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 energy diffusion.
2. Data and methods
2.1. Data
The dataset of solar PV projects that have received a feed-in tariff or one-time subsidy in Switzerland in 2011-2016 and have started operating in 2006 or later is used [40]. The feed- in tariff and one-time subsidy are support mechanisms for Swiss renewable electricity projects that supply electricity to the grid. The tariff compensates to the project owners the difference between the higher costs of renewable electricity generation and the lower reference price of
4 electricity in the market; the average market price in Switzerland in 2016 was 3.9 Rp./kWh (or about 3.9 US cents/kWh) [41]. The Swiss feed-in tariff for PV is generally differentiated by the type and capacity of technology and has been adapted throughout 2011-2016 in response to the falling costs of PV modules. In 2016, the PV projects had the average electricity generation costs of 10-18 Rp./kWh for 100kW systems, 14-26 Rp./kWh for 30kW, 18-31 Rp./kWh for 10kW, or 20-35 Rp./kWh for 6kW [42]. Hence, they received the feed-in tariffs of 16.4 Rp./kWh (1MW), 16.6 Rp./kWh (100kW, building-attached modules), 19.1 Rp./kWh (100kW, building-integrated modules), 19.5 Rp./kWh (30kW, building-attached modules), 22.4 Rp./kWh (30kW, building-attached modules), or a one-time subsidy of up to 30% of investment costs (10kW). The dataset includes 11’545 solar PV projects that in total constitute 544 MW of installed capacity and have produced 450 GWh of electricity in 2016. The dataset thus covers 34% of all solar PV generation in Switzerland [43]. The feed-in tariffs and one-time subsidies have been allocated on first-come first-served basis using technology-specific spending caps and have led to a long waiting list of operating solar PV projects; these waiting projects are therefore not in our dataset. Although the dataset does not cover all Swiss projects, it still includes a sufficiently large number for our investigation of federal policy-induced spatial patterns in PV diffusion. For the subsequent analysis, the projects are aggregated at the level of 2’222 Swiss municipalities using the location and postal codes of the project owners. The municipalities are the smallest independent administrative units in Switzerland, where socio-demographic and other municipal statistics are collected. Such a municipal-level aggregation therefore enables to account for the diversity in local contexts throughout Switzerland and is consistent with the resolution that is of interest for spatially-explicit energy systems models to inform policy making [21, 44, 45]. With the average area of 18 km² and the average diameter of 4.9 km, the municipalities are also small enough to allow a detailed spatial investigation. Figures SI1-4 in the Supplementary Information (SI) depict the spatial distribution and evolution of solar PV projects in 2011-2016 per municipality in total, per 1’000 inhabitants, per unit of estimated municipal electricity demand, and per unit of municipal land area. In order to investigate various factors that may contribute to spatial solar PV diffusion, the dataset is enriched with the data on exploitable solar PV potential, electricity demand, municipal and socio-demographic characteristics, and electricity prices. Exploitable PV potential [46, 47] is the amount of electricity that can be produced by solar PV, when considering the theoretical resource potential (i.e. solar irradiation), technical constraints (e.g. PV efficiency, availability and angle of roofs), and environmental and legislative constraints
5 (e.g. exclusion of heritage-protected buildings). Spatially-explicit estimates of solar PV potential are already publicly available for 80% of the Swiss buildings [48] and we used this data in our analysis. For the remaining 20%, we have applied the same methodology to estimate exploitable PV potential [48], using global annual irradiation as well as the areas, angles, and types of available rooftops. The total exploitable PV potential is thus estimated as 45.7 TWh/year in our analysis and it is equal to the estimates elsewhere [42], when excluding roofs below 10m2 and constraining the availability of roofs to 30-60% depending on the building type. The total and sectorial electricity demands per municipality are also added to the dataset from [21]. The municipal estimates have been calculated by downscaling the sectorial total electricity demand in Switzerland in 2016 [49, 50] in the basis of the number of inhabitants for the residential and transport demands, and the number of employees, electricity use intensities, and geographic location of 20 types of economic activities for industrial, commercial, and agricultural demands [49, 51]. Our total and sectorial electricity demands per municipality match well other existing estimates in Switzerland [52]. The dataset is also complemented with the latest municipal and socio-demographic data from the Swiss Federal Office of Statistics [53], including population size, population density, age distribution, average household size (all for the year 2014), the share of employees in the first, second, and third sectors, as well as social assistance rate (year 2013), the total land area, and the area shares for settlements, agriculture, forests, and unproductive land (year 2004/2009). The heterogeneity in political orientation is assessed using the data on party votes in the last National Council Elections [53] and aggregating these votes in three categories [16, 54]: liberal left (Social Democratic Party SP, Green Party GPS, Evangelical People's Party EVP, and Solidarity SoL), liberal right (Liberals FDP, Christian Democratic People's Party CVP, Green Liberal Party GLP, and Conservative Democratic Party BDP), and conservative (Swiss People's Party SVP and other small far right parties). On 21 May 2017, Switzerland has also held a referendum on the implementation of the Energy Strategy 2050 [55] that envisions extensive uptake of new renewable energy and energy efficiency. The municipal voting data are used here as a broad proxy of public support for the energy transition [56]. The electricity prices for 2016 [57] are included in the analysis for three types of typical consumers [58]: H4-catogery households with an annual electricity demand of 4.5 MWh/year (or approximately with 5 rooms, an electric stove, a tumbler, and without electric heating), C2- category small commercial business with the demand of 30 MWh/year and the maximum power load of 15 kW, and C5-category large industry with the demand of 500MWh/year, maximum load of 150 kW, medium voltage, and own transformer station. The total electricity prices are
6 considered without value added tax and, for detailed analysis, their energy and transmission and distribution (T&D) components are also considered separately. The prices of individual electricity utilities in Switzerland are matched to municipalities, using postcodes where these utilities are the primary suppliers [57].
2.2 Methods Two methods are used for investigating the spatial diffusion of solar PV projects in Switzerland: spatial analysis of hot spots and cold spots (Section 2.2.1) and step-wise regression (Section 2.2.2). These methods are applied on the afore-described spatially-explicit dataset (Section 2.1), using four types of indicators: the number of solar PV projects per municipality in total (Figure SI1), the number of PV project per 1’000 inhabitants (Figure SI2), per unit of estimated municipal electricity demand (Figure SI3), and per unit of municipal land area (Figures SI4). The number of PV projects was chosen as the primary variable because this is the most reliable kind of data in the dataset and because it is a proxy for general level of activity in each municipality, regardless of the installed capacity of these PV projects. The number of projects is also independent from the annual variations in electricity generation due to weather.
2.2.1. Spatial hot spots and cold spots of solar PV projects
In line with previous literature on regional spillover effects [27, 29, 31-33], some spatial grouping of solar PV projects throughout Switzerland can also be observed for all four of our 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 ( Figures SI1-SI4). We thus apply the ArcGIS hot spot analysis [59] that identifies statistically significant clusters of neighboring municipalities with high number of new PV projects per municipality (hot spots) and with low number of projects (cold spots). The areas that are neither hot spots nor cold spots are the areas where no non-random patterns in the number and proximity of PV projects can be statistically distinguished. After testing the various threshold distances for defining which municipalities shall be considered neighbors (Figures SI5-SI8), we have chosen the threshold distance of 10 km for the main analysis. This distance is about twice larger than the diameter of a Swiss municipality with an average area of 18 km2. The hot spot analysis is afterwards complemented with the calculation of Moran’s I, which is a metric for spatial autocorrelation. Finally, t-tests are conducted in order to understand what distinguishes the hot spots and cold spots from the other Swiss municipalities in terms of exploitable PV potential, electricity demand, socio-demographic and other municipal characteristics, and electricity prices.
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2.2.2. Stepwise regression
In order to explain the variance in spatial heterogeneity of solar PV diffusion using exploitable PV potential, electricity demand, socio-demographic and other municipal characteristics, and electricity prices, we conduct a stepwise regression. We have chosen stepwise regression in order to automatically select a set of predictive variables from our larger dataset from Section 2.1. The stepwise regression algorithm adds one variable after another by testing for the F-statistic and including into the regression model the variables that contribute the most to F-statistic at p<0.05. As the variables are selected automatically rather than based on theoretical considerations, they do not necessarily represent the drivers of the PV diffusion, but rather the elements that can be used for predicting this diffusion. Such predictive interpretation is especially useful in the modelling context because it can help identify small numbers of variables that could be used in order to model spatial PV diffusion more accurately. The comparable indicators of PV projects across the municipalities are chosen as the dependent variables: the number of PV projects per 1’000 inhabitants, per unit of municipal electricity demand, and per unit of municipal land area. These values are logarithmically transformed as log(y+1). After checking for the normality and heteroscedasticity of the residuals, ordinary least square regression is applied. In the absence of data on local policies or industrial innovation activities that could locally promote PV projects, we repeat the stepwise regression with 26 additional dummy variables that represent the Swiss cantons. In order to compare whether the regression findings from one sub-national region can be transferred to another one, the same procedure is repeated by additionally splitting the overall sample of Swiss municipalities into two of the large Swiss regions: German-speaking region (N=1’429) and French-speaking region (N=643). The independent predictive variables are chosen from the dataset in Section 2.1. The exploitable PV potential is recalculated per 1’000 inhabitants, electricity unit or land unit. The independent variables that describe the size of the municipalities, e.g. total electricity demand, total number of employees, or total land area, are excluded from the regressions because the comparative indicators are chosen as the dependent variables and because the size of a municipality is not a suitable unit to explain these comparative indicators. The sectoral electricity demand and the land use types are expressed in shares in order to be comparable across municipalities. All independent variables are standardized by subtracting the mean from each value and dividing it by the standard deviation for easier interpretation across many variables that differ substantially in units and orders of magnitude.
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3. Results
3.1 Spatial hot spots and cold spots of solar PV projects
Figure 1 shows the identified hot spots and cold spots in Switzerland in terms of the number of solar PV projects per municipality, per 1’000 inhabitants, per unit of municipal electricity demand, and per unit of municipal land area. Overall, it is visible that the spatial pattern in PV diffusion is uneven throughout Switzerland regardless the indicator used. The Swiss hot spots of above-average concentration of PV projects per municipality can be observed in the cantons of Glarus, St. Gallen, Appenzell Innerrhoden, Appenzell Ausserrhoden, Basel City, Luzern, Zug, and parts of Bern, Zurich, and Basel Land. Cold spots are located in the cantons of Vaud, Fribourg, Jura, and parts of Ticino. The spatial autocorrelation coefficient of Moran’s I is equal to 0.132 (p<0.001, 10km threshold distance). This value of Moran’s I therefore shows a measurable effect of spatial clustering because Moran’s I of 1 indicates perfect spatial clustering and Moran’s I of 0 shows perfect randomness. In principle, Figure 1 thus supports the findings in previous literature about the regional spillover effects beyond municipal boundaries [27, 29, 31]. To some extent, the hot spots in terms of the total number of PV projects per municipality in Figure 1 emerge from the interplay between the number of PV projects per municipality and the size of municipalities though. For example, in the south of Ticino or in the northwest of Vaud there is a larger cluster of very small municipalities and this could lead to the whole area being classified as a cold spot. We therefore conduct further hot spot analyses with comparative indicators per 1’000 inhabitants, per unit of electricity demand, and per unit of municipal land area.
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Figure 1. Spatial hot spots and cold spots of the number of solar PV projects in Switzerland in 2016, assuming the threshold distance of 10km
The spatial hot spots and cold spots in terms of PV projects per 1’000 inhabitants and per unit of electricity demand in Switzerland are relatively similar (Figure 1). This is not surprising because there are no large differences in composition of the electricity demand throughout Switzerland even if some areas are more industrialized than others [21]. Here, some smaller regional spillover effects can be observed again: Moran’s I is equal to 0.083 for PV projects per inhabitant and 0.074 for PV projects per unit of municipal electricity demand (in both cases at p<0.001, 10 km threshold distance). The hot spots in term of PV projects per 1’000 inhabitants or per unit of electricity demand are in the cantons of Bern, Solothurn, Basel Land and Graubünden, whereas the cold spots are in the densely populated cities of Zurich, Geneva and Basel City, as well as in less populated areas of Ticino and Schwyz. The spatial hot spots and cold spots in terms of PV projects per unit of municipal land area also hint to regional spillover effects with Moran’s I at 0.219 (p<0.001, 10 km threshold distance). Here the hotspots are in parts of the cantons of Bern, Basel Land, Aargau, Solothurn, Ticino, and Vaud, as well as in the urban cantons of Basel City, Geneva, and parts of Vaud.
10 Looking across all four indicators in Figure 1, the patterns are diverse, but parts of the cantons of Bern, Solothurn, Appenzell Ausserrhoden, and St. Gallen consistently appear as hot spots, whereas the south of the canton of Graubünden consistently appears as a cold spot. The large hot spots in terms of the total number of PV projects per municipality in the central and northeastern Switzerland and the cold spots in the cantons of Vaud and Jura cancel out, when looking at comparative indicators per 1’000 inhabitants or per unit of demand. The southern part of the canton of Ticino is a cold spot in terms of the number of PV projects per municipality, per 1’000 inhabitants, and per demand unit. Table 1 compares electricity demand, exploitable solar PV potential, municipal and other socio-demographic data, and electricity prices in these Swiss hot spots and cold spots from Figure 1 (95% and 99% confidence intervals only). 345 municipalities that are hot spots in terms of the number of PV projects per municipality also have statistically significantly higher number of projects per 1’000 inhabitants and per unit of municipal electricity demand or municipal land area. These hot spots in particular have higher exploitable PV potential, higher agricultural and industrial electricity demand, higher share of agricultural land, and lower share of forested land as compared to the areas that are neither hot spots nor cold spots. The cold spots in terms of the number of PV projects per municipality are characterized by statistically significantly lower exploitable PV potential, lower electricity demand of all types of consumers, lower share for settlement area, and higher share of unproductive land. The cold spots are also the areas with higher share of employees in the first sector like agriculture and forestry and higher social assistance rate. Their population has a higher share of children under 19 years old and a lower share of adults over 65 years old. Thus, the patterns in the Swiss hot spots and cold spots in terms of the number of PV projects per municipality can mostly be related to exploitable PV potential and the structure of electricity demand. The agricultural activity appears to be also important, but higher agricultural activity is characteristic to both hot spots and cold spots.
11 Table 1. Comparison of the average characteristics of the Swiss municipalities that are solar PV hot spots, cold spots and the municipalities with no non- random patterns in spatial PV distribution (99 % and 95 % confidence in Figure 1, 10 km threshold distance)
Number of PV projects Number of PV projects Number of PV projects Number of PV projects per municipality per 1’000 inhabitants per unit of electricity demand per municipal land area Parameter Units Hot Hot Hot Hot Other Cold spots Other Cold spots Other Cold spots Other Cold spots spots spots spots spots
Overall characteristics
Total number of municipalities municipality (m.) 345 1’526 351 294 1’751 177 294 1’780 148 579 1’337 306
Number of projects per municipality projects/m. 8.98*** 4.78 3.30*** 6.49 5.05 4.45 6.11* 5.09 4.68 6.66*** 5.04 3.10***
Number of projects per 1’000 inhabitants projects/1’000 inh. 3.43*** 2.35 2.30 2.99 2.13 1.80* 3.11 2.47 2.47 2.92 2.45 2.03
Number of projects per unit of municipal projects/GWh 0.629*** 0.467 0.485 0.563 0.494 0.394 0.596 0.486 0.403 0.570 0.481 0.415 electricity demand
Number of projects per unit of municipal projects/km2 0.778*** 0.577 0.438 0.638 0.584 0.527* 0.668 0.577 0.541 0.741 0.558 0.417 land area
Electricity demand and exploitable PV potential
Total electricity demand GWh/(yr·m.) 35.8 26.6 14.5** 33.9*** 23.3 41.4** 32.8*** 23.5 44.8*** 27.4 26.0 24.0
Residential electricity demand GWh/(yr·m.) 11.2 8.6 5.3* 9.7* 7.8 14.0*** 9.35* 7.87 14.9*** 8.78 8.50 8.05
Agricultural electricity demand GWh/(yr·m.) 0.67*** 0.43 0.33*** 0.54*** 0.44 0.36** 0.53*** 0.44 0.38* 5.29*** 4.47 3.14***
Industrial electricity demand GWh/(yr·m.) 12.6** 7.9 4.0*** 11.9*** 7.4 8.0 11.6*** 7.38 8.51 9.92* 7.59 6.21
Commercial electricity demand GWh/(yr·m.) 8.5 7.5 3.5* 9.27*** 5.8 15.6*** 9.0** 5.8 5.8*** 6.0 7.4 7.4
Transport electricity demand GWh/(yr·m.) 2.8 2.1 1.3* 2.45** 2.0 3.5*** 2.4* 2.0 3.8*** 2.2 2.1 2.0
Exploitable solar PV potential GWh/(yr·m.) 28.2* 20.4 13.9* 23.6*** 19.6 25.5** 22.3** 19.8 27.0** 22.0 20.9 16.5
Socio-demographic and other municipal characteristics
Population size inhabitants (inh.) 4’977 3’848 2’365 4’307** 3’455 6’232*** 4’158* 3’490 6’660*** 3’909 3’786 3’573
Population density inh./km2 455 439 328 380 414 587** 378 421 552 448 407 450***
Average household size inh./household 2.35 2.36 2.43** 2.32*** 2.38 2.39*** 2.32*** 2.38 2.37** 2.37** 2.38 2.43***
Total area km2 20.7 17.8 16.0 19.8 18.3 12.1*** 16.3* 18.7 13.0** 13.1*** 19.5 20.8
Share of settlement area % 15.0 15.3 12.4** 13.3 14.7 18.2*** 13.4 14.8 17.6 16.0 14.5 14.0
Share of agricultural area % 47.9* 44.6 50.0 45.2** 46.2 45.2** 45.9*** 46.1 45.1** 48.4*** 45.1 45.2***
Share of forested area % 30.6*** 33.6 29.4 34.6 32.0 33.3 35.2 32.0 33.9 32.5 32.9 30.8
12 Share of unproductive area % 6.5 6.5 7.7** 6.9 7.0 3.6*** 5.4*** 7.2 3.3*** 3.12*** 7.46 9.95***
Population share of 0-19 years old % 20.9* 20.7 23.1* 20.1 21.1 21.6*** 20.1 21.1 21.4*** 21.1** 20.9 21.5***
Population share of 20-64 years old % 60.4 61.0 60.7 60.8 60.8 61.5 60.7 60.8 61.4 61.0 60.9 60.6
Population share over 65 years old % 18.6 18.2 17.2*** 19.0*** 18.1 16.9 19.2*** 18.0 17.1 17.9*** 18.3 17.9
Social assistance rate % 1.76 1.78 1.82* 2.12*** 1.70 2.01 2.12*** 1.71 2.02 1.92 1.74 1.71*
Total number of employees employees/m. 2’920 2’402 1’156* 3’046*** 1’938 4’466*** 2’950*** 1’953 4’969*** 2’117 2’365 2’260
Share of employees in the first sector (raw 16.8 16.2 19.6 % 16.5 16.0 20.6*** 19.1** 16.8 13.7 20.3*** 16.6 13.1* material extraction)
Share of employees in the second sector % 27.0 26.4 23.2 27.2 26.7 22.8 27.1 26.2 23.3** 25.8 26.4 24.0* (manufacturing)
Share of employees in the third sector % 55.9 57.0 56.1* 53.2 56.6 62.9 53.4 56.7 62.7 56.0 57.0 56.7*** (services)
Political orientation and Energy Strategy 2050 vote
Liberal left % 21.1 23.3 27.2 21.9** 23.6 26.0*** 22.6 23.5 25.9 24.5** 22.7 25.5
Liberal right % 37.8 36.6 41.4 34.0 38.4 35.7*** 32.1 38.6 35.7 34.4 37.8 42.8
Conservative % 39.4 37.7 29.6*** 42.8 35.8 36.1* 43.7 35.7 35.8 39.4 37.0 30.5
Share of ‘yes’ votes for the Energy % 50.8*** 52.7 64.0* 48.2*** 54.9 56.4 48.2*** 55.0 56.1 51.3 53.7 61.6*** Strategy 2050
Electricity price
Total electricity price for H4 households Rp./kWh 21.53 20.70 21.65*** 22.23 20.95 19.25** 22.36 20.88 19.44** 21.12*** 20.83 21.37***
Total electricity price for C2 commercial Rp./kWh 20.78 20.12 20.93*** 21.43 20.30 19.03 21.56 20.25 19.13 20.48*** 20.20 20.76*** users
Total electricity price for C5 industrial Rp./kWh 15.74 15.76 18.99 15.63*** 16.37 16.37*** 15.57*** 16.38 16.29*** 15.60*** 16.16 18.03 users
Energy component in the price (H4) Rp./kWh 8.18** 7.97 8.80*** 8.45 8.12 7.71 8.50 8.10 7.78 8.09 8.05 8.57***
Energy component in the price (C2) Rp./kWh 8.16 8.10 9.02*** 8.54 8.24 7.98** 8.59 8.22 7.99 8.27 8.14 8.76*
Energy component in the price (C5) Rp./kWh 7.05 7.16 8.72*** 7.15 7.42 7.41* 7.19* 7.42 7.34* 7.14 7.29 8.30***
T&D component in the price (H4) Rp./kWh 10.98 10.41 10.18*** 11.41 10.42 9.36*** 11.51 10.37 9.48** 10.77** 10.39 10.20***
T&D component in the price (C2) Rp./kWh 10.39 9.77 9.26*** 10.67 9.73 8.89 10.78 9.69 8.99 10.05 9.75 9.44***
T&D component in the price (C5) Rp./kWh 6.78 6.58 7.77*** 6.64*** 6.82 6.87 6.56*** 6.83 6.88 6.56 6.77 7.36***
Note: Statistical significance when comparing hot spot (or cold spots) with the other municipalities that do not exhibit non-random spatial distribution of PV projects: * p≤0.05, ** p≤0.01, *** p≤0.001
13 The Swiss hot spots in terms of the number of PV projects per 1’000 inhabitants or per unit of estimated municipal electricity demand are also the areas with higher total, industrial and agricultural electricity demand and with higher share of population working in the first sector of raw materials extraction (agriculture and forestry). These hot spots yet have lower exploitable PV potential and total residential electricity demand. They also have higher share of population over 65 years old and higher social assistance rate as compared to the Swiss areas that are neither hot spots nor cold spots. The cold spots are characterized by statistically significantly lower total electricity demand, higher exploitable PV potential, but also higher population density, higher share of settlement area, lower share of productive land, and higher share of population under 19 years old. A similar pattern can be seen in terms of the number of PV projects per unit of municipal land area (Figure 1): the Swiss hot spots are municipalities with statistically higher agricultural and industrial electricity demand, higher share of agricultural land, and lower share of unproductive land than municipalities with no non-random patterns in spatial PV distribution. The cold spots are characterized by higher population density, lower agricultural electricity demand, lower share of agricultural land, as well as higher share of unproductive land. In sum, the findings hint again that the Swiss urban areas tend to be cold spots in terms of the number of PV projects per 1’000 inhabitants or per unit of municipal electricity demand. More rural areas that are especially active in agriculture are rather the hot spots in terms of both, the number of PV projects per 1’000 inhabitants and per unit of municipal electricity demand. The Swiss hot spots in terms of all four indicators of solar PV projects (Figure 1) have statistically significantly lower share of ‘yes’ votes for the energy transition that is envisioned in the Energy Strategy 2050. In the case of the number of PV projects per 1’000 inhabitants or per unit of electricity demand, the average share of ‘yes’ votes is even below the benchmark of 50%. The cold spots – the spatial aggregations of consistently lower number of PV projects – are statistically significantly more in favor of the Energy Strategy 2050. The cold spots also have higher share of liberal left and right voters, whereas the cold spots have lower share of conservative voters. The cold spots in terms of the number of PV projects per municipality and per unit of municipal land area are characterized by higher total electricity price and its energy and T&D components for industry as compared to the hot spots as well as to the other Swiss municipalities. In these cold spots, the total electricity price and its energy component tend to be higher for typical households and commercial users too, but the pattern is less clear because hot spots are also characterized by higher prices than the municipalities that are neither hot spots nor cold spots. In terms of the number of PV projects per 1’000 inhabitants or per unit of
14 municipal electricity demand, the cold spots have lower total electricity price and its T&D component for households, whereas the hot spots have lower total price and its T&D component for industry. In any case, we cannot observe a smooth and consistent pattern that higher electricity prices would be related to higher PV growth.
3.2. Stepwise regression
Table 2 shows the results of the stepwise regression in explaining the variance in the number of solar PV projects per 1’000 inhabitants as a result of exploitable solar PV potential, electricity demand, other municipal and socio-demographic characteristics, and electricity prices. In 12 iterations of stepwise regression, 43% of variance in the number of PV projects per inhabitant can be explained. Interestingly, the normalized solar PV potential per 1’000 inhabitants is not the primary predictive variable. The variation per inhabitant is, first of all, explained by the share of employees in the first sector of agriculture and forestry (positive effect) and the population density (negative effects). Both of these predictive variables account for 36% of the variance. Only then, the PV potential per inhabitant and the share of agricultural and forested land in the municipality contribute with positive effects, whereas the share of conservative voters and the share of commercial electricity demand contribute negatively. As noted in Section 3.1, the rural Swiss municipalities with heavy activity in the primary sector, especially agriculture, tend towards more PV projects per inhabitant as compared to the more urbanized areas. When cantons are added as dummy variables to the stepwise regression (Table SI2), the predictive power of the regression model increases slightly from 43% to 46%. The first four predictive variables remain the same as in Table 2 and afterwards the belonging of the municipality to several specific cantons starts contributing to predicting the variance: the cantons of Fribourg, Zurich, Schwyz, Ticino, Thurgau, and Nidwalden with negative effect, and the cantons of Solothurn, St. Gallen, and Appenzell Innerrhoden with positive effect. Some of these cantons are also identified, respectively, as hot spot or cold spots in terms of the number of PV projects per 1’000 inhabitants in Figure 1; the stepwise regression that is reported here does not consider spatial distribution.
15 Table 2. Stepwise regression results for the total number of PV projects per 1’000 inhabitants in all Swiss municipalities. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.260 0.356 0.380 0.404 0.412 0.420 0.424 0.428 0.430 0.432 0.435 0.437 Adj. R2 0.260 0.355 0.379 0.403 0.411 0.418 0.422 0.425 0.427 0.429 0.431 0.433 F-statistic 651.3 510.7 378.5 313.9 259.2 222.7 194.3 172.2 154.5 140.3 128.8 118.9 Regression constant -2.679*** -2.677*** -2.674*** -2.668*** -2.668*** -2.667*** -2.667*** -2.667*** -2.667*** -2.667*** -2.667*** -2.666*** Share of employees in the first sector, % .220*** .167*** .128*** .111*** .105*** .107*** .109*** .115*** .112*** .101*** .042 .046*
Population density, inhabitants/km2 -.127*** -.113*** -.115*** -.112*** -.113*** -.108*** -.109*** -.108*** -.102*** -.103*** -.103*** Solar PV potential, MWh/(year·inh.) .085*** .093*** .087*** .089*** .091*** .095*** .095*** .101*** .089*** .088*** Share of agricultural area, % .087*** .087*** .068*** .072*** .071*** .068*** .066*** .067*** .073*** T&D component in electricity price (H4), .037*** .039*** .067*** .073*** .089*** .088*** .086*** .084***
Rp./kWh Share of forested area, % .041*** .041*** .043*** .045*** .045*** .047*** .043*** Total electricity price (C2), Rp./kWh -.039*** -.046*** -.088*** -.089*** -.087*** -.091*** Share of conservative voters, % -.026*** -.024** -.026*** -.028*** -.027*** Energy component in electricity price (C2), .039** .039** .039** .042**
Rp./kWh Share of commercial electricity demand, % -.024** -.029*** -.030*** Share of agricultural electricity demand, % .069** .064** Population share over 65 years old, % .021** * p≤0.05, ** p≤0.01, *** p≤0.001
16 The results of the stepwise regression in terms of the number of PV projects per unit of municipal electricity demand (Table SI5) show that up to 54% of the variance can be predicted. As discussed in Section 3.1, the patterns here are similar to those for the number of PV projects per 1’000 inhabitants (Table 2), where agriculturally active municipalities again tend to have higher number of PV projects per demand unit. The share of employees in the first sector, the share of agricultural and forested land area, and the share of agricultural electricity demand have positive effects, whereas the population density has a negative effect. Exploitable solar PV potential comes as fourth in its predictive power, contributing merely with 1.5%-2% to explaining the variance. When cantons are added to the analysis as dummy variables, the predictive power of the stepwise regression model increases slightly to 57% (Table SI6). When a municipality is in the cantons of Fribourg, Zurich, Ticino, Schwyz, Geneva, Thurgau, Uri and Nidwalden, it has a negative effect on the number of PV projects per unit of municipal electricity demand. When the municipality is in the northwest cantons of Solothurn, St. Gallen, and Appenzell Ausserrhoden, this has a positive effect. The stepwise regression for the number of PV projects per unit of municipal land area (Table SI9) shows that up to 50% of variance can be explained in 13 stepwise iterations, especially by the exploitable PV potential per municipal land area as well as the shares of agricultural and forested land (all with positive effects). The municipalities with intensive agriculture also thus have a higher number of PV project per unit of municipal land area, even if the open-field PV is not considered in Switzerland due to social political constraints. The exploitable PV potential alone is the primary predictive variable, but it explains only 29% of the variance. The commercial electricity demand, the share of settlement areas or population density have negative effects. When cantons are added as dummy variables (Table SI10), the municipality’s belonging to some specific cantons have a lower number of PV projects per area unit: these cantons are Graubünden, Valais, Ticino, Fribourg, Uri, Jura, Vaud, Geneva, Schwyz, Zurich, Glarus, Obwalden, and Basel. Especially the cantons of Graubünden and Valais stand out because the respective dummy variables explain up to 4% of the variance.
3.3. Sub-national differences
The hot spot analysis in Figure 1 shows that solar PV projects have diffused differently across Switzerland, including its two large regions of the German- and French-speaking
17 municipalities. For example, in 1’429 German-speaking municipalities in central and northern Switzerland, there are 313 hot spot municipalities (at 99% and 95% confidence interval) in terms of the number of PV projects per municipality (22%) and only 73 cold spots (5%). In 643 French speaking municipalities in southwestern Switzerland, there are only 16 hot spots in terms of the number of PV projects per municipality (2%) and 275 cold spots (43%). The pattern is similar when analyzing the hot spots and cold spots in terms of PV projects per 1’000 inhabitants and per unit of municipal electricity demand: 19% of German-speaking municipalities are hot spots and 6% or 7% are cold spots, whereas 2% of the French-speaking municipalities are hot spots and 9% or 10% are cold spots. In terms of the number of PV projects per unit of municipal land area, 33% German-speaking municipalities and 17% of French- speaking municipalities are hot spots, and 6% of German-speaking municipalities and 31 % of French-speaking municipalities are cold spots. Having observed these regional differences, we additionally conduct stepwise regressions for German- vs. French-speaking sub-national regions in order to investigate to what extent the discovered predictive patterns in the spatial PV diffusion are transferable between the two regions. Table 3 compares the results of the stepwise regressions for the German- vs. French- speaking regions in terms of the number of PV projects per 1’000 inhabitants. Although the sample in both cases is smaller than in the main stepwise regression (Table 2), the predictive power increases both, for the German-speaking areas (45% as compared to 43%) and especially for the French-speaking regions (48%). But the predictive power is not as high as with cantons as dummy variables (47%; Table SI2). In all four models (Table 3), the same overall pattern can be observed: the agriculturally active municipalities have higher number of PV projects per 1’000 inhabitants and the densely populated municipalities have lower number of projects. The exploitable solar PV potential is the third, fourth or fifth predictive variable with positive effect, but it is not the primary variable. Other differences, such as voting behavior or electricity prices, play an even smaller role.
Table 3. Comparison of the stepwise regression outcomes for the number of solar PV projects per 1’000 inhabitants
Regression in 1’429 German- Regression in 643 French- Regression with cantons as Main regression (Table 2) speaking municipalities speaking municipalities dummy variables (Table SI2) (Table SI3) (Table SI4) R2=0.437 R2= 0.451 R2=0.490 R2=0.470 Adj. R2=0.433 Adj. R2= 0.446 Adj. R2=0.482 Adj. R2=0.464 + Share of employees in the + Share of agricultural + Share of employees in the - Population density first sector electricity demand first sector + Share of employees in the - Population density - Population density - Population density first sector + Solar PV potential + Share of agricultural area + Share of agricultural area + Solar PV potential
18 + Share of agricultural area + Solar PV potential - Share of liberal right voters + Share of agricultural area + T&D component in - Share of conservative voters + Solar PV potential - Canton of Fribourg electricity price (H4) + T&D component in + Share of forested area + Share of forested area - Canton of Zurich electricity price (H4) - Share of commercial -Total electricity price (C2) + Share of forested area + Canton of Solothurn electricity demand - Share of conservative - Share of commercial + T&D component in - Canton of Schwyz voters electricity demand electricity price (C5) + Energy component in + Population share over 65 + Canton of St. Gallen electricity price (C2) years old - Share of commercial - T&D component in + Share of forested area electricity demand electricity price (C2) + Share of agricultural + Share of ‘yes’ vote for - Canton of Ticino electricity demand Energy Strategy 2050 + Population share over 65 - Canton of Thurgau years old + Canton of Appenzell Innerrhoden - Canton of Uri + T&D component in electricity price (H4) + Share of transport electricity demand - Total electricity price (C2) - Canton of Nidwalden - Share of commercial electricity demand, Note: “+” marks positive effects of the variance in the independent variables on the dependent variable; “–” marks negative effects
In terms of the number of solar PV projects per unit of municipal electricity demand (Table 4), the region-specific stepwise regressions have slightly higher predictive power in the French-speaking municipalities at 60% as compared to 55% in the main regression. Here, the exploitable solar PV potential plays the most substantial role in both sub-national regions (with positive effect), whereas the share of electricity demand for transport sector is more decisive for the German-speaking municipalities and it is not a predictive variable for the French- speaking municipalities. In terms of the number of PV projects per unit of municipal land area (Table 5), the predictive power of the stepwise regression is particularly improved for the French-speaking municipalities (58%), but it is reduced for the German-speaking municipalities (50%) as compared to the main regression from Table SI9 (51%). The solar PV potential per area unit together with the share of agricultural land are the main predictors in all cases (positive effect). They also account for the increase in the predictive power of the model for the French-speaking municipalities. For instance, the exploitable PV potential and the share of agricultural land explain 49% of the variance in the French-speaking municipalities and the explain only 37%-39% in the other three models. When cantons are added as dummy variables to the stepwise regression, two of the most Alpine and sparsely populated cantons of Graubünden and Valais, as well as the urban cantons of Geneva and Zurich tend to have a lower number of PV projects per unit of municipal land area.
19 Table 4. Comparison of the stepwise regression outcomes for the number of solar PV projects per unit of electricity demand
Regression in 1’429 German- Regression in 643 French- Regression with cantons as Main regression (Table SI5) speaking municipalities speaking municipalities dummy variables (Table SI6) (Table SI7) (Table SI8) R2=0.547 R2= 0.549 R2=0.604 R2=0.575 Adj. R2=0.544 Adj. R2= 0.545 Adj. R2=0.598 Adj. R2=0.576 + Share of transport + Share of transport + Solar PV potential + Solar PV potential electricity demand electricity demand + Share of residential - Population density - Population density - Population density electricity demand + Share of transport + Solar PV potential - Population density + Solar PV potential electricity demand + Share of agricultural area + Share of agricultural area +Share of forested area + Share of agricultural area + T&D component in + Share of agricultural + Share of agricultural - Canton of Fribourg electricity price (H4) electricity demand electricity demand - Total electricity price (C2) - Share of conservative voters - Share of liberal right voters - Canton of Zurich + Share of agricultural + T&D component in +Share of agricultural area - Canton of Ticino electricity demand electricity price (H4) - T&D component in + Share of forested area + Share of forested area electricity price (C2) - Population share over 65 - Share of conservative voters + Canton of Solothurn years old + Energy component in + Share of forested area + Canton of St. Gallen electricity price (C2) + Share of industrial - Canton of Schwyz electricity demand + Population share over 65 + Share of agricultural years old electricity demand - Canton of Geneva + Canton of Appenzell Innerrhoden - Canton of Thurgau - Canton of Uri + T&D component in electricity price (H4) + Canton of Appenzell Ausserrhoden - Canton of Nidwalden - Total electricity price (C2) Note: “+” marks positive effects of the variance in the independent variables on the dependent variable; “–” marks negative effects
Table 5. Comparison of stepwise regression outcomes for the number of solar PV projects per unit of municipal land area
Regression in 1’429 Regression in 643 French- Regression with cantons as Main regression (Table SI9) German-speaking speaking municipalities dummy variables (Table municipalities (Table SI11) (Table SI12) SI10) R2=0.519 R2= 0.508 R2=0.587 R2=0.570 Adj. R2=0.515 Adj. R2= 0.503 Adj. R2=0.579 Adj. R2=0.564 + Solar PV potential + Solar PV potential +Solar PV potential + Solar PV potential + Share of agricultural area + Share of agricultural area + Share of agricultural area + Share of agricultural area - Share of commercial + Share of liberal left voters + Share of forested area - Canton of Graubünden electricity demand + Population share under + Share of forested area - Population density - Canton of Valais 19 years old - Share of agricultural + T&D component in - Share of agricultural - Share of settlements area electricity demand electricity price (C5) electricity demand, %
20 + Share of employees in the - Share of commercial + Share of liberal left voters - Canton of Ticino third sector electricity demand - Population density + Average household size - Population density + Share of forested area - Population share over 65 + Share of employees in the - Share of settlements area - Population density years old third sector - Share of liberal right + Share of employees in the - Share of commercial - Canton of Fribourg voters third sector electricity demand - Total electricity price (C5) + Share of forested area - Share of settlements area + T&D component in + Energy component in - Share of commercial electricity price (C5) electricity price (H4) electricity demand + Energy component in - Share of conservative + Share of employees in the electricity price (C2) voters third sector + Share of unproductive + Share of liberal left voters - Canton of Uri area - Share of unproductive - Canton of Jura area +Share of employees in the - Canton of Vaud second sector + Energy component in - Share of conservative electricity price (C5) voters - Canton of Geneva + Average household size - Canton of Schwyz + Share of liberal left voters - Canton of Neuchâtel - Canton of Zurich - Canton of Glarus + Canton of St. Gallen - Canton of Obwalden - Canton of Basel Stadt Note: “+” marks positive effects of the variance in the independent variables on the dependent variable; “–” marks negative effects
Spatial-explicit data on the cantonal and municipal policies to promote solar PV or other measures of energy transitions were not available for our analysis. The stepwise regression with cantons as dummy variables, therefore, can give hints about the potential role of such policies or local contexts. In the stepwise regressions for all three comparative indicators of the number of PV projects per 1’000 inhabitants, per unit of municipal electricity demand, and per unit of municipal land use area (Tables 3-5), it can be seen that the municipalities in the cantons of Fribourg, Zurich, Schwyz, and Ticino have consistently lower number of PV projects. The municipalities in the canton of St. Gallen tend to have a higher number of PV projects for all three indicators. For other cantons, the results vary depending on the indicator used. The observed pattern is not the same as in the hot spot analysis (Figure 1) because the stepwise regression does not account for the spatial correlations.
4. Discussion
21 Our empirical investigation of the feed-in tariff and one-time subsidy recipients among solar PV projects in Switzerland in 2016 reveals substantial spatial heterogeneity across the country. The total number of solar PV projects per 1’000 inhabitants varies from zero projects to 59 projects per 1’000 inhabitants (Figure SI2). Such large spatial variations can also be observed for the total number of PV projects per municipality (Figure SI1), or the relative number of PV projects per unit of municipal electricity demand (Figure SI3) or per unit of municipal land area (Figure SI4). We show that these spatial differences in most cases result from the urban-rural divide: the municipalities that are more involved in agriculture and forestry have higher number of PV projects on all our indicators, whereas the urban municipalities tend to have less PV projects. The exploitable solar PV potential – the main variable that is used in spatially-explicit bottom-up models – is also an important predictive variable, but it is secondary after this urban-rural divide except for the case of the number of PV projects per unit of municipal land area. Along the previous literature that emphasizes the importance of various socio-demographic and economic factors [27-31], we show that exploitable solar PV potential is still one of the most important predictors. As far as we are aware, our study is the first that took into account the exploitable PV potential after considering technical, environmental, and other constraints. Previous literature has been mostly limited to the theoretical potential, such as solar irradiation [30, 32, 33]. Further variables, such as the composition of the municipal electricity demand per sector, the share of employees by sector, or other socio-demographic characteristics explain further variance in solar PV diffusion too. Electricity prices and their energy or T&D components have been selected in some of the stepwise regression runs, but they would mostly come at late stages and explain relatively little additional variance. With the exception of exploitable PV potential, none of these independent variables are typically considered in spatially-explicit bottom-up energy models. Our methodological choice of the stepwise regression, however, allows us to conclude only about the predictive power and not about the explanatory power of these factors. Nonetheless, spatially-explicit models should not model the solar PV diffusion only on the basis of exploitable PV potential by scaling it according to the number of inhabitants, electricity demand, or land area. A much more detailed analysis that also considers the urban-rural divide, municipal and socio-demographic information needs to be conducted because otherwise modelled spatial PV patterns may be very different from the real-world spatial diffusion. Given the same federal-level policy to promote PV electricity, we show that some Swiss regions are still substantially faster in deploying PV projects than others. In the absence of systematic information about cantonal and municipal policies, active innovators in local markets, or other local imitative [29, 37, 39], generalizable patterns are at the moment difficult
22 to extract. Some of the cantons, however, can still be identified as rather systematically leading to higher or lower performance in terms of solar PV uptake (Tables 3-5). Our analysis also reveals large regional aggregations of solar PV hot spots and cold spots (Figure 1). These findings are to some extent in line with previous literature on regional spillover effects, especially when these spillover effects stretch beyond municipal and cantonal boundaries [29, 31-36]. In terms of the implications for the spatially-explicit models, we therefore conclude that PV diffusion patterns cannot be always directly transferred from one region to another without controlling for similarities and differences in the local contexts [15, 16]. Our empirical analysis has some limitations that could be addressed in future research. First, we only include the exploitable solar PV potential, electricity demand, municipal and socio-demographic characteristics, as well as electricity prices, but not the spatially-explicit data on PV installation costs, presence of local policies, and other characteristics of local markets. The cost variables are important to be considered in the future because they also exhibit spatial variation and hence may explain a portion of the spatial patterns observed. In 2016, the feed-in tariffs were uniform throughout the country for the same type and capacity class of PV plants. The productivity of PV plants differs across locations due to irradiation and hence affects the generation cost. The tariff paid by utilities for feeding electricity into the grid also varied in 2016 from below 4 Rp./kWh to above 20 Rp./kWh in 2016 [60]. Such data could be included in the dataset in the future in order to estimate the effects of economic drivers on spatial diffusion of PV projects. Second, our dataset is limited to the PV projects that are recipients of the feed-in tariff and one-time subsidy for electricity supplied to the grid in Switzerland in 2011-2016. The dataset does not include about 66% of all solar projects that were on the waiting list for the feed-in tariff or have never applied for it. In the future, the dataset could also be extended to cover all PV projects and other types of renewable electricity and heat plants in order to get a more holistic view on renewable energy. Third, our choice for stepwise regression is useful in order to automatically select the relevant predictors and proxies that can be used in models, but it does not guarantee that these predictors are the drivers of spatial diffusion. Future research should therefore apply theory-driven selection of explanatory variables and proceed with complete regression without conducting it stepwise. Fourth, like all other studies reviewed in this paper, we also investigate the PV diffusion in one country and its sub-national regions. Although we conclude with some new insights about the spatial patterns, we cannot interpret whether these patterns are applicable in other countries. The observed differences between German- and French-speaking regions show that extracted patterns cannot be unconditionally transferred even within the same country. Future research should thus create comparable
23 datasets and apply identical methodologies across several countries in order to draw internationally generalizable conclusions. Last, our analysis is descriptive because we investigate the spatial real-world patterns in how solar PV diffuses, but we do not explore the actual negative and positive impacts of uneven spatial diffusion. Future research could thus explore the distribution implications of spatial PV diffusion.
5. Conclusions and implications for spatially-explicit modes
In this paper, we present results of an empirical investigation of spatial diffusion of 11’545 solar PV projects that received feed-in tariff and one-time subsidy in Switzerland in 2016. We show that PV diffusion was spatially uneven throughout the country 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 or local energy markets, matter as well. Spatial diffusion patterns to some extent structurally differ across sub- national regions too and thus they cannot be directly transferred elsewhere without controlling for similarities and differences. Empirical investigations of real-world PV diffusion are therefore valuable in order to understand what can be generalized and where context-specific evidence is necessary. The recent trend to move from national-level bottom-up energy models to spatially- resolved models is meaningful because the observed large spatial heterogeneity in solar PV diffusion has associated impacts on generation costs or PV productivity. Theoretical or exploitable solar PV potential – a dimension that is considered in the majority of bottom-up energy models – is indeed one, but not the only key predictor of the number of PV projects. Our results show that, at least in the case of Switzerland, the practice of estimating the future spatial patterns of solar PV diffusion by proportionally scaling the PV potential on the basis of the number of inhabitants, estimated electricity demand or land area would not be sufficient to capture the real-world patterns and could yield inaccuracies. Other factors, especially the urban- rural divide and other municipal and socio-demographic characteristics, regional spillover effects, or local policies, should be considered too. If the well-known gap between modelled energy trends and real-world evolution of energy system is to be bridged [8-10] and if spatially accurate policy recommendations are to be drawn, the bottom-up models should represent the spatial heterogeneity of solar PV diffusion
24 more accurately. This could be done, for example, by at least applying different build rates for different sub-national regions or by modelling regional spillover effects. Broader aspects beyond technology, exploitable solar PV potential, economics, and national policy are notoriously difficult to quantify in the models though. Three research directions should be followed to improve that. First, similar empirical studies like ours should be conducted across many countries in order to develop robust quantitative functions of spatial PV diffusion or to at least broadly inform and validate spatially-explicit energy models. New modelling paradigms could then be explored in parallel, such as socio-technical energy transition models [9, 61] or qualitative-quantitative developments of socio-technical energy scenarios [62, 63]. Second, in order to conduct such empirical investigations, accessible data on the real-world spatial diffusion of solar PV need to be continuously collected. Some countries, like Switzerland, collect such data, but this is not universal globally. New methods that help to create solar PV databases from satellite imagery could therefore be applied [64, 65]. Third, such empirical evaluations should not be limited to solar PV only and it should be continuously collected, analyzed, and used to develop and validate spatially-explicit models for all renewable electricity and heat technologies.
Appendices Supplementary Information is available for this paper.
Acknowledgements This work was done in the scope of a Master thesis and, to some extent, was supported by the Swiss National Science Foundation Ambizione Energy grant No. 160563.
Competing interests The authors declare no competing interests.
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30 Spatially-explicit models should consider real-world diffusion of renewable electricity: solar PV example in Switzerland
Published 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])
Supplementary Information
S1
Figure SI1. The evolution of the number of solar PV projects per municipality in Switzerland in 2011-2016
S2
Figure SI2. The evolution of the number of solar PV projects per 1’000 inhabitants in Switzerland in 2011-2016
S3
Figure SI3. The evolution of the number of solar PV projects per unit of municipal electricity demand in Switzerland in 2011-2016
S4
Figure SI4. The evolution of the number of solar PV projects per unit of municipal land area in Switzerland in 2011-2016
S5
Figure SI5. Sensitivity of the outputs of hot spot analysis to the assumed threshold distance: the case of the number of solar PV projects per municipality in 2016
S6
Figure SI6. Sensitivity of the outputs of hot spot analysis to the assumed threshold distance: the case of the solar PV projects per 1’000 inhabitants in 2016
S7
Figure SI7. Sensitivity of the outputs of hot spot analysis to the assumed threshold distance: the case of the number of solar PV projects per unit of municipal electricity demand in 2016
S8
Figure SI8. Sensitivity of the outputs of hot spot analysis to the assumed threshold distance: the case of the number of solar PV projects per municipal land area in 2016
S9 Table SI1. Stepwise regression results for the total number of PV projects per 1’000 inhabitants in all Swiss municipalities. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.260 0.356 0.380 0.404 0.412 0.420 0.424 0.428 0.430 0.432 0.435 0.437 Adj. R2 0.260 0.355 0.379 0.403 0.411 0.418 0.422 0.425 0.427 0.429 0.431 0.433 F-statistic 651.3 510.7 378.5 313.9 259.2 222.7 194.3 172.2 154.5 140.3 128.8 118.9 Regression constant -2.679*** -2.677*** -2.674*** -2.668*** -2.668*** -2.667*** -2.667*** -2.667*** -2.667*** -2.667*** -2.667*** -2.666*** Share of employees in the first sector, % .220*** .167*** .128*** .111*** .105*** .107*** .109*** .115*** .112*** .101*** .042 .046*
Population density, inhabitants/km2 -.127*** -.113*** -.115*** -.112*** -.113*** -.108*** -.109*** -.108*** -.102*** -.103*** -.103*** Solar PV potential, MWh/(year·inh.) .085*** .093*** .087*** .089*** .091*** .095*** .095*** .101*** .089*** .088*** Share of agricultural area, % .087*** .087*** .068*** .072*** .071*** .068*** .066*** .067*** .073*** T&D component in electricity price (H4), .037*** .039*** .067*** .073*** .089*** .088*** .086*** .084***
Rp./kWh Share of forested area, % .041*** .041*** .043*** .045*** .045*** .047*** .043*** Total electricity price (C2), Rp./kWh -.039*** -.046*** -.088*** -.089*** -.087*** -.091*** Share of conservative voters, % -.026*** -.024** -.026*** -.028*** -.027*** Energy component in electricity price (C2), .039** .039** .039** .042**
Rp./kWh Share of commercial electricity demand, % -.024** -.029*** -.030*** Share of agricultural electricity demand, % .069** .064** Population share over 65 years old, % .021** * p≤0.05, ** p≤0.01, *** p≤0.001
S10 Table SI2 (part 1). Stepwise regression results for the total number of PV projects per 1’000 inhabitants in all Swiss municipalities with cantons as dummy variables. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.260 0.356 0.380 0.404 0.417 0.427 0.434 0.438 0.443 0.447 0.456 0.458 0.460 … Adj. R2 0.260 0.355 0.379 0.403 0.415 0.425 0.432 0.436 0.440 0.444 0.452 0.455 0.456 … F-statistic 651.3 510.7 378.5 313.9 263.9 229.4 201.9 180.0 162.6 149.0 140.2 129.7 120.7 … Regression constant -2.679 -2.677 -2.674 -2.668 -2.655 -2.643 -2.652 -2.649 -2.649 -2.655 -2.644 -2.638 -2.639 … Share of employees in the first sector, % .220 .167 .128 .111 .110 .108 .109 .109 .109 .113 .104 .102 .102 …
Population density, inhabitants/km2 -.127 -.113 -.115 -.116 -.113 -.111 -.112 -.112 -.112 -.110 -.110 -.110 … Solar PV potential, MWh/(year·inh.) .085 .093 .098 .093 .097 .096 .096 .097 .107 .110 .110 … Share of agricultural area, % .087 .093 .091 .088 .086 .086 .073 .061 .061 .061 … Canton of Fribourg -.186 -.196 -.187 -.190 -.190 -.173 -.179 -.185 -.184 … Canton of Zurich -.153 -.143 -.147 -.147 -.136 -.147 -.152 -.151 … Canton of Solothurn .145 .141 .141 .142 .129 .124 .125 … Canton of Schwyz -.251 -.251 -.232 -.238 -.244 -.243 … Canton of St. Gallen .143 .143 .135 .128 .130 … Share of forested area, % .032 .051 .050 .051 … Canton of Ticino -.209 -.215 -.214 … Canton of Thurgau -.104 -.102 … Canton of Appenzell Innerrrhoden .352 … * p≤0.05, ** p≤0.01, *** p≤0.001
S11 Table SI2 (part 2). Stepwise regression results for the total number of PV projects per 1’000 inhabitants in all Swiss municipalities with cantons as dummy variables. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 … 0.462 0.464 0.465 0.467 0.469 0.470 Adj. R2 … 0.458 0.459 0.460 0.462 0.463 0.464 F-statistic … 112.7 105.9 99.8 94.6 89.9 85.6 Regression constant … -2.637 -2.641 -2.640 -2.638 -2.637 -2.636 Share of employees in the first sector, % … .103 .101 .091 .091 .091 .083
2 Population density, inhabitants/km … -.110 -.110 -.108 -.104 -.105 -.101 Solar PV potential, MWh/(year·inh.) … .109 .106 .114 .115 .114 .118 Share of agricultural area, % … .060 .060 .058 .061 .060 .059 Canton of Fribourg … -.185 -.179 -.186 -.179 -.180 -.183 Canton of Zurich … -.153 -.129 -.132 -.140 -.144 -.145 Canton of Solothurn … .123 .120 .118 .110 .107 .104 Canton of Schwyz … -.245 -.243 -.243 -.254 -.258 -.252 Canton of St. Gallen … .128 .130 .135 .115 .111 .105 Share of forested area, % … .051 .051 .049 .047 .046 .046 Canton of Ticino … -.216 -.201 -.209 -.196 -.196 -.195 Canton of Thurgau … -.104 -.098 -.096 -.116 -.119 -.124 Canton of Appenzell Innerrhoden … .349 .358 .367 .345 .341 .337 Canton of Uri -.205 -.226 -.222 -.226 -.228 -.225 T&D component in electricity price (H4), .019 .020 .040 .041 .040
Rp./kWh Share of transport electricity demand, % .019 .022 .021 .020 Total electricity price (C2), Rp./kWh -.028 -.030 -.030 Canton of Nidwalden -.214 -.213 Share of commercial electricity demand, % -.018 * p≤0.05, ** p≤0.01, *** p≤0.001
S12 Table SI3. Stepwise regression results for the total number of PV projects per 1’000 inhabitants in German-speaking municipalities (N=1’429). The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.287 0.371 0.390 0.409 0.431 0.437 0.441 0.444 0.446 0.449 0.451 Adj. R2 0.287 0.370 0.389 0.407 0.428 0.434 0.437 0.440 0.442 0.445 0.446 F-statistic 508.9 372.3 268.6 217.5 190.3 162.6 141.3 125.1 112.4 102.1 93.5 Regression constant -2.668*** -2.659*** -2.651*** -2.642*** -2.616*** -2.619*** -2.617*** -2.616*** -2.617*** -2.617*** -2.607*** Population density, inh./km2 -.269*** -.194*** -.202*** -.182*** -.189*** -.187*** -.185*** -.179*** -.182*** -.177*** -.180*** Share of employees in the first sector, % .139*** .122*** .081*** .093*** .090*** .092*** .079*** .077*** .078*** .072*** Share of agricultural area, % .086*** .099*** .096*** .093*** .066*** .064*** .070*** .072*** .077*** Solar PV potential, MWh/(year·inh.) .089*** .110*** .102*** .107*** .116*** .114*** .114*** .118*** Share of conservative voters, % -.071*** -.071*** -.072*** -.075*** -.072*** -.070*** -.054*** T&D component in electricity price (H4), .031*** .032*** .032*** .026** .056*** .057*** Rp./kWh Share of forested area, % .043** .040** .035* .035* .037* Share of commercial electricity demand, % -.028** -.031** -.031** -.033** Population share over 65 years old, % .027* .028* .027* T&D component in electricity price (C2), -.035* -.035* Rp./kWh Share of ‘yes’ vote for Energy Strategy .033* 2050, % * p≤0.05, ** p≤0.01, *** p≤0.001
S13 Table SI4. Stepwise regression results for the total number of PV projects per 1’000 inhabitants in French-speaking municipalities (N=643). The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.260 0.365 0.410 0.445 0.460 0.477 0.484 0.490 Adj. R2 0.258 0.362 0.407 0.441 0.454 0.471 0.477 0.482 F-statistic 170.5 139.2 112.3 96.9 82.1 73.3 64.4 57.6 Regression constant -2.712*** -2.709*** -2.714*** -2.694*** -2.695*** -2.676*** -2.682*** -2.693*** Share of agricultural electricity demand, % .247*** .195*** .185*** .173*** .131*** .125*** .108*** .106***
Population density, inh./km2 -.092*** -.090*** -.097*** -.089*** -.086*** -.076*** -.079*** Share of agricultural area, % .093*** .093*** .089*** .062*** .062*** .057*** Share of liberal right voters, % -.092*** -.079*** -.073*** -.067*** -.058*** Solar PV potential, MWh/(year·inh.) .072*** .083*** .090*** .095*** Share of forested area, % .093*** .090*** .091*** Share of commercial electricity demand, % -.038* -.040** T&D component in electricity price (C5), .032*
Rp./kWh * p≤0.05, ** p≤0.01, *** p≤0.001
S14 Table SI5. Stepwise regression results for the total number of PV projects per unit of municipal electricity demand in all Swiss municipalities. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.321 0.416 0.489 0.505 0.520 0.527 0.532 0.536 0.536 0.541 0.543 0.545 0.546 0.547 Adj. R2 0.320 0.416 0.488 0.504 0.519 0.526 0.530 0.534 0.534 0.539 0.541 0.542 0.543 0.544 F-statistic 874.6 660.0 589.8 472.0 400.5 343.5 300.0 266.8 304.9 271.5 243.3 220.5 201.2 185.3 Regression constant -.416*** -.414*** -.412*** -.408*** -.403*** -.403*** -.404*** -.404*** -.404*** -.402*** -.402*** -.402*** -.402*** -.402*** Share of employees in the first sector, % .276*** .221*** .169*** .122*** .111*** .103*** .102*** .019 ------Share of transport electricity demand, % .152*** .150*** .133*** .121*** .128*** .132*** .143*** .145*** .139*** .137*** .136*** .160*** .163*** Population density, inh./km2 -.125*** -.114*** -.116*** -.113*** -.107*** -.108*** -.109*** -.110*** -.111*** -.110*** -.106*** -.106*** Solar PV potential, MWh/MWh .095*** .099*** .094*** .099*** .081*** .081*** .080*** .083*** .084*** .086*** .084*** Share of agricultural area, % .078*** .077*** .081*** .085*** .086*** .071*** .070*** .067*** .065*** .070*** T&D component in electricity price (H4), .041*** .075*** .072*** .072*** .073*** .078*** .093*** .092*** .090*** Rp./kWh Total electricity price (C2), Rp./kWh -.047*** -.045*** -.044*** -.043*** -.049*** -.092*** -.092*** -.095*** Share of agricultural electricity demand, % .097*** .114*** .119*** .125*** .123*** .130*** .131*** Share of forested area, % .036*** .038*** .040*** .041*** .038*** Share of conservative voters, % -.023** -.022** -.024** -.023** Energy component in electricity price (C2), .040** .039** .042** Rp./kWh Share of industrial electricity demand, % .031* .035* Population share over 65 years old, % .019* * p≤0.05, ** p≤0.01, *** p≤0.001
S15 Table SI6 (part 1). Stepwise regression results for the total number of PV projects per unit of municipal electricity demand in all Swiss municipalities with cantons as dummy variables. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.321 0.416 0.489 0.505 0.520 0.531 0.539 0.544 0.551 0.555 0.558 0.562 0.565 … Adj. R2 0.320 0.416 0.488 0.504 0.519 0.529 0.537 0.542 0.549 0.552 0.556 0.559 0.562 … F-statistic 874.6 660.0 589.8 472.0 400.5 347.9 307.9 275.5 251.3 229.4 211.8 196.5 183.6 … Regression constant -.416*** -.414*** -.412*** -.408*** -.403*** -.389*** -.377*** -.368*** -.364*** -.373*** -.380*** -.376*** -.378*** … Share of employees in the first sector, % .276*** .221*** .169*** .122*** .111*** .106*** .103*** .091*** .092*** .094*** .095*** .095*** .023 … Share of transport electricity demand, % .152*** .150*** .133*** .121*** .122*** .127*** .128*** .120*** .118*** .120*** .121*** .130*** … 2 Population density, inh./km -.125*** -.114*** -.116*** -.117*** -.113*** -.111*** -.111*** -.110*** -.110*** -.110*** -.111*** … Solar PV potential, MWh/MWh .095*** .099*** .110*** .107*** .119*** .125*** .127*** .127*** .126*** .110*** … Share of agricultural area, % .078*** .085*** .082*** .078*** .055*** .054*** .055*** .054*** .056*** … Canton of Friburg -.197*** -.208*** -.219*** -.207*** -.199*** -.193*** -.196*** -.177*** … Canton of Zurich -.152*** -.164*** -.162*** -.152*** -.144*** -.149*** -.147*** … Canton of Ticino -.177*** -.255*** -.243*** -.237*** -.240*** -.232*** … Share of forested area, % .048*** .045*** .047*** .046*** .047*** … Canton of Solothurn .122*** .129*** .125*** .126*** … Canton of St. Gallen .146*** .141*** .144*** … Canton of Schwyz -.232*** -.236*** … Share of agricultural demand, % .084*** … * p≤0.05, ** p≤0.01, *** p≤0.001
S16 Table SI6 (part 2). Stepwise regression results for the total number of PV projects per unit of municipal electricity demand in all Swiss municipalities with cantons as dummy variables. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 … 0.564 0.566 0.568 0.569 0.570 0.572 0.573 0.574 0.575
Adj. R2 … 0.562 0.563 0.565 0.566 0.567 0.568 0.569 0.569 0.570
F-statistic … 198.8 184.7 172.6 161.9 152.4 144.1 136.6 129.9 123.8 Regression constant … -.378*** -.374*** -.375*** -.370*** -.368*** -.372*** -.375*** -.373*** -.372*** Share of employees in the first sector, % … ------Share of transport electricity demand, % … .133*** .136*** .136*** .135*** .135*** .137*** .137*** .137*** .138*** 2 Population density, inh./km … -.112*** -.106*** -.106*** -.106*** -.106*** -.106*** -.106*** -.106*** -.105*** Solar PV potential, MWh/MWh … .109*** .109*** .110*** .112*** .111*** .108*** .107*** .106*** .108*** Share of agricultural area, % … .057*** .059*** .059*** .059*** .058*** .058*** .058*** .057*** .059*** Canton of Fribourg … -.174*** -.179*** -.179*** -.184*** -.185*** -.179*** -.176*** -.177*** -.171*** Canton of Zurich … -.147*** -.155*** -.154*** -.159*** -.160*** -.134*** -.131*** -.134*** -.139*** Canton of Ticino … -.234*** -.237*** -.237*** -.242*** -.243*** -.228*** -.225*** -.227*** -.216*** Share of forested area, % … .048*** .045*** .045*** .045*** .045*** .045*** .045*** .045*** .044*** Canton of Solothurn … .125*** .121*** .122*** .118*** .116*** .113*** .116*** .114*** .107*** Canton of St. Gallen … .145*** .140*** .142*** .136*** .134*** .137*** .140*** .138*** .122*** Canton of Schwyz … -.238*** -.242*** -.241*** -.246*** -.248*** -.245*** -.243*** -.245*** -.254*** Share of agricultural electricity demand, % .105*** .104*** .103*** .102*** .103*** .101*** .102*** .102*** .102***
Canton of Geneva -.149** -.149** -.153** -.154** -.136* -.134* -.136* -.107 Canton of Appenzell Innerrhoden .356** .351** .347** .357** .360** .357** .340* Canton of Thurgau -.085* -.086* -.080* -.077* -.079* -.095* Canton of Uri -.193* -.214* -.212* -.214* -.218* T&D component in electricity price (H4), Rp./kWh .019* .020* .019* .036* Canton of Appenzell Ausserrhoden .145* .143* .129 Canton of Nidwalden -.199* -.213* Total electricity price (C2), Rp./kWh -.023* * p≤0.05, ** p≤0.01, *** p≤0.001
S17 Table SI7. Stepwise regression results for the total number of PV projects per unit of municipal electricity demand in German-speaking municipalities (N=1’429). The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.325 0.415 0.485 0.508 0.522 0.537 0.543 0.545 0.547 0.549 Adj. R2 0.324 0.414 0.484 0.506 0.520 0.535 0.540 0.542 0.544 0.545 F-statistic 607.0 447.5 396.2 324.8 274.4 243.5 212.8 187.7 168.2 152.4 Regression constant -.369*** -.380*** -.375*** -.367*** -.375*** -.349*** -.352*** -.352*** -.350*** -.349*** Solar PV potential, MWh/MWh .340*** .242*** .168*** .163*** .082*** .107*** .100*** .100*** .100*** .103***
Population density, inh./km2 -.193*** -.196*** -.200*** -.189*** -.198*** -.195*** -.191*** -.192*** -.191*** Share of transport electricity demand, % .139*** .120*** .128*** .120*** .125*** .126*** .122*** .117*** Share of agricultural area, % .106*** .099*** .098*** .094*** .095*** .102*** .080*** Share of agricultural electricity demand, % .086*** .094*** .090*** .090*** .085*** .088*** Share of conservative voters, % -.067*** -.068*** -.066*** -.063*** -.064*** T&D component in electricity price (H4), .032*** .061*** .059*** .059***
Rp./kWh T&D component in electricity price (C2), -.035* -.037* -.037*
Rp./kWh Population share over 65 years old, % -.026* -.026* Share of forested area, % .035* * p≤0.05, ** p≤0.01, *** p≤0.001
S18 Table SI8. Stepwise regression results for the total number of PV projects per unit of municipal electricity demand in French-speaking municipalities (N=642). The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.373 0.459 0.527 0.553 0.578 0.592 0.604 Adj. R2 0.372 0.456 0.524 0.549 0.573 0.587 0.598 F-statistic 289.6 205.4 179.6 149.4 131.8 116.5 104.4 Regression constant -.454*** -.478*** -.471*** -.443*** -.430*** -.417*** -.425*** Solar PV potential, MWh/MWh .301*** .230*** .183*** .187*** .107*** .087*** .076***
Share of residential electricity demand, % .142*** .145*** .126*** .130*** .142*** .142*** Population density, inh./km2 -.085*** -.083*** -.077*** -.084*** -.085*** Share of forested area, % .120*** .119*** .110*** .075*** Share of agricultural electricity demand, % .126*** .128*** .131*** Share of liberal right voters, % -.071*** -.074*** Share of agricultural area, % .058*** * p≤0.05, ** p≤0.01, *** p≤0.001
S19 Table SI9 (part 1). Stepwise regression results for the total number of PV projects per unit of municipal land area in all Swiss municipalities. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.291 0.391 0.407 0.425 0.437 0.459 0.467 0.476 0.482 0.487 0.491 0.497 0.502 … Adj. R2 0.291 0.390 0.406 0.424 0.436 0.457 0.465 0.474 0.480 0.485 0.488 0.494 0.498 … F-statistic 760.8 594.4 424.0 341.9 287.1 260.8 231.0 209.9 190.7 175.3 161.8 151.6 142.5 … Regression constant -.399 -.380 -.383 -.385 -.383 -.389 -.389 -.390 -.391 -.392 -.394 -.395 -.395 … Solar PV potential, MWh/(year·km2) .262 .253 .221 .243 .239 .311 .307 .402 .395 .386 .376 .378 .378 …
Share of agricultural area, % .213 .227 .213 .184 .197 .198 .188 .176 .172 .178 .177 .180 … Share of agricultural electricity demand, % -.077 -.092 -.086 -.078 -.049 -.040 -.035 -.039 -.035 -.036 -.036 … Share of commercial electricity demand, % -.075 -.075 -.075 -.120 -.124 -.121 -.118 -.116 -.120 -.122 … Share of forested area, % .064 .109 .105 .099 .104 .097 .093 .097 .098 … Share of settlements area, % -.165 -.165 -.159 -.152 -.137 -.131 -.130 -.129 … Share of employees in the third sector, % .079 .087 .091 .095 .096 .106 .108 … Population density, inh./km2 -.103 -.100 -.102 -.094 -.092 -.092 … Population share over 65 years old, % -.043 -.043 -.041 -.051 -.051 … Share of liberal right voters, % -.040 -.038 -.028 -.030 … Total electricity price (C5), Rp./kWh -.032 -.045 -.100 … T&D component in electricity price (H4), .043 .040 …
Rp./kWh T&D component in electricity price (C5), .065 …
Rp./kWh * p≤0.05, ** p≤0.01, *** p≤0.001
S20 Table SI9 (part 2). Stepwise regression results for the total number of PV projects per unit of municipal land area in all Swiss municipalities. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 … 0.511 0.511 0.516 0.517 0.518 0.518 0.519 Adj. R2 … 0.508 0.508 0.512 0.513 0.514 0.514 0.515 F-statistic … 137.5 147.9 139.9 131.3 123.6 131.9 124.1 Regression constant … -.397 -.396 -.396 -.399 -.399 -.399 -.399 Solar PV potential, MWh/(year·km2) … .379 .379 .378 .375 .371 .371 .374 Share of agricultural area, % … .173 .173 .171 .171 .177 .176 .173 Share of agricultural electricity demand, % … -.041 -.041 -.030 -.031 .014 - - Share of commercial electricity demand, % … -.119 -.118 -.112 -.109 -.115 -.113 -.112 Share of forested area, % … .103 .103 .101 .106 .108 .108 .109 Share of settlements area, % … -.128 -.128 -.118 -.108 -.111 -.110 -.110 Share of employees in the third sector, % … .105 .102 .096 .093 .149 .133 .133 Population density, inh./km2 … -.095 -.096 -.107 -.106 -.102 -.102 -.103 Population share over 65 years old, % … -.048 -.046 -.046 -.044 -.045 -.045 -.042 Share of liberal right voters, % … -.027 -.029 -.018 -.017 -.017 -.017 -.016 Total electricity price (C5), Rp./kWh … -.240 -.251 -.273 -.272 -.268 -.269 -.350 T&D component in electricity price (H4), … .013 ------Rp./kWh T&D component in electricity price (C5), … .178 .189 .205 .204 .201 .202 .246 Rp./kWh Energy component in electricity price (C2), .096 .105 .102 .101 .100 .100 .079 Rp./kWh Share of liberal left voters, % .042 .040 .041 .040 .039 Share of unproductive area, % -.047 -.046 -.046 -.048 Share of employees in the second sector, % .049 .037 .038 Energy component in electricity price (C5), .070
Rp./kWh * p≤0.05, ** p≤0.01, *** p≤0.001
S21 Table SI10 (part 1). Stepwise regression results for the total number of PV projects per unit of municipal land area in all Swiss municipalities with cantons as dummy variables. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.291 0.391 0.419 0.434 0.452 0.464 0.489 0.497 0.507 0.514 0.521 0.529 0.534 … Adj. R2 0.291 0.390 0.418 0.433 0.450 0.462 0.487 0.495 0.504 0.512 0.518 0.526 0.531 … F-statistic 760.8 594.4 444.2 354.3 304.3 266.3 251.9 228.3 210.6 195.1 182.3 172.4 162.3 … Regression constant -.399*** -.380*** -.362*** -.348*** -.351*** -.339*** -.330*** -.332*** -.318*** -.325*** -.330*** -.331*** -.327*** … Solar PV potential, MWh/(year·km2) .262*** .253*** .241*** .230*** .197*** .203*** .201*** .300*** .300*** .339*** .357*** .362*** .358*** … Share of agricultural area, % .213*** .198*** .186*** .201*** .192*** .143*** .132*** .140*** .153*** .148*** .149*** .145*** … Canton of Graubünden -.409*** -.437*** -.431*** -.442*** -.437*** -.423*** -.436*** -.414*** -.367*** -.345*** -.354*** … Canton of Valais -.293*** -.316*** -.329*** -.327*** -.299*** -.312*** -.284*** -.261*** -.264*** -.272*** … Share of agricultural electricity demand, % -.080*** -.084*** -.077*** -.071*** -.073*** -.068*** -.077*** -.048*** -.048*** … Canton of Ticino -.276*** -.435*** -.468*** -.471*** -.430*** -.414*** -.414*** -.418*** … Share of forested area, % .099*** .097*** .092*** .116*** .116*** .112*** .112*** … 2 Population density, inh./km -.101*** -.104*** -.099*** -.100*** -.109*** -.107*** … Canton of Freiburg -.203*** -.201*** -.204*** -.203*** -.206*** … Share of settlements area, % -.099*** -.103*** -.103*** -.101*** … Share of commercial electricity demand, % -.049*** -.094*** -.092*** … Share of employees in the third sector, % .078*** .076*** … Canton of Uri -.437*** … * p≤0.05, ** p≤0.01, *** p≤0.001
Table SI10 (part 2). Stepwise regression results for the total number of PV projects per unit of municipal land area in all Swiss municipalities with cantons as dummy variables. The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 … 0.538 0.542 0.547 0.551 0.555 0.558 0.560 0.562 0.565 0.567 0.568 0.569 0.570 Adj. R2 … 0.535 0.538 0.543 0.546 0.550 0.553 0.555 0.557 0.560 0.561 0.562 0.563 0.564 F-statistic … 153.2 145.1 138.8 132.3 127.0 121.9 116.7 112.1 108.0 104.0 100.2 96.5 93.1
S22 Regression constant … -.320*** -.305*** -.286*** -.279*** -.276*** -.271*** -.271*** -.264*** -.253*** -.249*** -.256*** -.253*** -.252*** Solar PV potential, MWh/(year·km2) … .351*** .343*** .343*** .338*** .330*** .326*** .326*** .323*** .322*** .319*** .317*** .314*** .315*** Share of agricultural area, % … .142*** .150*** .145*** .150*** .142*** .141*** .142*** .139*** .138*** .137*** .140*** .138*** .139*** Canton of Graubünden … -.366*** -.382*** -.435*** -.441*** -.422*** -.429*** -.400*** -.409*** -.413*** -.421*** -.412*** -.422*** -.423*** Canton of Valais … -.282*** -.299*** -.368*** -.379*** -.369*** -.375*** -.345*** -.356*** -.362*** -.372*** -.362*** -.372*** -.374*** Share of agricultural electricity demand, % … -.050*** -.049*** -.039*** -.037*** -.044*** -.045*** -.043*** -.042*** -.045*** -.044*** -.042*** -.043*** -.043*** Canton of Ticino … -.424*** -.433*** -.455*** -.459*** -.437*** -.441*** -.418*** -.424*** -.433*** -.436*** -.428*** -.434*** -.435*** Share of forested area, % … .112*** .106*** .107*** .103*** .106*** .104*** .100*** .100*** .099*** .098*** .100*** .099*** .101*** Population density, inh./km2 … -.104*** -.102*** -.108*** -.097*** -.083*** -.081*** -.087*** -.088*** -.083*** -.084*** -.083*** -.081*** -.078*** Canton of Freiburg … -.212*** -.229*** -.268*** -.277*** -.306*** -.310*** -.324*** -.335*** -.346*** -.353*** -.345*** -.347*** -.348*** Share of settlements area, % … -.098*** -.090*** -.087*** -.084*** -.086*** -.083*** -.076*** -.073*** -.075*** -.072*** -.072*** -.071*** -.075*** Share of commercial electricity demand, % … -.089*** -.089*** -.085*** -.088*** -.078*** -.076*** -.072*** -.071*** -.071*** -.071*** -.071*** -.070*** -.070*** Zim3.sektorin … .068*** .072*** .064*** .067*** .065*** .066*** .063*** .060*** .064*** .063*** .064*** .063*** .064*** Canton of Uri … -.449*** -.462*** -.464*** -.465*** -.478*** -.486*** -.496*** -.504*** -.515*** -.519*** -.512*** -.515*** -.515*** Canton of Jura -.236*** -.249*** -.353*** -.363*** -.361*** -.363*** -.399*** -.428*** -.429*** -.447*** -.443*** -.444*** -.445*** Canton of Vaud -.096*** -.141*** -.154*** -.175*** -.180*** -.200*** -.214*** -.226*** -.234*** -.228*** -.229*** -.230*** Share of conservative voters, % -.047*** -.052*** -.048*** -.047*** -.036*** -.041*** -.037*** -.042*** -.040*** -.043*** -.044*** Canton of Geneva -.217 -.321 -.328 -.344*** -.352*** -.380*** -.383*** -.370*** -.371*** -.378*** Average household size, inh./household .044 .045 .051*** .051*** .054*** .054*** .052*** .051*** .051*** Canton of Schwyz -.257 -.233*** -.231*** -.246*** -.245*** -.234*** -.241*** -.242*** Share of liberal left voters, % .031** .037*** .038*** .041*** .044*** .040*** .039*** Canton of Neuchâtel -.211** -.220** -.232*** -.229*** -.228*** -.231*** Canton of Zurich -.093** -.093** -.085** -.086** -.089** Canton of Glarus -.540** -.536** -.539** -.540** Canton of St. Gallen .094* .089* .087* Canton of Obwalden -.271* -.271* Canton of Basel Stadt -.405* * p≤0.05, ** p≤0.01, *** p≤0.001
S23 Table SI11. Stepwise regression results for the total number of PV projects per unit of municipal land area in German-speaking municipalities (N=1’429). The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.275 0.373 0.407 0.440 0.454 0.475 0.483 0.489 0.496 0.502 0.504 0.506 0.508 Adj. R2 0.274 0.372 0.406 0.438 0.452 0.472 0.480 0.486 0.492 0.498 0.500 0.502 0.503 F-statistic 478.3 375.8 288.7 247.1 209.4 189.2 167.6 150.1 137.1 126.1 115.8 106.9 99.3 Regression constant -.334*** -.311*** -.287*** -.293*** -.296*** -.300*** -.292*** -.306*** -.308*** -.311*** -.305*** -.299*** -.299*** Solar PV potential, MWh/(year·km2) .278*** .272*** .230*** .481*** .435*** .431*** .415*** .434*** .445*** .460*** .463*** .475*** .466***
Share of agricultural area, % .220*** .217*** .195*** .212*** .195*** .192*** .225*** .229*** .194*** .187*** .185*** .190*** Share of liberal left voters, % .101*** .114*** .101*** .096*** .103*** .100*** .091*** .084*** .079*** .073*** .070*** Population density, inh./km2 -.301*** -.282*** -.258*** -.236*** -.207*** -.220*** -.219*** -.218*** -.232*** -.226*** Share of agricultural electricity demand, % -.069*** -.085*** -.094*** -.097*** -.071*** -.067*** -.075*** -.067*** -.067*** Share of commercial electricity demand, % -.077*** -.060*** -.065*** -.110*** -.103*** -.103*** -.107*** -.105*** Average household size, inh./household .063*** .059*** .054*** .058*** .065*** .061*** .055*** Share of settlements area, % -.144*** -.153*** -.187*** -.188*** -.191*** -.169*** Share of employees in the third sector, % .075*** .067*** .069*** .069*** .064*** Share of forested area, % .065*** .069*** .070*** .070*** Energy component in electricity price (H4), .024** .024** .023**
Rp./kWh Share of conservative voters, % -.026* -.029** Share of unproductive area, % -.047* * p≤0.05, ** p≤0.01, *** p≤0.001
S24 Table SI12. Stepwise regression results for the total number of PV projects per unit of municipal land area in French-speaking municipalities (N=642). The dependent variable has been logarithmically transformed and the independent variables have been standardized (Section 2.2.2).
R2 0.349 0.491 0.514 0.535 0.549 0.558 0.566 0.575 0.583 0.581 0.587 Adj. R2 0.348 0.488 0.511 0.531 0.544 0.552 0.560 0.568 0.575 0.574 0.579 F-statistic 260.4 233.5 170.8 138.9 117.2 101.1 89.5 81.1 74.1 83.0 75.5 Regression constant -.486*** -.495*** -.467*** -.577*** -.604*** -.601*** -.629*** -.616*** -.603*** -.577*** -.577*** Solar PV potential, MWh/(year·km2) .231*** .221*** .228*** .206*** .212*** .207*** .200*** .276*** .261*** .265*** .279***
Share of agricultural area, % .192*** .156*** .154*** .136*** .132*** .141*** .128*** .133*** .134*** .133*** Share of forested area, % .126*** .123*** .132*** .131*** .121*** .128*** .126*** .127*** .122*** Share of ‘yes’ vote for Energy Strategy .107*** .096*** .077*** .060* .054* .033 - -
2050, % Population share under 19 years old, % .066*** .069*** .070*** .065*** .067*** .069*** .062*** T&D component in electricity price (C5), .047** .047** .049*** .050*** .055*** .053***
Rp./kWh Share of liberal left voters, % .055** .059*** .064*** .070*** .072*** Population density, inh./km2 -.069*** -.068* -.070*** -.074*** Share of employees in the third sector, % .044* .051*** .083*** Share of commercial electricity demand, % -.056** * p≤0.05, ** p≤0.01, *** p≤0.001
S25 Table SI13. Comparison of the stepwise regression outcomes for the number of solar PV projects per unit of municipal electricity demand
Regression in German- Regression in French- Regression with cantons as Main regression (Table SI5) speaking municipalities speaking municipalities dummy variables (Table (Table SI7) (Table SI8) SI6) R2=0.547 R2= 0.549 R2=0.604 R2=0.575 Adj. R2=0.544 Adj. R2= 0.545 Adj. R2=0.598 Adj. R2=0.576 + Share of transport + Solar PV potential + Solar PV potential + Share of transport electricity demand electricity demand - Population density - Population density + Share of residential - Population density electricity demand + Solar PV potential + Share of transport - Population density + Solar PV potential electricity demand, + Share of agricultural area + Share of agricultural area +Share of forested area, + Share of agricultural area + T&D component in + Share of agricultural + Share of agricultural - Canton of Fribourg electricity price (H4) electricity demand electricity demand - Total electricity price (C2) - Share of conservative voters - Share of liberal right voters - Canton of Zurich + Share of agricultural + T&D component in +Share of agricultural area - Canton of Ticino electricity demand electricity price (H4) + Share of forested area - T&D component in electricity + Share of forested area price (C2) - Share of conservative voters - Population share over 65 + Canton of Solothurn years old + Energy component in + Share of forested area + Canton of St. Gallen electricity price (C2) + Share of industrial - Canton of Schwyz electricity demand + Population share over 65 + Share of agricultural years old electricity demand - Canton of Geneva + Canton of Appenzell Innerrhoden - Canton of Thurgau - Canton of Uri + T&D component in electricity price (H4) + Canton of Appenzell Ausserrhoden - Canton of Nidwalden - Total electricity price (C2) Note: “+” marks positive effects of the variance in the independent variables on the dependent variable; “–” marks negative effects
S26