energies Article A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition Jacob Hale and Suzanna Long *   Citation: Hale, J.; Long, S. A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition. Energies 2021, 14, 141. https://doi. org/10.3390/en14010141 Received: 1 December 2020 Accepted: 24 December 2020 Published: 29 December 2020 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional clai- ms in published maps and institutio- nal affiliations. Copyright: © 2020 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA; jmhy96@mst.edu * Correspondence: longsuz@mst.edu; Tel.: +1-573-341-7621 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. Keywords: time series forecast; life cycle thinking; energy transition; sustainability 1. Introduction Fossil fuel resources provide a majority of the world’s energy and subsequent carbon dioxide emissions [1,2]. In 1990, fossil fuels made up more than eighty-six percent of the total primary energy supply of the United States and its combustion resulted in more than four thousand eight hundred megatons of carbon dioxide emissions. By 2015, energy demands increased by almost an additional thirteen percent with carbon dioxide emissions increasing by more than an additional two and a half percent. During this time, renewables increased by less than two percent. When excluding biofuels and waste-to-energy sources, this increase is less than one percent. These findings demonstrate that portfolios are shifting, but not toward renewables resulting in an increase in already high carbon dioxide emissions. If this trend continues, the consequences associated with climate change will be further exacerbated [3]. To minimize further harm to the environment, fossil fuel dependent energy portfolios, especially those relying on coal, must be transitioned to renewable alternatives. Modern energy transitions are defined by a timely shift toward energy systems that address global energy challenges [4]. Transitions have received widespread scholarly attention from several perspectives such as socio-technical [5–8], existing system considera- tions [9–11], and environmental reform and governance [12–14], among others. An effective approach in quantitative studies is the use of time series forecasting methods to inform transition decision making. Energy forecasts primarily consist of three temporal horizons: short-, medium-, and long-term [15]. Short-term forecasts encompass studies from an hour to a week [16,17]. Medium-term forecasts include a month to five years [18–20]. Long- term forecasts cover periods from five to 20 years [21–23]. Forecasting is a data-driven method that relies on statistical procedures to derive relationships between variables [24]. Energies 2021, 14, 141. https://doi.org/10.3390/en14010141 https://www.mdpi.com/journal/energies