2017
DOI: 10.1016/j.esr.2017.02.001
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Hedging the risk of increased emissions in long term energy planning

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Cited by 18 publications
(10 citation statements)
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“…They pointed out different sources and dimensions of uncertainty in integrated energy planning in a city or territory, even involving renewables, and proposed a conceptual basis of uncertainty involving different types of uncertainty formalised in a coherent and holistic way. At the same time, Niet et al [94] warned against the risk of exceeding estimates of emissions savings in the case of uncertain technologies and indicated the need to incorporate risks in optimal models. Life cycle assessment (LCA) was also demonstrated to be helpful in evaluating the true environmental suitability of energy policies at the local level [66], because LCA considers secondary effects that alter the expected results and that of hidden long-term implications.…”
Section: Discussionmentioning
confidence: 99%
“…They pointed out different sources and dimensions of uncertainty in integrated energy planning in a city or territory, even involving renewables, and proposed a conceptual basis of uncertainty involving different types of uncertainty formalised in a coherent and holistic way. At the same time, Niet et al [94] warned against the risk of exceeding estimates of emissions savings in the case of uncertain technologies and indicated the need to incorporate risks in optimal models. Life cycle assessment (LCA) was also demonstrated to be helpful in evaluating the true environmental suitability of energy policies at the local level [66], because LCA considers secondary effects that alter the expected results and that of hidden long-term implications.…”
Section: Discussionmentioning
confidence: 99%
“…The OSeMOSYS model is well suited for this analysis and has been applied in similar studies, e.g. on the effects of carbon taxes (Lyseng et al, 2016), electricity trade (Taliotis et al, 2016;English et al, 2017;Pinto de Moura et al, 2017), and emissions uncertainty (Niet et al, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…Because energy infrastructure outlast any electoral or administrative cycle, such transparent information is critical for stakeholders including the public, that is, taxpayers and voters, and support organisations, like development banks. To this end, the analysis uses the Open Source Energy MOdelling SYStem (OSeMOSYS) which is an open source energy model generator that uses linear optimization techniques, and has global application (Fattori et al 2016, Löffler et al 2017, Niet et al 2017, Pfenninger et al 2018, Taliotis et al 2016, UN DESA 2016. It determines the cost-optimal long-term investment and operation required to satisfy an exogenously defined energy demand (Howells et al 2011).…”
Section: Model Descriptionmentioning
confidence: 99%