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Transnational municipal networks and climate change adaptation: A study of 377 cities


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Cities have increasingly recognised the risks posed by climate change and the need to adapt. To support climate action, cities have formed cooperative networks such as the C40 Cities Climate Leadership Group, the Global Covenant of Mayors and the International Council for Local Environmental Initiatives. However, a lack of scientific evidence exists when it comes to the actual impact of network participation, especially in the context of adaptation. This study is the first to test statistically the association between network membership and progress in adaptation planning in 377 cities globally. The results show that network members are more likely to have started the adaptation process than other cities, and that being a member of multiple networks is associated with higher levels of adaptation planning. Moreover, cities in wealthier countries are more likely to be more advanced in adaptation planning than others. We consider the possible explanations for these results based on the previous literature and information gathered from the networks. The main implications of our study are that network organisations should consider how to encourage the adaptation process among their members and the increased involvement of cities from lower-income countries.
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Transnational municipal networks and climate change adaptation: A
study of 377 cities
Milja Heikkinen
, Aasa Karimo
, Johannes Klein
, Sirkku Juhola
, Tuomas Yl
Helsinki Institute of Sustainability Science, Ecology and Environment Research Programme, Faculty of Biological and Environmental Sciences, PL 65
(Viikinkaari 2a), 00014, University of Helsinki, Finland
Faculty of Social Sciences, PL 54 (Unioninkatu 37), 00014, University of Helsinki, Finland
Geological Survey of Finland, Espoo, Finland, PL 96 (Vuorimiehentie 5), 02151, Espoo, Finland
Helsinki Institute of Sustainability Science, Faculty of Social Sciences, PL 54 (Unioninkatu 37), 00014, University of Helsinki, Finland
article info
Article history:
Received 26 June 2019
Received in revised form
3 December 2019
Accepted 6 February 2020
Available online 7 February 2020
Handling editor: Yutao Wang
Climate change adaptation
City networks
Adaptation planning
C40 network
Global covenant of mayors
Cities have increasingly recognised the risks posed by climate change and the need to adapt. To support
climate action, cities have formed cooperative networks such as the C40 Cities Climate Leadership Group,
the Global Covenant of Mayors and the International Council for Local Environmental Initiatives. How-
ever, a lack of scientic evidence exists when it comes to the actual impact of network participation,
especially in the context of adaptation. This study is the rst to test statistically the association between
network membership and progress in adaptation planning in 377 cities globally. The results show that
network members are more likely to have started the adaptation process than other cities, and that being
a member of multiple networks is associated with higher levels of adaptation planning. Moreover, cities
in wealthier countries are more likely to be more advanced in adaptation planning than others. We
consider the possible explanations for these results based on the previous literature and information
gathered from the networks. The main implications of our study are that network organisations should
consider how to encourage the adaptation process among their members and the increased involvement
of cities from lower-income countries.
©2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
1. Introduction
Cities are important actors in climate change adaptation (Revi
et al., 2014;van der Heijden, 2018;van der Heijden et al., 2018).
High hopes have been expressed about the benets of networking
between cities for climate action. With the support from inter-city
networks such as the C40 Cities Climate Leadership Group (C40),
the International Council for Local Environmental Initiatives (ICLEI)
and the Global Covenant of Mayors (GCoM), cities are pictured as
leading the way to a climate-safe future, combining ambitious
mitigation and adaptation efforts (C40, Solecki et al., 2018;van der
Heijden et al., 2019). Cities are also seen as the drivers of globally
sustainable development (UNEP, 2011;Barber, 2017;ICLEI, 2018;
Solecki et al., 2018;van der Heijden et al., 2019).
Meanwhile, the Intergovernmental Panel on Climate Change
(IPCC) Fifth Assessment Report (FAR) states that there is medium
condence, based on medium evidence and with medium agree-
ment, that horizontal learning through networks of cities benets
urban adaptation (Revi et al., 2014: 539). Despite the enthusiasm
surrounding these networks, there is little systematic evidence
concerning the effects of network participation (Wolfram et al.,
2019). There has been no research to show whether network
participation is associated with progress in planning of climate
change policies at the city level, especially when it comes to
adaptation (Fünfgeld, 2015;Woodruff, 2018).
We dene transnational municipal networks (TMNs) related to
climate change as organisations that aim to support cooperation
between cities to improve their climate change mitigation and
adaptation work. TMNs can require cities to adopt certain quanti-
tative or qualitative climate goals. They organise events, produce
information (e.g. reports on their membersclimate actions), offer
tools and/or resources and represent cities internationally. TMNs
originally concentrated on mitigation, but adaptation has increas-
ingly been on their agenda. Although some scholars have begun to
*Corresponding author.
E-mail addresses: milja.e.heikkinen@helsinki.(M. Heikkinen), aasa.karimo@
helsinki.(A. Karimo), johannes.klein@gtk.(J. Klein), sirkku.juhola@helsinki.
(S. Juhola), tuomas.yla-anttila@helsinki.(T. Yl
Contents lists available at ScienceDirect
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Journal of Cleaner Production 257 (2020) 120474
study their role in adaptation, it remains understudied (Juhola and
Westerhoff, 2011;Busch, 2015;Woodruff, 2018). The big question
regarding adaptation is whether these networks live up to the high
expectations to increase local adaptation efforts (Woodruff, 2018).
To our knowledge, this study is the rst effort to answer this
question through a statistical analysis of a global sample of cities.
We analysed the connection between TMN membership and
progress in climate adaptation planning worldwide using the
Adaptation Process Index (API), which is the rst global measure of
city-level climate change adaptation planning efforts, developed by
Araos et al. (2016b). We have employed their data set in this study,
analysing the connection between memberships in three key global
TMNsdC40, ICLEI and GCoMdand the progress of cities in their
adaptation planning processes.
The study covers 377 large cities (at least one million in-
habitants) for which the API is currently available (Araos et al.,
2016b). Large cities are of interest in climate adaptation research
due to their economic importance and the risks related to popu-
lation concentration (Hunt and Watkiss, 2011). Moreover, earlier
studies have found that large cities seem to be drivers in TMNs
(Woodruff, 2018).
Previous empirical studies have predominantly focused on a
handful of cities in wealthy western countries (van der Heijden,
2018), in the form of case studies of individual cities or limited
geographical areas (Kern and Bulkeley, 2009). The samples of the
few earlier large-N studies have either been from Europe (Reckien
et al., 2018) or from North America (Woodruff, 2018). The existing
studies also reect differences in focus. Reckien et al. (2018) re-
ported on the status and drivers of European city-level climate
change mitigation and adaptation planning in relationship to na-
tional- and international-level planning and legislation. Woodruff
(2018) analysed how factors like planning capacity affect the
probability of a city to join TMNs.
This study goes beyond the existing qualitative literature by
statistically testing the association between TMN participation and
climate adaptation enot just showing how some cities may benet
from these networks but testing whether this is the case at the
aggregate level and by seeing what kind of cities are more likely to
benet than others. We also go beyond the limited existing quan-
titative work by analysing a global sample which includes a sig-
nicant number of non-western cities, especially from China and
India. This approach provides new insights into the global role of
TMNs by signicantly widening the geographical scope of the
analysis compared to earlier studies.
We selected three TMNs
to analyse: C40, ICLEI, and GCoM
(formerly the Covenant of Mayors and the Compact of Mayors). C40
is a network of global mega-cities concentrating especially on
climate action. GCoM also concentrates on climate issues. ICLEI has
a broader aim to support the sustainability of cities, but it also
strongly promotes climate action, and adaptation was included in
its strategic plan for 2006.
These networks have been identied as
important global climate change-related TMNs in previous studies
(Kern and Bulkeley, 2009;Heidrich et al., 2016;Busch, 2015;Busch
et al., 2018;Woodruff, 2018;Reckien et al., 2018;van der Heijden
et al., 2019).
The C40 was established in London in 2005. Its target is to
develop and implement policies and programmes that generate
measurable reductions in both greenhouse gas emissions and
climate risks. The network promotes cities as leaders of change, and
cities need to pass an application process to become members
( According to Davidson and Gleeson (2015), C40
represents a new strategic urbanism phase of transnational urban
governance, because it ties together the most inuential and
economically-powerful mayors of global mega-cities to adopt a
more visible political stance. C40 also has several private sector
partners, like Bloomberg Philanthropies (
ICLEI is the oldest of the three networks, founded in 1990. When
compared with the C40, the climate agenda of ICLEI is more diverse,
targeting also smaller urban areas and connecting the work in
general themes of sustainability. It has an annual membership fee
(depending on the region, population and per capita gross national
income), but membership is open to all cities and regions (www.
GCoM differs from the other two. It is a combination of initia-
tives, including Covenant of Mayors (launched 2008), Compact of
Mayors (launched 2014) and Mayors Adapt (launched 2014). These
were combined in 2015. GCoM aims to give political support to the
climate work of the cities by supporting the engagementof mayors.
According to them, they are worlds largest global alliance for city
climate leadership with over 9000 members (https://www.
Although these networks operate independently, they have in-
terconnections. C40 and ICLEI had a role in creating the Compact of
Mayors, and they also work together on a range of projects, like
mitigation related Carbon Disclosure Project.
Next, we review the existing literature related to the impact of
TMNs, and draw our hypotheses, before describing our dataset and
the methods. In the results section, we have presented the average
and median APIs for cities with different combinations of network
memberships, as well as the results of statistical analysis. Our
ndings indicate that there is a statistically signicant connection
between network participation and starting the adaptation process,
and that being a member of multiple networks is associated with
higher levels of adaptation planning. Finally, we discuss this in light
of the existing literature, and draw a conclusion.
2. Literature review and hypotheses
The role of TMNs has been studied mostly from the point of view
of mitigation (Fünfgeld, 2015). We provide a short overview of
recongnized benets in Table 1. The benets are often connected to
information sharing, learning, shaping mitigation initiatives, and
increased resources. It has been found that networks help munic-
ipalities to act when state-level action is lacking, and that they act
as city advocates shaping the political environment and legal frame.
Networks may cause similar effects when it comes to adaptation
an Broto and Bulkeley, 2013).
Overall, we identify two specic topics in this discussion on
which we wish to shed light. First, claims have been made that
networking cities are pioneers of mitigation (Kern and Bulkeley,
2009), and the same may apply when it comes to adaptation.
Relatively strong claims state that networks do support local
climate adaptation (Revi et al., 2014;Woodruff, 2018), even though
little empirical evidence exists showing that networks have an ef-
fect on actual city-level climate action. Proving this claim is dif-
cult, and scholars should be critical of highly normative claims on
the impact of networks (van der Heijden, 2018). If city networks do
support local climate change adaptation, it would be reasonable to
expect members would have a higher API. Hence, we hypothesise
H1. Network members are more advanced in their climate change
adaptation planning processes than non-members.
We decided to leave 100 Resilient Cities out since it was founded 2013 and it
concentrates on resilience, a different concept than adaptation. Also, their future
plans are unclear:
M. Heikkinen et al. / Journal of Cleaner Production 257 (2020) 1204742
Many cities are members of more than one network. It is
possible that different networks offer different kinds of support,
making it reasonable to use resources in multiple memberships. If
networks do support climate change adaptation, it would be logical
to assume that the more networks a city participates in, the more
support it gets. Hence, we hypothesise that
H2. The more networks a city is a member of, the more advanced it is
in its adaptation planning process.
Second, TMNs seem to be biased towards the wealthy western
countries. When studying mitigation networks, Bansard et al.
(2017) found that cities in Europe and North America are over-
represented, and the cities which participate in several networks
connecting them together come from this region. Cities in wealthier
countries seem to end up at the core of networks dening best
practices, which may exclude the cities in less wealthy countries
and increase differences between cities (Kern and Bulkeley, 2009;
Shi et al., 2016). This is because cities in wealthy countries tend to
have higher overall administrative capacities to design policies, as
well as higher capacities to implement adaptation and mitigation
measures. For example, C40 may seem horizontal, but it is still
largely dominated by cities like New York and London (Acuto, 2013,
see also Bouteligier, 2013;Lee, 2018).
Cities with lower capacities may adopt passive roles, with their
membership becoming mostly symbolic (Kern and Bulkeley, 2009).
Not all members have access to the benets offered by the net-
works (Lee, 2015;van der Heijden, 2018), and although networks
have been active for some decades now, a great number of cities
have not joined (van der Heijden, 2018). On the other hand, it has
been argued that cities lagging in climate action reap the greatest
benets from the networks (Busch et al., 2018;Reckien et al., 2018).
Therefore, we test the hypothesis
H3. The wealthier the country in which a city is located, the more
advanced the city is in its adaptation planning process.
Our dataset is the rst global, large-N sample of city-level
climate adaptation measures. With these data, we can not only
test H3, but also control for wealth differences when testing H1 and
H2. Further, we can assess whether our two key independent var-
iables, network participation and wealth, are correlated.
3. Material and methods
We used data on 997 adaptation initiatives in 402 urban areas
around the world (Araos et al. (2016a,b). The dataset includes in-
formation about public adaptation planning in urban areas
more than one million people. The researchers considered material
in the following 13 languages, with a minimum of four cities per
language: English, Spanish, French, Chinese, Arabic, Russian,
German, Portuguese, Farsi, Korean, Japanese, Turkish, and Indone-
sian (Araos et al., 2016b).
Araos et al. (2016b) collected the data from climate change
planning documents in a web-based search using the Google search
engine. The search terms were climate changeand the citys
name. Thus, the search focussed on highly intentionaladaptation
policies as dened by Dupuis and Biesbroek (2013: p. 1480). The
data was collected between January 2 and March 29, 2014 (Araos
et al., 2016b). This method is consistent with other studies col-
lecting information about adaptation planning (Reckien et al., 2014,
2018;Lesnikowski et al., 2016).
3.1. Variables
Our dependent variable is the adaptation process index (API,
Araos et al., 2016b), drawn from the data described above. The in-
dex includes the following criteria: presence of climateprojections,
presence of vulnerability assessments, consideration of multiple
sectors, reassessment of development priorities in the face of
climate change, availability of climate change planning documents,
consultations and stakeholder engagement, management of bar-
riers and uncertainty, and monitoring and evaluation of adaptation
activities. The more criteria a city fullled, the higher its API. The
values range from 0 to 8. For further theoretical justications on
why these particular criteria are included in the API, see (Araos
et al., 2016b).
The main independent variable of interest is network partici-
pation. For each city in the dataset, we coded whether it is (or is
not) a member of a network on spring 2019, and if it is a member,
whether the city joined the network before 2014. We received the
information about memberships and when the cities joined
through personal communication with the networks, and through
webpage searches when necessary. Due to some missing data, we
had to drop some cities out of the analyses leaving us with 377
cities. We use network participation to explain the variation in API
in two ways: rst, by using each network membership as an in-
dependent variable separately, and then by combining network
memberships into one variable that measures the number of net-
works of which the city is a member.
As national-level control variables, we used location at the
continent level (Africa being the reference category), level of na-
tional adaptation legislation at the time based on Climate Change
Laws of the World database, 2017 (0 ¼no legislation,
1¼executive 2 ¼legislative) and gross domestic product. As a city
level control variable, we used the size of the city. We controlled for
location and GDP because earlier research found TNMs to be biased
towards wealthy countries in Europe and North America and cities
in wealthy countries have better resources for climate action, as
explained in section 2. We controlled for the existence of national
legislation because cities in countries in which legislation requires
cities to plan for adaptation are likely to be more advanced in their
adaptation planning, and for city size, because larger cities may
have better capacities for adaptation than smaller ones (cf. Reckien
Table 1
Overview of benets of networking. MLG ¼multi-level governance, SNA ¼Social Network Analysis.
Author(s): Year: Method(s): Scope: Main results concerning (potential) network benets:
Bulkeley et al. 2003 theoretical TMNs as part of MLG in Europe preliminarily: lobbying, information sharing, learning, policy initiative creation
Kern &Bulkeley 2009 case study TMNs as part of MLG in Europe pioneers benet: information sharing, learning, access to funding, legitimacy
Andonova et al. 2009 theoretical networks as transnational governance information sharing/diffusion, learning, possibly increased resources
Lee &van de Meene 2012 SNA learning in C40 learning, information sharing
Busch 2015 case study Inuence of TMNs in German cities information sharing, learning, goal setting, city advocate
Lee &Koski 2015 statistical methods mitigation in C40 member cities motivation of local policy &action, spills over to non-members
Busch et al. 2018 survey, interviews Inuence of TMNs in German cities information sharing, learning, possibly increased resources
The terms cityand urban areado not necessarily refer to areas dened by
administrative boundaries, but the data collection is based on the United Nations
denition of urban agglomeration.
M. Heikkinen et al. / Journal of Cleaner Production 257 (2020) 120474 3
et al., 2018).
Table 2 presents descriptive statistics of all variables used in the
analyses. Of the sample of 377 large cities, 202 were members of at
least one network, which is more than half of the sample. Out of
these 202 cities, 116 had joined at least one network before 2014
(30.77% of the whole sample and 57.43% of the network members).
Overall, the GCoM has the most members among the cities in our
sample with 150 members, 110 cities in our sample are members of
ICLEI and 74 are members of C40. The mean API score of the whole
sample is very low, only 1.58 on the scale from 0 to 8.
3.2. Models
To test binary relationships between network participation and
country-level GDP and API, we rst used one-way analysis of
variance (Raykov and Markoulides, 2013). We then analysed the
relationship further by using zero-inated negative binomial
regression models. We used this model type to take into account
the type and distribution of the dependent variable, the citys API.
The API can only take non-negative integer values that arise
from counting rather than ranking (Araos et al., 2016b). This kind of
count data is typically analysed using Poisson regression models
(Greene, 1994). However, the distribution of API across the cities
analysed is overdispersed and contains excessive zeros, which
creates a violation of the assumptions of a conventional Poisson
model. The zero-inated negative binomial model takes the large
number of zeros into account and allows for the variance of the
dependent variable to be greater than the mean, which is not the
case in traditional Poisson regression models for count data
(Greene, 1994).
Zero-inated count regression models are a mixture of a
generalized linear model for the dichotomous outcome, and a
conventional event-count generalized linear model, such as nega-
tive binomial regression (Desmarais and Harden, 2013). This is why
a zero-inated negative binomial model divides the analysis into
two distinct parts, a zero-ination model and a count model
(Greene, 1994). This means that two different processes are ana-
lysed at once, one analysing the probability of the dependent var-
iable getting a value zero (a reversed binomial model), and the
other analysing the distribution of the count variable including zero
(Greene, 1994). In case of the API, the two processes are whether a
city has started the adaptation process in the rst place, and if so,
how far along in the process it is. The expected value of the API is
thus expressed as a combination of both processes:
is the probability that a city has not started the adaptation
process and x
is the count component of the model (Zeileis et al.,
2008). We conducted the analysis using the pscl package in an R
environment for statistical computing.
The API includes two parts describing the awareness of
vulnerability and exposure: vulnerability and climate trend (Araos
et al., 2016b). It is not probable that vulnerability and/or exposure
that a city is unaware of will affect the adaptation planning process.
We did not include vulnerability as an explicit control variable,
because there is no reliable global index which would describe
vulnerability at the city level before 2014 (Araos et al., 2016b) and
vulnerability is partly covered by controlling for economic capacity,
since existing patterns of development have profound effects on
vulnerability (Shi et al., 2016).
4. Results
Our rst and second hypotheses are that network members
have higher APIs than other cities and that the more memberships,
the higher the API. Results of the analysis of variance showed a
Table 2
Variables used in the analyses.
Continuous variable information Minimum Maximum Mean Std. Deviation
Adaptation process index 0 8 1.58 2.47
GDP 2016 (in thousands) 0.80 87.86 22.54 18.19
Population (100 000) 10.02 369.33 31.14 37.91
Categorical variable information N Percent
C40 member (total) 74 19.89
C40 member 2014 or later 32 8.49
C40 member before 2014 42 11.14
ICLEI member (total) 110 29.18
ICLEI member 2014 or later 25 6.63
ICLEI member before 2014 85 22.55
GCoM member (total) 150 39.79
GCoM member 2014 or later 127 33.69
GCoM member before 2014 23 6.10
Member of 1 network before 2014 85 22.55
Member of 2 networks before 2014 28 7.43
Member of 3 networks before 2014 3 0.80
Level of adaptation legislation:
Executive 212 56.23
Legislative 47 12.47
Africa 46 12.20
Asia 185 49.07
Europe 34 9.02
Latin America 56 14.85
North America 50 13.26
Oceania 6 1.59
We also conducted the analyses using multilevel negative binomial regression
but ended up with the single level model because the model estimate for country
level variance was 0. This might be due to the fact that more than half of the cities
in our data are the only observation from their country.
M. Heikkinen et al. / Journal of Cleaner Production 257 (2020) 1204744
connection between network membership and adaptation plan-
ning progress, giving preliminary support to both hypotheses 1 and
Table 3 presents the means and analysis of variance in API for
the cities with different network membership combinations.
numbers show that there is a connection between network mem-
berships and higher APIs, especially when the city is member of
two or three networks, or when it is member in C40 or GCoM. Our
third hypothesis is that wealthier cities have higher APIs regardless
of network participation, so we also analysed the correlation be-
tween API and GDP. Our results indicate a signicant association
between these two variables with a Pearson correlation of 0.339
(p <0.000) between API and GDP.
Table 3 also presents the results from analysis of variance in GDP
for cities with different network membership combinations. Cities
that are not members of any of these networks have a signicantly
lower GDP compared to other cities. Specically, members of GCoM
and C40 have a higher GDP than non-members, but also members
of ICLEI that joined before 2014 have a statistically signicantly
higher mean GDP compared to cities that are not members.
These analyses offer preliminary support to all three hypothe-
ses. Wealth is connected to both network membership and the
adaptation planning progress. However, differences in GDP be-
tween network members and non-members raise a question of
whether these connections are independent or not. The results
from zero-inated negative binominal models give us a more
detailed picture (Table 4).
Table 4 presents the results of the zero-inated negative bino-
mial models (models 0 to 6). They also support our hypotheses.
Network members have been more likely to start their climate
change adaptation planning process than other cities (H1), and
being a member of multiple networks is connected to having a
higher API (H2). These results hold when wealth, geographical
location and legislation are controlled for. Also, cities in wealthy
countries have higher API scores when network memberships are
controlled for (H3). We also conducted the analyses using standard
negative binomial regression as a robustness check. However, ac-
cording to the Vuong test (Vuong, 1989) results, the zero-inated
model is a signicant improvement over the standard negative
binomial model (see supplement 2 and 3 for details).
The upper part of Table 4 shows the count part of the zero-
inated regression models, which are to be read like regular
count regression models, i.e. a one unit increase in an independent
variable results in exp(B) increase in the dependent variable. The
lower part of the table shows the zero-ination model, which in-
dicates the probability of the dependent variable (API) being a
certain zero. This indicates that the coefcient 1.87 for C40
members in Model 3 means that being a member of C40 decreases
the odds of having an API value of zero by exp (1.87) compared to
other cities.
We began regression modelling by estimating a null model that
includes only control variables, namely population, legislation, and
continent. Model 0 shows that indeed, there are differences in the
API of cities by continent. Asian and Latin American cities have
signicantly lower total APIs compared to the African cities in our
sample (Model 0, count model), and all but Asian cities have a
Table 3
Comparing means of API and GDP by network participation.
API GDP (in thousands)
Mean 95% condence interval Mean 95% condence interval
Lower Upper Lower Upper
No network memberships before 2014 0.89 0.68 1.10 18.80 16.84 20.77
Member of 1 network before 2014 2.21 1.62 2.79 29.24 24.83 33.64
Member of 2 networks before 2014 5.21 4.12 6.31 34.86 27.86 41.85
Member of 3 networks before 2014 4.33 5.71 14.37 43.02 27.44 58.60
ANOVA F Sig. F Sig.
Between groups 41.051 0.000 14.461 0.000
95% condence interval 95% condence interval
Mean Lower Upper Mean Lower Upper
Not a member of C40 1.14 0.91 1.37 21.56 19.58 23.54
Member of C40 before 2014 4.09 3.17 5.02 33.70 27.24 40.16
Member of C40 2014 or after 1.69 0.81 2.57 17.15 11.49 22.82
ANOVA F Sig. F Sig.
Between groups 3.,527 0.000 10.223 0.000
95% condence interval 95% condence interval
Mean Lower Upper Mean Lower Upper
Not a member of ICLEI 1.23 0.98 1.48 21.12 18.99 23.25
Member of ICLEI before 2014 2.64 1.99 3.29 28.35 24.21 32.48
Member of ICLEI 2014 or after 0.76 0.05 1.47 17.97 11.08 24.86
ANOVA F Sig. F Sig.
Between groups 13.169 0.000 6.094 0.002
95% condence interval 95% condence interval
Mean Lower Upper Mean Lower Upper
Not a member of CoM 1.01 0.77 1.25 16.77 14.98 18.56
Member of GCoM before 2014 5.22 3,98 6.45 43.45 39.04 47.86
Member of GCoM 2014 or after 1.78 1.33 2.24 29.07 25.35 32.79
ANOVA F Sig. F Sig.
Between groups 39.082 0.000 42.496 0.000
We analysed the means of API separately for cities with API >0. The results
point to similar direction. For details, please see the supplement.
M. Heikkinen et al. / Journal of Cleaner Production 257 (2020) 120474 5
signicantly lower probability of having an API value zero
compared to African cities (Model 0, zero-ination model).
The existence of national legislation on climate adaptation is not
signicant in any model. This is not surprising, since only ve out of
80 countries had legislation in place before 2014. The results con-
cerning the association between the existence of a national non-
binding strategic or guiding document (laws 2 in Table 4)are
mixed. The existence of a guiding document is associated with an
increased likelihood of having an API higher than zero, but nega-
tively associated with API in the count model when GCoM mem-
bership is included as a covariate.
Next, we test H3 on the association between the wealth of the
country that a city is located in and the citys API. The reason for
testing H3 before H1 and H2 is that adding GDP into the models at
this stage ensures that the wealth of the country is controlled for in
all subsequent models that test H1 and H2. Since H1 and H2 are our
primary hypotheses of interest, we wanted to present them rst in
the hypotheses section above.
The connection between GDP and higher API is statistically
signicant also when the citys location is controlled for (Model 1).
GDP remained signicant in all count and zero-ination models
(Models 2e6), indicating there is an association between GDP and
higher API regardless of network participation, and the probability
of having a value zero is lower in wealthier cities. All in all, these
results support H3.
H1 was tested in zero-inated Models 2 to 5 and count Models 2
to 5. The zero-inated Models 2 and 3 show that membership of
C40 and ICLEI decrease the probability of API being zero when a city
has joined the network before 2014. Model 4 shows that mem-
bership of GCoM is not signicant. The results are similar when all
memberships are in the same model (Model 5).
Count Models 2 and 3 show that membership of ICLEI or C40 is
not connected to higher API. However, cities which joined the
GCoM network before 2014 have signicantly higher API scores
Table 4
Parameter estimates and model t for zero-inated negative binomial models explaining adaptation process index.
Parameter estimates: count model Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Intercept 1.82 (0.23) *** 1.78 (0.23) *** 1.82 (0.24) *** 1.78 (0.23) *** 1.92 (0.25) *** 1.95 (0.25) *** 1.83 (0.24) ***
H1 C40 member before 2014 0.23 (0.14) 0.20 (0.14)
C40 member 2014 or after 0.03 (0.17) 0.03 (0.18)
ICLEI member before 2014 0.03 (0.10) 0.03 (0.10)
ICLEI member 2014 or after 0.07 (0.28) 0.05 (0.30)
GCOM member before 2014 0.62 (0.23) ** 0.60 (0.23) *
GCoM member 2014 or after 0.09 (0.13) 0.09 (0.14)
H2 Member in 1 network 0.09 (0.12)
Member in 2 networks 0.33 (0.15) *
Member in 3 networks 0.30 (0.31)
H3 GDP 2016 (in thousands) 0.01 (0.00) *** 0.01 (0.00) ** 0.01 (0.00) ** 0.01 (0.00) * 0.01 (0.00) * 0.01 (0.00) *
Population (100 000) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00)
Level of adaptation legislation: executive 0.19 (0.17) 0.30 (0.16) 0.33 (0.17) 0.30 (0.16) 0.43 (0.17) ** 0.45 (0.17) ** 0.37 (0.17) *
Level of adaptation legislation: legislative 0.28 (0.16) 0.24 (0.16) 0.26 (0.16) 0.23 (0.16) 0.11 (0.17) 0.13 (0.17) 0.18 (0.16)
Asia 0.44 (0.17) * 0.53 (0.17) ** 0.49 (0.18) ** 0.51 (0.18) ** 0.48 (0.18) ** 0.45 (0.19) * 0.46 (0.18) *
Europe 0.30 (0.22) 0.71 (0.24) ** 0.72 (0.24) ** 0.71 (0.24) ** 1.12 (0.30) *** 1.12 (0.30) *** 0.79 (0.25) **
Latin America 0.47 (0.23) * 0.53 (0.23) * 0.56 (0.23) * 0.54 (0.23) * 0.58 (0.23) * 0.60 (0.23) ** 0.56 (0.24) *
North America 0.36 (0.25) 0.98 (0.30) ** 0.99 (0.30) *** 0.98 (0.30) ** 1.04 (0.30) *** 1.06 (0.30) *** 1.01 (0.31) **
Oceania 0.24 (0.30) 0.73 (0.33) * 0.77 (0.32) * 0.72 (0.34) * 0.76 (0.33) * 0.79 (0.35) * 0.82 (0.34) *
Log(theta) 10.17 (62.98) 10.68 (44.10) 10.80 (39.43) 10.68 (44.32) 10.87 (38.33) 10.94 (35.26) 10.87 (38.38)
Parameter estimates: zero-ination model Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Intercept 3.96 (0.74) *** 5.22 (0.90) *** 5.39 (0.93) *** 5.29 (0.92) *** 5.11 (0.90) *** 5.19 (0.97) *** 5.28 (0.93) ***
H1 C40 member before 2014 1.87 (0.65) ** 1.75 (0.67) **
C40 member 2014 or after 0.62 (0.45) 0.71 (0.48)
ICLEI member before 2014 0.80 (0.33) * 0.79 (0.35) *
ICLEI member 2014 or after 0.44 (0.68) 0.36 (0.71)
GCOM member before 2014 1.05 (1.14) 0.59 (1.29)
GCoM member 2014 or after 0.15 (0.37) 0.35 (0.41)
H2 Member in 1 network 0.68 (0.33) *
Member in 2 networks 2.51 (0.87) **
Member in 3 networks 18.28 (6456.69)
H3 GDP 2016 (in thousands) 0.06 (0.02) *** 0.06 (0.02) *** 0.06 (0.02) *** 0.06 (0.02) ** 0.06 (0.02) ** 0.06 (0.02) **
Population (100 000) 0.02 (0.00) *** 0.02 (0.00) *** 0.01 (0.01) 0.02 (0.00) *** 0.02 (0.00) *** 0.01 (0.01) 0.01 (0.00) *
Laws 2 2.31 (0.62) *** 3.12 (0.70) *** 3.21 (0.72) *** 3.07 (0.70) *** 2.99 (0.70) *** 3.00 (0.73) *** 3.11 (0.71) ***
Laws 3 0.05 (0.63) 0.10 (0.70) 0.23 (0.72) 0.08 (0.73) 0.16 (0.76) 0.24 (0.82) 0.34 (0.79)
Asia 0.58 (0.45) 0.10 (0.48) 0.35 (0.50) 0.18 (0.48) 0.17 (0.49) 0.34 (0.52) 0.30 (0.50)
Europe 4.70 (0.83) *** 4.09 (0.88) *** 4.32 (0.91) *** 4.26 (0.89) *** 3.93 (0.95) *** 4.23 (0.99) *** 4.15 (0.90) ***
Latin America 1.35 (0.66) * 1.34 (0.71) 1.33 (0.73) 1.35 (0.73) 1.34 (0.72) 1.55 (0.77) * 1.35 (0.75)
North America 3.47 (0.77) *** 1.32 (0.92) 1.27 (0.96) 1.34 (0.92) 1.31 (0.92) 1.37 (0.95) 1.25 (0.95)
Oceania 5.21 (1.32) *** 3.69 (1.37) ** 3.50 (1.41) * 3.70 (1.46) * 3.70 (1.37) ** 3.67 (1.51) * 3.47 (1.44) *
AIC 999.68 978.64 973.37 979.28 977.67 975.77 970.94
Log Likelihood 480.84 468.32 461.68 464.64 463.83 454.88 458.47
N 377 377 377 377 377 377 377
***p <0.001, **p <0.01, *p <0.05.
M. Heikkinen et al. / Journal of Cleaner Production 257 (2020) 1204746
than others, controlling for geographic location and wealth (Model
4). These results are reproduced when all memberships are in the
same model (Model 5). This is because almost all of these cities are
members of the former Covenant of Mayors: out of 23 cities, 21 are
in western Europe. The remaining two cities are in Canada and
Mexico. In the dataset, 13 European cities are not members of
GCoM, 12 of them in Russia. Their average API is clearly lower than
among the European members of GCoM, which is probably why
controlling by continent has not made the result insignicant. The
effect of GCoM on API is not supported by standard negative
binomial models (Supplement 2), so this result needs to be inter-
preted with caution.
H2 was tested in Model 6 by studying whether membership of
multiple networks is associated with a higher API than member-
ship of just one network. Being a member of two networks before
2014 was signicantly connected to both higher API and a smaller
probability of having an API value equal to zero (Model 6). Being a
member of one network decreases the probability of a zero value
(Model 6, zero-ination part), but is not signicant in increasing API
(the count-part). Being a member of all three networks is not sig-
nicant, but this is probably because only three cities were mem-
bers of all three networks before 2014. These results are also
supported by standard negative binomial models (Supplement 2).
Network membership was not signicant in any of the models, if
the city had joined 2014 or later.
Adding GDP, memberships of C40 and GCoM, and the number of
network memberships improves the model t according to both
measures used (AIC and Log Likelihood). Adding ICLEI memberhip
improves only the Log Likelihood of the model. Analysis of Pearson
residuals indicate a reasonably good t to the data for all models
(Supplement 4).
5. Discussion
This article began with the observation that there are high ex-
pectations that TMNs will propel cities into ambitious climate
adaptation. Few studies, however, have looked into the validity of
this claim, and the ones that have are limited to a few cases or
specic geographic areas (Woodruff, 2018). We statistically tested
whether network members are more advanced in climate change
adaptation planning than other large cities, using a dataset that
includes cities from all over the world and estimating a set of zero-
inated negative binomial regression models.
Our results support the claim that TMN members are more
likely to have started climate change adaptation planning process
than other cities. Members of C40 and ICLEI are less likely to have
an API of zero than other cities when wealth, geographical location
and legislation are controlled for. Members of the GCoM network
have overall higher APIs than other cities. Being a member of two
networks is associated with higher API scores and being a member
of one of them increases the likelihood of a citys API being higher
than zero.
We found that cities in wealthier countries are members of
these networks more often than those in less wealthy countries.
Even though wealth partly explains higher API scores through its
effects on network membership, wealth also has a direct effect on
API (i.e. wealthy cities have a higher API scores when network
membership is controlled for).
Does all this mean that network membership supports cities in
their climate change adaptation planning? With the kind of cross-
national data used here, it is not possible to establish rmly the
direction of causality. However, we did test the hypotheses sepa-
rately for cities that joined before our data collection (2014) and
after, and these tests lent further support to the interpretation that
network membership does support the adaptation process. Those
cities that had joined networks before 2014 had been more likely to
have started the adaptation process than non-members, but this
did not apply to those cities which joined later. Also, being a
member of two networks before 2014 was connected to higher API,
while being a member of two after that date was not.
If it were the case that cities already active in adaptation would
be more likely to join the network, it would also be logical to nd an
association between higher APIs for cities that joined the networks
after Araos et al. (2016b) measured their adaptation progress in
2014. We found no such association.
Kern and Bulkeley (2009) found that the most active core
members of TMNs are often the founding members or those who
join early, while the cities joining only after their neighbours or
collaborators may adopt more passive roles. To establish whether
this is the case, or whether networks actually push cities to action,
would necessitate a full second round of data collection on adap-
tation initiatives in all cities included in our sample and analysis of
their API scores both before and after joining the network. This
should be done in future research.
In addition to the shortcomings imposed by our cross-sectional
dataset, this study has other limitations. First, we concentrated on
large cities. Results could be different for small or medium-sized
cities. Second, several large urban areas with adaptation activities
were not included in the dataset, since they did not offer infor-
mation in the thirteen languages used in the data collection. Third,
there is always the possibility of human error. Some cities may have
had adaptation documents that were not found, leading to under-
estimated API. Fourth, country-level factors like GDP and location
may not give a perfect picture about the capacities of the cities.
However, they are the best proxies for which reliable data were
available. Fifth, API is based on planning documents. Therefore, this
study does not reveal if cities implement these plans or not. Global
level analysis of implementation is another important topic for
future research. Also, independent adaptation by citizens, the pri-
vate sector and NGOs were beyond the scope of this study.
At the beginning, we noted that research on TMNs has often
focused on certain, often high-capacity, cities and regions (Bansard
et al., 2017) and may create an illusion about the cities on the front
line of adaptation, while in reality only a handful of cities partici-
pate (van der Heijden, 2018). The cities in less wealthy countries
may lack the necessary resources to join networks. Even among the
ones that do participate, not all are active (Kern and Bulkeley,
2009), nor gain access to the benets (Lee, 2015).
Our results show that many large cities remain outside the
global networks, especially in less wealthy countries. Also, they
show that cities located in poorer countries, especially outside the
networks, have advanced less in the adaptation process. This is
problematic, since urbanisation and population growth are rapid in
less wealthy countries (UN-DESA, 2015), where climate change
vulnerability is often high as well (Shi et al., 2016;van der Heijden,
2018). Considering the strong role cities from wealthy countries
have in the networks, it is probable that simply joining the net-
works is not a solution.
Overall, our results show that much improvement is needed in
adaptation planning in cities. This is true also for cities that belong
to TMNs and cities in wealthy countries. Each of the networks we
studied had many members with API scores of zero, and none of
them had an average API close to 8, the highest gure on our scale.
In our results, network membership was signicant even when
GDP was controlled for. This lends some support to Lee (2015)
argument that the attributes of the cities are more important
drivers of their climate policy practices than the attributes of the
host country. However, we did nd that wealth, as a country
attribute, does inuence adaptation planning progress indepen-
dent of the effect occurring through network membership. Wealth
M. Heikkinen et al. / Journal of Cleaner Production 257 (2020) 120474 7
has also been found to be a driver of national level adaptation
(Berrang-Ford et al., 2014). It has also been found that participation
in networks supports cities especially when they lack state-level
support (Bulkeley et al., 2003;Heidrich et al., 2016;Busch et al.,
2018;Reckien et al., 2018). In our data, all cities from less wealthy
countries with an API score of a perfect 8 participated at least in one
It has been noted that the climate plans of C40 members from
different contexts are remarkably similar, even though one could
assume that different practices are needed (Heikkinen et al., 2018).
While this similarity does not necessarily follow from network
membership (Heikkinen et al., 2018), previous studies have also
criticised networks for being too conservative and reinforcing the
status quo (Acuto, 2013;Bouteligier, 2013;van der Heijden, 2017;
Heikkinen et al., 2018). Also, a good solution may be less efcient or
even harmful when exported to another context (Gupta et al., 2015;
van der Heijden, 2017). It should be critically considered whether
traditional large-scale master planning is the best way to tackle
climate change-related risks in cities located in less wealthy
countries (Shi et al., 2016).
Therefore, networks should consider developing new ways to
act that would support context-based actions and more funda-
mental changes. C40 has taken steps in this direction by intro-
ducing personalised climate advisors. However, this is a costly
method and therefore not offered to all members (C40, 2017 per-
sonal communication
). This again raises the question of accessi-
bility to the benets (Lee, 2015), which may lead to a situation in
which the cities most needing support do not get it. Also, to have
greater impact, networks might want to consider how non-
members could benet from their work.
6. Conclusion
Our analysis shows that the networks do have potential to
support urban adaptation, but there is also room for improvement.
Even among network members, the average and median APIs are
not close to the maximum. Since API only measures the planning
process, there is a risk that performance is even worse when it
comes to actual action (see also Revi et al., 2014;Woodruff and
Stults, 2016). The main implication of these results is that TMNs
should consider how to further encourage adaptation among their
member cities. Our comparison of countries with different levels of
wealth and across geographic locations suggest that the networks
should also consider giving special attention to cities in the less
wealthy countries. Current and future mega-cities, facing a
considerable need for adaptation, are located there (Bulkeley et al.,
2011;Shi et al., 2016). It also seems that combining ambitious
emission reductions and sustainable well-being in these countries
is challenging (Sugiawan et al., 2019), which presents a further
challenge to TMNs operating there. Based on this study, the level of
adaptation process or networking in these countries is not high.
Thus, the networks should be careful in planning how to support
these regions so that cities are empowered to nd the solutions that
work best in their contexts.
Declaration of competing interest
The authors declare that they have no known competing
nancial interests or personal relationships that could have
appeared to inuence the work reported in this paper.
CRediT authorship contribution statement
Milja Heikkinen: Conceptualization, Methodology, Investiga-
tion, Project administration, Writing - original draft. Aasa Karimo:
Methodology, Formal analysis, Validation, Visualization, Writing -
original draft. Johannes Klein: Conceptualization, Writing - orig-
inal draft, Writing - review &editing. Sirkku Juhola: Conceptual-
ization, Writing - review &editing, Supervision. Tuomas Yl
Anttila: Conceptualization, Methodology, Writing - review &
editing, Supervision.
First, we thank Malcolm Araos and colleagues, who gave us the
access to their data. Data on climate change adaptation policies are
from the Tracking Research on Adaptation to Climate Change
Consortium (TRAC3). The analysis and ndings represent the work
and view of the authors. We thank the participants of ESG 2018
conference session Agency 18 eCities and City Networks as Agents
in Environmental Governance, Onerva Korhonen, Malcolm Araos,
the anonymous reviewers and the professional proofreaders for
their useful feedback. We take responsibility for remaining errors.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
This work was supported by the Tiina and Antti Herlin Foun-
dation, Finland [grant no. 20170005]; Kone Foundation, Finland
[grants no. 085319 and no. 090022]; and University of Helsinki
Research Funds, Finland.
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... However, local, national, or structural conditions limit climate city networks' ability to mobilise cities, casting doubt on whether these networks can promote urban climate action beyond nominal commitments and cities already willing to act (Gordon, 2018;Gordon & Johnson, 2018). Furthermore, the inclusiveness of city networks has also been questioned as larger and wealthier cities in the Global North continue to dominate the membership and management of city networks (Bouteligier, 2013;Davidson, Coenen, Acuto, & Gleeson, 2019;Heikkinen et al., 2020;Woodruff, 2018). Finally, it has been proposed that wealthier cities that are more advanced in climate action benefit more from participating in city networks than those less advanced in implementing climate action (Heikkinen et al., 2020). ...
... Furthermore, the inclusiveness of city networks has also been questioned as larger and wealthier cities in the Global North continue to dominate the membership and management of city networks (Bouteligier, 2013;Davidson, Coenen, Acuto, & Gleeson, 2019;Heikkinen et al., 2020;Woodruff, 2018). Finally, it has been proposed that wealthier cities that are more advanced in climate action benefit more from participating in city networks than those less advanced in implementing climate action (Heikkinen et al., 2020). This idea reduces the confidence in the ability of climate city networks to promote urban climate actions in the diversity of cities needed to achieve the global climate goals. ...
... Scholars have focused on individual city networks (e.g. Davidson, Coenen, & Gleeson, 2019 on C40), examined the diversity of commitments made by city networks (Bansard et al., 2017), and the outcomes of these networks for their members (Heikkinen et al., 2020;Woodruff, 2018). They have also explored the diverse roles of city networks in lobbying and networking national and international decision-makers, enabling the development and sharing of climate-related INTRODUCTION Introduction 4 knowledge, recognising best practices, and supporting the implementation of climate projects and policies by their members (Bellinson, 2018;Busch et al., 2018;Harman et al., 2015; T. Lee, 2019; T. Lee & Jung, 2018). ...
... We use the term TMNs for indicating transnational networks structured to guide and collaborate with volunteer LGs on climate change governance. TMNs offer knowledge-experience exchange; concrete projects and international recognition (Barbi and De Macedo 2019;Lee 2018); provide socialization, business interaction, and financial standards (Lee and Jung 2018); organize events and recommend tools and resources (Heikkinen et al. 2020); deliver monitoring and reporting (UCGL 2022); and carry out inspiration, target-plan provision, certification, and rewarding (Barbi and De Macedo 2019;Bulkeley et al. 2012;Gustavsson et al. 2009;Lee 2013;Lee and Koski 2014;Niederhafner 2013) for ...
... While LGs generally benefit from this membership (Busch et al. 2018), their impact in CCP-making and planning is bounded. For example, Heikkinen et al. (2020) pinpointed that member LGs of TMNs tend to start adaptation process earlier with higher levels of planning; however, these LGs happen to be in wealthier countries. This is particularly true for the early years of TMN establishments; while TMNs are considered as '… primarily networks of pioneers for pioneers' (Kern and Bulkeley 2009: 311), the recent research indicates that among the massive number of TMN members there are both frontrunners and laggards (Haupt et al. 2020;Kern 2019). ...
This paper qualitatively investigates one of the influential transnational municipal networks, Covenant of Mayors for Climate and Energy (CoM)’s position in three Turkish municipal governments in bridging the climate change science and climate change policy gap. In the last two decades, the importance of science-based policymaking for climate mitigation and adaptation and transnational municipal networks empowered by municipalities that guide city policies linked to international agreements has been recognized. In this paper, we argue that CoM has acted as a boundary-object in producing climate change policies and plans in Turkish municipal governments. However, CoM has done so to a certain extent; their effectiveness was limited due to the general atmosphere on climate change policies in Turkey. We substantiate this claim through a two-layer examination: a case-specific analysis of three municipalities and semi-structured interviews with thirteen experts in climate change policy-related issues.
... Formar parte de estas redes ha permitido iniciar las actividades de adaptación de las ciudades, especialmente si se es parte de más de una red. Sin embargo, son las ciudades de países con mayores ingresos las que tienen mayor avance (Heikkinen, Karimo, Klein et al. 2020). ...
En este libro que presentamos a continuación hay muchos tópicos de investigación y muchas reflexiones, ideas y experiencia sistematizada que han sido elaborados gracias a la generosidad y sapiencia de los autores, que nos respondieron con mucho entusiasmo y compromiso en esta tarea de repensar y de aportar en nuestro bicentenario. Habrá que reconocer que son ensayos y artículos sobre diversos temas relacionados con la sociedad y el ambiente, y todos tratados con esmero, integridad y maestría. Nuestro ha sido el trabajo de revisarlos, arbitrarlos y darles un diseño para su publicación. Les estamos muy agradecidos a los autores, y a la vez les señalamos a los lectores de este libro que las páginas que a continuación se presentan deben servir para comprender y transformar nuestra realidad social y ambiental.
... Public institutions have also promoted a resilient approach to planning from the international to the local level, such as the 2030 Agenda for Sustainable Development (United Nations, 2015). Other experiences come from non-profits, also spreading thanks to the adaptation support efforts of some Transnational Municipal Networks (TMNs) as the 100 Resilience Cities Network (100RC), Local Governments for Sustainability (ICLEI), C40 or the Global Covenant of Mayors for Climate & Energy (GCOM) (Heikkinen et al., 2020). Despite its success in literature and field experiences, putting resilience into practice is a complex objective to pursue, and this is closely related to the nature of the concept itself: resilience, especially in its urban and territorial understanding, is a multidisciplinary and complex concept by definition (Jabareen, 2013), that frames a "conceptual umbrella" fascinating but slippery and ambiguous . ...
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The challenge to make cities and human settlements inclusive, safe, and resilient, including mitigation and adaptation strategies against disaster, is a central issue in achieving sustainability. This research proposes a tool to measure local vulnerability from a multi-risk approach. The municipality of Moncalieri, Italy, was used as a case study within the research activities of the Responsible Risk Resilience Centre from the Polytechnic of Turin to test the vulnerability matrix. The tool consists of a mathematical framework for the territorial vulnerability assessment that integrates multiple indicators clustered into three factors defined as sensitivity, pressures, and hazards, weighted according to a participatory procedure. Space-dependent analyses using the Geographical Information System were developed from the multiple nested indicators to project the vulnerability index onto a homogeneous grid in the territory of interest. Thematic maps referring to the systemic vulnerability by different sensitivity components were generated. The tool not only contributes to increasing the awareness of territorial vulnerability but also offers support to resilience-based decision-making in designing technical measures of policies at a local scale. Further research is required to implement the framework in different scenarios and develop the model's temporal behaviour.
... Preparatory work attracts funds, increases social awareness, generates knowledge and helps gain technical capacities (Ford & King 2015;Heidrich et al. 2013;Neder et al. 2021;Olazabal et al. 2019a;Tilleard & Ford 2016). Implementation depends on effective collaborations across scales of governance (Betsill & Bulkeley 2006;Heikkinen et al. 2020;Sirkku 2016). Finally, adaptation also needs ex-post work to monitor, evaluate, correct and replicate successful processes (Arnott et al. 2016;Meerow & Woodruff 2020;Woodruff & Stults 2016). ...
Comparative studies of urban adaptation have evaluated the progress, means and scope of adaptation planning. Practice on the ground shows that the local politics of climate adaptation advance through various strategies to align different interests and spheres of action or disrupt mainstream practices, which translates into a wide range of interventions. This paper focuses on understanding the dynamics and tools that enable the institutionalisation of adaptation practices in local governments,i.e. the means through which adaptation practices, beyond plans and policies, are embedded in the routines of urban governance. It presents a framework to analyse the institutionalisation of adaptation that maps stages and tools with the potential to deliver adaptation in urban areas. Adaptation is framed as a learning process involving overlapping phases of recognition (of needs, capacities and actors), groundwork (knowledge generation) and action on the ground (change). The framework compares three Spanish local government initiatives (Bilbao, Barcelona and Madrid). The analysis shows that adaptation can be effectively incorporated into standard rules, norms and practices using combinations of tools and spatial and temporal scales. The coupled stages of recognition, groundwork and action highlight the importance of long-term learning processes to engage with the temporal dimensions of adaptation governance.
City officials increasingly maintain relations with foreign stakeholders, both public and private, a practice that is generally referred to as city diplomacy. In the past, city diplomacy activities focused on bilateral cultural and knowledge exchanges. Although this type of collaboration still exists, contemporary city diplomacy has become more dynamic and diverse, and increasingly includes an economic dimension. In addition, many cities currently prioritise becoming involved in a variety of multilateral inter-urban networks. Despite wide-ranging conversations on the challenges and opportunities of these new types of city diplomacy, theoretical reflections regarding the underlying processes and the potential consequences remain largely absent from the literature. In this article, we argue that the city diplomacy literature can therefore be enriched by engaging with concepts and debates developed in economic geography in two main ways. We first elaborate on contemporary varieties of urban entrepreneurialism and the extent to which these correspond with city diplomacy practices. We argue that city diplomacy contains elements of both traditional entrepreneurialism and managerialism. Second, we look at city diplomacy through the lens of uneven development, hypothesising that city diplomacy may entail a self-reinforcing effect in terms of enhancing socio-spatial differences between ‘superstar cities’ and ‘places that do not matter’ respectively.
The Green Belt and Road Initiative was proposed by the Chinese government to address the environmental challenges related to the Belt and Road Initiative (BRI). The BRI International Green Development Coalition (BRIGC), a flagship initiative of the Green BRI, aims to promote the BRI’s environmental performance through transnational governance measures. In this study, we analyzed the BRIGC from the perspective of orchestration theory. We developed a model of causal pathways to understand the micro-steps linking orchestrators, intermediaries, and targets. Furthermore, drawing on first-hand evidence collected through participatory observation, interviews, and focus groups, we identified four key pathways of green orchestration: green consumerism, green infrastructure, green finance, and green governance. We found that the orchestration processes were hampered by the lack of substantial input from the BRIGC, which undermined the intermediary-target relationship. We concluded with recommendations for the BRIGC to strengthen its orchestration capacity.
Cities face substantial risks of economic and physical losses from repeated exposure to climate hazards exacerbated by climate change. Drawing from the climate action and policy mix literatures, this study conceptualizes “climate action mix” defined as the diverse policy actions adopted by city governments to adapt to and mitigate the effects of climate hazards. This study makes a key contribution by analyzing the relation between the variety of hazards and the diversity of cities' climate action mixes. Deploying a modified Shannon diversity index, we develop a new measure of climate action mix by considering the breadth across different actions, and the depth of these efforts as measured by the progress along the policy cycle. We study an expansive range of mitigation and adaptation actions without imposing any domain or jurisdictional limitations in 162 cities across the United States. The analysis reveals a bifurcation in approaches where some cities have not adopted any policies, while others have a diverse mix of adaptation and mitigation actions in various stages of policy progression. We find that climate hazards drive local action—cities that experience multiple threats react by taking a diverse mix of climate actions. Cities broadly utilize global climate networks that offer policy learning opportunities and local networks that might promote a shared understanding of environmental threats leading to diverse climate action mixes. Finally, a city's capacity to develop climate adaptation and mitigation plans is positively related to a diverse portfolio of climate actions. Las ciudades enfrentan riesgos sustanciales de pérdidas económicas y físicas por la exposición repetida a los peligros climáticos exacerbados por el cambio climático. A partir de la literatura sobre la acción climática y la combinación de políticas, este estudio conceptualiza la "combinación de acciones climáticas" definida como las diversas acciones políticas adoptadas por los gobiernos de las ciudades para adaptarse y mitigar los efectos de las amenazas climáticas. Este estudio hace una contribución clave al analizar la relación entre la variedad de amenazas y la diversidad de combinaciones de acción climática de las ciudades. Al implementar un índice de diversidad de Shannon modificado, desarrollamos una nueva medida de la combinación de acciones climáticas al considerar la amplitud de las diferentes acciones y la profundidad de estos esfuerzos medidos por el progreso a lo largo del ciclo de políticas. Estudiamos una amplia gama de acciones de mitigación y adaptación sin imponer ningún dominio o limitaciones jurisdiccionales en 162 ciudades de los Estados Unidos. El análisis revela una bifurcación en los enfoques donde algunas ciudades no han adoptado ninguna política, mientras que otras tienen una combinación diversa de acciones de adaptación y mitigación en varias etapas de avance de la política. Descubrimos que los peligros climáticos impulsan la acción local: las ciudades que experimentan múltiples amenazas reaccionan tomando una combinación diversa de acciones climáticas. Las ciudades utilizan ampliamente las redes climáticas globales que ofrecen oportunidades de aprendizaje de políticas y redes locales que pueden promover una comprensión compartida de las amenazas ambientales que conducen a diversas combinaciones de acciones climáticas. Finalmente, la capacidad de una ciudad para desarrollar planes de adaptación y mitigación climática se relaciona positivamente con una cartera diversa de acciones climáticas. 气候变化加剧了气候危害,而反复暴露于这一危害的城市面临着巨大的经济和物质损失风险。基于有关气候行动和政策组合的文献,本研究将“气候行动组合”概念化为城市政府为适应和减轻气候危害影响而采取的多种政策行动。本研究通过分析危害种类与城市气候行动组合的多样性之间的关系,从而作出了重要贡献。通过使用一项经过修改的香农多样性指数,我们提出了一种新的气候行动组合衡量标准,即考量不同行动的广度以及由政策周期的进展加以衡量的行动深度。在没有施加任何领域限制或管辖限制的情况下,我们对美国162个城市的缓解和适应行动的范围进行了研究。分析揭示了行动存在的分歧,即一些城市没有采取任何政策,而另一些城市在政策进展的不同阶段采取了多样化的适应和缓解行动组合。我们发现,气候危害推动了地方行动——遭受多重威胁的城市通过采取多种气候行动组合来作出反应。城市广泛利用一系列提供政策学习机会的全球气候网络以及一系列可能促进关于环境威胁的共同理解的地方网络,这些举措导致了多种气候行动组合。最后,一个城市在制定气候适应和缓解计划方面的能力与多种气候行动组合呈正相关。
In a polycentric world, cities increasingly bear responsibility for implementing climate policies. To do so, they establish transnational city networks (TCNs), which produce ambitious imaginaries of the future of cities, such as ‘smart cities’ or ‘resilient cities’, based on ecological knowledge. This paper analyses Southeast Asian (SEA) cities’ participation in TCNs. First, this paper presents city networks operating in SEA. Then, drawing on a case study of Quezon City, this paper shows how SEA cities often position themselves in the network as knowledge consumers rather than (co)producers and prefer to learn from cities in the Global North. This research also shows how TCNs—with limited success—seek to counter this neo‐colonial knowledge flow model. The paper contributes to the literature on TCNs, arguing that the ongoing North–South imbalance needs to be addressed if networks are to promote viable models of future SEA cities. Identifying the patterns of knowledge flows inside TCNs, this study argues that networks should assist cities in imagining possible city futures beyond the experiences of the select world and global cities. TCNs should pay more attention to supporting their SEA members in looking ‘outwards’ to comparable cities worldwide rather than merely ‘upwards’ to global and mega‐cities.
Technical Report
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The KOKOSOPU project has aimed at a comprehensive evaluation of the national adaptation policy with particular emphasis on the national adaptation plan and the international policy development. In addition, future challenges related to societal development have been considered. Projections of climate change, Finland’s Climate Act and the strengthened adaptation policy in the EU emphasize the importance of the national adaptation policy. A key objective of the national adaptation plan 2014–2022 was to strengthen the adaptation capacity of the Finnish society. This objective is still relevant. The conditions for reaching the objective have, however, partly changed. First, cross border consequences of climate change are increasingly emphasized. Second, issues of justice and fairness with respect to the consequences of climate change and adaption actions are being identified as central. Third, greater weight is given to the overall sustainability of adaptation and climate action. The changing conditions for climate change adaptation should be reflected in the allocation of resources, in improved coordination within the administration and in co-operation between the public and private sectors. In addition knowledge and education should be improved, and resources provided for RDI and for monitoring and evaluation that supports continued improvement of adaptation activities.
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In what follows, three related topics are addressed to better understand the role of cities as units of polycentric urban climate governance. First, cities often set higher climate governance ambitions than the nation states they are in (Reckien et al., 2014). What explains this tendency of cities seeking to outperform and thus act independently of national governments? Second, cities are increasingly becoming sites and actors of experimentation with innovative governance instruments, including eco-financing and ‘urban laboratories’ (van der Heijden, 2016). What drives cities to experiment with innovative governance instruments in the first place? Third, cities have begun to break out of traditional top-down national-regional-local hierarchies and act in translocal networks (Acuto and Rayner, 2016). How do these networks seek to overcome regional and national barriers to climate governance, and what barriers do these networks raise themselves for cities in responding to climate change? Finally, whilst the literature on these three topics, and polycentric urban climate governance more broadly, has expanded rapidly since the early 2000s, it has a strong focus on a relatively small number of cities from the global North (Evans et al., 2016). This chapter therefore concludes with a reflection on how applicable it is for all cities in the world – including, crucially, those in the global South. It also identifies what further research is urgently required to understand and support the full potential that cities hold as actors in, and sites of, polycentric climate governance.
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This special issue contributes to scholarly debates about the role of cities in global climate governance, reflecting on the promise, limits, and politics of cities as agents of change. It takes an empirically-informed approach drawing on multiple diverse geographical and political contexts. Overall, the special issue aims to stimulate reflection and debate about where understanding and practice needs improvement to advance the role of cities in global climate governance. Key questions that are addressed in the special issue include: To what extent do real world experiences confirm or disconfirm the high expectations of cities as agents and sites of change in addressing global climate change as expressed in urban climate governance literature? In what ways do internal political dynamics of cities enable or constrain urban climate governance? How is climate governance in cities enabled and constrained by interactions with broader governance levels? In what ways can climate governance in cities be advanced through critical attention to the previous issues?
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Over the past decade, diverse urban governance innovations and experiments have emerged with the declared aim to foster climate change mitigation and adaptation, involving actors at multiple levels and scales. This urban turn in environmental governance has been accompanied by normative claims and high expectations regarding a leading role of cities in coping with climate change. However, while time pressures for effective action are growing, little is known about the social learning processes involved in such urban climate governance innovations, and what they actually contribute to achieve the required transformations in urban systems. Therefore, this special issue presents eight selected papers that explore learning in urban climate governance practices in a variety of local, national and international contexts. Their findings point to a more ambiguous role of these practices as they tend to support incremental adjustments rather than deeper social learning for radical systemic change. Against this backdrop we propose a heuristic distinguishing basic modes and sources in governance learning that aims to facilitate future empirical research and comparison, thus filling a critical theory gap. Using this framework for interpretation illustrates that urban climate governance learning urgently requires more openness, parallel processes, exogenous sources, as well as novel meta-learning practices.
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The Paris Agreement aims to limit global mean temperature rise this century to well below 2 °C above pre-industrial levels. This target has wide-ranging implications for Europe and its cities, which are the source of substantial greenhouse gas emissions. This paper reports the state of local planning for climate change by collecting and analysing information about local climate mitigation and adaptation plans across 885 urban areas of the EU-28. A typology and framework for analysis was developed that classifies local climate plans in terms of their alignment with spatial (local, national and international) and other climate related policies. Out of eight types of local climate plans identified in total we document three types of stand-alone local climate plans classified as type A1 (autonomously produced plans), A2 (plans produced to comply with national regulations) or A3 (plans developed for international climate networks). There is wide variation among countries in the prevalence of local climate plans, with generally more plans developed by central and northern European cities. Approximately 66% of EU cities have a type A1, A2, or A3 mitigation plan, 26% an adaptation plan, and 17% a joint adaptation and mitigation plan, while about 33% lack any form of stand-alone local climate plan (i.e. what we classify as A1, A2, A3 plans). Mitigation plans are more numerous than adaptation plans, but planning for mitigation does not always precede planning for adaptation. Our analysis reveals that city size, national legislation, and international networks can influence the development of local climate plans. We found that size does matter as about 80% of the cities with above 500,000 inhabitants have a comprehensive and stand-alone mitigation and/or an adaptation plan (A1). Cities in four countries with national climate legislation (A2), i.e. Denmark, France, Slovakia and the United Kingdom, are nearly twice as likely to produce local mitigation plans, and five times more likely to produce local adaptation plans, compared to cities in countries without such legislation. A1 and A2 mitigation plans are particularly numerous in Denmark, Poland, Germany, and Finland; while A1 and A2 adaptation plans are prevalent in Denmark, Finland, UK and France. The integration of adaptation and mitigation is country-specific and can mainly be observed in two countries where local climate plans are compulsory, i.e. France and the UK. Finally, local climate plans produced for international climate networks (A3) are mostly found in the many countries where autonomous (type A1) plans are less common. This is the most comprehensive analysis of local climate planning to date. The findings are of international importance as they will inform and support decision-making towards climate planning and policy development at national, EU and global level being based on the most comprehensive and up-to-date knowledge of local climate planning available to date.
In light of the relatively modest achievements of international climate change governance, high hopes are being placed on global city networks as an essential solution to problems in climate change adaptation and mitigation. The C40 Cities Climate Leadership Group, in particular, promotes itself as a network that enables cities to learn from each other in their efforts to confront climate change. Very little is known, however, about what kind of change the network promotes and how transformative the proposed solutions are. We assess the degree of (anticipated) change based on a stratified sample of twelve cities participating in the C40 network, signalled by adaptation and mitigation actions described in their policy documents. Our findings indicate that most proposed measures support the status quo, with the majority of actions focusing on infrastructure and technology, and only a few transformational climate measures are envisaged by the cities. © 2018
By sharing best practices and lessons learned among member cities, professional and learning networks have become prominent actors in supporting and shaping local climate change adaptation. I analyze the membership of 18 highly visible adaptation learning networks to determine what cities participate and if networks attract similar cities. I find that the formation of adaptation networks is driven by large, high-capacity cities. Adaptation networks include members of diverse sizes and planning capacity, however, cities with similar levels of social vulnerability and concern with climate change tend to participate in the same networks. Global and regional networks have different patterns of membership. These patterns of membership have important implications for diffusing climate change adaptation between cities.