IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i5p1964-d328433.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/5/1964/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/5/1964/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Anthony Halog & Yosef Manik, 2011. "Advancing Integrated Systems Modelling Framework for Life Cycle Sustainability Assessment," Sustainability, MDPI, vol. 3(2), pages 1-31, February.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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. 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).
    8. 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.
    9. 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.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aalto, Mika & KC, Raghu & Korpinen, Olli-Jussi & Karttunen, Kalle & Ranta, Tapio, 2019. "Modeling of biomass supply system by combining computational methods – A review article," Applied Energy, Elsevier, vol. 243(C), pages 145-154.
    2. Elias Hartvigsson & Erik Oscar Ahlgren & Sverker Molander, 2020. "Tackling complexity and problem formulation in rural electrification through conceptual modelling in system dynamics," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(1), pages 141-153, January.
    3. Gerrit Muller, 2021. "Applying Roadmapping and Conceptual Modeling to the Energy Transition: A Local Case Study," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    4. Jason Madan & Meghan Bruce Kumar & Miriam Taegtmeyer & Edwine Barasa & Swaran Preet Singh, 2020. "SEEP-CI: A Structured Economic Evaluation Process for Complex Health System Interventions," IJERPH, MDPI, vol. 17(18), pages 1-12, September.
    5. Eric Innocenti & Claudio Detotto & Corinne Idda & Dawn Cassandra Parker & Dominique Prunetti, 2023. "Spécification conceptuelle MR POTATOHEAD -Property Market Edition du système complexe d'un territoire touristique à deux marchés : application au territoire corse," Post-Print hal-04121402, HAL.
    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. Brailsford, Sally C. & Eldabi, Tillal & Kunc, Martin & Mustafee, Navonil & Osorio, Andres F., 2019. "Hybrid simulation modelling in operational research: A state-of-the-art review," European Journal of Operational Research, Elsevier, vol. 278(3), pages 721-737.
    8. Chong, Adrian & Augenbroe, Godfried & Yan, Da, 2021. "Occupancy data at different spatial resolutions: Building energy performance and model calibration," Applied Energy, Elsevier, vol. 286(C).
    9. Hana M Dobrovolny & Micaela B Reddy & Mohamed A Kamal & Craig R Rayner & Catherine A A Beauchemin, 2013. "Assessing Mathematical Models of Influenza Infections Using Features of the Immune Response," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-20, February.
    10. Zichong Lyu & Dirk Pons & Yilei Zhang & Zuzhen Ji, 2022. "Minimum Viable Model (MVM) Methodology for Integration of Agile Methods into Operational Simulation of Logistics," Logistics, MDPI, vol. 6(2), pages 1-28, June.
    11. Alireza Moumivand & Adel Azar & Abbas Toloie Eshlaghy, 2022. "Combined soft system methodology and agent‐based simulation for multi‐methodological modelling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(2), pages 200-217, March.
    12. Robert, Marion & Thomas, Alban & Sekhar, Muddu & Badiger, Shrinivas & Ruiz, Laurent & Raynal, Hélène & Bergez, Jacques-Eric, 2017. "Adaptive and dynamic decision-making processes: A conceptual model of production systems on Indian farms," Agricultural Systems, Elsevier, vol. 157(C), pages 279-291.
    13. Penny R. Breeze & Hazel Squires & Kate Ennis & Petra Meier & Kate Hayes & Nik Lomax & Alan Shiell & Frank Kee & Frank de Vocht & Martin O’Flaherty & Nigel Gilbert & Robin Purshouse & Stewart Robinson , 2023. "Guidance on the use of complex systems models for economic evaluations of public health interventions," Health Economics, John Wiley & Sons, Ltd., vol. 32(7), pages 1603-1625, July.
    14. Priscilla Avegliano & Jaime Simão Sichman, 2023. "Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-3.
    15. 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.
    16. Hansen, Samuel & Mirkouei, Amin & Diaz, Luis A., 2020. "A comprehensive state-of-technology review for upgrading bio-oil to renewable or blended hydrocarbon fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    17. Martin Comis & Catherine Cleophas & Christina Büsing, 2021. "Patients, primary care, and policy: Agent-based simulation modeling for health care decision support," Health Care Management Science, Springer, vol. 24(4), pages 799-826, December.
    18. Martins, Thales G.V. & Reis, Geraldo G. & Reis, Maria G.F. & Telles, Lucas A.A. & Lage, Mayara R. & Mendes, Gleidson G.C. & Pinto, Dayane L. & Castro, Nero L.M. & Lorenzon, Alexandre S. & Silva, Ricar, 2020. "Potential planting areas for native tree species in minas gerais state, Brazil, based on environmental variables and wood demand," Ecological Modelling, Elsevier, vol. 432(C).
    19. Christina Wulf & Jasmin Werker & Christopher Ball & Petra Zapp & Wilhelm Kuckshinrichs, 2019. "Review of Sustainability Assessment Approaches Based on Life Cycles," Sustainability, MDPI, vol. 11(20), pages 1-43, October.
    20. Khushboo E-Fatima & Rasoul Khandan & Amin Hosseinian-Far & Dilshad Sarwar & Hareer Fatima Ahmed, 2022. "Adoption and Influence of Robotic Process Automation in Beef Supply Chains," Logistics, MDPI, vol. 6(3), pages 1-20, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1964-:d:328433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.