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A systems approach to identify sets of indicators: Applications to coastal management
Ecological Indicators 23 (2012) 588–596
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
A systems approach to identify sets of indicators: Applications to coastal management Marcello Sanò a,b,∗ , Raúl Medina b a b
Griffith Centre for Coastal Management, Griffith University, Australia Environmental Hydraulics Institute “IH Cantabria”, Universidad de Cantabria, Spain
a r t i c l e
i n f o
Article history: Received 17 November 2010 Received in revised form 11 April 2012 Accepted 13 April 2012 Keywords: Integrated coastal zone management Stakeholders identification Systems thinking Analysis of relevance Principal components analysis
a b s t r a c t This paper proposes a systems approach and a set of steps to select critical variables to build a site-specific, problem-oriented and cost-effective set of indicators for Integrated Coastal Zone Management. The approach integrates three steps: (i) a method to systematically identify coastal stakeholders, called “Hydra”, (ii) a stakeholder-based conceptualization technique based on the use of matrices, and (iii) the analysis of the system structure and variables using structural analysis and multivariate statistics (Principal component analysis, PCA). In Section 2, a hypothetical example is used to illustrate the whole process, while real case studies are used in Section 3 for testing, including: (i) the application of the Hydra to the identification of coastal stakeholders for the Spanish Strategy for Coastal Sustainability, (ii) a stakeholder-based systems conceptualization exercise for an ICZM project in Egypt and (iii) the analysis of a dataset of sustainability indicators for coastal areas collected in Catalonia, Spain, using PCA. Results of the research show that the methodological framework is applicable both as an integrated approach or as independent steps to address the identification of cost-effective sets of indicators, oriented to site-specific problems, based on a broad systems perspective. Crown Copyright © 2012 Published by Elsevier Ltd. All rights reserved.
1. Introduction Integrated Coastal Zone Management (ICZM) is regarded as the pathway for the sustainable development of coastal systems in the 21st century, using an approach that integrates the management of natural processes with the improvement of economic efficiency, involving stakeholders throughout the process. ICZM theory calls for a systems approach to coastal zone management (Vallega, 1999; Kay and Alder, 2005), which considers the effect of different policy options on the whole coastal system, and the use of specific indicators as the way to measure the current condition of the system and the progress towards a desired state. The use of indicators to monitor state and progress of ICZM implementation has been explicitly suggested by the text of the chapter 17 of the Agenda 21 (UN, 1992). Moreover, the use of indicators is encouraged by the ICZM Protocol for the Mediterranean (UNEP, 2008), the most relevant legal instrument addressing ICZM in the Mediterranean countries. An extensive literature has been produced on indicators and ICZM, based on the research carried out by the coastal science
∗ Corresponding author at: Griffith Centre for Coastal Management, Griffith University, Gold Coast Campus Griffith University Q4222, Australia. Tel.: +61 755 528520; fax: +61 755 528067. E-mail address: m.sano@griffith.edu.au (M. Sanò).
community (Bowen and Riley, 2003; Ehler, 2003; Hanson, 2003; Henocque, 2003; Olsen, 2003; Rice, 2003; Pickaver et al., 2004; Sardá et al., 2005; Potts, 2006) and initiatives of government agencies (Scottish Executive Central Research Unit, 2001; IOC, 2003, 2006; Martí et al., 2006; NOAA, 2010). Most of these works propose broad and general sets of indicators to measure the state of the coast and its progress towards sustainability, a useful approach in comparing the state of ICZM implementation in different regions. On the other hand, while dealing with specific problems in particular coastal regions, problem-oriented indicators, based on a systems approach and combining stakeholders perspectives, should be identified and used to measure the state of the coast with respect to problems that are not reflected in generic indicator sets (FontalvoHerazo et al., 2007; Diedrich et al., 2010). While coastal management scholars have partially addressed these demands, sustainability sciences have developed theories linking systems approaches, the principles of sustainable development and the use of indicators as a way to measure the state and progress towards certain goals (Costanza, 1996; Ronchi et al., ˜ 2002; Castro-Bonano, 2002; Gallopín, 2003; Reed et al., 2005). For example Kelly (1998) supports the use of a systems approach to identify information (i.e. indicators) for sustainable development, arguing that other frameworks fail to capture important causal relationships and the system behaviour. The relevance of sustainability science to coastal management is specifically addressed by Cummins and McKenna (2010).
1470-160X/$ – see front matter. Crown Copyright © 2012 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2012.04.016
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The use of systems approaches in coastal management has been theorized in the past (Van der Weide, 1993; Vallega, 1999; Kay and Alder, 2005) and only recently there have been efforts to put these theoretical frameworks into practice (Robadue, 2005; Smith et al., 2008; Hopkins et al., 2011). From a theoretical perspective, Vallega (1999) gave a strong contribution to the application of the systems approach to coastal management by considering the coastal system as bi-modular: one module is the coastal ecosystem, comprising all the natural processes; the other module is the coastal community, comprising social and economic process. Following Vallega’s suggestions we embrace Berkes’s concept of social–ecological system (Berkes et al., 2000), which was also applied to coastal environments (e.g. Marin and Delgado, 2008). In the systems sciences context, we consider here the Systems Thinking approach (Sterman, 2000) as the most suitable approach for practical modelling of the coastal system and to identify coastal indicators. Systems Thinking is a discipline that provides tools to think holistically about problems and to focus on the components and relations of complex systems (Sterman, 2000). One of the paradigms of Systems Thinking is the mental model, the representation of reality in a stakeholder’s mind (Senge, 1990). Participatory techniques are quite common in the Systems Thinking literature (e.g. Vennix, 1999; Andersen et al., 2007) with specific applications to natural resource management (e.g. Antunes et al., 2006), and coastal management (e.g. Smith et al., 2008). In our methodology we chose to apply a participatory system conceptualization method as an initial step to identify indicators. This is followed by the application of two techniques for system analysis: one is the analysis of relevance (R) (Sanò, 2009) and the other is principal components analysis (PCA) (e.g. Bastianoni et al., 2008). The use of PCA to identify sets of indicators is not as common in the coastal management field as in other fields of knowledge such as econometrics (e.g. Peters and Butler, 1970) and psychology (e.g. Cooper and Kelleher, 1973). Despite this, some coastal scholars support its use (e.g. Jiménez and Van Koningsveld, 2004; Rice, 2003) and previous experiences exist in the literature: for example Sardá et al. (2005) use PCA to reduce the number variables to a strategic set of indicators; Shi et al. (2004) use PCA to eliminate overlapping information in basic indicators and to assign weights to variables of coastal sustainability. Our aim is to provide a methodological framework linking together stakeholder identification and engagement techniques, a system conceptualization approach and the analysis of the system structure and its variables to identify a critical set of indicators for coastal management. This framework is divided into three steps: 1. The Exploration of the System, corresponds to the identification and engagement of coastal stakeholders. For this purpose, we propose to use the so-called Hydra approach, where stakeholders are identified iteratively (Sanò et al., 2010) (Section 2.1). 2. The Modelling of the System corresponds to the use of a stakeholder-based approach for the identification of causal relationships between variables using matrices, which, in turn, are used to build a system conceptual model (details are described in Sanò, 2009) (Section 2.2). 3. The Analysis of the System, corresponds to (i) the analysis of the structure of system conceptual model, stored in matrices to identify a group of relevant variables (Sanò, 2009), and (ii) the multivariate analysis of this group of relevant variables using PCA to select a final set of variables to be used as critical indicators (Section 2.3). A simple example, based on a hypothetical coastal system made of 20 stakeholders, issues and variables (one issue and one variable per stakeholder) is used to illustrate the whole process, using a randomly generated dataset of variables. The methodological steps are
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then applied to real case studies and datasets from ICZM projects (Section 3). 2. Methodology As mentioned above, the methodology presented here includes three steps. To illustrate its application, we use here a hypothetical group of 20 stakeholders contributing to the construction of a set of indicators. The words issue, variable and indicator are used with different meanings: an issue is a specific problem identified by a stakeholder, a variable is the measurement which better represents the issue’s variability, and an indicator is the critical variable for the system to be used for its management. 2.1. Step 1. Exploration of the system ICZM deals most of the times with complex problems, characterized by multiple stakeholders, different points of view and different sets of alternative technical solutions (Olsen et al., 2009): this is called an unstructured problem (Thissen et al., 2008). A structured analysis is therefore needed and must be based on the contribution of experts and stakeholders, integrating knowledge with perception. While looking for variables representing a complex socialecological system, the systems approach should be applied from the beginning of the process, during the identification of the sectors and stakeholders of concern. The identification of stakeholders is not trivial as it is often based on each person mental model of the system’s functioning. Moreover, in remote areas, where the issues at stake are not so well identified as in more familiar environment, the identification of the right sectors and stakeholders is the key for the success of any initiative. Public bodies from the environmental sector (the so called problem-owner) should be responsible of identifying the interested stakeholders and set the initial boundaries to the coastal system in terms of number of sectors and stakeholders to be involved. This can be an authority responsible for ICZM initiatives at the national, regional, or local level. To facilitate the identification of stakeholders using a systems approach, we developed a simple technique called the Hydra (Sanò et al., 2010). The name comes from the Greek mythology: when Hercules fought Hydra, he found that every time he cut off a head, three more grew back in its place. In the same way, while doing a stakeholder inventory, the problem-owner can find out that every time he identifies a stakeholder, two or three more appear in the list. Using this approach, the problem owner is required to identify a preliminary list of relevant stakeholders. These stakeholders are asked in turn to identify missing stakeholders in the previous list. After a few rounds, all the relevant stakeholders are identified. This process is illustrated in our 20 stakeholders example by Fig. 1 where the problem owner (stakeholder number 1) identifies a group of six stakeholders (stakeholders numbers 2–7) who, in turn, identify other missing stakeholders and confirm the previous ones (stakeholders numbers 8–15). In the last round, five missing stakeholder are identified (stakeholders numbers 16–20). The process finalizes when no new stakeholder appears on the list. Different techniques are available for stakeholder engagement, and the choice should be based on the scale of the project and consequently on the number of stakeholders involved. When the number of identified stakeholders is high, public meetings and workshops are not advisable because of the difficulties in collecting valuable information. If the number of stakeholders is not high (let’s say, less than 30) or a bigger list is filtered according to their responsibilities or the importance of the sector they represent, a participatory workshop can be held to involve them in the concerned process, in this case, the identification of indicators.
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2.2. Step 2. Modelling of the system
Fig. 1. Stakeholders belonging to different components of the coastal system (e.g. ecological, physical, economic, and social) are identified using the Hydra approach. Each stakeholder identified one or more stakeholder.
In this context, participatory workshops are thought to involve stakeholders in the process to identify their own specific sectoral issues, together with the variables they consider as relevant and or they feel comfortable with. Coastal experts from different disciplines should be involved in the workshop, as a way to address and improve group’s understanding of specific issues and variables. Following the previous example, the hypothetical group of 20 stakeholders is invited to a workshop to identify issues and variables. During the workshop, experts provide information to the public. As a result, a list of issues and stakeholders is finally produced (Fig. 2). In the example, we proposed a simple model in which a stakeholder reports one issue and one corresponding variable. The result is a list of 20 issues and variables, one per stakeholder, the base for systems modelling and analysis.
Fig. 2 shows that the preliminary list of stakeholders, issues and variables built in the previous step still represents a reductionist view of the system, considering that causal relationships between variables are not yet identified. The system’s modelling process should be therefore completed through the identification of causal relationships between the systems variables. In our methodology, this is done by using specific interaction matrices filled by each stakeholder; the matrices are then superimposed (summed up) to build a combined matrix, representing all the relationships identified by the group. This, in turn, is used to build a causal loop diagram (CLD), using Systems Thinking notation (Sterman, 2000; García, 2006). Details of this technique are reported in Sanò (2009). This process can be visually illustrated with our 20 stakeholders example. In this case, each stakeholder is asked to fill a 20 × 20 matrix. This matrix can be easily translated into a causal loop diagram (Fig. 3). The superimposition of the 20 matrices, summing-up the values in the cells, is translated into a more complex causal loop diagram, representing a combined mental model for the group (Fig. 4). In this figure, the numbers appearing in the matrix represent the direct sum of the times each relationship was highlighted by participants. For example, the relationship between issue 2 and 8 appears as 13 meaning that 13 participant have identified it as a relevant direct relationship. 2.3. Step 3. Analysis of the system The first two steps of the methodology are necessary to build a comprehensive conceptual model of the system to be further analyzed using quantitative techniques. The objective of the analysis is to identify the critical variables of the system to be used as indicators for management. The analysis of the system proposed here includes two parts: • Analysis of the conceptual model, to calculate the relevance (R) of each variable of the system. • Principal components analysis (PCA), to select the variables accounting for the highest amount of information.
Fig. 2. A participatory workshop is held to build a list of issues and variables. Experts provide technical support and information to the group.
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Fig. 3. Each stakeholder fills an interaction matrix, on the left, which is then used to build a CLD, on the right. Plus and minus represent the so-called polarity, or the theorized directions of change, based on Systems Thinking notation (Sterman, 2000).
2.3.1. Analysis of the relevance (R) The calculation of R for a variable K represents the importance given to each relationship by the stakeholders’ group and of the level of influence of each variable on the system. R for a variable K is calculated using the following formula:
RK = (IKx + IKy ) × (AK + 1)
where R is the relevance of the variable K, Ix and Iy are the sum of the number of times each relationship was identified (calculated on the vertical and horizontal axes of the causal matrix), and A is the number arrows starting from each variable K. Details of this technique are reported in Sanò (2009). Continuing with our example, the calculation of R is the basis to order the 20 variables for their relevance (Table 1). A cut off point can then be used to select the most important variables for the system. In this case, we select and retain the first 10 variables in order of relevance R. These variables can be used as indicators or, if data is available, further analyzed using PCA.
2.3.2. Principal components analysis (PCA) Where datasets are available, multivariate statistics techniques such as principal component analysis (PCA) should be used to analyze the structure of a system. PCA can be used as a quantitative strategy to reduce the number of variables which compose the model to a more manageable set accounting for most of the variance of the original data, a useful approach when dealing with large and expensive datasets. PCA was chosen as it ensures orthogonality (independence) between the analyzed variables, and groups them into clusters of variables that can be further explored separately. Moreover, the objective of PCA is to identify the variables of a set which account for the largest amount of information in terms of its variance. In practice, performing PCA on n sets of variables returns n components (as much as the number of the initial variables) with the ultimate aim of retaining only the p < n components which account for as much variance as possible (usually >80%) in the set of variables: these are the principal components. Going back to our example, we kept 10 variables based on the values calculated for R. The application of PCA returns the variables with the highest factor loadings, that means the variables with the highest correlation with the principal components (for details on
Fig. 4. The shared matrix, on the left, sums-up the contribution of each stakeholder. Iy , Ix and A are parameters used to calculate the final relevance (see Section 2.3 of this paper and Sanò, 2009). The represented values were generated randomly for the purpose of the example. The resulting CLD, on the right, represents the shared model of the system’s structure and behaviour based on the stakeholders’ contribution.
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Table 1 Results of the calculation of R. Variables and issues with the highest R have a higher relevance for the system. In this example we choose to retain 10 variables (in grey) for further quantitative analysis using PCA. Variable
Ix
1 2 11 15 16 6 20 8 18 12 19 9 4 13 5 17 14 3 7 10 Total
Iy
A
182 166 158 171 148 171 139 113 175 136 120 118 94 107 118 122 126 74 44 86
173 167 143 123 158 131 122 185 70 124 147 127 121 105 146 85 79 160 138 64
16 14 14 14 13 13 14 12 14 13 12 12 13 13 10 12 12 8 10 12
2568
2568
251
R = (Ix + Iy ) × (A + 1) 5680 4662 4214 4116 3978 3926 3654 3576 3430 3380 3204 2940 2795 2756 2640 2484 2460 1872 1820 1800
the meaning of factor loading in PCA see Nardo et al., 2005). In this example, based on random data, we identified three principal components and identified three variables belonging to three orthogonal directions of variability (Fig. 5). The analysis of the system carried out using a simple example lead to a substantial reduction of the number of variables, from 20 to 10 after the analysis of the relevance R and from 10 to 3 after the use of PCA. The retained variables can be considered as relevant indicators for the system, based on the stakeholders’ perspective, and accounting for the maximum amount of information. These variables can be used as a cost-effective set of indicators for the system. 3. Applications In this section we report three different case studies where the application of each step was critical to explore, model or analyze the coastal system of concern. These case studies are based on three ICZM projects: (i) the Spanish Strategy for Coastal Sustainability, where we applied the Hydra to identify coastal stakeholders;
Fig. 5. Results of the PCA applied to the set of 10 variables. Variables 1, 11 and 18 have the highest correlation with the three components C1, C2 and C3. These can be considered as the most relevant variables for the system.
(ii) the Matruh–Sallum ICZM Plan in Egypt, in which we modelled the coastal system and analyzed the relevance (R) of different issues; (iii) the DEDUCE project, to test the effectiveness of PCA in reducing numbers of variables. Projects priorities did not allow the application of all the three steps in a sequence to one single project. However, the similarity of the three case studies allows the reader to visualize the connected steps illustrated in Section 2. 3.1. Exploration of coastal systems: the Spanish national stocktaking for ICZM In this case study we applied the Hydra approach to identify stakeholders for ICZM at the Spanish national level, a base for a successive in-depth survey. The Spanish Strategy for Coastal Sustainability (SCS) was an initiative to promote ICZM at the country’s national level, including a large number of activities, with stakeholder identification, classification and involvement a part of the whole process. In this paper we report the experience in stakeholders’ identification (Sanò et al., 2010). Stakeholders were identified in an iterative process where local offices (coastal districts) of the Directorate-General for the Coasts of the Ministry of the Environment prepared a short list of actors in each province of their competence. These stakeholders were asked to complete the list with any missing stakeholders. Finally these missing stakeholders were asked to complete the list with others still not included. The iterative process finished when no more actors appeared on the lists, usually after three rounds. At the end of the process, around 600 stakeholders were identified (Table 2). In this specific case study stakeholders were then asked to respond to a survey aiming at the identification of the main issues and sectors of concern for ICZM. This information was the base to select a group of stakeholders to be successively invited in a participatory planning process for each coastal stretch. The participatory process for the preparation of the SCS regional plans was not carried out as priority was given to carrying out other technical activities (see Sanò et al., 2010 for details). As a consequence, we decided to test the participatory modelling approach described in Section 2 to a different ICZM project, which was running in parallel in Egypt. 3.2. Modelling and analysis of coastal systems: ICZM planning in Egypt In this case study we modelled a coastal system in a remote area of Egypt, based on the contribution of experts and stakeholders, aiming at a reduced number of indicators, representative of the system. The Matrouh Sallum ICZM Plan (MSICZMP) was an ICZM cooperation project funded by the Spanish Agency for International Cooperation for Development (AECID) and executed by IH Cantabria for the Egyptian Environmental Affairs Agency (Cantabria, in press). The project included a set of activities distributed between a phase of analysis (2006–2007) and a planning phase (2008–2009): (i) inventory of stakeholders, (ii) diagnosis of the coastal system, (iii) identification of critical issues, (iv) prioritization of issues using systems thinking conceptualization, (v) analysis of sectoral plans, (vi) spatial analysis and zoning of the coastal area, and (vii) preparation of the final ICZM plan. Participatory activities, including workshops and surveys, were the basis of which to prepare to prepare the initial list of issues for system conceptualization, using the technique explained in Section 2.2. This list of issues was created with the contribution of the involved stakeholders, which included national and regional administrations, local representatives and academic experts. The list was the base for the construction of interaction matrices (Fig. 6) and a shared mental model representing the coastal system (Fig. 7).
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Table 2 Summary of the number and type of stakeholders identified at the regional and national level in Spain, using the Hydra approach. Level Andalusia Asturias Balearic Islands Basque Country Canary Islands Cantabria Catalonia Galicia Murcia Region Valencia Community National Total
Public administrations
NGOs
Total
52 10 13 27 20 22 36 43 10 30 10
16 6 2 4 5 3 8 11 3 6 3
Research organizations
Business sector 36 18 7 16 4 14 24 15 4 16 6
10 13 1 13 5 10 17 13 2 5 7
114 47 23 60 34 49 85 82 19 57 26
273
67
160
96
596
Fig. 6. Each participant to the exercise was asked to fill an interaction matrix, identifying interactions between variables, following CLD notation. Details of the methodology can be found in Sanò (2009).
Fig. 7. The resulting CLD represents the shared mental model of the system.
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Table 3 Issues and results of the calculation of the relevance (R) (Sanò, 2009). Issue
Ix
Iy
A
A+1
R
Coastal protectorates Agriculture and animal husbandry Climate change Urban expansion Beach erosion Sensitive coastal ecosystems Stakeholders engagement Roads and communication infrastructures Administrative coordination Fisheries and aquaculture Coastal legislation and setbacks Flooding Land property Water supply and sanitation Military areas Wastewater treatment and recycling Mine fields Eco-tourism Shoreline development Local tourism International resorts Industry Handcrafting and other traditions Waste management
255 192 182 209 176 177 196 170
228 225 127 220 169 227 148 166
6 3 4 2 2 1 1 1
7 4 5 3 3 2 2 2
3381 1668 1545 1287 1035 808 688 672
186 133 155 180 143 155 115 126 154 152 158 166 170 152 87 109
141 176 149 106 143 95 134 116 88 234 203 181 173 154 159 136
1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1
654 618 608 572 572 500 498 484 484 386 361 347 343 306 246 245
Table 4 Summary of the structure of the model of the coastal system used in the DEDUCE Project. Goals
Number of indicators
1. To control further development of the undeveloped coast as appropriate 2. To protect, enhance and celebrate natural and cultural diversity 3. To promote and support a dynamic and sustainable coastal economy 4. To ensure that beaches are clean and coastal waters unpolluted 5. To reduce social exclusion and promote social cohesion in coastal areas 6. To use natural resources wisely 7. To recognize the threat to coastal zones posed by climate change and to ensure appropriate and ecologically responsible coastal protection Total
Number of measurements
6
7
4
6
5
10
4
5
3
4
2 3
4 8
27
44
Source: modified from Martí et al. (2006).
The DEDUCE project (Martí et al., 2006) aimed to implement a set of indicators for coastal sustainable development in various European regions. This experience was strictly related with the Recommendation 413/2002/EC on ICZM and the work of the Working Group on Indicators and Data (WG-ID). The WG-ID proposed a core set of 27 indicators designed to monitor sustainable development of the coastal zone. This system of indicators is based on 7 goals, covering the components of the coastal system, which were recognized as important by this expert group (Table 4). This list of goals represents the model of the system used to identify and select the indicators thought to measure the level of sustainable development of the coastal zone. Both the construction of the model and the identification of its indicators were based on the group experts’ knowledge. The DEDUCE experience recognized the need to develop an integrated analysis of the model, to describe the types of relationships and to uncover causes and effects relations (Martí et al., 2006). PCA testing was carried out on datasets published by the Government of Catalonia, a Spanish region (Generalitat de Catalunya, 2008), which reports the evolution of the 27 indicators in coastal municipalities in the period 1980–2006. PCA was applied to each goal, independently from the other. As an example, we report the results of the analysis of goal 1. The complete analysis can be found in Sanò (2009). Goal 1 is represented in
The analysis of the relevance (R) was then used to select the critical issues that would finally address the preparation of the ICZM plan (Table 3). In this case, the most relevant issues corresponded to: the implementation of coastal protectorates; agriculture and animal husbandry; climate change; urban expansion; beach erosion. According to the scope of the project, these results were intended to be used to build a more detailed indicators system, through the identification of specific variables for each issue, to monitor the state of the coast and the progress of the ICZM plan implementation. However, lack of data or limited access to data due to institutional constraints did not allow us to proceed with a multivariate analysis using PCA. The use of multivariate techniques (PCA) was tested instead on the coastal indicators dataset of the DEDUCE project, in Catalonia, Spain. 3.3. Analysis of coastal systems using PCA: coastal sustainability indicators EU In this case study we applied PCA to an existing database of coastal sustainability indicators, to identify the most relevant indicators of the system.
Table 5 Results of the reduction of the number of variables for the DEDUCE database for the Catalonia Region. Goal
Initial number of variables
Available datasets
Filtering based on expert knowledge
Variables used in PCA
Final variables
Reduction based on PCA
1. To control further development of the undeveloped coast as appropriate 2. To protect, enhance and celebrate natural and cultural diversity 3. To promote and support a dynamic and sustainable coastal economy 4. To ensure that beaches are clean and coastal waters unpolluted 5. To reduce social exclusion and promote social cohesion in coastal areas 6. To use natural resources wisely 7. To recognize the threat to coastal zones posed by climate change and to ensure appropriate and ecologically responsible coastal protection Total
15
15
13
13
4
69%
11
8
3
3
1
67%
10
3
1
N/A
N/A
N/A
7
7
3
3
2
33%
5
4
4
4
2
50%
4 9
4 8
1 2
N/A 2
N/A 2
N/A 0%
61
49
27
25
11
56%
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this model by 13 variables, identified by the group of experts. The use of PCA identified previously undisclosed correlations between these variables, which belong to different orthogonal components of variability. The first three components account for more than 80% of the total variance of the system. The variables with the highest factor loadings are retained as the most critical for the system’s behaviour. Critical variables for the first component are (i) the number of road transits, (ii) the percentage of built-up land in the first km from the coastline, and (iii) the number of moorings. The critical variable for the second component is the coastal population. The number of variables for goal 1 can therefore substantially reduced from 13 to 4, meaning a 69% of reduction based on PCA results. The overall results of the PCA applied to the whole DEDUCE database is reported in Table 5. As we can see, PCA lead to a reduction of the number of variables of 56%. The results of this analysis demonstrated that a complex system of variables identified by a group of experts describing a coastal system can be substantially reduced without loosing much information. This outcome can be critical when coastal managers must take decisions on how to build a cost-effective set of indicators.
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time, improvements can be provided for each step of the process: • The Hydra approach for stakeholder identification should be tested against other similar methodologies; • Specific studies may address the effectiveness of the algorithm used for the calculation of the relevance R. • Multivariate techniques other than the PCA could be proposed to address the analysis of correlation and causality. Acknowledgements Authors would like to acknowledge: the Directorate General for the Coast of the Spanish Ministry of the Environment, for providing funds for the development of new approaches for the Spanish Strategy for Coastal Sustainability; the Spanish Agency for International Development Cooperation (AECID) for providing funds to carry out the Matruh–Sallum ICZM Project; the Environmental Department of the Government of Catalonia, for providing access to the database of the DEDUCE project; Mr. Daniel Ware from Griffith University for proof reading the final manuscript. This research is part of a PhD granted to Marcello Sanò by the University of Cantabria (2005–2009).
4. Conclusions In this paper we have proposed a systems approach to identify stakeholders, issues, variables and to finally select a reduced set to be used as indicators for management. We showed that a combination of (i) stakeholder identification techniques; (i) system conceptualization modelling; and (iii) quantitative analysis of the system variables can be used to deliver critical information for coastal management. In this process, initial omissions and misunderstanding can be avoided if stakeholders are identified in a systematic way, using approaches such as the Hydra. This approach helps problem owners to identify who and what really counts for the success of a specific initiative. The system conceptualization technique using combined matrices has proven to be useful to design a collective model of the coastal system, based on the contribution of the involved stakeholders. At the same time, the approach turned out to be useful to build consensus around coastal initiatives, to improve shared understanding and to increase the analytic capacity of coastal communities. The analysis of the system has shown to be useful to select the most representative variables based on the analysis of the relevance (R) and principal components analysis (PCA), when the corresponding datasets are available. This technique can be used to analyze the underlying structure of the system and to reveal relationships between groups of variables, by identifying independent (orthogonal) components. The results of the analysis can address the selection of the most significant and independent variables, accounting for the largest amount of information, to be used as a set of critical indicators or to be combined into a final composite index. Each of these steps can be applied independently or as an integrated approach. In Section 2 we show a theoretical example where all the steps are combined and integrated to deliver a set of critical indicators. Due to the complexity of ICZM projects, in Section 3 we report three case studies to show how to carry out each of the steps, as projects priorities did not allow application the entire process to a singe case study. However, the application of three different steps to three separate case studies showed how each step can be used as an independent tool to address the identification of indicators in complex socio-ecological systems. Further research should address the application of the whole methodological process to one, single case study. At the same
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Contents lists available at SciVerse ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
A systems approach to identify sets of indicators: Applications to coastal management Marcello Sanò a,b,∗ , Raúl Medina b a b
Griffith Centre for Coastal Management, Griffith University, Australia Environmental Hydraulics Institute “IH Cantabria”, Universidad de Cantabria, Spain
a r t i c l e
i n f o
Article history: Received 17 November 2010 Received in revised form 11 April 2012 Accepted 13 April 2012 Keywords: Integrated coastal zone management Stakeholders identification Systems thinking Analysis of relevance Principal components analysis
a b s t r a c t This paper proposes a systems approach and a set of steps to select critical variables to build a site-specific, problem-oriented and cost-effective set of indicators for Integrated Coastal Zone Management. The approach integrates three steps: (i) a method to systematically identify coastal stakeholders, called “Hydra”, (ii) a stakeholder-based conceptualization technique based on the use of matrices, and (iii) the analysis of the system structure and variables using structural analysis and multivariate statistics (Principal component analysis, PCA). In Section 2, a hypothetical example is used to illustrate the whole process, while real case studies are used in Section 3 for testing, including: (i) the application of the Hydra to the identification of coastal stakeholders for the Spanish Strategy for Coastal Sustainability, (ii) a stakeholder-based systems conceptualization exercise for an ICZM project in Egypt and (iii) the analysis of a dataset of sustainability indicators for coastal areas collected in Catalonia, Spain, using PCA. Results of the research show that the methodological framework is applicable both as an integrated approach or as independent steps to address the identification of cost-effective sets of indicators, oriented to site-specific problems, based on a broad systems perspective. Crown Copyright © 2012 Published by Elsevier Ltd. All rights reserved.
1. Introduction Integrated Coastal Zone Management (ICZM) is regarded as the pathway for the sustainable development of coastal systems in the 21st century, using an approach that integrates the management of natural processes with the improvement of economic efficiency, involving stakeholders throughout the process. ICZM theory calls for a systems approach to coastal zone management (Vallega, 1999; Kay and Alder, 2005), which considers the effect of different policy options on the whole coastal system, and the use of specific indicators as the way to measure the current condition of the system and the progress towards a desired state. The use of indicators to monitor state and progress of ICZM implementation has been explicitly suggested by the text of the chapter 17 of the Agenda 21 (UN, 1992). Moreover, the use of indicators is encouraged by the ICZM Protocol for the Mediterranean (UNEP, 2008), the most relevant legal instrument addressing ICZM in the Mediterranean countries. An extensive literature has been produced on indicators and ICZM, based on the research carried out by the coastal science
∗ Corresponding author at: Griffith Centre for Coastal Management, Griffith University, Gold Coast Campus Griffith University Q4222, Australia. Tel.: +61 755 528520; fax: +61 755 528067. E-mail address: m.sano@griffith.edu.au (M. Sanò).
community (Bowen and Riley, 2003; Ehler, 2003; Hanson, 2003; Henocque, 2003; Olsen, 2003; Rice, 2003; Pickaver et al., 2004; Sardá et al., 2005; Potts, 2006) and initiatives of government agencies (Scottish Executive Central Research Unit, 2001; IOC, 2003, 2006; Martí et al., 2006; NOAA, 2010). Most of these works propose broad and general sets of indicators to measure the state of the coast and its progress towards sustainability, a useful approach in comparing the state of ICZM implementation in different regions. On the other hand, while dealing with specific problems in particular coastal regions, problem-oriented indicators, based on a systems approach and combining stakeholders perspectives, should be identified and used to measure the state of the coast with respect to problems that are not reflected in generic indicator sets (FontalvoHerazo et al., 2007; Diedrich et al., 2010). While coastal management scholars have partially addressed these demands, sustainability sciences have developed theories linking systems approaches, the principles of sustainable development and the use of indicators as a way to measure the state and progress towards certain goals (Costanza, 1996; Ronchi et al., ˜ 2002; Castro-Bonano, 2002; Gallopín, 2003; Reed et al., 2005). For example Kelly (1998) supports the use of a systems approach to identify information (i.e. indicators) for sustainable development, arguing that other frameworks fail to capture important causal relationships and the system behaviour. The relevance of sustainability science to coastal management is specifically addressed by Cummins and McKenna (2010).
1470-160X/$ – see front matter. Crown Copyright © 2012 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2012.04.016
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The use of systems approaches in coastal management has been theorized in the past (Van der Weide, 1993; Vallega, 1999; Kay and Alder, 2005) and only recently there have been efforts to put these theoretical frameworks into practice (Robadue, 2005; Smith et al., 2008; Hopkins et al., 2011). From a theoretical perspective, Vallega (1999) gave a strong contribution to the application of the systems approach to coastal management by considering the coastal system as bi-modular: one module is the coastal ecosystem, comprising all the natural processes; the other module is the coastal community, comprising social and economic process. Following Vallega’s suggestions we embrace Berkes’s concept of social–ecological system (Berkes et al., 2000), which was also applied to coastal environments (e.g. Marin and Delgado, 2008). In the systems sciences context, we consider here the Systems Thinking approach (Sterman, 2000) as the most suitable approach for practical modelling of the coastal system and to identify coastal indicators. Systems Thinking is a discipline that provides tools to think holistically about problems and to focus on the components and relations of complex systems (Sterman, 2000). One of the paradigms of Systems Thinking is the mental model, the representation of reality in a stakeholder’s mind (Senge, 1990). Participatory techniques are quite common in the Systems Thinking literature (e.g. Vennix, 1999; Andersen et al., 2007) with specific applications to natural resource management (e.g. Antunes et al., 2006), and coastal management (e.g. Smith et al., 2008). In our methodology we chose to apply a participatory system conceptualization method as an initial step to identify indicators. This is followed by the application of two techniques for system analysis: one is the analysis of relevance (R) (Sanò, 2009) and the other is principal components analysis (PCA) (e.g. Bastianoni et al., 2008). The use of PCA to identify sets of indicators is not as common in the coastal management field as in other fields of knowledge such as econometrics (e.g. Peters and Butler, 1970) and psychology (e.g. Cooper and Kelleher, 1973). Despite this, some coastal scholars support its use (e.g. Jiménez and Van Koningsveld, 2004; Rice, 2003) and previous experiences exist in the literature: for example Sardá et al. (2005) use PCA to reduce the number variables to a strategic set of indicators; Shi et al. (2004) use PCA to eliminate overlapping information in basic indicators and to assign weights to variables of coastal sustainability. Our aim is to provide a methodological framework linking together stakeholder identification and engagement techniques, a system conceptualization approach and the analysis of the system structure and its variables to identify a critical set of indicators for coastal management. This framework is divided into three steps: 1. The Exploration of the System, corresponds to the identification and engagement of coastal stakeholders. For this purpose, we propose to use the so-called Hydra approach, where stakeholders are identified iteratively (Sanò et al., 2010) (Section 2.1). 2. The Modelling of the System corresponds to the use of a stakeholder-based approach for the identification of causal relationships between variables using matrices, which, in turn, are used to build a system conceptual model (details are described in Sanò, 2009) (Section 2.2). 3. The Analysis of the System, corresponds to (i) the analysis of the structure of system conceptual model, stored in matrices to identify a group of relevant variables (Sanò, 2009), and (ii) the multivariate analysis of this group of relevant variables using PCA to select a final set of variables to be used as critical indicators (Section 2.3). A simple example, based on a hypothetical coastal system made of 20 stakeholders, issues and variables (one issue and one variable per stakeholder) is used to illustrate the whole process, using a randomly generated dataset of variables. The methodological steps are
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then applied to real case studies and datasets from ICZM projects (Section 3). 2. Methodology As mentioned above, the methodology presented here includes three steps. To illustrate its application, we use here a hypothetical group of 20 stakeholders contributing to the construction of a set of indicators. The words issue, variable and indicator are used with different meanings: an issue is a specific problem identified by a stakeholder, a variable is the measurement which better represents the issue’s variability, and an indicator is the critical variable for the system to be used for its management. 2.1. Step 1. Exploration of the system ICZM deals most of the times with complex problems, characterized by multiple stakeholders, different points of view and different sets of alternative technical solutions (Olsen et al., 2009): this is called an unstructured problem (Thissen et al., 2008). A structured analysis is therefore needed and must be based on the contribution of experts and stakeholders, integrating knowledge with perception. While looking for variables representing a complex socialecological system, the systems approach should be applied from the beginning of the process, during the identification of the sectors and stakeholders of concern. The identification of stakeholders is not trivial as it is often based on each person mental model of the system’s functioning. Moreover, in remote areas, where the issues at stake are not so well identified as in more familiar environment, the identification of the right sectors and stakeholders is the key for the success of any initiative. Public bodies from the environmental sector (the so called problem-owner) should be responsible of identifying the interested stakeholders and set the initial boundaries to the coastal system in terms of number of sectors and stakeholders to be involved. This can be an authority responsible for ICZM initiatives at the national, regional, or local level. To facilitate the identification of stakeholders using a systems approach, we developed a simple technique called the Hydra (Sanò et al., 2010). The name comes from the Greek mythology: when Hercules fought Hydra, he found that every time he cut off a head, three more grew back in its place. In the same way, while doing a stakeholder inventory, the problem-owner can find out that every time he identifies a stakeholder, two or three more appear in the list. Using this approach, the problem owner is required to identify a preliminary list of relevant stakeholders. These stakeholders are asked in turn to identify missing stakeholders in the previous list. After a few rounds, all the relevant stakeholders are identified. This process is illustrated in our 20 stakeholders example by Fig. 1 where the problem owner (stakeholder number 1) identifies a group of six stakeholders (stakeholders numbers 2–7) who, in turn, identify other missing stakeholders and confirm the previous ones (stakeholders numbers 8–15). In the last round, five missing stakeholder are identified (stakeholders numbers 16–20). The process finalizes when no new stakeholder appears on the list. Different techniques are available for stakeholder engagement, and the choice should be based on the scale of the project and consequently on the number of stakeholders involved. When the number of identified stakeholders is high, public meetings and workshops are not advisable because of the difficulties in collecting valuable information. If the number of stakeholders is not high (let’s say, less than 30) or a bigger list is filtered according to their responsibilities or the importance of the sector they represent, a participatory workshop can be held to involve them in the concerned process, in this case, the identification of indicators.
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2.2. Step 2. Modelling of the system
Fig. 1. Stakeholders belonging to different components of the coastal system (e.g. ecological, physical, economic, and social) are identified using the Hydra approach. Each stakeholder identified one or more stakeholder.
In this context, participatory workshops are thought to involve stakeholders in the process to identify their own specific sectoral issues, together with the variables they consider as relevant and or they feel comfortable with. Coastal experts from different disciplines should be involved in the workshop, as a way to address and improve group’s understanding of specific issues and variables. Following the previous example, the hypothetical group of 20 stakeholders is invited to a workshop to identify issues and variables. During the workshop, experts provide information to the public. As a result, a list of issues and stakeholders is finally produced (Fig. 2). In the example, we proposed a simple model in which a stakeholder reports one issue and one corresponding variable. The result is a list of 20 issues and variables, one per stakeholder, the base for systems modelling and analysis.
Fig. 2 shows that the preliminary list of stakeholders, issues and variables built in the previous step still represents a reductionist view of the system, considering that causal relationships between variables are not yet identified. The system’s modelling process should be therefore completed through the identification of causal relationships between the systems variables. In our methodology, this is done by using specific interaction matrices filled by each stakeholder; the matrices are then superimposed (summed up) to build a combined matrix, representing all the relationships identified by the group. This, in turn, is used to build a causal loop diagram (CLD), using Systems Thinking notation (Sterman, 2000; García, 2006). Details of this technique are reported in Sanò (2009). This process can be visually illustrated with our 20 stakeholders example. In this case, each stakeholder is asked to fill a 20 × 20 matrix. This matrix can be easily translated into a causal loop diagram (Fig. 3). The superimposition of the 20 matrices, summing-up the values in the cells, is translated into a more complex causal loop diagram, representing a combined mental model for the group (Fig. 4). In this figure, the numbers appearing in the matrix represent the direct sum of the times each relationship was highlighted by participants. For example, the relationship between issue 2 and 8 appears as 13 meaning that 13 participant have identified it as a relevant direct relationship. 2.3. Step 3. Analysis of the system The first two steps of the methodology are necessary to build a comprehensive conceptual model of the system to be further analyzed using quantitative techniques. The objective of the analysis is to identify the critical variables of the system to be used as indicators for management. The analysis of the system proposed here includes two parts: • Analysis of the conceptual model, to calculate the relevance (R) of each variable of the system. • Principal components analysis (PCA), to select the variables accounting for the highest amount of information.
Fig. 2. A participatory workshop is held to build a list of issues and variables. Experts provide technical support and information to the group.
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Fig. 3. Each stakeholder fills an interaction matrix, on the left, which is then used to build a CLD, on the right. Plus and minus represent the so-called polarity, or the theorized directions of change, based on Systems Thinking notation (Sterman, 2000).
2.3.1. Analysis of the relevance (R) The calculation of R for a variable K represents the importance given to each relationship by the stakeholders’ group and of the level of influence of each variable on the system. R for a variable K is calculated using the following formula:
RK = (IKx + IKy ) × (AK + 1)
where R is the relevance of the variable K, Ix and Iy are the sum of the number of times each relationship was identified (calculated on the vertical and horizontal axes of the causal matrix), and A is the number arrows starting from each variable K. Details of this technique are reported in Sanò (2009). Continuing with our example, the calculation of R is the basis to order the 20 variables for their relevance (Table 1). A cut off point can then be used to select the most important variables for the system. In this case, we select and retain the first 10 variables in order of relevance R. These variables can be used as indicators or, if data is available, further analyzed using PCA.
2.3.2. Principal components analysis (PCA) Where datasets are available, multivariate statistics techniques such as principal component analysis (PCA) should be used to analyze the structure of a system. PCA can be used as a quantitative strategy to reduce the number of variables which compose the model to a more manageable set accounting for most of the variance of the original data, a useful approach when dealing with large and expensive datasets. PCA was chosen as it ensures orthogonality (independence) between the analyzed variables, and groups them into clusters of variables that can be further explored separately. Moreover, the objective of PCA is to identify the variables of a set which account for the largest amount of information in terms of its variance. In practice, performing PCA on n sets of variables returns n components (as much as the number of the initial variables) with the ultimate aim of retaining only the p < n components which account for as much variance as possible (usually >80%) in the set of variables: these are the principal components. Going back to our example, we kept 10 variables based on the values calculated for R. The application of PCA returns the variables with the highest factor loadings, that means the variables with the highest correlation with the principal components (for details on
Fig. 4. The shared matrix, on the left, sums-up the contribution of each stakeholder. Iy , Ix and A are parameters used to calculate the final relevance (see Section 2.3 of this paper and Sanò, 2009). The represented values were generated randomly for the purpose of the example. The resulting CLD, on the right, represents the shared model of the system’s structure and behaviour based on the stakeholders’ contribution.
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Table 1 Results of the calculation of R. Variables and issues with the highest R have a higher relevance for the system. In this example we choose to retain 10 variables (in grey) for further quantitative analysis using PCA. Variable
Ix
1 2 11 15 16 6 20 8 18 12 19 9 4 13 5 17 14 3 7 10 Total
Iy
A
182 166 158 171 148 171 139 113 175 136 120 118 94 107 118 122 126 74 44 86
173 167 143 123 158 131 122 185 70 124 147 127 121 105 146 85 79 160 138 64
16 14 14 14 13 13 14 12 14 13 12 12 13 13 10 12 12 8 10 12
2568
2568
251
R = (Ix + Iy ) × (A + 1) 5680 4662 4214 4116 3978 3926 3654 3576 3430 3380 3204 2940 2795 2756 2640 2484 2460 1872 1820 1800
the meaning of factor loading in PCA see Nardo et al., 2005). In this example, based on random data, we identified three principal components and identified three variables belonging to three orthogonal directions of variability (Fig. 5). The analysis of the system carried out using a simple example lead to a substantial reduction of the number of variables, from 20 to 10 after the analysis of the relevance R and from 10 to 3 after the use of PCA. The retained variables can be considered as relevant indicators for the system, based on the stakeholders’ perspective, and accounting for the maximum amount of information. These variables can be used as a cost-effective set of indicators for the system. 3. Applications In this section we report three different case studies where the application of each step was critical to explore, model or analyze the coastal system of concern. These case studies are based on three ICZM projects: (i) the Spanish Strategy for Coastal Sustainability, where we applied the Hydra to identify coastal stakeholders;
Fig. 5. Results of the PCA applied to the set of 10 variables. Variables 1, 11 and 18 have the highest correlation with the three components C1, C2 and C3. These can be considered as the most relevant variables for the system.
(ii) the Matruh–Sallum ICZM Plan in Egypt, in which we modelled the coastal system and analyzed the relevance (R) of different issues; (iii) the DEDUCE project, to test the effectiveness of PCA in reducing numbers of variables. Projects priorities did not allow the application of all the three steps in a sequence to one single project. However, the similarity of the three case studies allows the reader to visualize the connected steps illustrated in Section 2. 3.1. Exploration of coastal systems: the Spanish national stocktaking for ICZM In this case study we applied the Hydra approach to identify stakeholders for ICZM at the Spanish national level, a base for a successive in-depth survey. The Spanish Strategy for Coastal Sustainability (SCS) was an initiative to promote ICZM at the country’s national level, including a large number of activities, with stakeholder identification, classification and involvement a part of the whole process. In this paper we report the experience in stakeholders’ identification (Sanò et al., 2010). Stakeholders were identified in an iterative process where local offices (coastal districts) of the Directorate-General for the Coasts of the Ministry of the Environment prepared a short list of actors in each province of their competence. These stakeholders were asked to complete the list with any missing stakeholders. Finally these missing stakeholders were asked to complete the list with others still not included. The iterative process finished when no more actors appeared on the lists, usually after three rounds. At the end of the process, around 600 stakeholders were identified (Table 2). In this specific case study stakeholders were then asked to respond to a survey aiming at the identification of the main issues and sectors of concern for ICZM. This information was the base to select a group of stakeholders to be successively invited in a participatory planning process for each coastal stretch. The participatory process for the preparation of the SCS regional plans was not carried out as priority was given to carrying out other technical activities (see Sanò et al., 2010 for details). As a consequence, we decided to test the participatory modelling approach described in Section 2 to a different ICZM project, which was running in parallel in Egypt. 3.2. Modelling and analysis of coastal systems: ICZM planning in Egypt In this case study we modelled a coastal system in a remote area of Egypt, based on the contribution of experts and stakeholders, aiming at a reduced number of indicators, representative of the system. The Matrouh Sallum ICZM Plan (MSICZMP) was an ICZM cooperation project funded by the Spanish Agency for International Cooperation for Development (AECID) and executed by IH Cantabria for the Egyptian Environmental Affairs Agency (Cantabria, in press). The project included a set of activities distributed between a phase of analysis (2006–2007) and a planning phase (2008–2009): (i) inventory of stakeholders, (ii) diagnosis of the coastal system, (iii) identification of critical issues, (iv) prioritization of issues using systems thinking conceptualization, (v) analysis of sectoral plans, (vi) spatial analysis and zoning of the coastal area, and (vii) preparation of the final ICZM plan. Participatory activities, including workshops and surveys, were the basis of which to prepare to prepare the initial list of issues for system conceptualization, using the technique explained in Section 2.2. This list of issues was created with the contribution of the involved stakeholders, which included national and regional administrations, local representatives and academic experts. The list was the base for the construction of interaction matrices (Fig. 6) and a shared mental model representing the coastal system (Fig. 7).
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Table 2 Summary of the number and type of stakeholders identified at the regional and national level in Spain, using the Hydra approach. Level Andalusia Asturias Balearic Islands Basque Country Canary Islands Cantabria Catalonia Galicia Murcia Region Valencia Community National Total
Public administrations
NGOs
Total
52 10 13 27 20 22 36 43 10 30 10
16 6 2 4 5 3 8 11 3 6 3
Research organizations
Business sector 36 18 7 16 4 14 24 15 4 16 6
10 13 1 13 5 10 17 13 2 5 7
114 47 23 60 34 49 85 82 19 57 26
273
67
160
96
596
Fig. 6. Each participant to the exercise was asked to fill an interaction matrix, identifying interactions between variables, following CLD notation. Details of the methodology can be found in Sanò (2009).
Fig. 7. The resulting CLD represents the shared mental model of the system.
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Table 3 Issues and results of the calculation of the relevance (R) (Sanò, 2009). Issue
Ix
Iy
A
A+1
R
Coastal protectorates Agriculture and animal husbandry Climate change Urban expansion Beach erosion Sensitive coastal ecosystems Stakeholders engagement Roads and communication infrastructures Administrative coordination Fisheries and aquaculture Coastal legislation and setbacks Flooding Land property Water supply and sanitation Military areas Wastewater treatment and recycling Mine fields Eco-tourism Shoreline development Local tourism International resorts Industry Handcrafting and other traditions Waste management
255 192 182 209 176 177 196 170
228 225 127 220 169 227 148 166
6 3 4 2 2 1 1 1
7 4 5 3 3 2 2 2
3381 1668 1545 1287 1035 808 688 672
186 133 155 180 143 155 115 126 154 152 158 166 170 152 87 109
141 176 149 106 143 95 134 116 88 234 203 181 173 154 159 136
1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1
654 618 608 572 572 500 498 484 484 386 361 347 343 306 246 245
Table 4 Summary of the structure of the model of the coastal system used in the DEDUCE Project. Goals
Number of indicators
1. To control further development of the undeveloped coast as appropriate 2. To protect, enhance and celebrate natural and cultural diversity 3. To promote and support a dynamic and sustainable coastal economy 4. To ensure that beaches are clean and coastal waters unpolluted 5. To reduce social exclusion and promote social cohesion in coastal areas 6. To use natural resources wisely 7. To recognize the threat to coastal zones posed by climate change and to ensure appropriate and ecologically responsible coastal protection Total
Number of measurements
6
7
4
6
5
10
4
5
3
4
2 3
4 8
27
44
Source: modified from Martí et al. (2006).
The DEDUCE project (Martí et al., 2006) aimed to implement a set of indicators for coastal sustainable development in various European regions. This experience was strictly related with the Recommendation 413/2002/EC on ICZM and the work of the Working Group on Indicators and Data (WG-ID). The WG-ID proposed a core set of 27 indicators designed to monitor sustainable development of the coastal zone. This system of indicators is based on 7 goals, covering the components of the coastal system, which were recognized as important by this expert group (Table 4). This list of goals represents the model of the system used to identify and select the indicators thought to measure the level of sustainable development of the coastal zone. Both the construction of the model and the identification of its indicators were based on the group experts’ knowledge. The DEDUCE experience recognized the need to develop an integrated analysis of the model, to describe the types of relationships and to uncover causes and effects relations (Martí et al., 2006). PCA testing was carried out on datasets published by the Government of Catalonia, a Spanish region (Generalitat de Catalunya, 2008), which reports the evolution of the 27 indicators in coastal municipalities in the period 1980–2006. PCA was applied to each goal, independently from the other. As an example, we report the results of the analysis of goal 1. The complete analysis can be found in Sanò (2009). Goal 1 is represented in
The analysis of the relevance (R) was then used to select the critical issues that would finally address the preparation of the ICZM plan (Table 3). In this case, the most relevant issues corresponded to: the implementation of coastal protectorates; agriculture and animal husbandry; climate change; urban expansion; beach erosion. According to the scope of the project, these results were intended to be used to build a more detailed indicators system, through the identification of specific variables for each issue, to monitor the state of the coast and the progress of the ICZM plan implementation. However, lack of data or limited access to data due to institutional constraints did not allow us to proceed with a multivariate analysis using PCA. The use of multivariate techniques (PCA) was tested instead on the coastal indicators dataset of the DEDUCE project, in Catalonia, Spain. 3.3. Analysis of coastal systems using PCA: coastal sustainability indicators EU In this case study we applied PCA to an existing database of coastal sustainability indicators, to identify the most relevant indicators of the system.
Table 5 Results of the reduction of the number of variables for the DEDUCE database for the Catalonia Region. Goal
Initial number of variables
Available datasets
Filtering based on expert knowledge
Variables used in PCA
Final variables
Reduction based on PCA
1. To control further development of the undeveloped coast as appropriate 2. To protect, enhance and celebrate natural and cultural diversity 3. To promote and support a dynamic and sustainable coastal economy 4. To ensure that beaches are clean and coastal waters unpolluted 5. To reduce social exclusion and promote social cohesion in coastal areas 6. To use natural resources wisely 7. To recognize the threat to coastal zones posed by climate change and to ensure appropriate and ecologically responsible coastal protection Total
15
15
13
13
4
69%
11
8
3
3
1
67%
10
3
1
N/A
N/A
N/A
7
7
3
3
2
33%
5
4
4
4
2
50%
4 9
4 8
1 2
N/A 2
N/A 2
N/A 0%
61
49
27
25
11
56%
M. Sanò, R. Medina / Ecological Indicators 23 (2012) 588–596
this model by 13 variables, identified by the group of experts. The use of PCA identified previously undisclosed correlations between these variables, which belong to different orthogonal components of variability. The first three components account for more than 80% of the total variance of the system. The variables with the highest factor loadings are retained as the most critical for the system’s behaviour. Critical variables for the first component are (i) the number of road transits, (ii) the percentage of built-up land in the first km from the coastline, and (iii) the number of moorings. The critical variable for the second component is the coastal population. The number of variables for goal 1 can therefore substantially reduced from 13 to 4, meaning a 69% of reduction based on PCA results. The overall results of the PCA applied to the whole DEDUCE database is reported in Table 5. As we can see, PCA lead to a reduction of the number of variables of 56%. The results of this analysis demonstrated that a complex system of variables identified by a group of experts describing a coastal system can be substantially reduced without loosing much information. This outcome can be critical when coastal managers must take decisions on how to build a cost-effective set of indicators.
595
time, improvements can be provided for each step of the process: • The Hydra approach for stakeholder identification should be tested against other similar methodologies; • Specific studies may address the effectiveness of the algorithm used for the calculation of the relevance R. • Multivariate techniques other than the PCA could be proposed to address the analysis of correlation and causality. Acknowledgements Authors would like to acknowledge: the Directorate General for the Coast of the Spanish Ministry of the Environment, for providing funds for the development of new approaches for the Spanish Strategy for Coastal Sustainability; the Spanish Agency for International Development Cooperation (AECID) for providing funds to carry out the Matruh–Sallum ICZM Project; the Environmental Department of the Government of Catalonia, for providing access to the database of the DEDUCE project; Mr. Daniel Ware from Griffith University for proof reading the final manuscript. This research is part of a PhD granted to Marcello Sanò by the University of Cantabria (2005–2009).
4. Conclusions In this paper we have proposed a systems approach to identify stakeholders, issues, variables and to finally select a reduced set to be used as indicators for management. We showed that a combination of (i) stakeholder identification techniques; (i) system conceptualization modelling; and (iii) quantitative analysis of the system variables can be used to deliver critical information for coastal management. In this process, initial omissions and misunderstanding can be avoided if stakeholders are identified in a systematic way, using approaches such as the Hydra. This approach helps problem owners to identify who and what really counts for the success of a specific initiative. The system conceptualization technique using combined matrices has proven to be useful to design a collective model of the coastal system, based on the contribution of the involved stakeholders. At the same time, the approach turned out to be useful to build consensus around coastal initiatives, to improve shared understanding and to increase the analytic capacity of coastal communities. The analysis of the system has shown to be useful to select the most representative variables based on the analysis of the relevance (R) and principal components analysis (PCA), when the corresponding datasets are available. This technique can be used to analyze the underlying structure of the system and to reveal relationships between groups of variables, by identifying independent (orthogonal) components. The results of the analysis can address the selection of the most significant and independent variables, accounting for the largest amount of information, to be used as a set of critical indicators or to be combined into a final composite index. Each of these steps can be applied independently or as an integrated approach. In Section 2 we show a theoretical example where all the steps are combined and integrated to deliver a set of critical indicators. Due to the complexity of ICZM projects, in Section 3 we report three case studies to show how to carry out each of the steps, as projects priorities did not allow application the entire process to a singe case study. However, the application of three different steps to three separate case studies showed how each step can be used as an independent tool to address the identification of indicators in complex socio-ecological systems. Further research should address the application of the whole methodological process to one, single case study. At the same
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