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Towards Net Zero: Modeling Approach to the Right-Sized Facilities

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12 Scopus citations

Abstract

As the concentration of greenhouse gases (GHGs) in the atmosphere increases, the concerns about carbon emissions are growing. Several net-zero initiatives are taking place around the globe to achieve a balance between the GHGs put into the atmosphere and those taken out. While most efforts present a sectorized approach, this paper describes the importance of integrating information across different sectors for effective modeling of carbon emissions and holistic reduction opportunity analysis. Using the Idaho National Laboratory (INL) campus as a test case, this work provides a web-based tool for INL stakeholders to use when engaging in strategic planning to achieve carbon emissions reduction. This net-zero engineering support tool (NEST) uses historical data as foundational information for applying the modeling framework. Prediction of CO 2 emissions throughout project completion integrates various approaches and schedules aimed at energy conservation, fleet decarbonization, and other GHG reduction activities. Using NEST, stakeholders can visualize carbon emissions, electricity consumption, and costs for decision making when planning the pathway for reaching carbon net zero. The INL’s initiative to transition into an EV fleet was used to demonstrate the developed framework and the advantages of using NEST. It was shown that electrifying different fossil-fueled campus vehicles before 2030 with aggressive replacement schedules require high annual capital expenditure (CAPEX), which may not be available. The tool allows decision makers to test different replacement schedules and prioritize those that yield CAPEX below a certain threshold while meeting target milestones. While the demonstration focused on vehicle electrification, the developed framework lays the foundation for further quantitative analysis of other GHG reduction activities.

Original languageAmerican English
Article number163
Number of pages13
JournalSustainability
Volume15
Issue number1
Early online dateDec 22 2022
DOIs
StatePublished - Dec 22 2022

Keywords

  • data forecasting
  • data integration
  • data visualization
  • decarbonization
  • digital engineering
  • digital thread
  • digital twin
  • model-based systems engineering
  • net zero
  • smart cities
  • sustainability
  • systems of systems

INL Publication Number

  • INL/JOU-22-69078
  • 143610

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