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Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan

Author

Listed:
  • Takanobu Kosugi

    (Ritsumeikan University)

  • Yoshiyuki Shimoda

    (Osaka University)

  • Takayuki Tashiro

    (Environment Policy Bureau, City of Kyoto)

Abstract

This study investigates the factors influencing the diffusion of residential photovoltaic systems. Factors examined are related to social attributes, such as population structure and living environment within neighborhoods and those close by, together with a neighbor effect revealed as a positive spatial dependency of the diffusion. To examine these factors simultaneously, the study applies a spatial econometric analysis, taking advantage of the availability of cumulative data on installed residential photovoltaic systems and census-based social attributes in about 4000 census blocks in Kyoto City, which include 1.47 million people. Results include: (1) an observed neighbor effect, especially between census blocks within a radius of 1000 m; (2) evidence that diffusion is positively influenced in a census block by lower population density and higher number of household members, as well as by lower ratios of detached houses and lower population densities in nearby census blocks; and (3) indication that diffusion is positively influenced by a higher proportion of young people through various mechanisms. To further facilitate the diffusion, implementing non-economic measures designed in light of the observed neighborhood influences is recommended, in addition to conventional economic support measures.

Suggested Citation

  • Takanobu Kosugi & Yoshiyuki Shimoda & Takayuki Tashiro, 2019. "Neighborhood influences on the diffusion of residential photovoltaic systems in Kyoto City, Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 21(4), pages 477-505, October.
    • Handle: RePEc:spr:envpol:v:21:y:2019:i:4:d:10.1007_s10018-019-00239-5
    DOI: 10.1007/s10018-019-00239-5
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    Cited by:

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    3. Jianhua Zhang & Xiaolong Liu & Dimitris Ballas, 2023. "Spatial and relational peer effects on environmental behavioral imitation," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 25(4), pages 575-599, October.
    4. Emily Schulte & Fabian Scheller & Wilmer Pasut & Thomas Bruckner, 2021. "Product traits, decision-makers, and household low-carbon technology adoptions: moving beyond single empirical studies," Papers 2112.11867, arXiv.org.
    5. Oleg Kucher & Donald Lacombe & Sean T. Davidson, 2021. "The Residential Solar PV in the Mid-Atlantic: A Spatial Panel Approach," International Regional Science Review, , vol. 44(2), pages 262-288, March.
    6. Fabian Scheller & Isabel Doser & Daniel Sloot & Russell McKenna & Thomas Bruckner, 2020. "Exploring the Role of Stakeholder Dynamics in Residential Photovoltaic Adoption Decisions: A Synthesis of the Literature," Energies, MDPI, vol. 13(23), pages 1-31, November.
    7. Zhang, Jianhua & Ballas, Dimitris & Liu, Xiaolong, 2023. "Neighbourhood-level spatial determinants of residential solar photovoltaic adoption in the Netherlands," Renewable Energy, Elsevier, vol. 206(C), pages 1239-1248.
    8. Stewart, Fraser, 2021. "All for sun, sun for all: Can community energy help to overcome socioeconomic inequalities in low-carbon technology subsidies?," Energy Policy, Elsevier, vol. 157(C).
    9. Tao, Linwei & Hayashi, Kiichiro & Shiraki, Hiroto & Huang, Xiaoxun & Dem, Phub, 2024. "Exploration of determinants underlying regional disparity in rooftop photovoltaic adoption: A case study in Nagoya, Japan," Applied Energy, Elsevier, vol. 367(C).
    10. Lekavičius, V. & Bobinaitė, V. & Galinis, A. & Pažėraitė, A., 2020. "Distributional impacts of investment subsidies for residential energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    11. Pronti, A. & Zoboli, R., 2024. "Something new under the sun. A spatial econometric analysis of the adoption of photovoltaic systems in Italy," Energy Economics, Elsevier, vol. 134(C).
    12. Stewart, Fraser, 2022. "Friends with benefits: How income and peer diffusion combine to create an inequality “trap” in the uptake of low-carbon technologies," Energy Policy, Elsevier, vol. 163(C).

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    More about this item

    Keywords

    Peer effects; Spatial autoregression; Solar photovoltaic power generation; Demographic structure; Living environment;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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