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Ritchie_2018_Environ._Res._Lett._13_024012.pdf 2,55MB
WeightNameValue
1000 Titel
  • Defining climate change scenario characteristics with a phase space of cumulative primary energy and carbon intensity
1000 Autor/in
  1. Ritchie, Justin |
  2. Dowlatabadi, Hadi |
1000 Erscheinungsjahr 2018
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2018-02-07
1000 Erschienen in
1000 Quellenangabe
  • 13(2):024012
1000 Copyrightjahr
  • 2018
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1088/1748-9326/aaa494 |
1000 Ergänzendes Material
  • https://iopscience.iop.org/1748-9326/13/2/024012/media/ERL_024012_SD.docx |
  • https://iopscience.iop.org/1748-9326/13/2/024012/media/ERL_024012_%20SD_Methods.docx |
  • https://iopscience.iop.org/1748-9326/13/2/024012/media/ERL_024012_SD_Data%20on%20clusters.xlsx |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Climate change modeling relies on projections of future greenhouse gas emissions and other phenomena leading to changes in planetary radiative forcing. Scenarios of socio-technical development consistent with end-of-century forcing levels are commonly produced by integrated assessment models. However, outlooks for forcing from fossil energy combustion can also be presented and defined in terms of two essential components: total energy use this century and the carbon intensity of that energy. This formulation allows a phase space diagram to succinctly describe a broad range of possible outcomes for carbon emissions from the future energy system. In the following paper, we demonstrate this phase space method with the Representative Concentration Pathways (RCPs) as used in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The resulting RCP phase space is applied to map IPCC Working Group III (WGIII) reference case 'no policy' scenarios. Once these scenarios are described as coordinates in the phase space, data mining techniques can readily distill their core features. Accordingly, we conduct a k-means cluster analysis to distinguish the shared outlooks of these scenarios for oil, gas and coal resource use. As a whole, the AR5 database depicts a transition toward re-carbonization, where a world without climate policy inevitably leads to an energy supply with increasing carbon intensity. This orientation runs counter to the experienced 'dynamics as usual' of gradual decarbonization, suggesting climate change targets outlined in the Paris Accord are more readily achievable than projected to date.
1000 Sacherschließung
lokal climate change scenarios
lokal shared socioeconomic pathways (SSPs)
lokal representative concentration pathways (RCPs)
lokal decarbonization
lokal fossil fuel resources
lokal data mining
lokal energy transition
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0001-6679-3953|https://frl.publisso.de/adhoc/uri/RG93bGF0YWJhZGksIEhhZGkg
1000 Label
1000 Förderer
  1. Pacific Institute for Climate Solutions |
  2. Center for Climate and Energy Decision Making, Carnegie Mellon University |
  3. National Science Foundation |
  4. Social Sciences and Humanities Research Council of Canada |
1000 Fördernummer
  1. 36170–50280
  2. -
  3. SES-1463492
  4. -
1000 Förderprogramm
  1. Transition to a low GHG economy
  2. -
  3. -
  4. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Pacific Institute for Climate Solutions |
    1000 Förderprogramm Transition to a low GHG economy
    1000 Fördernummer 36170–50280
  2. 1000 joinedFunding-child
    1000 Förderer Center for Climate and Energy Decision Making, Carnegie Mellon University |
    1000 Förderprogramm -
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer National Science Foundation |
    1000 Förderprogramm -
    1000 Fördernummer SES-1463492
  4. 1000 joinedFunding-child
    1000 Förderer Social Sciences and Humanities Research Council of Canada |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6418832.rdf
1000 Erstellt am 2020-02-06T10:48:29.661+0100
1000 Erstellt von 218
1000 beschreibt frl:6418832
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet 2020-02-06T10:49:52.527+0100
1000 Objekt bearb. Thu Feb 06 10:49:40 CET 2020
1000 Vgl. frl:6418832
1000 Oai Id
  1. oai:frl.publisso.de:frl:6418832 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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