En-ROADS Technical Reference

Initialization, Calibration, Model Testing🔗

En-ROADS initializes and calibrates to available historical data, primarily provided by the following sources:

Energy and Emissions

  • Energy Information Administration (EIA) (2019)
  • International Energy Agency (IEA) World Energy Balances and World Energy Statistics (2023)
  • Energy Institute (EI) Statistical Review of World Energy (2023)
  • Global Carbon Budget (2023) (CO2 Energy Emissions and Land Use Change Emissions)
  • PRIMAP 2.4.2 (2023) (Non-CO2 GHG Emissions only)
  • Houghton and Nassikas (2017) (CO2 Land Use only)

Land Areas

  • Land Use Harmonization (LUH2) data (Hurtt et al., 2018)

GHG Concentrations, Radiative Forcings, Temperature Change, Sea Level Rise

  • National Oceanic and Atmospheric Administration (NOAA) concentrations (2024) and radiative forcings (2023)
  • Goddard Institute for Space Studies (GISS) GISTEMP4 Global Mean Estimates based on Land and Ocean Data 1880-2023 (2024)
  • Met Office Hadley Centre HadCRUT5.0.1.0 temperature 1850-2023 (2024)
  • National Aeronautics and Space Administration (NASA) satellite sea level rise (2023)

En-ROADS calibrates to projected values provided by the following sources:

  • International Energy Agency (IEA) WEO (2023)
  • Network for Greening the Financial System (2023)
    • GCAM 6.0 (U.S.)
    • REMIND-MAgPIE 3.2-4.6 (Germany)
  • SSP Version 2.0 scenarios (2018 - Available at: https://tntcat.iiasa.ac.at/SspDb)
    • Netherlands Environmental Assessment Agency (PBL). Integrated Model to Assess the Global Environment (IMAGE): Detlef van Vuuren, David Gernaat, Elke Stehfest
    • International Institute for Applied Systems Analysis (IIASA). Model for Energy Supply Strategy Alternatives and their General Environmental Impact - GLobal BIOsphere Management (MESSAGE-GLOBIOM): Keywan Riahi, Oliver Fricko, Petr Havlik
    • National Institute for Environmental Studies (NIES). Asia-Pacific Integrated Model (AIM): Shinichiro Fujimori
    • Pacific Northwest National Laboratory (PNNL). Global Change Assessment Model (GCAM): Kate Calvin and Jae Edmonds
    • Potsdam Institute for Climate Impact Research (PIK). REMIND-MAGPIE: Elmar Kriegler, Alexander Popp, Nico Bauer
    • European Institute on Economics and the Environment (EIEE). World Induced Technical Change Hybrid-GLobal BIOsphere Management (WITCH-GLOBIOM): Massimo Tavoni, Johannes Emmerling

“Calibration and validation comparisons.xlsx” and “Calibration and validation comparisons.pptx” provide output and figures demonstrating the strong fit to history and other modeling groups’ projections. Key comparison measures include GDP, total and source energy use and cost measures, GHG emissions and concentrations, and temperature change. Noteworthy, comparisons of primary energy of renewables depend on conversion assumptions which vary dramatically between sources.

Our default settings are guided primarily by history, WEO Current Policies, and NGFS Current Policies projections.

Land Calibration🔗

The land use change module is calibrated in the regional C-ROADS based on the Land Use Harmonization (LUH2) data prepared for the Climate Research Program Coupled Model Intercomparison Project (CMIP6). Our output for each land type strongly aligns with historical data. However, our projections suggest more farmland and less forest than do the LUH projections and those of the NGFS models. The differences are due to our accounting for the temperature effect on reducing crop yield, which translates to more farmland expansion to meet food demands. The other models do not account for that feedback.

Response to Actions🔗

Importing as data variables, En-ROADS also uses various scenario projections for model validation. Accordingly, there are necessary files, generated from data models, which must accompany the model. We test the model against the NGFS projections for their 6 scenarios. We set population and GDP per capita controls to follow the given NGFS trajectories and exogenously use the average of the models’ carbon price values for the given NGFS scenario, and assess the model output versus the IAMs' results.

An important caveat is that these other IAMs' assumptions other than carbon pricing are unknown. Accordingly, we force CDR and other GHG action to align with the NGFS projections for carbon removal and other GHG emissions. Reliably, for each scenario, the model captures the key dynamics of the NGFS models.

Although outdated now, we ran comparable assessments against all of the Shared Socioeconomic Pathway (SSP) of the IPCC's AR5 scenarios. Comparisons were against the output of 6 models for 5 SSP scenarios, each with up to 6 radiative forcing options, i.e., 1.9, 2.6, 3.4, 4.5, 6.0, and Baseline. Reliably, for each SSP storyline and RF level, the model captures the key dynamics of the SSP models.

Sensitivity Analyses🔗

Extreme Testing🔗

Sensitivity analyses provide insight into model robustness. Using a Latin grid, two tests for extreme conditions, one with standard controls and another with advanced controls, varied key actions. The extreme values for some variables are beyond the ranges available on the app but are tested for model robustness in Vensim. Output measures for each simulation were exported as a .csv file and assessed using an Excel workbook created to confirm reasonable model behavior.

Table 15.1 Sensitivity Analysis Definition (see Table 15.2 for normal slider ranges)
Variable Min Max*
Basic Controls
Source subsidy delivered coal tce 0 1000
Source subsidy delivered oil boe 0 1000
Source subsidy delivered gas MCF 0 20
Source subsidy delivered bio boe 0 1000
Source subsidy renewables kWh -0.1 0
Carbon tax initial target 0 1000
Annual improvement to energy efficiency of new capital stationary -1 5
Annual improvement to energy efficiency of new capital transport -1 5
Electric carrier subsidy stationary 0 100
Electric carrier subsidy with required comp assets 0 100
Percent available land for afforestation 0 100
Non afforestation Percent of max CDR achieved 0 100
Advanced Controls
Damage function on 0 1
No new coal 0 100
No new oil 0 100
No new gas 0 100
Utilization adjustment factor delivered coal 0 100
Utilization adjustment factor delivered oil 0 100
Utilization adjustment factor delivered gas 0 100
Table 15.2 Actual En-ROADS slider ranges (some values in Table 15.1 go beyond these limits)
Variable Min Max
Basic Controls
Source subsidy delivered coal tce 0 110
Source subsidy delivered oil boe 0 100
Source subsidy delivered gas MCF 0 5
Source subsidy delivered bio boe 0 30
Source subsidy renewables kWh -0.03 0
Carbon tax initial target 0 250
Electric carrier subsidy stationary 0 50
Electric carrier subsidy with required comp assets 0 50

Output variables for the sensitivity analyses include:

  • Final energy by each carrier for each end use[EndUseSector, Carrier]
  • Total Primary Energy Demand
  • Primary energy demand of coal
  • Primary energy demand of oil
  • Primary energy demand of gas
  • Primary energy demand of bio
  • Primary energy demand of nuclear
  • Primary energy demand of renewables
  • Primary energy demand of hydro
  • Market price of electricity
  • Market price of delivered fuels for nonelec carriers[Primary Fuels]
  • Adjusted cost of energy per GJ
  • CO2 emissions from energy
  • Temperature change from 1850

Varying Key Assumptions🔗

Additionally, using random triangular distribution, another set of sensitivity analyses tested the effects of varying key assumptions with actions. Results indicate that, regardless of these assumptions, the relative effect these actions have on the system are robust.

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