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Exploring energy policy scenarios to transition to a low carbon economy by 2050: A case study on the Northern Territory of Australia

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  • Hosein, Ada Shereen
  • Whale, Jonathan
  • Simsek, Yeliz
  • Urmee, Tania

Abstract

Transition to a low carbon economy has a unique set of circumstances which present both challenges and opportunities for the development of its energy policies and require customised energy planning solutions and policies. Each country begins its path to decarbonization from a different starting point. The Northern Territory (NT) of Australia has one of the highest rates of solar insolation in Australia which can be a catalyst for future energy development. This study explores the potential for long-range renewable energy (RE) planning and climate policies for the NT to transition to a low-carbon economy by 2050, while achieving sustainable economic growth by analysing three scenarios: business-as-usual (BAU), High Industry Growth (HIG) and Renewable Energy Export (REE). The results showed that slight variations in inputs and assumptions can affect the need and timing of appropriate policies to support a desired outcome or goal. In conclusion, a low-emissions economy based on an industry powered by renewable energy is possible with the support of a range of effectively timed energy and climate policies. Effective energy transition policies would need to be developed with careful consideration of the unique circumstances and existing barriers in the NT. The future could be a combination of elements in both HIG scenario and a REE scenario. If implemented and sequenced appropriately, energy policies can be effective in laying the groundwork for a smoother transition to low-carbon economy in the NT.

Suggested Citation

  • Hosein, Ada Shereen & Whale, Jonathan & Simsek, Yeliz & Urmee, Tania, 2023. "Exploring energy policy scenarios to transition to a low carbon economy by 2050: A case study on the Northern Territory of Australia," Energy Policy, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:enepol:v:180:y:2023:i:c:s0301421523002483
    DOI: 10.1016/j.enpol.2023.113663
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    Cited by:

    1. Li, Guozhu & Ding, Chenjun & Zhao, Naini & Wei, Jiaxing & Guo, Yang & Meng, Chong & Huang, Kailiang & Zhu, Rongxin, 2024. "Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network," Energy, Elsevier, vol. 293(C).

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