Climate action becomes credible when it is measurable. For companies, climate action often starts with building a robust greenhouse gas inventory that can stand up to scrutiny, inform a transition plan, and support better decisions across operations and value chains. For climate solution providers, it also means going one step further: translating a technology promise into a quantified climate impact narrative that investors can assess, challenge, and trust.
In this post, we share three client cases that demonstrate what “nature strategy in action” looks like in the climate space, from Scope 3 accounting in a real economy value chain to avoided emissions modelling designed for investment and scale.
1) RAR Açúcar: building a Scope 3 baseline that can drive decisions
For many companies, the majority of emissions do not sit inside direct operations. They sit upstream and downstream, across purchased goods, logistics, fuels and energy related activities, and the use of third party services. That is why Scope 3 is increasingly the core of carbon management.
What the challenge looked like
RAR Açúcar needed a structured way to quantify value chain emissions for the first time, establishing a Scope 3 baseline year and turning a complex network of suppliers and activities into an inventory that is consistent, auditable, and repeatable year after year.
What Impact Labs did
🔹Defined boundaries and reporting logic
- Clarified organisational and operational boundaries, then mapped the Scope 3 categories that are relevant to the business model
- Built an approach that follows recognised inventory principles, including consistency, transparency and completeness
🔹Ran a structured screening to prioritise effort
- Identified which Scope 3 categories required primary data requests versus high quality secondary estimation
- Documented assumptions and set a clear rationale for exclusions, when applicable, based on materiality and data feasibility
🔹Operationalised data collection and estimation
- Translated Scope 3 requirements into practical data requests that procurement, logistics and finance teams can realistically answer
- Selected and applied emissions factors aligned with the activity data available, while keeping an explicit record of factor sources, units, and conversion logic
- Built modular, traceable calculation tools and delivered targeted training to the Sustainability Lead, enabling autonomous, repeatable Scope 3 updates year after year.
🔹Delivered an improvement roadmap, not just an inventory
- Highlighted where better primary data would materially improve accuracy in future years
- Identified “quick wins” in data governance, supplier engagement, and internal ownership of emissions relevant datasets
Why this matters
A Scope 3 baseline is not the end state. It is the foundation for supplier engagement, transition planning, and credible reduction levers. When done well, it turns emissions reporting into a management tool.
2) A Sustainable Insulation Alternative: Quantifying Avoided Emissions
Avoided emissions refer to greenhouse gas emissions that do not occur because a product, service, or system replaces a more carbon-intensive alternative. Unlike direct emission reductions where emissions are measured at the source, avoided emissions are inherently comparative. They depend on a counterfactual: a clearly defined baseline scenario describing what would have happened in the absence of the intervention. This makes them conceptually powerful, but methodologically complicated.
What the challenge looked like
At the core of avoided emissions accounting lies a chain of assumptions. What technology, behavior, or system is being displaced? And at what scale? Over what timeframe? In which geography? Robust quantification requires a defensible hypothesis, a credible reference case, and transparency about uncertainty. It also requires addressing rebound effects, where efficiency gains or cost reductions lead to increased demand that partially offsets the intended climate benefit. Without these elements, avoided emissions risk becoming speculative claims rather than analytical insights.
For early-stage startups, the challenge is even sharper. Many operate in markets that are still emerging, with limited historical data and no stable incumbents to benchmark against. Their impact often depends on future adoption curves, policy developments, or infrastructure build-out that cannot yet be observed. As a result, avoided emissions estimates at this stage are less about precision and more about scenario credibility.
Why this matters for impact due diligence, and for the start up
Avoided emissions, when rigorously defined and transparently modeled, offer a way to evaluate potential climate leverage before markets fully materialize. The challenge for investors is not whether these estimates are uncertain, but whether that uncertainty is being surfaced, stress-tested, and priced appropriately. As climate capital moves from signaling to accountability, the ability to distinguish credible avoided-emissions pathways from speculative narratives will increasingly shape both risk management and long-term returns.
Avoided emissions are a decision and monitoring tool for directing capital toward the most promising climate solutions at scale. By translating future emissions prevention into structured, testable hypotheses, avoided emissions enable investors to compare system-level interventions with fundamentally different pathways and time horizons. While early-stage estimates are uncertain, they establish a disciplined framework for prioritizing climate leverage at entry and tracking whether the impact startups are delivering on their projected impact as they grow.
What Impact Labs did
🔹Definition of the use cases in different sectors and the associated reference scenario
- Mapped the most relevant application pathways where the solution can drive emissions reductions
- Defined what the product displaces in each pathway, and what a credible reference scenario looks like
🔹Built a comparative impact model for avoided emissions
- Structured a modelling approach that quantifies the difference between a baseline scenario and the pathway with the sustainable alternative
- Identified key parameters that drive the result, including performance assumptions, adoption rates, and system boundaries
🔹Stress tested the potential impact under different uncertainty levels
- Flagged the main uncertainty drivers and what evidence would be needed to validate them
- Identified where the model could be conservative, and where rebound effects or market constraints might affect outcomes
- Translated the analysis into decision ready insights for an investor audience, with clear “what to believe” and “what to verify” signals
Why this matters
Avoided emission captures something traditional carbon accounting struggles to see: the potential for structural change. While difficult to quantify, they are often where the largest climate leverage sits, at the point where entire systems are redesigned rather than optimized. The task ahead is not to dismiss avoided emissions because they are uncertain, but to develop frameworks that can handle uncertainty honestly, without inflating claims or obscuring assumptions.
3) A Tech service provider: Induced emissions methodology to pilot the climate impact of services sold
Not every climate impact story is about emissions reductions. Some services enable growth, and growth can increase emissions. In this case, the goal was to estimate the induced emissions associated with increased sales enabled by Marketing Mix Optimisation models, and to use that quantified signal to pilot how the company steers its climate impact, by comparing those induced emissions with the company’s footprint across Scopes 1, 2 and 3.
What the challenge looked like
The company needed a clear, decision grade way to translate a service driven commercial effect into carbon terms, under transparent assumptions. The key question was: how can induced emissions be quantified consistently, and used to guide priorities and targets as the approach is piloted and iterated.
What Impact Labs did
🔹Clarified objectives and the perimeter of accountability
- Framed the work around “avoided or induced emissions”, starting from the principle that the objective drives the required depth and evidence
- Defined practical perimeter options to make the methodology operational, including service line versus project comparison, absolute quantification versus comparison, and the feasibility of collecting client data
🔹Structured a robust quantification logic and stress tested the drivers
- Anchored the approach on the essential building blocks: scope and perimeter, reference scenario, and impact analysis questions, including the need for client data and justification of attribution
- Applied a structured quantification approach built around timeframe selection, baseline definition, quantifying both scenarios, quantifying the difference, and identifying uncertainties
- Explicitly integrated the main credibility risks that matter for services, including rebound effects, enabling effects, data quality, and attribution or allocation choices
🔹Designed a pilot pathway that is fit for steering
- Translated the methodology into an implementation sequence: validate objectives, select the perimeter, define scope, pilot on one or two cases, then scale
- Positioned pilots as the bridge between methodology and governance, enabling the company to test decision usefulness and refine targets as the approach matures
Why this matters
Induced emissions quantification helps organisations make visible how services and commercial levers can shift emissions in the real economy. It provides a consistent basis to compare signals over time, prioritise the most material use cases, and build an internal steering approach that links growth, targets, and climate impact.
What these cases have in common
Across these projects, three patterns show up again and again:
🔹 Boundaries decide outcomes
If organisational boundaries, system boundaries, and baselines are unclear, the numbers become non comparable.
🔹 Data quality is a strategy question
The right question is not “do we have perfect data”, but “what decisions do we need to make, and what level of data quality is required to make them responsibly”.
🔹 Credibility is built through transparency
Being explicit about assumptions, limitations, and uncertainty is not a weakness. It is what makes climate measurement usable in finance, governance, and strategy.
