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Lifecycle Assessment of Biomass Supply Chain with the Assistance of Agent-Based Modelling

Author

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  • Raghu KC

    (Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Lönnrotinkatu 7, 50100 Mikkeli, Finland)

  • Mika Aalto

    (Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Lönnrotinkatu 7, 50100 Mikkeli, Finland)

  • Olli-Jussi Korpinen

    (Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Lönnrotinkatu 7, 50100 Mikkeli, Finland)

  • Tapio Ranta

    (Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Lönnrotinkatu 7, 50100 Mikkeli, Finland)

  • Svetlana Proskurina

    (Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Yliopistonkatu 34, 53850 Lappeenranta, Finland)

Abstract

Even though biomass is characterised as renewable energy, it produces anthropogenic greenhouse gas (GHG) emissions, especially from biomass logistics. Lifecycle assessment (LCA) is used as a tool to quantify the GHG emissions from logistics but in the past the majority of LCAs have been steady-state and linear, when in reality, non-linear and temporal aspects (such as weather conditions, seasonal biomass demand, storage capacity, etc.) also have an important role to play. Thus, the objective of this paper was to optimise the environmental sustainability of forest biomass logistics (in terms of GHG emissions) by introducing the dynamic aspects of the supply chain and using the geographical information system (GIS) and agent-based modelling (ABM). The use of the GIS and ABM adds local conditions to the assessment in order to make the study more relevant. In this study, GIS was used to investigate biomass availability, biomass supply points and the road network around a large-scale combined heat and power plant in Naantali, Finland. Furthermore, the temporal aspects of the supply chain (e.g., seasonal biomass demand and storage capacity) were added using ABM to make the assessment dynamic. Based on the outcomes of the GIS and ABM, a gate-to-gate LCA of the forest biomass supply chain was conducted in order to calculate GHG emissions. In addition to the domestic biomass, we added imported biomass from Riga, Latvia to the fuel mixture in order to investigate the effect of sea transportation on overall GHG emissions. Finally, as a sensitivity check, we studied the real-time measurement of biomass quality and its potential impact on overall logistical GHG emissions. According to the results, biomass logistics incurred GHG emissions ranging from 2.72 to 3.46 kg CO 2-eq per MWh, depending on the type of biomass and its origin. On the other hand, having 7% imported biomass in the fuel mixture resulted in a 13% increase in GHG emissions. Finally, the real-time monitoring of biomass quality helped save 2% of the GHG emissions from the overall supply chain. The incorporation of the GIS and ABM helped in assessing the environmental impacts of the forest biomass supply chain in local conditions, and the combined approach looks promising for developing LCAs that are inclusive of the temporal aspects of the supply chain for any specific location.

Suggested Citation

  • Raghu KC & Mika Aalto & Olli-Jussi Korpinen & Tapio Ranta & Svetlana Proskurina, 2020. "Lifecycle Assessment of Biomass Supply Chain with the Assistance of Agent-Based Modelling," Sustainability, MDPI, vol. 12(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1964-:d:328433
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    References listed on IDEAS

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    2. Zhaoyuan He & Paul Turner, 2021. "A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities," Logistics, MDPI, vol. 5(4), pages 1-22, December.
    3. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    4. Shiyu Chen & Wei Wang & Enrico Zio, 2021. "A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains," Energies, MDPI, vol. 14(9), pages 1-27, May.
    5. Battuvshin, Biligt & Matsuoka, Yusuke & Shirasawa, Hiroaki & Toyama, Keisuke & Hayashi, Uichi & Aruga, Kazuhiro, 2020. "Supply potential and annual availability of timber and forest biomass resources for energy considering inter-prefectural trade in Japan," Land Use Policy, Elsevier, vol. 97(C).
    6. De Boeck, Kim & Decouttere, Catherine & Jónasson, Jónas Oddur & Vandaele, Nico, 2022. "Vaccine supply chains in resource-limited settings: Mitigating the impact of rainy season disruptions," European Journal of Operational Research, Elsevier, vol. 301(1), pages 300-317.
    7. Olli-Jussi Korpinen & Mika Aalto & Raghu KC & Timo Tokola & Tapio Ranta, 2023. "Utilisation of Spatial Data in Energy Biomass Supply Chain Research—A Review," Energies, MDPI, vol. 16(2), pages 1-23, January.
    8. Martinez-Valencia, Lina & Garcia-Perez, Manuel & Wolcott, Michael P., 2021. "Supply chain configuration of sustainable aviation fuel: Review, challenges, and pathways for including environmental and social benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    9. Simone Cornago & Yee Shee Tan & Carlo Brondi & Seeram Ramakrishna & Jonathan Sze Choong Low, 2022. "Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications," Sustainability, MDPI, vol. 14(11), pages 1-22, May.
    10. Tianran Ding & Wouter Achten, 2022. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/352782, ULB -- Universite Libre de Bruxelles.
    11. Islam Hassanin & Matjaz Knez, 2022. "Managing Supply Chain Activities in the Field of Energy Production Focusing on Renewables," Sustainability, MDPI, vol. 14(12), pages 1-33, June.

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