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

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.

[1]  Kenneth Gillingham,et al.  Peer Effects in the Diffusion of Solar Photovoltaic Panels , 2012, Mark. Sci..

[2]  Donna Heimiller,et al.  The transformation of southern California's residential photovoltaics market through third-party ownership , 2012 .

[3]  Johannes Rode,et al.  Does localized imitation drive technology adoption? A case study on rooftop photovoltaic systems in Germany , 2016 .

[4]  J. Richard Snape,et al.  Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid , 2016 .

[5]  Ritsuko Ozaki,et al.  Pro‐environmental products: marketing influence on consumer purchase decision , 2008 .

[7]  Samdruk Dharshing Household dynamics of technology adoption: A spatial econometric analysis of residential solar photovoltaic (PV) systems in Germany , 2017 .

[8]  N. Tanaka,et al.  Technology Roadmap- Nuclear Energy , 2010 .

[9]  Ziqiang Zhou,et al.  Spatio-Temporal Analysis and Forecasting of Distributed PV Systems Diffusion: A Case Study of Shanghai Using a Data-Driven Approach , 2017, IEEE Access.

[10]  A. Palm Local factors driving the diffusion of solar photovoltaics in Sweden: A case study of five municipalities in an early market , 2016 .

[11]  Nazmiye Balta-Ozkan,et al.  Regional distribution of photovoltaic deployment in the UK and its determinants: A spatial econometric approach , 2015 .

[12]  J. LeSage Introduction to spatial econometrics , 2009 .

[13]  Eiichi Endo Analysis of Dissemination of Residential PV Systems in Japan , 2014 .

[14]  Kenneth Gillingham,et al.  Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment , 2015 .

[15]  Ken’ichi Matsumoto,et al.  Effect of Subsidies on Introducing Residential Photovoltaic Systems , 2014 .

[16]  Carol Atkinson-Palombo,et al.  Peer effects in the adoption of solar energy technologies in the United States: An urban case study , 2019, Energy Research & Social Science.

[17]  E. Rogers Diffusion of Innovations , 1962 .

[18]  Calvin Lee Kwan,et al.  Influence of local environmental, social, economic and political variables on the spatial distribution of residential solar PV arrays across the United States , 2012 .

[19]  Roger Bivand,et al.  Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods , 2013 .

[20]  Shigeyuki Hamori,et al.  Impact of subsidy policies on diffusion of photovoltaic power generation , 2011 .

[21]  Sven Müller,et al.  The adoption of photovoltaic systems in Wiesbaden, Germany , 2013 .

[22]  Axel J. Schaffer,et al.  Beyond the sun—Socioeconomic drivers of the adoption of small-scale photovoltaic installations in Germany , 2015 .

[23]  Andrew O. Finley,et al.  spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models , 2013, 1310.8192.

[24]  V. Rai,et al.  Effective information channels for reducing costs of environmentally- friendly technologies: evidence from residential PV markets , 2013 .

[25]  Robert Margolis,et al.  Modeling photovoltaic diffusion: an analysis of geospatial datasets , 2014 .

[26]  Varun Rai,et al.  Solar Community Organizations and active peer effects in the adoption of residential PV , 2014 .

[27]  Roger Bivand,et al.  Comparing Implementations of Estimation Methods for Spatial Econometrics , 2015 .