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28 results for “GCAM”
Large Ensemble Dataset for Discovering Global Peak Water Limit of Future Groundwater Withdrawals Using 900 GCAM Runs
<h2><strong>Global Groundwater Withdrawals Peak Over the 21st Century </strong></h2> <p>The large ensemble dataset contains groundwater related model outputs from 900 scenarios modeled using <a href="http://jgcri.github.io/gcam-doc/toc.html">Global Change Analysis Model (GCAM)</a>. The scenario ensemble members include five Shared Socioeconomic Pathways (SSPs), four Representative Concentration Pathways (RCPs), five global climate model outputs, three groundwater depletion limits, two surface water storage expansion regimes, and two historical groundwater depletion trends.</p> <h3><strong>Journal Article</strong></h3> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a> </p> <h3><strong>Data Repository </strong></h3> <p>This <em><strong>data</strong></em> repository is to be used in combination with the <em><strong>main</strong></em> <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> containing all scripts and files for reproducing the experiment as well as the analysis and post-processing of the model outputs.</p> <p>Scripts and smaller files are provided in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz">GitHub meta-repository</a> whereas larger files are provided in this data repository. Please complete the repository by placing the files as described hereunder. Please find the GitHub meta-repository here: <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">https://github.com/JGCRI/niazi-etal_2024_nature-sustainability</a></p> <p>Descriptions of files:</p> <ol> <li><em><strong>gcam-5.7z</strong></em> contains the GCAM version used to simulate 900 scenarios of plausible futures. The model folder contains all necessary input files to reproduce the simulations. <ul> <li>The model is to be used in combination with the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a> to setup batch runs on cluster.</li> <li>Please navigate to <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model">model/</a> folder for other scenario-specific and model setup folders and files. <em><strong>gcam-5</strong></em> is to be extracted in the same directory (./<em>model/gcam-5/</em>). </li> <li>For the first-time users of GCAM, please follow guidance on <a href="http://jgcri.github.io/gcam-doc/toc.html">GCAM wiki</a> to setup GCAM or for background knowledge. </li> </ul> </li> <li><em><strong>crop_yeild.7z</strong></em>: This file contains inputs related to climate impacts on crop yields. This is to be downloaded and extracted in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/combined_impacts">model/combined_impacts/</a> folder. </li> <li><em><strong>outputs-all.7z: </strong></em>Key model outputs queried and collated from 900 GCAM runs are explained hereunder. The files could be downloaded individually (.csv files) or all at once in .7z format (<a href="../api/files/80b237d3-b22f-499f-8b8e-76c3846720a0/outputs-all.7z">outputs-all.7z</a>). These files are to be placed in the <a href="https://github.com/JGCRI/niazi-etal_202X_xyz/tree/main/model/outputs">model/outputs</a> folder of the <a href="https://github.com/JGCRI/niazi-etal_2024_nature-sustainability">meta-repository</a>. <ul> <li><em><strong>ag_prod_all_GW_scenarios.csv</strong></em> - Agricultural production across all scenario for 2050 and 2100 (tonnes)</li> <li><em><strong>prices_water_withdrawal_all.csv</strong> - </em>Water prices across all scenarios and years ($/km<sup>3</sup>)</li> <li><em><strong>global_irrigated_prod_by_crop.csv</strong></em> - All irrigated agricultural production for each crop across and scenarios all years (tonnes)</li> <li><em><strong>surface_water_production_all.csv</strong></em> - Runoff across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>groundwater_production_FINAL.csv</strong></em> - Groundwater withdrawals across all scenarios and years (km<sup>3</sup>)</li> <li><em><strong>water_withdrawals_desal_all.csv</strong></em> - Water withdrawals from desalination plants across all scenarios and years (km<sup>3</sup>)</li> </ul> </li> </ol> <h3><strong>Short introduction to the study</strong></h3> <p>Using 900 GCAM runs, this study finds that global groundwater withdrawals are expected to peak around mid-century, followed by a decline through 21st century, exposing about half of the population living in one-third of basins to groundwater stress, with cost and availability of surface water storage being the most significant driver of future groundwater withdrawals. This first-ever robust, quantitative confirmation of the peak-and-decline pattern for groundwater, previously only known for fossil fuels and minerals, raises concerns for basins heavily dependent on groundwater.</p> <p>Niazi, H., Wild, T.B., Turner, S.W.D., Graham, N.T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J.R., & Zhao, M. (2024). <a href="https://rdcu.be/dFpb5">Global peak water limit of future groundwater withdrawals</a>. <em>Nature Sustainability, 7</em>(4), 413–422. <a href="https://doi.org/10.1038/s41893-024-01306-w" rel="nofollow">https://doi.org/10.1038/s41893-024-01306-w</a></p> <p>Read full-text here: <a href="https://rdcu.be/dFpb5">https://rdcu.be/dFpb5</a></p> <h3><strong>Contact </strong></h3> <p>Please reach out to Hassan Niazi at <a href="mailto:hassan.niazi@pnnl.gov">hassan.niazi@pnnl.gov</a> for any questions. </p>
GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"
<p>GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"</p>
GCAM USA state-glu spatial boundaries with mapping/aggregation file
<p><strong>Data documentation: GCAM USA state-glu spatial boundaries with mapping/aggregation file</strong></p> <p> </p> <p><strong>Summary: </strong>These data products present a vector file which represents the intersections of state boundaries for USA with GCAM basin boundaries within USA and a mapping file that can be used to</p> <ol> <li>map basin level data to states within the USA region (based on area)</li> <li>map state level data to basins within the USA region (based on area)</li> </ol> <p>The vector file presents metadata to the user regarding the state name, state id, glu name and glu id along with a unique key for each polygon. These spatial boundaries can be reproduced/updated by updating the inputs and making use of the methodology described below.</p> <p><strong>CONTENTS:</strong> </p> <p><strong>gcamusa_state_glu_wgs84 </strong>folder contains the following,</p> <p><strong> shape_file</strong> folder contains the following,</p> <p> <strong>gcamusa_state_glu_wgs84.shp </strong></p> <p> <strong> </strong> column names in outputs:</p> <ul> <li><strong><em>key</em></strong>: Unique identifier for feature</li> <li><strong><em>state_id:</em></strong> Unique identifier for state (state geo id)</li> <li><strong><em>glu_id: </em></strong>Unique identifier for basin (basin number)</li> <li><strong><em>state_nm:</em></strong> State name</li> <li><strong><em>glu_nm: </em></strong>Basin name</li> </ul> <p> </p> <p> <strong> mapping_file </strong>folder contains the following,</p> <p> <strong>state_glu_mapping_WGS84.csv</strong></p> <p> column names in outputs:</p> <ul> <li><strong><em>state_id:</em></strong> Unique identifier for state (state geo id)</li> <li><strong><em>glu_id: </em></strong>Unique identifier for basin (basin number)</li> <li><strong><em>state_nm:</em></strong> State name</li> <li><strong><em>glu_nm: </em></strong>Basin name</li> <li><strong><em>state_area</em></strong>: Geometric area calculated for each state</li> <li><strong><em>glu_area</em></strong>: Geometric area calculated for basin/glu</li> <li><strong><em>intersection_area: </em></strong>Geometric area calculated for each basin-glu intersection</li> <li><strong><em>state_proportion: </em></strong>Share of glu value in a state</li> <li><strong><em>glu_proportion: </em></strong>Share of state value in a glu</li> </ul> <p><strong> input_files </strong>folder contains the following,</p> <p> </p> <ul> <li> <em>glu_boundaries_moirai_combined_3p1_0p5arcmin : </em>A shape file containing boundaries for the GCAM glu’s. source: <a href="https://zenodo.org/record/4014308#.X6nkQ2hKhaR">https://zenodo.org/record/4014308#.X6nkQ2hKhaR</a></li> <li><em>tl_2019_us_state.shp : </em>A shape file containing boundaries for USA states. Source: <a href="https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html">https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html</a></li> </ul> <p> </p> <p><strong>Methodology and reproducibility: </strong>The vector and mapping files were generated using the function located here- <a href="https://github.com/JGCRI/rgis/pull/7/commits/32ccb8cf8a30a66a84044da317b9041f492777be">https://github.com/JGCRI/rgis/pull/7/commits/32ccb8cf8a30a66a84044da317b9041f492777be</a></p> <p>In order to reproduce or update the outputs the user would have to follow the following steps:</p> <ol> <li>Clone the rgis package from GitHub - <a href="https://github.com/JGCRI/rgis">https://github.com/JGCRI/rgis</a></li> </ol> <p>Use the <em>get_intersection_fractions</em>() function. Set the <em>shpfile_1</em> parameter to the path to the latest state boundaries and set <em>shpfile_2</em> parameter to the path to the glu boundaries shape file. Set the <em>out_csv</em> parameter to the desired mapping file name and set the <em>out_shape_file</em> parameter name to the desired vector output name.</p>
Output Data: gcam_v5.4
<p>Output data for GCAM official release v5.4.</p> <p>Please cite the GCAM v 5.4 model which produced this dataset when using this data:</p> <ul> <li>Bond-Lamberty et al. 2021. JGCRI/gcam-core: GCAM 5.4 (gcam-v5.4). Zenodo. https://doi.org/10.5281/zenodo.5093192</li> </ul> <p> </p>
Demeter Input Files for 0.05 degree LULCC from GCAM SSP/RCP/GCM Scenario Runs
<p>Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM ) and a geospatial downscaling model (Demeter) under diverse Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05°×0.05°) and is spread under more diverse SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.</p> <p>The dataset includes the inputs for Demeter to produce projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). See <a href="https://github.com/JGCRI/chen_et_al_2020a">https://github.com/JGCRI/chen_et_al_2020a</a> for details on how to reproduce this experiment.</p> <p> </p>
GCAM-USA Scenarios for GODEEEP
<h1>GCAM-USA Scenarios for GODEEEP</h1> <p>This dataset contains a set of twelve future (2020-2050) scenarios modeled by <a href="https://gcims.pnnl.gov/modeling/gcam-global-change-analysis-model">GCAM-USA</a> for the <a href="https://godeeep.pnnl.gov">GODEEEP</a> project for the purpose of studying the effects of climate, socioeconomic change, technology change, current decarbonization incentives, and longer-term decarbonization policies on the U.S. energy-economy, the electricity grid, human well-being, and the environment.</p> <p>GCAM-USA is a version of the Global Change Analysis Model (GCAM) with state-level detail in the United States. GCAM-USA simulates the supply/demand dynamics and interactions of four systems (energy, water, agriculture and land use, and the economy) in 32 geopolitical regions in the world, including the 50 states and the District of Columbia within the U.S. It can be configured to include climate impacts on energy demands, water availability, and crop yields. The GCAM-USA scenarios for GODEEEP include business-as-usual (BAU) as well as net-zero (NZ) greenhouse gas emissions by 2050 policy scenarios. All the NZ policy scenarios include a carbon-free electricity system by 2035, also referred to as a "clean grid." Net-zero greenhouse gas emissions by 2050 requires a combination of solutions including carbon sequestration, new fuels, long- and short-term energy storage, and new technologies such as direct air capture that have not previously been included in GCAM-USA. The GCAM-USA scenarios for GODEEEP represent alternative combinations of assumptions for climate impacts, decarbonization policies, decarbonization incentives, and carbon capture and sequestration technology availability.</p> <p>GCAM-USA outputs are provided as XML databases, which can be read by the <a href="https://github.com/JGCRI/modelinterface">GCAM Model Interface</a> or packages such as <a href="https://github.com/JGCRI/gcamreader">gcamreader</a> for Python or <a href="https://github.com/JGCRI/gcamextractor">gcamextractor</a> for R.</p> <p>Summaries of each scenario are provided below. For additional discourse on the scenarios, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al 2023</a> and other upcoming papers to be announced on the <a href="https://godeeep.pnnl.gov">GODEEEP website</a>.</p> <h5>Abbreviations used in scenario names and descriptions</h5> <ul> <li><strong>BAU</strong>: Business-As-Usual. These scenarios represent the continuation of policies from the recent past and include major state-level clean energy policies but do not include any federal policies or incentives for a clean grid or a net-zero economy.</li> <li><strong>NZ</strong>: Net-Zero. These scenarios represent a <a href="https://www.whitehouse.gov/wp-content/uploads/2021/10/us-long-term-strategy.pdf">U.S. decarbonization goal</a> that requires a carbon-free electricity grid by 2035 and a net-zero greenhouse gas emissions economy by 2050.</li> <li><strong>IRA</strong>: Inflation Reduction Act. These scenarios include the <a href="https://www.whitehouse.gov/cleanenergy/inflation-reduction-act-guidebook/">IRA incentives</a> for energy efficiency as well as clean energy, transportation, and fuels between 2025 and 2035.</li> <li><strong>CCS</strong>: Carbon Capture and Sequestration. These scenarios assume that electricity generators equipped with CCS technology are available, whereas the other scenarios assume CCS technology is unavailable.</li> <li><strong>Climate</strong>. These scenarios include the dynamic effects of a climate pathway (<a href="https://www.nature.com/articles/s41597-023-02485-5">RCP8.5</a>) on heating and cooling degree days (HDD/CDD) in the period 2020-2050. Note that in scenarios without "climate" in their name, HDD/CDD are left static at their 2020 levels.</li> <li><strong>SSP2</strong>: <a href="https://doi.org/10.1016/j.gloenvcha.2015.01.004">Shared Socioeconomic Pathway 2</a>. A "middle of the road" socioeconomic pathway where population and economic growth trends follow historical patterns.</li> </ul> <h3>Scenario Descriptions</h3> <table> <tbody><tr> <th>#</th> <th>Name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>1</td> <td>bau</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>2</td> <td>bau_climate</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>3</td> <td>bau_ccs</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>4</td> <td>bau_ccs_climate</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>5</td> <td>bau_ira_ccs</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>6</td> <td>bau_ira_ccs_climate</td> <td> <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>7</td> <td>nz</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>8</td> <td>nz_climate</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are unavailable.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>9</td> <td>nz_ccs</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>10</td> <td>nz_ccs_climate</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does not include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> <tr> <td>11</td> <td>nz_ira_ccs</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>No climate impacts are considered.</li> </ul> </td> </tr> <tr> <td>12</td> <td>nz_ira_ccs_climate</td> <td> <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include the IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> <li>The socioeconomic change assumptions are consistent with SSP2.</li> <li>This scenario includes future climate impacts on heating and cooling degree days based on an RCP8.5 pathway.</li> </ul> </td> </tr> </tbody> </table> <h3>Acknowledgement</h3> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
gcam_7.0_ref
<p>Reference scenario for GCAM-v7.0.</p><p>Includes data up to 2050.</p><p>Used to test <a href="https://github.com/bc3LC/gcamreport">gcamreport</a> and to provide a <a href="https://bc3lc.github.io/gcamreport/articles/Step_By_Step_Full_Example.html">step-by-step example</a> of the package usage</p>
Output Data: gcam_v5p3_seasia
<p>Outputs of the Southeast Asia GCAM branch, cities configuration. Scenarios included are the reference (BAU) case, and two policy scenarios for Thailand ("low" and "high" sectoral policies).</p>
GCAM-China-v6 standard test scenarios (Mar 2024)
<p>GCAM-China-v6 test scenarios, including a reference scenario and a net-zero scenario till 2100.</p> <p>These are xml outputs of original scenarios, users can import them into GCAM scenario database using the "import scenario" function in model interface.</p>
A crop yield change emulator for use in GCAM and similar models: Persephone v1.0
<p>This is an archive of the raw data and analysis source code for the paper "A crop yield change emulator for use in GCAM and similar models: Persephone v1.0". The archive contains:</p> <ul> <li><strong>data.zip:</strong> All source code for analysis, input data for analysis, and results of analysis</li> <li><strong>persephone.proj : </strong>R project for ease of reproducing analysis</li> </ul>
Output data: gcam_v6p0_seasia
<p>Outputs of the Southeast Asia GCAM branch, cities configuration. Scenarios included are the reference (BAU) case, one policy scenario ("high" sectoral policies), and two carbon neutral 2050 scenarios (with and without an exogenous land use sink) for Thailand.</p>
Projecting Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).</p> <p>GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a></p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)</li> <li>residential energy consumption <em>per capita</em> by service and fuel, by state and income group, 2015-2045</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita</em> by service, fuel, and technology, by state and income group, 2015-2045</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2045</li> <li>residential heating service inequality (Eq.2), by state, 2015-2045</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZnoCCS (Net-Zero by 2050 without CCS)</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZnoCCS_climate</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)</span></p> <p>for income group<em> i</em> and service <em>j</em></p> <p> </p> <p><strong>Eq. 2</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)</span></strong></p> <p>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7227128">https://doi.org/10.5281/zenodo.7227128</a></p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Updated Projections of Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL). Compared to the first version of the dataset (<a href="https://zenodo.org/record/79880387">https://zenodo.org/record/79880387</a>), this updated dataset is based on model runs where the Inflation Reduction Act (IRA) are implemented in the model scenarios. In addition to the queried and post-processed key output variables related to residential energy sector in .csv tables, we also upload the full model output databases in this repository, so that users can query their desired model outputs.</p> <p>GCAM-USA operates within the Global Change Analysis Model (GCAM), which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a>.</p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of September 24, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2050 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the full model output databases and key output variables related to the residential energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2050 (Casper et al., 2023)</li> <li>residential energy consumption <em>per capita</em> by service, fuel, state, and income group, 2015-2050</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita </em>by service, fuel, state, and income group, 2015-2050</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2050</li> <li>estimated satiation gap (Eq.2), by service, state, and income group, 2015-2050</li> <li>residential heating service inequality (Eq.3), by state, 2015-2050</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZ (Net-Zero by 2050)</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZ_climate</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_{i,k} = \dfrac{\sum_j (service\ output_{i,j,k} * service\ cost_{j,k})}{GDP_{i,k}}\)</span></p> <p>for income group <em>i </em>and state <em>k</em>, that sums over all residential energy services <em>j</em>.</p> <p> </p> <p><strong>Eq. 2</strong></p> <p><span class="math-tex">\(Satiation\ Gap_{i,j,k} = \dfrac{satiation\ level_{j,k} - service\ output_{i,j,k}} {satiation\ level_{j,k}}\)</span></p> <p>for service <em>j</em>, income group <em>i</em>, and state <em>k</em>. Note that the satiation level and service output are per unit of floorspace.</p> <p> </p> <p><strong>Eq. 3</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality_j = \dfrac{S_j^{d10}}{(S_j^{d1} +S_j^{d2} + S_j^{d3} + S_j^{d4})}\)</span></strong></p> <p>for service <em>j </em>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality. Among the key output variables in this repository, we provide the residential <em>heating</em> service inequality output table as an example.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, K. C., Narayan, K. B., O'Neill, B. C., Waldhoff, S. T., Zhang, Y., & Wejnert-Depue, C. (2023). Non-parametric projections of the net-income distribution for all U.S. states for the shared socioeconomic pathways. <em>Environmental Research Letters</em>. http://iopscience.iop.org/article/10.1088/1748-9326/acf9b8.</p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin (Irrigated, Rain-fed)
<p>GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin resolution for year 2015 for irrigated and rain-fed GCAM crop breakout along with forest, urban, sparse, snow, shrub land classes. This run was generated for use by the `teleconnect` package (see <a href="https://github.com/IMMM-SFA/teleconnect">https://github.com/IMMM-SFA/teleconnect</a>). The following is the full README found in the zipped data resource:</p> <blockquote> <p>GCAM v5.2 to Demeter </p> <p>Title:<br> Demeter output for GCAM v5.2 with water constraints - Reference scenario</p> <p>Description:<br> Demeter run conducted using the base layer combining Mirca and Modis v6 type 5 to generate rain-fed and irrigated crops constrained to Modis crop area. GCAM projection split RockIceDesert into snow and sparse land classes.</p> <p>Building the Demeter base layer for use with GCAM allocated land classes and use types:<br> Described in the readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf document the docs directory of this data archive.</p> <p>GCAM Version: https://github.com/JGCRI/gcam-core/tree/gcam-v5.2 ; https://doi.org/10.5281/zenodo.3528353 </p> <p>GCAM Reference:<br> Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019.</p> <p>Demeter Reference:<br> Vernon, C.R., Le Page, Y., Chen, M., Huang, M., Calvin, K.V., Kraucunas, I.P. and Braun, C.J., 2018. Demeter – A Land Use and Land Cover Change Disaggregation Model. Journal of Open Research Software, 6(1), p.15. DOI: http://doi.org/10.5334/jors.208</p> <p>Run:<br> GCAM reference scenario with water constraints conducted by Sonny Kim (skim@pnnl.gov) originally retrieved from PNNL's Constance here: /pic/projects/GCAM/water_market/database_basexdbGCAM51WaterConstr. </p> <p>Contents:<br> teleconnect_agu2019<br> -- config_gcam5p1_watconstr_ref.ini (Demeter configuration file) <br> -- code (code to run Demeter pre-, run, and post-processing)<br> ---- README.txt (Description of run order and process for Demeter on Constance)<br> ---- demeter_preprocess.py (Python script to extract land data from the GCAM database and split RockIceDesert into snow and sparse)<br> ---- demeter_postprocessing.py (Python script to create fractional output of Demeter's native output in square kilometers)<br> ---- run_demeter.py (Python script to run Demeter)<br> ---- run_demeter_gcam5p1_watconstr_ref.sh (sbatch script to submit a Demeter run on Constance)<br> ---- run_postprocessing.sh (sbatch script to submit a post-processing run on Constance)<br> ---- run_preprocessing.sh (sbatch script to submit a pre-processing run on Constance)<br> ---- slurm-11504861.out (Slurm output from Demeter run)<br> -- GCAM <br> ---- database_basexdbGCAM51WaterConstr (GCAM output database)<br> -- inputs (input files used by Demeter) <br> ---- allocation <br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_constraint_alloc.csv (weighting of constraints)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_observed_alloc.csv (reclassification table for observed land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_order_alloc.csv (processing order for land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_projected_alloc.csv (reclassification table for GCAM land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_transition_alloc.csv (transition order for land classes)<br> ---- constraints<br> ------ 000_nutrientavail_hswd_5arcmin.csv (nutrient availability constraint weighted by grid cell)<br> ------ 001_soilquality_hswd_5arcmin.csv (soil quality constraint weighted by grid cell)<br> ---- observed<br> ------ gcam_reg32_basin235_modis_v6_2010_mirca_2000_5arcmin_sqdeg_wgs84_11Jul2019.csv (Demeter base layer)<br> ---- projected<br> ------gcam_5p1_watconst_reference.csv (output from demeter_preprocess.py from GCAM output)<br> ------gcam_5p1_watconst_reference_split.csv (output from demeter_preprocess.py from GCAM output with RockIceDesert split into snow and sparse land classes)<br> ---- reference (see https://github.com/IMMM-SFA/demeter)<br> ------ aezcoord.csv<br> ------ countrycoord.csv<br> ------ gcam_basin_lookup.csv<br> ------ gcam_regions_32.csv<br> ------ limits.csv<br> ------ query_land_reg32_basin235_gcam5p0.xml (land allocatio query)<br> ------ regioncoord.csv<br> -- for_teleconnect<br> ---- usa_demeter.csv (file used by the `teleconnect model` containing only 5-arcmin grid cells that are in GCAM region 1 (USA))<br> -- outputs (output files from Demeter run)<br> ---- ref_watconstr_2019-11-07_07h20m46s (output Demeter run directory)<br> ------ log_files (log file directory)<br> -------- logfile_ref_watconstr_2019-11-07_07h20m46s.log (log file from Demeter run)<br> ------ spatial_landcover_tabular<br> -------- landcover_2015_fraction.csv (fraction of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_sqkm.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_timestep.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -- docs <br> ---- readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf (creation of the Demeter base layer)</p> </blockquote>
Scenario Input files for "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM"
<p>The GCAM scenario input files needed for the experiments described in the paper "The Domestic and International Implications of Future Climate for U.S. Agriculture in GCAM".</p>
GCAM boundary spatial products from moirai v3.1
<p><strong>Summary</strong>- These data products present vector files for different representations of land area from the moirai land data system. Vector files are generated at 3 main spatial levels, namely country, region, basin. In addition to this, files are generated for different intersections for the 3 main categories, intersections for country and basin boundaries (country_basin), region and basin boundaries (region_basin) and region and country boundaries (region_country). Since the land data system does not generate land area information for all cells within the above mentioned boundaries (for water bodies for example), the vectors are presented for 3 main classes for each spatial category, land cells, cells with no land and combined. With all of the above mentioned combinations, the data products contain 18 different vector files.</p> <p><strong>Methodology- </strong>In generating these vector files, we used the land outputs from moirai as inputs along with separate inputs for the boundaries for the main spatial levels (country, basin and region). Combining the spatial boundaries with land inputs we generated 3 raster outputs (land, no land and combined) for each of the main spatial levels along with all the intersections. A unique key is assigned for each unique spatial boundary. We then converted these rasters to vectors through a process of polygonization where polygons were dissolved using the key and finally added all metadata (basin names, region names, country names) to each of the vector files. We also check and correct geometry errors in the polygons themselves.</p> <p> </p> <p><strong>CONTENTS:</strong></p> <p><strong>gcam_boundaries_moirai_3p1_0p5arcmin_wgs84 </strong>folder contains the following,</p> <p><strong> input_files </strong>contain the following,</p> <ul> <li><em>moirai_valid_land_area.bsq: </em>raster file containing actual land area by grid cell globally. <ul> <li>crs: EPSG:4326 WGS84 - World Geodetic System 1984</li> <li>resolution: 0.5 arc mins</li> </ul> </li> <li><em>Global235_CLM_5arcmin.bil</em>: raster file containing basin boundaries for all cells output by moirai <ul> <li>crs: EPSG:4326 WGS84 - World Geodetic System 1984</li> <li>resolution: 0.5 arc mins</li> </ul> </li> <li><em>GCAM_32_w_Taiwan.shp – </em>vector file containing boundaries for GCAM regions.</li> <li><em>GCAM_region_names.</em><em>csv</em>- Mapping file with details on GCAM region names (Used to fill in metadata)</li> <li><em>iso_GCAM_regID</em><em>.csv</em>- Mapping file containing details on individual country names by iso code. (Used to fill in metadata)</li> <li><em>basin_to_country_mapping</em><em>.csv</em> – Mapping file containing details on basin names by country and region. (Used to fill in metadata)</li> </ul> <p><strong>main_outputs </strong>contain the following,</p> <p>Contains files for each spatial boundary (country, region, country_basin etc), for each land category (land cells, no land and combined)</p> <p><strong><spatial>_boundaries_moirai_<land_category>_3p1_0p5arcmin.shp</strong></p> <p> column names in outputs:</p> <ul> <li><strong><em>key</em></strong>: Unique identifier for feature</li> <li><strong><em>reg_id</em></strong>: Unique identifier for region (region number)</li> <li><strong><em>ctry_id:</em></strong> Unique identifier for country (country number)</li> <li><strong><em>basin_id: </em></strong>Unique identifier for basin (basin number)</li> <li><strong><em>reg_nm</em></strong>: Region name</li> <li><strong><em>ctry_nm:</em></strong> Country name</li> <li><strong><em>basin_nm: </em></strong>Basin name</li> </ul> <p>See README in the zipped directory for a full reference.</p>
IM3 GCAM USA Dispatch Segment Calculation Examples (gcam v4.4)
<p>Input data needed to run scripts from gcam-usa v4.4 used to generate load segments as calculated in:</p> <ul> <li><a href="https://www.sciencedirect.com/science/article/pii/S2211467X1930104X">https://www.sciencedirect.com/science/article/pii/S2211467X1930104X</a></li> </ul> <p>Scripts Available at:</p> <ul> <li><a href="https://github.com/IMMM-SFA/gcam_usa_im3/tree/master/gcamUSADispatchWalkthrough">https://github.com/IMMM-SFA/gcam_usa_im3/tree/master/gcamUSADispatchWalkthrough</a></li> </ul> <p>Data Sources:</p> <ul> <li>FERC Form 714: https://www.ferc.gov/industries-data/electric/general-information/electric-industry-forms/form-no-714-annual-electric/overview </li> <li>EIA Monthly Enduse Data: https://www.eia.gov/electricity/data/browser/#/topic/5?agg=2,0,1&geo=g&freq=M&start=200101&end=201811&ctype=linechart&ltype=pin&rtype=s&maptype=0&rse=0&pin=</li> </ul>
GCAM-USA Raw Outputs for Khan et al. 2021 - Evolution of energy-water-agriculture interconnectivity across the U.S.
<p>GCAM-USA outputs for paper: Khan et al. 2021, Evolution of energy-water-agriculture interconnectivity across the U.S.</p>
GCAM-LAC v5.3-stash Output Data
<p>This dataset includes output data from the GCAM LAC (v5.3-stash) model. The GCM and RCP selected for the study of Colombia Energy-Water-Land Systems are GFDL-ESM2M and RCP 2.6.</p>
GCAM output files for Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming )
<p>Original GCAM output files for Iyer & Ou, et al. 2022 (Ratcheting of climate pledges needed to limit peak global warming)</p> <p>A "Naming Rule" file is included. More details and assumptions can be found in the original paper. GCAM documentation can be found at https://jgcri.github.io/gcam-doc/ </p>
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