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Long-term energy and emissions outlook for the road transport sector of Uzbekistan: Insights from an agent-based energy model

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  • Yakhshilikov, Jamshid
  • Cavana, Marco
  • Inoyatkhodjaev, Jamshid
  • Giarola, Sara
  • Leone, Pierluigi

Abstract

An agent-based energy model of Uzbekistan's road transport sector was developed to assess trajectories toward zero tailpipe emissions through 2050. The model captures heterogeneous decision-making among vehicle owners and evaluates scenarios combining technological progress and behavioral change. Findings show that without targeted policies, emissions exceed 41 million tonnes annually, and fossil fuels remain dominant by 2050. Scenarios incorporating carbon pricing, purchase subsidies, and regulatory mandates enable a full transition to electric and hydrogen vehicles. These outcomes are contingent on behavioral flexibility. Biomethane offers near-term mitigation by leveraging existing technologies designed for compressed natural gas but requires policy support for scale up production. Full decarbonization requires 79.4 PJ of electricity and 145.8 PJ of hydrogen annually by mid-century, along with coordinated action across governance levels. This includes investment in renewable electricity, hydrogen supply chains, and public awareness. The model highlights that technological progress alone is insufficient; only sustained policy intervention can overcome structural inertia and shift investments toward low-emission technologies. The study provides a foundation for designing a national transport decarbonization strategy aligned with climate goals.

Suggested Citation

  • Yakhshilikov, Jamshid & Cavana, Marco & Inoyatkhodjaev, Jamshid & Giarola, Sara & Leone, Pierluigi, 2025. "Long-term energy and emissions outlook for the road transport sector of Uzbekistan: Insights from an agent-based energy model," Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:energy:v:339:y:2025:i:c:s0360544225047486
    DOI: 10.1016/j.energy.2025.139106
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    References listed on IDEAS

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    1. Hao Zhang & Peifeng Zhu & Zhichao Yao & Qingling Wang, 2023. "An Agent-Based Model to Simulate the Diffusion of New Energy Vehicles," Complexity, Hindawi, vol. 2023, pages 1-9, March.
    2. Bin Xue & Xu Han & Haoqi Zhu & Qingtai Li & Yu Zhang & Ming Bai & Ying Li & Yiran Li & Meng Qin & Tasuku Nakajima & Wei Wang & Jian Ping Gong & Yi Cao, 2025. "Hydrogels with prestressed tensegrity structures," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    3. repec:gam:jeners:v:18:y:2025:i:24:p:6490-:d:1815666 is not listed on IDEAS
    4. Zhewei Zhang & Jun Yao & Chenyang Shen & Fengfeng Li & Changshun Deng & Taotao Zhao & Xuefeng Guo & Yan Zhu & Xiangke Guo & Nianhua Xue & Luming Peng & Weiping Ding, 2025. "Dynamic activation catalysts for CO2 hydrogenation," Nature Communications, Nature, vol. 16(1), pages 1-8, December.
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