That is the central argument of a major new perspective paper published in the journal Natural Hazards and Earth System Sciences. An interdisciplinary team led by Philip J. Ward of the Institute for Environmental Studies at Vrije Universiteit Amsterdam, together with Marleen C. de Ruiter, Kees Boersma, Jasper Verschuur, Nicole van Maanen and more than a dozen colleagues spanning climate risk, public health, behavioural science, crisis management and disaster governance, sets out five core challenges that stand between today’s fragmented risk research and what the United Nations has called Comprehensive Disaster and Climate Risk Management, or CRM. The paper, published as an invited highlight contribution, arrives in the wake of the Sendai Framework’s Mid-Term Review, which concluded bluntly that science is falling short in understanding complex multi-risks.

The authors’ starting point is a conceptual shift that has gathered momentum over the past decade. Risk, as the UN Office for Disaster Risk Reduction defines it, is a function of hazard, exposure and vulnerability. Traditional assessments treated these ingredients as static, studying each hazard in isolation. Researchers now recognise that hazards interact, triggering, amplifying or compounding one another, and that exposure and vulnerability themselves evolve over time, sometimes for decades. Slow-onset crises such as droughts and pandemics erode financial resources, education, health and social networks, while rapid-onset events like floods and earthquakes displace people into new exposures. A person displaced by flooding may encounter vector-borne disease; a community enduring an outbreak may face heightened exposure because its drinking water infrastructure has been damaged.

The links between climatic hazards and infectious disease are particularly striking. Flooding and high temperatures can expand breeding grounds for mosquitoes, amplifying malaria and dengue transmission, while floodwaters spread the bacteria responsible for cholera. A systematic review cited in the paper found that 58 percent of all known human infectious diseases, including Zika, Chikungunya and Ebola, have been aggravated by climatic hazards through more than 1,000 unique transmission pathways. Yet disaster risk models almost never include biological hazards, and health systems rarely plan for geophysical and meteorological shocks, leaving what the authors describe as double or even triple health burdens when physical injury, disease and mental health impacts coincide.

The first of the five challenges is observational: we lack comprehensive, high-quality data on how societies are impacted by, prepare for, and respond to multi-risks. Health scientists curate disease data, hydrologists curate flood and drought data, and seismologists curate earthquake data, each in disciplinary silos with incompatible standards. Spatial and temporal scales rarely match, so urban heat islands, flash floods and informal-settlement vulnerabilities fall below the resolution of global datasets. Vulnerability is captured in episodic snapshots such as censuses that quickly go stale, while impact databases like EM-DAT record only aggregated, largely tangible losses, overlooking mental health, social cohesion and long-term path dependencies. In many low- and middle-income countries, the sustainability of health information systems itself is threatened by budget cuts to external funding.

The second challenge follows directly: our understanding of why people, communities and institutions prepare, or fail to prepare, for complex multi-risks remains incomplete. Classic theories such as Expected Utility Theory, Prospect Theory, the Theory of Planned Behaviour and Protection Motivation Theory were largely built for single hazards and one-time decisions. In a multi-hazard world, households facing frequent floods and rare earthquakes may misallocate scarce resources, or conversely, experience with one risk may raise awareness of another. Systemic risks transmitted through food prices, supply chains and disease outbreaks compound the problem, because people perceive remotely materialising risks differently from local ones. Most empirical studies capture a single snapshot, so feedback loops between protective behaviour and declining risk perception remain theoretically hypothesised but empirically unverified.

Third, the capacity to simulate how current and future risk could be reduced is hampered by these observational and behavioural gaps. A review of roughly 70 large-scale risk models found that only two included dynamic vulnerability. Agent-based models and system-dynamics approaches have advanced for single hazards such as floods, droughts and outbreak response, but little work extends them to multi-risk settings. Validation is another bottleneck: disasters are inherently local processes, yet validation data arrive as coarse aggregates, leaving low-probability, high-impact events, which climate change may intensify, dangerously under-constrained. The authors call for simulations that couple hazards with dynamic exposure, vulnerability and human decision-making across societal groups, enabled by high-performance computing and richer empirical foundations.

Fourth, governance structures often reinforce the very silos that plague data and models. Mandates overlap or leave gaps when risks interact, political and budgetary cycles favour short-term action, and operational institutions are organised in isolated pillars that slow decision-making. Public participation, where it exists, is frequently semi-formal and structurally weak. The authors advocate poly-centric and net-centric governance approaches: adaptive, networked arrangements that distribute authority vertically and horizontally, connect state and non-state actors, and support collective sensemaking, with digital infrastructures for data interoperability and innovations such as citizen climate assemblies as potential test beds for deeper engagement.

The fifth challenge concerns knowledge exchange at the science, policy and practice interface. The multi-risk problem is, in the authors’ words, super wicked: complex, evolving and without definitive solutions. During the Covid-19 pandemic in the Netherlands, biomedical advice dominated early scientific guidance while social and economic perspectives were added later in parallel structures, illustrating how advisory architectures can entrench compartmentalisation. Research from Western, Educated, Industrialized, Rich and Democratic contexts travels more readily into policy than locally grounded and tacit knowledge, reinforcing epistemic injustices and blind spots about marginalised groups. Knowledge translation is often treated as linear dissemination rather than dialogue and co-production, and even co-located disciplines fail to integrate without deliberate frameworks and boundary objects such as participatory maps, outbreak scenarios and serious games.

Looking forward, the team sketches an ambitious agenda built on five pillars: a Disaster Risk Observatory integrating satellite imagery, census data, surveys and disaster forensics into a living system that tracks risk dynamics in near real time; a new generation of risk-behaviour theories incorporating learning, feedbacks and systemic interdependence; integrative modelling frameworks, notably agent-based, that are optimised locally but scalable globally; poly-centric governance for sharing actionable multi-risk data and models; and systematic study of the science-policy-practice interface itself, supported by knowledge brokers who span disciplinary and administrative boundaries. Crucially, the authors stress that the five challenges are mutually reinforcing, each compounding the others into a persistent actionability gap. Closing it, they argue, will require funding mechanisms and research efforts that reach well beyond the traditional one-to-four-year project cycle, engaging meaningfully with the Sendai Framework, the WHO Health Emergency and Disaster Risk Management Framework, and the global climate agenda.

Subject of Research: Comprehensive disaster and climate risk management and the scientific challenges of multi-risk assessment

Article Title: Invited Perspectives: Science for Comprehensive Disaster and Climate Risk Management

Article References: Ward, P. J., de Ruiter, M. C., Boersma, K., Cesuroglu, T., Clark, N., Comes, T., Dahal, A., Fransen, S., Verschuur, J., Anthonj, C., Balakrishnan, S., de Bruijn, J., Hendriks, E., Mena Fluhmann, R., Perlaviciute, G., Steg, L., van den Homberg, M. J. C., van Westen, C., Atun, F., … van Maanen, N. (2026). Invited Perspectives: Science for Comprehensive Disaster and Climate Risk Management. Natural Hazards and Earth System Sciences, 26(9), 4589-4609. https://doi.org/10.5194/nhess-26-4589-2026

Image Credits: AI Generated

DOI: 10.5194/nhess-26-4589-2026

Keywords: disaster risk management, climate risk, multi-hazard, cascading disasters, infectious disease, vulnerability dynamics, agent-based modelling, disaster governance, Sendai Framework, risk perception, knowledge exchange, Haiti earthquake

The research, published in the International Journal of Disaster Risk Science, analyzed 282 organizations that collaborated during the rainstorm, which killed 398 people, flooded 16 million hectares of crops, and caused direct economic losses exceeding 120 billion yuan. Drawing on 56 verified documents, including government investigation reports, press conference transcripts, and reconstruction plans, the team constructed an undirected network in which a tie existed whenever two or more organizations jointly participated in the same activity. Using the Louvain community detection algorithm, they found the network naturally divided into 22 organizational communities with a modularity of 0.6016, indicating substantially denser connections within groups than would occur by chance.

The study’s conceptual innovation lies in applying what network scientist Duxbury calls “micro effects on macro structures,” or MEMS. Previous emergency network research has mostly examined how individual or dyadic factors—such as similarity between two specific organizations—influence whether those two organizations form a tie. This study goes further, asking whether those same micro-level preferences scale up to shape macroscopic patterns: the clustering of many nodes into densely connected subgroups and the segregation of those subgroups from one another. Clustering itself can be beneficial, concentrating trust and coordination. Segregation, by contrast, is a structural obstacle—a scarcity of bridging ties that isolates communities and limits the flow of information and resources across the network.

The researchers distinguished three broad forms of homophily. Attribute homophily captures similarity in fundamental characteristics such as organizational type—government agencies, public institutions, state-owned enterprises, private firms, and nonprofits—and in responsibilities spanning flood control, transportation, medical services, telecommunications, and more. Location homophily reflects geographic proximity, measured through pairwise distances computed from longitude and latitude data. Institutional homophily concerns shared institutional environments: organizations answering to the same jurisdiction level (national, provincial, municipal, or county) or reporting to the same superior department. Drawing on transaction cost theory and the institutional collective action framework, the authors argue that similarity reduces information, negotiation, and enforcement costs, lowers the risk of defection and free-riding, and builds the trust that crisis conditions demand.

To test these ideas, the team employed exponential random graph models (ERGMs), estimated via Markov chain Monte Carlo, which can simultaneously evaluate endogenous structural mechanisms and exogenous organizational attributes. The results were striking. All five homophily indicators—type, responsibility, location, jurisdiction level, and affiliation—showed significant effects on tie formation, but their magnitudes differed dramatically. Affiliation homophily, the tendency of organizations under the same superior authority to collaborate, was by far the strongest driver, with a coefficient of 1.432. Responsibility homophily followed at 0.540, then type homophily at 0.308 and jurisdiction level homophily at 0.259. Notably, location homophily carried a tiny negative coefficient, suggesting that physical distance barely constrains collaboration in an era of digital coordination. Structural terms mattered too: transitivity, measured as geometrically weighted edgewise shared partners, was strongly positive at 2.083, confirming a powerful tendency toward triadic closure—organizations tend to partner with their partners’ partners.

The decisive evidence that homophily shapes macrostructure came from simulation. The team generated 100 networks under different model specifications and compared their modularity, as detected by the Louvain algorithm, with the observed network’s modularity of 0.602. Networks simulated with structural terms alone achieved average modularity of only 0.142, and networks combining structural and homophily terms reached 0.322. Networks simulated with homophily terms alone scored 0.411—the closest to reality. In other words, triadic closure alone reinforces local closure but cannot reproduce the clear community boundaries seen in the actual network; homophilous preference is the essential ingredient. When each homophily dimension was removed in turn, dropping affiliation homophily caused the largest decline in simulated modularity, identifying it as the primary engine of organizational clustering. This finding carries institutional meaning: in China’s centralized, hierarchical emergency management system, cross-provincial firefighting units and central state-owned enterprises in power and telecommunications clustered tightly despite geographic dispersion because they shared a common chain of command.

Yet the same system that produces cohesion also produces isolation. Using the 22 communities as units of analysis, the researchers applied a bias-corrected percentile bootstrap method to test whether homophily predicts four indicators of network segregation: the number of external ties a community maintains, the diversity of external communities it reaches, the efficiency of its cross-community pathways, and the share of its members entirely unable to reach other communities. The results were unambiguous. Only jurisdiction level homophily showed significant effects, and those effects consistently intensified segregation. Communities with a higher proportion of same-level members had significantly fewer external connections, reached fewer distinct outside communities, needed more steps to connect beyond their boundaries, and contained more organizations completely disconnected from other communities. Affiliation, type, responsibility, and location homophily showed no significant segregation effects once confidence intervals were considered.

Why does jurisdictional sameness breed isolation? The authors point to the hierarchical structure of China’s administrative system. Information and resources flow vertically—upward or downward through designated channels—while horizontal, cross-level communication lacks institutionalized channels and may carry compliance risks. Organizations therefore interact mainly with peers at their own administrative level, reducing external diversity and leaving some nodes stranded. The finding resonates with Ronald Burt’s theory of structural holes: brokers who span gaps between groups gain access to nonredundant information and drive innovation. When jurisdiction level homophily dominates interaction, it suppresses such brokerage, converting structural holes from opportunities into persistent fractures. Prior scholarship has documented homophily’s shadow side—it can hinder innovation, reproduce redundant skills, impede the integration of heterogeneous resources, and fragment regional responses—but this study provides rare quantitative evidence of how the same mechanism that builds community also constructs barriers.

The study’s most consequential conclusion is that different types of homophily are not interchangeable, as much prior tie-formation research implicitly assumed. Institutional homophily—especially shared affiliation—outweighs attribute and location similarity because it produces deeper alignment in authority boundaries, procedural standards, and reporting norms, which sustained, intensive collaboration requires. In a system emphasizing unified command and graded responsibility, organizations under the same authority share accountability chains, evaluation standards, and political risk, making co-affiliated units the safest partners under pressure. Attribute similarity reduces cognitive distance, and location similarity cuts logistical costs, but both are secondary to institutional compatibility in centralized governance contexts.

The policy implications are direct. The authors recommend leveraging affiliation homophily to strengthen unified coordination—clarifying the authority of emergency command organizations, standardizing operating procedures, and building unified command platforms—while simultaneously establishing flatter, cross-level collaboration mechanisms with direct and bypass reporting channels whose trigger conditions, authority boundaries, and review procedures are explicitly defined. The warning echoes earlier resilience research: highly hierarchical networks are vulnerable because if the central hub fails, the entire network risks collapse. An effective emergency network, the authors conclude, is like a well-planned city: it needs both the strong internal streets of cohesive communities and the open bridges between them. Homophily builds the streets; deliberate institutional design must build the bridges. Only then can disaster response achieve both the efficiency that comes from trusted, similar partners and the adaptability that comes from heterogeneous, cross-cutting connection.

Subject of Research: How homophily influences the clustering and segregation of organizational communities in emergency collaborative networks during China's Zhengzhou 7.20 extreme rainstorm.

Article Title: Exploring Homophily: How it Influences the Clustering and Segregation of Organizational Communities in Emergency Collaborative Networks

Article References: Xie, H., Zheng, S., He, A. J., & Xu, J. (2026). Exploring Homophily: How it Influences the Clustering and Segregation of Organizational Communities in Emergency Collaborative Networks. International Journal of Disaster Risk Science. https://doi.org/10.1007/s13753-026-00769-z

Image Credits: AI Generated

DOI: 10.1007/s13753-026-00769-z

Keywords: emergency collaborative network, homophily, organizational clustering, network segregation, exponential random graph models, institutional homophily, affiliation homophily, jurisdiction level homophily, Louvain community detection, Zhengzhou 7.20 extreme rainstorm, disaster governance, network modularity