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Article Spatially-explicit models should consider real-world diffusion of renewable electricity: Solar PV example in Switzerland THORMEYER, Christoph, SASSE, Jan-Philipp, TRUTNEVYTE, Evelina Abstract Spatially-explicit bottom-up energy models with detailed renewable energy representation are increasingly developed. In order to inform such models, we investigate spatial diffusion patterns of solar PV projects in 2′222 Swiss municipalities. Using a dataset of feed-in tariff and one-time subsidy recipients in 2016, we show that PV diffusion was spatially uneven throughout Switzerland in terms of four indicators: the number of PV projects per municipality, per 1′000 inhabitants, per unit of municipal electricity demand, and per unit of municipal land area. Urban-rural divide and exploitable solar PV potential are the key, but not the only predictors of the spatial heterogeneity in PV diffusion. The structure of the municipal economy, socio-demographic characteristics, regional spillover effects, and additional differences in local contexts, such as local policies, matter as well. Spatial diffusion patterns to some extent structurally differ across sub-national regions too, indicating that such empirical investigations are valuable in order to understand what can be generalized. We conclude with recommendations for [...] Reference THORMEYER, Christoph, SASSE, Jan-Philipp, TRUTNEVYTE, Evelina. Spatially-explicit models should consider real-world diffusion of renewable electricity: Solar PV example in Switzerland. Renewable Energy, 2020, vol. 145, p. 363-374 DOI : 10.1016/j.renene.2019.06.017 Available at: http://archive-ouverte.unige.ch/unige:119232 Disclaimer: layout of this document may differ from the published version. 1 / 1 Spatially-explicit models should consider real-world diffusion of renewable electricity: solar PV example in Switzerland This article is forthcoming in Renewable Energy 2019 Authors: Christoph Thormeyer2, Jan-Philipp Sasse1,2, Evelina Trutnevyte1,2* 1 Renewable Energy Systems, Institute for Environmental Sciences (ISE), Section of Earth and Environmental Sciences, University of Geneva, Switzerland 2 Institute for Environmental Decisions (IED), Department of Environmental Systems Science, ETH Zurich, Switzerland * corresponding author (Uni Carl Vogt, Boulevard Carl Vogt 66, CH-1211 Geneva 4, Switzerland; +41 22 379 06 62; [email protected]) Abstract Spatially-resolved bottom-up energy models with detailed renewable energy representation are increasingly developed. In order to inform such models, we investigate spatial diffusion patterns of solar PV projects in 2’222 Swiss municipalities. Using a dataset of feed-in tariff and one-time subsidy recipients in 2016, we show that PV diffusion was spatially uneven throughout Switzerland in terms of four indicators: number of PV projects per municipality, per 1’000 inhabitants, per unit of municipal electricity demand, and per unit of municipal land area. Urban-rural divide and exploitable solar PV potential are the key, but not the only predictors of the spatial heterogeneity in PV diffusion. The structure of the municipal economy, socio-demographic characteristics, regional spillover effects, and additional differences in local contexts, such as local policies, matter as well. Spatial diffusion patterns to some extent structurally differ across sub-national regions too, indicating that such empirical investigations are valuable in order to understand what can be generalized. We conclude with recommendations for developing and validating spatially-resolved energy models that capture realistic patterns in solar PV diffusion: create, maintain and analyze spatial data on PV projects and develop robust modeling functions that do not only rely only on PV potential. Keywords Renewable energy diffusion, spatial analysis, solar PV, spatially-explicit energy models 1 Graphical abstract Highlights • Diffusion of solar PV has been spatially uneven in 2’222 Swiss municipalities • Urban-rural divide and exploitable PV potential are the key predictors of spatial PV diffusion • Structure of the municipal economy, socio-demographic factors, and regional spillover effects matter too • Spatial patterns are not identical for all sub-national regions, emphasizing context dependency • Spatially-explicit energy models should be adapted and validated against real- world renewable data 2 1. Introduction Bottom-up energy system models with detailed renewable energy representation are widespread tools for informing renewable energy expansion, infrastructure planning, and policy design [1-3]. Such models rely on technical, resource and environmental constraints that are coupled with economic and policy drivers of technology diffusion in order to quantify future transition pathways of the energy system as a whole and its renewable energy components in particular. In recent years, there has been a shift towards spatially-resolved energy system models in order to better represent the spatial heterogeneity of renewable energy availability and performance [4-7]. At the same time, a significant amount of evidence has been compiled, showing that energy system models have represented the actual deployment of renewable energy very poorly even at an aggregated rather than spatially-explicit level [8-10]. An often- voiced argument is that existing models with their technical, resource, environmental, economic and policy considerations may not capture the real-world drivers and constraints of renewable energy diffusion comprehensively enough. Spatially-explicit analysis of renewable energy resources has therefore increasingly included other aspects: such as public and stakeholder acceptance and preferences [11-13], regional socio-demographic variations [14-16], landscape conflicts and socio-cultural constraints [17-19], or the need for regionally equitable distribution [20-22]. All of these studies are forward looking in nature as they quantify the maximum exploitable potential or what-if scenarios of renewable energy diffusion in the future. Yet, none of these studies compare their quantifications with the real-world evidence of how renewable energy has actually diffused in space and whether energy modelling can adequately capture that. Conceptual literature argues that heterogeneity in local contexts and uneven socio- demographic situations create diversity in how energy transitions [23] as well as general sustainability transitions unfold [24-26]. Empirical investigation of spatial diffusion data of solar PV, wind, hydropower, and biogas in the UK has revealed the importance of socio- demographic factors, locally available technical expertise, and spillover effects [27]. Due to availability of large datasets, most other empirical investigations of spatial renewable energy diffusion have focused on solar PV. In the UK, socio-demographic factors (e.g. population density, education, and housing type), electricity demand, and local air pollution have been shown to explain regional diversity in solar PV uptake [28]. In Germany, socio-demographic factors, economic incentives, and especially spillover effects have been found significant [29]. In the United States, the relevant factors have been identified as solar irradiation, electricity costs, and available incentives [30]; as well as the house size, electric vehicle ownership, and 3 spillover effects [31]. In Switzerland, the pre-existence of recent PV projects nearby have been found to particularly increase the PV adoption rate by households and to some extent by firms [32]. This Swiss study has also shown that two components are important in the spillover phenomenon: learning (word-of-mouth peer effect) and imitation (due to visibility). A subsequent study [33] has further revealed the importance of learning: the proximity to a German-French language border in Switzerland was shown to be a cultural barrier for learning through word-of-mouth effect and to reduce the rate of PV adoption. Peer effects [34-36], local market formation [37], and psychological drivers [38] are the other factors mentioned in literature on solar PV diffusion in developed countries. In addition, in Sri Lanka, the size and quality of housing, available incentives, and education have been used to explain spatial PV deployment [39]. In this study, we present an empirical investigation of spatial diffusion of 11’545 solar PV projects in 2’222 Swiss municipalities. Using a dataset of feed-in tariff and one-time subsidy recipients in 2016, we analyze to what extent the differences in exploitable solar PV potential, electricity demand, municipal and socio-demographic characteristics, and electricity prices determine the spatial heterogeneity in PV deployment. First, we observe whether a distinction can be made between sub-national regions with high and with low number of PV projects per municipality, per 1’000 inhabitants, per unit of municipal electricity demand, and per unit of municipal land area, and then we investigate what characterizes this distinction. Second, we investigate what variables (e.g. PV potential, socio-demographic factors, electricity prices) could be used as proxies or predictors in energy models for spatial patterns of PV diffusion. Third, we check whether the extracted diffusion patterns are generalizable and transferable between large sub-national regions. In the end, we conclude with implications of our findings to how spatially-explicit bottom-up energy system models could be improved with detailed and realistic representation of renewable