Xinjiletu YANG, Yanwei YUE, Weihong HAN, Huiyan YIN, Chunyan MAO

School of Economics and Management, Inner Mongolia University of Technology, Inner Mongolia 010051, China

Abstract [Objectives] The paper was to study the impacts of sand and dust storms (SDS) on regional economy. [Methods] In this paper, we combined the computable general equilibrium (CGE) model and the Monte Carlo method to examine the impact of SDS on the regional economy, with a focus on GDP, price index, employment rate, industrial structure and output, income and expenditure. We extended the standard CGE model, introduced the stochastic parameters into the production module, which had significant impact on economic output, and inserted the rate of change of the total labor supply and the expenditure share of early warning and protective measures into the income and expenditure module. [Results] SDS had significant impacts on regional GDP, employment rate, and industrial output from a macro perspective, and can reduce the income of residents and enterprises and increase expenditures from a micro perspective. The impact can be reduced by taking early warning and protective measures. [Conclusions] The protective measures taken for different grades of SDS have different effects.

Key words Stochastic CGE, Sand and dust storm (SDS), Regional economy, Inner Mongolia

1 Introduction

Sand and dust storms (SDS) are natural disaster events that occur in arid and semi-arid regions and have harmful impacts to socio-economy, human health, environment, and agro-ecosystems, particularly those living on and around dry belt[1]. Because SDS are characterized as weak horizontal visibility (<1 km), suddenness and short duration, people tend to underestimate its impact on regional economy compared with other meteorological disasters such as flood and drought[2]. Now, SDS pose a critical threat to regional economy and sustainable development. To have a thorough understanding of its effects, we must take a closer look at the different areas of society that are affected[3]. This article drew on the research on SDS by domestic and international scholars, and found that existing research on the impact of SDS mainly focuses on agriculture, transportation, and human health.

First of all, agriculture is influenced by SDS more than other economic sectors because of its necessary interactions with and dependence on the environment[4]. SDS can impose costs on the agricultural sector through losses in crops, because of plant destruction or yield reductions, and animal production, owing to livestock deaths or lower yields of meat or milk[2]. Taherietal.[5]established the disaster impact factor by combining weather change with agriculture, which is helpful to explore how to reduce the influence of SDS on agriculture.

Secondly, SDS seriously affect the development of transportation industry. Ahmady-Birganietal.[6]pointed out that when SDS occur, the visibility of the road is reduced, and it is difficult for the driver to judge the distance between vehicles and to confirm traffic signs and road facilities, easily causing traffic accidents. Within the airline industry, aircrafts are usually grounded during a severe dust event, and flights may be cancelled, delayed, or diverted to another location. Due to the inconvenience of transportation, the postal transportation and express service industry also suffers serious losses[7].

Finally, SDS, accompanied by strong winds, spread bacteria, viruses and other harmful substances carried in dust and pollen, becoming vectors of diseases. It can cause allergic dermatitis, rash and other symptoms in people with allergic constitution, has great harm to human cardiovascular system, and easily causes respiratory diseases, desert pneumoconiosis and coronary heart disease[8-9]. Wangetal.[10]found that after the occurrence of SDS, the number of outpatients to the department of eye, nose and throat increased significantly, and the increase rate was about 13.4%-27.8%.

Through literature review, the impact of SDS has not been investigated sufficiently. There is little research on the impact of SDS on industry and service industries. There are many types of agriculture, which are greatly affected by SDS, but existing research has not subdivided agriculture. Then, SDS affect human health, which indirectly affects labor efficiency and attendance, but most studies have not incorporated them in economic accounting, and the most important is that they all ignore early warning and protective measures can reduce the economic impact of SDS.

This paper drew on and complemented the application of stochastic CGE model in meteorological disasters, and Inner Mongolia was used as a case study to explore the impact of SDS on regional economy. First, depending on the ground visibility, we classified SDS weather into 5 grades: suspended dust, blowing sand, sand and dust storm (SDS), severe sand and dust storm (SSDS) and extreme severe sand and dust storm (ESSDS). Second, using the CGE model of input-output relationships and industry correlations, we established the mechanisms by which SDS affect production activities, and explored the relationship between SDS and labour supply, capital supply and prices, because the occurrence of SDS can be detrimental to the health of the workforce. Third, the paper illustrated important role of early warning and protective measures to reduce the impact of SDS.

2 Pathways

Inner Mongolia Autonomous Region is located in the north part of China, belonging to arid and semiarid areas of Central-East Asia. There are 6 deserts from west to east, with abundance sand and dust materials. Therefore, it is one of the main dust storm source regions in China[11]. The immediate economic impact of SDS in Inner Mongolia is significant, which would directly bring substantial damage to various industrial sectors and people’s lives. Therefore, a comprehensive analysis of the impact pathways of SDS in Inner Mongolia has implications for the management and prevention of SDS in other regions. Through reading and sorting out a large number of literatures, this paper summarized the impact pathways of SDS on the economic system, including planting, animal husbandry, transportation, health and social work and other industrial sectors and total supply of labor.

2.1 PlantingJiang[12]pointed out that after SDS occurred, a layer of dust covered the leaves of plants, which reduced photosynthesis. In 2010, a sand and dust storm in Gansu Province scraped the thickness of farmland soil up to 16.5 mm, and the number of crops covered by sand soil was 8.3%[12]. SDS accompanied by strong wind can impose costs on the agricultural sector through losses in crops, because of plant destruction or yield reductions.

2.2 Animal husbandryAnimal husbandry can be affected in several ways when the SDS occur. If the sector is using animals for milk production, there may be a decline during SDS, denying the income of producer with no compensatory reduction in costs[13]. SDS are accompanied by high winds and low temperatures, which lead to the damage of houses and yurt collapse, and the loss of animals, or generate a cost through death[14]. Finally, SDS destroy grassland, aggravate grassland desertification in Inner Mongolia, and affect grassland area, which leads to the death of a large number of livestock due to hunger, posing a threat to animal husbandry production and grassland ecological environment[15].

2.3 Transportation, storage and postalInner Mongolia has a vast territory, and the transportation, storage and postal industry play an important role in the economic development and social stability. SDS would cause traffic delays, making the normal operation of an expressway, air transportation and sea navigation impossible, and produce substantial economic losses[16]. On March 15, 2021, SDS occurred in most parts of Inner Mongolia. Among them, SSDS occurred in Alxa League, Bayannur City, Wuhai City, Ordos City, Baotou City and Hohhot City. SDS occurred in Ulanqab City and Xilinguole League, with the minimum visibility dropped below 500 m, and some cities adopted temporary traffic control and canceled all flights on that day.

2.4 Health and social work sectorDust in the atmosphere has a bad effect for human health owing to its biological, chemical, and physical properties[17]. In China, when the frequency and intensity of SDS increase, the number of hospital visits would increase, especially for respiratory, cardiovascular, pneumonia and hypertension diseases, which shows a positive correlation[18]. On April 15, 2021, an SDS hit Chifeng City, Inner Mongolia, and the number of hospital outpatients increased significantly. Due to the continuous influence of sand and storm weather, the meteorological department reminded the citizens to limit their outdoor activities to avoid damage to eyes and respiratory tract caused by dust.

2.5 Other industrial sectorsSince other industry sectors provide workers with indoor working areas, such as factories and office buildings, when the SDS occur, the building can effectively block access to sand and reduce the damage to production equipment, and the damage is much lower than in planting, animal husbandry, transportation, storage and postal sector[19]. However, under the action of the market mechanism, due to the internal correlation of the system, the scope and subject of damage will be further expanded, which will destroy the original economic equilibrium state, so the output value of other sectors will also be affected.

2.6 Total labor supplySDS seriously endanger the human health, and affect the work efficiency and attendance rate of the labor force. At the same time, SDS affect the output value of the industrial sector. When the output value decreases, the demand for labor force of enterprises decreases, and the labor supply changes in Inner Mongolia. When the number of employed people changes in the economic system, the output value of various sectors of the social economy would be affected due to the industry affiliation. Therefore, the indirect impact of SDS on the economy is realized through the change of labor supply[20].

3 Theoretical framework

As an effective policy analysis tool, the computable general equilibrium (CGE) model has been widely used in the fields of macroeconomics, finance, trade, income distribution, energy and environment, and household consumption. However, parameter values in the model are single, which do not reflect the real situation. There is a general recognition that the inclusion of stochastic parameters can better reflect reality. Because Monte Carlo method can test the robustness of parameters or exogenous variables on CGE experimental results, Monte Carlo is often combined with CGE model to analyze the impact of climate change on the economy[21-22].

Our model is closely related to traditional CGE model with several modifications. Firstly, we combined Monte Carlo with the CGE model, used the Monte Carlo method to randomly sample the occurrence probability of SDS, and calculated the impact parameters of SDS. Secondly, we expanded the production sector and the income and expenditure sector, introduced the impact parameters into the production module of model, and increased the rate of change of the total labor supply and the expenditure share of early warning and protective measures in the income and expenditure module. The model not only explained the relationship between SDS and production sectors and economic entities, but also constructed the relationship between labor and capital supply and prices. Finally, this paper used a stochastic CGE model, which is a more flexible equilibrium structure, with the economic model assumptions closer to economic reality.

3.1 Uncertainty analysisSDS is a kind of meteorological disaster, with uncertain occurrence time, place and grade, and different or the same grade of SDS weather can cause different damage to the affected area. Based on reality, the Monte Carlo method was used to analyze the uncertainty of the impact of SDS on the economy in Inner Mongolia[23-24]. Random samples of the number of SDS occurrences for 5 grades and GDP in Inner Mongolia from 1988 to 2017 were used.

(1)

whereZTi/2is the critical value of the standard univariate normal distribution.

0

(2)

whereaandvare parameters greater than zero, ifa<1,v<1, then the shape ofU,visU, the limit tends to infinite; ifa>1,v>1, the graph is normal distribution; ifa=v=1, the uniform density distribution is obtained. In this paper, the Monte Carlo method was used to determine the uncertainty of the stochastic parameters of the CGE model. It is necessary to continuously use the GAMS solution program, and the parameters are randomly sampled according to the preset probability distribution before implementation. Therefore, Stata software was used for random sampling, which is very easy to re-execute the GAMS program.

The CGE model includes 16 production departments and stochastic parameters for 5 grades of SDS. According to the calibration method of CGE model parameters, it can be divided into 80 stochastic parameters, and the value range and mean value of stochastic parametersX(a) of 5 grades of SDS can be calculated. The stochastic parameters are as follows: suspended dustX(1), blowing sandX(2), SDSX(3), SSDSX(4), ESSDSX(5). We used random number generator software to generate 1 000 random numbers for each of the 5 grades of stochastic parameters, which were then incorporated into the production function of the stochastic CGE model.

3.2 The modelThe onset of SDS has serious implications for the economy, but there is uncertainty as to which sectors are affected, hence it is necessary to divide the industry sector. The latest input-output table of Inner Mongolia in 2017 is used for sector integration and division. The 42 sectors in the input-output table are divided into 16 sectors, and all the models contain planting, forestry, animal husbandry, fishery, service products of agriculture, forestry, animal husbandry and fishery, metallurgy, chemical industry, energy industry, manufacturing and light industry, construction industry, transportation, storage and postal services, health and social work sectors, financial industry, real estate and other services.

The CGE model assumes a perfectly competitive market, and both producers and consumers take profit and utility maximization as decision objectives. Production is an important part of promoting economic development, and the impact of SDS on the production sector is mainly reflected in the production module. Production sector are modeled using nested constant elasticity of substitution (CES) functions to describe the relationship between capital, labor and input and output of SDS. The relationship between intermediate input and output is expressed by Leontief production function, and the stochastic parameterX(a) is added to the CES function of the industrial sector.

C=min{∑(Pi·Qi)}

(3)

(4)

where equation (3) is the cost function of the CES function, and equation (4) is the output function of the CES function, multiplyingX(a) in the basic structural equation. Here,Crepresents the cost;Pirepresents the commodity price;Qirepresents the commodity quantity;QArepresents the departmental GDP;X(a) represents the stochastic parameters;ajrepresents the share of production factors; Qjrepresents the commodity quantity produced by economic activities.

Since the total expenditure of society and residents is balanced in the model, when residents increase the expenditure on the protection, the expenditure of other departments will decrease. The expenditure on protection of residents was obtained through the Inner Mongolia Statistical Yearbook, including the expenditure on the prevention of SDS such as masks and goggles.

Household income (YH) from labor and capital income, enterprise transfer payment (transfrhent) and government transfer payment (transfrhgov)，enterprise income (YENT) from capital income, government income(YG) mainly come from various kinds of taxes[22].

SPSS regression was used to obtain the impact of SDS on the total labor supply, and the change ratio (L) was obtained (total labor supply rate of change: suspended dust 0.996 7, blowing sand 1.007 1, SDS 0.981 1, SSDS 0.862 5, ESSDS 0.738 8). At the total supply of labor, the impact of 5 grades of SDS on the total supply of labor was added, and the total labor supply (QLSAGGQ) was multiplied by the labor change ratio (L), which was incorporated in the total income of residents (YH).

3.3 TestThe consistency test results showed that the results calculated by CGE model were equivalent to the original data of the SAM table, ensuring the correctness of all equations written in the program. Due to the inconsistent origin of exogenous parameters in the model, the reasonableness of the parameters must pass the sensitivity test to ensure that the parameters fit the model. Taking production module as an example, the sensitivity of production substitution elasticity was tested. In the test, the elasticity of substitution between factors of production was varied between -50% and 50%, and the results showed that there was little change in macroeconomic indicators in this interval. When the elasticity of substitution was reduced by 50%, the simulation results hardly changed. The sensitivity test results showed that all the elasticity of substitution values had high reliability and can be used in the model.

The consistency and sensitivity tests show that the constructed model equations are correct; the SAM table data are not abnormal; the parameter calibration and model structure are reasonable; and the model structure is not affected even by external shocks.

3.4 Scenario assumptionsThe model took the situation in 2017 as the baseline scenario. According to the data of 2017, only suspended dust and blowing sand occurred in Inner Mongolia, and the economic activity was relatively stable compared with other years. The impact parameter of SDS was 1, and other variables remained unchanged.

The first simulation (scenario 1) inspected the impact of 5 grades of SDS and the change rate of total labor supply on the economy of Inner Mongolia, and obtained the impact of 5 grades SDS on GDP, price index, employment rate, total output of industrial sector, industrial structure, and income and expenditure of residents and enterprises.

Protection system is essential to help minimise losses of life and property from SDS. Based on scenario 1, scenario 2 was to set the protective expenditure of residents for different grades of SDS, to explore whether the economic impact can be reduced by setting early warning and protection measures. The steps taken to simulate scenario 2 are: (i) in scenario 2, 5 stochastic parametersX(a) are incorporated into the production function; (ii) the change rate of total labor supply affected by disasters is embedded toYH; (iii) based on scenario 1, set the expenditure of residents’ protective, the change rate of rural residents’ consumption expenditureshrhc(c) and urban residents’ consumption expenditureshrhr(c). The first-order matrices of the rate of change ofshrhc(c) andshrhr(c) areSSRH(c) andSSCH(c).

shrhc(c)=SSCH(c)·sam(c, ’C-HHD’)/EHCO

(7)

shrhc(c)=SSRH(c)·sam(c, ’R-HHD’)/EHRO

(8)

Since residents’ expenditures are balanced in the model, when residents increase their protection expenditures, the increased part will be reflected in the health and social work sectors, and residents’ expenditures on other service industries would decrease. At the same time, setting protective measures would cause new changes in the total labor supply, which isL*(Table 1). Compared with SDS without protective measures, total labor supply will increase after protective measures are set. When only suspended dust and blowing sand occur, the total labor supply change rate is 1, because this kind of weather is almost non-destructive, as long as protective measures are taken, it will not affect the labor attendance.

Scenario 3 was based on scenario 1 and scenario 2, and 1 000 random numbers were generated for 5 grades of stochastic parameters according to the range of values of the stochastic parameter. The simulation results of 5 grades of stochastic parameters were output, and the mean value and standard deviation, and the coefficient of variation from both were calculated. According to the magnitude of the coefficient of variation, the degree of dispersion of the variable value can be expressed, which can more truly reflect the impact of the stochastic SDS on the economy of Inner Mongolia in reality[26].

Table 1 Total labor supply rate of change (Increase protection)

4 Results

4.1 Scenario 1 and scenario 2

4.1.1GDP. Fig.1 demonstrates the impact of scenario 1 and scenario 2 on Inner Mongolia’s real GDP. It can be seen that the impact of scenario 2 on Inner Mongolia’s real GDP was significantly lower than that of scenario 1, and the effect of suspended dust and blowing sand was not obvious, while the effect of scenario 2 was more pronounced when SDS, SSDS, and ESSDS occurred. It shows that the stronger the grade of SDS, the more effective the early warning and protection measures are in reducing the impact of the SDS on the real GDP. Studies similar to ours have the same results that warning measures can assist the level of preparedness and minimize the loss of life and damage to productive resources.

Fig.1 The impact of SDS on real GDP of Inner Mongolia

4.1.2Price index. The price index is based on a period of time, reflecting changes in commodity prices. Setting price index=Nominal GDP/Real GDP, the ratio reflects the impact of SDS on the economic system price index.

Scenario 1 and scenario 2 simulated the impact of SDS on real GDP and nominal GDP of Inner Mongolia. The two values were equal, and the price index was 1, indicating that as a short-term impact, the impact of SDS on the price was limited. This is because the improvement of the ecological environment in Inner Mongolia has reduced the destructiveness of SDS; the ability of early warning and the deployment of government materials is improved, and it can guarantee the supply of commodities during the dusty period.

4.1.3Employment rate. Fig.2 shows the effects of scenario 1 and scenario 2 on the employment rate fluctuations in Inner Mongolia. It can be seen that the fluctuation of the employment rate in scenario 2 was significantly lower than that in experiment 1. The simulation results of scenario 2 showed that the set early warning and protection measures can effectively inhibit the decline of the employment rate, which was reduced by 0.020%, 0.092%, 0.282%, 0.392% and 0.713%, respectively. The effect of experiment 2 was better when SSDS and ESSDS occurred.

Fig.2 The impact of SDS on the employment rate in Inner Mongolia

4.1.4Total industrial output. Rate of decline in total industrial output mitigated by increased protective measures (Table 2), and the decrease rate of planting industry was alleviated by 0.001%, 0.004%, 1.09%, 0.79% and 0.86%, respectively. In scenario 2 when suspended dust and blowing sand occurred, animal husbandry output did not decrease but increased by 0.146% and 0.197%, respectively. Because the animal husbandry increases sand-proofing facilities and reduces grazing before low-grade SDS occurs, it reduces the spread of pathogens in the sand and reduces the likelihood of disease in cattle and sheep, giving the animal husbandry maximum protection. Compared with scenario 1, scenario 2 reduced the rate of decline of total output in transportation, storage and postal industry by 0.002%, 0.001%, 0.141%, 0.110% and 0.102%, respectively. The sector of health and social work increased output in scenario 2, because the SDS increased spending on citizens to buy dust prevention tools, such as dust masks, goggles and other windproof equipment, and increased residents’ respiratory and eye medical expenses.

Table 2 The impact of SDS on output decline of industrial sectors

4.1.5Industrial structure. Fig.3 compares the effect of scenario 1 and scenario 2 on the industrial structure of Inner Mongolia. As can be seen, when suspended dust and blowing sand weather occurred, the proportion of primary industry and tertiary industry increased in scenario 2, and the proportion of industry in scenario 2 was closer to the proportion of industrial structure in the base year. As other grades of SDS weather are more destructive, set warning and protection measures can not avoid impacts on industrial structure, but can effectively reduce industrial structure fluctuations.

Fig.3 The impact of SDS on industrial structure of Inner Mongolia

4.1.6Income and expenditure. Fig.4 compares the effect of scenario 1 and scenario 2 on income and expenditure of enterprises and residents in Inner Mongolia. Scenario 2 effectively reduced the impact of SDS on income and expenditure of enterprises and residents. The biggest difference between scenario 1 and scenario 2 was the dusty weather, suggesting that early warning and protective measures are most effective for SDS and the impact on income and expenditure is also minimal due to the minimal catastrophic of suspended dust and blowing sand. And SSDS and ESSDS weather are so destructive that protective measures can hardly alter the significant damage they cause to enterprises and residents, but we can effectively reduce it.

4.2 Scenario 3Scenario 3 incorporated the random numbers of the stochastic parameters of SDS into the model, randomized the stochastic parameters of different grades of SDS weather, and simulated the impact of scenario 3 on Inner Mongolia’s economy.

4.2.1GDP. The detailed impact of scenario 3 on the real GDP of Inner Mongolia is displayed in Table 3. The greater the coefficient of variation, the more dispersed the data. It can be seen from Table 3 that the coefficient of variation of scenario 2 was generally higher than that of scenario 1, indicating that protective measures increased the fluctuation of real GDP. This is because while set protection reduced the extent of SDS impacts, the change in set protection expenditure was greater than the impact on the productive sector, thus increasing the change in real GDP.

Table 3 Effect of randomization simulation on real GDP

4.2.2Price index. Scenario 3 simulated the impact of SDS on real GDP and nominal GDP in Inner Mongolia. The result was equal and the price index was 1, indicating that there was no impact on the price index in Inner Mongolia, and the randomization of SDS had no impact on commodity prices.

4.2.3Employment rate. The impact of scenario 3 on the employment rate in Inner Mongolia is summarized in Table 4. It is clear that the coefficient of variation of the impact of different grades of SDS on the employment rate fluctuated between 0.05 and 0.1, indicating that the randomness of SDS had little influence on the employment rate of Inner Mongolia. But the coefficient of variation of scenario 2 was larger than scenario 1, showing that the change in set protection expenditure outweighed the impact of the disaster on the production sector. In addition, different protective measures had inconsistent effects on different grades of SDS, and the effects of protective measures were also uncertain.

Note: a. The impact on enterprise income; b. The impact on enterprise expenditure; c. The impact on residents’ income; d. The impact on residents’ expenditure.

Table 4 Impact of randomized simulation on employment rate

4.2.4Total industrial output. Table 5 shows the impact of scenario 3 on the total output of industrial sectors in Inner Mongolia. It can be seen that the coefficient of variation was higher for the four industrial sectors of plantation, animal husbandry, transportation, storage and postal services and health and social work, indicating that the four sectors are subject to greater fluctuations in random SDS, and other industry sectors are minimally affected by randomization as they are largely unaffected by weather, regardless of whether the SDS is random or not. On the whole, the coefficient of variation of scenario 2 was greater than that of scenario 1, which is due to set protection measures to reduce the impact of SDS on the industrial sectors. However, there is uncertainty about the effect of protection measures, which makes the degree of fluctuation increase and the coefficient of variation increase.

4.2.5Industrial structure. Table 6 demonstrates the impact of scenario 3 on the industrial structure of Inner Mongolia. This table showed the coefficient of variation of the proportion of the tertiary industry, secondary industry and primary industry. The results are in line with the previous findings that SDS have a high impact on the primary industry and a low impact on the tertiary industry. Overall, the coefficient of variation of scenario 2 was greater than that of scenario 1, because set protective measures reduced the impact extent of SDS on industrial sectors, when the impact became smaller, the coefficient of variation became larger, which increased the degree of fluctuation.

4.2.6Income and expenditure. Table 7 shows the impact of scenario 3 on the income and expenditure of enterprises and residents in Inner Mongolia. As shown in Table 6, the coefficient of variation of corporate expenditure was greater than the coefficient of variation of corporate income, and residents showed the same characteristics, indicating that the randomness of SDS has greater fluctuations in the expenditure of enterprises and residents than in income. The coefficient of variation of scenario 2 was greater than that of scenario 1. As the increase in protective measures reduced the impact of SDS on income and expenditure, the degree of fluctuation increased as the impact became smaller.

Table 5 Impact of random simulation on the total output of industrial sector

Table 6 Impact of randomization simulation on industrial structure

Table 7 Impact of randomization simulation on income and expenditure of enterprises and residents

5 Conclusions

At the macro-level, SDS have a significant impact on real GDP and employment rate, and are positively correlated with SDS grade. SDS have different impact on the total output of various industrial sectors, which have the most significant impact on the production of planting, animal husbandry, transportation, storage and postal sector, and health and social work sector, and small or almost no impact on the output of other sectors. Second, at the micro-level, SDS reduce the income of residents and enterprises and increase their expenditures, while residents’ income and expenditure change less than enterprises. Different grades of SDS affect the industrial structure by changing the proportion of primary and tertiary industry, and thus proportion of secondary industry. Third, after setting early warning and protective measures, it can effectively reduce the impact of SDS on the economy, but the effects of protective measures under different grades of SDS are different. The effect of setting protective measures to suspended dust and blowing sand is not obvious, but to high-grade SDS is significant.

In sum, SDS could lead to a disruption in economic and social activity in numerous ways. Although SDS do not result in the kind of significant damage to infrastructure that is usually associated with natural hazards, such as earthquakes or hurricanes, the cumulative effects on society can be significant, as they occur more frequently than other natural hazards, so the impact of SDS on the regional economy can not be ignored. Now, an effective means of reducing the economic impact of SDS is early warning and protection, and timely warnings from the meteorological bureau to enhance the accuracy of reporting, as well as good scientific precautions by industry, are important for the stable development of the regional economy.