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Biofuel futures in road transport – A modeling analysis for Sweden

AbstractFirst and second generation biofuels are among few low-carbon alternatives for road transport that currently are commercially available or in an early commercialization phase. They are thus potential options for meeting climate targets in the medium term. For the case of Sweden, we investigate cost-efficient use of biofuels in road transport under system-wide CO2 reduction targets to 2050, and the effects of implementation of targets for an almost fossil-free road transport sector to 2030. We apply the bottom-up, optimization MARKAL_Sweden model, which covers the entire Swedish energy system including the transport sector. For CO2 reductions of 80% to 2050 in the Swedish energy system as a whole, the results of the main scenario show an annual growth rate for road transport biofuels of about 6% from 2010 to 2050, with biofuels accounting for 78% of road transport final energy use in 2050. The preferred biofuel choices are methanol and biomethane. When introducing additional fossil fuel phase-out policies in road transport (−80% to 2030), a doubling of the growth rate to 2030 is required and system CO2 abatement costs increases by 6% for the main scenario. Results imply that second generation biofuels, along with energy-efficient vehicle technologies such as plug-in hybrids, can be an important part of optimized system solutions meeting stringent medium-term climate targets.
Transportation, Road transport, Energy system, Environmental Science(all), Biofuels, MARKAL, CO2, Model
Transportation, Road transport, Energy system, Environmental Science(all), Biofuels, MARKAL, CO2, Model
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).48 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
