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Coherent estimations for residential photovoltaic uptake in Germany including spatial spillover effects

Author

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  • Jan Paul Baginski

    ()

  • Christoph Weber

    () (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen (Campus Essen))

Abstract

The share of solar energy in German electricity generation has increased strongly over recent years. This is largely due to guaranteed feed-in tariffs together with decreasing prices for solar panels. Residential PV systems play a decisive part providing households with a possibility to contribute to the Energiewende and benefit from the use of renewable energy. Their regional distribution varies distinctly across Germany implying different requirements in distribution grids as well as uneven utilization of national policy measures. Our paper focusses on the spatial diffusion of roof mounted PV systems and the underlying drivers in Germany. We extend previous findings not only by including additional explanatory variables but also by considering cross-regional spillover using spatial econometric models. Estimation results show that spatial dependence is a relevant determinant for explaining regional clusters of PV adoption. Recurrent visual perception or peer-effects might explain spatial autocorrelation as potential adopters follow decisions by actors in the proximity. Another reason for spatial dependence might be a concentration of craft skills or solar initiatives, which leads to an accelerated diffusion in a region and its surroundings. Whereas the first explanation corresponds to the specification of a spatial lag model, the latter is in line with a spatial error specification. However, our results indicate that although spatial lag is present, spatial dependence in the residuals has higher explanatory power. Hence, we suppose that spatial spillover is not mainly driven by social imitation but by unobserved regional characteristics. Notably, high values for solar radiation, the share of detached houses, electricity demand and inverse population density of a region favour the PV uptake.

Suggested Citation

  • Jan Paul Baginski & Christoph Weber, "undated". "Coherent estimations for residential photovoltaic uptake in Germany including spatial spillover effects," EWL Working Papers 1902, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
  • Handle: RePEc:dui:wpaper:1902
    as

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    References listed on IDEAS

    as
    1. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.
    2. Dastrup, Samuel R. & Graff Zivin, Joshua & Costa, Dora L. & Kahn, Matthew E., 2012. "Understanding the Solar Home price premium: Electricity generation and “Green” social status," European Economic Review, Elsevier, vol. 56(5), pages 961-973.
    3. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    4. repec:eee:enepol:v:109:y:2017:i:c:p:270-278 is not listed on IDEAS
    5. De Groote, Olivier & Pepermans, Guido & Verboven, Frank, 2016. "Heterogeneity in the adoption of photovoltaic systems in Flanders," Energy Economics, Elsevier, vol. 59(C), pages 45-57.
    6. Allan, Grant J. & McIntyre, Stuart G., 2017. "Green in the heart or greens in the wallet? The spatial uptake of small-scale renewable technologies," Energy Policy, Elsevier, vol. 102(C), pages 108-115.
    7. Rode, Johannes & Weber, Alexander, 2016. "Does localized imitation drive technology adoption? A case study on rooftop photovoltaic systems in Germany," Journal of Environmental Economics and Management, Elsevier, vol. 78(C), pages 38-48.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    residential photovoltaic; spatial econometrics; spatial spillover;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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