Modelling resilience, a proof of concept.
Date: 2025-03-11
Institutions: Joint Research Centre
Subjects: European security, air quality, antimicrobial resistance, community resilience, critical infrastructure security, economic policy, public health, report, risk management, statistical method

Modelling Resilience, 
a proof of concept 

ISSN 1831-9424 

Montanari, E., Mara, T., Rosati, R., Sangiorgi, M., 
Greidanus, H., Piccinini, P., Galariotis, I. 

2025 

 EUR 40256

 
  
 
This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
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JRC140046 

EUR 40256 

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ISBN 978-92-68-25484-4   ISSN 1831-9424   DOI: 10.2760/4529903  

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How to cite this report: European Commission: Joint Research Centre, Montanari, E., Mara, T. and Rosati, R., Modelling Resilience, a proof of 
concept, Sangiorgi, M., Greidanus, H., Piccinini, P. and Galariotis, I. editor(s), Publications Office of the European Union, Luxembourg, 2025, 
https://data.europa.eu/doi/10.2760/4529903, JRC140046.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ...................................................................................................................................................................................................... 3 

1  Introduction: Why do we need resilience? ........................................................................................................................ 4 

1.1  COVID lesson: the resilient response models ...................................................................................................... 4 

1.2  Resilience views across corporations and individuals..................................................................................... 5 

1.3  The importance of modelling to forecast resilience: a health-related example.............................. 6 

2  Literature & Data Review .......................................................................................................................................................... 9 

2.1  Resilience Dashboard Indicators (Benczur et al. 2023) - EU27 ................................................................ 9 

2.2  Special mention for the “World Risk Poll 2024” (Global) ........................................................................... 10 

2.3  EUROSTAT COVID-19 Dashboard & Indicators (EU27, (EUROSTAT 2024)) ..................................... 12 

2.4  OECD, IEA and World Bank Indicators (Global) ................................................................................................ 13 

2.5  The United Nations Sustainable Development Goals (SDGs, Global) .................................................. 14 

2.6  The INFORM Risk Index (Global) ((JRC) 2024) .................................................................................................. 14 

3  Methodology .................................................................................................................................................................................. 16 

3.1  Resilience: Capturing the target variable ............................................................................................................ 16 

3.2  Resilience: In search of the predictor variables ............................................................................................... 16 

3.3  Modelling challenges ...................................................................................................................................................... 16 

3.4  Statistical modelling reflections ............................................................................................................................... 18 

3.5  Analysis approaches ....................................................................................................................................................... 18 

3.6  Synthetic Indicators logistic & linear regression with endogenous Resilience .............................. 20 

3.7  Full Set of Indicators Linear regression with exogenous Resilience .................................................... 21 

3.8  Global Sensitivity Analysis and Shapley application..................................................................................... 22 

4  Results and Findings ................................................................................................................................................................. 25 

4.1  Synthetic Indicators Logistic Regression with endogenous Resilience ............................................... 25 

4.2  Full Set of Indicators Linear Regression with exogenous Resilience ................................................... 26 

4.3  Sensitivity Analysis with Shapley (Bayesian Sparse Polynomial Chaos Expansion PCE) ......... 29 

5  Conclusions ..................................................................................................................................................................................... 32 

References ............................................................................................................................................................................................. 33 

List of abbreviations and definitions ...................................................................................................................................... 35 

List of figures ....................................................................................................................................................................................... 36 

List of tables ......................................................................................................................................................................................... 37 

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Annexes ................................................................................................................................................................................................... 38 

Annex 1. Synthetic Indicator Data with Endogenous Resilience ....................................................................... 38 

2 

 
 
Abstract 

Enhancing European resilience and security is a European Commission priority for the near, mid, and 
long- term horizons.  The purpose of this report is to introduce a basic statistical model of resilience 
prediction based on indicators. It also includes  an advanced sensitivity  analysis using the Shapley 
method. 

Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. 
For this reason, resilience is often depicted on a timeline with three specific elements: anticipation, 
cushioning and adaptation - transformation, wrapping around one or multiple shocks or disturbances 
(European Commission 2020). 

Several European and International institutions track many indicators in the forms of dashboard and 
trend charts to resilience of such entities. For instance, the Resilience Dashboard at the Joint Research 
Center (Benczur et al. 2023) has compiled a structured dashboard accounting for 124 quantitative 
indicators for the past several years while EUROSTAT has been tracking indicators on their resilience 
dashboard. 

This  report  exploits  the  continuous  progress  made  within  JRC  and  proposes  others  drawn  from 
reputable international institutions and institutes such as the World Bank, United Nations and Lloyd's 
Register Foundation for enhanced prediction purposes.   

The application of logistic and linear regression models has shown numerous statistically significant 
indicators  relevant  for  resilience  prediction.    It  is  also  evident  that  more  nuanced  exploration  of 
continuous measures of resilience, as evidenced by its application to the World Risk Poll data technical 
report, produces substantial improvements in the statistical significance of indicators.  Not least, the, 
the deployment of the sensitivity analysis using the Shapley Model has revealed significant benefits 
for  identifying  the  contribution  of  individual  variables  to  the  variance  in  resilience  outcomes.  This 
method has highlighted the interconnected nature of a restricted number of indicators, such as active 
citizenship, years of life lost due to PM2.5, antimicrobial resistance, and the degree of specialisation 
of the economy, providing a more sophisticated understanding of their impact on resilience. 

3 

 
 
 
 
 
 
1 

Introduction: Why do we need resilience? 

The European Security Union Strategy (European Commission 2024a) highlights growing importance 
of  addressing  emerging  security  challenges  and  enhancing  societal  resilience  in  an  increasingly 
complex and interconnected world. As outlined in the seventh EU Security Union Report (EU, 2024), 
the  European  Union  has  implemented  a  comprehensive  approach  to  security  that  spans  multiple 
domains and recognizes the evolving nature of threats. 

Emerging  security  challenges,  such  as  cyber-attacks,  hybrid  threats,  and  foreign  information 
manipulation, have become more prominent and require innovative responses. The EU has responded 
by developing new tools and frameworks, like the EU Hybrid Toolbox (Council of the EU 2024) and 
measures  to  counter  foreign  interference,  particularly  in  the  context  of  protecting  democratic 
processes. 

Societal resilience has emerged as a crucial aspect of the EU's security strategy, recognising that a 
robust  and  adaptable  society  is  key  to  withstanding  and  recovering  from  various  threats.  This  is 
evident in the adoption of directives like the Critical Entities Resilience (CER) (European Commission 
2023a)  and  the  revised  Network  and  Information  Security  (NIS2)  (European  Commission  2023b), 
which aim to strengthen the resilience of critical sectors against both cyber and physical threats. 

By prioritising these areas, the EU aims to create a more secure environment for its citizens while 
maintaining the openness and values that characterise European societies. This balanced approach 
to security and resilience is crucial in addressing the complex and evolving threat landscape of the 
21st century.  

The Joint Research Centre (JRC) has outlined new priorities for 2024 (European Commission and Joint 
Research Centre 2023), with a strong emphasis on societal resilience and preparedness. This focus 
aims to enhance European societies' capacity to anticipate, adapt to, and effectively respond to multi-
faceted  and  interconnected  challenges  across  sectors  such  as  health,  environment,  food,  water 
management, and security. The JRC plans to harness anticipatory capabilities to proactively identify 
and address emerging security risks and vulnerabilities, enabling timely responses to potential crises. 
Additionally,  it  will  address  the  interconnected  and  cascading  nature  of  many  threats,  while 
contributing to strengthening the resilience of EU democratic institutions and social cohesion. 

The  JRC's  integrated  approach  to  security  and  defence  reflects  a  "whole-of-society"  perspective, 
encompassing critical infrastructure protection, public spaces/people protection, border security, and 
hybrid threats. It also aims to strengthen research on technology permeability between civil, space, 
and defence sectors. Other priorities include analysing the social, economic, and territorial impacts of 
transitions to foster fairness, studying migration patterns and their links to security, and supporting 
candidate  countries  in  adopting  EU  standards.  These  priorities  collectively  reinforce  the  JRC's 
commitment to building a more resilient, secure, and adaptable European Union.  

This report used the definition of resilience drawn from the JRC “2020 Strategic Foresight Report”: 
"Resilience is the ability not only to withstand and cope with challenges but also to undergo transitions, 
in a sustainable, fair, and democratic manner." 

1.1  COVID lesson: the resilient response models 

The COVID-19 pandemic and geopolitical instability demonstrate three key resilience aspects: 

1) Anticipation through horizon scanning 

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2) Cushioning via emergency policies 

3) Adaptation through systemic reforms 

The  need  for  developing  resilience  is  becoming  compelling  in  the  face  of  the  so-called  emerging 
volatile, uncertain, complex and ambiguous scenarios (VUCAS) exposing weaknesses in the ability to 
withstand unpredictable existential threats to the socio-economic fabric of the EU.  

1.2  Resilience views across corporations and individuals 

Resilience is often invoked by think tanks and consultancies  as one of the  key  factors  to improve 
response to a variety of threats including security ones in the medium and longer term. 

The three shock quantification parameters (depth, length, shape) correspond to Figure 1's resilience 
phases: 1) Depth maps to anticipation, 2) Length to cushioning, 3) Shape to adaptation. 

Figure 1. Key parameters for shock quantification. 

Source: (McKinsey 2020) 

Today’s government and corporate leader's mantra is to improve resilience to cope with a multitude 
of  unknown  and  unpredicted  threats  by  anticipating  and  establishing  cushioning  that  introduces 
future advantage at recovery. For example, Boston Consulting Group shows how resilient corporations 
have the merit to protect total shareholder returns as well as lives and livelihoods. 

5 

 
 
Figure 2. Crisis’ Phases and Resilient Response. 

Source: (Boston Consulting Group 2021) 

Improving resilience is also the mantra of the insurance business, “World Risk Poll” (Lloyd’s Register 
Foundation  2024)  by  Lloyd’s  Register  Foundation  goes  into  many  details  of  the  constituents  of 
resilience at various levels and dimensions. 

The World Risk Poll found that the U.S. ranks high for resilience on an individual basis. However, the 
country ranks lower for societal resilience, which measures confidence in U.S. institutions, like courts 
and elections, among other things. The poll found that no other country in the world has such resilient 
individuals living in a society perceived to be so fragile.  

Individual  resilience  measured  two  things:  a person’s  level  of  agency,  or  their  perceived  ability  to 
protect themselves or their family from a future disaster, and their education level. Societal rankings, 
on the other hand, measured reported discrimination and perceptions about the government’s care 
for individuals’ wellbeing and the standings of its institutions, like the military, courts and elections. 

While  looking  at  the  outcomes  from  Lloyd’s  study,  this  research  will  address  only  country  level 
resilience based on the dashboard data available (Benczur et al. 2023) aiming to address multi-level 
resilience in future publications. 

1.3  The importance of modelling to forecast resilience: a health-related 

example 

This report provides modelling of predictive analysis as described in the framework below focusing 
on the most significant subset of dashboard indicators (Benczur et al. 2023) to help and inform any 
future policy that may be determinant to build resilience in the most efficient way. 

6 

 
 
Table 1. Analysis Objective linked to Modelling 

Source MIT Horizon 

Prediction modelling is the basic technique to forecast future trends and identify potential risks or 
opportunities in our field using historical data. Predictive analysis, as outlined in the image, serves as 
our  primary  methodological  approach  because  it  enables  us  to  anticipate  potential  shifts  in  our 
subject matter without prescribing specific actions.  

A notable example of predictive analysis occurred in 1948 after Roosevelt's death from cardiac arrest, 
the Framingham Heart Study (framingham heart study 2024).  

This  long-term,  ongoing  cardiovascular  cohort  study  started  with  5,209  adult  participants  from 
Framingham,  Massachusetts.  The  study  has  continued  for  multiple  generations,  with  new  cohorts 
added over time to maintain its relevance and expand its scope.  

The key points about the study's timeline:  

—  1948: The original cohort study began.  

—  1971: The Offspring Study (second generation participants) was initiated.  

—  2002: The Third Generation Study began, enrolling grandchildren of the original participants.  

7 

 
 
The risk prediction model based on the Framingham study data has been updated several times since 
its  initial  development.  The  first  major  risk  prediction  model  was  published  in  1998,  using  data 
collected over the decades since the study's inception.  

While the study began in 1948, the risk prediction models and tools derived from its data have been 
developed  and  refined  over  time,  incorporating  new  findings  and  adapting  to  changing  health 
landscapes.    

Today and using 6 easily measurable indicators i.e. age, gender, cholesterol, blood pressure, smoking 
status,  diabetes  status, it is  possible  to  predict  whether  the  patient  would  develop  cardiovascular 
disease  over  the  next  10  years.  Notably,  blood  pressure  retained  as  a  leading  indicator  of 
cardiovascular risk that was previously considered a sign of strength. 

This example shows that it is possible to make meaningful predictions when the appropriate set of 
indicators is accurately identified and effectively incorporated into a statistical model. 

8 

 
 
2  Literature & Data Review 

This  report  focused  on  the  wealth  of  data  derived  from  JRC  Resilience  Dashboard  (European 
Commission 2020).  

This  report  also  compares  specific  databases  and  indicators  from  other  European  or  International 
institutions to identify any potential future opportunity and gap in the well-developed data universe. 

2.1  Resilience Dashboard Indicators (Benczur et al. 2023) - EU27 

This  data  originated  from  the  European  Commission's  Resilience  Dashboard  with  data  collection 
inception in 2021.  

In its simplest form the Resilience Dashboard (RDB) comprises eight synthetic indicators derived from 
the  full  set  of  124  quantitative  indicators  selected  by  their  capability  to  describe  an  element  of 
resilience or being a proxy of the same.  

The synthetic resilience indicators are split into four vulnerabilities and four capacities and aggregated 
across  the  following  main  thematic  dimensions  i.e.  Social  and  economic,  Green,  Digital  and 
Geopolitical.  

Below is the rendering of the synthetic vulnerabilities and capacities per thematic dimension  and 
split across the 27 EU member states. 

Figure 3. Synthetic Index Dashboard  

Source (Benczur et al. 2023) 

The RDB colour coding observed in Figure 3 is linked to the normalised quintile indicators values in 
Table 2 as following. 

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Table 2. Resilience Quintiles.  

Vulnerability (V) 

Capacity 

(C)Blue 

Light Blue 

Grey 

Orange 

Red 

1 

0.75 

0.5 

0.25 

0 

0 

0.25 

0.5 

0.75 

1 

Source (Benczur et al. 2023) and own elaboration 

A member state demonstrates resilience when ≥60% of indicators show blue (quintile 4-5), indicating 
superior performance across capacities and vulnerabilities. 

One of the initial issues in assessing the outcome through such inference is the endogeneity of the 
dashboard derived resilience outcome. Endogeneity refers to a situation where the indicators used in 
the  analysis  affect  both  the  outcome  variable  and  other  indicators  used  to  derive  the  outcome 
variable as it is shown in the multicollinearity analysis further below and as it is the case for the 
dashboard derived resilience. 

2.2  Special mention for the “World Risk Poll 2024” (Global) 

When researching for an independent source of resilience measure, this report found in the world risk 
poll 2024 (Lloyd’s Register Foundation 2024), a suitable input: this global survey provides an external 
perspective on resilience that is particularly useful because it offers data from beyond the European 
Union,  allowing  researchers  to  validate  and expand  their  correlations  across  a  wider  geographical 
context. Such resilience measure will be indicated as exogenous further in this report as it extends 
the opportunity of correlation verification. 

Lloyd’s conducts its poll on global resilience every two years through a third party, Gallup. The biennial 
reporting cycle (2022, 2024) publishes analysed data collected during preceding years (2021, 2023 
respectively). Having two reports lets us track trends over time, in addition to drawing conclusions 
about global resilience for 2022 and 2024 separately. Future reports will enable longitudinal analysis 
of global resilience evolution between reporting periods. 

10 

 
 
 
Figure 4. World Risk Poll 

Source: (Lloyd’s Register Foundation 2024) 

The World Risk Poll reports use a bottom-up data collection approach that makes it ideal to derive an 
exogenous measure for resilience. The process summarised is: 

—  Large-scale global survey: The 2022 report was based on 125,911 interviews in 121 countries 
and territories, while the 2024 report expanded to nearly 147,000 interviews in 142 countries 
and territories. 

—  Multilayered resilience measurement: Both reports assess resilience at four levels - individual, 
household, community, and societal. This type of multilevel analysis has great potential to 
enhance the granularity of the resilience modelling and analysis. 

—  Resilience Index: Both reports compile the various indicators into a global Resilience Index, 

scoring countries between 0 and 100. 

A few analysis points are here extracted from the World Risk Poll 2024 shows significant findings 
when resilience is measuring a multiple levels or granularity e.g. country vs. individual levels: 

—  Individual resilience fell globally, with significant drops in over a third (42) of countries 

measured in both years. 

—  The 2024 report measures 120 of the same countries surveyed in 2021, allowing for trend 

analysis. 

—  The 2024 report shows a slight increase in global resilience, with the average Resilience Index 
score rising from 55 in 2021 to 57 in 2023. While the 2022 report provided baseline data, the 
2024 report reveals that three-quarters of countries (90 of 120) saw no significant change in 
Resilience Index scores. 

—  Disaster experience: The 2024 report shows an increase in people experiencing disasters related 

to natural hazards, rising from 27% in 2021 to 30% in 2023. 

—  While the 2022 report focused more on regional differences, the 2024 report highlights 
employment status, household income, and age as strongly associated with resilience. 

In this report, the resilience index outcome for 2024 is used to correlate with the Resilience Dashboard 
Indicators. 

11 

 
 
2.3  EUROSTAT COVID-19 Dashboard & Indicators (EU27, (EUROSTAT 

2024)) 

The EUROSTAT COVID-19 dashboard is also an excellent framework for measuring societal resilience 
through  multiple  interconnected  indicators.  Beyond  the  classic  econometrics  indicators  well 
represented  in  the  Resilience  Dashboard  (Benczur  et  al.  2023),  the  EUROSTAT  dashboard  offers 
unique insights through its diverse economic and social metrics. The inclusion of dynamic indicators 
like  business  registrations,  bankruptcy  declarations,  and  economic  sentiment  provides  real-time 
signals of economic resilience, while mobility indicators such as flight numbers and tourism nights 
capture the recovery patterns in cross-border activities. 

This  multifaceted  approach  enables  a  more  nuanced  understanding  of  systemic  resilience  across 
different societal dimensions and the list keeps growing. 

Figure 5. European Statistical Monitor 

Source (EUROSTAT 2024) 

Several  additional  indicators  to  be  modelling  in  future  research  that  could  enhance  the  resilience 
assessment  framework  in  future  research  iterations.  Here  are  the  indicators  from  this  report 
comparison that are not explicitly represented in the current Resilience Dashboard: 

—  GDP 

—  Industrial production 

—  Inflation 

—  Economic Sentiment 

—  Business Registrations 

—  Bankruptcy declarations 

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—  Number of Flights 

—  Production in services 

—  Retail Trade 

—  Production in construction 

—  Government Debt 

—  Labour Market Slack 

—  Tourism Nights 

—  From Unemployment to Employment, From Unemployment to Inactivity, From Employment to 

Inactivity 

—  Excess Mortality 

2.4  OECD, IEA and World Bank Indicators (Global) 

The  Organisation  for  Economic  Co-operation  and  Development  (OECD),  the  International  Energy 
Agency (IEA), and the World Bank are paramount international organisations that collect, analyse, and 
maintain comprehensive datasets vital for understanding global socio-economic and environmental 
dynamics. 

The  OECD,  comprising  38  member  countries,  centres  on  economic  cooperation  and  development, 
providing standardised metrics across areas such as education, inequality, and infrastructure (OECD 
2024).  The  IEA,  an  autonomous  intergovernmental  organisation  within  the  OECD  framework, 
specialises in energy-related data and analysis, particularly in tracking renewable energy adoption 
and energy security (International Energy Agency 2024). The World Bank, through its extensive data 
collection  efforts  including  the  Worldwide  Governance  Indicators,  offers  crucial  insights  into 
governmental effectiveness, regulatory quality, and various development indicators across nations. 
These  organisations'  indicators  serve  as  fundamental  tools  for  policymakers,  researchers,  and 
institutions  in  assessing  and  building  resilience  across  economic,  social,  and  environmental 
dimensions (World Bank 2024). 

Several  crucial  indicators  that  could  enhance  the  resilience  assessment  framework  warrant 
consideration  for  future  research.  A  comprehensive  analysis  of  resilience  would  benefit  from  the 
inclusion of the following indicators. 

Table 3. International Organisations’ Relevant Indicators.  

Field 

Indicator 

Digital Resilience Metrics 

-  Cybersecurity preparedness indices 
-  Digital infrastructure robustness measurements 
-  Digital literacy and adoption rates 

Climate Adaptation Indicators 

-  Specific adaptation measures effectiveness 
-  Climate-related disaster recovery capabilities 

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Social Cohesion Metrics 

Supply Chain Resilience 

Mental Health and Wellbeing 

Urban Resilience 

Financial System Stability 

Ecosystem Resilience 

Innovation Ecosystem 

Intersectional Vulnerabilities 

-  Climate-resilient infrastructure assessments 

-  Social trust indices 
-  Community engagement levels 
-  Cultural diversity and integration measures 

- 
Trade partner diversity indices 
-  Critical resource availability tracking 
-  Supply chain redundancy measures 

-  Population-level mental health metrics 
-  Access to mental health services 
-  Work-life balance indicators 

-  City-level adaptive capacity measures 
-  Urban infrastructure flexibility 
-  Smart city implementation metrics 

-  Market stress indicators 
- 
Financial inclusion metrics 
-  Alternative finance accessibility 

-  Biodiversity indices 
-  Natural capital assessments 
-  Ecosystem services valuations 

-  Patent diversity metrics 
-  Startup survival rates 
- 

Innovation adoption speed 

-  Multidimensional poverty indices 
-  Social vulnerability assessments 
-  Cross-system risk measures 

Source: (OECD 2024; World Bank 2024), and Own elaboration 

These additional indicators would provide a more nuanced understanding of systemic resilience and 
better capture the interconnected nature of modern societal challenges in future research. 

2.5  The United Nations Sustainable Development Goals (SDGs, Global) 

Not  least,  the  United  Nations  SDG  indicators  were  reviewed  and  compared  to  the  Resilience 
Dashboard and found well-represented for what concerns resilience. 

2.6  The INFORM Risk Index (Global) ((JRC) 2024)  

The INFORM Risk Index is a tool that uses composite indicators to simplify complex crisis risk data 
and aid policymaking for crisis anticipation and preparedness. It highlights countries at high risk of 
humanitarian  emergencies  and  disasters  that  may  exceed  their  national  response  capacities, 
potentially  needing  international  assistance.  The  index  has  three  main  components:  hazard  and 

14 

 
 
 
 
exposure, vulnerability, and lack of coping capacity. Among  these, vulnerability and lack  of  coping 
capacity are particularly important for identifying indicators of resilience. 

Vulnerability: This measures what makes a population more vulnerable to hazards. Vulnerability is 
often linked to unmet basic needs and limited self-protection, focusing on: 

  The ability of individuals or households to afford safe living conditions and maintain 

well-being. 

  Factors like housing safety, access to healthcare, nutrition, and preventive medicine to 

reduce health risks during disasters. 

  Beyond income, addressing vulnerabilities requires improved development, education, 

and cultural awareness to enhance resilience. 

Lack of Coping Capacity: This evaluates how well a country can handle disasters through: 

—  Government actions to increase societal resilience. 

—  Success in implementing disaster response measures. 

—  Existing infrastructure and organized efforts to reduce disaster risks. 

15 

 
 
 
 
3  Methodology 

3.1  Resilience: Capturing the target variable 

The first step in policy-making analysis is to clearly and precisely define the objective, i.e. what we 
aim  to  understand,  analyse,  and  address  with  policy  recommendations.  In statistical  analysis,  this 
requires defining the target variable(s) with clarity and precision. This step is crucial for understanding 
the study's purpose and determining the best approach to model it effectively.  

The  term  "resilience"  is  inherently  complex,  making  it  challenging  for  researchers,  analysts,  and 
policymakers  to  define  a  clear  target  variable  for  accurate  analysis  and  predictions.  The  2020 
Strategic Foresight Report offers not only a useful definition but also divides resilience into four key 
dimensions:  a)  social  and  economic,  b)  geopolitical,  c)  green,  and  d)  digital.  This  approach  is  also 
followed in the 2021 Resilience Dashboards Report in which the resilience dashboards developed by 
the European Commission are presented in detail. By aligning with these dimensions and identifying 
relevant datasets, we can establish four distinct target variables corresponding to these categories. 
Therefore, one approach would be to define four target variables corresponding to the dimensions of 
resilience:  

1.  social and economic resilience; 

2.  geopolitical resilience; 

3.  green resilience and  

4.  digital resilience.  

Afterward, we can develop a single, comprehensive target variable for resilience that combines all 
the  previously  mentioned  target  variables  into  one.  This  structured  approach  allows  for  a  more 
detailed and comprehensive analysis of the multifaceted nature of resilience. 

3.2  Resilience: In search of the predictor variables  

The predictor variables could be identified through a deep literature review to determine the factors 
influencing resilience, the contexts in which they operate, and the levels of analysis they apply to. The 
2021  Resilience  Dashboards  Report  provide  a  strong  foundation  for  identifying  factors  that  may 
influence resilience. It presents 124 quantitative indicators representing vulnerabilities or capacities 
that  could  impact  future  societal,  economic,  digital,  and  green  transformations  or  help  manage 
potential new shocks. These indicators could be considered potential factors influencing resilience and 
warrant thorough exploration to determine their relevance and effectiveness as drivers of resilience. 

This step is crucial in the modelling phase because identifying the predictor variables will guide the 
search for relevant and reliable datasets. It will also help in selecting the most suitable models for 
making inferences and predictions about resilience. 

3.3  Modelling challenges 

Level of analysis: An essential aspect of modelling resilience is comprehensibly defining the level 
of analysis. Resilience operates at multiple levels, each with distinct characteristics and implications. 
For this report, we have identified three levels of analysis: 

16 

 
1. 

Individual: Focuses on personal adaptability and mental health support. 

2.  Community: Centers on local networks and support systems. 

3. 

Institutional: Examines the strength of governance, legal frameworks, and crisis 
management mechanisms. 

Clarifying  the  level  of  analysis  is  crucial  as  it  guides  the  selection  of  an  appropriate  modelling 
framework to make inferences and predictions about resilience. This multi-layered approach ensures 
a comprehensive understanding of the concept across different contexts. 

Data  management:  A  key  step  in  analysing  resilience  is  understanding  and  preprocessing  the 
available data. Once suitable data for the target and predictor variables is identified, the next steps 
involve data collection, exploratory data analysis and data cleaning and preparation. 

1.  Data Collection: 

(a)  Priority is given to open-source databases for unrestricted access. 

(b)  For proprietary survey data, collaborations with private companies may be 

necessary to obtain access. 

(c)  It is essential to store the collected data in a robust IT infrastructure to ensure easy 

and immediate access for researchers and policy analysts. 

2.  Exploratory Data Analysis (EDA):  After collection, exploring the data helps uncover 

patterns, trends, and anomalies. This involves: 

(a)  Creating visualizations such as histograms, scatterplots, and other graphical 

representations. 

(b)  Summarizing data using descriptive statistics. 

(c)  Detecting inconsistencies, errors, and outliers, which informs the data-cleaning 

process. 

3.  Data Cleaning and Preparation: 

(a)  Correct errors and handle missing or incoherent data. 

(b)  Identify and address outliers. 

(c)  Apply transformations to predictor variables, such as normalization or rescaling, to 

ensure consistency and suitability for modelling. 

These steps are crucial for ensuring the data's reliability and readiness for the subsequent modelling 
phases. 

Missing  data:  Resilience  modelling  will  rely  on  open-source  data  for  known  resilience  drivers  to 
estimate future societal risks and threats. The precision and performance of statistical models heavily 
depends  on  the  quality  and  coverage  of  the  data  used.  However,  datasets  often  face  significant 
challenges with missing data, which can undermine model reliability: 

1.  Bias in Estimates: Missing data is rarely random, which can lead to biased model 

estimates if not properly addressed (Honaker and King 2010). 

17 

 
2. 

Incomplete Data for Predictions: Predictions require complete data for all predictor 
variables in a given year, making missing data a critical issue. 

A common solution to missing data is imputation (Rubin 1987), which involves estimating missing 
values based on the distribution of observed data. While imputation is effective with small amounts 
of missing data,  it  becomes  less  reliable  as  data  gaps  increase.  Whether  using  single  or  multiple 
imputation techniques, careful consideration of the dataset’s characteristics is necessary to apply a 
robust and reliable imputation method for addressing missing data challenges. 

3.4  Statistical modelling reflections 

The selection of a suitable statistical model depends primarily on the objectives set by policymakers—
whether for inference, prediction, or both. A statistical model should be based on clear assumptions 
about the relationships between indicators and the model outcome. 

In this report, we propose a prototype statistical model as a foundational approach to: 

1.  Demonstrate how to model the effect of resilience drivers on resilience. 

2.  Provide policymakers with predictions based on these relationships between resilience and 

the drivers of resilience. 

A prototype model serves as the starting point for identifying the relationships between target and 
predictor variables and determining which variables are relevant to retain in the analysis. Once the 
prototype  model  is  finalized,  it  can  be  expanded  to  incorporate  greater  complexity  by  employing 
advanced techniques, such as: 

  Time Series Models: To analyse trends over time. 

  Longitudinal Models: To study changes in resilience across different time points. 

  Multilevel Models: To account for the hierarchical structure of data (e.g., individual, 

community, institutional levels). 

  Machine Learning Models: For handling large datasets and uncovering non-linear 

patterns. 

  Stochastic Models: Agent-based or Brownian motion dynamic modelling techniques 
may enable the creation of system that can adapt and learn much like the community 
or societies it models. 

This  progression  ensures  both  a  robust  understanding  of  resilience  and  actionable  insights  for 
policymakers. 

3.5  Analysis approaches 

This report utilises the logistic and linear regression t-stat approach and the Global Sensitivity Analysis 
(GSA) in steps of growing model complexity for analysing and determining the correlation and the 
most significant indicator(s) for any potential policy recommendation about resilience. 

The following considerations apply between t-stat and GSA applications: 

18 

 
1.  There are complementary strengths between logistic/linear regression t-stat, providing 

immediate statistical significance for indicators, and GSA, capturing complex interactions 
and non-linear effects. 

2.  The combination of the two methods offers a more comprehensive analysis with statistical 

validation using t-stats and broader system-wide understanding using GSA. 

3.  Each method has better field complexity applications, and, in this way, this report can 

address both simple and complex relationships between indicators and their interacting 
nature. 

The following comparative table provides a comprehensive review of the methods to be used in this 
report: 

Table 4. Comparative analysis of regression and sensitivity methods.  

Method 

Pros 

Cons 

Best Used For 

t-statistics 
in logistic 
& linear 
regression  

- Simple to interpret 
- Tests specific hypotheses 
- Well-established statistical 
significance 
- Works well for comparing 
means 

GSA 

- Captures non-linear 
relationships 
- Analyses multiple variables 
simultaneously 
- Identifies interaction effects 
- Quantifies importance of 
variables 

- Only for single variables 
- Assumes normal distribution 
- Limited to linear relationships 
- Can't handle interactions 

- Comparing means between 
groups 
- Testing specific hypotheses 
- Simple before/after 
comparisons 

- Computationally intensive- 
Requires more data 
- More complex to interpret- May 
need multiple methods 

- Understanding variable 
interactions 
- Ranking variable importance 
- Analysing complex systems- 
Model validation 

Source: (Duflo, Glennerster, and Kremer 2006), (Iooss and Prieur 2019), and own elaboration 

In addition to the t-stat approach and the GSA, Principal Component Analysis (PCA) may be used to 
reduce dimensionality and to identify patterns in data. This method handles multicollinearity and is 
computationally  efficient  while  it  assumes  linearity  and  is  sensitive  to  outliers.  It  was  already 
proposed and reviewed in the literature hence not pursued further.  

Furthering  the  explanation  on  the  logistic  and  linear  regression  t-stat  approaches,  this  report 
measures  the  statistical  significance  of  the  hypothesis  (H1)  that  any  indicator  is  predictive  of 
resilience where null hypothesis (H0) being no significance. The measure takes the name of p-value 
and it is compared to a maximum value of 5% to reduce the chance that the correlation occurs by 
chance. 

19 

 
 
Figure 6. Hypothesis test with p-value 

Source: (TestView 2024) 

3.6  Synthetic Indicators logistic & linear regression with endogenous 

Resilience 

This report  employs  logistic  regression as the first step and as  the extreme  simplified  analysis to 
determine the relationship between binary resilience outcomes and various synthetic indicators. This 
statistical  technique  allowed  us  to  estimate  the  probability  of  a  positive  (or  negative)  resilience 
outcome based on one or more predictor variables.  

Logistic regression is often used to model “expert assessment” with binary outcomes 0 or 1 while it 
has limited modulation of the outcomes and assumes independence among independent variables 
requiring multicollinearity verification (Williamson 2024). 

When using the data of the Resilience Dashboard, the logistic regression models the binary “resilience” 
using the eight indicators, be it vulnerability or capacity.  

The logistic regression equation is represented in the picture below in comparison to the classic linear 
regression. 

Figure 7. Regressions 

Source: (Sayad 2024) 

20 

 
 
 
Predictivity of each indicator is assessed through its statistical significance at 5%.  

This first logistic regression is performed using the following R code: 

# logistic regression model 

model <- glm(Resilience ~ Indicator, data = data, family = 
"binomial") 

# Summary of the model 

summary(model) 

R Simplified Logistic Regression. Source: Own elaboration 

Similarly,  this  report  attempted  to  run  multivariate  regression  using  all  synthetic  indicators.  The 
logistic regression of such procedure is: 

P(Y=1|X) = 1 / (1 + e^(-(β₀ +  Xᵢ Xj ...+ ε))) 

Where P(Y=1|X) is the probability of the outcome given the input indicators 

Xᵢ and Xᵢ are the paired predictor variables 

βᵢ are the regression coefficients. 

The results of such regression will not be presented in this report because inconclusive. 

3.7  Full Set of Indicators Linear regression with exogenous Resilience 

To complement our logistic regression analysis, this report has applied linear regression to examine 
continuous resilience measures from the World Risk Polls (Lloyd’s Register Foundation 2024). This 
method establishes correlations between quantitative resilience outcome and the full list of Resilience 
Dashboard indicators. 

Linear regression provides further insights into the direction and magnitude of relationships between 
variables,  allowing  to  create  better  predictive  models  and  identify  the  most  influential  factors 
contributing to resilience. 

Linear  Regression  is  therefore  a  more  nuanced  method  to  understand  of  relationships  between 
various resilience indicators and system outcomes (Brandon 2016) , quantified by: 

Y = β₀ + β₁X₁ + β2 USSR + β3 PIIGS + ε 

Where Y is the outcome variable resilience 

Xᵢ are the predictor variables 

βᵢ are the coefficients  

21 

 
 
USSR1 and PIIGS2 are dummy variables, i.e., they are categorical variable with value either 0 
or 1.  

ε is the error term. 

The  linear  regression  assumes  independence  among  independent  indicators,  so  it  requires 
multicollinearity verification. The linear regression is performed using the following R code: 

# Linear regression model 

model <- glm(Resilience ~ Indicator + USSR + PIIGS, data = data, 
family = "gaussian") 

# Summary of the model 

summary(model) 

R Linear Regression. Source: Own elaboration 

This report also applied a multivariate linear Regression for pairs of indicators, the general formula 
for it is: 

Y = β₀ + β₁ Xᵢ Xj ...+ ε 

Where Y is the outcome variable 

Xᵢ and Xᵢ are the paired predictor variables 

βᵢ are the coefficients 

ε is the error term. 

The results of such regression will not be presented in this report because inconclusive. 

3.8  Global Sensitivity Analysis and Shapley application 

When  analysing  complex  systems,  it  is  important  to  understand  how  different  input  factors  or 
variables  contribute  to  the  overall  output  or  outcome.  Global  sensitivity  analysis  is  a  powerful 
technique that helps us do this. It goes beyond just looking at the individual impact of each variable 
- it must also consider how the variables interact with each other to influence the result i.e. resilience. 

As described in the result further down, this report did not find conclusive results with multivariate 
regressions thus, we resorted to an advanced approach called the Shapley method and part of global 
sensitivity  analysis.  The  Shapley  method  is  named  after  the  mathematician  Lloyd  Shapley,  who 
developed this concept in the field of game theory. The key idea behind Shapley is to calculate a "fair" 

1 USSR takes value if the country is Bulgaria, Czechia, Poland, Romania or Slovakia else 0 
2 PIIGS takes value 1 if the country is Portugal, Italy, Ireland, Greece or Spain else 0 

22 

 
                                                 
 
contribution for each input variable, considering both its individual impact and how it interacts with 
the other variables. 

When trying to predict each country's overall resilience there might be many different factors that 
contribute to resilience, like the strength of the economy, the quality of infrastructure, the level of 
social cohesion, and so on. These factors do not act in isolation - they are  all interconnected  and 
influence each other in complex ways and in a non-linear way.  

The  Shapley method  allows  us  to  break  down the overall  resilience  score  and  assign  a  numerical 
"importance"  value  to  each  contributing  factor.  It  does  this  by  systematically  considering  all  the 
possible combinations of factors and measuring how the resilience score changes when each factor 
is added or removed. For example, this report will show four significant factors effecting resilience: 
A,  B,  C,  and  D.  The  Shapley  method  allow  us  to  assess  at  the  resilience  outcome  when  only  A  is 
present, then when A and B are present, then A, B, and C, and so on, up to all four factors.  In the 
context of our resilience example, the Shapley analysis might reveal that while factor A has the largest 
individual impact, the combination of factors B, C, and D together actually contributes more to the 
overall resilience score. This kind of nuanced insight can be extremely valuable for policymakers and 
researchers trying to understand and improve the resilience of socioeconomic systems. 

The final Shapley value for each factor represents its average marginal contribution. This gives us a 
clear understanding of which factors are the most important drivers of resilience, and how much each 
one contributes, both individually and through its interactions with the other factors. 

The key advantages of the Shapley method are: 

1. 

2. 

3. 

4. 

It accounts for interactions between variables. Traditional sensitivity analysis techniques 
may miss important synergies or antagonistic effects between factors. Shapley captures 
these complex relationships. 

It provides a fair and unbiased distribution of contributions. By considering all possible 
factor combinations, Shapley ensures that each variable's importance is assessed equitably, 
without favouring any one factor over another. 

It quantifies the relative importance of each factor. The Shapley values give us a numerical 
measure of how much each input variable influences the final output. This allows us to 
prioritise the most impactful factors. 

It works for both linear and non-linear models. The Shapley method can be applied to a 
wide range of modelling approaches, from simple linear regressions to more complex 
machine learning algorithms. 

In summary, global sensitivity analysis uses the Shapley method is a powerful tool for understanding 
how  different  factors  interact  to  influence  system-level  outcomes.  By  quantifying  the  fair 
contributions of each variable, it gives us a much richer and more accurate picture of what is driving 
the behaviour of the system we are studying. For college students and researchers alike, the Shapley 
method is an important addition to the toolbox of advanced analytical techniques. 

In  essence,  Shapley  calculates  “fairly”  each  feature's  contribution  to  the  model's  prediction  by 
considering  all  possible  combinations  of  features  and  assessing  how  the  model's  performance 
changes when each feature is added or removed. The Shapley value for indicator is calculated as: 

φᵢ(v) = Σ[S⊆N{i}] (|S|!(n-|S|-1)! / n!) [v(S ∪ {i}) - v(S)] 

Where N is the set of all indicators 

23 

 
S is the set or a subset of indicators 

v is the characteristic function 

n is the total number of indicators. 

(Iooss and Prieur 2019) 

The Shapley method addresses limitations of traditional sensitivity analysis techniques by accounting 
for  complex  interactions  between  variables.  This  advancement  not  only  quantifies  individual 
contributions  but  also  captures  the  intricate  interdependencies  among  variables,  leading  to  more 
nuanced insights into system behaviour. 

The  Shapley  value  allows  for  a  fair  distribution  of  contributions  among  indicators,  enabling 
researchers to identify key factors influencing resilience in socio-economic systems. By applying this 
method,  we  analyse  various  resilience  indicators.  These  indicators  serve  as  critical  inputs  for  our 
model, which aims to predict system responses under different scenarios of external shocks. 

24 

 
4  Results and Findings 

4.1  Synthetic Indicators Logistic Regression with endogenous Resilience 

Prior  to  running  any  regression,  this  report  verifies  the  collinearity  of  the  synthetic  indicators  as 
indicated in this multicollinearity matrix  heatmap.  A  correlation matrix  reveals  multicollinearity  by 
displaying  pairwise  correlation  coefficients  between  variables.  Correlation  values  (|R|  >  0.25-0.3) 
between  indicators  suggest  multicollinearity  thus  potential  redundancy  in  the  statistical  model's 
independent variables. 

The dark colours, either green or red, are indicative of relatively higher collinearity: 

Table 5. Synthetic Indicators Multicollinearity in Symmetric Correlation Matrix.  

R-values 

Green_V 

Digital_V 

Geopolitical_
V 

Social.and.econo
mic_C 

Green_C 

Digital_C 

Geopolitical_C 

Social.and.economic_
V 

5% 

10% 

-14% 

13% 

21% 

50% 

46% 

Green_V 

Digital_V 

-27% 

10% 

2% 

37% 

21% 

-18% 

-33% 

5% 

20% 

13% 

10% 

Geopolitical_V 

-5% 

-34% 

-20% 

10% 

Social.and.economic_
C 

Green_C 

Digital_C 

Source: Own elaboration 

40% 

54% 

11% 

32% 

-26% 

31% 

Using t-statistic thus measuring the number of standard deviations an estimated normalised outcome 
is  from  zero  (in  this  case  resilience),  this  report  essentially  quantifies  how  far  the  observed 
relationship deviates from the null hypothesis i.e. no effect of the indicator on resilience. The p-value 
represents  the  probability  of  obtaining  test  results  at  least  as  extreme  as  the  observed  results, 
assuming the null hypothesis is true, with values below 0.05 or 5% typically indicating statistically 
significant evidence to reject the null hypothesis. By regressing individual indicators, one can find the 
following summary significance table: 

Table 6. Synthetic indicators logistic regression’s results. 

By Single Indicator 

Stat Significance 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Social.and.economic_C3 

Digital_C 

Green_C 

Social.and.economic_V4 

Green_V 

Digital_V 

Geopolitical_V 

Geopolitical_C 

Source: Own elaboration 

5.00% 

5.00% 

10.00% 

None 

None 

None 

None 

None 

4.2  Full Set of Indicators Linear Regression with exogenous Resilience 

Prior to running any regression, this report has verified the collinearity of the 124 indicators and for 
practical purposes, the multicollinearity matrix is included in the report data section. The data frame 
structure including the added dummies is exemplified here following in picture. 

3 C is shorthand for capacity  
4 V is shorthand for vulnerability 

26 

 
 
                                                 
 
Table 7. Data frame structure for linear regression. 

Source: (Benczur et al. 2023), Own elaboration 

Similarly to what it is described in the previous chapter, this report used t-statistic thus measuring 
the  number  of  standard  deviations  an  estimated  normalised  outcome  is  from  zero  (in  this  case 
resilience), this report essentially quantifies how far the observed relationship deviates from the null 
hypothesis  i.e.  no  effect  of  the  indicator  on  resilience.  The  p-value  represents  the  probability  of 
obtaining test results at least as extreme as the observed results, assuming the null hypothesis is 
true, with values below 0.05 or 5% typically indicating statistically significant evidence to reject the 
null hypothesis. 

By regressing individual indicators, one can find the following summary significance table and the 
relative correlation measure R2: 

Table 8. Complete indicators linear regression’s results. 

By Single Indicator 

Stat Significance 

R2 

Life Years Lost PM2.5  

Urban Waste Water Treatment 

Armed.forces.personnel 

Average.scores.in.the.PISA.test 

Adult Participation  in Edu 

Antimicrobial  Resistance 

Active Citizenship 

Source: Own elaboration 

1.00% 

1.00% 

5.00% 

5.00% 

5.00% 

5.00% 

5.00% 

75% 

62% 

44% 

55% 

63% 

60% 

64% 

Other statistically significant indicators are cited here for completeness: 

—  E-waste recycling rate 

27 

 
 
—  E-passenger fleet 

—  Environmental patents per capita 

—  Extra EU import partner concentration 

—  Extra EU export partner concentration 

—  Fossil Fuel Subsidies 

—  Gender Employment Gap 

—  Gross Value added in environmental goods and services 

—  Import dependence in energy materials 

—  Net lending-borrowing 

—  Outward FDI partner concentration 

—  Organic Farming 

—  Soil Carbon content 

—  Water Exploitation 

Some of the results will not be surprising, the concept of indoor pollution can be linked to PM2.5 as 
well  as  other  harmful  pollutants  created  by  burning  gas  include  carbon  monoxide,  benzene, 
formaldehyde and particulate matter. A new study led by Spain's University Jaume I reveals that gas 
cookers are a significant source of indoor air pollution, expected to contribute to nearly 40,000 early 
deaths  and  hundreds  of  thousands  of  asthma  cases  across  Europe  each  year  (EPHA  2024).  The 
research highlights that nitrogen dioxide (NO₂) levels in homes using gas cookers often exceed WHO 
air quality guidelines, particularly in countries with high gas usage like the UK, France, and Italy. The 
European Public Health Alliance (EPHA) is urging EU policymakers to introduce stricter regulations and 
promote cleaner cooking alternatives to address this widespread but overlooked public health issue. 

Figure 8. Deaths from environmental issues in 2017. 

28 

 
 
 
Source: (Stanaway et al. 2017) 

This policy focus aligns with WHO (2017) data showing indoor air pollution causing approximately 1.6 
million deaths annually, making it the second-largest environmental cause of mortality after outdoor 
air pollution. 

The  urgency  for  action  is  further  supported  by  Lomborg  in  his  book  “Best  things  first”,  who 
demonstrates  that  clean  cooking  fuels  investments  yield  the  highest  benefit-to-cost  ratio  among 
various sustainable development interventions, surpassing even crucial areas like e-procurement and 
child  immunization.  This  economic  efficiency,  combined  with  the  substantial  mortality  burden, 
presents a compelling case for prioritizing clean cooking initiatives in both policy and development 
frameworks. 

Figure 9. Best Things First. 

Source. Lomborg 

4.3  Sensitivity Analysis with Shapley (Bayesian Sparse Polynomial Chaos 

Expansion PCE5) 

This report has conducted a sensitivity analysis on the Full Set of Indicators Linear Regression with 
exogenous Resilience. 

To  ascertain  the  relative  significance  of  the  indicators,  this  report  computed  Shapley  effects  to 
decompose  the  variance,  aggregation  all  other  variables  effects  in  unexplained.  The  quantitative 
analysis reveals that active citizenship contributes the highest  proportion at  32% of  the  variance, 
followed by years of life lost due to PM2.5 at 29%, antimicrobial resistance at 24%, and degree of 
specialisation  of  the  economy  at  7%.  Approximately  8%  of  the  variance  falls  under  unexplained. 
Notably, Shapley analysis highlights substantial correlation amongst the indicators set. 

5 Bayesian Sparse Polynomial Chaos Expansion (PCE) following Iooss & Prieur (2019) 

29 

 
 
                                                 
 
Figure 10. Variance decomposition via Shapley Effects. Around 8% is left unexplained. 

Source. Own elaboration 

The four variables demonstrating significant contribution to the exogenous output can be combined 
in the optimal model below while achieving an R-squared value of 92%: 

Y = P₀ + P₁(X₉₄) + P₁(X₆₁) + P₁(X₆₉) + P₂(X₈₄) × P₂(X₆₉) 

Where Y is the outcome variable resilience 

Pₖ(Xⱼ) denotes a kth-degree polynomial function of indicator Xⱼ . 

It is important to note that whilst the remaining indicators appear not to have direct impact on the 
exogenous  resilience,  they  may  exert  indirect  influence  through  their  correlations  with  the  four 
identified relevant variables corroborating the idea that the indicators are multicollinear. 

Further  investigation  utilising  Shapley  values  was  undertaken  to  elucidate  how  different  input 
variables  combine  to  influence  the  deviation  of  the  resilience  from  its  mean  (y  -  mean(y))  in  the 
following chart. This analysis revealed country-specific variations in the importance of variables. For 
instance,  in  one  country,  active  citizenship  emerged  as  the  most  significant  variable,  generally 
increasing its Resilience value above the mean. Conversely, for another country, years of life lost due 
to PM2.5 proved most influential, contributing to a reduction in Resilience below the mean value. 

For instance, for the first country the indicator "Active citizenship” seems to be the most important 
factor that contributes to increasing its resilience value while for the second country, the value of the 
"Years of Life Lost due to PM 2.5" variable is the most important negative contribution, meaning it 
tends  to  decrease the country's resilience. The chart allows  us  to  see  the relative importance  and 
impact of each factor on the resilience value for each of the 27 countries. 

30 

 
 
Figure 11. Decomposition of Resilience – Mean (Resilience) per key indicator per country. 

Source. Own elaboration 

This sensitivity analysis illuminates the intricate interplay of indicators contributing to the exogenous 
resilience.  Whilst  four  key  variables  have  been  identified,  their  high  correlation  suggests  complex 
relationships within the model. The Shapley value analysis further demonstrates that the importance 
of  these  variables  can  vary  markedly  between  countries,  underscoring  the  necessity  for  nuanced 
approaches in resilience planning and policy-making. 

31 

 
 
5  Conclusions 

The comprehensive study undertaken in this paper has underscored the immense value of the wealth 
of data from current dashboards and their trending indicators in the domain of societal resilience. 
The utilisation of the European Commission's Joint Research Centre (JRC) resilience dashboards, in 
conjunction with  data  from Lloyd's  Register  Foundation World  Risk  Poll  2024, has  provided  a  rich 
tapestry of quantitative measures that are crucial for understanding and improving the resilience of 
societies. 

The application of logistic and linear regression models has shown both benefits and limitations in 
this context. Logistic regression's simplicity and the ease with which it handles binary outcomes are 
invaluable  for  initial  assessments  of  resilience  factors.  However,  its  limitations  become  apparent 
when  addressing  the  complexity  of  societal  resilience,  which  involves  multiple,  often  interrelated 
factors. Linear regression, on the other hand, allows for a more nuanced exploration of continuous 
measures of resilience, as evidenced by its application to the World Risk Poll data report. However, 
this technique also assumes independence among predictors and is limited by its inability to capture 
nonlinear interactions without additional modifications. 

The deployment of the sensitivity analysis using the Shapley Model has revealed significant benefits. 
The Shapley Model, with its roots in cooperative game theory, has proven to be an insightful tool for 
identifying the contribution of individual variables to the variance in resilience outcomes. This method 
has highlighted the interconnected nature of variables, such as active citizenship, years of life lost 
due to PM2.5, antimicrobial resistance, and the degree of specialisation of the economy, providing a 
more sophisticated understanding of their impact on resilience report. 

Moreover, the Shapley Model has unveiled further opportunities available for expanding datasets. The 
ability  to  account  for  the  interactions  between  variables  has  paved  the  way  for  incorporating 
additional data sources, such as those from EUROSTAT, OECD, IEA, and the World Bank, into resilience 
models. These expanded datasets could enhance the resilience assessment framework, allowing for 
a more comprehensive and detailed analysis of resilience at multiple levels, including country and 
individual scales. 

There are burgeoning research opportunities in the field of security when accounting for multilevel 
resilience. This report’s findings suggest that future research should focus on the nuanced differences 
between country-level and individual-level resilience. The World Risk Poll 2024 has already begun to 
explore these dimensions, and further work could provide deeper insights into how different levels of 
resilience interact and influence each other's report. 

Lastly, the potential use of a stochastic approach in resilience modelling will be worth considering 
when  aiming to dynamic predictions that reflect the inherent  uncertainties and variabilities  within 
societal systems. This approach could complement the static models employed in this study, providing 
a more robust and comprehensive toolkit for policymakers and researchers aiming to enhance societal 
resilience in an ever-changing global landscape. 

While the current state of resilience modelling provides insights, there remains significant scope for 
the  advancement  of  methodologies  and  the  integration  of  richer  datasets.  The  combined  use  of 
logistic and linear regression models, sensitivity analysis through the Shapley Model, and the potential 
for stochastic approaches represents a powerful suite of tools for the ongoing effort to understand 
and strengthen the resilience of societies worldwide.. 

32 

 
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———. 2023b. “NIS2 Directive: Cybersecurity Legislation in the EU.” https://digital-

strategy.ec.europa.eu/en/policies/nis2-directive. 

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policy/priorities-2019-2024/promoting-our-european-way-life/european-security-union_en. 

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33 

 
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34 

 
 
 
 
List of abbreviations and definitions 

Abbreviations 

Definitions 

EU 

JRC 

OECD 

IEA 

NIS2 

PCA 

PCE 

RDB 

European Union 

Joint Research Centre 

Organisation for Economic Co-operation and Development 

International Energy Agency 

Directive on measures for a high common level of 
cybersecurity across the Union 

Principal Component Analysis 

Polynomial Chaos Expansion 

Resilience Dashboard 

VUCAS 

Volatile, Uncertain, Complex, Ambiguous Scenarios 

35 

 
 
List of figures 

Figure 1. Key parameters for shock quantification. ......................................................................................................... 5 

Figure 2. Crisis’ Phases and Resilient Response. ................................................................................................................ 6 

Figure 3. Synthetic Index Dashboard ........................................................................................................................................ 9 

Figure 4. World Risk Poll ............................................................................................................................................................... 11 

Figure 5. European Statistical Monitor ................................................................................................................................. 12 

Figure 6. Hypothesis test with p-value ................................................................................................................................. 20 

Figure 7. Regressions...................................................................................................................................................................... 20 

Figure 8. Deaths from environmental issues in 2017. ................................................................................................ 28 

Figure 9. Best Things First. .......................................................................................................................................................... 29 

Figure 10. Variance decomposition via Shapley Effects. Around 8% is left unexplained. ...................... 30 

Figure 11. Decomposition of Resilience – Mean (Resilience) per key indicator per country. ................. 31 

36 

 
 
List of tables 

Table 1. Analysis Objective linked to Modelling .................................................................................................................. 7 

Table 2. Resilience Quintiles. ...................................................................................................................................................... 10 

Table 3. International Organisations’ Relevant Indicators. ........................................................................................ 13 

Table 4. Comparative analysis of regression and sensitivity methods. ............................................................. 19 

Table 5. Synthetic Indicators Multicollinearity in Symmetric Correlation Matrix. ......................................... 25 

Table 6. Synthetic indicators logistic regression’s results. ........................................................................................ 25 

Table 7. Data frame structure for linear regression. .................................................................................................... 27 

Table 8. Complete indicators linear regression’s results. ........................................................................................... 27 

37 

 
 
 
Annexes 

Annex 1. Synthetic Indicator Data with Endogenous Resilience 

38 

 
 
 
BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

LT 

LU 

HU 

MT 

Social econ_V 

Green_V 

Digital_V 

Geopolitical_V 

Socialecon_C 

Green_C 

Digital_C 

Geopolitical_C 

Resilience 

0 

-0.75 

-0.25 

-0.25 

-0.25 

-0.25 

0 

-0.75 

-0.75 

0 

0 

-0.75 

0 

-0.25 

0 

-0.25 

0 

0 

-0.75 

0 

0 

-0.25 

0 

-0.75 

0 

-0.25 

0 

0 

-0.25 

-0.75 

-0.75 

-0.25 

-0.25 

-1 

0 

0 

0 

-0.75 

-0.25 

0 

0 

-0.25 

-0.25 

0 

-0.25 

-0.25 

-0.75 

0 

0 

-0.25 

0 

-0.25 

-0.75 

-0.25   

-0.75 

0 

-0.25 

-0.25 

-0.25 

0 

-0.25 

0 

-0.25 

-0.25 

0 

-0.25 

0 

-0.25 

0 

0 

0 

39 

0.75 

0.5 

0.5 

1 

0.75 

0.5 

0.75 

0.25 

0.5 

0.75 

0.5 

0.5 

0.5 

0.25 

0.25 

0.75   

0.5 

0.25 

0.75 

0.25 

0.5 

1 

1 

0.75 

0.5 

0.25 

0.75 

0.5 

0.75 

0.5 

0.25 

0.5 

0.5 

0.5 

0.25 

1 

0.25 

0.5 

1 

0.75 

0 

0.75 

0.25 

0.75 

0.75 

0.75 

0.25 

0.5 

0.5 

0.75 

0.75 

0.75 

1 

0.75 

0.5 

0.5 

0.25 

0.5 

0.5 

1 

0.25 

0.5 

0.5 

0.5 

0.5 

1 

0.5 

0.75 

1 

0.5 

1 

0 

0 

0 

1 

1 

0 

1 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

1 

 
 
1 

0.75   

0.25 

0.5 

0.5 

0.75 

0.5 

1 

1 

0.75 

0.75 

0.5 

0.75 

0.25 

0.75 

0.5 

0.75 

0.75 

0.75 

1 

0.75 

0.5 

0.75 

0.25 

0.5 

0.5 

1 

1 

0.75 

0.75 

0.5 

0.5 

0.5 

0.5 

0.75 

0.5 

0.5 

0.75 

0.75 

1 

0 

0 

0 

0 

1 

0 

1 

1 

1 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

EU27 

-0.25 

-0.25 

0 

0 

-0.75 

0 

-0.75 

-0.25 

-0.25 

0 

0 

-0.75 

0 

0 

0 

0 

-0.25 

0 

0 

0 

-0.25 

-0.25 

0 

-0.25 

-0.75 

-0.25 

0 

-0.25 

-0.25 

0 

0 

0 

-0.25 

-0.25 

0 

0 

0 

0 

0 

0 

40 

 
 
 
Getting in touch with the EU 

In person 

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address of the centre nearest you online (european-union.europa.eu/contact-eu/meet-us_en). 

On the phone or in writing 

Europe Direct is a service that answers your questions about the European Union. You can 
contact this service: 

— by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

— at the following standard number: +32 22999696, 

— via the following form: european-union.europa.eu/contact-eu/write-us_en. 

Finding information about the EU 

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You can view or order EU publications at op.europa.eu/en/publications. Multiple copies of free 
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EU law and related documents 

For access to legal information from the EU, including all EU law since 1951 in all the official 
language versions, go to EUR-Lex (eur-lex.europa.eu). 

EU open data 

The portal data.europa.eu provides access to open datasets from the EU institutions, bodies 
and agencies. These can be downloaded and reused for free, for both commercial and non-
commercial purposes. The portal also provides access to a wealth of datasets from European 
countries. 

 
 
 
 
 
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independent, evidence-based knowledge and 
science, supporting EU policies to positively 
impact society 

EU Science Hub 
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