DECARBONIZING CAMPUS MOBILITY: A COMPUTATIONAL INTELLIGENCE FRAMEWORK FOR EMISSION-AWARE URBAN TRANSPORT PLANNING

Khurram Iqbal; Perfshan Erum; Shujaat Ali; Khalid Bin Muhammad; Khalid Mahboob; Sarwat Ishaque; Hamza Birjees

doi:10.71146/kjmr434

Authors

Khurram Iqbal Department of Computing, FEST, Hamdard University, Karachi, Sindh, Pakistan Author
Perfshan Erum Department of Mathematics, NED University of Engineering and Technology, Pakistan Author
Shujaat Ali School of Electrical and Information Engineering, Tianjin University China Author
Khalid Bin Muhammad Faculty of Engineering, Science, Technology & Management, Ziauddin University, Karachi Author
Khalid Mahboob Department of Computer Science, Institute of Business Management, Karachi, Sindh, Pakistan Author
Sarwat Ishaque Computer Science and information System Department, Khadim Ali Shah Bukhari Institute, Karachi, Sindh, Pakistan Author
Hamza Birjees Department of Energy and Environment Engineering Dawood University of Engineering & Technology Karachi Author

DOI:

https://doi.org/10.71146/kjmr434

Keywords:

CO₂ Emissions, Data Analysis , Environmental Performance

Abstract

Currently, the majority of the cities throughout the world have been burdened with gases that are formed by automobiles. The rapid and continuous growth in industrialization and urbanization has caused much damage to the environment. The foremost source of environmental pollution in the city is mainly due to the industrial waste and traffic congestion. Carbon dioxide (CO2) emission is considered one of the major causes of global warming. In this article, a case study of the Institute of Business Management (IoBM) is presented. Data was gathered from 25th April 2017 to 20th March 2021 to gain information about all the types of cars parked inside the campus. This research purpose is to understand the models and interpret the CO2 emission rate of all the models of different companies. Lastly, this case study is an effort to suggest and recommend the major steps in controlling CO2 emissions that have been observed through the data.