KR20160010797A - A method for evaluating resilience cost index - Google Patents
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Abstract
Description
The present invention relates to a method for quantitatively comparing regional disinfection powers by using disinfection cost indexes and evaluating the efficiency of restoration activities.
Korea is exposed to various natural disasters. Especially, it is affected by heavy rains and typhoons. For example, in 2010 and 2011, 82.8% and 93.9% of property damage occurred due to heavy rainfall and typhoons, and all casualties were caused by heavy rains and typhoons (National Emergency Management Agency, 2011; 2012). Especially, when these disasters occur, it takes more restitution amount than damages, and it is necessary to pursue disaster prevention project and water supply project considering total amount of damage and restoration, but decision is still being made considering only the amount of damage. Also, when evaluating the economical efficiency of the dimension business, it is required to consider the reduction of the repair cost according to the dimension business as indirect benefit (Korea Development Institute, 2008). However,
The purpose of the present invention is to help decision making of disaster resource allocation by quantitatively comparing regional disaster power using disaster cost index and to evaluate efficiency of restoration activity by observing temporal trend of disaster cost index To provide a method to do so.
The method of evaluating the disinfection cost index according to the present invention is a method for evaluating the disinfection power of an area where a disaster occurs or a disaster is predicted by utilizing the disaster data of the whole country, And calculating a disinfection cost index defined by the following equation of the evaluation target area using the disaster data.
Here, L (t) is amount of damage, R (t) is recovered liquid, V (t) is the exposure parameter, t 0 and t f means the start and end time of each analysis.
According to the present invention, the disaster prevention cost index can be used to objectively make decisions in the disaster prevention policy determination, as well as to objectively analyze the effect of the disaster prevention policy.
FIG. 1 is a chart comparing the qualitative and quantitative evaluation factors of the resilience with the disassembly cost index.
2 is a view for explaining a disaster prevention scenario evaluation function of the disaster prevention cost index.
FIG. 3 is a graph showing the amount of damage due to domestic disasters and time variation of restoration amount.
FIG. 4 is a graph comparing the amount of damage and the amount of restoration.
FIG. 5 is a graph showing the amount of damage due to heavy rainfall and the amount of restoration.
In order to objectively support disaster management decisions within limited resources, we need a metric that can measure priorities. Considering the high recovery cost compared to the damage in case of disaster, it is necessary to consider not only the damage but also the restoration cost. In addition, since recent restoration activities are aimed at reducing future damage in addition to simply restoring the social system to its pre-existing condition, the weighing index should also be able to assess the interaction between the damage amount and the restoration amount. A quantitative explanation of this concept is the resilience cost index designed by Vugrin et al. (2010).
In order to measure the resilience of the national infrastructure, the present invention uses the resilience cost index to evaluate the disinfection ability of the national and local communities. To this end, the "resilience cost index" developed by Vugrin et al. (2010) "And the" disaster cost index "and the" resilience cost index ".
In the following, the definition of the disassembly cost index in the present invention and the disassembly cost index can be used as an index for comparing the disassembly power of a region or a country, We will explain that we can analyze the effect of restoration activities in Korea.
In recent years, there have been a number of studies in the United States focusing on the use of advanced disaster prevention systems to combine resilience with disaster management (ADB, 2013, Cutter et al., 2008). This is because the physical strengthening of the facilities has created the perception that the infrastructure or community functions exposed to various threats can not be maintained properly. In addition, disaster management organizations are interested in ways to quickly recover from disasters using less external resources, which is why the UN adopted the concept of resilience in the Code of Conduct, 2005-2015, "Building the Resilience of Nations and Communities to Disasters" It is also. However, despite its importance, the concept of resilience is still not properly agreed or amalgamated, and only a few studies attempt to quantify resilience (see Table 1). In Korea, the term "resilience" has been used as a disarming force in research to develop a diagnostic indicator of urban disaster power (National Disaster Prevention Research Institute Disaster Prevention Research Institute, 2010). In addition, it has been translated into resilience and resilience.
Among the methods in Table 1 that quantitatively studied resilience, the resilience cost index, designed by Vugrin et al. (2010), was designed to quantify the resilience of national infrastructure. Vugrin et al. (2010) It is defined as the ability of the system to shorten the time to perform below the target value without significantly reducing the system performance (SP) to the target system performance (TSP). The Recovery Dependent Resilience Cost (RDR) and the Optimal Resilience Cost (OR) can be distinguished by specific recovery activities. In addition, as shown in FIG. 1, Vugrin et al. (2010) have shown that resilience can be used to prevent an infrastructure from absorbing capacity to withstand external threat factors, adaptive capacity to cope with damage itself, And the restorative capacity to recover the restorative capacity (restorative capacity). Here, the absorption force will be durable, the adaptive force is explained as the magnetic force recovery force due to the internal change, the restoration force is the amount of the restoration resource or the quick adjustment force. More specific definitions of absorbency, adaptive force and restorative force are given in Table 2 below.
Table 2 shows that the resilience evaluation factor of Vugrin et al. (2010) is very similar to the functional goal of the urban disaster power announced by the Disaster Prevention Research Institute (2010) of the National Institute for Disaster Prevention Education.
The RDR in Table 1 is the sum of the performance degradation (TSP-SP) of the infrastructure system caused by external threat factors and the amount of recovery resources (ie, recovery effort) Is expressed. The influence of the system and the relationship between recovery effort and absorbency, adaptive force, and restoration force are as shown in FIG. The greater the resilience cost index, the less the system's resilience is interpreted.
On the other hand, the OR in Table 1 is a method of calculating the recovery effort that minimizes the sum of the system impact and recovery efforts. The resilience cost index designed by Vugrin et al. (2010) emphasizes the importance of recovery efforts in assessing resilience. It also shows that the recovery process is also considering interaction between system impact and recovery effort.
In the meantime, the resilience cost index of Vugrin et al. (2010) is designed to evaluate the resilience of the national infrastructure. However, the present invention modifies the resilience cost index so that it can be extended to assess the vulnerability of the state or community to disaster, It is defined as a cost index. The disassembly cost index defined in the present invention is as follows.
Where L (t) is amount of damage, R (t) is recovered liquid, V (t) is the exposure parameter, t 0 and t f indicates the start and end time of each analysis. The larger the cost of disaster cost index, the more disaster-prone costs are incurred compared to exposure factors, which may be a region or country vulnerable to disaster. Here, the disaster prevention cost is the total cost incurred due to the disaster, which is the sum of the damage incurred between t 0 and t f and the recovery cost required to recover it. The disaster prevention cost index obtained by normalizing the disaster prevention cost with the exposure factor.
In general, the disaster damage (L (t)) of a community or country is similar to the difference between TSP and SP of Vugrin et al. (2010), as large as the community deviates from its original state due to disaster (see Table 1) . The recovery amount (V (t)) corresponds to the RE of Vugrin et al. (2010) as the cost of returning the community to the desired state (TSP) after the disaster (see Table 1).
In the present invention, the equation (1) is calculated based on the amount of damage and the amount of restitution reported by the state. In the case of Korea, the amount of damage and restitution by region by disaster is relatively well reported in National Disaster Information Center. Details of damage amount and restoration amount will be described later.
On the other hand, the above equation (1) divides the sum of damage and restoration costs by the exposure factor (V (t)) in order to compare the dismantling power between areas with different socioeconomic scale. Here, the exposure factor is a vulnerable factor such as gross domestic product and population, and is also interpreted as disaster prevention resources. In other words, in areas with large in-house productivity or large populations, vulnerable factors exposed to disasters are large, but at the same time, there are many recovery resources and recovery personnel that can be mobilized during disasters.
In the present invention, the purpose of defining and exploiting the disaster cost index is ultimately to reduce disaster cost by reducing future losses and restoration costs. In other words, we choose t f -> ∞ in Eq. (1) and choose the optimal R (t) that minimizes the sum of the future damage and restoration costs, that is, the cost of disaster prevention. In this case, R (t) is the cost of the restoration activity that reflects the prevention and contrast effects.
For example, as shown in FIG. 2, when a disaster occurs in a community having a productivity of Y N , the community exhibits an absorptive capacity for a certain period of time to maintain its productivity. However, when the absorption capacity is exhausted, the productivity drops to Y D1 , and if the community has an adaptive capacity, it can restore some productivity without external resources. However, when this is done, the productivity drops again and external resources must be put in order to recover it.
Scenario 1 is a case where recovery resources are slowly applied at t 4, in which case a second disaster may occur before recovery is completed, and a second disaster may cause a drop in productivity.
In other words, the disaster cost index is a method to consider the amount of damages that accompany each disaster scenario and the cost of recovering the disaster scenarios. When establishing disaster prevention strategies, decision makers Can provide important information.
In addition, the disaster cost index as defined by Eq. (1) can be used not only to compare the dismantling capacity of different disaster prevention areas but also to enhance the disinfection effect of restoration activity (R (t)) It can also be used for analysis.
For example, the effect of reducing / preventing the recovery activity, that is, the effect of reducing the future loss (L (t)), becomes important, and the decision to reduce future damage by applying a large amount of recovery cost R (t) It can be seen that the amount of damages or the amount of restoration, that is, the cost of dismantling cost, is reduced within several years after a large amount of restoration solution is introduced as shown in the
Hereinafter, evaluation factors of the disassembly cost index will be described.
First, there are damages and restitution as evaluation factors.
FIG. 3 is a graph showing the damage amount and the restoration amount according to the disaster in Korea, and FIG. 4 is a graph showing a linear regression analysis of the damage amount and the restoration amount according to the disaster item based on the disaster annotation data.
Figure 3 shows the amount of damage and restoration data from 1979, but only the data after 2001 are analyzed to reflect the disaster type and recovery support policy that changed in the 2000s. The reason for analyzing the national disaster data is to evaluate the disaster cost index of Equation (1) by using the amount of damages and the amount of restoration reported in the disaster annual report. In addition, .
Referring to FIG. 4, the restoration amount corresponding to 1.47 times of the damage amount was taken on average in the past eleven years. In particular, in the buildings (1.97 times), ships (2.05 times) It can be seen that this was spent. In addition, the amount of damage and the amount of restoration show a high correlation (R 2 ) except for the other fields, which means private houses such as greenhouses, housing, etc. This shows that the slope of Fig. it means.
Table 3 summarizes the 6 disasters, the largest total disaster in 2001, and the major disasters in 2009, 2010, and 2011. For each disaster, the correlation between the damage amount and the restoration amount by 16 cities / districts was analyzed by using the linear regression equation. The slope and the correlation coefficient between the restoration amount and the damage amount are shown in Table 3. It can be seen that the recovery amount and the damage amount have a very high linear correlation and that the ratio of the recovery amount to the damage amount is determined by the disaster type. According to Table 3, Korea has a lot of restitution cost compared to the heavy rain damage. 3.83, 3.20, 3.06, 2.86, 2.53, 2.37, 2.15, 2.11, 2.09, 2.06, 1.93, which are higher than other disasters have.
In order to analyze the cause of the high recovery cost ratio occurring during the storm, we analyzed the restoration resources needed to recover the damage of the two storms, Most of the expenses were paid by the National Emergency Management Agency (33.3% of the total restoration costs) and the Ministry of Land, Transport and Maritime Affairs (43.9% and 35.2% of total restoration costs). 61.3% and 45.7% , And 91.5% and 89.1% of the expenses of the Ministry of Land, Transport and Maritime Affairs were used for river restoration. Based on these results, the ratio of the amount of restoration to the amount of damage to the river is shown in Table 4 below.
As shown in Table 4, the ratio of the amount of restitution to the amount of damage that occurred was 4.5 and 3.8, far exceeding the average (1.47) in Fig. And it can be concluded that the restoration cost of rivers and small river systems is very low considering that the restoration cost for restoration of the rivers / small rivers in Hwashi is high and the recovery cost is much higher than the damages.
Table 5 shows the ratio of total restitution to the total amount of damages by region in 2011.
Similar to the results in Table 3, it can be seen that the ratio of the amount of restitution to the amount of damage caused by heavy rainfall is the highest.
Table 7 shows the results of the analysis of the restoration costs of the seven storm events in 2011 compared to the damage amount by region.
It can be seen that the amount of restitution was particularly high in the June rainfall (26.67) and Jeju Island (8.22), and the heavy rainfall on July 7, 2008, compared to the amount of damages in Jeju Island (513.90) and Busan City (12.50). However, the amount of damage suffered in June was much higher than that of Gyeongsangnam-do (₩ 75,000) and Jeju Island (66,131,000 won) in Gyeonggi Province (5,823,006,000 won), Jeollanamdo Province (1,916,083,000 won) and Chungcheongbuk Province (1,787,874,000 won) Gyeongsangnam-do (58,260,671,000 won) and Chungcheongnam-do (20,974,832,000 won) suffered much damage compared to Jeju Island (1,043,000 won) and Busan City (600,000 won) These results show that the ratio of the amount of restitution to damage is influenced by the social, economic, political, and environmental characteristics of the area rather than the physical characteristics of the heavy rain, ie, the route and the radius of influence, .
In addition, Table 3 and Table 5 show that in the case of heavy snowfall in 2010 and 2011, a small amount of restoration is required compared to the amount of damage. This is because damage caused by heavy snow mainly occurs in agricultural areas such as vinyl houses, but compensation for facilities such as vinyl houses is compensated by wind and water damage insurance and crop injury insurance, .
The exposure factors will be described below.
In general, disaster risk is caused by three factors: hazards, exposures, and vulnerabilities (ADRC, 2005). Here, disaster is a natural or artificial threat such as typhoon and heavy rain. The exposure is an object that can be damaged by economic, social, cultural assets as well as people, and the degree of damage caused by disaster is defined as vulnerability . On the other hand, as mentioned above, exposure factors can also be interpreted as disaster prevention resources. Local inhabitants with large productivity or high population are vulnerable to disasters, but at the same time, there are many recovery resources and recovery workers that can be mobilized in the event of a disaster. By summing up this, the total gross area, area, and population can be used as exposure factors.
So far, from the 2001-2011 disaster analysis, it can be seen that Korea is paying high recovery cost per year compared to the amount of damage. In addition, considering the high recovery cost compared with the damage amount, it can be seen that the restoration cost is an important factor in evaluating the dismantling power. This is supported by the fact that as shown in Equation (1), the dismantlement cost index, which normalizes the sum of the damage amount and the restoration amount to the size of the exposure factor of the country or region, should be used for the dismantling force evaluation.
In the following, the dismantling cost index is utilized to evaluate the dismantling power of the national and local communities, and the effectiveness of the dismantling cost index is verified to compare the regional dismantling power.
1. Evaluation of National Disarmament
The sum of the damage and restitution of Table 3 can be used to evaluate the disinflation cost index in Equation (1). Because there is no comparative purpose between countries, it is not necessary to consider exposure factors. Table 3 lists disasters in descending order of magnitude of losses. Table 3 shows that, in general, the amount of restoration increases as the amount of damage increases, and the cost of disaster cost also increases in the same order. However, in 2010, the evaluation results using the damage priority and the disaster cost index differ from each other. In other words, in July, 2010, the recovery cost was higher than the damage amount of the heavy rainfall. In other words, in 2010, Korea was more vulnerable to heavy rainfall, and the disaster cost was also high.
In 2011, in spite of the small amount of damage, the June heavy rain required more restitution than the February 11 heavy snow, and the August heavy rain required more restitution than the January heavy snow. That is, priorities using disaster cost index can be different from those based on damage scale. However, in 2011 alone, the priorities based on the disaster cost index did not differ from the priorities in terms of damage scale, despite the large amount of damage caused by the heavy snowfall, which required a large amount of restoration to the damage caused by heavy rainfall.
2. Evaluation of local dismantling power
Equation (1) can also be used to compare and evaluate the disaster cost index of the area. (T 0 - t f ) is limited to the time of occurrence (t 0 ) of the specific disaster from the time of recovery (t f ) The results are shown in Tables 7 and 8, respectively.
Here, the area is listed in descending order of damage scale, and priority is given to the area where disaster reduction business takes priority because disaster prevention cost is high compared with exposure factor based on disaster cost cost index. Table 7 and Table 8 show that priorities judged on the basis of damage value and priorities judged on the basis of disaster cost index can be different. In particular, in Table 8, the rank determined by the sum of the damage amount and the restoration amount is also different from the damage amount ranking, and the ranking determined by the sum of the damage amount and the restoration amount depends on the restoration amount. This is because there are areas in the Jeonbuk area that require a lot of restitution compared to the damage.
There may be many reasons for the regional disparity in the cost of disaster for the same disaster. In particular, in the case of damage, regional deviations occur because disasters (eg, heavy rainfall, typhoon) are concentrated in a specific area, in addition to the vulnerability of the community (ie absorption and adaptability of Figure 1). For example, heavy rainfall in July 2011 (Table 7), central areas such as Gyeonggi-do and Gangwon-do and southern coastal areas of Gyeongsangnam-do, and typhoons in August 2011 (Table 8) It is crazy (Meteorological Agency, 2011). However, the ratio of the amount of restitution to the amount of damage seems to be due to the restoration power and social, economic, and political factors lower than the movement route and physical impact radius of the disaster. In order to properly manage the restoration cost, .
In addition, when the disinflation cost index is evaluated using Equation (1), the disinfection cost exponent value may become very small or large depending on the magnitude of the exposure factor. In this case, the exposure factor may be scaled by unit conversion of the exposure factor (eg population (thousand) population, GRDP (KRW) 1 billion). On the other hand, even when two communities with extreme exposure factors are compared, the disinflation cost index will still provide a meaningful result. For example, when comparing the dismantling capacity of an island village (eg, Ulleungdo) and a large city (eg, Seoul), the dismantling cost index of an island village may be relatively large due to too few or too few local gross production. However, even in this case, if the disaster prevention cost is large compared to the size of the economy or the population, it is reasonable to evaluate the disaster prevention power as low as possible, and the disaster prevention business should be implemented first. It can be interpreted as a disaster resource as well as a vulnerable person because it is costly compared to disaster prevention resources.
The following explains that the disinflation cost index can be used to analyze the effect of restoration activity through time variation of disinfection cost index. This is because, as shown in FIG. 2 and equation (1), many restoration costs spent in a particular area may have been used to enhance absorption and adaptability to reduce future damage.
As mentioned earlier, the high percentage of restitution versus damage can mean a low level of community restoration. In other words, it can be said that the restoration power of Hwashi rivers / small river basins in Korea is low and the restoration power of Jeollabuk-do is low in typhoon region in August, 2011. However, this judgment is based on the assumption that restoration costs are the costs of restoring the social system to pre-disaster conditions. In addition to restoring the system to its pre-disaster state, the actual restoration costs include absorptive capacity, May also include the costs incurred to improve resilience. In other words, when t f -> ∞ as in Eq. (1) and
In order to examine whether the high recovery volume ratio of Korea shows such an effect, the change of the dismantling cost index was analyzed. First, in order to evaluate whether the amount of restoration to the amount of damage caused by heavy rainfall strengthened the national disinflation ability of heavy rainfall, we examined the change of disinflation cost index for heavy rainfall in 2001-2011 as shown in Fig.
5 (a) shows the cumulative amount of damages and cumulative restitution at the end of the evaluation year (t f ) in the equation (1) as t 0 = 2001, and V (t) Did not do it. According to equation (1), the sum of accumulated damages and cumulative restitution is the disaster cost index from 2001 to t f . Referring to FIG. 5 (a), it can be seen that a large amount of restoration (1.9 times of the amount of the damage) was input due to the considerable amount of damage in 2006. In 2006, Korea suffered a total of 1.9 trillion won of property damage during a total of 16 damages, including typhoon Ewiniah (National Emergency Management Agency, 2007). Figure 5 (b) shows that a high amount of restoration was added to the amount of heavy rainfall every year. The ratio of restitution to damage has been steadily increasing since 2006, and the increase in damage and restoration from 2006 to 2008 seems to have been somewhat reduced, but it has been increasing since 2008. In other words, Korea's restoration activities are not strengthening national disarmament for heavy rainfall.
As described above, when determining the disaster prevention policy, quantitative methods are needed to compare and evaluate the disaster power so as to be helpful for objective decision making. In the case of Korea, it is considered that it is not reasonable to set the priorities of disaster prevention projects and dimension projects based on the amount of damages, considering the amount of restoration to the amount of damages per year. It would be reasonable to rank. In addition, in order to compare and evaluate the disaster prevention costs of different socioeconomic areas, it is necessary to normalize the disaster prevention cost by the gross production in the region, the population size, and the area. In addition, it is necessary to consider that the restoration amount can be utilized for the purpose of reducing future loss and ultimately enhancing the disinfection power. The present invention can be applied to a metering index which can cover all of these requirements, The It is proved that the disaster cost index can be used to compare the regional disaster power and analyze the national disaster power using the domestic disaster annual data. The efficiency of the recovery activity can be evaluated through the change of the disaster cost index .
In the case of Korea, it took a lot of restitution in the case of heavy rain, and in some areas it paid a particularly high amount of restoration, which would bring the community back to its pre-disaster state, This may be due to the fact that the cost of living is higher than the cost of living. In this study, we analyzed whether the restoration amount of Korea was used to strengthen the actual national disarming power and to lower the disarming cost index by using the 2001-2011 damage amount and restoration amount reported in the disaster annals. According to the Disaster Yearbook until 2011, Korea's restoration activities have not contributed to strengthening disaster prevention. It seems that the concept of resilience has only recently been introduced in Korea, and it has not been enough to consider the preventive / protective effect in the restoration process.
Future recovery activities will continue to take the preventive / evacuating effect and ultimately lower the disinfection cost index. Therefore, it is necessary to continuously monitor changes in the dismantling cost index proposed by the present invention in order to objectively evaluate their effectiveness. Also, in order to efficiently distribute limited disaster prevention resources, it is necessary to determine regional priorities by using the disaster cost index considering all the effects of damages, restoration costs, and exposure factors as supplementary data. In other words, the disaster cost index can be widely used both domestically and internationally as a criterion for objectively analyzing the effect of disaster prevention policy as well as helping objective decision making in disaster prevention policy decision.
Claims (2)
Obtaining disaster data relating to the evaluation target area,
And calculating a disinfection cost index defined by the following equation of the evaluation area using the disaster data.
Here, L (t) is amount of damage, R (t) is recovered liquid, V (t) is the exposure parameter, t 0 and t f means the start and end time of each analysis.
Wherein the exposure factor comprises a gross area, population or area in the area.
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| CN113505967A (en) * | 2021-06-04 | 2021-10-15 | 上海电力大学 | Power distribution network toughness improving method based on real-time fault rate |
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| 대한안전경영과학회지,제13권 제1호* * |
| 한국방재학회논문집 제9권 1호 2009년 2월 * |
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| CN112836334A (en) * | 2020-11-23 | 2021-05-25 | 武汉理工大学 | Reliability evaluation and post-disaster recovery method for marine network |
| CN113505967A (en) * | 2021-06-04 | 2021-10-15 | 上海电力大学 | Power distribution network toughness improving method based on real-time fault rate |
| CN113505967B (en) * | 2021-06-04 | 2022-05-20 | 上海电力大学 | Power distribution network toughness improving method based on real-time fault rate |
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