Assessing the Level of Urban Sustainability in the Capital of Pakistan: A Social Analysis Applied through Multiple Linear Regression

Abstract

It is reported that the rapid rate of urbanization has negative impacts on the environment, society, and economic conditions in the capital of Pakistan, which need to be addressed for sustainability. This study attempts to quantify the level of urban sustainability by using a Multiple Linear Regression Analysis in a social context. Primary data were collected through a random sampling survey, while secondary data were collected from the Capital Development Authority, public documents, and past studies or case reports. These data were associated with land values, property rent, the availability of commercial space, social security, the sense of belonging, and the frequency of urban flooding. The major sources of secondary data were the revised master plan of the city (2020–2040), urban gazette data, land value registers, property dealers’ records of real estate, and government documents. The results of the analyses show that there is a strong correlation between the selected variables and the urban sustainability of the area. These results were expressed through the coefficient of regression and coefficient of correlation through regression statistics, including R = 0.801 for economic sustainability, R = 0.822 for urban sustainability, and R = 0.905 for social sustainability. The results reveal that the level of urban sustainability is at risk due to overpopulation and the degradation of the ecosystem. It is concluded that there is a need for the implementation of a revised master plan in the area for the sustainable development of the city.

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“…As a statistical analysis method for studying nonlinear problems, multiple linear regression models play a very important role in statistics, and are widely used in fields such as data analysis, prediction, and modeling [5] [6] [7]. Its main functions include modeling and prediction, variable screening and impact analysis, control variable method, and model diagnosis.…”