Advanced Visualizations and Enhanced Sensitivity Analysis for 45Z Ethanol Cl Optimization
As momentum builds for a (hopeful) 45Z Clean Fuel Production Credit extension, the importance of every Carbon Intensity (Cl) point compounds. Scoring reductions should lead to potentially significant credits but not every capital improvement and process change will be feasible for implementation or an ROI even with a 45Z credit period that extends to 2031. This challenge has
led many biofuel producers away from simply exploring major projects like CCS or Combined Heat and Power but instead towards the question of What minor process or operational changes can we tweak by 1% here or 2% there and combine to yield a noticeable Cl reduction benefit?
That's exactly the question this sensitivity analysis set out to answer.
Using aggregated and anonymized production data from incite.aq's network of U.S.-based dry mill ethanol facilities, Incite.ag’s data science team utilized our deconstructed 45Z GREET model to run a complex series of “what-if” scenarios. The result? A clear, quantified look at how operational parameters—from ethanol yield and energy use to co-product outputs—can be strategically adjusted to drive down Cl scores.
Now no model is perfect. And in many cases theta are only as good as the input parameters that go into them. 45ZCF-GREET and GREET dependency cannot adjust for all of the economic, geographic, and operational constraints and priorities of a single plant. So while the analysis below is rock solid output directly from GREET, keep in mind that some of these adjustments may not be operationally feasible even when they are feasible via the model.
For our ethanol network, we hope you use this analysis as another marker in your strategic roadmap for real-world optimization—and that it can help your plant manager(s), technical teams, and executives make informed decisions based on actual 45Z GREET behavior.
Why Sensitivity Analysis?
Cl scoring under 45Z isn't a black box—but it can feel like one. With dozens of interlinked parameters and evolving regulatory requirements, it's easy to default to surface-level changes and miss deeper optimization opportunities. This analysis breaks down the components of the 45Z GREET model and isolates how much each operational parameter moves the needle—both alone and in combination. It's GREET, made tactical.
incite.ag Ethanol Network - Aggregated CI Impact by % of Parameter Increase
Parameter Analysis Insight: This chart shows the anonymized and aggregated Dry-Mill ethanol production data of the incite.ag network and answers the questions of "How would a 1% increase in a CI score-relevant, ethanol production input impact a fuel CI score?" Each row below shows a monthly production input / CI parameter and the scoring change (plus the optimal direction to adjust that parameter. EX: it is optimal to DECREASE one's electic use but increase your co-product yields)
Parameter Sensitivity Analysis - CI Reduction Potential Chart
Parameter Analysis Insight: This simplified visualization shows the statistically significant input parameters which impact fuel CI Scores. Parameters with greater potential impact values have a larger point on the chart. This visual emphasizes how the reduction in Natural Gas use and optimization related to feedstock (corn) CI scoring are the most statistically significant impacts one can make when seeking to reduce fuel CI scores.
Parameter Sensitivity Analysis - Parameter Impact Table
Parameter Analysis Insight: This table shows the impact of a 1% INCREASE in each parameter's base value on CI Score. Parameters with larger absolute impact values have a greater incfluence on CI Score. Negative impact values (green) indicate that increasing the parameter reduces CI Score, while positive values (red) indicate that increasing the parameter raises CI Score.
Parameter Sensitivity Analysis - Percentage Change Graph
Parameter Analysis Insight: New to reading charts like this? Parameters with steeper slopes have greater impact on CI Score. The chart below is a unique solution for visualizing how a CI Score changes with different percentage adjustments to key parameters. Green lines indicate parameters where increases reduce CI Score, while red lines indicate parameters where decreases reduce CI Score. For maximum CI Score reduction, focus on parameters with the steepest downward slopes at the percentage change levels you can realistically achieve.
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Takeaway #1
Some Small Changes Drive Big Cl Reductions:
One of the most impactful findings? The somewhat expected yet outsized impact of relatively modest improvements in a few high-leverage variables:
- Ethanol Yield: A 10% increase in gallons per bushel can drop Cl by up to 1.46 kg/MMBtu
- DDG Moisture: Increasing DDG moisture by just 10% can reduce Cl by up to 15.96 kg/MMBtu
- MWDG Moisture: Higher moisture in modified wet distillers grains also contributes to lower Cl, though at a smaller scale
These subtle process refinements bring disproportionate benefits in a GREET context.
Takeaway #2
Energy Inputs Still Matter—But with Diminishing Returns
Process fuel and energy use often draw attention in Cl conversations, and for good reason. This sensitivity analysis shows that while reducing natural gas and electricity consumption does help, the marginal benefit per unit is relatively small:
- A 5% reduction in fossil natural gas yields only a 0.004 kg/MMBtu drop in Cl
- Reducing electricity use by 5% changes Cl by just 0.008 kg/MMBtu
Please note specifically related to natural gas, this analysis is focusing on natural gas (as an input) in thousand mmbtu. Leading to a very high input number with low amount of change (1-10%). We are specifically analyzing this for CI Score impact as a whole and not individual outputs over 500 iterations. Analytically this targeted process means that we can only see what happens when this value changes compared to the resulting CI score. These findings don't mean energy efficiency or natural gas reduction isn't important—quite the opposite in fact, as natural gas is THE key input driver for reducing CI scores.
Takeaway #3
Synergies Between Parameters Compound Impact
When viewed in 3D, the relationship between ethanol yield, natural gas use, and co-product output reveals a valuable insight: interactions between parameters create compounding effects.
For example:
- Increasing ethanol yield while simultaneously decreasing natural gas use produces a significantly larger Cl reduction than adjusting either input alone.
- Maximizing co-product yields (dry distillers grains, MWDGs, corn oil) can enhance co-product credits and offset upstream emissions—amplifying the benefit of other optimizations.
This means a systems-thinking approach to Cl reduction beats a one-variable-at-a-time strategy. The best gains come from targeted, concurrent improvements.
Takeaway #4
Cl Score Composition Shows Where to Focus
A breakdown of Cl score components highlights where emissions are concentrated:
- Thermal energy (e.g., natural gas) and electricity dominate the emissions categories
- Co-product credits are essential in offsetting upstream and facility-level emissions
Understanding this balance helps plant operators shift from gut-feel decisions to evidence-based prioritization. If your thermal load is disproportionately high, reducing steam demand or switching fuel sources could offer better returns than marginal ethanol yield gains.
Takeaway #5
Optimization Potential Is Real—But Requires Context
When this model was ask to run a “optimal Cl reduction scenario” it identifies a combined set of parameter changes that, if implemented, could reduce Cl by up to 17.8%—from a baseline of 52.8 to a low of 43.4 kg/MMBtu.
But there's a caveat: this is a modeling exercise, not an operational blueprint. It is not taking into account all the the novel and innovative technology that is being piloted across the industry today and hasn't worked its way into the data sets at Argonne Labs. What looks good in GREET may not pencil out in the real world. Equipment limitations, operational priorities, labor constraints, and ROI considerations should always temper how these findings are applied on the ground.
Regardless, the analysis provides a baseline for evaluating tradeoffs—and elevates Cl conversations beyond guesswork.
Where This Goes Next
This report is part of a growing toolkit for ethanol producers who are serious about making Cl scoring a core part of their strategy. Whether you're: Preparing for 45Z monetization, Pursuing selective sourcing of low carbon feedstocks, Or simply trying to benchmark plant efficiency, sensitivity analysis like this offers a data-driven lens to guide your next moves.
Incite.ag's scoring team is available to walk through plant-specific modeling, simulate hypothetical adjustments, or accurate scoring and analysis of your actual operating data through 45Z-compliant systems.
incite.ag Staff
incite.ag
success@incite.ag
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Incite.ag guides producers across the agricultural supply chain to Turn Emissions into Income. Incite.ag’s CI scoring system unlocks novel revenue streams and empowers producers to take control of their unique CI Scores. Learn more by hitting the link below or reach out to the team directly at success@incite.ag or 815.373.0177.
