Global gridded scenarios of residential cooling energy demand to 2050

<p># ggACene (global gridded Air Conditioning energy) projections</p> <p>### Output AC and AC electricity gridded data</p> <p>This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply.</p> <p>### Input data and analysis replication</p> <p>The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (<a href="https://github.com/giacfalk/ggACene">https://github.com/giacfalk/ggACene</a>) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset.</p> <p>## Instructions<br>To reproduce the model and generate the dataset from scratch, please refer to the following steps:<br>- Download input data "replication_package_input_data.7z" by cloning the repository<br>- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)<br>- Open RStudio and adjust the path folder in&nbsp;the sourcer.R script<br>- Run the sourcer.R script to train the ML model, make projections, and represent result files<br><br></p> <p>### Figures replication package</p> <p>Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript.<br><br></p> <p>### Reference<br><br>Falchetta, G., De Cian, E., Pavanello, F., &amp; Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8</p>