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Accelerating residential decarbonisation: How stakeholder decision-making and socio-economic dynamics affect multi-decadal district heating network expansion

Author

Listed:
  • Cowley, Thomas
  • Hutty, Timothy
  • Hammond, Joseph
  • Brown, Solomon

Abstract

District heating networks (DHNs) are essential for decarbonising urban heating but face adoption barriers due to stakeholder complexities, infrastructure legacies, and consumer resistance. Existing models overlook the dynamic, iterative nature of infrastructure development and multi-stakeholder interactions. To address these limitations, this study develops an agent-based model (ABM) that continuously simulates DHN adoption and expansion. Unlike discrete optimisation approaches, this integrates evolving household decision-making, project developer strategies, and dynamic market conditions. Socio-economic factors, spatial dynamics, and temporal evolution are incorporated to provide a comprehensive understanding of DHN diffusion. The model projects that adoption in the studied urban area could reach 30.4% ± 2% of households by 2050. While this projection exceeds the UK's national target of 20% for DHN adoption, it highlights the significant potential to contribute to national decarbonisation goals. Heating bill savings range from 41.9% to 56.3%, while CO2 emissions are reduced by 24.3% compared to existing gas heated systems. Through the quantification of temporal dynamics and multi-stakeholder interactions, this study offers insights for urban energy transition strategies. These findings can inform policies that accelerate DHN adoption, mitigate risks associated with large-scale infrastructure investments, and contribute to sustainable urban heating strategies aligned with international climate goals.

Suggested Citation

  • Cowley, Thomas & Hutty, Timothy & Hammond, Joseph & Brown, Solomon, 2025. "Accelerating residential decarbonisation: How stakeholder decision-making and socio-economic dynamics affect multi-decadal district heating network expansion," Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:energy:v:326:y:2025:i:c:s0360544225019462
    DOI: 10.1016/j.energy.2025.136304
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    References listed on IDEAS

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    1. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    2. Kuntuarova, Saltanat & Licklederer, Thomas & Huynh, Thanh & Zinsmeister, Daniel & Hamacher, Thomas & Perić, Vedran, 2024. "Design and simulation of district heating networks: A review of modeling approaches and tools," Energy, Elsevier, vol. 305(C).
    3. Pagani, M. & Maire, P. & Korosec, W. & Chokani, N. & Abhari, R.S., 2020. "District heat network extension to decarbonise building stock: A bottom-up agent-based approach," Applied Energy, Elsevier, vol. 272(C).
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