Aligning your Climate Impact Model With Investors’ Expectations
For many climate-aligned investors (whether government, philanthropic, or commercial), measurable reduction of carbon emissions is a critical requirement for any investment decision. As a founder, communicating your impact is key to capturing the attention of your intended audience.
To help navigate the nascent field of climate impact modeling, we sat down with Jo Brickman. Jo is a Deputy Director at VertueLab and Director of their Impact Strategy group.
How will you have an impact?
Before you get into the nitty-gritty of building your model, articulate a) how your company drives impact objectives and b) the direct and indirect role that your company plays regarding emissions. In some cases, that role might be obvious. For example, electric vehicles (EVs) produce direct emission reductions. The units and assumptions are relatively clear — you can easily compare how much energy is consumed per mile by an EV vs an internal combustion engine. In other cases, the role might be less obvious, i.e. enabling technology like EV charging stations, or software to manage the deployment of EV charging infrastructure. The more indirect the impact, the more challenging it will be to model.
If not for your product, what would happen?
When designing your impact model, you must first establish what business-as-usual would look like between now and 2050. Without a baseline, you have nothing to measure your impact against. The baseline can be modeled by looking at what technologies you seek to replace (e.g. an EV instead of a gas-powered car) and/or displace (e.g. electric, autonomous buses that take cars off the road). Figure out what unit will measure both replacement and displacement and the potential for GHG emissions reduction when replaced and/or displaced.
What assumptions will you make? Based on what logic? And what data?
What you assume shapes your impact model. Establish two key sets of assumptions: (1) what the world will look like without your technology (as discussed above) and (2) how your technology will scale over time.
When in doubt, you might be tempted to design a model with as much detail as possible, seeking to compensate for what you don’t know. Steer clear of this rabbit hole. Greater detail often does not translate to more meaningful outputs and can instead create a misleading false precision, unnecessary complexity, costly overhead and a persistent headache for you.
Finally, keep in mind that, even with sound logic and assumptions, the quality of input data can still make or break your model. If you put garbage in, you will get garbage out.
Don't start from scratch
Save yourself a lot of trouble by building your analysis on what already exists. Start by looking for the most reputable third party data that you can find. Then, search for publications on similar technologies; these include academic articles from national labs and other research institutions. Google Scholar is your friend here. You may also be able to find publications from competitors in the same space.
Take a shortcut, when possible, by leveraging other models to see what they measure, what assumptions they make, and how they evaluate impact. Start by looking at platforms like Crane that host models for many climate-related technologies. You may find a technology just like yours; or, you may have to settle for an adjacent one. Either way, study them closely and repurpose what you can for your own model.
Be explicit and transparent
Write out your assumptions and the logic of your model (e.g. if this, then this; if this then this; etc). This will give you greater clarity on the logic you are using as well as provide transparency for investors. It will also make it easier to adjust the model in the future. Important questions to ask yourself include:
- What is the baseline? How did you establish it? How might the baseline change over the modeled time period due to macro shifts in the market? Can your technology match this shift by becoming more efficient in the future?
- Where did the numbers come from in your assumptions? Can you point to reputable sources? Or instead, to your own measurements?
- What are the policy or other market factors expected to be over the modeled time period? What impact might these have on your financial projections?
- Are you modeling the impact of the entire market for this type of product or just your own business? What implications does this have for your results?
- Will you model and report cumulative numbers for the entire time period of 30 years? Or, just the end year numbers in 2050?
Keep in mind that not all megatons are equal
While a megaton of carbon is a megaton of carbon, the broader social impact of any given megaton reduction will vary. For example, displacing natural gas in Scandinavia with wind has very different benefits than displacing charcoal cooking in Uganda with clean cookstoves. As you tell your story, don’t shy away from the additional impacts your technology or solution might have on society. What are the co-benefits? How might your product benefit communities on the frontline of climate disasters? How might it foster diversity and inclusion in decision-making? Registries like Gold Standard and Verra have done a lot of work to outline potential co-benefits and measurement methodologies, so don’t feel like you have to reinvent the wheel in this context either.
Jo Brickman is the Deputy Director, and the Director of the Impact Strategy Group at VertueLab. Jo has responsibility for the development and execution of strategic direction and programs at VertueLab. With the organization since 2010, she translated her previous decade of experience leading sustainable design at ZGF Architects into the creation of a collaborative research program focused on the sustainable built environment, and then into expanding the set of services offered to cleantech startups. Jo’s current focus as deputy director is on VertueLab’s organizational excellence, partnering with VertueLab’s executive director in essential internal firm leadership activities. In addition, Jo leads the organization’s efforts for impact measurement and management, ensuring that VertueLab programs and supported companies are designed and managed to achieve ambitious triple bottom line impact targets.