A spatial decision support system for multi-dimensional sustainability assessment of river basin districts: the case study of Sarno river, Italy

Land Use Policy 141 (2024) 107123 Available online 19 March 2024 0264-8377/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). A spatial decision support system for multi-dimensional sustainability assessment of river basin districts: the case study of Sarno river, Italy Giuliano Poli * , Stefano Cunt` o, Eugenio Muccio, Maria Cerreta University of Naples Federico II, Department of Architecture, Italy ARTICLE INFO Keywords: River basin management Spatial decision support system Network analysis Scenario planning Indicators-based assessment Multi-criteria decision analysis Sustainability assessment Socio-ecological systems Spatial misfits ABSTRACT Effective management of the complex Socio-ecological systems (SES) of the river basin, influenced by a range of social, economic, and environmental variables, requires multi-purpose Spatial Decision Support Systems (SDSSs) to identify and select balanced and sustainable regeneration scenarios. In many cases, the lack of cooperation between administrative institutions and the low level of participation of the stakeholders involved are the main obstacles to effective planning and regeneration practices and call for new forms of governance. However, to address these critical issues, few studies use quantitative approaches to assess the multidimensional resources of river basins in an integrated perspective in terms of their capacity to activate cooperation networks between river-crossed cities. This study aims to suggest a methodological approach combining Multi-Criteria Decision Analysis and Geographic Information Systems to achieve integration in pursuing sustainable regeneration pathways for a river basin district, analysing the case study of the Sarno River, composed of 20 administrative units, in Southern Italy. The specific purpose is to provide decision-makers with an SDSS to evaluate sustainability scenarios based on the multi-dimensional performances of River Basin District Units (RBDU). The results delivered four partial rankings of RBDU related to social, cultural, ecological, and economic scenarios and one overall ranking for an integrated sustainability scenario. Each ranking includes a clustering of municipalities according to three classes: Leader, Link and Follower. The outcome is twofold: to provide decision-makers with recommendations for supporting governance models based on sustainability pathways and to suggest valuable methods and tools for dealing with the spatial misfits, considered as the mismatch between the physical boundaries of a natural system and the management area of an organization. 1. Introduction The river basin, conceived as a complex Socio-ecological system (SES), is affected by a range of multidimensional variables, and thus requires multi-purpose Spatial Decision Support Systems (SDSSs) to select more balanced and sustainable regeneration scenarios for an effective management. Handling complex systems presents a challenge for practitioners as the outcomes of such systems result from interactions among different social and biophysical variables that are context-dependent. These components, indeed, may be common to many systems or unique to a particular system. The context-dependence of social-ecological dynamics makes it exceptionally difficult to draw general conclusions for governance, while generic design factors may also be too abstract to apply to a specific environment (Cox, 2011; Stoker, 2019; Gong and Tan, 2021). In this perspective, the European Water Framework Directive (WFD) released guidelines to promote territorial strategies based on cooperation among municipalities at different hydrological and institutional scalar dimensions (Hein et al., 2006), through the monitoring of existing resources and the co-production of new values derived from sustainable regeneration pathways (Chambers et al., 2021). Furthermore, the paradigm shift from a “hydrocratic” top-down model to a collaborative, decentralized, and multidimensional model becomes crucial when sustainable water management addresses local community needs and cooperation among decision-makers (Neto et al., 2020). In 2015, the Organisation for Economic Co-operation and Development (OECD) released the twelve Principles on Water Governance based on three drivers for sustainable water management: effectiveness, efficiency, and capacity to generate trust and engagement (OECD, 2015). Among the twelve, three Principles particularly concern this contribution, which aims to propose an integrated methodological approach to * Corresponding author. E-mail addresses: [email protected] (G. Poli), [email protected] (S. Cunt` o), [email protected] (E. Muccio), [email protected] (M. Cerreta). Contents lists available at ScienceDirect Land Use Policy journal homepage: www.elsevier.com/locate/landusepol https://doi.org/10.1016/j.landusepol.2024.107123 Received 27 April 2023; Received in revised form 1 November 2023; Accepted 26 February 2024 Land Use Policy 141 (2024) 107123 2 support River Basin Management (RBM) and refer to the need to manage water at suitable scales and to foster coordination between such scales (Principle 2); the requirement to provide, update and share knowledge through consistent, comparable and policy-relevant water data and information (Principle 5); and the need to promote methodologies for stakeholder engagement at bottom-up level (Principle 10). River Basin Management (RBM) is conceived as a set of tasks to understand, organise and manage complex SES (Holling, 2001; Gunderson and Holling, 2002; Folke, 2006; Gain et al., 2020), taking into account the multiple interactions between their physical, chemical, biological and socio-economic processes (Agramont et al., 2021; Wang et al., 2022). Indeed, it encompasses a range of activities, including water allocation and distribution, water quality monitoring and management, flood and drought management, ecosystem conservation and restoration, and stakeholder engagement and participation. To perform these activities, RBM uses methods and technical tools for assessing the ecological integrity of natural resources, such as water chemistry analysis, detection of potential ecological status, environmental monitoring of cumulative effects or disturbances affecting reservoirs, and quantitative analysis of ecosystem degradation. According to WFD article 2, a river basin district constitutes the primary unit of management of a river basin, which includes “the area of land and sea, made up of one or more neighbouring river basins together with their associated groundwaters and coastal waters” (European Communities, 2000). The heterogeneity of river-crossed administrative units, scarce environmental monitoring, and ineffectiveness of top-down regulation are some of the critical factors associated with the failure of RBM. Nevertheless, further issues related to RBM include, as the most recurring and entangled: ● Climate change, which accelerates the frequency and severity of floods and droughts by altering the natural flow patterns of rivers and should be better integrated into WFD (Quevauviller et al., 2012); ● Limited financial support, which prevents activities such as water quality monitoring, infrastructure maintenance, and water policy coordination among local and national institutions, NGOs, and other stakeholders (Kauffman, 2015); ● Complex political issues, such as conflicts over water rights and land use policies (Watson, 2004), non-operational cooperation between in-charge jurisdictions and problems of spatial fits (Moss, 2004); ● Lack of data at suitable temporal and spatial scales makes it difficult to develop evidence-based management plans (Thompson et al., 2018). However, the absence of cooperation between administrative units and scarce participation are the main obstacles to effective planning and regeneration practices, calling for new forms of governance (Newig et al., 2016). Furthermore, encouraging local stakeholders’ engagement to enhance evidence-based decision-making through fast, easy-to-use, replicable, and spatial assessment methodologies remains a challenge for identifying the multiple landscape values (Euler and Heldt, 2018). At the same time, an integrated, collaborative approach to water management that considers the complex social, cultural, economic, and ecological factors that influence water use and protection is needed in designing an efficient but sustainable river basin management plan (Zilio et al., 2018). Spatial qualitative and quantitative data integration is a crucial factor to consider in the context of RBM, especially in performancebased planning (De Toro et al., 2020; Pelorosso, 2020). SDSSs have been designed to provide decision-makers with formalized approaches for accessing, understanding, and interpreting information derived from data, models, and analyses, focusing on the geographic components of decision-making (Campagna, 2005; Sugumaran and Degroote, 2010; Cerreta et al., 2020). SDSSs represent a significant improvement over earlier Decision Support Systems (Simon, 1960; Rodela et al., 2017) by facilitating stakeholders’ access to decision problems, organizing multidimensional knowledge tiers, streamlining the process of linking data to decisions through geographical mapping (Keenan and Jankowski, 2019), and supporting choice through multi-criteria aggregation rules and spatial GIS techniques (Malczewski and Rinner, 2015; Cerreta et al., 2021). To the best of our knowledge, few studies have used quantitative approaches to assess river basin economic, social, environmental, and cultural resources in an integrated manner concerning their capacity to activate cooperation network between river-crossed cities (Ahmadi et al., 2019; Brillinger et al., 2022; Nucci et al., 2022). The methodological approach proposed in this study suggests a way to achieve integration in pursuing sustainable regeneration pathways for a river basin district, analysing the case study of the Sarno River, composed of 20 administrative units, in Southern Italy. This research aims to provide decision-makers with an SDSS to evaluate sustainability scenarios based on the multi-dimensional performances of river basin district units (RBDU). The research questions underlying this paper are as follows: ●Can SDSSs effectively boost decision-making processes for a sustainable RBM? ● Which methods and tools help collect and process spatially explicit data from local knowledge? ● How does a multidimensional performance assessment of a river basin district aid territorial clusters to emerge? Therefore, the article aims to show a geographic, indicators-based, and site-specific SDSS for assessing suitable scenarios to foster collaborative and multi-level governance between municipalities within a river basin district. The outcome is twofold: to provide decision-makers with recommendations for activating governance models based on pathways of sustainability and to suggest valuable methods and tools to deal with spatial misfits, considered as the mismatch between the physical boundaries of a natural system and the management area of an organization (Moss, 2012). The remainder of the article proceeds as follows: after the introduction, Section 2 presents the study area, collected data, and the methodological workflow related to the proposed SDSS. Section 3 aims to explain the SDSS procedural phases of "Intelligence" and "Design", along with data processing methods and tools. Section 4 expands on results by presenting the outcomes of the "Choice" phase. Finally, Section 5 concludes the article by discussing limitations, potentials, and further development. 2. Study area, data collection, and methodology 2.1. Study area The study area covers 333 km 2 and includes 20 administrative units with an overall population of approximately 1.300.635 inhabitants and an average population density of 1.818 people per km 2 (Fig. 1). It is located by the Sarno river basin, between the Metropolitan City of Naples and the Agro Nocerino-Sarnese, province of Salerno, in the Campania region (Southern Italy). Different landscape types shape the study area from mountain to coastal, alternating primary agricultural uses with complex cultivation patterns and dense urban cores. Natural reservoirs safeguard the focus area for an amount of approximately 60 km 2 , including two Regional parks and one National park, respectively referred to as “Regional Park of Monti Lattari”, “Regional Park of Sarno River”, and “Vesuvius National Park”. The Sarno River extends for around 24 km and originates in the Picentini mounts behind the Sarno town at approximately 30 m above sea level. It flows into the Bay of Naples between the two municipalities of Torre Annunziata and Castellammare di Stabia. Many secondary tributaries enrich Sarno’s hydrographic network, the most important of G. Poli et al. Land Use Policy 141 (2024) 107123 3 which are Solofrana and Cavaiola streams. Investigations carried out by the Basin Authority showed that the great environmental potential was counteracted by strong anthropic pressure. Approximately 180 km of riverbeds have been transformed into roads, and 98 km have been tombed, while 70% of the linear development of the entire network remains in its natural state. The authors, therefore, selected the Sarno river basin district as the testing area for the proposed methodological approach, due to its high spatial complexity, relevant governance gaps, high population density, and critical environmental conditions due to massive uses of chemical pesticides in agriculture and uncontrolled industrial development (Montuori et al., 2015). 2.2. Data collection Data collected and processed for this study were derived from opensource and authoritative sources; about the former, limitations due to missing data were esteemed to be acceptable compared to the costeffectiveness and the ease of information retrieval. The main opensource data were, thus, gathered with the following collection methods: documentation, observation, social media platforms, and miscellaneous reports. About the authoritative sources, the primary data were extracted from the Italian Ministry of the Interior dataset (2020); the Territorial Regional Plan of Campania Region (Italy); the Italian Ministry of Education dataset (2020), the Hydrogeological Structure Plan of Campania Region (Italy), and the Corine Land Cover EU projects (2018). 2.3. Methodology The methodological workflow, shown in Fig. 2, was structured into the three phases of decision-making conceived by Simon (1960) - Intelligence, Design, and Choice - to generalise the procedural steps and make them exportable, but with the concern of generating context-aware knowledge to inform local communities and decision-makers and developing shared future scenarios (Simon, 1960). The declared goal of the proposed methodological approach aims to provide decision-makers with an SDSS to evaluate sustainability scenarios based on multi-dimensional performances of RBDU. The operational objectives related to the main goal are the following: i) to collect data and engage stakeholders in developing sustainability scenarios; ii) to evaluate scenarios for administrative units to be ranked against the performance-based ability in pursuing them; iii) to develop a spatial tool in supporting the selection of the best-fit cooperation scenarios for groups of administrative units within the river basin district. Starting from the main goal, the methodological steps were defined: 1. Intelligence: the data collection phase, concerning decision-making problem structuring and definition of the 4 sustainability criteria for the classification of information. 2. Design: the data processing phase, concerning the construction of the 12 indicators core-set, the spatial visualisation of data and the planning of the 4 alternative scenarios with prioritisation of the indicator dimensions. 3. Choice: the data aggregation phase, concerning the evaluation of alternatives through TOPSIS method and the definition of ranking weighting methods for criteria and sub-criteria. The Outcome delivered four partial rankings of RBDU related to each scenario and one overall ranking for the sustainability scenario. Each ranking includes a clustering of municipalities obtained with the KMeans algorithm (Pelleg and Moore, 1999; MacKay, 2003) according to three classes: Leader, Link and Follower. After introducing the study context, a detailed description of SDSS’s phases has been provided in the following Sections 3 and 4. 3. SDSS phases, data processing, and tools 3.1. Intelligence The Intelligence phase concerned identifying the conditions that require a decision-making process, also known as problem structuring or formulation. In the context of Sarno River District management, several institutional levels are involved in the decision-making process. At the national level, the "Ministry of Ecological Transition" is responsible for policy-making and funding. At the regional level, the "Campania Region" implements national directives and oversees economic management and strategic planning. In particular, the region, through the "Sarno River Fig. 1. The study area. G. Poli et al. Land Use Policy 141 (2024) 107123 4 Basin Authority," which operates over 61 municipalities in the river basin, is responsible for hydrogeological risk reduction. In parallel, through the "Sarno River Regional Park Authority," it protects naturalistic values in a restricted area that includes the 11 municipalities of the Sarno River. The advisory and propositional unit of the Park Authority is the "Park Community," composed of the Presidents of the Campania Region, the Metropolitan City of Naples, the Province of Salerno and the Mayors of the municipalities in the Park area. Although it is an ideal decision-making forum where different levels of administration converge, it provides guidance without directly implementing actions on the territory. In fact, in accordance with the governing instruments of river basin management and environmental and landscape protection, the implementation of any interventions of urban regeneration and reconnection of the river system to urban systems is delegated to the urban planning instruments of respective municipalities. In this complex multi-scalar and multi-actor decision-making context, the focus was on addressing the key challenge of providing a tool that can inform collaborative decision-making processes by integrating the issues of hydrogeological risk, social vulnerability, landscape regeneration, and governance gaps, which were identified as the most important in structuring and broadening the decision-making problem based on the research questions. The goal was to provide institutional stakeholders, particularly from the regional to local level, with an SDSS that would serve as a starting point for a more efficient RBM. The primary data collection and selection operations were crucial in uncovering the context and highlighting the main local challenges. This was achieved using site-specific open-source and authoritative sources. In addition, institutional and local stakeholders were engaged to define potential scenarios by implementing Network Analysis (NA) (Chiesi, 2001), which helped identify the relational system through which local stakeholders interact. This enabled key criteria to be inferred and related indicators were selected as tools for monitoring the performance of district units. NA was particularly useful in analysing the relationships among local stakeholders (associations, third parties, institutions) who proposed sustainable solutions, projects, and regeneration actions at the river basin scale. NA provided visual representations of the river basin system, which could be valuable for communicating with stakeholders, including government agencies, communities, and environmental organizations. It supported in creating a shared understanding of the basin and its challenges. Furthermore, NA fits with the purpose of this study since it allows Decision-Makers to simulate and evaluate the potential outcomes of different management strategies before implementation. Fig. 3 illustrates the territorial network of 33 associations, civic committees, and groups with a declared interest in the Sarno River basin district, referring to four governance levels: local (municipality level), city-region (metropolitan city level), regional and national. Therefore, Fig. 2. The SDSS workflow. G. Poli et al. Land Use Policy 141 (2024) 107123 5 five primary scopes of the network were detected: environmental protection, cultural interest, civic promotion, sport, and welfare (Table A1). The indicators used to construct the SDSS informative tiers were selected to concisely express the relevant characteristics associated with the study area for assessing transformation and enhancement scenarios. Information needed to build the indicators was derived from spatially explicit and implicit data collected. Regarding the latter, data processing operations were carried out on homogeneous mapping units, considering the institutional perimeters of the municipalities within the Sarno RBDU provided by the Italian National Institute of Statistics (ISTAT). 3.2. Design According to the data combining methods classification provided by Malczewski and Jankowski (2020), the approach implemented in this study falls within Type 6, which relates to a mixed-use of Authoritative Geographic Data (AGD), Crowdsourced Geographic Data (CGD), and Crowdsourced Preference Data (CPD), referred to the use of social media platforms for data elicitation through photos, video, and text (Malczewski and Jankowski, 2020). AGD and CGD have informed a Scenario Planning (SP) approach to spark participative bottom-up planning by selecting development pathways linked to the four sustainability pillars (Hawkes, 2001; Soini and Birkeland, 2014) referred to as “Social”, “Cultural”, “Ecological”, and “Economic” scenarios. Furthermore, the spatial component of decision-making was conceived as one of the strengths to improve the communication of results that support the collaborative process of building networks of administrative units involved in RBM (Wissen Hayek et al., 2016). Following previous studies (Cerreta et al., 2020), statistical and geographical adjustments were addressed to make spatially explicit the selected indicators referring them to RBDU, corresponding with the NUTS 3 level (Eurostat, 2022). Gathered data have been, thus, transferred in the Geographic Information System (GIS) through Q-GIS open source software’s spatial analysis and geoprocessing tools. In particular, spatial components have been attached to raw data to build indicators i03, i04, i05, i08, and i11 with GIS (Table A2). Following Sub-Sections were addressed to explain in more detail the SP approach, indicators modelling, and processes to produce explicit spatial indicators from spatially implicit information. 3.2.1. Scenario planning approach According to Stewart et al. (2013), the term scenario, among different definitions, can relate to exploring future conditions or environments within the context of creating possible development pathways as a starting point for planning (Slaughter, 1996; Stewart et al., 2013). The primary characteristics of a decision-making problem addressed to the river basin district management are inherent to the co-existence of different and conflicting actors with incommensurable and divergent interests (i), the presence of important intangibles linked to local communities’ value systems (ii), and the relevance of key uncertainties linked to the multiple variables to be considered into decisions (Marshall, 1997). In this perspective, as argued by Mingers and Rosenhead (2004), the Scenario Planning (SP) approach is congruent with a Problem Structuring Method (PSM) since it allows multiple and conflicting viewpoints to be combined and considered together, enabling the evolving representation to contribute to an inclusive problem-solving process (Mingers and Rosenhead, 2004). The identification and implementation of partial or localised improvements, which is one of the features of PSMs, inform the decision-making process without the need for a global solution that would involve the merging of different interests. Meanwhile, SP simplifies multidimensionality by reducing data into a limited number of possible states (Schoemaker 1998). In this framework, SP was expected to be a valuable tool to imagine future development perspectives to trigger a participative planning process for the Sarno River district. In this meaning, scenarios were derived to include stakeholders’ priorities considering the four pillars of sustainability, explored and made explicit by the 12 spatial indicators. Fig. 3. The stakeholders’ network and governance levels. G. Poli et al. Land Use Policy 141 (2024) 107123 6 The stakeholders’ views were acknowledged during an institutional meeting, allowing four narratives of community scenarios to be inferred, each characterised by a priority sustainability criterion. The four scenarios relate to the promotion of policies for the Sarno River Basin districts’ management, depending on specific territorial development outlooks, as shown in Table 1: S1. Bottom-up social empowerment: a society-driven perspective, based on the promotion of bottom-up social policies in the fields of environmental protection, cultural interest, civic promotion, and welfare, maximising the Social criterion. S2. Territorial investments: economy-driven perspective, based on the resource management efficiency for sustainable development, maximising the Economic criterion. S3. Cultural promotion: culture-driven perspective, based on historical heritage conservation and awareness-raising, and human capital enhancement, maximising the Cultural criterion. S4. Ecosystem conservation: ecology-driven perspective, based on human impacts and environmental risk mitigation, and natural resources conservation and enhancement, maximising the Ecological criterion. CPD has informed S-MCDA in providing scenarios weights processed through the TOPSIS method (Yoon and Hwang, 1995). Consequently, the recommendation system based on partial and overall performance rankings of RBDU has been derived. 3.2.2. Social indicators A mapping of the main associations with environmental, cultural, civic engagement and welfare purposes, operating in the area and involved in projects to enhance the Sarno River basin has allowed the processing of social indicators through NA. Two metrics have been calculated to express the municipalities’ ability to network: •Degree Centrality (i01). •Eigenvector Centrality (i02). Degree Centrality (i01) is recognised as the most straightforward metric in the NA and represents the number of direct connections to other actors in the network. Its meaning refers to the importance of an actor within the entire network since the larger the number of adjacent nodes, the more relevant the node. In the examined network, a high Degree of Centrality refers to an increased number of projects an actor has managed as a leader involving other associations, highlighting its independence and capacity building within the network. Such a metric can be formalised through the Eq. (1). Eigenvector Centrality (i02) expresses the potential that derives from connections between a network node and those nodes that have the highest number of links and, thus, are well-connected within the graph. Such a metric seeks to identify the importance of a node without merely counting the number of its links but by analysing the quantity and quality of the connections with high-scoring nodes. In this study, Eigenvector Centrality has served the purpose to detect those administrative units with few but well-connected-to-leaders actors. The processed indicators were transferred into each RBDU by selecting the maximum relative value for both the metrics calculated within the municipal boundaries. The spatial representation of the social indicators in the GIS environment was provided in five classes using the classification method “Equal Intervals”, which is suitable to emphasise the relative number of connections belonging to one district unit compared to the others (Fig. A1). 3.2.3. Economic indicators The indicators of the economic dimension were selected by focusing on the expenditures incurred by the municipalities - expressed in per capita terms - contained in the final balance sheet of the municipalities and expressed for the three macro categories of investments, which in turn were identified considering the sustainability pillars. These categories (and related expenditure items) are: •Per capita expenditure on enhancing cultural heritage and activities (i03): historical heritage valorisation; cultural activities and interventions in the cultural sector. •Per capita expenditure on sustainable development and land and environmental protection (i04): soil protection; environmental protection, enhancement, and recovery; waste; integrated water service; protected areas, natural parks, nature protection and forestation; protection and enhancement of water resources; sustainable development of small municipalities in mountain areas; air quality and pollution control. •Per capita expenditure on social rights, social policies, and family (i05): interventions for childhood, kindergartens, disability, elderly, people at risk of social exclusion, families, right to housing; planning and governance of the network of social and health services; cooperation and associations; necropsy and cemetery service. As with the indicators of the social dimension, the collected data were reported to a univocal MMU that corresponds to each RBDU. The spatial representation of indicators in the GIS environment involved the selection of the “Natural breaks (Jenks)” classification method, which allowed for natural groupings of data for the creation of classes (Fig. A2). The resulting intervals were set to show maximum variance between individual classes and least variance within each class. 3.2.4. Cultural indicators Three indicators were selected to measure the cultural dimension, which on the one hand, quantify the built heritage of historical interest on a local scale, and on the other, identify the existing educational services. The indicators, both scaled up to the RBDU and spatially identified, are as follows: •Archaeological Sites and Heritage (i06), including the most relevant archaeological sites and parks in the Sarno valley (e.g., the Archaeological Park of Pompeii and its peripheral sites of Longola, Oplontis, Scafati, Stabiae). •Historical Centres (i07), which quantifies the area of the foundation cores of RBDUs in square kilometres, identifying and displaying their shape on a map. •Schools (i08), which measures the number of schools that have promoted educational activities in their territory on the topic of sustainability and have engaged in Sarno River valorisation projects. Regarding the spatial representation of indicator i05, the categorical classification method was chosen due to the reduced number of points for each RBDU. Indicators i07 and i08, instead, were classified according to the "Natural breaks (Jenks)" method (Fig. A3). Table 1 The four scenarios and related priority order of sustainability criteria. ID Scenario description Priority order according to sustainability criteria Social Economic Cultural Ecological S1 Bottom-up social empowerment I II III IV S2 Territorial investments II I III IV S3 Cultural promotion III IV I III S4 Ecosystem conservation IV III II I G. Poli et al. Land Use Policy 141 (2024) 107123 7 3.2.5. Ecological indicators The indicators of the Ecological dimension have been identified based on two main concerns: first, the risk associated with the coexistence between the river and the urban fabric, and second, the impact of human activities on the ecological level of surface water and soil. The following indicators, as shown in Fig. A4, have been scaled up to the RBDU: •Flood areas (i09): This indicator measures the areas historically affected by the river flooding phenomenon in square kilometres. •Hydrological Risk (i10): This indicator identifies the four hydrogeological risk classes - referred to as areas of very high (R4), high (R3), medium (R2) and moderate (R1) hydraulic risk - that the Italian territory is exposed to based on vulnerability and hazard. •Ecological status of surface water (i11): This indicator reconstructs the ecological status of surface water by using the average values of monitoring points on the river course. It refers to a synthetic index - namely LIMeco - that integrates some physical-chemical elements to express the river’s degree of naturalness in percentage terms. •Hemeroby impact level (i12): This indicator classifies the hemerobylevel-based to land use types based on the polygon features of the CLC, grouped according to the main land cover categories defined by the CORINE nomenclature (European Environment Agency, 2018). It expresses the degree of human impact on the land (Frondoni et al., 2011; Walz and Stein, 2014). Each municipality was assigned the hemeroby class found to prevail within the administrative limits. Following the classification proposed by Szilassi et al. (2017), the levels of hemeroby impact on soil - used to build the last-mentioned indicator - were divided into seven categories, as shown in Table A3. Since no ahemerobic areas are present in the Sarno river basin district, the authors divided the CLC levels characterizing the study area into six categories, excluding category one. 4. Results 4.1. Choice The Choice phase’s target was to combine scenario planning with MCDA in a GIS environment to derive overall and partial rankings for RBDU. The defined problem was modelled with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS), a compensatory multi-criteria analysis method (Yoon and Hwang, 1995) founded into the following elements: •Criteria, which have included four main domains referred to sustainability pillars in social, economic, cultural, and ecological terms. •Sub-criteria, which correspond to spatial indicators selected and which were processed as proxy-variables to specify the main criteria. •Alternatives, which have been identified with the RBDU of the decision-making problem. •Scenarios, which represent the conditions under which the bestperforming alternatives have been assessed against the selected criteria. In this phase, S-MCDA has allowed performance indicators related to RBDU to be aggregated through mathematical combinatorial rules and partial rankings and overall ranking to be derived as the output of SDSS. TOPSIS has allowed RBDU to be assessed concerning social, cultural, economic, and ecological indicators under conditions of the four scenarios (S1, S2, S3, S4). Therefore, TOPSIS was chosen as the best-fit MCDA method to integrate the spatial components of criteria and alternatives into classification maps based on the best/worst performance indicator values for each administrative unit. In addition, the possibility of implementing MCDA in a GIS environment is crucial in the context of RBDU management. Indeed, the geographical location of administrative units is very significant for decision-making, as they represent, with their ability to satisfy the evaluation criteria, the spatial alternatives against which policy and governance actions can be defined. The final ranking was obtained by ordering alternatives according to the relative closeness (C∗ i) through the Eq. (2). The ideal point algorithm - implemented through the Q-GIS vectormcda plugin released by Rocchi et al. (2020) - was used to perform the assessment and produce geographical choropleth maps with the ranking of Municipalities. Furthermore, to enable the comparison of the 4 scenarios’ index values resulting from the assessment performed through the TOPSIS method, the numerical indices were normalized so that values measured on different scales were adjusted to a common scale between 0 and 1. The weights were derived by applying two ranking order criteria weighting methods (Roszkowska, 2013), referred to as Rank Sum (RS) and Equal Weights (EW). At the level of criteria, RS can be formalised through Eq. (3), which allows the individual normalized ranks to be obtained by dividing by the sum of the ranks. The stakeholder’s priorities were turned into weights on a scale from 0.1 (least influential criterion) to 0.4 (criterion to be maximised). At the level of sub-criteria, EW - formalised through Eq. (4) - was used because no specific information about single indicator priority can be extracted at this stage since it would require the introduction of expert knowledge. The weights imputation with both weighting as mentioned above methods has been performed through the vector-mcda plugin. The K-Means algorithm was implemented in Python through the Machine Learning module sci-kit learn to cluster RBDU into three categories: Leader, Link, and Follower. The K-Means is a popular clustering algorithm used to group data points into k clusters based on their similarity. It starts by randomly selecting k initial cluster centres and then iteratively assigns each data point to the nearest cluster centre based on the chosen similarity measure. After all data points have been assigned, the algorithm recalculates the centre of each cluster based on the mean of the data points assigned to that cluster. This process of assigning data points and recalculating cluster centres is repeated until convergence, when the cluster assignments no longer change (Bock, 2007). 4.2. Spatial ranking partial results The results of the previous phases have allowed administrative units to be ranked against the performance-based ability in pursuing scenarios. To facilitate a critical reading of the results, a categorisation of municipalities by the three clusters mentioned above - referred to as Leader, Link, and Follower - was carried out for each scenario map. This addresses the purpose of supporting decision-making in a strategic planning context and thus identifying those municipalities that, due to their best performances, can lead the way in achieving the perspectives of the respective scenarios. Regarding the S1 scenario “Bottom-up social empowerment”, which maximised the weight of the Social dimension, the evaluation results show 10 Leader municipalities (led by Castellammare di Stabia), 7 Link municipalities and 3 Follower municipalities (with San Valentino Torio ranked last), as shown in Fig. 4. The municipalities of Cava de’ Tirreni and Sarno rank as Link municipalities for the S1 scenario despite their worse performances in the Social dimension since in calculating the index, they are compensated by performances in the Economic and Cultural dimensions for Cava de’ Tirreni and Cultural and Ecological dimensions for Sarno. On the other hand, with equal values for the Social dimension indicators, the municipality of San Giuseppe Vesuviano ranks higher than San Marzano because it performs significantly better on the other dimensions (in particular, Economic and Cultural). Regarding the S2 scenario “Territorial investment”, which maximised the weight of the Economic dimension, we obtain 6 Leader municipalities (led by San Giuseppe Vesuviano), 9 Link municipalities and 5 Follower municipalities (with San Valentino Torio ranked last), as G. Poli et al. Land Use Policy 141 (2024) 107123 8 shown in Fig. 5. With equal performances on the Economic indicators’ values, the municipality of Mercato San Severino ranks as Link by keeping a good values average across all dimensions (with a prevalence of Social and Ecological ones). In contrast, Scafati ranks as Leader thanks to aboveaverage performances, especially on Social and Cultural dimensions. Regarding the S3 scenario “Cultural promotion”, which maximised the weight of the Cultural dimension, there is only one Leader municipality (Castellammare di Stabia), 13 Link municipalities and 6 Follower municipalities (with Sant’Antonio Abate ranked last), as shown in Fig. 6. Despite the municipality of Castellammare di Stabia showing a slight gap in the average value of the Cultural indicators compared to Cava de’ Tirreni, it obtains a doubled score that ranks it first among Leaders, as it maximises the Social and Ecological dimensions by a large gap. Finally, concerning the S4 scenario “Ecosystem conservation”, which maximised the weight of the Ecological dimension, the ranking shows 4 Leader municipalities (led by Castellammare di Stabia), 9 Link municipalities and 7 Follower municipalities (with Angri ranked last) as shown in Fig. 7. It is noteworthy that the municipality of Sarno ranks as second in the scenario ranking, despite the worst performance in the Social dimension, while Torre Annunziata ranks among the Link municipalities, as it largely prevails in the Social dimension and moderately in the Economic one, despite the worst performance in the Ecological dimension. 4.3. Overall spatial ranking Performing the TOPSIS assessment against the indices of the municipalities for the 4 scenarios, giving equal weights, resulted in the final ranking describing the performance of the municipalities against the overall Sustainability scenario (Fig. 8). Furthermore, the spatialised results read in accordance with the Leader, Link and Follower classifications, obtained by applying the K-Means method, allow identification of territorial clusters. Concerning the municipalities that can be considered Leaders in the promotion and management of the river resource, two main clusters can be identified. The first comprises 4 municipalities, starting with those located at the river mouth, Castellammare di Stabia and Torre Annunziata, which are at the top of the ranking, then rising towards the municipalities of Pompei and Scafati. The second cluster is made up of 5 municipalities located in the inland area of the river system, starting with the upstream municipalities of Castel San Giorgio and Cava de’ Tirreni, which is home to the source of the Cavaiola torrent, and ending in the municipality of Nocera Inferiore, the most central in the valley. The municipalities that rank as Links constitute a main cluster composed of 4 municipalities located in the most central part of the valley and 3 detached municipalities, including Fisciano, among the last in the class, Nocera Superiore and Sarno, which, in addition to hosting the source of the Sarno river, ranks as first in its class. Follower class municipalities are concentrated in a single cluster of 3 municipalities (Poggiomarino, Striano and San Valentino Torio) located in the valley’s northern part, towards the Sarno river’s spring. 5. Discussion 5.1. Potentials and limitations of the SDSSs for Sarno River Basin District Several potentials arise from the integration of SDSS and RBM. First, SDSS boost RBM by providing decision-makers with spatial information Fig. 4. Partial ranking of RBDU according to Scenario 1 (Bottom-up Social empowerment). G. Poli et al. Land Use Policy 141 (2024) 107123 9 and tools to evaluate and visualise the complex data associated with river basins. In particular, SDSS supports RBM by integrating spatial and non-spatial data to provide a comprehensive view of the river basin, allowing decision-makers to identify areas of concern, such as water quality, quantity, and ecosystem health. Furthermore, it allows decisionmakers to simulate scenarios, visualise alternatives, and perform a multicriteria evaluation of various management options. This enables better-informed decisions to be made and helps to identify the most effective management strategies. The interpretation of river basins as SES, an expression of multilevel systems in which ecological and social elements interact through bidirectional interactions and feedback loops, underlines the need to make explicit the complex and dynamic interdependencies between social and ecological components and support the sustainable management of resources, based on integrated and multidimensional assessments. The preliminary findings of this study have highlighted the dual role of the evaluation process that underlies SDSS. Firstly, the purpose is to manage multidimensional resources and select scenarios that better meet complex and conflicting needs. Secondly, it addresses building incremental knowledge processes and mutual learning, which facilitate the identification and generation of new values in an integrated and collaborative perspective (Vilsmaier et al., 2015). Indeed, public involvement, supported by legislative instruments and improved in its procedures by technical/informatics tools capable of guiding participatory processes, has a positive impact on decision-making (Rega and Baldizzone, 2015). At the same time, evidence-based decision-making, if implemented by site-specific, geographic, and comprehensive indicators, stakeholders engagement and NA, generates an interface between policy and scientific research that aims to increase the level of multidimensional sustainability in planning decisions for RBM (F¨ orster et al., 2015). The methodological approach tested for the RBM of the Sarno River implements an incremental, dynamic, and iterative evaluation process, which involves different insights, leading to the exchange, synthesis, and co-production of knowledge and values in a complex decisionmaking context (Strang, 2007). The active involvement of expert groups and communities in decision-making is, therefore, an evaluation tool for self-learning and identifying new collective values (Closs and Antonello, 2011; Cerreta et al., 2021). In addition, the structured decision-making process allows for the testing of methodological procedures that can be scaled up in different contexts, using a multiscale approach that incorporates local instances and avoids overgeneralization of problems when the knowledge underlying the collected data is place-based and context-aware (Handoyo and Sensuse, 2017). The proposed methodology, structured according to the three phases of decision making, has been designed to facilitate its replicability and exportability to other geographic contexts within the framework of RBM. Specifically, the selected composite indicators involve the use of largely easy-to-find Authoritative Geographic Data, and partly of Crowdsourced Geographic Data (Najwer et al., 2022). The availability of the latter is conditional on the possibility of adapting the corresponding indicators selected as meaningful.This contingency could constitute a constraint or ultimately a possible obstacle to the immediate replicability and adaptability of the evaluation framework. Limitations of this study can be recognised in some aspects of the proposed methodological approach, which has been conceived to support the hypothesis of structuring a decision-making process for RBM according to the formal rules of SDSS. First, the amount and typology of selected indicators in the Knowledge phase requires multi-disciplinary expert validation to provide Fig. 5. Partial ranking of RBDU according to Scenario 2 (Territorial Investment). G. Poli et al. Land Use Policy 141 (2024) 107123 16 Figure A2. Economic Indicators spatial maps. Figure A3. Cultural Indicators spatial maps. G. Poli et al. Land Use Policy 141 (2024) 107123 17 Figure A4. Ecological Indicators spatial maps. APPENDIX B. 1. Eq. (1): DEGREE CENTRALITY d(i) = ∑ j mij (1) where mij =1 if a link among nodes i and j exists, and mij =0 if a link does not exist. 2. TOPSIS METHOD [Eq. (2): RELATIVE CLOSENESS] TOPSIS method (Technique for Order Preference by Similarity to Ideal Design) (Yoon and Hwang, 1995), main steps derived by Rocchi et al. (2020) Step 0 - Given a decisional problem, in which there are n alternatives and k attributes, a related “decisional matrix” can be constructed: G. Poli et al. Land Use Policy 141 (2024) 107123 18 Attributes Alternative designs y1 y2 a1 y11 y12 yk a2 y21 y22 … y1k … … … … y2k an yn1 yn2 ⋯ … in which yij is the value of the attribute yj for the alternative ai(i=1,…,n;j=1,…,k.). Step 1 - the normalized decisional matrix must be defined, whose elements are: zij =yij  ∑n i=1y2 ij √,i=1,…,n j =1,…k. Step 2 - the weighted normalized decisional matrix is subsequently defined, whose elements are: xij =wjzij,i=1,…,n;j=1,…,k. where wj is the weight of j-th attribute. Step 3 - an ideal point a∗and a worst point a−(nadir) are defined as follows: a∗={(maxixij∣j∈J),(minixij∣j∈J∧)∣i=1,…n}={x∗ 1,x∗ 2,…,x∗ k} a−={(minixij∣j∈J),(maxixij∣j∈J∧)∣i=1,…n}={x− 1,x− 2,…,x− k} where J is the set of the indices to be maximized (e.g. benefits) while Jˆis the set of the indices to be minimized (e.g. costs). Step 4 - the distance of the alternative from the ideal point a∗(i.e. an ideal or perfect alternative) is calculated as follows: S∗ i= ∑k j=1(xij −x∗ j)2 √,i=1,…,n and the distance from the negative ideal point a - is calculated as well: S− i= ∑k j=1(xij −x− j)2 √i=1,…,n Step 5 - The "relative closeness" of each design from the ideal point is calculated: C∗ i=S− i S− i+S∗ i i=1,…,n(2) Step 6 - Finally, ordering the alternatives according to C∗i, we obtain their ranking from the best to the worst; indeed, if C∗i>C∗j then ai "surpasses in degree" aj. 3. Eq. 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