**Bridging the Gap The Measure of Urban Resilience**

Printed Edition of the Special Issue Published in *Sustainability* Grazia Brunetta, Alessandra Faggian and Ombretta Caldarice Edited by

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**Bridging the Gap**

## **Bridging the Gap**

## **The Measure of Urban Resilience**

Editors

**Grazia Brunetta Alessandra Faggian Ombretta Caldarice**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Grazia Brunetta Politecnico di Torino, Responsible Risk Resilience Centre-R3C, Interuniversity Department of Regional and Urban Studies and Planning Italy

Alessandra Faggian Gran Sasso Science Institute Italy

Ombretta Caldarice Politecnico di Torino, Responsible Risk Resilience Centre-R3C, Interuniversity Department of Regional and Urban Studies and Planning Italy

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Sustainability* (ISSN 2071-1050) (available at: https://www.mdpi.com/journal/sustainability/ special issues/Urban Resilience sustainability).

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## **Contents**

#### **Angioletta Voghera and Benedetta Giudice**

Reprinted from: *Sustainability* **2019**, *11*, 3432, doi:10.3390/su11123432 ................ **255**

## **About the Editors**

**Grazia Brunetta** (Professor in Urban and Regional Planning, Politecnico di Torino, Interuniversity Department of Regional and Urban Studies and Planning). She is the project manager of the Responsible Risk Resilience Centre (R3C) of Politecnico di Torino, where she is the scientific coordinator of an interdisciplinary research team that works to enhance cities' forward resilient attitude. She is coordinator of the Risk Management and Adaptation working group of RESURBE—International Program on Urban Resilience. Her main research activities show a deep and constant involvement in the field of town and regional planning, with particular focus on innovation in the theories, methodologies and approaches for spatial planning, in both research and academic teaching activities. The main interests of her research deal with the following topics: (i) institutional innovation in urban, regional and environmental planning; (ii) evaluation methodologies in territorial planning; (iii) strategic assessment in spatial planning; (iv) local sustainable development in territorial planning approaches; (v) territorial resilience for spatial planning. Her research results have been collected in more than 200 national and international publications and she has been an invited speaker and guest in national and international conferences.

**Alessandra Faggian** (Professor of Applied Economics, Director of Social Sciences and Vice Provost for Research at the Gran Sasso Science Institute, L'Aquila, Italy). She is also a previous President of the North American Regional Science Council (NARSC), current co-editor of *Journal of Regional Science* and previous editor of the journal *Papers* in Regional Science. Prof Faggian's research interests lie in the fields of regional and urban economics, demography, labour economics and economics of education. Her publications cover a wide range of topics including migration, human capital, labour markets, creativity and local innovation and growth. She has co-authored over 80 academic publications. Her articles have appeared in journals such as *Oxford Economics Papers*, *Cambridge Journal of Economics*, *Feminist Economics*, *Regional Studies*, *Papers in Regional Science*, *Journal of Regional Science* and *The Journal of Economic Geography*. Alessandra is the 2007 recipient of the Moss Madden Memorial Medal by the Regional Science Association International: Irish and British section (RSAIBIS) for the best paper published in the year 2006 and the 2015 recipient of the Geoffrey Hewings Award by The North American Regional Council for outstanding research contribution by a young scholar in the field of regional science. In a recent ranking of the top 100 regional scientists in the world (Rickman and Winters, 2016), she was ranked 19th.

**Ombretta Caldarice** (Assistant Professor in Urban and Regional Planning, Politecnico di Torino, Interuniversity Department of Regional and Urban Studies and Planning). She has more than ten years of international experience in research, capacity-building and education on the role (and limits) of planning in complex and adaptive systems, assuming a comparative perspective within Europe. Her research tackles the challenge of how to make cities more resilient to climate change and other hazards. In particular, she works on three interrelated topics: (i) theories and practices of urban resilience; (ii) planning rules and urban codes; (iii) evaluation and assessment of vulnerabilities. She is an affiliated faculty member of the Responsible Risk Resilience Centre of the Politecnico di Torino, where she supports local stakeholders in the transition towards resilience. She is the junior coordinator of the Risk Management and Adaptation working group of RESURBE—International Program on Urban Resilience. She is lecturer and member of the expert committee of the Summer School on Urban Resilience, led by the University of Southern Denmark.

### *Editorial* **Bridging the Gap: The Measure of Urban Resilience**

#### **Grazia Brunetta 1, Alessandra Faggian 2 and Ombretta Caldarice 1,\***

Received: 14 January 2021; Accepted: 20 January 2021; Published: 21 January 2021

## **1. Understanding Resilience**

The concept of resilience has arisen as a "new way of thinking". It was applied in planning at the end of the last century as a concept that encourages policies to face stress factors and react by renewing and innovating cities. Resilience becomes instrumental in addressing both causes and effects of significant global challenges. As it motivates the transformative potentials of cities, resilience is commonly named "co-evolutionary resilience" [1] or, most recently, "transformative resilience" [2]. Following this more profound meaning, resilience is not only the opposite of vulnerability [3] but also a "broad concept", whose final purpose is to prevent and manage unforeseen events together with the improvement of the environmental and social quality of a territorial system [4]. In a nutshell, this approach characterises resilience as a territorial systems' capacity to respond systemically and dynamically to the present and future shocks related to significant global challenges through non-linear transformation processes. Such processes involve the natural and anthropic characteristics of a territorial system, their performance, quality, and functions [5]. Although the theoretical debate on resilience is deeply investigated, several methodological challenges remain mainly related to the concept's practical sphere. As a matter of fact, resilience is commonly criticised for being too ambiguous and empty meaning. At the same time, turning resilience into practice is not easy to do [6]. We need to measure resilience because its assessment allows consideration of what resilience is practical and what it is possible, and at which point resilience is realistically likely to fail. This will be arguably one of the most impactful global issues for future research on resilience [7].

The Special Issue "Bridging the Gap: The Measure of Urban Resilience" falls under this heading. To the best of our knowledge, it seeks to synthesise the state-of-the-art knowledge of theories and practices on measuring resilience. We were particularly interested in papers that address one or more of the following questions: "What are the theoretical perspectives of measuring urban resilience? How can urban resilience a property to be measured? What are the existing models and methods for measuring urban resilience? What are the main features that a technique for measuring urban resilience needs to guide proper adaptation and territorial governance? What is the role of measuring urban resilience in operationalising cities' ability to adapt, recover and benefit from shocks?"

#### **2. Measuring Resilience**

For over 40 years, resilience has emerged as a unifying concept in many disciplines linked to sustainability. Efforts to apply resilience within different fields have stimulated interest in measuring resilience, giving rise to various approaches, i.e., qualitative and quantitative methods, participatory assessments, statistical analyses, modelling and metrics [8]. As stated during the 2014 International Food Policy Research Institute (IFPRI) international conference, questions of what to measure, whom to measure, how often to measure, and what methods to use to capture resilience are still being debated. Simultaneously, if no clear guidelines on how to measure resilience reliably exist, decision-makers

will not make informed choices about how to use it. Resilience will miss the opportunity to be an organising principle, malleable but able to attract different interests and stakeholders together.

Basically, we can identify two main barriers in measuring resilience. The first one is a conceptual barrier, as it is difficult to measure something unless we know precisely what has to be measured. At the same time, the definition of resilience does not facilitate this. Coming to a common and shared understanding of what resilience means is a necessary starting point. As resilience is a continually changing process leading to the idea of dynamic non-equilibrium, the measure of resilience cannot be a single number or a result [9]. The second one is a methodological barrier, as it may not be easy to obtain reliable and meaningful data. As outlined by [10], there is a reliance on using the available data rather than data from a systematic approach. Although several frameworks have been proposed for "capturing resilience" in academic and public discourse, the existing techniques are limited to measuring specific disturbances. Less attention has been directed to consider resilience as a continually changing process. Moreover, the developed resilient metrics are set indicators of what is easy to measure rather than what is important [11].

Based on this introduction, this Special Issue of *Sustainability* aimed to cover urban resilience measurement by introducing pioneering approaches, discussing experimenting methodologies, and showing possible opportunities for the concept's development. In a very short time, the Special Issue attracted attention from the scientific research community, including 11 published papers, one of which is a review paper focused on comprehensive knowledge regarding multiple methods for measuring resilience. Long Nguyen and Akerar [12] conduct a systematic review of 77 different literature records published from 2000 to 2020, providing an investigation and a more comprehensive picture into the state-of-the-art on modelling, measuring, and visualising community resilience, summarising qualitative, quantitative, and hybrid approaches, and identifying critical points in building community resilience.

Six research papers of this Special Issue assume a methodological perspective that discusses new approaches and required changes to existing methods of measuring resilience. Feldmeyer and colleagues [13] present the result of a project funded by the German Federal Ministry of Education and Research named monitoring of adaptation measures and climate resilience in cities (MONARES). They identify 24 indicators to measure and monitor urban climate resilience for municipalities, assessing the requirements of indicators and implementing a mixed method for adapting global approaches to the local context. The article by Pilone, Demichela and Baldissone [14] presents a semi-quantitative methodology for assessing multiple risks to increase the awareness of municipal technicians. The methodology is based on the assignation of rates to the risks, revealing good feasibility in the results obtained for the interactions, and highlighting some problems neglected in the sectorial risk plans. Brunetta and Salata [15] want to move a step forward from theoretical works on measuring resilience, and particularly they work toward the application of a pioneering empirical methodology to measure and spatially represent the degree of vulnerability (as it is counterpoised to resilience, even if they act simultaneously). The paper by Rota, Bagliani and Feleting [16] proposes a taxonomy of regional resilience using a shift–share analysis based on the region's capacity of improving its employment rate during the pre-crisis period. The paper by Assumma and colleagues [17] investigates the role covered by the system dynamics model (SDM) and Lotka–Volterra models (LV) in supporting the decision-making process in the evaluation of resilience policies. Both the SDM and LV models may be considered reliable supporting tools for policy planning, thanks to their ability to predict possible future behaviours of selected key variables, thus helping stakeholders to identify and prioritise shared strategies for increasing resilience. Lastly, the paper by Mutani, Todeschi and Beltramino [18] aims to cover a research gap of the exiting simulation energy tools and models; namely, to translate the measurement of energy performance from the block of buildings or neighbourhood scale to the city level. The proposed methodology is based on buildings' energy balance, and it is able to carry out simulations at the territorial scale toward energy resilience.

The remaining four research papers of this Special Issue assume a case-study approach that reads and examines resilience measurement application, thoroughly identifying possible solutions to apply resilience in practice. Urso, Modica and Faggian [19] present Italian inner areas' response to the 2007–2009 Great Recession. The purpose of their paper is to analyse the potential structural change of Italian inner vs. non-inner areas, assessing their adaptive capacity. The authors found that urban poles and inner areas had different capabilities to re-adapt their local industrial compositions in response to the economic crisis with noticeable effects on their future resilience. In the paper by Mohabat Doost and colleagues [20], an empirical application to provide an overall assessment of the solar production capacity in the City of Moncalieri (Turin, Italy) is presented. Results demonstrate that the current minimum energy levels required by law are generally much lower than the effective potential solar energy production that each land use parcel-zone could effectively produce. Abastante and colleagues [21] support that a walkable city means creating a resilient and healthy city. Their paper presents case study research, the Main Campus of the Politecnico di Torino, as a fertile ground for studying a walkability assessment. Lastly, Voghera and Giudice [22] sustain that green infrastructure (GI) is a nature-based solution capable of enhancing the social–ecological quality of a specific territory, both in a sustainable and resilient way. Their paper attempts to fill the gap between evaluation methodology and planning tools compared to GI's indicators in Italy and France.

**Author Contributions:** Conceptualization: O.C., G.B. and A.F.; writing: O.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Review* **Modelling, Measuring, and Visualising Community Resilience: A Systematic Review**

#### **Hoang Long Nguyen and Rajendra Akerkar \***

Big Data Research Group, Western Norway Research Institute, P.O.Box 163, NO-6851 Sogndal, Norway<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>
Received: 14 August 2020; Accepted: 21 September 2020; Published: 24 September 2020

**Abstract:** The concept of community resilience receives much attention in studies and applications due to its ability to provide preparedness against hazards, to protect our life against risks, and to recover to stable living conditions. Nevertheless, community resilience is complex, contextual, multifaceted, and therefore hard to define, recognise, and operationalise. An essential advantage of having a complete process for community resilience is the capacity to be aware of and respond appropriately in times of adversity. A three-step process constituting of modelling, measurement, and visualisation is crucial to determine components, to assess value, and to represent information of community resilience, respectively. The goal of this review is to offer a general overview of multiple perspectives for modelling, measuring, and visualising community resilience derived from related and emerging studies, projects, and tools. By engaging throughout the entire process, which involves three sequential steps as we mentioned above, communities can discover important components of resilience, optimise available local and natural resources, and mitigate the impact of impairments effectively and efficiently. To this end, we conduct a systematic review of 77 different literature records published from 2000 to 2020, concentrating on five research questions. We believe that researchers, practitioners, and policymakers can utilise this paper as a potential reference and a starting point to surpass current hindrances as well as to sharpen their future research directions.

**Keywords:** community resilience; systematic overview; resilience modelling; knowledge representation; resilience assessment; information visualisation

#### **1. Introduction**

The word resilience originally stems from the Latin term "resiliere" that means to jump back or bounce back. The first careful consideration of the term resilience arose in the field of mechanics in 1858, followed by psychology in the 1950s, human ecology in the 1990s, and ending up with disaster risk reduction and climate change adaptation in the 2000s [1]. Resilience concentrates on improving the capacity of a system in the face of multiple hazards, rather than precluding or diminishing the loss of assets because of specified events. Resilience accepts the condition that a wide range of disruptive events—both stresses and shocks—may take place but are not inevitably foreseeable. This research topic has received significant interest from not only researchers but also practitioners and service-users. Recognising the importance of resilience, many definitions at multiple domains have been offered, as shown in Figure 1, including physical [2,3], social [4,5], ecological [6–8], economic [9], individual [10,11], and community [12,13]. According to mentioned literature, there is no commonly accepted way to define the concept of resilience formally; besides, several definitions are even overlapping with existing concepts [14], some of which are robustness, fault-tolerance, flexibility, survivability, and agility.

As the formal definition given by the United Nations Office for Disaster Risk Reduction (UNDRR), resilience is "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 [15]", in not only pre- but also post-disaster. During pre-disaster, we aim at anticipating vulnerabilities and risks proactively to mitigate harmful effects. On the other hand, the capability of valid and sufficient recovery is an essential objective in the post-disaster period [16]. Studies about resilience can help our societies in reducing disaster risk, adapting to climate changes, and coming up with strategies to develop more sustainably and efficiently.

**Figure 1.** Multiple domains of resilience.

In this paper, we focus on giving an overview of multiple perspectives regarding community resilience. Community resilience aims at representing the abilities of a local community as a complex system, including actions and interactions of local agencies, natural and built environments, critical infrastructures, and citizens, to reduce, withstand, and even turn back from impacts of hazards, as well as the competence to adapt and thrive themselves to be less vulnerable to future disasters and emergencies. There are more and more studies concentrating on building community resilience across various application domains (e.g., tourism [17], biodiversity management [18], energy [19], and mental health [20]) in either global [21] or regional levels, some of which are Brazil [22], Greece [23], and the United Kingdom [24]. Nonetheless, this research field still needs many efforts from researchers and practitioners to come up with comprehensive methodologies to model, measure, and understand community resilience. These three mandatory phases can support communities in proposing additional activities and new approaches to the comprehension of how to ensure that our communities can be better prepared, more flexible, and have the ability to bounce back promptly from an event, whatever form it may take.

Our motivation is to provide crucial knowledge regarding multiple methods for modelling, measuring, and visualising community resilience in this paper. For coming up with optimal decision-making criteria and strategies to make our communities resilient, we should focus on the entire process—all of these three phases. In particular, we address various components and properties to model community resilience; different qualitative, quantitative, and hybrid approaches for measuring resilience value; and several visualisation methods at the end to show resilience-related information. We believe that this paper can support not only academic researchers but also practitioners in recognising what frameworks are already out there and how we can build on them.

In this section, we introduced the problem and emphasised our motivation for conducting this review. The rest of this paper includes the following structure. In the next section, the necessary background will be given. Section 3 will provide vital information about the materials and methods to conduct this review. Further, Section 4 summarises different methodologies for modelling community resilience. Then, we will describe qualitative, quantitative, and hybrid approaches to

measure community resilience in Section 5. Section 6 will provide various visualisation techniques for representing resilience information. Finally, we will give some discussion, draw essential conclusions, and express future directions in the last section.

#### **2. Background**

Community resilience is a complicated concept that cannot be captured and turned into explicit knowledge effortlessly. What is generally accepted among researchers is the fact that community resilience tremendously depends on multiple components that affect and influence the overall resilience of a community [25]. Such elements can be related to particular risks, temporal and spatial contexts, and community features that resilience refers to (e.g., perception, hazards, and capacities). Even more complex, the term community resilience also has diverse meanings between communities by referring to different components of the community, including, but not limited to, the resilience of community infrastructure [26] and the resilience of social relationships [27]. Hence, it is necessary to identify, define, and describe the particular components and properties of community resilience in the process of modelling.

Based on components and properties defined in the modelling step, we can apply qualitative, quantitative, or hybrid methodologies to translate resilience dimensions, indicators, and proxies into tractable and understandable frameworks, expressions, formulations, or values. The target of qualitative methods is to provide detailed descriptions depending on specific contexts. To enable the ability to understand and transfer results, experts account for their viewpoints and perspectives [28] through case studies, grounded theories, interviews, ethnography, phenomenology, and hermeneutics [29]. It is ordinary to represent qualitative results as charts, diagrams, and other graphics by using visualisation methods. On the other hand, we measure quantitative value by paying attention to community resilience at a particular time point or by comparing resilience value before and after an event [30]. Generally, the community resilience value is appropriate for internal use. To compare a community with others, we may use their rank or percentile equivalent of the community resilience value; however, we have to ensure that the measurements should be taken in similar contexts. Our data should be comparable, comprehensible, measurable, and relevant [21] so that it is suitable for quantitative methodologies. Further, hybrid approaches are the integration of quantitative and qualitative methods; therefore, they can estimate both tangible and intangible value of community resilience.

Visualisation is the final puzzle piece to complete a big-picture of community resilience. In emergencies, especially in situations requiring immediate actions, we may face a massive amount of community resilience information. Visualisation is an effective and efficient solution that has the capacity to represent resilience-related information of communities in systematic forms without missing essential details [31]. We can also utilise information visualisation to discover latent patterns, which are arduous to recognise manually [32]. Additionally, emerging digital visualisation tools can involve end-users in many interactions (e.g., zooming in or out, employing dynamic charts, and changing visual appearances such as colours and shapes). With the support of disruptive technologies (e.g., machine learning and artificial intelligence) [33], we can leverage information visualisation to build recommender systems and dashboards for potential use in emergencies, disasters, and catastrophes as well.

#### **3. Methodological Approach**

This section describes in detail how we identify relevant and credible literature addressing resilience at different community levels. In the following sections, common themes are determined and summarised to generate insights into community resilience. The interest of this review is to find and evaluate studies, projects, and tools that draw upon new solutions for communities to model, measure, and visualise resilience.

#### *3.1. Research Question*

There is a need for a more transparent analytical overview and a selection of the studies, projects, and tools most relevant to what we can focus on in more detail. The results of this review will summarise and discuss the following research questions. Generally, different communities could benefit from this paper's much more comprehensive overview of:

We conduct this review study to fulfil the information required by communities in both static and dynamic phases. In the static phase, our target is to define what we have and what we suffer from. On the other hand, we aim at understanding whether those variables represent objects or contexts that we can work towards in the dynamic phase.

#### *3.2. Search Strategy*

Concerning geographic-based communities and resilience, the concept of community resilience may contain two proxies which are urban and rural resilience [34]. Urban resilience puts more focus on the ability of cities or urban systems to rebound from destruction [35], whereas rural resilience aims to conserve a satisfactory standard of living in rural areas [36]. For the sake of generalisation, we take into account studies, projects, and tools related to not only community resilience but also urban and rural resilience. We started this work by searching the published articles on Google Scholar, Scopus, Web of Science, and ScienceDirect, which are not limited to particular disciplines, using text strings "community resilience", "urban resilience", "rural resilience", "resilience assessment", and "resilience visualisation" and their combinations. Meanwhile, the systematic search of relevant projects and tools is conducted on Google search engine. We also check the reference lists of the selected articles to discover additional related work. Supplementary data sources involve our pre-existing knowledge of the literature.

#### *3.3. Eligibility Criteria and Selection Process*

To be included in this review, the inclusion criteria established that the literature must adhere to the following rules. No restrictions are imposed with regards to the time or country of publication.

On the contrary, we define the exclusion criteria used to filter literature that is not relevant for this study as follows.

After screening the data, full-text documents are collected to extract necessary study-specific parameters (e.g., type of resilience at community-based levels, number of resilience components, methodologies to assess resilience, and techniques for representing resilience information) for further analysis. Upon our search using the search strategy and inclusion criteria devised, we identify 77 studies, projects, and tools in the last 20 years, from 2000 to 2020, for inclusion in this review, as shown in Table 1.

**Table 1.** Resilience studies, projects, and tools at community-based levels.

**Table 1.** *Cont.*

#### **4. Modelling Community Resilience**

Determining and defining community resilience's components and properties is an essential step for further developing clear strategies and undertaking practical activities to attain resilience in our community. This section presents different studies that have been conducted to achieve a better understanding and clarification of the community resilience through modelling step.

#### *4.1. Defining Key Components*

Although the importance of modelling resilience is widely recognised and researched, proposing an appropriate number of resilience components is still a significant challenge. Researchers find out that short-term human memory works best when we have fewer elements to remember. People are usually good at remembering no more than seven different components [114]. The community resilience, therefore, almost encompasses from three to seven components. Noting that in most studies, the order of components does not reflect their importance.

Table 2 presents different studies, projects, and tools arranged by the number of components, their focuses, and years of publication. We use the year of publication instead of the year of study as it is relatively more accessible.

**Table 2.** Summary of community resilience components along with focuses and years of publication.

**Table 2.** *Cont.*

Figure 2 shows a diagram including nodes and edges, which represent resilience components and their relations based on the literature in Table 2, respectively. A connection exists among two components in case they co-occur in a model. For example, economy and institution are two of five indices defined in [53]; hence, there exists a relationship among these two nodes. Besides, the size of a node depicts the frequency of this component in the literature (i.e., a bigger node points out that this component appears more times than smaller ones). We may recognise from Figure 2 that society, economy, community, physical, resource, and infrastructure are mostly defined in different models.

**Figure 2.** Community resilience components and their relations.

#### 4.1.1. Less than Five Components

In [55], the emBRACE framework proposes the three community resilience domains, including resource and capacity, action, and learning followed by 17 different resilience indicators. Due to the nonstraightforward allocation property, a defined indicator can fit in not only one but also many dimensions. In addition, focusing on three components for modelling resilience [110], the authors build and verify the correlations of indicators through using the Mississippi county data. The combination of the refined indicators belongs to three community resilience components, which are economic development, social capital, and an additive index of community resilience. Meanwhile, in [112], the THRIVE tool of the Prevention Institute represents community resilience with three interconnected clusters, which are (i) social-cultural environment (people), (ii) physical/built environment (place), and (iii) economic/educational environment (equitable opportunity). This tool guarantees community resilience by increasing the quality of life and handling the biased distribution of health-related resources. Furthermore, social, economic, and environmental components are highly targeted and focused in [40,45,46].

Instead of using three components, the Community Disaster Resilience Framework (CDRF) addresses four different capital assets of a community comprising social, economic, physical, and human capital [82]. Similarly, Jordan and Javernick-Will [97] proposed four recovery indicators that are categorised as social, economic, environmental, and infrastructural. In addition, focusing on social and economic components, together with natural and institutional ones, the RDI [43] provides a better understanding of the connection between diversity in socio-ecological systems and its resilience. In [106], the C3 Living Design Project proposes a comprehensive action list, which can guide actions for a resilience present and future of communities, buildings, homes, and infrastructure, consisting of CV (community cohesion, social, and economic vitality), PH (productivity, health, and diversity), EW (energy, water, and food), and MA (material and artefact). In addition, Huang et al. [47] develop the assessment index system including four components, which are engineering, ecological, economic and social, to assess the changes in rural resilience.

Apart from that, the authors in [79] refer to community capacity and competence-based studies in social psychology and public health to develop the Communities Advancing Resilience Toolkit (CART). The CART describes four overlapping and interrelated domains of community resilience including (i) connection and caring, (ii) resource, (iii) transformative potential, and (iv) disaster management. A community with higher capability in these four defined domains can be more successful in reducing the harmful effects of disasters and other related difficulties. In a different approach [37], the Canadian Centre for Community Renewal (CCCR) focuses on people, organisation, resource, and community process. Among four dimensions, the people and organisation represent attitudes and behaviours of a community; the resource depicts awareness and use; and the community process portrays strategic thinking, participation, and action. These dimensions are further separated into 23 characteristics of resilience. In addition to the studies mentioned above, the authors in [84,115] model the community resilience with community connectedness, risk and vulnerability, available resources, and planning and procedures, which are logically overlapping and able to interact with each other. This demonstrates the equivalence among domains in constructing community resilience towards multiple disasters.

#### 4.1.2. From Five to Seven Components

By applying a five-components approach, the Zurich Flood Resilience Alliance (ZFRA) models community resilience with five capitals comprising human, social, physical, financial, and natural [94]. These five capitals can assist people in their development as well as enhance the ability to cope with and make a response to various flood-related shocks. Following [51], Simonovic and Peck propose the quantitative resilience framework, which combines economic, social, organisational, health and physical impacts, for dealing with climate change on coastal megacities. In [53], the authors propose five indices, which are social, economic, institutional, infrastructure, and community capacities,

to examine community-level resilience. With baseline conditions defined in this study, the authors can not only keep track of changes of resilience at a specific time in a particular place but also compare resilience among locations. The studies in [52,75] are similar; however, the authors extend their model by supplementing one more index that is the environmental capacity.

The similar idea can be found in [95] in which the International Federation of Red Cross and Red Crescent Societies (IFRC) describes six resilience indicators to fortifying community resilience including knowledge and health, society, infrastructure and service, economy, natural asset, and connectivity. These indicators are designed to effectively and efficiently support three critical constituents of the Framework for Community Resilience (FCR) that are (i) assisting communities towards risks promptly and proposing solutions to portray underlying vulnerabilities comprehensively, (ii) placing people and their demands in the centre, and (iii) being retrievable by people at anytime and anywhere. According to [96], The IMPROVER project provides physical, social, human, natural, economic, and institutional capitals as six crucial components along with the IMPROVER Societal Resilience Analysis (ISRA) (for qualitative measuring indicators) to self-assess and guarantee community resilience. In [76], the Bay Localize constructs the community resilience toolkit concentrating on six key components being composed of food, water, energy, transportation and housing, jobs and economy, and civic services. This toolkit is beneficial in helping communities facing risks and hazards in the area of climate change and peak oil. Following Alshehri et al. [85,86], the authors discuss social, economic, physical and environmental, governance, health and well-being, and information and communication dimensions. The featured contribution of these two studies is that the authors discovered the correlation between the six identified dimensions and 62 criteria (i.e., from seven to fourteen criteria connect to every dimension). In [83], Yoon et al. build a set of indicators to measure community disaster resilience index utilising human, social, economic, institutional, physical, and environmental factors that are related to vulnerability and capacity aspects of South Korea.

Concerning seven dimensions depicting community functionality, the PEOPLES framework is constructed in [102] to represent population and demographic, environmental and ecosystem, organised governmental services, physical infrastructure, lifestyle and community competence, economic development, and social-cultural capital. This framework can promote the empowerment of local planners, decision-makers, and stakeholders to evaluate and improve their community resilience in different temporal-spatial contexts.

#### 4.1.3. More than Seven Components

There are not many studies which are conducted in terms of using more than seven components. In [74], the authors leverage the top-down approach to put forward eight different indices for consideration, which are clustered into (i) coping capacity (i.e., social character, economic capital, infrastructure and planning, emergency services, community capital, and information and engagement) and (ii) adaptive capacity (i.e., governance, policy and leadership and community and social engagement). Along with each index is a set of measurable indicators. Hence, we can use either one number or sets of numbers to represent a resilience index in this study. Further, Barkham et al. [60] propose ten key components classified into two distinct themes that are vulnerability and adaptive capacity. The vulnerability includes climate, environment, resource, infrastructure, and community; whereas the adaptive capacity is made up of governance, institution, technical and learning, planning systems, and funding structures. Concerning this approach, a community is resilient in case it possesses low vulnerability and high adaptive capacity. In [89], the Community and Regional Resilience Institute (CARRI) defines Community Service Areas (CSAs) to support communities in realising strengths and shortages of resilience. The CSAs include 18 different aspects, some of which are communications, education, energy, and water, for improving community life and function together.

#### *4.2. Determining Community Resilience Properties*

Due to the diversity of definitions of community resilience and their components as we stated in the previous section, the properties of community resilience are therefore divergent as well. In this section, we describe different studies that sought to determine the properties of community resilience in various disciplines. In [94], the authors define four features of a resilient system taking into consideration assets, interactions and interconnections at the community level, including the robustness, redundancy, resourcefulness, and rapidity. These four properties are also determined for both physical and social systems in [30]. In another approach, the Bay Localize mentions the equity, quality, sustainability, and ownership as essential criteria for communities to adapt with resilience requirements related to climate change and peak oil [76].

Besides that, the simplicity, adaptation, dependency (i.e., not stand alone), and (future) orientation are defined as properties to guide the community in modelling resilience regarding a diverse range of philosophies [108]. Similarly, the authors in [88] propose four properties of community resilience involving the attribute, continuity, adaptation, and trajectory. Eventually, community resilience can be considered as a dynamic concept; wherefore, assigning a fixed value for a community over a long-term duration is inappropriate because it may change promptly [9,116]. Table 3 provides properties of community resilience and their descriptions in detail. A community resilience model should satisfy not all but at least some of these properties.

**Table 3.** Properties of community resilience and their descriptions.

#### **5. Measuring Community Resilience**

After modelling community resilience, it is indispensable to select appropriate methodologies for aggregating and assessing identified components to come up with general systems [39], comprehensive frameworks [44,65,103], management guidelines [56], innovative models [64,72], a resilience "value", a feasibility assessment [38], or underlying correlations among components [48]. To measure community resilience, we can apply either qualitative, quantitative, or combine these two methodologies as a hybrid one. Qualitative approaches, which are suitable for processes required professional experience of experts, are used to evaluate community resilience without providing a particular numerical descriptor. Apart from that, quantitative methods leverage numerical data along with statistical models to measure community resilience. From a practical perspective, both qualitative and quantitative approaches have proved beneficial and useful in measuring complex community resilience. Several appropriate methods for use include, for example, in-depth interview [46], semi-structured interview [62], observation [73], and survey [92]. Table 4 shows the summary of qualitative, quantitative, and hybrid approaches to measure community resilience.

#### *5.1. Qualitative Approaches*

Qualitative approaches can be applied either at (i) the framework level or at (ii) the component level. At the framework level, qualitative techniques aim at giving understanding into actions, themes, patterns, and overall structures of community resilience, for designing and developing processes, phases, or procedures pragmatically. They are usually designed in a step-by-step format to involve communities in sequences and activities, not only assessment but also engagement, implementation, planning, and others. On the other hand, we concentrate on more detailed and qualitative analyses of community resilience factors and their internal relationships at the component level [117]. Generally, a partial implementation of a framework-based approach can be considered as a component-based one. Qualitative methods are sometimes difficult to conduct due to the diversity of standards, interfaces, and coding.

#### 5.1.1. Framework Level

At the framework level, a completed process including continuous cycle or a sequential series of steps is defined and designed with the ultimate goal aiming at comprehending community resilience for effective development and implementation. Table 5 describes steps, stages, or phases of qualitative approaches at the framework level.

The IAP [61] comes up with six consecutive phases to evaluate climate risk, which are engagement, climate research and impacts assessment, vulnerabilities assessment, city resilience strategy, implementation, and monitoring and review. Along with each phase is the set of tools including objectives, guidance, questionnaires, and exercises. They help cities, local governments, and relevant stakeholders, either with a lot or little experience in climate change planning, to build urban resilience. In a similar manner, the Community Resilience System (CRS) also offers six stages (i.e., engagement, assessment, visioning, planning, implementing, and monitoring and maintaining) to support communities in understanding resilience, defining goals, creating strategies, deciding on tools and processes, and evaluating resilience [89]. To derive robust consequences, the authors describe appropriate steps for each stage in which each stage involves specific actions (together with related and supporting resources) required to accomplish.

In another approach, the CART [79] proposes a process, which encompasses assessment, feedback, planning, and action, to engage stakeholders in addressing community problems through field-tested surveys, key informant interviews, community conversations, and supplemental instruments. This toolkit contributes to empowering communities in leveraging their assets and strengths for overcoming multiple disasters. According to [77], the RAND Corporation aims at providing a roadmap to represent an essential step forward for determining the critical elements of community resilience. Based on eight levers, five core components and their interactions, the literature review, focus groups, and SME meetings are conducted for comprehending and strengthening community resilience. This proposed framework is suitable for various communities in reinforcing resilience concerning health security.

*Sustainability* **2020** , *12*, 7896

**Table 5.** Summary of steps, stages, or phases of qualitative approaches at the framework level.

#### 5.1.2. Component Level

At the component level, only resilience components are focused on and taken into account. According to [104], the authors first derive experiences from agro-pastoralist stakeholders through semi-structured interviews. In the following step, the theoretical thematic analysis, which is based on community resilience and social dilemmas frameworks, is applied for strengthening community

resilience with respect to the soil erosion reduction concerning five different domains (i.e., economic domain, social domain , cultural domain, governance, and environmental domain). By leveraging in-depth interviews, adding field observation and reading documents, Rahman and Kausel [105] determine planning capacity and social capacity of a community towards a tsunami based on eight essential resilience elements that are governance, society and economy, resource management, land use and structural design, risk knowledge, warning and evacuation, emergency response, and disaster recovery.

Further, the City Resilience Framework supplies a lens through which the cities' complication and the numerous factors that contribute to a city resilience can be acknowledged. To this end, they define 12 indispensable goals, which fit into four categories and seven qualities, as the backbone for the planning of a resilient city [49]. Cities can receive this framework as a compass to guide learning activities from literature, case studies, and other related areas. Equivalently, other authors also apply case study methodology to analyse, understand, and gain insights into community resilience with respect to community-based tourism [101] and evacuation route planning [63].

Referring to [99], this study spends six months to discover relevant and available capacities and resources of a community during a disaster through various resources that are semi-structured interviews, observation, informal conversations, and documentary and social media review. This qualitative research demonstrates the paramount importance of resilience capacities (i.e., local knowledge, sense of community, cooperation, organisation, social capital, and trust) in terms of responding to emergencies.

#### *5.2. Quantitative Approaches*

Quantitative approaches aim at measuring community resilience in recognisable ways to reduce the whims and opinions of analysts, experts, or other populations of the study. They can evaluate community resilience through the use of ordinal, interval, and ratio data obtained from surveys, observations, or secondary data. Towards qualitative approaches, the values of resilience components and their relationships need to be validated by discernible outcomes [111]. Based on components determined in the modelling step, a direct approach is to apply the composite index formula [108] as follows.

$$CR = \sum\_{j=1}^{|\mathcal{C}|} \sum\_{k=1}^{|I\_{\mathcal{C}\_j}|} i\_k \times w\_{k'} \forall j, k \in \mathbb{N}, j > 0, k > 0 \tag{1}$$

where *CR* represents community resilience, *C* is the set of resilience components, *Icj* is the set of indicators of component *cj*, and *ik*, *wk* denote for *k*th indicator and its weight, respectively. According to [54], the UNDRR identifies an ordinal scale in the range of [0, 3] (i.e., preliminary assessment) and the range of [0, 5] (i.e., detailed assessment) to evaluate ten different essentials, which are used to build resilient cities, including (i) three essentials regarding governance and financial capacity, (ii) five essentials related to planning and disaster preparation, and (iii) two essentials considering disaster response and post-event recovery. Local governments then define their weighting for each essential to reflect its importance and to assist the measurement.

As stated in [84], the authors identify a score range from 1 (low degree of resilience, it means the red zone) to 5 (high degree of resilience, it means the green zone) for every question in the scorecards. We obtain the final score by summing all the individual scores. If the overall score is above 99, our community is very resilient to disasters; if it is below 33, we are under the risk of preventing and recovering from disasters. We should especially put the greatest attention to a particular element in case its scores are significantly smaller than the others.

Instead of using an adding function, we can use an unweighted average of based scores [60] or standardised z-scores (due to the diversity of indicators' values) [82,110] on entire indicators. To compare the resilience among cities, the authors in [60] attempt to calculate the average one more time based on cities' scores. The precision of the resilience comparison highly depends

on the context similarity among cities at the time they are examined. As alternatives to explicit numbers, we can also use a priority rating (low/medium/high) [90,112], an effectiveness score range (A–F) [112], a vulnerability/capacity category (V/C), or an effect value (positive/negative) [83] for quantitative approaches.

On the other hand, the Analytic Hierarchy Process (AHP) is put to use in [98] to determine disaster-resilient indicators at the local level. The outcome-indicator score is further calculated based on criterion score and their weights. Besides, a six-point scale, which is extended from [118], is used to rank indicators for measuring process-indicator score. Level 0 represents the "absence of a clear and coherent activity/activities in an overall disaster risk reduction program", while level 5 refers to "a culture of safety exists among all stakeholders". Subsequently, the authors propose the weighted linear average (WLC) to measure composite indices based on these two evaluated scores.

Last but not least, several approaches attempt to capture dynamic resilience directly at the community level. Community resilience value that changes throughout an event due to risk perceptions of citizens or relationships between resilience components. To reflect the dynamic of community resilience, we can measure value at different time points [119] concerning the entirety components. In [51], Simonovic and Peck recognise that community resilience value can be dynamic in both time and space as well.

#### *5.3. Hybrid Approaches*

The measurement of community resilience in a variety of situations requires both qualitative and quantitative approaches [40,41] to capture perceptions, vulnerabilities, exposed values, and other resilience-related factors. A hybrid approach is one where both tangible and intangible elements are applicable [120] for enhancing analytical accuracy and deepening the understanding of community resilience. In [37], the authors harvest various information, which is related to characteristics of resilience, involving specific numbers, percentages, yes/no answers, opinions, and perceptions from interviews, organisation inventory, meetings, focus groups, and surveys. Similarly, both qualitative (i.e., literature review, group interview, and discussions) and quantitative (i.e., scales and surveys) data are usable in [42,109]. Nevertheless, we should keep in mind that hybrid approaches may require much effort and may be time-consuming in the data collection process.

The flexible combination of quantitative and qualitative approaches has been demonstrated in different studies. By mixing both methods, we can generally aggregate opinions of experts along multiple dimensions, indicators, and proxies. In [80], Cutter et al. combine the qualitative GIS (Geographic Information System) map and quantitative indicators to generate social vulnerability, built environment/infrastructure, hazard exposure, and hazards mitigation layers. The overlaying of these four layers provides a schematic representation of resilience baseline for communities. In a similar approach, the Bay Localize Community Resilience Toolkit [76] applies a scale from 0–4 to measure community-based resilience indicators. In consonance with rated values, the authors utilise the SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, which is an extremely helpful planning and problem-solving technique, to determine and define community's capabilities for overcoming challenges. Strengths and weaknesses are typically internal factors aiming at representing the conditions within our community. On the other hand, opportunities and threats are able to put our community in a clear picture of external influences [121].

In contrast, we can apply a quantitative measurement based on both quantitative and qualitative targets to come up with specific resilience indices [68]. Following this methodology, the following matrix

$$\begin{array}{c c c c c c} & \text{Prepar} & \text{Absorb} & \text{Recover} & \text{Adapt} \\ \text{Physical} & P-P & \text{Ab}-P & \text{R}-P & \text{Ad}-P \\ \text{Cogarithmic} & \begin{bmatrix} P-I & \text{Ab}-I & \text{R}-I & \text{R}-I & \text{Ad}-I \\ P-\text{C} & \text{Ab}-\text{C} & \text{R}-\text{C} & \text{Ad}-\text{C} \\ P-\text{S} & \text{Ab}-\text{S} & \text{R}-S & \text{Ad}-S \end{bmatrix} \\ & & \begin{bmatrix} P-S & \text{Ab}-S & \text{R}-S & \text{Ad}-S \end{bmatrix} \end{array} \tag{2}$$

utilises both qualitative and quantitative data in which qualitative values (obtained through personal communications with stakeholders) are placed at Prepare-Information (*P* − *I*), Prepare-Social (*P* − *S*), Recover-Information (*R* − *I*), and Adapt-Physical (*Ad* − *P*) positions [107].

According to [97], the authors make use of a three-round Delphi method to determine necessary resilience indicators. The first round begins with a comprehensive literature review to understand and derive a good set of indicators. Experts further evaluate each dimension in the second round in consideration of a five-point Likert type scale that is anchored with 1 (not applicable) and 5 (very important). Besides, the experts are also encouraged to provide their insights into other elements that are crucial for a community to be resilient to change and cope with disasters. All following rounds will continue until we acquire a general agreement of all panel members [85]. Besides, the Delphi method is also used to determine index weights for quantitative calculations [47]. It is noted that a Delphi technique can meet difficulties in case local communities or qualified respondents do not have adequate previous experience.

#### **6. Visualising Community Resilience**

This section explores different visualisation techniques to deal with various scales and units of analysis to enhance community resilience. In emergency circumstances, a mass amount of resilience-related information can be generated from diverse data sources. Hence, utilising multiple visualisation techniques to understand and illustrate this information is essential for a more detailed and complete resilience comprehension, community-based resilience planning, and decision-making processes. Besides, employing utilisation technologies can bring us valuable and actionable insights at the application level. Table 6 summarises different visualisation techniques to represent community resilience.

**Table 6.** Summary of community resilience visualisation techniques.

#### *6.1. Geospatial Information Visualisation*

In case geospatial information of community resilience is available, we can use a density map to highlight and demarcate critical locations [74] through different colour codes in which dark and cold colours usually indicate high resilience. In contrast, light and warm colours stand for low resilience. To show colours in a map, we are able to use either qualitative, sequential, or diverging scheme. The density map is advantageous in case many data points (or data lines) exist in a small geographic area. According to [71], the authors combine both numbers and colours to represent urban resilience indices and rankings for 50 Spanish province capitals following the standard deviation classification methodology of ArcGIS. However, the selection of red colour for high resilience areas may mislead readers because this colour is often associated with emergencies. With reference to [53], the authors depict the spatial distribution of disaster resilience and its components (i.e., social, economic, institutional, and infrastructure resilience) for 736 counties in the FEMA Region IV. The disaster resilience scores are expressed as standard deviations in order to emphasise high or low resilient counties extraordinarily. The authors further portray high and low resilient areas as dark blue and red, respectively.

In a similar approach, the authors in [75] visualise disaster resilience as well as six components based on a diverging scheme, from low (standard deviation < −1.5) to high resilience (standard deviation > 1.5). Furthermore, leveraging standard deviations [58,87], other studies create the density map to represent community resilience indices of Mississippi counties [110], disaster resilience indices of 11 local government areas (e.g., Greater Brisbane Area, Sunshine Coast, and others) [108], and community disaster resilience indices of 229 local municipalities in South Korea [83]. Despite the ability to present a holistic perspective of the resilience of a community and its neighbours, the density map shows the disadvantage if we want to represent all dimensions because each dimension will require a separate diagram.

Without tangible geospatial information, a bar chart can be the right selection [52,57] to visualise an overall value of resilience for various communities.

#### *6.2. Multidimensional Information Visualisation*

Stacked bar charts, spider charts (which is also known as radar charts), and radial stacked bar charts are beneficial for displaying multiple dimensions of community resilience. Among these three types, stacked bar charts are designed to concurrently compare the overall resilience between communities and recognise essential dimensions within a community. In [60], the authors use a stacked bar chart to display five aspects of vulnerability, five key themes of adaptive capacity, and overall resilience of 50 cities that have significant influence in the world. In another work, stacked bar charts are used to indicate top-ranking resilience dimensions by gender/age group, livelihood group, and level of intervention [81]. Despite that, one major disadvantage of a stacked bar chart is that we find it hard to compare a particular dimension of a community with others since they are not aligned with a common baseline.

On the other hand, spider charts help us to compare (i) resilience dimensions of a community over time or between communities by placing multiple polygons over or upon each other in a single diagram [105] and (ii) resilience dimensions with a defined standard [73]. Generally, spider charts can enable a better understanding of the strengths and weaknesses of resilience dimensions [104] and therefore very useful for high-level presentation of assessments. In [81], the CoBRA framework describes community attainment of resilience by illustrating five sustainable livelihood framework categories that are financial, human, natural, physical, and social by the current and crisis years. Likewise, Wardekker et al. [66] draw spider charts to elucidate the baseline and adaptation plans for flood-related resilience of Rotterdam based on ten resilience components (e.g., anticipation, robustness, flatness, and others). If measuring scales of axes are different, it would not seem helpful to compare resilience dimension across these axes. Besides, we should avoid concentrating too much on the polygons because the area and the shape of polygons can change depending on how we organise

the axes. We may use parallel coordinate charts as an alternative to spider charts. By extending the radial stacked bar chart, the authors in [49,69,100] express multiple indicators associated with defined dimensions required for a resilient community dexterously.

Furthermore, a hypercube has the advantage of providing a direct view of the relationships and correlations among resilient dimensions. Focusing on infrastructure resilience, Jovanovi´c et al. employ a three-dimensional space to visualise three resilience components including matrix-based indicators, complexity (level of detail), and smartness (big data analytics) [122] for healthcare infrastructure exposed to COVID-19 [123]. In another work, a resiliency cube is plotted to manifest the resilience of an urban road network in the time of earthquake [67]. Nevertheless, a hypercube may lose its clarity if there are so many resilient dimensions that need to be represented. A co-occurrence network [113] can be a suitable substitute in this condition.

#### *6.3. Dashboard*

A dashboard is a single screen summary of the analysis of different information. The use of dashboard holds great potential in the circumstance that we require multiple visualisations, which influence each other, to offer a comprehensive and engaging view of community resilience. Dashboards are also specialised in their dynamic and interactive capabilities. Infrastructure facility managers [124], local planning for resilience [56], or emergency managers [125] can utilise dashboards to derive critical insights for at-a-glance decision making and comprehensive strategies during a crisis.

To create a successful and helpful dashboard to represent community resilience, whether as an independent element or as a component of a specific framework, we should put our efforts in understanding our data, dealing with outliers, displaying meaningful results, and increasing semantic transparency. On the opposite, it is necessary to minimise response time, futile decorations, and redundant information.

#### **7. Discussion and Conclusions**

Acknowledging the importance of community resilience, researchers and practitioners have made significant attempts in not only studies but also practical matters. In particular, the objective of this paper is to provide an investigation and a more comprehensive picture into the state-of-the-art, accessible, and emerging works that are subjected to a three-step sequential process (i.e., modelling, measurement, and visualisation) to build community resilience. The modelling represents what is likely to be components and properties that communities should focus on to guarantee their resilience. Further, the measuring step assists communities in recognising where they are standing. Eventually, the visualisation aims at supporting communities in deriving insights into essential information promptly and precisely with minimum efforts. Based on this skeleton, communities can select most relevant approaches, which we mentioned in this review, to embed into their processes. For a successful resilience plan, communities should consider and follow all these three steps comprehensively. In addition to that, we want to mention critical points that were distilled herein for both research and practical uses.

addition, resilience capital/capacity well expresses the potential and abilities of a community to achieve something.

We are living in the fourth industrial revolution with the explosion of disruptive technologies that are essential and valuable for decision-making processes. In the next study, a comprehensive comparative analysis of how to utilise social networking services [127–129] and crowdsourced data for community resilience [130,131] will be taken into account. Besides, we will examine the interrelation and discuss open issues between cutting-edge technologies (e.g., machine learning, Internet of Things, and artificial intelligence) and community resilience. For example, the intelligent and adaptive use of machine learning methodologies to measure community resilience [132] can provide us with excellent opportunities for further development.

**Author Contributions:** Conceptualisation, H.L.N.; methodology, H.L.N.; validation, H.L.N. and R.A.; formal analysis, H.L.N.; investigation, H.L.N.; resources, H.L.N.; data curation, H.L.N.; writing—original draft preparation, H.L.N.; writing—review and editing, H.L.N. and R.A.; visualisation, H.L.N.; supervision, R.A.; project administration, R.A.; funding acquisition, R.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by the INTPART BDEM project (grant no. 261685/H30) and the Horizon 2020 RESILOC project (grant no. 833671).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Indicators for Monitoring Urban Climate Change Resilience and Adaptation**

#### **Daniel Feldmeyer 1,\*, Daniela Wilden 2, Christian Kind 3, Theresa Kaiser 3, Rüdiger Goldschmidt 4, Christian Diller 2 and Jörn Birkmann 1**

Received: 30 April 2019; Accepted: 21 May 2019; Published: 23 May 2019

**Abstract:** In the face of accelerating climate change, urbanization and the need to adapt to these changes, the concept of resilience as an interdisciplinary and positive approach has gained increasing attention over the last decade. However, measuring resilience and monitoring adaptation efforts have received only limited attention from science and practice so far. Thus, this paper aims to provide an indicator set to measure urban climate resilience and monitor adaptation activities. In order to develop this indicator set, a four-step mixed method approach was implemented: (1) based on a literature review, relevant resilience indicators were selected, (2) researchers, consultants and city representatives were then invited to evaluate those indicators in an online survey before the remaining indicator candidates were validated in a workshop (3) and finally reviewed by sector experts (4). This thorough process resulted in 24 indicators distributed over 24 action fields based on secondary data. The participatory approach allowed the research team to take into account the complexity and interdisciplinarity nature of the topic, as well as place- and context-specific parameters. However, it also showed that in order to conduct a holistic assessment of urban climate resilience, a purely quantitative, indicator-based approach is not sufficient, and additional qualitative information is needed.

**Keywords:** resilience; indicator; monitoring; climate change; climate adaptation

#### **1. Introduction**

Our society is facing multitudinous different challenges—in this paper we are focusing on two main challenges: climate change and urbanization. In 2015, 3.9 billion people were living in cities. By 2050, the population in cities is projected to reach up to 6.7 billion people [1]. Urban agglomerations will continue to grow and are increasingly threatened by the high uncertainty of climate change impacts [2]. In response to these impacts, cities are already implementing climate change adaptation measures in order to prepare for uncertain future changes. Adaptation to climate change and climate variability is not a new phenomenon [3]. However, steadily rising temperatures, increasing magnitude and frequencies of climate-induced extreme events, such as droughts, floods, storms or intense rainfall, as well as the growth of the global human population pose new adaptation challenges to humankind [3]. In our research, we use the term adaptation as defined by the United Nations Climate Change [4]: "Adaptation refers to adjustments in ecological, social, or economic systems in response to actual or expected climatic stimuli and their effects or impacts. It refers to changes in processes, practices, and

structures to moderate potential damages or to benefit from opportunities associated with climate change". Furthermore, the ability of adaptation is understood as part of resilience, as described by Folke et al. [5]. The concept of resilience can be attributed to Holling [6] and originates from ecology. He described resilience as the "measure of persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationship between population or state variables" [6]. The original concept of resilience gained increased importance in other disciplines, whereby the definitions of resilience were steadily differentiated, broadened and deepened. There are three main understandings of the character of resilience: "bounce back" which refers to the fast return to an equilibrium state of a system after a shock event, "bounce forward" which focuses on a system which should have capacities to be adapted to uncertainty and "both" which addresses the co-occurrence of the capacities for "bounce back" and "bounce forward" [7]. Meerow et al. [2] analysed 57 academic definitions of urban resilience, with particular regard to these fundamental understandings of urban resilience. The analysis showed that 35 definitions focus on "bouncing back", 15 on "bouncing forward" and only seven see both capacities as elementary for resilience. Figueiredo et al. [8] pointed out that the definitions shifted from an equilibrium-centred understanding of resilience towards an evolutionary/transformational understanding of resilience. Four main approaches to resilience can be identified: disaster risk reduction [9], socio-ecological [10], sustainable livelihoods [11] and the community-oriented approach [12]. Resilience can also be discussed on different scales (county, region, urban area, city, community and household) [8]. Even though it is important to take action on all scales, in this work we are focusing on cities—particularly in Germany—and are using the socio-ecological approach. Besides the definitions and understandings of resilience in academia, it is very important to also consider how practitioners interpret resilience. Practitioners and policy makers are a central part of the resilience-transformation process. Therefore, it is remarkable that the term resilience is interpreted in a much wider range of ways by practitioners than by academia [13].

Adaptation measures are implemented in different sectors of the city system. Since cities are complex and multifaceted systems, which in turn contain other systems, measuring the success of resilience-increasing activities poses a particular challenge. However, measurement is of great importance in order to be able to govern and steer the adaptation and transformation process. Every city has its specific context and needs, and its exposure to risk and vulnerability is dynamic and changes over time [8].

However, it is important to develop measurable indicators for different reasons. Indicators enable monitoring of the resilience-building process, as they provide regular and impartial feedback. They build an evidence base and make resilience more tangible for decision and policy makers as well as society at large. Furthermore, indicators can help to govern and steer the transformation process because they help to structure the new field of urban climate resilience. Clear indicators are not only important for the general measurement of resilience, but also for the analysis of whether adaptation measures were effective and whether the expected results were achieved [14]. Indicators also contribute to the credibility, transparency and accountability of the measures implemented. This in turn is very important for local policy makers to support further adaptation measures.

However, the development of indicators in this context poses particular challenges. In addition to the conceptual challenges of urban climate resilience, context specificity represents another challenge for the development of resilience indicators. Consequently, it is very important to consider how to include context specificity in the indicator set. Another fundamental consideration is in regard to the context-specific, dynamic and ever-changing nature of risk and vulnerability [8].

MONARES (monitoring of adaptation measures and climate resilience in cities), a project funded by the German Federal Ministry of Education and Research (BMBF), was initiated in order to address the main challenges of (1) developing a consistent understanding of resilience for both practitioners and academia, (2) shaping the adaptation and transformation process into a transparent process of governing and steering and (3) the use of resilience and adaptation measurements. The aim of MONARES is to create application-oriented methodologies for monitoring and evaluating local adaptation measures. As we are focusing on the special needs for cities in Germany, we are working together with 14 other projects of the funding initiative "Climate resilience through action in cities and regions" of the BMBF, who are focusing on climate change adaptation measures and urban resilience, as well as doing on-the-ground research in municipalities across Germany. These projects and cities differ considerably concerning scale (street, district, city, suburbs and region), inhabitants and type of adaptation measure (e.g., planning, physical infrastructure, capacity building or greening). Important commonalities of the projects are their interdisciplinary approach, the aim to enhance urban climate resilience and that they conduct on-the-ground research. However, the projects test many different pathways to improve resilience, and MONARES is focusing on how to measure the success and impact of these different projects and activities with a common set of indicators. In order to ensure applicability, we began to involve the projects at an early stage of our research. The first key step (Figure 1 Phase 1) before developing the indicators was to develop a framework [15] to describe urban resilience. Based on 19 frameworks described in the literature [16–34], our first draft was developed, which then was modified together with the projects. This process was indispensable as it resulted in a definition of urban resilience that is suitable for all projects so that there was agreement on common basic principles.

**Figure 1.** MONARES—research process.

Based on steps 1 to 3 as shown in Figure 1, the final definition of urban resilience in MONARES is as follows:

The climate resilience of a city depends on the ability of its sub-systems to anticipate the consequences of extreme weather and climate change, to resist the negative consequences of these events and to recover essential functions after disturbance quickly, as well as to learn from these events and to adapt to the consequences of climate change in the short and medium term, and transform in the long term. The more pronounced these abilities are, the more resilient a city is to the consequences of climate change. All abilities are important.

Based on this preliminary work, a four-step mixed-method approach (Figure 1 Phases 4–7) was designed to develop the indicators for urban climate resilience on which this paper focuses.

#### **2. Materials and Methods**

The exponential growth of literature concerning urban resilience contains a multitude of approaches, indicators and methods stressing the resistance of an urban system. The development of the method of this paper was guided by the questions: resilience for whom, for what and where [35]. A reflexive approach of input and feedback loops was developed in order to adapt and validate international indicators. A main challenge was to adapt the indicators to the specific context of German communities in the face of climate change.

#### *2.1. Literature Review: "Resilience Indicators"*

The selected frameworks (see Figure 1 Phase 1) were identified through an extensive literature review using the key search terms "resilience", "urban resilience", "climate resilience", "adaptive capacity + urban/city", "resistibility + urban" and "learning capacity + urban/city" (in German and English). Based on these frameworks and their operationalisation of resilience, an extensive list of indicators was deduced. These indicators were matched with the MONARES framework, developed in steps 1–3, which consists of dimensions and action fields (see Table 1).

**Table 1.** Dimensions and action field of the resilience framework.

As we have the aim to develop a user-friendly, applicable and transparent indicator set, we firstly reduced the indicators to two indicators per action-field. The two most important selection criteria were (1) context specificity of industrial nations, especially Germany, and (2) data availability. Context specificity is important because many of the indicators in the literature are suitable for the context of the Global South but not for the Global North, and even indicators that might be suitable for the Global North might not be suitable in the German context. The second criteria—data availability—is therefore important because municipalities have, on the one hand, good access to a lot of data but have, on the other hand, resource problems regarding time, finances and human resources. Action fields without literature-based indicators required the development of new ideas within the project. Given the available data, some action fields were difficult to measure without significantly neglecting the complexity of the action field.

#### *2.2. Survey to Assimilate the Indicators for Context Specificity*

Based on the literature review (see Figure 1 Phase 4) and the described selection process, an online-survey was developed (see Figure 1 Phase 5). The survey was used because, given that the indicators should be transparent and user-friendly, not only the scientific background is important, but a clear understanding of the indicators in the broad community is important also. The survey was sent to all persons who are working in one of the 14 projects mentioned above. 39 people answered the survey.

The main aim of the survey was to measure how participants assess the different indicators. They were requested to rate the importance of every indicator regarding urban climate resilience on a scale from one (low importance) to five (high importance). Each action field was represented by at least one indicator (Table 1). Besides the rating of indicators, the survey consisted of four chapters: First, some general background; Second, the context of urban climate resilience; Thirdly, the indicators; Fourthly, the possibility of extending the set of indicators by indicators without existing data sources, and some final remarks.

#### *2.3. Workshop Following the Survey*

As mentioned previously, the explanatory power of an indicator set of urban climate resilience is hugely dependent on the context, and therefore we discussed the results of the survey again with the 14 projects (see. Figure 1 Phase 6). Moreover, this feedback loop increases the transparency of the process and the robustness of the results. The workshop started with presenting the survey results and then the participants were split into two groups in order to create two independent feedback loops and cross-validation of the indicator set. For each group, a poster was prepared, listing all indicators included in the survey. The indicators that were ranked lower in the survey were written on the poster in light grey (compared to black), for an improved visualization of the survey results. Hence, both groups had the visual results to discuss and were asked to compare each pair in detail and find explanations for the survey results. In addition, the overall set remained visible, which allowed participants to keep the important question of the overall themes in mind. Therefore, indicators could be moved across the set or could become more important if they were deemed a missing piece in the mosaic. The guiding questions for this phase of the workshop were: (1) Are there enough indicators? (2) How many indicators are needed and sufficient? (3) Are the selected indicators the right ones or should they be changed? And (4) are there important gaps in the set that are yet to be filled?

#### *2.4. Finalizing the Indicators Set*

In Step 7 (see Figure 1) we analyzed the results of the workshop. Furthermore, expert interviews with practitioners were conducted with the aim to develop indicators in action fields where neither the literature review nor survey and workshop produced results. On this basis, we finalized the urban resilience indicator set.

#### **3. Results**

In our review of the academic literature, 19 indicator-based resilience frameworks were analyzed. Based on the indicators of these frameworks a list of 498 indicators (including duplicates) was generated. The indicator list was used as an important starting point for developing the MONARES Indicator Set (MIS). After screening the indicators through the lens of the MONARES-framework, some action fields remained empty and were filled by proposed indicators of the MONARES project-team. One to four

indicators were selected per action field in order to cover all topics and include sufficient redundancy. Table 2 shows the selected and proposed indicators.

#### *3.1. Survey about Resilience Indicators*

The survey was structured based on the results of Phase 4. The survey (Figure 1 Phase 5) was filled out by 39 respondents within the funding initiative "Climate resilience through action in cities and regions" of the BMBF. The overall mean perceived importance of the indicators was 3.63 within the complete range from one to five. Considering the complexity of the urban system and the interdisciplinary character of the indicator set, this rating was regarded as high. The median of four was also high. The standard deviation of 1.17 together with the entire evaluation range reflected the diversity of interpretations. Nevertheless, despite this diversity, these core numbers show that the indicators were overall judged as important. Splitting the indicators into the five main dimensions (Figure 2), the median shows that only the indicators within the dimension of economy were rated less important, they are rated in the middle of the range, which might indicate a slight indecisiveness. Several reasons could explain this, such as that the indicators selected were not covering the dimension in a satisfactory manner or that the dimension is perceived as unrelated to urban climate resilience. Those questions were discussed in the workshop (Figure 1 Phase 6) in detail.

**Figure 2.** Median importance of indicators grouped into five dimensions.

All top five ranked indicators had a median rating of 5. The mean values ranged from 4.4 to 4.6. Only two respectively three respondents did not rate the indicators, showing the general agreement regarding the importance. Nevertheless, regarding the minimum values, all had a large range from 2 to 5.

The set of five indicators in Table 3 shows that the three dimensions *environment*, *governance* and *society* were seen as particular important. The indicator rated as the most important was the environment indicator *cold air parcels*. Second and fourth ranked were *governance* indicators, namely *inter-o*ffi*ces working groups regarding risk, climate change and resilience* and *strategies against heavy rain and heat in plans*. Third and fifth ranked were two indicators from the dimension *society*. The respondents saw the importance of *experience with extreme events in the last five years* and *citizen information about heat, heavy rain and flooding* as particularly crucial for building urban resilience.

**Table 3.** The five indicators rated as most important in the survey.

Table 4 displays the five lowest ranked indicators in context of their relevance related to urban climate resilience. The overall lowest rated indicators were both from the *society* dimension, namely *voter turnout* and *number of associations*. The respondents did not think that they were relevant for measuring and monitoring urban resilience. The third lowest indicator was the *infrastructure* indicator *broadband access*. Fourth and fifth were two *economic* indicators measuring *ratio insolvencies to start-ups* and *share employees in largest sector*.

**Table 4.** Five lowest rated indicators.

Figure 3 displays boxplots of all indicators. The main tendency has already been shown in a more condensed form previously in Figure 2. *Share of nature conservation and protection areas (A\_c\_1)* was the lowest ranking in the dimension *environment*. The second indicator of the action field *biodiversity*, however, received high approval, which emphasised the perceived importance of *biodiversity* considerations for climate resilience in the urban context. *Settlement structure* (B\_a\_1&2) was seen as vital for structural climate change adaptation, similar to the first action fields of *soil and green spaces* (A\_a\_1-3).

*Energy* (B\_b\_1&2) indicators, in contrast, not only ranged from a rating of one to five, but the quartiles of the boxplot also show a comparably high range around the middle of the scale.

**Figure 3.** Box-plots of all indicators included in the survey (see Table 2 for indicator codes).

#### *3.2. General Workshop Results Regarding the MIS*

The discussion of the indicators during two discussion groups yielded important feedback on the overarching attributes and requirements of the MIS. They were mentioned several times from different persons and related to different indicators. Firstly, one important aspect was the size of the municipality and hence the scaling of the indicator. No universal scaling was found appropriate, since the different units and scales required indicator-specific scaling. Nevertheless, the scaling was seen as an important factor in order to reach the goal of acquiring indicators for municipalities and therefore an interpretable result on this level of administrative organization.

The overall discussion about applicability and feasibility was touched on in many ways from different angles, most prominently regarding data availability, numbers of indicators and total effort needed. The balancing of the loss of information related to simpler indicators or vice versa with more complex indicators with higher explanatory power but with an infeasibility to be handled by the target group was seen as a key challenge. Therefore, the participants agreed that the indicators should be based solely on existing data, thereby reducing the overall effort and simplifying the calculations and data management.

The idea of detailed factsheets describing the data source and calculation of the indicator and helping with the interpretation of the result was raised by participants and received wide support. Factsheets also help to communicate the meaning of an indicator to uninitiated persons, which was also mentioned as a crucial aspect.

The total number of indicators to be feasible was seen at around 25. Certain gaps were identified during the workshop due to the fact that specific expertise related to certain action fields was missing in the room, specifically regarding the action fields *energy, wastewater* and *civil protection*. Here, single expert interviews were carried out after the workshop to fill in the gaps.

#### *3.3. Indicator Specific Workshop Results*

Table 5 summarizes the process of indicator development during the three phases of the survey, the workshop and ending in the final set of indicators. The indicators highlighted in grey are those of the initial indicator set that were seen as important by survey respondents and therefore stayed on the list. The indicators highlighted in orange were updated or modified as a result of the survey and/or workshop. The yellow indicators were moved from one action field to another. The indicator *degree of soil sealing* was inverted to *degree of unsealed ground*, as sealing is not per se negative, even may even be desirable or unavoidable in urban areas. The *cold air parcels* was seen as an important factor of resilience but should be updated, adding cold air streams to the indicators. *Biodiversity* was discussed in contradictory ways, as it was not clear to the participants how it is related to climate hazards. Hence, the workshop resulted in representing urban biodiversity with the indicator *wetland and retention areas* in order to include flood protection arguments into the indicator of biodiversity.

Infrastructure was seen undoubtedly as a key area for achieving urban climate resilience, but also related to secondary data and its inherent complexity most difficult to quantify currently. *Accessibility of green spaces* was rather seen as an indicator of social justice and less as a settlement structural indicator and hence the second indicator *building density*, slightly lower ranked in the survey, was included instead. The *share of renewable energy* indicator focused strongly on climate protection and less on resilience factors, such as robustness and redundancy. These factors were seen to be better covered by the *diversity of renewable energy sources*. However, it was also argued that even conventional energy should be included in the indicator. This observation was followed by the consideration that no climate resilience can be achieved without climate protection in the long term. Therefore conventional energy sources cannot be regarded as a positive contribution to climate resilience in the long term. The action field of *telecommunication* was deleted in accordance with the participants' perception of this as being less important than the other action fields, lacking data and having low to no influence of the municipality. Instead, the action field *wastewater treatment* was included, as there was agreement on its importance additionally to the supply side. No specific indicator was defined in the workshop due to missing competence in this regard. *Transportation* was discussed as an important action field for municipalities, but participants agreed that its complexity cannot be covered by one indicator. Therefore, the action field remained as an action field of the framework, reminding of the importance of the topic and urging municipalities to consider and discuss it qualitatively.

The discussion around the *economic* dimension reflected the lower ranking of its indicators in the survey. The dimensions *environment* and *infrastructure* were seen to be more naturally linked to resilience than the *economic* dimension. Nevertheless, discussing the importance of a resilient economy for an urban system generated acceptance for the dimension and its components. This example illustrates one very important lesson of the workshop: the need for explanation and building a common understanding. *Innovation* was seen to be covered best by the *number of employees in research intensive companies* not by the *innovation index*. The *tax income from companies* was considered an important resource for the financial ability of the municipality to adapt. This indicator was part of the action field *municipal budget* in the survey and has since been moved to *business*. Similar to *energy,* a *diverse economy* was considered more robust, flexible and redundant when facing uncertainty of climate impacts. It was also discussed whether there might be sectors with crucial or higher relevance than others, but the group agreed that no single sector could be selected.

There was a general agreement on the importance and contribution of *society* to urban climate resilience, but less agreement on how to measure it quantitatively. Literature shows that the experience with extreme events contributes positively to citizens' resilience. In addition, *citizen information about heat, heavy rain and flooding* (Table 3) was amongst the top five rated indicators. However, regarding the spatial scale of municipalities, it was argued that information is not only provided by the local authority and therefore the indicator was not further considered. *Civil society* started an intense discussion on how to measure it and if the proposed indicators were adequate. In contrast to the survey, where the indicator *voter turnout* ranked higher, the workshop participants disliked this indicator, arguing that *voter turnout* nowadays cannot be seen as a proxy indicator for solidarity and community in Germany. The indicator *associations* was also critically reflected upon as being unable to capture *civil society* entirely. Still, the participants were in favour of the imperfect indicator *associations* instead of deleting the action field. In the survey, the dimension *governance* and its indicators were ranked high, and this result was confirmed in the workshop. Only one change was decided: replacing the *contact point for participation processes* with the *number of conducted participation processes*. Both were ranked very close in the survey with a mean of 3.3 and 3.4, respectively.

#### *3.4. Urban Climate Resilience Indicator Set*

Since even the diverse group of participants of the workshop did not cover all topics of the indicator set, experts were interviewed. Furthermore, the results of the survey and the results of the workshop were summarized and merged.

The final set of indicators is shown in Table 5 in the column MIS. Compared with the workshop set, the action field of *biodiversity* was seen crucial in its own right and better approximated by the indicator *nature conservation and protection areas*. Moreover, wetlands and retention areas were already covered by the *state of the water bodies* in line with the European Water Framework Directive regarding good ecological and chemical status. Hence, in order to create a balanced set of indicators, it was seen that the latter indicator added thematically more information and another aspect to the overall set. Secondly, the *air* action field was further developed, as *cold air parcels and flows* was difficult to interpret. The simple number or share of cold air parcels and streams were not clearly related to resulting air status. The *ventilation status* including the effects of air streams and cold air production parcels was therefore selected. For the *wastewater* action field introduced by the workshop, an expert interview recommended the indicator *share of adopted sewer system*. Another interview was conducted with the lower civil protection agency. The interviewee stressed the importance of volunteers across organizations, but as no data were gathered assessing the total numbers of volunteers, the most important one of the fire brigade was considered. Moreover, the municipality may have to consider this important topic even more in the future, as the principle of volunteers may be endangered due to demographic development. Finally, yet importantly, the *accessibility of hospitals* was interchanged with the *density of doctors*.

#### **4. Discussion**

The results from the work on indicators for monitoring urban climate resilience presented above yields a number of important insights and implications—with respect to previous studies but also for future research and for practitioners in this field.

Existing indicator sets are a good starting point, but adapting and extending them for the context at hand is crucial. There are numerous indicator sets for urban resilience; these provided a good basis from which the MONARES indicator set could be developed. However, many of the indicators analysed in the literature review were aimed at the context of developing countries. To adapt indicators identified in the review for the German context, four steps were important: (A) Disregarding indicators that do not allow sufficient distinction between cities, e.g., literacy rate is favoured as an indicator in many sources, but in Germany the literacy rate is rather high and differences between cities are marginal. (B) Disregarding indicators for which the data availability was rather limited in Germany. (C) Adding new indicators for action fields that are deemed important in the context of MONARES but which were not touched upon in the literature. (D) Focusing on municipalities as the key player for climate change adaptation. These level of municipalities require the set to be manageable in terms of data availability as well as size and complexity of the calculations.

Step A did not pose any major difficulties. Further, step B based on research concerning data availability did not cause problems. However, step C and D need to be examined in more detail.

First, the workshop clearly stated here the conflicting goals when discussing single action fields. It was felt that one indicator does not reflect the entirety of the topic, but at the same time all action fields were considered important and the total number of indicators should not exceed around 20, in order to stay manageable, which is far less than the proposed 52 indicators by the City Resilience Index (CRI) [22] and comparable to the core of 14 by the project Building Resilience Amongst Communities in Europe (embrace) [37] or Cutter's [43] core of 22. Since researchers, as well as practitioners, participated in our workshop, we had the impression that researchers tended to prefer larger, encompassing indicator sets. Compared with the scientists, practitioners were more in favour of concise and compact sets. The discussions in the workshop showed that persons with a research background had numerous ideas for new indicators for all dimensions, and advocated for their inclusion. During the workshop and its aftermath, practitioners working in municipalities displayed a different tendency—their perspective tended to focus more on how to handle the indicators in practice. Hence, what some researchers considered a concise indicator set was perceived by practitioners as overwhelming and too extensive. In order to find an adequate balance between a broad coverage and good usability in practice, it is important to involve both researchers and practitioners in the development of an indicator set. This finding is consistent with the literature and is one strength of the current study. Meerow and

Stults [13], for example, stress the need for including practitioners in the process. Consequently, the trade-off between practicability and completeness had to be balanced, leading to the fact that some indicators that were considered important were still sorted out in order to cover all action fields and still achieve a manageable amount of indicators.

Second, it was mentioned that the indicators just by title were not clear in terms of their effect on and relation to urban climate resilience, and were consequently rated around the middle. This fact was considered while developing the survey, but an in-depth explanation of indicators was removed from the survey in favour of including more indicators covering all action fields and in consideration of the time needed to fill out the survey. However, this lack of explanations meant that the disciplinary background of respondents affected the ratings.

Third, indicators from the dimension *environment* were met with relatively high consensus while indicators from the dimension *economy* were faced with more diverging opinions. The indicator selection was dependent on the conceptualization of urban resilience and the urban context. The results contribute to the gap between the understanding of urban resilience by scholars and practitioners [13]. This became apparent both in the survey and the workshop and shows that more research is warranted on what characterizes a climate resilience urban economy. Supporting evidence for this can be taken from the fact that much more has been published on climate resilience and environmental issues than on climate resilience and economic issues. Moreover, this discussion displayed the importance of a negotiation-focused approach for defining place-specific attributes of urban resilience and its measures [44].

Fourth, secondary data was seen as crucial for monitoring purposes in order to reduce resource expenditure by the administration. In other words, "The best indicator is inoperable if there is no feasible way to obtain the required data." [37]. Moreover, there was a strong request from the local administrations for more provision of data from the higher administrations. They argued that data handling, data collection and finances for these activities are lacking. They stressed the need for data provision to be handled at the higher level of administration to avoid scaling and data comparability issues. Hence, data availability for indicators on a municipal level is a strong limiting factor, especially when it comes to indicators concerning infrastructure and social aspects [45]. Parts of the infrastructure related to energy, transport and communication are owned or organized by entities on a higher administrative level, such as the national government or by private entities. This tends to lead to limited data availability when it comes to data with a sufficient resolution on a municipal level. Here it would be favourable if entities in charge of the respective infrastructure made access to data easier and provided data with a resolution that is suitable for analyses on a municipal level. Moreover, the discussion centred around technical measures and physical impacts and less about social drivers and demographic changes. The latter are seen as core aspects of the community's ability to resist unforeseen threats. Nevertheless, the intense discussion around the proxies suggested by literature displayed vividly the intricacy of social dynamics. New data and methods from the higher administration or crowd-sourced databases are needed to better understand and monitor the indicators [43].

Fifth, it is important to mention that a conflict of goals among indicators can arise and can lead to a competition for the scarce resources. These reciprocal processes cannot be completely avoided. For example: impervious surfaces are seen negative regarding heavy rain, fresh air and heat island effects, but they are necessary for a redundant infrastructure and other urban functions. Another example is provided by Meerow and Newell [35] who analysed the negative correlation of park access and stormwater management goals, concluding that resilience measures create winners and losers. This also requires transparency of the data and the method of the indicator definition to understand the root causes of the conflicting goals and find adequate solutions. Here the Rockefeller [22] approach seems like a black box because it is difficult to deduce what adaptation measures are used as a data basis, and indicator calculations are unclear. During the workshop, several practitioners mentioned consequently the necessity of transparency and the need for precise communication and non-scientific language.

Sixth, following the previous point, many indicator approaches are used to build a composite index for resilience [19,22,45–47], vulnerability [18,48–52] or risk [53–55]. Specifically, at the scale of urban resilience, indexing across the multitude of action fields was discussed critically. The different scales, topics and units appeared to not be logically linkable. Moreover, a combined index value was seen to not tell much about the level of resilience. It was seen as more important to see the contribution of each action field to the overall resilience. Also, considering the next step of adaptation measures, it is more relevant to have a resilience profile displaying specific topics to be addressed in the municipal context.

Working at the science-policy interface was challenging for all sides. The mixed method approach proved invaluable in finding a common language, tolerance and understanding. This created an environment that allowed for constructive criticism, which is indispensable for finding a compromise.

#### **5. Conclusions**

In this study, we developed an indicator set to measure and monitor urban climate resilience for municipalities, thereby assessing the requirements of indicators and implementing a method for adapting global approaches to the local context.

The mixed method approach proved to be essential for the process of indicator development. It provided an adequate frame and time to develop a mutual understanding across disciplines, researchers and practitioners, which is needed in order to select indicators or accept indicators from different fields of expertise. Transparency in the process and the inclusion of feedback builds acceptance and trust. The concept of resilience provided the required assembly hall and saw climate change as the imperative. Even the often-criticized ambiguity of the resilience concept was helpful as it created room for discussion. The number of 24 indicators based on secondary data balanced as well as possible the diverging interests. Amongst the indicators, conflict of goals is unavoidable. Making the conflicts visible is a helpful basis for making informed decisions, which is a strength of this indicator set. In general, the softer and more qualitative aspects of resilience are challenging. They were seen as crucial but very hard to assess by quantitative proxies based on secondary data. Still, representative surveys to cover them in more detail on a regular basis were rejected by municipalities as too expensive and labour-intensive.

Developing an indicator set tends to be easier than assessing the significance or validity of an indicator over time and it requires an extended period of observations to be able to make statements about the significance of a certain indicator. Nevertheless, in order to advance this field of research, it is necessary to pursue this path and start inquiries into the significance or validity of the numerous indicators that are permeating the ongoing discussions. In further research, the indicators need to be tested in reality, and there needs to be more research that addresses the validation of the indicators.

**Author Contributions:** Conceptualization: D.F., D.W., C.K., T.K., R.G., C.D., and J.B.; data curation: D.F., D.W.; funding acquisition: C.K., T.K., R.G., C.D., and J.B.; methodology: D.F., D.W.; writing—original draft: D.F., D.W.; writing—review & editing: D.F., D.W., C.K., T.K., R.G., C.D., J.B.

**Funding:** This research received no external funding.

**Acknowledgments:** The research leading to these results has received funding from the German Federal Ministry of Science and Education (BMBF) for the research Monitoring and Evaluation of climate resilience and urban adaptation measures (MONARES). The paper reflects only the authors' views, and the German Ministry is not liable for any use that may be made of the information contained herein.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

*and a Way Forward*; Zurich Flood Resilience Alliance: Zurich, Switzerland, 2014; Available online: http: //opim.wharton.upenn.edu/risk/library/ZAlliance-Operationalizing-Reslience.pdf (accessed on 16 May 2019).

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **The Multi-Risk Assessment Approach as a Basis for the Territorial Resilience**

#### **Eleonora Pilone \*, Micaela Demichela and Gabriele Baldissone**

Department of Applied Science and Technology, Politecnico di Torino, I 10129 Torino, Italy<EMAIL_ADDRESS>(M.D<EMAIL_ADDRESS>(G.B.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 18 March 2019; Accepted: 26 April 2019; Published: 7 May 2019

**Abstract:** The deep modifications to climate are currently provoking risks of increasing impact, that can cause unexpected consequences, interacting with other risks. However, the available planning regulations and instruments appear inadequate to face this challenge, most of all at a local scale. This paper presents a semi-quantitative methodology for the assessment of multiple risks, developed for the direct use of the municipality technicians, in order to increase their awareness towards multiple risks and unexpected events that could hit their territory. The methodology is based on the assignation of rates to the risks, and on a simple calculation of the binary interactions. It was tested on two Italian case studies, revealing a good feasibility in the results obtained for the interactions, and highlighting some problems neglected in the sectorial risk plans. The methodology is a background knowledge of the 'Responsible Risk Resilience Center' (R3C) of Politecnico di Torino, and it was furtherly developed through an in-depth analysis of the territorial vulnerabilities. This paper introduces two new indicators of sensitivity towards external risks, related to fire and flood risks, proposed for the application at a local scale. The indicators belong to a wider R3C framework in the phase of development to operationalize resilience.

**Keywords:** multi-risk; vulnerability; flood; fire; indicators

#### **1. Introduction**

The methodology for the semi-quantitative assessment of multiple risks at a local scale constitutes the background employed for the development of the framework and theories of the multidisciplinary research center of Politecnico di Torino 'R3C'—Responsible Risk Resilience Center. The R3C Project aims at designing and operationalizing an interdisciplinary research methodology to implement resilience in regional and urban systems. Within the project, an in-depth discussion around the epistemological meaning of resilience in different fields of application has been set up, comparing the theoretical approaches and their practical applications derived from the operational research carried out by urban and regional planners, social scientists, anthropologists, engineers, historicists, and ecologists [1].

The R3C group adheres to the definition of resilience as "the capacity of the system—and of all its socio-ecological, technical, and infrastructural components—to preserve or rapidly return to basic functionalities, responding to turbulence and/or shocks, of adaptation to climate change, and to transform the subset of components which limit the present and/or the future evolution capacity" [2]. In particular, the emerging idea of "territorial resilience" is introduced, as a concept capable of supporting the decision-making process, together with the tool needed for identifying vulnerabilities and guiding the transformation of socio-geographical areas [3]. In order to operationalize the "territorial resilience", it is essential to develop a framework for the measurement of the resilience itself. Measurement is

strictly related to the management of the risks impinging on an area, integrating the approaches of multi-risk assessment with climate modelling and the qualitative study of governance models [1].

The methodology described in this paper aimed at overcoming the common approach to risk analysis for single hazard factors [1], through the proposition of a multi-risk approach able to represent the mutual interaction of natural and anthropic stressors for the territory in a more useful view, for the development of an "operational resilience approach". The methodology was expressly developed and tested for the application at a local scale, because local authorities are on the front-line in facing the consequences of shocks and territorial changes, and often they do not have adequate instruments to cope with them. In fact, in Italy, as an example, the land use planning is delegated to municipalities, that are responsible both for the emergency planning and land use strategies. The operative tools available to the municipalities are the City Plan and the Municipal Emergency Plan. The first one aims at regulating urban and land functions, adapting the needs of urban development to the natural specificities of the territory (geomorphological, hydrological, etc.). The second one sets up the operational activities, the materials, capacities, and means to deal with possible emergencies, on the basis of the existing sectorial risk analysis. Both the plans implement and apply planning measures derived from the superordinate sectorial plans (seismic, flood, etc.), but, even if they share the same basic indications, they are not mutually linked in terms of long-term risk management, adaptation, and increase of resilience [4].

As a consequence of this planning structure, municipalities currently deal with multiple risks, but they merely implement contents from superior plans, without analyzing or correlating them in a systemic way. Additionally, the management of risks in a separate way, with different procedures, timings, and methodologies, makes it difficult for the municipalities to have a clear and updated concept of the actual hazards that threaten their territories, most of all for those deriving from the mutual influence and interaction between risks.

Till now, no mandatory rules require municipalities to evaluate the combined effects of risks; but the increasing effects of the climate change, together with the lack of resources for preventive and protective interventions, highlights the need of advanced approaches and tools for the identification of the areas more exposed to risks and risk interactions, to optimize and better address the use of resources, and to improve the actions related to adaptation and mitigation strategies.

However, the available methodologies for multi-risk assessment could present some problems for the application at a local scale (see [5] for a complete literature review). On one side, they still suffer some criticalities that need to be settled out, for example, as highlighted by Garcia-Aristizabal and Marzocchi [6], there are huge difficulties on the definition of a common metric for loss assessment, and the weighting of the different categories of exposed elements. On the other side, sometimes "specialists in various fields studying risks have failed to produce results in a form that could be useful to planners" [7]. Many methodologies for multiple risks are based on quantitative techniques for risk analysis; even if this mathematically rigorous approach can seem the most reliable one, the application to real cases usually require great simplifications, mainly related to the difficulties in obtaining the detailed information needed. Additionally, the high specialization level of this type of methodologies makes them hardly manageable for local administrations, that can have a limited technical preparation, and in many cases cannot afford the expenses for detailed risk investigations.

In order to address the problems above-mentioned, multi-risk projects like MATRIX [8] adopted a multi-level strategy, introducing the most technical phase of the methodology only after a first simpler phase. Analogously, the objective of the authors was the implementation of an easy-to-use risk screening instrument, based on a simplified methodology like an index approach, to allow the municipalities to directly evaluate the risks and possible risk interactions that affect their territory. After this, the Municipalities could define possible further actions, including the adoption of more specific risk-assessment procedures, in accordance with superior local authorities (provinces, regions).

The following paragraph presents the methodology proposed for this screening path. This approach was adopted as a baseline for the identification of the vulnerabilities of the territorial system and the measure of its resilience. As discussed in Section 3, a set of indicators of resilience are under development, able to estimate territorial vulnerabilities, and some of them are introduced in this paper.

#### **2. Materials and Methods**

#### *2.1. A Semi-Quantitative Methodology at a Local Scale*

The proposed methodology considers, in an integrated framework, the main risks on the territory and their possible interactions, in order to better orient further in-depth studies and interventions related to land use planning and emergency. Since the methodology was intended for a direct use from the municipalities' technicians, it recovered the simplified scheme adopted for the Italian plans related to the industrial risk, called E.R.I.R.—Elaborato Tecnico per il Rischio di Incidente Rilevante (Technical Plan for Major Risk accidents), composed by:

The risks to be taken into account were chosen following the concept of the "spatial relevance" stated in the ESPON project [9]: only risks that regularly or irregularly interest the same territorial area should be take into account, disregarding those that could take place everywhere. The methodology was developed for the risks more diffused in Italy: industrial, flood, and seismic risk, and, given the recent increase in extreme climatic events—violent rains, windstorms etc.—a climate related factor was also included. Each municipality should clearly consider also its main territorial criticalities, other than those included in the main model (i.e., volcanic risk, avalanches, wildfires, etc.).

A semi-quantitative approach was adopted, introducing a rating system common for all the main risks present on the territory. This type of approach, already employed in European projects [7] or regional methodologies [10], was chosen for its simplicity, which could allow its use also with low economical resources and technical skills. The adopted rating scale assumes different scores related to the possible impact of the risk/risks analyzed:

#### *2.2. Characterization of the Risks*

The first step of the proposed methodology consists of an in-depth analysis of the main territorial risks that insist on the territory of the municipality. An in-depth data collection has to be developed on the basis of existing sectorial plans, emergency plans, and through a direct investigation of the territory. In order to better understand and address the description of each risk, the risk characterization was based on three macro-categories, aimed at highlighting the characteristics of the analyzed risk which could mostly influence its dangerousness and its possible interaction with other events. The categories are:

The ratings defined in Section 2.1 were assigned to each risk based on these three macro-categories; a guideline for the assignation of the scores was defined. Climate related events were introduced

and rated, but they were evaluated through a simplified approach, related to the global tendencies, because an analysis of the local trends could present difficulties related to the data collection and interpretation. Table 1 below shows the guide for flood risk; it analyzes the functioning of individual regulatory subsystems or elements, such as water intakes, pumping stations, the water distribution network [11,12], and the events that occurred on the territory, both those reported in the flood plans through probabilistic approach, and the recent occurred ones.

**Table 1.** Guiding table for the assignation of the ratings to the flood risk.

#### *2.3. Risks Interactions*

The macro-categories SE, HE, and PM are the basis to assess the possible impact of risk interaction, because they determine the risk role in a possible risk interaction and provide useful indications on the possible plausible effects. However, the macro-categories have different levels of influence on the interaction, and different reliability in terms of data; therefore, different weights were attributed to express this variability. The weights (HE = 2, SE = 1, and PM = 0.5) were designed to obtain results in line with the general scale employed in the methodology (see Section 2.1.) and were validated through experts' judgement.

The binary risk interaction, intended as the impact a hazard factor could have on another one, should be assessed in the area of risk overlaying, where vulnerable environmental or territorial elements are present. The binary risk interaction is calculated through a weighted average of the values assigned to each category of the different risks, shown in Equation (1).

$$\mathbf{I} = \left[ (\mathbf{H} \mathbf{E}\_{\rm risk1} + \mathbf{H} \mathbf{E}\_{\rm risk2}) \right. \\ \left. \begin{aligned} \left( \mathbf{S} \mathbf{E}\_{\rm risk1} + \mathbf{S} \mathbf{E}\_{\rm risk2} \right) \ \* \ 1 + (\mathbf{P} \mathbf{M}\_{\rm risk1} + \mathbf{P} \mathbf{M}\_{\rm risk2}) \ \* \ 0.5 \right] \vert \mathbf{6} \end{aligned} \tag{1}$$

A dedicated binary interaction table was developed in order to simplify the assessment of the possible interactions: the values assumed by each risk macro-categories in the analyzed point of the territory are reported in the table; when a possible risk correlation was encountered, the formula of Equation (1) was applied.

The values of interaction obtained through the Table can be also assessed directly through a GIS (Geographic Information System): each risk factor can be represented on a single layer; then, it is possible to directly obtain the georeferenced value of the integrated risk intersecting the risk layers and making use of the "calculator" field.

Table 2 shows an example of an interaction table: in this case, on the territory under study, a major risk chemical plant (I) was present and flood (F), earthquake (E), and extreme climate events (C) could occur. The table shows the values assigned to each macro-category and it returns the results of the mutual interaction between the risks, where relevant. In the specific case, interactions with moderate effects could occur; in fact, despite the high dangerousness of the Seveso plant (due to the huge quantity of substances stored, presence of vulnerable items, etc. (SE = 3)), the natural risks had quite low values. The seismic risk was unlikely in the area (HE = 1), even if the poor quality of soil could enhance the SEISMIC EFFECTS (SE = 2), while the flood risk had been dramatically reduced through an effective system of protection (PM = −3); as a consequence, the possible binary interactions obtained a low value, tending towards moderate.

**Table 2.** Example of binary interaction table.

#### *2.4. Vulnerability and Compatibility Assessment*

The assessment of territorial and environmental vulnerabilities was based on the legislative indications of Ministerial Decree 09/05/2001 [13] and of D.G.R. 17/377 [14] for E.R.I.R. plan—plan for the safe planning of the areas around major risk plants. According to [13,14], the vulnerability is mainly identified as "exposure to the risks in terms of population"; the possible factors of sensitivity and coping capacity of the analyzed elements are not taken into account.

The proposed methodology recovered the classification of urban functions and strategic buildings in six different categories (see Table 3), assigned on the basis of the people density and mobility.

**Table 3.** DM 09/05/2001 territorial vulnerabilities.