During the past decade, Australian governments have widely promoted adoption of renewable energy technologies (RETs) and energy efficient measures (EEMs) by households and businesses as a way to reduce reliance on grid - supplied energy and shift energy use outside peak periods. However, it is not well understood what factors influence people taking up renewable energy technology and what leads to long-term changes in energy use.

The Alice Solar City (ASC) initiative was an important investment by the Australian Government and partner organisations during 2008–2013 to promote the adoption of solar energy technology and energy efficiency among households and business. The ASC was led by Alice Springs Town Council, and engaged with about 30% of Alice Springs households and over 200 businesses. The CRC-REP studied the role of the ASC in encouraging the adoption of solar energy technology and energy efficiency measures, and the subsequent impact on energy consumption.

This research study has analysed the implementation of energy efficiency measures (EEMs), solar renewable energy (RE) technologies and behaviour change initiatives brought about by the Alice Solar City (ASC) initiative. ASC was part of the Australian Government’s Solar Cities program, which trialled a range of innovative and sustainable energy solutions between 2008 and 2013. Households that registered for ASC received a home energy audit, from which a number of EEMs were recommended. Some of the EEMs were financially incentivised by ASC through energy efficiency vouchers (EEVs), which contributed to the cost of, among other things, households adopting photovoltaic (PV) technology or a solar hot water (SHW) system. Demographic information and data about household energy consumption were collected from participating households during their involvement in the program.

This study analysed the database repository of this information to discover the characteristics of ASC participants and what effects their participation in ASC and the EEMs they adopted had on their long-term energy use. The study found that households in freestanding houses, with fewer bedrooms, were more likely to be PV early adopters. The next strongest predictor of PV adoption was education level, with higher levels more likely to take up the technology. Demographic variables such as Aboriginal or Torres Strait Islander status, number of residents and the presence of children or the elderly in the household were only weakly correlated with PV adoption. There was a weak trend of increasing PV adoption with increasing income, which indicates that policy is best directed to the larger middle-income groups, which are only slightly less likely to take up PV energy, but who still have a greater effect on the total energy system.

The most heavily adopted EEV was SHW, with 908 participating households taking up the incentive from ASC and this resulting in a 10% fall in their energy use across the time they were in the program and over the longer term. The incentive for the PV was capped at 277 households, and that number was reached. This measure resulted in a net fall in electricity use of 34% for those households while they were in the program; this fall was also sustained over the longer term. The net change in energy use due to the PV system can be split into its components of energy production and energy consumption; in the long term, households increased their energy consumption by 6%, which was more than offset by the energy their PV system produced. This rebound effect of 15% observed for PV adopters (where a decrease in price results in an increase of consumption) is at the smaller range of rebounds observed in other studies. No rebound effect was observed for SHW adopters.

Economic analysis conducted showed that adoption of EEMs was not based on economic rational principles, with some heavily adopted EEMs having very long payback periods or negative internal rate of return. However, some EEMs were highly financially effective, if targeted to the appropriate households. The range of investment returns and the popularity of different incentivised EEMs offered through the program clearly indicate that economic effectiveness is only one consideration in EEM adoption. Other important considerations are popularity of product, perceived improvement in comfort, absolute up-front cost and support provided for the EEM adoption.

There was no statistically significant impact on electricity usage due to either the customer signing up to the ASC program or obtaining a personalised home energy audit. Overall, long-term change in energy use due to the ASC program was a net fall of 10%; a significant component of this is the adoption of PV and SHW. When these two items are excluded, the net fall in energy use due to participation in the ASC was 3%. While this 3% fall is not statistically significant when taken overall, some of the EEM-only adopters did have statistically significant reductions in energy use.

It is because these significant reductions are possible at the individual level that the program found that enhancing the ‘energy intelligence’ of motivated residents appears to be a critical prerequisite in the process of increasing energy efficiency of households. Carefully designing a package of small yet complementary changes can be an effective adaptation to improve the overall liveability for people in central Australia, particularly those living in remote communities. Understanding what drives different households to adopt RE technology will better inform strategies to ensure greater precision, and therefore effectiveness, in the targeting of future programs.