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Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks

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  • Li, Pei-Hao
  • Zamanipour, Behzad
  • Keppo, Ilkka

Abstract

To effectively meet the ambitious objectives set by the Paris Agreement, gaining a deeper understanding of the relationships between the key technologies involved in mitigation activities is pivotal. This research uses Bayesian Network (BN) methodology on a large ensemble of energy system model runs, aiming to shed light on the complex interdependencies, and related uncertainties, among the various technologies within the pathways. We specifically focus on tracking the evolution and interconnectedness of technology portfolios over time, enabling dynamic assessments of the impacts linked to specific deployment strategies. The results suggest that prioritizing early-stage transitions within the building sector is imperative and the consistent deployment of district heating emerges as a pivotal element in the long-term plans for decarbonisation. In the power sector, the rising trends in electrification and the substantial growth in low-carbon power plants and wind energy deployment, underscore the urgency for adaptable strategies within the power sector. Notably, the integration of bioenergy with carbon capture and storage (BECCS) also emerges as a crucial technology, offering a means to counterbalance emissions from carbon-intensive industries. The BN-based approach provides decision makers a powerful tool for comprehensive, informed, and systematic planning as they navigate towards a carbon-neutral future, but it is also crucial to acknowledge the reliance of our analysis on assumptions inherent in energy system models. Studies using different assumptions and model structures are needed to confirm the generalizability of our findings.

Suggested Citation

  • Li, Pei-Hao & Zamanipour, Behzad & Keppo, Ilkka, 2024. "Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks," Energy Policy, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:enepol:v:193:y:2024:i:c:s0301421524002933
    DOI: 10.1016/j.enpol.2024.114273
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    as
    1. Li, Pei-Hao & Pye, Steve & Keppo, Ilkka, 2020. "Using clustering algorithms to characterise uncertain long-term decarbonisation pathways," Applied Energy, Elsevier, vol. 268(C).
    2. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    3. Pizarro-Alonso, Amalia & Ravn, Hans & Münster, Marie, 2019. "Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Céline Guivarch & Thomas Gallic & Nico Bauer & Panagiotis Fragkos & Daniel Huppmann & Marc Jaxa-Rozen & Ilkka Keppo & Elmar Kriegler & Tamás Krisztin & Giacomo Marangoni & Steve Pye & Keywan Riahi & R, 2022. "Using large ensembles of climate change mitigation scenarios for robust insights," Nature Climate Change, Nature, vol. 12(5), pages 428-435, May.
    5. Hughes, Nick & Strachan, Neil & Gross, Robert, 2013. "The structure of uncertainty in future low carbon pathways," Energy Policy, Elsevier, vol. 52(C), pages 45-54.
    6. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    7. Céline Guivarch & Julie Rozenberg & Vanessa Schweizer, 2016. "The diversity of socio-economic pathways and CO2 emissions scenarios: Insights from the investigation of a scenarios database," Post-Print halshs-01292901, HAL.
    8. Moglianesi, Andrea & Keppo, Ilkka & Lerede, Daniele & Savoldi, Laura, 2023. "Role of technology learning in the decarbonization of the iron and steel sector: An energy system approach using a global-scale optimization model," Energy, Elsevier, vol. 274(C).
    9. Unruh, Gregory C., 2000. "Understanding carbon lock-in," Energy Policy, Elsevier, vol. 28(12), pages 817-830, October.
    10. Li, Francis G.N. & Trutnevyte, Evelina, 2017. "Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050," Applied Energy, Elsevier, vol. 189(C), pages 89-109.
    11. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
    12. Small, Mitchell J. & Wong-Parodi, Gabrielle & Kefford, Benjamin M. & Stringer, Martin & Schmeda-Lopez, Diego R. & Greig, Chris & Ballinger, Benjamin & Wilson, Stephen & Smart, Simon, 2019. "Generating linked technology-socioeconomic scenarios for emerging energy transitions," Applied Energy, Elsevier, vol. 239(C), pages 1402-1423.
    13. Pei-Hao Li & Steve Pye & Ilkka Keppo & Marc Jaxa-Rozen & Evelina Trutnevyte, 2023. "Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques," Climatic Change, Springer, vol. 176(7), pages 1-23, July.
    14. Morris, Peter & Vine, Desley & Buys, Laurie, 2015. "Application of a Bayesian Network complex system model to a successful community electricity demand reduction program," Energy, Elsevier, vol. 84(C), pages 63-74.
    15. Silva Herran, Diego & Tachiiri, Kaoru & Matsumoto, Ken'ichi, 2019. "Global energy system transformations in mitigation scenarios considering climate uncertainties," Applied Energy, Elsevier, vol. 243(C), pages 119-131.
    16. Moallemi, Enayat A. & de Haan, Fjalar & Kwakkel, Jan & Aye, Lu, 2017. "Narrative-informed exploratory analysis of energy transition pathways: A case study of India's electricity sector," Energy Policy, Elsevier, vol. 110(C), pages 271-287.
    17. Howells, Mark & Rogner, Holger & Strachan, Neil & Heaps, Charles & Huntington, Hillard & Kypreos, Socrates & Hughes, Alison & Silveira, Semida & DeCarolis, Joe & Bazillian, Morgan & Roehrl, Alexander, 2011. "OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development," Energy Policy, Elsevier, vol. 39(10), pages 5850-5870, October.
    18. Pye, Steve & Sabio, Nagore & Strachan, Neil, 2015. "An integrated systematic analysis of uncertainties in UK energy transition pathways," Energy Policy, Elsevier, vol. 87(C), pages 673-684.
    19. Chang, Yuanjiang & Wu, Xiangfei & Zhang, Changshuai & Chen, Guoming & Liu, Xiuquan & Li, Jiayi & Cai, Baoping & Xu, Liangbin, 2019. "Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 454-462.
    20. Li, Pei-Hao & Keppo, Ilkka & Strachan, Neil, 2018. "Incorporating homeowners' preferences of heating technologies in the UK TIMES model," Energy, Elsevier, vol. 148(C), pages 716-727.
    21. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
    22. Robert Gross & Richard Hanna, 2019. "Path dependency in provision of domestic heating," Nature Energy, Nature, vol. 4(5), pages 358-364, May.
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