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March 22, 2022

by F. Taheripour, S. Mueller, H. Kwon, M. Khanna, I. Emery, K. Copenhaver, M. Wang (Argonne National Laboratory)  The Systems Assessment Center and its collaborators provide a detailed technical review of a recently published article “Environmental Outcomes of the US Renewable Fuel Standard” by Lark et al. (2022). Our review explored modeling approach and data sources for land use changes, types of land conversions, and soil organic carbon changes, among other parameters, in the Lark et al. study, which resulted in significantly high greenhouse gas emissions of US domestic land use changes of corn ethanol presented in that study.  READ MORE  Download paper

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Excerpt from Argonne National Laboratory: Lark et al. (2022) recently published “Environmental Outcomes of the US Renewable Fuel
Standard” and addressed domestic land use change (LUC) of corn ethanol and associated greenhouse gas (GHG) emissions that are potentially caused by the U.S. Renewable Fuel Standard (RFS), as introduced in the 2005 Energy Policy Act and in the 2007 Energy
Independence and Security Act (EISA). To do so, they considered the corn ethanol volume changes and LUC between 2008 and 2016 1.

In their assessment, Lark et al. assumed a business-as-usual (BAU) scenario (representing the goals of RFS1 for ethanol volume, as adopted in the 2005 Energy Policy Act by Congress, between 2008 and 2016), a new scenario (representing the goals of RFS2 for ethanol volume, as
adopted in the 2007 EISA by Congress, between 2008 and 2016) to determine domestic LUC due to the RFS2. With no integrative modeling exercise, the authors simply calculated the average of the annual differences between the goals of RFS1 and RFS2 (5.5 billion gallons [Bgal]) and considered that volume of ethanol as the average annual contribution of RFS2 to new ethanol consumption between 2008 and 2016. Instead of using an integrated, coherent framework, as is the case with equilibrium models, in which changes in crop prices and associated LUCs at the intensive and extensive margin are determined simultaneously, Lark et al. applied a few loosely connected empirical methods to examine the impact of the RFS2 on three crops (corn, soybeans, and wheat). They estimated the short-term increases in commodity prices between 2008 and 2016 induced by the RFS2 and estimated that the prices of corn, soybeans, and wheat would increase by 30%, 20%, and 20%, respectively, due to an increase in the annual consumption of ethanol by 5.5 Bgal.

In the next step, Lark et al. used the Cropland Data Layer (CDL) from the United States Department of Agriculture (USDA) in combination with some other information on returns on cropland to estimate the probabilities of land transitions between cropland, pasture land, and Conservation Reserve Program (CRP) land. Using their projected increases in the prices of corn, soybeans, and wheat in combination with the estimated land transition functions, Lark et al. calculated that the area of corn plantation, adjusted for distiller’s dried grains (DDG), would increase by 2.8 million hectares (Mha) due to the RFS2, and that would lead to an increase in cropland area by 2.1 Mha. They showed that an overwhelming share of land conversion due to the RFS2 would be conversion of CRP land to active cropland. Lark et al. assigned a set of significantly large land use emissions factors to the CRP land conversion and likely double counted the N2O emissions in adding their LUC emissions to the rest of life-cycle analysis (LCA) emissions of corn-based ethanol, leading to the conclusion that the GHG emissions (commonly called carbon intensity) of ethanol are at least 24% higher than those of gasoline.

After a detailed technical review of the modeling practices and data used by Lark et al., we conclude that the results and conclusions provided by the authors are based on several questionable assumptions and a simple modeling approach that has resulted in overestimation of the GHG emissions of corn ethanol. In what follows, we present the general findings of our review.

Our review is organized in nine sections. In the first section we discuss their estimation of land conversions. The second section addresses systematic overestimation of soil organic carbon (SOC) changes by the authors. The issue of double-counting of N2O emissions in the Lark et al. LCA is discussed in the third section. In the fourth section we refer to some inconsistencies in results provided by Lark et al. We then discuss misattribution of ethanol volumes to the RFS2 by those authors in Section 5. The assessment of impacts of yield improvement and DDG offsets on the demand for cropland are addressed in Section 6. The estimation of price impacts of the RFS is discussed in Section 7. Section 8 outlines deficiencies in modeling land transition. Finally, we conclude our findings in Section 9.

1. Land Conversions
Land use changes identified by Lark et al. are likely representing conversion of fallow/idle land to crops rather than conversion of permanent grasslands and thus is unlikely to result in a large carbon debt upon conversion.

2. Systemic Overestimation of Soil Organic Carbon Changes
Lark et al. likely overestimated soil carbon loss by a factor of two to eight for land use change by apply carbon response functions that are relevant for conversion of native or undisturbed grassland to cropland and not for CRP and cropland pasture to cropland.

3. Double-counting of N2O Emissions and Omissions of Avoided Emissions
Lark et al. appeared to have double-counted the N2O emissions with fertilizer use for corn farming by adding 9 gCO2e/MJ of ethanol to the remaining LCA results of corn ethanol and overlooked that these were already included in the corn farming related emissions as is the case in most LCA calculations, such as those from the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model.

4. Inconsistencies in Results Obtained by Lark et al.
We note several findings reported by Lark et al. that are difficult to rationalize, as we illustrate in Figure 7. First, consider Panel A of Figure 7, which shows the projected changes in carbon intensity (CI) in cropland by county obtained from the Lark et al. results presented in their Figure 2. Panel A of Figure 7 reveals inexplicably negative CIs. Why are there negative changes in CI per hectare in cropland? When cropland increases, with no improvement in SOC due to land management or high carbon crops, carbon emissions should also increase, and when cropland declines, carbon emissions should decrease. A negative CI implies an error and that the results were not reviewed for accuracy. This figure also reveals extremely large values of the examined ratio up to more than 12 Gg CO2e/ha. These extreme CI values per hectare of land are not explained or justified.

5. Misattribution of Ethanol Volumes to the RFS2
Lark et al. attributed 5.5 Bgal of ethanol per year to RFS2 between 2008 and 2016 by comparing the volume under RFS2 and RFS1 without considering other drivers of ethanol production. The expansion in the biofuel industry (including corn ethanol and other biofuels), even in the short time period from 2008 to 2015, occurred due to many drivers, including but not limited to changes in non-RFS biofuels supporting policies (such as ban of MTBE in gasoline blends but needed oxygenate in gasoline blends and tax credits), changes in crude oil price, changes in demand for gasoline, the 10% blend rule and the blend wall issue, changes in livestock industry and its demand for feed crops and other feeds (e.g., DDG and meal products).

6. The Amount of LUC Attributed to Corn Ethanol Without Careful Consideration of Yield Increase and DDG Offsets

7. Estimation of Price Effects of the RFS2
The validity of picking the time period of 2006-2010 to assess price impacts with the 5.5 Bgal for RFS2 between 2008 and 2016 is questionable.

8. Modeling Land Transition
Lark et al. did not recognize cropland pasture as a sub-category of cropland in their analyses and perhaps treated this type of land as pasture land or fallow land. This misidentification and the method used by the authors to assess land return is likely to have artificially led to the additional demand for active cropland being met largely by CRP land and not by cropland pasture.

9. Conclusions
Over the past 15 years many papers have studied LUC due to biofuel production and policy. In the absence of any observed evidence, some early papers published on this topic claimed that producing ethanol in the U.S. would generate major deforestation in the country and elsewhere and that the emissions associated with the conversion of natural land to cropland would cause GHG emissions that would increase the carbon intensity of ethanol to a level higher than the carbon intensity of gasoline. Over time various studies showed that those early papers overstated the magnitude of deforestation due to biofuels. The more advanced analyses, relying on the recent actual observations on land use changes, showed that intensification in crop production due to yield improvement and cultivation of idled cropland, and shifting demand form unwanted feed crops to biofuels by-products have jointly eliminated the need for conversion of natural land to cropland for biofuel production and hence provided significantly lower estimates for land use change emissions due to biofuels.

In their recent publication, Lark et al. have at least clearly confirmed that there is no evidence supporting deforestation and conversion of natural land to crop production due to biofuels or any other driver. Hence, from this perspective they confirmed the findings of other recent publications that there is no evidence of deforestation in the U.S. due biofuels.

However, Lark et al. simply assumed that RFS was responsible for an expansion in ethanol consumption by 5.5 Bgal a year. With this premise and by using a few loosely connected empirical methods, the authors evaluated the impact of the assumed increase in ethanol volume on three crops (corn, soybeans, and wheat). Relying on the short-term increases in the prices of these commodities during the time period of 2006-2010, assuming that these price increases will sustain over 30-year time period, projecting return on cropland and pasture land using problematic outdated projections for future crop prices and many other variables, applying CDL data with low accuracy in detecting land use types, ignoring the fact that reduction in CRP land area was due to CRP funding cut by Congress, Lark et al. projected the assumed increase in ethanol consumption by 5.5 Bgal would lead to an increase in corn area by 2.8 Mha over 30 year time horizon and that increases the area of active cropland by 2.1 Mha. Their projection suggested that most of the expansion in active crop land comes from conversion of one type of unused cropland (CRP) to crop production. Regardless of the accuracy of this projection with respect to the type of unused land and its magnitude, the fact that area of active cropland could increase by cultivation of unused land in the U.S. due to additional demand for biofuels is not new finding and has been addressed and well noted in the existing literature. However, the type of unused cropland that has been cultivated is uncertain. Finally, while the existing literature concludes that marginal cropland is not a rich soil carbon content land, Lark et al. assigned very high emissions factors to CRP land and concluded that emissions due to LUC for corn ethanol was large. In addition, in a misunderstood LCA practice, involving double counting and neglecting various sources of emissions savings due to biofuel production, Lark et al. maintained that the carbon intensity of corn ethanol is larger than that of petroleum gasoline.

As presented above, in this technical review of Lark et al., we address a few key and apparent issues that need more careful examination of Lark et al. We highlight these issues below:

• The Lark et al. modeling approach that followed only short-run changes in 2008-16 in individual crops – corn, soybeans, and wheat – missed the long-run pattern of changes in the mix of crops and the combined effect across all crops produced in the U.S. This short- term analysis generated a higher demand for active cropland and overestimated land conversion from CRP to crop production than what is consistent with observed trends in data.

• The arbitrary choice of working with CDL data for the short-time segment of 2008-16 does not represent the U.S. long-term cropping pattern. The farming sector in some years deviates from its long-term pattern in response to short-term shocks in commodity prices and then returns to its long-term pattern when short-term price shocks disappear.

• The Lark et al. modeling approach is too limited to effectively consider the drivers of ethanol industry and its interaction with other industries including the cropping and livestock industries. The Lark et al. modeling approach projected an increase in the planted area of corn by 2.8 Mha by considering one single factor of 5.5 Bgal ethanol in the U.S. For the same time period, due to all drivers, the average of annual changes in the harvested area for corn may have been -0.25 Mha in the U.S. What can justify the difference between the Lark et al. projection and actual observations? Are there some factors that canceled out the impacts of RFS2? Or has the modeling practice missed some important drivers?

• The RFS has begun in 2005, continued until today, and could be continued in future. Picking an arbitrary time segment out of this long time period can lead to distorted results. Picking the time period of 2008-16 over which there was a major increase in crop prices (not only due to biofuels) and assigning the estimated land conversion for that period to a policy that will remain in place for a long time period can result in biased land use change attributable to the RFS. This biased attribution can certainly cause overestimated LUC magnitude for the RFS.

• The short-term changes in crop rotation does not reflect the long-run pattern of corn-soy rotation. Furthermore, Lark et al. with no justification picked a subset of their selected study regions to calculate changes in crop rotation. This selection eliminated areas with large shares in soybeans or wheat.

• Lark et al. did not recognized cropland pasture as a sub-category of cropland in their analyses and perhaps treated this type of land as pasture land or fallow land. This misidentification and the method used by them to assess land return artificially push the need for additional active cropland to CRP land.

• The CRP land left this program simply because there was no budget to keep them in the program. Assigning a portion expired CRP land to RFS (or any other biofuel) is problematic.

• To estimate probability of land transformation, Lark et al. used outdated and inaccurate projections for future crop prices and several other variables. In addition, in an ad hoc manner, they assumed costs of crop production remain constant over the 10-year projection period for the stream of expected returns on cropland. These made their land transformation projection questionable.

• We tested how much land expansion could be expected, given yield increases over the considered time frame as well as land offsets provided by DDG animal feed in order to meet the Lark et al. assumed 5.5 Bgal of ethanol stimulated by the RFS. Our analysis shows that at high level, yield increase on 2008 year corn acres over-compensate for ethanol demand. Even with ethanol production the 2008 corn footprint would still be down by 4.26 million acres. Ethanol demand may not drive an expansion above the 2008 year corn footprint but other factors including urban development may shift the corn footprint around.

• Our analysis of the cropland expansion data layer presented in Lark et al. supporting information revealed that areas identified by the authors as expansion to cropland may often be short-term fallow/idle lands (less than 10 years). In fact, many parcels identified by Lark et al in their “Cropland Expansion Layer” appear to be prime examples of land on the margin that is toggling between agriculture and fallow/idle state based on crop price signals. This would likely result in a systemic overestimation of SOC emissions for these parcels. Without such observation data to support their estimates, Lark et al. should have considered their results with high uncertainty.

• The authors missed the fact that corn ethanol LCA studies capture the N2O emissions from any change in nitrogen applied to corn in farming GHG emissions. As a result, they may have double-counted N2O emissions in their LUC emissions. They also failed to take
into account emissions savings due to avoided consumption and improvements in livestock industry induced by using biofuel by products.

• Lark et al. projected that in many counties area of cropland would increase largely (up to 2000 hectares for 1 hectare of changes in corn area). What justifies these magnificent changes? These large changes suggest that Lark et al. overestimated the land transformation elasticities.

• Lark et al. projected that the area of corn increases in 1,353 counties and decreases in 349 counties. In addition, their results showed changes in cropland in 126 counties with zero change in corn area. These odd results strongly suggest that the Lark et al. modeling approach may have considered reshuffles of crops among geographic locations of crop production with significant LUC emission implications.

1In their main manuscript, Lark et al. referred to 2008 to 2016 as the eight years of their assessment time period. From this specification, it seems that the authors refer to changes in eight years of 2008, 2009, 2010, …, 2015. However, in various other places of their paper and supplemental information (SI), they referred to 2009-2016 as their study period. In this note we refer to changes in the eight years from 2008 to 2015, unless we quote Lark et al. and perform analyses where they clearly reference 2016 as the end year.

2 First, we accessed data via https://zenodo.org/record/3905243#.YjeNMOrMJPb and downloaded the geodatabase of US_land_conversion_2008-16.gdb.zip. Second, we opened layer “ytc” (which is described on the webpage as areas in cropland expansion “areas converted to crop production between 2008 and 2016” with the year of expansion listed in the polygon) in ArcGIS software. Third, we loaded Lark et al. “cropland expansion” layer into Google Earth Engine (GEE) (See Figures 1-5) using a GEE javascript.

3 Based on data from FAOSTAT.   READ MORE

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Administration (FTA) feed Feed In Tariffs (FIT) feed prices Feedstock Flexibility Program for Bioenergy Producers feedstock logistics feedstock material feedstock prices Feedstocks feedstock storage feedstock transportation fermentation ferry fertilizer F Factor fiber Fiji Financing Finland Fischer-Tropsch/FT Fischer-Tropsch Synthetic Kerosene with Aromatics (FT-SKA Fischer-Tropsch Synthetic Paraffinic Kerosene (FT-SPK) Fischer-Tropsch Synthetic Paraffinic Kerosene with Aromatics (FT-SPK/A) fish feed fish oil fish waste fit for purpose flameleaf sumac flavors flax Fleets fleet turnover fleshings flex-fuel vehicles (FFV) Flightpath flight tests flixweed/tansy/herb-Sophia flood-prone soil Florida flue gas FOG (Fats/Oils/Grease) follow-the-crop food Food and Agriculture Organisation (FAO) Food and Drug Administration (FDA) food and fuel food policy food prices food processing waste food safety food security food vs biomaterials/bioplastics food vs fuel food waste for forage forage sorghum forecasts foreign oil Foreign Policy forest Forest Biomass for Energy forest biotechnology forest residue/waste Forest resources forestry Forest Service fossil carbon fossil fuel fracking fractionation fragrance France franchise fraud Freedom of Information Act (FOIA) free fatty acids (FFA) freight/cargo French French Guiana fructose fruit FT-SKA fuel fuel additives fuel cell electric vehicle (FCEV) fuel cells fuel economy fuel efficiency fuel injection fuel mixtures fuel molecules fuel oil fuel performance fuel prices Fuel Quality Directive (FQD) fuel registration Fuel Retailers fuel testing fuel transportation fuel use fuel wholesaler fully burdened cost fund funding fungus/fungi Furanics furfural fusel oils Future Farmers of America (FFA) Gabon Gambia gas-to-liquid (GTL) gasification gasoline gasoline-range hydrocarbons gasoline baseline gasoline consumption gasoline mandate gasoline markets gasoline price gas prices gas tax/highway user fee General Services Administration general waiver authority generators genetically engineered yeast cells genetically enhanced microbes genetically modified organism (GMO) genome Georgia Georgia (country) geothermal German Germany Ghana GHG (Greenhouse Gas Emissions) giant cane Giant King Grass Giant Reed/Arundo GIS glass tubing gliricidia sepium global rebound effect global warming global warming potential glucose glycerin glycerin standards glycerol goats Governance practices) Government Accountability Office (GAO) government investment government resources government subsidies grains grain sorghum/milo grain speculators GRAND-AM grants grants-local grants-state grapefruit grapes graphene graphite GRAS (generally regarded as safe) Grasses grasshoppers grease Great Green Fleet Great Lakes Greece green/black economy green bonds green chemistry Green Deal EU green economy green house facility Green Jobs Green New Deal Green Racing Green Recovery GREET Greenhouse Gases Regulated Emissions and Energy Use in Transportation Model Grenada gribble growers gua beans Guam guar Guatemala guayule Guerbet reaction Guinea Gulf states gulmohar Gumweed (grindelia squarosa) Guyana GWP h Haiti Halophytes harvesting harvest site processing Hawai'i hazardous waste hazelnut HBIIP Higher Blends Infrastructure Incentive Program HDCJ health health benefits health effects heat-tolerance heating oil/fuel heat of combustion heat of vaporization Heavy Duty Truck Rule heavy duty vehicles (HDV) hedging HEFA (Hydro-processed esters and fatty acids) HEFA50 helicopters hemicellulace enzymes hemicellulose hemicellulosic sugars Hemp hemp oil hemp seed herb hexanol HFO (Heavy Residual Fuel Oil) hibiscus high-octane/low-carbon (HOLC) liquid fuels high blend renewable fuels (HBRF) High Hydrogen Content Synthetic Paraffinic Kerosene (HHC-SPK) High Octane Fuel (HOF) High Octane Fuel Standard High Octane Gasoline (HOG) High Octane Vehicles (HOV) high performance regular high school project Highway Bill highway rights-of-way Highway Trust Fund history hog farmers hombayniya homogeneous-charge compression-ignition Honduras honey locust Honge tree nuts Hong Kong hops horticulture Housing and Urban Development (HUD) HPF (High Performance Fuels) HRJ (Hydrotreated Renewable Jet) human rights Hungary Hurricane Sandy HVO (Hydrotreated vegetable oil) HVO100 Hybrid aircraft hybrid buses hybrids Hydrocarbon-Hydroprocesed Esters and Fatty Acids (HC-HEFA-SPK) hydrocarbon fuels hydrodeoxygenation hydrodiesel hydrofaction Hydrogen/Renewable Hydrogen hydrogen aircraft hydrogenase hydrogenation hydrogenation-derived renewable diesel (HDRD) hydrogen fuel cells hydrogen leaks hydrogenolysis hydrogen pipeline hydrogen price hydrogen pumps hydrogen terminal hydropower Hydroprocessed fermented sugars to synthetic isoparaffins (HFS-SIP) hydroprocessing hydropyrolysis hydrothermal carbonization hydrothermal liquefaction (HTL) hydrothermal treatment Hydrotreated renewable diesel (HRD) hydrotreating hydrotreatment hydrous ethanol hypoxia zone Iceland Idaho Illinois illuppai ILUC (Indirect/Induced Land Use Change) import/export incinerator ash India Indiana Indian beech tree Indian grass indirect effects indirect emissions indirect fuel use change indium Indonesia industrial ethanol industrial gases industrial sugars industrial waste industrial waste gases IndyCar infographic Infrastructure inhibitors innovation insecticide/pesticide insects insurance integrated food/energy systems intellectual property Inter-American Development Bank inter-crop interactive map intercropping internal combustion engine internal combustion engine/gasoline engine ban International international balance of payments International Energy Agency (IEA) International Maritime Organization (IMO) International Monetary Fund (IMF) International Organization for Standardization (ISO) International Renewable Energy Agency (IRENA) International Sustainability and Carbon Certification model(ISCC) International Trade International Trade Administration International Trade Commission Internships inulin invasive species Investing investment tax credit ionic liquids Iowa IPCC Intergovernmental Panel on Climate Change Iran Iraq Ireland iridium iron IRS (Internal Revenue Service) IS 1460 ISO 8217 (marine distillate fuel standard) ISO 9000 isobutanol isobutanol price isobutanol pump price isobutene isomerisation isooctane isooctene isopropanol Israel Italy Ivory Coast JAA jackfruit Jamaica jamelão Japan jatobá Jatropha Jerusalem artichoke jet jet A Jet A-1 Jetfuel (Sustainable Aviation Fuel (SAF)) Jimmy Carter Jobs jojoba Jordan JP-4 JP-5 JP-8 JP-10 juniper Just A MInute Just Transition jute K-12 Education Kabakanjagala kalanchoe kamani Kansas Kans grass Karanja Kazakhstan kelp Kemiri Sunan kenaf Kentucky Kenya kerosene ketones kinggrass Kiribati Knowledge Discovery Framework Korea Kosovo kudzu kukui nut kulpa kusum Kuwait Kygryzstan labels labor policy Labrador lactose Lake County lamp oil landfill methane Landfills land ownership land 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M3 M15 M50 M100 ma macadamia macauba Macedonia machine learning Madagascar magnesium mahua Maine Malawi Malaysia Mali mallees Malta Malyasia mamona management changes mandate mandates mangaba manganese mango mangrove Manitoba mannose manure maple maps marginal land Marine/Maritime Bio and Renewable/Sustainable Fuel (SMF) Marine/Maritime Bio and Renewable/Sustainable Fuel (SMF) price Marine/maritime renewable fuel terminal/bunkering marine algae Marine Corps Marine Diesel Oil (MDO) Marine Gas Oil (MGO) market forces marketing markets/sales market share Mars Marshall Islands Maryland Masdar Institute Massachusetts mass balance standard Master Limited Partnership (MLP) Mauritius Mazda meat mechanics training medical waste MEEC membranes mergers and acquisitions mesquite methan methanation methane/biomethane methane leaks methanization Methanol/Biomethanol/Renewable Methanol methanol fuel cells methanol price Mexico Michelin GreenX Challenge Michigan micro-crop microalgae microbial 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naphtha/bionaphtha/renewable naphtha NASCAR National Academies of Science National Aeronautics and Space Administration (NASA) National Environmental Policy Act National Guard National Highway Traffic Safety Administration (NHTSA) National Institute of Standards and Technology (NIST) National Institutes of Health (NIH) National Laboratory National Oceanic and Atmospheric Administration (NOAA) National Oilheat Research Alliance National Park Service National Research Council National Science Foundation (NSF) national security National Security Council National Transportation Safety Board National Transportation Safety Board (NTSB) Native American tribes natural gas Natural Gas Act natural gas input natural gasoline natural gas prices natural gas vehicles Navy Nebraska neem negative carbon emissions neodymium Nepal net energy balance Netherlands Nevada New Brunswick Newfoundland Newfoundland and Labrador new fuel approval New Guinea New Hampshire New Jersey New Mexico New South Wales New York New Zealand next generation biofuels next generation vehicles NHRA drag racing Nicaragua nickel Niger Nigeria nipa sap nitrate leaching nitrates nitrogen Nitrogen fertiliser nitrogen starvation nitrous oxide (N2O) Niue NO2 noodles nopal North Africa North America North Carolina North Dakota Northeast northern catalpa Northern Ireland Northern Territory North Korea Northwest Territories Norway Nova Scotia NOx (nitrogen oxides) noxious weeds nuclear Nunavut nutraceuticals nutrient credit trading nutrient management nutrients nutrition nut shells oak oat hulls oats oat straw Obligated Parties/Point of Obligation (PoO) ocean-based energy Oceania octane octane price/value octanol Office of Energy Efficiency and Renewable Energy Office of Management and Budget (OMB) Office of Science and Technology Policy (OSTP) offtake agreements Ohio oil oil/gas terminals oil embargo oil exploration oil monopoly oil p oil price parity oil prices oil production oil refineries oil replacement Oils oil sands oil seed oil seed crops oil speculators oil spill oil subsidies oil taxes Oklahoma olefins oligomerization olive cake olive oil olive pits olives olive water Oman Omega-3s on-farm algae production on-farm biodiesel on-farm ethanol production on-farm natural gas production on-farm processing one pound waiver onion waste online courses Ontario OPEC (Organization of the Petroleum Exporting Countries) open fuel standard open pond opportunity zones optimized flex fuel vehicles orange peel orchard grass orchard prunings Oregon organic solar cells Organization for Economic Cooperation and Development (OECD) osage orange OSHA Overseas Private Investment Corporation overview overview/survey course oxygen oxygenate ozone Pakistan Palau palm palm biomass palm fatty acid distillate palm fiber palm fronds palm kernel palm kernel oil palm kernel shell palm oil palm oil mill effluent (POME) palm oil prices palm waste Paludiculture/peatland cultivation Panama pandas panic grass papaya paper Papua Indonesia Papua New Guinea paraffins Paraguay Paris Agreement parity partial waiver particulates pasture land Patent and Trademark Office (PTO) patents pathways Paulownia paulownia tree payments peaches peak oil peak oil demand peanuts/groundnuts peas pectin peela kaner pellets Pennsylvania pennycress/stinkweed pentane pentanol pentose pequi perennial grains perennial grasses Performance permitting Peru pest-tolerance pesticide-tolerance pests petition petroleum pharmaceuticals phase separation Philippines phosphorus photobioreactor photoelectrocatalysis photoelectrochemical photolysis photosynthesis phragmites pigeon pea pilot scale pine pineapple pine beetle pine nut pinion pipelines Pistacia chinensis PLA plant cell research plant cell walls plant oil plastic plastic-to-jet Plug-in Flex Fuel Hybrid Plug-in Hybrid Electric Vehicle (PHEV) plume grass podcasts Poland poli Policy politics pollinators pollution pollution control polyethylene polyfuel polymer polymerization polysaccharides pomace pomegranates pongamia pongamia pinnata poplar poppy population control Portable refinery Portugal poster sessions potamogeton potassium potato poultry litter/waste power-to-x/gas/liquid prairie grasses pre-processing precision farming/agriculture precursors/biointermediates premium gasoline Pretreatment pretreatment equipment price price of water prickly pear Prince Edward Island process flow diagram producer tax credit Production tax credit productivity project insurance propagating Propane/Biopropane/Renewable Propane propanol property insurance propylene protectionism protein protests proton exchange membrane (PEM) public comments public health policy Puerto Rico pulp Pulp/Paper Mill pump retrofit kit pumps/fueling station pungam Punnai tree pyrolysis Q-RIN QAP Qatar quality assurance Quality Assurance Plans (QAPs) quality improvement quantum dots Quebec Queensland quote of the week r R33 rabbits race radiata pine Rahal Letterman Lanigan Racing railroad rainforest rain tree RAND rare earth metal RD10 RD20 RD30 RD55 RD80 RD80B20 RD99 RD100 reclaimed mine lands recycled oil recycled plastics recycling red algae redcedar Reducing Emissions from Deforestation and Forest Degradation refineries reforestation Reformate regenerative braking regenerative farming Regulated Emissions regulations Regulations-Federal Regulations-State Regulatory Enhancement Growth Support (REGS) Reid vapor pressure (RVP) remediation remediation rice straw Renewable Chemical renewable chemical producer tax incentive renewable chemical production tax credit Renewable Diesel/Green Diesel/HVO/Paraffinic Diesel Renewable Diesel/Green Diesel price Renewable Diesel Production renewable diesel pumps renewable diesel tax credit renewable diesel terminal Renewable Energy Renewable Energy Directive (RED/RED II/REDIII) Renewable Energy Standard Renewable Energy to Fuels through Utilization of Energy-Dense Liquids (REFUEL) renewable fuel renewable fuel oil (RFO) Renewable 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management RJ-4 RJ-6 RME (rape methyl ester) RME180 RNA (Ribonucleic acid) roadmap rocket fuel Romania RON (Research Octane Number) rotation crops royalties RTP (rapid thermal processing) rubber rumen ruminants rural development Rural Energy for America Program (REAP) Rural Energy Self-Sufficiency Initiative Rural Renewable Energy Pilot Program Russia Russian olive rutabaga Rwanda ry rye Rye grass s saccharification SAF30 Safer and Affordable Fuel Efficient Vehicles (SAFE) safety safflower SAF pipeline sago pond weed SAIC SAK Salicornia salt-tolerant saltbush saltcedar sal tree salt water Sanctions Santa Monica sardine oil Saskatchewan Saudi Arabia sawdust scale up Scandinavia scholarships/fellowships Science Advisory Board (SAB) Science Policy Scotland scum sea level rise seaports seashore mallow seawater Seaweed/Macroalgae seaweed cultivation Section 526 Securities and Exchange Commission (SEC) seed-to-wheel seed husks Senegal Serbia sesame sewage Seychelles shale shale gas shale oil shark oil sheep shipping shipping containers shipworm Sierra Leone silica Silphie/cup plant/Indian cup silver silver maple simarouba Singapore sisal SK slash Slovakia Slovakia/Slovak Republic Slovenia sludge Small Business Administration small engines small refinery exemption (SRE) smog smokestack soap Social social benefit investing social cost social value social venture Society of Automotive Engineers (SAE) soi soil soil amendments soil carbon soil health soil microbial biomass solar energy solar energy-to-chemical conversion solaris solid oxide fuel cell (SOFC) Solomon Islands Solutions solvent liquefaction Somalia soot sorghum sorghum oil sorghum stover South Africa South America South Australia South Carolina South Dakota Southeast Asia Southern Africa South Korea South Pacific South Sudan Soviet Union SOx (Sulfur oxides) soybean prices soybeans soy meal Spain spartina specifications Spent Bleaching Earth Oil (SBEO) sprawl spruce Sri Lanka Stakeholders standards start-up State Department Statistics steam explosion steam methane reformation steam reformation steel stevia stillage storage tanks Straight (pure) Vegetable Oil (SVO or PVO) stranded assets Strategic Bioenergy Reserve STrategiv Petroleum Reserve straw students su sub-Saharan Africa sub-sim (substantially similar) subsid succinic acid sucrose Sudan sugar sugar-to-biodiesel sugar-to-farnesane sugar-to-jetfuel Sugar Beets/Energy Beets sugarcane sugarcane prices sugarcane straw Sugar kelp sugar palm sugar platform sugar prices sugars sugars-to-fats sugar standards sulfur Sumatra sunflower sunflower stalks supercritical fluid supercritical hydrolysis supply agreements supply chain Supreme Court surahart Suriname Sustainability Swaziland Sweden sweetgum sweet potatoes Sweet sorghum swine waste Switchgrass Switzerland sycamore syngas syngas/gas fermentation synthetic biology synthetic diesel synthetic gasoline synthetic kerosene synthetic liquified gas (SLG) synthetic methane/e-methane synthetic natural gas Syria Tailoring Rule Taiwan Tajikistan tall fescue tall oil tallow tallow tree tamanu/nyamplung Tamarix tank-to-wheel tank cars tankers tanker trucks Tanzania tariffs taro tar sands Tasmania tax benefit tax credit taxes tax incentives tax parity tax policy tea teach-the-teacher teacher teacher resources teacher training technical course Technical Readiness Levels techno-economic analysis technology transfer tech transfer telephone utility poles Tennessee termites terpenes terrestrial carbon testing Texas textbook Thailand theft therapeutics Thermal catalytic depolymerization (TCD) thermal deoxygenation thermocatalytic conversion thermochemical conversion thermochemical liquefaction Tibet Tier 3 Tier 4 tilapia tillage Timor-Leste tires tobacco tobacco tree Togo Tokyo toluene Tonga tool Toronto torrefaction Toxic Substances Control Act (TSCA) trade trade dispute/discrimination trade group trade organization Trade Policy trade secrets training trains transesterification transgenics transition Transportation Fuels Policy Transportation Fuels Policy--Municipal Transportation Fuels Policy--State Transportation Policy travel policy Treasury Department trees Trinidad and Tobago triticale truck trucks tubers tugboats tung tunicate Tunisia Turkey UCOME (Used Cooking Oil Methyl Ester) Uganda UK (United Kingdom) Ukraine UL (Underwriters Laboratory) ULSD (ultra low sulfur diesel) Ultra Low Sulfur Fuel Oil (ULSFO) underground storage tanks (UST) UNESCO United Arab Emirates (UAE) United Nations' International Civil Aviation Organization (ICAO) United Nations' Sustainable Development Goals (SDG) United Nations (UN) United States Auto Club Unleaded 88/E15 uranium urbanization urban sprawl Uruguay USAC US Agency for International Development (USAID) USAID US Army USDA Agricultural Research Service (ARS) used cooking oil (UCO) used motor oil used railroad ties US ethanol exports US Geological Survey US Product Safety Commission Utah utility model Uzbekistan value chain vanadium 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