ArticlePDF Available

Editorial. Risk-Based, Pro-Poor Urban Design and Planning for Tomorrow’s Cities

Authors:

Abstract and Figures

Tomorrow’s Cities is the £20m United Kingdom Research and Innovation (UKRI) Global Challenge Research Fund (GCRF) Urban Disaster Risk Hub. The Hub aims to support the delivery of the United Nation’s Sustainable Development Goals and priorities 1 to 3 of the Sendai Framework for Disaster Risk Reduction (DRR) 2015-2030. We work in four cities: Istanbul, Kathmandu, Nairobi, and Quito. We collaborate with local, national, and global organisations to strengthen disaster risk governance by undertaking integrated, multi-scale, and multi-disciplinary research to better understand natural multi-hazard risks and their drivers. Ongoing rapid urbanisation and urban expansion provide a time-limited opportunity to reduce disaster risk for the marginalised and most vulnerable in tomorrow’s cities. We aim to catalyse and support a transition from crisis management to pro-poor, multihazard risk-informed urban planning and people-centred decision-making in expanding cities worldwide. Tomorrow’s Cities is a fully-functioning, fully-funded international collaboration of communities, governance organisations, researchers, and risk professionals. We are developing our Phase 2 programme planned for 2021-24, which will build on the Phase 1 research and partnerships forged since our inception in early 2019. We seek global partners to co-produce and implement a new approach to risk reduction, through risksensitive design of tomorrow’s cities.
Content may be subject to copyright.
Journal Pre-proof
Editorial. Risk-Based, Pro-Poor Urban Design and Planning for Tomorrow’s Cities
Carmine Galasso, John McCloskey, Mark Pelling, Max Hope, Chris Bean, Gemma
Cremen, Ramesh Guragain, Ufuk Hancilar, Jonathan Menoscal, Keziah Mwang’a,
Jeremy Phillips, David Rush, Hugh Sinclair
PII: S2212-4209(21)00124-2
DOI: https://doi.org/10.1016/j.ijdrr.2021.102158
Reference: IJDRR 102158
To appear in: International Journal of Disaster Risk Reduction
Received Date: 24 February 2021
Accepted Date: 24 February 2021
Please cite this article as: C. Galasso, J. McCloskey, M. Pelling, M. Hope, C. Bean, G. Cremen, R.
Guragain, U. Hancilar, J. Menoscal, K. Mwang’a, J. Phillips, D. Rush, H. Sinclair, Editorial. Risk-Based,
Pro-Poor Urban Design and Planning for Tomorrow’s Cities, International Journal of Disaster Risk
Reduction, https://doi.org/10.1016/j.ijdrr.2021.102158.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition
of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of
record. This version will undergo additional copyediting, typesetting and review before it is published
in its final form, but we are providing this version to give early visibility of the article. Please note that,
during the production process, errors may be discovered which could affect the content, and all legal
disclaimers that apply to the journal pertain.
© 2021 The Author(s). Published by Elsevier Ltd.
1
Editorial. Risk-Based, Pro-Poor Urban Design and Planning for Tomorrow’s Cities
Carmine Galasso
1
(c.galasso@ucl.ac.uk), John McCloskey
2
, Mark Pelling
3
, Max Hope
4
, Chris Bean
5
, Gemma
Cremen
1
, Ramesh Guragain
6
, Ufuk Hancilar
7
, Jonathan Menoscal
8
, Keziah Mwang’a
9
, Jeremy Phillips
10
, David
Rush
2
, and Hugh Sinclair
2
1 University College London, UK
2 The University of Edinburgh, UK
3 King’s College London, UK
4 Leeds Beckett University, UK
5 Dublin Institute for Advanced Studies, Ireland
6 National Society for Earthquake Technology, Kathmandu, Nepal
7 Boğaziçi Üniversitesi, Istanbul, Turkey
8 Facultad Latinoamericana de Ciencias Sociales FLACSO, Ecuador
9 African Centre for Technology Studies, Nairobi, Kenya
10 University of Bristol, UK
Introduction
Tomorrow’s Cities
1
is the £20m United Kingdom Research and Innovation (UKRI) Global Challenge Research
Fund (GCRF) Urban Disaster Risk Hub. The Hub aims to support the delivery of the United Nation’s
Sustainable Development Goals and priorities 1 to 3 of the Sendai Framework for Disaster Risk Reduction
(DRR) 2015-2030 [1]. We work in four cities: Istanbul, Kathmandu, Nairobi, and Quito. We collaborate with
local, national, and global organisations to strengthen disaster risk governance by undertaking integrated,
multi-scale, and multi-disciplinary research to better understand natural multi-hazard risks and their
drivers.
Ongoing rapid urbanisation and urban expansion provide a time-limited opportunity to reduce disaster risk
for the marginalised and most vulnerable in tomorrow’s cities [2]. We aim to catalyse and support a
transition from crisis management to pro-poor, multi-hazard risk-informed urban planning and people-
centred decision-making in expanding cities worldwide.
Tomorrow’s Cities is a fully-functioning, fully-funded international collaboration of communities,
governance organisations, researchers, and risk professionals. We are developing our Phase 2 programme
planned for 2021-24, which will build on the Phase 1 research and partnerships forged since our inception
in early 2019. We seek global partners to co-produce and implement a new approach to risk reduction,
through risk-sensitive design of tomorrow’s cities.
Recognised need
The Sendai Framework for DRR 2015-2030 identifies an urgent need for a global effort by researchers,
practitioners, and governments to integrate science with action to support risk-sensitive decision
making [1]. The Hub aims to co-produce methodologies and guidelines for this action-oriented, pro-
poor, multi-hazard risk-based decision-making agenda.
1
https://www.tomorrowscities.org/
Journal Pre-proof
2
Understanding and acting on risk is complex. Risk
assessments are necessarily based on significant
simplifications of the underlying physical and social processes, they are difficult to validate, and the
reporting process often obscures caveats implicit in underlying assumptions (e.g., [3-4]). Technical
outputs may have an inappropriate impact due to inaccurate expectations and limited comprehension
(e.g., [5-6]).
Experience also shows that state-of-the-art risk modelling on its own is not sufficient to build risk
reduction into development planning and to support a movement to pro-poor, resilient actions (e.g., [7-
8]). Institutional inertia, exclusive decision-making structures, and competing interests can mean even
the best new knowledge is used only to enhance existing policy and practice (e.g., [9-10]).
This means that risk science has to be built on the best current methods and must also understand the
development context within which risk and resilience are positioned by competing actors in a city. It
must then be used to convene policy and practical spaces for new coalitions of interest to cohere and
bring pro-poor resilience into policy and action.
Current limitations
Current approaches to DRR decision support in both research and practice are hampered by historical
inertia, and display many common problems limiting their use in forwarding planning of urban
expansion and transformation (Figure 1). They typically:
Concentrate risk-quantification efforts on existing exposure and vulnerability rather
than on a better understanding of the consequences of today’s decisions on
tomorrow’s risk and resilience (e.g., [10-11]).
Neglect the dynamics of hazard, exposure, capacities, and vulnerabilities, treating each
as static over time, and ignoring their interactions and dependencies (e.g., [10]).
Avoid considerations of the drivers of governance, planning priorities, and broader
socio-economic processes in uneven vulnerability and risk creation (e.g., [12]).
Do not adequately synthesise vulnerability across different physical and social
contexts, qualitative and quantitative metrics, and local city-scale analysis (e.g., [13]).
Ignore current advances in physics-based natural-hazard modelling, deploying dated
representations of hazard that often rely on limited empirical data (e.g., [14-15]).
Employ only a limited set of risk metrics, emphasising asset value, providing
incomplete measures of the total impact of natural hazards, and undervaluing risk
experienced by marginalised communities (e.g., [16-18]).
Neglect disruption to socio-economic and technical networks and systems and to the
communities they serve (e.g., [19]).
Underemphasise social vulnerability as a policy domain for reducing urban disaster risk
(e.g., [20]).
Examine the impact of single hazards, overlooking interactions and dependencies
between hazards and human activity (e.g., [21-22]).
Exclude users from access to an examination of, or control over, underlying
assumptions or weightings in the risk quantification process (e.g., [23]).
Limit involvement of local stakeholders (e.g., [24]).
Journal Pre-proof
3
Figure 1. A conventional risk modelling framework. An exposure module contains details on the location and
characteristics of the (existing) inventory at risk, possibly including human exposure to death or injury. The hazard
module generally deals with a representative catastrophic single hazard, assessing the resulting hazard intensity
across a geographical area under consideration. The vulnerability module quantifies the susceptibility to damage or
other forms of loss to structures/infrastructure and their contents. Typically, vulnerability is confined to comprise
only direct economic losses, often described in terms of their repair/replacement costs. In some cases, social
aspects of vulnerability are also considered (often simplistically). The effects of natural hazards on coupled social-
engineered systems are conventionally studied using computational models representing the behaviour of each
asset in isolation. Moreover, current modelling approaches generally estimate risk using a snapshot of the
conditions at one point in time. The main output of a conventional risk model is a description of the annual
probability of exceeding certain economic loss levels and related statistics. Results are generally delivered to
decision makers through a one-way process in which many of the underlying assumptions and details of the various
modules/models are not adequately communicated and made accessible to end users.
Risk & Uncertainty in Tomorrow’s Cities
Existing threats from natural hazards, social drivers of risk, and the vulnerability of existing building stock,
housing and infrastructure, present a major challenge to the well-being of marginalised communities in the
world’s cities (e.g., [25]). However, Tomorrow’s Cities is dedicated not to the reduction of existing risks but
to the systematic and systemic reduction of risk in future development (e.g., [10]). We aim to advance
holistic assessments for multi-hazard risk within complex engineering- social systems and develop new
stakeholder partnerships for DRR.
Journal Pre-proof
4
We recognise the concept of potential risk, a property of yet-unbuilt infrastructure, yet-unknown socio-
economic characteristics, and as yet-unmade decisions, which can be reduced by modifying urban design
and planning as well as the institutions that deploy them. Extending existing evaluations based on the
economic value of physical assets to include the livelihood consequences of systems disruptions results in
more inclusive urban planning and action (e.g. [26]).
Our aim, therefore, is to develop a two-stage Tomorrow’s Cities Decision Support Environment (DSE) based
on detailed multi-hazard scenarios co-developed with stakeholders to provide 1) a transparent and rigorous
assessment of potential risk inherent in urban design, housing and infrastructure planning, around which 2)
decision makers and those at risk might consider the risk consequences of particular decisions (Figure 2).
Both stages elucidate the consequences of particular choices, and both provide opportunities to
foreground the perspective and experience of the at-risk poor. Using customised visualisation and multi-
faceted communication strategies, the selected scenarios also provide learning loops through which
different perceptions of risk can uncover novel risk metrics and modify risk models and assessments.
We bring a deep understanding of the inherent uncertainties involved in integrated social and physical
vulnerability analysis, and the expertise to deal with these in a sophisticated way (e.g., [27]). We embrace
uncertainty as an opportunity rather than a hindrance, ensuring the most effective deployment of models,
transcending simplistic single perspectives and exploring multiple, quantitative and qualitative approaches
to risk (e.g., [28]).
In this way, we use the convening power of interdisciplinary science and simulation, rather than the
frequently unspoken implication of scientific certainty, to enable inclusive decision making. Scenarios cover
a wide range of scales from single high-magnitude events through to repeated small disruptions,
connecting intensive risk to extensive or even every-day multi-hazards, and bridging the near-real-time
priorities of the urban poor with longer-term strategic planning. Disaggregation of impact on different
sectors of society (income classes, ages, genders, and marginalised communities) will help identify different
intervention options to reduce impacts. Further, by framing these analyses with assessments of social and
economic drivers of vulnerability and exposure through co-produced, participatory community-level
research, we build on established social impact and risk assessment methodologies.
Rather than usurping local decision authorities, these two DSE stages enable science to become a tool for
decision support in a collaborative environment , where decision makers and local partners are involved
early in framing and addressing the research questions. Rather than scientists and engineers providing
definitive forecasts, they provide a critical but supporting role in an ongoing multi-disciplinary process,
where local authorities and communities are integral to the risk analysis process.
Journal Pre-proof
5
Figure 2. The Tomorrow’s Cities approach to DRR in planned urban transformation or expansion. Draft urban plans,
including the design capacity of individual elements, social indicators, and models of networks and functioning
systems, are described in detail. These planning suggestions inform event scenarios, for which high resolution,
physics-based simulations of the important multiple hazards (incorporating detailed descriptions of uncertainty)
coupled with dynamic physical and social vulnerability analysis provide an array of potential risk metrics for
consideration and weighting. Multi-disciplinary teams integrate these metrics into impact assessments and detailed
descriptions of the consequences of the chosen events, using a wide range of expertise and methods. The entire
process is interactively co-produced by decision makers, facilitators, community representatives and technical
experts, using advanced visualisation and communication capabilities to facilitate deep understandings of the
consequences of different aspects of the original plans and of the impact assessments under different transparent
assumptions and planning options. Feedback loops, which potentially give decision-makers influence over all
aspects of the process, provide opportunities for interactive, evidence-based, and inclusive planning decisions.
Components of the Tomorrow’s Cities Decision Support Environment
The Tomorrow’s Cities DSE is built around complex, layered understandings of urban risk with particular
consideration for the priorities of marginalised urban populations who bear the brunt of disaster impacts.
This emphasis necessitates novel approaches to understanding risk and its quantification, built on a flexible
virtual space in which future development scenarios and detailed urban design and policy options can be
considered, evaluated, modified and rated by a range of stakeholders.
Journal Pre-proof
6
To achieve the Hub’s desired transition to multi-hazard urban risk-based planning, the Risk DSE must be
designed as a component of existing, wider Hub activity and be co-designed with city-level decision makers.
Most important is to integrate the best physical, engineering, and social science in the Risk DSE and co-
produce the methods with city-level urban planning partners within the context and at the fora in which
decisions are being made. It must be co-owned from the start by city actors and, in this way, will enable
inclusive, evidence-based policy advocacy and debate. Navigating this shift from risk management to
integrated risk-based planning as part of a city and community-level urban development is the Hub’s core
challenge. Existing Hub city institutional mapping can identify the needs of specific policy groups so that the
Hub understands how best to communicate and co-design the vision, mechanisms, and outputs of the Risk
DSE. Skills and capacity in risk sciences that are developing across the Hub combined with substantial Phase
2 funding, enable us to address this challenge confidently.
Building on research and partnerships developed in the Hub inception phase, Phase 2 of the Hub will bring
diverse stakeholders into shared processes of co-production, and the Tomorrow’s Cities DSE will open a
new decision-making space to explore novel evidence-based solutions to difficult policy challenges.
The DSE will:
Be co-produced with local, national, and global decision makers and research partners.
Have a global application but will succeed through city-level deployment and local action.
Concentrate on reducing the potential risk inherent in today’s decisions appropriately through
the design and planning of tomorrow’s built environment and social systems.
Foreground the role of governance, planning, and community capacity in risk
creation/reduction.
Enable assessing different policy and planning options in terms of their impact on various
economic, environmental, and social objectives.
Deploy state-of-the-art hazard and physical vulnerability models to develop validated
simulations around which multi-layered physics-driven scenarios can be constructed and
considered.
Deploy state-of-the-art social vulnerability assessments and examination of risk creation
processes through local, participatory methodologies and historical Forensic Investigations of
Disasters (FORIN) analysis.
Combine physical and social sciences in innovative integrated analyses to reveal the pathways
to, and drivers of vulnerability.
Employ a wide range of risk metrics, including those that capture the risk experience of
marginalised communities and the outcomes of socio-political and technical system analyses.
Account for the loss of function of systems and networks.
Examine the impact of multiple hazards and the resulting risk interdependencies.
Emphasise the use of appropriate visualisation to communicate the complexities of the
underlying modelling and its inherent assumptions.
Perform rigorous uncertainty modelling for each component of the risk assessment framework
and ensure open dissemination of the related results.
Provide all data and models as open-source tools, including complete documentation and
records of the development and previous case studies.
Tomorrow’s Cities have convened a group who are actively working on this approach to risk, building on
the Phase 1 research effort in our four cities.
We are now looking for partners to work with us to co-produce the details of the methodology to achieve a
good fit between the concept and its delivery.
Expressions of interest from and meetings with appropriate partners are welcomed and actively
encouraged.
Journal Pre-proof
7
References
[1] United Nations Office for Disaster Risk Reduction, UNDRR (2015). Sendai Framework for Disaster Risk
Reduction 2015-2030.
[2] United Nations, Department of Economic and Social Affairs, Population Division (2019). World
Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). New York: United Nations.
[3] Aitsi-Selmi, A., Murray, V., Wannous, C., Dickinson, C., Johnston, D., Kawasaki, A., ... & Yeung, T. (2016).
Reflections on a science and technology agenda for 21st century disaster risk reduction. International
Journal of Disaster Risk Science, 7(1), 1-29, https://doi.org/10.1007/s13753-016-0081-x.
[4] Fekete, A. (2012). Spatial disaster vulnerability and risk assessments: challenges in their quality and
acceptance. Natural hazards, 61(3), 1161-1178, https://doi.org/10.1007/s11069-011-9973-7.
[5] Gaillard, J. C., & Mercer, J. (2013). From knowledge to action: Bridging gaps in disaster risk reduction.
Progress in Human Geography, 37(1), 93-114, https://doi.org/10.1177/0309132512446717.
[6] National Research Council. 1996. Understanding Risk: Informing Decisions in a Democratic Society.
Washington, DC: The National Academies Press. https://doi.org/10.17226/5138.
[7] Intergovernmental Panel on Climate Change, IPCC (2018). An IPCC Special Report on the impacts of
global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways,
in the context of strengthening the global response to the threat of climate change, sustainable
development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J.
Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen,
X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].
[8] Baker, J. L. (Ed.). (2012). Climate change, disaster risk, and the urban poor: cities building resilience for a
changing world. The World Bank.
[9] Jasanoff, S. ed. (2004). States of knowledge: the co-production of science and the social order.
Routledge.
[10] Fraser, S., Jongman, B., Balog, S., Simpson, A., Saito, K., & Himmelfarb, A. (2016). The making of a
riskier future: How our decisions are shaping future disaster risk. Global Facility for Disaster Reduction and
Recovery.
[11] Gallina, V., Torresan, S., Critto, A., Sperotto, A., Glade, T., & Marcomini, A. (2016). A review of multi-
risk methodologies for natural hazards: Consequences and challenges for a climate change impact
assessment. Journal of Environmental Management, 168, 123-132,
https://doi.org/10.1016/j.jenvman.2015.11.011.
[12] Assmuth, T., Hildén, M., & Benighaus, C. (2010). Integrated risk assessment and risk governance as
socio-political phenomena: A synthetic view of the challenges. Science of the Total Environment, 408(18),
3943-3953, https://doi.org/10.1016/j.scitotenv.2009.11.034.
[13] Hallegatte, S., & Rozenberg, J. (2017). Climate change through a poverty lens. Nature Climate Change,
7(4), 250-256, https://doi.org/10.1038/nclimate3253.
[14] Bradley, B. (2019). Ongoing challenges in physics-based ground motion prediction and insights from
the 2010–2011 Canterbury and 2016 Kaikoura, New Zealand earthquakes. Soil Dynamics and Earthquake
Engineering, 124: 354-364, https://doi.org/10.1016/j.soildyn.2018.04.042.
[15] Paolucci, R., Mazzieri, I., Smerzini, C., Stupazzini, M. (2014). Physics-Based Earthquake Ground Shaking
Scenarios in Large Urban Areas. In: Ansal A. (eds) Perspectives on European Earthquake Engineering and
Journal Pre-proof
8
Seismology. Geotechnical, Geological and Earthquake Engineering, vol 34. Springer, Cham,
https://doi.org/10.1007/978-3-319-07118-3_10.
[16] Hallegatte, S., Vogt-Schilb, A., Bangalore, M., Rozenberg, J. (2017). Unbreakable : Building the
Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development. The World Bank.
[17] Markhvida, M., Walsh, B., Hallegatte, S., & Baker, J. (2020). Quantification of disaster impacts through
household well-being losses. Nature Sustainability, 1-10, https://doi.org/10.1038/s41893-020-0508-7.
[18] Verschuur, J., Koks, E. E., Haque, A., & Hall, J. W. (2020). Prioritising resilience policies to reduce
welfare losses from natural disasters: A case study for coastal Bangladesh. Global Environmental Change,
65, 102179, https://doi.org/10.1016/j.gloenvcha.2020.102179.
[19] Pescaroli, G., & Alexander, D. (2018). Understanding compound, interconnected, interacting, and
cascading risks: a holistic framework. Risk analysis, 38(11), 2245-2257, https://doi.org/10.1111/risa.13128.
[20] Vecere, A., Monteiro, R., Ammann, W. J., Giovinazzi, S., & Santos, R. H. M. (2017). Predictive models for
post disaster shelter needs assessment. International Journal of Disaster Risk Reduction, 21, 44-62.
[21] Selva, J. (2013). Long-term multi-risk assessment: statistical treatment of interaction among risks.
Natural hazards, 67(2), 701-722, https://doi.org/10.1007/s11069-013-0599-9.
[22] Gill, J. C., & Malamud, B. D. (2014). Reviewing and visualising the interactions of natural hazards.
Reviews of Geophysics, 52(4), 680-722, https://doi.org/10.1002/2013RG000445.
[23] Carpignano, A., Golia, E., Di Mauro, C., Bouchon, S., & Nordvik, J. P. (2009). A methodological approach
for the definition of multi-risk maps at regional level: first application. Journal of Risk Research, 12(3-4),
513-534, https://doi.org/10.1080/13669870903050269.
[24] Komendantova, N., Mrzyglocki, R., Mignan, A., Khazai, B., Wenzel, F., Patt, A., & Fleming, K. (2014).
Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: Feedback from
civil protection stakeholders. International Journal of Disaster Risk Reduction, 8, 50-67,
https://doi.org/10.1016/j.ijdrr.2013.12.006.
[25] Pelling, M., Maskrey, A., Ruiz, P., Hall, P., Peduzzi, P., Dao, Q. H., ... & Kluser, S. (2004). Reducing
disaster risk: a challenge for development.
[26] Burby, R. J., Deyle, R. E., Godschalk, D. R., & Olshansky, R. B. (2000). Creating hazard resilient
communities through land-use planning. Natural Hazards Review, 1(2), 99-106,
https://doi.org/10.1061/(ASCE)1527-6988(2000)1:2(99).
[27] Silva, V., Akkar, S., Baker, J., et al. (2019) Current Challenges and Future Trends in Analytical Fragility
and Vulnerability Modeling. Earthquake Spectra, 35(4): 1927-1952,
https://doi.org/10.1193/042418EQS101O.
[28] Latour, B. (2018). Down to Earth: Politics in the New Climatic Regime. Cambridge: Polity Press.
Journal Pre-proof
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
The authors declare the following financial interests/personal relationships which may be considered
as potential competing interests:
Journal Pre-proof
... This study addresses the aforementioned shortcomings of conventional seismic risk assessment and related DRM, providing a framework for investigating how geophysicsinformed policymaking may affect the seismic risk of different socio-economic groups (compared to spatially independent or more explicit income-based risk-mitigation interventions). The framework is adapted from that of Wang et al., (2023) -which focuses on the design of risk-sensitive, pro-poor policies and is based on the Tomorrow's Cities Decision Support Environment (TCDSE; Galasso et al. 2021;Cremen et al. 2023)-to: (1) explicitly integrate geophysical information; and also (2) consider the efficiency of policies in reducing earthquake impact on different populations (through a cost-benefit analysis), recognising the importance of funding constraints in real-life decision-making processes (Kenny 2012). We use the framework to assess policies in the earthquake-prone virtual urban testbed "Tomorrowville", which imitates a Global South urban setting through its physical and socio-economic characteristics . ...
... The performance of these policies is benchmarked against those that are explicitly income-based or more spatially independent. The framework is based on the Tomorrow's Cities Decision Support Environment for propoor risk-sensitive urban planning Galasso et al. 2021) and resulting approaches for designing relevant policies . ...
Article
Full-text available
Recent earthquake disasters have highlighted an urgent need for continuous advancements in approaches to reducing seismic risk. Decision-making on such strategies should consider subsurface geophysical information (e.g., seismic site response), given its direct link to seismic hazard. This may be particularly important in regions where the poorest in society often reside in areas with softer soils that lead to higher ground-motion amplifications. In this context, we propose a framework to support decision-making on earthquake risk policies, which explicitly integrates information on the geophysics of an urban system as well as its physical and social environment. The framework is based on the Tomorrow’s Cities Decision Support Environment, which was designed to support urban planning with a focus on pro-poor disaster risk reduction in countries of the Global South. It is further underpinned by a cost–benefit analysis, which facilitates the assessment of potential policies in terms of both their ability to reduce earthquake risk as well as their value for (often limited) money. We illustrate the framework using a well-established virtual urban testbed based on Global South cities, which reveals that geophysics-informed policy making can successfully lead to pro-poor earthquake risk reduction.
... As emphasised by Esmaiel et al. (2022), studies that combine spatial planning and disaster risk reduction are often overlooked due to the differing structures and objectives of the various institutions involved. The field of spatial planning has traditionally focused on static factors such as hazards, vulnerabilities and capacities, while neglecting the dynamic and interdependent nature of these elements and their relationship with exposure to disaster risks (Galasso et al., 2021;GFDRR, 2016). Concurrently, those engaged in spatial planning must consider the potential for landslides. ...
Article
Full-text available
Banyumas Regency is an area that with a high potential for landslide hazards. These pose a serious threat to spatial planning. This research aims to analyse land use in landslide-prone areas; to compare spatial planning strategies in the region; and to offer recommendations for effective spatial planning to reduce landslide risks. The application of a physical approach, incorporating ten parameters to assess landslide hazard levels, can be considered to be optimal. The research findings have significant implications for spatial planning and disaster risk management in Banyumas Regency. The analysis results indicate that certain community activity areas, such as residential, tourism and industrial zones, are located in areas with low to moderate landslide hazards. This demonstrates that the regency prioritises disaster risk considerations in land utilisation planning. The research has several limitations, including the lack of data validation and historical landslide data in Banyumas Regency concerning landslide hazard assessment. In addition, the study only focuses on landslide hazard areas in the central community activity zone, but does not include residential areas in other designated zones.
... Previous studies underscore the critical need for justice-oriented strategies and equity-focused interventions in seismic hazard and disaster risk management. Similarly, studies on risk-informed urban development planning and people-centered decision-making processes reveal a concerning trend of urban growth expanding into multi-hazard-prone and socially vulnerable areas [27,28]. These findings stress the urgent need for risk-informed urban design and planning approaches. ...
Article
Full-text available
This study investigates distributive environmental injustices in seismic risk exposure across urban areas of Ottawa-Gatineau and Montreal, testing the hypothesis that socially vulnerable communities face disproportionate seismic hazards. Using geographically aggregated data from Canada’s Probabilistic Seismic Risk Model and the 2021 national census, we examine spatial heterogeneity in the relationships between race/ethnicity, socioeconomic vulnerability, and seismic risk. Social vulnerability is measured through economic insecurity and neighborhood instability indices. The study uses separate geostatistical models to assess spatial heterogeneity and endogeneity. To address potential endogeneity in global ordinary least squares regression, we apply two-stage least squares regression with instrumental variables (e.g., rural areas, dwelling density) for robust global estimates. Spatial variability has been assessed using multiscale geographically weighted regression for more localized insights. Bivariate local indicators of spatial association cluster mapping further identify risk hotspots and high-risk socioeconomically disadvantaged areas for targeted interventions and disaster risk reduction programs. Findings reveal that recent immigrants, seniors, lone-parent households, and visible minorities are significantly associated with seismic risk in both regions. In Montreal, higher risk correlates with populations living alone, low-income individuals, those without a high school diploma, and non-official language speakers. In Ottawa-Gatineau, seismic risk is more strongly linked to seniors, visible minorities, and lone-parent families. Older housing consistently emerges as a critical built-environmental vulnerability. These results underscore the need for region-specific policies that integrate social and structural risk factors into disaster mitigation. The study contributes to environmental justice and social vulnerability literature, advocating for vulnerability-based risk management and targeted urban resilience strategies.
... About 1.1 billion people live in urban slums globally, which is expected to double in the next 25-30 years [36]. Hence, the most vulnerable and marginalised communities must be identified, continuously monitored [34], and considered in risk-informed urban planning to facilitate peoplecentred and pro-poor decision-making to reduce disaster risks [75][76][77]. ...
Article
Full-text available
Urbanisation activities such as landscape modifications are likely the dominant force controlling landslides in cities. Due to the lack of city-scale evidence, the urbanisation-landslide interaction has been commonly studied locally, focusing on individual landslides or road segments. We study the expansion of the metropolitan area of Medellín, Colombia, in 18-time steps since 1770 and explore the empirical relationship between urban expansion, landslide occurrences, and fatalities. Between the 1930s and 2023, Medellín’s population increased from about 120 thousand to 2.5 million inhabitants, while its area expanded from 9 km² to 107 km². Landslide occurrences have gradually increased since the 1930s with a volatile positive trend. In contrast, landslide fatalities have been fluctuating without a clear trend. Landslides have predominantly impacted the outskirts of the urban area, causing harm primarily to newly emerged neighbourhoods towards the city’s north, northeast, and west especially since 1960s. Thus, we argue that struggling settlement on Medellín’s margins has likely increased the city’s landslide hazards. Landslides kill 2 to 3.5 times more people per km² in informal neighbourhoods (categorised as Informal II and III) than in the most hazardous formal neighbourhood type. We recommend that land-use policies assess whether planned future land transformations could alter an area’s inherent hazard status rather than relying solely on existing hazard levels for permits.
... The classification tree model proposed herein could be immediately integrated into any disaster risk model with data on property damage, dwelling type, and tenure status. These estimates of displacement duration and return could be considered alongside other standard risk metrics (e.g., direct economic loss) to capture the human toll of disasters more holistically (e.g., Galasso et al., 2021;Markhvida et al., 2020). Survey responses from 11,715 households that experienced disaster displacement were used to fit predictive models for household displacement in three classes: emergency phase displacement (less than 1 month), recovery phase displacement (more than 1 month), and not returned (potentially permanent relocation). ...
Article
Full-text available
According to recent Household Pulse Survey data, roughly 1.1% of households were displaced due to disasters in the United States in recent years. Although most households returned relatively quickly, 20% were displaced for longer than 1 month, and 14% had not returned by the time of the survey. Protracted displacement creates enormous hardships for affected households and communities, yet few disaster risk analyses account for the time component of displacement. Here, we propose predictive models for household displacement duration and return for practical integration within disaster risk analyses, ranging in complexity and predictive power. Two classification tree models are proposed to predict return outcomes with a minimum number of predictors: one that considers only physical factors (TreeP) and another that also considers socioeconomic factors (TreeP&S). A random forest model is also proposed (ForestP&S), improving the model's predictive power and highlighting the drivers of displacement duration and return outcomes. The results of the ForestP&S model highlight the importance of both physical factors (e.g., property damage and unsanitary conditions) and socioeconomic factors (e.g., tenure status and income per household member) on displacement outcomes. These models can be integrated within disaster risk analyses, as illustrated through a hurricane scenario analysis for Atlantic City, NJ. By integrating displacement duration models within risk analyses, we can capture the human impact of disasters more holistically and evaluate mitigation strategies aimed at reducing displacement risk.
... As part of the Tomorrow's Cities project (see Galasso et al. 2021) Hen-Jones et al. (2024, studied three communities in the northern parts of Quito. A stochastic modelling approach was adopted to allow a wide range of combinations of potential landslide preparatory and triggering factors to be investigated. ...
Chapter
Full-text available
Quito, Ecuador, is a rapidly expanding multi-hazard environment at increased risk of rainfall-triggered landslides. In such cities, information on the impact of urbanisation on slopes and rainfall intensity-duration thresholds for triggering landslides can be valuable for resilient urban planning. However, rainfall thresholds are usually generated from empirical landslide data, which is often unavailable. This paper presents an overview of recent stochastic landslide modelling work, enabling regional sensitivity analysis and the development of rainfall thresholds based on tens-of-thousands of physics-based slope stability simulations. Typical landslide-prone communities in north Quito are characterised in terms of degree of urbanisation, slope geometry, soil properties and rainfall scenarios. Soil data came from the new Quito soil geotechnical database and drone mapping successfully resolved slope geometries and more detailed information such as construction materials and slope features. An overview of the modelling methodology is presented. The key findings of the simulation study are that slope cutting, followed by removal of vegetation are the key drivers of slope instability. These findings are discussed in the context of recommendations for land use policy in the studied areas.
... Despite representing slightly less than 9% of total natural disasters recorded in between 2000 and 2019, earthquakes accounted for 58% of the disaster-related 1.23 million lives lost. 1 To reduce the fatalities, modern building codes target Life-Safety objectives, thus inherently accepting damage to both the structural skeleton and the non-structural envelope/components. In fact, according to current seismic design philosophies, structural systems are designed to concentrate damage in specific regions, which are designed to deform plastically and provide global structural ductility. ...
Article
Full-text available
The growing concern over environmental impact and the significant improvement in the quality of engineered wood products have led to the rapid growth of the timber building industry in the last decades. Although traditional, yet recent, mass timber structural systems, such as cross‐laminated timber walls, can provide satisfactory seismic performance during earthquakes in terms of life‐safety, the crucial need for more resilient timber buildings has prompted the development of low‐damage high‐performance self‐centring and dissipative solutions based on unbonded post‐tensioned hybrid connections, referred to as Pres‐Lam technology. The flexibility of design and construction speed, combined with the enhanced seismic performance, create a unique potential towards an earthquake‐proof sustainable building system. Despite the growing popularity of the technology, a comprehensive framework for the fragility analysis, to be used in risk and loss modelling applications, has not yet been developed for both component and building levels. This article aims to develop a framework for assessing the fragility curves of moment‐resisting Pres‐Lam frame systems, at both structural system and connection levels, by using and comparing different approaches that involve nonlinear static (pushover) and time history dynamic analyses. A Python‐based parametric workflow was developed to evaluate fragility curves for a wide range of case‐study buildings. Particularly, three distinct structures were selected, and their fragility curves were evaluated utilizing alternative methodologies at a building structural‐system level. Finally, fragility models were fitted for individual structural connections using the results of time‐history analyses. These models are intended for use in a component‐based loss assessment.
Article
Full-text available
Quantified flood risk assessments focus on asset losses, neglecting longer-term impacts to household welfare via income and consumption losses. The extent of welfare losses depends upon resilience – the ability to anticipate, resist, cope, recover and learn from a shock. Here, we use a novel welfare loss modelling framework and perform a high-resolution spatial analysis in coastal Bangladesh to quantify welfare losses from a tropical cyclone under present and future climatic and socio-economic conditions. We further test various adaptation options that are intended to enhance resilience. Results show that poor households experience, on average, 7% of the asset losses, but 42% of the welfare losses. Combining dike heightening, post-disaster support and stronger housing can reduce welfare losses by up to 70%, and foster sustainable development by benefitting the poor, increasing resilience and demonstrating robustness under socio-economic and climatic uncertainties. Thus, a welfare-orientated perspective helps to identify adaptation options that enhance resilience and leave no-one behind.
Article
Full-text available
Natural disaster risk assessments typically consider environmental hazard and physical damage, neglecting to quantify how asset losses affect households’ well-being. However, for a given asset loss, a wealthy household might quickly recover, while a poor household might suffer major, long-lasting impacts. This research proposes a methodology to quantify disaster impacts more equitably by integrating the three pillars of sustainability: environmental (hazard and asset damage), economic (macro-economic changes in production and employment) and social (disaster recovery at the household level). The model innovates by assessing the impacts of disasters on people’s consumption, considering asset losses and changes in income, among other factors. We apply the model to a hypothetical earthquake in the San Francisco Bay Area, considering the differential impact of consumption loss on households of varying wealth. The analysis reveals that poorer households suffer 19% of the asset losses but 41% of the well-being losses. Furthermore, we demonstrate that the effectiveness of specific policies varies across cities (depending on their built environment and social and economic profiles) and income groups.
Article
Full-text available
In recent years, there has been a gradual increase in research literature on the challenges of interconnected, compound, interacting, and cascading risks. These concepts are becoming ever more central to the resilience debate. They aggregate elements of climate change adaptation, critical infrastructure protection, and societal resilience in the face of complex, high‐impact events. However, despite the potential of these concepts to link together diverse disciplines, scholars and practitioners need to avoid treating them in a superficial or ambiguous manner. Overlapping uses and definitions could generate confusion and lead to the duplication of research effort. This article gives an overview of the state of the art regarding compound, interconnected, interacting, and cascading risks. It is intended to help build a coherent basis for the implementation of the Sendai Framework for Disaster Risk Reduction (SFDRR). The main objective is to propose a holistic framework that highlights the complementarities of the four kinds of complex risk in a manner that is designed to support the work of researchers and policymakers. This article suggests how compound, interconnected, interacting, and cascading risks could be used, with little or no redundancy, as inputs to new analyses and decisional tools designed to support the implementation of the SFDRR. The findings can be used to improve policy recommendations and support tools for emergency and crisis management, such as scenario building and impact trees, thus contributing to the achievement of a system‐wide approach to resilience.
Book
Full-text available
"Economic losses from natural disasters totaled 92billionin2015.Suchstatements,alltoocommonplace,assesstheseverityofdisastersbynoothermeasurethanthedamageinflictedonbuildings,infrastructure,andagriculturalproduction.But92 billion in 2015.” Such statements, all too commonplace, assess the severity of disasters by no other measure than the damage inflicted on buildings, infrastructure, and agricultural production. But 1 in losses does not mean the same thing to a rich person that it does to a poor person; the gravity of a $92 billion loss depends on who experiences it. By focusing on aggregate losses—the traditional approach to disaster risk—we restrict our consideration to how disasters affect those wealthy enough to have assets to lose in the first place, and largely ignore the plight of poor people. This report moves beyond asset and production losses and shifts its attention to how natural disasters affect people’s well-being. Disasters are far greater threats to well-being than traditional estimates suggest. This approach provides a more nuanced view of natural disasters than usual reporting, and a perspective that takes fuller account of poor people’s vulnerabilities. Poor people suffer only a fraction of economic losses caused by disasters, but they bear the brunt of their consequences. Understanding the disproportionate vulnerability of poor people also makes the case for setting new intervention priorities to lessen the impact of natural disasters on the world’s poor, such as expanding financial inclusion, disaster risk and health insurance, social protection and adaptive safety nets, contingent finance and reserve funds, and universal access to early warning systems. Efforts to reduce disaster risk and poverty go hand in hand. Because disasters impoverish so many, disaster risk management is inseparable from poverty reduction policy, and vice versa. As climate change magnifies natural hazards, and because protection infrastructure alone cannot eliminate risk, a more resilient population has never been more critical to breaking the cycle of disaster-induced poverty.
Article
Full-text available
The assessment of shelter needs of displaced people in the aftermath of major earthquake events is one the main challenges that emergency responders currently have to face. Based on the scale of the disaster, the short-term shelter demand can turn into a temporary housing need for displaced population, which is a local government responsibility. The study presented in this paper is focused on a critical review of currently available methodologies and corresponding software packages that were developed specifically for estimating the number of displaced people and those who will most likely seek public sheltering and will need temporary housing. The main features and shortcomings of such tools are highlighted and interpreted with a view to future improvement and application in the disaster management field. Two software tools, ERGO-EQ and HAZUS-MH, have been identified as more exhaustive in considering all the different variables involved in the shelter needs estimation. For this reason, this study also includes a full application of those two software tools to a real case study. Specifically, the modelling of the February 22nd, 2011 Christchurch earthquake is presented, in which hazard, vulnerability and exposure (both physical and social) were characterized over a specific area of Christchurch urban area as input to the aforementioned software tools. The employed tools yielded different results in terms of dislocated people and shelter needs estimates, for which a brief discussion is presented on possible ways to improve and to better reflect the local conditions, in order to produce more realistic outputs.
Article
Full-text available
The first international conference for the post-2015 United Nations landmark agreements (Sendai Framework for Disaster Risk Reduction 2015–2030, Sustainable Development Goals, and Paris Agreement on Climate Change) was held in January 2016 to discuss the role of science and technology in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030. The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 aimed to discuss and endorse plans that maximize science’s contribution to reducing disaster risks and losses in the coming 15 years and bring together the diversity of stakeholders producing and using disaster risk reduction (DRR) science and technology. This article describes the evolution of the role of science and technology in the policy process building up to the Sendai Framework adoption that resulted in an unprecedented emphasis on science in the text agreed on by 187 United Nations member states in March 2015 and endorsed by the United Nations General Assembly in June 2015. Contributions assembled by the Conference Organizing Committee and teams including the conference concept notes and the conference discussions that involved a broad range of scientists and decision makers are summarized in this article. The conference emphasized how partnerships and networks can advance multidisciplinary research and bring together science, policy, and practice; how disaster risk is understood, and how risks are assessed and early warning systems are designed; what data, standards, and innovative practices would be needed to measure and report on risk reduction; what research and capacity gaps exist and how difficulties in creating and using science for effective DRR can be overcome. The Science and Technology Conference achieved two main outcomes: (1) initiating the UNISDR Science and Technology Partnership for the implementation of the Sendai Framework; and (2) generating discussion and agreement regarding the content and endorsement process of the UNISDR Science and Technology Road Map to 2030.
Article
The lack of empirical data regarding earthquake damage or losses has propelled the development of dozens of analytical methodologies for the derivation of fragility and vulnerability functions. Each method will naturally have its strengths and weaknesses, which will consequently affect the associated risk estimates. With the purpose of sharing knowledge on vulnerability modeling, identifying shortcomings in the existing methods, and recommending improvements to the current practice, a group of vulnerability experts met in Pavia (Italy) on April 2017. Critical topics related with the selection of ground motion records, modeling of complex real structures through simplified approaches, propagation of aleatory and epistemic uncertainties, and validation of vulnerability results were discussed, and suggestions were proposed to improve the reliability and accuracy in vulnerability modeling.
Book
The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for assessing the science related to climate change. It provides regular assessments of the scientific basis of climate change, its impacts and future risks, and options for adaptation and mitigation. This IPCC Special Report is a comprehensive assessment of our understanding of global warming of 1.5°C, future climate change, potential impacts and associated risks, emission pathways, and system transitions consistent with 1.5°C global warming, and strengthening the global response to climate change in the context of sustainable development and efforts to eradicate poverty. It serves policymakers, decision makers, stakeholders and all interested parties with unbiased, up-to-date, policy-relevant information. This title is also available as Open Access on Cambridge Core.
Article
This paper presents on-going challenges in the present paradigm shift of earthquake-induced ground motion prediction from empirical to physics-based simulation methods. The 2010–2011 Canterbury and 2016 Kaikoura, New Zealand earthquakes are used to illustrate the predictive potential of the different methods. On-going efforts in simulation validation and theoretical developments are then presented, as well as the demands associated with the need for explicit consideration of modelling uncertainties. Finally, discussion is also given to the tools and databases needed for the efficient utilisation of simulated ground motions both in specific engineering projects as well as for near-real-time impact assessment.
Article
Analysis of the economic impact of climate change typically considers regional or national economies and assesses its impact on macroeconomic aggregates such as gross domestic product. These studies therefore do not investigate the distributional impacts of climate change within countries or the impacts on poverty. This Perspective aims to close this gap and provide an assessment of climate change impacts at the household level to investigate the consequences of climate change for poverty and for poor people. It does so by combining assessments of the physical impacts of climate change in various sectors with household surveys. In particular, it highlights how rapid and inclusive development can reduce the future impact of climate change on poverty.