A consistent conceptual framework for applying climate metrics in technology life cycle assessment. (11th July 2017)

• Record Type: Journal Article
• Authors: Mallapragada, Dharik; Mignone, Bryan K
• Is Part Of: Environmental research letters. Volume 12:Number 7(2017:Jul.)
• Publication Date: 2017-07-11
• Subjects: climate change metrics -- greenhouse gas abatement -- methane -- natural gas -- technology assessment; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/aa7397
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: Comparing the potential climate impacts of different technologies is challenging for several reasons, including the fact that any given technology may be associated with emissions of multiple greenhouse gases when evaluated on a life cycle basis. In general, analysts must decide how to aggregate the climatic effects of different technologies, taking into account differences in the properties of the gases (differences in atmospheric lifetimes and instantaneous radiative efficiencies) as well as different technology characteristics (differences in emission factors and technology lifetimes). Available metrics proposed in the literature have incorporated these features in different ways and have arrived at different conclusions. In this paper, we develop a general framework for classifying metrics based on whether they measure: (a) cumulative or end point impacts, (b) impacts over a fixed time horizon or up to a fixed end year, and (c) impacts from a single emissions pulse or from a stream of pulses over multiple years. We then use the comparison between compressed natural gas and gasoline-fueled vehicles to illustrate how the choice of metric can affect conclusions about technologies. Finally, we consider tradeoffs involved in selecting a metric, show how the choice of metric depends on the framework that is assumed for climate change mitigation, and suggest which subset of metrics are likely to be most analytically self-consistent.