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Using fuzzy cognitive maps to promote nature-based
solutions for water quality improvement in developing-
country communities
Handling Editor: Zhen Leng
Abstract
An adequate strategy for water quality improvement must \nsider a range of political, economic, social,
technological, environmental, and legal (PESTEL) concepts. Nature-based solutions have emerged as
promising tools to improve water quality while considering these factors. In this context, fuzzy cognitive
maps and the PESTEL approach have been merged to i) identify the principal concepts that affect water
quality from different perspectives, and ii) theoretically explore the use of artificial floating islands as a
measure of nature-based solution combined with different policies, to find strategies to improve water
quality given local conditions. For this purpose, three Ecuadorian communities are used as scenarios. The
communities are located in different geographical regions, i.e., p
á
ramo (an alpine tundra ecosystem), coastal
mangrove, and tropical rainforest. From literature-based research, 40 PESTEL concepts are identified, then
local experts recognize relevant concepts related to water quality deterioration regarding local conditions,
and the communities develop fully democratic social cognitive maps. The cognitive map from the p
á
ramo
community is constructed with 17 concepts mainly driven by environment (23%) and economy (23%). The
major problem identified is natural water pollutants with the highest centrality value (ci
=
12.22). The
mangrove community uses 19 concepts influenced by policy (48%), and the major issue is human exposure
to environmental pollutants (ci
=
16.27). The rainforest community uses 15 concepts related to the
economy (40%), and the major problem is the violation of environmental legislation (ci
=
15.96). Our
pioneer work to predict the future of water management shows that in the worst-case scenario, more than
85% of concepts are affected in all communities. However, the implementation of policy strategies in
combination with artificial floating islands demonstrates a large potential for improving water quality. With
this study, we provide a novel, inclusive, and locally adapted framework to guide future water management
and contribute to achieving the Sustainable Development Goal SDG 6.
Q6
Kalina Fonsecaa,b,
∗
, kalina.fonseca@zeu.uni-giessen.de, Edgar Espitiaa,c, Lutz Breuera,d, Alicia Correaa
aCentre for International Development and Environmental Research, Justus Liebig University Giessen,
Senckenbergstrasse 3, 35390 Giessen, Germany
bWater Resources Management Group, Technical University of Cotopaxi (UTC), Av. Sim
ó
n Rodr
í
guez s/n Barrio
El Ejido Sector San Felipe, 050104, Latacunga, Ecuador
cDepartment of Earth and Water Sciences, Universidad Regional Amaz
ó
nica Ikiam, Km 8 road to Alto Tena,
Ecuador
dInstitute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use
and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390, Giessen, Germany
∗
Corresponding author. Centre for International Development and Environmental Research, Justus Liebig
University Giessen, Senckenbergstrasse 3, 35390 Giessen, Germany.
Q2
Keywords:
Data availability
Data will be made available on request.
1
Introduction
Healthy aquatic ecosystems (e.g., rivers, lakes, coastal waters, mangroves) provide local communities with valuable
ecosystem services ranging from food, biodiversity, energy, and water supply for consumption and recreation (Alam et
al., 2017; Brown et al., 2021; Grizzetti et al., 2016). However, impaired water quality contributes to the general
problem of water availability, threatening livelihoods and socio-economic growth (Beitl et al., 2019; Grigg, 2016).
Absent or inadequate water management is linked to the presence of water-related diseases such as cholera, diarrhea,
dysentery, hepatitis A, typhoid, or polio (WHO, 2019). Problems related to poor water quality are even more common
in developing countries (UNICEF, 2020), which often face economic water scarcity due to political, social,
institutional, or financial conditions (Gude, 2017). Under these circumstances, achieving target 6.3 of the Sustainable
Development Goals (
“
By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing
release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially
increasing recycling and safe reuse globally
”
) represents a significant challenge for communities in developing
countries which face water quality issues.
A holistic strategy for water quality improvement must consider the macro-environmental factors affecting its quality,
and the engagement of multisectoral stakeholders involved in water management decisions (Ghaboulian Zare et al.,
2022; Giordano et al., 2020; Grigg, 2016;
Ö
zesmi and
Ö
zesmi, 2004; Sansa et al., 2021; Wilson et al., 2018). The
exploration of macro-environmental factors based on the political, economic, social, technological, ecological, and legal
(PESTEL) concepts has gained increased attention in recent years (Iacovidou and Zorpas, 2022). With an in-depth
analysis, decision-makers can identify PESTEL concepts to be monitored as opportunities and threats in the
implementation of new strategies (De Sousa and Casta
ñ
eda-Ayarza, 2022) to solve water quality issues.
Strengthening the links between humans and nature increases the affordability of clean water by implementing nature-
based solutions, i.e., remediation methods that emulate natural processes. Among nature-based solution strategies,
Artificial Floating Islands (AFIs) have proved to be efficient in improving water quality at low operational costs, low
energy consumption, and with negligible environmental impact (Benvenuti et al., 2018; Fonseca et al., 2021; Martelo
and Borrero, 2012; Stewart et al., 2008). AFIs are composed of floating platforms that support the growth of sediment-
rooted emergent wetland plants, macrophytes, microbes, and related ecological communities such as algae, biofilms,
zooplankton, and small invertebrates (Colares et al., 2020; Yeh et al., 2015). By using AFIs, polluted aquatic
ecosystems are restored as water passes beneath the floating mat through the following mechanisms: plant root uptake
of metals and nutrients, biofilm development, extracellular enzyme release, contaminant settling, and binding, as well as
suspended matter flocculation enhancement (Yeh et al., 2015). AFIs as a potential strategy accompanied in combination
with water management policies are more feasible to be implemented in small, low-income communities, especially in
developing countries where treatment infrastructure is inadequate or limited to large cities (ONU, 2018). Several studies
(Fonseca et al., 2020; Kusin et al., 2019; Lu et al., 2015; Nuruzzaman et al., 2021; Stewart et al., 2008; Yeh et al., 2015
) have demonstrated the potential of AFIs to clean polluted water from natural and anthropogenic sources in the
communities within the scope of the aforementioned. However, there is a lack of experimental data (Colares et al., 2020
) and multidisciplinary studies that engage stakeholders and consider PESTEL concepts to design AFIs implementation
for the improvement of water quality (Benvenuti et al., 2018; Fonseca et al., 2020; Negrete et al., 2019.; Yeh et al.,
Water quality,Nature-based solutions,PESTEL,Fuzzy cognitive maps,Community engagement
Abbreviations
No keyword abbreviations are available
2015). Moreover, several AFIs projects face sustainability issues due to insufficient community engagement and
multisectoral planning, limited funds, and technical support (Watkins et al., 2017).
Fuzzy Cognitive Maps (FCMs) are tools to analyze complex systems from the perception of stakeholders. They can
extract qualitative key information to support decision-making in ecosystem management (Adriaenssens et al., 2004;
Vergini and Groumpos, 2021). FCMs are based on human reasoning and linguistic approach to deal with vague, and
uncertain data that are interpreted in fuzzy rule-based models.
This study aims to develop a decision-making framework that identifies the principal concepts that affect water quality
at the community level and explores the implementation of AFIs together with policy interventions to improve water
quality. We implemented the framework in three Ecuadorian communities with diverse socio-cultural backgrounds and
located in different geographical regions with different hydroclimatic and physiographic conditions. Yet they all share
the common ground of water quality issues and that water and its associated ecosystem services provide livelihood, and
food and also play a crucial role in family income generation (Calle et al., 2018; Delgado-Aguilar et al., 2017; Garc
í
a et
al., 2019). Based on a FCM approach, we will answer the three following questions: (1) What are the PESTEL
concepts related to water quality deterioration at the community level considering multi-sectoral stakeholder
perceptions? (2) How can local water quality be improved if communities implement AFIs? (3) Which policies of the
PESTEL concepts can be combined with AFIs to strengthen their effectiveness to improve water quality?
2
Materials and methods
2.1
Study area
This study analyses three communities in Ecuador (Fig. 1): Chilla Chico in the Cotopaxi province, located in the
p
á
ramo of the Andes (Fig. 1a), Mogollon in the Guayas province, a community characterized by mangroves at the
Pacific Coast (Fig. 1b), and Awayaku in the Napo province, situated in the Amazon rainforest region (Fig. 1c).
Hereinafter referred to as the p
á
ramo, mangrove, and rainforest community, respectively.
alt-text: Fig. 1
Fig. 1
i
Images are optimised for fast web viewing. Click on the image to view the original version.
The Par
á
mo community is home to approximately 1,900 low-income indigenous people who subsist on the farming of
potatoes, corn, and beans at 3,000
m above sea level (m.a.s.l.) (O
ñ
a et al., 2021; Ter
á
n Cazar, 2017). In this area, the
p
á
ramo ecosystem provides water mainly for human consumption and irrigation. However, pollutants have impaired
the ecosystem, including the community irrigation ponds. These contaminants mainly include arsenic and other heavy
metals from the Iliniza Volcanic Complex (Fonseca et al., 2020); nitrogen and phosphorus from livestock and fertilizer
application; and fecal coliforms from the feces of all warm-blooded animals and humans (Fonseca and Clairand, 2018).
In the mangrove community, 1,750 inhabitants largely depend on fishing along the Mogollon estuary bank, one of the
most important tributaries of the Salado estuary (Hechavarr
í
a Hern
á
ndez et al., 2018). The Salado estuary is
characterized by extensive tidal bays within river mangroves that provide nursery habitat and feeding grounds for
numerous species of fish, crustaceans, mollusks, and wading birds (Calle et al., 2018). High concentrations of heavy
metals such as cadmium, copper, chromium, and lead (Fern
á
ndez-Cadena et al., 2014), fecal coliforms, and low
dissolved oxygen content (Negrete et al., 2019) are present in these coastal estuarine systems due to the accelerated
urban development in the last eight decades (Calle et al., 2018).
The rainforest community, is a small community of 700 indigenous people from the Kichwa Rukullacta group, located
in the upper tributaries of the Napo River between 520 and 1,240
m a.s.l. . The community integrates subsistence
agriculture with tilapia and carachama fishing as a source of economic income (GADR San Pablo, 2015; Pueblo
Kichwa de Rukullakta, 2018). In the Napo River, concentrations of cadmium, lead, copper, zinc, and mercury have
increased, mainly due to legal and illegal small-scale mining, sewage discharges, fish farming, and non-functional
landfills (Capparelli et al., 2020).
All three communities are exposed to impaired water quality along the water-soil-crop-food chain, but its potential
impact on public health is unknown. Nevertheless, several diseases are linked to water contaminated with heavy metals
and feces from untreated or inadequately treated sources (Qian et al., 2022). The main toxic effects of heavy metals on
humans include nephrotoxicity, neurotoxicity, hepatotoxicity, skin toxicity, and cardiovascular toxicity (Mazumder,
2008; Mitra et al., 2022; Srivastava et al., 2012). Moreover, gastro-enteric epidemics caused by water contaminated
with human and animal feces are a major health concern, not only due to considerable human morbidity and mortality
Study area, (a) Chilla Chico community, (b) Mogollon community, (c) Awayaku community, (d) location of Ecuador in South
America, and (e) location of the study communities (black dots) and respective provinces (red surfaces) in Ecuador.
but also because of the alarming rate of spread of drug-resistant bacteria (Some et al., 2021). In the three communities,
interviews and workshops are held to build FCMs based on the perception of multi-sectorial stakeholders regarding
water quality improvement.
2.2
Fuzzy cognitive maps
The FCM approach is a semi-quantitative method that depicts expert knowledge, causal reasoning, and stakeholder
perceptions by signed directed graphs (Gray et al., 2015; Moosavi et al., 2021; Santoro et al., 2019). By applying the
principles of fuzzy logic and cognitive maps, different sources of knowledge can facilitate the decision-making
processes (Shahvi et al., 2021). This knowledge is framed in three main steps forming the FCM approach: a list of
concepts, drawing causal relationships among these concepts by cognitive maps, and estimating the fuzzy inference
Fig. 2.
alt-text: Fig. 2
Fig. 2
i
Images are optimised for fast web viewing. Click on the image to view the original version.
2.2.1
List of concepts
The procedure first involves identifying concepts and then selecting the most important ones to include in the map.
Different approaches can be used, such as selecting a list of preliminary concepts from the literature (Morone et al.,
2021) or from meetings with experts (
Ö
zesmi and
Ö
zesmi, 2004). Subsequently, a close-ended survey is conducted to
validate the concepts based on the opinion of the experts, who rate the level of connection between the concepts and
the aim of the study using a unipolar Likert scale (Ghaboulian Zare et al., 2022; Morone et al., 2021).
2.2.2
Drawing of social cognitive maps
The principal output of the FCM approach is a mental map, structured by nodes and bidirectional connections between
nodes. Nodes are concepts, representing physical quantities or abstract ideas, while connections represent the
influencing degree (Cij) of the cause of concept i on the effect concept j (Kosko, 1986). The stakeholders participating
in the map construction decide when a connection exists by assigning a status value of concept represented as a number
Flowchart to build FCMs as a decision-making framework to improve water quality at community level. NbS
=
nature-based
solutions.
within [0, 1], and subsequently weights (wij ) to concept pairs (between
−
1 and 1). When wij is positive, there is a
positive influence of concept Ci on concept Cj. On the contrary, when wij is negative, there is a negative influence of
concept Ci on concept Cj. When wij is equal to zero, it is assumed that there is no relationship between concepts (
Kosko, 1986). The degree of influence among the concepts is coded into an adjacency matrix (aij ) (Chen and Chiu,
2021). This matrix is structured by three concept types (i.e., receiver, driver, and ordinary) that interact with each other.
The receiver concepts accept input from others, being most influenced by others (
Ö
zesmi and
Ö
zesmi, 2004). The
policy strategies or policy drivers are driver concepts in the FCM, ideal candidates to manipulate the system due to their
nature of sending stimuli and not receiving incoming connections (Ghaboulian Zare et al., 2022; Gray et al., 2014;
Morone et al., 2021; Solana-Guti
é
rrez et al., 2017). Concepts with both driving and receiving features are called
ordinary concepts (Gray et al., 2015;
Ö
zesmi and
Ö
zesmi, 2004). A large number of concepts and connections indicate
a major degree of interaction between concepts in the cognitive map (Gray et al., 2014). Following the above
guidelines, the stakeholders can build two types of cognitive maps: individual or social. According to Mourhir (2004)
and
Ö
zesmi and
Ö
zesmi (2004) to build social cognitive map compared to the individual one, it consumes less time and
resources, as it can be produced collaboratively by stakeholders in a workshop and facilitates social learning.
One way to evaluate the complexity of a FCM model is by using graph theory indices (e.g., density, hierarchy index,
outdegree, indegree, centrality) that are used to analyze the concept contribution in the cognitive map. The number of
concepts (N) and connections (C) represent the density of the map (D) (Nikas et al., 2019). When the stakeholders
identify a large number of causal relationships among concepts, the density of the map (Eq. (1)) is high (Blacketer et
al., 2021;
Ö
zesmi and
Ö
zesmi, 2004; Shahvi et al., 2021).
The hierarchy index (h) represents a democratic or hierarchical map. When the hierarchy index (Eq. (2)) is equal to 1,
the map is fully hierarchical. On the contrary, when h is equal to 0, the system is fully democratic (Mourhir, 2021;
Ö
zesmi and
Ö
zesmi, 2004).
The outdegree indice (Eq. (3)) corresponds to the row sum of absolute values v of a concept i. It describes the
cumulative strengths of connections (aij ) leaving the concepts.
The indegree indice (Eq. (4)) is calculated as the sum of the absolute weights of the incoming FCM graph edges.
A high value of and zero of classifies the concept as a driver, while a high value of and zero of
classifies the concept as a receiver (
Ö
zesmi and
Ö
zesmi, 2004; Solana-Guti
é
rrez et al., 2017). The outdegree and
indegree together describe the centrality (ci) of the system (Eq. (5)), which represents the overall importance of a given
(1)
(2)
(3)
(4)
concept in the causal flow of the cognitive map (Ghaboulian Zare et al., 2022; Nikas et al., 2019;
Ö
zesmi and
Ö
zesmi,
2004; Schiavon et al., 2021).
2.2.3
Fuzzy inference
The fuzzy inference system involves three stages i.e., the natural dynamic simulation, worst-case scenario, and
scenarios with policy drivers (Gray et al., 2015). In the natural dynamic simulation, the receiver and ordinal concepts
are activated (0 means no-activate and 1 means activate) to predict a scenario without external influences, known as the
steady-state (Eq. (6)) (Adriaenssens et al., 2004). Based on the collective stakeholder knowledge, each activated
Concept contributes its weight to activate its descendent, interacting with each other (Solana-Guti
é
rrez et al., 2017).
That means a steady-state snapshot of how the concepts and linkages of the system, given the current structure, would
be resolved in the absence of change or intervention, at different periods (Chen and Chiu, 2021; Kosko, 1986; Lopolito
et al., 2020).
That is, is the status value of concept Ci at period t; is the status value of concept Cj at period t; is the status
value of concept Ci at period t+1; is the corresponding fuzzy relation degree between Cj and Ci; and is a
sigmoid threshold function (Eq. (7)).
Specifically, > 0 determines its steepness (Vergini and Groumpos, 2021; Xiao et al., 2012). The sigmoid function
transforms the status value of each node into the interval [0, 1] at each iteration (Chen and Chiu, 2021), providing non-
negative values that are easy to compare and allow to reach steady-state scenario (Lopolito et al., 2020).
In the second stage, a worst-case scenario is estimated in which the concept with the highest centrality value (Eq. (5)) is
clamped to a maximum value of 1 (Eq. (6) and Eq. (7)) to understand its influence on the others. The outcome of this
scenario is compared with the steady-state to understand the relative changes (Gray et al., 2015).
Finally, the third stage determines what may result if drivers concepts or policy drivers are implemented. A similar
procedure to the one described in the previous step is followed. The only difference is that the policy drivers are now
set at their maximum value (1) (Morone et al., 2021). The policy effect is assessed by calculating the difference of the
policy intervention and the steady-state scenarios.
2.3
Implementation of the FCM approach in the communities
Following the steps described in section 2.2, the FCMs were co-created with the communities. A flowchart for its
implementation is presented in Fig. 2.
Step 1 identification of the main concepts affecting water quality and policy driversA literature review was conducted
(Morone et al., 2021). We selected receiver and ordinary concepts related to water quality issues under the PESTEL
approach, and a driver concept based on nature-based solutions to improve the water quality. We used various
databases (Scopus, ScienceDirect, SpringerLink, Web of Science) to search for terms that include a combination of
keywords related to water pollution, water management-governance, water and SDGs, water treatment technology,
water economy, and water users.Studies were included in the review if they fulfilled the following criteria: are written
in English to focus on the literature of international impact; are studies from 2000 to 2022, i.e., since the term water
(5)
(6)
(7)
governance first appeared concerning the range of political, social, economic, and administrative systems (Baumgartner
and Pahl-Wostl, 2013); and investigate the link between water deterioration and PESTEL approach. Initially, 50
research papers were identified with these criteria, based on the title and abstract. Subsequently, the papers were
examined to extract concepts related to our study goal. Twenty-two papers met this purpose (Table 1).We identified 40
initial concepts and the community experts validate their PESTEL linkage according to the local situation of each
community. The group of local experts ( ) was chosen based on their knowledge and influence in water
management and decision-making. They invited other specialists following the snowball sampling approach. Thus, the
final number of experts was 10 in p
á
ramo and mangrove, respectively, and 8 in the rainforest community. Socialization
meetings were held face-to-face or in remote mode to explain the objective and steps of the methodology (Fig. 2). After
that, closed-ended surveys were sent to the 28 experts to answer the question: Which of the 40 concepts do you think is
definitely connected when considering the deterioration of water quality according to water use (agriculture and
aquaculture, respectively)? Using a score unipolar Likert scale 5-point (i.e. from 5
=
definitely connected to
1
=
definitely NOT connected). In addition, experts were asked to add missing concepts and policy drivers to improve
water quality. Concepts and policy drivers added by experts were coded according to the community: p
á
ramo (P),
mangrove (M), and rainforest (R), plus (E) for experts and the number to which it belongs in the PESTEL table.
Finally, to identify the representative concepts based on the experts' opinions, we conducted an exploratory analysis of
the survey responses by assessing the frequency and standard deviation of responses. The concepts with the highest
averages and the lowest standard deviations were selected, to reflect the highest degree of connection among the
opinions of the experts.To include the perception of the local communities, three workshops were held in December
2021, one in each community with the participation of 10 people. In the workshops, the concepts and policy drivers
defined by the experts were debated in a fully democratic and participative atmosphere. In the first part of the
workshop, each concept and driver policy (DAFIs ) were explained and posted on the wall for the community to
remember as they built the FCMs. Besides, local communities added the missing concepts and policy drivers according
to their perception, which were coded in the same way as for experts, with the only difference being the letter (C) for
communities.
Step 2 Drawing of Social Cognitive MapsThe instructions to draw the social cognitive maps were explained to the
local communities by video. The community received a sheet of paper with the predefined concepts (ordinary and
receiver) printed at random and asked to rate the degree of cause-effect influence of the concepts. The rating was
performed using arrows and rating the degree of influence between these connections using expressions via a 6-degree
scale, ranging from
−
3 (strong negative) to +3 (strong positive) (Nyaki et al., 2014). To end, the group average
perception was assessed (Gray et al., 2014). Finally, the cognitive maps provided by the communities were converted
into adjacency matrices (
Ö
zesmi and
Ö
zesmi, 2004) to analyze indices of graph theory (Eq. (1)-5)) (Mourhir, 2021)
using the FCMapper software (available: http://www.fcmappers.net/joomla/).
Step 3 Fuzzy inferenceUsing the calculation algorithm presented in Eq. (6) and (7) (FCMapper), the model was run to
get the steady-state and make a comparison with the worst-case and policy intervention scenarios. In the first stage, the
worst-case scenario represents the maximum influence of the main concept that affect water quality at the community
level on the other PESTEL concepts. For the second stage, the communities were asked to score policy drivers (via a 6-
degree scale as ) to understand how they might mitigate unwanted outcomes. In this policy intervention scenario,
the drivers were introduced individually i.e., the drivers were set to 0 except the selected one, to isolate and assess the
role of each policy driver on the community model. The value for the simulated scenario was set to 1, urging the system
to fully adopt the policy measure implemented (Morone et al., 2021). Finally, the policy effect was calculated using the
difference between the steady-state and each policy intervention.
Appendix A
step 2
alt-text: Table 1
Table 1
PESTEL concepts and policy drivers, extracted from literature review.
Poli tical (P)
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
Concepts Id Studies
Environmental education focused on key ecosystems P1Sarkar et al. (2007)
Land use planning P2Gyawali et al. (2013)
Decision-makers without adequate training P3Apostolaki et al. (2019)
Short-term environmental projects without results P4San Llorente Capdevila et al. (2020)
Few legitimate conservation policies for key ecosystems P5Rahm et al. (2013)
Experts in decision-making P6San Llorente Capdevila et al. (2020)
Initiatives to accelerate progress on the SDGs P7Alcamo (2019)
Strong institutions P8Davies & Mazumder (2003)
Economic (EC)
Concepts Id Studies
Fish production EC1Pulford et al. (2017)
Food Security EC2Grigg (2016)
Costly treatments of water EC3Pilon-Smits (2005)
Inhabitants incur medical costs EC4Grigg (2016)
Water demand for economic activities: crop and fishing EC5Gyawali et al. (2013)
Support to sustainable productive projects EC6Li et al. (2021)
Economic growth EC7Brockwell et al. (2021)
Water quality for food production EC8Pulford et al. (2017)
Socia l (S)
Concepts Id Studies
Stakeholders involvement S1Wilson et al. (2018)
Population growth S2Brockwell et al. (2021)
Lack of community cohesion S3Wilson et al. (2018)
Persistent equity issue S4Grigg (2016)
Poverty rate S5Grigg (2016)
Sustainable communities S6Davies & Mazumder (2003)
Social well-being S7Brockwell et al. (2021)
Achieve gender equality S8Anderson et al. (2021)
Technolog ical (T)
Concepts Id Studies
Alternative water technology T1Sarkar et al. (2007)
Local development technology T2Rolfe & Harvey (2017)
Innovation in the water sector T3Nyiwul (2021)
Environmental (EV)
Concepts Id Studies
Natural water pollutants EV1Samal et al. (2011)
Anthropogenic water pollutants EV2Oladipo et al. (2021)
Climate change EV3Moosavi et al. (2021)
3
Results
3.1
Identification of the main concepts affecting water quality
3.1.1
P
á
ramo community
The community experts chose 14 out of 40 initial concepts PESTEL, then added two new ones, and the community
added one concept (Table 2a). In total 17 concepts related to political 18%, environmental 23%, social 18%,
technological 0%, economic 23%, and legal 18% factors (Fig. 6a). The community identified as the most influential
concept, i.e., with the highest centrality value EV1 (ci
=
12.22) natural water pollutants (Table 2a). The community
linked the presence of EV1 mainly as a cause of the decline of S7 (
−
1), and increase of EC3 (+1) (Fig. 3). The impact
of EV1 on the other concepts is relatively minor (Fig. 3). Concerning the highest outdegree of the model, P3: Decision-
makers without adequate training (od
=
10.25), reveal a strong influence on other concepts; however, our focus is on
the centrality value as it controls the dynamics of the whole community model. In addition, Table 2a shows that EC3:
Costly treatments of water, EC5:Water demand for economic activities: crop, L3: Legal actions by users, and L4: Water
governance crisis have the highest steady-state value; nonetheless, their outdegrees are lower as well as influence on the
model. The most influenced concepts, determined via indegree scores, are L3: Legal actions by users and EC7:
Economic growth, in decreasing order.
Waterbody restoration EV4Moosavi et al. (2021)
Conventional treatments EV5Sarkar et al. (2007)
Ecosystem conservation EV6Brockwell et al. (2021)
Human exposure to environmental pollutants EV7Hartmann et al. (2018)
Safe water EV8Maurice et al. (2019)
Legal (L)
Concepts Id Studies
Right to live in a healthy environment L1Valladares & Boelens (2019)
Environmental law violation L2Wilson et al. (2018)
Legal actions by users L3Maurice et al. (2019)
Water governance crisis L4Rolfe & Harvey (2017)
Dissemination of water laws to communities L5Wilson et al. (2018)
Poli cy dri ver (D)
Concepts Id Studies
Artificial Floating Island DAFIs Yeh et al. (2015)
alt-text: Table 2
Table 2
PESTEL concepts and policy drivers with their outdegree, indegree, centrality, steady-state value and type identified for the (a)
p
á
ramo, (b) mangrove, and (c) rainforest communities. In addition to the existing 40 PESTEL concepts, further concepts for the six
PESTEL factors P, EV, S, EC L and D are added for each community p
á
ramo (P), mangrove (M) and rainforst (R) by either experts (E)
or communities (C). For example: EVPC1 is an additional concept for the environmental factor in the p
á
ramos identified by the
community. DAFIs is the policy driver.
(a) P
á
ramo community
PESTEL Concepts Outdegree Indegree Centrality Steady- Type
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
state
val ue
Poli tical
P3: Decision-makers without adequate
training 10.25 1.00 11.25 0.87 Ordinary
P4: Short-term environmental projects
without results 9.33 2.66 11.99 0.97 Ordinary
P5: Few legitimate conservation policies for
key ecosystems 4.76 1.99 6.75 0.92 Ordinary
Environmental
EV1: Natural water pollutants 8.00 4.22 12.22 0.99 Ordinary
EV2: Anthropogenic water pollutants 5.27 4.48 9.75 0.99 Ordinary
EV3: Climate change 3.78 2.82 6.60 0.96 Ordinary
EV PC1 : Landslides 2.21 3.62 5.83 0.98 Ordinary
Socia l
S2: Population growth 3.40 1.13 4.53 0.40 Ordinary
S3: Lack of community cohesion 4.28 1.25 5.53 0.89 Ordinary
S7: Social well-being 1.33 8.49 9.82 0.00 Ordinary
Economic
EC3: Costly treatments of water 1.33 9.02 10.35 1.00 Ordinary
EC5: Water demand for economic activities:
crop 3.90 4.85 8.75 1.00 Ordinary
EC7: Economic growth 0.00 6.31 6.31 0.00 Receiver
EC PE1 : Increased agriculture in p
á
ramo 6.46 1.98 8.44 0.93 Ordinary
Legal
L3: Legal actions by users 0.00 8.35 8.35 1.00 Receiver
L4: Water governance crisis 3.69 7.10 10.79 1.00 Ordinary
LPE1 : Laws that contradict each other 2.99 1.71 4.70 0.92 Ordinary
Poli cy dri vers
DAFIs : Artificial floating islands 12.18 0 12.18 0 Driver
DPE1 : Positive political influence of
community leaders 8.20 0 8.20 0 Driver
D PE2 : Government institutions specialized
in water issues 11.38 0 11.38 0 Driver
D PC1 : Reinforce training in environmental
education 9.07 0 9.07 0 Driver
(b) Mangrove comm unity
PESTEL Concepts Outdegree Indeg ree Centrality
Steady-
state
val ue
Type
Poli tical PME1 : Absence of water pollution control by
government 6.63 1.98 8.61 0.92 Ordinary
Environmental EV2: Anthropogenic water pollutants 8.63 6.00 14.63 0.66 Ordinary
EV4: Waterbody restoration 4.31 8.66 12.97 0.00 Ordinary
EV5:Conventional treatments 9.93 1.00 10.93 0.64 Ordinary
EV6: Ecosystem conservation: Mangrove 4.33 7.13 11.46 0.02 Ordinary
EV7: Human exposure to environmental
pollutants 7.00 9.27 16.27 0.98 Ordinary
EV ME1 : Water contamination from mining, oil,
and aquaculture activities 9.26 1.66 10.92 0.67 Ordinary
EV ME2 : Waste disposal problems 0.00 3.00 3.00 0.97 Receiver
EV ME3 : Poor waste management by the
municipality 6.77 5.20 11.97 0.94 Ordinary
EV MC1 : Mangrove habitat lose 8.26 0.66 8.92 0.74 Ordinary
Socia l
S2: Population growth 6.66 4.77 11.43 0.08 Ordinary
S7: Social well-being 0.00 11.33 11.33 0.00 Receiver
SME1 : Irregular settlement in mangrove zones 7.96 1.00 8.96 0.83 Ordinary
SME2 : Access to safe water, food and energy 4.87 8.66 13.53 0.01 Ordinary
Economic
EC3: Costly treatments of water 5.32 8.04 13.36 1.00 Ordinary
EC4: Inhabitants incur medical costs 0.00 10.56 10.56 1.00 Receiver
EC8: Water quality for food production 4.93 8.66 13.59 0.01 Ordinary
EC ME1 : Lack of a sewer system 7.09 4.78 11.87 0.99 Ordinary
Legal L2: Environmental law violation 6.56 6.15 12.71 0.98 Ordinary
Poli cy dri vers
DAFIs : Artificial floating islands 10.29 0 10.29 0 Driver
DME1 : Corporate environmental responsibility 8.13 0 8.13 0 Driver
DME2 : Education in promoting environmental
awareness 6.91 0 6.91 0 Driver
DMC1 : Payment for ecosystem services 7.74 0 7.74 0 Driver
(c) Rainforest community
PESTEL Concepts Outdegree Indegree Centrality
Steady-
state
val ue
Type
Environmental
EV2: Anthropogenic water pollutants 8.66 6.12 14.78 0.99 Ordinary
EV3: Climate change 0.33 2.16 2.49 0.73 Ordinary
EV6: Ecosystem conservation 5.26 6.77 12.03 0.02 Ordinary
EV7: Human exposure to environmental
pollutants 5.99 6.39 12.38 0.97 Ordinary
Socia l
S2: Population growth 6.13 2.31 8.44 0.40 Ordinary
S7: Social well-being 0.00 7.01 7.01 0.01 Receiver
SRC1 : Lack of environmental education 6.32 1.95 8.27 0.69 Ordinary
Technolog ical T1: Alternative water technology 5.56 5.49 11.05 0.01 Ordinary
Economic
EC1: Fish production 5.25 5.26 10.51 0.08 Ordinary
EC2: Food Security 2.39 5.02 7.41 0.14 Ordinary
EC3: Costly treatments of water 5.14 8.00 13.14 0.94 Ordinary
EC4: Inhabitants incur medical costs 2.82 2.72 5.54 0.90 Ordinary
EC8: Water quality for fishing production 3.75 6.27 10.02 0.02 Ordinary
EC RE1 : Low purchasing power 8.82 3.61 12.43 0.93 Ordinary
Legal L2: Environmental law violation 9.31 6.65 15.96 1.00 Ordinary
Poli cy dri vers DAFIs : Artificial floating islands 10.48 0 10.48 0 Driver
DRE1 : Better educational level of community
members 7.62 0 7.62 0 Driver
3.1.2
Mangrove community
The experts chose 11 out of 40 PESTEL and added seven new concepts, and the community one (Table 2b). Hence,
the model is composed of overall 19 concepts which can be grouped as political 48%, environmental 21%, social 21%,
technological 0%, economic 21%, and legal 5% (Fig. 6b). The community identified the major water quality issue in
view of human exposure to environmental pollutants EV7 (ci
=
16.27 which belongs to the environmental concept
group (Table 2b). The community connected the EV7 concept primarily as a cause of the decline of EC8 (
−
1), and
increase EC4 (+1) (Fig. 4). The impact of EV7 on the other concepts is relatively minor (Fig. 4). Moreover, in Table 2
a, EV5: Conventional treatments has the highest outdegree value (od
=
9.93); however, the concept is not perceived as
the most important in the model i.e., it has a lower centrality value than EV7. Also, EC3: Costly treatments of water
and EC4: Inhabitants incur medical costs have the highest steady-state value, but the low outdegrees indicate a slight
influence on the model. Finally, the community perceived as the most affected concepts, regarding indegree score, S7:
Social well-being, EC4: Inhabitants incur medical cost, and EVE9: Waste disposal problems.
DRE2 : Environmental education programs 7.61 0 7.61 0 Driver
DRC1 : Community committee to denounce non-
compliance with the law to the government 7.52 0 7.52 0 Driver
alt-text: Fig. 3
Fig. 3
The main problem and its impact on PESTEL concepts identified in the social cognitive map by the p
á
ramo community (full map in
). Blue lines represent the positive influence of concept Ci on concept Cj, and orange lines represent the negative
influence of concept Ci on concept Cj. Line thickness indicates the degree of influence between components.
i
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appendix B(1)
alt-text: Fig. 4
Fig. 4
i
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3.1.3
Rainforest community
The experts selected 13 out of 40 PESTEL concepts and add one more. The community proposed an additional one (
Table 2c). The model consists of 15 concepts, split into political 0%, environmental 26%, social 20%, technological
7%, economic 40%, and legal 7% concepts (Fig. 6c). The community connected L2 with a high impact on EV7 (+1),
and a decrease in EV6 (
−
1) (Fig. 5). The impact of L 2 on the other concepts is relatively minor (Fig. 5). The
community identified (L2) as being most relevant from the perspective of centrality, outdegree, and steady-state value
(ci
=
15.96, od
=
9.3, and steady-state
=
1), and S7: Social well-being is perceived, regarding indegree score, as the
concept most affected (Table 2c).
The main problem and its impact on PESTEL concepts identified in the social cognitive map by the mangrove community (full map
in appendix B(2)). Blue lines represent the positive influence of concept Ci on concept Cj, and orange lines represent the negative
influence of concept Ci on concept Cj. Line thickness indicates the degree of influence between components.
alt-text: Fig. 5
Fig. 5
The main problem and its impact on PESTEL concepts identified in the social cognitive map by the rainforest community (full map
in appendix B(3)). Blue lines represent the positive influence of concept Ci on concept Cj, and orange lines represent the negative
influence of concept Ci on concept Cj. Line thickness indicates the degree of influence between components.
i
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alt-text: Fig. 6
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3.2
Identification of the main policy drivers to improve water quality
We proposed DAFIs as a nature-based solution to remove contaminants and therefore improve the water quality in the
three communities. Local experts suggested two additional policy drivers and the community one, this is described in
Table 2 (a), (b), and (c). Thus, the most influential drivers according to outdegree scores were in three communities the
DAFIs , followed by DPE2: Government institutions specialized in water issues for the p
á
ramo community (od
=
11.38);
DME1: Corporate environmental responsibility for the mangrove community (od
=
8.13), and D RE1: Better educational
level of community members in the rainforest community (od
=
7.62).
3.3
Analysis of drawn social cognitive maps
In all communities, the majority of identified concepts are ordinary, and only four are policy drivers. The mangrove
community listed a slightly higher number of concepts in relation to the other communities, which indicates a larger
amount of problems. Meanwhile, the rainforest community identified a higher number of connections that show a better
interaction between concepts. A hierarchy index h of 0.35, 0.40, and 0.40 for the p
á
ramo, mangrove, and rainforest
community, respectively, depict that the developed maps are fully democratic. According to the density (D) ranging
between 0.3 and 0.5, participants did not identify a large number of causal relationships among the concepts (Table 3).
3.4
Fuzzy inference: Comparison between steady-state and simulated worst-case scenario
To better understand how PESTEL concepts might respond to a change without policies, the most influential concepts,
i.e. the concepts with high centrality value were set as high (=1) ( ). At the moment that the concepts EV 1
Fig. 6
Concepts from PESTEL perspective in the (a) p
á
ramo community (b) mangrove communities and (c) rainforest community.
alt-text: Table 3
Table 3
Analysis of social cognitive maps from graph theory.
P
á
ramo community Mangrov e community Rainforest community
No. of concepts (N)17 19 15
No. of connectio ns (C) 91 123 103
No. of receiver concepts 2 3 1
No. of ordinary co ncepts 15 16 14
Number of policy drivers (driv er co ncepts) 4 4 4
Hiera rchy index (h)0.35 0.40 0.40
Density (D)0.31 0.35 0.46
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
Appendix C
in p
á
ramo, EV7 in mangrove, and L2 in rainforest were set to 1, the remaining concepts were affected by 89%, 90%,
and 87.5%, respectively. Besides, in the communities, different levels of alteration were identified in population growth
(S2), anthropogenic water pollutants (EV2), and social well-being (S7). In the p
á
ramo and mangrove communities, S2
shows a moderate decrease, where the zones are most populated, while for rainforest community shows a weak
decrease. In relation to EV2, the decrease is very weak in the p
á
ramo community because the main problem in the area
is natural contamination, and a very weak increase in the rainforest if the violation of the law persists. In the mangrove,
there is a decrease, as it is taken into account only from the perspective of contamination towards humans and not from
the environmental factor. Finally, S7 shows a weak decrease in the three communities (Fig. 7).
3.5
Fuzzy inference: Comparison between steady-state and DAFIs with additional policies
The impact of the DAFIs and the other drivers policy in the communities were examined in isolation in the fuzzy
inference estimation of of the FCM approach ( ).
The results depicted in Fig. 8a show that the policies identified in p
á
ramo community as most important to reduce the
natural water pollutants (EV1) are DAFIs , DPC1 , DPE2, and DPE1, in decreasing order. In addition, considering that
water plays a rol in the economic income of farming families, the policies DAFIs and D PE2 could improve the water
quality as well as crops quality and contribute the economic growth (EC7). In the mangrove community (Fig. 8b), the
implementation of DAFIs could decrease the human exposure to environmental pollutants (EV7), and increase the
access to safe water, food and energy (SME2). In the rainforest community (Fig. 8c), the DAFIs has positive impact on
social well-being (S7), and environmental law violation (L2) can be faced with any of the 4 policies.
alt-text: Fig. 7
Fig. 7
Results of the worst-case scenario for the p
á
ramo community, mangrove communities and rainforest communities. Results are only
shown for concepts with moderate to strong changes and which have been proposed for all three communities. Information on all
concepts is given in appendices C and D. Numbers at bars indicate the absolute number of concepts identified.
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step 2 Appendix D
alt-text: Fig. 8
Fig. 8
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4
Discussion
We co-designed together with local experts and community members from three communities in Ecuador, a decision-
making framework to identify what affects water quality, and potential strategies and policies to improve its condition.
Furthermore, we developed an explanatory model that analyses the current status and simulates future water quality
scenarios.
Common issues in developing countries are the limited number of scientific studies that identify strategies to improve
water quality. In addition, water quality data is often not available because it is either limited in public access or
expensive to measure (
Ö
zesmi and
Ö
zesmi, 2004). FCMs offer an alternative approach to developing strategies for
improving water management, through a formal assessment of linguistic data (Adriaenssens et al., 2004).
To obtain reliable FCMs the participation of local experts that share experience and knowledge of the system under
consideration is essential. These can include specialists from governmental organizations, non-governmental
organizations (NGOs), universities, consultants, and communities (Blacketer et al., 2021; Ghaboulian Zare et al., 2022;
Giordano et al., 2020; Gray et al., 2015; Morone et al., 2021; Mourhir, 2021; Santoro et al., 2019). For example, the
Comparison between steady-state and change rate concepts by clamping each policy to 1 in the (a) p
á
ramo community, (b) mangrove
communities, and (c) rainforest community.
study of Giordano et al. (2020) considers several stakeholders in the process of designing and implementing nature-
based solutions as a key driver for enhancing community involvement and institutional cooperation. Regarding the
number of experts to be consulted, we involved more than eight per community, satisfactorily meeting the
recommendations of seven to 15 participants reported elsewhere (G
ó
mez Mart
í
n et al., 2020; Videira et al., 2014).
Building democratic FCMs requires recognition of local problems and solutions to improve water quality from the
perspectives of local experts and community members. For the p
á
ramo community, where economic and environmental
concepts were identified as critical influencers, the water is a strategic, economic, and political resource, therefore its
access and management are a source of conflict, and power (Pila, 2018). Many rural and indigenous communities in
Cotopaxi face socio-economic problems such as high poverty rates, water scarcity, lack of access to irrigation, and
unequal distribution of land and water (Partridge, 2016). These factors have determined, as in other Andean
communities, to use the ecosystem as a natural resource and capital for family sustenance (United Nations
Development Programme, 2021. The p
á
ramo ecosystem itself has been used for thousands of years for different human
activities, facing unprecedented anthropogenic pressures (Correa et al., 2020). The need to generate income through
activities such as cultivation, intensification of livestock grazing, Pinus plantations, clearing activities, and tourism, may
significantly alter the hydrological processes of the p
á
ramo ecosystem, affecting directly the water supply function (
Buytaert et al., 2006; Garc
í
a et al., 2020). Regarding the main problem recognized by locals (centrality index), the
people from the p
á
ramo community identified natural water pollutants. The presence of As with Fe is widespread from
natural geological origins in multiple zones of Cotopaxi (Bundschuh et al., 2021; Morales-Simfors et al., 2019),
confirming what the locals perceive. In addition, the community linked the presence of natural pollutants mainly as a
cause of declining social well-being (
−
1), similar to finding in Bundschuh et al. (2021), and increased water treatment
costs (+1) as stated in Joseph et al. (2019) (Fig. 3).
In our findings from the mangrove community, the political influence is critical to the water quality. In the same line,
Beitl et al. (2019) highlight that the mangrove ecosystem in Ecuador has been undervalued and converted to other uses
by ineffective policies. The results obtained in the study of Pazmi
ñ
o Manrique et al. (2018) indicate that the
management of marine areas, including mangroves, was not well-positioned within the Ecuadorian political agenda. Its
management model is confusing and its implementation is slow and yields poor results. In the mangrove community
human exposure to environmental pollutants was the key identified problem which has a major impact on the medical
expenses of the inhabitants (+1), and decreases the water quality for food production (
−
1) (Fig. 4). In water, exposure
to heavy metal(loid)s through ingestion of tap water and incidental ingestion of surface water, results in unacceptable
risk levels (carcinogenic and non-carcinogenic) for human health in adults and children in contaminated areas on the
Ecuadorian coast (Jim
é
nez-Oyola et al., 2021). In food, concentrations of cadmium and mercury above the limits
considered safe for human consumption established by the European Union have been found in yellowfin tuna caught
off the Ecuadorian coast (Ara
ú
jo and Cede
ñ
o-Macias, 2016). In the same line, Fern
á
ndez-Cadena et al. (2014)
reported the presence of several metals (Cd, Cu, Cr, and Pb), and Calle et al. (2018) concentrations of total mercury
(THg), which exceeded the permissible level by Ecuadorian legislation (Asamblea Nacional, 2014) in the Salado
estuary. This is due to urban sprawl, industrial growth of Guayaquil city, and inefficient water management practices (
Calle et al., 2018; Vinueza et al., 2021). Some of these elements can bioaccumulate and possibly biomagnify
throughout the food web, being potential risks to human health (Calle et al., 2018).
The rainforest community perceives the economic concept as an important concept influencing water quality, which is
confirmed by different studies (Cammelli et al., 2020; Garrett et al., 2017) that state that the majority of the people
residing in forested regions remain impoverished due to environmental degradation and low-income. Therefore, the
increasing pressure on land use over the last decades can be responsible for leaving water reservoirs unprotected,
resulting in a runoff of pollutants and nutrients into such reservoirs and increasing the water treatment costs, as shown
by Danelon et al. (2021) for the Brazilian case, including the Amazon forest. Finally, in the rainforest community, the
violation of environmental legislation (Buccina et al., 2013) was the most influential problem, and has a high impact on
human exposure to environmental pollutants (+1) and a decrease in ecosystem conservation (
−
1) (Fig. 5). A large
majority of people living in the Amazon region have no access to drinking water distribution systems and collects water
from rain, wells, or small stream (Maurice et al., 2019). The lack of access to good quality water violates the
inhabitant's right to live in a healthy environment according to the Ecuadorian constitution (Asamblea Constituyente,
2008). In addition, in the same Ecuadorian constitution, the nature has the right to integral respect for its existence and
for the maintenance and regeneration of its life cycles, structure, functions, and evolutionary processes (Asamblea
Constituyente, 2008). However, Capparelli et al. (2020) found that in the zone of the rainforest community,
Q3
anthropogenic activities are introducing metals to the aquatic ecosystem, as some metals were up to 500 times above the
maximum permissible limits for the preservation of aquatic life established by Ecuadorian and North American
guidelines.
Our exploration to anticipate the future of water management at the community level with D AFIs and complementary
policy drivers (Ghaboulian Zare et al., 2022), leads us to suggest the use of two scenarios: the worst-case scenario and
a managed policy scenario for the three communities. In the worst-case scenario of the three communities, more than
85% of the concepts are changing. However, these changes range from very weak to medium, and no concept
experiences a strong change ( ). But this is not necessarily always the case. Gray et al. (2015) use the worst-
case scenario idea in the community model of the bushmeat trade in Tanzania and create a scenario in which the
concept of the increased human population is characterized
“
as high
”
.
Concerning the reaction of the system to the proposed policies, the implementation of AFIs as nature-based solutions
leads to a decrease in natural and anthropogenic water pollutants in the p
á
ramo community. Our findings are in line
with the study of Ladislas et al. (2015) who show that AFIs can effectively remove some heavy metals, such as
cadmium, nickel, and zinc from stormwater ponds. Also, the results of Wang et al. (2020) reveal the purification
abilities of AFIs for carbon, nitrogen, and phosphorus. In the mangrove community, the AFIs could increase access to
safe water, food, and energy, and improve the water quality for food production. In accordance with Karstens et al.
(2018), the goal of AFIs in coastal habitats is the restoration and rehabilitation as well as the local enhancement of
water quality by nutrient absorption and removal. Due to their generally low cost and simple construction, AFIs are
used to treat wastewater from secondary effluents, stormwater, and agricultural runoff (Benvenuti et al., 2018; Colares
et al., 2020). Regarding food production and energy, the plants harvested from very large AFIs can be processed into
biogas, bio-fertilizer, biomaterials, or even food for animals and humans (Yeh et al., 2015). In the rainforest community,
the AFIs have a positive impact on ecosystem conservation and as an alternative to common water treatment
technologies. Sharma et al. (2021) and Nuruzzaman et al. (2021) state that the AFIs are a cost-effective and eco-
friendly phyto-technology that potentially benefits ecosystem quality preservation and landscape conservation. As
reported by [Instruction: TO DC: Remove gs link for "UNESCO"]UNESCO and UNESCO, 2019, nature-based
solutions offer some of the most effective and sustainable ways to improve water security, supporting the achievement
of SDG target 6.3. They also offer additional benefits to the communities in which they are applied, such as improved
agriculture, job creation, climate resilience, and the achievement of several other SDGs.
In our communities, various water quality problems can be addressed with a technical solution such as AFIs. However,
community members outlined complementary policy drivers or combinations with AFIs which are needed to
demonstrate significant changes in water quality. Strategies such as the positive political influence of community leaders
(Pila, 2018), the government institutions specializing in water (Davies and Mazumder, 2003), and reinforcing training
in environmental education (Su
á
rez-Perales et al., 2021) can be applied to the problem of a few legitimate conservation
policies in the p
á
ramo community. For the environmental law violation in the mangrove community, corporate
environmental responsibility (Hambira and Kolawole, 2021), education in promoting environmental awareness (
Su
á
rez-Perales et al., 2021), and payment for ecosystem services (Fu et al., 2018) are key aspects to improve the water
quality. Moreover, to achieve social well-being in the rainforest community, a better education level of communities
members (Al Amin et al., 2021), environmental education programs (Su
á
rez-Perales et al., 2021), and community
committees to denounce non-compliance with the law to the government (Buccina et al., 2013) are presented as
potentially promising concepts.
5
Conclusions
In our study, the FCM method proves to be a useful tool to identify concepts that influence water quality based solely
on a linguistic description from local participants. Using diverse sources of knowledge from people in three
communities located in key ecosystems in Ecuador, we identify issues that locals relate to poor water quality such as
natural pollutants, human exposure to environmental pollutants, and violation of environmental legislation.
The application of FCMs offers a promising method to analyze and identify AFIs as nature-based solutions for water
quality improvement. However, not all the problems recognized by the communities are solved by this option.
Therefore, model simulations combining AFIs with different policies show better and more comprehensive options for
improving water quality and ensuring future sustainable water governance. Some of the main policies suggest
Appendix C
reinforcing training and educational programs to promote environmental awareness, demand corporate environmental
responsibility, implement payment for ecosystem services, and organize community committees to denounce non-
compliance with the law.
We suggest that future studies should consider three aspects of the community FCMs: the criteria that determine the
necessary participation of stakeholders, the policy driver associated with the nature-based solution, and the use of
understandable rating scales for the degree of influence between concepts. Related to the criteria of participation, FCMs
encourage stakeholders to take part in water management decisions. Nevertheless, participants should fulfill the
following criteria: In the communities, the water must play a crucial role in family income generation and, in the case of
experts, their knowledge, experience, and influence on water decision-making. In this way, it is possible to identify the
real problems and propose adequate strategies based on the internal mental models of stakeholders. Regarding the
policy driver, we propose AFIs as a nature-based solution taking into account the economic water scarcity of
communities, although for future studies we recommend exploring theoretically other nature-based solutions that also
cover the problem of physical water scarcity. This approach suggests that for scoring the degree of cause-effect
influence of concepts, simple scales may adequately capture our objective, otherwise the community may perceive long
scales as a complex methodology.
Our results show that the simple nature of the FCMs provides a novel, inclusive, and locally adapted framework to
understand how water quality could be improved, guiding future water management and contributing to achieving the
SDGs, particularly target 6.3 in developing countries. Furthermore, as pioneering work in the region, our study has the
potential to be replicated in different communities in developing countries, where access to data is limited, but the
motivation and knowledge of stakeholders exist.
Funding
This project was funded by the German Academic Exchange Service (DAAD) from funds of Federal Ministry for
Economic Cooperation (BMZ), SDGnexus Network (grant number 57526248), program
“
exceed -
Hochschulexzellenz in der Entwicklungszusammenarbeit
”
.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Acknowledgments
The first author would like to acknowledge Justus Liebig University Giessen, Technical University of Cotopaxi,
Decentralized Autonomous Government (GAD) of the province of Cotopaxi/YakupakWasi, and the Ministry of the
Environment, Water and Ecological Transition of Ecuador (MAAE) for providing the facilities for developing this
research. The authors would like to thank the Communities of Chilla Chico, Mogollon, Awayaku, and Pueblo Kichwa
de Rukulla PKR for their active participation in this study. Special thanks to Juan S. Acero Triana for his suggestions
and comments. Finally, thanks to Nataly Marisol Llugsha Moreta, Marcelo Fernando Amores Palma, and Dennys
Villegas for their assistance in the development of this work.
Appendix A
Participation of multisectoral stakeholders in water management decisions
Q4
CRediT authorship contribution statement
Kalina Fonseca : Conceptualization,
Methodology,
Software,
Data curation,
Writing – original draft,
Writing –
review & editing.
Edgar Espitia : Visualization,
Writing – review & editing.
Lutz Breuer : Supervision,
Project
administration,
Funding acquisition,
Writing – review & editing.
Alicia Correa : Supervision,
Writing – review &
editing.
Stakeholder s Catego ry Function Expert
’
role Number
experts
Invited
partici pants
Total
of
experts
Participants
of
community
P
á
ramo
Decentralized
Autonomous
Government (GAD) of
the province of
Cotopaxi/YakupakWasi
Irrigation water
management in
the province of
Cotopaxi,
including
p
á
ramo areas
Director of the
irrigation and
drainage
1 2
10 10
Cotopaxi Technical
University -
Environmental
Engineering
Department
Training of
professionals in
the management
and protection of
water, soil and air
Director of the
project: nature-
based
solutions in
the p
á
ramo of
Cotopaxi
1 2
Independent
professional Consultant
Specialist in
conventional
water treatment
plants and
irrigation
reservoirs
1 2
NGO
“
Plan
Internacional"
Promote
associativity,
sustainable
production, and
fair trade
General
manager in
Cotopaxi
1 0
Mangrove
Ministry of the
Environment, Water
and Ecological
Transition of Ecuador
(MAAE).
Salado Estuary
and Santay
Island recovery
Project (PRESIS)
Manager of
project PRESIS 1 2
10 10
University of
Guayaquil
Training of
professionals in
biotechnology,
biodiversity, and
conservation of
natural resources
Director of
biology
department
1 0
Municipal Water and
Sewerage Company of
Guayaquil (EP
EMAPAG)
Consultant
Specialist in
conventional
water treatment
plants
1 2
ONG
“
Misi
ó
n
Asistencia"
Promotes the
protection of
people and
communities
against the
harmful effects of
natural or
anthropogenic
phenomena
Executive
director 1 2
Rainforest Decentralized
Autonomous
Government (GAD) of
Application of
environmental
management
policies in the
Coordinator of
the
environmental
management
1 1 8 8
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
Appendix B
the province of
Archidona
territory of
Archidona
and control
department
Amazon Regional
University Ikiam-
Department of
Hydrology
Training of
professionals in
management and
protection of
water
Coordinator of
department of
hydrology
1 2
Independent
professional Consultant
Specialist in
conventional
water treatment
plants
1 0
NGO
″
180 GRADOS"
Development of
social projects
with a gender
approach
Executive
director 1 1
Social cognitive map constructed in the workshop by the p
á
ramo community. Blue lines represent
positive influence of concept Ci on concept Cj, and orange lines represent a negative influence of
concept Ci on concept Cj. Line thickness indicates the degree of influence between components.
(1)
alt-text: Image 1
i
Images are optimised for fast web viewing. Click on the image to view the original version.
Cognitive map constructed in the workshop by the mangrove community. Blue lines represent positive
influence of concept Ci on concept Cj, and orange lines represent a negative influence of concept Ci
on concept Cj. Line thickness indicates the degree of influence between components.
(2)
i
Images are optimised for fast web viewing. Click on the image to view the original version.
Appendix C
alt-text: Image 2
Social cognitive map constructed in the workshop by the rainforest community. Blue lines represent
positive influence of concept Ci on concept Cj, and orange lines represent a negative influence of
concept Ci on concept Cj. Line thickness indicates the degree of influence between components.
(3)
alt-text: Image 3
i
Images are optimised for fast web viewing. Click on the image to view the original version.
(1) Results of worst-case scenario in the p
á
ramo community. Factors with an increasing trend are listed in the left
column, factors with a decreasing trend in the right column. 1
=
strong change (red color); 2
=
medium change (blue
color); 3
=
weak change (purple color); 4
=
very weak change (grey color).
(2) Results of worst-case scenario in the mangrove community. Factors with an increasing trend are listed in the left
column, factors with a decreasing trend in the right column. 1
=
strong change (red color); 2
=
medium change (blue
color); 3
=
weak change (purple color); 4
=
very weak change (grey color).
Concept EV 1: Natural water pollutants
% of Concept Changed 89
Concept Strength
(Positiv e) Concept Strength
(Negativ e)
P3: Decision-makers without adequate training 4 EV2: Anthropogenic water
pollutants 4
P4: Short-term environmental projects without
results 4 AC1: Landslides 4
P5: Few legitimate conservation policies for key
ecosystems 3 S 2: Population growth 2
EV3: Climate change 4 S7: Social well-being 4
S3: Lack of community cohesion 3 EC7: Economic growth 4
EC3: Costly treatments of water 4 EC PE1 : Increased agriculture in
p
á
ramo 4
EC5: Water demand for economic activities: crop 4
L3: Legal actions by users 4
L4: Water governance crisis 4
LPE1 : Laws that contradict each other 4
i
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purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
Concept EV 7: Human exposure to environmental pollutants
% of Concept Changed 90
Concept Strength
(Positiv e) Concept Strength
(Negativ e)
PME1 : Absence of water pollution control by government 4 EV2: Anthropogenic water
pollutants 2
EV5:Conventional treatments 3 EV4: Waterbody restoration 4
EV6: Ecosystem conservation: Mangrove 4 S2: Population growth 2
EV ME1 : Water contamination from mining, oil, and
aquaculture activities 4 S7: Social well-being 4
EV ME2 : Waste disposal problems 4 EC3: Costly treatments of
water
4
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
(3) Results of worst-case scenario in the rainforest community. Factors with an increasing trend are listed in the left
column, factors with a decreasing trend in the right column. 1
=
strong change (red color); 2
=
medium change (blue
color); 3
=
weak change (purple color); 4
=
very weak change (grey color).
Appendix D
(1) Comparison between steady-state and change rate concepts by clamping each policy to 1 in the p
á
ramo community.
EV ME3 : Poor waste management by the municipality 2 EC8: Water quality for food
production 3
EC ME1 : Lack of a sewer system 4 ECME1 : Lack of a sewer
system 4
SME1 : Irregular settlement in mangrove zones 4
SME2 : Access to safe water, food and energy 4
EC4: Inhabitants incur medical costs 4
L2: Environmental law violation 4
Concept Scenari o: L2: Enviro nmental law viola tion
% of Concept Changed
87.5
Concept Streng th
(Positiv e) Concept Strength
(Negativ e)
EV2: Anthropogenic water pollutants 4 EV3: Climate change 4
EV7: Human exposure to environmental
pollutants 3 EV6: Ecosystem conservation 3
SRC1: Lack of environmental education 2 S2: Population growth 3
ECRE1: Low purchasing power 3 S7: Social well-being 4
T1: Alternative water technology 4
EC1: Fish production 3
EC2: Food Security 3
EC3: Costly treatments of water 4
EC4: Inhabitants incur medical costs 3
EC8:Water quality for fishing
production 3
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
PESTEL Concepts
DAFIs :
Artificia l
Floa ting
Islands
DPE1: Positive pol itica l
influence of community
leaders in water
management.
DPE2: Government
Institutions
Specialized in
Water Issues
DP1: Reinforce
training in
environmental
educatio n
Poli tical P3: Decision-makers
−
53.2%
−
49.9%
−
53.2%
−
49.9%
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
(2) Comparison between steady-state and change rate concepts by clamping each policy to 1 in the mangrove
community.
without adequate
training
P4: Short-term
environmental
projects without
results
−
73.2%
−
69.3%
−
75.4%
−
69.2%
P5: Few legitimate
conservation
policies for key
ecosystems
−
80.2%
−
78.9%
−
79.4%
−
78.1%
Environmental
EV1: Natural water
pollutants
−
66.1%
−
53.9%
−
56.7%
−
59.6%
EV2:
Anthropogenic
water pollutants
−
67.9%
−
56.4%
−
62.7%
−
66.4%
EV3: Climate
change
−
59.5%
−
49.5%
−
48.0%
−
55.0%
EVPC1 : Landslides
−
53.6%
−
36.1%
−
45.7%
−
48.6%
Socia l
S2: Population
growth 2.7% 1.9% 3.3% 0.4%
S3: Lack of
community
cohesion
−
21.0%
−
21.0%
−
28.1%
−
24.6%
S7: Social well-
being 31.0% 19.6% 25.6% 25.5%
Economic
EC3: Costly
treatments of water
−
42.6%
−
26.1%
−
35.2%
−
32.8%
EC5: Water demand
for economic
activities: crop
−
51.5%
−
36.5%
−
44.4%
−
46.0%
EC7: Economic
growth 36.6% 23.4% 36.3% 29.3%
ECPE1: Increased
agriculture in
p
á
ramo
−
65.5%
−
68.2%
−
62.7%
−
69.3%
Legal
L3: Legal actions
by users
−
20.4%
−
12.6%
−
15.6%
−
13.4%
L4: Water
governance crisis
−
21.1%
−
16.1%
−
22.3%
−
17.3%
LPE1: Laws that
contradict each
other
−
80.7%
−
79.0%
−
80.6%
−
79.0%
PESTEL Concepts DAFIs : DEM1: Corporate DME2 : Education in DMC1:
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
(3) Comparison between steady-state and change rate concepts by clamping each policy to 1 in the rainforest
community.
Artificia l
Floa ting
Islands
Environmental
Responsibility
promo ting
environmental
awareness
Payment for
ecosy stem
servi ces
Poli tical
PME1 : Absence of water
pollution control by
government
−
28.2%
−
32.5%
−
29.9%
−
37.4%
Environmental
EV2: Anthropogenic water
pollutants
−
49.4%
−
51.0%
−
50.0%
−
46.6%
EV4: Waterbody restoration 50.5% 40.2% 41.3% 42.4%
EV5:Conventional
treatments
−
40.7%
−
32.9%
−
35.8%
−
33.8%
EV6: Ecosystem
conservation: Mangrove 39.5% 31.3% 33.2% 31.7%
EV7: Human exposure to
environmental pollutants
−
33.2%
−
22.5%
−
24.8%
−
22.5%
EV ME1 : Water
contamination from mining,
oil, and aquaculture
activities
−
57.5%
−
57.6%
−
56.9%
−
56.2%
EV ME2 : Waste disposal
problems
−
31.5%
−
41.8%
−
40.5%
−
40.8%
EV ME3 : Poor waste
management by the
municipality
−
9.4%
−
8.2%
−
10.3%
−
12.9%
EV MC1 : Mangrove habitat
lose
−
49.2%
−
48.8%
−
50.0%
−
52.7%
Socia l
S2: Population growth 50.7% 41.0% 41.9% 44.5%
S7: Social well-being 58.9% 38.8% 43.9% 45.9%
SME1 : Irregular settlement
in mangrove zones
−
26.1%
−
26.0%
−
32.0%
−
31.0%
SME2 : Access to safe water,
food and energy 78.3% 63.9% 64.6% 71.0%
Economic
EC3: Costly treatments of
water
−
5.1%
−
4.5%
−
4.4%
−
4.4%
EC4: Inhabitants incur
medical costs
−
5.2%
−
3.4%
−
3.6%
−
3.7%
EC8: Water quality for food
production 55.3% 42.4% 45.9% 46.2%
EC ME1 : Lack of a sewer
system
−
1.3%
−
1.7%
−
1.6%
−
1.7%
Legal L2: Environmental law
violation
−
24.7%
−
32.2%
−
23.7%
−
27.2%
i
The table layout displayed in this section is not how it will appear in the final version. The representation below is solely
purposed for providing corrections to the table. To preview the actual presentation of the table, please view the Proof.
PESTEL Concept
DAFIs :
Artificia l
Floa ting
Islands
DRE1:Better
educatio nal level of
community
members
DRE2:
Environmental
educatio n
programs
DRC1: Community committee to
denounce non-complia nce with
the law to the government
Environmental
EV2:
Anthropogenic
water pollutants
−
14.7%
−
9.2%
−
9.5%
−
10.0%
EV3: Climate
change 4.3% 3.8% 3.8% 4.1%
EV6: Ecosystem
conservation 80.0% 71.9% 73.4% 67.8%
EV7: Human
exposure to
environmental
pollutants
−
44.9%
−
35.5%
−
39.1%
−
34.8%
Socia l
S2: Population
growth 44.1% 42.1% 42.6% 41.8%
S7: Social well-
being 91.4% 89.6% 90.1% 88.1%
SRC1 : Lack of
environmental
education
−
35.4%
−
38.1%
−
38.7%
−
29.6%
Tecnological T1: Alternative
water technology 35.5% 21.1% 21.9% 18.8%
Economic
EC1: Fish
production 81.8% 75.3% 75.7% 75.0%
EC2: Food
Security 83.9% 83.6% 83.7% 83.3%
EC3: Costly
treatments of
water
−
28.7%
−
16.7%
−
18.2%
−
13.8%
EC4: Inhabitants
incur medical
costs
−
77.0%
−
76.1%
−
74.9%
−
74.4%
EC8:Water
quality for
fishing
production
76.3% 66.0% 67.2% 63.8%
EC RE1 : Low
purchasing power
−
85.3%
−
82.7%
−
82.2%
−
82.4%
Legal
L2:
Environmental
law violation
−
5.8%
−
4.9%
−
5.0%
−
6.3%
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i
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Highlights
•FCMs offer a novel and locally adapted framework to improve water quality management.
•NbS plus policy strategies are useful to achieving the SDG 6at the community level.
•A strategy to improve water quality in communities must consider PESTEL concepts.
•Experts and communities can identify key strategies to solve water quality problems.
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