Exploration of determinants underlying regional disparity in rooftop photovoltaic adoption: A case study in Nagoya, Japan

Linwei Tao, Kiichiro Hayashi, Hiroto Shiraki, Xiaoxun Huang and Phub Dem

Applied Energy, 2024, vol. 367, issue C, No S0306261924008523

Abstract: The ongoing transition towards a regional strategy of rooftop photovoltaic (PV) promotion has prompted the examination of the mechanisms and drivers underlying spatial disparity of PV distribution. However, this regional investigation has been suffering from the scarcity of high-resolution PV location data and insufficient exploration of spatial heterogeneity in determinants at finer scales. This study proposed a comprehensive methodological flow to explore the heterogeneous determinants under diverse spatial distribution patterns of intra-city rooftop PV adoption at the census-block level: 1) establish a rooftop PV location dataset by applying deep learning techniques to aerial imagery and analyze its spatial distribution patterns; 2) summarize potential determinants from socioeconomic, technical, living environment, and demographic perspectives through literature review, aggregating data at a census-block level; and 3) examine the relationship between the determinants and spatial disparity in rooftop PV adoption using statistical regression models. The spatial analyses revealed an uneven rooftop PV distribution, characterized by spatial clusters with spatially varied determinants. PV adoption positively correlated with residential land, technical potential, building density, detached house ratio, population density, and age group of 0–19. Conversely, commercial land and age groups of 40–59 and 60–100 exhibited a negative impact. A bidirectional effect was observed in land price and age group of 20–39, whereas homeownership ratio and electricity expenditure were insignificant. Insights from this study will benefit local policymakers to scientifically tailor region-specific strategies, facilitating effective PV promotion for clean energy development.

Keywords: Regional rooftop PV adoption; Spatial distribution analysis; Determinant investigation; Negative binomial regression; Deep learning detection (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:367:y:2024:i:c:s0306261924008523

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DOI: 10.1016/j.apenergy.2024.123469

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