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6 results for “Clean Cooking”
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Cooking demand data for clean cooking simulation.
<p>This will be a dataset of clean cooking data collected hourly to allow for clean cooking simulation. Currently this is dummy data but it will soon be uploaded with live working data.</p>
Dataset for Nigeria clean cooking scenarios and impacts study
<p>This is the dataset and tool used for analysis in the journal article</p> <p>Yetano Roche, M. et al., (2024). Towards clean cooking energy for all in Nigeria: Pathways and impacts. Energy Strategy Reviews, 53, 101366,<br><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.esr.2024.101366" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.esr.2024.101366</span></a></p> <p>Abstract:</p> <p>Over 175 million Nigerians rely on the use of traditional biomass for cooking, and it is estimated that more than 128,000 people died in Nigeria in 2019 from household air pollution related to these fuels. There is currently a gap in the study of possible pathways to meet Nigeria's goals in clean cooking and in understanding the health and climate impacts that different pathways can bring about. We explore clean cooking access scenarios for Nigeria until 2060 under a business-as-usual scenario, a moderate climate mitigation scenario, and an ambitious transformative scenario. We carry out a disaggregation at the state level for the period up to 2030 to better guide shorter-term policy development. Our analysis shows that under an ambitious scenario where 85 million households achieve access to clean cooking by 2060, annual premature deaths due to exposure to household air pollution would decrease by 7 % compared to 2018 levels. A baseline scenario, on the other hand, sees a dramatic 77 % increase, resulting in 209,000 people dying prematurely, of which 94,000 children under 5. Furthermore, we find that woodfuel removals from forestland would lead to a tripling of carbon dioxide emissions from land use change, reaching 602 Mt CO2 by 2060. Our findings stress the vital importance of a clean cooking transition in Nigeria and underline the urgent need for immediate acceleration in national efforts regarding access to clean cooking for all.</p> <p>Keywords: Clean cooking; Climate change; Health; Long-term scenarios; Net-zero; Nigeria</p>
Applied Implementation Research for Clean Cooking in Cambodia
ClinicalTrials.gov study NCT06942715. IPD Sharing: YES. Countries: 1. Publications: 0.
Cooking Costs for Clean Cooking modelling
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