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 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 .

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 assessment 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 implemented 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.

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 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 resource 5 .

PcfWebUI

We introduce PcfWebUI, a data-driven tool developed to support companies in their decarbonization 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 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). Another important chart is the scope graph that shows data grouped by scopes, providing a view of emissions according to different scope categories.

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 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 efficiency benchmarking data. Finally, We will integrate our novel SPARQL query generation approach into PcfWebUI for a more user-friendly experience [6].