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Don’t let perfection be the enemy of progress
“This summer is what climate change looks like, scientists say”, this article on Politico headlines. Compared to 2019, more companies disclose their carbon emissions, but reduction targets – and impactful measures – are still missing. How can we make progress here, while not letting perfectionism get in our way?
What’s there to measure?
When you sell products, then your product falls under “scope 3 emissions”. Besides indirect emissions like employee commuting or capital goods, scope 3 includes your product: everything from cultivating the raw materials (f.e. growing cotton), up to the manufacturing (electricity used for sewing or dyeing) and use of finished goods (washing your t-shirt). It’s what makes scope 3 quite complex and challenging for all – okay, most – of us. Especially when there are many third party activities (expelling carbon) that your organization is accountable for. Also, scope 3 is huge. It represents 90% of the total corporate emissions.
We can’t reduce what we haven’t measured
Companies need to collect data, but that data collection shouldn’t become a goal in itself. It’s a means for effective footprint reduction. Our time should not be soaked up by collecting perfect figures if that keeps us from moving forward. If data collection causes stagnation in action, what value are we exactly creating? We need to get going.
What value are you creating if all your time is getting soaked up by collecting perfect figures?
How to gain meaningful insights instead of perfect data
There are several ways to better understand the environmental footprint of our products (or service). Life cycle assessments (LCAs) are a good example. A life-cycle assessment literally assesses the environmental impact (including carbon emissions, water, energy, land use, toxicity) of every stage in a product’s life cycle. It includes what raw materials are used, where they’re extracted, what production processes took place where, and how the product’s transported. Conducting a LCA is valuable, but it is also a costly and time consuming exercise.
Is an LCA something for you?
- Do you have a complex product? If you sell professional cameras for example, an LCA could be a great move, because the product contains many different, technical components. All with their own impact that should be retrieved.
- Do you have a relatively straightforward product, but with many different variants? Let’s say basic apparel? It might be interesting to look at a variation of the LCA’s that’s scalable: because only a few inputs (raw materials) are used, but with quite some varieties (in origin, fiber, manufacturing process or color).
Scalable LCA’s: here’s how to get it done.
In order to make things a bit more tangible, there are many opportunities to create more automated LCA’s or product environmental footprints that are scalable. Imagine there are mostly 8 steps included to assess the life-cycle of a t-shirt:
- fiber farming
- yarn manufacturing
- fabric manufacturing
- wet processing
- garments manufacturing
- recycle and disposal
It takes quite some time and effort to map the environmental footprint of each and every step for a specific product and to engage your supply chain partners to come to the right data. However, for all of these steps described, we could build the 3 to 10 “most likely scenarios”. For example:
- fiber farming
- conventional cotton from South India
- conventional cotton from China
- “Better Cotton” from South India
- organic cotton from Turkey
- wet processing
- pre-treatment, dyeing light color, finishing
- pre-treatment, dyeing dark color, finishing
- pre-treatment, ozone washing, finishing
This “scenario building” enables us towards a “modular approach”. We could “swap” scenarios for every step, making sure to collect data and include specific scenarios relevant for your organization. We can make almost endless varieties, while optimizing and fine-tuning the data along the way.
A “modular approach” is more scalable and enables us to optimize and fine-tune the data along the way
Is this perfect? No, but this “scenario swapping” is a scalable approach to get started with scope 3. It enables us to identify emission hotspots.. The data isn’t flawless, but we like to choose “from zero to progress” over “from zero to perfect”. We can still fine-tune our data along the way. Let’s make progress while outsmarting perfectionism.
Reduce what you measure, and reduce while you measure
In short: start reducing. It’s true that you can only create a strategy when you have the data. For you can’t reduce what you haven’t measured. But it’s definitely about finding that balance between collecting insights and action.
We need to reduce, while ensuring that perfectionism doesn’t come between us and progress.
Once we identify the impactful actions that we need (to meet our reduction targets), we can plan them, allocate resources, budget them and engage the teams and people that we need in the phase of implementation. And ofcourse we need to stay critical, describe what we do and how we do it, define assumptions and improvements. In the end, the progress is also in educating ourselves with methods of efficient data collection and fine-tuning the type of data we use.
We can help you with our “carbon approach”, which is focused on identifying emissions hotspots and building an actionable reduction strategy. This way you’ll reduce emissions where your biggest impact is. Depending on your needs, we can engage our network of partners to collect the right insights and data on the intensity level needed to meet your ambitions.