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Handling data complexity of Automating PCF calculations and integration with decision making framework on Decarbonization using Marginal Abatement Cost Curve(MACC)
Linked Sessions:
Main Presenter: Suvayan Guha Ray
Co-Authors: Rasool, Shaikh Akhtar Ananda Sekar Deepa Govindarajan Suchira Sen Mahabala PS Chandran, Drishya Pooja RV Mohammed Abdulrahman Al-akil Mohammed Al Maghrabi
The chemical industry offering products and solutions for many downstream industries has a key role towards achieving decarbonization in several value chains. Due to the industry’s nature of relying on resource- and emissions-intensive operation, it is often challenged by demands on producing products with minimal emissions. As such, data on Product carbon footprint is becoming a critical enabler towards action and progress on value chain emission reductions. With data on PCF becoming significant in its usage for regulatory driven needs as well as for customer decision making on feedstock and supplier choices, there is a dual need for scaling PCF calculations while achieving compliance with evolving standards and guidelines.
PCF Automation tool has to be built with emphasis to data quality. Again careful choices on simplification of large data, documented procedures in compliance with rule-books (e.g. TfS, CBAM, ISCC-CFC) is to be given due importance. Further, flexibility to sources of data, mechanisms for review checks, design elements for data transparency are important amongst other technical aspects that range from the specificity of PCF standards and guidelines along with the IT complexity of data integration pipelines related required transformations. This work presents a detailed overview of various technical elements that are necessary for a robust PCF automation capacity building and some of the best-practices and lessons learnt from its implementation. Necessary case studies has been introduced wherever has relevance to explain the concepts. These includes, but not limited to, examples of allocation rules, handling of CO2 capture as part of emissions inventory for compliance with various rule-
books, data mapping strategy to primary and secondary data. The work also builds on this to outline how PCF Automation Tool based data can drive decarbonization strategy formulation by integrating its outcome with MACCs (Marginal abatement cost curves). Also, some of the futuristic considerations on applying machine learning techniques to PCF data handling and processing are addressed in this work.
