This repository contains the general code for the Nepal case study developed using the geospatial cost-benefit clean cooking tool, OnStove. OnStove calculates the net-benefits of different stove options in a given geography and compares all stoves to one another with regards to their net-benefit. In this study, we linked outputs from OnStove with a spatial multicriteria analysis (MCA) based on the methods of the Energy Access Explorer (EAE).

Introduction

OnStove is developed by the division of Energy Systems at KTH together with partners. OnStove, is a geospatial raster-based tool that determines the net-benefits of different cooking solutions, for each raster grid cell of a given study area. The tool takes into account four benefits of adopting clean cooking: reduced morbidity, mortality, emissions and time saved, as well as three costs: capital, fuel, and operation and maintenance (O&M). In each grid cell of the study area the stove with the highest net-benefit is chosen.

OnStove produces scenarios depicting the “true” cost of clean cooking. The scenarios' benefits and costs produced by the tool are to be interpreted as the benefits and costs one could expect if the clean cooking transition were to happen now (overnight change). Results from OnStove can be interpreted as an upper bound of net-benefits following a switch to cleaner stoves. This can give a sense of the cost of inaction. OnStove can be used by planners and policymakers to identify the potential benefits
different interventions could cause in their systems.

Installation

Install a python distribution using Miniconda (recommended) or Anaconda

Downloading the source code

Open an Anaconda Prompt or a Command Prompt and download the source code with:

conda install git
git clone https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal.git

or you can download as a zip file from the GitHub repository. The repository contains the folder structure and scripts needed to run the entire analysis.

Installing OnStove with conda

The easiest way of installing and using OnStove is through conda. The OnStove Nepal model, uses version 0.1.6 of the tool. After installing a distribution of conda, open an Anaconda Prompt or a Command Prompt. You can install the environment by either running:

conda create -n onstovenepal -c conda-forge -c bioconda onstove==0.1.6 snakemake-minimal

Or by using the provided environment.yaml file in the root folder:

conda env create -f environment.yaml

After a few minutes, you will have a new conda environment called onstovenepal with OnStove installed on it. To use it open an Anaconda Prompt, and activate the environment with:

conda activate onstovenepal

Now your environment onstovenepal is available to use.

Important

Note that you always need to activate the environment before conducting any analysis.

Input data

All GIS and socio- and techno-economic data for the model, can be downloaded from the permanent repository at DOI: 10.5281/zenodo.10641858. Download the data and extract it inside the 1. Data and 2. Scenario inputs based on the information provided in the repository.

Running the analysis

In the Anaconda Prompt, change your current directory to the 3. Scripts folder inside the path where you have the OnStove Nepal project, using the command cd:

cd <replace-with-folder-path>/3.\ Scripts

Once you are in that folder you have two different ways you can run the analysis:

Running manually with Jupyter lab

Open a Jupyter lab session running the following in the anaconda prompt:

jupyter lab

Double-click on one of the notebooks inside the 3. Scripts folder and follow the steps described there. The order of the analysis is:

1. DataProcessor.ipynb - reads and processes all raw geospatial data needed for the analysis.
2. OnStove.ipynb - runs the OnStove model for Nepal for one selected scenario.
3. MCA.ipynb - runs the MCA analysis based on the results of the OnStove model, to prioritize actions based on the government's goals.

Running with the automated workflow through Snakemake

You can run the entire analysis using the Snakemake automated workflow. The workflow will allow you to run the entire model very easily, going from raw data processing, up to results and visualizations creation. To read more about The OnStove workflow, please visit the post about the development of the OnStove automated workflow.

To run the workflow type in the anaconda prompt:

snakemake -n

This will perform a dry run that will tell you what are the different steps that will be performed without running them. This is useful to check that your model input files are in the right places and to see which will be the expected output files. Then to start the analysis run:

snakemake --cores 3

Where --cores 3 tells the computer how many processes it is allowed to run in parallel. This is directly linked with the number of scenarios.

Results

After the runs finish, all results will be available in the 4. Results folder. You can read more about the results and access them at the permanent repository at DOI: 10.5281/zenodo.10643983, and in the related paper.

Documentation

Access the latest OnStove documentation in read the docs.