Below is the text version of the Co-Optima Capstone webinar, "What environmental and economic benefits might be realized by co-optimizing fuels and engines for medium-duty and heavy-duty commercial vehicles?" Watch the webinar recording.
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Troy Hawkins: Good afternoon, and thanks for joining this instance of our Capstone series of webinars. I am Troy Hawkins from Argonne National Laboratory, and I co-lead the Co-Optima Analysis Team together with Avantika Singh. I will be presenting on the analysis we've done for heavy-duty fuels and engines, specifically addressing the question, "What environmental and economic benefits might be realized by co-optimizing fuels and engines for medium- and heavy-duty vehicles?" Watch the webinar.
This presentation is the fourth presentation in the Capstone series. It follows a similarly titled presentation by Avantika Singh, which focused on light-duty fuels and engines. If you attended that one, you will see the complementarity between these two. This presentation also builds on the second Capstone webinar delivered by Bob McCormick and Chuck Mueller, which focused on the potential for Co-Optima approaches for medium- and heavy-duty vehicles. I will revisit those themes and examine them from some new angles.
I would like to begin by thanking the Bioenergy Technologies Office for the opportunity to do this work, and thanking Alicia Lindauer, Kevin Stork, Jim Spaeth, and Trevor Smith, our technology managers, as well as the Co-Optima leadership team, Dan Gaspar, Anthe George, Bob McCormick, and Robert Wagner, for their invaluable input and guidance. I would like to acknowledge the team behind this work, which I will show at the end of the presentation, but this is not just my work, this is the work of many in Co-Optima.
Co-Optima researchers conduct research at the fuel/engine interface with the aim of optimizing fuel properties and engine technologies to more quickly identify technology options to improve performance. Co-Optima seeks to identify biomass and waste-derived blendstocks, which provide a performance advantage for advanced engines, which would then create a market pull for biofuels. The presentation is organized as shown on the left. First, I will provide some background on the potential for co-optimized fuels and engines for medium- and heavy-duty vehicles. Next, I will provide some additional background on the goal for this work, followed by key takeaway findings from the research. After that, I will review the research approach developed through the Co-Optima medium- and heavy-duty analysis, and then share some of the most notable outcomes. Finally, I will speak about the next steps for this effort.
The motivation for our work on heavy-duty fuels and engines is that society needs cost-effective, clean, low-carbon powertrains for applications that require long-range, rapid re-energizing, lightweight, and compact size. These characteristics are particularly important for freight-hauling applications, such as long-haul trucking, which has been the focus of our analysis, as well as rail and transoceanic shipping. What are the options for meeting this need? Well, there has been significant recent discussion of the potential for electrification. Electrification faces significant challenges associated with the cost, weight, and space required for batteries, as well as potential challenges associated with sourcing critical materials for batteries and their potential recovery through battery recycling. Additionally, a significant supply of clean electricity is required to make electrified transportation sustainable. Providing this electricity, as well as charging vehicles, requires a large amount of new infrastructure to enable widespread, clean, and rapid recharging. Similarly, fuel cells are still expensive. Hydrogen vehicles face challenges associated with hydrogen supply, delivery, and refueling. Hydrogen as a fuel is also significantly less energy-dense than diesel.
Meanwhile, current methods of producing hydrogen by steam methane reforming are energy and carbon intensive, while green hydrogen requires significant amounts of low-carbon electricity and infrastructure. As a result, heavy-duty vehicles and other freight-hauling applications continue to be powered with diesel engines. Unfortunately, these fuels have high lifecycle carbon dioxide emissions and degrade air quality due to emissions of particulate matter and nitrogen oxides. As a result, a number of policies, such as the federal Renewable Fuel Standard, and California and Oregon's Low-Carbon Fuel Standard, with other similar standards considered in Washington, Colorado, South Dakota, Minnesota, Iowa, and New York.
However, when run on biofuels, the emissions from internal combustion engines can be decreased. This is particularly attractive as drop-in biofuels could provide an immediate benefit for the existing vehicle fleet, while, as Bob McCormick and Chuck Mueller described in the second Capstone webinar, advanced engine technologies used in combination with performance-advantage biofuels have the potential to significantly reduce engine-out emissions of particulate matter, or soot, and nitrogen oxides.
Why diesel? Diesel compression ignition engines have many desirable attributes. They are cost effective, inherently high efficiency, the ignition timing is easy to control, they can be used with a range of fuels, they have high torque and power density, low cyclic variability, they are durable and reliable, low emissions of un-combusted hydrocarbons and carbon monoxide. However, the challenges for diesel are that when used with conventional diesel, they have significant carbon dioxide emissions. Although heavy-duty vehicles run on diesel use emissions control systems, policies focused on improving air quality seek further reductions in emissions of soot, or particulate matter, and nitrogen oxides, which are challenging to achieve with current technologies.
To improve on current diesel engine technologies, Co-Optima is seeking new, sustainable fuel/engine combinations that can leverage the benefits of compression ignition engines while addressing the concerns. The consortium is focused on liquid fuels, particularly those compatible with existing fuel-distribution infrastructure. Through interactions between the Co-Optima biofuel production researchers and those focused on engine modeling and testing, the team identifies promising blendstocks and down selects based on a range of criteria. Research is focused on non-food-based biofuel feedstocks to avoid competing with food uses and to avoid land-use change impacts. Most feedstocks considered in this program are waste or terrestrial biomass or algae, which can be produced on marginal lands unsuitable for conventional agriculture. We assessed the full well-to-wheels lifecycle impacts for the down-selected biofuels options. In addition, we analyze and provide insights on the potential value of biofuels in the market, including the potential benefits of re-optimizing refineries considering the additional degrees of freedom offered by bioblendstocks with advantageous properties. We considered consumer choice in the potential sales of vehicles with co-optimized engines, and finally, we considered the environmental and socioeconomic impacts at the national scale.
Ultimately, Co-Optima analyses provide data, tools, and knowledge, which enable informed decisions and could support the uptick of biofuels in the market. Finally, I'll note this presentation is focused on the opportunities and impacts for Co-Optima strategies in medium- and heavy-duty vehicles. The goal of this effort is to identify cost-effective, low-carbon, drop-in biofuels for diesel engines, and quantify their potential cost and benefits at scale. Research in Co-Optima has aimed at answering three fundamental questions. You heard about the first one in the Capstone webinar by Dan and Jim in March, a recording of which is available on the Co-Optima website. Today, we're focused on what will work in the real world, and I'll be focusing on the heavy-duty sector.
Specific to this presentation, the goal is to inform research and development and commercialization decisions. Two specific subgoals: we evaluate selected blendstocks to understand their environmental benefits and tradeoffs, economic drivers in price, and scalability potential; and we also quantify benefits and tradeoffs at scale, the potential for purchase by consumers, and environmental and employment effects. The impact of these analyses is that stakeholders understand the cost and benefits of co-optimized fuel/engine strategies, and can make informed decisions regarding commercialization and further R&D.
Next, I'll share a few key takeaways. Up front, I would like to say low-carbon biofuels could be produced at near-competitive prices to enable clean diesel vehicles. We are well on the path to achieving this goal. With further development, biofuels for diesel engines could be produced for $3.00 to $4.00 per gallon gasoline equivalent, with lifecycle greenhouse gas emissions greater than 60 percent less than diesel. High-cetane, low-sulfur biofuels have the potential to drive blending by refineries to meet stringent specifications, such as California diesel fuel. Biofuels and ducted fuel injection, which was the subject of the second webinar, have the potential to significantly reduce nitrogen oxide and particulate matter emissions, enabling clean diesel vehicles. Decarbonizing heavy-duty transportation is challenging and costly. Low-carbon biofuels offer a path forward with favorable, marginal carbon dioxide abatement cost.
Next, I'll go through some slides describing the research approach. I'll break this out into sections, first talking about the cost, greenhouse gas, environmental impacts, and scalability assessment, then moving on to the refineries analysis, and finally the consideration of consumer purchases and impact at scale. Overall, the research approach will quantify the cost and environmental effects of using bioblendstocks in advanced diesel engines, model vehicle choice and effects at scale. Here you can see the steps I'll walk through. First, focused on the biofuel production, here we are answering the question, "What are the costs, greenhouse gas emissions, and environmental effects of diesel-compatible biofuels?" TEA and LCA help identify promising fuels. This project occurs in the context of down-selecting performance-enhancing fuels. First, based on properties identified by the Advanced Engine Development and Fuel Properties teams of the Co-Optima consortium, targeted blendstocks by the High-Performance Fuels Team, and measured by the Fuel Properties Team. Production pathways are developed for down-selected candidates beginning with promising feedstocks, such as low-value biomass, and waste. Process models are developed in consultation with the High-Performance Fuels Team. In this process, we endeavor to consider a diverse set of production methods, chemical structures, and feedstocks.
On this slide, you can see a simplified illustration of the analysis approach for the TEA and LCA. On the top, you can see some of the inputs that go into performing TEA and LCA. In the middle, you can see some of the feedbacks between the TEA and the LCA. You can also see which labs in the consortium are involved in the TEA—NREL and Pacific Northwest National Lab.
Results are iterated in discussion with our stakeholders and external advisory board, Co-Optima leadership and team leads, and the High-Performance Fuels Team. Next, we evaluate metrics to classify the scale-up potential of Co-Optima bioblendstocks. We consider economic, environmental, and technological readiness metrics. We look at two cases: a current baseline, the state of the technology today, as well as a future target case. We considered 19 metrics, and we tried to classify them as favorable, neutral, unfavorable, or unknown.
Here you can see some of the metrics that we are using. I won't get into the exact criteria for each, but these bioblendstocks are classified based on objective and clearly communicated criteria. The three tables show the criteria that are used. The cost metrics on the left are calculated through TEA. These include baseline and target costs and their ratio, which is an indication of technological readiness level. We also consider price dependency on co-products, market competition for the bioblendstock and its precursors, and feedstock cost. Environmental metrics, in the middle, are calculated through LCA. These include carbon efficiency and conversion yield for the baseline and target cases, as well as reductions in the lifecycle greenhouse gas emissions, fossil energy use, and water consumption relative to conventional fuel. Scalability or technological readiness metrics are shown on the right. These include reliability of the data sources, sensitivity of production process to the feedstock type, and robustness to feedstock variability, as well as the blending behavior with conventional fuel, the extent to which the bioblendstock has already undergone testing toward certification, and current legal limitations to the allowable blend level.
Next, I'm going to move into discussing how we do the refinery benefits analysis. Here we are addressing the question, "What value can diesel-like biofuels provide the refining industry?" Here the basis is that with the availability of these performance advantaged bioblendstocks, refineries re-optimize to blend those biofuels. We benchmark against a business-as-usual case, and we quantify the refinery-wide cost of blending biofuels as well as the environmental performance of refinery products. With this, we identify fuel properties that would generate market pull from refiners and determine cost and sustainability implications of blending these bioblendstocks at refineries.
This slide gives a bit more detail on the economic modeling and the refinery process modeling. We see how Co-Optima bioblendstocks could be integrated at the refinery to produce ultra-low-sulfur diesel. The team developed models for three different refinery configurations using Aspen PIMS, which is the most prevalent industry tool for refinery planning and operations. We built three models. The first is a highly complex, large refinery; the second is an average-complexity medium refinery; and the third is a small, simple refinery. They are all based on PADD 3 conditions, which contain more than 50 percent of the nation's refining capacity.
The Aspen PIMS models are coupled with an LCA model to provide the environmental impacts. Here we're looking at whether re-optimizing the refinery and perhaps increasing the intensity at certain units cause tradeoffs, and to what extent those offset the benefits of the bioblendstocks. We call this the Refinery Products LCA Tool. It's an Excel-based tool to inform the carbon intensity of refinery emissions.
We also look at other impact categories, input bioblendstocks cases, and the business-as-usual case from Aspen PIMS. It uses emission factors that are derived from the University of Calgary's Prelim model. I would also use the supply chain emission factors for fuels and bioblendstocks from the GREET model, and we have coupled it with a volatile-organic-compounds calculator addressing the supply chain of conventional fuels. Then, as an output, it gives an overview of the refinery. It considers refinery-wide impacts, per-unit product impacts, and end-use emissions. That includes the full lifecycle from upstream input supply chains through use.
I'm moving through each of the steps in how we do the analysis, and then next, I'll come back and hit each of these again to talk about the results for each. Next, we're going to talk about adoption and scale up. The question that we're addressing here is, "Would co-optimized biofuels and engines sell in the heavy-duty market?" For this, we're using a model that projects the heavy-duty fleet using a vehicle choice model. The model is a new version of the National Renewable Energy Lab's ADOPT model. ADOPT was previously configured for light-duty vehicles, and as a result of this project and another parallel project, it was extended to include heavy-duty vehicles. For this analysis that I'm presenting today, we're focusing on Class 8 heavy-duty vehicles like long-haul freight trucks. The scenarios start with all existing Class 8 tractor options, which captures the diversity of performance characteristics and pricing. Each year, new vehicles are introduced to the market based on the bestsellers. In this analysis, obviously we introduce co-optimized vehicles using mixing-controlled compression ignition engines with different Co-Optima bioblendstocks. Sales estimates are used for future vehicle evolution, and then sales are placed into a stock model to estimate the total fleet greenhouse gas emissions and petroleum consumption.
With that, we move on to the economy-wide benefits. Here we're addressing the question, "What are the environmental and societal effects of heavy-duty biofuels and engines at scale?" To address that question, we use an integrated model, or a series of models, that were existing prior to Co-Optima, most of them, but brought together specifically for this purpose, so this way of integrated modeling is new to the Co-Optima program. The models are listed here. I apologize for the acronyms, but at the top is the ADOPT model that I previously mentioned at the National Renewable Energy Lab. That is coupled with the BSM, or Biomass Scenario Model, which addresses the question, "How will the biofuels industry grow?" It looks at capacity build-out of biofuel production infrastructure. Those two feed into a model that we've put together at Argonne specifically for Co-Optima, Bioeconomy AGE, which looks at the energy and environmental impacts of biofuels. Bioeconomy AGE, in turn, is informed by GREET supply chains for the fuels, as well as downstream emissions, and GREET is, in turn, informed by the TEA process models. On the upper right is the Bioeconomy Input-Output Model, which is another National Renewable Energy Lab model which addresses, "What are the economic impacts from biofuel plant construction and operation?" It addresses the growth and net job benefit over time. With all of these together, we have a fairly complete picture of what impacts would occur from the scale-up of biofuels.
In the next section of the presentation, I'm going to move on to notable outcomes. I'm proud of what we've accomplished in this project and I'm looking forward to sharing these. At a high level, we have found that low-carbon biofuels could be produced at near-competitive prices. We were able to identify the potential for market pull by refineries due to favorable properties of bioblendstocks, and we found that biofuels can reduce greenhouse gas emissions for trucks already on the road, while new trucks with advanced engines could realize additional benefits from reduced emissions of nitrogen oxides and particular matter. In contrast to the research on light-duty vehicles, the research for heavy-duty vehicles did not yet confirm the potential for significant fuel efficiency gain. This outcome was not surprising, as the potential for increased efficiency in compression ignition engines is limited.
Now I'll step through these again to present the results. Once again, here we're looking at, "What are the costs, greenhouse gas emissions, and environmental effects and scalability of diesel-compatible biofuels?" Our screening, at a very high level, identified 13 promising MCCI, or mixing-controlled compression ignition, biofuels. These results are presented in a forthcoming report on the top 13 blendstocks for mixing-controlled compression ignition diesel engines, which presents the process through which the fuels are evaluated, first through identification of promising blendstocks based on structure property relationships, followed by fuel property testing and further screening through the TEA LCA and assessment of scalability considerations, as I've described. Out of this process, the 13 bioblendstocks identified are shown on the screen including the hydrocarbons farnesane, Fischer-Tropsch diesel, renewable diesel from hydrothermal liquefaction of wet waste—such as sewage, sludge, and manure, algae, and algae-wood blends—isoalkanes made from ethanol, isoalkanes made from food waste, and hydroprocessed esters and fatty acids, often abbreviated as HEFA and commonly referred to as renewable diesel. Esters are short-chain esters from oilseed crops, fatty acid methyl esters, abbreviated as FAME and commonly known as biodiesel, fatty acid fusel esters, and ethers: 4- butoxyheptane, polyoxymethylene ethers, or POMEs, alkoxyalkanoates, and fatty alkyl ethers.
I apologize for the small size on this slide. These are the screening results. It compiles the results of our cost, environmental, and scalability screening analyses. The cones correspond to the criteria I described earlier in the presentation, and each row provides the results for a candidate mixing-controlled compression ignition bioblendstock. The colored dots reflect the results of the assessment, once again, based on the criteria I mentioned earlier. Favorable results are shown in green, neutral in blue, unfavorable in orange, and gray is used to depict cases where not enough data were available for a determination. I just want to call out a few things, and I'll go into some more detail in the following slides. For the top-performing pathways, both the HEFA, or renewable diesel, and the FAME, or biodiesel, are approved fuels already produced at commercial scale from a variety of fats, oils, and greases feedstock. They both achieve a greater than 60 percent greenhouse gas emission reduction and favorable economics, although currently the feedstocks can be expensive, limited in supply, and face market competition.
The hydrothermal liquefaction pathways, and there are multiple feedstocks used through HTL, offer the potential to achieve greater than 60 percent greenhouse gas emission reductions, favorable economics depending on the feedstock, and can utilize a variety of sludge and wet waste feedstocks. However, the technological readiness level is lower than other pathways, and some of the fuel properties are yet unknown. Fischer-Tropsch diesel is a commercialized process already approved as a fuel additive and is capable of using less costly and more abundant lignocellulosic feedstocks. Across the pathways, the economic metrics are largely favorable or neutral, indicating that many of these bioblendstocks could be produced at cost, which are within reason for consideration in particular in connection with the future where they are incentivized by policy. Some of the pathways show the potential for target minimum fuel selling prices below $4.00 per gallon of gasoline equivalent, which is the metric we use by setting the energy in the bioblendstock equal to that of a gallon of gasoline.
The environmental results are mixed, with 11 of the pathways achieving greater than 60 percent reductions in greenhouse gas emissions, while the results indicate water consumption may be a challenge. The technological readiness metrics are also mixed with the production processes for many of these pathways still at a low TRL. The associated research and development efforts are mostly still at the bench scale, and more information is needed to understand blend behavior and regulatory limits. It's important to note the efforts to collect these data are underway. Of the top 13 candidates shown here in bold, the analysis and preliminary experimental results indicate the bioblendstocks identified have potential to reduce particulate matter and nitrogen oxide emissions by somewhere in the range of 50 to 99 percent. They are likely compatible with existing fuel infrastructure in the legacy suite and could reduce greenhouse gas emissions by 60 to 90 percent per unit energy substituted.
As part of the cost screening, we examined the contributions to the bioblendstock costs in greenhouse gas results. Here you see the costs, or minimum fuel selling price results. Each bar reflects a bioblendstock listed on the left of the figure. The contributions are shown as segments in the bars, with the production stages shown in the legend broken out by operating and capital expenses. In many cases, the production processes make more than one product, and in these cases a credit corresponding to the value of the co-product is shown in purple as a negative contribution to the price. The resulting price is shown as a black dot on each bar. As these results are at the rough screening level, exact prices are not shown on the minimum fuel selling price axis; rather, the gray region is used to indicate the classification of pathways into the favorable, neutral, and unfavorable categories. From this figure, we can see that feedstock costs contribute significantly to bioblendstock prices. This highlights the importance of research beyond the conversion process to optimize feedstock production and logistics upstream of the biorefinery.
Additionally, to address this, research is focused on pathways using waste feedstocks, which are available at very low prices and which, in some cases, benefit from avoided tipping fees or avoided conventional management requirements. Examples include the hydrothermal liquefaction of sewage sludge and manure and isoalkanes produced from food waste. The chemical pathways did not fair as well in the cost analysis, which is in part due to some of the conventions used in the analysis, which may be addressed in future research. First, the sodium hydroxide caustic used in the pre-treatment adds cost, as well as the glucose used in the enzyme production. Further, the use of lignin to produce additional products could potentially reduce the bioblendstock cost for the biochemical pathways. Here we did not include lignin valorization to maintain a conservative result and avoid confounding results due to uncertainties in lignin conversion efficiency. In general, we focused on pathways not dependent on co-products for the fuel price, again, to provide consistent, conservative estimates and avoid large uncertainties. Fischer-Tropsch diesel is a notable exception, with hydrocarbons and wax being sold as co-products.
Similarly, we provide a contribution analysis for the greenhouse gas results shown in the figure here. The features of this figure are similar to the previous. In this case, lifecycle greenhouse gas emissions are shown on the horizontal axis. We find that there are biofuel pathways with the potential to deliver significant greenhouse gas reductions. However, the level of greenhouse gas reductions compared with conventional diesel are not guaranteed. This is something that was surprising to some in the Co-Optima team who would have previously assumed advanced biofuels to be inherently low carbon. The dashed line in the figure shows the greenhouse gas level corresponding to a 60 percent reduction from conventional diesel, which is the EPA's criteria for advanced biofuels and BETO's target for the Co-Optima consortium. It should be noted that CO2 from fuel combustion is emitted from the figure because it originates from biogenic carbon, which was taken up, or would have been emitted anyway from the biomass and waste feedstocks for fuel production. The values below the axis reflect both co-product credits and, in the case of isoalkanes from food waste, avoided methane emissions, which would have occurred had the food waste not been used to produce fuel.
Some of the themes for the greenhouse gas results are similar to those for cost. Feedstocks contribute significantly to many pathways, but the biochemical pathways are affected by the use of sodium hydroxide for feedstock preprocessing and other chemical inputs to the conversion process. Another key takeaway message from these results is that there are a variety of feedstocks and pathways which could provide low-carbon, diesel-like biofuels. In other words, there is not a single winner. Finally, feedstock production, sodium hydroxide pre-treatment, chemical inputs, and using methane-emitting waste feedstocks are all opportunities to improve the greenhouse gas emission profiles of future fuel production pathways.
To combine the greenhouse gas and cost perspectives, I wanted to briefly present this figure, which shows the two results together, with the cost results sorted to match the greenhouse gas ordering. We can see that while there is a positive correlation between the greenhouse gas and cost results, there are some tradeoffs, particularly for some of the lowest greenhouse gas pathways for which costs are slightly higher than for other pathways that meet the 60 percent reduction threshold but with slightly higher greenhouse gas emissions.
In the next two slides, I wanted to highlight the results for a couple pathways to give a flavor of the detailed findings we report in the “top 13” report. This slide presents the hydrothermal liquefaction process developed at Pacific Northwest National Laboratory. Hydrothermal liquefaction can utilize several types of feedstocks, and due to its use of water as a solvent, it is particularly suitable for processing wet feeds such as algae or wet waste. All of the HTL (the abbreviation for hydrothermal liquefaction) pathways evaluated under Co-Optima showed greater than 60 percent greenhouse gas emission reduction versus conventional diesel. However, only the wet waste and algae-wood-blend feedstocks offered favorable costs. Using wet waste feedstocks offered the most favorable economics and environmental metrics for all of the evaluated pathways, although feedstock availability may be limited. As this research continues, it addresses the cost and operability challenges posed by the use of high-pressure reactors for HTL.
Co-Optima evaluated multiple mixing-controlled compression ignition bioblendstocks produced through carboxylic acid, volatile fatty acid intermediates produced via fermentation. Those were 5-ethyl-4-propylnonane, 4- butoxyheptane, and hexyloxy heptanes, hexyl hexanoate, and mixed isoalkanes from volatile fatty acids. Pathways using corn stover feedstocks were unable to achieve greater than 60 percent greenhouse gas emission reduction and favorable economics without lignin valorization due to the high cost and greenhouse-gas-intensive biomass deconstruction. When food waste was used as a feedstock to produce the volatile fatty acids, the minimum fuel selling price was reduced, and greenhouse gas emissions dropped sharply. Additional reductions in cost and emissions could be achieved through the valorization of lignin to co-products.
I had also mentioned we focused in later analysis on pathways from waste feedstocks. This slide shows results from a deep dive on pathways to produce renewable diesel from hydroprocessing fats, oils, and greases, and by hydrothermal liquefaction of swine manure. We found renewable diesel from these wastes could be produced with very low lifecycle well-to-wheel greenhouse gas emissions for reasonable costs below $4.50 gallon of gasoline equivalent. The figure below shows how the results for the hydrothermal liquefaction pathway depend on the swine manure management practices, which are displaced by the fuel production process. When U.S. average management practices are considered, which include significant emissions from disposal pits, the offset emissions are significant, resulting in a very negative greenhouse gas profile. If we consider improved management practices with our estimate of the maximum amount of methane-flaring, which could be achieved using best practices, the result is still negative but not as much so.
If we consider diverting swine manure that is currently being anaerobically digested with methane capture in electricity generation, the displacement credit is minimal, and the lifecycle greenhouse gas emissions no longer meet the 60 percent reduction criteria. I should mention that this case is unlikely; we show it here to help understand the potential range of results under different conditions. Overall, the swine manure HTL pathway offers greater than 100 percent greenhouse gas reductions, and a price of about $3.60 per gallon of gasoline equivalent, which could be reduced further with economies of scale achievable in a larger facility. The fats, oils, and greases hydroprocessing pathway offers greenhouse gas reductions around 87 percent, with a price around $4.40 per gallon of gasoline equivalent.
Next, I'll move to the refinery benefits analysis. This figure compares the environmental performance of the refinery product slate after re-optimizing for the Co-Optima bioblendstocks. The analysis compares the impacts for producing a slate of refinery products, re-optimizing the refinery for blending these blendstocks with advantageous properties. The results are benchmarked against a business-as-usual refinery case. The solid bars show the case for the general U.S. market, while the hatched results are California diesel formulations required to meet California's emission standards. We ran results for a wide range of scenarios. Results for the West Texas Intermediate crude price is $60 per barrel in the year 2040, considering each Co-Optima fuel blended at 11.4 volume share in diesel are shown here. The breakeven value represents the value we estimate the Co-Optima bioblendstocks would offer refiners due to their ability to increase refinery profitability. We see that the results for California diesel formulations offer significantly higher prices. This analysis reveals that the prices that could be achieved are significant, especially in the California scenario. In the next slide, I'll talk about the environmental benefits.
This figure compares the environmental performance of the refinery product slate after re-optimizing for Co-Optima bioblendstocks. We see that the greenhouse gas benefits of blending Co-Optima fuels outweigh the potential tradeoffs. Here we are looking at a couple different cases. On the vertical axis, you can see the lifecycle greenhouse gas emissions. The stacks in the bar show the different contributions over the entire lifecycle, and then the three cases within each fuel type relate to the different blending cases at the refinery, so first the business-as-usual, and then the hexyl hexanoate case, HEX, and the POME case. If you go to the far right, for the ultra-low-sulfur diesel, you can see the benefits of the cases where we're blending Co-Optima blendstock, or the HEX and POME, with the ultra-low-sulfur diesel. You can see also in that case the results that say HEX but don't have the gray bar below the axis reflect the portion of the fuel in those cases that are not blended with the Co-Optima bioblendstock. We don't blend with the entire feed. Then in the other cases, the reformulated gasoline, regular and premium, reformate, and kerosene and jet fuel, you can see the potential tradeoffs. For example, in the RGE premium case, we can see increased impacts in the POME case due to the reconfiguration of the refinery. But what we find is that the benefits of blending the bioblendstocks on the backend more than offset any tradeoffs due to re-optimizing the refinery. These results are for greenhouse gas emissions. We also have the ability to produce results for other emissions categories and lifecycle impacts.
Next, I'm going to move on to the adoption and scale-up results. Some of this work is still underway, so I'm presenting some of our intermediate results. This is the outcome of an analysis that we did recently but based on a paper that was published about a year ago. It is looking at the economic benefit to truck operators for the reduced nitrogen oxide and particular matter control costs associated with using Co-Optima's fuels and engines. What we found was that with the DFI technologies (ducted fuel injection), we can achieve greater 80 percent reductions in engine-out NOx and PM, which translates to a $4,500 to $5,000 lifetime cost reduction. This is due to reduced use of exhaust fluid and downsizing the selective catalytic reduction system. Those results and the contributions are shown on the right, most of the reduction is coming from the selective catalytic reduction reducing the need to operate it.
In the next two slides, I wanted to look at the cost potential for reducing greenhouse gas from today's fleet using Co-Optima strategies, and to give some perspective comparing to current results for battery technologies. Battery technologies have not been the focus of Co-Optima, so we are pulling from other work to do this. This figure compares the environmental performance of the fuels across a range of scenarios. Going down the scenarios, we can see the first bars are for conventional diesel, and everything is scaled to 100 percent so that we could fit the cost and greenhouse gas results on the same axis. The next three results are for three different Co-Optima cases with a blend level of 30 percent, and then the bottom three cases are three different sets of assumptions from a report on battery electric vehicles—actually, the combination of a recent total-cost-of-ownership report and a lifecycle assessment out of Argonne for medium- and heavy-duty vehicles. We can see that the costs for Co-Optima fuels are slightly higher than those for diesel. On the greenhouse gas reductions, this is for the blend, you can see the darker red is the greenhouse gas from the conventional diesel and the lighter red is from the effective blending of Co-Optima fuels. The reduction comes from the blend.
Moving down the figure, we can see that the cost for battery electric vehicles today is significantly higher than conventional vehicles and Co-Optima vehicles, but the projections show that cost coming down steeply in the future. For the greenhouse gas results, I should mention that we had results that were consistent that we could use, but they did not include the vehicle cycle emissions, or the emissions from producing the batteries for the battery electric vehicles, so these are under-estimates for the BEV results. Drop-in biofuels can reduce greenhouse gas emissions from today's fleet and particulate matter and NOx emissions in co-optimized engines at a cost that is potentially competitive with electric trucks in the near term.
Moving to this figure, and this one is a bit complicated, and in the interest of time, I'll go through it rather quickly. The red lines show the marginal cost of CO2 abatement for Co-Optima fuels. The blue lines and green lines show two different sets of cases for battery electric vehicles, with the blue being future and more optimistic assumptions and the green being more like the current assumptions. The different blue and green lines reflect different carbon intensity of the electricity, and the different red lines reflect different Co-Optima fuels. In one of the cases, the AAEE case reflects a higher assumption about the incremental engine costs. The horizontal axis shows the price of diesel in dollars per gallon. As the price of diesel goes up, the relative cost of these other technologies goes down, and you can see that in the lines. Typically, the price of a LCFS credit today is around $200 per tonne CO2. On this chart, we can see that much of the range falls below that level, for both Co-Optima and battery technologies. Again, we see from this that biofuels could offer attractive greenhouse gas abatement costs for heavy-duty vehicles, and that both biofuels and electrification offer complementary strategies for distinct applications. The comparison between the two really depends on what we assume will happen with the price of battery electric vehicles and the carbon intensity of the grid in the future.
Finally, I am going to move on to the economy-wide benefits and wrap up. I will very quickly present results for a study where we looked at allocating biomass resources to decarbonized transportation; we looked at the potential biofuel supply for the heavy-duty sector, lifecycle greenhouse gas emissions, and land use change. What we found is significant greenhouse gas reductions could be achieved by allocating available biomass across the sectors—heavy-duty transportation as well as aviation and marine fuels—without significantly increasing land use. All of the land use increase that you see there is marginal land. It does seem that even with competition amongst different sectors for the biomass feedstocks, still significant greenhouse gas reductions could be achieved in the heavy-duty sector using biofuels.
Next, these results show that biofuel production provides jobs. These are preliminary results and will be integrated in the full scale of analysis. Employment for biofuel production is on par with conventional fuels, and a shift from petroleum fuels to biofuels would not cause net job loss, and then there could be significant construction jobs associated with biofuel infrastructure, although those jobs only occur during the build-out.
What are the next steps for this work? We want to look at how we can decarbonize transportation and end NOx and PM emissions at an acceptable cost. I mentioned that we are in the midst of completing the integrated analysis for the medium- and heavy-duty vehicles. This is something where we'll have results for Class 8 trucks this year and moving into medium-duty and partially electrified drivetrains in our analysis next year. The report shown in the lower left here is the one that was the integrated analysis for light-duty vehicles. We'll have a similar report next year for the Class 8 trucks and moving forward for the medium-duty and electrified scenarios. Moving forward, we want to realize the potential of these Co-Optima strategies by further reducing carbon intensity, increasing the blend level, looking at the requirements to achieve net-zero criteria air pollutants, and leveraging existing production and distribution infrastructure.
Finally, I would like to acknowledge the team. You can see the photo from one of our annual meetings here, and some of those from DOE who contributed to this work, and also to the leadership team, who I mentioned at the beginning of the presentation, and finally the analysis team, who are the minds behind the work presented here. I will end with the slide here for the next two Capstone seminars. Thank you very much for your attention.
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