The most accurate estimate to date for the impact of the carbon price on retail electricity prices reveals the burden will vary considerably, depending on a household鈥檚 location.
Tasmanians, with a relatively low carbon footprint, are set to gain significantly from the carbon price once tax and pension changes are factored in, while Queenslanders 鈥� heavily dependent on coal to generate electricity 鈥斅爓ill wear the biggest increase in power prices, according to economic models run on 鈥渟upercomputers鈥� at .
The study estimates an average 8.9 per cent increase for retail electricity prices in the five eastern states, due to the carbon price 鈥斅燽elow the 10 per cent rise estimated by the federal . This runs counter to claims that Treasury has underestimated the impact of the carbon price on the economy.
Based on the , the GCI study produced the following estimated increases in retail electricity prices in the five eastern states covered by the .
National: 1.97c/KwH - up 8.9 per cent
Queensland: 2.15c/KwH - up 10.4 per cent
New South Wales: 1.99c/KwH - up 8.7 per cent
South Australia: 2.05c/KwH - up 8.6 per cent
Victoria: 1.94c/KwH - up 8.5 per cent
Tasmania: 0.79c/KwH - up 3.8 per cent
鈥淲hether or not these increases in prices are regarded as high or low is, of course, a matter of opinion," said , Program Leader Renewable Energy at the at 国产探花.
"However, these estimates are the most reliable currently available and should be the focus of ongoing debates and discussions about the household impact of the carbon price.鈥�
The wholly independent study, funded by the , was recently completed by the in 国产探花鈥檚 . It is the first study in Australia to employ an agent-based modelling methodology to closely simulate the behaviour of traders and generators operating in the national electricity market.
鈥淲ith the use of supercomputing power, this approach to modelling has produced very realistic simulations of economic behaviour and its effects, 鈥� Professor Foster said. 鈥淚t contrasts markedly with other popular modelling approaches that rely on sets of unrealistic assumptions that are included to make the modelling manageable.鈥�
Full technical details of the modelling can be found at
Enquiries: Professor John Foster, Director of the , Program Leader Renewable Energy, , ph +61 7 3365 6780, 0418 119 320 j.foster@uq.edu.au
Media: Robert Mackay-Wood, , ph +61 7 3346 9041, 0410 491 159 r.mackaywood@uq.edu.au
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