| Issue |
E3S Web Conf.
Volume 48, 2018
The 4th International Workshop on UI GreenMetric World University Rankings (IWGM 2018)
|
|
|---|---|---|
| Article Number | 03007 | |
| Number of page(s) | 5 | |
| Section | Issues and Innovation in Managing Energy | |
| DOI | https://doi.org/10.1051/e3sconf/20184803007 | |
| Published online | 08 August 2018 | |
Evaluation of electricity consumption and carbon footprint of UI GreenMetric participating universities using regression analysis
1
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
2
School of Environmental Science, Universitas Indonesia, Kampus UI Salemba, Jakarta 10430, Indonesia
3
Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
* Corresponding author: apresekal@gmail.com
UI GreenMetric as sustainability-based university rankings has received a worldwide acceptance since its initiation in 2010. One of the criteria for this ranking is the annual electricity consumption of participating Universities. There are some challenges in evaluating the overall data, i.e. some electricity consumption information is missing or may not accurately represent the real condition. There is various information that can be used to calculate the university rank associated with electricity consumption. On the other hand, some external data sources from World Bank on the annual electricity consumption per capita for every country is highly correlated with the electricity consumption in every University. This paper aims to show our evaluation and prediction of the annual electricity consumption from participating university using regression analysis based on the available data of UI GreenMetric and relevant external information. This is conducted using regression analysis on the data submitted in 2017 and the predicted KWH based on the number of full-time student and staff in the university. The result shows that some universities are consuming more electricity than the average KWH used per-capita in their country. The result also shows that the prediction cannot be used accurately, especially for the carbon footprint. This evaluation may help universities to improve their policy in reducing the electricity consumption and the greenhouse gas emission reduction policy, and mainly helps UI GreenMetric to speed up the verification process when necessary
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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