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.
