GCAM-USA electricity demand results for National Climate Assessment 5

Khan, Zarrar; Zhao, Mengqi; Ahsan, Hamza; Wolfram, Paul; Snyder, Abigail; Kyle, Page; Rice, Jennie; Vernon, Chris; Ou, Yang; Binsted, Matthew; Iyer, Gokul

doi:10.57931/1962861

Title: GCAM-USA electricity demand results for National Climate Assessment 5

Dataset
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Abstract

Overview This dataset includes GCAM-USA v 5.3 outputs for the percent change in electricity demand in the U.S. from 2020 to 2050 and from 2020 to 2100 for the thermodynamic global warming scenario "RCP8.5_hotter" and the SSP5 socioeconomic scenario. These results were produced as part of the Integrated Multisector, Multiscale Modeling (IM3) project. Detailed Information The electricity demand is calculated based on the IM3 GCAM-USA simulations. For the purpose of reproducibility, we provide the following data: 1. Raw data: the annual electricity demand for CONUS simulated by IM3 GCAM-USA for the scenario RCP8.5 Hotter - SSP5. 2. R scripts: process raw data, calculate percent change of electricity demand from 2020 to 2050 and from 2020 to 2100, and plot the data over CONUS. 3. Results: figures provided for NCA-5 and the corresponding data table from the R scripts.

Authors:

;

Joint Global Change Research Institute (JGCRI); Pacific Northwest National Laboratory
Pacific Northwest National Laboratory
Joint Global Change Research Institute (JGCRI)

Publication Date:: Thu Mar 23 00:00:00 EDT 2023

Research Org.:: MultiSector Dynamics - Living, Intuitive, Value-adding, Environment

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Subject:: Climate Change; Electricity Demand; NCA; TGW; WRF

OSTI Identifier:: 1962861

DOI:: https://doi.org/10.57931/1962861

Citation Formats


                    Khan, Zarrar, Zhao, Mengqi, Ahsan, Hamza, Wolfram, Paul, Snyder, Abigail, Kyle, Page, Rice, Jennie, Vernon, Chris, Ou, Yang, Binsted, Matthew, and Iyer, Gokul. GCAM-USA electricity demand results for National Climate Assessment 5.  United States: N. p., 2023. 
        Web.  doi:10.57931/1962861.

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                    Khan, Zarrar, Zhao, Mengqi, Ahsan, Hamza, Wolfram, Paul, Snyder, Abigail, Kyle, Page, Rice, Jennie, Vernon, Chris, Ou, Yang, Binsted, Matthew, & Iyer, Gokul. GCAM-USA electricity demand results for National Climate Assessment 5.  United States.  doi:https://doi.org/10.57931/1962861

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                    Khan, Zarrar, Zhao, Mengqi, Ahsan, Hamza, Wolfram, Paul, Snyder, Abigail, Kyle, Page, Rice, Jennie, Vernon, Chris, Ou, Yang, Binsted, Matthew, and Iyer, Gokul. 2023.  
"GCAM-USA electricity demand results for National Climate Assessment 5".  United States.  doi:https://doi.org/10.57931/1962861.  https://www.osti.gov/servlets/purl/1962861. Pub date:Thu Mar 23 00:00:00 EDT 2023

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@article{osti_1962861,

  title        = {GCAM-USA electricity demand results for National Climate Assessment 5},

  author       = {Khan, Zarrar and Zhao, Mengqi and Ahsan, Hamza and Wolfram, Paul and Snyder, Abigail and Kyle, Page and Rice, Jennie and Vernon, Chris and Ou, Yang and Binsted, Matthew and Iyer, Gokul},

  abstractNote = {Overview This dataset includes GCAM-USA v 5.3 outputs for the percent change in electricity demand in the U.S. from 2020 to 2050 and from 2020 to 2100 for the thermodynamic global warming scenario "RCP8.5_hotter" and the SSP5 socioeconomic scenario. These results were produced as part of the Integrated Multisector, Multiscale Modeling (IM3) project. Detailed Information The electricity demand is calculated based on the IM3 GCAM-USA simulations. For the purpose of reproducibility, we provide the following data: 1. Raw data: the annual electricity demand for CONUS simulated by IM3 GCAM-USA for the scenario RCP8.5 Hotter - SSP5. 2. R scripts: process raw data, calculate percent change of electricity demand from 2020 to 2050 and from 2020 to 2100, and plot the data over CONUS. 3. Results: figures provided for NCA-5 and the corresponding data table from the R scripts.},

  doi          = {10.57931/1962861},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Mar 23 00:00:00 EDT 2023},

  month        = {Thu Mar 23 00:00:00 EDT 2023}

}

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DOI: https://doi.org/10.57931/1962861

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