For companies, investors, and sustainability teams, one of the hardest questions in climate strategy is not only how much carbon has already been emitted, but how much could be avoided in the future if a solution scales.

That is where forward-looking greenhouse gas impact modeling becomes important. Unlike traditional carbon accounting, which often focuses on historical emissions, avoided emissions modeling looks at the potential impact that climate technologies, products, and services can have over a future timeframe. This matters for organizations evaluating decarbonization solutions, investments in climate technologies, and claims about future emissions trajectories.

In a recent Net Zero Compare conversation, Karol Kaczmarek spoke with Gigi Alsaadi, a Vancouver-based sustainability consultant working to accelerate climate solutions through impact assessment and field-building. She is the Co-Founder of KanataQ, a sustainability solutions marketplace and market intelligence startup. Today she shares insights as the CRANE Fellow at Prime Coalition, a U.S.-based nonprofit, where she develops resources for investors and innovators to help catalyze high-impact yet overlooked climate technologies.

Note: The views expressed in this blog and podcast recording are Gigi’s own and may not necessarily reflect the views or positions of Prime Coalition.

🎥 Watch the Full Conversation: The full interview with Gigi Alsaadi goes deeper into how forward-looking greenhouse gas impact modeling works in practice. The conversation is useful for sustainability professionals, ESG teams, investors, and business decision-makers who want to better understand avoided emissions, data limitations, and the role of open-access tools such as CRANE. The article below summarizes the main themes, but the full recording provides additional nuance around methodology, data, and how impact analysis can support capital allocation.

Why Forward-Looking Impact Measurement Matters

Traditional carbon accounting usually focuses on what has already happened. It helps companies understand their historical greenhouse gas footprint, including direct emissions, purchased energy emissions, and value chain emissions. This work is essential for reporting, compliance, and operational management, but it does not always answer a key question for climate innovation: what could this solution avoid in the future?

That is where forward-looking impact measurement becomes useful. For early-stage climate technologies, historical deployment data is often limited. But investors and entrepreneurs still need a structured way to assess whether a solution could materially reduce emissions if it scales.

A key distinction is the difference between carbon accounting and avoided emissions analysis. Carbon accounting looks backward: it asks how much greenhouse gas a company or activity has already emitted. Avoided emissions analysis looks forward: it asks how emissions might differ between two possible futures: one where a climate solution scales, and one where the incumbent technology or process continues.

In simple terms, impact modeling compares a world with the solution against a world without it. The core question is practical: if this solution grows, how many tons of CO₂ could be avoided?

Gigi used solar panels as a helpful example. Decades ago, solar had far less deployed impact than it does today. If decision makers had only looked at historical emissions reductions at that point, they may have underestimated its future role in displacing higher-emission power generation. The same logic applies to many newer climate technologies today.

This does not mean future impact should be accepted without scrutiny. It means the model must clearly explain what is being compared, what assumptions are being made, and what would likely happen without the solution. For climate technology investors, that distinction matters: a solution may have limited historical data today, but still have significant future relevance if its impact pathway is credible.

Why Incumbent Scenarios Are Central

Avoided emissions modeling depends heavily on the incumbent scenario, one which would likely occur if the climate solution did not exist or did not scale. This matters because an impact claim is only meaningful if the comparison is credible. If a technology claims to avoid emissions, it must be clear what it is replacing, what would otherwise happen, and whether the same emissions reductions would have occurred anyway.

For example, if a new energy solution is compared against a high-emission incumbent, the impact may appear large. But if the real-world alternative would already be a lower-carbon option, the impact may be smaller. The credibility of the baseline directly affects the credibility of the claim.

Gigi also highlighted related issues such as additionality and double counting. Additionality asks whether the emissions reduction would truly not have happened without the solution. Double-counting occurs when multiple parties claim the same emissions reduction. Both can distort impact claims if they are not handled carefully.

For companies and investors, this means that avoided emissions numbers should never be treated as standalone figures. The assumptions behind them matter just as much as the headline number.

What CRANE Does

The CRANE Tool was discussed as an open-access tool designed to support this type of forward-looking impact analysis. CRANE was launched by Prime Coalition and developed in partnership with Rho Impact. It was designed as free and transparent software to help investors and innovators assess early-stage climate technologies, and its methodology has been aligned with Project Frame’s consensus-built guidance.

The tool includes more than 200 default technology models across categories such as sustainable aviation, alternative proteins, smart thermostats, and others. Each model includes default market and emissions data, which can help users start an assessment faster.

The key point is that CRANE is not meant to be a corporate footprinting tool in the same way that many Scope 1, 2, and 3 reporting systems are. Its purpose is to estimate the potential emissions impact of climate solutions themselves. That makes it relevant for investors assessing climate technologies, but also for companies trying to understand whether a product or service can credibly support decarbonization goals.

Why Open-Access Tools Matter

Since the organization’s inception in 2014, Prime Coalition could be perceived as a pioneer in helping advance practical approaches for assessing the future emissions impact of climate solutions. At a time when forward-looking impact assessment was still far less developed, Prime’s work helped shape how investors and innovators could evaluate the potential climate impact of emerging technologies.

Open-access tools like CRANE later helped translate Project Frame’s methodology into a practical, standardized approach that a wider group of users could apply. If every investor, startup, or company uses a similar method, claims become easier to compare, thus reducing fragmentation. Open-access tools also support transparency: if users can inspect models easily, they can better understand how the numbers were produced. This makes it easier to challenge assumptions, improve models, and raise methodological standards over time.

Gigi also noted that CRANE is now available through the free CRANE Tier (you are invited to explore it here) on Rho Impact’s Koi.eco, an AI-accelerated platform that brings users larger technology libraries and more data. This helps reduce barriers to impact measurement while supporting impact claims and making greenwashing harder.

How Impact Modeling Supports Investment Decisions

Of course, the key goal of forward-looking impact analysis is to make informed investment decisions. Gigi explained that impact analysis can enter the investment process across all stages. During sourcing and screening, investors can use impact models to identify priority sectors or companies. During due diligence, they can test whether a company’s impact remains credible under different assumptions. After investment, they can work with portfolio companies to refine strategy and increase emissions reduction potential.

She also described a simple underlying logic: net unit impact multiplied by market scale equals total emissions impact. In practice, this means investors need to understand both the emissions reduction per unit and the realistic scale a technology could reach.

This is where analysis assumptions become critical. For example, an early-stage technology with strong unit impact but limited market adoption may have a weaker investment case than a technology with moderate unit impact but large potential scale.

Prime Coalition has collectively mobilized more than $322 million into catalytic investment for climate solutions. In this context, impact modeling helps connect capital with technologies that may otherwise struggle to receive funding, especially in areas where risk tolerance is needed.

For business leaders, this is a useful reminder that emissions impact is increasingly tied to financial decision-making. Investors are increasingly diligent in understanding how the investment’s GHG impact is calculated, whether its market is scalable, and whether the model holds up under scrutiny.

Common Weaknesses in Climate Impact Claims

One of the biggest challenges in forward-looking impact analysis is data availability. Early-stage climate technologies often have limited historical data, especially when they are first-of-a-kind solutions or rely on new processes, materials, or systems without established life cycle assessment data.

Gigi explained that in these cases, analysts often need to use proxies, benchmarks, and informed assumptions. That does not make the work less useful, but it does mean uncertainty must be handled carefully. This is where sensitivity analysis becomes important. Instead of relying on one optimistic projection, analysts can model low, medium, and high impact trajectories to show a more realistic range of possible outcomes.

The conversation highlighted other common weaknesses:

• Lack of transparency. Gigi described seeing spreadsheets with numbers but limited explanation of where those numbers came from. If a claim does not clearly explain its key assumptions and data sources, it becomes difficult to evaluate.

• Overly optimistic market scaling. Startups often have ambitious growth expectations, and that optimism can feed into inflated impact projections. While ambition is part of innovation, impact models need to reflect realistic adoption pathways for their technologies.

• Inconsistent system boundaries. A credible comparison must assess the climate solution and the incumbent on a comparable basis (e.g., both include the same lifecycle emission boundaries). Otherwise, the model may compare apples-to-oranges, and overstate or understate its impact.

• Lack of review. Independent review, or at least a transparent audit trail, can improve confidence, especially when a model is used for investor reporting, procurement decisions, or public claims.

These issues are not signs of bad intent. Often, they reflect the fact that impact modeling is technically demanding and still developing. But the consequences are real: poor assumptions can lead to poor investment, procurement, and strategy decisions. A model with clearly documented uncertainty is often more useful than a confident number with no explanation behind it.

Practical Lessons for Companies and Investors

For sustainability teams, procurement leaders, CFOs, and climate investors, the conversation points to several practical lessons:

• Clarify the analysis parameters. Forward-looking impact modelling is only meaningful with appropriate comparisons, transparent assumptions, and well-documented numbers.

• Look for uncertainty ranges. Sensitivity checks with low, medium, and high scenarios are often more useful than a single projection.

• Treat impact modeling as iterative. As technologies mature and better data becomes available, models should be updated.

• Use forward-looking impact analysis as a decision-support tool. It should help organizations make better choices, not provide a false sense of certainty.

Conclusion

Gigi Alsaadi’s conversation with Net Zero Compare made one point especially clear: credible climate impact depends on defensible assumptions, credible methodology, and realistic projections.

For companies, investors, and sustainability teams, the practical lesson is to look beyond headline impact numbers. Forward-looking GHG impact modeling is not a replacement for traditional carbon accounting. It answers a different question: not only what emissions have already occurred, but what emissions could be avoided if a solution scales. For business decision-makers, that distinction matters. Climate strategy depends not only on ambition, but on the quality of the analysis behind each decision.