Digital Data Ecosystems for the Verification of Corporate Carbon Emission Reporting

Titelangaben

Körner, Marc-Fabian ; Strüker, Jens:
Digital Data Ecosystems for the Verification of Corporate Carbon Emission Reporting.
2023
Veranstaltung: Bayreuth Digital Science Conference , 10.02.2023 , Bayreuth.
(Veranstaltungsbeitrag: Kongress/Konferenz/Symposium/Tagung , Poster )

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Abstract

Carbon taxes and emission trading systems have proven to be powerful decarbonization instruments; however, achieving our climate goals requires to reduce emissions drastically. Consequently, the EU has adopted regulatory measures, e.g., the expansion of the ETS or the carbon border adjustment mechanism (CBAM), which will require carbon emissions to be managed and priced more precisely. However, this capability is accompanied by unprecedented challenges for companies to report their carbon emissions in a more fine-granular and verifiable manner. These requirements call for an integrated and interoperable systems that can track carbon emissions from cradle to grave by enabling a digital end-to-end monitoring, reporting, and verification tooling (d-MRV) of carbon emissions across value chains to different emitters. To establish such systems, our research proposes recent advances in digital science as central key-enablers. In detail, our research aims to design digital data ecosystems for the verification of corporate carbon emission reporting that accounts for regulatory requirements (cf. CBAM and EU-ETS) and that prevents greenwashing and the misuse of carbon labels that are currently highly based on estimations. Accordingly, it will be possible to digitally verify and report a company’s – and even a specific product’s – carbon footprint. Therefore, we apply several digital technologies that fulfill different functions to enable digital verification: While distributed ledger technologies, like Blockchains, may be used as transparent registers – not for the initial data but for the proof of correctness of inserted data – zero-knowledge proofs and federated learning may be applied for the privacy-preserving processing of competition-relevant data. Moreover, a digital identity management may be used for the identification of machines or companies and for governance and access management. Against this background, we also refer to data spaces as corresponding reference architectures. Overall, we aim to develop end-to-end d-MRV solutions that are directly connected to registries and the underlying infrastructure.