Abstract
Energy procurement for global companies grows more challenging with improved sustainable energy opportunities, increasing greenhouse gas compliance reporting, electricity market regulatory changes, and customer pressure for companies to be sustainable. With so many dimensions to the challenge, it is difficult to simultaneously weigh the options and make a fully informed decision. In this case study, we develop a multi-criteria model to support global companies’ sustainable energy procurement decisions. The model synthesizes calculated and judgment-based metrics for both environmental and business considerations using the Analytica decision modeling software. In order to facilitate the use of this powerful modeling platform for multi-criteria strategy applications, we implement two new generic modules, one for multi-attribute utility and one for decision-analytic strategy tables. We then apply the model to a real-world case study for a multinational company, using a stochastic framework with Monte Carlo simulation to create a range of values for the various strategies. The model can be replicated for similar energy purchasing decisions, while the modules can be reused for a wide range of decision modeling situations.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The Analytica code for the MAUT and Strategy Table modules and the full model with associated case study data are available from https://github.com/donald-jenkins001/Strategic-Sustainable-Energy-Analytica-Model.
References
Analytica (2018) Tornado Plots—Analytica Wiki. https://wiki.analytica.com/Tornado_Plots. Accessed 7 Sep 2020
Ayyub BM (2001) Elicitation of expert opinions for uncertainty and risks. CRC Press, Boca Raton
Baxter R (2021) Energy storage financing: project and portfolio valuation. Sandia National Lab.(SNL-NM), Albuquerque
Brede M, De Vries BJM (2013) The energy transition in a climate-constrained world: regional vs. global optimization. Environ Model Softw 44:44–61. https://doi.org/10.1016/j.envsoft.2012.07.011
Capehart BL, Turner WC, Kennedy WJ (2006) Guide to energy management system, fifth. The Fairmont Press Inc, Lilburn
Dowlatabadi H (1998) Sensitivity of climate change mitigation estimates to assumptions about technical change. Energy Econ 20:473–493. https://doi.org/10.1016/s0140-9883(98)00009-7
Eschenbach TG, McKeague LS (1989) Exposition on using graphs for sensitivity analysis. Eng Econ 34:315–333. https://doi.org/10.1080/00137918908902996
Esty DC, Winston AS (2006) Green to gold: how smart companies use environmental strategy to innovate, create value, and build competitive advantage. Yale University Press, London
Ewing B, Baker E (2009) Development of a green building decision support tool: a collaborative process. Decis Anal 6:172–185. https://doi.org/10.1287/deca.1090.0146
Finnerty N, Sterling R, Contreras S et al (2018) Defining corporate energy policy and strategy to achieve carbon emissions reduction targets via energy management in non-energy intensive multi-site manufacturing organisations. Energy 151:913–929. https://doi.org/10.1016/j.energy.2018.03.070
Frostell BM, Sinha R, Assefa G, Olsson LE (2015) Modeling both direct and indirect environmental load of purchase decisions: a web-based tool addressing household metabolism. Environ Model Softw 71:138–147. https://doi.org/10.1016/j.envsoft.2015.05.014
Galbreth MR, Ghosh B (2013) Competition and sustainability: the impact of consumer awareness. Decis Sci 44:127–159. https://doi.org/10.1111/j.1540-5915.2012.00395.x
Google (2011) Google’s green PPAs: what, how, and why. http://www.google.com/green/pdfs/renewable-energy.pdf. Accessed 7 Sep 2020
Guikema S, Milke M (1999) Quantitative decision tools for conservation programme planning: practice, theory and potential. Environ Conserv 26:179–189
Ibáñez-Forés V, Bovea MD, Pérez-Belis V (2014) A holistic review of applied methodologies for assessing and selecting the optimal technological alternative from a sustainability perspective. J Clean Prod 70:259–281. https://doi.org/10.1016/j.jclepro.2014.01.082
Kandakoglu A, Frini A, Ben Amor S (2019) Multicriteria decision making for sustainable development: a systematic review. J Multi-Criteria Decis Anal 26:202–251. https://doi.org/10.1002/mcda.1682
Keeney RL, Raiffa H, Meyer RF (1993) Decisions with multiple objectives: preferences and value trade-offs. Cambridge University Press, Cambridge
Keisler JM (2002) Attribute-based differentiation of alternatives. J Multi-Criteria Decis Anal 11:315–326. https://doi.org/10.1002/mcda.338
Kumar A, Sah B, Singh AR et al (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain Energy Rev 69:596–609. https://doi.org/10.1016/j.rser.2016.11.191
Lerche N, Wilkens I, Schmehl M et al (2019) Using methods of multi-criteria decision making to provide decision support concerning local bioenergy projects. Socioecon Plan Sci 68:100594. https://doi.org/10.1016/j.seps.2017.08.002
Lumina (2018) Analytica software (ver 5.0). https://lumina.com. Accessed 28 Mar 2018
Maizlish N, Woodcock J, Co S et al (2013) Health cobenefits and transportation-related reductions in greenhouse gas emissions in the San Francisco Bay Area. Am J Public Health 103:703–709. https://doi.org/10.2105/AJPH.2012.300939
Martínez-García M, Valls A, Moreno A, Aldea A (2018) A semantic multi-criteria approach to evaluate different types of energy generation technologies. Environ Model Softw 110:129–138. https://doi.org/10.1016/j.envsoft.2018.04.003
McKay MD, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239. https://doi.org/10.1080/00401706.2000.10485979
McKinley G, Zuk M, Höjer M et al (2005) Quantification of local and global benefits from air pollution control in Mexico City. Environ Sci Technol 39:1954–1961. https://doi.org/10.1021/es035183e
McNamee P, Celona JN (2008) Decision analysis for the professional, Fourth. SmartOrg, Incorporated
Morgan MG, Henrion M, Small M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, Cambridge
Mydock S, Pervan SJ, Almubarak AF et al (2018) Influence of made with renewable energy appeal on consumer behaviour. Mark Intell Plan 36:32–48. https://doi.org/10.1108/MIP-06-2017-0116
Porter ME (1998) On competition. Harvard Business School Press, Boston
Senbel M, McDaniels T, Dowlatabadi H (2003) The ecological footprint: a non-monetary metric of human consumption applied to North America. Glob Environ Chang 13:83–100. https://doi.org/10.1016/S0959-3780(03)00009-8
Stadler M, Marnay C, Azevedo IL, et al (2009) The open source stochastic building simulation tool SLBM and its capabilities to capture uncertainty of policymaking in the U.S. building sector. In: 32nd IAEE international conference—energy, economy, environment: the global view
Sterling R, Coakley D, Contreras S, et al (2017) A systematic decision support framework and prioritization method for energy projects in industrial organisations. In: 10TH international conference on sustainable energy and environmental protection. University of Maribor Press
Strantzali E, Aravossis K (2016) Decision making in renewable energy investments: a review. Renew Sustain Energy Rev 55:885–898. https://doi.org/10.1016/j.rser.2015.11.021
The Climate Group (2019) Budweiser puts 100% renewable electricity message at the heart of super bowl commercial. https://www.theclimategroup.org/news/budweiser-puts-100-renewable-electricity-message-heart-super-bowl-commercial. Accessed 26 Jan 2020
Tylock SM, Seager TP, Snell J et al (2012) Energy management under policy and technology uncertainty. Energy Policy 47:156–163. https://doi.org/10.1016/j.enpol.2012.04.040
Wang JJ, Jing YY, Zhang CF, Zhao JH (2009) Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew Sustain Energy Rev 13:2263–2278. https://doi.org/10.1016/j.rser.2009.06.021
Winston A, Favaloro G, Healy T (2017) Energy strategy for the C-suite. Harv Bus Rev 95(1):139–146
Funding
The authors did not receive any financial support from any organization for the submitted work.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by DJJ. The initial draft of Sects. 3.1 and 3.2 were written by JMK, with DJJ writing the initial draft for remaining sections. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
Donald J. Jenkins reports a relationship with Enel-X North America that included employment through the end of August 2019. Enel X is an energy services company operating in many countries with a unit based in Massachusetts supporting customers in North America. No funds or support were provided by Enel-X in the conduct of this research. Jeffrey M. Keisler reports that he is on the editorial board of Environment Systems and Decisions.
Rights and permissions
About this article
Cite this article
Jenkins, D.J., Keisler, J.M. A decision analytic tool for corporate strategic sustainable energy purchases. Environ Syst Decis 42, 504–520 (2022). https://doi.org/10.1007/s10669-022-09866-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10669-022-09866-y