Quality data ensures greener LCAs. So How Do We Get It?

How carbmee helps businesses decarbonize their value chain and enable decision-making based on data about their purchases and sourcing 🤲🌎


When faced with the challenge of decarbonization, many companies struggle — or even fail — to successfully analyze their supply chain emissions. This then causes a major delay in ensuring greener practices: Scope 3 emissions are responsible for up to 80% of total corporate greenhouse gas emissions.

Luckily, technology is here to help.

We created the carbmee platform to help businesses decarbonize their value chain and enable decision-making based on data about their purchases and sourcing. How do we do it?

 

LCA and ERP: Decarbonization Starts Here

First, we take data from the company’s Enterprise Resource Planning (ERP) system; that is, the system used to store a company’s data from a wide range of business processes and activities. Then, we connect it to established environmental databases to accurately calculate the carbon footprint. The underlying methodology is life cycle assessment (LCA).

If you’re not familiar with the jargon, LCA is a methodology used to assess the environmental impact of a product or service’s life cycle, including its natural resource use, human health, and ecological consequences. LCA takes the entire life cycle into account, from “cradle to grave”. Emissions can occur at any stage, such as resource extraction, production, transport, during use, or end-of-life. This includes energy and materials across the whole value chain: raw material extraction, processing, manufacture, distribution, usage, final disposal and recycling, for example.

Only with proper LCA documentation (i.e. looking at the entire value chain) are companies able to measure and take action towards their decarbonization process.

However, if the available data is flawed or imprecise, they will fail to yield valuable results.

The consequence of procuring a product or service can be greater than you would intuitively think. That’s where carbmee comes in.

 

Data Quality at carbmee

At carbmee, we try to ensure a high level of data quality by following these principles:

  • only using established and high-quality datasets from trusted databases;
  • using real-life data from companies’ ERP systems;
  • involving the suppliers in the data collection so that data is not only based on the purchased materials, but also specific to each supplier.

It’s not only about data quality though: it’s also about what makes the datasets relevant for an efficient and greener life cycle assessment. If we look at the wrong topics, we might compromise the result of the study.

 

Data Quality Topics for LCA

Managing and describing data quality is an integral part of any scientific undertaking, and decarbonization and LCA are no exception. Even with the best intentions, doing the right thing is difficult if the data that is used for decision-making is of poor quality.

If LCA methodology is the best way towards informed decision making, what is its data quality dependent on?

 

1. Time-related Coverage

That is, the age of datasets and the minimum length of time over which data should be collected. At carbmee, we use current datasets by using the latest version of our underlying environmental databases. The data of our foreground system is taken from the company’s ERP system and thus available in real-time — as fresh as data could possibly be.

 

2. Geographical Coverage

Location plays a big part in data quality. The geographical area from each data (for unit processes) should be collected to satisfy the goal of the LCA study. By implementing a location choosing service into our platform, we ensure that the user gets the dataset with the best geographical fit.

 

3. Reliability and Completeness

This means the relevance of the quality of the underlying measurements. What do they represent? And how much? We use only established sources of LCI datasets for our platform. The foreground data is directly taken from the companies ERP system and this relies on measured real-life events.

 

4. Technological Correlation

How well does the technology described in the dataset match the technology that is supposed to be modelled? By choosing the dataset with the best fit and further adjusting it with company-specific data, we can even improve the technological fit further: so it is better than using only the generic dataset. In other words, we are aiming for accuracy first, and then improving the precision.

We are confident that the data we gather for our platform will enable us for meaningful decision-making and to do the right thing.

 

A Few Self-Critical Words

We are aware that by looking only at the carbon footprint, there is a risk of overlooking environmental impacts that occur in other categories like eutrophication or ecotoxicity, to just name a few.

Next to climate change, the biggest environmental problem of our time is the loss of biodiversity and ecosystem services. However, by setting up a platform like carbmee, and focusing on one problem only at first, we hope to do this well before expanding the scope of our offering.

Our aim is to play a major role in future procurement decisions within companies. Expanding the scope of our calculation from only one category (climate change) to a comprehensive range of several categories will only take a few mouse clicks and is certainly something we are aiming for in the future.

Are you ready to join us?

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