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

Author

Listed:
  • 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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    1. Mirkouei, Amin & Haapala, Karl R. & Sessions, John & Murthy, Ganti S., 2017. "A mixed biomass-based energy supply chain for enhancing economic and environmental sustainability benefits: A multi-criteria decision making framework," Applied Energy, Elsevier, vol. 206(C), pages 1088-1101.
    2. Chris Davis & Igor Nikolić & Gerard P. J. Dijkema, 2009. "Integration of Life Cycle Assessment Into Agent‐Based Modeling," Journal of Industrial Ecology, Yale University, vol. 13(2), pages 306-325, April.
    3. S Robinson, 2008. "Conceptual modelling for simulation Part II: a framework for conceptual modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(3), pages 291-304, March.
    4. Cambero, Claudia & Sowlati, Taraneh, 2014. "Assessment and optimization of forest biomass supply chains from economic, social and environmental perspectives – A review of literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 62-73.
    5. S Robinson, 2008. "Conceptual modelling for simulation Part I: definition and requirements," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(3), pages 278-290, March.
    6. Santos, Andreia & Carvalho, Ana & Barbosa-Póvoa, Ana Paula & Marques, Alexandra & Amorim, Pedro, 2019. "Assessment and optimization of sustainable forest wood supply chains – A systematic literature review," Forest Policy and Economics, Elsevier, vol. 105(C), pages 112-135.
    7. Susie Ruqun Wu & Xiaomeng Li & Defne Apul & Victoria Breeze & Ying Tang & Yi Fan & Jiquan Chen, 2017. "Agent†Based Modeling of Temporal and Spatial Dynamics in Life Cycle Sustainability Assessment," Journal of Industrial Ecology, Yale University, vol. 21(6), pages 1507-1521, December.
    8. Anthony Halog & Yosef Manik, 2011. "Advancing Integrated Systems Modelling Framework for Life Cycle Sustainability Assessment," Sustainability, MDPI, vol. 3(2), pages 1-31, February.
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    Cited by:

    1. Dimitra Kaffe, 2026. "Comparative Life Cycle Assessment of Agro-Industrial Residues: Environmental Impacts of Grape Marc and Brewer’s Spent Grain Combustion," Circular Economy and Sustainability, Springer, vol. 6(2), pages 1-47, April.
    2. Longo, Sonia & Cellura, Maurizio & Luu, Le Quyen & Nguyen, Thanh Quang & Rincione, Roberta & Guarino, Francesco, 2024. "Circular economy and life cycle thinking applied to the biomass supply chain: A review," Renewable Energy, Elsevier, vol. 220(C).
    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. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Laurynas Virbickas & Irina Kliopova & Edgaras Stunžėnas, 2025. "Environmental Impact Assessment of Logging Residue Utilization for Increased Bioenergy Production from Scots Pine Forest Stands in Lithuania Using a Life Cycle Approach," Sustainability, MDPI, vol. 17(18), pages 1-21, September.
    10. 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.
    11. 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.
    12. Youngjin Kim & Sojung Kim, 2025. "Optimization and Simulation in Biofuel Supply Chain," Energies, MDPI, vol. 18(5), pages 1-24, February.
    13. 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).
    14. 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.

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