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Modelling future uptake of solar photo-voltaics and water heaters under different government incentives

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  • Higgins, Andrew
  • McNamara, Cheryl
  • Foliente, Greg

Abstract

To accelerate the adoption of technologies to reduce energy consumption and greenhouse gas (GHG) emissions in the residential sector, government policy makers offer a range of fiscal instruments and incentives. Despite the high costs of these schemes, methods are lagging to systematically evaluate their likely effectiveness amongst a geographical landscape of heterogeneous consumers. To address this need, a model was developed for spatial adoption of technologies such as water heaters and solar photo-voltaic panels (PVs), across housing stock, given government policy incentives. By combining features of choice modelling, Multi-Criteria Analysis (MCA) and diffusion models, it provides a capability to analyse future adoption patterns of the competing technology options under a range of features for purchase timing and choice. The model was implemented across 2.7million residential dwellings in the State of New South Wales (NSW) of Australia to estimate future stock of PV and water heater options at geographical units of 250 households. Validation against actual numbers of PV installations at each postcode showed the model was effective at identifying high versus low adoption locations. Application to a wide range of policy scenarios, ranging from feed-in tariffs to upfront rebates, showed substantial differences in their effectiveness to accelerate uptake, and the government expenditure required.

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  • Higgins, Andrew & McNamara, Cheryl & Foliente, Greg, 2014. "Modelling future uptake of solar photo-voltaics and water heaters under different government incentives," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 142-155.
    • Handle: RePEc:eee:tefoso:v:83:y:2014:i:c:p:142-155
    DOI: 10.1016/j.techfore.2013.07.006
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    1. Karakaya, Emrah, 2016. "Finite Element Method for forecasting the diffusion of photovoltaic systems: Why and how?," Applied Energy, Elsevier, vol. 163(C), pages 464-475.
    2. Rezvani, S. & Bahri, P.A. & Urmee, T. & Baverstock, G.F. & Moore, A.D., 2017. "Techno-economic and reliability assessment of solar water heaters in Australia based on Monte Carlo analysis," Renewable Energy, Elsevier, vol. 105(C), pages 774-785.
    3. Chang, Keh-Chin & Lin, Wei-Min & Leu, Tzong-Shyng & Chung, Kung-Ming, 2016. "Perspectives for solar thermal applications in Taiwan," Energy Policy, Elsevier, vol. 94(C), pages 25-28.
    4. Higgins, Andrew & Grozev, George & Ren, Zhengen & Garner, Stephen & Walden, Glenn & Taylor, Michelle, 2014. "Modelling future uptake of distributed energy resources under alternative tariff structures," Energy, Elsevier, vol. 74(C), pages 455-463.
    5. Gautam, Abhishek & Chamoli, Sunil & Kumar, Alok & Singh, Satyendra, 2017. "A review on technical improvements, economic feasibility and world scenario of solar water heating system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 541-562.
    6. Karakaya, Emrah, 2014. "Finite Element Model of the Innovation Diffusion: An Application to Photovoltaic Systems," INDEK Working Paper Series 2014/6, Royal Institute of Technology, Department of Industrial Economics and Management.
    7. Rana, Anber & Sadiq, Rehan & Alam, M. Shahria & Karunathilake, Hirushie & Hewage, Kasun, 2021. "Evaluation of financial incentives for green buildings in Canadian landscape," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. van Blommestein, Kevin & Daim, Tugrul U. & Cho, Yonghee & Sklar, Paul, 2018. "Structuring financial incentives for residential solar electric systems," Renewable Energy, Elsevier, vol. 115(C), pages 28-40.

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