In this report for Transport and Environment, we describe the biofuel policy frameworks and targets of the nine leading global producers and consumers: the USA, Brazil, the EU+UK, Indonesia, China, India, Argentina, Canada, and Thailand. The report links these countries’ biofuel feedstock demand to ecological risks, to the carbon opportunity cost of using extra land for agriculture, and to the greenhouse gas implications of relying on biofuels to displace fossil fuels.
We calculate that about 32 Mha of cropland is currently devoted to biofuel feedstock production after accounting for co-product allocation. The benefit of this, as conventionally calculated (i.e. ignoring ILUC), is a 233 MtCO2e/year emissions saving compared to an equivalent amount of fossil fuels. But returning this land to its natural state — and replenishing its above- and below-ground carbon — could provide a much larger carbon sink of 428 MtCO2e/year. While rewilding of agricultural land on this scale is not currently plausible, these numbers underscore the importance of thinking about land use in ways that aren’t readily captured by conventional lifecycle analysis.
Under present policy targets, biofuel consumption in the nine study countries is set to increase from 104 Mtoe in 2023 to 150 Mtoe in 2030. Given existing and future feedstock slates, and constraints on advanced and residual feedstock supply, we conclude that over 90% of this is likely to come from crops: that means an extra 20 Mha devoted to biofuels worldwide. Accounting for the range of crops grown in each country, we calculate that if ILUC is taken into account, biofuel policy in 2030 will increase emissions from transport fuels by 34 MtCO2e/year compared to 2023.
The EU+UK and Thailand are found to have adopted policies that are reducing biofuel-associated emissions down over time. The USA, India, and Indonesia, by contrast, are expected to expand use of high-ILUC palm and soybean oils, and so their biofuel policies are likely to be harming rather than benefitting the climate.
https://www.cerulogy.com/wp-content/uploads/2019/01/thumb-nails_9.png300300Cato Sandfordhttps://www.cerulogy.com/wp-content/uploads/2021/05/cerulogy-logo-156h.jpgCato Sandford2025-10-09 15:07:272025-12-16 19:19:51Diverted harvest: Environmental Risk from Growth in International Biofuel Demand
Clean fuel standards (CFSs) are regulations which reward or penalise transport fuels based on their lifecycle greenhouse gas emissions — in particular, their emissions with respect to a ‘compliance standard’ which tightens each year. A number of CFSs are already in operation around the world.
Our paper published in the journal Energy Policy examines a hypothetical CFS covering the USA’s road and aviation segments. We model two transport decarbonisation scenarios: one with an emphasis on road electrification, and the other with an emphasis on next-generation liquid fuels and CCS. Both are designed to be consistent with a net-zero-2050 target for the USA’s economy as a whole, with road transport achieving a 87-94% carbon intensity cut and aviation ∼84% (excluding contrails). The electrification-heavy scenario offers faster and deeper emissions cuts, as well as being a significantly cheaper decarbonisation option.
The development and results of these scenarios provide context for considering CFS design issues: the ambition of targets, how to drive investment, and the need for restrictions on certain biofuel feedstocks.
https://www.cerulogy.com/wp-content/uploads/2018/04/thumb-nails_6.png300300Cato Sandfordhttps://www.cerulogy.com/wp-content/uploads/2021/05/cerulogy-logo-156h.jpgCato Sandford2025-09-05 09:05:012025-09-08 17:29:51A federal clean fuel standard (CFS) for the USA
The EU’s ReFuelEU Aviation and the UK’s SAF Mandate place requirements on fuel suppliers to bring alternative aviation fuels to market. In parallel, financial support for alternative fuels comes from the EU’s reimbursement scheme, which subsidises some fraction of the cost difference paid by airlines, and the UK’s guaranteed strike price (GSP), which shields SAF producers from variability in market prices.
This report examines the policy frameworks in the two regions and reviews their strengths and weaknesses. It finds that the structure and targeting of their complementary support mechanisms will deliver diverging outcomes. The EU’s reimbursement scheme will benefit airlines and reduce the costs to flyers, but will do little to motivate next-generation fuel producers. In contrast, the UK’s GSP delivers clear reassurance to fuel producers and is more likely to stimulate investment in the industry by facilitating finance availability and reducing the cost of capital.
The report presents illustrative model scenarios and concludes by distilling major challenges and recommendations for the two policy frameworks.
The EU’s mandate trajectory and its slightly obscure system of non-compliance penalties could be reviewed, and a joined-up approach adopted for targeting resources towards supporting investment in more sustainable alternative fuels.
For the UK, there are some important details to iron out about how price-setting works in the GSP; and a potentially vacillating balance of alternative fuel supply and demand from the EU may pose challenges for compliance and costs.
https://www.cerulogy.com/wp-content/uploads/2016/12/thumb-nails_15.png300300Cato Sandfordhttps://www.cerulogy.com/wp-content/uploads/2021/05/cerulogy-logo-156h.jpgCato Sandford2025-07-24 14:18:342025-07-24 14:18:34Staying Aloft: Support Mechanisms for ‘Sustainable Aviation Fuels’ in the United Kingdom and European Union
This report examines the climate and environmental risks associated with biofuel policies in Sweden, Finland, and Norway. While these countries are often recognised as climate leaders, their support for certain biofuel feedstocks, including palm derivatives, used cooking oil, and animal fats, raises concerns about indirect land use change, deforestation, and peatland degradation. The findings suggest that without further safeguards, Nordic biofuel demand could drive deforestation and peat loss amounting to tens of thousands of hectares annually by 2030.
Finland consumes particularly high volumes of PFAD – palm fatty acid distillates – in its biofuel sector. PFAD is a valuable by-product of palm oil refining, and is linked to the expansion of oil palm plantations in Southeast Asia. Sweden and Norway have taken steps to exclude PFAD, but continue to rely heavily on residual oils, which come with their own environmental challenges. In all three countries, current policy frameworks do not fully account for indirect emissions or the global displacement effects of feedstock use.
This report provides country-specific recommendations to align Nordic biofuel policy with climate and biodiversity goals, including capping high-risk feedstocks, supporting the development of advanced alternatives, and accelerating transport electrification. The analysis underscores the importance of embedding strong environmental safeguards in biofuel policy to ensure that efforts to decarbonise transport do not undermine broader sustainability objectives.
How e-fuels can mitigate biodiversity risk in EU aviation and maritime policy
This report, commissioned by Opportunity Green on behalf of the Skies and Seas Hydrogen-fuels Accelerator Coalition (SASHA), explores the biodiversity risks associated with the EU’s efforts to decarbonise aviation and maritime transport. The ReFuelEU Aviation and FuelEU Maritime regulations aim to engender a rapid transition away from fossil fuels and towards alternative fuels; but this raises concerns for nature protection, potentially undermining the EU’s biodiversity commitments under the Biodiversity Strategy for 2030 and the Nature Restoration Regulation. Cerulogy’s report assesses how different fuel pathways – biofuels from crops, residues and waste oils, and synthetic e-fuels – compare in terms of pressure on land, habitats, species, and ecosystems.
Cerulogy modelled alternative fuel demand in the aviation and maritime segments to 2050. We considered four scenarios representing different dominant fuel production technologies: cellulosic residues, cellulosic crops, lipids, and electrofuels. For each scenario, we estimated feedstock and land requirements, and developed a biodiversity risk framework to evaluate land-use change, habitat degradation, species loss, pollution, and agrochemical use. To assess policy coherence, we examined trade-offs and synergies between the EU’s transport decarbonisation goals and its nature and biodiversity policy framework.
Our findings show that, while all fuel pathways carry some environmental risk, electrofuels may represent the lowest overall risk to biodiversity, largely due to their minimal land footprint and reduced pressure on ecosystems, species, and habitats. Even biofuels derived from residues and wastes may have implications for nature when scaled to meet growing fuel demand. The EU’s current approach risks locking in high-impact fuel systems unless it also addresses total energy use in aviation and shipping. Until policymakers are ready to confront demand growth in these hard-to-decarbonise sectors, support for options like electrofuels may be the clearest path for the EU to aligning its climate and biodiversity goals.
In this paper for the International Council on Clean Transportation we consider the industrial development implications of the targets for alternative aviation fuel supply that are being introduced throught he UK’s SAF Mandate.
https://www.cerulogy.com/wp-content/uploads/2024/03/thumb-nails-44.png300300Chris Malinshttps://www.cerulogy.com/wp-content/uploads/2021/05/cerulogy-logo-156h.jpgChris Malins2024-09-05 16:48:262024-10-15 11:06:08Vertical Take-off? Cost Implications and Industrial Development Scenarios for the UK SAF Mandate
Fatty acid methyl ester (FAME), or biodiesel as it is more commonly known, is a diesel substitute produced by reacting methanol with vegetable oil, and is the second most widely used bio-additive in America fuels (behind ethanol added to gasoline). As shown in Figure 1, U.S. biodiesel consumption has increased more than a hundredfold since 2001[1]. The main driver of this rapid expansion of the biodiesel industry has been the federal Renewable Fuel Standard (RFS), under which biodiesel produced from soy oil canola oil, yellow grease and tallow qualifies as an advanced renewable fuel.
Figure 1 Rising biodiesel consumption in the U.S.
Source: EIA Monthly Energy Review, Table 10.4 https://www.eia.gov/totalenergy/data/monthly/index.php#renewable
For many people, the word biodiesel conjures up an image of restaurant grease being collected and recycled as fuel. In fact, about two thirds of U.S. biodiesel is produced from food-grade unused vegetable oils, and only an eighth is actually produced from recycled restaurant oil. Most of the food-grade vegetable oil turned into biodiesel in the U.S. is soy oil (about 55% of total biodiesel), and the rest is largely canola oil (about 10% of total biodiesel production). According to the EPA’s analysis for the RFS, soy biodiesel use has associated lifecycle greenhouse gas emissions of 40 grams of carbon dioxide equivalent released for every megajoule of energy in the biodiesel (gCO2e/MJ). More recent analysis[2], which provides the basis of values included in Argonne National Laboratory’s GREET 2017 model, estimated an even lower value of 29 gCO2e/MJ, even when including emissions from potential land use changes in the U.S. and internationally (indirect land use change, ILUC).
Figure 2. GHG intensity of soy biodiesel, as reported by Chen et al. (2018)
This combination of rapid increase in renewable fuel use and relatively low lifecycle greenhouse gas emissions appears to be a good news story for the climate. However, dig a little deeper into the analysis underneath the numbers and the picture gets much murkier. In fact, it turns out there’s a very real possibility that U.S. government agencies have systematically overstated the environmental benefit of using soy biodiesel, and that soy biodiesel should not qualify as an advanced fuel under the RFS.
Soy in the EPA’s analysis for RFS
This article will mostly focus on some issues in the soy biodiesel analysis from GREET, but before we get into that it’s worth taking a moment to revise how soy biodiesel qualified as an advanced biofuel in the first place. The magic number to be counted as an advanced biofuel in the RFS was 46 gCO2e/MJ. Provided soy biodiesel’s assessed greenhouse gas intensity was below that number it would qualify for support as an advanced fuel alongside biodiesel from waste and residual oils and imported sugarcane ethanol.
While we are now used to the idea that soy biodiesel counts as an advanced biodiesel, this result was anything but inevitable ten years ago. Indeed, the preliminary lifecycle analysis undertaken by the EPA for the RFA concluded that when land use change emissions were included soy biodiesel had a larger climate impact than fossil fuels, assessed at 95 gCO2e/MJ. In the final analysis, the EPA presented results for three years: 2012; 2017 and 2022. In general, we would expect that over time processes would become more efficient and yield will increase, and indeed we see that the lifecycle emissions results for soy biodiesel in 2012 and 2017 were both above the threshold value – only by 2022 was soy biodiesel expected to achieve the requisite climate benefit. This is shown in Figure 2. The reduction in expected emission intensity from 2012 to 2022 is rather significant – a 17 gCO2e/MJ reduction.
Figure 3 Evolution of soy biodiesel GHG intensity results in RFS analysis
So, how did the negative preliminary result turn into the positive (for soy biodiesel, at least) Unpacking the results reveals a number of interesting features. Figure 3 shows the results from the final RFS analysis decomposed into constituent emissions. There are three features of the analysis that are particularly interesting. Firstly, there are credits of 8 gCO2e/MJ given in 2022 for domestic land use change and for domestic rice methane, and a credit of 6 gCO2e/MJ from international livestock emissions. Without any one of these credits (assuming the rest of the analysis remained the same), soy biodiesel would not have qualified as an advanced biofuel under the RFS. Secondly, the international land use change emissions fall by 30% from 2017 to 2022, avoiding 19 gCO2e/MJ. Without this reduction, soy biodiesel would also have failed to qualify as an advanced biofuel.
Figure 4 Decomposition of RFS lifecycle results by type of emissions Source: https://www.regulations.gov/document?D=EPA-HQ-OAR-2005-0161-3173
The three large emissions credits is each somewhat surprising. The rice methane credit is based on a prediction from the FASOM model that 8% of increased soy acreage in 2022 would replace U.S. rice paddies.[3] This is about the only place in the biofuel discussion that you’ll see anyone claim that biofuels strongly impact rice production. Indeed, the modeling with FAPRI undertaken for the RFS final analysis shows no significant reduction in paddy area, and during discussions of the 2008/09 food price crisis it was repeatedly stated that rice markets could not be strongly impacted by biofuel demand.
The domestic land use change emissions credit is arguably even more surprising. In general in the land use change discussion it is accepted that increased agricultural area will generally result in losses of carbon from biomass and soils in the converted landscapes. It is, to say the least, counter-intuitive to find a result in which a large expansion of soybean area apparently increases total carbon storage in U.S. landscapes rather than reducing them.
The third credit, for a reduction in international livestock methane emissions, is also somewhat surprising. A significant reduction in beef production is predicted in Latin America by the model, with soybean area in Brazil expanding at the expense of pastureland. While this seems a plausible prediction, the co-product of soy oil production, soy meal, is widely used as a livestock feed. It is therefore not clear that increased area devoted to soybeans, even at the expense of pastureland, should result in reduced livestock raising. Indeed, some biofuel producers have sought to characterize the biofuel industry as producing ‘fuel and feed’ because increased oilseed crushing can support increased availability of high protein animal feed. One way in which increased vegetable oil production could be delivered without producing large quantities of animal feed would be to use palm oil instead of soy oil to meet increased demand – but the EPA modeling does not predict a large palm oil response.
Finally, why are the international land use change emissions calculated for 2022 so much lower than for the other years given? It turns out that the reduction is primarily due to a large assumed reabsorption of carbon in the Amazon in the modeling, in the period 2023 to 2051, which is absent in the modeling of 2012 and 2017[4]. It’s unclear why the carbon change dynamics are so different in the 2022 results than the earlier results, but it suggests that the model may be predicting an unrealistic carbon sequestration dynamic in the 2022 results.
Of course, the points we’ve identified here are only in the areas in which it seems that the EPA modeling may have underestimated emissions, and it would be possible to identify places in the model where one assumption or another may have tended to overestimate soy biodiesel emissions. Still, there are enough questionable outcomes documented in the modeling that it’s worth at least asking whether the results may have been unduly favorable to soy biodiesel, and whether soy biodiesel was rather lucky to make it into the advanced biofuel category.
Soy in GREET
While the oddities in the EPA lifecycle analysis are interesting, the EPA numbers have not been updated since the final RFS rule was adopted, whereas the lifecycle values bundled in the GREET lifecycle analysis tool from Argonne National Laboratory are regularly updated. As we noted above, these numbers are now rather more favorable to soy biodiesel even than the EPA numbers were, having been reduced to 29 gCO2e/MJ in the analysis by Chen et al. (2018) – but it turns out that these values may also be giving a misleadingly positive impression.
Allocations
The first issue in the GREET analysis relates to the way that emissions are divided between the oil and the meal that are produced when soybeans are crushed. In the analysis by the EPA, this step is not necessary – the availability of soy meal is fed back into the models used in the emissions assessment, and allows for reductions in the total amount of additional crop cultivation required in the assessment. The results in GREET deals the co-products in the same way for the land use change analysis through the GTAP model, but for agricultural emissions GREET uses a system of allocation.
The purpose of allocation is to fairly divide the emissions associated with a process between different outputs of that process. The three most common allocation approaches in lifecycle analysis are:
Market value: emissions are allocated between co-products in proportion to the amount of each co-product produced multiplied by a typical price for that material. The advantage of market value is that it allocates more emissions to higher value co-products, and thus best corresponds to the economic decision making of producers. The main disadvantage is that prices are variable, and so there is no single correct answer as to which set of prices should be used, meaning that results could change over time.
Energy content: emissions are allocated between co-products based on chemical energy content. This approach is used by the European Union for its lifecycle analysis. It has the advantage that energy content is often a reasonable proxy for the value of a co-product (especially where materials are used for food or feed or directly for energy), but the disadvantage that it is not appropriate for co-products whose use does not require any chemical energy content.
Mass: emissions are allocated between co-products based on mass. This is the approach taken in GREET. It has the advantage that it can be applied very widely to any material co-products (it is not appropriate for power or heat as co-products) but the disadvantage that the mass of a material is often not strongly correlated to its value. For instance, it would not seem appropriate to allocate emissions to residues with very little value based on their mass.
GREET uses mass allocation for oilseed crushing, giving the justification in Chen et al. (2018) that, “meals are not an energy product and mass is not subject to market value vacillation”. This rationale is fine insofar as it goes, but it ignores the fundamental weakness of using a mass-based allocation, which is that it results in a disproportionate allocation of emissions to heavy, low value products. While soy meal is clearly not normally burned for energy, the energy in the meal is metabolized by animals when it is used in feed rations, and so the argument against using energy allocation is disingenuous at best. Indeed, in the European Union it is standard to use energy allocation in these calculations.
As can be seen in Figure 4, using the mass allocation for soybean crushing results in allocating only half as many emissions to the oil as would be allocated on a value basis. Given that soy meal is sold for animal feed, and the animal feed sector does not generally use lifecycle accounting as part of its decision making, the result of this is that a large chunk of the emissions associated with soybean production simply disappears from the GREET lifecycle analysis. Using mass allocation gives a misleading impression of the balance of the meal and oil in the value of the soybean, and reduces the stated GHG intensity of soy biodiesel by about 10 gCO2e/MJ. This issue is particularly important in the case of soy biodiesel because the soybean has a much larger fraction of its mass go to the co-product than any other major biofuel crop. The use of mass allocation therefore also skews any comparison of the environmental credentials of biofuels in favor of soy biodiesel.
Figure 5 Comparing the results of different allocations between soy oil and meal
Source: GREET 2017
Indirect land use change
The use of mass allocation has been a feature of the GREET lifecycle analysis for many years. In contrast, the land use change emissions estimates included in GREET regularly change, and the model used to estimate ILUC emissions for GREET (called GTAP) is regularly updated.
In 2009, the GTAP framework was used to assess the ILUC emissions from soy biodiesel for the regulatory analysis under the California Low Carbon Fuel Standard (LCFS). At that time, the estimate for ILUC emissions for soy biodiesel was 62 gCO2e/MJ, higher than those estimated by EPA. By 2018, however, an ILUC value of 6.3 gCO2e/MJ from Chen et al.[5] was integrated into GREET 2017. The downward trajectory of soy biodiesel ILUC estimates from GREET over this period is shown in Figure 6. What accounts for this 90% drop in estimated emissions, and are the new values more credible than the older California estimates?
Figure 6 Point ILUC factor estimates for US soy biodiesel obtained with variants of the GTAP-BIO model
Part of the answer is that a series of amendments to the GTAP modeling framework has been made since 2009 in a way that in several cases was effectively designed to reduce the output ILUC values. A forthcoming paper[6] shows in detail that a number of innovations have been introduced to the GTAP framework in the interim in ways that inevitably resulted in lower estimated ILUC emissions, but that were not well supported by available evidence.
As an example, a 2017 paper introduced a regionally differentiated approach to set the response of crop yields to increased demand, replacing a uniform global parameter that had been used previously. This paper provided no evidence that the previous response had been too weak on average, and provided no new econometric evidence for the regional values adopted. This adjustment could have been implemented in a way that added sophistication to the model but avoided changing the average global yield response. Instead, the new parameters were raised for more than half the regions considered, including the regions most important to the analysis. This model change therefore resulted in lower ILUC estimates, despite presenting no evidence that the ILUC estimates actually ought to be lower.
Similarly, in 2017 a new model feature was introduced allowing cropping intensity to increase (i.e. allowing the model to assumed that one piece of land may be harvested two or more times in a year). Making this change would obviously result in reduced ILUC estimates, but the evidentiary basis for setting the new parameters was exceedingly weak. Previously, some commentators had argued that this cropping intensity response was already accounted for implicitly in the model in the yield response, but no consideration was given to whether the yield parameters should therefore be adjusted when the new feature was added. The picture that emerges is of a willingness to edit the model in ways that reduced the output values, and a corresponding resistance to making any amendments that would increase the ILUC estimates. It is therefore very hard to have confidence that the reductions in expected land use change between the older modeling and the latest modeling represent an improved understanding, rather than simply reflecting the underlying opinion of the modeling teams doing the work.
Alongside changes in the GTAP economic model itself, the Chen et al. (2018) ILUC assessment reflects the use of a new set of emissions assumptions for the amount of carbon released following any given change in land use. The result in Chen et al. (2018) that has been made the default ILUC value for soy biodiesel in GREET (6.3 gCO2e/MJ) is based on emission factors from a model called ‘CCLUB’. If instead the emission factors developed by the California Air Resources Board are used, the results is quite different, 18.3 gCO2e/MJ. It turns out that the CCLUB model uses some very questionable assumptions about soil carbon in particular that explain much of this difference. The use of an incorrect land use history in soil carbon modelling for conversion of ‘cropland pasture’ to permanent cropping results in large carbon credits that are almost certainly not justified. The model also systematically understates the importance of peat drainage emissions when palm oil expands in Indonesia and Malaysia, justified by an inappropriate use of analysis from the EPA’s final RFS rule.
The choice of emission factors alone cuts 12 gCO2e/MJ from the lifecycle estimate in GREET. It’s hard to make a numerical assessment of the overall impact of poorly justified model changes in GTAP, but these likely shave further tens of grams of emissions from the reported numbers.
The palm oil connection
Above, we noted that the CCLUB emissions model has resulted in a systematic underestimation of peat drainage emissions associated with palm oil expansion. In the current GREET modeling, the expansion of oil palm in Southeast Asia accounts for only a small fraction of the global increase in vegetable oil production to meet increasing U.S. demand for soy oil for biodiesel – the model assumes that the principle response is for soybean area and hence soybean oil production to increase. Even so, the peat emissions are an important term, and one of the major differences between results using different sets of emission factors.
There is evidence, however, that GTAP may significantly underestimate the link between U.S. soy oil demand and palm oil production elsewhere in the world. The GTAP model is calibrated based on known trade patterns. There has been relatively little import of palm oil to the United States in the past, and therefore the model assumes that there will be relatively little in future. However, there is evidence that the link between the soy and palm oil markets is stronger than this, and that palm oil exports are actually quite responsive to the use of soy oil in the U.S. The basic idea is that because soybeans are grown in large part in order to produce soy meal, soybean area may be unresponsive to increases in demand for vegetable oils alone. In contrast, the oil palm industry produces very little meal, and therefore oil palm area responds only to vegetable oil demand. The hypothesis is therefore that the palm oil supply will increase even if other vegetable oils are used for biodiesel. In this case, as a limited supply of soy oil is increasingly diverted to biodiesel production, palm oil will be imported to the U.S. to meet supply shortfalls in other markets).
A recent paper presenting an econometric assessment of the linkage between soy oil demand and palm oil imports[7] provided support for this hypothesis, suggesting that the connection between soy oil consumption and palm oil production may be much stronger than GTAP has previously been calibrated to assume. If so, this would likely imply that ILUC emissions from soy biodiesel are larger than previously believed, because the oil palm industry is very strongly associated with tropical deforestation and peat loss[8].
Conclusion
Soy biodiesel is currently treated as an advanced biofuel under the RFS, and recent lifecycle analysis of soy oil by Chen et al. (2018) (corresponding to the default soy biodiesel emissions given in GREET 2017) concluded that, “soy biodiesel could achieve … 66–72% reduction in overall GHG emissions, relative to its petroleum counterpart.” This article explains that the picture for soy biodiesel is in fact much murkier than these results suggest. Both the EPA modeling and the modeling for GREET include a number of modeling assumptions that seem very likely to be unduly favorable, and result in published lifecycle GHG intensity values that are misleadingly low.
In the case of GREET 2017, we have shown that the use of unduly favorable mass based allocation saves about 10 gCO2e/MJ, the use of a flawed emission factor model saves another 10 gCO2e/MJ, and a series of optimistic modeling assumptions in GTAP likely account for published ILUC values being at least a further 10-20 gCO2e/MJ below a reasonable estimate. Recalibrating the modeling to more accurately reflect the link between soy and palm oil markets would further increase the estimated GHG intensity. Taken together, we can conclude that the value of 29 gCO2e/MJ for soy biodiesel given by Chen et al. (2018) could easily be 40 gCO2e/MJ too low, and therefore that soy biodiesel may well deliver much less than a 50% GHG emissions reduction compared to fossil diesel.
Acknowledgement
This article was supported by the National Wildlife Federation.
[1] There is a regular annual drop off in biodiesel blending during the winter months as biodiesel solidifies at higher temperatures than fossil diesel, and thus biodiesel blending must be reduced in cold weather.
[2] Henceforth referred to as “Chen et al. (2018)”. R. Chen, Z. Qin, J. Han, M. Q. Wang, F. Taheripour, W. E. Tyner, D. O’Connor, and J. Duffield, “Life cycle energy and greenhouse gas emission effects of biodiesel in the United States with induced land use change impacts,” Bioresour. Technol., vol. 251, pp. 249–258, 2018.
[6] Malins, C., Plevin, R., Edwards, E. (2019). Accentuating the positive – how robust are reductions in modeled estimates of the indirect land use change from conventional biofuels?
For over ten years, indirect land use change modeling has been an important part of assessing the environmental impact of U.S. biofuel policy. While several models have been developed to undertake these assessments, notably the FAPRI-FASOM model used by the Environmental Protection Agency (EPA) in determining the lifecycle emissions of fuels supported by the Renewable Fuel Standard (RFS) (U.S. Environmental Protection Agency, 2010), the most prolific use of a single model has been the GTAP computational general equilibrium model, used for regulatory analysis by the California Air Resources Board (California Air Resources Board, 2014), in the Argonne National Laboratory’s GREET model (Argonne National Laboratory, 2017) and in a sequence of independent analyses by researchers. The GTAP model itself does not include land use change emission factors, and thus areal land use change results output from GREET must be coupled to emission factor models to produce ILUC results in terms of carbon dioxide emissions per unit of energy produced.
One notable feature of the ILUC factor results reported using the GTAP model over the past decade is that they have shown a tendency to reduce over time, as shown for in the figure below for corn ethanol.
Point ILUC factor estimates for US corn ethanol obtained with variants of the GTAP-BIO model
A forthcoming academic paper[1] will discuss in detail the underlying analytical reasons for these reductions, and queries whether the changes to the model and emissions factors that have driven these changes have been adequately justified. While assessing modeling choices is an interesting exercise, it is also enlightening to compare reported model results to observed changes in U.S. agriculture. One source for observed data on U.S. land use changes that may be associated with the growth of the corn ethanol industry is a series of papers by researchers at the University of Wisconsin (including Lark, Salmon, & Gibbs, 2015; Wright et al., 2017), which use data from the USDA cropland data layer (CDL). Here, we compare ILUC results for corn that are documented by Qin, Dunn, Kwon, Mueller, & Wander (2016) against the observed land use changes in the U.S. in the period 2008-2012 documented in Wright et al. (2017).
Qin et al. (2016) give one of the lowest corn ILUC numbers available in the literature, as low as 2.1 gCO2e/MJ at the bottom end of the interval given (the range reflects different yield and tillage assumptions), but are these results consistent with observed land use changes? Below, the reported model outputs from the ‘Corn2’ scenario in Qin et al. (2016)[2] are considered, and compared to agricultural statistics and the results of the analysis presented in (Wright et al., 2017) for the years 2008-2012, during which ethanol productions increased by 4.2 billion gallons. It is important to note up front that it is always difficult to compare observed land use changes to the results of indirect land use change models. It is generally not possible to definitively identify whether a given land use change would have occurred in the absence of biofuel demand. In contrast, in ILUC modeling we know by hypothesis that all land use changes reported are due to biofuel demand. Apparent inconsistencies between observed land use data and ILUC models may have several reasonable explanations. The results noted below cannot refute the results of the modeling, but do perhaps suggest that some model assumptions ought to be reexamined.
Qin et al. (2016) consider an increase of biofuel demand by 11.59 billion gallons (the ‘shock’), which is about equivalent to the increase in ethanol production in the U.S. between 2004 and 2015 (based on data from the EIA[3]), and assess how much land use change was likely to have been caused by that increase. Given average corn production[4] and ethanol refinery[5] yields for the period 2011-2015, producing a billion gallons of ethanol requires about a million hectares of corn. The net cropland expansion in the U.S. modeled by Qin et al. (2016) is much less than this however, only 160 thousand hectares per billion gallons of additional corn ethanol demand. Almost all of the U.S. land use change (154 thousand hectares per billion gallons) was modeled coming from a category of land identified as ‘cropland pasture’ – defined by the USDA as land that at some previous point was cropped, and is still suitable for cropping, but since then has been use as pasture. A further 112 thousand hectares of land use change per billion gallons are predicted outside the U.S. That the net cropland expansion modeled should be less than the absolute cropland requirement is not surprising to anyone familiar with ILUC modeling. There are several effects that reduce net requirements for additional land – notably these include the use of co-products from ethanol production (distillers’ grains) as animal feed, reducing demand for food[6], and the possibility that agricultural productivity increases (Malins, Searle, & Baral, 2014). While it is not surprising that the net land requirement is less than the gross requirement, it is clear that GTAP is modelling a strong role of responses other than land use change in meeting additional corn demand, given that the net land demand is only about a quarter of the gross.
Wright et al. (2017), looking at satellite data, identify 1.1 million hectares of land within 50 miles of ethanol refineries being converted from non-crop status to cropping in the period 2008-2012, with most of this land (700 thousand hectares) converted to corn or soybean cropping. A further 600 thousand hectares of land from 50 to 100 miles from ethanol refineries was converted from non-crop statuses, though over half of this was planted with other crops. Further than 100 miles from corn ethanol refineries only 20% of newly converted land was planted with corn or soybeans[7]. Wright et al. (2017) argue that depending on the mix of rotations adopted (how much land remains in corn-soy rotation and how much is given over to continuous corn) then their analysis would be consistent with between 500,000 and 1 million hectares of previously uncropped land being converted for corn production within 100 miles of ethanol refineries over the period considered. If all of this additional corn area could indeed be attributed to the corn ethanol mandate, that represents somewhere between 130 thousand and 260 thousand hectares of land use change to corn production for every billion gallons of additional ethanol demand in that period, comparable to the modelled values. Data from the AFDC[8] shows that during this period use of corn for non-fuel applications reduced, so it is not unreasonable to assume that most conversion of uncropped land to corn production in this period was a response to the corn ethanol mandate. Preliminary analysis by the same group (Lark et al., 2019) estimates that 1.1 million hectares of land conversion since 2005 is associated with the ethanol mandate of the RFS2, about 200 thousand hectares per billion gallons.
Another comparison point comes from related work supported by the National Wildlife Federation (Hendricks, 2018), which aims to provide an economic assessment of the impact of the RFS2 on land use changes in the period 2008-2016 using data from the National Resources Inventory (NRI). This assessment attributes 1.2 million hectares of net new cropland in the period 2009-2016 to the RFS, predominantly coming from conversion of former CRP land. If all of this was associated with the corn mandate, it would be equivalent to about 190 thousand hectare per additional billion gallons of ethanol production – part of this expansion ought to be associated to the biodiesel mandate, and so the overall conclusion is quite comparable to the U.S. land use change modeled by (Qin et al., 2016).
While the realized rates of land use change for corn ethanol production identified in these studies are comparable to those modeled by Qin et al. (2016), there is evidence that the role of the cropland pasture land category as a source of cropland may be dramatically overstated by the GTAP modeling. For example, using the same dataset as considered by Wright et al. (2017), this study finds that 22% of newly cropped land in the period 2008-2012 was converted from “long-term (20 + year) unimproved grasslands”. This compares to the (Qin et al., 2016) result in which at most 5% of converted grasslands could have possibly have fallen into the long-term unimproved category[9]. The cropland pasture category in GTAP does not directly correspond to any single land category in the University of Wisconsin assessments, but the observations certainly suggest that assuming that over 90% of new cropland associated with ethanol demand comes from cropland pasture is likely to be a considerable over-estimate. The economic analysis by (Hendricks, 2018) also gives a very different result to the GTAP modeling. This economic analysis suggests that over 95% of grassland converted to cropping due to RFS was former CRP land. As CRP land is not pastured, it does not fall under the definition of cropland pasture in the agricultural census, and indeed GTAP has a separate category for CRP land introduced at the same time as the cropland pasture category.
Lark et al. (2015) also document rates of forest land conversion to cropland that appear to be larger than anticipated by Qin et al. (2016)[10], although it is even more difficult for these relatively small forest area changes to draw a firm conclusion about whether this should be attributed to the ethanol mandate.
These discrepancies regarding the source of new land are potentially important in the ILUC analysis, because different land use changes have very different assumed carbon losses in the modeling. In particular, when using the CCLUB emission factors included in the GREET model the conversion of cropland pasture to corn is assumed to result in an increase in carbon sequestration, whereas Lark et al. (2015) argue that the conversion to cropland they identify is likely to be associated with very significant carbon dioxide emissions. This fundamentally different interpretation seems to arise primarily due to the use of a rather questionable modeling assumption in the development of the CCLUB emission factors. In CCLUB, soil carbon under the cropland pasture land use is modeled using DAYCENT and treating cropland pasture as if it has uniformly been cropped before 1951, then used as pasture for 25 years before spending the last 35 years being farmed for a generic crop. This land use history is almost the opposite of the cropland pasture definition (previously cropped but then used as pasture for several years. Combining this difficult to justify modeling choice with the high rates of cropland pasture conversion modeled with GTAP by Qin et al. (2016) leads to a dramatic reduction in the predicted ILUC factor.
The importance of these emission factor assumptions can be further illustrated by comparing the average carbon loss per hectare assumed for conversion of grassland to cropland in Qin et al. (2016) (for the Corn2 scenario with CCLUB emission factors) with average values estimated by Spawn, Lark, & Gibbs (2012) for grassland conversion in the Lark et al., (2015) results. Spawn et al. (2012) estimate an average loss of about 50 tonnes of carbon per hectare for conversion of new land to corn agriculture. Qin et al. (2016) in contrast assume an average increase by 13 tonnes of carbon stored per hectare.
Summary
Recent analysis with the GTAP model has suggested that ILUC emissions from corn ethanol may be much lower than was calculated in either the initial or revised ILUC analysis for the California Air Resources Board, or by the US Environmental Protection Agency. The results from Qin et al. (2016) using GTAP with the CCLUB emission factor model have even been integrated into the GREET lifecycle analysis model. Comparing these GTAP results to land use changes identified by other sources, however, suggests that total U.S. land use changes may be slightly underestimated, and strongly suggests that the role of cropland pasture as a land source may be significantly exaggerated. These features of recent GTAP modeling, combined with highly questionable assumptions about carbon sequestration when cropland pasture is converted to corn cropping have likely led to significant underestimates of likely ILUC emissions.
References
Argonne National Laboratory. (2017). The Greenhouse gases, Regulated Emissions, and Energy use in Transportation Model. Retrieved August 2, 2018, from https://greet.es.anl.gov/
California Air Resources Board. (2014). Appendix I – Detailed analysis for indirect land use change. Sacramento, CA. Retrieved from https://www.arb.ca.gov/regact/2015/lcfs2015/lcfs15appi.pdf
Hendricks, N. P. (2018). The Impact of the Renewable Fuels Standard on Cropland Transitions.
Lark, T. J., Hendricks, N. P., Pates, N., Smith, A., Spawn, S. A., Bougie, M., … Gibbs, H. K. (2019). IMPACTS OF THE RENEWABLE FUEL STANDARD ON AMERICA ’ S LAND AND WATER. In American Academy for the Advancement of Science (AAAS) Annual Meeting (pp. 1–13). Washington DC.
Lark, T. J., Salmon, J. M., & Gibbs, H. K. (2015). Cropland expansion outpaces agricultural and biofuel policies in the United States. Environmental Research Letters, 10, 44003. Retrieved from http://stacks.iop.org/1748-9326/10/i=4/a=044003
Malins, C., Searle, S. Y., & Baral, A. (2014). A Guide for the Perplexed to the Indirect Effects of Biofuels Production. International Council on Clean Transportation. Retrieved from http://www.theicct.org/guide-perplexed-indirect-effects-biofuels-production
Qin, Z., Dunn, J. B., Kwon, H., Mueller, S., & Wander, M. M. (2016). Influence of spatially dependent, modeled soil carbon emission factors on life-cycle greenhouse gas emissions of corn and cellulosic ethanol. GCB Bioenergy, 8(6), 1136–1149. http://doi.org/10.1111/gcbb.12333
Spawn, S. A., Lark, T. J., & Gibbs, H. K. (2012). U.S. cropland expansion released 115 million tons of carbon (2008-2012). In America’s Grasslands Conference 11/15/2017, Fort Worth, TX. Fort Worth. Retrieved from http://www.gibbs-lab.com/wp-content/uploads/2017/11/spawn_Summary.pdf
U.S. Environmental Protection Agency. (2010). Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis, 2010(February 2010), 1109. http://doi.org/EPA-420-R-10-006., February 2010
Wright, C. K., Larson, B., Lark, T. J., Gibbs, H. K., Salmon, J. M., Gibbs, H. K., … Gibbs, H. K. (2017). Recent grassland losses are concentrated around U. S. ethanol refineries. Environmental Research Letters, 12(4), 044001. http://doi.org/10.1088/1748-9326/aa6446
Acknowledgement
This article was supported by the National Wildlife Federation.
[1] Malins, C., Plevin, R., Edwards, E. (2019). Accentuating the positive – how robust are reductions in modeled estimates of the indirect land use change from conventional biofuels?
[2] Referred to in the CCLUB module of GREET as the ‘Corn 2013’ scenario.
[9] Of a total 1.9 million acres of conversion of cropland pasture plus other grassland in GTAP-CCLUB, 95% was from cropland pasture.
[10] In 2008-12 nineteen thousand hectare of forest conversion for every additional billion gallons of ethanol demand, about half of it identified by (Wright et al., 2017) as within 100 miles of ethanol refineries compared to 6 thousand hectares per billion gallons in (Qin et al., 2016).
https://www.cerulogy.com/wp-content/uploads/2017/07/thumb-nails_3.png300300Chris Malinshttps://www.cerulogy.com/wp-content/uploads/2021/05/cerulogy-logo-156h.jpgChris Malins2019-03-29 18:15:422022-10-31 20:27:41Comparing GTAP ILUC results to observations of ethanol related land use change
Indirect land use change, often abbreviated to ILUC, refers to the expected expansion of agricultural area (and subsequent release of carbon from biomass and soils) when biofuel policies increase demand for agricultural commodities. In 2008, research led by Tim Searchinger using the FAPRI economic model[1] suggested that accounting for these ILUC emissions might eliminate the presumed climate benefit of using corn ethanol, but more recent analyses have suggested that the ILUC effect is much smaller than originally suggested. ILUC cannot be measured directly, because by definition it is an indirect effect where causality is mediated by market mechanisms, and so researchers have developed complex systems of economic modeling to produce scenarios for the way we might expect the agricultural system to react to increased biofuel demand.
In the U.S., one of these models has taken an increasingly dominant role in ILUC analysis – GTAP, the computable general equilibrium economic model of the Global Trade Analysis Project. Results from GTAP are used in the Low Carbon Fuel Standard regulation in California, and have been integrated into the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model of the Argonne National Laboratory. With a steady stream of results, updates and model adjustments, the ILUC research using GTAP is probably the most prolific ILUC modeling strand in the world in terms of the number of papers published and ILUC estimates recorded.
In 2009 when the California Air Resources Board first regulated for ILUC emissions, they estimated the ILUC from corn ethanol production at 30 gCO2e/MJ[2] and the ILUC from soy biodiesel at 62 gCO2e/MJ. More recent GTAP estimates, however, have been as low as 10 gCO2e/MJ for both fuels (e.g. Chen et al., 2018; Qin, Dunn, Kwon, Mueller, & Wander, 2016). The figure below shows the downward tendency over time in ILUC estimates reported for soy biodiesel using GTAP. A reduction by 20 gCO2e/MJ or more in estimated ILUC emissions can significantly affect our understanding of how much climate benefit a fuel delivers, and by affecting regulatory treatment of biofuels a lower lifecycle carbon emissions value can have large financial implications.
Point ILUC factor estimates for US corn ethanol obtained with variants of the GTAP-BIO model
Given the very large reported reductions in estimated ILUC in some of the lower results compared to the initial CARB assessments, up to a 60% reduction for corn ethanol and a 90% reduction for soy biodiesel, it is useful to understand why the numbers have fallen so far. A forthcoming paper co-authored by Chris Malins of Cerulogy reviews in detail innovations introduced to the modeling framework since 2009, and concludes that the evidentiary basis for many of the changes that have led to reduced ILUC estimates is weak.
The review looks in particular at changes made to the model in five areas over the past decade, reviewing the strength of evidence for the changes made and the impact of the changes: the intensive yield response; the role of cropland pasture; cropping intensity change; yield at the extensive margin; and the emission factors associated with land use changes.
Intensive yield change
The intensive yield change refers to the amount that agricultural yields can be increased in response to increased demand. A strong intensive yield response means less extra land is needed to produce biofuel feedstock. The GTAP modelers have generally preferred a value of 0.25[3] as a central estimate for this parameter, based on analysis of historical corn yields. There have however always been highly divergent expert opinions on this question. For example, economist Steve Berry argued in a report for the Air Resources Board that there is no robust econometric evidence for the value of 0.25, and that ta value of 0.1 would be more appropriate given the data available.[4]
While the Air Resources Board ended up using a reduced average yield response value in its 2014 regulatory modeling (compared to the value of 0.25 used in its 2009 work), subsequent studies with GTAP have continued to be based on the parameter of 0.25. Indeed, since 2017 the response has effectively been increased even further, due to a model amendment which implemented differentiated yield response by region, but increased it in more regions than it was reduced in. These asymmetric changes were made despite presenting no evidence that the previous yield response had been too weak on average.
For assessing the ILUC of soy biodiesel, it is also important to note that there is some evidence in the literature that the yield response for soybeans is much weaker than that for corn. Applying differentiated yield responses for other crops would therefore likely have increased ILUC results, especially for soy biodiesel. It is doubly unfortunate for the soy analysis that while regional yield responses have been implemented in a way that strengthens the yield response for all feedstocks, but no action has been taken to differentiate in the modeling the yield response of different crops.
Cropland pasture
Cropland pasture is a land category in USDA reporting that refers to areas that are currently pastured but that have been used for annual crops in the (relatively) recent past. This land category was added to the GTAP database for the U.S. and Brazil in 2010, presumed to have lower carbon stocks than other land types. The model assumes that conversion of cropland pasture to crop production is easier than conversion of forest or permanent pasture, and so introducing this land category immediately reduced ILUC emissions. Since then, a sequence of amendments to model structure and parameters have continually increased the importance of cropland pasture conversion in the model results.
While cropland pasture has taken on a central role in the GTAP modeling, the evidence that it is so readily converted to cropland is actually rather patchy. Statistical data on cropland pasture area is complicated by the fact that the wording of USDA questionnaires has changed, which USDA believe may have caused a large reduction in reported areas. Despite the lack of evidence to support such a major change to the modeling, a large elasticity of cropland pasture yield to rent was subsequently introduced, also without any direct evidence to support the value chosen. Changes made in 2013 to regionalize the land use changes associated with price increases further reduced the likelihood of forest or permanent pasture conversion in many regions, based on weak evidence, thus again increasing the role of cropland pasture still further. An alternative regional approach suggested in a 2012 paper would have given a more balanced result, but has subsequently been ignored.
Cropping intensity
Cropping intensity refers to the number of times a given area of land can be harvested in one year. Traditional annual cropping includes only one harvest, but especially in warmer climates it may be possible to grow two or even more crops in a single year. This possibility was not explicitly included in the original GTAP ILUC modeling, although some experts argued that it was taken into account through the yield response. Since 2017, increased cropping intensity has been supported by GTAP modeling, and as one might expect has further reduced predicted ILUC emissions. Most countries do not directly report on cropping intensity, and thus analysis of cropping intensity changes has tended to rely on comparing harvested and planted area data, even though datasets may not be readily comparable. No analysis has been presented that robustly demonstrates a link between demand or price increases and increased cropping intensity – major weaknesses in one of the studies used to justify introducing a strong response are documented in the appendix to a previous paper.[5]
Extensive yield
In general, we expect that farmers will preferentially plant the best land available to them, and therefore that yields will be higher on land already being farmed than on areas where agriculture expands. In the earlier GTAP modeling it was assumed that new agricultural land would have a yield two thirds that achieved on land already farmed, but this was revised based on work published in 2010. A global assessment was undertaken of expected ‘net primary production’ (NPP) using an ecosystem model, and it was assumed that the difference modeled in NPP between areas under cropping and areas under natural cover would indicate the likely difference between yields on existing land and newly farmed land. Using the new system generally resulted in increased assumed yields on newly farmed land, but questions remain about whether the analytical approach used was appropriate, and whether it had adequate resolution. In particular, it has not been explained why for many regions the new results seemed to run counter to economic logic – notably, why would farmers not have been more successful in identifying and farming the most promising land? These questions have never been resolved.
Emission factors
The economic model provides a prediction for which types of land may be converted due to biofuel demand, but turning this into an emissions estimate requires making assumptions about the carbon stock changes following land use changes. Since 2014, the ILUC estimates included in the GREET model have not by default used the emission factors developed for the California Air Resources Board, instead relying on the Carbon Calculator for Land Use Change from Biofuels Production (CCLUB).[6] This model results in systematically lower ILUC estimates than are obtained when using alternative carbon stock change values.
One major reason for the difference comes back to cropland pasture. The CCLUB modeling assumes that there are significant increases in carbon sequestration when cropland pasture is converted to annual cropping. The basis for this is a somewhat baffling underlying modeling decision to treat cropland pasture as if it was under annual crops from 1976 to 2010, and then converted to corn or soy agriculture in 2011. This directly contradicts the definition of cropland pasture, which of course must have been used for pasture immediately before conversion. This odd modeling choice is compounded by some difficult-to-justify assumptions about increased carbon sequestration in corn and soy agriculture compared to other annual crops. The upshot is that cropland pasture conversion, which in real life almost certainly results in carbon losses, is treated as a carbon gain. For the soy biodiesel pathway, there are also difficult-to-justify modeling choices made that result in underestimated emissions from peat drainage associated with oil palm expansion in Southeast Asia – for instance averaging prevalence of peat soils across administrative districts with no reference to their size or suitability for palm agriculture, so that the city of Jakarta is weighted equally with areas on the oil palm frontier in Kalimantan.
Conclusions
The more detailed review of these issues paints a picture of a systematic and chronic willingness within the community that has developed the ILUC modeling in GTAP to make modeling decisions that reduce ILUC estimates, even where the evidence is weak. The resulting downward bias in model outcomes is compounded by an apparent corresponding resistance to invest time in model amendments that would increase ILUC estimates, even where the evidence base is relatively strong. Even if strong evidence was available for the modeling changes made, choosing to focus only on changes that reduce ILUC emissions would gradually introduce a downward bias into the results. When the bar is set low for the quality of supporting evidence, this bias manifests itself in the results very quickly.
The result is a long-term optimism bias pushing reported ILUC emissions ever lower, so that it is impossible to conclude with any confidence to what extent the reported reductions represent a real improvement in our understanding of ILUC and to what extent they reflect a subjective decision by a small group of modelers that the numbers ought to be smaller.
ILUC emissions are important – they are used in regulatory analysis, and our understanding of ILUC informs our understanding of whether biofuel policies are effective tools to reduce climate change. New, lower ILUC results are often presented as having great policy importance – one recent paper that presented a lower ILUC value for corn ethanol concluded that, “it is important to note the importance of the new results for the regulatory process. …[because] the current estimate values are substantially less than the values currently being used for regulatory purposes.”[7]
On the contrary, reviewing the development of the GTAP model leads not to the conclusion that regulators ought to unquestioningly adopt the most recent analysis, but that it is vital that any regulatory assessment of ILUC emissions should be balanced and should not rely too heavily on the work of any single modeling group. The California process has always involved public consultation and expert workgroups, providing a degree of balance. In contrast, the ILUC results used in the GREET model are not tempered by any formal consultative process. Any future revisions to regulatory ILUC values must involve an open and honest assessment of the evidence base for all model features, and should ensure that possible model changes get equal consideration, regardless of whether they would be expected to increase ILUC emissions outcomes or reduce them.
References
California Air Resources Board. (2009). Proposed Regulation to Implement the Low Carbon Fuel Standard, Volume I, Staff Report: Initial Statement of Reasons. Sacramento, CA: California Air Resources Board. Retrieved from http://www.arb.ca.gov/regact/2009/lcfs09/lcfsisor1.pdf
California Air Resources Board. (2014). Appendix I – Detailed analysis for indirect land use change. Sacramento, CA. Retrieved from https://www.arb.ca.gov/regact/2015/lcfs2015/lcfs15appi.pdf
Chen, R., Qin, Z., Han, J., Wang, M. Q., Taheripour, F., Tyner, W. E., … Duffield, J. (2018). Life cycle energy and greenhouse gas emission effects of biodiesel in the United States with induced land use change impacts. Bioresource Technology, 251, 249–258. http://doi.org/10.1016/j.biortech.2017.12.031
Hertel, T. W., Golub, A. A., Jones, A. D., O’Hare, M., Plevin, R. J., & Kammen, D. M. (2010). Effects of US Maize Ethanol on Global Land Use and Greenhouse Gas Emissions: Estimating Market-mediated Responses. BioScience, 60(3), 223–231. http://doi.org/10.1525/bio.2010.60.3.8
Qin, Z., Dunn, J. B., Kwon, H., Mueller, S., & Wander, M. M. (2016). Influence of spatially dependent, modeled soil carbon emission factors on life-cycle greenhouse gas emissions of corn and cellulosic ethanol. GCB Bioenergy, 8(6), 1136–1149. http://doi.org/10.1111/gcbb.12333
Taheripour, F., Cui, H., & Tyner, W. E. (2017). An Exploration of agricultural land use change at the intensive and extensive margins: implications for biofuels induced land use change. In Bioenergy and Land Use Change (pp. 19–37). Retrieved from https://books.google.co.uk/books?hl=en&lr=&id=vWk9DwAAQBAJ&oi=fnd&pg=PA19&dq=An+Exploration+of+agricultural+land+use+change+at+the+intensive+and+extensive+margins&ots=DCLdhoHgYh&sig=heg7uMycBk6hpQ4W0q0jQFl9Ugc
Taheripour, F., & Tyner, W. E. (2013a). Biofuels and Land Use Change: Applying Recent Evidence to Model Estimates. Applied Sciences, 3, 14–38. Retrieved from http://www.mdpi.com/2076-3417/3/1/14
Taheripour, F., & Tyner, W. E. (2013b). Induced Land Use Emissions due to First and Second Generation Biofuels and Uncertainty in Land Use Emission Factors. Economics Research International, 2013, 1–12. http://doi.org/10.1155/2013/315787
Taheripour, F., Zhao, X., & Tyner, W. E. (2017). The impact of considering land intensification and updated data on biofuels land use change and emissions estimatesmen. Biotechnology for Biofuels, 10(1), 1–16. http://doi.org/10.1186/s13068-017-0877-y
Tyner, W. E., Taheripour, F., Zhuang, Q., Birur, D. K., & Baldos, U. (2010). Land Use Changes and Consequent CO2 Emissions due to US Corn Ethanol Production: A Comprehensive Analysis. Department of Agricultural Economics, Purdue University, 1–90. Retrieved from http://www.transportation.anl.gov/pdfs/MC/625.PDF
Acknowledgement
This article was supported by the National Wildlife Federation.
[2] Grams of additional carbon dioxide equivalent emissions due to indirect land use change for every megajoule of biofuel produced.
[3] This is the elasticity of yield to own price, the fraction by which the yield of crops increases for every 100% increase in the price of that crop.
…you’ll never believe the regulatory mistakes the EU is at risk of making in the RED II*
In a blog post just under a year ago I heralded the generally positive qualities of the European Commission’s proposal for a revised Renewable Energy Directive for the period 2021-2030 (RED II). Since then, both the European Parliament and the European Council have started to develop their positions on the legislation. Plenty of organisations have proffered opinions on some of the big picture questions posed by the Directive (what should be the level of ambition for advanced biofuels, what cap would be appropriate for food-based fuels and such), and I don’t intend to revisit any of those questions today. Instead, I’d like to talk about a few details in the draft regulatory language that might represent a real problem down the line if the Directive is implemented. Without further ado therefore, and embracing the clickbait conventions of the zeitgeist, here’s a Cerulogy listicle:
Three crazy details in the 13th November European Council draft of RED II that could have regulators (and environmentalists and investors) tearing their hair out in the years to come
1. An inadequate definition of ‘low iLUC risk’ biofuels
The proposed RED II caps the extent to which food-based biofuels can be used to meet renewable energy targets at 7%, but it creates a get-out for food-based fuels that meet the definition of being, ‘low indirect land-use change-risk biofuels and bioliquids’. The idea of low ILUC-risk biofuels has been around for years, and it’s fundamentally a sensible idea. If displacing existing uses of food-commodities causes ILUC, why don’t we just avoid displacing existing uses? It’s an idea that was supported in the Gallagher Review, and that I myself have spoken in favour of on several occasions. The problem (as I pointed out in a blog for the ICCT a few years ago) is that it’s not enough to just say that a biofuel has low ILUC risk – this has to be robustly demonstrated, and demonstrating it is complex. If the oversight systems don’t work, you will very quickly end up certifying biofuels as ‘low ILUC risk’ even though they are nothing of the kind. Unfortunately, the current language of RED II is incredibly vague on the subject. The definition given for a low ILUC risk biofuel is that the feedstock must be ‘produced within schemes which reduce the displacement of production for purposes other than for making biofuels and bioliquids’. The definition doesn’t say how much displacement should be reduced. To avoid ILUC completely it would have to be reduced by 100%, but what about a scheme that reduces displacement by only 10%, or only 1%? Given the proposed language, Member States may find it difficult to put robust requirements in their national implementations. Before you know it, the EU will be up to its neck in palm oil from replanting programmes getting certified as low ILUC risk – even if the programmes deliver only modest yield improvements and the true ILUC impact of the palm oil is still very high indeed. The solution? The language of the Directive should be revised from ‘reducing’ displacement to ‘avoiding’ displacement, and a delegated act should be added for the European Commission to define robust protocols to evaluate schemes. Further discussion of the challenges of ILUC mitigation is available in this ICCT paper.
2. Double counting renewability of electrofuels
The proposed Directive allows electrofuels (fuels that are produced by electrolysing water to produce hydrogen and reacting the hydrogen with carbon monoxide or dioxide to produce hydrocarbons, referred to as ‘renewable fuels of non-biological origin’ in the proposed Directive), to be counted towards the transport sector obligation for renewable energy use. This is reasonable enough – liquid fuels produced from renewable electricity present much lower sustainability risks than most biofuels, and electrofuel technologies would provide a valuable additional option for producing low-carbon drop-in liquid fuels. The problem is that way that electrofuels are counted towards overall Member State renewable energy targets. Whereas advanced biofuels are counted based on the energy content of the fuel, it is proposed that electrofuels would be counted based on the amount of electricity required to produce them. Because the efficiency of conversion of electricity to drop-in fuels will be only about 40% with currently available technologies, that means that electrofuels would count two and a half times more to overall targets than advanced biofuels would. Why is this a problem? Because that means that if a Member State uses electrofuels instead of biofuels for the transport target, there is a reduced requirement for renewables in heat and power, reducing the true ambition of the renewables target. Indeed, the less efficient electrofuel production is the less renewable energy in heat and power will be needed to meet targets. The solution? Count electrofuels in the overall Member State target by the energy delivered to transport, not the energy used to produce them.Watch this space for a forthcoming Cerulogy report on electrofuels.
3. No mechanism to deal with developing ILUC science
If there’s one thing that everyone seems to agree on about the ILUC discussion since 2009, it’s that it created uncertainty for investors considering entering the biofuel market. The draft Directive seems to be trying to create certainty by writing in ILUC values and sticking to them – the Annexes include estimated average values, and ranges, for three groups of biofuels (starchy, sugary and oily). Anything without a value is assigned an ILUC estimate of zero, and there appears to be no accommodation for the values to be revised to reflect new evidence. Also, while the initial proposal provided a general power for Member States to “distinguish between different types of biofuels and bioliquids produced from food and feed crops”, the latest Council edit restricts that power to “categories set out in Annex VIII”. That appears to mean no distinction between different feedstocks in a category. Given that more recent ILUC analysis for the Commission suggests, for instance, that palm oil biodiesel could have four times the ILUC of some other vegetable oils, it seems both shirt-sighted and ill-judged to preclude Member States from taking evidence on differences in ILUC impact into consideration. More generally, while there are certainly those in industry who believe that locking in ILUC estimates in this way reduces future uncertainty, I disagree. Rather than preventing ILUC from becoming an issue again in the 20s, I think this rigidity almost guarantees it. By preventing the Commission from adjusting to new science through delegated acts, the proposed Directive guarantees that every time new evidence on ILUC comes to like, there will be calls to reopen the Directive entirely with new legislation. For the advanced alternative fuels industry, that increased risk that the Directive could be subjected to wholesale revision before 2030 can only be a barrier to fundraising. The solution? Provide greater leeway for Member States and/or the Commission to react appropriately to existing and new evidence on ILUC and other sustainability problems.
Fingers crossed for good outcomes as the process moves forward!
* Clickbait titles are just the worst aren’t they?
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