Renewable Diesel vs Sustainable Aviation Fuel: why such a difference in adoption rates?

At first glance, RD and SAF appear nearly inseparable—often co-produced in HEFA systems, governed by overlapping policy frameworks, and sharing many investors, incentives, and technologies. Traditional analysis sees them as market twins.  Yet, in this analysis out from SAF adoption leader and hard-working champion IAG, the numbers for SAF lag far behind renewable diesel.

Converting this chart to gallons, think around 130 million gallons or so for these players, total — taking in to account there are more players than these top 10 and American and United hadn’t yet reported. Any way you look at it, renewable diesel is in the billions, SAF in the millions.  

In the third quarter of last year, renewable diesel accounted for nearly 65 percent of distillate fuel consumed in California, the US’s largest market, according to EIA. Market shares for RD in the west coast states are expressed in the 20-70% market share range. SAF is in the sub-2% range. Here’s a chart for that.

The challenge with techno-economic analysis and traditional technology adoption models

Traditional techno-economic analysis views renewable diesel and SAF as scalable, lower-carbon fuel option that benefit from infrastructure compatibility and regulatory support. HEFA (Hydrotreated Esters and Fatty Acids) technology, in particular, is celebrated for its streamlined process—feedstock goes in, drop-in diesel comes out. TEA emphasizes ROI, capital intensity, carbon intensity, and policy stacking.

GTESI analysis, based on the Genral Theory of Evolutionary Systems and Information, widens the lens. GTESI reveals a critical divergence in persistence dynamics.

It asks not just: “Does this pencil?” but “Will this persist?” Through its core vectors—Inverse Persistence Ratio (IPR), Symbolic Compression Divergence (SCD), Trust Ritual Failure Index (TRFI), and Entropy Export Deficit (EED)—GTESI tracks what most forecasts miss: the structural, symbolic, and thermodynamic dynamics that determine whether an industry stabilizes or snaps under pressure.

Looking at Renewable Diesel

Renewable Diesel benefits from alignment across all four GTESI vectors. It’s easy to explain (“used oil becomes fuel”), integrates smoothly into existing infrastructure, and supports high-frequency trust rituals—from earnings calls to truck-stop pump expansion. Thermodynamically, it’s a relatively low-complexity, entropy-manageable system.

In GTESI terms, renewable diesel currently thrives not because of a singular breakthrough, but because of its high alignment across multiple dimensions. Narrative simplicity (“used cooking oil becomes jet fuel”), ritual stability (from earnings calls to plant openings), and infrastructure integration (brownfield refinery conversions) create a resilient ecosystem. This coherence enhances persistence.

But GTESI also sees stress ahead: feedstock scarcity could strain symbolic cohesion and raise entropy burdens. If producers must work harder to explain the system—or defend cost premiums—the balance between symbolic trust and operational reality may fracture.

Turning to SAF

SAF, by contrast—especially non-HEFA pathways like ethanol-to-jet—suffers from narrative strain (high SCD), operational friction (high EED), and rituals that remain speculative rather than confirmatory (moderate TRFI). Even HEFA-SAF, while technically feasible, runs into symbolic mismatch: the aviation sector demands ultra-purity, rigorous carbon accounting, and future-proof scaling—conditions that exaggerate minor process inefficiencies into major trust gaps.

GTESI insight: the key difference isn’t chemistry—it’s symbolic load and entropy logistics. RD is a good-enough solution deployed at scale. SAF, especially ATJ, is an aspirational system under narrative stress. One moves. One hesitates.

Until SAF finds stronger ritual coherence and entropy control, GTESI predicts its persistence will remain conditional—dependent on subsidies, not structure.

Renewable Diesel’s GTESI scorecard

I. GTESI Risk Vector Scores

II. Sector Patterns

• Symbolic Compression Matches Operational Reality: HEFA benefits from simplicity in narrative (“vegetable oil becomes jet fuel”) and engineering. This reduces SCD and increases symbolic trust.

• Strong Ritual Encoding: Players like Marathon, Neste, and Valero participate in recurring symbolic events—earnings calls, policy wins, plant expansions—that reinforce trust and belief.

• Thermodynamic Coherence: Fewer unit ops, existing infrastructure, and acceptable energy input profiles allow for manageable entropy flows.

• Path Dependency Advantage: Success of early plants led to rapid institutional investment, media amplification, and regulatory reinforcement—creating a strong positive feedback loop.

III. GTESI Case highlights

• Marathon’s Infrastructure Fit: Co-processing and brownfield conversion reduce symbolic and physical friction. High ritual coherence with traditional refining.

• Lifecycle Performance: Shows alignment between feedstock emissions and carbon credit performance—a strong thermodynamic-symbolic linkage.

• Capital Formation: Unlike speculative DAC or ATJ, HEFA capital investment maps well to thermodynamic reality, with multiple FID-stage or operating plants.

IV. GTESI Sector Forecast

• Short-Term (1–3 years): Continued growth driven by policy, energy security narrative, and path dependence.

• Mid-Term (4–7 years): Strain likely due to feedstock competition and policy rebalancing—potential SCD inflection point.

• Watch for: Signs of EED strain—i.e., increasing efforts to justify expansion despite input scarcity and rising LCFS friction.

V. Takeaways

• Renewable Diesel succeeds not because it is perfect, but because it exhibits high coherence across GTESI dimensions.

• It is symbolically credible, thermodynamically tolerable, and ritually reinforced.

• The model flags future fragility, not current collapse. But watch for IPR-SCD decoupling if feedstock cost exceeds symbolic justification.

SAF’s GTESI scorecard

I. GTESI Risk Vector Scores

II. Sector Patterns

• Complexity Overload: Unlike HEFA, the ATJ process contains 4–6 major unit operations, each with high sensitivity to feedstock, energy cost, and purity. GTESI flags this as a scaling constraint.
• Symbolic Trust Mismatch: The ethanol economy has symbolic baggage (corn, subsidies), which is not easily harmonized with aviation’s high-purity, low-risk image.
• Entropy Re-import Problem: Process generates water and dilutes carbon value. 2.1 gallons of ethanol → 1 gallon of jet is a net information dilution event.
• Delayed IPR Emergence: With no successful large-scale plant, rituals are mostly anticipatory, not confirmatory. System remains speculative.

III. GTESI Highlights from Source Data

• Gevo LCA Report (CRA): Despite a well-argued emissions case, the loss of volume and carbon credits in the ETJ pathway undermines the narrative payoff.
• Saddler & Comer: Complexity of scaling ethanol-based SAF and low carbon intensity loss confirm high SCD and entropy reinjection.
• ATJ Tech Papers: Frequent reliance on “future scale” and “policy bridge” arguments reveals symbolic gaps in present trust structures.

IV. GTESI Sector Forecast

• Short-Term (1–3 years): Modest gains via policy support, demonstration plants. TRFI will rise or collapse based on real project delivery.
• Mid-Term (4–7 years): Without large-scale operation and favorable LCA crediting, GTESI predicts narrative exhaustion and funder fatigue.
• Watch for: Projects slipping from FID to indefinite delay; loss of trust rituals (e.g., silent partners, vanished policy enthusiasm).

V. Takeaways

• ATJ is not a failure, but its symbolic momentum exceeds its thermodynamic coherence.
• It has potential, but must rapidly close the gap between narrative and entropy export reality.
• GTESI sees an IPR trap ahead: persistence is only likely with extraordinary trust rituals, breakthrough tech compression, or unusual policy fortification.

Summary: What GTESI Reveals That Traditional Analysis Misses

Why Persistence Matters More Than Yield

As engineers and scientists, we’re trained to look for process efficiency, cost per unit, and scalable integration. Techno-Economic Analysis (TEA) is our go-to: it tells us if a system pencils out under known assumptions.

But many technologies that look brilliant on paper stall—or collapse—at the point of market entry, scale-up, or system shock. The culprit often isn’t the chemistry or the cost. It’s something harder to quantify:

• Misaligned incentives
• Symbolic breakdowns (“green” tech that isn’t trusted)
• Fragile coordination mechanisms
• Hidden entropy that the system can’t export

These are not business errors. They are thermodynamic imbalances in disguise. GTESI (General Theory of Evolutionary Systems & Information) offers a structured, rigorous way to evaluate systems not just by how they work, but by whether they will persist.

Traditional Metrics vs. GTESI Diagnostics

GTESI Is Rigorous, Not Philosophical

• Grounded in thermodynamics (entropy, dissipation, Landauer limits)
• Builds on information theory (compression, trust encoding)
• Validated by empirical collapse and persistence case studies
• Makes falsifiable predictions (e.g., early market collapse flags, symbolic decay curves)

It is not speculative theory. It is an evaluative system that complements TEA the way structural analysis complements material strength testing.

Why Engineers Should Care

GTESI helps answer:

• Why did that rival process with worse yield win market trust?
• Why did that tech fail even after pilot success and funding?
• Why do some companies scale under chaotic conditions while others stall at the ribbon-cutting?

GTESI provides a diagnostic lens for:

• Early-stage tech screening
• Go-to-market strategy
• System integration risk
• Policy resilience assessment

What do its concepts and acronyms mean?

IPR: Inverse Persistence Ratio: “Value without memory.”

IPR measures the gap between valuation persistence (e.g., market cap, investor enthusiasm) and operational memory (e.g., track record, cash flow, physical plant, ecosystem stability). A high IPR means price is sticking around — but the foundation is eroding. It’s like watching smoke in the sky when no-one is yelling “fire!”. Warning sign of symbolic inflation, over-valuation, or collapse risk.

SCD: Symbolic Compression Divergence: “When your story breaks from your system.”

SCD tracks the misalignment between public narrative (press releases, investor calls, strategic decks) and internal motion (technical progress, team stability, delivery timelines). A high SCD means the symbolic layer is leaking entropy — the story is losing coherence. Early indicator of reputational fragility, trust erosion, or memetic drift.

TRFI: Trust Ritual Failure Index: “Rituals keep systems sane.”

TRFI monitors the health of symbolic trust rituals: SEC filings, earnings calls, guidance cycles, leadership continuity, board signaling. A rising TRFI signals missed filings, ambiguous metrics, unexplained personnel shifts — cracks in the ceremonial foundation. When ritual breaks down, systems lose legitimacy — with investors, partners, regulators.

EED: Entropy Export Deficit: “Adaptation stalls, pressure builds.”

EED scores how well a system is exporting entropy — through innovation, expansion, alliances, new markets. A high EED means the system is hoarding entropy instead of offloading it — a pressure cooker instead of a pressure valve. Strong predictor of layoffs, retrenchment, sudden pivots, or collapse.

Core GTESI Concepts

How GTESI Works: A Diagnostic Tool

GTESI evaluates the motion-memory balance in a system. GTESI doesn’t replace financial models — it explains why they fail when they do. A healthy system shows

• Entropy exported (not bottled)
• Symbols that match reality
• Rituals that maintain trust
• Compression that enables repetition (manufacturing, contracts, team function)

A brittle system shows:

• IPR: Inverse Persistence Ratio — symbols outlasting performance
• SCD: Symbolic Compression Divergence — narrative drift
• TRFI: Trust Ritual Failure Index — cadence breakdowns, filings missed, metrics blurred
• EED: Entropy Export Deficit — innovation that fails to scale or simplify

More GTESI insights