Spatial Dependence of Solar Photovoltaic Systems: Data Gathering

Process, Related Issues and Preliminary Results

Sergio Copiello

Department of Design and Planning, IUAV University of Venice, Dorsoduro 2206, Venice, Italy

Keywords: Solar Photovoltaic, Spatial Energy, Spatial Data, Peer Effects, Neighborhood Effects.

Abstract: In a previous study (Copiello and Grillenzoni, En. Proc., 2017), we have proven the solar photovoltaic

capacity in Italy to be characterized by spatial dependence. In that research, the units of analysis were the

Italian provinces, which correspond to level 3 of the European NUTS (Nomenclature of territorial units for

statistics) classification. Here we focus on new data encoded according to the Italian townships, namely, the

municipalities corresponding to level 2 of the European LAU (Local administrative units) classification.

The change of scale is a huge challenge, due to both the difficulty to find reliable information and the time-

consuming definition of the proximity structure of the units: while the provinces are about 100, the Italian

municipalities are several thousands, and each one shares the borders with many others. In particular, three

neighboring regions - Veneto, Trentino-Alto Adige, and Friuli-Venezia Giulia, in North-eastern Italy - and

their 1,121 towns are considered in this study, which primarily aims to delve into the issues related to the

data gathering process. As far as the preliminary findings are concerned, we find more clues about the role

played by the so-called neighborhood and peer effects.

1 INTRODUCTION

During the last four decades, in the Western

economies, the energy production and consumption

model has faced several changes, which imply that

producers and consumers have experienced shifts in

the energy mix. For instance, it deserves mentioning

the progressive substitution of oil products with

natural gas, which nowadays is the primary source to

produce electricity, as well as to heat buildings, in

several countries (Copiello, 2017). Moreover, it is

worth recalling the ongoing transition toward the

renewables. Under this framework, the last ten years

have seen a sizeable increase in the amount of solar

photovoltaic (PV) generation, which is about to

supply a 10% share of the primary energy used in

the residential sector (Copiello, 2017). The upward

trend in PV energy production is expected to go on

during the next years. According to the Short-Term

Energy Outlook published by the Energy

Information Administration (July 2017), in the U.S.,

the large-scale PV electricity generation should

increase by 38% in 2017 and 19% in 2018, while the

small-scale PV electricity generation will experience

a growth of 32% and 29%, respectively. As far as

long-term trends are concerned, the 2014 edition of

Technology Roadmap: Solar Photovoltaic Energy

published by the International Energy Agency

envisions that 16% of total electricity generation will

be met by PV systems in 2050, in comparison to 2%

in 2020 and 7% in 2030.

The ever-greater role played by PV systems has

drawn the attention of the scholarly research, which

has been engaged in analyzing the determinants of

their adoption and deployment. Following a

promising research strand focusing on neighborhood

and peer effects, in a previous study we proven the

spatial dependence that characterizes the installation

of PV capacity in Italy (Copiello and Grillenzoni,

2017b). In that research, the units of analysis were

the Italian provinces, which correspond to level 3 of

the European NUTS (Nomenclature of territorial

units for statistics) classification. Here we focus on

new data encoded according to the Italian townships,

namely, the municipalities corresponding to level 2

of the European LAU (Local administrative units)

classification. In particular, three neighboring

regions - Veneto, Trentino-Alto Adige, and Friuli-

Venezia Giulia, in North-eastern Italy - and their

1,121 towns are considered (Figure 1). The dataset

consists of all the PV systems that have been

installed - both by households and companies, on the

Copiello, S.

Spatial Dependence of Solar Photovoltaic Systems: Data Gathering Process, Related Issues and Preliminary Results.

DOI: 10.5220/0006665400150022

In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 15-22

ISBN: 978-989-758-292-9

Figure 1: Area of analysis: the municipalities in North-eastern Italy.

buildings’ rooftop or on the ground - during the

period 2005-2016, thanks to the subsidies provided

by the Italian laws named “Conto Energia” (Palmer

et al., 2015).

The main purpose of this study is to delve into

the issues related to the data gathering process,

particularly the stage meant to define the proximity

structure characterizing the units of analysis.

Moreover, we aim to discuss the preliminary

empirical evidence, as we find more clues about the

role played by the so-called neighborhood and peer

effects.

The remainder of this paper is organized as

follows. Section 2 provides a brief literature review

about the drivers of the adoption of PV systems,

with a specific focus on the few studies dealing with

the topic of spatial patterns. Section 3 is devoted to

discuss the data gathering process and the related

issues, particularly as regards the proximity structure

of the observations. Section 4 describes the

preliminary results we achieve, stressing

the additional clues of spatial dependence. Finally,

Section 5 outlines the conclusions of the

analysis.

2 LITERATURE REVIEW

The literature argues that the choice to adopt PV

systems depend on a set of influential parameters.

Balcombe et al. (2013) provide a summary of 18

earlier and contemporary studies that relate to the

motivations and barriers for the adoption of

microgeneration energy technologies, including both

solar thermal and solar PV. Half of these studies

concerns the UK, and most of the remaining

involves continental Europe’s countries. The

reviewed literature agrees in identifying the role

played by environmental concerns and financial

aspects. As far as the latter are concerned, the will to

save money due to lower energy bills is a significant

incentive, although counteracted by the expectation

of high upfront and operating costs, not to mention

long payback times and unclear impact on property

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

value. It looks like other motivations and barriers do

matter, although there is a lack of consensus about

their importance. Additional determinants emerging

from the literature review performed by Balcombe et

al. (2013) are as follows: age; household size; home

ownership or tenancy; social class; income;

education.

The survey performed by Sardianou and Genoudi

(2013) focusing on the residential sector confirms

some of the above findings: the consumers’

willingness to adopt renewable energy sources is

affected by age, education, income, electricity cost,

and perceived installation and maintenance costs.

The same authors claim that a tax deduction is more

likely to support the acceptance of the renewables

than an energy subsidy. However, it should be

considered that the above results stem from a small

sample, and are characterized by a low goodness of

fit.

Within the domain of the renewable energies, the

research strand that focuses on the adoption of PV

points out the significance of the following factors to

distinguish between early innovators, potential

adopters, majority adopters, and rejecters: the per-

capita income more than sunlight intensity (Schaffer

and Brun, 2015); the costs to be incurred and their

ratio to the expected benefits (Vasseur and Kemp,

2015); the built environment as well as the property

ownership structure (Schaffer and Brun, 2015;

Graziano and Gillingham, 2015; Balta-Ozkan et al.,

2015; Sommerfeld et al., 2017).

Alongside the above empirical evidence, another

phenomenon came to light following specific

studies. The literature suggests that the adoption of

the renewables, and especially the deployment of

solar PV across a country, may be encouraged by a

kind of emulation within communities and between

neighbors. Let us quote the Schelly’s (2014) words:

“Adoption of technological innovations is arguably

promoted through [a] form of informal information

sharing. [...] it is not simply information, but

particular communities of information. [...] For

some, individuals within their neighbourhood or

community provided inspiration” (p. 188). Actually,

during the last few years, a promising research

strand has focused on the occurrence of peer effects

and neighborhood effects in order to explain the

adoption of renewable energy sources, and

especially PV systems. That research branch sinks

its roots in the idea that spatial dependence is a key

driver for the diffusion of technological innovations

across territories and regions (Anselin, 1988; Keller,

2002; Schaffer and Brun, 2015).

Bollinger and Gillingham (2012) found that

social interactions - namely, peer effects - play a

major role in explaining the diffusion of PV panels

in California. Their analysis points to the

significance of two phenomena that occur within the

same zip code area and give rise to social spillovers:

the visibility of the PV panels is the former, the

influence of word of mouth is the latter. Other

studies show evidence that PV adoption is affected

by the number of similar systems that have been

previously installed in the same area or, more to the

point, in the recent past and in the immediate

surroundings (Müller and Rode, 2013; Schaffer and

Brun, 2015; Graziano and Gillingham, 2015; Balta-

Ozkan et al., 2015; Palm, 2016; Rode and Weber,

2016; Dharshing, 2017; Zhao at al., 2017, Copiello

and Grillenzoni, 2017b). Let us consider the words

of Müller and Rode (2013) that get to the heart of

the matter: “imitation of spatially close precursors is

indeed an explaining factor in PV adoption; [...]

results confirm a localized peer effect in the

adoption of PV” (p. 527). Similarly, Graziano and

Gillingham (2015) “find clear evidence of spatial

neighbor effects (often know as ‘peer effects’) from

recent nearby adoptions that diminish over time and

space” (p. 816). Balta-Ozkan et al. (2015),

Dharshing (2017), and Copiello and Grillenzoni

(2017b) confirm the occurrence of regional spillover

effects. Rode and Weber (2016) show the

occurrence of localized emulative behavior. Zhao et

al. (2017) claim that the deployment of PV systems

may be described by clusters that tend to spread in

the surrounding areas.

3 DATA GATHERING PROCESS

AND RELATED ISSUES

3.1 Proximity Structure

In order to investigate the occurrence of

neighborhood and peer effects, the identification of

the proximity structure that characterizes the unit of

analysis is the most time-consuming process we had

to deal with. It relies on the following stages:

 use of search engines to find the list of

adjoining municipalities for each analyzed

township;

 replacement of the adjoining municipalities’

names with the codes provided by the

National Institute of Statistics;

Spatial Dependence of Solar Photovoltaic Systems: Data Gathering Process, Related Issues and Preliminary Results

Figure 2: Excerpt from the proximity structure dataset.

 use of the codes to extrapolate the data

concerning the solar photovoltaic capacity in

the adjoining municipalities, which are then

summed to calculate the total (Figure 2).

As far as the first stage is concerned, in North-

eastern Italy, the number of municipalities

surrounding each township highly varies, from a

minimum of 1 to a maximum of 21. On average, the

number of municipalities sharing their boundaries is

equal to 7. Figure 3 shows a selection of complex

neighborhoods. For instance, Verona - one of the

chief town in the Veneto region - is surrounded by

16 medium-sized townships. That situation is

common to other chief towns, but it also occurs in

rural and mountainous areas. Another unusual

feature is that several municipalities are composed

by at least two not contiguous territories. The issue

is further complicated because, contrary to what is

commonly thought, the municipal boundaries are not

stable at all. During the last decade, several changes

have taken place, mainly due to the need to reduce

the number of local administrative units, so as to

achieve saving in public expenditure. In the

Trentino-Alto Adige region alone, 50 municipalities

have disappeared: after having merged themselves,

they have brought 18 new larger townships into

being. Most of the mergers occurred in the last few

years and became effective in January 2016. Instead,

the figures on the installation of solar photovoltaic

systems mainly refer to the ex-ante situation.

Therefore, we had to keep track of both the

following aspects: the photovoltaic capacity installed

in the municipalities according to their former

boundaries (before the mergers), and their currently

neighboring towns (after the mergers). Another

distinguishing feature concerns the Alto Adige area -

namely, the province of Bolzano - where the

municipalities are identified by two names. Since it

is legally designated as a bilingual region, the former

name is in Italian, while the latter is in German.

Unfortunately, several sources use only one of the

names to label the data they provide, hence we had

to face matching problems when assembling the

dataset.

Leaving the above specific problems aside, the

identification of the proximity structure entails, at

least, two other issues, which have wide significance

and strong ability to affect the results. The former is

how we define the concept of proximity, namely,

what is the assumptions - and the measures - which

we rely on to distinguish the near spatial units from

the distant ones. The latter consists in the sort of

truncation the proximity structure is sometimes

subjected to.

As far as the first topic is concerned, to quote the

words of Tobler (1970), “everything is related to

everything else” (p. 234) and, more to the point,

“everything is related to everything else, but near

things are more related than distant things” (p. 236).

In this study, we assume that the energy-related

behavior in a municipality may be affected by what

happens in the adjoining municipalities. Therefore,

here we establish a relationship between proximity

and administrative borders, suggesting to translate

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

Figure 3: Selection of complex neighborhoods.

the concept of proximity into practice according to

the shared boundaries between the analyzed

municipalities. But why not to assume that the local

behavior may be somehow affected by what happens

in all the surrounding municipalities within a radius

of, let us say, 50 kilometers? And why not to

consider all the municipalities within the same

province or region? Each of the above option is

arbitrary and, although we prefer to adopt the first

solution, the third one could be preferred for

simplicity’s sake. However, it looks hard to sustain

that a specific way to define the proximity structure

should certainly prevail among several available

alternatives. Moreover, one should be aware that the

above remarks are not free of consequences for the

results. In other words, the empirical findings on the

occurrence of spatial dependence phenomena, in

turn, also depend on how the spatial relationships

between the units of analysis are defined.

As regards the second topic, in our case study,

the claim that “everything is related to everything

else” is somehow violated by the presence of the

national borders, where the spatial relationships find

an unexpected interruption. For instance, in the

province of Bolzano, the town of San Candido

borders on the Austrian town of Sillian. The two

towns are not situated on the opposite slopes of high

mountains, instead they are both located along the

Drava River in the Puster Valley. Moreover, they are

well linked by a primary road, and border controls

are no more carried out thanks to the Schengen

Agreement, not to mention that more than 80% of

the inhabitants in the Italian town of San Candido

are German native speakers. The same situation can

be found in several other municipalities, especially

in the northern Alto Adige, at the Austrian border,

and in the north-eastern Friuli, at the Austrian and

Slovenian borders. Therefore, there is no reason to

neglect the occurrence of cross-border relationships

and dependencies, except that we have no data on

the installed photovoltaic systems outside of Italy.

Obviously that data can be searched for, but we must

consider that they have a different nature and origin.

Indeed, we are analyzing the photovoltaic systems

that were subsidized according to a sequence of

Italian laws (Palmer et al., 2015). That laws were

stimulated by the European Directive 2001/77/CE.

The same happened in Austria, but according to

different detailed rules, as well as to different timing

and subsidies.

3.2 Other Parameter

The data concerning the installed PV capacity, both

in each municipality and in the adjoining ones, are

juxtaposed with variables belonging to the following

clusters (Table 1): geoclimatic aspects (surface area,

latitude, altitude, solar radiation); demography

(inhabitants and population density); economy

(income); social and behavioral aspects (waste

recycling rate). The underlying hypotheses are as

follows. The PV capacity is expected to be fostered

by a lower latitude and the corresponding higher

solar radiation, while it is expected to be limited by

unfavorable geographic conditions, such as smaller

surface area and higher altitude. The number of

inhabitants and the population density are

anticipated to be positively related to the installed

PV capacity, since individuals and families are

important targets of the policies providing incentives

and subsidies for the renewables. Also, the

disposable income is expected to be positively

related to the installed capacity, because the

adoption of PV systems involves the ability to incur

investment costs, even in presence of public grants.

The waste recycling rate is assumed as a proxy of

the adoption of innovative and responsible behavior,

hence we expect that the more the individuals and

households are prone to recycle, the higher should

be the installed PV capacity.

The above variables have some limitations,

especially with regard to the reference period, which

is not homogeneous. In particular, the data on solar

radiation refer to several years ago. They stem from

a research performed by ENEA, the former Italian

institute for research on nuclear energy, now

Spatial Dependence of Solar Photovoltaic Systems: Data Gathering Process, Related Issues and Preliminary Results

National agency for new technologies, energy and

sustainable economic development. The average

radiation on monthly and yearly basis is extrapolated

from EUMETSAT maps acquired during the period

1995-1999. The results are published only for the

towns with more than 10 thousand inhabitants.

However, the yearly solar radiation for different

locations, according to their latitude and longitude,

may be estimated using a web-based calculation

tool.

Table 1: Summary of the parameters.

Code Parameter Unit of

measure

Oipc Overall installed power

capacity

Oipc

s-1

Oipc in the surrounding

towns

Oipc_05-10 Oipc 2005-2010 kW

Oipc_05-10

s-1

Oipc 2005-2010 in the

surrounding towns

Oipc_11-16 Oipc 2011-2016 kW

Oipc_11-16

s-1

Oipc 2011-2016 in the

surrounding towns

Area Municipality surface area km2

Lat Latitude degrees

Alt Altitude m

Rad Global solar radiation MJ/m2

Inhab Number of inhabitants

Dens Population density inhab/km2

Inc Per capita disposable

income

Euros per

capita

Recycl Share of urban wastes

recycled

4 PRELIMINARY RESULTS

We base our preliminary findings on the following

regression model, from which we expect useful

suggestions in order to develop further studies:

Ln Oipc =





Ln Oipc

s-1



Ln X +



(1)

where



is the constant,



and



are the regression

coefficients, X is the vector of the independent

variables, and



is the error term. We use a double

logarithmic model since in the previous study it

proved to fit better the data (Copiello and

Grillenzoni, 2017b). Moreover, it allows dealing

with the possible non-linear relationships between

the parameters. Since the Ordinary Least Squares

(OLS) estimates are affected by heteroscedasticity,

as shown by the cone-shaped scatterplot of the

residuals (Figure 4), we opt for using

heteroskedasticity-robust Weighted Least Squares

(WLS) (Copiello and Grillenzoni, 2017a). The

results are summarized in Table 2.

Figure 4: Cone-shaped scatterplot of the residuals.

Due to their implications, two empirical findings

are worth attention. The first is that, contrary to the

expectations, the deployment of solar PV

installations has little or nothing to do with latitude

and solar radiation. The second is that several clues

of neighborhood and peer effects arise from the

analysis.

Table 2: Summary of the results.

Dependent Oipc

Parameter





T-stat P-value

const. 6.1309 2.375 0.0177

Oipc

s-1

0.4579 13.58 0.0000

Area 0.8493 21.42 0.0000

Dens 0.8313 22.40 0.0000

Inc -1.0487 3.843 0.0001

Adj. R

0.6471

The relationship between PV systems and

geoclimatic variables is quite weak with regard to

the installed capacity, on the one hand, and both

latitude and solar radiation, on the other hand. In

Figure 5 the values of these variables are subdivided

into quartiles. The correlation values are -0.38 and

0.45, respectively. It looks like the reason is the

strong development of the PV capacity in Alto-

Adige. Despite being an entirely mountainous region

characterized by a solar radiation of 4,661 MJ/m2 on

average, the more northern area of analysis has an

SMARTGREENS 2018 - 7th International Conference on Smart Cities and Green ICT Systems

Figure 5: Relationship between PV systems and geoclimatic variables.

installed photovoltaic capacity of 1,975 kW (54

kW/Km2), which are nearly the same one can find in

the Friuli region (2,151 kW, 77kW/km2), one degree

of latitude to the south. To go to the root cause of

that empirical finding, at least two hypotheses can be

put forward. Firstly, the geoclimatic data may not

tell the whole story, since during the winter a not

negligible share of the solar radiation is lost in the

Po Valley due to the recurrent presence of dense and

persistent fog. Secondly, the propensity to adopt PV

systems in Alto Adige may be ascribed to the

influence of the neighboring Austria, where the

government subsidies have started earlier.

The second hypothesis paves the way to the main

aim of this study, which is to check whether the

deployment of PV capacity is driven by

neighborhood effects, that is to say, whether the

phenomenon is bolstered by emulation. The

relationship between the installed capacity in each

municipality and the corresponding installed

capacity in the adjoining townships is positive and

high. Therefore, if we aim to understand the

deployment of the PV capacity and generation in a

territory, then we should consider not only

geoclimatic and socio-economic factors of that same

territory, but also what happens with regard to the

adoption of PV systems in the surroundings.

5 CONCLUSIONS

In this follow-up study, we analyze data encoded at

Spatial Dependence of Solar Photovoltaic Systems: Data Gathering Process, Related Issues and Preliminary Results

the municipal level, hence disaggregated at level 2

of the European LAU (Local administrative units)

classification. Here we find new empirical evidence

of the spatial dependence characterizing the

deployment of PV capacity and generation,

confirming our previous findings and the claims of

the few studies that have so far looked at this

promising research strand. We may conclude that

some energy-related behavior, signally those

concerning the adoption of renewable energy

sources, spread themselves across the space due to

phenomena of emulation between neighbors and

peers that can be caught and expressed according to

proximity measures.

However, further developments are required: by

enlarging the dataset in order to include additional

variables, by testing other proximity measures, and

by defining not only spatial but also spatio-temporal

regression models.

ACKNOWLEDGEMENTS

Statistical analysis is performed using the packages

R v. 3.3.2 and gretl v. 2017b. Spatial data

representation is made using the software QGis v.

2.14.9.

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