Exploring preferences for household energy technology adoption from a spatiotemporal perspective: Evidence from demographics, the economy, and the environment

Wei Liu, Chuanmin Guo, Xuechen Gui, Dong Li and Xinyu Chen

Energy, 2025, vol. 319, issue C

Abstract: To understand the preference for heating and cooling technology (HCT) adoption and promote appropriate HCTs, this study conducts a comprehensive spatiotemporal analysis of HCT adoption in 300,043 houses between 2011 and 2019 across 108 city councils in New South Wales, Australia. First, this study establishes a driving indicator system with respect to demographics, economy, and environment. Next, this study employs a three-dimensional trend analysis to create spatial distribution trend maps and uses Moran's I to analyse spatial autocorrelations via ArcGIS to examine the spatiotemporal aspects of technology adoption. The results indicate that, over time, HCT adoption has stabilised and air conditioning is expected to continue as the primary choice in the future. Spatially, despite disparities in HCT adoption across cities, air conditioning remains the predominant choice. This study then uses ordinary least squares, geographically weighted regression and multiscale geographically weighted regression (MGWR) to establish the relationships between the driving indicators and HCT adoption rates via ArcGIS, with the results of the MGWR being the most significant. The MGWR results indicate significant impacts at various spatial scales. Among the 20 parameters examined, 16 exhibited significant effects, with the economic and environmental indicators having the most pronounced impacts. The MGWR results also emphasise the significance of socioeconomic contexts in different cities. In general, to encourage technology adoption, it is crucial to reduce adoption costs, increase green energy efficiency standards, and promote industrial upgrading. The results of this study can predict HCT adoption and offer valuable insights for future technology promotion recommendations.

Keywords: Household; Technology adoption; Spatiotemporal analysis; Driving indicator; Multiscale geographically weighted regression (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:319:y:2025:i:c:s0360544225005997

DOI: 10.1016/j.energy.2025.134957

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