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\"How did Gross Domestic Product (GDP) become the world's most influential indicator? Why does it still remain the primary measure of societal progress despite being widely criticised for not considering well-being or sustainability? Why have the many beyond-GDP alternatives not managed to effectively challenge GDP's dominance? The success of GDP and the failure of beyond-GDP lies in their underlying communities. The macro-economic community emerged in the aftermath of the Great Depression and WWII. This community formalised their 'language' in the System of National Accounts (SNA) which provided the global terminology with which to communicate. On the other hand, beyond-GDP is a heterogeneous community which speaks in many dialects, accents and languages. Unless this changes, the 'beyond-GDP cottage industry' will never beat the 'GDP-multinational'. This book proposes a new roadmap to 2030, detailing how to create a multidisciplinary Wellbeing and Sustainability Science (WSS) with a common language, the System of Global and National Accounts (SGNA)\"-- Provided by publisher.

Increased interest in managing resilience has led to efforts to develop standardized tools for assessments and quantitative measures. Resilience, however, as a property of complex adaptive systems, does not lend itself easily to measurement. Whereas assessment approaches tend to focus on deepening understanding of system dynamics, resilience measurement aims to capture and quantify resilience in a rigorous and repeatable way. We discuss the strengths, limitations and trade‐offs involved in both assessing and measuring resilience, as well as the relationship between the two. We use a range of disciplinary perspectives to draw lessons on distilling complex concepts into useful metrics. Measuring and monitoring a narrow set of indicators or reducing resilience to a single unit of measurement may block the deeper understanding of system dynamics needed to apply resilience thinking and inform management actions. Synthesis and applications. Resilience assessment and measurement can be complementary. In both cases it is important that: (i) the approach aligns with how resilience is being defined, (ii) the application suits the specific context and (iii) understanding of system dynamics is increased. Ongoing efforts to measure resilience would benefit from the integration of key principles that have been identified for building resilience.

Background The coronavirus pandemic has had a profound impact on organization and delivery of care. The challenges faced by healthcare organizations in dealing with the pandemic have intensified interest in the concept of resilience. While effort has gone into conceptualising resilience, there has been relatively little work on how to evaluate organizational resilience. This paper reports on an extensive review of approaches to resilience measurement and assessment in empirical healthcare studies, and examines their usefulness for researchers, policymakers and healthcare managers. Methods Various databases (MEDLINE, EMBASE, PsycINFO, CINAHL (EBSCO host), Cochrane CENTRAL (Wiley), CDSR, Science Citation Index, and Social Science Citation Index) were searched from January 2000 to September 2021. We included quantitative, qualitative and modelling studies that focused on measuring or qualitatively assessing organizational resilience in a healthcare context. All studies were screened based on titles, abstracts and full text. For each approach, information on the format of measurement or assessment, method of data collection and analysis, and other relevant information were extracted. We classified the approaches to organizational resilience into five thematic areas of contrast: (1) type of shock; (2) stage of resilience; (3) included characteristics or indicators; (4) nature of output; and (5) purpose. The approaches were summarised narratively within these thematic areas. Results Thirty-five studies met the inclusion criteria. We identified a lack of consensus on how to evaluate organizational resilience in healthcare, what should be measured or assessed and when, and using what resilience characteristic and indicators. The measurement and assessment approaches varied in scope, format, content and purpose. Approaches varied in terms of whether they were prospective (resilience pre-shock) or retrospective (during or post-shock), and the extent to which they addressed a pre-defined and shock-specific set of characteristics and indicators. Conclusion A range of approaches with differing characteristics and indicators has been developed to evaluate organizational resilience in healthcare, and may be of value to researchers, policymakers and healthcare managers. The choice of an approach to use in practice should be determined by the type of shock, the purpose of the evaluation, the intended use of results, and the availability of data and resources.

Resilience as a concept is multi-faceted with complex dimensions. In a disaster context, there is lack of consistency in conceptualizing social resilience. This results in ambiguity of its definition, properties, and pathways for assessment. A number of key research gaps exist for critically reviewing social resilience conceptualization, projecting resilience properties in a disaster-development continuum, and delineating a resilience trajectory in a multiple disaster timeline. This review addressed these research gaps by critically reviewing social resilience definitions, properties, and pathways. The review found four variations in social resilience definitions, which recognize the importance of abilities of social systems and processes in disaster phases at different levels. A review of resilience properties and pathways in the disaster resilience literature suggested new resilience properties—“risk-sensitivity” and “regenerative” in the timeline of two consecutive disasters. This review highlights a causal pathway for social resilience to better understand the resilience status in a multi-shock scenario by depicting inherent and adaptive resilience for consecutive disaster scenarios and a historical case study for a resilience trajectory in a multiple disaster timeline. The review findings will assist disaster management policymakers and practitioners to formulate appropriate resilience enhancement strategies within a holistic framework in a multi-disaster timeline.

In disaster studies, the measurement of resilience has developed through several conceptual phases, reflecting broader transformations in disaster risk research. It is clear that the initial approaches, rooted in ecology and engineering, defined resilience primarily as a system’s ability to absorb shocks and return to equilibrium. Although such perspectives provided clarity and measurability, they often reduced complex social realities to simplified technical indicators. Indeed, this did not provide comprehensive and precise insights into levels of resilience to different types of disasters. Over time, critics emphasized that resilience cannot be captured solely through stability or recovery speed, since communities are not mechanical systems but socio-political entities shaped by governance, culture, and inequality. In contrast, second-generation models introduced multidimensionality, incorporating economic, institutional, infrastructural, and environmental factors. Nevertheless, these frameworks frequently faced problems of comparability, data availability, and context sensitivity. Composite indicators, such as the Baseline Resilience Indicators for Communities (BRIC), sought to operationalize resilience systematically. However, they faced limitations in weighting procedures, variable selection, and the neglect of qualitative dimensions such as trust, solidarity, and adaptive learning. Similarly, the Disaster Resilience of Place (DROP) model provided valuable theoretical grounding, yet its transferability across diverse socio-political settings remained a challenge. According to various theoretical analyses, resilience measurement continues to face methodological and practical limitations, such as conceptual ambiguity (the absence of a universally accepted definition), indicator overload (large sets of variables that reduce analytical precision), underdeveloped statistical assessments (overlooking the dynamic, process-oriented nature of resilience), and contextual gaps (insufficient adaptation to local governance and cultural conditions). To address these challenges on a theoretical level, research was conducted in Serbia applying a qualitative empirical approach. 19 experienced local-level disaster management practitioners were interviewed. Semi-structured interviews and thematic analysis revealed how resilience is perceived and operationalized in practice, highlighting the gap between formal strategies and local realities. Findings indicate limited awareness, fragmented institutional cooperation, and an over-reliance on central authorities. Such results underscore the need for flexible, participatory, and context-sensitive measurement models. By critically tracing the evolution of resilience measurement – its achievements and shortcomings – this research underscores that resilience cannot be fully understood through indicators alone. A synthesis of quantitative rigor and qualitative insight is required to link global frameworks with local experiences. Such a comprehensive approach not only improves measurement but also strengthens governance and societal capacities, which are essential for addressing future risks.

How communities respond to and recover from damaging hazard events could be contextualized in terms of their disaster resilience. Although numerous efforts have sought to explain the determinants of disaster resilience, the ability to measure the concept is increasingly being seen as a key step toward disaster risk reduction. The development of standards that are meaningful for measuring resilience remains a challenge, however. This is partially because there are few explicit sets of procedures within the literature that outline how to measure and compare communities in terms of their resilience. The primary purpose of this article is to advance the understanding of the multidimensional nature of disaster resilience and to provide an externally validated set of metrics for measuring resilience at subcounty levels of geography. A set of metrics covering social, economic, institutional, infrastructural, community-based, and environmental dimensions of resilience was identified, and the validity of the metrics is addressed via real-world application using Hurricane Katrina and the recovery of the Mississippi Gulf Coast in the United States as a case study.

Reducing disaster risk and enhancing resilience are major global societal challenges. To inform this challenge, understanding resilience at the community level is especially important because the impact of disasters and the potential for resilient development are particularly acute at this scale. The last decade has seen a surge in efforts in measuring resilience to a variety of hazards, yet measurement frameworks lack empirical validation and widespread application. To bridge this information gap, we provide analysis into an unprecedented dataset: a standardized, empirically validated approach to community flood resilience measurement, applied in over 290 communities across 20 developing countries. The analysis is based on the Flood Resilience Measurement for Communities (FRMC) framework and tool designed to provide a holistic approach to measuring community flood resilience and to support implementation of resilience-strengthening interventions. Our analysis starts with an assessment of the validity and reliability of the data and leads into querying whether and how to organize the wealth of information of community contexts into a discrete set of clusters. Although we appreciate that fostering resilience has to be strongly context-aware, we also present a taxonomy related to flood risk and socioeconomic community characteristics, which, using multinomial and random forest methods, leads us to identifying five distinct community clusters based on their resilience profiles and capital scores. This clustering taxonomy provides a way to group communities by similarities and differences between absolute and distributional resilience levels and socioeconomic community characteristics. These clusters may serve as a resource for further examining efforts for building resilience, analyzing resilience dynamics over time, and informing policy options across the world.

The paper presents the model resilience measurement based on the complex network theory and analyzes the resilience of project portfolio network considering risk propagation. The model can be used to evaluate the resilience and improve its success probability of projects in the portfolio network. Firstly, the research analyzes the dynamic changes of the portfolio network derived from the construction of the project portfolio matrix, as well as the main factors to be taken into consideration in measuring the resilience of the project portfolio. Further, the research measures the resilience of the project portfolio network according to the node attributes (project) and the relationship attributes (the relationship that one project will impact another in the portfolio network) respectively. Then, to integrate the resilience of the projects and influence relationship between projects, the research proposes the dynamic PageRank algorithm to analyze the resilience of the project portfolio based on the analysis of traditional PageRank algorithm. In addition, resilience is not only affected by the projects and its influence relationship between them, but is also affected by risk propagation. Therefore, the research presents a model for analyzing the portfolio network resilience considering multiple risk propagation. Finally, a research and development project portfolio are taken as an example to demonstrate the effectiveness of the method presented in this research. Our approach can be used by managers to identify the scores of project resilience capacity in portfolio network. Our method explicitly allows to uncover the most resilient projects considering the resilience of project (node) and their influence relationship (network structure), and the risk propagation. Utilizing the outcomes of this research can enhance the capacity of the whole project portfolio to manage risks and improve the success probability of the whole project portfolio by enhancing network resilience.

Academics and practitioners have become more interested in the operationalization and measurement of social-ecological resilience. An analysis of how social-ecological resilience has been operationalized and measured is crucial to understanding systems complexity and dynamics and for clarifying empirical cases of monitoring programmes in ways that enrich their utility and explanatory power. The literature shows that social-ecological resilience has been operationalized using the concepts of adaptability and absorption of disturbance. In addition, diversity and connectivity are principles that have been studied. Climate change in rural coastal regions is the most common stressor that has been studied, and the human dimension of such systems is the dominant focus. Systems interactions, feedbacks and thresholds are rarely identified or assessed. In addition, attributes of the system primarily using indicators are preferred over analysing causal relationships with models. Answering the question of what this resilience is for is a very important aspect of defining the system and the method for assessing resilience.

As urbanization and informatization progress, human societies increasingly face unforeseeable public safety risks, necessitating a comprehensive governance system for early risk prevention. This paper introduces a model for community public safety resilience governance grounded in the adaptive cycle model and formulates research hypotheses. We derive a mathematical formula for calculating the safety disaster risk value using risk management theory, which aids in determining the resilience value and risk level of communities. The proposed governance model assesses community resilience through a four-faceted approach that encompasses natural disasters, accidents, public health events, and social security incidents. The public safety resilience of Community L in N city, Jiangsu Province, was evaluated using this model. Furthermore, it was determined that urban communities in NJ City, WX City, and XZ City had resilience governance scores of 77.655, 73.18, and 73.475, with scores exceeding 70 points. An analysis of 16 subject cities revealed that only five are currently in the renewal stage, representing an optimal state of high resilience and low risk. The remaining 11 cities face varying degrees of challenges. To prevent systemic decay, it is crucial to implement customized public safety governance strategies for different urban types.