PCFWebUI: Data-driven WebUI for holistic decarbonization based on PCF-Tracking⋆ Ajay Kumar1 , Marius Naumann2 , Kevin Henne2 and Mohamed Ahmed Sherif1 1 Paderborn University, Data Science Group, Pohlweg 51, D-33098 Paderborn, Germany 2 Paderborn University, Department of Energy System Technologies, Warburger Straße 100, D-33098 Paderborn, Germany Abstract The pursuit of corporate greenhoue gas neutrality has become increasingly critical due to heightened sus- tainability expectations, rising energy and CO2e costs, and stricter regulatory requirements. Key drivers, such as the mandated reduction of greenhouse gas emissions by 65% by 2030 compared to 1990 levels and the goal of achieving climate neutrality by 2045, necessitate immediate action toward decarbonization. In this paper, we introduce PcfWebUI, a data-driven tool developed to support companies in their decar- bonization journey. PcfWebUI is built on real company data integrated into a knowledge graph, enabling efficient tracking and management of product carbon footprints (PCF), facilitating strategic planning and accurate monitoring to help companies meet stringent climate targets and progress toward climate neutrality. A demo of our system is publicly available at https://climatebowl.demo.dice-research.org/. Keywords Knowledge Graphs, PCF Tracking, Energy Efficiency 1. Introduction Achieving greenhouse gas (GHG) neutrality poses a complex challenge for companies, driven by political pressures, stakeholder demands, and rising energy costs. Essential drivers such as the mandatory reduction of GHG emissions by 65% by 2030 relative to 1990 and the attainment of net zero emissions by 2045 underscore the need for companies to embark on the path to decarbonization promptly [1]. The allocation of GHG emissions to specific sources and the identification of suitable measures are complex due to the intricate interactions within the production systems. Current digital approaches exhibit significant gaps, particularly in the comprehensive aggregation and assessment of GHG emissions. Furthermore, they fall short in sufficiently developing reduction measures across the entire value chain. Addressing these challenges requires a holistic and digital approach. The foundational step towards reducing GHG emissions is achieving transparency in emissions across both specific sites and the entire SEMANTICS24: 20th International Conference on Semantic Systems, September 17-19, 2024,Amsterdam ⋆ You can use this document as the template for preparing your publication. We recommend using the latest version of the ceurart style. * Corresponding author. † These authors contributed equally. $ ajayk@campus.uni-paderborn.de (A. Kumar); marius.naumann@upb.de (M. Naumann); khenne2@mscould.uni-paderborn.de (K. Henne); mohamed.sherif@uni-paderborn.de (M. A. Sherif) € https://dice-research.org/AjayKumar (A. Kumar); https://dice-research.org/MohamedAhmedSherif (M. A. Sherif)  0009-0009-2400-1984 (M. Naumann); 0000-0002-9927-2203 (M. A. Sherif) © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR ceur-ws.org Workshop ISSN 1613-0073 Proceedings value network. This transparency is starting point for the development of decarbonization measures. In this paper, we present PcfWebUI1 Based on real companies’ data integrated into knowledge graph (KG), PcfWebUI enables efficient tracking and management of the Product Carbon Footprints (PCF), helping companies to identify potentials to reduce GHG emission as well as derive and assess specific measures. The system’s data-driven approach ensures accurate monitoring and strategic planning, supporting companies in meeting stringent climate targets and advancing toward GHG neutrality. PcfWebUI aims to simplify the process of identifying and implementing energy efficiency (EE) measures. This is the focus of our collaborative project Climate bOWL2 . 2. PCF-Tracking in context of industrial decarbonization The holistic decarbonization of corporate activities can be divided into the three phases: target definition, carbon accounting and action planning [2]. Defining reduction targets and translating them into a reduction pathway is the foundation of any decarbonisation strategy. It is necessary to create transparency about the company’s product-specific greenhouse gas emissions through carbon accounting. The general methodical procedure is defined in the Greenhouse Gas Protocol (GHGP) or standard DIN EN ISO 14064, the product-specific accounting is specified in standard DIN EN ISO 14067 [3]. The accounting of all input and output flows for the relevant process modules of a product is recorded in a life cycle inventory and then subjected to an impact assess- ment in order to quantify the GHG potential measured in GHG equivalents [4]. By analyzing the PCF, it becomes clear which process modules in the life cycle inventory have the greatest GHG potential. The aggregation of process modules according to criteria such as scope categories or life cycle phases makes it possible to evaluate the influence of individual or aggregated process modules on the overall PCF. This analysis is important because it defines the starting point for the development of decarbonization measures by identifying the emission-intensive process modules. In the third phase of action planning, measures for GHG reduction are derived for the identified drivers of the PCF. In principle, decarbonization efforts can be strategically imple- mented following the principles of avoidance, reduction, replacing and offsetting [5]. There is a need to automate the individual steps of PCF tracking and the development of measures in order to ensure a holistic approach and accelerate industrial decarbonization [2]. The Climate bOWL research project is therefore investigating the development of a digital support system that automates the PCF accounting and analysis as well as the derivation of reduction measures based on the methodological framework described. 3. Climate bOWL Knowledge Graph The foundation of PcfWebUI’s robust functionality lies in its use of real companies’ data from the Climate bOWL project, integrated into knowledge graph (KG). For generating our KG, we 1 Demo: https://climatebowl.demo.dice-research.org. Please be aware that for data protection purposes, our public demo exclusively utilizes synthetic data. This ensures that no real company data is used or exposed. 2 https://dice-research.org/ClimatebOWL Company Product Transport :hasEmissionsfactor THGEmissions :hasProduct :hasProcess :hasEmissionsfactor :hasEmissionsfactor Material :hasDocumentation Process :hasCategory :hasCategory :hasDataQualityAssessment Documentation :hasOutput :hasInput :hasReferenceFlow Energy :hasAnotherTimesampReferenceFlow :hasAllocation :hasDatavalidation Flow :hasCategory Allocation DataValidation ReferenceFlow DataQualityAssessment Figure 1: The Climate Bowl Ontology (CLBO) deployed Python scripts to convert data from Excel files into the KG triples. Our KG conversion scripts are available from the project Github3 . The resulted triples is then hosted using Apache Jena4 . To facilitate advanced querying, we also provided a SPARQL endpoint to our KG, allowing users to perform complex queries and extract more specific information. The Climate Bowl Ontology (CLBO), see Figure 1, is designed to facilitate the aggregation, evaluation, and prioritization of GHG emission reduction measures throughout the value chain in industrial settings. CLBO defines key concepts and their relationships relevant to industrial GHG emission reduction. Key entities include companies, products, processes, flows, and documentation standards. Properties describe the relationships between these entities, enabling a structured approach to tracking and analyzing GHG emissions and identifying potential reduction measures. For more details see the climatebowl ontology online resource5 . 4. PcfWebUI We introduce PcfWebUI, a data-driven tool developed to support companies in their decar- bonization journey. The system emphasizes improving material and energy efficiency as the first step, followed by substituting energy sources and compensating for residual emissions. PcfWebUI enables efficient tracking and management of PCF facilitating strategic planning and accurate monitoring to help companies meet stringent climate targets and progress toward climate neutrality. The key components of PcfWebUI are: 1. Query Component. The query component is designed to allow users to efficiently search and retrieve specific information related to their PCF data. This component includes a 3 https://github.com/dice-group/ClimateBowl-KGConverter 4 https://jena.apache.org/ 5 http://w3id.org/dice-research/climatebowl/ontology field for SPARQL queries that utilizes our integrated KG to get detailed data for a product. We built the query component using React6 for the front-end and Apache-Jena for the back-end. The query component provides instantaneous feedback, displaying results as users refine their queries. 2. PCF Tracking Component. The PCF tracking component enables users to monitor and manage PCF comprehensively. This component presents a table with different parameters for a single product, where the data for this table comes from the KG using the SPARQL query in the query component. As shown in Figure 2, the Flow column ("fluss" in German), there is a drop-down feature that allows users to choose for different materials or energy sources. The Flow column includes a searchable drop-down that retrieves data from our KG, allowing users to find materials or energy sources quickly. This component dynamically reflects changes in emission factors in real-time, indicated by a green arrow if emissions decrease and a red arrow if emissions increase. 3. Analytics Component. The analytics component of PcfWebUI responsible for analyzing PCF data and generating insights. This component uses the updated data from the PCF Tracking table, including all user updates, to display various analytical graphs. One of the key graphs is the life cycle phases graph, where emission data is divided into different life-cycle phases of the product and compares old and new emissions (See an example in the lower part of Figure 2). Another important chart is the scope graph that shows data grouped by scopes, providing a view of emissions according to different scope categories. 5. Conclusions and Future Work Corporate climate neutrality is imperative due to strict regulations, rising energy and CO2e costs, and increased sustainability expectations. PcfWebUI is a pivotal tool, enabling efficient tracking and management of product carbon footprints with a data-driven approach. By integrating real company data into a KG, PcfWebUI enhances material and energy efficiency, energy source substitution, and residual emissions compensation. Despite challenges in data availability, secure exchange, and PCF tracking complexity, PcfWebUI offers a robust foundation for companies to meet decarbonization targets and achieve GHG neutrality. In future work, we will enhance PcfWebUI by developing advanced features for EE bench- marking, measure derivation, and prioritization to guide companies in decarbonization. We will also integrate a recommendation system for energy reduction and decarbonization based on PCF and energy efficiency benchmarking data. Finally, We will integrate our novel SPARQL query generation approach into PcfWebUI for a more user-friendly experience [6]. Acknowledgments. This work has been supported by the Ministry for Economic Affairs, Innovation, Digitalisation and Energy of North Rhine-Westphalia (MWIDE NRW) within the project Climate bOWL under the grant no 005-2111-0020, and by the German Research Foun- dation (DFG) within the project INGRID under the grant no NG 105/7-3. This work has been supported within the project "WHALE" (LFN 1-04) funded under the Lamarr Fellow Network programme by the Ministry of Culture and Science of North Rhine-Westphalia (MKW NRW). 6 https://react.dev/ Figure 2: PCF-tracking UI overview References [1] European Commission, European climate law, 2021. [2] M. Naumann, M. Ostermann, N. Buchenau, J. Oetzel, F. Schlosser, H. Meschede, T. Tröster, Energy efficiency improvement in manufacturing industry: A review based on a framework for holistic decarbonization (not published), submitted in Renewable and Sustainable Energy Reviews (2024). [3] R.-H. Hechelmann, A. Paris, N. Buchenau, F. Ebersold, Decarbonisation strategies for manufacturing: A technical and economic comparison, Renewable and Sustainable Energy Reviews 188 (2023) 113797. doi:10.1016/j.rser.2023.113797. [4] International Organization for Standardization, Din en iso 14040: Environmental manage- ment - life cycle assessment: Principles and framework, 2009. URL: https://www.iso.org/ standard/37456.html. [5] M. Finkbeiner, V. Bach, Life cycle assessment of decarbonization options—towards scientifi- cally robust carbon neutrality, The International Journal of Life Cycle Assessment 26 (2021) 635–639. doi:10.1007/s11367-021-01902-4. [6] H. M. Zahera, M. Ali, M. A. Sherif, D. Moussallem, A.-C. Ngonga Ngomo, Generating sparql from natural language using chain-of-thoughts prompting, in: SEMANTiCS, 2024.