No abstract

To date, atmospheric concentrations of sulfur hexafluoride (SF6) are the most potent among the greenhouse gases identified by the Intergovernmental Panel on Climate Change (IPCC) and are still rising. In the EU-28, SF6 has been banned from several applications, however, an important exception is gas-insulated electrical switchgear (GIS) for which cost-effective and environmentally sound alternatives were unavailable when the F-Gas regulation was last revised in 2014. To date, after some recent innovations, we argue that the phasing out of SF6 could spur the accelerated development of alternatives with a lower carbon footprint. In the EU-28, the SF6 amount in switchgear is unclear. In this paper, we estimated the SF6 amount to be between 10,800 and 24,700 t (with a mode at 12,700 t) in 2017, resulting in 68 to 140 t of annual emissions from operational leakage only, corresponding to 1.6 to 3.3 Mt of CO2-eq. We additionally calculated the potential greenhouse gas savings over the lifecycle of one exemplary 145 kV gas-insulated switchgear bay upon replacing SF6 by decafluoro-2-methylbutan-3-one (C5-FK) and heptafluoro-2-methylpropanenitrile (C4-FN) mixtures. Projecting these results over the EU-28, a phase-out scenario starting from 2020 onwards could reduce the carbon footprint by a median of 14 Mt of CO2-eq, over a period of 50 years. Extrapolation to medium voltage could be assumed to be of a similar magnitude.

No abstract

No abstract

While previous research has focused on developing prospective LCI databases that build upon projections from integrated assessment models (IAMs), until now only attributional databases have been developed. To construct consequential LCI databases, a novel approach is required that can be applied consistently on a large scale. To this end, the heuristic approach from Bo Weidema was selected as a basis for this study. This approach has been validated before with historical data and was adapted in this study to identify the marginal suppliers in a prospective context. The different steps within the approach were analyzed and alternative techniques for each step within the heuristic method were proposed. The techniques were tested out on the future electricity sector using projections from two IAMs (IMAGE and REMIND). Results showed how sensitive the results are to which technique is selected in each step. The most sensitive step is the selection of the time interval, with even small changes resulting in a noticeable difference. In addition the results also showed a substantial difference between the IAM projections. The relevance and goals of the alternative techniques for each step were discussed to guide users on forming the heuristic method for their study.