1200 word literature review for 15 Articles + Questions
Sam k5
PrioritizationofResilienceInitiativesforClimateRelatedDisastersintheMetropolitan.pdf
Risk Analysis, Vol. 42, No. 5, 2022 DOI: 10.1111/risa.13823
Prioritization of Resilience Initiatives for Climate-Related Disasters in the Metropolitan City of Venice
Marta Bonato,1,2,3 Beatrice Sambo,1,2 Anna Sperotto,1,2,4 James H. Lambert,5
Igor Linkov,6,7 Andrea Critto,1,2,∗ Silvia Torresan,1 and Antonio Marcomini1,2
Increases in the magnitude and frequency of climate and other disruptive factors are placing environmental, economic, and social stresses on coastal systems. This is further exacerbated by land use transformations, urbanization, over-tourism, sociopolitical tensions, technologi- cal innovations, among others. A scenario-informed multicriteria decision analysis (MCDA) was applied in the Metropolitan City of Venice integrating qualitative (i.e., local stakeholder preferences) and quantitative information (i.e., climate-change projections) with the aim of enhancing system resilience to multiple climate-related threats. As part of this analysis, dif- ferent groups of local stakeholders (e.g., local authorities, civil protection agencies, SMEs, NGOs) were asked to identify critical functions that needs to be sustained. Various policy initiatives were considered to support these critical functions. The MCDA was used to rank the initiatives across several scenarios describing main climate threats (e.g., storm surges, floods, heatwaves, drought). We found that many climate change scenarios were considered to be disruptive to stakeholders and influence alternative ranking. The management alterna- tives acting on physical domain generally enhance resilience across just a few scenarios while cognitive and informative initiatives provided resilience enhancement across most scenarios considered. With uncertainty of multiple stressors along with projected climate variability, a portfolio of cognitive and physical initiatives is recommended to enhance resilience.
KEY WORDS: Climate change; critical functions; risk management; scenario-based preferences; sys- tems engineering; uncertainty analysis; Venice
1Fondazione Centro Euro-Mediterraneo sui Cambiamenti Cli- matici (Fondazione CMCC), c/via Augusto Imperatore 16, Lecce, 73100, Italy.
2University of Ca’ Foscari, Via Torino 155, Venezia Mestre, 30170, Italy.
3Helmholtz-Centre for Environmental Research - UFZ, 15 Per- moserstraße, Leipzig, 04318, Germany.
4Basque Centre for Climate Change (BC3), Scientific Campus of the University of the Basque Country, Building 1, Barrio Sarriena 48940, Leioa, Bizkaia, Spain.
5University of Virginia, Charlottesville, VA, USA. 6Engineer Research and Development Center, U.S. Army Corps of Engineers, Concord, MA, USA.
7Carnegie Mellon University, Pittsburgh, PA, USA. ∗Address correspondence to Andrea Critto, Department of En- vironmental Sciences, Statistic and Informatic, University of
1. INTRODUCTION
Climate change is compounding with various threats both to natural and human coastal sys- tems (Intergovernmental Panel on Climate Change [IPCC], 2021), by increasing the frequency, dura- tion, and intensity of many types of climate-related extreme events (European Environment Agency [EEA], 2017; IPCC, 2021, 2014). Extreme events can act as triggering factors for disasters (IPCC, 2021). According to UNDRR (United Nations Office for Disaster Risk Reduction), over the last 20 years
Ca’ Foscari, Via Torino 155, 30170 Venezia Mestre, Italy; an- [email protected]
931 0272-4332/22/0100-0931$22.00/1 © 2021 The Authors. Risk Analysis pub- lished by Wiley Periodicals LLC on behalf of Society for Risk Analysis.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
932 Bonato et al.
90% of major disasters have been caused mainly by floods, storms, heatwaves, droughts, and other climate-related extreme events (Wallemacq & Be- low, 2015). Given the importance of understanding the relationship between climate change and other stressors, fostering a coherent integration of Climate Change Adaptation (CCA) and Disaster Risk Re- duction (DRR) concepts is becoming a global and European priority. The current European Union (EU) development policy context (i.e., the adoption of the 2030 Agenda for Sustainable Development (United Nations [UN], 2015), the Paris Agreement on Climate Change (United Nations Framework Convention on Climate Change [UNFCCC], 2015), the Sendai Framework (UNDRR, 2015)) recognizes how a stronger integration of CCA and DRR could help in addressing important EU Societal Challenges (e.g., climate action), supporting the achievement of multiple Sustainable Development Goals (SDGs) (UN, 2015) (e.g., SDG6, SDG7, SDG9, SDG13, SDG14, and SDG15) and disaster risk reduction targets (UNDRR, 2015).
Through the concept of resilience, we are able to enhance the integration of CCA and DDR (Howes, 2015), by incorporating traditional risk assessment into a wider framework that embraces strategies of adaptation to improve disaster risk management (see, e.g. Bostick, Connelly, Lambert, & Linkov, 2018; Bostick, Holzer, & Sarkani, 2017; Connelly, Lambert, & Thekdi, 2016; Donnan et al., 2020; Hamilton, Lam- bert, Keisler, Holcomb, & Linkov, 2013; Hamilton, Lambert, & Valverde, 2015; Karvetski & Lambert, 2012; Karvetski, Lambert, & Linkov, 2009; Karvet- ski, Lambert, Keisler, & Linkov, 2011; Karvetski, Lambert, Keisler, Sexauer, & Linkov, 2011; Lambert et al., 2012; Lambert, Wu, You, Clarens, & Smith, 2013; Parlak, Lambert, Guterbock, & Clements, 2012; Quenum, Thorisson, Wu, & Lambert, 2019; Schroeder & Lambert, 2011; Thorisson, Lambert, Cardenas, & Linkov, 2017; You, Connelly, Lambert, & Clarens, 2014; You, Lambert, Clarens, & McFar- lane, 2014). Various definitions of resilience (Fox- Lent, Bates, & Linkov, 2015) have been explored in several and distinct contexts, from ecology (Gunder- son, Allen, & Holling, 2012; Holling, 1973; Walker, Holling, Carpenter, & Kinzig, 2004), to engineering (Ganin et al., 2016; Holling, 1996), to disaster risk reduction (Rose, 2004). In the disaster risk reduc- tion context, resilience is defined as “the ability of a system, community or society exposed to hazards to resist, absorb, accommodate, adapt to, transform and recover from the effects of a hazard in a timely
and efficient manner, including through the preserva- tion and restoration of its essential basic structures and functions through risk management” (UNDRR, 2009). Several efforts (Bostick et al., 2017; Hosseini, Barker, & Ramirez-Marquez, 2016; Linkov et al., 2018; Linkov et al., 2014) identify resilience as “emer- gent system property” arising from the complex in- teractions among components of the system under analysis including the physical components and the social, institutional, and informational services that enable their effective use.
Especially in the context of climate related- disasters, characterized by low probability, high con- sequence risks, and uncertainty, this concept can be particularly useful to bound the system under study, identifying critical system functionalities relevant for stakeholders and add the consideration of longer- term horizons (Taarup-Esbensen, 2020) in the risk re- duction and adaptation processes.
In other words, resilience offers the opportu- nity of bringing a different perspective that other- wise may be missed by traditional risk analysis ap- proaches: the ability to understand the capacity of system to recover from a massive external shock (Linkov, Trump, & Fox-Lent, 2016). While it is in- herently impossible to foresee a highly uncertain and infinitely diverse future, resilience can improve sys- tem capacity to quickly cope and adapt to multiple climate change related stressors (Terzi et al., 2019).
On the other hand, a robust resilience analysis cannot be performed aside from the consideration of the type of events which may potentially occur. In this sense risk assessment can supplement resilience analysis with new insights about unknown and po- tentially surprising types of events as well as “cause– effect” relationships between stressors in a way that targeted and more efficient measures can be pro- posed (Aven, 2017).
This sort of complementary relationship between resilience and risk has been recognized and discussed by several authors from both fields (Linkov et al., 2016; Park, Seager, Rao, Convertino, & Linkov, 2013; Trump, Florin, & Linkov, 2018). While these stud- ies explored the topic at a conceptual level, this arti- cle finally shows how such integration of risk assess- ment into resilience analysis can be operationalized in practice, specifically merging together an expert- based assessment of climate change risks and local stakeholders’ preferences and choices regarding the system under analysis.
For this purpose, the scenario-based multicrite- ria decision analysis (MCDA) methodology initially
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 933
developed by Linkov and Lambert (Bostick et al., 2017; Fox-Lent, & Linkov, 2018; Linkov et al., 2018) was further adapted to permit the integration of bottom-up qualitative information (i.e., goals, alter- natives and constraints of local actors), typically em- ployed in resilience analysis, with quantitative met- rics derived by top-down climate change risk assess- ment into a unique assessment framework. Given its adaptive and iterative nature, we employed the pro- posed approach to pursue the following research ob- jectives: (i) explore local actors’ priorities in terms of critical components of the system that should be protected and the typology of risk management mea- sures to be implemented; (ii) assess how such prior- ities are likely to be disrupted/impacted by foreseen climate change scenarios; (iii) reprioritize proposed initiatives accordingly in order to identify the best set of measures to enhance the overall resilience of the system toward multiple climate-related disasters. The methodology was implemented and tested with the local actors of the Metropolitan City of Venice in the frame of the BRIDGE project, a bilateral cooper- ation between Italy and the United States funded by the Italian Ministry of Foreign Affairs and Interna- tional Cooperation. The area is extremely vulnerable to different type of climate related extreme events (Barbi, Formentini, Monai, Rech, & Zardini, 2007; Biolchi et al., 2019; Ferrarin, Chiggiato, Schroeder, & Zaggia, 2019; Lionello et al., 2020), due to its ge- ographical, geomorphological, and climatic charac- teristics (Međugorac, Pasarić, & Güttler, 2021). Such kind of events in the last decade have increased in frequency and intensity (Caporalini, Deuss, & Godlewski, 2020; Lionello et al., 2020; Međugorac et al., 2021; Morucci, Coraci, Crosato, & Ferla, 2020), calling urgently for the implementation of risk man- agement strategies and the adoption of a tailored climate change adaptation plan compatible with the economic development of the area and shared by the numerous actors and visions involved.
2. METHODOLOGY
The scenario-informed multicriteria methodol- ogy proposed aims at the integration of different data (i.e., priorities on critical functions and risk manage- ment initiatives and climate-related scenarios) to an- alyze the interconnectivity of different domains and stages of coastal resilience and support disaster risk management. Given the specificities of the analyzed case study and the multiplicity of involved interests (see Section 2.1), the methodology was codeveloped
step by step with local actors involved at different stages of the disaster risk management cycle (see Sec- tion 2.2). A structured participative process was im- plemented starting with stakeholder’s analysis, a first round of individual semistructured interviews, and stakeholders’ selection. Based on this, a small group of stakeholders was invited to take part in a work- shop which in addition to providing them with the most up-to date climate information and scenarios expected in the case study allowed to elicit their per- spectives and preferences on different input data (i.e. the critical functions, risk management initiatives), as well as allowed to collect the relevance values neces- sary for the different steps of the methodology (see Section 2.3).
2.1. Resilience Issues and Challenges in Venice
The case study is represented by the Metropoli- tan City of Venice and its lagoon located in the North-East of Italy, along the Adriatic coast (Fig. 1).
It represents a coastal-urban system that is facing multiple challenges related both to global change phenomena and socioeconomic dynamics. The Metropolitan City of Venice is a densely urban- ized and populated area with 842 942 inhabitants in 2020 (Italian National Institute of Statistics [IS- TAT], 2021), of which 255 609 reside in the munic- ipality of Venice. A variety of economic activities are conducted in this area, such as fishery, aquacul- ture, agriculture, and maritime shipping. Moreover, a fundamental role is played by tourism: Venice it- self is one of the most visited destinations in Italy and Europe with 12 million touristic presence in 2019 (Regione Veneto, 2021). Due to its natural charac- teristics, there is also a need to ensure environmen- tal protection in this area; the Venice lagoon is it- self a UNESCO site since 1987 where several areas are safeguarded at regional or national level or un- der Natura 2000 protection (Regione Veneto, 2021). The climatic and geographic conditions contribute to make the Metropolitan City of Venice naturally vulnerable to climate related extreme events. In re- cent years, frequent high tides, pluvial floods, wind- storms, drought, and heat waves have become more frequent and intense (Caporalini et al., 2020; Gallina et al., 2020; Lionello et al., 2020) leading to both sig- nificant environmental and economic losses. In 2007 a severe pluvial flood event hit the municipality of Venice with more than 260.4 mm of precipitation in 24 hours (Barbi et al., 2007), causing widespread
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
934 Bonato et al.
Fig 1. Case study area.
flooding in urban and agricultural areas close to the Venice Lagoon; the main damages were recorded in Mestre and in the nearby agricultural areas located among the coast (Rossa et al., 2010). In 2015, a tor- nado struck the area of Riviera del Brenta causing damage to about 320 residential buildings (Zanini, Hofer, Faleschini, & Pellegrino, 2017). In October 2018, the Vaia storm hit the Veneto region with heavy precipitation and strong winds, causing damage to forests and to the boundary areas. Finally, the city of Venice is periodically subjected to sudden and frequent high tides. In 2019, due to a simultane- ous occurrence of extreme high tide, strong Scirocco winds, and heavy rainfalls, the water level reached the height of 187 cm, becoming the second highest recorded high water and causing the flooding of 88% of the city (Ferrarin et al., 2019). The extreme high tide caused significant damage to residential struc- tures, economic activities, cultural heritages, trans- port facilities and, unfortunately, also caused the loss of one human life. Climate change, which affects the frequency and intensity of extreme events, tends to increase the occurrence of weather-related disasters (World Economic Forum [WEF], 2021). Accordingly, there is the need to plan for adaptation by imple- menting a set of risk management initiatives to in- crease the overall resilience of the Metropolitan City
of Venice toward disasters that involve climate and the variety of other stressors.
2.2. Stakeholders
The scenario-based methodology presented in this article took advantage of a strong engagement of local stakeholders of the Metropolitan City of Venice. Stakeholders play an important role by pro- viding their perspectives and identifying needs, which is useful to improve the decision-making process. According to Bostick et al. (2017), the participative process is essential for the implementation of the methodology for building resilience in coastal areas and for the codevelopment of information, which can be practically used for climate change adaptation and disaster risk management planning. The engagement process should be as inclusive as possible, involving actors covering different sectors and different stages and domain of disaster risks and resilience manage- ment. This ensures that personal perspectives and bias are not going to outweigh the final results, that needs and perspectives of minority groups are taken into account (Luyet, Schlaepfer, Parlange, & Buttler, 2012), and that different systems connection and in- terdependencies are fully understood (Walker, An- deries, Kinzig, & Ryan, 2006).
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 935
Table I. List of Institutions and Organizations Participating in the 2019 Workshop with their Sector(s) of Expertise
Level Institution No. Expertise Sector(s)
National authorities Italian Ministry of Transports and Infrastructures
2 Manufactured, Economic
Italian Institute for Environmental Protection and Research (ISPRA)
2 Natural, Social
Regional authorities Regional Civil Protection Agency 1 Social, Manufactured Regional Agency for Environmental
Prevention and Protection (ARPAV)-Metereological Service
1 Natural
Local authorities Venice Municipality-Environmental Department
1 Natural
Metropolitan City of Venice- Environmental, Civil Protection and Agriculture Department
1 Natural, Social, Manufactured, Economic
Independent authorities Consortium for the protection of the Venetian lagoon (Consorzio Venezia Nuova)
1 Manufactured, Natural, Economic
Veneto Orientale Reclamation Consortium 1 Manufactured, Natural, Economic Research institutions Consortium for Coordination of Research
Activities concerning the Venice Lagoon system (CORILA)
1 Natural, Cultural
Euro-Mediterranean Centre on Climate Change (CMCC)
1 Natural, Social
Parks Lega Italiana Protezione Uccelli Italian (LIPU)
1 Natural
NGOs Venice Resilience Lab 1 Cultural, Social, Natural We are here Venice 1 Cultural, Social, Natural
Stakeholders to be involved were selected among an initial list of thirty authorities and local actors (e.g. national, regional and local authorities, research institutions, regional meteorological offices, environmental protection agencies, nongovernmen- tal organizations [NGOs] and small and medium- sized enterprises [SMEs], sector representatives) which actively work at different stages of disaster risk management and represent different fields of exper- tise (e.g. civil protection, environmental protection, cultural heritage management, primary, secondary, and tertiary sectors). A first contact with local stake- holders was established by means of semistructured telephone interviews aimed at understanding what types of data they were using, and what type of works they were carrying out in the field of disaster risk management. After this first semistructured inter- view, a small group of stakeholders coming from dif- ferent levels of institutions and organizations were selected and invited to take part in a local workshop called “Building the Resilience of the Metropolitan City of Venice and its Lagoon to Disasters,” held on October 30, 2019. Although attention was paid to in- vite a group of local actors as heterogenous as pos-
sible, in the end only 15 participated. As described on Table I, manufactured, social and natural sectors are well represented by participants, while cultural and economic sectors are slightly less represented. Through focus groups discussions and an individual questionnaire, we collected local actors’ preferences on input data (e.g., the critical functions, risk man- agement initiatives and scenarios). Such results have been elaborated after the workshop and included in the analysis as described in the following Section 2.3.
2.3. The Methodological Framework for Resilience Assessment
Environmental decisions are complex and built upon multidisciplinary approaches and knowledge. Considering that various types of decisionmak- ers are relying on the integration of experimen- tal tests, models, and tools, it is necessary to find a methodology flexible enough to incorporate the huge amount of data and heterogenous information available (Huang, Keisler, & Linkov, 2011). Accord- ingly, in this article a scenario-informed multicrite- ria methodology is applied to support disaster risk
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
936 Bonato et al.
Fig 2. Methodological framework for resilience assessment in the Metropolitan City of Venice (Adapted from Bostick et al., 2017; Linkov et al., 2018).
management, exploiting available data to analyze the interconnectivity of different domains and stages of coastal resilience. MCDA and scenario planning have been jointly used to bring together different lo- cal stakeholders to discuss and plan adaptation to climate-related extreme events. The incorporation of scenario-based preferences to risk analysis is intro- duced by Schroeder and Lambert (2011), while sub- sequent efforts extended the approach to both risk and resilience (e.g., Thorisson et al., 2017). The ap- proach of this article (Fig. 2) is based on the re- silience assessment methodology originally devel- oped by Fox-Lent and Linkov (2018) and Linkov et al. (2018), which is here adapted to include top- down quantitative information and assessments (e.g., climate change projections coming from regional cli-
mate models and impact analysis). These are neces- sary to characterize future climate change scenarios and related risks for the coastal area of interest, thus providing the basis for the prioritization of a set of risk management initiatives against a set of “plausi- ble futures” based on assumptions about climatic and socioeconomic development.
The methodology applied relies on different it- erative steps (Fig. 2). The first requires the iden- tification of key critical functions (i.e., subsystems and processes that are affected by climate related- extreme events) for the system under analysis, which will form the basis for risk management initiatives comparison. Later, the relative importance of each critical function within the system (e.g., no, low, medium, and high relevance) is assessed by the
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 937
Fig 3. Resilience matrix for characterizing the dimensions of initiatives for coastal systems. Adapted from Fox-Lent et al., 2015 and Bostick et al. (2017).
stakeholders and subsequently the qualitative rele- vance value of the critical functions is converted to a relevance value weight (Supporting Information, Appendix I).
In the second step (Fig. 2) a set of risk management initiatives aimed at enhancing the overall resilience of the system to climate-related extreme events are selected and proposed for prioritization. Risk management initiatives can in- volve the allocation of resources, policies, structural and nonstructural interventions encompassing differ- ent risk-management stages and belong to several domains as described in Fig. 3.
Following Thorisson et al. (2017), for each risk management initiative identified, the impact on each of the critical functions previously identified is as- sessed by local stakeholders by assigning an impact value (e.g., no, low, medium, and high impact). The impact value score indicates how much the project initiative could impact the critical function: a low impact value score means that the project initia- tive has low or no impact on that particular critical function, while a higher value means that the ini- tiative has a significant impact on the critical func- tion. Also in this case, the qualitative impact value of the risk management initiatives on the critical functions is subsequently converted in a quantitative score (Supporting Information, Appendix I). A mul-
ticriteria value function (Supporting Information, Appendix I, Equation A1) is then used to incor- porate the weights representing the relative impor- tance of the critical functions and the scores repre- senting the impact of the risk management initiatives on the critical functions, generating a first prioritiza- tion among risk management initiatives based solely on stakeholders’ priorities. The set of risk manage- ment initiatives identified are then evaluated against different possible scenarios with the goal to identify initiatives which are robust across a range of plausi- ble futures (Fig. 2). Different scenarios can describe both climatic (e.g., precipitation or temperature in- crease) or nonclimatic (e.g., urbanization, population growth) factors, which affect key critical functions and are defined based on the best available knowl- edge or the diverse view of stakeholders. Based on literature review and expert knowledge, a first assess- ment of which critical functions would be impacted by each of the considered scenarios is conducted. Then, an assessment of the impact of each selected scenario on critical functions (e.g., low, medium, or high impact) is performed. This influence can be as- sessed based on available information, using quanti- tative data or expert judgement. The introduction of scenarios results in a reranking of risk management initiatives which modifies the previous assessment of stakeholders’ preferences.
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
938 Bonato et al.
Table II. Description of the Capital Categories and of the Respective Critical Functions Considered for the Study Area
Capital Critical Function Critical Function Description
Manufactured capital C1-Housing Urbanized areas of residential type. C2-Infrastructures Transport and communication systems (road network, rail network,
airports, and port areas) and energy lines. Social capital C3-Population Population at the census section level, providing information on age,
genre, and population density. Cultural Capital C4-Cultural sites Historical centers, museums, and roads of historical-environmental
value representing the historical and cultural heritage of the society and providing a recreational service for resident population and tourists.
Natural Capital C5-Forests and semi-natural areas
Wooded areas and seminatural environments particular important for biodiversity conservation and for the provision of various ecosystem services (e.g., regulation of air quality, timber production, etc.).
C6-Beaches Beaches and the associated vegetation important for their environmental value linked to the presence of priority habitats, and for the various services they provide, including the recreational bathing one.
C7-Wetland and Water bodies
Wetlands, an interface environment between the land and aquatic ecosystem, and the hydrographic network. Both are of particular importance for maintaining biodiversity and providing water services.
C8-Green urban areas Furnishing greenery (historic gardens, urban parks, neighborhood green spaces, tree-lined avenues) and functional greenery (for sports, education, health) serving as recreational and leisure areas for the resident population and providing various regulatory services (e.g., air quality regulation in urban areas and rainwater infiltration).
Economic capital C9-Primary sector Gross Value-Added product (GVA) associated to agricultural areas, comprising all the arable areas, the permanent crops, and the pastures.
C10-Secondary Sector GVA associated with industrial areas. C11-Service sector GVA of the service sector (e.g., commercial activities, tourism, etc.).
3. RESULTS
In the following sections, the results of the application to the case study of the Metropolitan City of Venice of the scenario-informed multicriteria methodology presented in Section 2.3 are described.
3.1. Critical functions identification and weights assessment
For the Metropolitan City of Venice and its la- goon, the set of critical functions considered was se- lected in relation with the scope of the analysis, the spatial scale and based on the perspective and prior- ities of the local stakeholders involved. Selected crit- ical functions belong to five different capital typolo- gies commonly used in climate risk and sustainability assessment (Goodwin, 2003):
• Manufactured capital: material assets or real es- tate built and human-made.
• Social capital: factors that constitute human capital at the individual and collective level.
• Cultural capital: tangible artefacts and immate- rial aspects of culture.
• Natural capital: natural resources and pro- cesses, renewable and nonrenewable, that pro- duce goods and services for human wellbeing.
• Economic capital: various economic sectors, which produce an income and allow the ex- change of previous types of capital.
Accordingly, a set of 11 critical functions was se- lected and summarized in Table II along with the different capitals and with their correspondent de- scription.
For each critical function, the qualitative rele- vance value assigned by local stakeholders was con- verted into a quantitative one (0—no relevance, 1— low relevance, 2—medium relevance, and 3—high relevance). The mean value that stakeholders gave to each critical function during the workshop (Sec- tion 2.2) was considered as the overall weight to each of them. Resulting scores assigned during the work- shop are reported in Table III.
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 939
Table III. Critical Function Assessment
Critical Functions Relevance Weights
c1: Housing High relevance 3 c2: Infrastructures High relevance 3 c3: Population High relevance 3 c4: Cultural sites Medium relevance 2 c5: Forests and semi-natural areas Medium relevance 2 c6: Beaches Medium relevance 2 c7: Wetland and Water bodies High relevance 3 c8: Green urban areas Medium relevance 2 c9: Primary sector Medium relevance 2 c10: Secondary sector Medium relevance 2 c11: Service sector Medium relevance 2
From Table III, it is possible to observe that housing, infrastructures and population have been weighted with the highest priorities. For these critical functions, stakeholders during the discussion agreed on assign a priority importance independently of their sector of expertise as they are strongly related with human life and well-being. Wetlands and wa- ter bodies were also scored with high relevance as even stakeholders not directly involved in the natu- ral sector recognized their value not only in natural terms (e.g., unique ecosystems and providers of sup- porting and regulating ecosystem services) but also in their importance as infrastructure in the context of the Venetian Lagoon (e.g., channels for naviga- tion). However, none of the critical functions were weighted with low or no relevance.
3.2. Risk Management Initiatives Selection and Assessment
Stakeholders of the Metropolitan City of Venice suggested a set of eleven risk management initiatives, belonging to the different resilience stages and re- silience domains (Section 2.3) which are described in Table IV. For each risk management initiative and critical function intersection the qualitative im- pact value, representing the impact that a specific initiative can have on the critical function, was con- verted in a quantitative one (0—low or no impact, 1—medium impact, and 2—high impact). Table V summarizes the impact value scores assigned to each critical function of the Metropolitan City of Venice. Importantly, each stakeholder assessed the 11 risk- management initiatives only against the critical func- tions belonging to its sector/sectors of expertise (e.g., stakeholders who are expert in natural systems as- sessed only the critical functions belonging to the nat-
ural capital), allowing us to have a more competent judgement. All the scores assigned to an intersec- tion were averaged and subsequently rounded to the nearest integer.
Most of the initiatives are considered to have a medium or high impact on the analyzed critical func- tions, particularly on population, housing, infrastruc- tures, and cultural sites. On the contrary, the impact scores that they assigned are generally lower for the critical functions belonging to the natural and eco- nomic capitals, for which, in fact, different initiatives are also scored with no or low impact. These results can be explained by the fact that stakeholders be- longing to natural and economic capitals confirmed, also in the assessment of the risk management initia- tives, the priority importance that they give to the hu- man capital. In addition, although many stakehold- ers belonged to the natural sector of expertise, not a lot of risk management initiatives specifically tar- geted for such capital were suggested and thus were available for them to evaluate with high scores.
3.3. Description of Scenarios and Impact on Critical Functions
In the workshop, a set of climate change scenar- ios was proposed by the experts and discussed to- gether with stakeholders in order to identify the most representative ones for the case study area. The final set of scenarios, identified as the most prominent by local stakeholders, are:
• S1: increase frequency of storm surge events. • S2: increase frequency of pluvial flood • S3: increase frequency of heat waves. • S4: increased frequency of drought conditions.
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
940 Bonato et al.
Ta bl
e IV
. R
is k
M an
ag em
en tI
ni ti
at iv
es D
es cr
ip ti
on
R is
k M
an ag
em en
tI ni
ti at
iv es
R is
k M
an ag
em en
tI ni
ti at
iv es
D es
cr ip
ti on
R es
ili en
ce St
ag e
R es
ili en
ce D
om ai
n
p 1 :I
nf or
m at
io n:
co m
m on
go od
D at
a co
lle ct
io n
an d
an al
ys is
,i nf
or m
at io
n pr
od uc
ti on
an d
sh ar
in g
in or
de r
to de
ve lo
p th
e kn
ow le
dg e
ba se
re qu
ir ed
to id
en ti
fy cl
im at
e ch
an ge
im pa
ct ho
ts po
ts ,e
na bl
e cl
im at
e ad
ap ta
ti on
,a nd
pl an
op ti
on s.
In fo
rm at
io n
ca n
in cl
ud e
re po
rt s,
ri sk
an d
vu ln
er ab
ili ti
es m
ap s,
st at
is ti
cs ,c
lim at
e ch
an ge
pr oj
ec ti
on s,
kn ow
le dg
e pl
at fo
rm s,
an d
ne tw
or ks
.
P re
pa re
dn es
s, R
es po
ns e
In fo
rm at
io n
p 2 :G
re en
an d
bl ue
in fr
as tr
uc tu
re s
ne tw
or ks
N et
w or
ks of
na tu
ra la
nd se
m in
at ur
al la
nd sc
ap e
el em
en ts
th at
bu ild
up on
ec os
ys te
m s
to m
ee tg
lo ba
lc ha
lle ng
es ,s
uc h
as ri
sk re
du ct
io n
fo r
st or
m s,
la nd
sl id
es ,a
nd flo
od s.
T he
se so
lu ti
on s
in cl
ud e,
fo r
in st
an ce
,t he
m ai
nt en
an ce
of ve
ge ta
te d
du ne
s, w
et la
nd s,
an d
sa lt
-m ar
sh es
re st
or at
io n,
be ac
he s
re co
ns tr
uc ti
on ,t
he cr
ea ti
on of
gr ee
n co
rr id
or s
in ur
ba n
an d
ag ri
cu lt
ur al
ar ea
s, es
ta bl
is hm
en t,
an d
re st
or at
io n
of ri
pa ri
an bu
ff er
s.
P re
ve nt
io n
P hy
si ca
l
p 3 :U
pd at
in g
an d
im pl
em en
ta ti
on of
pl an
s an
d re
gu la
ti on
s
U pd
at in
g an
d im
pl em
en ti
ng pl
an s
an d
re gu
la ti
on s
to gu
id e
te rr
it or
ia la
nd ur
ba n
pl an
ni ng
,a s
w el
la s
la nd
an d
na tu
ra lr
es ou
rc e
m an
ag em
en tw
hi ch
pl ay
a si
gn ifi
ca nt
ro le
in ri
sk pr
ev en
ti on
.S uc
h in
it ia
ti ve
s in
cl ud
e, fo
r in
st an
ce ,
lim it
in g
th e
ur ba
ni za
ti on
in flo
od pr
on e
ar ea
s, en
co ur
ag in
g flo
od an
d dr
ou gh
tr is
k- se
ns it
iv e
la nd
us e
an d
m an
ag em
en tp
ra ct
ic es
,s et
ti ng
th e
lim it
s fo
r w
at er
ab st
ra ct
io ns
,c on
st it
ut in
g na
tu re
an d
bi od
iv er
si ty
pr ot
ec te
d ar
ea s.
P re
ve nt
io n
C og
ni ti
ve
p 4 :A
da pt
at io
n an
d op
ti m
iz at
io n
of w
at er
in fr
as tr
uc tu
re s
an d
su pp
ly
A da
pt at
io n
an d
op ti
m iz
at io
n of
w at
er in
fr as
tr uc
tu re
s an
d su
pp ly
sy st
em s
to in
cr ea
se th
ei r
ef fic
ie nc
y un
de r
w at
er sc
ar ci
ty an
d dr
ou gh
tc on
di ti
on s.
Su ch
in it
ia ti
ve s
in cl
ud e,
am on
g ot
he rs
,a ct
io ns
su ch
as w
at er
le ak
co nt
ro ld
ur in
g tr
an sp
or t,
di ff
er en
ti at
io n
of w
at er
su pp
ly so
ur ce
s, cr
ea ti
on of
re se
rv oi
rs ,a
nd im
pr ov
em en
to fi
rr ig
at io
n ef
fic ie
nc y.
P re
ve nt
io n,
R es
po ns
e P
hy si
ca l
p 5 :A
da pt
at io
n of
hy dr
au lic
de fe
ns e
st ru
ct ur
es A
da pt
at io
n or
im pr
ov em
en to
fp hy
si ca
la nd
en gi
ne er
ed st
ru ct
ur es
to st
re ng
th en
an d
en ha
nc e
th ei
r pr
ot ec
ti on
ca pa
ci ti
es an
d m
ee ts
af et
y re
qu ir
em en
ts un
de r
ch an
gi ng
co nd
it io
ns (e
.g .,
se a
le ve
lr is
e in
cr ea
se ,s
ev er
flo od
an d
st or
m su
rg e,
st ro
ng w
in ds
ex tr
em e
te m
pe ra
tu re
s) .S
uc h
in it
ia ti
ve s
in cl
ud e
th e
co ns
tr uc
ti on
or ra
is in
g of
em ba
nk m
en ts
,q ua
ys id
es ,p
ub lic
pa ve
d ar
ea s,
di ke
s, da
m s,
sy st
em of
bu lk
he ad
s lo
ca te
d in
th e
L ag
oo n
in le
ts (e
.g .,
M O
du lo
Sp er
im en
ta le
E le
tt ro
m ec
ca ni
co [M
O SE
]) .
P re
ve nt
io n
P hy
si ca
l
p 6 :E
m er
ge nc
y re
sp on
se ar
ra ng
em en
ts P
ut ti
ng in
pl ac
e ph
ys ic
al an
d st
ru ct
ur al
ar ra
ng em
en ts
to re
sp on
d to
em er
ge nc
ie s
an d
lim it
in g
th e
im pa
ct of
th e
ev en
ts on
pe op
le ,m
at er
ia ls
,a nd
bu ilt
st ru
ct ur
es .O
ns it
e in
it ia
ti ve
s in
cl ud
e fo
r in
st an
ce th
e in
st al
la ti
on of
te m
po ra
ry fo
ot br
id ge
s, ca
se ,p
um ps
,a nd
m ob
ile bu
lk he
ad s
on pr
iv at
e bu
ild in
gs do
or s.
P re
ve nt
io n,
R es
po ns
e P
hy si
ca l (C
on tin
ue d)
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 941
Ta bl
e IV
. (C
on ti
nu ed
)
R is
k M
an ag
em en
tI ni
ti at
iv es
R is
k M
an ag
em en
tI ni
ti at
iv es
D es
cr ip
ti on
R es
ili en
ce St
ag e
R es
ili en
ce D
om ai
n
p 7 :E
ar ly
w ar
ni ng
sy st
em s
E nh
an ci
ng th
e pr
ep ar
ed ne
ss of
de ci
si on
m ak
er s
an d
pr iv
at e
in di
vi du
al s
to cl
im at
e- re
la te
d na
tu ra
lh az
ar ds
an d
th ei
r re
ad in
es s
to ha
rn es
s fa
vo ra
bl e
co nd
it io
ns by
m ea
ns of
th e
ti m
el y
an d
sy st
em at
ic m
on it
or in
g, di
ss em
in at
io n
an d
co m
m un
ic at
io n
of re
le va
nt in
fo rm
at io
n on
di ff
er en
tt yp
es of
ad ve
rs e
ev en
ts .E
ar ly
w ar
ni ng
sy st
em s
ca n
be de
ve lo
pe d
fo r
flo od
,h ig
h w
at er
, he
at w
av es
,a nd
dr ou
gh ta
nd ba
se d
on di
ff er
en tm
ed ia
(e .g
., bu
lle ti
ns ,m
ob ile
A pp
s, ac
ou st
ic si
gn al
s, em
ai ls
).
P re
pa re
dn es
s In
fo rm
at io
n
p 8 :E
nv ir
on m
en ta
le du
ca ti
on an
d aw
ar en
es s
A ct
io ns
pr om
ot in
g th
e pu
bl ic
aw ar
en es
s fo
r th
e al
te re
d co
nd it
io ns
un de
r cl
im at
e ch
an ge
w it
h th
e fin
al ai
m to
ac hi
ev e
lo ng
-t er
m la
st in
g be
ha vi
ou ra
l ch
an ge
s. T
he se
ac ti
on s
ca n
in cl
ud e
en vi
ro nm
en ta
le du
ca ti
on pr
og ra
m s,
aw ar
en es
s ca
m pa
ig ns
de ve
lo pe
d th
ro ug
h di
ff er
en tk
in d
of m
ed ia
an d
m ea
ns (e
.g .,
te le
vi si
on ,i
nt er
ne t,
ne w
sp ap
er s,
le ct
ur es
), an
d ta
rg et
in g
di ve
rs e
pu bl
ic gr
ou ps
(e .g
.c hi
ld re
n, st
ud en
ts ,c
it iz
en s)
.
P re
ve nt
io n,
P re
pa re
dn es
s So
ci al
p 9 :C
it iz
en sc
ie nc
e T
he in
vo lv
em en
to fc
it iz
en s,
m an
y of
w ho
m ha
ve no
sp ec
ifi c
sc ie
nt ifi
c tr
ai ni
ng ,
in sc
ie nt
ifi c
re se
ar ch
— w
he th
er co
m m
un it
y- dr
iv en
re se
ar ch
or gl
ob al
in ve
st ig
at io
ns .I
nv ol
ve d
ci ti
ze ns
ca n
sp an
ov er
di ff
er en
tg ro
up s
(e .g
., st
ud en
ts ,f
ar m
er s,
fis he
rm en
,c hi
ld re
n, el
de rl
y pe
op le
) an
d di
ff er
en t
ac ti
vi ti
es in
cl ud
in g
en vi
ro nm
en ta
ld at
a co
lle ct
io n
an d
ob se
rv at
io n,
pa ss
iv e
se ns
in g
us in
g se
ns or
s in
st al
le d
on m
ob ile
s or
ot he
r de
vi ce
s, pa
rt ic
ip at
or y
se ns
in g
w he
re pa
rt ic
ip an
ts ac
ti ve
ly pa
rt ic
ip at
e to
m ea
su re
m en
tc am
pa ig
ns (i
.e .,
A cq
ua A
lt a
K id
s D
is co
ve ry
).
P re
ve nt
io n,
P re
pa re
dn es
s, R
es po
ns e
So ci
al
p1 0:
C iv
il pr
ot ec
ti on
m ac
hi ne
pl an
ni ng
P re
pa ra
ti on
an d
tr ai
ni ng
of ci
vi lp
ro te
ct io
n vo
lu nt
ee rs
,p ro
to co
ls ,e
qu ip
m en
t, an
d co
nt in
ge nc
y pl
an s
to be
us ed
in th
e ev
en to
fa n
em er
ge nc
y. P
re pa
re dn
es s,
R es
po ns
e C
og ni
ti ve
p 1 1:
P la
ns an
d st
ra te
gi es
fo r
re st
or at
io n
an d
re co
ve ry
of hi
st or
ic al
ar ea
s
D ra
ft in
g of
pl an
s an
d st
ra te
gi es
fo r
re st
or at
io n
an d
re co
ve ry
of hi
st or
ic al
ar ea
s, co
ns id
er in
g co
ns er
va ti
on ne
ed s
un de
r fu
tu re
cl im
at e
sc en
ar io
s. P
re ve
nt io
nR ec
ov er
y an
d R
eh ab
ili ta
ti on
C og
ni ti
ve
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
942 Bonato et al.
Ta bl
e V
. A
ss es
sm en
to fE
ac h
R is
k M
an ag
em en
tI ni
ti at
iv e
w it
h R
es pe
ct to
E ac
h C
ri ti
ca lF
un ct
io n
C ri
ti ca
lf un
ct io
ns R
is k
M an
ag em
en tI
ni ti
at iv
es c 1
: H
ou si
ng c 2
:I nf
ra s-
tr uc
tu re
s c 3
:P op
u- la
ti on
c 4 :
C ul
tu ra
l Si
te s
c 5 :F
or es
ts an
d Se
m i-
na tu
ra l
A re
a c 6
: B
ea ch
es
c 7 :
W et
la nd
s an
d W
at er
B od
ie s
c 8 :G
re en
U rb
an A
re as
c 9 :
P ri
m ar
y Se
ct or
c 1 0:
Se co
nd ar
y Se
ct or
c 1 1:
Se rv
ic e
Se ct
or
p 1 :I
nf or
m at
io n:
co m
m on
go od
1 1
2 2
1 1
2 1
2 2
2
p 2 :G
re en
an d
bl ue
in fr
as tr
uc tu
re s
ne tw
or ks
1 1
1 1
2 1
2 1
1 1
1
p 3 :U
pd at
in g
an d
im pl
em en
ta ti
on of
pl an
s an
d re
gu la
ti on
s
2 2
2 1
1 1
1 1
2 2
2
p 4 :A
da pt
at io
n an
d op
ti m
iz at
io n
of th
e w
at er
ne tw
or k
an d
su pp
ly
1 2
2 1
1 0
2 1
2 1
1
p 5 :A
da pt
at io
n of
hy dr
au lic
de fe
ns e
st ru
ct ur
es
2 2
2 2
1 2
2 1
2 1
0
p 6 :E
m er
ge nc
y re
sp on
se ar
ra ng
em en
ts 2
2 2
2 1
1 1
1 1
1 2
p 7 :E
ar ly
w ar
ni ng
sy st
em s
2 1
2 2
0 1
1 1
1 2
2
p 8 :E
nv ir
on m
en ta
l ed
uc at
io n
an d
aw ar
en es
s
1 1
2 1
2 1
1 1
1 1
1
p 9 :C
it iz
en sc
ie nc
e 1
1 2
1 1
1 1
1 1
1 1
p 1 0:
C iv
il pr
ot ec
ti on
m ac
hi ne
pl an
ni ng
2 2
2 2
1 1
1 1
1 2
2
p 1 1:
P la
ns an
d st
ra te
gi es
fo r
re st
or at
io n
an d
re co
ve ry
of hi
st or
ic al
ar ea
s
2 1
2 2
0 0
0 1
0 0
0
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 943
The selected narrative climate change scenarios were then characterized by experts by a set of Cli- mate Extreme Indices (CEIs), commonly used as “proxies” for hazards characterization (Mysiak et al., 2018), and derived from the outputs of Regional Cli- mate Models (RCMs) available for the case study for the future medium term period (i.e. 2021–2050) considering the Representing Concentration Path- way (RCP) 8.5. For more details about CEIs used see the Appendix II of Supporting Information (Table A1).
The degree of impact (low, medium, or high) that each climate change scenario would have on each of the critical function has been assessed based on the results of a climate change risk study performed by Sambo (2020) (Table VI). If a critical function was considered not to be impacted by a specific cli- mate change scenario the degree of impact has not been assessed and this result as a blank spot in Ta- ble VI. The qualitative impact values of the climate change scenarios on the critical functions were then converted into quantitative ones (1—no impact, 3— low impact, 5—medium impact, and 7—high impact). These values are used as α multiplier that increases the weight of a critical function under a specific cli- mate change scenario (Equation A2, Supporting In- formation, Appendix I). These scores were finally used to perform Equation A3 (Supporting Informa- tion, Appendix I) obtaining in this way a new climate change scenario-based ranking of risk management initiatives.
The results show how increased frequency of storm surge events is affecting all the critical func- tions, and mainly with a high impact. The increased frequency of pluvial flood is going to affect (with medium or low impact) all the critical functions char- acterized by artificial and, thus, nonpermeable sur- faces, which are therefore more pressured by heavy precipitation events. The increased frequency of heat waves is going to affect only the human components of the coastal system, for which is expected a low im- pact. Finally, the increased frequency of drought con- ditions is going to affect population and secondary sector, which are both expected to be medially im- pacted. Drought conditions are going to slightly af- fect (i.e., medium impact) also the primary sector.
3.4. Risk Management Initiatives Prioritization
The use of the scenario-based multicriteria methodology described in Section 2.3 allowed a rank- ing of the 11 risk management initiatives previously
Ta bl
e V
I. Im
pa ct
C la
ss ifi
ca ti
on fo
r th
e Sc
en ar
io s
C on
si de
re d
on E
ac h
C ri
ti ca
lF un
ct io
n
s 1 :S
to rm
Su rg
e s 2
:P lu
vi al
F lo
od s 3
:H ea
tW av
es s 4
:D ro
ug ht
c 1 :H
ou si
ng H
ig h
Im pa
ct M
ed iu
m Im
pa ct
c 2 :I
nf ra
st ru
ct ur
es H
ig h
Im pa
ct M
ed iu
m Im
pa ct
c 3 :P
op ul
at io
n M
ed iu
m Im
pa ct
L ow
Im pa
ct L
ow Im
pa ct
M ed
iu m
Im pa
ct c 4
:C ul
tu ra
ls it
es H
ig h
Im pa
ct M
ed iu
m Im
pa ct
c 5 :F
or es
ts an
d se
m i-
na tu
ra la
re as
M ed
iu m
Im pa
ct c 6
:B ea
ch es
H ig
h Im
pa ct
c 7 :W
et la
nd s
an d
w at
er bo
di es
H ig
h Im
pa ct
c 8 :G
re en
ur ba
n ar
ea s
H ig
h Im
pa ct
c 9 :P
ri m
ar y
se ct
or H
ig h
Im pa
ct L
ow Im
pa ct
c 1 0:
Se co
nd ar
y se
ct or
L ow
Im pa
ct M
ed iu
m Im
pa ct
M ed
iu m
Im pa
ct c 1
1: Se
rv ic
e se
ct or
M ed
iu m
Im pa
ct L
ow Im
pa ct
L ow
Im pa
ct
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
944 Bonato et al.
Fig 4. Risk management initiatives baseline rankings (diamonds) and the ranges of rankings associated to the four climate change scenarios (horizontal bars)
identified (Table IV) by stakeholders as the most prominent ones for the case study area. The rank- ing is to be intended as the order of the initiatives in term of their effectiveness in improving the overall resilience of the analyzed system.
In Fig. 4, diamonds display the baseline ranking of the risk management initiatives. As explained in Section 2.3, the baseline ranking is calculated taking only into consideration values scores that stakehold- ers gave to the critical functions and the scores that they assigned to the impact of the initiatives on criti- cal functions. Accordingly, the baseline ranking does not consider the effect of climate change scenarios. Among the top five positions we can find initiatives belonging both to the physical (i.e., Adaptation of hydraulic defence structures [P5] and Emergency response arrangements [P6]), cognitive (i.e., Civil Protection machine planning [P10] and Updating ad implementation of plans and regulations [P3]), and information (i.e., Information: common good [P1]) domains. Considering the resilience stages, the initiatives placed in the top five positions are dealing
mainly with prevention (i.e., P3, P5, and P6) and with preparedness and response stages (i.e., P10, P1).
The last positions, instead, are occupied by nature-based solutions (i.e., Green and blue infras- tructures [P2]), social initiatives (i.e., Environmental education [P8], Citizen science [P9]), and a very spe- cific cognitive initiative dealing with historical and cultural heritage sites (i.e., Plans and strategies for restoration and recovery of historical area [P11]). Re- garding the resilience stages, the initiatives in the last positions are distributed among all the stages, also because various of these initiatives fall themselves into different resilience stages.
The horizontal bars associated to each diamond in Fig. 4 show the ranges of rankings that each risk management initiative can take due to the integra- tion of the four additional climate change scenarios into the assessment. Independently from the position in the baseline, a small range bar means that an initia- tive is stable across the different climate change sce- narios, on the contrary a large range bar means that an initiative widely changes its position depending on
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 945
Table VII. Risk Management Initiatives Ranking for the Baseline, for Single Climate Change Scenarios and Across All the Scenarios
Ranking
Ordered Risk Management Initiatives Baseline Storm Surge Pluvial Flood Heat
Waves Drought Multiple Scenarios
Adaptation of hydraulic defense structures (P5) 1 1 4 6 3 3 Updating and implementation of plans and
regulations (P3) 2 2 2 1 1 1
Civil Protection machine planning (P10) 2 2 1 1 4 2 Information: common good (P1) 4 5 6 3 2 4 Emergency response arrangements (P6) 5 4 3 4 7 5 Early warning systems (P7) 6 7 5 5 5 6 Adaptation and optimization of the water network
and supply (P4) 7 6 7 7 6 7
Green and blue infrastructures networks (P2) 8 8 11 10 10 10 Environmental education and awareness (P8) 8 9 8 8 8 8 Citizen Science (P9) 10 10 9 9 9 9 Plans and strategies for restoration and recovery of
historical areas (P11) 11 11 9 11 11 11
the considered scenario. Most stable initiatives are represented by updating and implementation of plans and regulations (P3), adaptation and optimization of the water network and supply (P4), environmental education and awareness (P8), citizen Science (P9). adaptation of hydraulic defense structures (P5), infor- mation common good (P1), and emergency Response arrangements (P6) instead largely vary their position when climate change scenarios is introduced.
Rankings of initiatives are quite different when one climate change scenario at time is considered. Table VII shows risk management initiatives rank- ings for the baseline, for a single climate change sce- nario and across all the scenarios. Looking at the physical initiatives, we can see that adaptation of hy- draulic defense structures (P5), which in the baseline is on top, stays on top positions for storm surge’s sce- nario but it drops positions when considering pluvial flood, heat waves, and drought events (it drops from position 1 to position 4, 6, and 3, respectively). In the same way, emergency response arrangements (P6) slightly advances in the ranking (from position 5 to positions 4, 3, and 4 respectively) when considering the storm surge, pluvial flood, and heat waves scenar- ios individually; however, it drops in position when considering just drought. On the contrary, adapta- tion and optimization of the water network and sup- ply (P4), slightly advances in the ranking considering the drought scenario. Green and blue infrastructures (P2) is already in a low position, and it drops further for pluvial flood, heat waves, and drought. Looking at cognitive and informative initiatives we can see how
information: common good (P1), updating and im- plementation of plans and regulations (P3), and Civil protection machine planning (P10) remain in a high- ranking position across almost all the considered sce- narios. Early warning systems (P7) is already quite stable and always positioned in the middle of the rankings. Even the initiatives belonging to the social domain, such as Environmental education (P8) and Citizens science (P9), remain quite stable for all the four scenarios, ranging from position 8 to 10, never appearing among the priority ones. P8 drops only for storm surge from position 8 to 9, while P9 rises from 10 to 9 for all the scenarios except for storm surges, where it remains at the same position.
When considering multiple scenarios, the overall ranking differs from the ones obtained considering single scenarios individually. Comparing the overall ranking with the baseline one, few shifts in positions can be observed as updating and implementation of plans and regulation (P3) moves in first position re- placing a rather strong physical initiative, adaptation of hydraulic defense structures (P5), green and blue infrastructures networks (P2), and citizen science (P9) are reversed in the ranking.
3.5. Scenario Disruptiveness
Together with the prioritization of the risk man- agement initiatives, an assessment of the scenarios’ influence on stakeholders’ priorities was performed. The calculation of disruption scores is described by Thorisson et al. (2017) and Schroeder and Lambert
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
946 Bonato et al.
Table VIII. Sum of Squares Error Values for Quantifying the Disruptiveness of the Four Scenarios
Scenario SSE
Heat waves 35 Pluvial flood 33 Drought 24 Storm surges 5
(2011). Understanding the disruptiveness of the cli- mate change scenarios will help stakeholders in the determination of project initiatives to discuss given the scenarios of greater concern (Bostick et al., 2017). Some climate change scenarios may have very lit- tle impact on the risk management initiatives rank- ing in the baseline scenario, but others may com- pletely change the prioritization. This effect was captured by the scenario’s disruptiveness metrics (Equation A4, Supporting Information, Appendix I). Table VIII displays the sum of squared error scores for the four considered scenarios. A higher sum of squared error scores would indicate that a particu- lar scenario has increased influence on changing risk management initiatives’ ranking. Therefore, the sce- narios are classified according to their degree of in- fluence on the ranking of project initiatives based on the sum of square error scores.
The most influential scenario according to this disruptiveness metric is the heat waves. When com- paring the priority orders of the initiatives under this scenario and under the baseline scenario, changes in the ranking occur for eight out of 11 initiatives. While most of them are characterized by small shifts in the ranking (less or equal to two positions), signif- icant changes (more o equal to three position) occur for adaptation of hydraulic defense structures (P5), which moves from position 1 to position 6. Nine out of 11 initiatives change their position in the drought scenario when comparing with the baseline one, but none of them are characterized by significant alter- ations. Instead, low metric scores characterize storm surge, due to small shifts in the ranking for only four initiatives out of 11.
We can observe the same trend when analyzing the disruptiveness of the scenarios separately on the ranking of initiatives belonging to different domains (Fig. 5). According to this metric storm surge sce- nario is not disruptive at all, as it does not change the position of any of the initiatives in the four domains. This can be explained by the fact that storm surge is already perceived as the most severe threat in Venice,
taking into account also the exceptional high water events occurred in these last years, and for this rea- son, the measures have been proposed with this per- ception and specifically with the aim of cope with this hazard. The drought scenario influences the change on the ranking position of the initiatives belonging to the physical and cognitive domains, while the plu- vial flood scenario the ones belonging to the physical, cognitive, and information domain. Heat waves influ- ence the change on the ranking position of the ini- tiatives belonging to physical and cognitive domains. Heat waves and pluvial flood are the most disruptive scenarios, which results, in fact, with the largest areas in the graph (Fig. 5).
4. DISCUSSION
The above results suggest how stakeholders have varying perspectives and priorities for adaptation and resilience. Such preferences indirectly reflect on the choice and ranking of the risk management ini- tiatives. The positions of risk management initiatives in the baseline ranking depend on their impact on the critical functions and on the relevance stakehold- ers assigned to that particular critical function. Initia- tives appearing in the top five positions, in fact, are those providing most beneficial effects on the man- ufactured (i.e., housing, infrastructures) and social (i.e., population) capital, priority critical functions for the case study according to stakeholders. Many of the risk management initiatives that appear in the top positions (Table VII) have a positive impact also on the cultural and economic sectors, although these sectors were initially underrepresented among the in- volved stakeholders (Table I).
Moreover, the workshop format allowed for an open exchange between the involved local actors en- abling a support between each other in emphasiz- ing a certain shared priority or a mitigation of their positions. The opportunities to share, among stake- holders and between stakeholders and the experts, knowledge, and expertise and to enable learning pro- vided by the workshop format, are recognized to be necessary to build effective disaster risk management (O’Brien, O’Keefe, Gadema, & Swords, 2010).
Climate scenarios substantially influence the ranking of risk management initiatives and disrupt stakeholders’ priorities. However, such changes can be more or less pronounced, depending on the sta- bility of selected initiatives to different scenarios. The results suggest how initiatives belonging to the physical domain, despite being in top positions, are
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 947
Fig 5. Disruptiveness of scenarios: (a) storm surge, (b) pluvial flood, (c) heat waves and (d) drought (with diagrams adapted from Lambert et al. (2013) and You, Connelly et al. (2014), and You, Lambert et al. (2014).
generally less stable. These results can be explained by the fact that physical initiatives are usually de- signed and implemented targeting very specific typologies of extreme climate events (i.e., storm surge, flood, etc.) (Royal Society, 2014). For exam- ple, the design of hydraulic defense structures and the implementation of emergency response arrange- ments including a set infrastructural projects like the MOSE – (MOdulo Sperimentale Elettromec- canico) or temporary solutions (e.g. footbridges, pumps and mobile bulkheads on private buildings doors) are specifically designed for the protection of the Metropolitan City of Venice from storm surge and high waters events, while lacking any ability to increase the system resilience in relation with other kinds of hazard (i.e. drought, heatwaves). The trig- gering flood threshold for raising the MOSE barrier is a negotiated level involving maritime commerce
and social factors, such that frequent low-level floods will still occur in the Metropolitan City of Venice. Also, physical initiatives adopting a nature-based approach (e.g., the implementation of green and blue infrastructure networks) are quite unstable. As recalled by Calliari, Staccione, and Mysiak (2019) nature-based solutions are “living” solutions whose effectiveness is determined both by the magnitude of the threats, as well as their ability to adapt to en- vironmental and anthropogenic pressures to which they are exposed. Climate change, in particular, can alter ecosystems and their services, and may under- mine the performance of green and blue solutions relying on them (Seddon et al., 2020).
On the contrary, cognitive, informative, and social initiatives, seem to be more stable under changing conditions, as they maintain their position when climate change scenarios are introduced. Such
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
948 Bonato et al.
initiatives, as opposed to physical ones, are not aimed at coping with specific hazard typologies but rather they are thought to increase the overall resilience of the system across different possible adverse events exploiting the power of institutions, the sharing of knowledge and the public involvement. Initiatives re- lated with implementation of plans and regulations (e.g., P3) as well as based on the use of early warning systems (e.g., P7), acting on the way that people per- ceive and react to extreme events, no matter of which type they are, are prone to promote behavioural changes that, on long term horizon, have greater po- tential to enhance resilience overall (IPCC, 2012).
Also, initiatives directly involving citizen in sci- entific research, data collection, and observations (i.e., Acqua Alta Kids Discovery) gain position rela- tive to the baseline suggesting that such kind of initia- tive becomes fundamental when climate change sce- narios come in place. Recent reports from both IPCC (2014)and Royal Society (2014) confirm that social approaches are vital in building resilience as raising the awareness and knowledge of specific community groups toward climatic phenomena make them also more supportive toward other kind of adaptation op- tions (Tompkins & Adger, 2004).
Different climate scenarios, when analyzed one by one, lead to a specific prioritization of the set of risk management initiatives. At the same time, when the scenarios are analyzed together, the prioritization of the same initiatives can be totally different.
However, given the large uncertainty in predict- ing which hazard scenarios may occur in a particu- lar area, the best option is to build overall resilience of coastal systems in the face of a range of adverse events. For this reason, it is fundamental to select ini- tiatives which are optimal from a multihazard per- spective. Results suggest that, in the Metropolitan City of Venice, options suitable to cope with multi- ple hazards are cognitive (i.e., updating and imple- mentation of plans and regulation [P3]) and social ones (i.e., citizen science [P9]). Both of them, in fact, gain positions in the ranking, and the former one es- pecially replaces a rather strong physical initiative (i.e., adaptation of hydraulic defense structures [P5]) at the top position. As demonstrated by the Royal Society (2014), physical initiatives have the lowest potential to adapt to multiple adverse events and thus nonphysical initiatives are preferable in a con- text of high uncertainty as the one induced by climate change.
Heat waves was the scenario with the highest po- tential to disrupt stakeholders’ priorities while storm
surge was the least influential one. Although, climate change scenarios for the case study have been intro- duced to stakeholders only at a late stage, it must be considered that they may have already experienced climate change effects and it tends to influence their decisions (Bronfman, Cisternas, Repetto, Castañeda, & Guic, 2020) and, indirectly, the selection and rank- ing of risk management initiatives. In the Metropoli- tan City of Venice, given the intensity of the high- water events occurred in the last years, storm surge is in fact perceived as the most severe threat. Ac- cordingly, most of the initiatives which have been proposed are targeting this hazard. This vision was explored in the workshop with local stakeholders, stressing the importance of providing decisionmakers with relevant information to overcome their biases and to select strategies for resilience, maximizing ef- ficiency and economic efforts given a set of scenarios.
5. CONCLUSIONS
In this article, a scenario-informed multicriteria methodology to support the analysis and priori- tization of risk management initiatives aimed at enhancing resilience towards multiple climate re- lated stressors was developed. The methodology was applied and tested to the case study of the Metropoli- tan City of Venice in Northern Italy considering several representative scenarios of climate-related extremes (e.g., storm surges, pluvial floods, heat waves, drought) that could impact different coastal systems and functions (i.e., natural, cultural, social, and economic).
A feature of this analysis is the integration of top-down information, quantitative data derived from regional climate change model projections and impact analysis, together with a bottom-up qualitative information elicited from local actors and stakeholders in a workshop, into a final tai- lored framework for resilience assessment. Various stakeholders were involved across sectors of exper- tise and fields of interest; these include national, regional, and local authorities, independent au- thorities, research institutions, parks, and NGOs. Stakeholders identified the priorities and necessities for the resilience-enhancing initiatives. On the other hand, they have been presented to different climate change scenarios, having the opportunity to improve their wealth of knowledge about the most prominent hazards for the case study area.
Independently from their sector of exper- tise, stakeholders agree on assigning a priority
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 949
importance to critical functions belonging to man- ufactured (i.e., housing, infrastructures) and social (i.e., population) capital, as they are strongly related with human life and well-being. As a consequence, risk management initiatives that appear in the top five positions in the baseline ranking are those that have a major impact on such critical functions. These are adaptation of hydraulic defense structures (P5), emergency response arrangements (P6), civil protec- tion machine planning (P10), updating ad implemen- tation of plans and regulations (P3), and information: common good (P1), which belong to the physical, cognitive, and information domains. The use of scenario-based preferences allows understanding of how each of the four considered climate-change scenarios (i.e., storm surge, pluvial flood, heat waves, and drought) is prone (or not) to disrupt stakehold- ers’ priority for risk management. For all scenarios, initiatives belonging to the physical domain are generally less stable, while cognitive, informative, and social initiatives seem to be more stable under changing conditions. However, when comparing the priority orders of the risk management initiatives under the heat wave scenario and under the baseline scenario, changes in the ranking occur for a higher number of initiatives (eight out of 11) with respect to changes in the ranking under other scenarios. According to the disruptiveness metric, heat waves is, in fact, the most influential scenario.
From the results also emerge that different sce- narios, when analyzed one by one, lead to a specific prioritization of the set of risk management initia- tives; meanwhile, when the scenarios are analyzed to- gether, the prioritization of the same initiatives can be totally different. The necessity to adopt a mul- tihazard approach to disaster risk management and climate change adaptation is confirmed by these out- comes. In fact, the initiatives preferred to increase the overall resilience considering more than one sce- nario can be more efficient in case of high uncer- tainty than sectorial initiatives that are targeted for a specific hazard. Implementing initiatives strongly oriented to cope with single hazard could lead to an increase of a risk toward other kind of hazards (i.e., maladaptation) thus undermining efforts and re- sources invested for risk reduction. Accordingly, a portfolio of risk-management initiatives should be used to enhance the resilience of the system includ- ing physical initiatives to cope with large scale and in- tense events together with cognitive and social ones which can be flexible enough to be effective against a range of hazards.
In future analyses, we recommend iteration by addressing how the stakeholders’ preferences for re- ceptors and initiatives might change. It must be con- sidered that stakeholders may have prior knowledge and perceptions of some specific hazards that could occur in the case study area, and this could have in- fluenced the score allocation; in fact, they could have some a priori ideas on climate change future scenar- ios. It can be seen from the workshops results: most of involved stakeholders perceived storm surge as the most severe threat for the case study area and in fact most of the proposed risk management initiative pro- posed are specifically designed to cope with this kind of hazard.
The approach of this effort could be improved considering additional terrestrial, coastal, and ma- rine climate-related extreme and scenarios (e.g., wa- ter quality alteration, river flooding, sea level rise, increase sea surface temperature). Moreover, socioe- conomic scenarios describing the future evolution of socioeconomic dynamics (e.g., urbanization, popula- tion growth, migrations, tourism) could be integrated to take into account the effect of their interaction with climatic drivers in exacerbating the risk and vul- nerability towards disasters. This could be done by integrating outputs derived from land use change model, economic models, and demographic projec- tions. At the same time, the methodology can be enriched included additional critical functions (i.e., electric network, navigation channel, migrants, fish- ing valleys, hydric resources, resident population) to better describe site-specific peculiarities and pro- cesses as suggested by stakeholders during the work- shop.
A key outcome of the proposed methodology is an identification of scenarios that are most and least disruptive to the prioritization of risk management initiatives; such understanding contributes to overall resilience of the coastal systems. It is an essential piece of knowledge in the analysis of the resilience of the Metropolitan City of Venice to climate-related and other factors including the current pandemic (i.e., COVID-19), over-tourism and depopulation. These outputs can then be used to support the implementation of climate change adaptation and disaster risk reduction policies and strategies. The participative process initiated within the BRIDGE project represent a step towards the establishment of a community of practice for disaster risk reduction and climate change adaptation in the Metropolitan City of Venice: through the workshops, diverse ac- tors involved in the resilience building cycle had the
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
950 Bonato et al.
opportunity to sit at the same table and to discuss to reach agreement on a shared set of measures to be implemented. Most of actors involved were interested in staying engaged in future steps of the process, giving room to the hope that the cooperation exercise performed during the workshop could be reflected in the reality in the implementation of a shared local plan for climate change adaptation.
Some of the initiatives proposed for prioritiza- tion were already part of existing plans (i.e., geo- morphological plan of the Venice lagoon) or de- rived from implemented adaptation projects and pilots (i.e., LIFE Seresto, LIFE Vimine). Testing their efficiency toward a set of multiple scenarios, provided practitioners with new insights and recom- mendations to be considered for future efforts, like for instance, adopting a multihazard perspective to DRR and the need for a stronger inter-sectoral coor- dination in climate change adaptation.
ACKNOWLEDGMENTS
The research leading to these results has received funding from the Italian Ministry of Foreign Affairs and International Cooperation in the frame of the Project BRIDGE—Building resilience of society to disasters: improved methodologies and solutions for Italy and USA (2019–2021).
The authors would like to thank all the local stakeholders of the Metropolitan City of Venice for the fruitful collaboration, discussions, and supply of data. We also thank the two anonymous reviewers for their precious comments, which helped to improve the quality of the manuscript.
Open Access Funding provided by Universita Ca’ Foscari within the CRUI-CARE Agreement.
REFERENCES
Aven, T. (2017). How some types of risk assessments can support resilience analysis and management. Reliability Engineering & System Safety, 167, 536–543.
Barbi, A., Formentini, G., Monai, M., Rech, F., & Zardini, F. (2007). Meteo-climatic analysis of the pluviometric event of 26 September 2007 in the area of Venice (Analisi Meteo- Climatica Dell’Evento Pluviometrico del 26 Settembre 2007 nel Veneziano). Teolo, Italy: ARPAV Regional Department for Territorial Safety, Teolo Meteorological Center.
Biolchi, S., Denamiel, C., Devoto, S., Korbar, T., Macovaz, V., Scicchitano, G., … Furlani, S. (2019). Impact of the October 2018 storm Vaia on coastal boulders in the northern Adriatic Sea. Water (Switzerland), 11(11), 1–23. https://doi.org/10.3390/ w11112229
Bostick, T. P., Holzer, T. H., & Sarkani, S. (2017). Enabling stake- holder involvement in coastal disaster resilience planning. Risk Analysis, 37(6), 1181–1200. https://doi.org/10.1111/risa.12737
Bostick, T. P., Connelly, E. B., Lambert, J. H., & Linkov, I. (2018). Resilience science, policy and investment for civil infrastruc- ture. Reliability Engineering and System Safety, 175(January 2017), 19–23. https://doi.org/10.1016/j.ress.2018.02.025
Bronfman, N. C., Cisternas, P. C., Repetto, P. B., Castañeda, J. V., & Guic, E. (2020). Understanding the relationship between direct experience and risk perception of natural hazards. Risk Analy- sis, 40(10), 2057–2070. https://doi.org/10.1111/risa.13526
Calliari, E., Staccione, A., & Mysiak, J. (2019). An assessment framework for climate-proof nature-based solutions. Science of the Total Environment, 656, 691–700.
Connelly, E. B., Lambert, J. H., & Thekdi, S. A. (2016). Robust investments in humanitarian logistics to support disaster re- silience of sustainable community supply chains. ASCE Natural Hazards Review, 17(1), 11.
Donnan, R. C., Edwards, C. R., Iyer, A. R., Karamete, T., My- ers, P. F., Olson, S. E., … Lambert, J. H. (2020). Enterprise resilience of maritime container ports to pandemic and other emergent conditions. In Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, 2020, pp. 1–6. https://doi.org/10.1109/SIEDS49339.2020.9106638
European Environment Agency. (2017). Climate change adapta- tion and disaster risk reduction in Europe. Enhancing coher- ence of the knowledge base, policies and practices. EEA Re- port. https://doi.org/10.1007/s13398-014-0173-7.2
Ferrarin, C., Chiggiato, J., Bajo, M., Schroeder, K., Zaggia, L., & Benetazzo, A. (2019). VENEZIA : l’acqua alta eccezionale del 12/11/2019. Analisi preliminare dei dati e descrizione della fenomenologia. CNR-Ismar Venezia.
Fox-Lent, C., Bates, M. E., & Linkov, I. (2015). A matrix ap- proach to community resilience assessment: An illustrative case at Rockaway Peninsula. Environment Systems and Decisions, 35(2), 209–218. https://doi.org/10.1007/s10669-015-9555-4
Fox-Lent, C., & Linkov, I. (2018). Resilience matrix for compre- hensive urban resilience planning. Lecture Notes in Energy, 65, 29–47. https://doi.org/10.1007/978-3-319-75798-8_2
Gallina, V., Torresan, S., Zabeo, A., Critto, A., Glade, T., & Marco- mini, A. (2020). A multi-risk methodology for the assessment of climate change impacts in coastal zones. Sustainability (Switzer- land), 12(9), https://doi.org/10.3390/su12093697
Ganin, A. A., Massaro, E., Gutfraind, A., Steen, N., Keisler, J. M., Kott, A., … Linkov, I. (2016). Operational resilience: Concepts, design and analysis. Scientific Reports, 6, 19540.
Godlewski, G. B., Caporalini, L., & Deuss, B. (2020). “Acqua Alta” and the need for the Mo.S.E. project: Managing the coastal flood in Venice. Student report, Delft University of Technology. http://resolver.tudelft.nl/uuid:ea34a719-79c1-4c6e- b886-e0d92407bc9d
Goodwin, N. R. (2003). Five kinds of capital: Useful con- cepts for sustainable development. Medford, MA: Tufts University.
Gunderson, L. H., Allen, C. R., & Holling, C. S. (2012). Founda- tions of ecological resilience. Washington, DC: Island Press.
Hamilton, M. C., Lambert, J. H., Keisler, J. M., Holcomb, F. H., & Linkov, I. (2013). Research and development priorities for energy islanding of military and industrial installations. ASCE Journal of Infrastructure Systems, 19(03), 297–305.
Hamilton, M. C., Lambert, J. H., & Valverde, L. J. (2015). Climate and related uncertainties influencing research and development priorities. ASCE-ASME Journal of Risk and Uncertainty in En- gineering Systems. Part A: Civil Engineering., 1(2), 8.
Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4, 1–23. https://doi. org/10.1146/annurev.es.04.110173.000245
Holling, C. S. (1996). Engineering resilience versus ecological re- silience. Engineering within Ecological Constraints, 31(1996), 32.
Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016). A review of definitions and measures of system
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
Prioritization of Resilience Initiatives for Climate-Related Disasters in Venice 951
resilience. Reliability Engineering and System Safety, 145, 47–61. https://doi.org/10.1016/j.ress.2015.08.006
Howes, M. (2015). Disaster risk management and climate change adaptation: a new approach. In J. P. Palutikof, S. L. Boulter, J. Bernett, D. Rissik (Eds.), Applied studies in climate adaptation. Wiley, Oxford, pp. 407–414.
Huang, I. B., Keisler, J., & Linkov, I. (2011). Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of the Total Environment, 409(19), 3578– 3595. https://doi.org/10.1016/j.scitotenv.2011.06.022
IPCC. (2012). Managing the risks of extreme events and disas- ters to advance climate change adaptation. A special report of working groups I and II of the intergovernmental panel on climate change (Vol. 9781107025). Cambridge, UK and New York: Cambridge University Press. https://doi.org/10.1017/ CBO9781139177245.009
IPCC (2021). Climate Change 2021: The Physical Science Ba- sis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. May- cock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cam- bridge University Press. In Press.
IPCC. (2014). Climate Change 2014: Synthesis Report. Contribu- tion of Working Groups I, II and III to the Fifth Assessment Re- port of the Intergovernmental Panel on Climate Change [Core Writing Team, R. K. Pachauri and L. A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.
ISTAT. (2021). Popolazione residente al 1° gennaio: Veneto. Available at: http://dati.istat.it/Index.aspx?DataSetCode= DCIS_POPRES1
Karvetski, C. W., & Lambert, J. H. (2012). Evaluating deep un- certainties in strategic priority-setting with an application to fa- cility energy investments. Wiley Journal Systems Engineering, 15(4), 483–493.
Karvetski, C. W., Lambert, J. H., Keisler, J. M., & Linkov, I. (2011). Integration of decision analysis and scenario planning for coastal engineering and climate change. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 41(1), 63–73. https://doi.org/10.1109/TSMCA.2010.2055154
Karvetski, C. W., Lambert, J. H., Keisler, J. M., Sexauer, B., & Linkov, I. (2011). Climate change scenarios: Risk and impact analysis for Alaska coastal infrastructure. International Journal of Risk Assessment and Management, 15(2–3), 258–274. https: //doi.org/10.1504/IJRAM.2011.042120
Karvetski, C. W., Lambert, J. H., & Linkov, I. (2009). Emergent conditions and multiple criteria analysis in infrastructure prior- itization for developing countries. Journal of Multi-Criteria De- cision Analysis, 16(5–6), 125–137. https://doi.org/10.1002/mcda. 444
Lambert, J. H., Karvetski, C. W., Spencer, D. K., Sotirin, B. J., Liberi, D. M., Zaghloul, H. H., … Linkov, I. (2012). Priori- tizing infrastructure investments in Afghanistan with multia- gency stakeholders and deep uncertainty of emergent condi- tions. ASCE Journal of Infrastructure Systems, 18(2), 155–166.
Lambert, J. H., Wu, Y. J., You, H., & Clarens, A., & Smith, B. (2013). Climate change influence on priority setting for trans- portation infrastructure assets. ASCE Journal of Infrastructure Systems, 19(1), 36–46.
Linkov, I., Bridges, T., Creutzig, F., Decker, J., Fox-Lent, C., Kröger, W., … Thiel-Clemen, T. (2014). Changing the resilience paradigm. Nature Climate Change, 4(6), 407–409. https://doi. org/10.1038/nclimate2227
Linkov, I., Fox-Lent, C., Read, L., Allen, C. R., Arnott, J. C., Bellini, E., … Hyde, I. (2018). Tiered approach to resilience as- sessment. Risk Analysis, 38(9), 1772–1780.
Linkov, I., Trump, B. D., & Fox-Lent, C. (2016). Resilience: Ap- proaches to risk analysis and governance. In Florin, M. V.,
& Linkov, I. (Eds.). IRGC resource guide on resilience. Lau- sanne: EPFL International Risk Governance Center (IRGC). https://doi.org/10.5075/epfl-irgc-228206
Lionello, P., Barriopedro, D., Ferrarin, C., Nicholls, R., Orlic, M., Raicich, F., … Zanchettin, D. (2020). Extremes floods of Venice: Characteristics, dynamics, past and future evolution. Natural Hazards and Earth System Sciences, 1–34. https://doi.org/10. 5194/nhess-2020-359
Luyet, V., Schlaepfer, R., Parlange, M. B., & Buttler, A. (2012). A framework to implement Stakeholder participation in environ- mental projects. Journal of Environmental Management, 111, 213–219. https://doi.org/10.1016/j.jenvman.2012.06.026
Međugorac, I., Pasarić, M., & Güttler, I. (2021). Will the wind as- sociated with the Adriatic storm surges change in future cli- mate? Theoretical and Applied Climatology, 143(1–2), 1–18. https://doi.org/10.1007/s00704-020-03379-x
Morucci, S., Coraci, E., Crosato, F., & Ferla, M. (2020). Extreme events in Venice and in the North Adriatic Sea: 28–29 October 2018. Rendiconti Lincei, 31(1), 113–122. https://doi.org/10.1007/ s12210-020-00882-1
Mysiak, J., Torresan, S., Bosello, F., Mistry, M., Amadio, M., Marzi, S., … Sperotto, A. (2018). Climate risk index for Italy. Philo- sophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2121), https://doi.org/ 10.1098/rsta.2017.0305
O’Brien, G., O’Keefe, P., Gadema, Z., & Swords, J. (2010). Ap- proaching disaster management through social learning. Dis- aster Prevention and Management: An International Journal, 19(4), 498–508. https://doi.org/10.1108/09653561011070402
Park, J., Seager, T. P., Rao, P. S. C., Convertino, M., & Linkov, I. (2013). Integrating risk and resilience approaches to catastro- phe management in engineering systems. Risk Analysis, 33(3), 356–367. https://doi.org/10.1111/j.1539-6924.2012.01885.x
Parlak, A. I., Lambert, J. H., Guterbock, T. M., & Clements, J. L. (2012). Population behavioral scenarios influencing radio- logical disaster preparedness and planning. Accident Analysis and Prevention, 48, 353–362. https://doi.org/10.1016/j.aap.2012. 02.007
Quenum, A., Thorisson, H., Wu, D., & Lambert, J. H. (2019). Resilience of business strategy to emergent and future condi- tions. Journal of Risk Research, 1–19. https://doi.org/10.1080/ 13669877.2018.1485172
Regione Veneto. (2021). Movimenti turistici nel Veneto: Venezia. Available at: http://statistica.regione.veneto.it/ banche_dati_economia_turismo_turismo1.jsp
Regione Veneto. (2021). Parchi e Aree Protette in Veneto: Provin- cia di Venezia. Available at: https://www.regione.veneto.it/web/ agricoltura-e-foreste/sic-zps-venezia
Rose, A. (2004). Defining and measuring economic resilience to disasters. Disaster Prevention and Management, 13(4), 303–314. https://doi.org/10.1108/09653560410556528
Rossa, A. M., Laudanna Del Guerra, F., Borga, M., Zanon, F., Settin, T., & Leuenberger, D. (2010). Radar-driven high- resolution hydro-meteorological forecasts of the 26 September 2007 Venice flash flood. Journal of Hydrology, 394(1–2), 230– 244. https://doi.org/10.1016/j.jhydrol.2010.08.035
Royal Society. (2014). Resilience to extreme weather. London, UK: Royal Society.
Sambo, B. (2020). Building the resilience of coastal systems to ex- treme events: The case of the Metropolitan City of Venice and its lagoon. (Unpublished Master dissertation) University of Ca’ Foscari, Venice, Italy.
Schroeder, M. J., & Lambert, J. H. (2011). Scenario-based multi- ple criteria analysis for infrastructure policy impacts and plan- ning. Journal of Risk Research, 14(2), 191–214. https://doi.org/ 10.1080/13669877.2010.515314
Seddon, N., Chausson, A., Berry, P., Girardin, C. A. J., Smith, A., & Turner, B. (2020). Understanding the value and limits of nature- based solutions to climate change and other global challenges.
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
952 Bonato et al.
Philosophical Transactions of the Royal Society B, 375(1794), 20190120.
Taarup-Esbensen, J. (2020). A resilience-based approach to risk assessments—Building resilient organizations under arctic con- ditions. Risk Analysis, 40(11), 2399–2412. https://doi.org/10. 1111/risa.13535
Terzi, S., Torresan, S., Schneiderbauer, S., Critto, A., Zebisch, M., & Marcomini, A. (2019). Multi-risk assessment in mountain re- gions: A review of modelling approaches for climate change adaptation. Journal of Environmental Management, 232, De- cember(2018), 759–771. https://doi.org/10.1016/j.jenvman.2018. 11.100
Thorisson, H., Lambert, J. H., Cardenas, J. J., & Linkov, I. (2017). Resilience analytics with application to power grid of a devel- oping region. Risk Analysis, 37(7), 1268–1286. https://doi.org/ 10.1111/risa.12711
Tompkins, E. L., & Adger, W. N. (2004). Does adaptive manage- ment of natural resources enhance resilience to climate change? Ecology and Society, 9(2), 10. https://doi.org/10.5751/ES-00667- 090210
Trump, B. D., Florin, M.-V., & Linkov, I. (2018). IRGC resource guide on resilience. (Vol. 2). Lausanne, Switzerland: EPFL In- ternational Risk Governance Center (IRGC). https://doi.org/ 10.5075/epfl-irgc-262527
UN. (2015). Transforming our world: The 2030 agenda for sustain- able development. Resolution adopted by the UN General As- sembly on 25 September 2015.
UNDRR. (2015). Sendai framework for disaster risk reduction 2015–2030. Geneva, Switzerland: United Nations Office for Disaster Risk Reduction.
UNFCCC. (2015). Adoption of the Paris agreement, Bonn: https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf
UNDRR. (2009). Disaster risk reduction. Chemical & Engineering News, 64, https://doi.org/10.4324/9781351138444-34
Walker, B. H., Anderies, J. M., Kinzig, A., & Ryan, P. (2006). Ex- ploring resilience in social-ecological systems: Comparative stud-
ies and theory development. Melbourne, Australia: CSIRO Pub- lishing.
Walker, B., Holling, C. S., Carpenter, S. R., & Kinzig, A. (2004). Resilience, adaptability and transformability in social– ecological systems. Ecology and Society, 9(2), 5.
Wallemacq, P., & Below, R. (2015). The human cost of natural dis- asters: A global perspective. Analysis, Centre for Research on the Epidemiology of Disasters, March 2015, 1–56.
World Economic Forum. (2021). The global risks report 2021: 16th edition. Weforum.Org. Retrieved from http://www3.weforum. org/docs/WEF_The_Global_Risks_Report_2021.pdf
You, H., Lambert, J. H., Clarens, A. F., & McFarlane, B. J. (2014). Quantifying the influence of climate change to priorities for in- frastructure projects. IEEE Transactions on Systems Man and Cybernetics: Systems, 44(2), 133–145.
You, H., Connelly, E. B., Lambert, J. H., & Clarens, A. F. (2014). Climate and other scenarios disrupt priorities in several man- agement perspectives. Environment Systems and Decisions, 34(4), 540–554. https://doi.org/10.1007/s10669-014-9525-2
Zanini, M. A., Hofer, L., Faleschini, F., & Pellegrino, C. (2017). Building damage assessment after the Riviera del Brenta tor- nado, northeast Italy. Natural Hazards, 86(3), 1247–1273. https://doi.org/10.1007/s11069-017-2741-6
SUPPORTING INFORMATION
Additional supporting information may be found on- line in the Supporting Information section at the end of the article.
Supplementary Material
15396924, 2022, 5, D ow
nloaded from https://onlinelibrary.w
iley.com /doi/10.1111/risa.13823 by C
su - N orthridge, W
iley O nline L
ibrary on [13/10/2023]. See the T erm
s and C onditions (https://onlinelibrary.w
iley.com /term
s-and-conditions) on W iley O
nline L ibrary for rules of use; O
A articles are governed by the applicable C
reative C om
m ons L
icense
