Table 2.

Comparison of data requirements, availability and uncertainty for fuels ALCA and CLCA*.

ALCA	CLCA
data requirements
1. data specific to the activity within the LCA boundary (e.g. amount of fuel produced, amounts of material and energy inputs consumed in the farming, production and refining processes, transport of fuels, amounts of waste generated)	in addition to the data required to perform the ALCA, CLCA inputs include: 1. market impact data relating to increased biofuels production (e.g. price elasticity data for biofuel inputs and outputs, response of world markets to increased production of biofuels and biofuel co-products)
2. data on the environmental impacts of manufacture and supply of finished fuels and energy inputs	2. technical response data to increased biofuel production (e.g. crop yield response to increase in demand and price of biofuel inputs, crop technology potential, impact of increased farming inputs, impact and potential of modified farming practices, potential and impact of disused, abandoned, marginal and degraded land, impact of biofuel-driven sustainability criteria on farming outputs)
data availability
type 1 inputs: theoretically readily available data which can be monitored over time, to create average values for input to the LCA. The actual availability, quality and accuracy of these data depend on three key factors:   — accuracy of the measurements made (e.g. accuracy of weighbridge used to measure the amount of wheat entering an ethanol mill)   — rigorousness of the monitoring process, which may be supplemented with a verification process   — ease of access to commercially sensitive information	type 1 inputs: many potential sources (e.g. US Department of Agriculture or Organization for Economic Cooperation and Development). However, all have yet to fully factor in the effects of the imminent expansion of the biofuels market; not only in terms of increased demand for agricultural products for biofuel feedstocks, but also in terms of the effects on some agricultural markets of the large quantities of biofuel co-products which will be entering the animal feed markets (e.g. distiller's dried grains with solubles)
type 2 inputs:   — at present, there are many examples where producers have not yet carried out accurate LCAs for their products. In these cases, biofuel LCA practitioners are required to use ‘average’ or default values   — if a biofuel producer identifies and selects an input with very low embodied environmental impacts (e.g. chooses his feedstock from a farmer that minimizes GHG emissions), hence removing that material from the market, causing other potential users of the same type of material to use more carbon-intensive suppliers. This creates an indirect impact in itself, and brings in the need for a CLCA approach	type 2 inputs: the available data are based mainly upon historic trends and in most cases are dependent upon well-understood technical factors.  However, because these data are likely to be derived from historic trends, the impacts of the emerging biofuels market itself on their future values cannot be factored in
data uncertainty
the greatest uncertainties in input data are where direct, accurate measurement has not yet been carried out, or is prohibitively expensive, or where values vary significantly over time. The most striking example is the uncertainty of nitrous oxide emissions from soils, which can vary dramatically from region to region and even between adjacent fields; while default values are available, there is still considerable uncertainty in these default values [32]. Units used for reporting quantities of fuels and materials consumed may vary among industries and regions, challenging the reporting and documentation in fuel LCA models.  Nonetheless, like quantities should be reported in a consistent manner (perhaps repeated in metric units)	type 1 inputs:   — projected supply and demand data:  data influenced by multiple factors, many of which are interdependent and may be influenced by biofuel expansion itself   — price elasticity data: based on historic trends. Any structural change in world trade could induce changes that cannot be reflected from the historic data type 2 inputs:   — crop yield response to increase in demand and price: three factors affect this input:    — crop technology potential: new techniques in breeding and selection are constantly emerging. The rate of development and potential of new, as yet unknown, technologies is impossible to predict accurately    — increased farming inputs: difficult to forecast and highly variable. Fertilizer use is particularly difficult to project where very different application rates and methods are common within and between farms in the same region and the data would also be sourced from economic models which cannot reflect real-time farming practice     — farm practices: the likely impact of improved farming practices is also uncertain and difficult to model. The main area of uncertainty is the interrelationship between market factors and the implementation of the improved practices   — potential of disused, abandoned, marginal and degraded land: significant potential for cropland expansion into this type of land [33,34].  Although most models do not yet factor in the potential impact of biofuels expansion on this type of land, any impact will be strongly policy-driven. The inevitable uncertainty about the extent and the direction of government policies in different world regions is certain therefore to add significant uncertainty to modelling this parameter. (See above for more detail)   — impact of biofuel-driven sustainability criteria on farming outputs: European biofuel suppliers must comply with strict sustainability criteria.  There are as yet no data available on the likely impacts of this requirement and it is not factored into existing modelling capability