An automated personal carbon footprint calculator for estimating carbon emissions from transportation use

Ajufo, Chiamaka Ann Marie and Bekaroo, Girish ORCID: https://orcid.org/0000-0003-1753-4300 (2021) An automated personal carbon footprint calculator for estimating carbon emissions from transportation use. icARTi '21: Proceedings of the International Conference on Artificial Intelligence and its Applications. In: icARTi 2021, 09-10 Dec 2021, Virtual Event, Mauritius. . [Conference or Workshop Item] (doi:10.1145/3487923.3487935)

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Abstract

Transportation is one of the biggest menaces to the planet, releasing several million tons of gases into the atmosphere on an annual basis. The growing use of transportation has expanded the concentration and release of these gases, which affect the environment in a number of ways such as depletion of the ozone layer, air pollution and more seriously, global warming and climate change. Among the different modes, road transportation is a significant contributor of greenhouse gas as it ejects dangerous gases directly into the atmosphere, and these emissions are predicted to increase drastically over the years. As such, it is essential to track and monitor emissions from transportation activities in an attempt to reduce the global emissions of greenhouse gases, through carbon footprint calculators. However, most of these calculators do not solely focus on transportation and the ones that do, require a substantial amount of effort and manual input. this paper investigates acceptance of an automated personal transportation-based carbon footprint calculator and its accuracy in monitoring and reducing carbon emissions. As part of this study, a mobile application called TCTracker was implemented using Global Positioning System (GPS) functionality and built-in artificial intelligence (AI) features. The acceptance of the tool was evaluated using the Technology Acceptance Model whereby involving forty users to evaluate four constructs notably, perceived ease of use, perceived usefulness, perceived enjoyment, and intention to use. Among these constructs, perceived ease of use and perceived usefulness had the highest scores, to also depict the acceptance of the tool, while also sustaining interest in carbon footprint tracking.

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