Assessing non-marginal variations with consequential LCA: Application to European energy sector

Abstract

Many policies are being designed to mitigate impacts of human activities on the environment. An environmental evaluation of these policies should include assessments of their impacts according to all known environmental impacts. Moreover, because policies may indirectly affect regions or economic sectors not initially targeted by these policies, indirect environmental consequences should be included in environmental balances. Life cycle analysis (LCA) is a holistic method made to assess environmental impacts caused by products or services according to various environmental damage categories. However, the ability of LCA to model environmental consequences due to a change is restricted to marginal changes occurring in small life cycles. New methodological developments are needed to study major changes and their environmental consequences as they may happen when a policy is applied at large scale. For that purpose, the economic general equilibrium model GTAP has been used to predict global economic perturbation that would be caused by two different European energy policies (bioenergy policy and business as usual policy). LCA was then used to assess environmental impacts due to European energy generation and perturbation of world economy. Despite the bioenergy policy involves more energy from renewable technologies which are expected to be less polluting, results show that due to rebound effects, bioenergy policy results in more environmental impacts. Combining both GTAP and LCA improves environmental assessment made with GTAP because it allows computing environmental impacts according to products life cycles instead of using economic sector emission factors and because emissions and extractions from environment are related to impacts on environment. Regarding LCA method, this new approach allows studying significant changes affecting large systems with a global modeling of economy in a time dependent environment. However, more work is needed to evaluate this new approach, especially uncertainty should be studied.