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Article

The Evaluation of Sustainable Development Projects in Marginal Areas: An A’WOT Approach

by
Rubina Canesi
* and
Chiara D’Alpaos
Department of Civil Environmental and Architectural Engineering (ICEA), University of Padova, Via Venezia 1, 35131 Padova, Italy
*
Author to whom correspondence should be addressed.
Land 2024, 13(5), 601; https://doi.org/10.3390/land13050601
Submission received: 4 April 2024 / Revised: 20 April 2024 / Accepted: 22 April 2024 / Published: 30 April 2024
(This article belongs to the Special Issue The Other Perspective. City Networks and Territorial Justice towards a Landscape Ethics)
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Abstract

:
The increasing urbanization trend, projected to reach 70% of the global population residing in cities by 2050, underscores the pivotal role of cities in achieving the Sustainable Development Goals (SDGs) set by the 2030 Agenda for Sustainable Development (UN, 2015) and combating climate change. Nonetheless, the 2023 report by the United Nations Human Settlements Programme (UN Habitat) reveals an alarming gap in achieving SDG 11 “Sustainable cities and communities” by 2030. This gap highlights the urgent need for transformative shifts in urban policies and investments to prevent cities from becoming centers of global disparities, including socio-economic inequalities, digital divide, and spatial fragmentation, particularly in marginal areas. Marginal areas suffer indeed from conditions of sub-optimality in planning capacity, valuable decision-making, and project implementation. The inadequate planning, management, and governance of marginal areas, coupled with suboptimal investments, can severely compromise their socioeconomic condition. Planning efforts frequently fall short in achieving long-term sustainability goals due to localized and short-sighted decision-making processes, particularly evident in marginal areas. It is crucial, though, to support their public administrations in the achievement of the SDG 11 targets and in their responsive participation in the calls for the allocation of public funding. In this paper, we provide a theoretical and methodological approach to evaluate urban regeneration projects in marginal areas. In detail, we develop an A’WOT approach, which combines a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to the Analytic Hierarchy Process (AHP), to rank alternative urban development projects.

1. Introduction

According to 2022 estimates by the United Nations Human Settlements Programme (UN Habitat), by 2050, 70% of the world population will live in cities [1]. As a result, cities and urban areas play a fundamental role in implementing the Sustainable Development Goals set by the 2030 Agenda for Sustainable Development [2] and fighting climate change. Nonetheless, the 2023 UN Habitat report reveals that the world is far from achieving SDG 11 “Sustainable cities and communities” by 2030 [3,4,5]. Significant shifts in current urban policies and investments are needed to prevent cities from becoming grounds for global divides, which include ever-growing socio-economic inequalities, digital divide, and spatial fragmentation. These divides risk being exacerbated in marginal areas, which are regions characterized by limited access to economic opportunities, essential services, and social infrastructure, often leading to socio-economic disparities and exclusion from mainstream development processes.
The inadequate planning, management, and governance of many urban areas coupled with poor investments can greatly diminish the quality of life of local populations. Despite the numerous advantages stemming from economies of scale, due to urban areas’ high population density that allows for more efficient provision of public services, which lower costs per capita, policy-makers and urban planners are at the forefront in addressing the challenges posed by environmental degradation, inequalities and social segregation, service provision, and urban health and aging. In addition, the COVID-19 pandemic provides an austere reminder that cities must enhance their resilience to get prepared for unpredictable events.
Despite the concept of sustainability and sustainable development, expressed in the Brundtland report in 1987 [6], the conceptualizations of sustainability principles remain rather limited in Governments’ policies. Urban development projects entail actions aimed at transforming or/and improving a specific area within a city border and encompass the construction of new buildings and infrastructures, as well as the redevelopment or revitalization of existing structures and public spaces [7]. These projects indeed affect the three pillars of sustainability: economics, environment, and society [8,9,10,11,12]. Nonetheless, planning efforts have often proven to be unsuccessful in the long term due to a lack of global sustainability. Local projects are frequently developed and undertaken under the driver of contingent needs, which are in turn constrained by tight deadlines and a below-strength project team. These challenges, which lead to an inefficient and sometimes myopic identification of interventions to be undertaken [13,14], become harsher the smaller the project territorial scale. Marginal areas indeed suffer from conditions of sub-optimality in planning capacity, valuable decision-making, and project implementation. On top of that, the assessment of project sustainability in urban contexts has always represented a critical issue in planning and public decision-making, due to the vagueness of the concept of sustainability and the complexity of the valuation of urban development by its very nature [15].
It is crucial, though, to support marginal areas both in their pathway towards the achievement of the SDG 11 targets and in their responsive participation to the calls for the allocation of public funding.
The objective of this paper is to provide a valuation framework to support public administrations operating in marginal areas in the ranking of urban development projects, which potentially can participate in the calls for the allocation of financial resources at the national or EU level. The aim of public administrations is indeed to finance projects that generate positive impacts locally and are sustainable in the long run, according to the three pillars on which the sustainability paradigm is grounded.
In detail, we develop an A’WOT approach, which combines a Strengths, Weak-nesses, Opportunities, and Threats (SWOT) analysis to the Analytic Hierarchy Process (AHP), to rank alternative urban development projects to be undertaken in a marginal area located in Central Italy.
The remainder of the paper is organized as follows: Section 2 includes the methodological background focusing on the A’WOT approach. It also delves into case studies, detailing the SWOT analysis conducted for each project. Additionally, it outlines the decision-making framework through the SWOT matrix and elucidates the evaluation process employed to select A’WOT criteria. Section 3 illustrates the main findings and discusses the alternatives’ final ranking. Finally, Section 4 summarizes the findings and discloses limitations and possible future directions of the research.

2. Urban Sustainable Development Projects in Marginal Areas

The concept of marginal areas is complex and multifaceted within the realm of urban studies and sustainable development. In contrast to urban areas, which are known for their dense population, extensive infrastructure, commercial vigor, and residential expansion, marginal areas are often characterized by a combination of physical, social, and economic challenges that render them less desirable or functional compared to more central or prosperous parts of a city or region. The connection between urban and marginal areas lies in their spatial proximity and interdependence. Marginal areas serve as buffers between urban centers and rural landscapes, providing essential services, such as food production, natural resource management, and recreational spaces. Additionally, they often experience spillover effects from urban development. Understanding the intrinsic nature of marginal areas is crucial for devising effective strategies for urban sustainable development. These areas indeed are often challenged by accentuated vulnerabilities (e.g., limited access to essential services, poor infrastructure, digital divide, and social inequalities) and require targeted interventions to improve their livability and resilience.
The concept of marginality encompasses an intricate phenomenon, influenced by various interconnected environmental and socio-economic factors. As such, the definition and utilization of marginal land can vary significantly depending on the geographical region, national context, and organizational perspective [16,17,18].
Marginal areas, also called Inner Peripheries, are defined as national territories facing challenges of access to basic services of general interest [19]. They are characterized as multidimensional phenomena that exacerbate the impacts of various socio-economic processes leading to detachment from external territories and networks. Marginal areas are distinguished by their level of disconnection, rather than their geographical proximity to the central region of Europe. Shared among marginal areas is the fact that their overall performance, development levels, access to services, or quality of life of the population tend to be relatively lower compared to neighboring territories. According to ESPON, a European Union funded program, the three conditions that can result in marginal areas are as follows: (i) existing as an “enclave” with restricted access to economic activity centers; (ii) facing insufficient access to vital services, stemming from factors like geographic isolation, shifts in service delivery methods, or austerity measures; (iii) facing the absence of “relational proximity”, resulting in exclusion from the economic mainstream due to limited engagement with wider networks. These circumstances are defined by social and institutional dynamics within individuals, groups, businesses, or organizations, rather than purely by geographic factors.
According to the Italian National Strategy for Inner Areas (SNAI), marginal areas are characterized by notable remoteness from primary and advanced service centers (e.g., health, education, mobility, etc.), yet they boast abundant environmental resources (e.g., water, agricultural systems, forests, natural and human landscapes, etc.) and cultural assets (e.g., archaeological sites, historical settlements, abbeys, small museums, craft centers, etc.) [20]. These areas typically lack adequate access to essential services, infrastructures, and economic opportunities, which in turn leads to disparities in living conditions and quality of life compared to more privileged parts of the city [16]. Marginalization in urban contexts can manifest in various forms, including the following: (i) physical degradation, (ii) lack of services and infrastructures, (iii) social exclusion and inequality, (iv) economic deprivation due to depopulation, a high unemployment rate, and poverty, and (v) environmental vulnerability [21,22,23,24,25].
Italy, like many other countries in Europe and worldwide, grapples with the challenges posed by marginal areas within its urban and rural landscapes. In Italy, marginal areas are often affected by a combination of economic stagnation, social exclusion, and environmental degradation, particularly in southern regions, in which the population density is low. The development of these regions is crucial for achieving balanced territorial growth and fostering sustainable development across the country. The SNAI 2014–2020 constitutes one of the strategic intervention lines of the European Structural Funds for the programming cycle 2014–2020. It is supported both by European funds for the co-financing of local development projects and by national resources [20]. The aim of the SNAI strategy is to mitigate, over the medium term, the demographic decline observed in specific regions of the country. These regions are identified by SNAI as those situated farthest from primary and advanced essential service hubs. SNAI’s primary goal is to generate fresh income prospects and enhance accessibility to crucial services for residents, with a primary focus on local public transport, education, social and health services, economic diversification, and social inclusion.
Efforts to promote urban sustainable development in marginal areas encompass a range of interventions aimed at addressing the root causes of marginalization while enhancing the resilience and well-being of local communities. These projects often adopt a holistic approach that integrates environmental, social, and economic dimensions of sustainability. The Italian National Recovery and Resilience Plan (NRRP), also supported by the fund for marginal areas, established in 2021 by the Territorial Cohesion Agency, aims to support and finance marginal areas by promoting sustainable development, social inclusion, and economic growth. Key priorities for NRRP fund allocation in marginal areas include the following: (i) infrastructure development to improve transportation networks, access to water and sanitation facilities, energy systems, and digital connectivity; (ii) citizens’ empowerment and economic support for initiatives aimed at promoting entrepreneurship, innovation, and job creation, including access to financing skills development programs and local industries and businesses; (iii) social inclusion and territorial cohesion to address social inequalities, promote access to education, healthcare, and social services, and empower marginalized communities to participate in decision-making processes; (iv) environmental sustainability to promote environmental conservation, sustainable land management, and climate resilience actions (e.g., reforestation, ecosystem restoration, and mitigation of climate change effects) [26].
Marginal areas represent complex urban spaces facing a combination of physical, social, and economic challenges. Understanding the dynamics of marginalization is essential for formulating effective strategies for urban sustainable development. Supporting public administrations in their decision-making process in ranking urban development projects can enhance the sustainability and effectiveness of investments in these areas.

3. Materials and Methods

Based on the decision-maker’s (i.e., the municipality) objective, in the structuring phase of the decision-aiding process, the decision problem was formulated as a ranking problem. Any decision problem is indeed a partition of a set of actions A (e.g., the projects under investigation) under some properties and is reduced in aggregating some ordering relations applied to set A [27,28,29]. Nonetheless, within the proposed valuation framework, the set A was partitioned into ordered classes via pairwise comparisons, where the primitive is the binary relation (“at least as good as”):
≽ ⊆ A × A
which results in a partitioning of A in [A1],…[An], such that
A j ≥ A i ⇔ j ≥ i   a n d   ∀ x ∈ A j ,   y ∈ A i : x   ≽ ′   y  
To solve the ranking problem, we implemented an A’WOT model to aggregate preferences and obtain a global preference structure with respect to any alternative (i.e., a global value judgment based on each alternative), which in utility/value functions in which preference structures are weak orders [30,31,32].
The A’WOT analysis represents the fusion of two decision-making approaches widely adopted in decision-making and strategic planning: the Analytic Hierarchy Process (AHP) and the SWOT analysis. The AHP, initially introduced by Saaty in the 1980s [33,34], is renowned for its structured approach to complex decision-making and provides a systematic method for evaluating and prioritizing criteria based on pairwise comparisons. Saaty and Kearns emphasize the utility of multicriteria methodologies in facilitating analytical planning processes. They underline the intricate nature of systems, hierarchies, and prioritization within planning endeavors, characterizing planning as a distinctive mode of decision-making. They also review conventional planning methods, delineating their limitations, and propose how these can be mitigated through the adoption of a systems-oriented, multicriteria perspective [35].
On the other hand, SWOT analysis is based on internal and external evaluation criteria, also defined as SWOT categories, and offers a comprehensive assessment of SWOT categories: namely, internal Strengths and Weaknesses, which can contribute to achieving objectives, and external Opportunities and Threats, which can prevent objectives from being achieved [36,37,38]. SWOT analysis is extensively utilized as a tool for analyzing both internal and external environments, providing a systematic approach to support strategic decision-making in various contexts. SWOT analysis excels in identifying key factors for analysis to support the decision-making. However, it primarily provides qualitative assessments [39]. To rectify this limitation and enhance the quantitative foundation of strategic planning processes, the A’WOT method has been proposed in the early XXI [40]. A’WOT is a hybrid approach that integrates SWOT analysis with the Analytic Hierarchy Process.
In the realm of strategic management and decision support systems, the merger of AHP and SWOT analysis combines the strengths of both methodologies. By integrating the rigor of AHP’s quantitative analysis with the qualitative insights of SWOT analysis, the A’WOT method offers a robust framework for strategic decision-making and a priority setting. This synergy enables decision-makers to leverage the depth of the SWOT analysis while incorporating the analytical rigor and prioritization capabilities of the AHP. We opted to employ an A’WOT model due to the synergistic benefits derived from combining these two distinct features. The AHP method facilitates a clear and efficient ranking of the proposed alternatives. However, identifying criteria and sub-criteria can prove complex, particularly when involving diverse groups of experts. Conversely, SWOT analysis assists in this process by establishing criterion levels and enhancing the identification of sub-criteria, aligned with its four factors. This is especially advantageous in urban design contexts, where decision-makers consistently apply SWOT logic to plan programmatic decisions.
The integration of the AHP within a SWOT framework aims to assess, systematically, SWOT categories and factors and render them comparable in terms of their relative importance [40]. The SWOT framework establishes the fundamental structure for analyzing a decision-making scenario, while the AHP supplements the SWOT analysis with a robust analytical decision-making tool [41]. Through the assessment of SWOT categories and factors, it becomes possible to discern, for instance, whether particular weaknesses demand primary attention or threats may surpass future cumulative opportunities [41,42,43].
Once the significance of various SWOT categories is established, alternatives can be ranked according to the overall strategic decision-making problem setting.
The A’WOT is therefore a hybrid method that unfolds according to the following steps (Figure 1):
  • Phase I: Problem structuring.
  • Phase II: SWOT analysis.
  • Phase III: Construction and validation of the hierarchy.
  • Phase IV: Pairwise Comparisons and aggregation of experts’ judgments and consistency index calculation.
  • Phase V: Final ranking of alternatives and sensitivity analysis.
In this study, we implement an A’WOT approach to rank, from best to worst, urban development projects. This tool holds potential value for public administrations in their decision-making processes to select strategic projects for potential candidacy in public funding allocation. In detail, we develop and implement an A’WOT model to rank three alternative projects to be undertaken in a marginal area, defined by the National Strategy for Marginal Areas [20], and located in Central Italy [15].The three projects consist of an Urban Digital Innovation Center (Project#1), the implementation of a Renewable Energy Community District (Project#2), and a Center for Active Ageing (Project#3). The three projects encompass three distinct potential alternative projects that the municipality may undertake in coherence with NRRP funds in the marginal area under investigation (Table 1). The first alternative (Project#1) consists of a vacant building to be redeveloped into an Urban Digital Innovation Center. This project involves demolishing a section of the current structure and constructing an Innovation-Lab dedicated to art, photography, and digital media, as well as co-studying and co-working spaces. Additionally, the complex will feature a performance hall, a library, a cafeteria, and a restaurant. The second alternative (Project#2) consisting of the implementation of a Renewable energy community district, aims to install photovoltaic panels on around 21 public buildings, such as schools, libraries, cultural centers, and municipal offices, engaging additional private residential users. Finally, the last alternative (Project#3) plans to redevelop a currently vacant building into a Center for Active Ageing. This project intends to undertake significant renovations and repurposing of the building. This will include enhancements to seismic resilience, facility upgrades, and energy efficiency improvements. The refurbished structure will accommodate an elderly center, senior housing services, a primary health care facility, a library, and a recreational center. Each project has an initial investment cost of approximately four million euros, to be completed in less than two years to match the NRRP spending horizon [15]. Table 1 outlines a summary description of the alternatives, the assets that are subject to renovation (i.e., existing improvements), and a concise demographic statistical overview of the municipality area in which the three alternative projects might be undertaken. As to area, Table 1 reports its Density (inhabitants/km2), Population Trend (%), Age Index (%), Average Age (Yrs), share of inhabitants who are not Italian citizens, i.e., Foreigners Percentage (%), Unemployment rate (%), and average number of the economically dependent population per 100 economically productive population, i.e., the Dependency ratio (%), Mortality index (%), and Vacant buildings index (%).
As outlined previously, the evaluation process is articulated into five stages. The initial stage involves the implementation of the SWOT analysis and the construction of the SWOT matrix to delineate the decision problem. Specifically, SWOT categories were disaggregated into distinct factors, representing pivotal facets for the evaluation of urban development projects. To ascertain these SWOT factors, we conducted an extensive literature review on the main drivers of urban development projects and urban quality [15,45]. Additionally, we identified and interviewed a panel of experts comprising local authorities, academics, and technical stakeholders engaged in redevelopment projects. Figure 2 details the SWOT factors identified:
  • S1—Energy Performance: it relates to the capability of the project to improve the energy efficiency of existing buildings.
  • S2—Cohesion, Equal Opportunities, and Education: it relates to the capability of the project to increase education, training, and qualified work levels and promote social cohesion or engagement.
  • S3—Digitalization: it relates to the capability of the project to raise digital skills or digital connectivity.
  • W1—Project Operation and Maintenance Costs: it relates to the operation and maintenance costs of the project.
  • W2—Funding Constraints: it relates to the opportunity of applying for public funding to cover initial investment costs.
  • W3—Investment Timing: it relates to the project timing and duration.
  • O1—Job Creation: it relates to the capability of the project to increase local employment rates throughout the project life cycle.
  • O2—Neighborhood Urban Quality: it considers the capability of the project to improve urban quality and quality of life in the surrounding neighborhood.
  • O3—Circular Economy: it relates to the capability of the project to use large shares of local or circular products out of the total materials used.
  • T1—Demographic Decline: it relates to whether the local demographic decline can affect the project’s successful implementation.
  • T2—Absence of Complementary Services: it relates to the lack of public services complementary to the project (e.g., public transport, elderly care services, public spaces, etc.), which can affect the project’s cost-effectiveness.
  • T3—Regulatory Risks: it relates to possible changes in regulation, which can affect business or property use in the project area.
According to the proposed framework (Figure 1), in Phase I, the decision problem was structured by defining the problem statement (i.e., ranking problem) and identifying actors, stakeholders, objectives, points of view, and alternatives.
In Phase II, a SWOT analysis was performed for the three alternative urban development projects and Strengths, Weaknesses, Threats, and Opportunities were identified (Figure 2). In Phase III, SWOT categories and factors were structured into a hierarchy (Figure 3).
The uppermost tier of this hierarchy represents the overarching goal namely, the ranking of urban development projects based on their potential for implementing successful and sustainable valorization strategies in the marginal area of interest. Subsequently, at lower hierarchical levels, criteria (SWOT categories) and sub-criteria (SWOT factors) are delineated. Finally, at the base of the hierarchical structure, the alternatives under investigation (i.e., the three urban development projects) are located. The hierarchy was validated by the decision-maker and the panel of experts. Once the hierarchy was structured, in Phase IV and Phase V, the AHP was implemented. In detail, relations within the hierarchical structure and the relative importance of criteria, sub-criteria, and alternatives are determined through pairwise comparisons. Elements at each hierarchical level are pairwise comparisons with respect to their control criterion or sub-criterion (i.e., their parent node). In other words, alternatives are pairwise-compared against each SWOT factor, each SWOT factor is pairwise-compared against its parent SWOT category, and finally, SWOT categories are pairwise-compared against the goal (see example in Figure 4).
In pairwise comparisons, the relative importance of hierarchical elements is expressed via sematic judgments, which are converted into value judgments according to the Saaty’s fundamental scale (Table 2).
Pairwise comparisons are used to fill pairwise-comparison matrices of rank n (1), namely square matrices of preferences, in which the dominance coefficient a i j represents the relative importance of the element on row i against the element on column j [34,46]. To clarify, the value of a i j corresponds to the relative importance of a specific criterion, sub-criterion, or action A i when compared to another criterion, sub-criterion, or action A j . A score of 1 denotes equal importance between the two components, while a score of 9 indicates the extreme importance of component i over component j. Pairwise comparison matrices take the form of square n × n positive reciprocal matrices of preferences (Figure 5). The comparisons along the main diagonal are set to 1 due to the reflexive nature of the binary preference relation, while the elements within the lower part of the sub-matrix are reciprocals of the elements within the upper triangle (i.e., a j i = 1 a i j ) :
The principal eigenvector à la Perron-Frobenius of each pairwise-comparison matrix represents the node priorities (i.e., weights). To test the consistency of judgements, the consistency index CI is calculated [33,34,47]:
CI = λ m a x − n n − 1
where λ m a x is the maximum eigenvalue and n is the rank of the pairwise-comparison matrix, respectively. For consistency to be satisfied, CI < 0.1.
Finally, the ranking of alternatives, based on global priorities (i.e., global weights), is obtained by implementing a weighted-sum aggregation procedure throughout the hierarchical levels [34].

4. Results and Discussion

Conducting Phases IV and V, as previously mentioned, we facilitated a focus group session, led by a moderator, to validate the hierarchy and local and global priorities through dynamic and collaborative discussions. The focus group involved experts with diverse expertise to include as many varieties of thinking as possible. Brainstorming sessions aided in the consensus-building process based on the final set of weights, which were derived by calculating experts’ judgment geometrical means according to group decision-making theory [48,49]. Specifically, as above mentioned, local priorities were determined using the eigenvalue approach to pairwise comparisons and calculating the geometric mean, which permits a synthesis of experts’ judgements to be made and represents, consequently, the representative judgement of the entire panel of experts. Finally, group local priorities were then aggregated within the hierarchy to establish global priorities among criteria and sub-criteria [50,51,52]. Table 3 displays criteria and sub-criteria priority vectors. The weighted geometric mean aggregation method was employed to compute global priorities, effectively capturing preference information embedded within the local pairwise comparison matrices of alternatives. The A’WOT model within SuperDecision V3.2 Software was employed to obtain priority vectors and finalize the ranking. As shown in Table 3, Strengths and Threats Criteria play a major role, with priorities of 0.4515 and 0.3072, respectively, followed by Opportunities and Weaknesses Criteria, at 0.1366 and 0.1047. It is important to highlight that when evaluating Strengths and Opportunities, attributing a higher priority to one alternative over another is equivalent to its potential based on these two factors. Conversely, when evaluating Weaknesses and Threats, attributing a higher priority to an alternative over another is equivalent to maximizing its potential in mitigating these two factors.
This ranking of criteria is rather intuitive for urban development processes, wherein the public administration relies on the project’s Strengths and Opportunities to ensure cost-effective public spending. Additionally, it reveals the intention of the public administration to minimize the project’s potential negative impacts on the surrounding neighborhood, by preferring intervention strategies that mitigate Threats and Weaknesses. Based on the direct inspection of Table 3, it emerges that a high relative importance has been attributed to Cohesion, Equal Opportunities, and Education (S2) and to Complementary Services Absence (T2), with an overall priority (i.e., global weight) of 0.2829 and 0.2099, respectively. Energy Performance (S1) follows closely behind with a global weight of 0.1263. Finally, in Table 4 we reported the priority vectors of alternatives with respect to each sub-criterion, along with the final ranking of the projects, calculated based on both normal and ideal global priorities.
According to the results obtained from the implementation of the proposed A’WOT model, Project#1 exhibits the highest potential for a successful implementation of the public administration’s urban development and public-assets valorization strategies. This is primarily attributable to the project’s involvement in the redevelopment of a currently abandoned and vacant asset that is undergoing degradation, which in turn results in negative externalities affecting the surrounding neighborhood. Additionally, this project is characterized by its swift intervention, offering employment opportunities and fostering cohesion within the local population. Furthermore, it addresses the provision of complementary and digitalization services, which are crucial for the cohesive and comprehensive development of marginal areas. Project#2 is ranked second. This project is appealing for its capacity to improve the energy performance of existing buildings, while also providing an opportunity for social aggregation. However, it faces challenges related to timing and the opportunity to raise the employment rate. Project#3 is last in the ranking due to its susceptibility to the high rates of local depopulation prevalent in the marginal areas of the country, posing a significant threat to its success and effectiveness.

5. Conclusions

The ranking of urban development projects is a complex process, which requires a profound knowledge of their peculiarities and involves multiple decision criteria, actors, and stakeholders whose objectives are often conflicting and competing. In this paper, we provided an A’WOT model for ranking urban development projects in marginal areas. A’WOT provides a reliable tool to support policy-makers in identifying priorities through a multi-criteria approach. The A’WOT approach indeed couples the SWOT analysis with the AHP, thus benefitting from a strategic planning and management perspective provided by the former and the analytically determined priorities of the SWOT factors that in turn become commensurable thanks to the latter.
In detail, we considered three alternative projects: Project#1, Project#2, and Project#3. Project#1 consists of the redevelopment of a vacant building into an Urban Digital Innovation Center, whereas Project#2 involves the implementation of a renewable energy community district, and, finally, Project#3 aims to redevelop a currently vacant building into a Center for Active Ageing. Our findings show that, according to experts’ judgement, Project#1 is ranked as first, due to its potential for a successful implementation of the public administration’s urban development strategy and public-assets valorization strategy. By contrast, Project#3 is final in the ranking as it is more exposed to the threats of local depopulation compared to the other alternatives. Indeed, the projects’ Strengths and Threats are valued as the more important criteria. Although our results are grounded in experts’ judgements, they can have interesting policy implications and contribute to clarify the accountability, transparency, and legitimacy of the underlying public decision process.
The proposed multicriteria approach has been applied to assist a municipality located in a marginal area in ranking alternative projects to apply for PNRR funds. We opted for adopting an A’WOT approach, which is acknowledged in the literature to be effective in ranking projects by addressing their strengths and weaknesses simultaneously based on their impacts on the territory, both positive (opportunities) and negative (threats). Although the A’WOT method implements complex evaluation techniques, through a set of actions meant to engage stakeholders, its results are indeed transparent and easy to understand by decision-makers and stakeholders and, consequently, it favors the co-construction of valorization strategies and fosters quality governance processes. These processes are instrumental in enhancing the efficiency of administrative programmatic choices while aligning with the conscious needs of the territory. With the aid of integrated evaluation methodologies, like SWOT and AHP, collaborative actions can be formulated and comprehended by stakeholders engaged in the territory’s valorization process. This facilitates more effective public decision-making, thereby advancing the collective goals of enhancing the territory. Despite its positive aspects, the implementation of the model revealed certain weaknesses. Utilizing AWOT necessitates a comprehensive grasp of both AHP and SWOT methodologies. This complexity can present challenges for stakeholders who are unfamiliar with these techniques, resulting in extended evaluation and discussion times within focus groups.
Further research will include the development of a first-hand list of SWOT categories and factors, which match NRRP requirements, to be adopted in the valuation of urban development projects in marginal areas.

Author Contributions

Conceptualization, R.C. and C.D.; methodology, R.C. and C.D.; software, R.C.; formal analysis, R.C.; data curation, R.C.; writing—original draft preparation, R.C. and C.D.; writing—review and editing, R.C. and C.D. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by NOP on Research and Innovation (PON Ricerca e Innovazione) 2014–2020, Asse IV–Action IV.4 and Action IV.6 “Research Contracts on Green Themes” (Ministry of University and Research MUR—DM 1062/2021), Towards green transition: new indicators for the assessment of territorial projects development (TGTIndex), Cod. 19-G-14046-1. The views and opinions expressed are solely those of the authors.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. UN Habitat. World Cities Report 2022. Envisaging the Future of Cities. 2022. Available online: https://unhabitat.org/sites/default/files/2022/06/wcr_2022.pdf (accessed on 27 March 2024).
  2. UN. Transforming Our World: The 2030 Agenda for Sustainable Development. 2015. Available online: https://sustainabledevelopment.un.org/content/documents/21252030 Agenda for Sustainable Development web.pdf (accessed on 3 April 2024).
  3. UN Habitat. Rescuing SDG 11 for a Resilient Urban Planet. 2023. Available online: https://unhabitat.org/sites/default/files/2023/11/sdg_11_synthesis_report_2023_executive_summary_2023.pdf (accessed on 27 March 2024).
  4. Zhong, C.; Guo, H.; Swan, I.; Gao, P.; Yao, Q.; Li, H. Evaluating trends, profits, and risks of global cities in recent urban expansion for advancing sustainable development. Habitat. Int. 2023, 138, 102869. [Google Scholar] [CrossRef]
  5. Gao, J.; O’Neill, B.C. Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nat. Commun. 2020, 11, 2302. [Google Scholar] [CrossRef] [PubMed]
  6. UN. Report of the World Commission on Environment and Development—Our Common Future. 1987. Available online: https://sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf (accessed on 27 March 2024).
  7. Lahoz, C.F.; Blasco, J.A. Hamburg at the Forefront of the Active Cities. In Intersecting Health, Livability, and Human Behavior in Urban Environments; IGI Global: Hershey, PA, USA, 2023; pp. 59–82. [Google Scholar]
  8. Canesi, R.; Marella, G. Towards European Transitions: Indicators for the Development of Marginal Urban Regions. Land 2023, 12, 27. [Google Scholar] [CrossRef]
  9. Canesi, R. Urban Policy Sustainability through a Value-Added Densification Tool: The Case of the South Boston Area. Sustainability 2022, 14, 8762. [Google Scholar] [CrossRef]
  10. Trovato, M.R.; Giuffrida, S.; Collesano, G.; Nasca, L.; Gagliano, F. People, Property and Territory: Valuation Perspectives and Economic Prospects for the Trazzera Regional Property Reuse in Sicily. Land 2023, 12, 789. [Google Scholar] [CrossRef]
  11. Giuffrida, S.; Trovato, M.R.; Falzone, M. The information value for territorial and economic sustainability in the enhancement of the water management process. In Computational Science and Its Applications—ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science; Gervasi, O., Murgante, B., Misra, S., Borruso, G., Torre, C.M., Rocha, A.M.A., Taniar, D., Apduhan, B.O., Stankova, E., Cuzzocrea, A., Eds.; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
  12. Trovato, M.R.; Giuffrida, S. The protection of territory from the perspective of the intergenerational equity. In Integrated Evaluation for the Management of Contemporary Cities; SIEV 2016. Green Energy and Technology; Mondini, G., Fattinnanzi, E., Oppio, A., Bottero, M., Stanghellini, S., Eds.; Springer: Cham, Switzerland, 2018. [Google Scholar] [CrossRef]
  13. D’Alpaos, C.; Dosi, C.; Moretto, M. Concession length and investment timing flexibility. Water Resour. Res. 2006, 42, W02404. [Google Scholar] [CrossRef]
  14. Canesi, R.; Gallo, B. Risk Assessment in Sustainable Infrastructure Development Projects: A Tool for Mitigating Cost Overruns. Land 2023, 13, 41. [Google Scholar] [CrossRef]
  15. Canesi, R. A multicriteria approach to prioritize urban sustainable development projects | Un approccio multicriteri per il ranking di progetti urbani sostenibili. Valori e Valutazioni 2023, 2023, 117–132. [Google Scholar] [CrossRef]
  16. Kang, S.; Post, W.M.; Nichols, J.A.; Wang, D.; West, T.O.; Bandaru, V.; Izaurralde, R.C. Marginal Lands: Concept, Assessment and Management. J. Agric. Sci. 2013, 5, 129–139. [Google Scholar] [CrossRef]
  17. Ali, S.A.; Tallou, A.; Vivaldi, G.A.; Camposeo, S.; Ferrara, G.; Sanesi, G. Revitalization Potential of Marginal Areas for Sustainable Rural Development in the Puglia Region, Southern Italy: Part I: A Review. Agronomy 2024, 14, 431. [Google Scholar] [CrossRef]
  18. Dagel, K.C. Defining Drought in Marginal Areas: The Role of Perception. Prof. Geogr. 1997, 49, 192–202. [Google Scholar] [CrossRef]
  19. Noguera, J.; Ortega-Reig, M.; del Alcázar, H. PROFECY—Processes, Features and Cycles of Inner Peripheries in Europe; Final Report; ESPON: Luxembourg, 2017; Available online: https://www.espon.eu/sites/default/files/attachments/D5%20Final%20Report%20PROFECY.pdf (accessed on 1 April 2024).
  20. SNAI. Strategia Nazionale delle Aree Interne SNAI. Dipartimento Per le Politiche di Coesione—Presidenza del Consiglio dei Ministri. 2013. Available online: https://www.agenziacoesione.gov.it/strategia-nazionale-aree-interne/ (accessed on 20 February 2023).
  21. Tesitel, J.; Kusová, D.; Bartos, M. Non-marginal parameters of marginal areas. Ecol. J. Ecol. Probl. Biosph. 1999, 18, 39–46. [Google Scholar]
  22. Agyeman, J.; Bullard, R.D.; Evans, B. Just Sustainabilities: Development in an Unequal World. MIT Press: Cambridge, MA, USA, 2003. [Google Scholar]
  23. Buck, N.; Gordon, I.R.; Harding, A. Changing Cities: Rethinking Urban Competitiveness, Cohesion and Governance; Bloomsbury Publishing: New York, NY, USA, 2017. [Google Scholar]
  24. Amin, A. Lively Infrastructure. Theory Cult. Soc. 2014, 31, 137–161. [Google Scholar] [CrossRef]
  25. Mehretu, A.; Pigozzi, B.W.; Sommers, L.M. Concepts in social and spatial marginality. Geogr. Ann. Ser. B 2000, 82, 89–101. [Google Scholar] [CrossRef]
  26. NRRP. Piano Nazionale di Ripresa e Resilienza; Presidenza del Consiglio dei Ministri: Rome, Italy, 2021. [Google Scholar]
  27. Öztürk, M.; Tsoukiàs, A.; Vincke, P. Preference modelling. In Multiple Criteria Decision Analysis: State of the Art Surveys; Figueira, J., Greco, S., Ehrgott, M., Eds.; Springer: New York, NY, USA, 2005; pp. 27–59. [Google Scholar]
  28. Tsoukiàs, A. On the concept of decision aiding process: An operational perspective. Ann. Oper. Res. 2007, 154, 3–27. [Google Scholar] [CrossRef]
  29. Colorni, A.; Tsoukiàs, A. What is a decision problem? Preliminary statements. In Algorithmic Decision Theory, Proceedings of the Third International Conference, ADT, Bruxelles, Belgium, 12–14 November 2013; Perny, P., Pirlot, M., Tsoukiàs, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 139–153. [Google Scholar] [CrossRef]
  30. Moretti, S.; Öztürk, M.; Tsoukiàs, A. Preference modelling. In Multiple Criteria Decision Analysis: State of the Art Surveys; Greco, S., Ehrgott, M., Figueira, J.R., Eds.; Springer: New York, NY, USA, 2016; pp. 43–95. [Google Scholar] [CrossRef]
  31. Bouyssou, D.; Marchant, T.; Pirlot, M.; Tsoukiàs, A.; Vincke, P. Modelling preferences. In Evaluation and Decision Models with Multiple Criteria: Case Studies; Bisdorff, R., Dias, L., Meyer, P., Mousseau, V., Pirlot, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 35–87. [Google Scholar] [CrossRef]
  32. Bouyssou, D.; Pirlot, M. Preferences for multi-attributed alternatives: Traces, dominance, and numerical representations. J. Math. Psychol. 2004, 48, 167–185. [Google Scholar] [CrossRef]
  33. Saaty, T.L. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 1977, 15, 234–281. [Google Scholar] [CrossRef]
  34. Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation; McGraw-Hill International: New York, NY, USA, 1980. [Google Scholar]
  35. Saaty, T.L.; Kearns, K.P. Analytical Planning: The Organization of System; Pergamon Press Ltd.: Oxford, UK, 1985. [Google Scholar]
  36. Helms, M.M.; Nixon, J. Exploring SWOT analysis—Where are we now? A review of academic research from the last decade. J. Strategy Manag. 2010, 3, 215–251. [Google Scholar] [CrossRef]
  37. Hill, T.; Westbrook, R. SWOT analysis: It’s time for a product recall. Long Range Plan. 1997, 30, 46–52. [Google Scholar] [CrossRef]
  38. Sarcina, A.; Canesi, R. Renewable Energy Community: Opportunities and Threats towards Green Transition. Sustainability 2023, 15, 13860. [Google Scholar] [CrossRef]
  39. Falcone, P.M.; Tani, A.; Tartiu, V.E.; Imbriani, C. Towards a sustainable forest-based bioeconomy in Italy: Findings from a SWOT analysis. For. Policy Econ. 2020, 110, 101910. [Google Scholar] [CrossRef]
  40. Kurttila, M.; Pesonen, M.; Kangas, J.; Kajanus, M. Utilizing the analytic hierarchy process (AHP) in SWOT analysis—A hybrid method and its application to a forest-certification case. For. Policy Econ. 2000, 1, 41–52. [Google Scholar] [CrossRef]
  41. Bottero, M.; D’Alpaos, C.; Marello, A. An application of the A’WOT analysis for the management of cultural heritage assets: The case of the historical farmhouses in the aglie castle (Turin). Sustainability 2020, 12, 1071. [Google Scholar] [CrossRef]
  42. Kangas, J.; Pesonen, M.; Kurttila, M.; Kajanus, M. A’WOT: Integrating the AHP with SWOT analysis. In Proceedings of the Sixth International Symposium on the Analytic Hierarchy Process (ISAHP), Bern, Switzerland, 2–4 August 2001; pp. 2–4. [Google Scholar]
  43. Pesonen, M.; Kurttila, M.; Kangas, J.; Kajanus, M.; Heinonen, P. Assessing the priorities using A’WOT among resource management strategies at the Finnish Forest and Park Service. For. Sci. 2001, 47, 534–541. [Google Scholar] [CrossRef]
  44. De Felice, F.; Petrillo, A. Absolute measurement with analytic hierarchy process: A case study for Italian racecourse. Int. J. Appl. Decis. Sci. 2013, 6, 209–227. [Google Scholar] [CrossRef]
  45. D’Alpaos, C.; Andreolli, F. Urban quality in the city of the future: A bibliometric multicriteria assessment model. Ecol. Indic. 2020, 117, 106575. [Google Scholar] [CrossRef]
  46. Saaty, T.L. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process; RWS Publications: Pittsburgh, PA, USA, 2006. [Google Scholar]
  47. Saaty, T.L. Decision-making with the AHP: Why is the principal eigenvector necessary. Eur. J. Oper. Res. 2003, 145, 85–91. [Google Scholar] [CrossRef]
  48. Xu, Z. On consistency of the weighted geometric mean complex judgement matrix in AHP. Eur. J. Oper. Res. 2000, 126, 683–687. [Google Scholar] [CrossRef]
  49. Grošelj, P.; Stirn, L.Z. Acceptable consistency of aggregated comparison matrices in analytic hierarchy process. Eur. J. Oper. Res. 2012, 223, 417–420. [Google Scholar] [CrossRef]
  50. Dong, Q.; Saaty, T.L. An analytic hierarchy process model of group consensus. J. Syst. Sci. Syst. Eng. 2014, 23, 362–374. [Google Scholar] [CrossRef]
  51. Banzato, D.; Canesi, R.; D’Alpaos, C. Biogas and Biomethane Technologies: An AHP Model to Support the Policy Maker in Incentive Design in Italy. In Smart and Sustainable Planning for Cities and Regions; Bisello, A., Vettorato, D., Laconte, P., Costa, S., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 319–331. [Google Scholar] [CrossRef]
  52. Krejčí, J.; Stoklasa, J. Aggregation in the analytic hierarchy process: Why weighted geometric mean should be used instead of weighted arithmetic mean. Expert. Syst. Appl. 2018, 114, 97–106. [Google Scholar] [CrossRef]
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Figure 1. A’WOT flowchart. Authors’ elaboration from De Felice et al. [44].
Figure 1. A’WOT flowchart. Authors’ elaboration from De Felice et al. [44].
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Figure 2. SWOT factors identified for the projects under investigation. Authors’ elaboration.
Figure 2. SWOT factors identified for the projects under investigation. Authors’ elaboration.
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Figure 3. Hierarchy. Authors’ elaboration.
Figure 3. Hierarchy. Authors’ elaboration.
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Figure 4. Example of pairwise-comparison questions for the evaluation of sub-criteria O1, O2, and O3 with respect to criterion “Opportunities”. Source: SuperDecision Software V-3.2.
Figure 4. Example of pairwise-comparison questions for the evaluation of sub-criteria O1, O2, and O3 with respect to criterion “Opportunities”. Source: SuperDecision Software V-3.2.
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Figure 5. Pairwise-comparison matrix.
Figure 5. Pairwise-comparison matrix.
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Table 1. Alternatives and marginal area characterization.
Table 1. Alternatives and marginal area characterization.
DescriptionAssetAsset SizeTarget PopulationCosts
Project#1Urban Digital Innovation CenterFormer school building requalification2000 m2 of gross floor area (GFA)Youth, young families, young entrepreneurs, youth not in employment, education or training (NEET), adolescents, and young individuals facing challengesEUR 4,200,000
Project#2Renewable Energy Community-PV plantsPrivate and Public buildings requalification20,000 m2 of roof areaPrivate citizens, private enterprises, and municipal public facilities.EUR 3,500,000
Project#3Center for Active AgeingFormer school building requalification2600 m2 of
GFA		Seniors, young families, children, and youth experiencing difficult circumstances.EUR 4,100,000
Marginal area characterization
Density (inhabitants/km2)12.95
Population trend 2023/2022, variation over the previous year (%)−3.34%
Age index *166
Average Age (Yrs)60.6
Foreigners Index **4.40%
Unemployment rate9.2%
Dependency ratio ***110
Mortality index34
Vacant buildings index ****11.5
* Age index = number of elderly population (≥65 years) per 100 individuals younger than 14 years old. ** Foreigners Index = (number of non-Italian citizens)/(total population). *** Dependency Ratio = 100 × [young population (0–14 yrs) + elderly population (≥65 yrs)]/population (15–64 yrs). **** Vacant buildings index = number of vacant and abandoned buildings/total existing buildings.
Table 2. Saaty’s fundamental scale. Source: Saaty, 1980.
Table 2. Saaty’s fundamental scale. Source: Saaty, 1980.
Intensity of ImportanceDefinitionExplanation
1Equal importanceTwo activities contribute equally to the objective
3Moderate importanceExperience and judgment strongly favor one activity over another
5Strong importanceExperience and judgment strongly favor one activity over another
7Very strong or demonstrated importanceAn activity is strongly favored, and its dominance demonstrated in practice
9Extreme importanceThe evidence favoring one activity over another is of the highest possible order of affirmation
2, 4, 6, 8Intermediate values between the two adjacent judgmentsWhen compromise is needed
ReciprocalsIf activity has one of the above numbers assigned to it, when compared with another activity, then this latter has the reciprocal value when compared with the formerA reasonable assumption
Table 3. Criteria and sub-criteria priority vectors.
Table 3. Criteria and sub-criteria priority vectors.
CriteriaCriteria PrioritiesSub-CriteriaSub-Criteria Priorities
Strengths0.4515S1—Energy Performance0.2797
S2—Cohesion, Equal Opportunities, and Education0.6267
S3—Digitalization0.0936
Weaknesses0.1047W1—Project Operation and Maintenance Costs0.4054
W2—Funding Constraints0.4806
W3—Investment Timing0.1140
Opportunities0.1366O1—Job Creation0.3764
O2—Neighborhood Urban Quality0.4742
O3—Circular Economy0.1494
Threats0.3072T1—Demographic Decline0.1998
T2—Absence of Complementary Services0.6833
T3—Regulatory Risks0.1169
Table 4. Alternatives’ priority vector and final ranking.
Table 4. Alternatives’ priority vector and final ranking.
Sub-CriteriaAlternatives
Project#1Project#2Project#3
S10.16670.66670.1667
S20.52780.13960.3325
S30.57140.14290.2857
W10.20000.60000.2000
W20.44440.11110.4444
W30.55840.12200.3196
O10.55840.12200.3196
O20.62500.13650.2385
O30.20000.60000.2000
T10.16920.44340.3874
T20.47210.44430.0836
T30.20000.60000.2000
Overall Priority (Normals)0.42200.33160.2464
Overall Priority (Ideals)1.00000.42200.1407
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Canesi, R.; D’Alpaos, C. The Evaluation of Sustainable Development Projects in Marginal Areas: An A’WOT Approach. Land 2024, 13, 601. https://doi.org/10.3390/land13050601

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Canesi, Rubina, and Chiara D’Alpaos. 2024. "The Evaluation of Sustainable Development Projects in Marginal Areas: An A’WOT Approach" Land 13, no. 5: 601. https://doi.org/10.3390/land13050601

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Canesi, R., & D’Alpaos, C. (2024). The Evaluation of Sustainable Development Projects in Marginal Areas: An A’WOT Approach. Land, 13(5), 601. https://doi.org/10.3390/land13050601

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