Conference Agenda
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Daily Overview |
| Session | |
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Renewable Energy 4: Evidence from Different Choice Modelling Location: B133 Session Chair: Kumai Kokash, University of Kassel | |
| Presentation 1 | |
Public support for solar power development in Japan: a choice experiment 1: Tokyo University of Marine Science and Technology, Japan; 2: Kyushu University, Japan Solar photovoltaic (PV) deployment is expanding rapidly in Japan, yet concerns about landscape change, disaster risk, and biodiversity impacts may undermine social acceptance. This study quantifies residents’ preferences for solar PV promotion policies using a discrete choice experiment. Choice tasks varied five attributes: visibility of solar panels within residential areas, changes in landslide risk, negative effects on wildlife, additional CO2 emission reductions (3–7%), and a monthly increase in the electricity bill (50–2,000 yen). We estimate a mixed logit model with random coefficients to capture preference heterogeneity and derive willingness-to-pay (WTP) measures. To examine observed heterogeneity, we regress individual-specific parameters on sociodemographic characteristics, and we assess geographic clustering in WTP using hot spot analysis. The results show strong aversion to localized externalities. Median WTP to avoid increased landslide risk and negative wildlife impacts is −47.6 yen/month and −41.3 yen/month, respectively. Respondents value climate benefits, with positive WTP for additional CO2 reductions (1.99 yen/month per percentage point). Landscape visibility has no significant average effect but exhibits substantial heterogeneity, indicating that visual impacts are highly context dependent. Cost produces the largest disutility and varies widely across individuals. Spatial analyses reveal non-uniform geographic patterns in WTP. The findings suggest that socially sustainable solar expansion requires stricter attention to disaster and ecological risks, transparent communication of expected CO2 reductions, and geographically targeted siting and mitigation strategies. | |

