Research on Climate Resilience Assessment and Enhancement Strategies for Hebei Province in Response to Climate Change

Abstract

Enhancing climate resilience is imperative for cities to mitigate the effects of global warming and the rising frequency of extreme weather events. This paper develops an evaluation index system for urban climate resilience in Hebei Province, based on data from 11 cities within the province. It evaluates the levels of climate resilience and identifies their limiting factors using the entropy weight method, an urban climate resilience assessment model, and an obstacle degree model, with a focus on four dimensions: ecological resilience, economic resilience, social resilience, and infrastructure resilience. The results indicate that (1) spatial variations in climate resilience across cities in Hebei Province are minimal, with the majority of cities exhibiting climate resilience levels within the moderate resilience category. (2) The majority of regions display low ecological and infrastructure resilience (0.1–0.3), while economic resilience is distributed across three tiers, with regional variations; social resilience remains moderately resilient (above 0.3). (3) Among the social resilience factors, C₃ and C₈ exhibit the highest obstruction levels, emerging as key barriers. (4) In order to effectively respond to climate change risks and challenges in a scientific manner, differentiated implementation of climate response strategies, the core of which lies in identifying the dominant vulnerability dimensions of different cities and accurately applying policies, such as Shijiazhuang, Baoding, Xingtai, Handan, and other cities with fragile ecological resilience, should comprehensively deepen the construction of sponge cities to alleviate urban flooding and the heat island effect.

1. Introduction

Since the beginning of the 21st century, global climate change, characterized by the frequency and intensity of high temperatures and extreme weather events, has become one of the major environmental challenges [1,2,3]. Its widespread and far-reaching impacts pose a serious threat to the natural ecosystems and socioeconomic systems of cities [4]. The Intergovernmental Panel on Climate Change (IPCC) forecasts a rise in global average temperatures of 2.6 °C to 4.8 °C by the close of the twenty-first century, with climate warming remaining a persistent challenge in the years to come [5]. In recent years, as the world’s largest developing country and carbon emitter, China is facing significant impacts of climate change. According to the “Blue Book on Climate Change in China (2025)” released by China, the warming trend of the climate system continues. Since the 1990s, global ocean warming, rising sea levels, and glacier melting have significantly accelerated. China is a sensitive and significantly affected area of global climate change, with a warming rate higher than the global average during the same period, and an increasing number and intensity of extreme weather and climate events [6]. Climate warming has intensified the frequency and intensity of extreme climate events, such as the extreme high temperature and drought in the Yangtze River basin in 2022 [7], and the “July 20” extremely heavy rainstorm in Henan Province in 2021 [8], all of which pose a serious threat to agriculture, infrastructure, and the life safety of residents. Therefore, how to enhance the ability of cities to resist and adapt to climate change has become an urgent task for the global scientific community and policy makers.

The concept of “resilience” was first proposed by HOLLING [9] and was introduced into the field of ecology in the mid- to late 20th century, with the continued penetration of the concept of urban resilience and the related use of resilience in urban planning continuing to emerge [10]. Urban resilience [11,12] refers to the ability of an urban system to maintain basic functions, adapt to changes, and achieve sustainable recovery in the face of multiple pressures such as natural disasters, climate change, and socioeconomic shocks [13]. The current research on urban resilience by foreign scholars is mainly reflected in the urban resilience connotations; for example, in 2019, Ribeiro et al. [14] defined four basic connotations of urban resilience: resistance, recovery, adaptation, and transformation; Suárez et al. [15] defined urban resilience from a socio-ecological system perspective, developed a methodological framework for measuring urban resilience, and defined an urban resilience index. As research on urban resilience deepened, international studies gradually shifted toward evaluating assessment methods and frameworks. For instance, Kusumastuti et al. [16] established an urban resilience assessment framework for natural disasters based on the United Nations International Strategy for Disaster Reduction (UNISDR) definition of resilient cities and Simpson’s community resilience assessment methodology. Early domestic research on urban resilience primarily focused on resilience level measurement [17,18,19], spatiotemporal evolution [20,21,22], and the influencing factors of resilience [23,24,25]. Subsequent domestic research has increasingly focused on the coupling and coordination of urban resilience with other disciplines. For instance, Li Zhiyuan et al. [26] constructed a comprehensive evaluation index system for urban resilience and tourism environmental carrying capacity in the Yangtze River Economic Belt, assessing their development levels. Liu Hailong et al. [27] conducted spatiotemporal evolution analyses and impact factor studies by coupling and coordinating the concepts of urban resilience and innovation efficiency. However, these studies have focused on the interrelationships of internal urban systems and have relatively neglected external, violent, and uncertain climate shocks. Climate risks are characterized by extremes, uncertainty, the co-existence of slow onset and sudden onset, and chain reactions, which pose challenges to urban systems that have not been adequately considered in urban resilience research. Some of the existing resilience studies have also mentioned climate factors, but most of them treat climate factors as a common external variable or marginalize them, leading to a gap in our knowledge of the real vulnerability and adaptive capacity of cities in the face of increasingly severe climate threats. Therefore, this paper starts from the perspective of climate resilience, measures climate resilience levels in Hebei Province, and proposes enhancement strategies, with a view to promoting climate resilience theory development.

Climate resilience is a relatively recent concept, with research emerging later both domestically and internationally. “Urban resilience” is a comprehensive concept that emphasizes the overall capacity of urban systems to cope with various types of perturbations (economic crises, public health events, etc.). Climate resilience [28], on the other hand, refers specifically to the ability of cities to withstand, adapt, and transform in response to climate-related shocks and stresses (extreme weather, urban heat island effect [29], persistent drought, etc.). A unified definition of climate resilience remains absent. In the literature on urban climate change, the majority of research on climate resilience has been framed around keywords such as “climate change” [30,31], “urban resilience studies with climate as the backdrop” [32], “urban thermal environment [33,34,35,36] “, and “land surface temperature [37]”, all of which serve as precursors to climate resilience research. Current research worldwide primarily examines urban resilience from the perspective of disaster studies, focusing on phenomena such as heatwaves [38], heat vulnerability [39,40,41], floods [42], and droughts [43]. However, studies focusing on individual disaster risks or isolated dimensions of resilience often fail to provide comprehensive evaluations of climate resilience, lacking systematic and scientifically grounded assessment frameworks. In recent years, significant progress has been made in climate resilience research, including territorial spatial planning based on climate resilience [44] and agricultural food production [45]. Nevertheless, a lack of quantitative assessment methods for measuring urban climate resilience persists. Research using quantitative evaluation methods can more scientifically and intuitively reflect a city’s resistance and recovery capacity in the face of meteorological disaster impacts. This article explores the four resilience dimensions of “ecological, economic, social, and infrastructure resilience”, which is an operationalization and extension of the core concepts of “exposure, sensitivity, and adaptability” to climate risks emphasized in the IPCC report. For example, infrastructure resilience is directly related to a city’s “exposure”, while social and economic resilience determines a city’s “adaptability”. This provides the theoretical basis for this paper to assess resilience from the four dimensions of ecological, social, economic, and infrastructural resilience.

This study investigates 11 cities in Hebei Province, developing an assessment model and evaluation index system for urban climate resilience across four dimensions: ecological, economic, social, and infrastructure resilience. By integrating the entropy weight method with the urban climate resilience assessment model, the study conducts a comprehensive analysis of the climate resilience levels in Hebei. ArcGIS10.8 analytical methods are utilized to investigate the spatial patterns of climate resilience across the province. Finally, an obstacle model is employed to identify the “key barrier factors” affecting climate resilience across various cities in Hebei. Based on these findings, optimization strategies are proposed to mitigate urban climate disaster risks and enhance the capacity of cities to resist and adapt to climate hazards [46,47], offering critical guidance for improving climate resilience.

2. Research Area Overview

Hebei Province (36°05′–42°40′ N, 113°27′–119°50′ E) is composed of 11 prefecture-level cities: Shijiazhuang, Tangshan, Qinhuangdao, Handan, Xingtai, Baoding, Zhangjiakou, Chengde, Cangzhou, Langfang, and Hengshui (as shown in Figure 1) [48]. The province has a coastline of 487 km and covers a total area of 188,800 km². Hebei Province is situated in the northern part of the North China Plain, bordered by the Bohai Sea to the east, the Taihang Mountains to the west, and the Inner Mongolia Plateau to the north. It serves as the core region of the Beijing–Tianjin–Hebei Cooperative Development Strategy [49]. The terrain exhibits a distinct step-like distribution, with the Damshang Plateau, Yanshan-Taipei Mountains, and North China Plain arranged sequentially from northwest to southeast, resulting in an elevation difference exceeding 2800 m. The region experiences a temperate monsoon climate, characterized by high temperatures and rainfall in the summer, and cold, dry conditions in the winter. In recent years, as a typical resource-dependent economic province, Hebei has encountered significant climate resilience challenges. In 2023, the province’s average annual temperature reached 13.2 °C, 1.0 °C above the normal (12.2 °C) and 0.6 °C higher than in 2022 (12.6 °C), marking the highest recorded temperature in history since meteorological observations began. This temperature ranked among the highest in the country. The province’s average annual precipitation in 2023 was 648.1 mm, representing a 28.2% increase from the normal level and a 13.8% rise compared to 2022, which was also a high-precipitation year.

The number of days of high temperature and heavy rainfall has gradually increased. From the point of view of the number of days of high temperature (daily maximum temperature ≥ 35 °C), high incidence of high-temperature events was located in the central and the southern part of Hebei Province, with the average annual number of high-temperature days in most areas being in the range of 7–20 days, and a few areas having an average annual number of high-temperature days of more than 20 days (e.g., Figure 2). In terms of cities, Handan, Xingtai, and Hengshui all have an annual average of more than 20 days of high temperature, with Handan’s annual average of 26.33 days of high temperature ranking first in Hebei Province; Zhangjiakou, Chengde, and Qinhuangdao all have an annual average of fewer than 7 days of high temperature, with Qinhuangdao’s annual average of 4.63 days of high temperature ranking last in Hebei Province. Extreme weather and climate events occur frequently; high temperatures, heavy rainfall, high winds and dust, strong snowfall, and other disasters have a greater impact, and the degree of loss caused by meteorological disasters is the heaviest in the past 10 years. Therefore, assessing the climate resilience of Hebei Province and proposing enhancement strategies with a view to improving the resistance of cities in Hebei Province will be a major impetus to promoting sustainable economic development, social progress, and safeguarding people’s safety and well-being in Hebei Province.

In summary, Hebei Province is a representative region in China, highly vulnerable to climate risks within the broader context of global climate change. The climate risks encountered in Hebei Province are characterized by their compounded nature and elevated frequency. The primary risk arises from the escalating intensity, frequency, and duration of heatwaves and high-temperature events, resulting in recurrent and prolonged meteorological and agricultural droughts, which pose enduring threats to agricultural production and ecological security. Furthermore, the risk of extreme heavy precipitation events, along with the associated urban flooding and basin-wide floods, has markedly intensified. Short-duration heavy rainfall frequently exceeds the capacity of urban drainage systems and river flood control mechanisms, leading to considerable economic losses. Moreover, agricultural production is subjected to various stresses from agrometeorological disasters, including hot dry winds and frost. These risk factors are interwoven and compounded, exacerbating the vulnerability of Hebei Province’s socioeconomic and natural ecosystems. Systematic measures are urgently required to strengthen the region’s climate resilience.

3. Data and Methods

3.1. Data Source

This article follows the basic principles of accessibility, representativeness, and scientificity, and constructs an evaluation index system for urban climate resilience in Hebei Province around four dimensions: ecological resilience, economic resilience, social resilience, and infrastructure resilience [50]. A total of twenty-five climate resilience indicators were selected, as detailed in

Table 1. Evaluation index system of urban climate resilience in Hebei Province.

Goal Level	Guideline Level	Indicator Level	Interpretations	Indicator Attributes	Weights
Urban climate resilience	Ecological Resilience (0.2008)	Greening coverage in built-up areas (A1) Parkland area per capita (A2) Sewage treatment rate (A3) Ratio of good air quality days (A4) Soil erosion control area (A5)	Urban ecological regulation capacity	Positive Positive Positive Positive Positive	0.0305 0.0227 0.0227 0.0391 0.0856
	Economic Resilience (0.2803)	Per capita GDP (B1) Per capita fiscal revenue (B2) Number of patents granted (B3) Local general public finance budget expenditure (B4) Average annual disposable income per urban resident (B5) Financial investment in science and technology (B6)	Government capacity to regulate resources and household economic buffers	Positive Positive Positive Positive Positive Positive	0.0483 0.0431 0.0503 0.0406 0.0384 0.0594
	Social Resilience (0.3172)	population density (C1) Disaster prevention and emergency management expenditure (C2) Food production per capita (C3) Number of employees in public administration and social organizations (C4) Number of urban and rural residents guaranteed minimum subsistence allowance (C5) Number of beds in medical institutions per 1000 population (C6) Health technicians per 1000 population (C7) University students per 10,000 population (C8) Percentage of vulnerable population (elderly + children) (C9)	Reflecting the Government’s capacity for disaster preparedness	Negative Positive Positive Positive Negative Positive Positive Positive Negative	0.0201 0.0396 0.0467 0.0390 0.0194 0.0258 0.0348 0.0528 0.0388
	Infrastructure Resilience (0.2015)	Total water resources (D1) Road network density (D2) Density of drainage pipes (D3) Density of water pipes (D4) Total water supply (D5)	Reflects the capacity of the city to prevent flooding and the coverage of the water supply system	Positive Positive Positive Positive Positive	0.0508 0.0087 0.0193 0.0447 0.0777

. This study includes 11 cities in Hebei Province. The indicator data primarily come from the Hebei Statistical Yearbook (2021–2023) and the Hebei Statistical Bulletin on National Economic and Social Development, with supplementary data sourced from the China Urban Construction Statistical Yearbook and the China Urban Statistical Yearbook.

3.2. Data Processing

The positive and negative indicators in the raw data were standardized, and the missing values of the raw data were supplemented by interpolation with the following formulas [51]:

Positive indicator:

$$X_i=\left(x_i-x_{min}\right)/\left(x_{max}-x_{min}\right)$$

(1)

Negative indicator:

$$X_j=\left(x_{max}-x_j\right)/\left(x_{max}-x_{min}\right)$$

(2)

In the formulas, $X_i$ and $X_j$ represent the normalized values, $x_i$ and $x_j$ denote the original data, and $x_{min}$ and $x_{max}$ are the maximum and minimum values of the original data matrix.

3.3. Research Methods

(1)

Entropy weight method

The entropy weight method is an objective weighting technique grounded in information entropy, determining weights by assessing the dispersion of indicator values. This method is applied in multi-indicator comprehensive evaluations to assign weights to each indicator. The primary advantage of the entropy weight method lies in its weight determination, which is entirely dependent on the internal structural relationships within the sample data. This method effectively mitigates biases stemming from subjective human judgment, enhances the objectivity of evaluation results, and is suitable for comprehensive evaluations involving multiple indicators [52,53].

(1) There are m cities to be evaluated and n evaluation indicators, forming a raw data matrix:

$$X={\left\{x_{ij}\right\}}_{m\times n}\left(0\ll i\ll m,0\ll j\ll n\right)$$

(2) Calculate the weight of the first city for indicator:

$$p_{ij}=X_{ij}{{\displaystyle \sum }}_{i=1}^m{X}_{ij}$$

(3) Calculate the entropy value of the indicator:

$$e_j=-k{{\displaystyle \sum }}_{i=1}^m\left(p_{ij}\ln p_{ij}\right),k=1/\ln m,e_j\in \left[0,1\right]$$

(4) Calculate the coefficient of variation:

$$g_i=1-e_i$$

(5) Calculate weights for evaluation indicators:

$$w_j{=g}_i/{\sum }_{j=1}^n{g}_i$$

(6) Calculate the Comprehensive Climate Resilience Score:

$$R={{\displaystyle \sum }}_{j=1}^n{w}_j\times X_{ij}$$

(7) Calculate the composite scores for each resilience system in climate resilience:

$$Q_1={{\displaystyle \sum }}_{l=1}^r{a}_l{x}_l, Q_2={{\displaystyle \sum }}_m^p{b}_m{y}_m, Q_3={{\displaystyle \sum }}_n^q{c}_n{z}_n, Q_4={{\displaystyle \sum }}_o^t{d}_o{u}_0$$

(3)

$Q_1$ indicates the comprehensive score for ecological resilience; $Q_2$ indicates the composite score for economic resilience; $Q_3$ indicates the composite score for social resilience; $Q_4$ indicates the composite score for infrastructure resilience. $a_l,b_m,c_n,d_o$ represent the weight of each indicator in the four resilience systems, $x_l,y_m,z_n,u_o$ denote the standardized values of each indicator in the four resilience systems, and $r,p,q,t$ indicate the number of indicators in the four resilience systems.

(2)

Urban Climate Resilience Assessment Model

This paper calculates the level of urban climate resilience from four dimensions: ecological resilience, economic resilience, social resilience, and infrastructure resilience. The formula is as follows:

$${\mathrm{U}}_{\mathrm{s}}={\mathrm{A}}_1\times {\mathrm{X}}_{\mathrm{s}}+{\mathrm{A}}_2\times {\mathrm{Y}}_{\mathrm{s}}+{\mathrm{A}}_3\times {\mathrm{Z}}_{\mathrm{s}}+{\mathrm{A}}_4\times {\mathrm{K}}_{\mathrm{s}}$$

(4)

$${\mathrm{X}}_{\mathrm{s}}={{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{m}}{\mathrm{W}}_{\mathrm{t}}{\mathrm{x}}_{\mathrm{t}}$$

(5)

$${\mathrm{Y}}_{\mathrm{s}}={{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{m}}{\mathrm{W}}_{\mathrm{t}}{\mathrm{y}}_{\mathrm{t}}$$

(6)

$${\mathrm{Z}}_{\mathrm{s}}={{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{m}}{\mathrm{W}}_{\mathrm{t}}{\mathrm{z}}_{\mathrm{t}}$$

(7)

$${\mathrm{K}}_{\mathrm{s}}={{\displaystyle \sum }}_{\mathrm{t}=1}^{\mathrm{m}}{\mathrm{W}}_{\mathrm{t}}{\mathrm{k}}_{\mathrm{t}}$$

(8)

In the formula, ${\mathrm{U}}_{\mathrm{s}}$ represents the overall climate resilience level of a city, ${\mathrm{A}}_1$, ${\mathrm{A}}_2$, ${\mathrm{A}}_3$, ${\mathrm{A}}_4$ denote the respective weights for ecological resilience, economic resilience, social resilience, and infrastructure resilience; ${\mathrm{X}}_{\mathrm{s}}$, ${\mathrm{Y}}_{\mathrm{s}}$, ${\mathrm{Z}}_{\mathrm{s}}$, ${\mathrm{K}}_{\mathrm{s}}$ represent the ecological resilience level, economic resilience level, social resilience level, and infrastructure resilience level, respectively; ${\mathrm{x}}_{\mathrm{t}}$, ${\mathrm{y}}_{\mathrm{t}}$, ${\mathrm{z}}_{\mathrm{t}}$, ${\mathrm{k}}_{\mathrm{t}}$ represent the values after normalization for the t-th indicator of ecological resilience, economic resilience, social resilience, and infrastructure resilience, respectively.

(3)

Obstacle Degree Model

To thoroughly investigate the “key barrier factors” influencing climate resilience levels across different cities in Hebei Province and subsequently formulate targeted enhancement strategies, this study employs a barrier model for calculation. A higher barrier value indicates a stronger impediment to the coordinated development of the system, while a lower value signifies a weaker impediment. The formula is as follows [54,55]:

$${\mathrm{I}}_{\mathrm{ij}}=1-{\mathrm{X}}_{\mathrm{ij}}$$

(9)

$${\mathrm{F}}_{\mathrm{ij}}={\mathrm{W}}_{\mathrm{i}}\times {\mathrm{W}}_{\mathrm{ij}}$$

(10)

$${\mathrm{O}}_{\mathrm{ij}}={\displaystyle \frac{{\mathrm{I}}_{\mathrm{iJ}}\times {\mathrm{F}}_{\mathrm{ij}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{I}}_{\mathrm{ij}}\times {\mathrm{F}}_{\mathrm{ij}}}}$$

(11)

$${\mathrm{U}}_{\mathrm{i}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{{\mathrm{n}}_{\mathrm{i}}}{\mathrm{O}}_{\mathrm{ij}}$$

(12)

${\mathrm{I}}_{\mathrm{ij}}$ is the degree of deviation of the j-th indicator of the i-th city, ${\mathrm{X}}_{\mathrm{ij}}$ is the standardized value of the j-th indicator of the i-th city; ${\mathrm{F}}_{\mathrm{ij}}$ is the factor contribution of the j-th indicator of the i-th city, ${\mathrm{W}}_{\mathrm{i}}$ is the weight of the i-th guideline level, ${\mathrm{W}}_{\mathrm{ij}}$ is the weight of the j-th indicator of the i-th city; ${\mathrm{O}}_{\mathrm{ij}}$ is the degree of obstruction of the j-th indicator of the i-th city; ${\mathrm{U}}_{\mathrm{i}}$ is the degree of obstruction of the i-th guideline level.

4. Analysis of Results

4.1. Climate Resilience Assessment Results for Hebei Province

This study utilizes the entropy weight method alongside an urban climate resilience assessment model to calculate the overall climate resilience level of Hebei Province. It then performs a comprehensive evaluation of climate resilience across cities within the province and analyzes the spatial distribution of urban climate resilience. To more effectively reflect the climate resilience levels (CI) of cities in Hebei, an equidistant classification method is employed to categorize resilience levels within the range of 0 to 1. If CI ≥ 0.7, the resilience level is categorized as strong resilience; if 0.5 ≤ CI < 0.7, it is categorized as relatively strong resilience; if 0.3 ≤ CI < 0.5, it is categorized as moderate resilience; and if 0 ≤ CI < 0.3, it is categorized as low resilience.

4.1.1. Analysis of the Overall Climate Resilience Assessment Results for Hebei Province

As shown in Figure 3, from a spatial perspective, the spatial variation in climate resilience among cities in Hebei Province is relatively small, with most cities’ climate resilience levels concentrated in the moderate resilience category (0.3 ≤ CI < 0.5). Regarding the overall spatial distribution, Shijiazhuang and Tangshan demonstrate comparatively strong climate resilience, ranking as the top two cities in Hebei Province. This suggests that these two cities have superior conditions in infrastructure development, government management, medical facilities, and economic development compared to others. Consequently, they exhibit a stronger capacity to respond to and recover from meteorological and flood disasters, resulting in more robust urban climate resilience. Cities with moderate resilience are predominantly concentrated in north-central Hebei, whereas those with low resilience are mainly clustered in the southern part of the province. This reflects the uneven development among cities in Hebei, resulting in a spatial pattern characterized by higher resilience in the north and lower resilience in the south. Currently, the majority of cities in Hebei Province fall within the low-to-moderate resilience categories. This necessitates government guidance and resource allocation in urban development to strengthen each city’s ecological regulation and self-recovery capabilities.

4.1.2. Analysis of the Assessment Results for the Climate Resilience Subsystem

As shown in Figure 4, the ecological resilience level of most areas in Hebei Province is low, ranging from 0.1 to 0.3. Only a few regions, such as Chengde and Zhangjiakou, exhibit higher ecological resilience composite scores (0.459 and 0.392, respectively), which places them in the medium resilience category (above 0.3). The overall spatial pattern demonstrates low resilience in the central and southern parts of the province, while the northern region shows higher resilience. The low ecological resilience in most areas of Hebei Province can be attributed to its early development, which heavily relied on the exploitation of natural and mineral resources. This, coupled with excessive deforestation, resulted in significant ecological degradation, including soil erosion and water runoff. As a consequence, the ecological resilience against climatic hazards remains low.

Economically developed regions in Hebei Province, such as Shijiazhuang and Tangshan, demonstrate higher levels of economic resilience, categorized within the “relatively strong resilience” range of above 0.5. In contrast, less developed areas such as Zhangjiakou, Chengde, and Hengshui exhibit lower resilience levels, ranging from 0 to 0.3. The resilience ratings of cities in Hebei Province encompass three distinct tiers, highlighting regional disparities. A distinct spatial pattern emerges, characterized by higher resilience in central areas and lower resilience in peripheral regions. Economic resilience in Hebei shows a clear correlation with the levels of urban economic development. For example, Shijiazhuang and Tangshan, as the two major central cities in the province with the highest levels of economic development and strongest economic strength, also demonstrate high economic resilience.

Most regions in Hebei Province demonstrate moderate levels of social resilience, with scores above 0.3. Shijiazhuang demonstrates the highest social resilience at 0.618, indicating comparatively strong resilience, while Xingtai has the lowest at 0.28, indicating comparatively low resilience. Overall, social resilience is relatively evenly distributed across Hebei Province, with minimal variation. The high social resilience in most areas of Hebei Province can be attributed to policy drivers and institutional advantages. As a key component of the national strategy for “Beijing-Tianjin-Hebei Coordinated Development,” this initiative prioritizes economic growth, ecological conservation, and public services as its core objectives. This approach has significantly enhanced the region’s overall resilience to risks and recovery capacity.

Only Shijiazhuang in Hebei Province exhibits a medium level of infrastructure resilience, ranging from 0.3 to 0.5, while the remainder of the province demonstrates a low level, below 0.3, and in some areas, even below 0.1. No significant differences are observed in the levels of infrastructure resilience across the cities and towns in Hebei Province. A key factor contributing to the overall low level of infrastructure resilience in Hebei Province is the inadequate current infrastructure, accelerated urbanization, significant increases in impervious surfaces, and extensive surface hardening.

4.2. Study on Climate Resilience Barriers in Hebei Province

4.2.1. Barrier Factor Analysis

This study computes the impedance level for each climate resilience indicator in Hebei Province using the impedance model. The impedance factor with the highest ranking within each resilience subsystem is chosen for statistical analysis (

Table 2. Barrier factors and barrier levels for urban climate resilience in Hebei Province.

Region	Ecological Resilience	Economic Resilience	Social Resilience	Infrastructure Resilience
Shijiazhuang	A5 (0.2627)	B1 (0.2038)	C3 (0.4683)	D1 (0.1625)
Tangshan	A5 (0.2611)	B3 (0.1425)	C3 (0.4082)	D5 (0.1597)
Qinhuangdao	A5 (0.1886)	B6 (0.2106)	C3 (0.3587)	D5 (0.1532)
Cangzhou	A5 (0.2191)	B6 (0.1743)	C8 (0.3437)	D5 (0.1904)
Xingtai	A5 (0.1633)	B6 (0.1742)	C8 (0.3398)	D5 (0.1675)
Baoding	A5 (0.1570)	B1 (0.1789)	C8 (0.3443)	D5 (0.1754)
Zhangjiakou	A1 (0.0597)	B6 (0.2086)	C8 (0.3113)	D5 (0.1892)
Handan	A5 (0.1817)	B1 (0.1469)	C8 (0.3747)	D5 (0.1397)
Langfang	A5 (0.2462)	B6 (0.1623)	C3 (0.3889)	D5 (0.2055)
Hengshui	A5 (0.2005)	B6 (0.1546)	C8 (0.3740)	D5 (0.1825)
Chengde	A1 (0.0602)	B6 (0.2290)	C8 (0.3407)	D5 (0.2190)

).

As shown in the table, the highest-ranked barrier factors in ecological resilience are A₅ and A₁. Among these, A₅ dominates most regions except Zhangjiakou and Chengde, making it the “key barrier factor” affecting Hebei Province’s ecological resilience. Economic resilience features three factors with relatively high obstruction levels, B₁, B₃, and B₆, with B₆ being the primary obstacle. Social resilience ranks C₃ and C₈ as the highest-ranked factors, with C8 being the primary obstacle. Infrastructure resilience ranks D₁ and D₅ as the highest-ranked factors, with D₅ being the primary obstacle.

As demonstrated in the table, each barrier factor in social resilience is the most significant relative to the other factors. In Shijiazhuang, Tangshan, and Qinhuangdao, the primary barrier factor is C₃ (per capita food production); however, in other regions, the most obstructive factor is C₈ (number of university students per 10,000 people).

4.2.2. Resilience Subsystem Failure Analysis

An analysis of climate resilience barriers in Hebei Province was conducted across four dimensions: ecological resilience, economic resilience, social resilience, and infrastructure resilience (Figure 5). As illustrated, the primary barrier to climate resilience in Hebei is social resilience, with most barrier levels clustered around 0.5. This suggests that among the subsystems of ecological, economic, and infrastructure resilience, the social resilience subsystem has the greatest influence on urban climate resilience in Hebei. Economic resilience is the next most significant barrier, with values around 0.3 across most regions, except for Shijiazhuang and Tangshan. Ecological and infrastructure resilience have the least influence, with some cities, such as Chengde and Zhangjiakou, even recording ecological resilience values below 0.1.

4.2.3. Enhancement Strategy

As global climate change intensifies, urban climates are increasingly characterized by instability and uncertainty. Given the spatial variations in climate resilience across cities in Hebei Province, it is crucial to implement differentiated climate response strategies while simultaneously advancing ecological, economic, social, and infrastructure resilience. This integrated approach seeks to enhance climate resilience and capacity for sustainable development in cities across Hebei Province.

(1) According to the climate resilience map of Hebei Province, the low resilience of cities in the southern region can primarily be attributed to its location in the hinterland of the North China Plain, where water scarcity is a significant issue, groundwater overexploitation is prevalent, and the ecological foundation is relatively fragile. Additionally, the region is more vulnerable to flooding caused by extreme precipitation, and there is a compounded risk of drought and heatwaves, putting further strain on the ecosystem’s carrying capacity and resilience. Furthermore, some of the older urban infrastructure (e.g., drainage networks and energy systems) in southern cities were constructed earlier and may not have been designed to accommodate the current intensity of extreme weather events. The urban regeneration process is not keeping pace with the rapidly evolving climate risks. For example, cities such as Xingtai and Hengshui in the southern region exhibit lower climate resilience levels, recorded at 0.28 and 0.288, respectively. Both Xingtai and Hengshui are located in the low-lying plains of the Haihe River Basin, rendering them high-risk areas for droughts and floods. They also face long-term pressures related to water scarcity and relatively slow economic development, which weakens urban resilience to climate hazards and consequently lowers their climate resilience levels. Therefore, Xingtai and Hengshui should promote comprehensive, watershed-based sponge city development. This entails not only constructing rain gardens and permeable pavements within urban areas, but also restoring and developing wetlands, floodplains, and flood detention basins (such as Hengshui Lake) in upstream regions (e.g., the western mountainous areas of Xingtai), urban peripheries, and along river corridors. At the same time, extreme water resource coordination and conservation strategies must be implemented to ensure water supply during climate-related disasters and enhance resilience.

(2) According to the analysis of the climate resilience subsystem in Hebei Province, most cities exhibit low ecological and infrastructure resilience, generally below 0.3. The foundation of ecological resilience lies in the protection, restoration, and enhancement of natural ecosystems, enabling them to buffer climate impacts and provide critical regulatory services. Therefore, coastal cities such as Qinhuangdao and Tangshan should prioritize the restoration of coastal wetlands, lagoons, and salt marshes, while establishing coastal shelterbelt systems. Zhangjiakou and Chengde, covering the Bashang Plateau and Yanshan-Taihang Mountain regions, serve as water conservation zones and ecological barriers for the Beijing–Tianjin–Hebei region. These cities should focus on implementing strategies such as deep water conservation, targeted forest quality enhancement, and mine ecological restoration. Cities such as Shijiazhuang, Baoding, Xingtai, and Handan must comprehensively advance the construction of sponge cities to alleviate urban flooding and mitigate heat island effects. The foundation of infrastructure resilience lies in enhancing the design standards, redundancy, and intelligent response capabilities of lifeline systems (energy, water, transportation, and communications), ensuring uninterrupted operation or rapid recovery in the event of shocks. An integrated emergency command platform should be established to cover the entire province, consolidating data from meteorological, water conservancy, transportation, public security, and other departments, thereby enabling coordinated responses and precise resource allocation during extreme weather events.

(3) According to the barrier degree model analysis, the factors C₃ (per capita grain yield) and C₈ (number of college students per 10,000 people) in social resilience show the highest barrier degrees, thereby identifying them as key barrier factors. This suggests that these factors exert the most significant influence on urban climate resilience. Cities with high per capita grain production, such as Hengshui, Handan, and Xingtai in the core areas of the high-yield plain, possess efficient agricultural systems but are more vulnerable to climate risks. The primary objective is to enhance engineered adaptation capacity by upgrading high-standard farmland water conservancy infrastructure, promoting water-saving technologies such as smart drip irrigation and sprinkler systems to mitigate drought stress, and improving flood control and drainage systems. In cities with a high number of college students per capita, such as Shijiazhuang and Langfang, efforts should concentrate on enhancing climate change awareness and leadership among highly educated populations. By leveraging public education and volunteer organizations, this abundant human capital can be converted into social capital for community climate action, thereby enhancing society’s collective capacity for responding to climate risks and improving adaptive capabilities.

5. Conclusions and Discussion

5.1. Conclusions

This paper focuses on cities in Hebei Province as the research subject, analyzing climate resilience from four dimensions: ecological, economic, social, and infrastructural resilience. It evaluates the level of climate resilience and its hindering factors using the entropy weight method, the urban climate resilience assessment model, and the obstacle degree model. The key conclusions are as follows:

(1) In terms of overall urban climate resilience, spatial variations among cities in Hebei Province are relatively small. Most cities display moderate resilience levels (0.3 ≤ CI < 0.5), with Shijiazhuang and Tangshan ranking as the top two cities in the province for climate resilience, demonstrating relatively high levels of resilience.

(2) From the perspective of the climate resilience subsystem in Hebei Province, most regions display low ecological resilience levels, ranging from 0.1 to 0.3. Urban economic resilience spans three levels across the province, with regional variations forming a spatial pattern that exhibits higher resilience in central areas and lower resilience in peripheral regions. Most areas in Hebei Province demonstrate moderate social resilience levels, all exceeding 0.3. Only Shijiazhuang’s infrastructure resilience falls within the moderate resilience range (0.3–0.5), whereas the infrastructure resilience of other regions remains at low levels, all below 0.3.

(3) Concerning the factors influencing climate resilience, the two barrier factors, C₃ (per capita grain production) and C₈ (number of college students per 10,000 people), within social resilience demonstrate the highest barrier levels, designating them as primary barrier factors. The primary obstacle system for climate resilience in Hebei Province is social resilience, with most obstacle levels concentrated around 0.5. This suggests that the urban social resilience subsystem of the province exerts the greatest influence on urban climate resilience, relative to ecological resilience, economic resilience, and infrastructure resilience.

5.2. Discussion

Research on urban resilience strategies in the context of climate change is essential for enabling cities to address climate risks, strengthen their adaptive capacity, and ensure sustainable urban development. Since 2014, academic research on resilient cities has experienced significant growth, with climate change emerging as a key focus within the field. However, the majority of existing studies on climate resilience are theoretical in nature, with empirical research being notably scarce. Moreover, the existing literature seldom employs quantitative methods to assess urban climate resilience.

Compared with established studies on climate resilience in Chinese cities, the contributions of this study are mainly as follows:

(1) The majority of existing studies on climate resilience are theoretical in nature, with empirical research being notably scarce. Moreover, the existing literature seldom employs quantitative methods to assess urban climate resilience. In this regard, the contribution of this study is mainly in the quantitative deepening of the indicator system and methodology. First, at the level of the indicator system, although existing studies have emphasized the multidimensionality of climate resilience, empirical assessments are predominantly confined to easily quantifiable dimensions, such as the economy and infrastructure, due to data availability. In contrast, this study develops a four-dimensional framework encompassing economic resilience, social resilience, ecological resilience, and infrastructural resilience. This comprehensive selection of indicators more effectively captures the complex composition of urban climate resilience. At the methodological level, the existing literature on urban climate resilience predominantly features qualitative analyses or static indicator rankings, with a notable lack of in-depth analysis of resilience barriers. This study not only quantitatively assesses the resilience levels of 11 cities in Hebei Province using the entropy weight method and the urban climate resilience assessment model, but also introduces a barrier degree model, which accurately identifies the dominant barrier factors that affect resilience enhancement in different cities.

(2) The theoretical contribution of this paper is mainly reflected in the organic combination of the relatively macroscopic climate resilience theory with the multidimensional assessment and spatial pattern analysis at the regional level, and a focus on the differentiated paths of cities with different levels of development within the provincial area. The climate resilience frameworks in existing studies (e.g., UNISDR) are mostly guiding principles. In this study, the framework is translated into a system of quantifiable indicators of ecological–social–economic–infrastructural aspects, and a climate resilience study is carried out for Hebei Province, which deepens the application of resilience theory in the provincial complex unit. Through multidimensional quantitative assessment and spatial visualization, this study transforms the abstract concept of climate resilience into a comparable management tool for cities, identifies the priority areas of action for cities in the process of moving towards climate-resilient development, and provides a theoretical basis and practical handholds for coordinating resilience building at the provincial level.

(3) The findings of this paper are deeply integrated with national policy revelations and combined with China’s national strategy of “Beijing-Tianjin-Hebei Cooperative Development”, the construction of “new-type urbanization”, and the goal of “dual-carbon”. It is pointed out that enhancing the climate resilience of cities is an important support to ensure the sustainable development of the Beijing–Tianjin–Hebei urban agglomeration, as well as an intrinsic requirement to promote the new urbanization centered on human beings and to achieve high-quality development. Also closely echoing the Sustainable Development Goals (SDG 11 and SDG 13) at the international level, this paper’s assessment of urban infrastructure resilience and social resilience provides a complementary perspective for measuring the progress of Chinese cities in terms of SDG 11. This study responds directly to the call for SDG 13 (Climate Action), especially its core requirements of “integrating climate change responses into national policies, strategies, and planning” and “enhancing the capacity to respond to climate-related disasters”. This study fits well with the pathway of “climate resilient development” emphasized in the IPCC’s Sixth Assessment Report (AR6), and our four-dimensional framework encompasses the core systems of resilience articulated by the IPCC (social, economic, infrastructural, and ecological).

This study also presents several limitations: (1) In constructing the indicator system, it failed to include indicators related to climate and emergency management. Future research should engage in more detailed and comprehensive studies on climate resilience indicators and aim to ensure the objectivity of the collected data, thereby enhancing the realism of the research outcomes. (2) This paper lacks research on climate risk of cities, with only a single study of urban climate resilience; in future, we should fully consider the combination of climate risk and urban climate resilience research, with more consideration of climate data in the data source. (3) The research object of this paper is the cities in Hebei Province, and the research area is small; in the future, we should increase the case study of climate resilience cities or expand the research area, for example, through the expansion of the research scale of city groups and countries, and pay attention to the combination of theory and empirical evidence, so that the conclusions of the guidance of urban planning have more practical significance.