Development of Integrated Urban Resilience Framework for Punjab, Pakistan

Abstract

Climate change impacts and disaster events are drastically increasing because of the devastating anthropogenic activities against nature. This has made our cities, towns, villages and remote areas much more vulnerable to the ensuing hazards. Over the world, extensive efforts have been undertaken to reduce the susceptibility of the human being through the development of strategies and policies, frameworks for vulnerability, risk analysis, resilience, and robust models to calibrate resilience. In line with said pattern, a concept of resilience was introduced in 1973 by Holling to explain the countering feature which described as how an ecosystem is bounced back to its original state after having been encountered by any stresses and shocks. Various frameworks have been explored to measure the resilience of urban area in terms of socio-economic, disaster risk reduction, climate change, and hazards. The research in hand is one of such initiatives that will address sustainable development goals (SDGs) priority areas 9 and 11 particularly in the circumstances when there is no existing framework as such at present to address the resilience in Punjab, Pakistan. The aim of this research is to; 1) review prevailing urban resilience frameworks to conceptualize integrated urban resilience framework (IURF), 2) model integrated urban resilience framework (IURF) at the district and household level using multivariate techniques, and 3) to operationalize these models using rubric scales. The methodology to conduct this research consists of three parts i.e. conceptualization, data collection, and modeling. In conceptualization, the literature was reviewed from 2000 to 2016 on empirical studies describing how to measure resilience in terms of urban sociology, urban economy, urban vulnerabilities, urban disaster risk reduction and climate change. This process helped in identifying key indicators and dimensions for our study. Secondly, data collection included a collection of data from experts and field pertaining to urban resilience at the district level in Punjab and household level in Muzaffargarh. Subsequently, the data gathered was screened and analyzed using SPSS software. In this, Multivariate techniques were applied to model the dimensions of urban resilience. The techniques used were exploratory factor analysis and regression analysis. To measure the level of urban resilience of each district of Punjab, data on all dimensions, including indicators was collected from relevant departments based on rubric scales. To operationalized household resilience model, a data of 221 sample size was collected from the urban area of district Muzaffargarh. xvi The urban resilience can be measured by using two models, i.e. Composite Index based and regression analysis based. In composite index model, the value of coefficients will be changed based on new data, whereas in the case of regression model they remain same but only values of independent variables i.e. factors vary. The urban resilience modeling revealed that there are six factors or dimensions playing a vital role in making urban area resilient against disaster concerning both natural hazards and climate change. These are enumerated as institutions, infrastructures, community awareness, Hazards, Vulnerability & Risk Assessment (HVRA), social, and economic. These factors explained about 74 % of total variance. Based on the variance table of our data, the ranking of dimensions exhibits institutions (51%), community awareness (8.2%), social (4.36%), infrastructure (4.22 %), Hazards, Vulnerability & Risk Assessment (HVRA) (3.38%), and economy (2.737 %) of total urban resilience respectively. The household resilience modeling revealed that there were seven key factors that play a vital role in measuring household resilience. These are social, awareness and participation, civil society organizations, public institutions, economic, house structure and critical facilities. The total variance explained by these factors is 63.76% of total characteristics of household resilience. Comparing both models, regression analysis model is found more powerful as it can be efficiently used with the only variation of responses or field data against factors while for composite index model needs to perform factor analysis separately for each data set. The constants used in this model are not same for all data while coefficients used in the regression model are same for all data sets. The regression model is a predictive model as compared to the composite index model. Concerning research and modeling, the urban resilience encompasses the top three main dimensions, including physical infrastructure, community awareness and hazards, vulnerability and risk assessment. The household resilience at the bottom specifically relates to civil society organizations, public institutions and socioeconomic characteristics as critical factors. As such, the respective dimensions at said urban and household levels qualify to be augmented for building of resilient and sustainable communities.