**2. Analysis of the vulnerability of Chinese urban agglomerations and urban continuous belts**

#### **2.1 Theoretical model**

According to the concept of urban vulnerability, relevant indicators are selected to establish an urban vulnerability evaluation index system, as shown in **Table 1**.

**41**

**Table 1.**

coping ability.

with internal and external factors [8].

*Urban vulnerability evaluation index system.*

*Climate Resilience, Megalopolis Vulnerability and Spatial Distribution*

**Code Indicator layer Positive and** 

emissions

dioxide emissions

wastewater emissions

unemployment rate

coverage rate

foreign capital

foreign currency household deposits of financial institutions

of science and education expenditure in fiscal expenditure

*x*<sup>4</sup> Fiscal deficit Positive 0.0097

*x*<sup>10</sup> Freight volume Negative 0.0754 *x*<sup>11</sup> Passenger volume Negative 0.1162 *x*<sup>12</sup> Urban road area Negative 0.0836

*x*<sup>1</sup> Industrial dust

*x*<sup>2</sup> Industrial sulfur

*x*<sup>5</sup> Urban registered

*x*<sup>6</sup> Financial institution loan balance

*x*<sup>8</sup> Per capita disposable income of urban residents

*x*<sup>7</sup> Urban green

*x*<sup>9</sup> Actual use of

*x*<sup>13</sup> Domestic and

*x*<sup>14</sup> The proportion

*x*<sup>3</sup> Industrial

**negative**

Positive 0.0857

Positive 0.0577

Positive 0.0575

Positive 0.0325

Positive 0.1631

Negative 0.0070

Negative 0.0423

Negative 0.1217

Negative 0.0904

Negative 0.0572

**Weight**

Urban vulnerability refers to the sensitivity it exhibits when faced with the influence of multiple factors inside and outside the urban system and the strength of its coping ability to adapt to this sensitivity. The higher the sensitivity of the urban system, the stronger the urban vulnerability. The higher the response capacity of the urban system, the smaller the urban vulnerability. Therefore, urban vulnerability is a function of the sensitivity and coping ability of the urban system when faced

( ) *<sup>S</sup>*

In the formula, V represents the urban vulnerability index. S represents the urban sensitivity index and R represents the urban response capability index. Urban vulnerability is directly proportional to sensitivity and inversely proportional to

*<sup>R</sup>* V f S,R = = (1)

*DOI: http://dx.doi.org/10.5772/intechopen.95253*

**layer**

Sensitivity index(S)

Coping Ability Index(R)

**Target layer Criterion** 

Urban vulnerability index(V)


#### *Climate Resilience, Megalopolis Vulnerability and Spatial Distribution DOI: http://dx.doi.org/10.5772/intechopen.95253*

#### **Table 1.**

*Urban vulnerability evaluation index system.*

Urban vulnerability refers to the sensitivity it exhibits when faced with the influence of multiple factors inside and outside the urban system and the strength of its coping ability to adapt to this sensitivity. The higher the sensitivity of the urban system, the stronger the urban vulnerability. The higher the response capacity of the urban system, the smaller the urban vulnerability. Therefore, urban vulnerability is a function of the sensitivity and coping ability of the urban system when faced with internal and external factors [8].

$$\mathbf{V} = \mathbf{f}\left(\mathbf{S}, \mathbf{R}\right) = \frac{\mathbf{S}}{R} \tag{1}$$

In the formula, V represents the urban vulnerability index. S represents the urban sensitivity index and R represents the urban response capability index. Urban vulnerability is directly proportional to sensitivity and inversely proportional to coping ability.

According to the calculation method of the urban vulnerability evaluation index system [7], the acquired data is processed first. Due to the diversity of data types in the evaluation index system, the dimensions and magnitudes of different index data have certain differences [9]. Therefore, it is necessary to further non-dimensionalize the acquired data to eliminate its impact on urban vulnerability evaluation indicators. In this evaluation indicator system, all indicators have a positive correlation with the sub-target level [10]. The standardization method of deviation of the data is selected to process the original data without dimension. The standardization of the dispersion of the data is a linear transformation of the original data, so that the result falls into the interval [0,1]. The conversion function is as follows:

$$X\_{\circ} = \frac{\boldsymbol{\pi}\_{\circ} - \min\{\boldsymbol{\pi}\_{\circ}\}}{\max\{\boldsymbol{\pi}\_{\circ}\} - \min\{\boldsymbol{\pi}\_{\circ}\}} \tag{2}$$

Second, the entropy method is used to determine the weight of each index in the urban vulnerability evaluation index system. The specific process is as follows:

1.Quantify all indicators with the same measurement. Calculate the proportion of the j-th index value of the i-th evaluation object, the calculation method is as follows:

$$p\_{\boldsymbol{\psi}} = \frac{\boldsymbol{\varkappa}\_{\boldsymbol{\psi}}}{\sum\_{i=1}^{m} \boldsymbol{\varkappa}\_{\boldsymbol{\psi}}} \tag{3}$$

2.Calculate the information entropy *<sup>j</sup> e* :

$$e\_j = -\frac{\mathbf{1}}{\ln m} \sum\_{i=1}^{m} \left( p\_{\circ} \ln p\_{\circ} \right) \tag{4}$$

In the formula, 0 ≤ *<sup>j</sup> e* ≤ 1; when *pij* = 0, *<sup>j</sup> e* = 0.

3.Calculate the difference coefficient *<sup>i</sup> g* :

$$\mathbf{g}\_i = \mathbf{1} - \mathbf{e}\_j \tag{5}$$

The smaller the entropy value, the greater the difference between indicators.

4.Calculate the index weight *wj* :

$$\mathbf{w}\_{\circ} = \frac{\mathbf{g}\_{\circ}}{\sum\_{i=1}^{n} \mathbf{g}\_{\circ}} \tag{6}$$

**43**

*Climate Resilience, Megalopolis Vulnerability and Spatial Distribution*

Xuancheng

Greater Bay Area. The relevant indicator data comes from the 2019 data China City Statistical Yearbook, the 2019 provincial and municipal statistical yearbooks and statistical bulletins. Due to the availability of data, the calculation does not include Hong Kong, Macau Special Administrative Region, Dingzhou and Xinji county-level cities. For individual missing values, replace with the mean value of the city group

Hebei Province; Anyang in Henan Province

Beijing; Tianjin; Baoding, Tangshan, Langfang, Shijiazhuang, Qinhuangdao, Zhangjiakou, Chengde, Cangzhou, Hengshui, Xingtai, Handan, Dingzhou, Xinji in

Shanghai; Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, Taizhou in Jiangsu Province; Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, Jinhua, Zhoushan and Taizhou in Zhejiang Province; Hefei, Bengbu, Wuhu in Anhui Province, Ma'anshan, Tongling, Anqing, Chuzhou, Chizhou,

Hong Kong; Macau; Guangzhou, Shenzhen, Zhuhai, Foshan, Zhongshan, Dongguan, Zhaoqing, Jiangmen, Huizhou in Guangdong Province

Combining the relevant index data of the studied cities and using the urban system vulnerability evaluation index model, the 2018 Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Guangdong-Hong Kong-Macao Greater Bay Area are calculated to obtain the vulnerability index

The central cities and economically underdeveloped cities of the three major urban agglomerations are relatively vulnerable areas in the urban agglomerations, and are low-sensitive and high-response areas. The vulnerability of the urban system is divided into 4 levels according to the clustering results. Among the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou is a very vulnerable area with a vulnerability index of 0.45, a sensitivity index of 0.1, and a coping capacity index of 0.34; Shenzhen is a low-vulnerability area; other areas are not vulnerable. Among the Yangtze River Delta urban agglomerations, Shanghai and Suzhou are vulnerable areas with a vulnerability index of 0.57 and 0.43, of which Shanghai's vulnerability index and sensitivity index are 0.18 and 0.39, respectively. Hangzhou and Nanjing are vulnerable areas. Other areas are not fragile areas. In the Beijing-Tianjin-Hebei urban agglomeration, Beijing is a very vulnerable area, with the vulnerability index and sensitivity index being 0.18 and 0.44, respectively. Tianjin and Tangshan are lowvulnerability areas. Other areas are not vulnerable areas. The vulnerability structure of the central cities of China's three major urban agglomerations is obvious. Their economic development is in a leading position in the urban agglomerations and the country, and from the perspective of urban infrastructure, cities have a strong ability to deal with vulnerability. Therefore, these cities are vulnerable. Therefore, urban sensitivity can be reduced through environmental protection policies (**Table 3**) [12].

**3. Measures to improve the climate adaptability of megacities**

According to the results of the second section, the fragility structure of the central cities of the three major urban agglomerations is obvious. Their economic

*DOI: http://dx.doi.org/10.5772/intechopen.95253*

**City group Cities**

Beijing-Tianjin-Hebei City Group

Yangtze River Delta City Group

Guangdong-Hong Kong-Macao Greater

*Definition of the study area.*

Bay Area

**Table 2.**

where the city is located (**Table 2**).

of each city in 2018. As shown in **Figure 1**.

**2.4 Result analysis**

#### **2.2 The indicator system**

According to the concept of urban vulnerability [11], relevant indicators are selected to establish an urban vulnerability evaluation index system, as shown in **Table 1**.

#### **2.3 Research scope and data sources**

The research scope includes the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration and the Guangdong-Hong Kong-Macao

*Climate Resilience, Megalopolis Vulnerability and Spatial Distribution DOI: http://dx.doi.org/10.5772/intechopen.95253*


**Table 2.** *Definition of the study area.*

Greater Bay Area. The relevant indicator data comes from the 2019 data China City Statistical Yearbook, the 2019 provincial and municipal statistical yearbooks and statistical bulletins. Due to the availability of data, the calculation does not include Hong Kong, Macau Special Administrative Region, Dingzhou and Xinji county-level cities. For individual missing values, replace with the mean value of the city group where the city is located (**Table 2**).

## **2.4 Result analysis**

Combining the relevant index data of the studied cities and using the urban system vulnerability evaluation index model, the 2018 Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Guangdong-Hong Kong-Macao Greater Bay Area are calculated to obtain the vulnerability index of each city in 2018. As shown in **Figure 1**.

The central cities and economically underdeveloped cities of the three major urban agglomerations are relatively vulnerable areas in the urban agglomerations, and are low-sensitive and high-response areas. The vulnerability of the urban system is divided into 4 levels according to the clustering results. Among the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou is a very vulnerable area with a vulnerability index of 0.45, a sensitivity index of 0.1, and a coping capacity index of 0.34; Shenzhen is a low-vulnerability area; other areas are not vulnerable. Among the Yangtze River Delta urban agglomerations, Shanghai and Suzhou are vulnerable areas with a vulnerability index of 0.57 and 0.43, of which Shanghai's vulnerability index and sensitivity index are 0.18 and 0.39, respectively. Hangzhou and Nanjing are vulnerable areas. Other areas are not fragile areas. In the Beijing-Tianjin-Hebei urban agglomeration, Beijing is a very vulnerable area, with the vulnerability index and sensitivity index being 0.18 and 0.44, respectively. Tianjin and Tangshan are lowvulnerability areas. Other areas are not vulnerable areas. The vulnerability structure of the central cities of China's three major urban agglomerations is obvious. Their economic development is in a leading position in the urban agglomerations and the country, and from the perspective of urban infrastructure, cities have a strong ability to deal with vulnerability. Therefore, these cities are vulnerable. Therefore, urban sensitivity can be reduced through environmental protection policies (**Table 3**) [12].
