https://link.springer.com/article/10.1007/s10584-017-2051-8
Climatic Change <https://link.springer.com/journal/10584> pp 1–13 The road to achieving the long-term Paris targets: energy transition and the role of direct air capture - Authors <https://link.springer.com/article/10.1007/s10584-017-2051-8#authors> - Authors and affiliations <https://link.springer.com/article/10.1007/s10584-017-2051-8#authorsandaffiliations> - Adriana MarcucciEmail author <[email protected]> - Socrates Kypreos - Evangelos Panos - - <[email protected]> <http://orcid.org/0000-0002-0427-9120> - - - - 1. 1. 2. 2. Article First Online: 19 August 2017 <https://link.springer.com/article/10.1007/s10584-017-2051-8#article-dates-history> - 1Shares <http://www.altmetric.com/details.php?citation_id=24086363&domain=link.springer.com> - 5Downloads Abstract In this paper, we quantify the energy transition and economic consequences of the long-term targets from the Paris agreement, with a particular focus on the targets of limiting global warming by the end of the century to 2 and 1.5 °C. The study assumes early actions and quantifies the market penetration of low carbon technologies, the emission pathways and the economic costs for an efficient reduction of greenhouse gas (GHG) emissions such that the temperature limit is not exceeded. We evaluate the potential role of direct air capture (DAC) and its impact on policy costs and energy consumption. DAC is a technology that removes emissions directly from the atmosphere contributing to negative carbon emissions. We find that, with our modelling assumptions, limiting global temperature to 1.5 °C is only possible when using DAC. Our results show that the DAC technology can play an important role in realising deep decarbonisation goals and in the reduction of regional and global mitigation costs with stringent targets. DAC acts a substitute to Bio-Energy with Carbon Capture and Storage (BECCS) in the stringent scenarios. For this analysis, we use the model MERGE-ETL, a technology-rich integrated assessment model with endogenous learning. Electronic supplementary material The online version of this article (doi:10.1007/s10584-017-2051-8 <https://doi.org/10.1007/s10584-017-2051-8>) contains supplementary material, which is available to authorized users. -- You received this message because you are subscribed to the Google Groups "geoengineering" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/geoengineering. For more options, visit https://groups.google.com/d/optout.
