ENVIRONMENTAL RISK ASSESSMENT OF GROUND HANDLING OFFICES: CASE STUDY OF NEW ISLAMABAD AIRPORT

Abstract

A detailed framework to quantify carbon footprint and environmental risk assessment based on an airport office environment in Pakistan has been presented in this study. As recognition of climate change is increasing, organizations that have recently started to examine their carbon footprint might lack the resources and skilled individuals to conduct a comprehensive risk assessment. To address this gap, a set of quantification methods have been presented in this study that categorizes the emission sources from an office into various categories. According to the findings, the organization's annual carbon footprint was 24642.97 tonnes of CO2e, with main emissions coming from the printing devices and airport ramp vehicles usage. The study also revealed that this strategy can be used to estimate total emissions as well as identify significant emission sources. Now a days, environmental risk assessment is an effective decision-making approach to reduce the environmental impacts and consequences of diverse activities to accomplish sustainable development. The likelihood-severity matrix approach, which is known as a quantitative approach for risk assessment, is one of the most applied environmental risk assessment techniques. Numerical assumptions of the likelihood and severity of risk occurrence are extremely challenging with this technique because these components are related with high degree of uncertainty. Hence, a risk analysis that considers the uncertainties associated with emission from office operations is required to assess existing risks and prioritize them for further precautionary measures and decisions to reduce, mitigate, and/or potentially eliminate the involved risks. Therefore, this study provides a risk assessment model based on Fuzzy set theory principles to analyze risk occurrences in an office environment. To evaluate the validity of Fuzzy risk model, the results of Fuzzy risk assessment are compared to those of conventional risk assessment. Study findings discovered that the Fuzzy logic model has a high potential to accurately model the risk evaluation associated with uncertainty.