A Critical Review of Community Resilience Building Framework: Contexts of Climate Change and Disasters in Bangladesh

The concept of community resilience in the contexts of climate change and disasters draws increasing attention and interest from practitioners and researchers in recent development discourse. This paper provides a critical review of six selected frameworks of community resilience building operationalized in Bangladesh over the span of years. In other words, this study aims to contribute to the understanding of resilience through a systematic analysis of the dimensions and indicators of community resilience frameworks. The analysis shows that comprehensive and effective community resilience frameworks should incorporate the missing components linked to fundamental elements of good governance, economic growth, environmental sustainability, social transformation, and capacity development. The paper concludes by highlighting a few other areas of grave concern that need more appropriate attention, considering the severe threats posed by climate change and natural disasters in line with sustainable development goals. Finally, this study recommends further research regarding the effectiveness of these frameworks in different climatic and disaster contexts that can lead the concept into a new dimension of community resilience and sustainability.

Deterministic and fuzzy-based methods to evaluate community resilience

Community resilience is becoming a growing concern for authorities and decision makers.This paper introduces two indicator-based methods to evaluate the resilience of communities based on the PEOPLES framework.PEOPLES is a multi-layered framework that defines community resilience using seven dimensions.Each of the dimensions is described through a set of resilience indicators collected from literature and they are linked to a measure allowing the analytical computation of the indicator’s performance.The first method proposed in this paper requires data on previous disasters as an input and returns as output a performance function for each indicator and a performance function for the whole community.The second method exploits a knowledge-based fuzzy modeling for its implementation.This method allows a quantitative evaluation of the PEOPLES indicators using descriptive knowledge rather than deterministic data including the uncertainty involved in the analysis.The output of the fuzzy-based method is a resilience index for each indicator as well as a resilience index for the community.The paper also introduces an open source online tool in which the first method is implemented.A case study illustrating the application of the first method and the usage of the tool is also provided in the paper.

A Nationwide Approach on Measuring Households’ Resilience by Constructing Climate Resilient Livelihoods Index (CRLI) in Rural Bangladesh

This study developed households’ Climate Resilient Livelihoods Index (CRLI) in Bangladesh. CRLI indicators were selected based on the Adequacy of Human livelihood conditions for Well-being and Development (AHEAD) framework and FAO resilience tools. The study was designed on cross-sectional data through a country-wide primary survey of 26,925 rural households. At first, we performed logistic regression to gauge the significance and intensity of different livelihood indicators on any specific livelihood indicator. Secondly, we scored each household with the set criteria of different livelihoods accessibility, if any households fulfill the set criteria was “scored 1” and if not “scored 0”. After scoring the households, eight different scores for each household were summed up to construct a composite score of “CRLI”. If any household scored 0 - 2 was considered as low resilient, if any household scored 3 - 5 was considered as moderate resilient and if any household scored 6 - 8 was considered as highly resilient. Additionally, we used ArcMap to visualize the percentage of households in districts with different resilience categories. Findings revealed that nationally 1.7% of households were low resilient, 60% of households were moderate resilient and only 11.48% of households were high resilient. More specifically, only 1.7% of households failed to secure any of the climate-resilient livelihood indicators, and only 0.06% of households secured all of them. Findings also revealed that food secured households had better adaptive capacity due to ensuring access to basic services, more financial capabilities, lower dependency ratio, and physical connectivity. In contrast, households with social safety net coverage had food insecurity, less financial ability, higher dependency ratio, lower education, and income sources. Among 64 counties, Cox’s Bazar, Bandarban, Chuadanga, Barguna, Bhola, Patuakhali, Narail, Kurigram, Sunamganj, Jamalpur, and Netrokona were the most vulnerable in terms of low CRLI. On the other hand, more than 25% of high resilient households were located in Dhaka, Gazipur, and Munshiganj counties. These findings would propel the government to devise appropriate steps in terms of more investment in area-specific local communities for enhancing regional resilience.

Integrated Model for Resilience Evaluation of Power-Gas Systems Under Windstorms

Integrated power-gas systems(IPGS)have developed critical infrastructure in integrated energy systems.Moreover,various extreme weather events with low probability and high risk have seriously affected the stable operation of IPGSs.Due to close interconnectedness through coupling elements between the power system(PS)and natural gas system(NGS)when a disturbance happens in one system,a series of complicated sequences of dependent events may follow in another system.Especially under extreme conditions,this coupling can lead to a dramatic degradation of system performance,resulting in catastrophic failures.Therefore,there is an urgent need to model and evaluate resilience of IPGSs under extreme weather.Following this development trend,an integrated model for resilience evaluation of IPGS is proposed under extreme weather events focusing on windstorms.First,a framework of IPGS is proposed to describe states of the system at different stages under disaster conditions.Furthermore,an evaluation model considering cascading effects is used to quantify the impact of windstorms on NGS and PS.Meanwhile,a Monte Carlo simulation(MCS)technique is utilized to characterize chaotic fault of components.Moreover,time-dependent nodal and system resilience indices for IPGS are proposed to display impacts of windstorms.Numerical results on the IPGS test system demonstrate the proposed methods.

An indicator model for assessing community resilience to the COVID-19 pandemic and its validation: A case study in Hong Kong

The COVID-19 outbreak had a significant negative impact on the world,and the fifth wave of COVID-19 in Hong Kong brought a considerable shock to Chinese society.There is a growing call for more resilient cities.However,empirical evidence and validation of modeling studies of resilience indicators for urban community responses to the COVID-19 pandemic still need to be provided.In this study,a resilience assessment indicator model comprising 4 subsystems,7 indicators,and 12 variables was developed to assess the resilience of Hong Kong communities in response to COVID-19(i.e.,Resilience Index).Furthermore,this study utilized regression models such as geographically weighted regression(GWR)and multiscale GWR(MGWR)to validate the resilience model proposed in this study at the model and variable levels.In the regression model,the Resilience Index and the individual variables in the resilience model are explanatory variables,and the outcomes of the COVID-19 pandemic(confirmed cases,confirmation rate,discharged cases,discharge rate)are dependent variables.The results showed that:(i)the resilience of Hong Kong communities to the COvID-19 pandemic was not strong in general and showed some clustered spatial distribution characteristics;(i)the validation results at the model level showed that the Resilience Index did not explain the consequences of the COvID-19 pandemic to a high degree;(ii)the validation results at the variable level showed that the MGWR model was the best at identifying the relationships between explanatory variables and the dependent variable;and(iv)compared with the model-level assessment results,the variable-level assessment explained the consequences of the COvID-19 pandemic better than the model level assessment results.The above analysis and the spatial distribution maps of the resilience variables can provide empirically based and targeted insights for policymakers.

Measuring the resilience of an airport network

Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characteristics after the disturbance and measure the robustness of the network with respect to connectivity. The dynamic processes occurring at the node and link levels are often ignored. Here we analyze airport network resilience by considering both structural and dynamical aspects. We develop a simulation model to study the operational performance of the air transport system when airports operate at degraded capacity rather than completely shutting down. Our analyses show that the system deteriorates soon after disruptive events occur but returns to an acceptable level after a period of time. Static resilience of the airport network is captured by a phase transition in which a small change to airport capacity will result in a sharp change in system punctuality. After the phase transition point, decreasing airport capacity has little impact on system performance. Critical airports which have significant influence on the performance of whole system are identified, and we find that some of these cannot be detected based on the analysis of network structural indicators alone. Our work shows that air transport system’s resilience can be well understood by combining network science and operational dynamics.