The greenhouse gas emissions performance of cellulosic ethanol supply chains in Europe

Abstract

Background: Calculating the greenhouse gas savings that may be attributed to biofuels is problematic because production systems are inherently complex and methods used to quantify savings are subjective. Differing approaches and interpretations have fuelled a debate about the environmental merit of biofuels, and consequently about the level of policy support that can be justified. This paper estimates and compares emissions from plausible supply chains for lignocellulosic ethanol production, exemplified using data specific to the UK and Sweden. The common elements that give rise to the greatest greenhouse gas emissions are identified and the sensitivity of total emissions to variations in these elements is estimated. The implications of including consequential impacts including indirect land-use change, and the effects of selecting alternative allocation methods on the interpretation of results are discussed.

Results: We find that the most important factors affecting supply chain emissions are the emissions embodied in biomass production, the use of electricity in the conversion process and potentially consequential impacts: indirect land-use change and fertiliser replacement. The large quantity of electricity consumed during enzyme manufacture suggests that enzymatic conversion processes may give rise to greater greenhouse gas emissions than the dilute acid conversion process, even though the dilute acid process has a somewhat lower ethanol yield.

Conclusion: The lignocellulosic ethanol supply chains considered here all lead to greenhouse gas savings relative to gasoline An important caveat to this is that if lignocellulosic ethanol production uses feedstocks that lead to indirect land-use change, or other significant consequential impacts, the benefit may be greatly reduced.Co-locating ethanol, electricity generation and enzyme production in a single facility may improve performance, particularly if this allows the number of energy intensive steps in enzyme production to be reduced, or if other process synergies are available. If biofuels policy in the EU remains contingent on favourable environmental performance then the multi-scale nature of bioenergy supply chains presents a genuine challenge. Lignocellulosic ethanol holds promise for emission reductions, but maximising greenhouse gas savings will not only require efficient supply chain design but also a better understanding of the spatial and temporal factors which affect overall performance.

Figure 1

Combined greenhouse gas and cost model schematic.

Figure 2

Supply chain inputs, outputs, and greenhouse gas allocation methods.

Figure 3

Estimated greenhouse gas emissions from cellulase manufacture.

Figure 4

Carbon emissions from ethanol production for base-case supply chains. Greenhouse gas emissions arising from the use of fossil fuels, fertiliser, and so on, are apportioned between ethanol and solid fuel on the basis of energy content.

Figure 5

The variation in embodied carbon emissions estimates for alternative allocation methods and locations. The example shown is for the spruce enzymatic hydrolysis supply chain (Spruce-EH).

Figure 6

The variation in embodied carbon emission estimates for alternative locations and onsite versus offsite enzyme production. The example shown is for the spruce enzymatic hydrolysis supply chain (Spruce-EH); all fossil carbon emissions are allocated to ethanol and residual solid fuel in proportion to their energy content.

Figure 7

The variation in supply chain greenhouse gas emissions owing to increased transport distances due to increasing plant capacity. The example shown is for the enzymatic hydrolysis process, all fossil carbon emissions are allocated to ethanol and solid fuel in proportion to their energy content.

Figure 8

Greenhouse gas emissions for supply chains including consequential impacts (indirect land use change and fertilizer replacement). The example shown assumes an enzymatic hydrolysis process, EU 27 electricity, and that emissions are allocated to ethanol and surplus solid fuel in proportion to their energy content.

Figure 9

The sensitivity of supply chain greenhouse gas performance to variations in emission factor assumptions – softwood enzymatic process. The example shown assumes EU27 electricity, and that emissions are allocated to ethanol and solid fuel in proportion to their energy content.

Figure 10

The sensitivity of supply chain greenhouse gas performance to variations in emission factor assumptions – softwood dilute acid process. The example shown assumes EU27 electricity, and that emissions are allocated to ethanol and solid fuel in proportion to their energy content.