Life Cycle Modelling and GHG Reporting in the IT Age

Main Presenter: Julian Baehr 

Co-Authors: Liselotte Schebek Laura Göllner-Völker

Session: Poster Session 2

Life Cycle (LC) modelling is the state-of-the-art methodology to quantify and report environmental indicators like global warming potential (GWP). However, due a growing pool of methodological LC approaches, ever growing data demands and constantly enhancing sustainability legislation on national and international level, successful LC modelling requires an interdisciplinary consortium of LC experts, data engineers and political scientists. Within a jungle of methodological standards, footprints, norms, protocols and pathfinders, companies and their stakeholders face great difficulty choosing the right methodology to obtain correct indicators required to report on sustainability issues. The growing number of political and corporate reporting schemes increases uncertainty since indicators to be reported are based on varying methodological principles. To achieve the Paris Agreement climate targets, it will be of utmost importance that companies, organizations and authorities measure and
report their greenhouse gas (GHG) emissions regularly and correctly. Therefore, clear methodological guidance on underlying LC approaches of indicators is required.
Within an interdisciplinary project with expertise from LC modelling, IT and political sciences existing political and corporate reporting schemes are evaluated regarding the underlying LC principles in view of developing a typology of indicators and related LCA approaches. The obtained typology is intended to serve two overarching aims. First, it shall disclose the indicators that can be obtained by following a certain LC approach and second, it reveals data demands required to calculate the corresponding indicator. Having a clear picture about required data needs is a first step to tackle the daunting task of data acquisition. The better the understanding of required data, the more realistic is the automatization of data management. Manual data acquisition from countless sources like primary data, literature and LC databases is not only time consuming but also error-prone and often – due to a lack of required information – based on assumptions. The implementation of novel IT
approaches and machine learning in LC modelling as part of sustainability management offers great potential to automize manual acquiring, sorting, processing and structuring of data to obtain real-time impact results which are adjustable with minimal effort.
an outcome of the project shall be, to understand how LC data acquisition can be automated using advanced and innovative IT-based solutions like blockchain or internet of things (IoT). This shall help companies to report their GHG emissions with less effort, decreasing uncertainty and in view of the interest and needs of diverse stakeholders.

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