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  Sectoral performance analysis of national greenhouse gas emission inventories by means of neural networks

Ganzenmüller, R., Pradhan, P., Kropp, J. P. (2019): Sectoral performance analysis of national greenhouse gas emission inventories by means of neural networks. - Science of the Total Environment, 656, 80-89.
https://doi.org/10.1016/j.scitotenv.2018.11.311

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Ganzenmüller, Raphael1, Autor              
Pradhan, Prajal1, Autor              
Kropp, Jürgen P.1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Annual greenhouse gas emissions have increased more than threefold between 1950 and 2014, posing a major threat to the integrity of the entire earth system and subsequently to humankind. Consequently, roadmaps towards low-carbon pathways are urgently needed. Our study contributes to a more detailed understanding of the dynamics of country based emission patterns and uses them to discuss prospective low-carbon pathways for countries. As availability of databases on sectoral emissions substantially increased, we employ machine learning techniques to classify emission features and pathways. By doing so, 18 representative emission patterns are derived. Overall emissions from seven sectors and for 167 countries covering the time span from 1950 to 2014 have been used in the analyses. The following significant trends can be observed: a) increasing per capita emissions due to growing fossil fuel use in many parts of the world, b) a decline in per capita emissions in some countries, and c) a shift in the emission shares, i.e., a reduction of agricultural and land use contributions in certain regions. Using the emission patterns, their dynamics, and best performing countries as role models, we show the possibility for gaining a decent human development without significantly increasing per capita emissions.

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 Datum: 2019
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.scitotenv.2018.11.311
PIKDOMAIN: RD2 - Climate Resilience
eDoc: 8271
Research topic keyword: Mitigation
Research topic keyword: 1.5/2°C limit
Research topic keyword: Global Commons
Model / method: Machine Learning
Regional keyword: Global
Organisational keyword: RD2 - Climate Resilience
Working Group: Urban Transformations
 Art des Abschluß: -

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Titel: Science of the Total Environment
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 656 Artikelnummer: - Start- / Endseite: 80 - 89 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals444