2019). For example, the destruction of vegetation and the increase in impervious surfaces have caused changes in hydrological processes. Further, climate-change impacts have further weakened the flood capacity and water storage capacity of rivers near cities (Duan et al. 2024). In highly urbanized areas, even small-scale rainfall events can cause significant flood losses (Poussin et al. 2015; Huang 2019). Second, due to rapid urbanization, many urban layouts have shifted from traditional horizontal development to large-scale expansion of underground spaces (Waghwala & Agnihotri 2019; Zheng et al. 2023). These spaces include urban underground railways, underground shopping malls, garages and other low-lying areas. These underground spaces have indeed saved urban space and improved people's lives. However, their low-lying and semi-enclosed nature makes them vulnerable to floods.

In the context of climate change, the YREB faces the challenge of balancing urbanization with FPC when improving urban flood resilience. This requires achieving coordinated development between the two subsystems. However, this is a major challenge. This paper concludes that the coordination of these two subsystems is influenced by several factors (Tonne et al. 2021), mainly affected by the FPC subsystem. Possibly due to the numerous mountainous areas, insufficient investment in flood protection and incomplete urban flood prevention measures (Zheng et al. 2023), the coordination distribution of the YREB has obvious characteristics. Specifically, the minimum value of coupling coordination is primarily concentrated in the upstream, while the maximum value is mainly concentrated in the downstream. Obviously, there are significant differences in the urbanization development foundation and economic level in these regions, which may further lead to differences in the coordination degree (Liu et al. 2022). In addition, benefiting from more effective local government policies and optimized flood prevention systems, all regions have achieved coordinated development of urbanization and FPC since 2020. However, Figure 7 shows that the changes in the coupling coordination degree within the same region are not significant. This implies that the change in the coupling coordination degree is a gradual process, which is different from the results of previous studies. It is possible that there are similarities and correlations in their coupling coordination mechanisms (Zhou et al. 2023).

The coupling coordination of the two subsystems in the YREB shows obvious spatial differentiation and dynamic evolution characteristics. These characteristics are influenced by various factors, including the level of urbanization development, human and financial inputs for flood prevention, differences in flood risk and uneven spatial change. The Dagum Gini coefficient decomposition results (Table 4) show that the largest contribution is from inter-regional differences, which occur in the range [66.84, 88.73]. This is different from the conclusion of previous studies that ‘intra-regional differences and hypervariable density are the primary factors influencing the coordination relationship between urbanization and people's well-being in the YREB’ (Liu et al. 2023). Therefore, it is recommended that the YREB addresses the spatial imbalance between regions to reduce development disparities.

This study argues that there is indeed an interaction between FPC and urbanization, but it is not consistently positive or negative. This differs from the conclusion of previous studies, which suggest that ‘FPC will be improved to a certain extent when urbanization develops to a certain degree’ (Liu et al. 2022). On the positive side, the growth of FPC indicates that local governments are paying more attention to environmental protection and flood prevention, which helps to achieve the dual goals of economic development and environmental protection (Yuan et al. 2023). This is precisely the goal of the high-quality development of urbanization (Pervaiz & Hummel 2023). Strengthening urban flood management can promote a virtuous cycle of urbanization development, as evidenced by the coupling and decoupling results.

There is a dynamic decoupling relationship between relative decoupling and absolute decoupling in the FPC subsystem and the urbanization subsystem. The decoupling state is mainly characterized by strong decoupling. This means that the growth of FPC in most regions is not fully dependent on the development of urbanization. It also means that rapid urbanization may not necessarily bring higher economic and social benefits from flood prevention. This only occurs when urbanization resources and FPC subsystems are rationally allocated (Zhang & Li 2020). These results may be related to the construction of regional water networks, such as the improvement of water infrastructure networks and the optimization of flood disaster reduction systems (Poussin et al. 2015; Liu et al. 2022).

It should be noted that the decoupling state in Guizhou and Jiangxi is not ideal, and there is still a significant amount of strong negative decoupling. The decoupling state indicates a positive increase in urbanization levels and a decrease in FPC. This finding differs from previous studies (Tenaw & Hawitibo 2021; Yuan et al. 2023). It is clear that the regional imbalance and unevenness of FPC remains a major issue.

Besides analyzing the coordination relationship between urbanization and FPC from both coupling and decoupling perspectives, this study also focuses on the following three policy strategies to improve urban flood resilience under climate change.

First, it is necessary to simultaneously strengthen the coordination between urbanization and FPC. We should improve the cooperation mechanism for flood prevention and strengthen the construction of urban flood prevention infrastructure. For example, constructing the underground pipe gallery may enhance the ability of urban disaster prevention and mitigation. In addition, rational planning of urban layout and active promotion of sponge-city construction are effective measures to reduce flood risks.

Second, to reduce the regional gap in the coordinated development of urbanization and FPC, combining spatial Markov results, regions with high coupling coordination should be promoted to exert their own spatial spillover effects. This may help to further narrow the gap between upstream and downstream.

Finally, non-engineering measures are used to supplement engineering measures. For instance, long-term flood insurance and reinsurance policies could be developed in flood-prone areas of the YREB.

This paper has some limitations. Although the coordination of FPC and urbanization has been analyzed from the decoupling and coupling perspectives, the internal coordination mechanism has not been analyzed. It is hoped that the coordination mechanism will be further analyzed in future research.

In addition, the coordination relationship between urbanization and FPC is influenced by various factors. Future research needs to be improved by integrating the progress of flood prevention practice with the advancement of scientific research. For example, future research could be combined with other indicators or applied in different urban agglomerations. This may provide a better understanding of the interrelationship between urbanization, hydrology and flood risk.

This study analyzes the coordination relationship between urbanization and FPC in the YREB, with the aim of accelerating the construction of flood-resilient cities. The methods used include the coupling coordination degree model and the Tapio decoupling model. The research results may have some practical implications for flood risk management and flood response in relevant government departments. The main conclusions are as follows:

• (1) The composite index of urbanization and FPC in the YREB shows an overall fluctuating upward trend, with significant regional differences. Jiangsu and Zhejiang provinces have a higher composite index for both subsystems than other regions. Furthermore, the FPC in the downstream is stronger.

• (2) The change in the coupling coordination degree of the YREB is relatively small and has not fully reached the level of excellent coordination in 2022. The overall spatial distribution is characterized by a gradual development from west to east.

• (3) Inter-regional differences are the primary cause of the imbalance affecting coordination. The number of damaged reservoirs and other indicators are important factors limiting the coordination between urbanization and FPC.

• (4) The decoupling states are mainly strong decoupling, strong negative decoupling and expansion negative decoupling. The growth trend of coupling coordination is not consistent and there are some negative interactions.

This research was funded by Sichuan Philosophy and Social Sciences (Grant No. SC23E044), Panzhihua Advanced Manufacturing Technology Key Laboratory (Grant No. 2022XJZD01), Sichuan Key Provincial Research Base of Intelligent Tourism (Grant No. ZHYR23-02), and the Hubei Key Laboratory of Construction and Management in Hydropower Engineering (China Three Gorges University) Open Fund (Grant No. 2024KSD07).

Z.W. and S.H. designed and developed the manuscript. Y.C. and C.D. helped to prepare the best possible quality figures and write the text. J.L. contributed to methodology and data curation. X.Z. and P.W. contributed to the conceptualization, visualization and reviewing and editing of the writing.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.