Supporting Materialfor Searchinger, Beringer, Strong,

Does the world have low carbon bioenergy potential from the dedicated use of land?

Why Table 1 Does Not Focus on BiodieselEthanol is much more likely to achieve net benefits in GHGs than biodiesel counting land use change because its yields of biofuel per hectare are substantially higher than soybean and rapeseed biodiesel of Edwards et al., 2014. (Biodiesel emissions from oil palm per hectare can be comparable with maize ethanol (Evans et al., 2015), but requires lands suitable for tropical rainforests, and so has much higher land use opportunity costs.) In addition, estimates of cellulosic ethanol production emissions are substantially lower than biodiesel, also increasing the opportunity to have net benefits (Edwards et al., 2014).

Sources and assumptions used to prepare table 1 “Potential benefits and opportunity costs per hectare of dedicating land for biofuels.”We have deliberately chosen yields and production emissions for biofuels

Maize ethanol assumes maize yields of 10 t/ha/y, roughly the U.S. average1, and 50% savings compared to gasoline ignoring land use change. Such 50% savings for ethanol, excluding land use exceed the most recent estimates by the European Commission Joint Research Committee (JRC) A recent analysis by the Joint Research Center of the European Commission (Edwards et al., 2014) estimates maize production emissions at 68.9 gCO2/MJ with DDGS as animal feed equivalent to a 21% reduction in emissions relative to fossil fuels. Our assumption of a 50% reduction is therefore highly optimistic. Some analysis report possible emission reductions from corn ethanol of 50% (Wang et al., 2012) or even up to 70% (studies cited in Evans et al., 2015), but they assume no emissions from land use change or improved process management and technologies in the future. As the Evans supplement shows, many of these studies projecting high emissions reductions also assume that the added energy in the fermentation process is due to bioenergy, which is counted as carbon neutral (Searchinger et al., 2015a).

We assume no net production emissions of cellulosic ethanol, and therefore 100% greenhouse gas savings of gasoline emissions based on the most variation of the GREET model cited in Table S2 of Evans et al., 2015. Zero emissions is possible only because of electricity by-product credits.

Gasoline emissions are 87.1 gCO2eq/MJ based on recent estimates by the Joint Research Center (Edwards et al., 2014).

All cellulosic ethanol assumes yields of 379 liters per dry ton, a value at the upper end of results from recent techno-economic studies on cellulosic ethanol production

1 www.nass.usda.gov

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(Humbird et al., 2011) and above values applied in recent assessments of future biomass potentials (U.S. Department of Energy, 2016).

We show calculations for yields of perennial grasses are and yields either (a) at the highest rates for any region of the U.S. used by the U.S. EPA in rulemaking as discussed in (Plevin, 2010) , (b) at rates for Miscanthus estimated by a research group highly optimistic about Miscanthus (Hudiburg et al., 2015).

Some papers claiming large bioenergy potentials use even more optimistic assumptions of high future biomass yields and conversion efficiencies (Evans et al., 2015; Popp et al., 2014; Xue et al., 2016). However, we agree with Searle & Malins (2014) that these projections extrapolate inappropriately from small tests, and generally fail to acknowledge that in the real world, farmers do not come close to achieving the yields of researcher (Lobell et al., 2009). In addition, if conversion efficiencies are higher, then less biomass will be available for electricity co-products, whose benefits are needed for the assumption of zero net production emissions. This calculation assumes no loss of biomass during drying, which can be substantial (Searle et al., 2016)).

We also show carbon sequestration rates for perennial crops adding .6 tC/h/y for soil carbon sequestration based on a meta-analysis of studies for grass bioenergy crops planted on cropland (Harris et al., 2015). Although these rates seem plausible, they assume that perennial crops will be perpetually grown on this land. Farmers will generally remain free to plant alternative crops as prices changes over time, which would cause much of this carbon to be lost. As a result, it is unlikely soil carbon gains would average these levels across fields. These soil carbon gains are also unlikely to occur if perennial grasses are planted into grasslands.

For humid tropical forests, seasonal tropical forests and humid savannas, we used figures from the supplement in Gibbs et al. (2008) assuming loss of vegetative carbon and 25% of soil carbon.

For clearing of temperate forests, we used soil and vegetative carbon stock averages as estimated and cited in in Pan et al. (2011), Table S.4 and assumed loss of vegetative carbon and 25% of soil organic carbon (SOC) within the top meter of the soil (Gibbs et al., 2008; Searchinger et al., 2008). Recent meta-analysis of land use change impacts on soil organic carbon (SOC) stocks report observed losses between 25% and 40% following the conversion of primary tropical forest to agricultural land(Don et al., 2011; Guo and Gifford, 2002; Wei et al., 2014), but these estimates are often for soil depths less than 1 meter. In the temperate zone, the conversion of native forests and grasslands results in SOC losses between 30% and 50% (Poeplau et al., 2011; Wei et al., 2014) but again with highly variable depths. The variability in the data and differences in depths do not permit a truly rigorous, quantitative analysis of this percentage. Because soil carbon tends to decline at deeper depths, our assumption regarding SOC losses in the top meter following the conversion of natural vegetation into agricultural land is a reasonable, average interpretation of this data.Our assumptions are also conservative, because many temperate forest stocks have been cut and are re-growing and we do not count foregone carbon sequestration.

We choose 1 tC/ha/y for grassland regeneration. This figure is consistent with figures selected by Righellato & Spracken (2007), reported by Conant et al. (2001), and with calculation of Conservation Reserve Program Lands set forth in Gelfand et al. (2011). However, several other more recent papers suggest potentially much higher rates in early years of reestablishing grasses, perhaps depending on management. For

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example, Machmuller et al. (2015) found an average of carbon accumulation of 8 tC/ha/y over the first six years of conversion of cropland on three farms in the Southeastern United States to management intensive grazing.

For carbon sequestration rates for reforesting tropical forests, we show an average calculated by a recent meta-analysis for above-ground vegetation with an average of 3.1 tC/ha/y over 20 years (Poorter et al., 2016), and adjust that to account for below-ground vegetation based at ~20% on average (Martin et al., 2013). The IPCC used a range of numbers from 2.5-4. (IPCC, 2000).

For temperate forests, we use a figure of 3 tC/ha/y based on a range of numbers from 2.5-4.5 tC/ha (IPCC, 2000).

Uncertainties in cellulosic energy crops and ethanol yieldsUnderstanding the full GHG mitigation potentials of cellulosic ethanol is challenging, because there is still hardly any commercial-scale cultivation of cellulosic energy crops as well as cellulosic ethanol production. Expectations of high biomass yields and conversion efficiencies are still based on small-scale field trials with uncertain transferability to commercial production.

It is unlikely that high yield levels of short rotation coppice or C4 grasses such as Miscanthus or switchgrass observed on small field trials under favorable conditions and management practices are possible under large-scale, industrial cultivation. Main reasons lower commercial-scale yields are cultivation on less suitable land or suboptimal clone suitability for a site. Plants in the center of large, high-density plantations will also receive less sunlight compared to the edges which reduces productivity. To avoid energy-intensive post-harvest drying of the biomass, some perennial grasses are left on the field over winter and harvested after senescence. Miscanthus and switchgrass loose between 20 and 50% of their biomass during this drying period. Mechanical harvesting necessary on large plots is also less effective than hand-harvesting typical to field trials leading to reduced yield levels even if energy crops reach the same level of productivity. First observations from semi-commercial scale field trials indicate that yields from large scale cellulosic energy crop plantations may reach only 20-50 % of yields levels technically possible (Searle and Malins, 2014).

Many scientific analyses of bioenergy use observations from test sites and may therefore overestimate production and mitigation potentials of biofuels and other bioenergy technologies. For example, two recent studies assume average Miscanthus yields between 19.5 and 31.4 tDM/ha for temperate regions (Dwivedi et al., 2015; Evans et al., 2015), while the analysis of Searle et al. (2014) suggests that commercial scale yields may only range from 7 to 15 tDM/ha. As noted in the main text, modeling for the U.S. Environmental Protection Agency for rulemaking of switchgrass yields estimated highest regional yields in the wetter, southern portions of the U.S. at 9tDM/ha, and much lower values elsewhere.

Similar uncertainties exist for cellulosic ethanol production when the technology moves from pilot projects to industrial scale application. A recent assessment of GHG mitigation values from biofuels applied a range of possible conversion values between 310 and 434 l/tDM to account for this uncertainty (Evans et al., 2015). Another assessment found conversion values between 254 and 420 l/tDM in recent techno-economic studies (Humbird et al.,

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2011). For our analysis, we did not include biomass loss from drying and used an intermediate ethanol conversion efficiency of 379 l/tDM.

Sources and assumptions used to prepare table 2 “Greenhouse gas savings of bioenergy pathways relative to hard coal (EU-average) and natural gas (EU-mix).”

Forest growth rates for temperate forests are from Luyssaert et al. (2007) and Xu et al. (2014). We assumed 70% of above-ground vegetation is harvested for bioenergy.

See discussion below of biomass yields from short-rotation coppice cellulosic energy crops.

We used a range of estimates of biomass supply chain emissions and reference fossil fuel emissions from Giuntoli et al. (2017). In all cases, we assumed that biomass would be converted to wood pellets before used.

We used a GHG emission factors for hard coal (EU-average with marginal refinery emissions for HFO and Diesel) of 112.3 (g CO2eq/MJ fuel) (Giuntoli et al., 2017).

We calculated the additional carbon storage from biomass cultivation as sum of average vegetation carbon stock a wood growing cycle, but assumed that growth rates in the first half of that cycle match growth rates in the second half. We also accounted for soil carbon sequestration by adding a belowground component at 35% of the aboveground value (Jackson et al., 1996; Mokany et al., 2006).

We used efficiency numbers for conversion from biomass to the different energy outputs as described in the information for table 3.

Sources and assumptions used to prepare table 3 “Comparison of efficiencies of different bioenergy pathways and solar equivalences.”

Our .2% photosynthetic conversion efficiency into harvested, above-ground biomass (total solar radiation to energy in biomass) is purely illustrative as the ratios among different solar pathways for use of the biomass remains the same regardless of this efficiency – each pathway will scale proportionately. The figure of 0.2% would be what could be expected in one of the better cells of the dark green area shown in Figure 1.

We use an intermediate value for cellulosic ethanol conversion efficiency of 45%. See discussion below about uncertainties in cellulosic energy crop yields and ethanol conversion efficiencies.

Efficiencies of different bioenergy conversion technologies were taken from Edwards et al. (2014). For electricity, we use a value of 30%, typical for conventional biomass power plants. For residential and industrial heat, we use values of 90% and 80%, respectively, typical for small small-scale pellet stoves and large-scale biomass-fired boilers.

For combined heat and power (CHP), we used a CHP plant efficiency for of 85% with an efficiency of 30% for electricity production and 55% for heat production (BASIS – Biomass Availability and Sustainability Information System, 2015). To account for

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periods of low heat demand, including the summer (Keen and Apt, 2016), we assume that only 50% produced for residential heating is actually used. For the example of CHP with the heat used in industrial processes we assume a heat utilization factor of 80%.

Solar equivalent pathways include PV (see discussion of solar conversion efficiencies below), the combination of PV and heat pumps, and solar industrial heat. For residential heat we assume that 2/3 (https://www.eea.europa.eu/data-and-maps/figures/households-energy-consumption-by-end-uses-5) of the PV electricity is used to run a heat pump with a coefficient of performance of 3 (Marek Miara et al., 2011). For industrial solar heat we use an efficiency range of 30-60% (Haagen, 2012). For industrial heat production with heat pumps from PV electricity we also assume that heat accounts for about two-thirds of total energy consumption in industry (IEA-ETSAP and IRENA, 2015).

Analysis of Solar Conversion Efficiencies Calculations of solar conversion efficiencies of Brazilian sugarcane and highest estimates of U.S. cellulosic ethanol

These numbers require information only about the solar radiation received in an area of production, the crop or biomass yields, the quantities of biofuels per ton of crop, and the energy of the biofuel. Brazilian sugarcane ethanol numbers assume average solar radiation of 2,000 kilowatt hours per square meter per year in Brazilian sugarcane producing areas based on solargis global solar radiation map, which yields 72,000 GJ/ha/yr. (Map available at <http://solargis.info/doc/_pics/freemaps/1000px/ghi/SolarGIS-Solar-map-World-map-en.png > ). This number, which measures solar radiation available for PV, directly contributes to the energy production of PV but only is used for the relative conversion efficiency of cellulosic ethanol, whose energy content is estimated directly from yields. To assure a consistent analysis of efficiency, both PV and ethanol must use the same measure of solar radiation.

Estimated output from sugarcane ethanol also assumes a yield of 80 metric tons of sugarcane per hectare/yr, dry matter content of 27 percent, and an energy content of 17 GJ/tDM, for 367.2 GJ/ha/yr. If sugarcane is produced every year, then it generates a 0.51 percent efficiency of the energy in sugarcane relative to solar radiation, but if it is produced only seven of eight years to factor in replanting, the efficiency is 0.45 percent. Assuming 75 liters per ton of sugarcane and 23.4 MJ/l, that results in 140 GJ/ha/yr of energy in ethanol. The result is 0.19 percent assuming both annual production and 100 percent of fossil fuels used in production are offset by an electricity energy credit from burning sugarcane bagasse. The energy in the biomass other than sugar, the bagasse, is therefore counted in this net calculation.

Calculations of cellulosic ethanol in the U.S. at the highest point in the U.S. are based on Department of Energy Projections reported United States in Geyer et al. (2013), and the assumption of 379l liters/tDM in biomass, compared to our calculation of 11 percent efficiency of PV discussed below and using the solar map cited above.

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Calculations of net photovoltaic conversion efficiency

In 2014, the U.S. Department of Energy, as expressed in its PVWatts calculator website assumed that new PV cells for homeowners would convert 16 percent of solar radiation into electricity. We calculate that translates into a net operating basis for a home of 11 percent. This figure for rooftop solar assumes a 16 percent photovoltaic cell, a 20 percent loss in actual operation of a rooftop solar installation, including losses from conversion of DC power to AC power and a further 11 percent cost for paying back the energy used to construct and install the system. Photovoltaic efficiencies and payback times are from Fthenakis (2012), and the 20 percent efficiency loss is based on typical conversion cost figures using the PVWatts calculator created by the National Renewable Energy Laboratory of the U.S. Department of Energy.

Our assumption of 11% net rooftop PV efficiency is conservative given that the average efficiency of commercial PV modules increased to 17% and typical performance ratios of PV systems now range between 80 and 90% (ISE, 2016).

Calculations for a PV farm solar farm differ somewhat from calculations for rooftop solar. One calculation for brazil assumes a 16 percent efficient solar PV cell, a 10 percent loss in efficiency for DC/AC conversion, a 50 percent “coverage factor,” and a 10 percent payback cost for the energy involved in construction and installation, yielding an overall efficiency of 6.5 percent, which is more than 30 times the net solar conversion efficiency of sugarcane into ethanol in Brazil (0.2 percent) and maize into ethanol in the United States (0.15 percent). The “coverage factor” represents the average spacing that commercial solar PV systems commonly have between solar cells to avoid shading when they tilt cells to maximize the reception of sunlight (Ong 2013). The 10 percent loss in DC/AC efficiency is based on NREL estimates for average effects (personal communication with Paul Denholm, September 11, 2014).

The biggest loss of land use efficiency in the above calculation is the coverage factor, which is largely related to the tilt of solar panels. Technically, solar farms could be structured to achieve almost a 100 percent coverage factor with no tilt of solar panels, but the cost per cell will rise because of the lack of tilt to maximize solar radiation per cell. The cheaper solar cells become, the more economically worthwhile it is to sacrifice tilt for greater energy per square meter. Farms can also space solar panels and use the land in between.

Sources and assumptions used to prepare table 3 “Comparison of efficiencies of different bioenergy pathways and solar equivalences.”

Our .2% photosynthetic conversion efficiency into harvested, above-ground biomass (total solar radiation to energy in biomass) is purely illustrative as the ratios among different solar pathways for use of the biomass remains the same regardless of this efficiency – each pathway will scale proportionately. The figure of 0.2% would be what could be expected in one of the better cells of the dark green area shown in Figure 1.

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We use an intermediate value for cellulosic ethanol conversion efficiency of 45%. See discussion below about uncertainties in cellulosic energy crop yields and ethanol conversion efficiencies.

Efficiencies of different bioenergy conversion technologies were taken from Edwards et al. (2014). For electricity, we use a value of 30%, typical for conventional biomass power plants. For residential and industrial heat, we use values of 90% and 80%, respectively, typical for small small-scale pellet stoves and large-scale biomass-fired boilers.

For combined heat and power (CHP), we used a CHP plant efficiency for of 85% with an efficiency of 30% for electricity production and 55% for heat production (BASIS – Biomass Availability and Sustainability Information System, 2015). To account for periods of low heat demand, including the summer (Keen and Apt, 2016), we assume that only 50% produced for residential heating is actually used. For the example of CHP with the heat used in industrial processes we assume a heat utilization factor of 80%.

Solar equivalent pathways include PV (see discussion of solar conversion efficiencies above, the combination of PV and heat pumps, and solar industrial heat. For solar combined heat and power, we assume that 2/3 of the PV electricity is used to run a heat pump based on typical splits of residential energy use in Europe (https://www.eea.europa.eu/data-and-maps/figures/households-energy-consumption-by-end-uses-5), and the heat pump has coefficient of performance of 3, which means it produces 3 times as much energy in heat for each unit of energy in electricity used (Marek Miara et al., 2011). For industrial solar heat we use an efficiency range of 30-60% (Haagen, 2012). To compete industrial combined heat and power from fossil fuel use with that output from solar, heat accounts for about two-thirds of total energy consumption in industry (IEA-ETSAP and IRENA, 2015) and is supplied by industrial solar heat while PV provides the electricity.

Global Mapping of PV vs. Bioenergy Efficiencies The global solar energy vs bioenergy comparative calculation was based on a GIS

(geographic information systems) analysis, which compared the net energy output of potential bioenergy production against the output of photovoltaics. The area analyzed excluded area covered permanently by ice and the driest deserts because such areas could not produce bioenergy although they could produce solar energy.

Biomass production was estimated by cell using a modified version of the LPJmL model (Beringer et al., 2011; Searchinger et al., 2015b) that simulates energy crop productivities comparable to NPP. This model adjusts LPJmL biomass production to match the NPP of the native vegetation of a cell. In genera agricultural biomass production rarely exceeds that of native vegetation (Field et al., 2008; Haberl et al., 2013). We further assumed production of 379 liters per metric ton of biomass as discussed above, and that all energy used to produce and transport biomass and refine it into ethanol would be either provided by the biomass itself or offset by electricity byproducts. Using ethanol, these assumptions imply that 47 percent of the gross energy in the biomass becomes useable energy.

For PV production, this analysis used a global data set of Global Horizontal Irradiance (GHI) available from the U.S. National Renewable Energy Laboratory. The GHI is the total

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solar radiation received by a horizontal PV cells and is a weighted sum of the Direct Normal Irradiance (DNI) and the diffuse light (all sunlight that comes to the panel from other areas of the sky except the narrow beam from the sun. (https://eosweb.larc.nasa.gov/sse/global/text/22yr_swv_dwn).

We used a net efficiency of 10% for solar radiation. This efficiency is based on the 17% PV efficiency of standard PV cells today, and an 85% performance ratio (halfway between standard 80% and 90% ratios today) (ISE, 2016), plus our estimate from above that 11% of the energy generated by the PV is used to pay back the energy used to produce and install the PV. We then further assume a coverage factor of 78%. As noted above, coverage factors can vary greatly for PV in practice, in part because PV is typically installed on infertile land, for which land area needs are not a concern. As the primary purpose of this analysis is to compare PV on land that might grow bioenergy reasonably well, we assume that some effort would be made not to use land unnecessarily. Where tilting is still desired, for example, solar arrays, can be spaced to allow grazing to occur between arrays, and as they become cheaper, tilting becomes less important. With space constraints varying from 50% to nearly full coverage, we deliberately chose 78% in part to generate an even 10% to avoid creating a false sense of precision in this analysis.

Although we are counting energy used for PV to obtain net efficiencies, we are not incorporating production energy use into the efficiencies for bioenergy.

This analysis calculated that on 73 percent of the world’s land, the useable energy output of PV would exceed that of bioenergy by a ratio of more than 100 to 1. For the remaining quarter of the world’s land, the average ratio is still 85 to 1 and the lowest ratio is 40 to 1. This relatively “better” land for bioenergy consists primarily of areas whose native vegetation would have been dense forest, and which today includes the world’s densest remaining tropical forests and the North American and European areas of the world’s best farmland. This land is therefore the land most valuable for carbon storage, food, and timber. If energy production chose from the top 25 percent of land with the highest efficiency advantage for PV, the minimum ratio of PV to bioenergy production would be 5,000 to 1.

This analysis should be viewed only as illustrative. At finer resolution, much land would neither be suitable for biomass production nor PV, such as some steeply sloped land.

Other comparisons of PV versus bioenergy efficienciesGeyer (2013), using optimistic estimates of potential biomass yields, estimated that PV

would produce more than 80 times the electricity of bioenergy per hectare of land, using a PV rated efficiency of 9 percent common in 2005, over most of the United States. Adjusting that figure to the commercial typical PV cell today of 16 percent would raise that increased efficiency to a multiple of over 140 for most of the United States.

Fthenakis and Kim (2009) performed a land use analysis for electricity production using a life-cycle approach, which means that they calculated not just direct land demands but also indirect land demands, such as the land used in mining materials or disposing of materials. They estimated solar energy from PV from a power plant to be roughly 250 square meters per gigawatt hour, depending on the type of solar energy system (e.g., a solar thermal tower

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was the highest land user, a sophisticated PV system was the lowest, and rooftop PV had almost no land use). By comparison, the most efficient form of biomass-generated electricity in the most efficient location using fast-growing willows required more than 12,600 square meters per gigawatt hour, even assuming high yields of 15 tons of dry matter per hectare per year. For the most efficient bioenergy location in the United States, PV would generate 50 times more energy per hectare. That figure is for a high-yielding, bioenergy location.

For other estimates, see MacKay (2010); and Edwards et al. (2010), Table 9.2. The disparity has been growing because of rapid increases in the solar conversion efficiency of PV.

Discussion of GLOBIOM model analysisGLBOBIOM's low ILUC estimates of 11 gCO2/MJ for maize ethanol is partially the result of

reduced food consumption estimates and substantial yield gains due to higher prices spurred by ethanol. However, it also substantially results from GLOBIOM's estimate that most of the additional cropland that would occur globally due to European maize ethanol would occur in Europe and come either from a category of existing "other natural land" or would be future abandoned cropland that would revert only to that same category, i.e., not forest. GLOBIOM estimated the vegetative carbon content of that land at only 7.1 tC/ha. Thus, conversion of such existing lands would incur that carbon costs and reversion of land to that category would sequester .35tC/ha/y, which is close to carbon-free land.

To put this number in perspective, other studies have estimated that Europe’s long-existing grassland, for which soil carbon sequestration rates should have reached a near equilibrium, is sequestering carbon at .7 tC/ha/y, twice the rate (Soussana et al., 2014). We also estimate that 80% of existing European cropland was naturally forest (with other lands including many carbon rich lands, such as wetlands and native European grasslands). By estimates presented here, the vegetative component of regeneration of abandoned land as forest in Europe would be 1.4-2.3 tC/ha/y (Luyssaert et al., 2007; Xu et al., 2014).

To make this estimate, GLOBIOM had to estimate that farming this land would be more profitable than other global land, which meant it would need to be highly productive so that its yield to cost ratio was better than other land. Among other advantages, farming this land would have to be more profitable than farming abandoned land that would otherwise revert to forest. The analysis offered no evidence that farmers have been converting this category of land. Why did GLOBIOM make this calculation?

One answer appears to be that GLOBIOM assigned land a yield based on lands within 600 separate regions of the European Union, on average 7,370 square kilometers. These regions are far larger than the areas of “other natural land,” which tend to be fragmented and disbursed. If these regions have lands with other productive potential, GLOBIOM is in effect assigning to “other natural land” the yield potential of more productive land. In effect, the basic method of the model converts low productivity land with low carbon potential into high-yielding land for crop production.

In addition to this heavy weighting of "other natural land," the modelers also chose low estimates of forest carbon sequestration, using the IPCC's lowest estimate of aboveground

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net biomass growth of 1.4tC/ha/y out of a range up to 2.1 tC/ha/y for temperate forests (IPCC, 2006). The GLOBIOM number is 45-74% of our best estimate of total carbon sequestration in temperate European forests today including the belowground components in vegetation and soils.

Overall, the GLOBIOM analysis contains a wide range of assumptions that are both inherently implausible and at best have effectively no evidentiary foundation but that result in the assumption both that extremely low carbon cost land will be the land expanded because of bioenergy and that this land will produce high yields.

Bioenergy and harvesting existing forestsBy basic principles of mathematics, the effect of harvests on carbon over a broader

landscape is the sum of the effects on individual stands. If the harvest of each stand and its regrowth results in increases in carbon in the atmosphere over any given period, e.g. 50 years, calculating effects at the landscape cannot be better.

Despite this fact, there are continued claims that a landscape analysis can show benefits where a stand analysis does not. It is true that trying to provide a fixed quantity of wood using a larger forest can allow different kinds of harvest. However, if the stand analysis looks at the full range of harvest options – and papers typically do look at a range of harvest options – then the stand analysis incorporates the effects of the different harvests. However, if harvests occur over the course of time in different stands, the payback period – the period in which using biomass instead of fossil fuels actually increases emissions – will actually become longer. This increase occurs because even when the stands harvested in the first year regrow enough to payback the carbon debt, stands harvested in later years have not yet regrown enough to pay back their carbon debts. Analyses such as Mika & Keeton (2015) and Mitchell et al. (2012) correctly employ a landscape approach. By contrast, Buckholz et al.(2011) and Davis et al. (2012) incorrectly treat the net growth of unharvested forest stands, which would occur without bioenergy, as canceling out the net carbon costs of bioenergy harvests even though the decision not to harvest stemwood would have greater benefits.

Wang et al. (2015) finds benefits not in this way but through use of an economic model, which claims that so long as landowners know that bioenergy will be used over the long-term, it will be in their economic interest to plant more forests, which will result in net carbon gains. Putting aside the economic basis for these claims, this analysis by itself ignored the costs of carbon implications of converting agricultural land to forest. The model set forth in the study by itself treated this land as a carbon-free asset. However, for a net calculation, the authors cited a study of the emissions from indirect land use change by the GTAP model to claim that if they incorporated that cost from another model, the result would still be GHG reductions, which raises the question of where the GTAP model obtains its benefits. As discussed elsewhere in this paper and in Searchinger et al. (2015) and its supporting material, the low ILUC in the GTAP model occurs in large part because of large estimates of reduced food consumption. Even if true, this estimate of reduced food demand cannot be used to claim bioenergy provides benefits as part of a global strategy that also meets growing food demands. The GTAP model also relies heavily on claims that new agricultural land, globally, will result from a category called cropland pasture, whose conversion in turn appears to have minimal effect on livestock production. In this way,

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pasture – even pasture capable of good crop production -- is a low or no-carbon cost category of land.

List of additional studies finding long-term greenhouse gas emission increases from forest bioenergy

Bernier, P., and D. Paré. “Using Ecosystem CO2 Measurements to Estimate the Timing and Magnitude of Greenhouse Gas Mitigation Potential of Forest Bioenergy.” GCB Bioenergy 5, no. 1 (January 1, 2013): 67–72. doi:10.1111/j.1757-1707.2012.01197.x.

Holtsmark, B. “Harvesting in Boreal Forests and the Biofuel Carbon Debt.” Climatic Change 112, no. 2 (May 1, 2012): 415–28. doi:10.1007/s10584-011-0222-6.

Hudiburg, T. W., B. E. Law, C. Wirth, and S. Luyssaert. “Regional Carbon Dioxide Implications of Forest Bioenergy Production.” Nature Climate Change 1, no. 8 (2011): 419–23. doi:10.1038/nclimate1264.

Manomet Center for Conservation Sciences. “Massachusetts Biomass Sustainability and Carbon Policy Study: Report to the Commonwealth of Massachusetts Department of Energy Resources.” Brunswick, Maine, 2010.

McKechnie, Jon, Steve Colombo, and Heather L. MacLean. “Forest Carbon Accounting Methods and the Consequences of Forest Bioenergy for National Greenhouse Gas Emissions Inventories.” Environmental Science & Policy 44 (December 2014): 164–73. doi:10.1016/j.envsci.2014.07.006.

McKechnie, J., S. Colombo, J. Chen, W. Mabee, and H. L. MacLean. “Forest Bioenergy or Forest Carbon? Assessing Trade-Offs in Greenhouse Gas Mitigation with Wood-Based Fuels.” Environmental Science & Technology 45, no. 2 (2011): 789–95. doi:10.1021/es1024004.

Mitchell, S. R., M. E. Harmon, and K. E. B. O’Connell. “Carbon Debt and Carbon Sequestration Parity in Forest Bioenergy Production.” GCB Bioenergy 4, no. 6 (2012): 818–27. doi:10.1111/j.1757-1707.2012.01173.x.

Stephenson, A. L., and D. J. C. MacKay. “Life Cycle Impacts of Biomass Electricity in 2020.” UK Department of Energy and Climate Change, 2014. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/349024/BEAC_Report_290814.pdf.

Zanchi, G., N. Pena, and N. Bird. “Is Woody Bioenergy Carbon Neutral? A Comparative Assessment of Emissions from Consumption of Woody Bioenergy and Fossil Fuel.” GCB Bioenergy 4, no. 6 (2012): 761–72. doi:10.1111/j.1757-1707.2011.01149.x.

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ReferencesBASIS – Biomass Availability and Sustainability Information System, 2015. Report on

conversion efficiency of biomass.Beringer, T., Lucht, W., Schaphoff, S., 2011. Bioenergy production potential of global

biomass plantations under environmental and agricultural constraints. GCB Bioenergy 3, 299–312. doi:10.1111/j.1757-1707.2010.01088.x

Buchholz, T., Canham, C.D., Hamburg, S.P., 2011. Forest biomass and bioenergy: opportunities and constraints in the Northeastern United States. Cary Institute of Ecosystem Studies.

Conant, R.T., Paustian, K., Elliott, E.T., 2001. Grassland management and conversion into grassland: effects on soil carbon. Ecol. Appl. 11, 343–355.

Crutzen, P.J., Mosier, A.R., Smith, K.A., Winiwarter, W., 2008. N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels. Atmospheric Chem. Phys. 8, 389–395.

Davis, S.C., Dietze, M., DeLucia, E., Field, C., Hamburg, S.P., Loarie, S., Parton, W., Potts, M., Ramage, B., Wang, D., Youngs, H., Long, S.P., 2012. Harvesting Carbon from Eastern US Forests: Opportunities and Impacts of an Expanding Bioenergy Industry. Forests 3, 370–397. doi:10.3390/f3020370

Don, A., Schumacher, J., Freibauer, A., 2011. Impact of tropical land-use change on soil organic carbon stocks – a meta-analysis. Glob. Change Biol. 17, 1658–1670. doi:10.1111/j.1365-2486.2010.02336.x

Dwivedi, P., Wang, W., Hudiburg, T., Jaiswal, D., Parton, W., Long, S., DeLucia, E., Khanna, M., 2015. Cost of Abating Greenhouse Gas Emissions with Cellulosic Ethanol. Environ. Sci. Technol. 49, 2512–2522. doi:10.1021/es5052588

Edwards, R., Larivé, J.F., Rickeard, D., Weindorf, W., 2014. Well-to-Tank Report Version 4.a: JEC Well-to-Wheels Analysis, JRC Technical Reports. European Commission, Joint Research Centre, Institute for Energy and Transport.

Edwards, R., Mulligan, D., Marelli, L., 2010. Indirect land use change from increased biofuels demand: comparison of models and results for marginal biofuels production from different feedstocks. European Commission, Joint Research Centre, Institute for Energy, Publications Office of the European Union, Luxembourg.

Evans, S.G., Ramage, B.S., DiRocco, T.L., Potts, M.D., 2015. Greenhouse Gas Mitigation on Marginal Land: A Quantitative Review of the Relative Benefits of Forest Recovery versus Biofuel Production. Environ. Sci. Technol. 49, 2503–2511. doi:10.1021/es502374f

Field, C., Campbell, J., Lobell, D., 2008. Biomass energy: the scale of the potential resource. Trends Ecol. Evol. 23, 65–72. doi:10.1016/j.tree.2007.12.001

Fthenakis, V., 2012. How long does it take for photovoltaics to produce the energy used? Natl. Assoc. Prof. Eng. Mag. 16–17.

Fthenakis, V., Kim, H.C., 2009. Land use and electricity generation: A life-cycle analysis. Renew. Sustain. Energy Rev. 13, 1465–1474. doi:10.1016/j.rser.2008.09.017

Gelfand, I., Zenone, T., Jasrotia, P., Chen, J., Hamilton, S.K., Robertson, G.P., 2011. Carbon debt of Conservation Reserve Program (CRP) grasslands converted to bioenergy production. Proc. Natl. Acad. Sci. 108, 13864–13869. doi:10.1073/pnas.1017277108

Geyer, R., Stoms, D., Kallaos, J., 2013. Spatially-Explicit Life Cycle Assessment of Sun-to-Wheels Transportation Pathways in the U.S. Environ. Sci. Technol. 47, 1170–1176. doi:10.1021/es302959h

12

Gibbs, H.K., Johnston, M., Foley, J.A., Holloway, T., Monfreda, C., Ramankutty, N., David Zaks, 2008. Carbon payback times for crop-based biofuel expansion in the tropics: the effects of changing yield and technology. Environ. Res. Lett. 3, 034001. doi:10.1088/1748-9326/3/3/034001

Giuntoli, J., Agostini, A., Edwards, R., Marelli, L., 2017. Solid and gaseous bioenergy pathways: input values and GHG emissions. Calculated according to the methodology set in COM(2016) 767 (No. EUR 27215 EN).

Guo, L.B., Gifford, R.M., 2002. Soil carbon stocks and land use change: a meta analysis. Glob. Change Biol. 8, 345–360. doi:10.1046/j.1354-1013.2002.00486.x

Haagen, M., 2012. Master Thesis The Potential of Fresnel Reflectors for Process Heat Generation in the MENA Region. University of Kassel.

Haberl, H., Erb, K.-H., Krausmann, F., Running, S., Searchinger, T.D., Kolby Smith, W., 2013. Bioenergy: how much can we expect for 2050? Environ. Res. Lett. 8, 031004. doi:10.1088/1748-9326/8/3/031004

Harris, Z.M., Spake, R., Taylor, G., 2015. Land use change to bioenergy: A meta-analysis of soil carbon and GHG emissions. Biomass Bioenergy 82, 27–39. doi:10.1016/j.biombioe.2015.05.008

Hudiburg, T.W., Davis, S.C., Parton, W., Delucia, E.H., 2015. Bioenergy crop greenhouse gas mitigation potential under a range of management practices. GCB Bioenergy 7, 366–374. doi:10.1111/gcbb.12152

Humbird, D., Davis, R., Tao, L., Kinchin, C., Hsu, D., Aden, A., Schoen, P., Lukas, J., Olthof, B., Worley, M., Sexton, D., Dudgeon, D., 2011. Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol. Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover (No. NREL/TP-5100-47764). National Renewable Energy Laboratory, Golden, CO.

IEA-ETSAP, IRENA, 2015. Solar Heat for Industrial Processes (Technology Brief E21).IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the

National Greenhouse Gas Inventories Programme. Published: IGES.IPCC, 2000. Land use, land-use change, and forestry: summary for policymakers : a

special report of the Intergovernmental Panel on Climate Change. WMO (World Meteorological Organization) : UNEP (United Nations Environment Programme), Geneva.

ISE, 2016. Photovoltaics Report. Fraunhofer Institute for Solar Energy Systems.Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996. A

global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411.Keen, J.F., Apt, J., 2016. Are high penetrations of commercial cogeneration good for

society? Environ. Res. Lett. 11, 124014. doi:10.1088/1748-9326/11/12/124014Lobell, D.B., Cassman, K.G., Field, C.B., 2009. Crop Yield Gaps: Their Importance,

Magnitudes, and Causes. Annu. Rev. Environ. Resour. 34, 179–204. doi:10.1146/annurev.environ.041008.093740

Luyssaert, S., Inglima, I., Jung, M., Richardson, A.D., Reichstein, M., Papale, D., Piao, S.L., Schulze, E.-D., Wingate, L., Matteucci, G., Aragao, L., Aubinet, M., Beer, C., Bernhofer, C., Black, K.G., Bonal, D., Bonnefond, J.-M., Chambers, J., Ciais, P., Cook, B., Davis, K.J., Dolman, A.J., Gielen, B., Goulden, M., Grace, J., Granier, A., Grelle, A., Griffis, T., Grünwald, T., Guidolotti, G., Hanson, P.J., Harding, R., Hollinger, D.Y., Hutyra, L.R., Kolari, P., Kruijt, B., Kutsch, W., Lagergren, F., Laurila, T., Law, B.E., Le Maire, G., Lindroth, A., Loustau, D., Malhi, Y., Mateus, J., Migliavacca, M., Misson, L., Montagnani, L., Moncrieff, J., Moors, E., Munger, J.W., Nikinmaa, E., Ollinger, S.V., Pita, G., Rebmann, C., Roupsard, O., Saigusa, N., Sanz, M.J., Seufert, G., Sierra, C., Smith, M.-L., Tang, J., Valentini, R., Vesala, T., Janssens, I.A., 2007. CO2

13

balance of boreal, temperate, and tropical forests derived from a global database. Glob. Change Biol. 13, 2509–2537. doi:10.1111/j.1365-2486.2007.01439.x

Machmuller, M.B., Kramer, M.G., Cyle, T.K., Hill, N., Hancock, D., Thompson, A., 2015. Emerging land use practices rapidly increase soil organic matter. Nat. Commun. 6, 6995. doi:10.1038/ncomms7995

MacKay, D., 2010. Sustainable energy - without the hot air, Reprinted. ed. UIT Cambridge, Cambridge.

Marek Miara, Danny Günther, Thomas Kramer, Thore Oltersdorf, Jeannette Wapler, 2011. Heat Pump Efficiency Analysis and Evaluation of Heat Pump Efficiency in Real-life Conditions. Fraunhofer ISE, Freiburg.

Martin, P.A., Newton, A.C., Bullock, J.M., 2013. Carbon pools recover more quickly than plant biodiversity in tropical secondary forests. Proc. R. Soc. Lond. B Biol. Sci. 280, 20132236. doi:10.1098/rspb.2013.2236

Mika, A.M., Keeton, W.S., 2015. Net carbon fluxes at stand and landscape scales from wood bioenergy harvests in the US Northeast. GCB Bioenergy 7, 438–454. doi:10.1111/gcbb.12143

Mitchell, S.R., Harmon, M.E., O’Connell, K.E.B., 2012. Carbon debt and carbon sequestration parity in forest bioenergy production. GCB Bioenergy 4, 818–827. doi:10.1111/j.1757-1707.2012.01173.x

Mokany, K., Raison, R.J., Prokushkin, A.S., 2006. Critical analysis of root : shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96. doi:10.1111/j.1365-2486.2005.001043.x

Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., Ciais, P., Jackson, R.B., Pacala, S.W., McGuire, A.D., Piao, S., Rautiainen, A., Sitch, S., Hayes, D., 2011. A Large and Persistent Carbon Sink in the World’s Forests. Science 333, 988–993. doi:10.1126/science.1201609

Plevin, R., 2010. Review of final RFS2 analysis. Energy and Resources Group, University of California at Berkeley, Berkeley, CA.

Poeplau, C., Don, A., Vesterdal, L., Leifeld, J., Van Wesemael, B., Schumacher, J., Gensior, A., 2011. Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach. Glob. Change Biol. 17, 2415–2427. doi:10.1111/j.1365-2486.2011.02408.x

Poorter, L., Bongers, F., Aide, T.M., Almeyda Zambrano, A.M., Balvanera, P., Becknell, J.M., Boukili, V., Brancalion, P.H.S., Broadbent, E.N., Chazdon, R.L., Craven, D., de Almeida-Cortez, J.S., Cabral, G.A.L., de Jong, B.H.J., Denslow, J.S., Dent, D.H., DeWalt, S.J., Dupuy, J.M., Durán, S.M., Espírito-Santo, M.M., Fandino, M.C., César, R.G., Hall, J.S., Hernandez-Stefanoni, J.L., Jakovac, C.C., Junqueira, A.B., Kennard, D., Letcher, S.G., Licona, J.-C., Lohbeck, M., Marín-Spiotta, E., Martínez-Ramos, M., Massoca, P., Meave, J.A., Mesquita, R., Mora, F., Muñoz, R., Muscarella, R., Nunes, Y.R.F., Ochoa-Gaona, S., de Oliveira, A.A., Orihuela-Belmonte, E., Peña-Claros, M., Pérez-García, E.A., Piotto, D., Powers, J.S., Rodríguez-Velázquez, J., Romero-Pérez, I.E., Ruíz, J., Saldarriaga, J.G., Sanchez-Azofeifa, A., Schwartz, N.B., Steininger, M.K., Swenson, N.G., Toledo, M., Uriarte, M., van Breugel, M., van der Wal, H., Veloso, M.D.M., Vester, H.F.M., Vicentini, A., Vieira, I.C.G., Bentos, T.V., Williamson, G.B., Rozendaal, D.M.A., 2016. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214. doi:10.1038/nature16512

Popp, A., Rose, S.K., Calvin, K., Van Vuuren, D.P., Dietrich, J.P., Wise, M., Stehfest, E., Humpenöder, F., Kyle, P., Van Vliet, J., Bauer, N., Lotze-Campen, H., Klein, D., Kriegler, E., 2014. Land-use transition for bioenergy and climate stabilization: model comparison of

14

drivers, impacts and interactions with other land use based mitigation options. Clim. Change 123, 495–509. doi:10.1007/s10584-013-0926-x

Righelato, R., Spracklen, D.V., 2007. Carbon Mitigation by Biofuels or by Saving and Restoring Forests? Science 317, 902–902. doi:10.1126/science.1141361

Searchinger, T.D., Edwards, R., Mulligan, D., Heimlich, R., Plevin, R., 2015a. Do biofuel policies seek to cut emissions by cutting food? Science 347, 1420–1422. doi:10.1126/science.1261221

Searchinger, T.D., Estes, L., Thornton, P.K., Beringer, T., Notenbaert, A., Rubenstein, D., Heimlich, R., Licker, R., Herrero, M., 2015b. High carbon and biodiversity costs from converting Africa’s wet savannahs to cropland. Nat. Clim. Change 5, 481–486. doi:10.1038/nclimate2584

Searchinger, T.D., Heimlich, R., Houghton, R.A., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D., Yu, T.-H., 2008. Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change. Science 319, 1238–1240. doi:10.1126/science.1151861

Searle, S., Petrenko, C., Baz, E., Malins, C., 2016. Crops of the Biofrontier: In search of opportunities for sustainable energy cropping. Washington D.C.: The International Council on Clean Transportation.

Searle, S.Y., Malins, C.J., 2014. Will energy crop yields meet expectations? Biomass Bioenergy 65, 3–12. doi:10.1016/j.biombioe.2014.01.001

Soussana, J.-F., Klumpp, K., Ehrhardt, F., 2014. The role of grassland in mitigating climate change, in: A. Hopkins, R.P. Collins, M.D. Fraser, V.R. King, D.C. Lloyd, J.M. Moorby, P.R.H. Robson (Eds.), EGF at 50: The Future of European Grasslands, Grassland Science in Europe. Aberystwyth, pp. 75–87.

U.S. Department of Energy, 2016. 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy, Volume 1: Economic Availability of Feedstocks (No. ORNL/TM-2016/160). Oak Ridge National Laboratory, Oak Ridge, TN. doi:10.2172/1271651

Wang, M., Han, J., Dunn, J.B., Cai, H., Elgowainy, A., 2012. Well-to-wheels energy use and greenhouse gas emissions of ethanol from corn, sugarcane and cellulosic biomass for US use. Environ. Res. Lett. 7, 045905. doi:10.1088/1748-9326/7/4/045905

Wang, W., Dwivedi, P., Abt, R., Khanna, M., 2015. Carbon savings with transatlantic trade in pellets: accounting for market-driven effects. Environ. Res. Lett. 10, 114019. doi:10.1088/1748-9326/10/11/114019

Wei, X., Shao, M., Gale, W., Li, L., 2014. Global pattern of soil carbon losses due to the conversion of forests to agricultural land. Sci. Rep. 4, 4062. doi:10.1038/srep04062

Xu, B., Yang, Y., Li, P., Shen, H., Fang, J., 2014. Global patterns of ecosystem carbon flux in forests: A biometric data-based synthesis: Carbon flux in forests. Glob. Biogeochem. Cycles 28, 962–973. doi:10.1002/2013GB004593

Xue, S., Lewandowski, I., Wang, X., Yi, Z., 2016. Assessment of the production potentials of Miscanthus on marginal land in China. Renew. Sustain. Energy Rev. 54, 932–943. doi:10.1016/j.rser.2015.10.040

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