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54 results for “decarbonization”
Dataset for the paper entitled "Implications of Uncertainty in Technology Cost Projections for Least-Cost Decarbonized Electricity Systems"
<p>This dataset contains model codes, post-process scripts, and model output data used to support findings in the paper entitled "Implications of Uncertainty in Technology Cost Projections for Least-Cost Decarbonized Electricity Systems". </p><p>Please contact Lei Duan (leiduan@carnegiescience.edu) for any questions. </p>
Dataset for "Long-term implications of reduced gas imports on the decarbonization of the European energy system"
<p>A dataset containing results for the paper "Long-term implications of reduced gas imports on the decarbonization of the European energy system". See also the associated Github repository: https://github.com/TimToernes/Reduced-gas-imports </p>
Land Use Trade-offs in Decarbonization of Electricity Generation in the American West
<p>This Zenodo archive includes the code and data used to produce the publication Patankar et al. (2022): Land Use Trade-offs in Decarbonization of Electricity Generation in the American West.</p> <p>This analysis requires five types of scripts/modeling efforts that take in three types of inputs, as described below. The code for the GenX model is not included here as it is available at https://github.com/GenXProject/GenX with further documentation available at https://genxproject.github.io/GenX/dev/. </p> <p> **Inputs**<br> 1. Solar and wind candidate project area (CPA)<br> 2. Renewable resource profiles<br> 3. Real and routing cost surface data<br> **Scripts and Models**<br> 1. PowerGenome - Power system data compilation software<br> 2. GenX Power system model along with the Modeling to Generate Alternatives (MGA) algorithm<br> 3. Transmission network building<br> 4. Downscaling for new solar and wind capacity <br> 5. Land use analysis presented in the paper</p> <p>For information on the description of the datasets, scripts and modeling efforts, see the README.md file. </p>
Dataset for use with "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization"
<p>This sqlite file contains the input data and results used in "Technology standards vs. carbon taxes: comparing emissions reductions and system costs for US decarbonization." The file is a version of the nine region Open Energy Outlook database for use with the Temoa model. The database is continually updated. Updated versions can be found in the Temoa GitHub repository, https://github.com/TemoaProject/oeo.</p>
Data for the Article "Maturity Assessment of Grid-Scale Flexibility and Energy Storage Services Towards a Decarbonized Europe"
<p>This dataset includes the data collected form various stakeholders regarding grid-scale flexibility and energy services as part of the SINNOGENES Project.</p>
Dataset: Global Electricity Sector Decarbonization Modelling
<p>This dataset contains the supporting materials for the PhD thesis "Global Electricity Sector Decarbonization Modelling". The following list of data packages and files are compiled from this research outputs.</p> <p><strong>Data Package 1: The main output datasets</strong><br> File 1: Definition of the zones in this study<br> File 2: Statistics on land cover pixels updated from OSM<br> File 3: Validation of the eligibility masks with existing sites<br> File 4: Validation of the estimated CF profile for EU countries<br> File 5: Summary of the available area, technical potential and capacity potential in this study<br> File 6: Aggregation of the estimated 10-year hourly CF into 288 month-hour profile<br> File 7: The projection of electricity demand profile for each zone in 288 month-hour profile<br> File 8: The overall aggregated output covariance matrices for each country</p> <p><strong>Data Package 2: The estimated 10-years hourly capacity factor of all available technologies and capacity factor tranches in every zone in the world</strong></p> <p><strong>Data Package 3: The solutions of optimal climate-related energy mix with different objectives in global electricity systems</strong></p> <p><strong>Data Package 4: The modelling results of electricity sector decarbonization pathways by country/district</strong></p> <p><strong>Data Package 5: Python scripts of the electricity sector decarbonization modelling framework</strong></p>
ERL-118550 Data: Rebates and Grid Decarbonization from the Inflation Reduction Act Promote Equitable Adoption of Energy Efficiency Retrofits
<p>The authors have self-reported an issue in how they used RSMeans 2019 City Cost Index (CCI) data to adjust for regional cost differences. The publicly available webpage stated these data could be used to “adjust for cost differences when compared to the national average, show cost differences between cities, compare cost differences between quarters of the same year, or adjust costs to Canadian cities.” However, the RSMeans Data and Engineering Department later clarified that these values “were intended to show how much CCI values changed for each city at the start of 2019 compared to the values in our 2019 book.” Nevertheless, our capital cost estimates closely align with several peer-reviewed studies and publicly available data sources. Based on our review, we do not believe our method significantly affected the study’s overall findings or conclusions. Further discussion is provided in the manuscript’s Limitations section and Appendix S5 of the Supplementary Materials.</p> <p>Peer-reviewed article available here: https://iopscience.iop.org/article/10.1088/1748-9326/adb765</p> <p>Article DOI: 10.1088/1748-9326/adb765</p>
Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset
<p>Dataset of the project Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset</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>
Air Quality-Related Equity Implications of U.S. Decarbonization Policy
<p>This repo includes supporting material for the publication:</p> <p>Paul Picciano, Minghao Qiu, Sebastian Eastham, Mei Yuan, John Reilly, Noelle E. Selin. Air Quality-Related Equity Implications of U.S. Decarbonization Policy. <em>Nature Communications (2023).</em></p> <p>Please download and unzip the file <strong>"climate_policy_pollution_equity.zip"</strong>. Please see README below for a full description of the scripts and data included in this repo. </p> <p>For correspondence on the publication, please contact Noelle Selin selin@mit.edu.</p> <p>If you have questions about this data repo, please contact Minghao Qiu mhqiu@stanford.edu or Paul Picciano pauldpicciano@gmail.com</p> <p> </p> <p><strong>README</strong></p> <p>The materials in this repository allow users to reproduce the main results and figures of the paper. The analysis is performed using R (version 4.3.0). </p> <p><strong>-- Scripts:</strong></p> <ul> <li><strong><em>initial_setup.R</em>: </strong> Used to load R packages, set up file paths, variable names, and the functions used in other scripts. Please load this script first before running other scripts.</li> <li><strong><em>Figure1 - 5.R</em>: </strong> Used to generate figures 1 - 5 in the main paper. </li> <li><strong><em>optimization_scenarios.R: </em></strong><em>Used to generate the optimization scenarios and 5000 possible emission reduction scenarios that achieve the same level of CO2 reductions. The results generated by this script are then used to generate Figure 5.</em></li> </ul> <p> </p> <p><strong>-- Data:</strong></p> <ul> <li><strong><em>emission_aggregate_scenarios.csv</em>: </strong> Nationally-aggregated emissions of CO2 and non-CO2 species by different sectors under the three main emission scenarios (scenarios number 1 - 3 as labeled in the paper). The unit of CO2 emissions is billion metric tons. Units of non-CO2 emissions are million metric tons. Used to plot Figure 1.</li> <li><strong><em>InMAP_pm25_conc_scenarios.rds</em>: </strong> PM2.5 concentration for each InMAP grid cell due to emissions from each sector (and combined sectors) under different emission scenarios. InMAP simulates annual mean PM2.5 concentration (unit: μg/m3). Used to plot Figure 2.</li> <li><em><strong>US_shapefiles.RData</strong></em>: Shape files of US states. Used to plot Figure 2.</li> <li><em><strong>exposure_main_scenarios.csv: </strong></em>Population-weighted PM2.5 for each population group xxx under different scenarios (i.e. AvgExposure_xxx, units of exposure: μg/m3); disparities in population-weighted PM2.5 between each population group xxx and the total population (i.e. AvgDisparity_xxx, units of disparity: %). Used to plot Figures 3 and 4. Rows 2-4 correspond to the main scenarios (scenario numbers 1 - 3). Rows 5 - 16 correspond to the sensitivity scenarios (scenario number 4 as labeled in the paper).</li> <li><strong><em>source_emission_baseline_2030.rds: </em></strong>Emissions from each individual source as projected by the baseline 2030 scenario (scenario number 2). Used as input data for the optimization analysis.</li> <li><strong><em>source_emission_cap50_2030.rds: </em></strong>Emissions from each individual source as projected by the cap50% scenario (scenario number 3). Used as input data for the optimization analysis.</li> <li><strong><em>minority_exposure_by_sector.rds: </em></strong>Changes in population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group between the cap50% scenario and baseline 2030 scenario. Used to plot Figure 5.</li> <li><em><strong>optimization_scenarios.rds: </strong></em>Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under different optimization scenarios (scenarios number 5 -10). This file is generated by <strong><em>optimization_scenarios.R</em></strong> and further used to plot Figure 5.</li> <li><em><strong>cap50_nation_total_5000draws.rds: </strong></em>Population-weighted PM2.5 exposure and disparities for the racial/ethnic minority group under 5000 possible scenarios that achieve the same level of CO2 reductions without any constraints (i.e. the same as the "nation-total" scenario). This file is generated by <strong><em>optimization_scenarios.R</em></strong> and further used to plot Figure 5.</li> </ul> <ul> </ul> <p> </p>
Code: Decarbonization Employment and Energy Systems (DEERS) Model
<p>The Decarbonization Employment and Energy Systems (DEERS) model is a data-driven framework for estimating labor market pathways of large-scale, low-carbon energy-supply infrastructure development. The DEERS model is designed as a tool to inform regional and national workforce and infrastructure planning and policy-making in the U.S. The model simulates the distribution of labor effects over time and across economic sectors, resource sectors, occupations, and geography for multi-decadal energy-supply system transition scenarios. The model is used to estimate employment demand and wages, as well as experience, education, and training requirements, across domestic energy supply chains. We also incorporate time-variant factors, such as labor productivity and wage inflation, which are especially important in the context of emerging labor markets and long-term transitions. The DEERS model is adaptable to different energy system contexts and readily coupled with regional and downscaled macro-energy system modeling outputs. It can also be used to explore modifiable workforce and infrastructure planning and policy decisions, such as high road labor policies, siting domestic manufacturing facilities, creating just transition funds, and changing fossil fuel exports over time.</p>
Net-zero 1.5 °C sectorial pathways for G20 countries: energy and emissions data to inform science-based decarbonization targets
<p><span>This data for global, regional (EU-27), and country-specific (G20 member countries) energy and emission pathways required to achieve a defined carbon budget of under 450 Gt/CO2, developed to limit the mean global temperature rise to 1.5°C, over 50% likelihood. The data were calculated with the 1.5°C sectorial pathways of the One Earth Climate Model—an integrated energy assessment model devised at the University of Technology Sydney (UTS). </span></p> <p><span>The data consist of the following six zip-folder datasets (refer to Section 2 for an explanation of the data):</span></p> <p><span>1. </span><span>Appendix folder: Each file contains one worksheet, which summarizes the overall 1.5°C scenario.</span></p> <p><span>2. </span><span>Sector folder (XLSX): Each file contains one worksheet, which summarizes the industry sectors analysed.</span></p> <p><span>3. </span><span>Sector folder (CSV): The data contained are the same as those described in point 2.</span></p> <p><span>4. </span><span>Sector emissions folder: Each file contains one worksheet, which summarizes the total annual emissions for each industry sector.</span></p> <p><span>5. </span><span>Scope emissions folder (XLSX): Each file contains one worksheet, which summarizes the total annual emissions for each industry sector—with the additional specificity of emission scope. </span></p> <p><span>6. </span><span>Scope emissions folder (CSV): The data contained are the same as those described in point 5.</span></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>
Model version, input data, results, and processing scripts for Speizer et al., "Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways"
<p>Includes the files needed to run the scenarios, analyze the outputs, and produce the figures for Speizer et al., "Rapid implementation of mitigation measures can facilitate decarbonization of the global steel sector in 1.5°C-consistent pathways." </p>
Data from: Empirical evidence for the potential climate benefits of decarbonizing light vehicle transport in the U.S. with bioenergy from purpose-grown biomass with and without BECCS
Open the record for dataset details and reuse information.
Net-zero 1.5 °C sectorial pathways for G20 countries: energy and emissions data to inform science-based decarbonization targets
Open the record for dataset details and reuse information.
Data accompanying the paper "Early habitability and crustal decarbonation of a stagnant-lid Venus"
<p>Produced data from a coupled interior-atmosphere model depicting Venus' early evolution assuming stagnant-lid convection, CO<sub>2</sub> degassing, weathering, carbonate burial, and crustal decarbonation. The data accompanies the paper "Early habitability and crustal decarbonation of a stagnant-lid Venus", submitted to JGR: Planets.</p>
Replication package for the paper "Modeling Europe's role in the global LNG market 2040: balancing decarbonization goals, energy security, and geopolitical tensions"
<p>This package contains folders and files with code and data used in the study described in the paper. Further information can be found on <a href="https://github.com/sebastianzwickl/lng-trade-europe" target="_blank" rel="noopener">GitHub</a>.</p>
Decarbonization pathways promote improvements in cement quality and reduce the environmental impact of China's cement industry
<p>The data show the results of the papre "Decarbonization pathways promote improvements in cement quality and reduce the environmental impact of China’s cement industry".</p>
Code and input data related to "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector"
<p>Input data and code for the submitted article: "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector" <br><br>by Alissa Ganter <sup>1,†</sup>, Paula Baumann <sup>1,2,†</sup>, Veis Karbassi <sup>2</sup>, Giovanni Sansavini <sup>1,*</sup></p> <p><sup>1 </sup>Reliability and Risk Engineering, Institute of Process and Energy Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland</p> <p><sup>2 </sup>School of Business and Economics, RWTH Aachen University, Kackertstraße 7, 52072 Aachen, Germany</p> <p><sup>† </sup>These authors contributed equally</p> <p><sup>*</sup>Corresponding author: sansavig@ethz.ch</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.