The pursuit of corporate greenhoue gas neutrality has become increasingly critical due to heightened sustainability 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 decarbonization journey. PcfWebUI is built on real company data integrated into a knowledge graph, enabling eficient 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/.
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 eficient 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 eficiency (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 [
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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.
We introduce PcfWebUI, a data-driven tool developed to support companies in their decarbonization journey. The system emphasizes improving material and energy eficiency as the ifrst step, followed by substituting energy sources and compensating for residual emissions. PcfWebUI enables eficient 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 eficiently search and retrieve specific information related to their PCF data. This component includes a 3https://github.com/dice-group/ClimateBowl-KGConverter 4https://jena.apache.org/ 5http://w3id.org/dice-research/climatebowl/ontology ifeld 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 diferent 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 diferent 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 diferent 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 diferent 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 eficient 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 eficiency, energy source substitution, and residual emissions compensation. Despite challenges in data availability, secure exchange, and PCF tracking complexity, PcfWebUI ofers 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
benchmarking, 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 eficiency benchmarking data. Finally, We will integrate our novel SPARQL
query generation approach into PcfWebUI for a more user-friendly experience [