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The RSB GHG accounting scheme Feasibility of a metamethodology and way forward

A report prepared by

E4tech

For the Roundtable on Sustainable Biofuels

Version 4.1

8 October 2009

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E4tech authors: Ausilio Bauen François Vuille Philip Watson Kathrine Vad

Contact: François Vuille Avenue Juste‐Olivier 2 1006 Lausanne Switzerland franç[email protected] +41 21 331 15 79

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Contents

Executive Summary ................................................................................................................................... 6

1 CONTEXT AND OBJECTIVES ................................................................................................... 8

1.1 Context of the project ............................................................................................................................ 8

1.2 Objectives of the project ........................................................................................................................ 8

2 LIFECYCLE ASSESSMENT APPROACH TO GHG ACCOUNTING FOR BIOFUELS ....... 9

2.1 Introduction to LCA as a methodology to calculate GHG emissions of biofuels ........................................ 9

2.2 Key methodological choices in LCA studies ............................................................................................ 10 2.2.1 Consequential vs. attributional LCA ....................................................................................................... 10 2.2.2 System boundaries ................................................................................................................................ 11 2.2.3 Understanding the impacts of co‐products ........................................................................................... 11 2.2.4 Greenhouse gas and their global warming potential ............................................................................ 12 2.2.5 Land‐use change .................................................................................................................................... 13

2.3 Importance of input data ...................................................................................................................... 15

3 GHG ACCOUNTING METHODOLOGIES IN REGULATORY SCHEMES ........................ 17

3.1 The European Renewable Energy Directive (RED) .................................................................................. 17 3.1.1 Context and objectives of the scheme .................................................................................................. 17 3.1.2 GHG saving thresholds ........................................................................................................................... 17 3.1.3 Reporting under the RED scheme .......................................................................................................... 18 3.1.4 GHG saving calculation methodology .................................................................................................... 18 3.1.5 Input data .............................................................................................................................................. 21 3.1.6 Chain default values .............................................................................................................................. 22

3.2 The Californian Low Carbon Fuel Standard (LCFS) .................................................................................. 25 3.2.1 Context and objectives of the scheme .................................................................................................. 25 3.2.2 Carbon intensity thresholds ................................................................................................................... 25 3.2.3 Reporting under the LCFS scheme ......................................................................................................... 26 3.2.4 Carbon intensity calculation methodology ............................................................................................ 27 3.2.5 Input data .............................................................................................................................................. 30 3.2.6 Chain default values .............................................................................................................................. 31

3.3 The United Kingdom Renewable Transport Fuel Obligation (RTFO) ........................................................ 32 3.3.1 Context and objective of the scheme .................................................................................................... 32

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3.3.2 GHG saving thresholds ........................................................................................................................... 32 3.3.3 Reporting under the RTFO scheme ........................................................................................................ 32 3.3.4 GHG saving calculation methodology .................................................................................................... 34 3.3.5 Input data .............................................................................................................................................. 37 3.3.6 Chain default values .............................................................................................................................. 37

3.4 The U.S. 2009 Renewable Fuel Standard (RFS2) programme .................................................................. 40 3.4.1 Context and objective of the scheme .................................................................................................... 40 3.4.2 GHG saving thresholds ........................................................................................................................... 40 3.4.3 Reporting under the RFS2 scheme ........................................................................................................ 41 3.4.4 GHG saving calculation methodology .................................................................................................... 41 3.4.5 Input data .............................................................................................................................................. 45 3.4.6 Chain default values .............................................................................................................................. 46

4 RELEVANT ASPECTS OF NONREGULATORY INITIATIVES ........................................ 48

4.1 GBEP .................................................................................................................................................... 48

4.2 Roundtable on Sustainable Palm Oil (RSPO) .......................................................................................... 49

4.3 Comparison of methodological choices in other reviewed studies ......................................................... 49

5 COMPARISON OF EXISTING REGULATORY SCHEMES ................................................. 52

5.1 Introduction ......................................................................................................................................... 52

5.2 Attributional vs. consequential LCA ....................................................................................................... 52

5.3 System boundaries ............................................................................................................................... 53 5.3.1 Type of fuels and end use ...................................................................................................................... 53 5.3.2 Breadth of biofuel chain analysis ........................................................................................................... 54 5.3.3 Depth of biofuel chain analysis .............................................................................................................. 54 5.3.4 Land‐use change .................................................................................................................................... 55

5.4 Metric .................................................................................................................................................. 56

5.5 Fossil fuel references ............................................................................................................................ 57

5.6 Treatment of co‐products ..................................................................................................................... 58

5.7 Direct land‐use change .......................................................................................................................... 59

5.8 Tank‐to‐wheel emissions ...................................................................................................................... 61

5.9 Input data ............................................................................................................................................. 63 5.9.1 N fertiliser production emissions ........................................................................................................... 63

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5.9.2 N2O emissions from agricultural soils .................................................................................................... 65 5.9.3 Transportation ....................................................................................................................................... 65 5.9.4 Conversion process ................................................................................................................................ 67

5.10 Chain default values ............................................................................................................................. 69

5.11 Reporting scheme ................................................................................................................................. 70

5.12 Summary of convergences and discrepancies ........................................................................................ 71

6 CONCLUSIONS AND RECOMMENDATIONS FOR A WAY FORWARD ........................ 73

6.1 Options for developing a GHG accounting methodology and for its use ................................................. 73

6.2 Option A – Develop an regulation‐compliant RSB GHG accounting meta‐methodology .......................... 73

6.3 Option B – Develop RSB's own GHG accounting methodology ............................................................... 74 6.3.1 Option B1 – RSB‐developed GHG accounting methodology ................................................................. 75 6.3.2 Option B2 – RSB certification scheme ................................................................................................... 75 6.3.3 Option B3 – Multi‐scheme reporting tool .............................................................................................. 75

6.4 Option C – Adopt an existing GHG accounting methodology .................................................................. 76

6.5 Recommendations ................................................................................................................................ 76

7 REFERENCES .............................................................................................................................. 79

Annex 1. Discounting of future emissions................................................................................ 82

Annex 2. Additional information on the RED methodology ............................................... 84

Annex 3. Additional information on the Californian LCFS ................................................. 89

Annex 4. Additional information on the UK RTFO ................................................................ 91

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Executive Summary

The principal objective of this report is to discuss the feasibility of a meta‐methodology that would be compatible with existing regulatory approaches to account for lifecycle GHG emissions of biofuels.

A systematic study of the methodologies and input data used by existing regulatory schemes (Chapter 3) and non‐regulatory initiatives (Chapter 4) has been performed. This enabled the identification of areas of consensus and divergences between schemes (Chapter 5), and led to the conclusion that existing regulatory schemes do not present sufficient level of compatibility to enable the development of a meta‐methodology.

Three alternative strategies were then identified where the RSB has the potential to add value to existing GHG accounting practices more broadly. These options have been assessed against criteria such as scientific rigour, potential for adoption by stakeholders and regulatory schemes, and ease of development and implementation (Chapter 6). This information is meant to enable the RSB stakeholders to take an informed decision as to the strategy to pursue in relation to the GHG accounting of biofuels.

One of the proposed options forward is for the RSB to develop its own GHG accounting methodology and certification scheme, possibly coupled to the development of a computational reporting tool. Given the RSB’s recognised position as a leader in the biofuel sustainability area, the RSB methodology should be based on the scientific state‐of‐the‐art and be as rigorous and accurate as possible. The methodological ingredients given in Table 1 below have been identified as those offering the required scientific rigour, without jeopardising implementability of an RSB certification scheme.

Table 1 – Overview of the recommendations for the development of an RSB GHG accounting methodology and reporting scheme

Methodological ingredient

Recommendations for development of a best-practice methodology and reporting scheme

Type of LCA approach

We recommend the RSB adopt a mixed approach of attributional and consequential LCA. The general framework should be attributional (based on the assessment of direct impacts), while taking into consideration the consequences of causing indirect land-use change, using co-products for biofuel production and substituting other fuels and products by biofuels and their co-products.

Type of fuels and end use

The scope of the GHG accounting methodology will depend on what the objectives of the RSB scheme are. However, it should at least cover all biofuels used for road transport.

Breadth of biofuel chain analysis

We recommend the RSB calculate the carbon intensities of biofuels on a well-to-wheel basis, as this is necessary to accurately determine the carbon saving potential of biofuels.

Depth of chain analysis

We recommend the RSB exclude from the LCA calculations emissions from the construction of plants, infrastructures and transportation systems and seeding material production.

Metric We recommend to use g CO2-eq / MJ of fuel as the metric, but to include tank-to-wheel emissions and the effect of vehicle efficiency in the calculations.

Fossil fuel references We recommend the RSB differentiate between diesel and gasoline type fuels, and calculate the carbon intensity of these reference fossil fuels using the actual RSB accounting methodology.

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Table 1 – Continued

Methodological ingredients

Recommendations for development of an RSB accounting methodology and reporting scheme

Treatment of co-products

We recommend using system expansion as a co-product treatment method. Allocation based on economic value should be used as a fall back approach when necessary data or information is lacking, as this is the closest allocation approach to system expansion.

Direct land-use change (LUC)

We recommend using annualisation as a method to calculate carbon emissions from direct land-use change. The choice of time horizon to use for the annualisation is somewhat subjective. In order to ensure all biofuel feedstocks are treated equally, the same time period should be used for LUC caused by any feedstock. A good metric would be to choose a time horizon that reflects the average planning cycle of feedstock cultivation. In this regard, a 15 to 20 years period seems a reasonable choice, which would also correspond to the RSB principles on GHG emissions. We recommend not using annualisation period longer than 20 years as this does not correspond to a foreseeable future and appears to optimistic. Ultimately, the choice of time horizon will depend on stakeholders’ opinion and on what is considered as international best-practice (e.g. IPCC approach).

Indirect land-use change

Consideration of indirect land-use change (iLUC) is beyond the scope of the present project.

Tank-to-wheel emissions

We recommend that the RSB recognise the carbon neutrality of biofuels, and thus not assign any combustion-related CO2 emissions to biofuels. However, we advise taking into account non-CO2 emissions (CH4 and N2O) from biofuel combustion, as these are not carbon neutral and may become significant in the carbon balance of advanced biofuels. The RSB should consider evidence on what the best scientific practice would be to account for these emissions. So far as fossil fuels are concerned, we recommend the RSB take into account all GHG emissions due to their combustion. Finally, we recommend including vehicle energy efficiency in the methodology, but differentiate between fuels only once sufficient scientific evidence becomes available. The RSB should monitor evidence in this area.

Input data The selection of data sources will depend on the actual scope of the RSB scheme. Overall, RSB experts should probably classify possible data sources based on their level of accuracy and authoritativeness. For data that might be considered less certain, a conservative approach could possibly be used.

Chain default values

Chain values result from the LCA calculation and are therefore entirely defined by the methodological choices and input data used in the GHG accounting methodology. As such, there is no choice to be made at the level of the chain values. However, there are schemes in which the chain default values used for reporting are not equal to the calculated chain values, but are chosen in a more arbitrary way in order to better meet specific policy objectives. We recommend RSB use the calculated chain values as default values as this adds to the scheme simplicity and transparency.

Reporting scheme

The reporting approach depends on the actual objectives of the scheme, hence no universal best-practice can be derived as to how reporting should be performed. However, should the RSB objective be either to incentivise the improvement of biofuel production or to create a standard that only the best-in-class biofuels can meet, then the reporting party should be offered the possibility to report its actual input data, with default input data set at a conservative level. Threshold levels should be set as to encourage low carbon biofuels. Should the RSB envisage implement a biofuel certification scheme, then we recommend RSB develop guidance and tools to enable the industry to efficiently report the carbon intensity and savings of biofuels.

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1 Context and objectives

1.1 Context of the project

The Roundtable on Sustainable Biofuels (RSB) is developing a standard for sustainable biofuels, and is currently defining its approach to and criteria for greenhouse gas (GHG) saving for Version One of this standard. There are three key elements to consider when defining this approach:

• the level of GHG saving biofuels will be required to achieve;

• the methodology used to calculate the level of GHG saving;

• compatibility with existing GHG accounting methodologies and regulatory schemes.

Several regulatory schemes have recently been, or are about to be implemented (e.g. UK's Renewable Transport Fuel Obligation, EU’s Renewable Energy Directive, U.S. EPA’s Renewable Fuel Standard, the Californian Low Carbon Fuel Standard). These schemes have already defined GHG saving requirements for policy reasons and lifecycle assessment methodologies to estimate these savings.

In this context, the RSB has to decide between different strategic options regarding the development of its GHG saving approach. The favoured option is to design a meta‐methodology and certification standard that would be compliant with all the different accounting approaches used in existing regulatory schemes. This option might however prove not feasible due to the possibly incompatible nature of existing accounting methodologies. An alternative way forward, although somewhat less ambitious, could be for the RSB to establish a set of best‐practice characteristics which GHG accounting methodologies and reporting schemes should possess in order to be compliant with the RSB standard. Then, there are all the intermediate options, ranging from the development of an additional accounting methodology and reporting scheme based on what the RSB considers as the best methodological ingredients, through to the development of a GHG accounting methodology or reporting tool that would be compliant with some, but not all, of the existing regulatory schemes.

In order for the RSB Steering Group to take informed decisions on its strategy forward and on the ingredients for its GHG accounting methodology, the RSB seeks to understand the methodological differences amongst existing accounting approaches and reporting schemes. This will enable the RSB to identify opportunities to add value to existing GHG accounting practices, while offering implementation options to its sustainability standards.

1.2 Objectives of the project

There are two key objectives of this project:

• to provide RSB with an overview of the characteristics of existing accounting methodologies and regulatory schemes and to discuss their relative merits and inter‐compatibilities;

• to discuss the feasibility of a ‘meta‐methodology’ approach, and outline possible options and their key elements. If the meta‐approach is deemed to be unfeasible, alternative strategies – both at the level of GHG accounting methodology and at that of biofuel certification schemes – will be identified and their advantages and disadvantages discussed.

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2 Lifecycle assessment approach to GHG accounting for biofuels

Lifecycle Assessment (LCA) of a product (e.g. a biofuel) is the evaluation of the inputs, outputs and the potential environmental impacts of this product throughout its lifecycle (ISO, 2006), i.e. from raw material extraction and acquisition, through energy conversion, material production and manufacturing, to use, end‐of‐life treatment and final disposal.

Although LCA is widely recognised as providing the best framework for assessing the potential environmental impacts of products and despite harmonisation efforts made by ISO (the ISO 14040 series), no internationally standardised methodology for LCA currently exists. The lack of a common methodology means there is scope for a range of subjective methodological choices which fuels debate about best practice use and interpretation.

Section 2.1 gives an overview of the main steps of an LCA, while Section 2.2 provides insight on the methodological choices that have to be made, and the impact they can have on LCA results.

2.1 Introduction to LCA as a methodology to calculate GHG emissions of biofuels

LCA studies are usually performed in four steps, stage 2 and 3 being the actual ‘calculation’ steps:

Step 1 – Goal and Scope Definition. In this initial step, the goal of the study is defined in terms of intended application and audience, and reasons for carrying out the study. The definition of the scope of the study includes determining the product system to be studied, the functional unit1, the system boundaries and co‐product treatment procedures and the limitations and assumptions. The scope definition should ensure the breadth, depth and detail of the study are compatible and sufficient to address the stated goal (ISO, 2006).

Step 2 – Lifecycle Inventory Analysis (LCI). This step involves collection of data on the processes considered within the system boundaries, including inputs and outputs of the processes and their related environmental emissions. The output of this stage is an inventory table containing all environmental emissions. In LCA studies that focus on the impact of a biofuel on climate change, the inventory table will contain all the different GHGs emitted during the entire lifecycle of this biofuel.

The amount of data required to perform an LCA is typically very large. For a typical biofuel chain composed of 4 or 5 process steps (raw material cultivation and harvesting, feedstock transport to the conversion plant, biomass conversion into a biofuel, transport to the distribution point), data on around one hundred inputs would be required just to assess the lifecycle GHG emissions. The quantity of data is much larger if the scope is extended beyond GHG emissions to all environmental burdens. These data requirements often raise issues regarding both the practicality of carrying out LCA studies (due to, for example, the availability and ease of collection of the data) and the accuracy of their results (due to, for example the transparency, reliability, accuracy, and currency of the data used). Depending on the objective of the LCA, the complexity of carrying out the inventory analysis often requires some form of trade of between scientific rigour and practicality.

1 The functional unit is the quantified performance of a product for use as a reference unit (ISO, 2006).

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Step 3 – Lifecycle Impact Assessment (LCIA). The impact assessment phase of LCA is aimed at evaluating the significance of potential environmental impacts using the emission inventory. In general, this process involves calculating the impact of each input, output and/or emission in the specific environmental categories being considered (ISO, 2006).

In the case of GHG accounting of a biofuel, the different GHGs considered (e.g. CO2, CH4, N2O) are aggregated, using global warming potentials2, to calculate the carbon intensity3 of the biofuel, i.e. the amount of ‘carbon dioxide equivalent’ (CO2‐eq) per unit of biofuel.

Step 4 – Interpretation. In this final step, the findings from the inventory analysis and the impact assessment are interpreted and conclusions are drawn with reference to the defined goal and scope, and recommendations may be provided (ISO, 2006).

To put the LCA results into perspective, a comparison of the calculated impact can be made with that of a reference model. For biofuels, GHG emissions are usually compared to those of reference fossil fuels that the biofuels are replacing. This comparison can yield GHG emission savings achieved by given biofuels – which is of particular interest in schemes imposing minimum GHG saving thresholds.

2.2 Key methodological choices in LCA studies

2.2.1 Consequential vs. attributional LCA

Two fundamentally different approaches to LCA exist, the attributional and the consequential LCA. In theory, these two approaches are designed for different purposes.

An attributional LCA estimates the environmental burdens of a product system that can be ‘blamed’ on or ‘attributed to’ the delivery of a given amount of a product. Attributional LCAs do not explore any impacts that a product may have outside the product system. For example, the results of an attributional lifecycle accounting of GHG emissions from a biofuel would describe the GHG emissions of the average biofuel production in a geographic area.

In contrast, a consequential LCA is an estimate of the global change in environmental burdens and resource flows that result from the production of an additional unit of a product. A consequential approach to LCA considers all factors that could influence or be influenced by an increase in the production of a biofuel, including economic and environmental feedback loops. Crucially, this may involve assessing impacts beyond just the product system of immediate concern (e.g. indirect changes in land use caused by biofuel production). A very broad consequential LCA could, for example, describe and quantify the effects of oil or fertiliser price changes or the effects that investments in technology or climatic feedback loops might have (Winrock International, 2009).

2 The global warming potential (GWP) of a GHG is a measure of its contribution to the greenhouse effect, relative to that of CO2 – see Section 2.2.4 for further explanation. 3 The carbon intensity of a fuel is a measure of the quantity of GHG being emitted during the production and/or use of a certain amount of this fuel (which can be measured per unit of energy or mass).

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In practice, consequential LCAs are typically more resource intensive to carry out than attributional, however they are considered to be of greater scientific rigor. In addition, views expressed about potential indirect impacts of biofuels (e.g. indirect land‐use change) suggest that the general public tend to take a consequential view when deciding whether or not they believe biofuels to be a ‘good thing’. Most LCAs carried out today are based on an attributional framework, although some aspects are dealt with using a consequential approach (e.g. indirect land‐use change and system expansion for co‐product treatment). These mixed approaches are a strategy for balancing scientific rigour with practicality.

2.2.2 System boundaries

Setting system boundaries precisely is an important part of an LCA study, since they can have a significant effect on the LCA results. For a biofuel the following aspects should be defined in particular:

• Supply chain: does the LCA include the entire supply chain from seed production through biofuel use in an engine to useful work delivered at the wheel (i.e. well‐to‐wheel analysis), or only a portion of it (e.g. well‐to‐tank)?

• Does the scope include any biofuel, i.e. any bioenergy product made from biomass, or is it restricted to, for example, liquid biofuels or biofuels for transport?

• Are ‘embodied’ emissions, such as the construction and maintenance of farming machinery, the construction of fertiliser plants and the construction and maintenance of roads included in the analysis? Or can they be neglected on the basis that they make an insignificant contribution to the final result?

2.2.3 Understanding the impacts of co‐products

In all biofuel production chains, processes are encountered that produce several products, for example the biofuel product, and one or more co‐products or by‐products4 sold into other markets (all are referred to as co‐products in this document). Interpreting the impact co‐products have on a product lifecycle is one of the major challenges that LCA practitioners face.

There are two main co‐product treatment methods that are based on fundamentally different principles: system expansion and allocation (the latter approach further breaks down into a number of different sub‐categories). They all have their advantages and disadvantages, and are often best suited for certain chains, but not for all. This is one of the reasons why no consensus has yet been reached on a preferred method for treating co‐products. The main co‐product treatment methods are described below.

System expansion (also called substitution). System expansion is the method of co‐product treatment used in consequential LCA. It is based on two key principles (1) that the emissions which occur during and upstream of the processing step that produces the co‐product (e.g. biofuel plant) are almost always the consequence of demand for one of the products produced by the process and therefore should all

4 A co‐product is a product produced along with the main product and that has similar financial revenues as the main product. A by‐product is a product produced along with the main product and that has smaller financial revenues than the main product (RSB, 2008).

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be attributed to that product; and (2) any change in GHG emissions that occur as a results of a co‐product (e.g. waste management, additional processing requirements, displacement of another product) should also be included in the biofuel’s carbon intensity.

Thus, in a system expansion method, all the environmental emissions of the biofuel system are allocated to the main product, and the avoided emissions are then withdrawn from these environmental emissions.

The substitution approach is considered to be scientifically the most rigorous approach, but has limitation in terms of practicability as it often proves to be a very complex and potentially uncertain approach:

• First it should be determined which product (energy, feed, fertiliser, etc.) a given co‐product substitutes for. The more pathways and geographical origins of biofuels are considered, the more difficult it is to ensure that the default substitutions considered truly reflect the industry’s situation.

• Second it should be determined how much GHG emissions can actually be saved with the avoided production of the substituted product (usually done by performing a lifecycle assessment of the substituted product). This calculation step falls outside the system boundaries and can prove extremely difficult to evaluate on an average basis.

Allocation (or partitioning). The principle behind partitioning is to allocate the environmental emissions between the main product and different co‐products through multiplying ratios calculated with some physical or economic property of these products (e.g. mass, energy content, market value).

2.2.4 Greenhouse gas and their global warming potential

Global warming potential (GWP) is a measure of how much 1 kg of a greenhouse gas is estimated to contribute to global warming relative to 1 kg of CO2. This contribution is measured by assessing the global mean ‘radiative forcing5’ of a gas over a specified time period. The choice of time period is subjective, however, there is reasonable consensus within biofuel LCAs that GWPs should be evaluated over a 100 year timeframe. The IPCC has calculated and updated in each of its different assessment reports the GWP of the main GHGs (see Table 2). These GWPs are used in LCA studies to convert CH4 and N2O emissions into CO2‐equivalents.

5 Put simply ‘radiative forcing’ is a measure of the amount heat trapped (more accurately, the net change in irradiance at the tropopause) in the earth’s atmosphere by a gas. See IPCC (2007) for further explanation.

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Table 2 – The three main GHGs and their ‘100 year’ global warming potentials as assessed in different IPCC Assessment reports (source: IPCC, 2007, 2001 and 1995)

CH4 N2O CO2

2nd IPCC Assessment report 21 310 1

3rd IPCC Assessment report 23 296 1

4th IPCC Assessment report 25 298 1

2.2.5 Land‐use change

Land which is currently not used for agricultural production (e.g. forests, native grasslands) is typically a large store of carbon. This carbon is stored in above and below ground biomass of the plants, as well as in the soil and in leaf matter and other residue on top of the soil. Converting this land to agricultural production results in the loss of this significant carbon sink. When considering land‐use change as a contributor to a biofuel’s lifecycle GHG emissions, two different types are usually considered: direct land‐use change (LUC) and indirect land‐use change (iLUC).

Direct land‐use change refers to land being cleared specifically for the purpose of growing biofuels. This land‐use change from one type of ‘use’ (e.g. forest land) to another (biofuel feedstock production) tends to generate large changes in its carbon stock. This effect has been extensively studied over the last few years and default values of GHG emission factors are available. Furthermore, direct land‐use change is relatively easy to trace back.

Indirect land‐use change may occur if new biofuel feedstock crops are grown on existing agricultural land. This is because displacing other crops (e.g. that were being grown for another use such as food or feed) from the land may cause supplies of those crops to decrease, leading to a rise in price of those crops and their substitutes. This would also happen if a portion of existing crops was diverted into biofuel production. In both cases, this may stimulate production elsewhere, for which new land may be brought into production. There is considerable uncertainty over the magnitude of the indirect impact. This will depend on the area and type of land brought into agricultural production globally as an indirect result of the use of land for energy crops. This in turn depends on the potential for improved crop yields on existing and new land. If the new areas brought into production have high carbon stocks (e.g. forests) carbon may be released. These potential CO2 emissions are indirectly caused by the biomass cultivation which displaced the former use, and hence, could be allocated to it. The amount of potential CO2 emissions may be considerable, depending where and how the displacement might occur.

Since indirect land‐use changes occur well outside normal geographic and temporal boundaries of analysis, consequences of iLUC are particularly difficult to assess. Most initiatives that assess the carbon intensity of biofuels have not yet integrated iLUC in their calculations, although the large global warming potential of iLUC has now been recognised.

Detailed discussion of iLUC is outside the scope of this report.

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Discounting of future emissions. Land‐use change does not result in one large instantaneous release of GHG emissions. Many of the emissions (or the foregone sequestration6) occur over a significant period of time that can last for several decades. Figure 1 represents a typical emission profile for land‐use change.

Figure 1 – Land conversion emissions profile (Source: Courtis, 2009). This figure assumes that all above‐ground carbon is released in year 1 due to burning of native vegetation to clear the land for cultivation; the majority of below‐ground release occurs over the first 5 years followed by a much slower release over the next 15 years;

finally, forgone sequestration6 occurs over the entire project period.

Given the scale of the emissions resulting from land‐use change (that can completely off‐set the positive impact of the biofuel produced), it is crucial to determine how land‐use change emissions should be accounted for over time. In particular, the following aspects should be considered:

• Reference land use: in order to assess the differences in carbon stocks between two land uses, a reference land use has to be defined. This could be, for example, the land use immediately prior to conversion of land to biofuel feedstock production, or the land use in a given reference year. The latter approach is typically preferred within biofuel LCAs because it is perceived as plausible to claim that any land‐use change prior to a certain data was not caused by biofuels (since there were not in widespread production).

• Project time horizon: the timeframe of the project (i.e. the number of years during which feedstock for biofuel will be grown on the cleared land) influences the allocation of the land‐use change emissions. Allocating total emissions over a shorter period produces higher values than if they are distributed over a longer time frame.

• Impact time horizon: the period of time or the point in the future at which it is appropriate to compare the relative global warming effects of different fuels. Choosing a short impact horizon (e.g. 20 to 30 years) places an emphasis on achieving early emissions reductions which may be

6 Foregone sequestration is a kind of emissions penalty corresponding to the loss of maturing forests and grasslands and their carbon sequestration capacity as plants grow each year (Butler, 2008).

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appropriate if one assumes that irreversible effects of global climate change may occur if GHG emissions are not reduced quickly.

• Discounting of future emissions: to discount future emissions means to weight the importance of future emissions against emissions happening today. Just as for the allocation problem, many different discounting methods have been proposed to date (California Environmental Protection Agency, 2009). Several calculation approaches are discussed in Annex 1.

2.3 Importance of input data

The data used to carry out an LCA is extremely important because the accuracy of the study is very dependent on the quality of the input data. There are several ways in which data uncertainty can affect the output of an LCA:

• Aggregation level. Data found in LCA databases or default values in biofuel schemes all have a certain degree of aggregation, that is, they represent an average of more detailed data. For example, crop yield or fertiliser input data is rarely used on a field‐by‐field or farm‐by‐farm basis. Instead it is averaged over regions, countries, continents or globally. Inevitably, the use of aggregated data leads to greater uncertainty in the final result.

• Data availability. Some data might not be available at all and need either to be constructed from similar data used in existing database. This can lead to large uncertainties depending on how influential of the data.

• Scientific uncertainty. Some processes that emit GHGs are less well understood than others. A particularly important example for biofuel chains is nitrous oxide (N2O) emissions from agricultural soil. This source of emissions is very complex and hence extremely difficult to capture accurately with models, which makes any calculations uncertain. N2O emissions depends on a range of factors including soil type, daily climate and tillage practices (E4tech 2008) and can vary by more than two orders of magnitude (Joint Research Centre, 2007a).

The following data categories can be distinguished:

• Material input data. This includes all materials (machinery, building materials, chemicals, etc.) that are consumed during the lifecycle of the fuel. Examples include fertilisers and pesticides for agricultural production of biomass and chemicals used in the conversion processes. The information needed is the type of material and the amount consumed during each lifecycle process.

• Energy input data. This category includes any type of energy (fuel, electricity and heat) consumed during the lifecycle of the studied fuel. Examples include diesel input to farming machinery, steam and electricity input to biofuel conversion. The information needed is the type of energy, the amount consumed during each lifecycle process and its origin (e.g. for electricity, is it electricity from the grid or from a natural gas fired CHP). Energy input to transport is excluded from this category.

• Yield. For each process, the yield (or efficiency) of the process is needed (e.g. agricultural yield, conversion efficiency, but also losses during transport and/or storage, etc.).

• Transportation input data. For each transportation step, information on transport distance, transport mode, type of fuel consumed and fuel intensity (fuel used to move one tonne one km) is needed.

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• Emission factors. Emission factors are a special type of input data. Each factor is associated with a material or energy inputs and represents the carbon intensity of that product. Emission factors are thus used to convert the material and energy input into GHG emissions.

• GHG emissions from agricultural soils. GHG emissions (particularly N2O) due to the use of fertilisers on agricultural soils are important sources of GHG emissions for biofuels.

• Tank‐to‐wheel input data. In the tank‐to‐wheel analysis, two specific pieces of data are required: the tailpipe emissions of the fuel (if any) and the efficiency of the vehicle in which the fuel is used.

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3 GHG accounting methodologies in regulatory schemes

3.1 The European Renewable Energy Directive (RED)

3.1.1 Context and objectives of the scheme

Recognising the need to further promote the use of renewable energy, the European Commission (EC) has recently introduced, through the Renewable Energy Directive (RED) a mandatory target for renewable energy of a 20 % share of total energy supply and a 10 % minimum target for the share of biofuels in transport fuel consumption by 2020. The RED, published in June 2009, defines the support mechanisms to the use of renewable energies.

For biofuels7 and bioliquids8 (hereafter referred to simply as biofuels), the RED sets out mandatory sustainability criteria that must be met in order for biofuels to be eligible for government support and to be counted towards the 10% target. Among these criteria, the RED defines minimum GHG emission saving thresholds to be achieved by any biofuel if it is to be eligible for support in the European Union.

Furthermore, the RED describes the methodology to be used for the calculation of these emission savings. The methodology is based on the lifecycle accounting of emissions occurring during the production and use of a fuel. The methodology applies to all biofuels, irrespective of their origin (region, type of feedstock, etc.) or final use (heat, electricity or road transport).

3.1.2 GHG saving thresholds

The RED will require all biofuels supplied in the EU to achieve, initially, a minimum saving of 35 % compared with emissions from fossil fuels. While the RED must be transposed into Member State legislation by December 5th, 2010, in practice the actual implementation date is likely to vary by country.

There is a ‘grandfathering’ clause in the Directive which means that biofuel plants in operation prior to January 23, 2008 will not have to meet the threshold until April 2013. From January 2017, the threshold will increase to 50% for all plants, except those that enter operation after this date, which will face a 60% threshold from the beginning of 2018.

To define these thresholds, the EC took into account both the availability of different biofuel chains likely to contribute to reaching the 10 % biofuel target and the GHG savings that would be achieved by these biofuels. The European Commission found that, with very high saving thresholds (50 % or more in 2010), too few biofuel chains would be eligible and the 10 % biofuel target would not be reachable. With a low saving threshold (10‐15 % in 2010), the impact of GHG emissions would be limited. Thus the 35 % threshold was a trade off between these two scenarios (Commission of the European Communities, 2008).

7 The RED defines biofuels as “liquid or gaseous fuel for transport produced from biomass” (EC, 2009). 8 The RED defines bioliquids as “liquid fuel for energy purposes other than for transport, including electricity and heating and cooling, produced from biomass” (EC, 2009).

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3.1.3 Reporting under the RED scheme

The RED will require economic operators9 to report on their compliance with sustainability criteria, including GHG savings. To enable this report, the EC has published default carbon intensities and GHG emission savings for a list of common biofuel production pathways. Where the default GHG saving from a production pathway lies below the GHG saving threshold set for compliance, producers wishing to demonstrate that their product is nevertheless compliant can do so by carrying out their own GHG saving calculations using actual data from a part or all of their supply chain.

Economic operators will report to their Member State, who will then report to the European Commission. The actual reporting mechanisms are not yet known, as they will be defined by Member States.

3.1.4 GHG saving calculation methodology

Table 3 summarises methodological aspects of the RED carbon intensity calculations, the main points of which are discussed further below. Table 3 – Summary of key aspects of the RED GHG emission calculation methodology for fossil and biofuels (source: EC, 2009)

Methodological aspects Selected approach Additional information / remark

Goal and Scope Definition

Type of LCA Attributional

Fuel chains considered Liquid and gaseous biofuels used for transport and liquid biofuels used for energy purposes other than transport (including electricity and heating and cooling)

Default values have been calculated for common 1st and 2nd generation biofuel chains.

System boundaries Well-to-Wheel Direct emissions from the construction of infrastructure, plants and transportation machinery (truck, ship, planes) are not included.

Fossil fuel reference Latest available average from the fossil part of petrol and diesel consumed in the Community (reported under Directive 98/70/EC).

If not available, the values to be used are:

• For any type of biofuels (no distinction is made between gasoline and diesel substituting fuels): 83.8 g CO2-eq / MJ.

• For bioliquids used for electricity production: 91 g CO2-eq / MJ.

• For bioliquids used for heat production: 77 g CO2-eq / MJ.

• For bioliquids used for cogeneration: 85 g CO2-eq / MJ.

9 The Renewable Energy Directive does not define which industries are included under the term ‘economic operators’ – it is therefore likely that the question of precisely who will be required to report on compliance with sustainability criteria will be left to individual Member State policies.

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GHGs included CO2, CH4 and N2O Conversion to CO2-eq using 100 year global warming potential from the 3rd IPCC report

Unit of carbon intensity g CO2-eq / MJ of fuel

Lifecycle Inventory Analysis – Well to Tank

Allocation method Allocation based on energy content (defined as LHV of co-products) for most co-products.

Wastes (produced by the biofuel chain) and biogenic CO2-eq from fermentation are treated by system expansion. Export electricity is treated by an adapted system expansion procedure.

Direct land-use change Not yet implementable, as a calculation method for carbon stocks of different land use types is still missing.

Discounting of future emissions by annualised emissions over 20 years. Bonus of 29 g CO2-eq / MJ if biomass is produced on degraded land (more information in Annex 2).

Baseline for LUC The reference land use shall be the land use in January 2008 or 20 years before the raw material was obtained, whichever was the later.

ILUC The European Commission is currently establishing their approach to iLUC.

Discussion of iLUC is out of the scope of this report

Lifecycle Inventory Analysis – Tank to Wheel

Emissions from the use of biofuel

The RED does not take into account any GHG emissions from the combustion of biofuels or bioliquids.

Emissions from the use of fossil fuel

Not applicable as the methodology only concerns biofuels. A separate methodology for fossil fuels will be defined under the Fuel Quality Directive.

The carbon intensity of fossil fuels is defined as indicated in a previous row called ‘Fossil fuel reference’.

Energy efficiency of vehicles

The carbon intensity of transport fuels can be adjusted to take into account differences in vehicle energy efficiency for different fuels.

But such adjustments will only be allowed where evidence of the differences is provided. The default assumption is that biofuels have no impact on the energy efficiency of vehicles.

Lifecycle Impact Assessment

Emission savings calculation EF: total emissions from the reference fossil fuel

EB: total emissions from the biofuel or bioliquid

Data

Data aggregation level There is no specific requirement for the level of data aggregation defined within the methodology.

Existing default values are effectively global; however, Member States are required to review consistency with these default values (for agricultural production only) at a NUTS 213 level. In addition, the Commission is considering the feasibility of a similar approach for non-EU feedstocks.

Key input data sources Mainly JEC WTW study (JEC, 2007).

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Type of Lifecycle Assessment. The approach taken by the RED methodology is fairly typical of biofuel LCA. It is based on an attributional framework complemented by some consequential aspects. The treatment of excess electricity from cogeneration (taken into account through a modified system expansion method) and the approach taken when using the agricultural co‐product for biofuel production (agricultural co‐products, if used, are assigned the emissions due to the use of extra fertiliser to replace the missing nitrogen in the soil) are example of these consequential aspects.

The choice of some consequential elements of the methodology appears to be the result of policy makers trying to avoid the creation of perverse incentives, and changes in GHG emissions that fall outside the boundary of the biofuel system, but which can very clearly be ‘blamed’ on the biofuel. For example, the GHG emissions saved by exporting excess electricity from a biofuel plant are clearly attributable to the biofuel (since the biofuel plant would otherwise not exist, and the electricity would not otherwise be generated). However, the European Commission decided to adapt the usual system expansion procedures to avoid creating situations in which biofuel producers could receive a credit for exporting electricity from a cogeneration (‘combined heat and power’) plant that was oversized to meet the needs of the biofuel plant. It should be noted that, if strictly followed, the normal system expansion approach would have avoided creating incentive anyway (see the discussion of determining versus non‐determining co‐products in, for example, E4tech (2008) and Weidema (2003)).

System boundaries. The system boundaries defined by the RED are fairly standard: well‐to‐wheel (WtW) excluding the processes that have negligible contributions to the results (e.g. construction of infrastructure, plants and transportation machinery). For a few specific feedstocks, the fuel chain has been adapted as follows:

• For biofuels produced from wastes (waste wood, used cooking oil) the feedstock production process is not taken into account.

• For agricultural co‐products used as feedstocks, the feedstock production process is also excluded but the consequences of using the co‐product, instead of leaving it on the field, is considered. The only example of this case in the RED is wheat straw, where the extra fertiliser is considered, as using wheat straw for biofuels instead of ploughing it back into the agricultural field required extra N fertiliser input for wheat production.

Treatment of co‐products. The basic co‐product allocation method defined in the RED is that of emission allocation on the basis of energy content (determined by the lower heating value). Exceptions to this allocation method include: excess electricity produced by cogeneration of an agricultural co‐product or of a fossil fuel, wastes and biogenic CO2 produced during fermentation routes to bioethanol and used for industrial applications.

The choice of the European Commission to implement an energy content‐based allocation has been justified on the grounds of practicality:

• The method is geographically and temporally stable, i.e. the energy content of a product does not vary with location or time.

• It is easy and inexpensive to implement: a list of product lower heating values can be published, and the accurate use of these values can be easily verified.

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However, this method has a serious drawback insofar as it does not capture accurately the full GHG impact of a co‐product that is not used in the energy sector. One example out of many is that of the nitrogen fertiliser which is co‐produced during biogas production from manure. N fertiliser will never be used for its energy content, but will substitute for fertilisers. During the biogas generation via fermentation of dry manure, 0.34 g of N fertiliser is produced for each MJ of biogas (values published by the RED10). If the energy content‐based partitioning method is applied, 3 % of the upstream emission is allocated to the N fertiliser11. If a system expansion method is applied, 2 g CO2‐eq can be withdrawn from the upstream emissions, corresponding to 19 % of the upstream emissions.

Direct land‐use change. Annualised emissions from carbon stock changes caused by land‐use change are calculated by dividing total emissions equally over 20 years. The specific guidelines of the RED on calculating the change in carbon stocks due to land‐use change can be found in Annex 2.

The RED includes a CO2 credit of 29 g CO2‐eq / MJ (also called bonus) for the conversion of severely degraded or contaminated land into biomass production. This is an incentive to avoid indirect land use change as well as direct land use change that causes significant carbon stock loss. The bonus may be large enough to encourage conversion of degraded land (RED default values are between 4 and 70 g CO2‐eq / MJ) particularly when the GHG saving threshold increases to 50 % in 2017.

Emissions from the use of biofuels. The RED assumes that the emissions due to burning of biofuels are equivalent to the CO2 which the biofuel took up during growth. Therefore, the CO2 taken up during growth and the emissions from combustion are not considered in this scheme. This is a fairly typical approach in other lifecycle assessments of biofuels.

3.1.5 Input data

Most of the RED default input data is based on the well‐to‐wheel studies conducted by the Joint Research Centre, EUCAR and CONCAWE (referred to as the JEC WTW studies) (Joint Research Centre et al., 2007a). The JEC study, which was first published in 2003, is an extensive analysis of many different (fossil and bio‐) fuel pathways. It has been widely reviewed and is now considered an authoritative source of well‐to‐wheel data.

N2O emissions from agricultural soils. In the JEC WTW studies, N2O emissions from soils are based on the result of detailed analysis using the DeNitrification‐DeComposition (DNDC) model12. The DNDC model is a biogeochemical model which is capable of taking into account the main factors which influence the rate of N2O emission from soils. This approach differs significantly from most biofuel LCAs which typically rely on the so‐called IPCC ‘Tier 1’ approach which simply assumes the rate of N2O emissions is proportional to the rate of nitrogen fertiliser applied.

10 The Institute for Energy at the European Joint Research Centre has published a spreadsheet detailing the relevant input data to calculating default GHG emissions from biofuels according to the RED. This spreadsheet is available at: http://re.jrc.ec.europa.eu/biof/html/input_data_ghg.htm. 11 Using an energy content for N fertiliser of 49 MJ / kg (Fehrenbach, 2008). 12 For more information, please see http://www.dndc.sr.unh.edu/.

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This complex emissions estimate reduces the error margin on this very sensitive parameter to about 30%, mostly from the component of emissions from leached nitrogen, for which the IPCC procedure is used. JEC’s values are probably somewhat higher than those calculated using default IPCC values (depending on fertilizer assumptions). The IPCC procedure assumes that emissions are proportional to the nitrogen fertilizer rate. JEC’s results, on the other hand, indicate that soil type, climate, and ground cover are more important than the fertilizer rate.

For crops grown outside of Europe (i.e. sugar cane and soya bean), soil emissions were calculated from the nitrogen fertiliser additions using IPCC default coefficients (Joint Research Centre et al., 2007b). As the soils model used in the detailed calculations did not include short‐rotation forestry in its crop‐list IPCC default factors were used. This is not likely to be very influential, as soil emissions from short‐rotation forestry are low anyway so that their additional uncertainty stays moderate (Joint Research Centre, 2007a).

3.1.6 Chain default values

Chain default values were published in the RED for a selection of chains (see Annex 2 of this report). This list will be updated as other chains become an important part of the biofuels and bioliquids mix in Europe.

For each chain, the RED calculated two values: a ‘typical value’ and a ‘default value’:

• The typical value is an estimate of the representative GHG emission for a particular biofuel production pathway.

• The default value is calculated by multiplying the typical value for processing emissions by 1.40 to make the default values conservative.

The RED requires Member States to specify which of their regions (based on NUTS 2 regions13) have agricultural practices at least as good as the published default values. Biofuels sourced from outside these regions must prove their compliance with the GHG saving criteria using actual data. The European Commission is exploring the feasibility of a similar approach from non‐EU produced feedstock.

13 NUTS stands for nomenclature of units for territorial statistics. It is a hierarchical classification of administrative boundaries developed by Eurostat. The idea behind NUTS is to provide a common designation for different levels of administrative geographic boundaries across the European Union, regardless of local language and naming conventions. The NUTS levels are defined in terms of minimum and maximum population sizes; NUTS 2 corresponds to populations between 800,000 and 3,000,000 inhabitants.

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Figure 2 – Typical and default carbon intensity of bioethanol for different feedstocks and different production

pathways as calculated by the RED (source: E4tech, based on EC, 2009)

Figure 3 – Typical and default carbon intensity of biodiesel for different feedstocks and different production

pathways as calculated by the RED (source: E4tech, based on EC, 2009)

Figure 2 and Figure 3 compare the typical and default carbon intensities for bioethanol and biodiesel. These figures show that the default values are systematically conservative. However, the lower the contribution of processing emissions to the fuel chain’s carbon intensity, the smaller the difference between typical and default values: the default values are less conservative for the ‘best’ chains. This means that the incentive to improve the GHG performance of biofuels increases with the carbon intensity.

0 20 40 60 80

Sugar beet

Wheat (process fuel not specified)

Wheat (lignite in CHP)

Wheat (natural gas in boiler)

Wheat (natural gas in CHP)

Wheat (straw in CHP)

EU Corn (natural gas in CHP)

Sugar cane

Wheat straw

Waste wood

Farmed wood

Carbon intensity [g CO2eq / MJ bioethanol]

Typical GHG emissions

Default GHG emissions

0 20 40 60 80

Rape seed

Sunflower

Soybean

Palm oil (process not specified)

Palm oil (methane capture at oil mill)

Waste oil

Waste wood (Fischer‐Tropsch)

Farmed wood (Fischer‐Tropsch)

Carbon intensity [g CO2eq / MJ biodiesel]

Typical GHG emissions

Default GHG emissions

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Aggregation level and accuracy. The geographical scope of the RED is quite wide as it includes the 27 Member States of the European Union, and the data aggregation level is very large, which carries a naturally large risk of inaccuracy:

• The default values create an implicit global level of data aggregation. However, the data itself is not usually based on ‘average’ EU data, but on specific supply chains which have been considered to be representative (e.g. the default value for maize bioethanol is based on Romanian maize production). This means, for example, that Brazilian and Pakistani sugar cane ethanol have the same "sugar cane ethanol" default value of 24 g CO2‐eq / MJ. By comparison, these two specific products are considered in the RTFO scheme where they are attributed default value of respectively 24.8 and 115 g CO2‐eq / MJ. This potential for significant uncertainty in carbon intensities is somewhat reduced for EU produced biofuels by the requirement to assess which NUTS 213 regions achieve the same or better performance than the default biofuel chain (see Section 3.1.5 above on input data). A similar approach is under scrutiny by the European Commission for non‐EU countries.

• The scheme does not differentiate between the different EU countries in terms of distribution of the biofuels, i.e. transport distances are the same for all European countries. Since it is not known which biofuels will be consumed in which region, this averaging approach might generate large inaccuracies regarding the impact of transporting the biofuels to the country of consumption.

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3.2 The Californian Low Carbon Fuel Standard (LCFS)

3.2.1 Context and objectives of the scheme

The Californian Low Carbon Fuel Standard (LCFS) is a regulatory scheme with the objective of reducing lifecycle GHG emissions of the transport sector in California by 10 % by 2020. The LCFS is only one of several actions to reduce GHG emissions from the transportation sector towards achieving Governor Schwarzenegger’s long term goal of reducing GHG emissions by 80% by 2050. The LCFS is designed to provide a durable framework that uses market mechanisms to spur the steady introduction of lower carbon fuels.

The LCFS framework establishes performance standards that fuel producers and importers must meet each year beginning in 2011. They must report all fuels supplied and track the fuels’ carbon intensity through a system of “credits” and “deficits”. Credits are generated from fuels with lower carbon intensity than the standard, while deficits result from the use of fuels with a carbon intensity that is higher than the standard. Carbon intensities are calculated on a lifecycle basis using the LCFS reporting methodology.

A regulated party meets its compliance obligation by ensuring that the amount of credits it earns (or otherwise acquires from another party) is equal to, or greater than, the deficits it has incurred. Credits may be banked and traded within the LCFS market to meet obligations. This credit based approach is designed to provide flexibility for the regulated parties, as it offers them several mechanisms by which they can to meet the regulatory requirements.

The LCFS methodology is still under development. In March 2009, the Air Resources Board14 (ARB) published its Proposed Regulation to Implement the Low Carbon Fuel Standard. As of September 2009, this document is still in public review.

3.2.2 Carbon intensity thresholds

The LCFS achieves GHG emission reductions by incrementally reducing the allowable carbon intensity of transportation fuel supplied in California. The scheme does not limit the actual carbon intensity of individual batches or types of fuels, but requires regulated parties to comply with an annual standard for the total amount of fuel they provide.

One standard is established for gasoline and the alternative fuels that can replace it. A second similar standard is set for diesel fuel and its replacements. Each standard is set to achieve an average 10% reduction in the carbon intensity of the state‐wide mix transportation fuels by 2020. It is interesting to note that the reference gasoline fuel is a blend of Gasoline with 10% by volume corn‐derived ethanol (95.85 g CO2‐eq / MJ). However, due to the poor overall carbon performance of corn‐derived ethanol, the carbon intensity of this E10 blend is virtually identical to that of the pure gasoline based on the average crude oil supplied in California (95.86 g CO2‐eq / MJ).

14 The Air Resource Board is California’s public institution responsible for attaining and maintaining air quality and for dealing with air pollution problems.

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An interesting feature of the LCFS scheme is that the compliance schedules are “back‐loaded”; that is, there are more reductions required in the last five years, than in the first five years (see Figure 4). This schedule allows for the development of advanced fuels that are lower in carbon than today’s fuels and the penetration of more efficient advanced vehicle technologies such as plug‐in hybrid electric vehicles, battery electric vehicles, fuel cell vehicles, and flexible fuel vehicles.

Figure 4 – LCFS compliance schedules for gasoline and diesel fuel. Source: California Environmental Protection

Agency (2009a)

Furthermore, the LCFS does not limit the carbon intensity of individual fuels that are supplied to the Californian market. Thus, they rely on market mechanisms to balance out the very worst fuels supplied through the supply of very low carbon fuels.

3.2.3 Reporting under the LCFS scheme

The proposed regulation has several different methods for establishing carbon intensities. With these different methods, no fuel is excluded from the LCFS unless specifically exempted. The first method, referred to as Method 1, establishes default values for a number of specified fuel pathways. Regulated parties may choose to use the default pathways to calculate credits and deficits.

Under specified conditions, regulated parties may also obtain approval from ARB’s Executive Officer to either modify the CA‐GREET model inputs to reflect their specific processes (Method 2A) or to generate a completely new fuel pathway using CA‐GREET (Method 2B). For both Method 2A and 2B, there is a scientific defensibility requirement for the regulated party to meet before the Executive Officer can approve new values. Additionally, for Method 2A, the LCFS requires the modified input values to result in a decrease in the well‐to‐tank carbon intensity of at least 5 g CO2‐eq / MJ.

Although the LCFS reporting approach theoretically offers complete freedom to the regulated parties to calculate the actual carbon intensity of their own biofuels, the lack of modularity of the proposed reporting system might reduce reporting accuracy. Regulated parties have the relatively easy option of using a predefined chain default value (i.e. Method 1, which might not necessarily be an accurate reflection of their production system in all the process steps) or defining their chain using either Method

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2A and 2B, which might be an intensive undertaking given the requirement to get Executive Officer approval. This means that:

• If a regulated parties’ production process is more carbon intensive than that of the default chain, they can simply use the default value. The scheme therefore provides no incentive to improve the production process.

• If a production process is less carbon intensive than that of the default chain, regulated parties may not necessarily wish to go through a potentially significant administrative effort to have their specific chain accepted, unless they are certain to make a significant saving on the default chain.

• As method 2A requires a substantial change in carbon intensities, it removes incentives for incremental improvements in the GHG emissions of the production chain.

3.2.4 Carbon intensity calculation methodology

To assess the direct emissions of transport fuels, the LCFS uses the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model, modified for use in California (CA‐GREET). To assess the emissions from land‐use changes, a global trade model has been specifically developed. The principal characteristics of the LCFS accounting methodology are summarised in Table 4 and further discussed in the following sections. Table 4 – Summary of key aspects of the LCFS GHG emission calculation methodology for fossil fuels and biofuels (Source: California Environmental Protection Agency, 2009a; California Environmental Protection Agency, 2009b)



Type of LCA Attributional A consequential approach is taken for the treatment of co-products and by considering the impact of iLUC

Fuel chain considered All transportation fuels used in California, except liquefied petroleum gas (LPG), racing fuel and fuel used in interstate locomotives, ocean-going vessels, aircraft and military tactical vehicles.

This includes fossil fuels, 1st and 2nd generation liquid biofuels, gaseous fuels (hydrogen, natural gas, SNG, etc.), as well as electricity (for use in electric vehicles).

System boundaries Well-to-wheel Direct emissions from the construction of infrastructure, plants and transportation systems (truck, ship, planes, etc.) are not included. Emissions from seed production for crops grown to produce biofuels are also excluded.

Fossil fuel reference For the diesel standard: ultra low sulphur diesel (94.71 g CO2-eq / MJ )

For the gasoline standard: gasoline with 10% by volume corn-derived ethanol (95.85 g CO2-eq / MJ)

As the LCFS is a program to reduce the overall GHG intensity of the fuel system, the fuel system in place is considered as the reference system. This is why E10 was considered as the gasoline baseline, instead of pure gasoline.

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System boundaries. The system boundaries of the LCFS are typical for a well‐to‐wheel study, excluding processes which have a minor impact on carbon intensities, for example the construction of plant, infrastructure (e.g. roads), and transportation systems (tractor, truck, ship, etc.) and the production of


GHGs included CO2, CH4 and N2O

VOC and CO

Conversion to CO2-eq using global warming potential from the 4th IPCC report

Conversion to CO2-eq using molecular weight ratios



Allocation method Mostly system expansion. Where system expansion was not feasible, allocation based on energy content is used.

Direct LUC Included. The LCFS methodology treats direct and indirect land-use change jointly.

Indirect LUC Included LCFS was the first regulatory scheme to consider iLUC in its calculation.

Discussion of iLUC is out of the scope of this report


Emissions from the use of the biofuel

For biofuels, the net CO2 released from the combustion is considered ‘carbon neutral’ and assigned a value of zero.

When biofuel is blended with fossil fuel, tailpipe emissions are assigned to both the bio- and the fossil fuel based on the energy contributions of each fuel.

Emissions from the use of the fossil fuel

Combustion-related CO2 emissions are calculated based on the carbon content of the fuel. Other tailpipe emissions (CH4 and N2O) are calculated based on the EMFAC and the MOBILE6 models.


The vehicle efficiency is considered to be the same for biofuel and the fossil fuel they substitute.


Emission savings calculation

Scheme based on carbon intensity calculation and not emission savings.

Input data

Data aggregation level Aggregation performed at regional level for US produced biofuel and at country level for biofuels produced outside the US

Input data sources Mainly data from the GREET model, adapted to Californian conditions.

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seeding materials. Figure 5 shows the standard fuel lifecycle, this chain is then adapted for specific chains. In the case of biofuels produced from wastes (used cooking oils, tallow), the feedstock recovery process is excluded.

Figure 5 – Processes included in the system boundaries as defined by the LCFS (source: California Air Resources

Board, 2009a)

Treatment of co‐products. System expansion is the main approach used for the treatment of co‐products, with the aim to achieve a scientifically accurate approach.

At the time of writing, 4 out of the 8 biofuel chains published by the Air Resources Board have used a system expansion method (corn ethanol, sugarcane ethanol, farmed trees to ethanol, forest waste to ethanol). In the soybean to biodiesel, soybean to renewable diesel, waste cooking oil to biodiesel and tallow to renewable diesel chains, an allocation method based on energy content is used.

Emission from the fuel combustion. The last emission component in the well‐to‐wheel pathway is the tank‐to‐wheel emissions resulting from the use of the fuel in an internal combustion engine. Combustion CO2 from biofuels are treated in the typical manner: since the biomass feedstock was produced by ‘capturing’ CO2 from the atmosphere, the net CO2 released from the use of the biofuel is considered ‘carbon neutral’ and assigned a value of zero.

However, if the biofuel is blended with fossil fuel, a proportion of the non CO2‐tailpipe GHG emissions are attributed to the biofuel to account for the fact that CO2‐uptake during biomass growth does not compensate for CH4 or N2O emissions during biofuel combustion. For instance, since ethanol is blended with gasoline for use as California Reformulated Gasoline (CaRFG), tailpipe emissions data from the use of this fuel is used to calculate the GHG impact from the relevant species in tailpipe exhaust. For corn ethanol, a proportional amount is thus attributed based on the energy contributions of ethanol in CaRFG.

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These emissions are unlikely to cause a significant difference in the overall lifecycle GHG emissions of biofuels, as they represent less than 1 % of the total GHG emissions (which is negligible compared to the level of uncertainty of the results). However, as tailpipe emissions (CO2 and non‐CO2) of fossil fuels are taken into account, the non‐CO2 combustion‐related emissions of biofuels should also be taken into account.

Adjustment for vehicle efficiencies. The carbon intensities of certain fuels need to be adjusted to account for lower (or higher) end use efficiencies for these fuels relative to the baseline fuels. This is captured by using an Energy Economy Ratio (EER). The EER is defined as the ratio of the distance traveled per unit energy input for a fuel of interest to the distance traveled per unit energy for a reference fuel. Each EER is specific to one fuel‐vehicle combination. The EER for biofuel is however always 1, meaning that the carbon intensity of the biofuel is no impacted by the vehicle efficiency.

Land‐use change. The LCFS will be the first regulatory scheme to consider both direct and indirect land‐use change. To assess the emissions from LUC, the LCFS uses a global trade model to estimate the GHG emissions impact. In general, the model evaluates the worldwide land use conversion associated with the production of crops for fuel production. Different types of land use have different rates of storing carbon. Multiplying the changes in land use by an emission factor for each type of land results in an estimate of the GHG emissions impacts of land use change.

However, direct and indirect land‐use change impacts are not differentiated, but treated within a global approach and they cannot be disaggregated. Discussion of indirect land use change falls outside the scope of this report.

3.2.5 Input data

The input data for calculating the direct impact of the fuel chains are taken directly from the GREET model and adapted to match Californian practice (CA‐GREET). GREET uses input data regarding energy use, material inputs and their resulting GHG emissions that are typical data (i.e. average data) taken from published authoritative sources (e.g. US Department for Agriculture), or specifically generated by conducting surveys of typical practices (e.g. farming practices for US corn production) wherever relevant published data is not available.

Agricultural soil emissions. Of particular significance are the emissions from agricultural soils which are considered in the following way:

• The LCFS uses the IPCC average N2O emissions estimates based on nitrogen fertiliser application.

• The LCFS also considers CO2 emissions from agricultural soils due to the use of lime. These emissions are calculated based on the amount of carbonate in the applied lime (California Environmental Protection Agency, 2009a).

Averaging for emissions from soils is mostly performed at a country level, except for specific US corn production data that are aggregated at regional level.

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Specific default carbon intensities have been calculated for a number of different biofuel chains. Multiple pathways are being developed that represent differences in how and where the fuel is produced. The data aggregation is performed at country level, except for US corn which is differentiated on a regional basis.

Although the number of default values for biofuel chains that have been calculated to date is very limited (see Figure 6), the LCFS is continuing to develop carbon intensity values for additional pathways and the proposed regulation itself provides for a public process to modify or add other pathways (see Section 3.2.3).

Figure 6 – Proposed default carbon intensities for the different fossil and biofuels calculated by the LCFS,

including land‐use change (see Table 23 and Table 24 in Annex 3 for the precise carbon intensity values) (source: E4tech, based on California Air Resources Board, 2009a)

0 50 100 150

Gasoline

Ethanol from corn

Ethanol from sugarcane

Diesel

Biodiesel from soy

Biodiesel from used cooking oil

Carbon intensity [g CO2eq / MJ fuel]

Min

Variation

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3.3 The United Kingdom Renewable Transport Fuel Obligation (RTFO)

3.3.1 Context and objective of the scheme

In the context of the UK’s Kyoto Protocol commitment to reduce CO2 emissions by 12.5 % as compared to 1990 levels, and the UK government’s own targets from the 2003 Energy White Paper of a 20 % reduction in CO2 emissions by 2010 and 60% carbon saving by 2050, the UK government has introduced the Renewable Transport Fuels Obligation (RTFO) in 2008. The RTFO is a regulation that requires suppliers of fossil fuels to ensure that a certain percentage (2.5 % in 2008/2009) of the road transport fuels they supply in the UK market is renewable fuels.

Under the RTFO, the fuel suppliers are required to report the volume of biofuels supplied together with their carbon intensities (as well as information about the fuel’s compliance with the sustainability meta‐standard). This is done using the calculation methodology and a Carbon Calculator reporting tool that have been developed by the RTFO.


The RTFO scheme has been in force since in April 2008. No compliance threshold has been set for the GHG saving that different biofuels must meet, however, the Government did set (non‐binding) indicative targets for obligated parties (40 % annual GHG saving of fuel supplied in 2008/09).

3.3.3 Reporting under the RTFO scheme

Carbon intensities are calculated on a lifecycle basis. Default values for some 150 biofuel chains (based on different fuels, feedstocks and countries of origin) have been calculated to be used by the regulated parties for reporting on the carbon intensity of the biofuels they supply on the UK market. In the case of new biofuels or new feedstocks or production pathways for existing biofuels being introduced into the UK market on a significant scale, the RFA develops new fuel chain and default values for these fuels.

The default values calculated in the RTFO are conservative, but fuel suppliers can provide additional qualitative or quantitative data to improve the accuracy of the calculation and thus make the default values less conservative. This is illustrated in Figure 7. High level default values (where little is known about the origin of the biofuel) represent conservative GHG savings; but typical default factors (where the calculation includes more detailed information) are less conservative in order to encourage the supply of information. Table 5 provides an example of this approach. Table 5 – RTFO conservative default value for various bioethanol chains

Country Feedstock Type of default value Example

Unknown Unknown Fuel default value Default value of bioethanol: 115 g CO2-eq / MJ

Unknown Known Feedstock default value Default value of bioethanol produced from corn: 108 g CO2-eq / MJ

Known Known Feedstock & origin default value

Default value of bioethanol produced from French corn: 49 g CO2-eq / MJ

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This flexible calculation method provides a practical, cost‐effective and credible reporting system. Suppliers are also required to report on the type of information used in their calculations through reporting the accuracy levels 0 to 5 as illustrated in Figure 7.

Figure 7 – Hierarchy of default values under the RTFO scheme (source: RFA, 2009b)

The RFA has noted the usefulness of the hierarchical approach to default values: information is sometimes unavailable on the feedstock and/or origin of the fuel supplied. However, the RFA has seen an increase in the feedstock and origin (i.e. level 2 on Figure 7) default values used, showing that the industry is adapting to the legislation and has set up the procedures to collect simple information about its products. Finally, there has been a limited amount of actual data (level 5 on Figure 7) reported, probably due to a lack of incentive under the RTFO to carry out more detailed data collection.

Reporting tool. The actual reporting is carried out using a software tool called the Carbon Calculator15 which can help fuel supplier prepare monthly reports to the RFA. The RTFO Carbon Calculator is a stand‐alone software programme which is pre‐loaded with all of the default values set in the RTFO (for all of the levels shown in the diagram above). The user chooses the type of fuel supplied, the feedstock and the country of origin in a user‐friendly interface. The default values for the chain can then be replaced with any known actual values (e.g. natural gas consumption at a biodiesel plant) and information about land use change and broader sustainability criteria can be reported. He can also easily change some input data values into actual values (if known) and report on land‐use change (if known).

It should be noted that the RTFO Carbon Calculator is currently being updated (changing input values and modifying methodological choices) to meet the requirements of the recently published European

15 The Carbon Calculator tool can be downloaded at http://www.renewablefuelsagency.org/carboncalculator.cfm.

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Renewable Energy Directive. The revised version of the software will be available for the start of the 2010/11 RTFO year in April 2010.


The GHG accounting methodology of the RTFO only applies to biofuels. The carbon intensities of fossil fuels were slightly adapted from the JEC well‐to‐wheel study (JEC, 2007). Table 6 presents the methodological aspects of the RTFO biofuels carbon intensity calculations, the main points are further detailed below.

Table 6 – Summary of key aspects of the RTFO GHG emission calculation methodology for fossil fuels and biofuels (source: E4tech, 2008; RFA, 2009b)



Type of LCA Mainly attributional Consequential approach taken for the treatment of co-product by system expansion

Fuel chain considered All biofuel for transport This includes: bioethanol and ETBE, FAME biodiesel, hydrotreated vegetable oil, biogas and pure plant oil.

System boundaries Well-to-wheel Direct emissions from the construction of infrastructure, plants and transportation machinery (truck, ship, planes) are not included. Emissions from seed production for crops grown to produce biofuels are also excluded.

Emissions occurring after the refinery are also excluded.

Fossil fuel reference Gasoline: 84.8 g CO2-eq / MJ

Diesel: 86.4 g CO2-eq / MJ

GHGs included CO2, CH4 and N2O Conversion into CO2-eq using global warming potentials from the 3rd IPCC report



Allocation method System expansion is the preferred method

When system expansion, not feasible, allocation based on market value is applied

Direct LUC Included within boundaries of methodology, but not included in default values

If information is available, fuel suppliers are legally required to integrate land-use change into the carbon intensity calculations.

Baseline for LUC Use of the land on 30 November 2005

Indirect LUC Not included



Biofuels are assumed to be carbon neutral.

The GHG emissions release during the combustion of biofuels are off-set by the CO2 uptake during the biomass growth

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System boundaries. The RTFO methodology description (E4tech, 2008) sets out a procedure to define biofuel chains, including where to start, what processes should be included, and the end point of the chains. Figure 8 presents a typical chain.

A characteristic peculiar to the RTFO is that the end point of the chains is the ‘duty‐point,’ i.e. the point at which the fuel will be required to pay UK excise duty as a road fuel (typically the refinery ‘gate’). In practice, this means that the final step in the chain is a fuel transport and storage step. The duty‐point was chosen as the end point of the system boundary because that is when the fuel supplier reports the required information to the RFA.

However, it was estimated that the processes undergone by a biofuel after the duty point (e.g. transport, filling station) would be roughly the same as for the fossil fuel that it displaces. For fossil fuels, the exclusion of the process after the duty‐point means that the JEC fossil fuel reference values have been lowered the carbon intensities by approximately 1 g CO2‐eq / MJ.



The combustion-related CO2, CH4 and N2O emissions for fossil fuels were taken from the JEC well-to-wheel study

JEC used the vehicle simulation tool ADVISOR, developed by NREL

Energy efficiency of vehicle

Considered to be the same whether the vehicle runs on neat fossil fuel or on a fossil fuel / biofuel blend


Emission savings calculation 100 CIbf: carbon intensity of the biofuel

CIff: carbon intensity of the fossil fuel displace by the biofuel

Data

Data aggregation level Country-specific default carbon intensities Possibility of calculating more accurate (less aggregated) carbon intensities

Input data sources Various sources including FAO, JEC, etc.

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Figure 8 – Biofuel lifecycle steps (in blue) and example of the oil seed rape to biodiesel chain (in white) (source:

RFA, 2009b)

Treatment of co‐products. In order to represent as accurately as possible the GHG impacts of co‐products, the preferred co‐product treatment method in the RTFO methodology is system expansion.

However, as the RTFO addresses many different chains, in some cases, it was not possible to identify what product is substituted by a particular co‐product, while in other situations it was very difficult to assess the carbon intensity of the substituted product. In these cases, allocation based on market values was used as a fall back option. This allocation method was chosen because it was deemed to be the closest to system expansion in principle (as it incorporates some market mechanisms through the price of the co‐products).

The market values used for the allocation procedure are based on three year rolling averages (updated annually). Furthermore, within one conversion plant, if allocation based on market value is required for one of the co‐product, it must be used for all co‐products, including energy co‐products (E4tech, 2007).

Direct land‐use change. Fuel chain default values do not include direct land use change. This is because the systems providing assurance on the provenance of fuels are in the very early stages of development, and it is felt as an overly conservative approach to apply an assumed land‐use change carbon impact ‘penalty’. However, when information on previous land use can be supplied, there are default values for assessing the input of land use change..

There is no incentive for fuel suppliers to report or source information on the previous use of the land. However, a fuel supplier would commit a criminal offence by not reporting known information.

Fuels suppliers who report on land‐use changes are asked to report on how the land used to produce a biofuel was being used in 30 November 2005 – based on four categories: cropland, forest land, grassland with agricultural use and grassland without agricultural use (definitions of the categories can be found in Annex 4) (E4tech, 2008). The IPCC “Tier 1” methodology (IPCC, 2006) is then used to calculate the changes in carbon stocks in biomass, dead organic matter and soils. This carbon stock

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change is then annualised over a 20‐year period to provide a land‐use carbon intensity estimate added to the fuel chain carbon intensity.

Emission from the fuel combustion. Biofuels are not assigned any emissions during the combustion step in the RTFO, as the CO2 released during combustion is considered to be off‐set by the CO2 taken‐up during the feedstock’s growth. In contrast, emissions of CO2, CH4 and N2O due to the combustion of fossil fuels are taken into account in the carbon intensity calculations.

Adjustment for the vehicle efficiency. The model is built so that it can, in theory, account for the vehicle efficiency. However, in the absence of evidence to the contrary, the efficiency of a vehicle running on neat fossil fuels and the efficiency of a vehicle running on a blend of fossil and biofuel are assumed to be the same in the RTFO calculations.

3.3.5 Input data

A detailed list of input data sources are provided in (RFA, 2009b). Unlike the schemes, that started from a comprehensive set of data (GREET or JEC), input data for the RTFO was collected through a bottom up approach. Thus, many different data sources were used, including statistical information from international organisations (e.g. FAO on yield and fertiliser input), other LCA studies on biofuels (e.g. JEC), etc.

Agricultural soil emissions. The “tier 1” approach developed by the IPCC (2006) that links N2O emissions to the amount of nitrogen fertiliser used was adopted by the RTFO. This method was chosen because it is simple to use, does not require large amounts of data collection and it makes N2O emissions proportional to the driving factor which can most practically be influenced by the biofuel industry (i.e. amount of synthetic nitrogen fertiliser application) (E4tech, 2008).


As detailed in Section 3.3.3, conservative default values are calculated for each biofuel type based on the knowledge the fuel supplier has of its supply chain (especially about the feedstock and its country of origin). The process for setting these higher‐level default values is as follows (E4tech, 2008):

• Feedstock and origin default values were set using single default values and default fuel chains.

• Feedstock default values were set equal to the feedstock and origin default value from the country which has the highest carbon intensity (provided the fuel from this feedstock and origin is likely to make up 5% or more of the UK biofuels market).

• Fuel default values were set equal to the feedstock default value from the feedstock which has the highest carbon intensity (again provided the fuel from this feedstock and origin is likely to make up 5% or more of the UK biofuels market).

Figure 9 and Figure 10 show the feedstock and origin default values of bioethanol and biodiesel respectively as calculated under the RTFO. These figures show wide differences between default carbon intensities of chain using the same feedstock but from different country of origin. Several reasons explain these differences: the transport distances from the country of origin to the UK are different;

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production processes between different countries can also be different; and finally emission factors of the energy used for biofuels production (electricity, natural gas, coal, etc.) vary between countries.

Figure 9 – Feedstock and origin default carbon intensities of bioethanol as calculated under the RTFO (source:

E4tech, based on RFA, 2009b)

0 20 40 60 80 100 120 140

CanadaFrance

GermanyHungary

SpainUkraine

United KingdomSpain

UKBrazil

MozambiquePakistan

South AfricaPakistan

South AfricaUK

FranceUSA

Sweden

Carbon intensities of bioethanol [g CO2eq / MJ bioethanol]

Wheat

Barley

Sugar beet

Sugar cane

Molasses

Corn

Sulphite liquor

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Figure 10 – Feedstock and origin default carbon intensities of Fatty‐Acid Methyl Ester (FAME) biodiesel as

calculated under the RTFO (source: E4tech, based on RFA, 2009b)

0 20 40 60 80 100

AustraliaCanadaFinlandFrance

GermanyPolandUkraine

United KingdomUnited States

ArgentinaBrazil

CanadaSpainUSA

IndonesiaMalaysia

United KingdomDenmark

United KingdomUnited States

ArgentinaChinaFranceRussian …Ukraine

United StatesIndia

PhilippinesIndonesia

IndiaUSA

Carbon intensity of biodiesel [g CO2eq / MJ biodiesel]

Oilseed rape

Soy

Palm

Used cooking oil

Tallow

Sunflower Oil

Coconut (ME)

Jatropha (ME)

Corn oil (ME)

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3.4 The U.S. 2009 Renewable Fuel Standard (RFS2) programme

3.4.1 Context and objective of the scheme

In 2007, the United States’ Energy Independence and Security Act (EISA) was passed. The EISA establishes new volume obligations for four categories of renewable fuels (cellulosic biofuel, biomass‐based diesel, advanced biofuel, and total renewable fuel16) that must be used in transportation fuel each year in the United States. The EISA also includes new definitions and criteria for both renewable fuels and the feedstocks used to produce them, including new GHG emission thresholds for renewable fuels, as compared to those of average petroleum fuels used in 2005.

Under the EISA, the U.S. Environmental Protection Agency (EPA) is required to promulgate regulations implementing changes to the National Renewable Fuel Standard program (commonly known as the RFS2 program) which will go into effect on January 1, 2010. As part of this, the EPA analysed lifecycle GHG emissions savings from increased renewable fuels use. The regulatory purpose of the lifecycle GHG emissions analysis is to determine whether renewable fuels meet the GHG thresholds for the different categories of renewable fuel as defined under the EISA (EPA, 2009b).

The EPA has carried out the lifecycle analysis of a range of biofuels currently expected to contribute significantly to meeting the volume mandates of EISA through to 2022. The EPA is now seeking peer reviews of the methodology and input data used, so as to assure the most robust assessment of lifecycle GHG performance for the final rule. The methodology, input data and chain carbon intensities considered in this report are thus only preliminary propositions from the EPA, and may change in the final rule (EPA, 2009b).


The RFS2 has defined specific GHG emission thresholds for each of four types of renewable fuels (cellulosic biofuel, biomass‐based diesel, advanced biofuel and total renewable fuel), requiring a percentage improvement compared to a baseline set by fossil gasoline and diesel. The EISA required (EPA, 2009b):

• 20 % reduction in lifecycle GHG emissions for any renewable fuel produced at new facilities (those constructed after enactment)17;

• 50 % reduction in order to be classified as biomass‐based diesel or advanced biofuel;

• 60 % reduction in order to be classified as cellulosic biofuel.

16 EISA defines as follows these four categories: (1) cellulosic biofuel = domestically produced cellulosic ethanol; (2) Biomass‐based diesel = a majority of fatty‐acid methyl ester (FAME) biodiesel and a smaller portion of non‐co‐processed renewable diesel; (3) advanced biofuel = imported (sugarcane) ethanol + a smaller amount from co‐processed renewable diesel; (4) total renewable fuel volume = corn ethanol. 17 Biofuels produced at facilities that were constructed before enactment of the RFS2 programme automatically qualifies for the ‘renewable fuel’ category following the grandfathering provision of the programme.

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The RFS thresholds do not, as such, constitute compliance levels for transportation biofuels. In this sense, the scheme does not exclude certain biofuels from being supplied in the US if they do not meet the GHG performance set by the thresholds.

However, there is a global requirement that the volume mandates be met through the use of renewable fuels that meet the given thresholds (EPA, 2009b). In order for renewable fuels to qualify, they must meet or exceed these minimum GHG reduction thresholds (EPA, 2009a).

3.4.3 Reporting under the RFS2 scheme

Under the RFS2 program, each U.S. transport fuel producers and importers (referred to as obligated parties) will be required each year to provide a certain volume of each biofuel category. The biofuels are identified through their associated Renewable Identification Number (RIN).

RINs are generated by biofuel producers and importers and are transferred along with renewable fuel through the distribution system. RINs have a valid life of 2 years and can be traded among obligated parties. Obligated parties have the responsibility of acquiring sufficient RINs each year to meet their renewable fuel obligations.


The GHG calculation methodology has been developed by the U.S. EPA. Table 7 provides an overview of this methodology, while key aspects are further discussed in the next paragraphs.

Table 7 – Summary of key aspects of the EPA GHG emission calculation methodology for fossil fuels and biofuels (source: EPA, 2009c)



Type of LCA Partly consequential, partly attributional Attribution approach for:

• GHG emissions from fossil fuels;

• GHG emissions from the conversion of agricultural products into biofuel.

Consequential approach for:

• GHG emissions from agricultural production of biomass;

• Treatment of co-products.

Fuel chain considered All liquid transportation fuels produced from biomass

This includes fuel intended for use in road vehicles and non-road vehicles such as locomotives and marine engines and vessels.

System boundaries Well-to-wheel Direct emissions from the construction of infrastructure, plants and transportation machinery (truck, ship, planes) are not included.

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Fossil fuel reference Gasoline in 2005: 98 395 g CO2-eq / mmBtu

Low sulphur diesel in 2005: 96 843 g CO2-eq / mmBtu

Gasoline in 2005 (unit conversion): 93.26 g CO2-eq / MJ

Diesel in 2005 (unit conversion): 91.79 g CO2-eq / MJ

GHGs included CO2, CH4 and N2O Conversion into CO2-eq using global warming potentials from the 2nd IPCC assessment report

Unit of carbon intensity g CO2-eq / mmBtu


Allocation method System expansion

Direct LUC Included The EPA methodology does not differentiate between direct and indirect land-use change.

Indirect LUC Included Discussion of iLUC is out of the scope of this report



CO2 emissions from biomass-based fuels combustion are not included in their lifecycle emissions results.

Combustion related CH4 and N2O emissions for biofuels are based on EPA MOVES model results.


CO2 emissions from combustion of fossil fuels are estimated based on their carbon content.

Combustion related CH4 and N2O emissions for fossil fuels are based on EPA MOVES model results.


Considered to be the same whether the vehicle runs on neat fossil fuel or on a fossil fuel / biofuel blend


Emission savings calculation 100 Change: percent change from 2005 petroleum

baseline

Embiof: net present value of lifecycle GHG emissions per million British thermal unit of biofuel

Emff: net present value of lifecycle GHG emissions per million British thermal unit of fossil fuel

Data

Data aggregation level Country-level aggregation for most data used in the direct emissions analysis

The agricultural models distinguish crop production by region in the US

Input data sources GREET GREET was also used to perform some of the calculations (see Figure 11).

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Type of LCA. To quantify the lifecycle GHG emissions associated with the increase in renewable fuel mandated by EISA, the US EPA compared the differences in total GHG emissions between two future scenarios:

• The ‘business as usual scenario’ estimates volume of different renewable fuels based on the likely fuel pool in 2022 without EISA, as predicted by the Energy Information Agency’s Annual Energy Outlook for 2007.

• The ‘EISA volumes scenario’ which considers the volumes of renewable fuels as mandated by EISA for 2022.

The EPA approach does not calculate the carbon intensity for each gallon of biofuel based upon its unique production characteristics. Rather, it determines the overall aggregate impacts across sections of the economy in response to a given volume change in the amount of biofuel produced. In the case of agricultural impacts, the impact on the entire U.S. agricultural system that would result from expanded demand for biofuel feedstock was assessed. Those impacts were then normalized by dividing total impacts over the renewable fuel volume change between the business as usual case and the EISA volumes.

Similarly, the typical emissions impact of a type of biofuel production facility (e.g., a plant that uses the dry mill process to turn corn starch into ethanol) were estimated. The emissions assessment from a typical facility was then ascribed to all biofuel produced across facilities using that same basic technology.

The EPA methodology does not take a purely attributional or consequential approach. The GHG emissions of fossil fuels were estimated using an attributional methodology. But for biofuels, the methodology is more complex. The agricultural production stage and the impact of co‐products were assessed using global market models that take into account far reaching consequences of an increase in biofuel production. This is a typical consequential approach. For other lifecycle stage however, such as the conversion process, a more classical attributional approach was followed.

There are several important implications associated with this methodology:

• First, this methodology does not distinguish the emission performance between biofuel production plants using the same basic production technology and type of feedstock.

• Second, the GHG impacts per gallon of biofuel are calculated for the specific production volumes defined by EISA. These impacts could prove widely different if the real volumes happen to be very different from those considered in the study. To mitigate this risk, the EPA plans to revisit its targeted volume on a regular basis in case mandated volumes cannot be reached or get largely exceeded.

• Third, by focusing on 2022, this analysis does not track how biofuel GHG emission performance may change over time between now and 2022.

• Finally, several of the lifecycle emission impacts for one fuel are interrelated with those of another fuel, in particular the land‐use changes. This makes it not possible to differentiate the contribution of the land‐use change to one fuel vs. another.

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System Boundaries. The EPA has used many different models to assess different parts of the fuel chains and their direct and indirect impacts on GHG emissions. Figure 11 summarises the different models and how they are linked to each other.

Nevertheless, some parts of the lifecycle of the fuels have specifically excluded from the system boundaries. These are the infrastructure related GHG emissions (e.g. the energy needed to manufacture the tractor used on the farm) and the facility construction‐related emissions (e.g. steel or concrete needed to construct a refinery) (EPA, 2009c).

Figure 11 – System boundaries, models and data sources used by the EPA calculation methodology (source: EPA,

2009d)

Emissions from fuel combustion. CO2 emissions from biofuels combustion are not included in their lifecycle emissions results as these are considered carbon neutral (EPA, 2009d). However, the combustion of biofuels is considered to result in net additions of CH4 and N2O to the atmosphere (EPA, 2009d). On the other hand, CO2, CH4 and N2O emissions are all taken into account when calculating the tailpipe emissions of fossil fuels.

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Combustion‐related CO2 emissions are based on the carbon content of the fossil fuel. Combustion related CH4 and N2O emissions for both biofuels and fossil fuels are based on the EPA‐developed Motor Vehicle Emission Simulator (MOVES) (EPA, 2009d).

The EPA is the only scheme that systematically includes CH4 and N2O emissions of biofuel combustion. However, these emissions are unlikely to cause a significant difference in the overall lifecycle GHG emissions of biofuels, as they represent less than 1 % of the total GHG emissions.

Adjustment for the vehicle efficiency. The EPA methodology assumes that vehicle energy efficiency will not be affected by the presence of renewable fuels (i.e. efficiency of combusting one MJ of bioethanol is equal to the efficiency of combusting one MJ of gasoline). Therefore, every MJ of renewable fuel produced is directly compared to one MJ of the fossil fuel that it displaces.

Some studies have shown that, because of the increased octane content of bioethanol (and particularly E85), vehicle efficiency may be slightly improved with use of E85 vs. gasoline. This would imply that one MJ of ethanol would actually displace slightly more than one MJ of gasoline because of improved engine efficiency. However, these studies have not been considered conclusive enough for the EPA to include in their analysis at this point. This is a point that will be considered for the final rule and may add a sensitive issue.

Treatment of co‐products. Co‐product treatment is mainly carried out through system expansion (EPA, 2009d). Especially for co‐products influencing the animal feed market and the energy market, the EPA was modelling these global markets, and took into account the possibility of a market saturation by the co‐products. The EPA is the first scheme to consider a market saturation when applying the system expansion method.

However, some co‐products (e.g. from refineries) were taken into account through an energy content‐based allocation method.

Direct land‐use change. Land‐use change impacts are included in the carbon intensity calculations performed by the EPA. However, direct and indirect land‐use change impacts were not differentiated, but treated within a global approach and cannot be disaggregated. The methodology is described under the indirect land‐use change report and thus falls outside the scope of this report.

Two discounting methods are proposed by the EPA to account for GHG emissions due to biofuel‐induced land‐use change. One option assumes a 30‐year time period for assessing future GHG emissions impacts and values equally all emission impacts, regardless of time of emission (30‐year annualised method). The second option assesses emissions impacts over a 100‐year time period and discounts future emissions at 2 % annually (EPA, 2009b).

3.4.5 Input data

To quantify the emission factors associated with different steps of the production and use of various fuels (e.g. extraction of petroleum products, transport of feedstocks, production and transport of agricultural sector), the analysis tool GREET model was used. GREET has been under development for several years and has undergone extensive peer review and multiple updates. Of the available sources

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of information on lifecycle GHG emissions of fossil energy consumed, the EPA believes that GREET offers the most comprehensive treatment of emissions from the covered sources (EPA, 2009c).

To estimate the GHG emissions associated with renewable fuel production, a detailed ASPEN‐based process models developed by USDA and DOE’s National Renewable Energy Laboratory (NREL) was used.

N2O emissions from agricultural soils. For N2O emissions from U.S. soils, the EPA used the CENTURY and DAYCENT models, developed by Colorado State University. The DAYCENT model simulates plant‐soil systems and is capable of simulating detailed daily soil water and temperature dynamics and trace gas fluxes (CH4, N2O, NOX and N2O). The CENTURY model is a generalized plant‐soil ecosystem model that simulates plant production, soil carbon dynamics, soil nutrient dynamics, and soil water and temperature (EPA, 2009c). For N2O emissions outside the U.S., however, the IPCC approach was used.

The EPA concluded that the CENTURY and DAYCENT models are compatible with the IPCC, but more accurate, especially on a regional scale. At a national scale, the use of the CENTURY and DAYCENT models is not expected to have big influence on the results of EPA’s GHG calculations.


Figure 12 and Figure 13 show how EPA’s lifecycle GHG emission estimates vary by lifecycle stage for each biofuel analysed for two different discounting methods.

The EPA results indicate that emissions produced during the fuel production stage can vary significantly for corn ethanol depending on the type of facility used to convert corn into ethanol. Clearly the choice of fuel production technology can be used as a measure to reduce the climate impact of corn ethanol production.

Conversion of cellulosic feedstock (e.g. corn stover or switchgrass) to ethanol creates a net sequestration of carbon during the fuel production stage. Ethanol is fermented with the cellulosic portion of the biomass, while process energy is generated through the unfermentable portion (mainly lignin) of incoming biomass. Based on NREL estimates, the process is assumed to generate excess electricity. Biomass fired electricity generation reduces GHG emissions by offsetting other forms of electricity production.

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Figure 12 – Lifecycle GHG results (including indirect emissions) using a 100‐year net present value discounting

method with a 2 % discount rate (source: EPA, 2009d)

Figure 13 – Lifecycle GHG results (including indirect emissions) using a 30‐year annualisation method (source: EPA, 2009d)

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4 Relevant aspects of non‐regulatory initiatives

Some relevant learning may also be gained from studying non‐regulatory initiatives, regarding the way GHG emissions are considered. Of particular interest are those approaches by the Global Bioenergy Partnership (GBEP) and by the Roundtable on Sustainable Palm Oil (RSPO), as well as various academic studies.

4.1 GBEP

Context. The GBEP has recognized that the biofuels community utilizes a wide range of LCA methodologies and data analysis techniques that all have their own merits and limitations. While there is considerable overlap in guiding principles and in some methodologies, the GBEP has come to the conclusion that the diversity of bioenergy production systems and implemented methodological approaches preclude the possibility of applying a single methodology to all bioenergy systems, world‐wide.

Recognizing this fact, GEBP determined that its most useful contribution to biofuels LCA would be to provide a common framework for LCA reporting, rather than developing a common methodology.

GBEP approach. The GBEP has therefore developed a framework that allows for comparison of existing LCA employed by independent scientists, industrial groups, and technical agencies, and provides a reference for the development of future analyses. The framework is intended to provide a template for LCA that is transparent and that can be applied to a wide range of bioenergy systems. It does not set data standards and does not specify particular emissions models.

The goal of the framework is to ensure that countries and organizations can evaluate GHG emissions associated with bioenergy in a consistent manner, using methods appropriate to their circumstances, conditions and systems of production.

GBEP tool. The version zero of the framework consists of 10 step questionnaire that guides the user through the ideal characteristics of a full LCA appropriate for bioenergy production and use, including emissions due to land‐use change, biomass feedstock production, co‐products treatment, transport of biomass, processing into fuel, transport of fuel, fuel use and replacement.

The methodological framework is intended to be a practical product for the end user. For this reason it strikes a balance between inclusive detail and ease of application. The downside to this flexibility is that the framework is not, in itself, an LCA model. It rather is a sort of flexible “checklist” intended to provide a list of pertinent questions for countries and institutions to compare the various existing methodologies dedicated to assessing GHG emissions of bioenergy systems in a transparent way.

Possible applications of the GBEP tool. GBEP expects its framework to have many potential applications. It is claimed that the framework could be used by governments that have implemented GHG emissions standards for biofuels, in order to present their methods in a manner that is transparent and intelligible to all stakeholders. The framework can also be applied by biofuels producers and manufacturers of products that use biofuels in order to support claims of GHG reductions relative to fossil fuels. Non‐

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government organizations can also make use of the framework to evaluate GHG reductions included in their voluntary sustainability analyses of biofuels.

However, in its current status of development, the GBEP framework is only a questionnaire. In this sense, it seems to be lacking a final step that draws conclusions about the most appropriate methodological characteristics that would turn it into a more useful resource.

There are no specific lessons that the RSB could learn from the GBEP appraoch, nor any conclusions to be drawn regarding the development of its accounting methodology.

4.2 Roundtable on Sustainable Palm Oil (RSPO)

The Roundtable on Sustainable Palm Oil (RSPO) has defined a set of principles and criteria that, if applied, should guarantee sustainable production of palm oil.

When the RSPO Principles & Criteria were first agreed in 2005, GHG emissions from palm oil production were not a subject of particular attention within RSPO and as a result, there is no GHG saving requirement in the current version of their standards. The RSPO has nevertheless recognized that sustainability of palm oil production can only be claimed when explicit consideration has been given to GHG emissions. In this regard, the RSPO is currently working on amending its standards.

Therefore, there are no useful lessons to be learnt or information to be exploited from the RSPO in the context of this present project.

4.3 Comparison of methodological choices in other reviewed studies

Many reviewed LCA studies have been carried out to assess the GHG emissions of biofuels. It is outside the scope of this project to consider each of them in detail. However, it is of interest to identify any trend in methodological choices and try to explain the differences in the results obtained.

When comparing reviewed LCA studies that focus on GHG accounting of biofuels, there is a degree of consensus regarding preferred methodological choices (see Table 8). Most studies:

• Consider a well‐to‐wheel approach, thus including the utilisation phase of the fuel.

• Have adopted g CO2‐eq / km travelled as a functional unit (in agreement with the WtW approach).

• Use system expansion as the prime approach to treat the co‐products.

• Those that consider land‐use change have all adopted the IPCC guidelines.

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Table 8 – Comparison of methodological choices in reviewed studies (source: Gnansounou et al., 2009)

Despite the degree of consensus on the methodological choices, the magnitude of the discrepancies among the results from the different studies is tremendously high (see Figure 14). Quantitative investigations of the differences in the results from one study to the other are not straightforward due to: the lack of transparency concerning inventory data, the assumptions made to complement unavailable data, modeling choices about system definition and boundaries, functional units, reference systems and specific choices regarding the treatment of co‐products within a given approach (Gnansounou 2009).

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Figure 14 – Well‐to‐wheel GHG emission savings for a range of 1st and 2nd generation biofuels (excluding land‐use change) compared with gasoline or fossil diesel (source: OECD, 2008 based on IEA and UNEP analysis of 60 published lifecycle analysis studies giving either ranges (shown by the bars) or specific data (shown by the

dots)).

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5 Comparison of existing regulatory schemes

5.1 Introduction

The objective of this chapter is to identify the commonalities and differences between the existing methodologies and regulatory schemes and discuss the implications of the different methodological and data choices on the calculated GHG performance.

This enables the identification of areas where consensus has been or is likely to be achieved, and discussion about the opportunities for a meta‐methodology to be built, given the compatibilities between existing methodologies and regulatory schemes.

Each section of this chapter discusses one key methodological aspect of existing regulatory schemes. At the end of each section, two summary boxes conclude on:

• the degree of compatibility between the schemes regarding the methodological aspect examined.

• what approach could be considered as a best‐practice regarding the methodological aspect considered.

A clear distinction is again made between the LCA methodologies and the reporting schemes.

5.2 Attributional vs. consequential LCA

The methodologies developed and applied to measuring biofuel GHG emissions to date are largely based on attributional approaches to LCA. However, all schemes (and in particular the EPA approach) do treat specific aspects using a consequential approach which better reflects the reality, in particular:

• treating co‐products by system expansion;

• taking into account the consequences of using co‐products as feedstock for biofuel production;

• considering indirect land‐use change (iLUC);

• calculating a GHG emission reduction by substituting fossil fuels with biofuels.

Since policy‐makers and consumers need to be informed about the consequences of their decisions, using a consequential approach is highly relevant. However, to date there is no agreed and accepted methodology applied to biofuels for a fully consequential LCA analysis.

In addition, there will not be a fixed consequence or single conclusion from a consequential LCA as global economic circumstances change over time. Basing a regulatory scheme on a fully consequential framework is probably an unrealistic ambition, both in terms of methodological development and scheme implementation.

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Degree of compatibility between regulatory schemes

All existing schemes base their methodology on an attributional LCA framework, with some aspects treated through a consequential approach. However, the consequential components differ between the schemes. They will be discussed in dedicated sections below.

Recommendations for development of a best‐practice methodology and reporting scheme

We recommend the RSB adopt a mixed approach of attributional and consequential LCA. The general framework should be attributional (based on the assessment of direct impacts), while taking into consideration the consequences of causing indirect land‐use change, using co‐products for biofuel production, substituting other fuels and products by biofuels and their co‐products.

5.3 System boundaries

5.3.1 Type of fuels and end use

Each regulatory scheme has defined the fuels to which they apply. These fuels differ by their type (liquid, gaseous, electricity), their origin (biomass or fossil) and their end uses (road transport, other transportation systems, electricity, heat, cooling).

Table 9 shows that the scope is different for each scheme. The LCFS has the widest scope in terms of fuel type as it considers all fuels used in transport, which include liquid and gaseous biofuels, electricity, hydrogen etc. The RED, on the other hand, has the widest scope in terms of end use as it includes transport and electricity, heating and cooling. However, all schemes apply to liquid biofuels for transport.

Table 9 – Comparison of the fuels included in each scheme

RED LCFS RTFO RFS2

Liquid or gaseous biofuels for transport & liquid biofuel for heat, cooling and/or power.

Fuels for transport, except LPG, propane, racing fuel and fuel used in interstate locomotives, ocean-going vessels, aircraft and military tactical vehicles

Liquid or gaseous biofuels for transport

Liquid biofuels for transport


Each scheme applies to a different set of fuels. Liquid biofuels for transport are the common denominator.

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The scope of the GHG accounting methodology will depend on what the objectives of the RSB scheme are. However, it should at least cover all biofuels used for road transport.

5.3.2 Breadth of biofuel chain analysis

All the regulatory schemes considered perform their calculations on a well‐to‐wheel basis, which in effect encompasses the lifecycle analysis from the production of the feedstock18 to the consumption of the fuel in vehicles.

It is necessary to consider the actual use phase of the biofuel (i.e. the tank‐to‐wheel analysis) if the objective is to determine the carbon saving potential of biofuels, or biofuel blends, compared to neat fossil fuels. A well‐to‐tank study is appropriate only if the objective of the scheme is to compare different production pathways of the same biofuel, as the pathway does not influence the composition of the fuel and thus the tank‐to‐wheel part of the study. This is not the objective of most regulatory schemes that focus on GHG saving potentials.


There is consensus among regulatory schemes to base GHG accounting methodologies on a well‐to‐wheel analysis.


We recommend the RSB calculate the carbon intensities of biofuels on a well‐to‐wheel basis, as this is necessary to accurately determine the carbon saving potential of biofuels.

5.3.3 Depth of biofuel chain analysis

All schemes have a cut‐off value for processes that are considered as small contributors to overall GHG emissions. This cut‐off is usually taken at 1% of the total GHG emissions, which means that the construction of plants, infrastructures (e.g. roads, pipelines) and transportation systems (tractor, truck, ship, etc.) are excluded from the calculations. In most schemes, the production of seeding materials for biomass production is also excluded, although the RED includes it for some chains.

Several studies have analysed the effect of this cut‐off. Winrock International (2009) used a study by Macedo et al. (2008) to compare embodied energy in equipment manufacture and buildings and found that it is usually low in comparison to energy flows associated with energy production. The EPA (2009d)

18 For the waste materials, the production of the feedstock is usually not included.

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also analysed the effect of including farm equipment production emissions. The EPA concluded that this would decrease the GHG saving of corn ethanol by approximately 1 % in comparison to gasoline. However, for a fair comparison of fossil and biofuel, including emissions from production of farm equipment leads to the inclusion of emissions from the production of fossil fuel extraction/production machinery. The net effect of this would be a slight increase in both the biofuel and petroleum fuel lifecycle results and a smaller or negligible effect on the comparison of the two.


These is a consensus among regulatory schemes on excluding processes that contribute less than 1 % to the total GHG emissions. But, the exact processes excluded are not always the same: e.g. only the RED includes the production of seeding material.


We recommend the RSB exclude from the LCA calculations emissions from the construction of plants, infrastructures, transportation systems and seeding material production.

5.3.4 Land‐use change

All schemes except the RTFO and the RED include both direct land‐use change (LUC) and indirect land‐ use change (iLUC) in their calculation. A final decision regarding iLUC has not been made for the RED (see Table 10). It should be noted that the RTFO includes LUC in the system boundaries, however the GHG impact of this effect is not included in the calculation of the chain default values.

Table 10 – Summary of land‐use change aspects taken into account by the regulatory schemes

Scheme Direct land-use change Indirect land-use change

RED Under discussion

LCFS

RTFO ( ) X

EPA

Legend: Included in the default values

( ) Included in the scheme, although not in the default values

X Not included in the scheme

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Provided the RED decides to include indirect land‐use change in its calculations, then a system boundary that would include both LUC and iLUC is compatible with all regulatory schemes (with the exception of the RTFO but this scheme will be replaced by the RED in the short term).


There is a scientific consensus that both direct and indirect LUC can yield significant, if not dominant, GHG emissions. We therefore recommend the RSB consider GHG emissions from both direct and indirect land‐use change in its accounting methodology. See Section 5.7 for more details.

5.4 Metric

When comparing emissions of biofuels with those of fossil fuels, it is of utmost importance to consider the same relevant service from both systems. In the case of transport fuels, since mobility is concerned, this service must be related to mechanical energy, in other words, to the distance travelled (Gnansounou, 2009). A relevant unit is therefore g CO2‐eq / km travelled, which is the unit considered by most academic studies (EMPA, etc.).

In spite of this, all the regulatory schemes considered g CO2‐eq / unit energy (MJ or Btu) as a metric. This unit has the clear advantage of enabling the GHG performance of biofuels to be directly compared per unit of energy supplied. This approach is well suited to regulatory schemes that have the fuel suppliers or producers as regulated parties. However, this metric does not explicitly acknowledge the potential for changes in vehicle efficiency from using different fuels, even though this can still be taken into account.

Any change in vehicle efficiency from using biofuel blends should be taken into account in calculating the overall GHG impact. JEC (2008) assumes that the energy efficiency of vehicles when using low biofuel blends fuels would be the same as when using fossil fuel. This assumption is contradicted by the results of a 2007 study co‐sponsored by the U.S. Department of Energy and American Council for Ethanol (Shockey & Aulich, 2007) which suggests that using gasoline blended with ethanol in specific cars models can increase mileage per gallon compared to using unblended gasoline. There is no widespread agreement on these issues. Adoption of a different assumption to using a comparable energy basis has not been seen to‐date within methodologies (Winrock International, 2009).


The use of g CO2‐eq / MJ as a metric is compatible with all regulatory schemes.

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We recommend to use g CO2‐eq / MJ of fuel as the metric, but to include tank‐to‐wheel emissions and the effect of vehicle efficiency in the calculations (see section 5.8 below).

5.5 Fossil fuel references

The choice of reference fossil fuels and their respective carbon intensity differ significantly between schemes (see Table 11). Differences of up to 15 % are observed between the carbon intensities of these reference fuels. This can be explained by differences in the supply chain and by the fact that these carbon intensities are themselves the result of LCA studies that are based on different assumptions and methodological approaches.

This, however, not only makes the development of a meta‐approach difficult, but it also precludes any direct benchmarking between the compliance thresholds of different schemes.

Table 11 – Summary of the reference fossil fuels in the regulatory schemes (all carbon intensities are expressed in g CO2‐eq / MJ)

Schemes Diesel-type Gasoline-type

Definition Carbon intensity Definition Carbon intensity

RED Not specified A 83.8 Not specified A 83.8

LCFS Ultra low-sulphur diesel 94.7 Reformulated gasoline mixed with corn-derived ethanol at 10 % by volume

95.9

RTFO Diesel 86.4 Gasoline 84.8

RFS2 Low-sulphur diesel 91.8 Gasoline 93.3 A The RED differentiates the fossil fuels based on their use, not if their type.


The large discrepancies regarding the reference fossil fuels and their carbon intensities among the schemes preclude the selection of a reference fuel that would be compatible with all approaches.


We recommend the RSB differentiate between diesel and gasoline type fuels, and calculate the carbon intensity of these reference fossil fuels using the actual RSB accounting methodology.

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5.6 Treatment of co‐products

There is no ‘correct’ way of solving the multi‐functionality problem, even not in theory (Guinée et al., 2004). For this reason, there is no internationally agreed approach to co‐product treatment, although system expansion is usually considered the most scientifically sound approach and has been adopted by the majority of regulatory scheme (RTFO, LCFS, EPA) and academic studies (ADEME, JEC, etc.). ISO, in its 14040‐14049 series on LCA, does not specify a single method to follow, but specifies that, where possible, treatment by allocation should be avoided and system expansion used instead (ISO, 1998).

In spite of that, the RED has opted for an allocation approach based on energy content, which in itself precludes the development of a meta‐methodology. LCA results have been reported to be highly sensitive to the allocation method when evaluating the carbon intensity of biofuels. For instance, allocation by energy content (as in the RED) results in a substantially more favourable net calculated carbon intensity for chains that yield co‐products with a high energy value (e.g. bagasse from sugar cane ethanol), than a system expansion approach or allocation by market value (Winrock International, 2009). For instance, this is one of the key reasons why sugar cane ethanol has a lower carbon intensity in the RED (24 g CO2‐eq / MJ) than in the LCFS (27.4 g CO2‐eq / MJ).

The following section reviews the respective merits and drawbacks of the different approaches to co‐product treatment.

System expansion. Although system expansion can be considered the most scientifically sound approach to the treatment of co‐products, this approach nevertheless carries drawbacks.

First, system expansion is the least practical approach to implement in a regulatory scheme, as it is often difficult to determine what the co‐products are substituting (i.e. during definition of default biofuel chains) and to verify claims that are made. With the advent of future biorefineries that are expected to produce a much larger number of co‐products, substitution may become an even more difficult and time consuming approach.

Second, estimating the impact of the substituted product also proves complex as this can vary significantly from case to case, which introduces uncertainty into default values. It is important to emphasise that while the uncertainty can be significant, it is also manageable, i.e. it can be reduced over time as more information about methods is gathered.

Third, using system expansion assumes that the volume of co‐product is less than the volume of product it substitutes for. If the quantity of bioethanol from corn produced is small, the quantity of DDGS (co‐product of corn ethanol) produced will also be small, and it will substitute a small part of the pig fodder on the market. But as the bioethanol production grows, so will the DDGS production. Substitution will be possible up to up to a point when the DDGS has substituted all the pig fodder and the left‐over DDGS will have to find another use or become a waste. Thus the amount of co‐product that can be taken into account by system expansion is limited, and should be considered as such (as done in e.g. the RFS2 scheme).

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Allocation. The key advantage of allocation is simplicity: as it is based on a fixed characteristic of the co‐products (such as mass, energy content or price), it is easier to implement and to verify than substitution.

The main issue with allocation is that it cannot accurately represent the GHG impact of co‐products. For example, allocation based on energy content might hardly be justified for treating co‐products that are used for non‐energy purposes such as fertiliser or animal feed.

Risk in using multi‐treatment approach. Several schemes are combining different approaches to co‐product treatment, so that the most appropriate method can be selected in each specific case. For instance, the RTFO and the LCFS complement a mainly substitution‐based approach by allocation by market value and by energy content respectively. Such an approach carries the risk of treating different biofuels on an unfair basis, but no real alternative exist when information is lacking. This risk can be mitigated over time by improving understanding of co‐product markets and their GHG emissions, and thus replacing the allocation methods by system expansion.


All schemes but the RED use system expansion. This alone precludes the development of an overarching methodology as no approach to co‐product treatment can be chosen that can be simultaneously compatible with system expansion and allocation.


We recommend using system expansion as a co‐product treatment method. Allocation by economic value should be used as a fall back approach when necessary data or information is lacking, as this is the closest allocation approach to system expansion.

5.7 Direct land‐use change

Direct land‐use change is included in all four regulatory schemes considered. However, the approach used is fundamentally different between the European schemes (RTFO, RED) that use a bottom‐up approach and the U.S. schemes (LCFS, EPA) that are based on a global top‐down analysis:

• The RTFO and RED determine the land‐use change of a particular plot of land based on the previous use of this land at a given reference date. Table 12 provides a comparison of the accounting methodologies for LUC of the RED and the RTFO.

• The EPA and LCFS rather use a scenario‐type approach to determine the surface of lands that might be converted (directly or indirectly) based on the volumes of biofuels that are expected to be produced in the future. In this global approach, the impacts of direct and indirect LUC are treated jointly and cannot be disaggregated.

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Table 12 – Comparison of the direct land‐use change accounting methodologies of the RED and the RTFO

RED RTFO

Reference land use Land use in January 2008 or 20 years before the raw material was obtained, whichever the later.

Land use on 30 November 2005.

Carbon stocks included Not yet defined

Biomass Dead organic matter Soils

Methodology used to calculate changes in carbon stocks

Not precisely defined but will be based on IPCC guidelines (2006). Tier 1 methodology of the IPCC (2006).

Discounting method 20-year annualised 20-year annualised

Additional aspect Bonus of 29 g CO2-eq / MJ applied if land is converted from severely degraded or heavily contaminated land to agricultural land.

As emissions due to land‐use change (direct and indirect) occur over several years and with changing intensity, schemes have to decide whether or not to discount future emissions and the time horizons to calculate annual GHG emissions in g CO2‐eq / MJ. While the RED, LCFS and RTFO have all opted for the same annualisation method (see Table 13), the EPA has analysed both the annualisation and net present value, but has not yet published its final decision (see Annex 1 for a description of the different discounting methods). The Net Present Value (NPV) method received as a critique that discounting rates should only be applied to economic parameters, not to GHG emissions (ICF International, 2009).

The opening of grasslands and forests for crop production creates a large initial release of carbon from the soil. The carbon balance is regained over increasing years of subsequent biofuel production from those lands to replace petroleum transportation fuels. The shorter the timeline for analyzing lifecycle GHG emissions, the shorter the time for biofuels to overcome the initial carbon debt. European schemes (RED and RTFO) consider a horizon of 20 years, whereas American schemes (LCFS and EPA) consider 30 years, which means that the carbon impact will be considerably higher in the European schemes.

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Table 13 – Comparison of discounting methods and time horizons between the RED, LCFS, RTFO and EPA methodologies (NPV refers to Net Present Value). For a full explanation of each of the discounting methods, please refer to Annex 1.

RED LCFS RTFO EPA A

Discounting method

Annualisation Annualisation Annualisation Annualisation NPV (2%)

Project time horizon [year]

20 30 20 30 n/a C

Impact time horizon [year]

n/a B n/a B n/a B n/a B 100

A EPA has not yet made a final choice as to the discounting methodology, but nevertheless published carbon intensities using both an annualisation and a net present value methods. B The annualisation method does not require the definition of any impact time horizon. C The NPV method does not require the definition of any project time horizon.


The approaches used to calculate the GHG emissions from direct land‐use change (LUC) vary widely between schemes. Not only the calculation models and data used are different, but the actual accounting methods differ fundamentally.


We recommend using annualisation as a method to calculate carbon emissions from direct land‐use change. The choice of time horizon to use for the annualisation is somewhat subjective. In order to ensure all biofuel feedstocks are treated equally, the same time period should be used for LUC caused by any feedstock.

A good metric would be to choose a time horizon that reflects the average planning cycle of feedstock cultivation. In this regard, a 15 to 20 years period seems a reasonable choice, which would also correspond to the RSB principles on GHG emissions. We recommend not using annualisation period longer than 20 years as this does not correspond to a foreseeable future and appears to optimistic.

Ultimately, the choice of time horizon will depend on stakeholders’ opinion and on what is considered as international best‐practice (e.g. IPCC approach).

5.8 Tank‐to‐wheel emissions

The treatment of the use phase can be split into three independent aspects when comparing the approach taken by different schemes:

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• Combustion‐related CO2 emissions;

• Non‐CO2 combustion‐related GHGs;

• Vehicle efficiency.

Combustion‐related CO2 emissions. All regulatory schemes consider that the CO2 released on combustion of the biofuel is negated by the CO2 taken up while growing the crop. Thus only fossil fuels are assigned combustion‐related CO2 emissions, often calculated based on their carbon content.

Non‐CO2 combustion‐related GHGs. Both biofuel and fossil fuel emit tailpipe GHGs (CH4, N2O) due to imperfect combustion. Only the LCFS and EPA methodologies assign tailpipe emissions to biofuels whereas the RTFO, EPA and most other studies assign tailpipe emissions to fossil fuels only. In the draft version of the California Air Resources Board model for example a TtW emission of 0.78 and 0.82 g CO2‐

eq / MJ combustion for respectively diesel and gasoline vehicles related to CH4 and N2O emissions. These tailpipe emissions are small in comparison to current levels of lifecycle GHG emissions of biofuels. However, with the advent of 2nd generation biofuels, tailpipe emissions could become significant and should not be neglected.

Vehicle energy efficiency. All the regulatory schemes consider that there is not enough scientific evidence to date on the possible influence of biofuel on vehicle engine efficiency. Thus, no scheme currently differentiates between vehicles running on fossil fuels, or a blend of fossil‐ and bio‐fuels.


There is a reasonable consensus between the schemes regarding tank‐to‐wheel emissions. Small divergences nevertheless exist on the way schemes account for non‐CO2 tailpipe emissions (CH4, N2O), but these currently only generate small differences in the overall GHG balance of biofuels.


We recommend the RSB recognise the carbon neutrality of biofuels, and thus not assign any combustion‐related CO2 emissions to biofuels. However, we advise taking into account non‐CO2 emissions (CH4 and N2O) from biofuel combustion, as these are not carbon neutral and may become significant in the carbon balance of advanced biofuels. The RSB should consider evidence on what the best scientific practice would be to account for these emissions.

So far as fossil fuels are concerned, we recommend the RSB to take into account all GHG emissions due to their combustion.

Finally, we recommend RSB include vehicle energy efficiency in the methodology, but differentiate between fuels only once sufficient scientific evidence becomes available. The RSB should monitor evidence in this area.

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5.9 Input data

Some input data have greater influences on the final results than others, and their accuracy are thus more important. However, some of the most influential input data also happen to be the most uncertain ones, thus generating large uncertainties in the final results. In the following sections, the most important input data and their impact on the GHG emissions in the different accounting methodologies are discussed.

5.9.1 N fertiliser production emissions

Two pieces of data are necessary to assess the influence of the use of nitrogen fertiliser in agricultural practice: the amount of fertiliser used and the emission factor associated with the fertiliser production.

The amount of fertiliser used depends heavily on the crop grown, the soil type, the local climate and agricultural practices. Since the data aggregation level and geographical scope differ widely between schemes and given the large number of underlying choices and their local nature, it is not surprising to see wide ranging default values between schemes (see Table 14).

The emission factors for N fertiliser production also vary heavily between different LCA schemes, ranging from 1.3 kg to 6.8 CO2‐eq / kg N (see Table 14). These differences are mainly due to the fact that:

• Schemes consider different types of fertiliser. If one considers e.g. sugarcane ethanol from Brazil, the RTFO uses urea as N fertiliser, whereas the RED considers ammonium nitrate.

• Even if the same fertiliser type is used, fertiliser emission factors are themselves calculated through an LCA approach based on different methodological approaches (Winrock International, 2009).

The large differences in default values for fertiliser use and associated emission factor lead to differences in GHG emission accounting ranging from 50% to 300% depending on the biofuel considered (see Table 14). These differences do not show any systematic features between the schemes. This example illustrates the challenge in selecting input default value when trying to develop a meta‐methodology that would be compliant with all existing schemes.

Even for well‐studied chains such as the Brazilian ethanol, there is no consensus on the emission factor of N fertiliser, as each scheme considers a different type of fertiliser to be used: the RED does not specify the type (and thus uses the highest emission factor), the LCFS uses an emission factor calculated as the weighted average of 3 types of N fertiliser, and the RTFO considers urea to be used as N fertiliser, thus using the lowest emission factor.

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Table 14 – Comparison of N fertiliser use, emission factors and GHG emissions associated with the fertiliser production for different type of crop grown at the same location.

Crop / location Input data type RED LCFS RTFO

Corn / Europe Amount used [kg N / t corn] 14.8 -- 20.0 A

Emission factor [kg CO2-eq / kg N input] 6.1 -- 6.8 B

GHG emissions [kg CO2-eq / t corn] 89.7 -- 135.7

Corn / USA Amount used [kg N / t corn] -- 16.5 16.8

Emission factor [kg CO2-eq / kg N input] -- 2.9 F 6.8 B

GHG emissions [kg CO2-eq / t corn] -- 48.3 114.0

Sugar cane / Brazil Amount used [kg N / t sugar cane] 0.9 C 1.1 1.1

Emission factor [kg CO2-eq / kg N input] 6.1 2.9 F 1.3 D

GHG emissions [kg CO2-eq / t sugar cane] 5.5 3.2 1.5

Soya bean / Brazil Amount used [kg N / t soya bean] 2.9 E -- 3.9

Emission factor [kg CO2-eq / kg N input] 6.1 -- 1.3 D

GHG emissions [g CO2-eq / t soya bean] 17.4 -- 5.2

Sunflower / Europe Amount used [kg N / t sunflower seed] 16.0 -- 54.5-56.5 (country

dependent) Emission factor [kg CO2-eq / kg N input] 6.1 -- 6.8

GHG emissions [g CO2-eq / t sunflower seed] 97.0 -- 371-384 (country

dependent)

Oilseed rape / Europe Amount used [kg N / t rapeseed] 44.1 -- 42.9-61.1 (country

dependent) Emission factor [kg CO2-eq / kg N input] 6.1 -- 6.80

GHG emissions [g CO2-eq / t rapeseed] 268 -- 291-415 (country

dependent) A Amount of N fertiliser used in France. The RTFO scheme provides country‐specific values; France is the only European country that the RTFO considers as producing corn. B Emission factor of ammonium nitrate fertiliser. C The RED does not provide country specific data, but region‐specific data. This value is the N fertiliser amount used outside of Europe to grow sugar cane. No further information is provided on the geographical origin of the sugar cane. D Emission factor of urea fertiliser. E The RED does not provide country specific data, but region‐specific data. This value is the N fertiliser amount used outside of Europe to grow soya beans.

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F Emission factor of N fertiliser calculated as the weighted average of 3 types of N fertiliser as modelled in CA‐GREET.

5.9.2 N2O emissions from agricultural soils

N2O emissions from soils have a very large, potentially dominant, impact on the overall GHG emissions of agricultural products. These account for 10% to 80% of the GHG balance of biofuels (Smeets et al, 2008). In spite of this, few detailed models are available to accurately calculate these emissions, which also makes N2O emissions one of the most uncertain data in the LCA of biofuels.

Many studies currently rely on default IPCC N2O emission factors. These IPCC emission factors link the amount of N fertiliser used with the N2O emissions from agricultural soils. Thus, even if some schemes use the same model, the results can be different because of differences in assumption regarding the N fertiliser use. Some more complex biogeochemical models for N2O are available in the EU and US, and these are used by some schemes (e.g. the RED).

This situation yields large differences in N2O soil emission factors between schemes (up to almost 400%), as illustrated in Table 15. It is interesting to note that these differences do not show a systematic bias between biofuel chains. For instance, while the RTFO and RED tend to agree on the N2O emission related to the cultivation of soy bean in Brazil, the RED has a much lower default value than the RTFO for the EU Corn, and a much higher value for Brazilian sugar cane. This makes the design of any meta‐methodology particularly challenging.

Table 15 – Selection of default values for the N2O emission from soil in different schemes (unit: kg CO2‐eq / t feedstock)

Crop / location RED LCFS RTFO

Corn / Europe 70 -- 123

Corn / USA -- 137 103

Sugar cane / Brazil 39 6.8 8.0

Soy bean / Brazil 235 -- 243

Sunflower / Europe 193 -- 336-348 (country dependent)

Oilseed rape / Europe 328 -- 264-376 (country dependent)

5.9.3 Transportation

The influence of feedstock and biofuel transportation on the overall GHG emissions of biofuels is usually small (see Figure 15).

Several factors play a role in calculating the GHG emission from transportation:

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• Transport modes. There is a general consensus between accounting methodologies that trucks running on diesel are used for short to medium transportation distances, while rail or ships are used for medium to long transportation distance. Divergence can be found on the capacity of the different transport modes used (15 or 40 t for trucks for example) but this is unlikely to cause major differences in the overall GHG emission of biofuels.

• Transport distances. Given the low impact of transport on the overall GHG emissions of biofuels, it is usually accurate enough to average transport distances at country level by specifying the country of origin of the feedstock and the country of biofuel consumption. This is the approach taken by the RTFO, LCFS and EPA schemes. The RED has chosen to divide the world in two regions (namely "Europe" and "outside of Europe"), which may results in significant inaccuracies on the overall GHG balance. If one considers, for instance, coconut‐based biodiesel, the RTFO distinguishes between three countries of origin for the feedstock (India, Indonesia and the Philippines). The difference in transport distances between these countries to the UK generates differences of 8% in the GHG balance of this biofuel (3 g out of 42 g CO2‐eq / MJ biodiesel).

• Fuel intensity. Fuel intensity (i.e. amount of fuel consumed per ton‐kilometre) of the different transportation modes vary widely between schemes. These differences can stem from, for example, the use of transport modes with different capacities, or from whether a scheme considers the variation of fuel intensities between countries (e.g. European or North American trucks are usually more efficient than trucks from developing countries).

• Emission factors. Emissions from the different transportation modes have been widely studied and are now well known and generally agreed upon. Differences in this input data between different schemes are thus generally low.

• Return trip. All schemes account for the return trip of the empty vehicle. This is done either by increasing the distance travelled or by increasing the fuel intensity.

Overall, the divergences in accounting methodologies on transport modes and distances only cause minor differences in the carbon intensities of biofuels. However, with the advent of 2nd generation biofuels and the improvement of agricultural practices, the relative GHG impact of transport will gain in significance. This might generate larger relative differences between schemes.

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Figure 15 – Carbon intensity of different biofuel chains (various feedstock origins), as calculated with the RED

methodology. The importance of the different stages (cultivation, processing and transport and distribution) has been identified.

5.9.4 Conversion process

Although the actual conversion processes are usually very similar amongst plants producing the same biofuel, there are nevertheless parameters in the production pathways that may generate significant differences in the GHG balance of the product. The key influencing factors is the amount of input energy and the source of this energy. This is illustrated in Figure 16 in which 4 pathways to produce bioethanol from wheat are compared that only differ by the type of fuel and heating system used to generate the steam and electricity consumed by the conversion process.

This example shows that it is crucial that GHG accounting methodologies differentiate between biofuel chains that use variations over the production processes. In spite of that, schemes differ widely as to how many pathways they consider for each biofuels. For instance, while the EPA considers 25 different pathways for the production of bioethanol from corn, the LCFS, RTFO and RED only consider respectively 11, 3 and 1 chains. The less pathways considered, the larger the inaccuracy of the default value as compared to real values.

0 20 40 60

Sugar beet ethanolWheat ethanol (fuel not specified)

Wheat ethanol (lignite in CHP)Wheat ethanol (nat gas in boiler)Wheat ethanol (nat gas in CHP)Wheat ethanol (straw in CHP)

Corn ethanol, Community produced Sugar cane ethanolRape seed biodieselSunflower biodieselSoybean biodiesel

Palm oil biodiesel (process not specified)Palm oil biodiesel (methane capture at mill)

Waste oil biodieselBiogas from organic part of MSW

Biogas from wet manureBiogas from dry manure

Wheat straw ethanolWaste wood ethanol

Farmed wood ethanolWaste wood Fischer‐Tropsch diesel

Farmed wood Fischer‐Tropsch diesel

Carbon intensity [g CO2eq / MJ biofuel]

Cultivation

Processing

Transport and distribution

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In order to improve the accuracy of the reporting process, the RTFO, RED, and LCFS allow the regulated parties to use their own data instead of the predefined default. However, it is not clear today to what extend there will be sufficient incentives under the various schemes for the regulated parties to actually report their real values.

Figure 16 – Example of the influence of the type of energy used in the conversion process for the wheat to

ethanol chain as calculated in the RED


Overall, very little consensus can be observed between the schemes on input data. In particular, large differences in emission factors and data regarding agricultural practices and conversion processes are observed between the accounting methodologies, due to the difference in scope, hypotheses and calculation approaches, which results in large differences in the carbon intensities calculated.


The selection of data sources for the input data will depend on the actual scope of the RSB scheme. Overall, RSB experts should probably classify possible data sources based on their level of accuracy and authoritativeness. For data that might be considered less certain, a conservative approach could possibly be used.

0 20 40 60

Wheat ethanol (lignite in CHP)

Wheat ethanol (nat gas in boiler)

Wheat ethanol (nat gas in CHP)

Wheat ethanol (straw in CHP)

Carbon intensity [g CO2eq / MJ bioethanol]

Cultivation

Processing

Transport and distribution

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5.10 Chain default values

Table 16 shows that the default carbon intensities of similar biofuel chains differ largely between methodologies. This is somewhat expected given the wide differences in methodological approach and input data discussed in the previous sections. This certainly confirms the difficulty in trying to build an overarching methodology.

Attempting to directly compare the chain default values between different schemes amounts to comparing apples with pears and may thus lead to the drawing of misleading conclusions. It should indeed be kept in mind that these chain values are the result of complex LCA calculation approaches that are based on different system boundaries, methodological choices, aggregation level and input data.

All these differences in input parameters influence the final GHG balance to some degree. Given that the parameters can hardly be disaggregated, it is difficult to estimate how big an influence each individual parameter has on the LCA results.

Table 16 – Comparison of chain default values as published by the RED, LCFS and RTFO (land‐use change excluded)

Chain RED LCFS RTFO

Corn / Europe 37 49

Corn / USA 47-75 A 108

Sugar beet / Europe 33 50

Sugarcane / Brazil 24 12-27 B 25

Wheat / Europe 26-57 E 58-103 C

Oilseed rape / Europe 46 45-61 C

Palm oil / Outside of Europe

32-54 D 47

Soya bean / Europe 50 48-78 C

Soya bean / USA 27 58

Sunflower / Europe 35 55-62 C A Depending on chain characteristics such as U.S. region of origin of the feedstock, type of conversion energy, and dry or wet DGS production. B Depending on type of conversion (e.g. use of bagasse to produce heat, credit for extra heat production, etc.). C Depending on country of origin of the feedstock. D Depending on conversion process characteristics (e.g. methane capture at oil mill). E Depending on type of conversion energy used.

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A wide range of chain default values is observed between the schemes. This reflects the largely different input parameters (methodological choices and input data) used in the GHG calculations.


Chain values result from the LCA calculation and are therefore entirely defined by the methodological choices and input data used in the GHG accounting methodology. As such, there is no choice to be made at the level of the chain values.

However, there are schemes in which the chain default values used for reporting are not equal to the calculated chain values, but are chosen in a more arbitrary way in order to better meet specific policy objectives. We recommend use the calculated chain values as default values as this adds to the scheme simplicity and transparency. This aspect will be discussed in the next section.

5.11 Reporting scheme

Reporting approach. The approach to reporting on the GHG performance of biofuels differs widely between schemes and reflects the differences in their respective objectives:

• Reporting based on typical default values. This approach consists of publishing typical default values that are obtained from input data that are supposed to reflect the most widely used production practices. This is the approach used by the Californian LCFS. The main issue with this approach is that there is no incentive to improve all those biofuels that have carbon performances below the typical default values (or to report actual data).

• Reporting based on conservative default values. In this approach, conservative default values are published that can be amended to real values by those fuel suppliers that possess sufficient information about their supply chain. This should maximise information communicated by regulated parties in exchange of a ‘reward’ consisting in higher GHG performance reported. This approach is typically of that taken by the RTFO and the RED. While the RTFO calculates its chain default values based on conservative input data, the RED multiplies its typical chain default values (obtained from typical input data) by a pre‐determine multiplication factor.

• No GHG reporting. The RFS2 program is the only scheme where reporting parties do not actually report on the GHG performance of their fuel. In such an approach, the level of reporting accuracy depends on the number of independent production chains considered for each biofuel.

Compliance thresholds. The RED, LCFS and RFS2 have set thresholds for the GHG emission savings of bio‐ or low carbon fuels compared to the GHG emissions of fossil fuels. Since the GHG accounting methodologies and reference fossil fuel used differ widely between schemes, it is not possible to make any meaningful comparisons between the thresholds.

A common feature can however be found between the schemes. The thresholds are usually back‐loaded, i.e. they get more and more severe with time (e.g. RED and LCFS). This gives a few years for the

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novel technologies such as second generation biofuels to be developed, as these are expected to be much less carbon intensive than first generation biofuels.

Reporting tool. RTFO is the only regulatory scheme that is implemented at the time of writing, and thus the only scheme to have a defined reporting tool.


The approach to reporting is specific to each scheme, with little compatibility between them.


The reporting approach depends on the actual objectives of the scheme, hence no universal best‐practice can be derived as to how reporting should be performed.

However, should the RSB objective be either to incentivise the improvement of biofuel production or to create a “gold” standard that only the best‐in‐class biofuels can meet (see Section 0), then the reporting party should be offered the possibility to report its actual input data, with default input data set at a conservative level. Threshold levels should be set as to encourage low carbon biofuels.

Should the RSB envisage implement a biofuel certification scheme, then we recommend RSB develop guidance and tools to enable the industry to efficiently report the carbon intensity and savings of biofuels.

5.12 Summary of convergences and discrepancies

Table 17 provides an overview of the methodological choices made by the different regulatory schemes and other authoritative initiatives, while Figure 17 gives a view of the level of consensus in these choices. It can be concluded that, although consistent background assumptions and approaches are meet for certain aspects (e.g. type of LCA, metric, etc.), most methodological aspects (e.g. treatment of co‐products, land‐use change) and input data differ widely between regulatory schemes.

On a broader level, various other stakeholders (including companies, NGOs, academics) have recognised the importance of developing sustainability standards including criteria on GHG emissions. However, mutual differences are also visible in the strictness, extent and level of detail of these criteria, due to various interests and priorities (van Dam et al., 2008).

All in all, a weak level of compatibility exists between the regulatory schemes, which makes the development of an overarching methodology virtually impossible. This aspect is further discussed in the next chapter.

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Table 17 – An overview of key parameters in existing models and methodologies (source: Winrock International, 2009)

Figure 17 – Level of consensus among the regulatory schemes on approaches to specific methodological aspects and input data

Approach to specific methodological aspects Type of LCA

Type of fuel and end use

System bounda-ries

Metric Reference fossil fuel

Co-product allocation

Inclusion of iLUC

Approach to LUC

Tank-to-wheel phase

Reporting scheme

Input data

Agricultural production Transport

Conversion Tank-to-wheel phase

Chain default values

Fertiliser production

N2O soil emissions

LUC Level of details

Input data

Legend: Green: quite consistent background assumptions

Orange: some discrepancies observed, it affects results to some extent

Red: high inconsistency area, it affects results significantly

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6 Conclusions and recommendations for a way forward

6.1 Options for developing a GHG accounting methodology and for its use

The RSB has developed a set of sustainability standards for biofuels that have received considerable interest from stakeholders in different segments of the biofuel industry. The RSB now wishes to develop and implement a biofuel certification scheme that would build on the RSB sustainability standards. This would encompass the development of an RSB accounting methodology to calculate the GHG performance of biofuels.

However, several regulatory schemes have already been (or are going to be) introduced at global level to assess the sustainability of biofuels and their potential to mitigate climate change. These regulatory schemes all encompass GHG accounting methodologies. This certainly constrains the market space for the RSB and raises issues regarding the positioning of an RSB approach and its compatibility with existing regulatory initiatives.

In this context, the RSB can envisage the following strategic options, in relation to GHG accounting:

• Option A – Develop an RSB meta‐methodology for accounting GHG emissions that is compliant with existing regulatory approaches.

• Option B – Develop an RSB GHG accounting methodology, which would be built on what the RSB considers to be the best available methodological practices and input data.

• Option C – Adopt an existing GHG accounting methodology (e.g. RED, LCFS), i.e. that which most closely aligns with what RSB stakeholders believe to be the best approach (i.e. as identified in Option B), or that which the RSB believes is most likely to become the most widespread method.

These options, which can all become part of a certification scheme and encompass additional activities the RSB could build around it, need to be assessed against a set of criteria, so that their respective merits and drawbacks can be compared and the best option identified. The most important criteria to be met are:

• Scientific rigour (i.e. ensure an accurate assessment of the impact of biofuels on GHG emissions);

• Fairness (i.e. ensure that all biofuels are treated equally);

• Ease of development and implementation;

• Compatibility with RSB sustainability standards;

• Potential for recognition by existing regulatory schemes;

• Scope for deployment.

The selected GHG accounting methodology, or set of methodologies, would be the basis on which biofuels would be certified within the RSB certification scheme.

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6.2 Option A – Develop a regulation‐compliant RSB GHG accounting meta‐methodology

As a possible path forward, the RSB is questioning whether a meta‐methodology for biofuels could be built that would be compatible with the existing regulatory schemes. By compatibility it is meant that any regulated party would be able to report the GHG performance of their biofuels using the RSB meta‐scheme rather than the regulatory approach under which they fall, and still be compliant with the latter.

Prior to discussing this possibility, it should be realised that the UK‐RTFO is meant to be superseded by the EU‐RED scheme in 2011, and the EPA is not per se a GHG reporting scheme, as the regulated parties have to report the volume of the different fuels they supply, but not their actual GHG performances. Therefore, a meta‐scheme would, in effect, only need to comply with the GHG accounting methodologies of the RED and LCFS schemes (at least for the time being).

Unfortunately, even though the number of schemes to comply with is currently limited to two, the development of a meta‐approach that is methodologically compliant in every aspect with the RED and LCFS is not feasible for two reasons:

• First, the RED and LCFS GHG accounting methodologies differ too widely as to their methodological choices (see Chapter 5), which makes the building of a meta‐approach virtually unfeasible. For instance, it is impossible to treat co‐products in a way that is compatible both with system expansion and allocation.

• Second, the input data and aggregation level used by the RED and LCFS are not sufficiently consistent for enabling a meta‐approach to rely on a set of agreed upon values to be used as input. Even if the RED and LCFS accounting methodologies would be fully identical, the large discrepancies observed between their respective input data (see Section 5.9) would generate large differences in the LCA results.

Although the development of a meta‐approach is not possible from a purely methodological point of view, there are two alternative ways of building an overarching regulatory‐compliant meta‐approach. If methodological compliance is not an objective per se, but only a matching of the actual LCA results is required, the following two approaches can be considered:

• choose the input data of the meta‐methodology in a sufficiently conservative way so as to render all the output chain default values compliant with (that is higher than) those chain default values of the RED and LCFS (Option A1);

• adapt the GHG performance thresholds of the meta‐scheme to make these systematically tighter than that those limits set in the RED and LCFS (Option A2).

These options are not considered further as they:

• are not scientifically and methodologically rigorous, as data is arbitrarily set and comparison between schemes and with thresholds is not valid nor possible;

• do not provide a methodology for accurately and fairly comparing biofuel chains, as they are mainly stratagems to comply with the results and thresholds of existing methodologies and schemes;

• do not provide any reason for the industry to adopt the RSB approach.

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6.3 Option B – Develop RSB's own GHG accounting methodology

6.3.1 Option B1 – RSB‐developed GHG accounting methodology

An alternative to building a meta‐methodology is to develop a new GHG accounting methodology that would be fully compliant with the RSB sustainability standards and with what are considered to be the best available methodological ingredients and input data (see Chapter 5).

This option could build on the wealth of information on methodological choices and data available, as well as the principles governing the RSB’s sustainability standards. However, this approach entails several drawbacks regarding the implementation of the methodology:

• This RSB approach would not be methodologically compliant with the existing regulatory schemes and could therefore a priori not gain formal recognition by the LCFS and RED.

• The potential for uptake of the RSB methodology could be limited due to the existence of methodologies that are part of regulatory schemes. Therefore, its added value to the industry and to policy‐makers would need to be considered carefully. Countries that have not yet implemented any regulatory schemes might prefer adopting GHG accounting methodologies that have already been enforced in reporting schemes as these might be perceived more practicable. Also, there may be no incentive for industry to use methodologies that are not part of a regulatory framework.

Combining this RSB methodology with a certification scheme could however provide an attractive option, as described in the next section.

6.3.2 Option B2 – RSB certification scheme

The RSB‐developed GHG accounting methodology could be coupled with appropriately defined performance thresholds and RSB certification scheme. This might both respond to a demand from the RSB stakeholders and be of interest to countries that envisage implementing a regulatory scheme.

If made sufficiently attractive (in particular in terms of reporting efforts and accessibility), the RSB scheme could attract a critical mass of certified parties and might gain general public recognition as a standard. This could put the RSB in a strong position to ultimately influence and claim formal recognition by existing regulatory schemes.

A further advantage of this approach is that it could potentially be coupled to a gold‐standard type certification. To create such a RSB gold standard, tighter thresholds could simply be assigned to each performance threshold already defined in the RSB certification scheme. While potentially a wide set of biofuels would obtain certification within the RSB scheme, only the best‐in‐class biofuels (i.e. those with lowest carbon intensity) might be compliant with the stricter gold‐standard thresholds. Such a gold certification scheme could be viewed by the industry and other stakeholders as providing a better and stricter performance standard for biofuels. It would offer opportunities for product differentiation that might appeal to biofuel producers and suppliers, even for those that already have to report under existing regulatory schemes. This approach can be likened to that of the Gold Standard for the Clean Development Mechanism (CDM).

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The development and maintenance of a certification scheme based on an own methodology, would require additional efforts compared to a certification scheme based on an existing approach, but could easily build on the wealth of information available through other schemes.

6.3.3 Option B3 – Multi‐scheme reporting tool

An interesting option for the RSB could be to develop a calculation tool for the application of its own calculation methodology, and possibly for other calculation methodologies, especially for those part of regulatory schemes. This would be of significant added value to those intending to apply the methodologies, would limit errors in their application, and would generate visibility for RSB’s activities.

A user‐friendly computational tool could be developed that would serve as a universal tool for all regulated parties to report on the performance of their biofuel under the relevant schemes (e.g. LCFS, RED) and under the RSB scheme. It would contain the methodologies, default values and performance thresholds of each scheme. The reporting party could simply indicate with which scheme to comply, and the tool would automatically switch to the requested one and guide the user through a step‐by‐step user‐friendly reporting process. Since this meta‐reporting tool would build upon the exact GHG accounting methodologies defined by the regulatory schemes, it is thus intrinsically compatible with these, which would facilitate formal recognition.

Furthermore, the tool could:

• be updated to integrate new schemes included in new regulatory frameworks;

• provide guidance on ways to qualify for the RSB certification scheme;

• offer a platform to promote the RSB certification scheme.

It is also worth noting that the development of such a common tool would foster the lack of, but equally demonstrate the need for harmonisation between schemes, which is precisely one of the objectives of the RSB.

6.4 Option C – Adopt an existing GHG accounting methodology

Instead of developing its own GHG accounting methodology, the RSB could decide to adopt a methodology part of an existing regulatory scheme. The selection criteria could be to choose the methodology which:

• most closely aligns with what RSB stakeholders believe to be the best methodology to accurately assess the GHG footprint of biofuels;

• is likely to become the most widespread methodology.

If enough consensus could be generated around a particular methodology, the RED methodology for example, then the RSB could decide to adopt this methodology, use it as part of its certification scheme, and possibly develop a calculation tool based on it to assist with its widespread implementation.

However, none of the methodologies currently proposed as part of the regulatory frameworks (RED and LCFS) provides an option that:

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• is likely to become a globally accepted scheme;

• closely matches what the RSB considers the best available methodological ingredients and reporting approach;

• enables to draw value from the RSB sustainability standards or offers flexibility to integrate the view of the RSB stakeholders.

6.5 Recommendations

Table 18 provides a synthetic view of the merits and drawbacks of each of the proposed options:

• Option A1 and A2 aim at building a meta‐methodology, but can be discarded because of their poor scientific rigour, lack of overall coherence, and unlikely recognition by regulatory schemes.

• Option B offers the RSB the opportunity to develop its own GHG accounting methodology based on what it considers to be the best available methodological ingredients.

o Option B1 does not allow for recognition by existing schemes and is therefore likely not to find synergies with the implementation of existing schemes in Europe and the USA.

o Option B2 adds value to the RSB accounting methodology by developing an associated reporting scheme. It offers opportunities in terms of developing a more accurate performance standard for biofuels that the industry and governments may wish to adopt. It also carries the opportunity to jointly develop, and with minimal additional effort, a “gold” standard that might gain global recognition and offer the reporting parties with potential for product differentiation. This option would benefit from and to the broad stakeholder base and international nature of the RSB.

o Option B3 could add value to the RSB certification scheme by offering a platform to promote it. This would bring the greatest potential for attracting global recognition by helping the industry with the implementation of reporting under regulatory schemes.

• Option C is clearly an option with minimal risk, but would constrain the RSB to the choice of a methodology that at present would have limited geographical reach and possibly stakeholder acceptability. It also does not exploit the potential for the RSB to provide international leadership. As with option B, a calculation tool could be developed around the adopted methodology that could provide outreach for the RSB.

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Table 18 – Summary of proposed options

Options Scientific rigour

Fairness Ease of development and imple-mentation

Compatibility with RSB sustainability standards

Recognition by existing regulatory scheme

Scope for deployment

Option A1 - Meta-GHG accounting methodology based on conservative input data.

-- - - -- -- --

Option A2 - Meta-reporting scheme based on tight GHG performance thresholds.

- + + - - --

Option B1 – RSB-developed GHG accounting methodology.

++ ++ + + -- -

Option B2 – RSB certification scheme based on RSB accounting methodology.

++ ++ - ++ + +

Option B3 – RSB certification scheme and multi-scheme reporting tool.

++ ++ -- ++ ++ ++

Option C – Adoption of an existing regulatory GHG-accounting methodology and development of a compliant reporting tool.

+ + ++ -- + +

Legend: ++ Excellent performance of the proposed strategy with regard to the specific criterion + Good performance of the proposed strategy with regard to the specific criterion - Poor performance of the proposed strategy with regard to the specific criterion -- Very performance of the proposed strategy with regard to the specific criterion

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7 References

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Fehrenbach, H. (2008) Sustainability requirements for biofuels – German Perspectives: GHG Accounting Methodology and Default Data According to the Biomass Sustainability Ordinance (BSO), presentation of the ifeu at Brussels of January the 25th, 2008

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Annex 1. Discounting of future emissions

Annualised emissions. The first, and simplest, discounting method is an averaging approach which adds all CO2 emissions attributable to land‐use change (which may include emissions credits for land reversion if deemed appropriate) and then divides that total by the total fuel production over the assumed project horizon. The resulting land‐use change carbon intensity value can then be added to the fuel’s direct carbon intensity value.

∑

Net present value. The second method utilizes a net present value (NPV) calculation to discount future emission so that a ton of emissions occurring today is weighted more heavily than a ton of emissions occurring in the future. This approach is analogous to methods used for economic analysis of projects. The discount rate selected for the NPV calculation determines how earlier emissions will be weighted relative to emissions occurring later. A positive discount rate weights carbon emissions today as having larger impacts than future emissions and results in a larger burden on biofuels due to their initial “burst” of emissions from land‐use change. To calculate the contribution of land‐use change to the carbon intensity of a biofuel, the annual GHG emissions flows for that biofuel and of the reference fossil fuel are first discounted to an NPV. Then, the ratio of the “NPV emissions” of the biofuel and the fossil fuel is multiplied by the carbon intensity of the fossil fuel thereby providing a single value for the carbon intensity of the biofuel. Using this method, one must choose an appropriate discount rate to reflect the cost or damage caused by earlier, rather than later emissions of GHG.

Fuel Warming Potential. The third method involves the calculation of a “physical” Fuel Warming Potential to compare fuels with different GHG time emission profiles. This value is calculated for the studied fuel by comparing its cumulative radiative forcing against that of a reference fossil fuel. The ratio provides an estimate of the global warming impact of the fuel relative to the reference fuel. When this ratio is multiplied by the carbon intensity of the reference fuel, it provides the carbon intensity for the biofuel.

where:

ai is the radiative forcing efficiency

Ci(t) is a time dependent abundance of CO2 in the atmosphere

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CIi is the carbon intensity of fuel i

FWIbiofuel is the Fuel Warming Intensity or carbon intensity of the biofuel

Comparison of discounting methods. Figure 18 shows the carbon intensity due to land‐use change only, assuming the emission profile shown on Figure 1 and different impact horizons. For any impact horizon, the average or annualized method gives lower carbon intensity values than the other methods. The Fuel Warming Potential yields the highest carbon intensity values for low impact horizon whereas the net present value gives the highest carbon intensity for long impact horizons. The breakeven point is at an impact horizon of 50 years.

Figure 18 – Comparison of time accounting methods for the emission profile shown on Figure 1

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Annex 2. Additional information on the RED methodology

List of chains and their typical and default values, as published in the RED. Table 19 – Typical and default carbon intensities of biofuels and bioliquids

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Table 20 – Typical and default carbon intensities for future biofuels and bioliquids that were not on the market or were only on the market in negligible quantities in 2008

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Table 21 – Typical and default greenhouse gas emission savings for biofuels if produced with no net carbon emissions from land‐use change

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Table 22 – Typical and default greenhouse gas emission saving for future biofuels that were not on the market or were on the market only in negligible quantities in January 2008, if produced with no net carbon emissions from land‐use change

Land‐use change emission calculation guidelines. For the calculation of those emissions the following rule should be applied:

3.6641

201

where

el = annualised greenhouse gas emissions from carbon stock change due to land‐use change (measured as mass of CO2 equivalent per unit biofuel energy);

CSR = the carbon stock per unit area associated with the reference land use (measured as mass of carbon per unit area, including both soil and vegetation). The reference land use shall be the land use in January 2008 or 20 years before the raw material was obtained, whichever was the later;

CSA = the carbon stock per unit area associated with the actual land use (measured as mass of carbon per unit area, including both soil and vegetation). In cases where the carbon stock accumulates over more than one year, the value attributed to CSA shall be the estimated stock per unit area after 20 years or when the crop reaches maturity, whichever the earlier;

P = the productivity of the crop (measured as biofuel or bioliquid energy per unit area per year);

eB = bonus of 29 g CO2‐eq / MJ biofuel if biomass is obtained from restored degraded land under the conditions provided below.

The bonus of 29 g CO2‐eq / MJ shall be attributed if evidence is provided that the land:

• was not in use for agriculture or any other activity in January 2008; and

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• falls into one of the following categories:

• severely degraded land19, including such land that was formerly in agricultural use;

• heavily contaminated land20.

The bonus of 29 g CO2‐eq / MJ shall apply for a period of up to 10 years from the date of conversion of the land to agricultural use, provided that a steady increase in carbon stocks as well as a sizable reduction in erosion phenomena for land falling under (i) are ensured and that soil contamination for land falling under (ii) is reduced.

There are no guidelines provided yet for the calculation of land carbon stocks. The RED specifies that the Commission shall adopt, by 31 December 2009, guidelines for these calculations drawing on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories — volume 4.

19 “Severely degraded land” means land that, for a significant period of time, has either been significantly salinated or presented significantly low organic matter content and has been severely eroded 20 “Heavily contaminated land” means land that is unfit for the cultivation of food and feed due to soil contamination.

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Annex 3. Additional information on the Californian LCFS

Table 23 – Default carbon intensities for gasoline and the fuel that replace gasoline for different pathways calculated by the LCFS up to today (source: California Environmental Protection Agency, 2009a)

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Table 24 – Default carbon intensities for diesel and the fuel that replace diesel for different pathways calculated by the LCFS up to today (source: California Environmental Protection Agency, 2009a)

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Annex 4. Additional information on the UK RTFO

Definition of land‐use categories

Cropland: This category includes cropped land, including rice fields, and agro‐forestry systems where the vegetation structure falls below the thresholds used for the Forest Land category.

Forest land: Land spanning more than 0.5 hectare with trees higher than 5 metres and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural (or urban) land use.

Grassland (and other wooded land not classified as forest) with agricultural use: This category includes rangelands and pasture land that are not considered Cropland but which have an agricultural use. It also includes systems with woody vegetation and other non‐grass vegetation such as herbs and brushes that fall below the threshold values used in the Forest Land category and which have an agricultural use. It includes extensively managed rangelands as well as intensively managed (e.g., with fertilization, irrigation, species changes) continuous pasture and hay land.

Grassland (and other wooded land not classified as forest) without agricultural use: This category includes grasslands without an agricultural use. It also includes systems with woody vegetation and other non‐grass vegetation such as herbs and brushes that fall below the threshold values used in the Forest Land category and which do not have an agricultural use.

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The RSB GHG accounting scheme - globalbioenergy.org€¦ · The RSB GHG accounting scheme ... 12 2.2.5 Land‐use ... a standard that only the best-in-class biofuels can meet, then