Siemens on Monday launched a set of tools to help businesses track their product carbon footprint (PCF), including emissions data from all of their suppliers. According to the US Environmental Protection Agency, such scope 3 emissions, also known as value chain emissions, often represent the majority of an organization's total greenhouse gas emissions.

Siemens' new solution comprises software called SiGreen and a distributed ledger system, called the Estainium network, that lets cross-industry partners securely exchange reliable emissions data. By taking actual, accurate emissions data -- rather than industry averages as other tools use -- the Siemens system aims to make it easier for organizations to assess their true carbon footprint. 

The new tools should serve the growing number of enterprises that are prioritizing sustainability measures for a number of different reasons. Companies are facing pressure to reduce their carbon footprint from employees, investors, customers and regulators. 

"The pressure on organizations to meet environmental, social and governance (ESG) criteria is more widespread than most finance leaders might realize -- 85% of investors considered ESG factors in their investments in 2020," according to Gartner. 

However, gathering emissions data from supply chain partners often requires them to disclose strategically important information. That's why NGOs like WRI, GHG Protocol and GIZ have recommended distributed ledger-based systems. Data is shared directly within the scope of customer-supplier relations without having to hand it over to a third party or share information about their own supply chains.

The Estainium network supports the creation and exchange of Verifiable Credentials from third parties to ensure the trustworthiness of the information shared. To verify the values reported by a supplier, customers can subject them to a so-called Verifiable Proof against the corresponding Credential via the IDUnion blockchain. 

Meanwhile, SiGreen makes use of real data rather than industrial average values.

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