Digital technologies for precise carbon balancing in timber procurement

European Journal of Forest Research, vol. 144, pp. 1043–1061

Abstract

Abstract Detailed tracking of all relevant– natural and man-made– activities in the forest enables real-time monitoring of carbon dynamics, providing immediate feedback on the carbon impact of specific forestry operations. Carbon is sequestered and emitted at many points along the wood supply chain as well as its upstream and downstream processes. The spatial, technical, and organizational aspects of the associated process steps are very heterogeneous, making them complex to capture in the real world and in the information world. This article initially provides an overview of digital technologies for precise carbon balancing in timber procurement. We then show how to use these technologies to capture and consolidate data at the stem section level to enhance the precision of carbon accounting. This granular approach allows for detailed accounting of carbon sequestration and emissions, which is crucial for developing effective carbon management strategies. We propose technical building blocks and an architecture to consider this in an integrated approach. The architecture is based on Digital Twins to individually acquire data with Internet of Things technologies for networking. Data sovereignty among the participating stakeholders is ensured through a dataspace approach yielding a “Dataspace Forestry 4.0”. Integrated human-machine interfaces give stakeholders access to relevant data, and simulation technologies allow them to explore process variants to support decision-making.

Authors 11

1. Martin Hoppen corresponding Aachen Institute for Man-Machine Interaction

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Man-Machine Interaction, Aachen, Germany

2. Simon Baier

   Institut für Oberflächen- und Schichtanalytik GmbH

   Affiliation as printed

   iFOS GmbH, Aindling-Hausen, Germany

3. Lennart Schinke Aachen Institute for Man-Machine Interaction

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Man-Machine Interaction, Aachen, Germany

4. Martin Ziesak

   Institut für Oberflächen- und Schichtanalytik GmbH

   Affiliation as printed

   iFOS GmbH, Aindling-Hausen, Germany

5. Lukas Schreiber

   Institut für Oberflächen- und Schichtanalytik GmbH

   Affiliation as printed

   iFOS GmbH, Aindling-Hausen, Germany

6. Arthur Wahl Aachen Institute for Man-Machine Interaction

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Man-Machine Interaction, Aachen, Germany

7. Jiahang Chen Aachen Institute for Man-Machine Interaction

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Man-Machine Interaction, Aachen, Germany

8. Anil Riza Bektas Aachen Institute for Man-Machine Interaction

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute for Man-Machine Interaction, Aachen, Germany

9. Frank Heinze

   Institut für Forschung und Transfer

   Affiliation as printed

   RIF Institute for Research and Transfer, e.V., Joseph-von-Fraunhofer-Straße 20, 44227, Dortmund, Germany

10. Michael Schluse Aachen Institute for Man-Machine Interaction

    RWTH Aachen University

    Affiliation as printed

    RWTH Aachen University, Institute for Man-Machine Interaction, Aachen, Germany

11. Jürgen Roßmann Aachen Institute for Man-Machine Interaction

    RWTH Aachen University

    Affiliation as printed

    RWTH Aachen University, Institute for Man-Machine Interaction, Aachen, Germany