2012

Soimakallio, Sampo

Ambitious climate change mitigation requires the implementation of effective and equitable climate policy and GHG emission reduction measures. The objective of this study was to explore the significance of the uncertainties related to GHG emission reduction measures and policies by providing viewpoints on biofuels production, grid electricity consumption and differentiation of emission reduction commitments between countries and country groups. Life cycle assessment (LCA) and macro-level scenario analysis through top-down and bottom-up modelling and cost-effectiveness analysis (CEA) were used as methods. The uncertainties were propagated in a statistical way through parameter variation, scenario analysis and stochastic modelling. This study showed that, in determining GHG emissions at product or process level, there are significant uncertainties due to parameters such as nitrous oxide emissions from soil, soil carbon changes and emissions from electricity production; and due to methodological choices related to the spatial and temporal system boundary setting and selection of allocation methods. Furthermore, the uncertainties due to modelling may be of central importance. For example, when accounting for biomass-based carbon emissions to and sequestration from the atmosphere, consideration of the temporal dimension is critical. The outcomes in differentiation of GHG emission reduction commitments between countries and country groups are critically influenced by the quality of data and criteria applied. In both LCA and effort sharing, the major issues are equitable attribution of emissions and emission allowances on the one hand and capturing consequences of measures and policies on the other. As LCA and system level top-down and bottom-up modelling results are increasingly used to justify various decisions by different stakeholders such as policy-makers and consumers, harmonization of practices, transparency and the handling of uncertainties related to methodological choices, parameters and modelling must be improved in order to avoid conscious misuse and unintentional misunderstanding.