Odianosen Asibor, JudeClough, Peterali Nabavi, SeyedManovic, Vasilije2024-05-272024-05-272023-10-17Asibor, Jude Odianosen; Clough, Peter; Nabavi, Seyed ali; Manovic, Vasilije (2023). Data: A Machine Learning Approach for Country-level Deployment of Greenhouse Gas Removal Technologies. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.21187129https://dspace.lib.cranfield.ac.uk/handle/1826/21708This dataset consists of the applied normalised input data for the selected bio-geophysical and technoeconomic variables of each of the 182 countries assessed in the study. These input variables include the mean quarterly temperature (Q1T, Q2T, Q3T, Q4T), the mean quarterly precipitation (Q1P, Q2P, Q3P, Q4P), water availability (WA), forestation land use (FLu). agricultural land use (AgLU), low carbon energy availability (LCEA), Geological storage potential (GSP), Biomass availability and Gross national income per capita (GNI). Details on data sources, preprocessing and normalisation methodology are presented in Sections 2.2 and 2.3 of the paper as well as in the Supplementary Information document. The dataset also includes the suitability category results that was obtained for each country for the five GGR methods assessed in the study. This is also the data that was used for plotting the global maps in Figure 4.CC BY 4.0'greenhouse gas removal''BECCS''DACCS''Machine Learning''Net zero''Climate Change mitigation'Dataset10.17862/cranfield.rd.21187129