Urban Vulnerability under the Extreme High Temperatures in the Chengdu-Chongqing Area, Western China

Abstract

The frequent occurrence of extreme high-temperature events in the summer under global climate change poses a serious threat to Chinese society. An urban vulnerability evaluation system for counties in the Chengdu-Chongqing Area was constructed to calculate the urban vulnerability and distribution characteristics of each district. In this study, a vulnerability-contribution model was used to analyze the types of urban vulnerability in the Chengdu-Chongqing Area. Additionally, combined with the optimal parameter geographic detector (OPGD) model, the main influencing factors and interactions of urban vulnerability were explored. The results show that: ① The urban vulnerability of the Chengdu-Chongqing Area is high in the east and low in the west, with vulnerability degree mostly below the medium degree. ② Exposure contributes more than 50% to severe and general urban vulnerability in the region, while adaptability contributes the highest proportion to mild urban vulnerability, reaching 47.53%. ③ From the factor perspective, the impact ratio of high-temperature days on urban vulnerability is 39.1%, and the interaction between various meteorological factors and social factors produces an enhancement effect, with the highest interaction q-value reaching 0.7863.

1. Introduction

The sixth assessment report of the Intergovernmental Panel on Climate Change (IPCC) noted that in the past 70 years, the frequency of heat waves and droughts has increased, and the future probability of potential hazards of heat waves, droughts, and their combined disasters has also increased [1,2]. High-temperature and heat wave-related disasters threaten the sustainable development of economy, health, and natural ecology [3,4,5]. It is pointed out that taking urgent action to address climate change and the impacts is one of the goals of sustainable development in 2015 [6]. In recent years, the scope of the impact has expanded, with more areas being exposed to high-temperature and hot environments [7,8]. After 2007, the increase in the number of related official documents in China, such as the “Notice on Further Strengthening Summer Heat Prevention and Cooling Work in the Workplace”, indirectly confirms increasingly severe high-temperature events.

The number of studies on group health vulnerability under extreme high-temperature conditions has increased in recent years [9,10,11,12]. It has been shown that excessive environmental temperature increases the risk of death [13]. Racial genetic differences may have different adaptive capacities to high temperatures, while linguistic barriers may make it more difficult to access assistance during a disaster [14]. The research on the vulnerability of social operational capacity and sustainable development management under heat waves is also improving constantly. Urbanization and increased population density have led to an increase in the vulnerability [15], and there are significant differences in heat waves between urban and rural areas [16], so the population exposure under the high temperatures should be taken seriously [17]. The studies on the vulnerability of high-temperature heat waves in China include different scales such as the whole country [18], the Yangtze River Economic Belt [19], and specific cities [20]. With the normalization of extreme heat disasters, engineering technology, organizational systems, and politics should be combined to improve capabilities of pre-disaster warning, disaster rescue, and post-disaster resettlement [21]. A high-precision and dynamic high-temperature health stress warning system needs to be continuously improved, and the goals of cooling and health should be included in the national spatial planning goals. International, domestic, and community cooperation should be strengthened to alleviate the threat of high temperatures [22]. Overall, scholars have made rich research achievements on the vulnerability of regional extreme high temperatures, which can provide prevention suggestions for large-scale heat wave disasters.

The Chengdu-Chongqing region is economically developed in western China with a complex terrain and unique climate [23]. It is similar to the Yangtze River Delta urban agglomeration [24] and some urban belts in Western countries [25] in population, economy, and scale. The Chengdu-Chongqing region is close to the southern edge of the North-South Transitional Zone, bordering Gansu Province, Shaanxi Province, Hubei Province, and Guizhou Province. The latitude of the region is around 30° N, which is similar to the important hubs such as the Wuhan urban agglomeration and the Tokyo–Osaka transportation line in climate and latitude. Meanwhile, it is an important stronghold of the ancient Three Kingdoms culture and a hub area of the South and North Silk Road, with an important cultural background. Studies have shown that the heat island area in the downtown area of Chengdu increases during the daytime, and the variation rate of air temperature may be higher than the global average while the intensity of the heat island effect is positively correlated with population [26,27]. The index related to minimum temperature is more sensitive to urbanization [28]. Similarly, the central and southwestern parts of Chongqing were the areas with the most severe heat wave disasters in the past 60 years [29]. Luo et al. [30] pointed out that the impact of three-dimensional variables (altitude) on surface temperature in mountainous cities is 2.3% higher than that in plain cities. Yang et al. [31] and Yin et al. [32] explored the risk and response measures of high-temperature disasters in the Chengdu-Chongqing Economic Zone and Nanchong, which have considered socio-economic indicators, social environmental indicators, and climate characteristics when conducting risk assessments, which can reflect social vulnerability.

With the development of society, the amount of refrigeration equipment such as air conditioning is increasing, and the aging population is also intensifying. However, at the same time, the global average annual temperature is also constantly rising, and most cities around the world have been hit by heat waves. In the dynamic pattern of intensified climate change and socio-economic development in the new era, the potential risk of heat wave disasters may differ. Therefore, it is necessary to determine new indicators based on these changes to analyze the high-temperature response capacity of key cities and regions. The Chengdu-Chongqing region holds an important strategic position in the development and economic construction of western China [33], with high representativeness in geographical location and economic scale. In summary, this study considered the potential driving effect of the Chengdu-Chongqing Economic Circle on the surrounding areas. The Chengdu-Chongqing Economic Circle was expanded to cover some surrounding areas (Chengdu-Chongqing Area) and has been used as the research object.

2. Research Areas and Methods

2.1. Overview of the Research Area and Data Sources

The Chengdu-Chongqing Area (27°43′ N–32°54′ N, 101°48′ E–110°6′ E) defined in the study includes Sichuan Province and Chongqing. The total area is nearly 2.4 × 10⁵ km². The area is mainly composed of basins, surrounded by mountains, with an average elevation below 1500 m. Some areas in the west are plateau mountains, while the east is parallel valleys. Additionally, it has a subtropical monsoon climate, with a vegetation type of subtropical evergreen broad-leaved forest, but extreme high temperatures have occurred frequently in recent years [34].

The population of Chengdu Chongqing region is about 100 million, and the regional GDP in 2021 has exceeded 7 trillion yuan and it is showing an upward trend, which plays an important role in strategic economic and ecological status (shown in Figure 1).

Meteorological data were obtained from the National Meteorological Science Data Center (http://data.cma.cn/, accessed on 20 September 2023). The daily maximum temperature data from 48 meteorological stations in the Chengdu-Chongqing Area (including 33 in Sichuan and 15 in Chongqing) from 1961 to 2020 were selected for this research. The outliers were eliminated by using the 3σ method and the original data quality control code, and the missing values were supplemented by linear interpolation. Finally, the daily maximum temperature data for 60 years were obtained. The distribution of the meteorological stations and terrain overview are shown in Figure 1. The socioeconomic data are taken from the statistical yearbook (2020) and social questionnaire survey of each area.

2.2. Research Methods for Urban Vulnerability

2.2.1. Identification of High-Temperature Heat Waves and High-Temperature Days

High-temperature heat waves refer to weather processes where the temperature remains abnormally high during a certain period. A general definition of a high-temperature day (a day with a maximum temperature higher than 35 °C is considered a high-temperature day) [1] is adopted because of the small spatial span and non-significant elevation differences. During heat waves (a duration of 3 or more days with high temperature), the severity and degree of harm caused by high-temperature disasters are often expressed in terms of high temperature intensity. It can be expressed as the cumulative number of temperatures in the part where the daily highest temperature is higher than the absolute threshold (35 °C) during the heat wave process [35] (in degrees Celsius). The formula for calculating high temperature intensity is as follows:

$$Q= \frac{{{\displaystyle \sum }}_{i=1}^m\left[\left(\overline{T_i}-35\right)\times \left(d_i\right)\right]}{m}$$

(1)

where Q is the intensity of high-temperature heat waves in a certain year (°C); $\overline{T_i}$ is the daily average of the highest temperature during the i-th heat wave of a certain year; m is the total number of heat wave frequencies that occur in the region in a certain year; and $d_i$ is the duration of the i-th heat wave.

In addition, Nearest Neighbor is used for interpolation of temperature-related data in various districts and counties. Previous studies have shown that this method has high accuracy in interpolation of meteorological elements.

2.2.2. Construction of the Indicator System and Data Processing

The definition of vulnerability in catastrophology refers to the (potential) loss caused by natural disasters to the disaster-bearing entity. Usually, the loss is the result of the joint action of various factors such as nature, society, and economy [36]. This study focuses more on the impact of high temperature heat waves on social operations, and the framework proposed by IPCC [37] provides guidance for the construction of the indicator system in this article. Xie et al. [38] noted that indicators should include both small-scale residents and large-scale regions as much as possible. Therefore, the 13 indicators (shown in

Table 1. Urban vulnerability evaluation index system in the Chengdu-Chongqing Area.

Target	Dimensional Layers	Weight	Index (Driving Factors)
urban vulnerability	Exposure	0.388	Number of hot days (X1)
		0.343	Intensity of heat wave (°C) (X2)
		0.032	Green space for each inhabitant (X3)
		0.237	Frequency of heat waves (X4)
	Susceptibility	0.083	Proportion of rural population (X5)
		0.744	Population density (X6)
		0.054	Proportion of the elderly population (X7)
		0.119	Gender ratio (X8)
	Adaptability	0.099	Number of air conditioners at home (X9)
		0.410	Quantity of regular heatstroke medication/food (X10)
		0.189	Proportion of medical staff (X11)
		0.226	Per capita number of medical treatments (X12)
		0.076	Per Capita GDP (X13)

) were comprehensively considered for vulnerability measurement. The weights of each factor in Table 1 are determined by the entropy method to avoid subjectivity caused by artificial settings.

Due to dimensional differences in various statistical indicators, range standardization is used here to perform dimensionless processing on the data of each indicator. The forward formula is as follows:

$$b_{\mathrm{ij}}=\frac{a_{ij}-a_{jmin}}{a_{jmax}-a_{jmin}}$$

(2)

The calculation formula for negative indicators is:

$$b_{\mathrm{ij}}=\frac{a_{jmax}-a_{ij}}{a_{jmax}-a_{jmin}}$$

(3)

Above, b_ij, a_ij, a_jmax, a_jmin are the standardized value, original value, maximum value, and minimum value of the j-th indicator of the i-th research object, respectively.

The multiplication and division method can better reflect the combined changes in various dimensional layers [38], and the urban vulnerability index (UVI) is calculated using the multiplication and division method to characterize urban vulnerability. The formula is as follows:

$$UVI=\frac{EI\times SI}{AI}$$

(4)

$$EI={{\displaystyle \sum }}_{i,j=1}^n{W}_{ij}{I}_{ij} SI={{\displaystyle \sum }}_{i,j=1}^n{W}_{ij}{I}_{ij} AI={{\displaystyle \sum }}_{i,j=1}^n{W}_{ij}{I}_{ij}$$

(5)

The UVI represents the urban vulnerability index, while the EI, SI, and AI represent the exposure index, sensitivity index, and adaptability index, respectively. W_ij represents the weight of the j-th indicator in the i-th region, and I_ij represents the standardized value of the j-th indicator in the i-th region (each index is categorized into 5 degrees by the natural breakpoint method).

2.2.3. The Division of Vulnerability Levels

The sample is smaller when the districts were selected as the subject of the study. So, the severe, general, and mild urban vulnerability districts and counties were defined here (severe, general, and mild vulnerability levels are different from the above vulnerability degree). The specific division is shown in

Table 2. Interval division of vulnerability levels in the Chengdu-Chongqing Area.

Vulnerability Level	Division Basis
serious	1.9001 < UVI
general	1.0465 < UVI ≤ 1.9001
mild	0 ≤ UVI ≤ 1.0465

.

2.3. Research Methods for Determining the Influencing Factors of Urban Vulnerability

2.3.1. Contribution Analysis Method

This article aims to reveal the impact of exposure, sensitivity, and adaptability on urban vulnerability, clarify the vulnerability factors of each district and county, and further quantify the contribution of the three dimensions to urban vulnerability based on vulnerability assessment, referring to the methods of Zhang et al. [20]. The calculation formula is as follows:

$$C_{mn}=\frac{W_n\times I_{mn}}{{{\displaystyle \sum }}_{n=1}^3{W}_n\times I_{mn}}\times 100\%$$

(6)

In the formula, C_mn represents the contribution of n dimensions in the m-th region, and W_n represents the weight of the n-th dimension. I_mn is the standardized value of the n indicator in layer m. The weight of the dimensions is determined by using the entropy method, with the weights of the exposure, sensitivity, and adaptability dimensions being 0.1697, 0.2834, and 0.5469, respectively.

2.3.2. Optimal Parameter-Based Geographical Detector

To further explore the impact of various factors and their combinations on vulnerability in different dimensions, the optimal parameter geographic detector (OPGD) [39] was used to eliminate subjectivity in partitioning continuous variables and detecting spatial heterogeneity, revealing the driving factors behind vulnerability [40]. First, the q-value (q ∈ [0, 1]) of each continuous factor is calculated under different grading methods and different numbers of discontinuities. The larger the value is, the stronger the spatial differentiation of regional vulnerability, and the stronger the explanatory power of the influencing factor. Then, equal breaks, natural breaks, quantile breaks, geometric breaks, and standard deviation breaks are used to classify and set the classification level as 3 to 9. The parameter combination with the largest q value was selected as the discretization standard to detect and identify the influence of a single factor on urban vulnerability.

$$q=1-\frac{{{\displaystyle \sum }}_{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}=1-\frac{SSW}{SST}$$

(7)

In the equation, q represents the explanatory power of the factor, h represents the stratification of the explanatory variable or the dependent variable, N_h and N represent the number of units in layer h and the entire region, respectively, and ${\sigma }_h^2$ and ${\sigma }^2$ represent the variance of layer h and the whole region value, respectively.

The joint effects of two factors on urban vulnerability are analyzed through interaction detection. The main types include nonlinear attenuation q(X₁∩X₂) < min(q(X₁), q(X₂)), single-factor nonlinear attenuation min(q(X₁), q(X₂)) < q(X₁∩X₂) < max(q(X₁), q(X₂)), double-factor enhancement q(X₁∩X₂) > max(q(X₁), q(X₂)), independent q(X₁∩X₂) = q(X₁) + q(X₂), and nonlinearity enhancement q(X₁∩X₂) > q(X₁) + q(X₂) [41]. The specific discrimination method can be found in reference [27].

3. The Results

3.1. Spatial Pattern of Regional Vulnerability

The exposure degree, sensitivity degree, and adaptability degree of each district were quantified in the Chengdu-Chongqing Area through the EI, SI, and AI. The calculation of each indicator is shown in Formula (5), and the final calculation results are brought into Formula (4) to obtain the urban vulnerability index of each district and county. This article uses the natural breakpoint method to divide the calculation results of each dimension into five degrees: low, medium-low, medium, medium-high, and high (Figure 2).

Overall, the percentages of districts with exposure indices ranging from high to low were 20, 32, 42, 35, and 27, accounting for 12.82%, 20.51%, 26.92%, 22.44%, and 17.31%, of the total number of districts, respectively. From the spatial distribution of the exposure index, the areas with high exposure indices gradually increase from northwest to southeast in a ladder shape, mainly concentrated in Chongqing, showing a trend of relative aggregation, in which the southwest of Chongqing (such as Yuzhong District, Yubei District, Jiulongpo District, Banan District, etc.) is the most concentrated, and the northeast of Chongqing also has high exposure index districts (such as Wanzhou District, Wuxi County, etc.). This finding is highly consistent with the conclusions of Mao and Wang [42]. There is no area with a high exposure index in Sichuan, and the area with a high exposure index is mainly located in the border area between Sichuan and Chongqing (such as southern Nanchong, Dazhou, southern Guang’an, and some districts in Luzhou). Areas and counties with medium and low exposure are mainly located in Sichuan, and there are only three areas and counties with low or lower exposure indices in Chongqing (Qianjiang District, Youyang Tujia and Miao Autonomous County, and Xiushan Tujia and Miao Autonomous County). The previous methods were uesd to measure the exposure under high-temperature conditions [17,18] and the exposure in green space areas [21]. It has been noted that topography, atmospheric circulation, and vegetation are important factors affecting high-temperature exposure [42,43]. Compared with Sichuan, Chongqing is closer to the east and is more easily controlled by the Western Pacific Horse latitudes in summer. It is sunny, hot, and less rainy [44]. In addition, the parallel mountains and valleys in eastern Sichuan in the north prevent some cold air in the north from flowing southward [42], resulting in strong high-temperature sustainability.

From the perspective of the sensitivity exponential distribution, the Chengdu-Chongqing Area has a low sensitivity overall. The areas with high sensitivity indices are Wuhou District and Yuzhong District, which are highly consistent with the locations of Chengdu and the main urban areas of Chongqing. The areas with higher sensitivity indices were distributed only in the main urban area of Chengdu (mainly the Qingyang District, Jinniu District, Jinjiang District, and Chenghua District) and not in other locations in the Chengdu-Chongqing Area. The districts with medium or low vulnerability indices account for a significant proportion of the total area. From a quantitative perspective, the number of districts with vulnerability indices ranging from high to low is 2, 4, 3, 57, and 90, accounting for 1.28%, 2.56%, 1.92%, 36.54%, and 57.69%, respectively, of the total number of districts.

From the exponential distribution of adaptability, there is no obvious trend in the horizontal distribution of the overall adaptability, showing a broken patch distribution. Among them, the number of districts with below-medium adaptability indices is the largest in the Chengdu-Chongqing Area. The districts with medium adaptability indices are mostly around the city center, while the districts with medium-high and high adaptability indices are mainly the main urban areas of cities at various levels (such as Luojiang District and Da’an District). Notably, there are no districts with high adaptability indices in Chengdu and Chongqing, which are all districts with high adaptability indices. In terms of quantity, the numbers of districts with high to low adaptability indices are 2, 13, 59, 61, and 21, accounting for 1.28%, 8.33%, 37.82%, 39.10%, and 13.46% of the total, respectively.

In terms of the spatial characteristics of the vulnerability index, the overall distribution is high in the east and low in the west. The vulnerability indices are arranged from high to low, with proportions of 2.56%, 6.41%, 25.00%, 31.41%, and 34.62%, respectively. There were only 33.97% of the districts with moderate or greater vulnerability indices, of which there were only four districts with high vulnerability indices, which were distributed in both Sichuan and Chongqing, including Anju District of Suining city, Gulin County of Luzhou city, Kaizhou District of Chongqing, and Zhongxian County of Chongqing city. There are 10 districts with high vulnerability indices, 7 of which are located in Sichuan (such as Daying County, Pengxi County, and Anyue County) and 3 of which are located in Chongqing (Shapingba District, Yuzhong District, Pengshui Miao, and Tujia Autonomous County). The areas and counties with medium vulnerability image indices are mostly located in the eastern part of the Chengdu Plain, while the areas and counties with medium-low and low vulnerability degrees are mainly located on the western edge of the Chengdu Plain.

3.2. Main Causes of Regional Vulnerability

3.2.1. Statistics on Different Causes of Vulnerability

The three dimensions of exposure, sensitivity, and adaptability comprehensively determine the urban vulnerability of the Chengdu-Chongqing Area. Here, the causes of vulnerability in each district and county are classified into three categories from a dimensional perspective, namely, high-frequency vulnerability caused by high exposure, vulnerability caused by high social sensitivity, and vulnerability caused by insufficient adaptability. The specific discrimination method calculates the contribution degree of different dimensions of each district and county according to Formula (6). If the contribution degree of the corresponding dimension accounts for the largest proportion, it is the corresponding cause of vulnerability.

Figure 3 shows that the high vulnerability in the central and eastern parts of the Chengdu-Chongqing Area is almost entirely caused by high-temperature exposure. The changes in the western region, especially the Chengdu Plain, Ya’an, and its surrounding areas, are mainly due to insufficient adaptability. However, there are very few districts with high sensitivity that lead to vulnerability, which are mainly scattered in the main urban areas of Chengdu and Chongqing. In terms of quantity, there are a total of 96 vulnerable districts caused by high exposure, accounting for 61.54%; 6 vulnerable districts caused by high sensitivity, accounting for 3.85%; and 54 vulnerable districts due to insufficient adaptability, accounting for 34.61%.

3.2.2. Regional Distribution of Different Causes of Vulnerability

From the perspective of districts with different levels of high-temperature urban vulnerability, out of the 14 severely vulnerable districts, 13 have high-temperature exposure, and only 1 has high social sensitivity. Out of 39 general urban vulnerability districts, a total of 36 have high-temperature exposure, and 2 have high social sensitivity. Out of 103 mild urban vulnerability districts, 47 have high-temperature exposure, 2 have high social sensitivity, and 54 have insufficient adaptability. According to the average contribution of each district and county type (Figure 4), the average contribution of the exposure index in both the severe and general urban vulnerability districts exceeded 50%, reaching 62.02% and 54.67%, respectively. Among the mild urban vulnerability districts, the average contribution degree of adaptability was the highest, accounting for 47.53%.

High and medium-high vulnerability (severe urban vulnerability level) districts and counties, as well as medium vulnerability (general urban vulnerability level) districts, are mostly affected by high temperature exposure, while the main cause of low and medium-low vulnerability (mild urban vulnerability level) districts is insufficient adaptability. The number of vulnerable areas in the Chengdu-Chongqing Area due to high sensitivity is relatively low.

3.3. Exploration of the Driving Factors of Urban Vulnerability

3.3.1. Discretization of the Continuity Factor

The academic community has conducted many studies using geographic detectors (GDs) in areas such as ecological vulnerability, urban waterlogging risk, and human habitat vulnerability, proving the feasibility of using evaluation factors as independent variables to detect the aforementioned objects [45,46,47,48,49,50]. The Chengdu-Chongqing Area covers mountains, plains, and other major geomorphic areas, has a subtropical monsoon climate, has stable socioeconomic growth, and is representative of both natural and social conditions. Therefore, the selection of independent variable factors can be found in the relevant research on other urban agglomerations in China. The UVI is taken as the dependent variable, and 13 evaluation indicators under each district and county are selected as the independent variables. The specific processing results are shown as follows (Figure 5).

As an example, for X₁, the geometric interval method has a significantly higher q-value when classified as 5 compared to the other methods. Therefore, the optimal parameter selection for high-temperature days (X₁) in geographical detectors should be based on the geometric interval divided into five categories. Different driving factors have different spatial discretization methods to maximize their q-values, and this parameter combination that maximizes q-values is the optimal parameter for geographic detectors. Given the impact of space, this article provides parameter selection for the top eight impact factors with the highest q-values. The specific parameter division and setting results are shown in

Table 3. Classification method and number of stages for impact factors.

Factors	Methods	Intervals	Factors	Methods	Intervals
X1	geometric	5	X8	quantile	8
X2	equal	8	X9	natural	8
X3	quantile	8	X10	quantile	9
X4	sd	8	X11	quantile	9
X5	quantile	9	X12	natural	8
X6	quantile	9	X13	natural	9
X7	sd	9	-	-	-

.

3.3.2. Analysis of a Single Driving Factor

By using OPGD to conduct single factor-driven detection of urban vulnerability in the Chengdu-Chongqing Area to identify its explanatory power, we find that the significance of each factor corresponds to the q-value as follows (

Table 4. The correspondence between q values and the significance of each factor.

Variable	q-Value	Sig.	Variable	q-Value	Sig.
X1	0.390771	0.000	X8	0.043767	0.493
X2	0.356134	0.000	X9	0.173176	0.001
X3	0.067794	0.194	X10	0.123695	0.030
X4	0.348757	0.000	X11	0.102308	0.067
X5	0.247505	0.000	X12	0.076069	0.162
X6	0.119297	0.031	X13	0.070002	0.539
X7	0.072545	0.300	-	-	-

). After removing the factors with insufficient significance, the explanatory power of the remaining factors is as follows (Figure 6):

Overall, the q-value of X₁ is 0.391, indicating that the explanatory power of the high-temperature days factor for overall urban vulnerability reaches 39.1%, which is the highest among all factors. The q-values of X₂ and X₄ are 0.356 and 0.349, respectively, both reaching a high level of explanatory power. At the socioeconomic level, the explanatory power of each factor in descending order is the proportion of the township population (X₅), the number of air conditioners at home (X₉), the number of regular heatstroke medications/food (X₁₀), and the regional population density (X₆), with corresponding q values of 0.248, 0.173, 0.124, and 0.119, respectively. Single-factor detection revealed that natural factors such as high-temperature days, intensity, and frequency of heat waves play a dominant role in the level of urban vulnerability in the Chengdu-Chongqing Area, especially high-temperature days, which have the strongest explanatory power for regional urban vulnerability, while socioeconomic factors such as population density have relatively weak explanatory power for regional urban vulnerability. This conclusion also maintains high consistency with the results of the contribution in the previous text.

3.3.3. Analysis of Interaction Driving Factors

To further explore the interactions between various factors, the OPGD interaction detector was used for detection. The results are as follows (Figure 7). Most factors show a nonlinear enhancement trend (interaction types are identified with “2” in Figure 7a). The factor interactions with explanatory powers exceeding 70% are X₄∩X₆, X₂∩X₆, X₄∩X₉, and X₂∩X₉. Among them, the interaction between average heat wave frequency and regional population density has the strongest explanatory power, and the trend shows nonlinear enhancement, with a q-value of 0.7863, indicating that the synergy of the two factors can explain 78.63% of urban vulnerability. Therefore, it is necessary to focus on adjusting population density and arranging heat relief facilities in this area to reduce urban vulnerability.

In addition, after the interaction between various socio-economic factors (X₅~X₁₃) with the intensity and frequency of heat waves (X₂, X₄), the q-value of the interaction ranged from 0.397 to 0.7863 (higher than the q-value under a single socio-economic factor), the interaction between socio-economic factors (X₅~X₁₃) with X₂ and X₄ showed an enhanced signal. In other words, the differences in social factors alone have less impact on the divergence of urban vulnerability in the context of high temperatures, but the divergence of urban vulnerability is more pronounced with the addition of heat wave intensity (X₂) and heat wave frequency (X₄).

It is worth noting that the interaction between high-temperature days (X₁) and the most factors show single-factor nonlinear weakening, showing only a double-factor enhancement with X₅ and X₆. Except for X₅ and X₆, the q-values of the interactions between X₁ and the other factors ranged from 0.2316 to 0.3897, which was lower than its explanatory power. The reason may be that the number of high-temperature days in a region increases beyond a certain threshold, and the impact of high-temperature days on urban vulnerability decreases due to the increased operational intensity of cooling equipment and medical systems in the region. In addition, it is also possible that the increase in the number of high-temperature days may cause urban vulnerability to reach a peak in the region, resulting in paralysis and no longer increasing.

4. Discussion

The evaluation indicators are improved in this study to make the indicator system more in line with the current development status of the area. In the exploration of influencing factors, a more objective OPGD model was used to determine the explanatory power of each influencing factor on urban vulnerability, and their interaction modes were accurately identified. Within the study area, there are differences in exposure levels, sensitivity, and adaptability among different counties. Overall, the vulnerability is higher in the east and lower in the west. In addition, extreme high temperatures make a greater contribution to severe social vulnerability, while mild social vulnerability is often associated with insufficient adaptability. Therefore, for the eastern part of the research area, reducing direct solar thermal radiation is very important. This requires the government (meteorological department) to carry out artificial rainfall when necessary and take measures for high temperature warnings. From the results of factor detection, in addition to natural factors, the proportion of rural population and the equipment of heat dissipation facilities will affect extreme high temperatures in urban systems. Therefore, it is necessary to continue to promote the rural revitalization plan, strengthen rural infrastructure construction, and increase the deployment scope of heat dissipation facilities. In fact, the threshold for cooling demand varies among different regions, which leads to differences in energy consumption (especially electricity) between regions under extreme high temperatures [51,52]. So, the energy reserves will also affect the adaptability indicators to a certain extent (areas with fewer backup energy sources are more sensitive to high-temperature attacks, such as power outages, resulting in an increase in UVI). In addition, the number of days with high temperatures has the strongest explanatory power for the spatial distribution of vulnerability. However, some factors (e.g., X₄, X₅) weakened their explanatory power after the interaction with X₁, which may be related to the increase in high-temperature days leading to the peak of urban vulnerability in the region.

The main influencing factors of urban vulnerability under high temperature include natural factors such as high-temperature exposure and sunshine duration, as well as socioeconomic factors such as population density, the urban green space ratio, and heatstroke prevention capacity [53]. The scope of extreme climate events caused by ocean surface temperature anomalies [53,54] is also highly likely to increase, so the differentiation of urban vulnerability under the combined effects of socioeconomic and natural factors may be more significant. There is research indicating that Sichuan Province lacks adaptability in the face of flood disasters [55]. This is similar to the conclusion of this article, which is that the number of counties with increased urban vulnerability due to insufficient adaptability exceeds one-third. In terms of spatial distribution of vulnerability, the study points out that highly vulnerable areas are concentrated in the center areas of Chengdu and Chongqing, as well as the rurales in the northwest of Chongqing [31]. The results are similar to the above conclusions. However, this study found that the distribution area of counties with exposure-induced vulnerability is larger. On the one hand, this difference may be due to the longer meteorological data in this article (frequent occurrence of severe high temperature weather in recent years). On the other hand, differences in evaluation indicators and methods may also be one of the reasons for the differences. So, if the method needs to be applied to other regions, it is necessary to consider the special geographical differences of other regions (including natural and social factors).

At present, there are still limitations in the indicators of exposure, sensitivity, and adaptability in the study of urban heat wave vulnerability [56]. The evaluation system here may not always emphasize the mitigating effect and the concern for outdoor workers [57,58]. However, these indicators can meet the research needs at the county level. To make research more refined, human activities (e.g., increased CO₂ emissions due to human activities) as well as land use types could be considered. In addition, the smallest unit of this study is the county. The data accuracy is slightly lower than that of the kilometer grid dataset (1 km × 1 km), and the temperature data can only be obtained from some national stations and the site density and coverage area are limited, which may make it difficult to match the mountainous terrain. Therefore, the impact of mountainous terrain on temperature and heat has not been fully considered, which leads to a reduction in the impact of ventilation [29] and altitude [30] on temperature. However, considering that the population concentration areas in most counties are at low altitudes and close to plain areas, the impact of errors on the results is negligible on the county scale.

In subsequent research, optimization can be made from aspects such as evaluation systems and data to compensate for the above shortcomings. The studies on the risk of high-temperature heat wave disasters still mainly focused on the number of high-temperature days and the intensity of heat waves [59,60,61]. However, the indicator of humidity may be an important influencing factor of high-temperature heat wave disasters in some areas. Moreover, land use types also continue to be altered [62]. The ability to absorb heat and long-wave radiation is different between built-up land (e.g., hard concrete surfaces) and agricultural land (e.g., paddy fields). Therefore, different land use types have different impacts on regional warming. In the future, improving the selection of influencing factors can comprehensively evaluate the threat of high-temperature heat waves in the context of global climate change [63]. In addition, against the backdrop of economic recovery and the gradual control of the COVID-19 pandemic, the increase in outdoor population mobility has the potential to expand the population base suffering from sensitive diseases such as hypertension. Therefore, groups with high exposure and sensitivity, such as the tourism industry [56], high-temperature sensitive patients [64], and outdoor workers [65], should also be considered at the sensitivity level. Finally, with the establishment of more automatic weather stations and the development of real-time weather information sharing systems, the problem of insufficient station density and the lack of consideration for the impact of terrain on temperature in future research can gradually be solved.

5. Conclusions

With the vulnerability evaluation index system, contribution model, and OPGD, the high-temperature disasters and the impacts in the Chengdu-Chongqing Area were analyzed. The results are as follows:

① The exposure index increases from northwest to southeast. The sensitivity index is low in most areas, but the sensitivity indices of central Chengdu and central Chongqing are approximately 0.79. The adaptability index exhibited significant regional differences and low concentrations. The vulnerability index is high in the east and low in the west, with significant differences among districts and counties.

② The vulnerable districts caused by high exposure have the greatest proportion and the largest area, with a total of 96, followed by the vulnerable counties with insufficient adaptability, accounting for 34.61%, and the vulnerable counties with insufficient sensitivity have a relatively small number, accounting for only 3.85%. The average contribution of exposure levels to general and severe urban vulnerability areas and counties exceeds half, with the highest proportion being the average contribution of adaptability in mild urban vulnerability areas and counties.

③ The explanatory power of climate factors on the spatial differentiation of urban vulnerability is stronger than that of socioeconomic factors, with the highest explanatory power being the number of high-temperature days, reaching 39.1%. The combined effect of climate factors and regional socioeconomic factors had a greater impact on the urban vulnerability of the Chengdu-Chongqing Area.