Estimates of life cycle impacts are subject to uncertainty. In life cycle assessments, there is uncertainty in input data (parameter uncertainty), in normative choices (sce­nario uncertainty) and mathematical relationships (model uncertainty) (Huijbregts et al. 2003; Lloyd and Ries 2007). As the focus in this chapter is largely on com­paring fuels, model uncertainty tends to be rather similar for all fuels, which is favourable to the comparative value of life cycle assessments.

Parameter uncertainty may be limited by using relatively good quality inven­tories of emission and resource use data, such as the JLCA-LCA inventory from Japan (Suguiyama et al. 2005) and the Ecoinvent database (cf. Zah et al. 2007), as well as recent peer-reviewed research into emissions and resource use. In this way, uncertainties in contributions of industrial and transport activities to impacts of bio­fuels, especially in industrialized countries, can be limited. However, uncertainties about industrial activities in some developing countries may remain relatively large because of major uncertainty about fuels, energy efficiency and environmental tech­nology (e. g. Reijnders and Huijbregts 2008a; de Vries 2008). Uncertainties linked to the fate of C and N in cropping and harvesting feedstocks are relatively large. In the case of N2O emissions associated with intensive cropping, uncertainty in net greenhouse gas emissions may well be ±20% (Reijnders and Huijbregts 2008b), and uncertainties about changes in C stocks of ecosystems may also be quite substantial.

Normative choices are an important source of uncertainty. One of these choices relates to the time that land will be used to produce biofuel. Choices regarding this time are important in calculating net greenhouse gas emissions due to land use change (e. g. Reijnders and Huijbregts 2008a; Wicke et al. 2009). Allocation in the case of multi-output production is another normative choice that is important. As explained in Chap. 2, there are three major ways to allocate. The first is based on prices, the second on physical categories, such as energy or weight, and the third on subtracting avoided processes (also called substitution). Apart from choosing the basis for allocation, there may also be other matters to consider. Take, for example, the conversion of lignocellulose in dried distillers grains with or without solubles [DDG(S)] to ethanol. Lignocellulosic outputs of ethanol production such as dried distillers grains are currently an ingredient of animal feed (Taylor-Pickard 2008). If one goes back in history, there have been advocates for replacing ingredients that had high starch contents by lignocellulosic ingredients, such as DDG(S), in ani­mal feed (e. g. Sarkanen 1976). Now, when these lignocellulosic components are diverted to the production of transport fuel, will this give rise to an increase in the starch content of animal feed? And should the effects thereof on the net emission of greenhouse gases be allocated to lignocellulosic ethanol, and if so to what ex­tent? Also, an objection has been raised against considering dried distillers grains as a product output of ethanol production, to which non-product outputs should be allocated (Patzek 2004). According to Patzek (2006) dried distillers grains should become an input in cropping and spread on the fields to diminish the need for nitro­gen fertilizer, decrease soil erosion and improve the energy efficiency of cropping. Patzek (2006) has also argued that if there is any crediting at all of dried distillers grains, the energy credit should be somewhat negative.

The complications of dealing with co-products such as DDG(S) and alloca­tion may well seem so problematic that no ‘iron-clad’ estimate of net greenhouse gas emissions associated with biofuels from multi-output processes seems feasible. Only limited study has been made as to the differences in estimated environmental impacts of transport biofuels following from the different approaches to allocation. Eickhout et al. (2008) found that the substitution approach and allocation on the basis of energy-generated outcomes for ethanol and biodiesel were in the range of ±15%. Curran (2007) looked at the impact of different ways of allocation (based on price, weight, volume and energy) on the relative environmental ranking of con­ventional gasoline and bioethanol and found that this ranking was the same in all instances. On the other hand, Reijnders and Huijbregts (2005) found that alloca­tion based on either price or energy may lead to a difference in the environmental ranking of fossil-fuel- and manure-based electricity. And Malga and Freire (2006) did show that in the case of wheat ethanol, different ways to allocate have a major influence on results.

In the following, we will indicate what type of time frame and allocation has been used in arriving at specific results.