The drivers of GHG emissions : a novel approach to estimate emissions using nonparametric analysis

dc.contributor.authorMagazzino, Cosimo
dc.contributor.authorCerulli, Giovanni
dc.contributor.authorHaouas, Ilham
dc.contributor.authorUnuofin, John Onolame
dc.contributor.authorSarkodie, Samuel Asumadu
dc.contributor.emailu22006517@tuks.co.zaen_US
dc.date.accessioned2024-04-11T06:37:37Z
dc.date.available2024-04-11T06:37:37Z
dc.date.issued2024-03
dc.description© 2023 The Author(s). Published by Elsevier B.V. on behalf of International Association for Gondwana. Research. This is an open access article under the CC BY license.en_US
dc.description.abstractThe rising levels of global GHG emissions underpin climate change, hence, taking an appropriate inventory of the drivers and patterns of anthropogenic emissions remains crucial to mitigating global climate effects. However, there are conflicting views in the literature on the relationship between respective drivers and GHG emissions due to the lack of robust analysis that accommodates the interaction of all significant drivers. We use novel estimation techniques to decipher the 26-year inventory of GHG occurrences and simultaneous assessment of interactions in 50 countries stratified based on socioeconomic developments over the period 1990–2018. This study highlights different drivers of GHG emissions under broader categories such as population, economic development, forest density, and agricultural practices. Non-parametric estimations roughly confirm the magnitude of the influence of forests, agriculture, and land-use intensity on GHG emissions, ultimately tracking the most significant emission sinks.en_US
dc.description.departmentChemical Engineeringen_US
dc.description.librarianhj2024en_US
dc.description.sdgSDG-07:Affordable and clean energyen_US
dc.description.sdgSDG-08:Decent work and economic growthen_US
dc.description.sponsorshipThe National Research Foundation of South Africa.en_US
dc.description.urihttps://www.elsevier.com/locate/gren_US
dc.identifier.citationMagazzino, C., Cerulli, G., Haouas, I. et al. 2024, 'The drivers of GHG emissions: a novel approach to estimate emissions using nonparametric analysis', Gondwana Research, vol. 127, pp. 4-21, doi : 10.1016/j.gr.2023.10.004.en_US
dc.identifier.issn1342-937X
dc.identifier.other10.1016/j.gr.2023.10.004
dc.identifier.urihttp://hdl.handle.net/2263/95476
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2023 The Author(s). Published by Elsevier B.V. on behalf of International Association for Gondwana Research. This is an open access article under the CC BY license.en_US
dc.subjectGreenhouse gas (GHG) emissionsen_US
dc.subjectLasso regressionen_US
dc.subjectClimate change mitigationen_US
dc.subjectLand useen_US
dc.subjectForestryen_US
dc.subjectSustainable development goals (SDGs)en_US
dc.subjectSDG-07: Affordable and clean energyen_US
dc.subjectSDG-08: Decent work and economic growthen_US
dc.titleThe drivers of GHG emissions : a novel approach to estimate emissions using nonparametric analysisen_US
dc.typeArticleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Magazzino_Drivers_2024.pdf
Size:
1.98 MB
Format:
Adobe Portable Document Format
Description:
Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: