Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks

Pei Hao Li, Behzad Zamanipour*, Ilkka Keppo

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
28 Downloads (Pure)

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.

Original languageEnglish
Article number114273
Number of pages21
JournalEnergy Policy
Volume193
DOIs
Publication statusPublished - Oct 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian networks
  • Decarbonisation pathways
  • Energy system model
  • Uncertainty

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