Emission Factor Mapping Service
Automated Emission Factor Mapping ensures reliable mapping of emission factors to your products, minimizing manual errors.
Why It Matters
Collecting accurate Scope 3.1 emissions data is vital for a complete emissions inventory. However, the complexity of supply chains and the limited availability of emissions factors pose significant challenges. Addressing these barriers is crucial for achieving progress in emissions reduction and meeting sustainability goals.
How it works
Our Emission Factor Mapping Service helps you close data gaps and address inconsistencies in Scope 3.1 emissions reporting by automatically mapping chemical substances using CAS IDs. This eliminates the need for time-consuming manual matching, providing you with consistent and accurate emissions data. If certain chemicals are missing from the database, we focus on closing those data gaps: we offer our modeling on demand services or, alternatively, provide similar chemical substances (proxys) with the support of our automated proxy suggestion tool.
Emission Factor Mapping
Automated Mapping Based on CAS-IDs
First, we identify all products in your Scope 3.1 inventory that have emission factors available in the Carbon Minds database. This mapping is based on CAS IDs, numerical identifiers for chemical substances. Remaining data gaps are addressed in the next steps.
Prioritization of data gaps
Invest in data quality where it matters most
Prioritize data gaps based on their expected impact on your Scope 3.1 results. High-priority data gaps are filled using our on-demand modeling service leading to higher data quality, while low-priority gaps will be filled with proxies
Modeling On Demand
Higher quality
With our Modeling on Demand service, we model the missing emission factors for you using our third-party certified methodology, allowing you to fill data gaps with representative data.
Proxy Mapping
Lower quality
For less critical data gaps, our proxy suggestion engine identifies the substances in our database most similar to your target based on molecular structure, quantifying the similarity. You can use these suggestions to select suitable proxies.