Carbon Dioxide Removal (CDR)

Last Updated: 2025-11-10

Modeling CDR as Avoided Emissions

To date, there are no commonly used frameworks that bridge carbon dioxide removal (CDR) assessments and avoided emissions assessments. While both types of assessments are relevant to forecasting the impact of emissions interventions, they are typically treated as entirely separate categories of investment during impact evaluation.

Given Koi's fundamental approach — one which aligns with all major avoided emissions frameworks — we have extended the avoided emissions framework to include strategies for modeling CDR. CDR presents a unique opportunity to build standardized datasets from the extensive scientific literature and high-quality verification protocols that exist. We've developed novel CDR datasets and methods that bring CDR assessments into concordance with avoided emissions perspectives and metrics.

The two assessment types are compatible but require translation, including breaking down the most studied CDR processes into their foundational elements: carbon footprints, value chain phases, materials, energy conversion, and costs. Our main goal is to present Koi's users and the wider avoided emissions forecasting community with a logical and practical method for handling CDR as though it is any other mitigation-focused climate technology.

Apples-to-apples comparisons are what we're all after. Any approach offered must be intuitive and stand up to scrutiny of a variety of practitioners while the standards setters (GHG Protocol, ISO) catch up. Most of all, it needs to be useful and lead to better decisions for the climate and society.

We expect the approach offered here to change over time, and we invite feedback on what works, where it falls short, and what we can do to improve it. Feedback can be sent via email at hello@koi.eco or through our public roadmap.

Implementation in Koi

Koi's CDR models follow the same fundamental arithmetic as all other scenario models in Koi, ensuring consistency and comparability across diverse technologies and markets. For a given year, the core formula remains:

CDR Impact Formula

Annual Emissions Reduction=

Unit Impact×Scale

Where:

Unit Impact = Baseline Emissions Intensity - Solution Emissions Intensity
Scale = Units of functional output deployed
Emissions Intensities = Net mass of CO₂e emitted per functional unit

Breaking Down CDR Components

For CDR solutions, the Unit Impact represents the net carbon removal achieved per functional unit. This is calculated by comparing:

Solution Emissions Intensity: The net CO₂e emissions per functional unit of the CDR solution (accounting for both removal and any associated emissions)
Baseline Emissions Intensity: The CO₂e emissions per functional unit in a counterfactual scenario (typically zero for atmospheric CO₂)

Solution Scale

Solution Scale represents the size of the CDR market, measured in functional units (e.g., tonnes of CO₂ removed, USD_rce deployed, facility capacity).

This mathematical approach ensures that CDR solutions can be evaluated alongside traditional avoided emissions technologies using the same consistent framework, enabling fair comparisons and scalable analysis across entire portfolios.

Functional Units and Market Analysis

Understanding Functional Units

The phrase "functional unit" is LCA jargon, representing a mandatory methodological decision in any life cycle footprint study. The functional unit establishes a fair point of comparison between conventional activities and new approaches — they must "do the same thing." It is also an important consideration for having parameters in units where the arithmetic is compatible with Koi's fundamental approach discussed above.

Functional units can be simple (tons of beef), esoteric (lumen-hours, revenue passenger kilometers), or abstract (PJ-equivalent of chemicals, as used by the International Energy Agency to normalize across thousands of chemicals based on energy requirements rather than final mass).

CDR Functional Unit Selection

For CDR solutions, we face unique challenges in functional unit selection wherein the market size must be expressed in units that cancel with the GHG performance functional unit to arrive at avoided emissions. Here are the primary options and the implications within an avoided emissions analysis:

CDR Functional Unit Comparison

Market for Assessment	Total Market Size Options*	Key Limitations
Total sequestered CO₂e
Mass of CO₂e that can be sequestered Used in Koi:	Planetary CO₂, anthropogenic emissions, suitable geographic areas	Sequestration potential becomes unrealistically large, due to the unconstrained total market size, unless the available market is highly geographically constrained
Net sequestered CO₂e
Net mass of CO₂e that can be sequestered Used in Koi:	Carbon markets	Always results in GHG intensity = -1 due to numerator being set to the inverse (see Unit Impact formula)
USD_rce (removal cost equivalent)
Future purchases for that type of CDR Used in Koi:	CCUS financial market size (broadly or technology-specific)	Contains implicit assumption that removal cost is same for implementer and buyer
* Based on relevant, measurable data

Why We Avoid Mass-Based Functional Units for CDR

We avoid using CO₂ sequestered (total or net) as functional units for two key reasons:

Ill-defined market limits: Total potential ranges from total recoverable atmospheric CO₂ (extremely large) to suitable geologic formations (more constrained but still problematic)
Logical dead ends: As a result of all Koi GHG intensities being measured with a numerator in units of CO₂e emitted (net), using net CO₂e sequestered as the denominator results in an uninformative ratio of -1 tCO₂e/tCO₂e net removal (one equals one). This prevents the comparison of CDR effectivity between solutions which vary widely in their net sequestration performance.

The Financial Approach: USD_rce

The financial approach unlocks high-quality data sources and enables measurement of industrial activity in USD_rce (removal cost equivalent) terms. This approach:

Leverages existing data: Cost of removal and CCUS market forecasts are readily available
Enables comparisons: Allows comparison across different CDR technologies and industrial activities
Provides practical utility: Aligns with how markets actually function and value CDR services

Note: There are many market projections on CCS, CCUS, and carbon credits, and it's still anyone's guess which of these will turn out to be accurate. Publicly available sources that we provide in Koi and which serve as the basis for our category-based scenario modeling are listed in the Available CDR Templates section below.

The numerator is always net CO₂e emitted, thereby normalizing all models against one another for equal comparisons across different CDR technologies and approaches.

Example from Conventional Industries

Widget Manufacturing Case Study

Consider a sector that produces 1,000,000 widgets per year. This conventional process emits 0.1 tCO₂e per widget, totaling 100,000 tCO₂e annually. Now imagine a new, CDR process compatible with the widget industry that costs $200/tCO₂e removed (inclusive of capture, transport, and storage).

Converting to USD_rce Functional Units

To express production in units equivalent to the cost of removal, we calculate:

Carbon Neutrality Cost Calculation

0.1 tCO₂e/widget×$200/tCO₂e removed

$20/widget = 20 USD_rce/widget

Result: Each widget requires $20 in removal cost equivalent to achieve carbon neutrality

This dollar amount represents the cost of carbon removal, not the normal production costs or market price of the widgets themselves.

Converting Intensities to USD_rce

As mentioned earlier, most models in Koi just use a widget — a discrete, measurable product or process — as their functional unit. In a simplified typical case, we might represent the intensities as:

Baseline scenario intensity=0.1 tCO₂e/widget

Solution scenario intensity=0 tCO₂e/widget(it is a very effective CDR process!)

To fit the CDR module construct though, we must convert these intensities into USD_rce equivalent:

Baseline=0.1 tCO₂e/widget×widget/20 USD_rce=0.1 tCO₂e/20 USD_rce=0.005 tCO₂e/USD_rce

Solution=0 tCO₂e/widget×widget/20 USD_rce=0 tCO₂e/20 USD_rce=0 tCO₂e/USD_rce

Note that even though the baseline does not contain the CDR process, it must be converted into a removal cost equivalent for the arithmetic to work. Multiplying both the baseline and solution by the cost of carbon removal normalizes each emissions intensity to be compatible with the financial market size. Conceptually, both the baseline and solution now represent the emissions intensity for a number of widgets expressed in equivalent CDR cost units. The fact that the solution is equal to 0 means that the CDR technology is highly (idealistically) effective, whereas the baseline shows the cost of this technology/per widget, if the CDR was totally ineffective.

This approach allows us to work backwards from reported removal costs to arrive at normalized intensities, not measured in industry-specific units (kWh, tonnes of steel, etc.) but according to the unit economics of capture and storage pathways themselves.

Calculating Unit Impact

The difference in unit emissions becomes:

Unit Impact=0.005-0=0.005 tCO₂e/USD_rce

Since we assumed 100% effectiveness, this value equals the inverse of the solution cost ($200/tCO₂e). In reality, even the most effective CDR solutions typically achieve at most 90% net emissions reduction without becoming cost-prohibitive.

Market Forecasting

Now that intensities are represented in USD_rce, we can multiply by anticipated market demand — what the world is likely to pay for this type of removal. This approach:

Simplifies market sizing: No need to know specific sector widget counts
Enables cross-industry comparison: Different industries can be compared using the same functional unit
Leverages existing data: Uses reported removal costs and market forecasts

Koi CDR Models

Thanks to best practices in describing system boundaries and major phases in peer-reviewed LCA literature, we can represent the value chain sources and sinks of emissions in discrete components. We have developed model templates for a variety of CDR solutions (14 and counting). These models can be used as-is to assess a particular CDR technology, or fine-tuned for company-specific innovations.

Genuinely unique CDR pathways/technologies are uncommon, and we have found that most companies in this area are seeking specific performance or price breakthroughs somewhere in the value chain of known (if still developing) pathways. Therefore, the 14 provided templates cover significant ground in terms of CDR company coverage.

Default Baseline Modeling

The baseline represents a counterfactual future scenario, and its selection is crucial for accurate impact assessment. This is inherently a subjective decision that can be modified in Koi as needed for any scenario model, allowing users to explore different assumptions and scenarios. Koi's CDR models use different baseline approaches depending on the carbon source:

Atmospheric and Oceanic Carbon Removal

For technologies that remove carbon from atmospheric or oceanic sources, the default baseline is No Activity — representing zero emissions. This reflects the counterfactual scenario where no CDR technology exists to remove carbon from these natural reservoirs.

Note: So what does it mean for the baseline intensity to be 0 tCO₂e/USD_rce? Not much — this is an expedient for our calculation given current functionality in our platform.

Fossil Fuel Carbon Removal

For CDR technologies that capture carbon from fossil fuel sources (coal, oil, natural gas), the baseline represents the emissions of conventional industrial activities without CDR implementation. This baseline is derived by:

Analyzing CDR-enabled value chains from peer-reviewed LCA literature
Removing CDR-specific components including additional energy requirements, material inputs, and sequestration phases for the baseline
Calculating a conventional baseline emissions intensity normalized to the USD_rce functional unit

This approach allows measurement of baseline industrial "production" in removal cost equivalent terms.

Available CDR Templates

Koi currently provides CDR templates for 14 carbon removal technologies, organized below by capture method and application. Each template serves as the foundation for company-specific models in Koi. Additional CDR templates, and expanded CDR datasets, are under development.

Point-Source CCS

Membrane-Based Capture

Calcium Looping & Mineralization

Direct Air Capture & Bio-Based

References

Koi's approach to CDR modeling builds on work from the following sources:

Market Forecast References

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