The starting point of the analysis is a deceptively simple observation: hazards are not evenly distributed across the globe, and neither is attention. Some regions have built deep expertise in the one or two threats they face most often, while other potential hazards go unmanaged entirely. The all-hazards approach, formally born in the late 1970s when the United States National Governors’ Association articulated the principles of comprehensive emergency management, was meant to fix exactly this blind spot. The idea was that governments at every level should prepare for and respond to all kinds of hazards rather than treating each threat in isolation. In practice, the United States could not fully implement the approach until the 1980s, and even then regions clashed over how to apply it, with law enforcement officials and firefighters sometimes disagreeing within the same jurisdiction about how a common plan should work.

Today, the vocabulary of all-hazard management, sometimes called multi-hazard, all-risk, or societal resilience management, appears in national strategies around the world, and globalization has spread emergency management information across borders faster than ever. Yet the review finds that the field still has not addressed the concept comprehensively. Researchers have repeatedly shown that existing practices fall short on interagency coordination, resource allocation, decision-making, stakeholder communication, policy implementation, and institutional learning. Alternative proposals, such as emphasizing functional management of communication and transportation systems across all hazards, have helped, but the gap persists. Among all hazard families, the review argues, wind is where the gap is widest, because practitioners discuss air pollution, hurricanes, and wildfires while disregarding wind direction as a meteorological driver, pollinator decline as an ecological disturbance, and localized indigenous wind phenomena such as gap winds and dust devils.

The scientific framing here matters. The study treats all wind hazards as climatological hazards generated by atmospheric processes rather than as isolated variables. Damaging winds arise from interactions among boundary-layer dynamics, surface roughness, land-sea thermal contrasts, synoptic circulation, convective instability, and wind-field variability. Wind itself is simply air moving from high-pressure to low-pressure areas, and the larger the pressure difference, the faster it moves, but that moving air also carries pollutants, moisture, heat, and pollen. Recent climate science has reinforced how much long-term atmospheric circulation patterns, shifting thermal gradients, and regional climate variability control the frequency, intensity, and geography of hazardous wind events, which means effective management must combine short-term meteorological forecasting with long-term climate adaptation. Empirical and modeling studies now allow intensity, persistence, direction, and circulation patterns to be quantitatively analyzed through observational datasets, synoptic climatology, and numerical modeling, giving hazard managers a far more robust scientific foundation than a decade ago.

To organize the evidence, the review distinguishes two analytical categories. External hazard-oriented management addresses the observable hazards within the current boundary of wind: air pollution, hurricanes, and wildfires. Internal meteorological process management addresses the unexpected or unobservable processes behind them, including wind direction as a driver, wind-related ecological disturbance, and location-specific wind systems that modify rather than constitute hazards. Conceptually, these are treated as a linked sequence rather than equivalent categories: meteorological drivers shape the conditions under which hazards emerge, hazards produce direct physical impacts or ecological disturbances, and those disturbances in turn modify vulnerability and subsequent risk. The method behind the paper was a structured narrative review, supported by the PRISMA 2020 flow diagram, that systematically identified, screened, and synthesized English-language literature spanning climatology, meteorology, engineering, emergency management, and public policy, with iterative thematic coding applied across three units of analysis: international organizations, developed countries, and developing countries.

The results reveal a field that performs well on the visible half of the problem. At the international level, the Sendai Framework for Disaster Risk Reduction 2015-2030, backed by the United Nations Office for Disaster Risk Reduction, encourages all-hazard management across all sectors, while the Paris Agreement targets the climate change that drives air pollution. International non-governmental organizations have pushed early action, from the Cameroon Red Cross prioritizing emergency preparedness to Emirates Nature-WWF in the United Arab Emirates promoting rapid air quality improvement and Fire and Emergency New Zealand responding quickly to pollution events. In developed countries, the United States has built hurricane and flood capability, with Hurricane Katrina in 2005 exposing the failures of fragmented all-hazard planning and Hurricane Sandy in 2012 demonstrating the payoff of prior planning, incident command systems, coordination, and leadership. European nations, despite the rarity of tropical systems there, have managed the health and social consequences of extratropical cyclones such as Vince in 2005 and subtropical storm Alpha in 2020, delivering humanitarian logistics from mental to physical health services.

Developing countries tell a parallel story centered on fire. Africa has been described as a wildfire continent, with roughly 70 percent of the 400 million hectares burned worldwide each year located in African savannas and grasslands, and those fires, many too small for satellite detection, disrupt the global atmosphere, carbon cycle, and ecosystems. South American nations including Brazil, Argentina, Chile, and Uruguay, frequently threatened by heatwave- and drought-driven wildfires and major timber exporters besides, have invested in advanced weather technology and land-use planning, while Southeast Asian countries have long battled wildfires in the Lower Mekong Basin. Yet across all three stakeholder groups, the review finds, internal meteorological processes are managed only through improvised alternatives rather than systematic frameworks, leaving the underlying drivers of wind-related risk largely unexamined.

Why bother closing that gap? The review identifies three major benefits of genuine all-hazard management. First, it provides a robust reference for all stakeholders across every phase of the emergency management cycle, from prevention and preparedness through response and recovery. Second, it enables cooperation among stakeholders who otherwise work in silos, including governments, businesses, voluntary organizations, mass media, and local communities, building professional networks that hazard-specific management rarely achieves. Third, it accumulates disaster response capacity for local communities, a process that helped create the professional emergency management profession itself in the twenty-first century. Focusing only on visible risks, the paper warns, misses crucial opportunities for early detection, forecasting, prevention, and recovery. Wildfire research makes the point concretely: air stability, wind direction, and wind speed are key factors controlling ember movement, fire intensity, and fire spread, so monitoring those meteorological processes directly improves forecasts and emergency response before dangerous conditions fully materialize.

The policy implications are deliberately framed as directions to test rather than mandates to impose. Drawing on the principle of progressiveness from the Emergency Management Institute’s 2007 principles of disaster management, the review suggests that practitioners could use fine-scale wind direction patterns, wind-driven pollinator decline, and localized indigenous wind knowledge to identify subtle signals, anticipate cascading impacts, and inform early warning indicators. Emergency managers might assess whether micro-level wind direction data supports interagency collaboration during wind-related incidents; institutions could explore incorporating pollinator-wind interactions into climate adaptation and biodiversity monitoring; and local authorities might test whether indigenous wind knowledge strengthens land-use planning and community risk education. The author is explicit that these recommendations are complementary possibilities, not conclusions requiring immediate revision of the Sendai Framework or other international instruments, and that feasibility must be evaluated within existing risk assessment, early warning, and preparedness programs while accounting for different institutional capacities and regional hazard contexts.

The review also acknowledges its own limits. It did not explore the complex interrelationships between wind hazards and other natural or anthropogenic hazards, and language and accessibility constraints, including the exclusion of non-English publications and reliance on paywalled literature, may have underrepresented regions with different approaches to wind hazard management. Still, as what the author describes as a pioneering attempt to study all-wind-hazard management comprehensively, the paper offers a conceptual framework that connects climatology, disaster risk reduction, and emergency management from a single analytical viewpoint. The next steps it proposes are practical: tabletop exercises, seminars, drills, and eventually full-scale exercises that test whether incorporating both external hazards and internal meteorological processes improves coordination under broader wind-hazard scenarios. If those tests succeed, the humble daily movement of air, the same force that carries pollen between plants and shapes local winds known only to the communities that live with them, could finally take its place alongside the hurricane in the world’s disaster plans, reducing human loss, economic damage, and the mental stress that follows in the wake of every wind-driven catastrophe.

Subject of Research: All-hazard management of wind hazards and internal meteorological processes in disaster risk reduction

Article Title: All-hazard management approach for wind

Article References: Ha, K.-M. (2026). All-hazard management approach for wind. Theoretical and Applied Climatology, 157(10), Article 624. https://doi.org/10.1007/s00704-026-06559-3

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06559-3

Keywords: wind hazards, all-hazard management, disaster risk reduction, meteorology, climatology, emergency management, Sendai Framework, wildfires, hurricanes, air pollution, pollinator decline, resilience

The study set out to answer a deceptively simple question: how does the science of building LCA actually handle resilience, the capacity of a structure to withstand hazards and recover its function afterward? Rios conducted a systematic review of 40 original LCA studies spanning four hazard types: heat, flood, wind, and seismic events. Each study was mapped against a diagnostic grid built from two established resilience frameworks. The first, from Bruneau and colleagues, describes resilience through four dimensions: robustness, the strength to withstand a shock; redundancy, the presence of substitutable systems; rapidity, the speed of restoration; and resourcefulness, the capacity to mobilize people and materials for recovery. The second, from Rehak and colleagues, divides the resilience timeline into four phases: preparation, system response, recovery, and adaptation.

The results of that mapping are strikingly lopsided. Nearly every study in the review clustered in a single cell of the grid: robustness during the preparation and recovery phases. In practice, this means researchers have become adept at asking how hazard-resistant design choices change the initial bill of materials, and at converting predicted damage into repair quantities that are added to the inventory as extra embodied carbon. Seismic studies, aided by the mature FEMA P-58 performance-based earthquake engineering pipeline, are the most internally consistent. Heat studies, powered by building energy simulation with future climate projections, form a second coherent camp. Flood studies are the most fragmented, with four distinct integration mechanisms, from depth-damage functions to insurance-based input-output conversion, coexisting without cross-fertilization. Wind studies, only three in total, borrow from both the seismic and insurance modeling traditions.

What the review found missing is more consequential than what it found present. The rapidity dimension appeared only as partial evidence in two seismic papers that treated time-to-repair as an output but never translated recovery duration into environmental flows. The redundancy and resourcefulness rows of the evidence map were entirely empty: no study modeled backup systems as a design variable, evaluated the environmental cost of built-in spare capacity, or asked what emissions would be avoided by systems that prevent damage escalation. The system response phase, the chaotic period during and immediately after a hazard when a building is operating below its functional threshold, was nearly absent across all hazard groups. In other words, the environmental cost of the disruption itself, the emergency fuel, the temporary shelter, the evacuation transport, falls outside every LCA system boundary in the reviewed literature.

The root of the problem, Rios argues, lies in the functional unit, the quantified description of service against which all environmental flows are normalized. In standard practice, that unit is static: one square meter of office space for fifty years, for example. This definition assumes the building delivers its service completely and continuously, an assumption that hazard exposure systematically violates. If a building loses functionality for six months after a flood, dividing its total life cycle impacts by the full fifty-year service life, as if those six months of failed delivery had never occurred, overstates its environmental efficiency and understates the cost of its fragility. The review found that not a single one of the 40 studies defined the functional unit in terms of sustained service delivery through disruption events.

To close this gap, the paper introduces a resilience-extended LCA framework, or RES-LCA, built on a reformulation of the functional unit as time-integrated functionality. Expressed mathematically as the sum over time of the functionality level Q multiplied by the duration of each time step, the new unit captures the cumulative service actually delivered over the building lifetime under all conditions, including disruption. When a building performs perfectly, the formulation reduces exactly to the conventional one. The difference appears only when service fails, which is precisely when resilience needs to be measured. Under this reformulation, the four resilience dimensions acquire distinct signatures in the functionality curve: robustness governs the magnitude of the initial drop, rapidity the slope of recovery, redundancy the residual service maintained during the disruption, and resourcefulness the shape and duration of the recovery trajectory.

The framework also extends the LCA inventory in two directions. A proposed system response module captures the environmental cost of compensatory services deployed while a building operates below functional threshold, flows that currently escape every assessment. An adaptation module captures the embodied impacts of post-event improvement works, embedding the bounce-forward logic in which a rebuilt building outperforms its pre-disaster baseline. Meanwhile, the often-optional Module D, normally limited to credits for reuse and recycling, is repositioned as a system-wide account of the avoided burdens generated by resilience investments. The approach builds on earlier work by Pizzol, who showed that representing resilience requires reformulating the functional unit and expanding the system boundary, and applies that insight within the existing EN 15978 modular structure that practitioners already use.

An illustrative example shows how the reformulation can flip conventional conclusions. Consider two identical residential buildings in a coastal flood zone, one at grade and one elevated on a concrete podium whose upfront embodied carbon is 30 percent higher. Both experience a single 100-year flood. The elevated building suffers no damage; the at-grade building spends two months nearly non-functional and four more months partially functional, requiring repairs and generating displacement-related emissions. Under conventional LCA, the elevated design appears environmentally preferable only if the avoided repair carbon exceeds the podium premium. Under RES-LCA, the compensatory service emissions of the disrupted building enter the calculation, and for a serious flood event, any non-zero displacement burden is enough to tip the balance toward the resilient design. The framework, in effect, makes the environmental value of resilience visible for the first time.

Perhaps the most provocative implication is what this means for the long-assumed trade-off between resilience and sustainability. The review confirms empirically that the tension is real in current practice: timber has the lowest embodied carbon but the highest flood vulnerability, green roofs reduce operational impacts but increase seismic repair flows, and passive cooling lowers energy use but raises overheating risk in extremes. Yet the study argues that this trade-off is, in part, an artifact of a measurement system that undercounts the environmental costs of service disruption. When environmental performance is defined in terms of service actually delivered, resilient buildings cease to look like costly deviations from efficiency and instead emerge as systems that deliver more reliable service per unit of environmental burden. Sustainability and resilience, long treated as competing agendas, become aligned.

The author acknowledges significant limitations. Data on compensatory service flows, phased recovery materials, and adaptation timelines are scarce and inconsistently reported, and no standardized method exists for quantifying building-level resourcefulness. The framework addresses only the technical dimension of resilience, leaving community-scale interdependencies and the social and organizational domains of the TOSE framework for future work, and the functionality trajectories it requires carry substantial uncertainty under a changing climate. Even so, the core message stands: as climate hazards intensify and billions in public infrastructure funding become tied to resilience criteria, the tools used to certify environmental performance must learn to count what happens when buildings fail. A green building that cannot keep its occupants sheltered through a disaster, the study suggests, is not as green as the spreadsheet claims.

Subject of Research: Integrating hazard resilience into building life cycle assessment through time-integrated functionality

Article Title: Resilience in building life cycle assessment: a critical review and framework for time-integrated functionality

Article References: Rios, F. C. (2026). Resilience in building life cycle assessment: a critical review and framework for time-integrated functionality. Journal of Industrial Ecology. https://doi.org/10.1007/s44498-026-00152-7

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00152-7

Keywords: life cycle assessment, building resilience, embodied carbon, functional unit, seismic hazard, flood, heat waves, wind hazards, sustainability, climate adaptation, redundancy, EN 15978

The core of the system is a two-layer architecture that separates the geometry of pathfinding from the judgment of how cautious to be. The lower layer generates candidate trajectories using the A* algorithm, a classic graph-search method dating back to a landmark 1968 paper by Hart, Nilsson and Raphael, which finds minimum-cost paths by exploring the most promising branches of a grid first. What makes the Inha team’s implementation distinctive is the structure of its cost function. Every candidate path is scored by a weighted combination of two terms: a distance term that favors shorter routes and shorter required times of arrival, and a risk term that penalizes exposure to hazards. Those hazards come in two flavors, static and dynamic. Static risk is the urban landscape itself, the buildings that a low-altitude air taxi must thread between. Dynamic risk is the weather, specifically wind and gust severity, which can change from one minute to the next and turn a safe corridor into a dangerous one.

The upper layer, and the true innovation of the study, is the AI pilot itself: a reinforcement-learning agent trained with the deep deterministic policy gradient algorithm, or DDPG, a technique introduced by Lillicrap and colleagues in 2016 that excels at continuous control problems. The agent’s single job is to choose a continuous weighting factor, denoted lambda, which controls the relative influence of the distance and risk terms inside the A* cost function. In other words, the AI does not draw the path directly. Instead, it decides how much the path planner should care about speed versus safety, given the current operational context. When the battery is healthy and the air is calm, the agent can push lambda toward efficiency, letting the vehicle fly direct, fast routes. When the state of charge falls or gusts intensify, the agent shifts the weighting toward risk avoidance, and the planner responds by carving out longer but safer detours around buildings and turbulent zones.

One design decision stands out as particularly consequential. Battery state-of-charge and wind information are fed to the policy as continuous observations, not as discrete emergency flags. This choice matters because it changes the character of the system’s behavior at the boundary between normal and abnormal flight. A flag-based system, in which an emergency is declared when some threshold is crossed, tends to produce abrupt, discontinuous responses: the trajectory is one thing before the threshold and something quite different after it. The learned policy, by contrast, produces a lambda that responds smoothly and monotonically to gradually degrading conditions. As the battery drains cell by cell or the wind stiffens knot by knot, the safety weighting tightens proportionally, and the planned path bends incrementally away from hazard. Yet the researchers report that the same policy still reacts decisively when a genuine emergency arrives without warning, snapping to a strongly safety-dominant configuration when the situation demands it.

Validation came through two complementary evaluation strategies. The first was a Monte Carlo benchmark, in which the AI-assisted system was pitted against rule-based baselines and against a planner with a fixed lambda value across many randomized scenarios. Monte Carlo testing is the standard way to expose brittleness in autonomous systems, because it samples the space of possible conditions rather than testing a handful of hand-picked cases. The second was a controlled context-sweep case study, in which a single operational variable, such as battery level or wind severity, was systematically varied while the system’s responses were recorded. This sweep allowed the team to verify the smooth, monotonic behavior of the weighting factor directly, confirming that the agent’s decisions track the operational context in an interpretable way rather than jumping erratically between modes.

Performance was judged on three metrics that map cleanly onto what a passenger would actually feel and what an operator would actually care about. Path length measures efficiency, the extra distance flown to reach the destination. Risk exposure quantifies how much of the flight is spent near buildings or in severe wind and gust conditions. Trajectory smoothness captures how gentle the resulting path is, a property with direct consequences for passenger comfort, vehicle dynamics and control authority. Smoothness is not a cosmetic concern; certification frameworks for piloted aircraft, including the long-standing military flying-qualities specification MIL-F-8785C, treat handling qualities as central to airworthiness, and a replanning system that produced jagged, erratic paths would be unusable in practice regardless of how safe it was on paper.

The study situates itself within a decade of growing interest in flight-deck decision support for advanced air mobility. NASA researchers, including Karr, Ballin, Barrows and colleagues, have developed autonomous operations planners and flight path management automation specifically for high-density urban environments, and flight evaluations of such systems have already been conducted. The NASA urban air mobility maturity level scale, described by Goodrich and Theodore in 2021, provides the community’s roadmap for how these operations might scale from today’s limited demonstrations to high-density fleets. What the Inha work adds to this lineage is the learned, context-sensitive middle layer: rather than encoding the safety-efficiency trade-off as a fixed engineering choice made in advance, the system lets a trained policy make that choice in flight, informed by the actual state of the vehicle and the atmosphere.

The choice of DDPG as the learning algorithm is also worth appreciating on its own terms. Reinforcement learning agents come in many forms, but many of the most successful ones, including the policy-gradient methods behind celebrated game-playing systems, operate over discrete action spaces. Trajectory weighting does not fit that mold. The appropriate balance between speed and safety is not one of several options but a dial with infinitely many settings, and DDPG was designed precisely for such continuous action spaces, using an actor network to propose actions and a critic network to evaluate them. Related work has already shown the algorithm’s usefulness in safety-critical planning, including an improved DDPG model for emergency fire-escape path planning published in 2024, and the Inha team’s application extends that idea into the three-dimensional, hazard-rich environment of urban airspace.

The implications for the emerging air taxi industry are considerable. Certification authorities and operators alike have struggled with the question of how much autonomy to trust in the cockpit, and a system like this one offers a middle path: the AI does not fly the vehicle, it advises the trajectory planner, adjusting a single, physically meaningful parameter that a human pilot or ground operator could inspect and, in principle, override. The researchers describe their AI pilot as a decision-support tool for both gradual context changes and emergency responses during urban air mobility operations, a framing that keeps the human in the loop while offloading the relentless, second-by-second arithmetic of risk and distance. The work was supported by the National Research Foundation of Korea, and the authors report no competing interests.

Challenges remain before such a system carries passengers. The study validates the concept in simulation, and the gap between Monte Carlo benchmarks and flight test in a real city, with real air traffic, real sensor noise and real regulatory scrutiny, is the widest gap in aviation. The authors note that the data supporting the findings are available upon reasonable request, which will help other groups scrutinize and extend the approach. Still, the conceptual contribution is clean and potentially durable: treat the safety-efficiency trade-off not as a design constant but as a learned function of the operational context, and let a continuous, smoothly responding policy turn that function into concrete flight paths. As fleets of electric vertical-takeoff vehicles move closer to routine operation over the world’s cities, the flights that matter most will not be the ones that go according to plan. They will be the ones that do not, and the machines that can quietly, smoothly find a better way home may prove to be the technology that finally makes urban flight trustworthy.

Subject of Research: AI-based emergency trajectory replanning for urban air mobility vehicles

Article Title: AI-Based Pilot Assistance System for Emergency Trajectory Replanning in Urban Air Mobility

Article References: Yu, J., & Choi, K. (2026). AI-Based Pilot Assistance System for Emergency Trajectory Replanning in Urban Air Mobility. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01258-9

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01258-9

Keywords: urban air mobility, AI pilot, reinforcement learning, DDPG, A* algorithm, trajectory replanning, flight path management, safety-efficiency trade-off, wind hazards, battery state of charge, path planning, decision support