Published February 9, 2024 | Version v1.0

Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"

  • 1. ROR icon KTH Royal Institute of Technology
  • 1. ROR icon KTH Royal Institute of Technology
  • 2. ROR icon Clean Cooking Alliance
  • 3. ROR icon Nepal Open University
  • 4. ROR icon World Resources Institute

Description

This repository includes input data to run the OnStove Nepal model presented in the paper "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis" DOI: https://doi.org/10.1016/S2542-5196(24)00209-2.

The code and automated workflow to run the model can be found in the Github repository https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal. All result files and figures can be downloaded from the permanent repository https://doi.org/10.5281/zenodo.10643983.

The "GIS_input_data/" 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 "1. Data" folder in your project. 

The "Scenario_inputs/" 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 supplementary material of the related publication in the link https://doi.org/10.1016/S2542-5196(24)00209-2. To run the model extract the scenario data inside your "2. Scenario inputs" folder in your project. 

Files

GIS_input_data.zip

Files (353.2 MB)

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Additional details

Related works

Is supplement to
Journal article: 10.2139/ssrn.4726269 (DOI)

Software

References

  • Ramirez, Camilo and Khavari, Babak and Oberholzer, Alicia and Ghimire, Bhoj Raj and Mishra, Bhogendra and Sinclair-Lecaros, Santiago and Mentis, Dimitris and Gurung, Anobha and Khatiwada, Dilip and Nerini, Francesco Fuso, Achieving Nepal's Clean Cooking Ambitions: An Open Source Spatially Explicit Approach. Available at SSRN: https://ssrn.com/abstract=4726269 or http://dx.doi.org/10.2139/ssrn.4726269