Information diffusion-based risk assessment of natural disasters along the Silk Road Economic Belt in China

Journal Pre-proof Information diffusion-based risk assessment of natural disasters along the Silk Road Economic Belt in China

Yu Xiaobing, Yu Xianrui, Li Chenliang, Ji Zhonghui PII:

S0959-6526(19)33614-5

DOI:

https://doi.org/10.1016/j.jclepro.2019.118744

Reference:

JCLP 118744

To appear in:

Journal of Cleaner Production

Received Date:

04 April 2019

Accepted Date:

05 October 2019

Please cite this article as: Yu Xiaobing, Yu Xianrui, Li Chenliang, Ji Zhonghui, Information diffusionbased risk assessment of natural disasters along the Silk Road Economic Belt in China, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.118744

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Journal Pre-proof Information diffusion-based risk assessment of natural disasters along the Silk Road Economic Belt in China Yu Xiaobing, Yu Xianrui, Li Chenliang,Ji Zhonghui School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China Abstract: The Belt and Road Initiative launched by China has attracted considerable attention. However, little attention has been paid to the risk assessment of natural disasters for regions along the route. Due to the frequency of disasters and harsh natural environment, areas along with the Silk Road Economic Belt are vulnerable to natural disasters. In this study, an evaluation model is proposed involving evaluation indicators, the relative damage calculation and information diffusion theory. The risk of natural disasters including flood, drought, convective storm, and low temperature is respectively assessed by this model. As a result, it is found that the probability of natural disasters is negatively correlated with the relative damage, and the higher the relative damage, the lower the risk probability. Besides, drought has the greatest impact on crop yield among the studying areas, followed by flood. Although the impact of convective storm and low temperature is not as large as the flood and drought, they also occur frequently and cannot be ignored. Furthermore, the empirical studies from Sent Viet Nam and Philippines have demonstrated that our model can be applied into other countries along the Belt and Road to make the risk assessment. Keywords: the Belt and Road; the Silk Road Economic Belt; information diffusion; risk assessment; natural disasters 1. Introduction The Belt and Road Initiative was first proposed by General Secretary Xi Jinping in September 2013, which specifically referred to the Silk Road Economic Belt and the 21st Century Maritime Silk Road. The purpose of this initiative is to further strengthen cooperation and partnership with other countries in the world, especially developing ones along the route. It aims to seek and uphold mutual benefit and accommodate the interests of all parties. Until now, the Belt and Road Initiative has involved more than 65 countries of Asia, Europe and Africa, and the related economic volume has been over 21 trillion US dollars (Peng et al., 2017). It has become the most popular initiative for providing various countries and regions with international collaborative platform. Facing the global economic downturn, China's Belt and Road initiative is proposed to reinforce the trade and investment between countries, particularly the major projects concerning infrastructure construction and energy investment. The outlook for Belt and Road initiative will be promising if the related projects are carried out smoothly. However, the regions along with the Silk Road Economic Belt are vulnerable to natural disasters like flood, drought and low temperature. It will do great damage to local residents and affect the regular operation of economy when natural disasters occur (Guo et al., 2018; Zhai and Feng, 2008). That greatly influences the implementation and operation of the Belt and Road initiative. Participants could suffer huge losses instead of gains. Therefore, making the risk assessment is essential to the reduction of losses for countries vulnerable to natural disasters. However, major studies about natural disaster of the Belt and Road concentrate on the statistical

Journal Pre-proof description via ArcGIS spatial analysis tool, and the superposition analysis method (Li et al., 2014; Chen et al., 2016; Peng et al., 2017; Mao et al., 2018; Wu et al., 2019). Little attention has been given to the natural disaster risk assessment for participants of this initiative, particularly the quantitative evaluation of disaster risk. In this paper, we make a quantitative assessment of the risk of natural disasters. It should be noted that disasters mean damage, disruption and widespread. Nowadays, the most consistent definition for disaster appeared to be ‘Disaster is the widespread disruption and damage to a community that exceeds its ability to cope and overwhelms its resources’(Mayner and Arbon, 2015). In spite of long history, the term risk is still vague. The origin of the word risk was fear or adventure in the history. At the beginning of the 17th century, risk became prominent in mathematics and led to probability theory(Frosdick, 1997; Hale, 1999; Heckmann et al., 2015). Here, risk is the probability of disasters that result in loss Our study extends the existing literature through the quantitative analysis and main contributions are as follows: (1)The main natural disasters flood, drought, convective storm, and low temperature are discussed along the “Belt and Road”. (2)An evaluation model is proposed. The model mainly consists of evaluation indicators, the relative damage calculation and information diffusion theory. Three indicators include affected area, covered area and demolished area. They are adopted to calculate the value of the relative damage. Information diffusion theory is used to evaluate risk of four natural disasters and give quantitative results. (3)Empirical studies have demonstrated that drought and flood are two main disasters. The evaluation model can be extended to other countries along the “Belt and Road”. 2. Literature review Risk assessment is vital for the improvement of risk management. Many scholars have studied the risk assessment from different fields. For supply chain management, motivations for the development of a comprehensive supply chain network design methodology are provided and discussed (Klibi et al., 2010). Green opportunistic supply chain is designed in a lean and agile manufacturing setting (Golpîra et al., 2017). In addition, there is a lot of research about the natural disaster risk assessment (Dhakar et al., 2017; Silva and Schneider, 2017; Zaharia et al., 2017; Kossobokov and Nekrasova, 2018; Rana and Routray, 2018; Fakhruddin et al., 2019; Pathak and Dodamani, 2019). For instance, based on the disaster and development-related vulnerability, an integrated disaster and climate risk assessment is provided by ranking a set of infrastructure sector indicators. (Fakhruddin et al., 2019). Mathematical policy analysis techniques are applied into the study of risk assessment about earthquakes and tsunamis occurred in Japan and Indonesia (Parwanto and Oyama, 2014). Geo-spatial early warning decision support system is created in order to demonstrate the assessment of natural disasters (Damalas et al., 2018). Moreover, some literature focuses on the flood risk assessment. An index-based method is employed to the evaluation of vulnerability to flood of rural communities in Malawi, which indicates the susceptibility affects flood vulnerability to a large extent (Mwale et al., 2015). There is a huge difference among municipalities in how they conduct flood risk assessments (Norén et al., 2016). Table 1 enumerates the major contributions of previous literature about the flooding risk assessment. Furthermore, drought hazard management has also attracted scholar's attention. Drought Management Plan approved by the Segura River Basin Authority intents to impose stricter water

Journal Pre-proof restrictions on residents (Gómez Gómez and Pérez Blanco, 2012). Drought risk and food security related with climate change in the Mediterranean region is studied in detail based on the spatially distributed hydrological model (Gampe et al., 2016). Analysis of groundwater-level fluctuations on multiannual and seasonal scales improves the forecast about the risk of groundwater drought hazard (Krogulec, 2018). Table 1 Major contributions about the flooding risk assessment Reference

Major contributions

(Falter et al., 2015)

An original approach integrating the complete flood risk chain is presented to assess flood risk in Germany.

(Foudi et al., 2015)

Expected annual loss and damage-probability curves are applied into the flooding risk evaluation in the downstream cities of the Ebro River Basin in Spain.

(Maia et al., 2016)

A digital terrain model is developed to evaluate the potential risks of sea level rise and coastal flooding in southern Brazil, which can identify the area most vulnerable to flooding.

(Löschner et al., 2017)

Both climate change and settlement development significantly increase future levels of flood risk.

(Silva et al., 2017)

Coastal flood is discussed by combining GIS-based inundation analysis over the last 35 years and policy recommendations are provided in terms of management and safety decisions.

(Talbot et al., 2018)

(Xu et al., 2019)

Extreme floods result in losses in almost every ecosystem service considered in the study but small floods have neutral or positive effect on half of the ecosystem services. The compound effects of rainfall and storm tides on coastal flood risk have been well researched compared with the extensively studies about the dependence between rainfall and storm tides.

In addition, risk assessments about other natural disasters are also investigated such as earthquake and typhoon (Feng and Luo, 2009; Lee and Chi, 2011; Ye et al., 2016; Erdik, 2017; Li et al., 2019). Referring to the conception of economic elasticity, a regression model based on the records of direct economic loss caused by earthquake in China is proposed to evaluate risk (Wu et al., 2019). Analysis shows that the uncertainty of the seismic risk decreases with the earthquake magnitude while the variability of seismic resilience increases with the earthquake magnitude (Pavel and Vacareanu, 2016). A new approach for tsunami risk assessment has been implemented by the Mexican government to address the investment of public infrastructure (Jaimes et al., 2016). Moreover, resource endowment and environmental capacity of China affect international investment, which also needs to make assessment of environmental risk (Duan et al., 2018; Huang, 2019). After the proposal of the Belt and Road Initiative, there is a growing concern about the related study (Huang, 2016; Du and Zhang, 2018; Bleischwitz et al., 2019; Hafeez et al., 2019). Consumption of renewable and traditional fossil energy are investigated by employing vector error correction model, which indicates the relationship between energy consumption and economic development varies from different subgroups (Liu and Hao, 2018). As an often-overlooked aspect of the Belt and Road, the impact on water resource management has been studied for perfecting the research content (Howard and Howard, 2016; Zhang et al., 2018;Williams, 2019). Besides, the

Journal Pre-proof electricity consumption negatively contributes to environmental quality with the introducing the Environmental Kuznets curve (EKC) hypothesis (Saud et al., 2019). Other important studies touching on the Belt and Road are involved in Table 2. Table 2 Studies about the Belt and Road initiative Reference

Major contributions

(Fan et al., 2016)

Regional cooperation should strengthen local intellectual property protection, reduce tax burdens, and eliminate commercial barriers.

(Chen et al., 2018)

The increase in cultivated land is mainly concentrated in Central and Eastern Europe and Southeast Asia, while China’s cultivated land suffers most among the sixty-four countries.

(Lee et al., 2018)

Expected impacts on trade and implications on structural changes in transportation systems, port networks, and international logistics have been comprehensively discussed.

(Liu et al., 2018)

Future population growth and urbanization hotspots along the routes are identified via the methods including spatial autocorrelation analysis and hierarchical cluster analysis.

(Rauf et al., 2018)

Environmental degradation occurs in all countries, whereas the results for energy consumption are complicated.

(Chan and Reiner, 2019)

Security of supply and access to the retail market are the key elements for vertical integration on the basis of cases about eighteen manufacturers in four Maritime Silk Road countries.

(Flint and Zhu, 2019)

The potential implication of the Belt and Road initiative in terms of economy and politics is discussed by using a political economy approach.

Nevertheless, the current research about the Belt and Road initiative mainly focuses on the aspects of economic development such as such as energy consumption and investment risk. Studies about the natural disasters along the route remain to be further explored. Once hit by natural disaster, the related trade, logistics, investment and finance will be affected inevitably. For most developing countries along the route, the impact of natural disaster is even greater. Thus, our study makes the risk assessment of natural disaster for regions along the route of the Belt and Road. However, most previous literature on disaster risk assessments is related with large-scale probabilistic statistical samples. Due to the difficulty of carrying out disaster statistics in reality, obtained data inevitably possess the characteristics such as short chronological sequence and poor continuity, which could lead to the inaccuracy and instability when conducting risk assessments. For the defect of the large-sample statistical model, an evaluation model is proposed based on information diffusion method. Information diffusion applies fuzzy information to handle small samples combined with corresponding diffusion functions (Zou et al., 2012a; Liu et al., 2018). It is widely used by many scholars in the risk assessment of natural disasters ( Liu et al., 2010; Li et al., 2012; Zou et al., 2012b ; Xu et al., 2013; Hao et al., 2014; Bai et al., 2015; Wu et al., 2015; Li et al., 2015). In view of that, our study utilizes information diffusion to assess the risk of natural disasters, which can help reduce the negative impact caused by natural disasters and promote the development of the Belt and Road Initiative.

Journal Pre-proof 3. Study area and Data resources 3.1 Study area Main regions along with the Silk Road Economic Belt in China are selected as study areas respectively, Guangxi, Chongqing, Yunnan, Gansu, Shaanxi, Ningxia, Qinghai, Xinjiang and Sichuan in Fig.1. The landform of study area is complex including mountain, hill, valley, plateau and other terrains. Also, chosen regions span multiple climatic zones with distinct climatic differences. Precipitation is unevenly distributed in time and space, which leads to frequent natural disasters, especially droughts and floods. For instance, Gansu is located in the inland of China, with relatively harsh natural conditions. Natural disasters seriously affect agricultural production, restrict the development of economy, and pose a great threat to people's lives and property.

Fig.1. The Study area 3.2. Data resources This paper analyzes the influences of four common natural disasters on selected areas namely, flood, drought, convective storm, and low temperature. To ensure the credibility and impartiality of data, a secondary data set is collected from China Statistical Yearbook, China Agricultural Statistics from 2001-2016 and the information of Disaster Management Database of the Plantation Management Department of the Ministry of Agriculture. 4. Methodology 4. Evaluation model Evaluation involves different disasters, such as flood, drought, convective storm, and low temperature. These disasters have different influences. Each disaster also has different impact which depends on the scales of the disaster. Therefore, it becomes a complicated problem. In order to solve the problem, a disaster evaluation model is proposed, which mainly includes evaluation indicators, the relative damage calculation and information diffusion theory in Fig.2.

Journal Pre-proof Evaluation indicators Affected area Flood

Covered area Demolished area Affected area

Drought

Covered area Demolished area

Disasters

Affected area Convective storm

relative damage

Information diffusion theory

Disaster risk

Covered area Demolished area Affected area

Low temperature

Covered area Demolished area

Fig.2 The proposed evaluation model The output of the evaluation indicators is the input of the relative damage calculation and the output of the relative damage calculation is the input of information diffusion theory. The final result of information diffusion theory is risk. The risk is based on the probability theory. In the following, each of the part is introduced in detail. 4.1 Evaluation Indicators There are three indicators including affected area (A), covered area(C) and demolished area (D), which is adopted in disaster information statistics by agricultural department in China. Generally, the loss of crop yield exceeding ten percent is recorded as the affected area(A) while more than thirty percent is considered as the covered area(C). Furthermore, the demolished area (D) represents the crop yield reduced by over eighty percent because of natural hazard (Wu et al., 2014). In our study, 𝐴𝑘𝑖𝑗 represents the affected crop area of the ith disaster in jth year of kth region. Similarly, 𝐶𝑘𝑖𝑗 indicates the covered area of the ith disaster in jth year of kth region and 𝐷𝑘𝑖𝑗 means the demolished area of the ith disaster in jth year of kth region (𝑖 = 1,2,3,4;𝑗 = 1,2,3,...,16;𝑘 = 1,2,…,9). The subscript 𝑖 indicates the type of natural disasters including flood, drought, convective storm and low temperature in order while j indicates the year from 2001 to 2016. Also, 𝑘 means the study areas namely, Guangxi, Yunnan, Chongqing, Sichuan, Shaanxi, Ningxia, Gansu, Qinghai and Xinjiang. 4.2 The relative damage A direct comparison of crop losses of the disaster areas is not appropriate because the original crop planting area of each region is different. Hence, a production reduction method is proposed to estimate disaster reduction of grains (Zhang et al., 2009). Based on the previous three indicators, the loss of grain can be calculated as follows: 𝑌𝑘𝑖𝑗 = [𝐷𝑘𝑖𝑗 × 0.8 + (𝐴𝑘𝑖𝑗 ― 𝐶𝑘𝑖𝑗) × 0.1 + (𝐶𝑘𝑖𝑗 ― 𝐷𝑘𝑖𝑗) × 0.3] × 𝑦𝑘𝑗 (1) 𝑘 𝑘 𝑘 𝑘 = (0.1 × 𝐴𝑖𝑗 +0.2 × 𝐶𝑖𝑗 +0.5 × 𝐷𝑖𝑗) × 𝑦𝑗 According to the method, the affected area (A) includes covered area(C) and demolished area (D) is contained by covered area(C). Therefore, when it comes to the value of loss of grain, the overlapping part needs to be deleted as expressed in Eq.(1). 𝑌𝑘𝑖𝑗 represents the loss of crop of the ith disaster in jth year of kth region, and 𝑦𝑘𝑗 is the per unit grain production in jth year of kth province. The total disaster damage to crops can be obtained as follows:

Journal Pre-proof 𝑇𝑘𝑗 =

∑

4

𝑌𝑘𝑖𝑗

(2)

𝑖=1

Where 𝑇𝑘𝑗 is the sum damage to crops of four natural disasters in jth year of kth region. It is necessary to test the independence of different disaster data. Statistic approach is used and Pearson correlation coefficients are adopted in Eq. (3). 16

𝑟(𝑇𝑘𝑗',𝑇𝑘𝑗) =

∑𝑚 = 1(𝑇𝑘𝑗'𝑚 ― 𝑇𝑘𝑗')(𝑇𝑘𝑗𝑚 ― 𝑇𝑘𝑗) 16

∑𝑚 = 1(𝑇𝑘𝑗'𝑚 ― 𝑇𝑘𝑗')(𝑇𝑘𝑗𝑚 ― 𝑇𝑘𝑗)

(3)

Where parameters j and j’ indicates two different disasters, m indicates the data series from 1 to 16. The range of r is between -1 and 1. If r is very close to 0, two disasters are not related to each other and can be considered as independence. In order to compare the degree of grain loss caused by different disasters in different years, we define 𝑌𝑘𝑖𝑗 'as the relative damage, which indicates the ratio of the loss of grain to the total damage. It can be calculated as follows: 𝑌𝑘𝑖𝑗 '

=

𝑌𝑘𝑖𝑗 𝑇𝑘𝑗

, 𝑖 = 1,...,4;𝑗 = 1,...,16; 𝑘 = 1,...,9

(4)

4.3 Information diffusion theory The aim of information diffusion is to maximize the use of valid information and improve the accuracy of risk assessment when the sample size is not large enough. The principle of information diffusion is as follows (Huang and Shi, 2002): If X = {x1, x2, ..., xn} is a given sample to estimate the relationship R of the universe V, and x1, x2, ..., xn are observation samples, and each of them carries the corresponding information. Let V = {v1, v2, ..., vm} be the domain of disaster index. According to Eq.(5), the ith observation sample 𝑥𝑖 spreads the information to all points in the disaster domain V: (𝑥𝑖 - 𝑣𝑗)2 1 𝑓𝑖(𝑣𝑗) = 𝑒𝑥𝑝 [ ] 𝜌 2𝜋 2𝜌2

(5)

Where the value of 𝑥𝑖 is 𝑌'𝑖𝑗 obtained in Section 4.2, and 𝜌 is the diffusion coefficient, which is determined by the maximum value b, the minimum value a, and the number of samples m (Chatman, 1986). Due to the number of samples is 16, the value of 𝜌 will be calculated by formula 1.4208(𝑏 - 𝑎)/(𝑚 - 1) (Huang and Shi, 2002), and the value of a and b is the minimum values and the maximum values of the relative damage amount 𝑌'𝑖𝑗. Then let 𝐴 = 𝑚𝑎𝑥 {𝑓(𝑣𝑗)} 1≤𝑗 ≤𝑚

𝑢𝑥(𝑣𝑗) =

𝑓(𝑣𝑗) 𝐴

(6) (7)

Where ux(vj) is the information distribution after normalization, which becomes a fuzzy set of membership functions of a single-valued sample u. In the process of calculating risk, it is necessary to ensure that the importance of each set of samples is identical, so the information diffusion is performed on the jth sample uj, and the related attaching function of the fuzzy subset can be represented as follows:

Journal Pre-proof

𝑢𝑥𝑖(𝑣𝑗) =

𝐹𝑖(𝑣𝑗) 𝑚 𝐹 (𝑣 ) =1 𝑖 𝑗

∑𝑗

, 𝑖 = 1,...,4;𝑗 = 1,...,16.

(8)

Let 𝑢𝑥𝑖 be the normalized information distribution of sample xi. If we let x1, x2, ..., xn be the n specified observation values, then the function can be called the information quantum diffused from sample of X = {x1, x2, ..., xn} to the observation point of 𝑣𝑗. It can be represented as: 𝑚

∑ 𝑢 (𝑣 )

𝑙(𝑣𝑗) =

𝑥𝑖

(9)

𝑗

𝑖 =1

In general, 𝑙(𝑣𝑗) is a non-positive integer, so the risk probability of the sample membership 𝑣𝑗 can be estimated as follows: 𝑝(𝑣𝑗) =

𝑙(𝑣𝑗) (10)

𝑛 ∑𝑖 = 1𝑙(𝑣𝑗)

The function 𝑝(𝑣𝑗) is the frequency value of the sample appearing on the point of 𝑣𝑗, and the value can be taken as the estimated value of the probability. Then, the probability value beyond 𝑣𝑗 should be: 𝑛

𝑃(𝑣 ≥ 𝑣𝑗) =

∑ 𝑝(𝑣 )

(11)

𝑗

𝑘 =𝑗

Where 𝑃(𝑣 ≥ 𝑣𝑗) represents the probability of surpassing the probability risk, which is used to estimate the disaster risk. 4.4 Analysis of disaster risk in Guangxi In order to elaborate the algorithm more clearly, we will use the example of Guangxi to demonstrate the impact of disasters and analyze the reason behind. Step 1: According to Eq. (2) and Eq. (4), the damage caused by flood, drought, convective storm and low temperature disasters in Guangxi and the damage 𝑌𝑖𝑗 are shown in Table 3, and the results of the relative damage are also presented in Table 3. 700 600 500 400 300 200 100

flood

drought

convective storm

low temperature

Fig.3. The damage in Guangxi

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

0

Journal Pre-proof

Table 3 The damage and relative damage in Guangxi Year

2001

2002

2003

2004

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Flood

473.66

0.8646

537.46

0.8740

132.48

0.1732

251.37

0.2941

Drought

28.40

0.0518

64.09

0.1042

349.97

0.4575

556.73

0.6514

C storm

15.57

0.0266

11.52

0.0187

43.16

0.0564

20.87

0.0244

Low t

31.17

0.0569

1.86

0.0030

239.26

0.3128

25.64

0.0300

Year

2005

2006

2007

2008

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Flood

305.29

0.3783

130.33

0.2877

151.50

0.3025

444.92

0.5223

Drought

477.71

0.5919

302.79

0.6685

340.93

0.6808

117.38

0.1378

C storm

20.66

0.0256

16.61

0.0367

7.88

0.0157

20.22

0.0237

Low t

3.38

0.0042

3.24

0.0071

0.46

0.0009

269.37

0.3162

Year

2009

2010

2011

2012

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Flood

152.85

0.3037

149.67

0.2037

118.55

0.2373

81.45

0.6603

Drought

341.53

0.6785

571.03

0.7771

188.81

0.3780

36.90

0.2991

C storm

8.35

0.0166

5.02

0.0068

1.72

0.0034

4.54

0.0368

Low t

0.64

0.0013

9.10

0.0124

190.44

0.3813

0.47

0.0038

Year

2013

2014

2015

2016

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Damage kg/km2

Relative damage

Flood

20.27

0.1901

64.78

0.7543

129.80

0.6780

50.09

0.4657

Drought

33.49

0.3139

5.56

0.0647

61.80

0.3199

17.89

0.1663

C storm

26.43

0.2478

8.47

0.0986

1.55

0.0080

15.88

0.1476

Low t

26.47

0.2482

7.07

0.0823

0.00

0.0000

23.70

0.2204

The average annual damage of flood, drought, convective storm and low temperature is 199.66 (kg/km2), 218.44 (kg/km2), 14.22 (kg/km2) and 52.02 (kg/km2) respectively. The average relative damage of flood, drought, convective storm and low temperature is 0.4490, 0.3964, 0.0496 and 0.1051. As is shown in the Fig.3, it can be concluded that flood and drought are main disasters in Guangxi, and the impact of the convective storm and the low temperature is relatively slight. And the disaster reduction of grains was severe before 2011. As flood and drought are main disasters in Guangxi, Pearson correlation coefficients of main disasters are computed and listed in Table 4. These coefficients are very small. It can be observed that the flood is independent of drought, convective storm and low temperature. Table 4 The Pearson correlation coefficients of main disasters in Guangxi Flood Flood

Drought 0.15

Convective storm

Low temperature

0.087

0.188

Journal Pre-proof Drought

0.15

0.185

-0.013

Step 2: Let X = {x1, x2, ..., xn} be a given sample to estimate the relationship R of the universe V, and the value of X is the each year of the relative damage 𝑌'𝑖𝑗, which are observation samples. Let V = {0,0.025,0.05, …,1} be the domain of disaster index. For example, 𝑓1(𝑣1) = 𝜌 (𝑥1 - 𝑣1)2 2𝜌2

]

1

=

𝑒𝑥𝑝 [ -

(0.8646 - 0)2

],

2𝜌2

𝜌 2𝜋

where

1

𝑒𝑥𝑝 [ -

2𝜋

𝜌 = 1.4208 × (0.8740 - 0.1901)/(16 -

1) = 0.0648, thus 𝑓1(𝑣1) = 1.28 × 10 ―38. Step 3: Using Eq. (8) to normalize the information distribution of X. Step 4: The information quantum diffused from sample of X = {x1, x2, ..., xn} to the observation point of 𝑣𝑗 can be obtained according to Eq. (9). Step 5: Calculate the risk probability of the sample membership 𝑣𝑗. Taking the flood as an example, the function 𝑝(𝑣𝑗) is the frequency value of the sample appearing on the point of 𝑣𝑗, and the results are shown in Table 5. Table 5 The risk probability of the membership 𝑣𝑗 𝑝(𝑣𝑗) 𝑝(𝑣𝑗) 𝑝(𝑣𝑗) 𝑝(𝑣𝑗)

0.025

0.050

0.075

0.100

0.125

0.150

0.175

0.200

0.0013

0.0033

0.0069

0.0128

0.0210

0.0307

0.0405

0.0490

0.225

0.250

0.275

0.300

0.325

0.350

0.375

0.400

0.0553

0.0588

0.0593

0.0562

0.0500

0.0420

0.0342

0.0280

0.425

0.450

0.475

0.500

0.525

0.550

0.575

0.600

0.0241

0.0223

0.0213

0.0203

0.0190

0.0178

0.0176

0.0189

0.625

0.650

0.675

0.700

0.725

0.750

0.775

0.800

0.0211

0.0232

0.0242

0.0236

0.0221

0.0206

0.0199

0.0204

0.825

0.850

0.875

0.900

0.925

0.950

0.975

1.000

0.0215 0.0221 0.0212 0.0182 0.0138 0.0091 0.0052 0.0026 Step 6: Apply the Eq. (11) to calculate the probability value beyond 𝑣𝑗 (0,0.025,0.05, …,1) and the results are shown in Table 6, and the vertical axis is the value of 𝑝(𝑣𝑗). 𝑝(𝑣𝑗)

1.5 1 0.5 0 10% flood

20%

40% drought

60%

70%

convective storm

80%

100% low temperature

Fig.4. The probability of surpassing the probability risk As is shown in the Fig.4, flood and drought are the high frequency disaster in Guangxi. When the relative damage amount exceeds 40%, the estimated damage risk of the convective storm and the low temperature is already 0, so the frequency of these two disasters is relatively low in the future. There are two reasons to explain this phenomenon. Firstly, natural environment may lead to this result. Guangxi has a unique natural geography, especially in karst and mountainous

Journal Pre-proof landform. That brings about the shortage of the water storage capacity and limited drainage capacity. It is prone to the short-term surge of stagnant water, and the severe water shortage is also likely to happen in the dry season, which affects the normal cultivation of crops. Meanwhile, the time of the monsoon advances and retreats are different, resulting in uneven distribution of seasonal rainfall. The statistics of rainfall have revealed that the precipitation accounts for 50% of the whole year in May, June and July, while spring accounts for 30% and autumn accounts for 15%. It only accounts for about 5% in winter, which probably leads to a large proportion of drought occurrence in spring, autumn and winter, and the severe flooding in summer. Secondly, influence of human factors also counts. The influx of immigrants and unrestricted natural growth has gradually sharpened the contradiction between people and land from the end of the Qing Dynasty in Guangxi. As a result, it is unsuitable for planting. The gradual deterioration of the ecological environment has also made the problem of soil erosion increasingly severe. Table 6 The probability of surpassing the probability risk Probability

Flood

Drought

Convective storm

Low temperature

0.05

0.9982

0.9550

0.4833

0.6287

0.10

0.9880

0.8841

0.1877

0.4656

0.15

0.9541

0.8051

0.1152

0.4191

0.20

0.8829

0.7369

0.0728

0.3997

0.25

0.7787

0.6784

0.0482

0.3359

0.30

0.6606

0.6139

0.0025

0.2359

0.35

0.5544

0.5397

0.0000

0.1118

0.40

0.4781

0.4679

0.0000

0.0328

0.45

0.4259

0.4053

0.0000

0.0038

0.50

0.3823

0.3531

0.0000

0.0001

0.55

0.3431

0.3105

0.0000

0.0000

0.60

0.3076

0.2675

0.0000

0.0000

0.65

0.2676

0.2109

0.0000

0.0000

0.70

0.2202

0.1427

0.0000

0.0000

0.75

0.1745

0.0804

0.0000

0.0000

0.80

0.1340

0.0372

0.0000

0.0000

0.85

0.0921

0.0135

0.0000

0.0000

0.90

0.0488

0.0035

0.0000

0.0000

0.95

0.0169

0.0006

0.0000

0.0000

1.00

0.0026

0.0000

0.0000

0.0000

5 Results and discussion 5.1 Results 5.1.1 Risk analysis of flood disaster Probability risk values are used to reflect distribution of flood risk in nine selected regions every year. The whole risk of nine areas is depicted in Fig.5. Different areas have different risk. As shown in Fig. 6(a), the risk value of all regions exceeds 0.5 when 𝑣𝑗 ≥ 10%.So, the flood disaster with 𝑣𝑗 ≥ 10% occurs every two years at least in most areas. The flood in Guangxi provinces (0.9880) will cause damage each year. In addition, the risk value is also high in Chongqing (0.9234), Sichuan (0.9618), and Shanxi (0.8551). According to Fig.6(b), the risk value

Journal Pre-proof varies from 0 to 0.8 when 𝑣𝑗 ≥ 25%, and it is high in Sichuan (0.8209), Guangxi (0.7787) and Chongqing (0.7737). It is lower than 0.2 in Gansu (0.1706), Ningxia (0.1423) and Xinjiang (0.1314). It is very

Fig.5. The results of flood disaster risk

(a)

(c)

(b)

(d)

Journal Pre-proof Fig.6 The distribution of flood risk with different relative damage low in Qinghai (0.0104). As seen from Fig.6(c), the risk value is from 0 to 0.7 when 𝑣𝑗 ≥ 45%, and the risk value is below 0.01 in Xinjiang (0.0989), Yunnan (0.0008) and 0.000 in Ningxia, Gansu and Qinghai. The maximum appears in Chongqing (0.6695) and Sichuan (0.5559). In Fig.6(d), when 𝑣𝑗 ≥ 65%, the risk value is below 0.3 in most regions except for Chongqing (0.3702), Guangxi (0.2676), Sichuan (0.0819) and Shanxi (0.0819). In short, the distribution of risk levels is coincident, and the centers of areas with relatively large or small risk values are constant, which means the analysis above is reasonable. Moreover, the risk value decreases with the increase of the risk level. 5.1.2 Risk analysis of drought disaster Fig.7 pictures the changing trend of the drought disaster risk. According to the forecast, the risk values of all regions are higher than 0.8 when 𝑣𝑗 ≥ 15% in Fig.8(a). Especially in Shaanxi and Gansu, the risk value has been maintained 1.0 when 5% ≤ 𝑣𝑗 ≤ 20%, which means the drought with 𝑣𝑗 ≥ 15% occurs in Shaanxi and Gansu every year. According to Fig.8(b), the risk value is from 0 to 0.9 when 𝑣𝑗 ≥ 35%, and all of them are higher than 0.5, so the drought disaster with 𝑣𝑗 ≥ 35% occurs every two years at least. The maximum appears in Gansu (0.9827), Shanxi (0.8717), Ningxia (0.8549) and Yunnan (0.8479). As shown in Fig.8(c), the risk value is from 0 to 0.5 when 𝑣𝑗 ≥ 65%, and it is still high in Ningxia (0.5904), Gansu (0.5076) and Yunnan (0.4121). The values of risk remaining areas are lower than 0.1 in Xinjiang (0.0670). In Fig.8(d), the risk value is from 0 to 0.3 when 𝑣𝑗 ≥ 75%, and the regions with a risk value of 0-0.1 expands further, but areas with high risk values are consistent with those above.

Fig.7. The results of drought disaster risk

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(a)

(b)

(c) (d) Fig.8.The distribution of drought risk with different relative damage 5.1.3 Risk analysis of convective storm disaster Risk estimation values are used to reflect distribution of convective storm disaster every year. The whole risk of convective storm disaster is shown in Fig.9, which demonstrates that convective storm has lower impact than flood and drought disasters. The risk value of Xinjiang province is the highest no matter what the relative damage is. When 𝑣𝑗 ≥ 5%, the risk value is up to 0.9551. As depicted in Fig.10(a), Xinjiang is located in the arid western part of Northwest China. Due to the harsh natural environment, Xinjiang is prone to extreme cold weather in winter, which leads to the convective storms. The risk of convective storm in Gansu is a little lower than Xinjiang. It ranks the second, which is up to 0.9455 when 𝑣𝑗 ≥ 5%. On the contrary, areas with a small risk are scattered in Guangxi, Sichuan and Chongqing. The risk values of three areas are 0.4833, 0.7492 and 0.7080 when 𝑣𝑗 ≥ 5%. The risk value decreases rapidly with the increase of the risk level. When 𝑣𝑗 ≥ 35%, the risk value of three areas is almost 0. This finding indicates that the risk level of convective storm is lower in these areas. These areas belong to subtropical monsoon climate. The frequency of convective storm is much lower.

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Fig.9. The results of convective storm disaster risk

(a) (b) Fig.10.The distribution of convective storm disaster risk with different relative damage 5.1.4 Risk analysis of low temperature disaster The risk value of low temperature disaster is presented in Fig.11. The values are much lower than the values of other three disasters. It indicates that low temperature has the least impact on studying areas among four natural disasters. The risk value is below 0.1 when 𝑣𝑗 ≥ 35% except Guangxi, Yunnan, Qinghai and Xinjiang. It is high in Xinjiang and Yunnan provinces. However, the risk value of both provinces is lower than 0.9 when 𝑣𝑗 ≥ 5%, which is presented in Fig.12(a). From above discussion, it can be found that the risk value of other three disasters are higher than 0.9. The highest risk value is in Xinjiang, which is up to 0.8674. It becomes much lower (0.2018) when 𝑣𝑗 ≥ 25%. On the contrary, the risk value of Guangxi, Chongqing, Sichuan, Shaanxi, Gansu and Qinghai provinces are much lower compared with Xinjiang and Yunnan provinces. The minimum appears in Chongqing city, which is just 0.4986. When 𝑣𝑗 ≥ 25%, the risk value is lower than 0.1 as demonstrated in Fig.12(b). Therefore, it can be concluded that the risk of low temperature disaster is much smaller. That is related to the location and climate of Chongqing city. Chongqing city is located in upper reaches of the Yangtze River with the subtropical monsoon humid climate. Therefore, low temperature disaster occurs seldom. Sichuan province has similar features with Chongqing city. So the risk is also very low.

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Fig.11. The results of convective storm disaster risk

(a) (b) Fig.12. The distribution of low temperature disaster risk with different relative damage 5.2 Discussion According to the above analysis, it can be found that these nine regions along the Silk Road Economic Belt are greatly influenced by natural disasters. Drought disaster has the greatest impact and flood disaster ranks the second among these disasters. The influence of low temperature and convective storm is relative smaller compared to drought and flood disasters. (1) The recurrence interval of drought disaster is demonstrated in Fig.13. When the relative damage of drought disaster is more than 10%, all the provinces along the Silk Road Economic Belt are mainly encountered once in one year. Gansu is hit by drought annually as well as Qinghai and Yunnan when the relative damage is more than 40%. In the case of once in two years, the drought disaster also reaches to the risk probability which is more than 30% in Chongqing. The Ningxia is the highest, which is up to 70%. It further indicates that drought disaster has a great impact on crop production. When the relative drought damage is more than 50%, recurrence interval increases rapidly in Xinjiang. This indicates that the probability drought occurs in the province is relative smaller.

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Fig.13. The recurrence interval of drought disaster (2) As shown in Fig.14, the impact of flood disaster is not as great as that of drought disaster. When the risk probability of relative disaster damage is more than 5%, all the provinces are mainly influenced once in one year to two years. Guangxi, Sichuan, and Chongqing have smaller recurrence interval than the rest areas, which indicates that floods are more likely to occur in these areas. Furthermore, Xinjiang, Qinghai, Ningxia and Gansu have bigger recurrence interval than the rest, which means the flood disaster has smaller probability. The recurrence interval of two remaining provinces Yunnan and Shaanxi is between above two groups.

Fig.14. The recurrence interval of flood disaster (3) The impact of convective storm disaster on crop yields is relatively small compared to drought and flood disasters as presented in Fig.15. When the relative storm disaster is more than 5%, most regions encounter convective storm once a year to two years including Xinjiang, Qinghai, Gansu, Ningxia, Shaanxi, Yunnan, Sichuan and Chongqing. Besides, the recurrence interval of Guangxi is up to 2.068 years when the relative storm is more than 5%. Chongqing, Guangxi and Sichuan have reached to 100-year disaster with 30% risk probability. The trend of Qinghai, Xinjiang and Gansu provinces is similar to each other. The recurrence interval is relative smaller than the other provinces. It indicates that the three provinces suffer from convective storm disaster more frequently. The finding is consistent with the real observations.

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Fig.15. The recurrence interval of flood disaster (4) From Fig.16, it can be noticed that low temperature disaster has the smallest impact on crop yield. When the risk probability of relative damage is more than 5%, low temperature attacks all studying areas yearly or biennially. However, the recurrence interval of eight regions increases slowly except Shaanxi. Shaanxi has the lowest risk level among all studying areas, which has reached to a once-in-a-century chance with 25% risk probability. The recurrence interval increases rapidly when the relative damage is more than 35%. When the relative low temperature damage is less than 35%, the trend of Guangxi, Qinghai, Yunnan and Xinjiang is similar.

Fig.16. The recurrence interval of low temperature disaster While processing and analyzing the disasters of small sample size in China, we extend the proposed model to other countries along the Silk Road Economic Belt. In this paper, Vietnam and Philippines are taken as the research object for the frequent and severe natural disaster. Related data can be obtained from ED-DAT containing the international disaster data (https://www.emdat.be/). The data are listed in Table 7. To study the economic losses caused by flood, we have calculated the disaster risk and the results are shown in Fig.17.

Journal Pre-proof Table 7 The disaster data of Vietnam and the Philippines Vietnam

Philippines

Year

Total disaster(million)

Flood disaster(million)

Total disaster (million)

Flood disaster(million)

2001

171.9

28.5

107.061

97.361

2002

284.2

35.827

15.829

6.664

2003

105

26.4

42.302

35.229

2004

38

8

138.867

138.867

2005

346.37

218

2.515

2

2006

1099

34.94

347.281

330.921

2007

981

1.5

16.815

10.215

2008

673.5

27.844

481.202

441.625

2009

1065.2

0

962.017

932.698

2020

704.7

332

335.087

284.42

2011

219.002

219.002

730.025

527.238

2012

372.8

0

993.467

918.137

2013

1552.73

579

12371.35

10136.56

2014

10.7

10

1062.899

1062.899

2015

6966.8

0

1965.966

1881.367

2016

846.437

145

180.074

170.754

2017

3162.655

1308.4

140.8759

10.87492

2018

245

0

325.655

0

1 0.8 0.6 0.4 0.2 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%

0

Vietnam

Philippines

Fig.17. The probability of surpassing the probability risk As shown in the Fig.17, the flood is the high frequency disaster in Philippines from 2001 to 2018, and the flood has caused large economic losses in Philippines. According to the “Global Risk Report” issued by the UNU in Germany, Philippines is the third vulnerable country when faced natural disasters and climate change among the countries evaluated. Impacted by Monsoon typhoon from June to December, abundant rainfall has led to the outbreak of floods. Different from the Philippines, Vietnam is affected by Tropical monsoon, and flood disaster occurs suddenly with high intensity, which often causes huge losses in the short term. But the precipitation is not as much as Philippines in the long term. Although different countries have distinct index to describe the damage caused by natural disasters, the proposed model still can be used to make evaluation.

Journal Pre-proof 6. Conclusion The Silk Road Economic Belt areas have complex geological structures and climatic conditions that lead to the frequent occurrence of natural disasters. In order to figure out the natural disasters risk along the Silk Road Economic Belt, an evaluation model is proposed. The model consists of evaluation indicators, the relative damage calculation and information diffusion theory. This paper selects nine regions in China along the Silk Road Economic Belt as research objects, namely Guangxi, Chongqing, Yunnan, Gansu, Shanxi, Ningxia, Qinghai, Xinjiang and Sichuan. The proposed model is applied to make risk assessment of four main disasters including flood, drought, convective storm and low temperature combined with the data of disaster loss. The risk of these regions under different probability of relative damage is calculated, and the frequency of disasters is reckoned by the evaluation results. Our study can provide some useful references for the development of the Silk Road Economic Belt. Besides, the method is also extended to evaluate natural disasters in Vietnam and Philippines and results have demonstrated that the method is also effective. The empirical study has revealed that flood and drought are main natural disasters among selected regions. Convective storm and low temperature disaster also occur in many areas along the Silk Road economic belt. Driven by the initiative of the “Belt and Road”, countries and regions along the route can draw on the similarities between the natural environment and the types of disasters, and they can improve disaster prevention system. As an advocate, China can demonstrate its mature technology for disaster prevention to these countries along the “Belt and Road”. Acknowledgement This research was funded by the China Natural Science Foundation (No.71974100, No.71503134, No.41501555), Major Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu province (2019SJZDA039) and Qing Lan project in the Jiangsu province. References References Bai, C.-Z., Zhang, R., Hong, M., Qian, L.-x., Wang, Z., 2015. A new information diffusion modelling technique based on vibrating string equation and its application in natural disaster risk assessment. International Journal of General Systems 44(5), 601-614. Bleischwitz, R., Geng, Y., Xu, T., 2019. Trade impacts of China’s Belt and Road Initiative: From resource and environmental perspectives. Resources Conservation and Recycling Volume 150. Chan, J.H., Reiner, D., 2019. Evolution in inter-firm governance along the transport biofuel value chain in Maritime Silk Road countries. Transportation Research Part E: Logistics and Transportation Review 122, 268-282. Chatman, E.A., 1986. Diffusion Theory: A Review and Test of a Conceptual Model in Information Diffusion. Journal of the AMERICAN SOCIETY FOR INFORMATION SCIENCE 37(6), 377-386. Chen, D., Yu, Q., Hu, Q., Xiang, M., Zhou, Q., Wu, W., 2018. Cultivated land change in the Belt and Road Initiative region. Journal of Geographical Sciences 28(11), 1580-1594. Chen, M., Liu, W., Yeerken, W., Gong, Y., 2016. The impact of the Belt and Road Initiative on the pattern of the development of urbanization in China. Mountain Research, (5): 637–644. (in Chinese) Damalas, A., Mettas, C., Evagorou, E., Giannecchini, S., Iasio, C., Papadopoulos, M., Konstantinou, A., Hadjimitsis, D., 2018. Development and Implementation of a DECATASTROPHIZE platform and tool for the management of disasters or multiple hazards. International Journal of Disaster Risk

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Journal Pre-proof Information diffusion-based risk assessment of natural disasters along the Silk Road Economic Belt in China Yu Xiaobing, Yu Xianrui, Li Chenliang,Ji Zhonghui School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China Xiaobing Yu： [email protected]

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