Abstract
Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.
Recommended Citation
J. Hale and S. Long, "A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition," Energies, vol. 14, no. 1, MDPI, Dec 2020.
The definitive version is available at https://doi.org/10.3390/en14010141
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Keywords and Phrases
Energy transition; Life cycle thinking; Sustainability; Time series forecast
International Standard Serial Number (ISSN)
1996-1073
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
29 Dec 2020
Comments
The authors gratefully acknowledge the DOE SUNSHOT GEARED program for partially funding this research through DOE Project DE-EE0006341.
This article belongs to the Special Issue Integrated Approaches for Enterprise Sustainability.