Evaluating the monetary values of greenhouse gases emissions in life cycle impact assessment

Abstract

It is commonly acknowledged that greenhouse gas (GHG) emissions from anthropogenic sources accelerate climate change impacts. Efforts are made by governments and companies to reduce GHG emissions via policies and actions. In order to determine which actions to prioritize among many options, benefits of emission reductions are often monetized, to compare with the costs of action or with benefits that can be obtained from other actions. Life cycle assessment (LCA) is a commonly used tool to assess the amount of GHGs emitted over the life cycle of a service, policy or product system. However, the damage modelling of GHGs in life cycle impact assessment (LCIA) and its monetary values have not been separately evaluated. This hinders the application of LCA in relevant decision contexts. This study evaluates the cause-effect chains and associated monetary values of GHG in three LCIA methods LIME2, EPS2015 and ReCiPe2016. Among these three, EPS2015 covers most damage categories, including the ones on human health, ecosystem and social assets. ReCiPe2016 does not include social assets damages and LIME2 does not consider ecosystem damages in climate change impact. Human health damages are well estimated in all three methods, contributing to 70–97% of the GHG monetary values. The lack of data is a clear obstacle across methods. Further research is needed to develop comprehensive and robust modelling approach for ecosystem damages, which are not well covered in current LCIA methods. Moreover, due to the scope of environmental LCA, there is a lack of consideration on socio-economic consequences, which may not be negligible for climate change. The resulting monetary value of GHG, expressed in per tonne CO2-eq are 16, 160 and 140 US$2017 respectively in LIME2, EPS2015 and ReCiPe 2016. These monetary values are reasonable for use in decision contexts where LCA is applied. Further research is, however, needed to reduce the current uncertainty of at least 1–2 orders of magnitude.