Abstract
Risk assessment of climatic events and climate change is a globally challenging issue. For risk as well as vulnerability assessment, there can be a large number of socioeconomic indicators, from which it is difficult to identify the most sensitive ones. Many researchers have studied risk and vulnerability assessment through specific set of indicators. The set of selected indicators varies from expert to expert, which inherently results in a biased output. To avoid biased results in this study, the most sensitive indicators are selected through sensitivity analysis performed by applying a non-linear programming system, which is solved by Karush-Kuhn-Tucker conditions. Here, risk is assessed as a function of exposure, hazard, and vulnerability, which is defined in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), where, exposure and vulnerability are described via socioeconomic indicators. The Kolmogorov-Smirnov statistical test is applied to select the set of indicators that are the most sensitive for the system to assess risk. The method is applied to the Bangladesh coast to determine the most sensitive socioeconomic indicators in addition to assessing different climatic and climate change hazard risks. The methodology developed in this study can be a useful tool for risk-based planning.
Highlights
A non-linear programming system with inequality and equality constraints is designed from 27 (23 socioeconomic and 4 hazards) possible parameters by using normalized scores of each parameter. This non-linear programming problem is solved by KKT conditions and it gives the rank of indicators during the risk minimization process
The sensitivity analysis made in this study shows that non-linear programming is effective to
The sensitivity analysis made in this study shows that non-linear programming is effective to select the most sensitive indicators for risk assessment
Summary
Climate change’s impact has become the most important threat to human civilization. For the past few decades, climate change has increasingly affected the lives of people and all climate sensitive sectors. Sensitivity analysis provides a powerful means of learning about the degree of sensitivity of all the socioeconomic parameters of the system It primarily studies the degree of impact of each indicator on the composition of the indices [19]. The tool can be used for the identification of uncertainties to prioritize additional research or data collection [24] Various methods such as the differential method, analysis of variance (ANOVA), linear regression analysis (RA), response surface method (RSM), mutual information index (MII), fourier amplitude sensitivity test (FAST), Sobol’s method [25], and non-linear programming [26] can be used to perform sensitivity analysis. A statistical test is performed to select the most sensitive indicators, which can be used to assess the risk in the selected system
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