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Nuclear power can reduce emissions and maintain a strong economy: rating Australia’s optimal future electricity-generation mix by technologies and policies


Hong, S and Bradshaw, CJA and Brook, BW, Nuclear power can reduce emissions and maintain a strong economy: rating Australia's optimal future electricity-generation mix by technologies and policies, Applied Energy, 136 pp. 712-725. ISSN 0306-2619 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Elsevier Ltd.

DOI: doi:10.1016/j.apenergy.2014.09.062


Legal barriers currently prohibit nuclear power for electricity generation in Australia. For this reason, published future electricity scenarios aimed at policy makers for this country have not seriously considered a full mix of energy options. Here we addressed this deficiency by comparing the life-cycle sustainability of published scenarios using multi-criteria decision-making analysis, and modeling the optimized future electricity mix using a genetic algorithm. The published ‘CSIRO e-future’ scenario under its default condition (excluding nuclear) has the largest aggregate negative environmental and economic outcomes (score = 4.51 out of 8), followed by the Australian Energy Market Operator’s 100% renewable energy scenario (4.16) and the Greenpeace scenario (3.97). The e-future projection with maximum nuclear-power penetration allowed yields the lowest negative impacts (1.46). After modeling possible future electricity mixes including or excluding nuclear power, the weighted criteria recommended an optimized scenario mix where nuclear power generated >40% of total electricity. The life-cycle greenhouse-gas emissions of the optimization scenarios including nuclear power were <27 kg CO2-e MW h−1 in 2050, which achieves the IPCC’s target of 50–150 kg CO2-e MW h−1. Our analyses demonstrate clearly that nuclear power is an effective and logical option for the environmental and economic sustainability of a future electricity network in Australia.

Item Details

Item Type:Refereed Article
Keywords:future electricity mix, genetic algorithm, nuclear power, renewable energy, decarbonization
Research Division:Engineering
Research Group:Resources engineering and extractive metallurgy
Research Field:Nuclear engineering (incl. fuel enrichment and waste processing and storage)
Objective Division:Energy
Objective Group:Energy transformation
Objective Field:Nuclear energy
UTAS Author:Hong, S (Dr Sanghyun Hong)
UTAS Author:Brook, BW (Professor Barry Brook)
ID Code:97917
Year Published:2014
Web of Science® Times Cited:21
Deposited By:Biological Sciences
Deposited On:2015-01-21
Last Modified:2018-04-04

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