DEM311: Emergency and Disaster Management in Context - Assessment Answer

Introduction

Quantitative disaster risk appraisal approaches utilise numerical models and measurable examination to assess the probability and extent of debacles. The subjectivity and ambiguity of qualitative risk assessments are diminished by these methods, which provide a methodical and uniform approach to catastrophe risk assessment. These philosophies give a methodical and normalised way to deal with calamity risk evaluation, permitting leaders to designate assets and focus on risk-decreasing procedures (Li et al. 2012). This essay will compare and contrast various quantitative disaster risk assessment approaches using three quantitative risk assessment models as examples. In this review, we examine the advantages and disadvantages of three quantitative methods for assessing catastrophe risk: cost-benefit analysis, risk equations, and multi-criteria analysis.

Discussion

Strengths of Quantitative Disaster Risk Assessment Methods

One of their strengths is the ability of quantitative methods for disaster risk assessment to evaluate risks that are complex and interdependent. Numerous danger types, their recurrence, power, and spatial conveyance, as well as the weakness and openness of networks and resources, can be considered by these models (World Health Organization, 2014). This provides a comprehensive view of the environment prone to catastrophe and can assist in locating high-risk locations that call for immediate assistance and resources.

The ability to evaluate the efficacy of risk reduction measures and track progress toward disaster risk reduction goals is another strength of quantitative disaster risk assessment methods (World Health Organization, 2014). This is accomplished by modelling the effects of various risk reduction scenarios, such as implementing early warning systems or constructing protective infrastructure. This can assist policymakers and practitioners in making evidence-based choices and allocating resources to the most effective risk-reduction strategies.

Finally, quantitative methods for disaster risk assessment offer a framework for making decisions based on objective measurements and empirical data (Li et al. 2012). Policymakers and practitioners are able to base their decisions on the evidence and data provided by the models, which reduces subjectivity and uncertainty.

Examples of Quantitative Risk Assessment Models

Each of the various quantitative risk assessment models has its own set of advantages and disadvantages.

Risk (R) = Hazards (H) * Vulnerability (V) * Exposure (E)

The connection between exposure, vulnerability, hazards, and risk is depicted in this model. Earthquakes, floods, and terrorist attacks are examples of hazards that can harm people or the environment. Vulnerability refers to qualities of persons or assets that render them vulnerable to harm, such as age, socioeconomic level, or a lack of infrastructure (Pasman and Reniers, 2014). The physical placement and closeness of persons or assets to dangers are referred to as exposure.

According to Chang et al. (2021), by incorporating a variety of factors that contribute to the risk of disaster, this model provides a comprehensive view of risk. Additionally, it is simple to comprehend and widely utilised by practitioners and policymakers to guide strategies for reducing disaster risk. The fact that this formula provides an easy-to-understand method for estimating the risk of a disaster is one of its strengths (Chang et al. 2021). By taking into account the three aspects of exposure, vulnerability, and hazards, it is possible to assist decision-makers in determining which populations and regions are most vulnerable and in giving priority to measures to reduce risk. It is widely applicable in disaster risk management due to its adaptability to various hazard types, sectors, and scales. In contrast, the author also stated that this model doesn't catch the intricacy and reliance of calamity risk factors, for example, the flowing impacts of dangers, or the effect of social and political variables on weakness (Pasman and Reniers, 2014). Hazards, vulnerability, and exposure are also weighted based on subjective judgments. Furthermore, the formula fails to account for the potential interactions or feedback loops between the factors, which could affect the overall risk.

The Risk (R) = Hazards (H) * Vulnerability (V) * Exposure (E) formula is still a useful tool for calculating disaster risk despite these limitations. By providing a simple and adaptable framework for calculating disaster risk, it can assist decision-makers in identifying locations and individuals at high risk and prioritising initiatives to reduce risk (Wei et al. 2021). Be that as it may, it is basic to perceive the recipe's restrictions and possible predispositions and supplement it with other quantitative and subjective strategies to guarantee a complete and exact evaluation of calamity risk.

Multi-criteria Analysis

"Multi-criteria analysis (MCA)," which uses a multi-dimensional framework to evaluate various risk mitigation strategies, is another quantitative method for assessing disaster risk. MCA loads and scores different measures given their relative significance, including social, financial, and natural variables (Wei et al. 2021). The formula for MCA can be represented as:

MCA Score = Σ(weighted score for each criterion)

Where:

Σ = sum of all weighted scores for every criterion

Weighted score = score for every criterion * weight allocated to the criterion

The result of the formula provides decision-makers with a ranked list of measures based on how well they meet multiple criteria to achieve the desired outcomes.

According to Skilodimou et al. 2019, one of MCA's key strengths is its ability to assess several perspectives and trade-offs, enabling decision-makers in selecting the most effective and cost-efficient risk-reduction solutions. One of MCA's strengths is its clear and methodical approach to assessing and selecting various risk reduction approaches. By taking into account various principles and allocating burdens to each standard, pioneers can ensure that the activities picked are agreed with the best outcomes and requirements (Skilodimou et al. 2019). Additionally, MCA can assist in determining trade-offs between various criteria and locating areas where additional data or analysis may be required for disaster risk management. However, MCA has a few downsides, for example, the way that it depends on emotional information sources and may have predispositions or conflicting weighting rules. Another issue is that the interconnectedness and complexity of risk reduction measures may not be fully taken into account by MCA (Nsengiyumva et al. 2018). For instance, the synergistic effects of certain measures when combined may not be fully reflected by the individual criteria used in MCA. MCA may also ignore changes in the natural or socioeconomic environment, for example, which could affect the effectiveness of the measures.

Cost-benefit Analysis

According to Sharma and Ravindranath, 2019, the cost-benefit analysis approach of catastrophe risk assessment evaluates the costs and benefits of various risk mitigation methods. The effectiveness of this approach is determined by calculating a net present value and taking into account the financial costs and benefits The net present value, or NPV, is calculated as follows:

∑(Benefits) = the amount of the multitude of advantages coming about because of the execution of a gamble decrease measure, determined in present worth terms

∑(Costs) = the amount of the relative multitude of expenses related to the execution of the action, additionally determined in present worth terms of each measure.

One of the main advantages of cost-benefit analysis is its capacity to justify the economics of risk-reduction measures. It enables decision-makers to justify the allocation of resources and prioritise measures based on their economic efficiency. This is especially important when there are limited resources and decision-makers must choose between various measures. One of the main advantages of cost-benefit analysis is that it can clearly explain the economics behind risk-reduction measures (Sharma and Ravindranath, 2019). This makes it possible for decision-makers to provide justifications for the distribution of resources and to prioritise measures based on how cost-effective they are. In any case, cost-benefit analysis has impediments, including the powerlessness to catch non-financial advantages like social and ecological effects, as well as its dependence on precise information data sources and assumptions (Pasman and Reniers, 2014). At the point when measures with both monetary and non-financial impacts are assessed utilising cost-benefit analysis, moral issues emerge. For example, it may not be moral to carry out an action that helps a well-off populace yet adversely affects the climate.

Concussion

To conclude, quantitative strategies for disaster risk evaluation give decision-makers a deliberate and standard methodology for surveying and estimating fiasco gambles. This makes it feasible for them to focus on risk-reducing efforts and assign resources. The fact that they are unable to take into account the complexity and interrelationships of various risk factors, the uncertainty and variability that are associated with disaster risks, and the constraints imposed by the quality and availability of data are just a few of the disadvantages of these approaches. It is essential to acknowledge these limitations and take into consideration the particular context and factors that affect disaster risk when using these methods to help make decisions.

References

Chang, M., Cui, P., Dou, X., & Su, F. (2021). Quantitative risk assessment of landslides over the China-Pakistan economic corridor. International Journal of Disaster Risk Reduction, 63, 102441. Retrieved from: https://www.sciencedirect.com/science/article/pii/S2212420921004027 [Retrieved n 20.4.2023]

Li, K., Wu, S., Dai, E., & Xu, Z. (2012). Flood loss analysis and quantitative risk assessment in China. Natural Hazards, 63, 737-760. Retrieved from: https://link.springer.com/article/10.1007/s11069-012-0180-y [Retrieved n 20.4.2023]

Nsengiyumva, J. B., Luo, G., Nahayo, L., Huang, X., & Cai, P. (2018). Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. International Journal of environmental research and public health, 15(2), 243. Retrieved from: https://www.mdpi.com/259328 [Retrieved n 20.4.2023]

Pasman, H., & Reniers, G. (2014). Past, present and Future of Quantitative Risk Assessment (QRA) and the incentive it obtained from Land-Use Planning (LUP). Journal of loss prevention in the process industries, 28, 2-9. Retrieved from: https://www.academia.edu/download/45154757/Past_present_and_future_of_Quantitative_20160427-21590-1dy42uw.pdf [Retrieved n 20.4.2023]

Sharma, J., & Ravindranath, N. H. (2019). Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change. Environmental Research Communications, 1(5), 051004. Retrieved from: https://iopscience.iop.org/article/10.1088/2515-7620/ab24ed/meta [Retrieved n 20.4.2023]

Skilodimou, H. D., Bathrellos, G. D., Chousianitis, K., Youssef, A. M., & Pradhan, B. (2019). Multi-hazard assessment modelling via multi-criteria analysis and GIS: a case study. Environmental Earth Sciences, 78, 1-21. Retrieved from: https://link.springer.com/article/10.1007/s12665-018-8003-4 [Retrieved n 20.4.2023]

Wei, Y., Jin, J., Cui, Y., Ning, S., Fei, Z., Wu, C., ... & Tong, F. (2021). Quantitative assessment of soybean drought risk in Bengbu city based on disaster loss risk curve and DSSAT. International Journal of Disaster Risk Reduction, 56, 102126. Retrieved from: https://www.sciencedirect.com/science/article/pii/S2212420921000923 [Retrieved n 20.4.2023]

World Health Organization. (2014). Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and 2050s. World Health Organization. Retrieved from: https://apps.who.int/iris/bitstream/handle/10665/134014/9789241507691_eng.pdf [Retrieved n 20.4.2023]

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