Data: A Machine Learning Approach for Country-level Deployment of Greenhouse Gas Removal Technologies

Date published

2023-10-17 16:41

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Volume Title

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Cranfield University

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Dataset

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Citation

Asibor, 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.21187129

Abstract

This 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.

Description

Software Description

Software Language

Github

Keywords

'greenhouse gas removal', 'BECCS', 'DACCS', 'Machine Learning', 'Net zero', 'Climate Change mitigation'

DOI

10.17862/cranfield.rd.21187129

Rights

CC BY 4.0

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Funder/s

Petroleum Technology Development Fund