Climate change is leading to systemic and existential impacts, and evidence is mounting that these can result in the displacement of human populations. There is a rapidly growing demand for comprehensive risk assessments that include displacement and its associated costs to inform humanitarian response and national planning and coordination. However, owing to complex causation, missing and incomplete data, and the political nature of the issue, the longer-term economic impacts of disaster- and climate-related displacement remain largely hidden. Current approaches are rarely ex ante and prospective and do not consider systemic risk management. Not surprisingly, response-based approaches have shown mixed results, repeatedly demanding substantial resources while not addressing the root causes of displacement. Climate change is not only affecting the intensity, frequency, and duration of hazards that trigger displacement but is also eroding already fragile livelihoods and ecosystems, acting as an aggravator of existing vulnerability and contributing to chronic poverty and conflict in affected countries ([ 1 ][1]). Although disaster risk reduction as a cross-sectoral issue has gained considerable attention over the past two decades, disaster displacement risk is still not fully integrated in national policies and planning. Out of 46 countries included in the 2020 Internal Displacement Index, most acknowledge disaster displacement in principle and have climate policies or national adaptation plans in place. However, only 27 recognize the link between the gradual impacts of climate change and displacement ([ 2 ][2]). With an evidence-based, longer-term vision and investments, climate-related displacement— the forced movement of people in response to a hazard—can be averted and replaced by a range of measures such as planned relocation that is voluntary (at least to a large degree) and financially supported, or by building the resilience of at-risk populations, reducing vulnerability to such an extent that moving is not required. What is missing is a risk-informed framework for country-led, forward-looking approaches to make the case for substantial investment in effective risk reduction, durable solutions for those displaced, and the prevention of new displacement. Applied risk science, using probabilistic models and large empirical datasets compiled over the years, combined with insights from local empirical research and community assessments, now offers the opportunity for a step change in informed decision-making. For example, the shift from deterministic disaster risk assessments, based on historical data, to state-of-the-art probabilistic modeling used by the insurance industry, calibrated with historical data but including randomness to encompass all possible scenarios, presents a notable advance in risk science that is yet to be fully applied to displacement risk. New tools and risk modeling platforms, such as CLIMADA run by ETH Zürich or CAPRA of the World Bank, can now be adapted for displacement risk assessments. Further, assessing the social and economic cost of displacement can provide incentives for transformational action and change, from mere response to disaster displacement to proactively addressing vulnerability and exposure, thereby reducing displacement risk. Disaster displacement is a global reality and everyday occurrence. Millions of disaster displacements have been systematically recorded since 2008—on average, 24.5 million new movements every year ([ 3 ][3]). Weather-related hazards account for almost 90% of all these displacements ([ 2 ][2]), with climate change and the increasing concentration of populations in areas exposed to storms and floods, coupled with socioeconomic drivers of vulnerability, meaning that more people are at risk of being displaced. Demographic, historical, political, and socioeconomic factors determine whether people can withstand the impacts of a physical hazard or environmental stressor or have to leave their homes. Climate change interacts with all of these factors, particularly where resources and the capacities of humans and systems are already stretched ([ 4 ][4]). For example, sea level rise results in loss of land in coastal areas and low-lying atolls of island states, forcing communities to retreat or leave the land altogether. Salinization can reduce crop yields, undermine arable land and freshwater availability, and force people to move. Increasing temperatures affect soil moisture and degradation, which make the soil susceptible to nutrient loss and erosion, thereby destroying the livelihood basis for rural communities. Glacial retreat and melt, loss of biodiversity, and land and forest degradation mean decreased ecosystem services and provisioning services, pushing people to move. Because climate change can also alter the intensity, frequency, and duration of hazard events, climate anomalies and more devastating sudden-onset disasters may follow. Most of the impacts of climate change only result in displacement for those vulnerable to them. This essential point is repeatedly forgotten, with important policy implications ([ 5 ][5]). A prosperous farmer with access to drip irrigation and fertilizers, reliable buyers, loans, and insurance will not be as affected by changes in rainfall patterns as a smallholder subsistence farmer relying on the regularity of seasonal rains or a pastoralist in search of pasture for his herd. An urban dweller with an office job and regular income will not need to leave his home because of the loss of mangroves, which are providing sustenance to millions in coastal communities. Nonetheless, although individual vulnerability leads to a risk of adverse displacement outcomes, disaster and climate risks are increasingly becoming systemic because high-level and widespread impacts may ripple through social and economic networks, incurring further adverse micro and macro impacts and disruptions ([ 6 ][6]). Climate change is thus a displacement trigger in its own right (e.g., loss of coastlines through sea level rise and coastal erosion), a visible aggravator (e.g., when livelihoods are eroded because of soil degradation and loss of ecosystem services), and a hidden aggravator (e.g., increasing the intensity of cyclonic winds and shifting rainfall patterns that result in floods). But the impacts of climate change interact with broader changes in the physical and social environment, resulting in potentially rising costs associated with future displacement. ![Figure][7] Global disaster displacement risk relative to population size Average Annual Displacement (AAD) risk is a compact metric that represents the estimated effect, accumulated over a long time frame, of future small to medium and extreme events and estimates the likely displacement associated with them on a yearly basis for sudden-onset hazards such as tsunamis, cyclonic winds, storm surges, and riverine floods. See ([ 10 ][8]) for details. Each country's AAD risk relative to its population size is shown (expected annual displacements / 10,000 people). Country income group classification from the World Bank. GRAPHIC: N. DESAI/ SCIENCE BASED ON B. DESAI ET AL. Disaster displacement often undermines the welfare and well-being of affected individuals and communities and can also incur a substantial social and economic burden on countries. Although many countries have begun to plan for t he risk of extreme events in one way or another, governments typically do not formally account for displacement risk and their associated costs in national development plans and annual budgets of line ministries. Even without taking into account the aggravating forces of climate change, there is growing evidence that displacement not only severely disrupts the lives of those forced to flee their homes but also has an economic impact on local communities and national economies ([ 7 ][9]). The direct cost of providing every internally displaced person (totaling more than 55 million in 2020) with support for housing, education, health, and security has been estimated at US$370 per person per year, accumulating to more than US$20.5 billion for 2020 ([ 2 ][2]). These figures are mostly based on information available from protracted conflict-related displacement situations because the economic impacts of displacements linked with disasters and climate change usually go unrecorded. A key knowledge gap exists here because only limited event-based or nationally aggregated data is available on how long people remain displaced after a disaster, despite ample evidence that this type of displacement is often long-term and can become protracted ([ 2 ][2]). These impacts can add up to billions of dollars worldwide. Each time one person is displaced, even for a few days, costs arise for transportation, shelter, food and nonfood items, and the loss of income if the person cannot continue their usual work. Adding in long-term consequences, such as lack of schooling, training, and on-the-job experience, increases this economic impact. These costs should be on national balance sheets but are instead most often borne by communities themselves, by local governments that have to divert already limited development funds to response, and by humanitarian actors. In the face of increasingly severe disaster- and climate-related displacement, these costs are only set to rise. The highest economic impacts usually stem from the loss of income and the need to provide displaced people with shelter and health care. Disaster-resilient housing and livelihoods, as well as strong primary health care systems, are also where investments are needed most ahead of disaster events to reduce displacement and enable lasting solutions. By nature of its mandate, humanitarian response is not set up to invest in resilient livelihoods or infrastructure and service development. It is not only low-income nations that are at risk of economic impacts due to displacement. During the 2019–2020 bushfires in Australia, the loss of economic production as a result of people missing just one day of work during displacement was estimated to be about US$510 per person ([ 8 ][10]). These costs add up, particularly if a disaster causes considerable housing destruction, which may delay people from returning to their homes for months. The cost of covering housing needs resulting from Australia's Black Summer bushfires was estimated to be between US$44 million and US$52 million for a year, posing a substantial financial burden, given that previous recovery efforts indicate that it can take people between 1 and 4 years to rebuild their homes ([ 8 ][10]). These numbers and examples from across the globe highlight that we need to get better at understanding and assessing the nature and scale of disaster displacement risk. The coverage and detail of relevant datasets have improved, and various models and approaches exist at regional and global scales, although their time frames, methods, and resulting estimates vary enormously. For example, the World Bank, using a gravity model and new data on climate change, water availability, and crop production, has estimated that slow-onset climate hazards such as water scarcity and declining crop yields could lead to more than 100 million additional internal migrants in Latin America, South Asia, and sub-Saharan Africa by 2050 should neither accelerating climate impacts nor unequal development be adequately addressed ([ 9 ][11]). In many such assessments, there is a strong focus on environmental stressors and hazards, and on climate change's impacts on their intensity and frequency. This may have potentially resulted in inflated numbers and certainly in an inflated perception regarding the role of climate change in the dynamics of human mobility and forced movements today and in the coming decades. Estimates from a probabilistic model that takes housing rendered uninhabitable as a proxy for displacement in sudden-onset disasters, such as floods and cyclones, suggest that an average of around 14 million displacements can be expected each year (a conservative approach that is highly likely to be an underestimate) ([ 10 ][8]). This displacement risk is heavily concentrated in the Asia-Pacific region, where both exposure and vulnerability are high. Even in relative terms—that is, numbers of potential displacements in relation to population size—displacement risk is high not only for South and East Asia but also for Pacific and other small island states (see the first figure). Climate change as well as changes in population size and composition and of key social and economic indicators all affect how this displacement risk may change in the future. According to probabilistic, spatially explicit risk modeling that uses ensembles of climate models and hydrodynamic modeling to quantify flood hazard, is calibrated on past events, and incorporates commonly used climate change and development scenarios, rapidly increasing exposure due to population growth may be the largest driver of displacement risk in the future ([ 11 ][12]). Nevertheless, this strong role of population size should not overshadow the fact that the substantial increase related to climate change is not trivial (see the second figure). New assessments show that we can expect a 50% increase in displacement risk related to floods for each degree of temperature increase ([ 11 ][12]). Although, currently, various epistemic uncertainties need to be reckoned with, such projections serve to illustrate the future burden to consider in a rapidly warming and changing climate. Beyond probabilistic and deterministic disaster displacement risk models, there are other modeling approaches that can increasingly be put to the task. Agent-based network models can assess individual-level impacts and costs through a bottom-up methodology that can reflect how shocks to one part of a system (community, economy, country, or region) can cascade through the whole system and also spill over into other systems ([ 12 ][13]). Further, a system dynamics approach can describe in a relatively comprehensive manner the relationships between a wide range of dimensions and indicators, although it requires granular datasets that are often unavailable and is highly cost- and labor-intensive to develop. ![Figure][7] Change in flood displacement risk Shaded areas show different scenarios of flood displacement risk based on a range of climate and hydrological models, relative to historical baseline. The width of the shading represents an estimate of the uncertainty induced by natural climate variability and limitations in current understanding of the climate system and hydrological systems. Dashed lines show the average values across models. Historical baseline is defined by the average flood hazard frequency and intensity from 1976 to 2005, combined with population data for 2000. RCPs reflect different trajectories of variation in atmospheric GHG concentrations. SSPs reflect different scenarios of global socioeconomic development. Modified from ([ 11 ][12]). GRAPHIC: KELLIE HOLOSKI/ SCIENCE BASED ON KAM ET AL. ([ 11 ][12]) Finally, integrating risk estimates with analysis of public finance allows quantification of the relevance and “additionality” of internal displacement impacts on governments' (and often donors') budgets. First attempts at undertaking this analysis, adapting the International Institute for Applied Systems Analysis (IIASA) catastrophe simulation model (CatSim) in support of public financing strategies in pre- and postdisaster contexts, have shown that the cost of internal displacement can substantially increase national and global budget gaps (fiscal gaps) and the chance of budget crises ([ 13 ][14]). F or example, in Bangladesh, a disaster with a return period of 50 years can be expected to incur costs related to internal displacement of nearly US$4.1 billion per year of subsequent displacement; a smaller magnitude but more frequent disaster with a return period of 10 years would incur more than US$1 billion. The estimated possible amount of funding that the country may be able to divert from existing development budgets and credit buffers adds up to just over US$1 billion of fiscal resilience, which means that Bangladesh is likely to be unable to cover the costs associated with internal displacement for events that occur every 10 years on average. Further estimates of such costs can provide the basis for making the case for preventive action and for developing appropriate financial instruments such as national reserve funds, enhanced social protection schemes, and catastrophe bonds, as well as regional or global sovereign insurance pools ([ 14 ][15]). Beyond these first steps in developing basic estimates of the costs, further work is required to better understand who bears these and how benefits from improved policies would be distributed across different segments of society. Comprehensive risk assessments that account for displacement risk and estimate its economic costs signal a need to improve coordination on budget allocations and cooperation in program execution across ministries and public and private sectors. This would enable the explicit inclusion of these contingent risks into budget stress-testing procedures and other risk-management planning processes. It would also provide incentives for managing risk with an ex ante approach, because it anticipates the ex post consequences and trade-offs involved in responding to shocks ([ 13 ][14]). Risk assessments should help communities and local and national governments grappling with immediate displacement risk or the prospect of intensifying natural hazards or loss of territory or habitats. More financing must be made available for localized, granular displacement risk assessments, which municipalities can use to inform urban development plans, zoning regulations, and local building codes and for forward-looking, long-term planning for relocation where necessary. Recent attempts at providing a measure for displacement risk and its impacts are only the first step. In the coming years, further investment should build on the promises of longer-term risk modeling and couple its results with impact assessments so that countries can build displacement estimates into their multiyear development plans ([ 15 ][16]). Understanding needs and priorities in the decision-making processes of affected populations, institutional capacities, and socioeconomic dynamics, even if less systematically assessed, will be at least as important at indicating what the future holds. Given the scope and complexity of the problem, a pluralistic methodological setup is required to contribute to a better understanding of displacement risk and to inform effective policy and response under a broad range of circumstances. 1. [↵][17]Intergovernmental Panel on Climate Change (IPCC), Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, Eds. (IPCC, 2014). 2. [↵][18]Internal Displacement Monitoring Centre (IDMC), Global Report on Internal Displacement 2021 (2021); [www.internal-displacement.org/global-report/grid2021][19]. 3. [↵][20]IDMC, Global Internal Displacement Database; [www.internal-displacement.org/database][21]. 4. [↵][22]1. M. Brzoska, 2. C. Fröhlich , Migr. Dev. 5, 190 (2016). [OpenUrl][23][CrossRef][24] 5. [↵][25]Economics of Climate Adaptation (ECA), “Shaping climate-resilient development: A framework for decision-making. A report of the Economics of Climate Adaptation Working Group” (ECA, 2009); [www.ethz.ch/content/dam/ethz/special-interest/usys/ied/wcr-dam/documents/Economics\_of\_Climate\_Adaptation\_ECA.pdf][26]. 6. [↵][27]1. C. Raymond et al ., Nat. Clim. Chang. 10, 611 (2020). [OpenUrl][28] 7. [↵][29]1. S. Ambrus 1. C. Cazabat, 2. L. Yasukawa , “Unveiling the cost of internal displacement. 2020 report,” S. Ambrus, Ed. (IDMC, 2020); [www.internal-displacement.org/sites/default/files/publications/documents/IDMC\_CostEstimate\_final.pdf][30]. 8. [↵][31]1. J. Lennard 1. E. du Parc, 2. L. Yasukawa , “The 2019–2020 Australian bushfires: From temporary evacuation to longer-term displacement,” J. Lennard, Ed. (IDMC, 2020); [www.internal-displacement.org/sites/default/files/publications/documents/Australian%20bushfires_Final.pdf][32]. 9. [↵][33]1. K. K. Rigaud et al ., “Groundswell: Preparing for internal climate migration” (World Bank, 2018); . 10. [↵][34]IDMC, “Global disaster displacement risk – A baseline for future work” (2017); [www.internal-displacement.org/publications/global-disaster-displacement-risk-a-baseline-for-future-work][35]. 11. [↵][36]1. P. M. Kam et al ., Environ. Res. Lett. 16, 044026 (2020). [OpenUrl][37] 12. [↵][38]1. A. Naqvi, 2. F. Gaupp, 3. S. Hochrainer-Stigler , OR Spectrum 42, 727 (2020). [OpenUrl][39] 13. [↵][40]IDMC, IIASA, “Points of no return: Estimating governments' fiscal resilience to internal displacement” (IDMC, 2020); [www.internal-displacement.org/sites/default/files/publications/documents/201903-fiscal-risk-paper.pdf][41]. 14. [↵][42]1. J. Linnerooth-Bayer, 2. S. Hochrainer-Stigler , Clim. Change 133, 85 (2015). [OpenUrl][43] 15. [↵][44]1. S. Hochrainer-Stigler et al ., Int. J. Disaster Risk Reduct. 24, 482 (2017). 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Habitability loss is increasingly recognized as an important dimension of climate risk assessment and one with complex linkages to migration. Most habitability assessments, like climate risk assessments more generally, are based on “top-down” approaches that apply quantitative models using uniform methodologies and generalizable assumptions at global and regional scales, privileging physical sciences over social science–informed understandings of local vulnerability and adaptive capacity. Many assessments have focused on a single climate hazard threshold (such as permanent inundation or the 1-in-100-year flood), and a subset have implied that outmigration may be one of the few viable adaptation responses ([ 1 ][1]). There is a risk that such climate determinism minimizes the potential for human agency to find creative, locally appropriate solutions. Although top-down modeling can serve a useful purpose in identifying potential future “hot spots” for habitability decline and potential outmigration, only by integrating “bottom-up” insights related to place-based physical systems and social contexts, including potential adaptive responses, will we arrive at a more nuanced understanding. This integrated framework would encourage development of policies that identify the most feasible and actionable local adaptation options across diverse geographies and groups, rather than options that are deterministic and one-size-fits-all and encourage binary “migrate or not” decisions. We propose a set of recommendations centered around building the research and assessment knowledge base most needed to inform policy responses around habitability loss and migration. We define habitability as the environmental conditions in a particular setting that support healthy human life, productive livelihoods, and sustainable intergenerational development. Climate change may undermine one or more of the following associated, interacting, dimensions of habitability: basic human survival ([ 2 ][2]), livelihood security ([ 3 ][3]), and societies' capacity to manage environmental risks ([ 4 ][4]). Rapid rates of climate change and departures from historical variability ranges can increase risks, especially when coupled with nonclimate stressors. In such instances, threats to habitability may be evident in changing flows of human migration, whether forced or voluntary ([ 5 ][5]). Most habitability assessments have relied on outputs from top-down models. This approach is conducive to system-level prediction, producing quantitative outputs that are globally comparable, such as single physical hazard thresholds that are either assumed or empirically based. Much recent work reflects a blend of long-term, high-resolution historical climate data where available, combined with projections across a large suite of global climate models driven by multiple representative concentration pathways (RCPs) representing trajectories of greenhouse gas concentrations. Another critical element is inclusion of extreme events, often expressed as a frequency of occurrence or a magnitude associated with a given recurrence period. In turn, top-down demographic and economic models, which form the basis for the shared socioeconomic pathways (SSPs) projecting global socioeconomic trajectories, provide a picture of future population and development that can also inform projections of people and assets at risk. Climate projections can also drive sectoral impact assessments—for example, empirically by extending historical statistical relationships between climate variability and the affected sector. More commonly, projections from standardized climate simulations drive sectoral impact models that dynamically simulate key features, such as crop growth. Top-down migration models use relative changes in sectoral impacts across regions along with other information as a means of projecting future population flows. Thus, these models project responses to habitability changes in regions where varying conditions may lead to outmigration, inmigration, or both. The standardized nature of top-down methods facilitates comparisons—for example, of regions most at risk of crossing habitability thresholds associated with a climate hazard, and when. The top-down perspective can also reveal large-scale trends and interconnected features of global systems. However, there are several limitations. First, local and regional geophysical and sector-specific factors can drive hazards and risks at scales missed by global analyses. Second, less-modeled, place-specific characteristics of populations, such as health and socioeconomic status, shape both exposure and vulnerability. Third, adaptation choices and activities are embedded in historical context and culturally specific individual and community values and objectives that cannot easily be incorporated in models. Fourth, high-impact outcomes—associated, for example, with compound extreme events and abrupt changes in climate, ecological, and social systems—may be underestimated because of top-down model limitations such as the inability to credibly resolve evolving correlation structures across variables, space, and time, and key system sensitivities and feedbacks within and across systems ([ 6 ][6]). For example, climate phenomena teleconnected across great distances may lead to “breadbasket” failures in key food-producing regions and price shocks that can seriously reduce food security among vulnerable populations far away from the regions experiencing the climate stress. Fortunately, top-down approaches are increasingly being paired with bottom-up approaches that offer a specificity that can help address these challenges. Bottom-up conceptual and/or computational modeling of complex adaptive systems can be designed to simulate the local experience of losing habitability over time. In the breadbasket case above, models of local responses can be paired with global models of international food trade that set boundary conditions. For example, agent-based models (ABMs) set up simulations with agents empirically calibrated to behaviorally respond to changing environmental conditions: the loss of assets and livelihood opportunities, threats to life, and changing structure of social networks. Modeling can be trained on local data to understand and predict important feedbacks at higher spatial and temporal resolution than is possible with global models. ABMs can be calibrated to examine a range of individual-actor preferences and test the effect of local decision-making to plausibly depict tradeoffs among adaptation options, including migration ([ 7 ][7]). As another bottom-up example, qualitative information can be coproduced with diverse stakeholders, including subject matter experts, to explore high-impact scenarios and local solutions that will be missed by top-down approaches. Of course, bottom-up approaches have their limitations as well. For example, their specificity makes it difficult to compare across geographies and groups, and individual methodological decisions can appear arbitrary. Furthermore, bottom-up computational models such as ABMs are still limited by a lack of empirical data with which to calibrate model parameters. Here, we walk through the habitability challenges of two climate hazard examples, demonstrating the strengths and limitations of top-down approaches and how bottom-up perspectives lead to different policy-relevant insights. ### Sea level rise and extreme sea level events Recent years have seen growing complexity and nuance in assessments. Global assessments have supplemented climate model outputs by considering a broad range of sea level change components and including, for example, expert elicitation as a means of estimating low-probability, high-consequence outcomes ([ 8 ][8]). High-spatial-resolution digital elevation models and consideration of changes in the frequency and intensity of societally relevant metrics such as recurrence intervals and extreme values of coastal high water have been integrated into global products. Using many of the above advances, Kulp and Strauss estimated that the number of people exposed annually to coastal flooding under constant population could increase from 250 million people today to, by 2100, 310 million to 420 million under an intermediate scenario to 380 million to 630 million under a high-end scenario ([ 1 ][1]). Other studies have included changes in storms, hyper-local positive correlations between population density and subsidence, population projections consistent with SSP-RCP combinations, and assets at risk. Additional refinements have focused on specific coastal locations, adding critical context at the expense of global information. For example, Storlazzi et al. framed their assessment of tipping-point risks to atolls around two metrics—annual overwash events that threaten infrastructure, and salinization of groundwater—that are specifically relevant for atolls given their small size, uniformly low elevation, and relative isolation and found that habitability is threatened in most atoll islands by the middle of the 21st century, far sooner than permanent-inundation–based studies would suggest ([ 9 ][9]). Some local studies have included dynamic interaction between coastal waters and adjacent landforms. Other local and regional studies have considered social dimensions of human vulnerability, as well as in situ adaptation, using empirically calibrated agent-based livelihood decision models that span multiple climate, RCP, and SSP scenarios ([ 7 ][7]). The three dimensions of habitability demonstrate why no single coastal flood metric threshold can be determined in a top-down way. For the direct survivability dimension, key factors include future flood control, feasibility of evacuation, and the stochasticity of individual storms. For livelihood, saline intrusion, for example, could benefit some sectors such as specialized aquaculture, even as it harms most sectors and people. And for the societal resilience dimension, large-scale factors such as levels of inequity, strength of governance and social networks, and quality of infrastructure will be critical. As sea levels rise and coastal flooding becomes more common, social, economic, and political factors in some locations will conspire to induce sudden loss of habitability far sooner than physical hazard–based thresholds such as permanent inundation would suggest, as risk perception and long-term economic viability shift. For example, increases in insurance premiums could negatively affect asset values and tax revenues, leading to deteriorating infrastructure and services. The timing of such threshold-crossing cannot be predicted on the basis of top-down models alone. In some instances, shocks can lead to rapid learning, adjustment, and in situ adaptation, at least temporarily. ![Figure][10] Frequent exceedance by 2100 of historically rare climate thresholds Under the high-emissions scenario RCP8.5, at most coastal locations extreme sea level events historically defined as 1-in-100-year events are projected to range in frequency from once per year to more than 10 times per year due to the effects of sea level rise alone. Only point locations where historical event data are available are shown. Projected number of days per year by 2100 exceeding a 33°C wet bulb globe temperature (WBGT) in a high-emissions scenario are also depicted. Under standard assumptions of wind and solar radiation, a WBGT of 33°C corresponds to a wet bulb temperature of roughly 31.5°C. [Sea level data are from figure 4.12 in ([ 8 ][8]); WBGT data are from fig ure 3 in ([ 12 ][11]).] GRAPHIC: N. DESAI/ SCIENCE BASED ON HORTON ETAL. ### Extreme heat Most assessments of future heat hazards have considered temperature only, although recent efforts are increasingly adopting a compound events framework—for example, considering how co-occurring extremes of high temperature and high humidity can modulate threats to habitability. Humid heat is particularly harmful to human health and the ability to engage in outdoor activities. Sherwood and Huber described a wet bulb temperature of 35°C as a threshold above which humans could not survive beyond approximately 6 hours owing to physiological and thermodynamic limits on the ability to cool through perspiration ([ 2 ][2]). Model-based studies have projected that this threshold could be crossed in the Persian Gulf and South Asia during the second half of the 21st Century under a high-emissions scenario ([ 10 ][12]). However, a finer-scale study found that this threshold has already been briefly crossed multiple times in populous cities. Although an absolute habitability threshold exists for the survivability dimension of extreme humid heat, some people will lose their ability to thermoregulate at much lower wet bulb temperatures. Mortality rates of the elderly, those with chronic health conditions, and those involved in strenuous activity rise dramatically well below the 35°C wet bulb threshold. In terms of the livelihood dimension, at ∼3.5°C of global warming above preindustrial levels, de Lima et al. project that in Sub-Saharan Africa and Southeast Asia increases in humid heat may decrease agricultural labor productivity by 30 to 50%, leading to larger agricultural sector impacts than are associated with direct temperature and CO2 effects on crops ([ 11 ][13]). However, air conditioning and other adaptations will enable—indeed, have enabled—some people to continue to live in places that exceed the 35°C threshold. Such an outcome increases inequity because those with no option but to work outdoors, or no access to affordable air conditioning, would be forced to migrate. And even for those with air conditioning, the third dimension of habitability—society's capacity to manage environmental risks—will be tested in unforeseen ways because it will be critical that air conditioning not fail. Sea level rise and extreme humid heat are far from the only climate hazards that have been assessed in the literature for potential habitability thresholds. For example, changes in surface moisture fluxes as mean precipitation and temperature shift are projected to have large impacts on dryland agriculture, fire regimes in forests, and water availability downstream from snow and glacier reservoirs. These and other hazards and impacts may overlap and interact across scales to affect habitability in complex ways, such as by potentially increasing the risk of conflict. Areas where current-day rare extreme sea level and humid heat events will occur with high frequency by the end of the century under a high emissions scenario of sea level rise and warming are identified in the figure ([ 8 ][8], [ 12 ][11]). The two metrics, corresponding to the current 1-in-100-year extreme sea level event and a wet bulb globe temperature of 33°C, respectively, are emblematic of top-down approaches. They thus represent an important point of entry for engagement with the bottom-up insights described above, as a step toward more nuanced habitability and migration assessments. Migration may result from threats to survival, upended livelihoods, or the breakdown in the collective capacity to adapt ([ 5 ][5]). However, research on climate change and migration makes clear that an even broader set of factors undergird migration decision-making. A decision to move is ultimately a personal or household judgment on factors that include local habitability. Involuntary migration occurs when people lack agency about the key dimensions of mobility, including the timing, destination, or duration of mobility or whether to migrate at all. Where agency is extremely low, involuntary migration may take different forms, including temporary or permanent displacement and distress migration. Distress migration—mass migration or displacement related to rapid deterioration in local circumstances—is a humanitarian concern because of the need for emergency interventions to avoid poor outcomes. Distress migration has been a common phenomenon throughout history but has risen and fallen on the global policy agenda largely as a function of whether or not wealthy industrialized countries are destinations. Also of humanitarian concern is the phenomenon of involuntary immobility, in which people are unable to move without help—the population most likely to require assistance relocating under managed retreat programs. Avoiding distress migration and involuntary immobility in favor of safe and orderly migration, as advanced by the Global Compact on Migration, is now a global policy priority, and the Compact calls on governments to “strengthen joint analysis and sharing of information to better map, understand, predict, and address migration movements” as a result of climate change impacts—all of which are essential aspects of habitability assessment. Many assessments posit some form of forced migration as an inevitable outcome of declining habitability. Yet, environmental stress rarely directly results in migration but works through a complex array of economic, demographic, social, and political proximate determinants that both initiate and sustain or modify flows. In any given population exposed to climate risks, different segments of the population respond to hazards differently and at different points in time, and as such, migration evolves with habitability through time. Whereas some may be able to migrate from deteriorating conditions without assistance, others may become immobile owing to limited options and insufficient resources, suffering progressive impoverishment and vulnerability unless social protection or planned relocation efforts are implemented ([ 5 ][5]). In situ adaptation, facilitated migration, and improving reception of migrants in (largely urban) destination areas are often more appropriate policies in these regions. Managed retreat has been proposed as a strategy for regions with declining habitability, but as a largely technical package of responses that includes buyouts, incentives, and planned relocation, among others, it does not currently translate well to most developing-world circumstances. The relationship between habitability and migration may be counterintuitive, as illustrated by the lack of evidence for migration away from low-lying delta areas despite acute risks ([ 7 ][7]). Migration itself affects habitability for those who are unable or unwilling to leave increasingly vulnerable circumstances, either positively, such as through incoming remittances, or negatively, such as through outmigration of the working-age demographic stratum and subsequent changes in economic dynamism and livelihood options. Flows may begin owing to entrenched poverty and environmental risks and then be sustained as migrant social networks lower barriers for those who initially remained behind. Although migration offers possibilities for advancing human well-being, as multiple dimensions of habitability are compromised, resulting forced migration will negatively affect human well-being. Migrants risk new constraints in urban informal settlements, and displaced persons may become permanently disconnected from their original communities and livelihoods in resettlement communities or refugee camps ([ 13 ][14]). Although top-down assessments oversimplify likely migratory responses to habitability declines, this does not necessarily imply that migration flows are overestimated. Multiple factors are driving migration in developing regions to varying degrees, including poor governance, perceived lack of opportunities, conflict, individual extreme events, and in some cases, climate-catastrophic discourses that add to a sense of hopelessness ([ 14 ][15]). Deeper and more contextualized understandings of migration dynamics aid in policy design, but the threats that result from declining habitability in combination with other drivers are real and may lead to substantial displacement of populations across a range of spatial scales. Top-down, threshold-based habitability assessments can serve a critical role in helping to identify priority regions and groups for integrated bottom-up work while revealing interactions in global systems that cannot be gleaned from the bottom-up work alone. Integration not only leads to better predictions of when and where habitability may diminish but also can be used to inform adaptation responses that themselves help preserve or restore habitability. Bottom-up assessments by definition provide finer, local resolution, and their richness of detail means that they require diverse participation and methods. To date, most locales have not been subject to such integrated habitability assessment. We thus encourage transdisciplinary, long-term coupled top-down and bottom-up habitability assessment [for example, ([ 15 ][16])] to complement and augment efforts such as the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), which has contributed so much to our understanding of potential future climate impacts on sectors such as agriculture, water, ecosystems, and health. Initial model intercomparison could focus on what regions and groups face diminishing habitability under different model configurations. Particularly where models agree on potential habitability hot spots, bottom-up modeling experiments could be conducted and compared on specified challenges to human survival, livelihoods, and capacity to manage risk, although standardization would be needed. The Intergovernmental Panel on Climate Change (IPCC) and national efforts can also help to develop this still inchoate middle space between top-down and bottom-up approaches to habitability and migration. Migration is emerging as a cross-cutting theme throughout the current IPCC assessment, and a special report on habitability and migration would both advance the knowledge base and showcase emerging methodologies. As one example, a climate change detection and attribution dimension would help inform dialogues about loss and damage under the Paris Agreement. Likewise, a discussion on migration across the Reasons for Concern commonly used in IPCC assessments ([ 5 ][5]) would allow us to distinguish how climate-induced migration, distress or otherwise, is distinct from other forms of migration. The complexity of the assessment challenge calls for a holistic, people-centric approach in which models, data aggregation, and ethnographic work are all advanced. Sectors such as engineering, hydrology, and reinsurance, that have historically been overreliant on physical models and hazard thresholds, operate at a scale that is ripe for habitability-relevant innovations at the interface between top down and bottom up. In this middle space, models could be used to examine policy scenarios instead of learning occurring exclusively from costly, time-consuming, real-world policy interventions that may put vulnerable people at risk. Greater communication among modelers will be key, and models must be validated with on-the-ground local research. To support migration and habitability modeling specifically, this would include data on when, where, and why people have moved or considered moving, how they define habitability, and the policy conditions that determine mobility outcomes ([ 14 ][15]). Furthermore, bottom-up research must account for the place-specific characteristics of populations—such as assets, livelihood opportunities, and social networks—that shape both exposure and adaptation. Investments in place-based social science thus help address data gaps, providing ground-truthing that will strengthen simulations of the outcomes of interventions. Investments in early-warning systems could help to anticipate where distress migration may happen, a key step in informing policy. The shortcomings of adaptation planning and policy at current risk levels in wealthy countries hint at the global challenges ahead in a changing climate. In the United States, for example, federal and local risk assessments—let alone policies—are not presently centrally coordinated or comparable. There is woefully insufficient funding available for bottom-up adaptation efforts from the better-financed federal level. Policies toward population mobility—whether planned, internal responses or immigration from other countries—vary from inconsistent over time to incoherent and sometimes inhumane. Coproduction of knowledge across diverse groups will be a precondition for any breakthroughs. In some instances, a starting point may be to bring preexisting top-down habitability and migration assessments to communities, provided that community feedback is collected and integrated iteratively and before key policy decisions are made. In other instances, stakeholder engagement may begin with fewer top-down, nonprobabilistic approaches that can be developed with communities, such as storylines and scenarios. Storylines and scenarios lend themselves to exploration of the uncertainties that most influence habitability locally (for example, the potential for changing correlation structures in models) and which adaptation strategies should be explored for which groups. Deeper stakeholder engagement, coupled with the other recommendations above, thus provides a foundation for colearning, iteration, and developing flexible approaches to the challenge of diminishing habitability. To the extent that top-down, threshold-based approaches are used to define habitability universally, there is a risk of assuming a high likelihood of uniform outmigration or concluding with blanket policy recommendations around managed retreat. Basing assessments on nuanced definitions of habitability and integrating top-down with bottom-up approaches could encourage a broader range of policies tailored to specific locations and groups, including regions that have been put forth as likely receiving areas. A focus on the dimensions of habitability presented here, and bottom-up approaches, will invariably alter top-down projections of migration. Under wetbulb temperatures exceeding 35°C, high levels of outmigration from the Persian Gulf may be avoided if air conditioning is widely available and alternative livelihood options develop for those who would otherwise work outdoors. However, there will be regions where social tipping points and a sense of prevailing pessimism about the future—for example, owing to evolving risk perception or disinvestment by the private or public sectors—could contribute to outmigration far sooner and more suddenly than top-down habitability threshold–based methods would suggest. Global, regional, and national migration policies themselves will also play an important role in facilitating or impeding migration. What is already clear is that climate change will result in shifting population distributions and that this process will overall be harmful to the most vulnerable, including those who may be “trapped” in deteriorating circumstances. For the reasons described here, and as a matter of climate justice, many semi-arid regions, much of the tropics, and some low-lying deltas and islands should be high priorities for integrated transdisciplinary work on habitability risks and major investments in adaptation. But only by taking into account the complexities described here will we avoid climate determinism and instead implement proactive policies on adaptation and migration that in particular will address the needs of the most vulnerable. 1. [↵][17]1. S. A. Kulp, 2. B. H. Strauss , Nat. Commun. 10, 4844 (2019). [OpenUrl][18] 2. [↵][19]1. S. C. Sherwood, 2. M. Huber , Proc. Natl. Acad. Sci. U.S.A. 107, 9552 (2010). [OpenUrl][20][Abstract/FREE Full Text][21] 3. [↵][22]1. T. Tanner et al ., Nat. Clim. Chang. 5, 23 (2015). [OpenUrl][23] 4. [↵][24]1. J. Barnett, 2. W. N. Adger , Annu. Rev. Environ. Resour. 43, 245 (2018). [OpenUrl][25] 5. [↵][26]1. R. McLeman et al ., Clim. Change 165, 24 (2021). [OpenUrl][27] 6. [↵][28]1. N. 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For decades, insurers have used such factors as age and credit score to determine the prices paid by individuals. Now many maintain that driving habits are a fairer gauge of a person's accident risk. In a deal announced early Thursday, Cambridge Mobile said it had closed on the purchase of TrueMotion for an undisclosed price. After combining, Cambridge Mobile is to provide telematics services to 21 of the 25 largest auto insurers in the U.S. based on premiums, with clients including some of the largest auto insurers in Australia, Canada, Japan, South Africa and the U.K. A pre-markets primer packed with news, trends and ideas. The deal paves the way for the two Boston-area firms to combine workforces to improve existing offerings sold to car insurers and invent new products.

Why analytics are vital for prosperous businesses? Descriptive analytics: review data with stats to tell you what happened in the past. It helps a business understand better how it is performing by providing context to help stakeholders interpret information. This can be in the form of data visualizations like graphs, charts, reports, and dashboards. Predictive analytics takes past data and feeds it into a machine learning model that considers key trends and patterns.

The European Commission's (EC) proposed Artificial Intelligence (AI) regulation – a much-awaited piece of legislation – is out. While this text must still go through consultations within the EU before its adoption, the proposal already provides a good sense of how the EU considers the development of AI within the years to come: by following a risk-based approach to regulation. Other use-cases such as FRT for authentication processes are not part of the list of high-level risks and thus should require a lighter level of regulation. While technology providers have to maintain the highest level of performance and accuracy of their systems, this necessary step isn't the most critical to prevent harm. The EC doesn't detail any threshold of accuracy to meet, but rather requires a robust and documented risk-mitigation process designed to prevent harm.

Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored for such applications due to their simple design and training efficiency. We propose a modified RVFL network that avoids computationally expensive matrix operations during training, thus expanding the network's range of potential applications. Our modification replaces the least-squares classifier with the Generalized Learning Vector Quantization (GLVQ) classifier, which only employs simple vector and distance calculations. The GLVQ classifier can also be considered an improvement upon certain classification algorithms popularly used in the area of Hyperdimensional Computing. The proposed approach achieved state-of-the-art accuracy on a collection of datasets from the UCI Machine Learning Repository - higher than previously proposed RVFL networks. We further demonstrate that our approach still achieves high accuracy while severely limited in training iterations (using on average only 21% of the least-squares classifier computational costs).

3D model generation from single 2D RGB images is a challenging and actively researched computer vision task. Various techniques using conventional network architectures have been proposed for the same. However, the body of research work is limited and there are various issues like using inefficient 3D representation formats, weak 3D model generation backbones, inability to generate dense point clouds, dependence of post-processing for generation of dense point clouds, and dependence on silhouettes in RGB images. In this paper, a novel 2D RGB image to point cloud conversion technique is proposed, which improves the state of art in the field due to its efficient, robust and simple model by using the concept of parallelization in network architecture. It not only uses the efficient and rich 3D representation of point clouds, but also uses a novel and robust point cloud generation backbone in order to address the prevalent issues. This involves using a single-encoder multiple-decoder deep network architecture wherein each decoder generates certain fixed viewpoints. This is followed by fusing all the viewpoints to generate a dense point cloud. Various experiments are conducted on the technique and its performance is compared with those of other state of the art techniques and impressive gains in performance are demonstrated. Code is available at https://github.com/mueedhafiz1982/

Federated learning (FL) is a distributed learning paradigm in which many clients with heterogeneous, unbalanced, and often sensitive local data, collaborate to learn a model. Local Differential Privacy (LDP) provides a strong guarantee that each client's data cannot be leaked during and after training, without relying on a trusted third party. While LDP is often believed to be too stringent to allow for satisfactory utility, our paper challenges this belief. We consider a general setup with unbalanced, heterogeneous data, disparate privacy needs across clients, and unreliable communication, where a random number/subset of clients is available each round. We propose three LDP algorithms for smooth (strongly) convex FL; each are noisy variations of distributed minibatch SGD. One is accelerated and one involves novel time-varying noise, which we use to obtain the first non-trivial LDP excess risk bound for the fully general non-i.i.d. FL problem. Specializing to i.i.d. clients, our risk bounds interpolate between the best known and/or optimal bounds in the centralized setting and the cross-device setting, where each client represents just one person's data. Furthermore, we show that in certain regimes, our convergence rate (nearly) matches the corresponding non-private lower bound or outperforms state of the art non-private algorithms (``privacy for free''). Finally, we validate our theoretical results and illustrate the practical utility of our algorithm with numerical experiments.

Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems. Entity linking on short text (e.g., single sentence or question) poses particular challenges due to limited context. While prior approaches use either heuristics or black-box neural methods, here we propose LNN-EL, a neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to using rules, LNN-EL performs competitively against SotA black-box neural approaches, with the added benefits of extensibility and transferability. In particular, we show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even scores resulting from previous EL methods, thus improving on such methods. For instance, on the LC-QuAD-1.0 dataset, we show more than $4$\% increase in F1 score over previous SotA. Finally, we show that the inductive bias offered by using logic results in learned rules that transfer well across datasets, even without fine tuning, while maintaining high accuracy.

Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multitask framework. We provide a thorough analysis to gain insights into model performance.