Brandão M, Kirschbaum MUF, Cowie AL, Hjuler SV (2019). Quantifying the climate change effects of bioenergy systems: comparison of 15 impact-assessment methods across three case studies. Global Change Biology Bioenergy 11: 727–743.
Ongoing concern over climate change has led to interest in replacing fossil energy with bioenergy. There are different approaches to quantitatively estimate the climate change effects of bioenergy systems. In the present work, we have focused on a range of published impact assessment methods that vary due to conceptual differences in the treatment of biogenic carbon fluxes, the type of climate change impacts they address and differences in time horizon and time preference. Specifically, this paper reviews fifteen different methods and applies these to three hypothetical bioenergy case studies: (a) woody biomass grown on previously forested land; (b) woody biomass grown on previous pasture land; and (b) annual energy crop grown on previously cropped land. Our analysis shows that the choice of method can have an important influence on the quantification of climate change effects of bioenergy, particularly when a mature forest is converted to bioenergy use as it involves a substantial reduction in biomass carbon stocks. Results are more uniform in other case studies. In general, results are more sensitive to specific impact assessment methods when they involve both emissions and removals at different points in time, such as for forest bioenergy, but have a much smaller influence on agricultural bioenergy systems grown on land previously used for pasture or annual cropping. The development of effective policies for climate change mitigation through renewable energy use requires consistent and accurate approaches to identification of bioenergy systems that can result in climate change mitigation. The use of different methods for the same purpose: estimating the climate change effects of bioenergy systems, can lead to confusing and contradictory conclusions. A full interpretation of the results generated with different methods must be based on an understanding that the different methods focus on different aspects of climate change and represent different time preferences.
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