Network Diffusion of Green Technology in
Post-Fukushima JapanDavid Castells-Quintana
Department of Applied Economics, Universitat Autònoma de Barcelona
Alvaro Domínguez

Asian Growth Research Institute
Felipe Santos-Marquez
Technische Universität Dresden

Working Paper Series Vol. 2024-02
March 2024

The view expressed in this publication are those of the author(s) and do
not necessarily reflect those of the Institute.

No part of this article may be used or reproduced in any mannerwhatsoever without written permission except in the case of brief
quotations embodied in articles and reviews. For information, pleasewrite to the Institute.

Asian Growth Research InstituteNetwork Diffusion of Green Technology in
Post-Fukushima JapanDavid Castells-Quintana1 , Alvaro Domı́nguez2 , and FelipeSantos-Marquez3
1

Department of Applied Economics, Universitat Autònoma de Barcelona. ∗2 Asian Growth Research Institute. †3 Technische Universität Dresden. ‡March 25, 2024

Abstract
We study the diffusion of adoptions of green technologies in Japanafter the 2011 Fukushima incident. We find that, on average, municipalities within a 120 km radius of a given nuclear power plant adopted
green technology at a higher rate than those outside that radius. We
then rely on a network diffusion model to analyze the direction, speed,
and order in which municipalities adopted said technology. Next, we
perform a counterfactual analysis by targeting key spreaders to alter
the diffusion process. Finally, we propose a novel targeting method
accounting for possible “bottlenecks” preventing the propagation process in the network.
Keywords: Energy Transition, Networks, Technology Diffusion
JEL classification: C15, O33, P11, P18, Q42

1

Introduction

Given the increasing threat of climate change, energy transitions from carbon
and fossil-based sources to greener and renewable ones have become a major∗

E-mail: david.castells.quintana@uab.cat
E-mail: alvdom123@gmail.com‡
E-mail: felipe.santos@tu-dresden.de†1

global need and goal. The need for more sustainable energy is recognized by
the Sustainable Development Goals 7, on affordable and clean energy, and11, on sustainable cities and communities. However, energy transitions arecostly and require time. While the capacity of renewable energy sources has
increased substantially in recent years, renewables still represent less than
30% of all global electricity generation and only around 11% of global primary energy. Wind electricity generation, in particular, has shown one of thehighest increases among all renewable power technologies but represents onlyaround 7% of electricity generation worldwide. At this pace of transition, weare still far from meeting the goal of global Net Zero Emissions by 2050, with
only 3 of 50 components evaluated as fully on track (International Energy
Association, 2023).1 Furthermore, energy transitions are occurring at different speeds across world regions and sectors, with some countries showing
no progress and with important technological and economic obstacles to be
overcome. In this regard, a better understanding of the local pace and diffusion in the adoption of renewable energy sources can, therefore, be of greatvalue to improve policy design that fosters and accelerates needed energy
transitions worldwide.
This paper empirically studies the spatial spread of green energy transitions at the local level. We focus on Japan and explore nuclear-to-windenergy transitions triggered by the Fukushima Nuclear Incident (FNI) in
2011. To do so, we build a novel panel dataset combining gridded data onlights, population, vegetation greenness, and pollution, aggregated at themunicipal level and matched with the location of wind farms and nuclear
plants. Our dataset includes 1742 municipalities with observations from 2001
to 2020. Using panel-data econometric techniques, we explore the connection between the proximity to nuclear power plants and the spread of the
adoption of wind energy technology (WET). We then model and simulate
the diffusion of WET through a network, taking into account how adoption
coordination at the local (municipal) level may impact at a higher level (i.e.,
regional or national). By explicitly considering the network structure, we areable to identify bottlenecks that may hinder the diffusion process and thus
inform policy design.
We look at post-Fukushima Japan for various reasons. First, because
the FNI works as a natural experiment, enabling us to identify the causal
effects of phasing out nuclear technology on WET diffusion. Second, due tothe relevance of the Japanese case, different energy sources, including fossil
1

Solar, electric vehicles, and lighting are the only components evaluated as on track. By
country, poorer regions of the globe are clearly lagging behind. See the full report on
https://www.iea.org/topics/tracking-clean-energy-progress.

2

fuels, nuclear, and renewables, have been extensively adopted with leading
global technologies. Finally, due to the availability of rich, fine-grained geolocated data of WET adoption along with other factors for Japan for over
20 years.
We find that, on average, municipalities within a 120 km radius of agiven nuclear power plant adopted green technology at a higher rate thanthose outside that radius. This work fits in the discourse surrounding the
local and global policy agenda on carbon neutrality.2
Our paper relates to several strands in the literature. First, we relate to
the increasing literature on energy transitions, especially to those papers analyzing local adoption of greener technologies (Hall and Helmers, 2013; Popp
et al., 2011; Rode and Weber, 2016). Second, we relate to the literature
studying network diffusion of new technologies (Acemoglu et al., 2011; Beaman et al., 2021), especially in the energy sector (Halleck-Vega et al., 2018).
Finally, we relate to papers studying the energy consequences of exogenous
shocks, in particular, the Fukushima event in Japan in 2011 (Okubo et al.,2020; Rehdanz et al., 2017; Kawashima and Takeda, 2012).
We contribute to the literature through diverse avenues. First, by empirically analyzing a specific energy transition (nuclear to wind), exploitingrich fine-grained data in Japan, and benefiting from the natural experimentthat the Fukushima incident provided. Differently from other papers analyzing this incident, we employ causal inference methods to study the effect of
this incident on the adoption of wind energy technology. Second, we benefit from our detailed data by integrating two complementary methodologies,
namely Difference-in-Differences (Diff-in-Diff) and network analysis. This
allows us to first capture the effect of an exogenous shock (i.e., the FNI) on
the adoption of wind energy to then study the subsequent diffusion mechanism. Finally, by better understanding the structure of progressive adoptionof newer technologies, we provide insights that might improve policy designin the allocation of resources to foster the diffusion of these technologies.
The rest of the paper is structured as follows. In Section 2, we review theliterature. In Section 3, we provide some insights about the Japanese context, describe our data, and derive stylized facts. In Section 4, we perform
regression analysis to estimate the causal impact of the FNI on WET adop2

For instance, according to the Ministry of Economy, Trade and Industry of Japan (METI):“In October 2020, Japan declared that it aims to achieve carbon neutrality by 2050.
Carbon neutrality by 2050 cannot be realized through ordinary efforts. It is necessary
to significantly accelerate efforts toward structural changes in the energy and industrial sectors and undertake bold investment for innovation.” https://www.meti.go.jp/
english/policy/energy_environment/global_warming/ggs2050/ (Accessed September 20, 2023).

3

tion. In Section 5, we implement a network model to explain the diffusion of
WET in Japan. Finally, section 6 concludes and derives policy implications.

2

The spatial diffusion of energy transitions:
literature review□ Energy transitions: pace and determinants
One key dimension of climate change mitigation is that of energy transition.
In this regard, there is an increasing branch of the literature focusing on
the pace and determinants of energy transitions (see for instance Hall andHelmers (2013); Popp et al. (2011); Rode and Weber (2016); Halleck-Vegaet al. (2018). While some papers have taken a country-level perspective,
others have delved into subnational dynamics, analyzing energy transitions
at a more local level (see for instance Blanchet (2015), for Berlin; Bayulgen(2020), for the US; Oudes and Stremke (2018), for Italy; Balta-Ozkan et al.
(2021), for the UK). This literature has highlighted the relevance of several
contextual factors, including civil preferences and demands, as well as policy
designs to foster the spread of greener energy sources.
Regarding the type of energy transition, earlier studies tended to focus on
solar energy. But, in contrast to solar, wind energy is not on track to meet
the Net Zero Emissions target by 2050; productivity has to rise, costs haveto go down, and the average annual generation growth rate needs to increase
to about 17% (International Energy Association, 2023). Some studies havefocused on the deployment of wind sources at the local level (see for instance,
Frantál and Nováková (2019), for the Czech Republic; Kiunke et al. (2022),
for Germany; ).
Most of the studies mentioned above have implicitly analyzed energy
transitions by analyzing the deployment of renewable sources but have notexplicitly looked at actual transitions from one energy source to another (i.e.,fossil to nuclear, fossil to renewables, nuclear to renewables, including solar
and/or wind). And few papers have actually focused on nuclear-to-windtransitions (Hong et al. (2018), for Sweden; Cherp et al. (2017), comparing
Germany and Japan).
Finally, a fundamental aspect of energy transition is not only the adoption of greener technologies but also their diffusion in space. In this regard,
recent papers in regional science have put the focus on the spatial processof energy transitions, where spatial distances and proximities play a pivotal
role (Caragliu and Graziano, 2022).
□ Networks in the spatial diffusion of green technologies
4One way to study the spatial diffusion of energy transitions, especially at
local levels, is by analyzing the role of networks. Networks allow us to model
the structure of relations or interactions through which certain behaviors
spread. In this regard, we can study diffusion processes by modeling how
agents, through their interactions and decisions, propagate a particular behavior in a network (e.g., green energy usage). In our case, this allows us
to learn how local interactions and coordination among municipalities cancreate agglomeration effects and thus have a global impact on the adoption
and spread of renewable energies.
The classical work of Morris (2000) formalizes how, through a network,
two alternative actions can be played in equilibrium. Our model builds uponsome of Morris’s definitions and expands on them to analyze other questions. Other authors, such as Acemoglu et al. (2011), study the adoption oftechnologies using a similar model. In our case, we do so, too, but considering weights on the network’s links to capture spatial influence from players’
distances from each other. Cabrales et al. (2011) present a model that simultaneously explores network formation and productive efforts. Paired agents
create spillovers, which are multiplicative in both agents’ efforts. This differsfrom our study since we do not consider paired players resulting from a network formation process as a condition for diffusion to take place. Instead,
we work with a fixed network where diffusion occurs from the coordinationof actions due to the incentives that agents derive from neighboring agents’actions.
A related but different strand of the literature studies how such diffusion may be altered/maximized through the proper “targeting” of influential
or important nodes, given their relative position in the network. Workssuch as Kempe et al. (2003), Banerjee et al. (2013), Tsakas (2017), Galeotti
et al. (2020), Beaman et al. (2021), Alexander et al. (2022), and Jackson andStorms (2023) study this issue and propose various alternatives for targetingsuch agents. In this work, we propose a different targeting method considering “bottlenecks” that may arise in a network, preventing the spread of atechnology usage. Galeotti and Rogers (2013) investigate the dynamics of aharmful state in a population split in two. Their study derives conditions
under which a planner can suppress this state contingent on the level of groupinteraction. Conversely, we consider a unified population and seek ways aplanner can propagate green energy adoption within it.

5□ Energy transitions in Japan
Japan is a good case study to analyze energy transitions. As mentioned
already, Japan is a leading country in terms of energy technologies, where
several energy sources, from fossil-fuel-based to nuclear to renewables, havebeen extensively deployed. Thus, several papers have empirically analyzed
the Japanese case (Fraser, 2019). Some studies have already analyzed theimpact of the FNI on Japan’s energy markets and energy transition (Okubo
et al., 2020; Rehdanz et al., 2017; Kawashima and Takeda, 2012). Some ofthese studies consider the role of spatial factors, such as the distance to the
Fukushima nuclear power plant and other plants has also been taken into
account. For instance, Okubo et al. (2020) report that individuals living up
to 30 km from a Nuclear power plant run by Tokyo Electric Power Company(TEPCO ) have a higher preference for renewables in the energy mix. Additionally, Rehdanz et al. (2017) report that the willingness to pay (WTP)
for renewables increases with the proximity to Fukushima, while for the nuclear share, the WTP decreases for municipalities close to Fukushima. Close
to what we do, Mochizuki and Chang (2017) have empirically shown how
the Fukushima disaster was an opportunity for the diffusion of solar energy
across Japanese communities.
However, compared to previous studies, we empirically analyze the intensity and geographical extent of energy transitions given the exogenous shockof the FNI. We also study why some municipalities transit and others do not.
And by doing so, relying on network analysis, we provide insights on how tobetter design policy interventions that optimize and accelerate the adoption
and diffusion of renewable energy sources.

3

Energy production and transition in Japan:
context and data

In Japan, coal and oil have been used to produce over 65% of its energy needsfor over 30 years. Although there had been a decreasing trend in the usageof fossil fuels, this was reversed after the 2011 Fukushima Nuclear Incident(FNI). Before the incident, the government projected that about 40% of theenergy mix would come from nuclear sources by 2030. Nevertheless, as of2020, projections stand at about 20% and thus show a 20% decline in predisaster planning (Hughes, 2021).
In terms of renewable energy, there has been a recent rise in its sharein Japan’s electricity mix. Such a rise has been mainly dominated by the
installation of solar photovoltaic (PV) units. The deployment of solar PVs

6

at higher rates than other renewable energy sources may be explained bytechnical and nontechnical components (Hughes, 2021), and is in line witha lower global adoption of wind vs solar. In Japan, technical components
are related to the high capital and maintenance costs associated with WET
deployment in a country with such a mountainous geography. Non-technicalreasons can be associated with the lobbying power of the PV industry, as
suggested by Li et al. (2019). Despite the growth of the installed capacityof renewables, fossil fuels continue to have the largest importance in energy
generation. In fact, from 2010 to 2015, the share of electricity generated fromthermal coal increased from 21 to 31 percent (Hughes, 2021).
To explore the effect of the FNI on the expansion of adoption of windfarms, we build a detailed panel dataset combining gridded data on nighttime lights, population, normalized difference vegetation index, and pollution, matched with the location of nuclear plants and wind farms. Our
dataset includes information for 1711 Japanese municipalities from 2001 to
2020.
For energy production data, we rely on the “Wind Power” database forJapan3 . This dataset contains information on the geolocation of wind farms.
It also includes other information, such as the year the wind farms werecommissioned and the number of turbines in each farm. Using this data, we
calculate the number of wind farms per municipality from 2001 to 2020. For
nuclear, we rely on the Global Power Plant Database (2018). This databaseincludes the geolocation (longitude-latitude) of each plant. Appendix A provides more information on the construction of energy variables.
We match our data on wind farms and nuclear plants with other data
aggregated at the municipal level. For air pollution, we obtain data forozone concentration and PM2.5 concentration. This data comes from Brauer
et al. (2016) and was obtained via GeoQuery (Goodman et al., 2019). For
population, we rely on GHS population grid multitemporal estimates, andfor green cover, we use the normalized difference vegetation index, both from
GeoQuery (Goodman et al., 2019). We also use data on night-time lightsfrom Li et al. (2020). This dataset provides a harmonization between the
DSMP and VIIRS time series, allowing us to have data from 1992 to 2020.
Table A.1 in the Appendix provides definitions and sources from thedifferent variables considered, while Table A.2 provides descriptive statistics
for our main variables of interest. In the rest of this section, we highlightstylized facts for our key variables.
3

Data available from http://www.thewindpower.net/ .

7

3.1

Wind energy production and diffusion

Figure 1 shows a map with the location of all wind farms in the databasefor which both commission year and geolocation are available. The figure
shows that wind farms are mostly located in coastal areas and in the fourlargest islands of Hokkaido, Honshu, Shikoku, and Kyushu. Coastal areas
seem the most appropriate for the development of onshore wind farms, given
the mountainous geography of the largest islands. Figure 2 provides a barplot
showing the evolution in the total number of wind farms over time. This plotshows how the adoption of wind farm technology followed a rapid growth up
to the year 2000, slowing down afterward until the early 2010s and then risingagain rapidly.
Figure 1: Location of wind farms and commission years

Note: Maps were created using data from the “Wind Power” database for Japan. Only
wind farms for which geolocation and commissioned years are available are considered.

3.2

Nuclear plants

Figure 3 shows the location of the 16 Japanese nuclear power plants. As weare interested in spatial patterns of substitution of energy sources, the figure
also shows buffers of a 100 km radius surrounding all plants.

4

The post-Fukushima adoption of wind energy: a Difference-in-Differences approach

In this section, we use our panel data set to explore the connection between
the proximity to nuclear power plants and the spread of adoption of Wind
8

Figure 2: Evolution in the number of wind farms

Note: The figure was created with data from the “Wind Power” database for Japan. Only
wind farms for which geolocation and commissioned years are available are considered.

Figure 3: Location of nuclear plants in Japan

Note: Location of 16 Japanese nuclear power plants. Geolocation data was taken from the
Global Power Plant Database. The circles represent 100 km radius buffers surrounding
each nuclear power plant.

Energy Technology (WET). We do this relying on econometric analysis andbenefiting from the exogenous shock that the Fukushima incident in 20119

represented.

4.1

Did Fukushima increase the adoption of greener
energy sources?

We begin by assessing to what extent the 2011 Fukushima incident translated into an increase in the adoption of green technologies (namely wind) inareas surrounding nuclear power plants. To do so, we rely on Difference-inDifferences (Diff-in-Diff) approach., as specified in equation 1:log(W F )rt = βTrt + δXrt + γt + θr + εrt ,(1)

where (W F )rt is the number of wind farms in municipality r at time t,
and Trt is our treatment dummy, which takes a value of 1 if municipality r
is at a distance below a given threshold (i.e., 60, 90 or 120 km) from any
nuclear reactor for all years after 2011 (the year of the Fukushima incident).
Xrt is a vector of controls. γt are time-specific fixed effects, while θt aremunicipality-specific fixed effects. As we include country-specific fixed effects,
our panel-data specification exploits the within-countries evolution over time,
controlling for time-specific fixed effects.
Our identification of β rests on the natural experiment that the Fukushima
accident represented. The assumption is that after the Fukushima incident,
municipalities closer to nuclear power plants had higher incentives for energy
transition away from nuclear sources. Public confidence in nuclear energy
generation plummeted after the Fukushima incident, and the authorities responded by shutting down most of the country’s 50 operational power reactors. We also expect that as reactors have slowly resumed operation, theeffect that we measure may also show a decreasing trend. In any case, ourability to identify a causal effect depends on whether the parallel trends
assumption holds for the trend of wind farm development in treated andnon-treated municipalities. We argue that such an assumption holds as we
conduct event study analysis and compare pre-treatment trends for treatment and control groups.
Table 1 presents the results of the Diff-in-Diff specification. Results inTable 1 show the regressions estimated based on Equation 1 for three different
models. Column (1) shows the estimates for a model in which only thetreatment dummy is considered and controlling for municipality-fixed effects.
The coefficient estimate suggests that treated municipalities have, on average,
a 2% higher number of wind farms. To control for yearly shocks that affectall municipalities, time-fixed effects are included in column (2). The point
estimate in (2) with the full set of two-way fixed effects is halved from the10

value in (1) to 0.01. Lastly, including the full set of controls in column (3)yields a point estimate that remains statistically significant with a value ofapproximately 1.2%.
Table 1: Regression estimates of the Difference-in-Differences model

Dependent Variable:
Model:
Variables
treat2log(wind farms+1)
(1)
(2)
(3)
0.0214∗∗∗(0.0031)0.0104∗∗(0.0049)

0.0119∗∗(0.0050)-0.0070∗∗∗(0.0019)-0.0008(0.0006)
5.7 × 10−8
(4.78 × 10−7 )0.0023(0.0026)-0.0073∗∗(0.0032)−2.3 × 10−6
(1.84 × 10−6 )

Yes

Yes
Yes

Yes
Yes

0.90594

0.90673

0.90719

ozone
pm2.5
pop
log(lights+1)
lights pc
ndvi mean
Fixed-effects
asdf idyear
Fit statistics
R2

Clustered (asdf id) standard-errors in parentheses
Signif. Codes: ***: 0.01, **: 0.05, *: 0.1
Note: this table reports the regressions estimates based on Equation (1).

4.2

Event study

The Difference-in-Difference estimation provides the average treatment effect.
Nevertheless, it is possible that the effect becomes less or more pronounced
11over time, or that it takes some time for the effect to kick in. For thesereasons, we perform a simple event study to capture dynamic treatment
effects. This is done using the model in Equation 2:log(W F )rt =

−2
X
τ =−q

τ
γτ Drt+mXτδτ Drt+ δXrt + γt + θr + ϵrt

(2)

τ =0

Where γτ are the coefficients for the years before treatment, also known
as leads, and δτ are the after-treatment coefficients, also known as lags. The
coefficient for one year before treatment is omitted, which makes it the reference year. Xrt is a vector of controls, γt are time-specific fixed effects, andθr are municipality-specific fixed effects.
The estimates of the coefficients for the leads and lags for municipalities
treated in a radius of 120 km are shown in Figure 2. Figures XX and XX
in the Appendix show the estimates for treatment groups defined by 90 and
150 km radius around all nuclear power plants. All the pre-treatment coefficients shown in Figure 2 are not statistically different from zero, suggestingthat control and treatment groups exhibit parallel trends before the FNI.
In contrast, during the treatment period, municipalities in the vicinity of
nuclear reactors had, on average, a higher number of wind farms for mostpost-treatment years; point estimates are close to the 1% reported using thediff-in-diff estimator. Nevertheless, the coefficient estimates for 9 and 10
years after the FNI are not statistically different from zero at conventionalsignificance levels. The insignificant levels for later years suggest that, as nuclear reactors have been reactivated, municipalities in proximity to nuclear
plants have not developed more wind farms compared to control municipalities. This change in later years may also reflect that not only incentives tobuild turbines have spread through the country in line with national-level
policies aimed at reaching carbon neutrality but also that larger investments
may be flowing to offshore turbines in line with the underlying higher capacity in offshore areas in Japan.

12Figure 4: Event study estimates

Note: this figure shows the point estimates for the leads and lags based on equation (2).
The treatment status is based on the distance from the centroid of a municipality to theclosest nuclear power plant. Municipalities at distances lower than 120 km are assigned
to the treatment group.

5

Network diffusion of wind energy adoption
in Japan

5.1

Network inference

To model the diffusion of adoptions of energy within the network wheremunicipalities are interconnected, we first need to establish its structure.
Since we lack prior knowledge of the network topology, we use the following
approach to infer it. First, we define a radius of 120 kilometers from a given
nuclear power plant. Any municipality within this distance is consideredconnected to the respective power plant. If two or more municipalities areconnected to the same power plant, they are also linked to one another in
the network. We show these connections in the first graph of Figure 5, where
power plants are depicted as red nodes and municipalities as black nodes.4
Similarly, if a municipality is positioned within 120 kilometers of two or more
power plants, it is connected to all of them.
Subsequently, we remove the red nodes, symbolizing the nuclear power
plants, from the network, reflecting the scenario where these power plants
have ceased operations. The remaining graph, which includes only munici4

The links do not necessarily represent functional or operational connections between
municipalities and power plants, but rather the spatial proximity or influence of thepower plant on the nearby municipalities in a geographical context.

13

palities, becomes the central focus of our analysis. This approach allows us
to account for the shut down of nuclear power plants and concentrate exclusively on the underlying network of interactions between municipalities,
which is critical for our simulations.
Figure 5: Inference of the Network

In the following section, we describe the network model we use to study
the diffusion process of technology adoption. The idea is that this spread
results from coordinated efforts between different municipalities at the local
level, which in turn have a global impact. This phenomenon is related to thefact that the network’s defining factor is the geographical proximity to nuclear
power plants. The presence of these may have contributed to the clusteringof economic and social activities in their vicinity. This clustering could be
attributed to various factors, such as job opportunities, infrastructure, orother elements associated with the presence of power plants.

5.2

The Model

We consider a weighted network represented as a graph G = (N, E, w), where:• N = {1, 2, . . . , n} is the set of agents or nodes5 in the network.
5

In this paper we will refer to agents, nodes, and municipalities interchangeably.

14• E is the set of edges connecting different agents, with no self-loops.
More specifically, we denote ij ∈ E as the existence of an edge or link
between nodes i and j. Additionally, the network is undirected, i.e.,
ij = ji, ∀ij ∈ E.
• w is a function that assigns weights to the edges, w : E → R+ ∪ {0}. In
other words, w assigns a non-negative real number to each edge ij ∈ E.
We define the set of neighbors of agent i ∈ N as Ni (G) = {j | ij ∈ E}. An
important measure in weighted networks is the strength of node i, defined
as:
X
Si =
wij
j∈Ni (G)

where wij represents the weight or distance between nodes i and j in the
network. A weight of 0 indicates the absence of a connection between nodes.
This measure gives the influence that node i receives from its neighbors
within the network based on the weights of its connections. Meanwhile, letidenote the average strength of node i, where |Ni (G)| representsS̄i = |NiS(G)|the degree or number of (unweighted) connections of node i.6

5.3

Seed Set & Thresholds

At the initial iteration (k = 0), a subset of individuals Ψ(0) ⊆ N is selected
as the seeds. These represent the set of agents initially activated (i.e. those
who adopted the green technology) at this time.
At the next iteration, a node i ∈ N will consider, with equal probability,
the adoption of a new technology (i.e., determined through a 50% Bernoulli
trial) if a fraction q ∈ (0, 1] of her neighbors is in the seed set:Pj (Ψ(0) ∩ Dij (G))
P
≥ q ⇒ i ∈ Ψ(1)(3)
j Dij (G)
where Dij (G) = w1ij · S̄i represents the spatial influence of node i’s neighborhood, so that closer neighbors, captured through w1ij , will have a stronger
influence than those farther away. Equation 1 tells us that agent i will contemplate whether to adopt the new technology if at least a fraction q of herneighbors is within the seed set at k = 0. If an agent rejects adoption in period k, she will have the opportunity to reconsider in period k + 1. This will
6

While Si and |Ni (G)| may both represent the weighted degree of a node, we distinguishthem to represent the weighted and unweighted versions of degree, respectively.

15

continue until she decides to adopt. This mechanism will work in delaying
the diffusion process.
Following Morris (2000), we can interpret this model as agents playing
a coordination game. Their payoffs come from whether or not they match
behavior with each of their neighbors:
Agent jAgent i

Adopt T ech
Adopt T echa, a
Do not Adoptc, b

Do not Adopt
b, c
d, d

with a > c and d > b, thus coordinating is better than not doing so. In thisd−bgame, a specific threshold exists, such that if at least a proportion q = a−c+d−b
of an agent’s neighbors adopts the behavior, then the agent’s best response
is to also choose to do so. At this precise threshold, the agent remains
indifferent, while in all other cases, it has a clearly defined best response.
Usually, it is unlikely for the threshold to be precisely met. Nevertheless,certain rational thresholds, say q = 1/2, are discussed in the literature. For
example, individuals might tend to conform to most of their friends’ actions,making room for these rational thresholds. Unless specified otherwise, when
there is a tie, we will assume that an agent adopts a behavior if exactly q of
their neighbors follow it.
For k ≥ 0, we generalize the condition for adopting technology as:
P Sk−1j ({ t=0 Ψ(t)} ∩ Dij (G))
P
≥ q ⇒ i ∈ Ψ(k).
(4)
j Dij (G)This general condition ensures that for any period k, agent i will consider
adopting the new technology if the weighted proportion of neighbors within
the union of seed sets up to that period exceeds or equals q for that agent.
Additionally, we define a subset H ⊆ N to be cohesive if:P
j (H ∩ Dij (G))
P
> 1 − q, ∀i ∈ H.
(5)
j Dij (G)Equation 5 says that a set of agents makes a cohesive set H if, for eachmember of the set, the weighted proportion of neighbors in H is strictly
greater than the threshold q.

16

5.4

Equilibrium

A non-empty seed set Ψ∗ is considered a fixed point (an equilibrium) of thethreshold model if:
Ψ(0) = Ψ∗ ⇒ Ψ(k) = ∅, ∀k > 0.

(6)Equation 6 states that if the initial seed set Ψ(0) is equal to Ψ∗ , thenthe set of activated agents will become empty (Ψ(k) = ∅) for all subsequent
iterations (k > 0). In other words, an innovation initiated at Ψ∗ cannot
propagate further through the network.
For a graph G with threshold value q, an adopter set Ψ∗ is considered afixed point if and only if its complement, (Ψ∗ )c = N \ Ψ∗ , forms a cohesive
set:
Fixed Point: Ψ∗ ⇔ (Ψ∗ )c is cohesive.
This means that a set Ψ∗ is a fixed point if, when it adopts the innovation,
its complement (Ψ∗ )c (the non-adopting agents) forms a cohesive set. In otherwords, the non-adopting agents are interconnected in a way that prevents
further adoption of the innovation (think of a closed village or a tightly-nit
community not accepting anything coming from “the outside”). Therefore,
members of a cohesive set H cannot satisfy Equation 4 unless there exists(at least) an individual inside H who has previously adopted the innovation.

5.5

Results

Relying on the definition to infer networks from subsection 6.1, we obtain
three sub-networks or components: The first with 798 municipalities, thesecond with 109, and the third with 259. The first component contains
mostly municipalities from the central area of Japan (island of Honshu),
the second from the north, Hokkaido, and the third from the south, Kyushu.
The structure of the networks is because of the geographic position of nuclear
power plants within these islands. Due to, in some instances, power plants
being located at distances above 120 km from others in different islands,
components do not have connections between them.
In order to run the simulations, we need to obtain threshold values, q,with higher values implying a more stringent condition for diffusion to occur.
We proceed by finding a q such that if we add ϵ > 0 to it, there is nodiffusion (i.e., the maximum value at which diffusion still happens, with any
slight increase beyond this point preventing any further diffusion). Then, we
multiply the obtained q by 0.25, 0.5, and 0.75. This way, we obtain three
additional thresholds along with the original q: Thresholds 1, 2, 3, and 4.
17

These four values allow us to assess the speed and extent of the diffusion at
different stringency levels.
Additionally, we may ask how to maximize the spread of an event ofinterest (in our case, the adoption of green technologies). To do this, different
methods of “targeting” have been proposed. Some of the most common ones
consist of optimally allocating the initial seeds based on different centrality
measures such as Closeness, Betweenness, and Eigenvector. By using some
centrality, the central planner can find the best positions in the network forthe diffusion to be maximized. We can observe this in Figure 6. In it, we
see that if the initial seeds are as those of the graph to the left, then an“optimal” allocation would be that in the graph to the right. Given the
network structure, we redistribute the same number of seeds so that they are
better positioned to diffuse the technology.
Figure 6: Seeding in a Network

We proceed to run simulations for each of the components utilizing thecriteria for thresholds mentioned above. Also, we run these simulations using
the original seeds (i.e., the municipalities that adopted the technologies in thereal world first) and alternative ones given by seeds allocated through different centrality measures.7 Figure 7 below presents the results after 100 runs
for the first component. In these graphs, the “Normal” seeds correspond to
the real original adopters. We can see that as we move from the first threshold to the fourth one, the runs take, on average, more time to end. Further,each seeding curve shifts to the right and becomes more variable. With thefirst threshold, the number of adoptions peaks almost at the beginning, but
7

The idea of using these other seeds is to analyze alternative “if” or “counterfactual”
scenarios to the real one.

18

higher values of q delay (and even reduce) the number of adoptions at eachstep of the simulation. The results for the other components are shown inAppendix B.
Figure 7: Number of Adopters for Each Threshold (First Component)

In Figure 8 below, we can observe the cumulative mean adoption of thetechnologies for the first component. For the lowest values of q (threshold1), regardless of the seeding type, we reach full adoption of the technology(although at different speeds/number of steps). When we increase the valueof q to that of threshold 2, the Normal seeding is the only one that attains
full adoption. The other seeding strategies reach approximately 92.5% of
adoption. This same situation repeats when we raise q again, as shownin the graph for threshold 3. Interestingly, for the maximum value that q
can attain before no more diffusion occurs (threshold 4), the Normal seeding
experiences a noticeable drop to below 20%, whereas the Eigenvector seeding
strategy is more successful, achieving above 50% adoption.
Why does the Normal seeding strategy generally outperform the otherones? This can be attributed to the network structure, where within eachcomponent, there are municipalities situated between two or more nuclear
power stations. An illustrative example can be seen in the two central nodes
in each graph depicted in Figure 6. These municipalities essentially act as“gatekeepers” for the diffusion process. In simpler terms, if one side of the
network wants to communicate with the other, it must do so through these19Figure 8: Cumulative Mean Adoptions of Technology (First Component)

central nodes.
The situation described above can lead to complications in the diffusion
process, as these municipalities may serve as bottlenecks that disrupt the
propagation of technologies. For instance, if a municipality requires morethan eight neighbors to adopt to decide to do so itself, the diffusion process
on the left side of the graph in Figure 6 will be partial or incomplete. Furthermore, in the case of the “Optimal” seeding on the right side of the graph,diffusion will not occur at all!
When running simulations for the first component of the network (shownin Figure 9), we encounter the same issue. In this scenario, we have fourteen
initial seeds for the Normal case. Throughout each run or period, we observe
the diffusion process continue until it halts by the end of period 10. Notably,
this diffusion remains partial, as the upper right side of the network retains
its original state – only the initial seeds have adopted the technology, withno other nodes changing their statuses. This observation confirms that thetwo municipalities connecting this large cluster of municipalities to the restof the network inhibit the diffusion process.
To solve this kind of problem, we propose an alternative type of seeding
method. We define this targeting and show its results in Appendix B.
20

Figure 9: Bottlenecks in First Component

6

Conclusions and policy implications

In this paper, we have empirically explored the adoption and spatial diffusionof wind energy. To do so, we have focused on Japan, building a dataset combining detailed gridded data on lights, population, vegetation greenness, andpollution, aggregated at the municipal level and matched with the location ofwind farms and nuclear plants for more than 1711 municipalities in over the
1990-2020 period. Using panel-data econometric techniques, we have shownhow the exogenous shock that the Fukushima incident of 2011 represented
led to an increase in the adoption of wind farms, especially in municipalities
close to nuclear plants.
By relying on a network diffusion model, we show how the coordinationof adoptions of green technologies through the local interaction of municipalities has impacts at the national level. By running simulations, we areable to analyze the alternative diffusion paths and timings of said adoption
in the network. Naturally, one would be inclined to ask if we can change
the outcomes of the spread of technology by targeting specific municipalities,
given their position in the network. When we apply traditional targeting
methods, we observe that these do not provide any improvement due to theexistence of specific municipalities that work as bottlenecks in the propagation of technologies. We then propose a new type of targeting that takesthese bottlenecks into consideration. Our results show that not only does21

it allow for a faster diffusion, but it also relies on fewer seeds or starting
points, thus solving an essential economic problem: maximizing outcomeswhile minimizing costs.
Our findings suggest that considering the network of influences of municipalities and targeting specific ones based on their relative positions canhelp policymakers attain desired outcomes more efficiently. In our case, thistranslates into better and faster adoption of greener technologies such as
WET.

22

References
Acemoglu, D., Ozdaglar, A., and Yildiz, E. (2011). Diffusion of innovations
in social networks. In IEEE Conference on Decision and Control andEuropean Control Conference, pages 2329–2334, Orlando, FL, USA. IEEE.
Alexander, M., Forastiere, L., Gupta, S., and Christakis, N. A. (2022). Algorithms for seeding social networks can enhance the adoption of a publichealth intervention in urban india. Proceedings of the National Academyof Sciences, 119(30):e2120742119.
Balta-Ozkan, N., Yildirim, J., Connor, P. M., Truckell, I., and Hart, P.
(2021). Energy transition at local level: Analyzing the role of peer effects
and socio-economic factors on uk solar photovoltaic deployment. Energy
Policy, 148:112004.
Banerjee, A., Chandrasekhar, A. G., Duflo, E., and Jackson, M. O. (2013).
The Diffusion of Microfinance. Science, 341(6144):1236498.
Bayulgen, O. (2020). Localizing the energy transition: Town-level politicaland socio-economic drivers of clean energy in the united states. Energy
Research & Social Science, 62:101376.
Beaman, L., BenYishay, A., Magruder, J., and Mobarak, A. M. (2021). Can
Network Theory-Based Targeting Increase Technology Adoption? American Economic Review, 111(6):1918–1943.
Blanchet, T. (2015). Struggle over energy transition in berlin: How do grassroots initiatives affect local energy policy-making? Energy Policy, 78:246–
254.
Brauer, M., Freedman, G., Frostad, J., Van Donkelaar, A., Martin, R. V.,
Dentener, F., Dingenen, R. V., Estep, K., Amini, H., Apte, J. S., Balakrishnan, K., Barregard, L., Broday, D., Feigin, V., Ghosh, S., Hopke, P. K.,
Knibbs, L. D., Kokubo, Y., Liu, Y., Ma, S., Morawska, L., Sangrador,
J. L. T., Shaddick, G., Anderson, H. R., Vos, T., Forouzanfar, M. H.,
Burnett, R. T., and Cohen, A. (2016). Ambient Air Pollution Exposure
Estimation for the Global Burden of Disease 2013. Environmental Science& Technology, 50(1):79–88.
Cabrales, A., Calvó-Armengol, A., and Zenou, Y. (2011). Social interactions
and spillovers. Games and Economic Behavior, 72(2):339–360.

23

Caragliu, A. and Graziano, M. (2022). The spatial dimension of energy
transition policies, practices and technologies. Energy Policy, 168:113154.
Cherp, A., Vinichenko, V., Jewell, J., Suzuki, M., and Antal, M. (2017).
Comparing electricity transitions: A historical analysis of nuclear, windand solar power in germany and japan. Energy Policy, 101:612–628.
Frantál, B. and Nováková, E. (2019). On the spatial differentiation of energy
transitions: Exploring determinants of uneven wind energy developmentsin the czech republic. Moravian Geographical Reports, 27(2):79–91.
Fraser, T. (2019). Japan’s resilient, renewable cities: how socioeconomics
and local policy drive japan’s renewable energy transition. Environmental
Politics.
Galeotti, A., Golub, B., and Goyal, S. (2020). Targeting Interventions in
Networks. Econometrica, 88(6):2445–2471.
Galeotti, A. and Rogers, B. W. (2013). Strategic immunization and groupstructure. American Economic Journal: Microeconomics, 5(2):1–32.
Goodman, S., BenYishay, A., Lv, Z., and Runfola, D. (2019). GeoQuery: Integrating HPC systems and public web-based geospatial data tools. Computers & Geosciences, 122:103–112.
Hall, B. H. and Helmers, C. (2013). Innovation and diffusion of clean/green
technology: Can patent commons help? Journal of Environmental Economics and Management, 66(1):33–51.
Halleck-Vega, S., Mandel, A., and Millock, K. (2018). Accelerating diffusion of climate-friendly technologies: A network perspective. Ecological
Economics, 152:235–245.
Hong, S., Qvist, S., and Brook, B. W. (2018). Economic and environmental
costs of replacing nuclear fission with solar and wind energy in sweden.
Energy Policy, 112:56–66.
Hughes, L. (2021). Energy Policy in Japan: Revisiting Radical Incrementalism. In Pekkanen, R. J. and Pekkanen, S. M., editors, The Oxford
Handbook of Japanese Politics, pages 376–394. Oxford University Press, 1
edition.
Jackson, M. O. and Storms, E. C. (2023). Behavioral communities and theatomic structure of networks. arXiv preprint at arXiv:1710.04656.
24

Kawashima, S. and Takeda, F. (2012). The effect of the Fukushima nuclear accident on stock prices of electric power utilities in Japan. Energy
Economics, 34(6):2029–2038.
Kempe, D., Kleinberg, J., and Tardos, É. (2003). Maximizing the spreadof influence through a social network. In Proceedings of the ninth ACM
SIGKDD international conference on Knowledge discovery and data mining, pages 137–146.
Kiunke, T., Gemignani, N., Malheiro, P., and Brudermann, T. (2022). Key
factors influencing onshore wind energy development: A case study fromthe german north sea region. Energy Policy, 165:112962.
Li, A., Xu, Y., and Shiroyama, H. (2019). Solar lobby and energy transitionin Japan. Energy Policy, 134:110950.
Li, X., Zhou, Y., Zhao, M., and Zhao, X. (2020). A harmonized global
nighttime light dataset 1992–2018. Scientific Data, 7(1):168.
Mochizuki, J. and Chang, S. E. (2017). Disasters as opportunity for change:
Tsunami recovery and energy transition in japan. International journal ofdisaster risk reduction, 21:331–339.
Morris, S. (2000). Contagion. The Review of Economic Studies, 67(1):57–78.
Okubo, T., Narita, D., Rehdanz, K., and Schroeder, C. (2020). Preferences
for Nuclear Power in Post-Fukushima Japan: Evidence from a Large Nationwide Household Survey. Energies, 13(11):2938.
Oudes, D. and Stremke, S. (2018). Spatial transition analysis: Spatially
explicit and evidence-based targets for sustainable energy transition at
the local and regional scale. Landscape and Urban Planning, 169:1–11.
Popp, D., Hascic, I., and Medhi, N. (2011). Technology and the diffusion ofrenewable energy. Energy Economics, 33(4):648–662.
Rehdanz, K., Schröder, C., Narita, D., and Okubo, T. (2017). Public preferences for alternative electricity mixes in post-Fukushima Japan. Energy
Economics, 65:262–270.
Rode, J. and Weber, A. (2016). Does localized imitation drive technology
adoption? A case study on rooftop photovoltaic systems in Germany.
Journal of Environmental Economics and Management, 78:38–48.
Tsakas, N. (2017). Diffusion by imitation: The importance of targeting
agents. Journal of Economic Behavior & Organization, 139:118–151.
25