Environmental Research Letters

The integrity of forest-based climate solutions and carbon credits requires persistent carbon storage, but climate change is increasing the risk of natural disturbances that release carbon back into the atmosphere. Using global satellite data, disturbance modeling, and machine learning, we provide the first spatially explicit and scenario-based maps of long-term probability of carbon loss in global forests under different disturbance severities and climate scenarios. We find that North American conifer forests, tropical rainforests, and Asian (sub)tropical dry forests face the greatest risks, and that Eurasian temperate forests, African (sub)tropical dry forests face the lowest. Globally, the likelihood of reversals over 100 years is 31%-42% across all scenarios. Our work helps to maximize the benefits of forest-based climate solutions by informing more strategic project placement and more robust reversal-risk compensation mechanisms, such as buffer pools, and highlights critical additional science to better understand and manage risks of these essential climate solutions. Plain Language SummaryForests can help slow and lessen climate impacts. However, in places this benefit is becoming less reliable as climate change increases natural disturbances such as wildfires, drought, storms, and insect outbreaks, which can release stored carbon back into the atmosphere. In this study, we created the first scenario-based global maps of risks and found that the risk of carbon loss is widespread and highly variable across regions, with especially high vulnerability in North American conifer forests, tropical rainforests, and Asian tropical and subtropical dry forests. Our study highlights the importance of considering disturbance risks when siting forest projects for climate mitigation, and developing protocols for carbon markets, such as in voluntary programs and under the UNFCCC Paris Agreement. Key PointsO_LIA demographic model framework estimates the reversal risk from natural disturbances over 100 years in global forests C_LIO_LISpatially explicit maps under different severity scenarios show variation in the integrated 100-year risk of carbon reversal C_LIO_LISpatially explicit maps estimate the required buffer pool needed to compensate for disturbance-driven reversals in global forests C_LI

It remains challenging to quantify recent changes in forest carbon due to lags in forest inventory measurements. The national U.S. forest inventory remeasures plots every five to ten years, so quantifying current carbon stocks using inventory data requires extrapolating from the last time plots were measured. We address this extrapolation challenge by fusing spatially explicit fire disturbance and canopy cover data from Landsat with forest inventory data using a statistical model. We produce annual estimates of live forest carbon across the Western U.S. from 2005 to 2022, and find that live forest biomass increased from 2005 to 2015, and then declined by 5% from 2015 to 2022 -- a signal missed by both official U.S. reporting and Earth system models. The trend reversal was driven primarily by increasing tree mortality from wildfire, and secondarily by slowing rates of carbon accumulation in undisturbed areas. Our results highlight the importance of accounting for rapidly changing disturbance regimes, and can help to improve jurisdictional carbon accounting and inform the extent to which federal and state climate mitigation strategies can rely on land to achieve net-zero emissions targets. Significance statementPolicy makers need to accurately and rapidly assess the status of the land carbon sink in order to make land management decisions and to assess progress towards climate commitments. However, lags in on-the-ground measurements make it challenging to do so, and it remains an open question whether Western U.S. forests are a net sink or a source of carbon. We fuse on-the-ground forest measurements with remote sensing data to show that live biomass is net declining in Western U.S. forests, and that this trend is driven primarily by increasing wildfire activity. This result challenges the idea that jurisdictions can rely on the land to offset fossil emissions, and supports tracking land carbon trends separately from fossil emissions inventories.

Tropical agricultural frontiers continue to drive native vegetation loss while supplying global commodity demand; yet it remains unclear how alternative governance pathways reshape where expansion occurs across interconnected biomes. Here, we use a spatially explicit land-use model to evaluate how contrasting governance regimes, with and without moderate livestock intensification, affect land-use futures across the Brazilian Amazon, Cerrado, and Pantanal through 2030. We compare Governance Inertia, Collaborative Governance, and Integrated Governance to assess their effects on native vegetation conversion, the land-use origins of soybean expansion, and landscape structure. Under scenarios in which conversion from native vegetation is explicitly constrained, projected soybean expansion remains nearly constant ([~]6.2 Mha) but is redirected away from native vegetation and toward already converted lands. Relative to Governance Inertia, constrained-governance scenarios avoid approximately 13.5 Mha of native vegetation loss and require an estimated 13.9% increase in livestock occupation rate to maintain production with a smaller pasture footprint. These shifts are also associated with modest but consistent improvements in vegetation continuity and reduced fragmentation. Our findings show that the effectiveness of zero-deforestation trajectories depends not only on limiting expansion, but on governing where land-use demand is absorbed across tropical frontier systems.

Background: Surgical care centralization in the U.S. delays access and increases carbon emissions. Global targets suggest patients live within 2-hours of a surgical facility. This study quantifies the environmental impact of travel for cataract surgery in rural Michigan and models the potential emissions reductions from decentralizing surgical and follow-up services. Methods: A retrospective, cross-sectional study analyzed electronic medical records from a rural Michigan ophthalmology practice (March-November 2023). We calculated travel distances using population-weighted centroids and estimated emissions using U.S. Department of Energy vehicle data. A k-means clustering model optimized additional facility placement, and a gradient analysis identified optimal numbers for decentralization points, for emissions reductions. Results: The 920 patients traveled a median of 55.45 km (IQR: 43.33-88.20 km) for surgery and 55.07 km (IQR: 43.54-87.82 km) for follow-up visits, generating Total Surgical Access Emissions (TSAE) of 57,168 kgCO2; (median of 59.20 kgCO2; IQR: 32.31-81.87) under the centralized model. The k-means decentralization model and gradient analysis identified 7 hospitals and 9 clinics, respectively, as the optimal expansion points, reducing emissions by 34.07% (19,475 kgCO2 saved) and 39.52% (22,590 kgCO2; saved). The Surgical Access Carbon Impact (SACI) model demonstrated that achieving two-hour access to clinic services reduced excess emissions by 54.7%. Sensitivity analyses using fuel-efficient vehicles (Toyota Prius and Tesla Model 3) or reducing follow-up visit frequency reduced emissions by 54.03% (30,888 kgCO2) and 25.83% (14,768 kgCO2), respectively. Conclusion: Decentralizing surgical services in rural U.S. settings could cut travel-related emissions by up to 40%, significantly reducing healthcare-related carbon footprints while improving timely access to care. The SACI metric provides a novel framework for integrating environmental sustainability into U.S. health policy and service planning

Tropical dryland ecosystems are highly biodiverse and fragmented and are experiencing significant anthropogenic and climatic changes. With increasing extremes in temperature and precipitation, coupled with significant alteration, these ecosystems are at greater risk of increased exposure and vulnerability to climatic change; however, little work has quantified the climatic shifts occurring within these ecosystems globally. Here, we aim to fill this gap by using the ERA-5 reanalysis and CHIRPS precipitation data to quantify changes in essential climatic variables in tropical drylands since 2000. Overall, we find that regional pressures differ, with tropical dry forests, savannas, and shrublands becoming hotter and drier in the Neotropics and parts of the Afrotropics and Australasia. By contrast, the tropical dry forests in the Indomalayan, Oceania, and Nearctic are experiencing hotter and wetter conditions. Globally, though, these ecosystems are experiencing more change than the global average, suggesting they may be approaching tipping points in their resilience, ultimately shrinking the area where they can survive.

Dengue fever risk and burden has increased globally in the past decade, with record-breaking outbreaks driving high case numbers, outbreaks increasing in existing transmission suitable regions, and occurring in new locations. A combination of global change processes, including climate change, have provided the environmental backdrop for introductions and resurgences of mosquito-transmitted dengue virus. Understanding shifts in exposure risk is integral to public health preparedness. This study provides global mapping of the thermal suitability of dengue transmission for CMIP6 climate scenarios, across a range of general circulation models (GCMs), and we created spatially explicit demographic projections of transmission risk using year-matched RCP-SSP frameworks for demographic and emissions scenarios. Globally, poleward shifts in projected distributions of suitability for transmission for both Ae. aegypti and Ae. albopictus suitability are shown in both the near term (2030s) and longer term (2050). Under a 'middle of the road' climate scenario (CMIP6 SSP2-4.5), regions in Africa and Asia are the major areas driving increases in year-round (12 months) population at risk (PAR) through 2050, with an anticipated net gain in 932 million people at risk for Ae. aegypti transmission and 24 million for Ae. albopictus, which includes multiple regions losing areas of year-round suitability as temperatures exceed the higher thermal boundary for transmission. In contrast, the estimated net increase in PAR for one or more months of transmission suitability at a global scale by 2050 is 3.29 billion people for Ae. aegypti transmission and 3.30 billion for Ae. albopictus transmission. This snapshot of a 'middle-of-the-road' combination of climate and demographic driven increases in potential dengue transmission exposure emphasizes the importance of both expanding suitability in new areas, and growing populations in areas approaching and becoming exposed year-round. Globalization, urbanization, and shipping will continue to provide the potential for introductions into newly suitable areas as season lengths increase, sparking outbreaks in unexposed populations. This is compounded and becomes ever more probable as the number of people and places at year-round risk also increases. This project provides all global gridded outputs for onward mapping and reuse, to add to the toolkit to anticipate and prepare for prevention and response to dengue in a changing world.

Environmental changes are reshaping the distribution and seasonal dynamics of vector-borne diseases, with important implications for public health. Tick-borne encephalitis virus (TBEV) and West Nile virus (WNV) cause growing concern in Europe, with rising case numbers and ever-expanding circulation areas. The transmission risk of TBEV and WNV follows characteristic seasonal patterns, driven largely by weather-dependent activity of their arthropod vectors. The relative roles of climate and land-use change on the seasonal dynamics and spread of these diseases and their vectors remain, however, poorly quantified. Here, we assess the spread and phenology of TBEV and WNV in response to historical and future climate and land-use changes across Europe. We developed spatiotemporal species distribution models (SDMs) for the viruses and their primary vector species, generating monthly environmental suitability predictions from the 1970s to 2050s. Virus models incorporated vector suitability as a nested predictor to capture the dependence of virus occurrence on vector presence. To disentangle drivers of observed changes, we applied counterfactual historical simulations, attributing shifts in seasonal transmission risk to climate or land-use changes. Historical attribution results show that land-use changes mainly affected absolute vector suitability, whereas climatic changes drove shifts in seasonal transmission risk. Transmission risk is projected to rise continent-wide for both TBEV and WNV over the coming decades. Further, TBEV is projected to undergo pronounced phenological shifts, with a dominant spring peak and a delayed autumn peak extending into October. Prolonged seasonal transmission windows are projected to create hotspots that both intensify and expand across large regions. Taken together, our findings underscore the need for coordinated transnational efforts to manage the projected health burden of TBEV and WNV across Europe, and support upstream prevention by providing climate-informed guidance on intervention timing and spatial prioritisation.

Invasive plants pose a major environmental problem, threatening biodiversity, altering ecosystem functions, and causing economic loss. Climate change is altering environmental conditions, potentially facilitating the spread of invasive plant species, posing challenges for ecosystem management and biodiversity conservation. Accurate predictions of invasive species distributions are therefore essential for effective monitoring and early intervention. Species distribution models (SDMs) have become an important tool for predicting species habitats, but many studies rely on traditional machine learning approaches, focus on single-species predictions and overlook uncertainty associated with future climate scenarios. This study aims to evaluate the performance of a deep learning-based SDM framework, Deepbiosphere, for predicting both native and invasive plant species distributions on a regional scale, the US state of Michigan, and to assess how climate scenario uncertainty influences spatial predictions of invasive species risk particularly on two focal invasive species. Results show that Deepbiosphere outcompeted other baseline models by on average of 10.98% with a mean AUC-ROC of 0.79 across 1553 vascular plant species. For two invasive species Rhamnus cathartica and Ailanthus altissima, Deepbiosphere respectively improved modeling accuracy by an average of 56.41% and 74.99%, suggesting its enhanced predictive capability for invasive species. Current predictions indicated that R. cathartica is already broadly suitable across much of Michigan, whereas A. altissima is currently more restricted to southern regions. Under future climate scenarios, both species were projected to expand northward, with a particularly strong expansion signal for A. altissima. Prediction uncertainty was spatially heterogeneous, where general circulation models (GCMs) were the dominant source of uncertainty across most of the state. By integrating citizen science, remote sensing, and deep learning, we produced high-resolution risk-uncertainty maps for key invasive species and highlighted the importance of explicitly mapping uncertainty to support more informed invasive species management under climate change.

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

Background Increasingly accessible satellite imagery provides scalable measures of the built and natural environment relevant to population health. However, whether such imagery can capture subnational variation in mortality and life expectancy remains unclear. We therefore assessed its predictive value for regional mortality and life expectancy across OECD regions. Methods We conducted an ecological, cross-sectional prediction study using 2023 data from OECD Territorial Level 3 (TL3) regions. Annual cloud-masked composites from the Harmonized Landsat and Sentinel-2 collection were processed in the Google Earth Engine, tiled at 224 x 224 pixels, and encoded with the pretrained Prithvi foundation model to derive region-level satellite embeddings. For each outcome, we trained LightGBM regressors for a country-only baseline, a satellite-only model, a combined model (country + satellite), and a final contextual model that additionally included prespecified socioeconomic and environmental covariates. Performance was evaluated using 10-fold outer cross-validation with held-out test folds; R2 was the primary metric. Results The analytic sample comprised 2,414 OECD TL3 regions across 38 countries, for which 939,959 satellite image tiles were processed. In paired bootstrap comparisons, adding satellite features to country indicators improved predictive performance for all outcomes, with incremental R2 ranging from 0.097 to 0.233. The final contextual model achieved R2 values of 0.78 (95% CI, 0.74-0.81) for crude mortality, 0.87 (0.84-0.89) for age-adjusted mortality, 0.86 (0.82-0.88) for infant mortality, and 0.76 (0.69-0.84) for life expectancy. In SHAP analyses, the aggregated satellite image effect consistently ranked among the top predictors across outcomes. Conclusion Satellite imagery captures subnational environmental heterogeneity relevant to regional mortality and life expectancy beyond country identity alone. Earth observation may therefore provide a scalable, complementary data source for characterizing geographic disparities in population health.

West Nile virus dynamics are shaped by hydrological conditions that influence mosquito habitat and pathogen transmission, but identifying causal relationships is difficult in managed landscapes where irrigation decouples local water conditions from precipitation, complicating climate-disease inference. We address this challenge using a 21-year panel of more than 19 million Culex tarsalis mosquitoes from Californias Central Valley, applying fixed-effects panel models to estimate how surface water availability affects mosquito abundance and infection rates while accounting for spatial differences and shared temporal variation. We find that wetter conditions lead to higher mosquito abundance but slightly lower infection rates, suggesting divergent responses of vector population growth and pathogen amplification. These patterns are consistent across multiple hydrological measures, including drought indices, soil moisture, surface water, and evapotranspiration. Effects are strongest in water-limited regions, where hydrological variability is greatest and buffering by snowmelt-fed river systems is weakest. Overall, hydrological conditions exert contrasting effects on key components of West Nile virus dynamics, and these relationships are strongly conditioned by human water management. Our results highlight how irrigation decouples local hydrological conditions from broader climatic variability, underscoring the need for fine-scale hydrological data and panel-based approaches to identify drivers of disease dynamics in human managed landscapes.

Statewide tracking of urban tree canopy change is essential for evaluating progress toward policy targets, but detecting real change requires both high-resolution mapping and rigorous uncertainty estimation. We produced a four-year canopy cover time series for all California census-designated places using 60-cm NAIP aerial imagery and a U-Net deep learning model trained with semi-automated LiDAR-derived labels and manually annotated tiles. Canopy cover and change were estimated using stratified, error-adjusted area estimation, enabling comparisons across years. Statewide canopy cover showed a modest negative trend from 2016 to 2022 (Sens slope: -0.60% per year), but confidence intervals included zero across all groups and climate zones, indicating that trends were not statistically distinguishable from no change. Urban canopy cover was consistently lower than non-urban canopy by approximately six percentage points, and canopy cover was highest in the Northern California Coast and lowest in the Southwest Desert. Residential parcels accounted for 55-56% of canopy within incorporated urban areas across all years, indicating that statewide canopy increase goals will require engagement with private landowners. Error adjustment substantially altered canopy estimates relative to raw pixel-count totals, with direct implications for AB 2251 canopy tracking where baselines and targets drawn from unadjusted maps may not reflect true canopy extent. This open-source workflow is transferable to future NAIP acquisition years and other U.S. states, providing a scalable framework for long-term urban forest monitoring.

Forests are essential to the global carbon cycle with light use efficiency (LUE) as a key parameter for assessing carbon sequestration capacity. However, the variations and drivers of LUE remain inadequately understood. Using remote sensing data, we analyzed global LUE patterns across five forest types and identified the main drivers. The global average annual LUE of forests is 0.93 {+/-} 0.36 g C MJ-1 during the period 2001-2022, with an increasing trend of 0.0034 g C MJ-1 yr-1. Among forest types, evergreen broadleaf forests exhibited the highest LUE, followed by evergreen needleleaf forests. Deciduous broadleaf forests and mixed forests showed similar levels, while deciduous needleleaf forests exhibiting the lowest LUE. Variations in LUE were jointly driven by plant traits and climatic conditions, with generalized linear models explaining 86% and 98% of spatial and temporal LUE variations, respectively. These findings highlight the critical role of plant traits and climate in shaping forest LUE, providing insights for enhancing carbon cycle models and informing forest management strategies in the context of global change.

ContextArtificial light at night (ALAN) has profound impacts on individual organisms and entire communities. Still, humans tend to underestimate the true biological (spatial) footprint of ALAN, in part because of our limited sensitivity to light compared to other organisms. ObjectivesWe sought to demonstrate how far ALAN can reach into dark spaces at levels that can impact organismal behavior and physiology using a fundamental physical law, the inverse square law. MethodsWe created a spatially explicit model of light spread on real landscapes, parameterized using increasingly available landscape-scale vegetation data to account for attenuation through forests and blocking by topographic relief. ResultsLighting types common in rural areas can produce biologically important effects more than 1 kilometer from the source, and effects of large lights might stretch 3 kilometers or more. The footprint of a light is determined by the complex and multidimensional interaction between characteristics of the light itself and the environment. For example, attenuation through a dense forest might decrease the footprint of a light more than 90% compared to the same light on a grassland. In complex environments, even small changes in light placement and characteristics can lead to large changes in the biological footprint of the light. ConclusionsDesigners and land stewards must account for lighting type, brightness, directionality, and reflected light to create ecologically responsible lighting. Vertical vegetation and topography strongly influence the propagation of biologically detrimental light, and environmental context is vital when planning and installing lights to minimize the biological impacts.

Biochar amendments are increasingly applied to improve soil physical functioning and support carbon dioxide removal, but their effects on intrinsic soil thermal properties remain poorly characterised. We conducted the first global systematic meta-analysis of 19 independent studies, 231 control-biochar comparisons, and 529 property-specific effect sizes to test how biochar changes soil heat transfer and storage. Biochar reduced thermal conductivity by 17.6% (95% CI, -22.7 to -12.2), thermal diffusivity by 11.0% (-14.5 to -7.3), and volumetric heat capacity by 8.3% (-12.3 to -4.1). Gravimetric heat capacity showed no significant overall response (+3.3%; -7.6 to 15.4) but was supported by fewer studies. Negative responses were directionally consistent for thermal conductivity, diffusivity, and volumetric heat capacity. Moderator analyses showed that responses were most consistently associated with post-application bulk density and changes in bulk density, while application rate modulated response magnitude and soil texture constrained context dependence. Co-variation among thermal conductivity, thermal diffusivity, and volumetric heat capacity matched expected physical dependencies, indicating coordinated structural reorganisation rather than independent shifts in isolated parameters. These estimates describe intrinsic conductive and storage properties; field-scale soil temperature responses may also be modified by albedo, evaporation, vegetation, and surface energy balance. Improved integration of soil thermal measurements with moisture dynamics, structural changes, and carbon cycling is essential to accurately represent biochar effects in soil and land-surface models.

Nepal's rangelands provide multiple benefits, including support for pastoral livelihoods and alpine biodiversity, regulation of water and soil nutrients, and sequestering carbon. Climate change and anthropogenic pressures are altering these rangelands, leading to vegetation and biodiversity change. However, national-scale assessments of rangeland change are limited in Nepal. This study quantified rangeland changes at multiple spatial scales and assessed the climatic and non-climatic drivers of rangeland change. About 80.7% of Nepal's high-altitude rangeland (> 2,000m) outside protected areas showed no significant change. Among areas exhibiting significant annual maximum NDVI trends, 383,281 ha (18.6%) showed positive and 14,702 ha (0.7%) showed negative trends, corresponding the ratio of increase in vegetation greenness and decline in vegetation greenness to 26:1. Climate predicted positive trends covered 627,184 ha (30.5%), whereas residual trends caused by non-climatic drivers covered 94,656 ha (4.6%). Climate induced negative trends covered 47,609 ha (2.3%) while residual trends were observed in 6,260 ha (0.3%). Negative trend pixels were concentrated mainly within the 3,000 to 5,000 m elevation band, with Karnali Province recording the highest proportional climate predicted decline in vegetation greenness (3.4%). At the municipality scale, rangeland change showed no significant relationship with grazing pressure derived from gridded livestock data, suggesting that grazing pressure alone did not explain the non-climatic vegetation signal. These spatially explicit, nationally consistent results identify where rangeland change is occurring and help distinguish climatic and non-climatic drivers of rangeland vegetation change, providing evidence to support targeted rangeland management under Nepal's federal governance structure.

Native vegetation protection is a key strategy for delivering both biodiversity and climate benefits, and protected areas have been widely adopted to keep tropical biomes standing. Yet deforestation is driven by interrelated environmental and social factors, and the effectiveness of protected areas varies considerably across space. Here, we evaluated the impact of 802 protected and conserved areas in the Brazilian Amazon on preventing vegetation loss and avoiding carbon emissions over the past 40 years using statistical matching to address the location bias of protection. We found that protected areas were effective throughout the study period, reducing the probability of deforestation per km2 by an average of 0.5 percentage points per year. While the Amazon biome lost 14% of its native vegetation between 1986 and 2024, protected areas prevented the deforestation of 290,436 km2, nine times their actual internal loss. They also stored 45,336 Mt of carbon in 2016 (61% of the Amazon stock) and prevented the emission of 7,300 Mt of CO2 by 2024. Deforestation inside the areas and remoteness reduced their impact, while areas that were initially more preserved were more effective. Area size and age had no influence over impact once we analyzed the amount of avoided deforestation per size and age. Impact also varied across Brazilian states, highlighting the role of regional context. All three protection categories (conservation units, indigenous lands, and quilombola territories) showed a positive mean impact, indicating that each, in aggregate, contributes to reducing deforestation. These findings provide robust evidence of the substantial role of Amazonian protected areas in habitat conservation and climate mitigation, while underscoring that this contribution remains undervalued. We advocate for strategically expanding protection to areas of greatest potential impact, and for securing adequate funding to ensure protected areas can fulfill that potential.

Abstract Background: Despite considerable reductions in under-five mortality during the Millennium Development Goal era, progress towards Sustainable Development Goal (SDG) 3.2 remains uneven across Africa. Identifying countries at greatest risk of missing the target is essential for prioritizing interventions and resource allocation. Methods: A Bayesian spatial forecasting ecological study was conducted using 2024 country-level data from 49 African countries obtained from UNICEF. Spatial dependence was assessed using Global Moran's I and Local Indicators of Spatial Association. Bayesian structured additive regression models with Gaussian, Gamma, and Exponential likelihoods were fitted using Integrated Nested Laplace Approximation (INLA) and compared using the Deviance Information Criterion (DIC), Watanabe-Akaike Information Criterion (WAIC), and conditional predictive ordinates. Posterior exceedance probabilities were estimated, an SDG Failure Index (SFI) and a Priority Intervention Index (PII) were developed, and Bayesian posterior predictive simulations were performed to estimate country-specific probabilities of attaining SDG 3.2 by 2030. Results: Significant spatial clustering of under-five mortality was observed with (Moran's I = 0.355, p < 0.001), and hotspots in Benin, Cameroon, and Nigeria. The Gamma model provided the best fit (DIC = 114.92; WAIC = 111.71). Diarrhoea was the only significant predictor (posterior mean=0.030; 95% credible interval: 0.004-0.056). Twenty-three countries (46.9%) were classified as high risk, whereas only five (10.2%) had achieved SDG 3.2. West Africa recorded the highest mean mortality (7.05%) and North Africa the lowest (1.64%). Bayesian projections indicated that only five countries were likely to achieve SDG 3.2 by 2030, while 41 (83.7%) were unlikely to do so. Conclusion: Considerable geographical inequalities in under-five mortality persist across Africa, and most countries remain off-track for achieving SDG 3.2 by 2030. The integration of exceedance probability mapping, the SDG Failure Index, the Priority Intervention Index, and Bayesian probability forecasting provides a practical framework for monitoring progress and prioritizing countries requiring accelerated action towards achieving SDG 3.2.

BackgroundA structural governance failure sits at the intersection of international biodiversity law and the digital genomics revolution. The Convention on Biological Diversity (CBD) and the Nagoya Protocol on Access and Benefit-Sharing (ABS) were designed to ensure that countries of biological origin share equitably in commercial benefits from their genetic resources. Critically, these instruments apply exclusively to non-human genetic resources: plants, animals, fungi, and microbiota. Human genetic resources are deliberately excluded from the CBD and Nagoya ABS framework and are governed separately through bioethics instruments, including the World Health Organization (WHO) framework and the Declaration of Helsinki. This study focuses on non-human digital sequence information (DSI), nucleotide and protein sequence data derived from non-human organisms deposited in open-access databases, which underpins industries generating over USD 1.56 trillion in annual revenue. Africa, hosting approximately 25% of global terrestrial species and nine of the worlds 36 biodiversity hotspots, provides a disproportionate share of the genetic resources from which non-human DSI is derived, yet receives negligible monetary returns because digitisation severs the traceability chain that ABS governance requires. Human genomic data is presented here solely as a secondary indicator of Africas broader infrastructure; it does not constitute the legal basis for Africas modelled allocation share under the Cali Fund. ObjectivesThis study systematically characterises (i) Africas non-human biodiversity endowment as the basis for Cali Fund claims; (ii) ABS governance readiness across 54 African Union (AU) member states; (iii) the commercial trajectories of non-human DSI-dependent industries and projected Cali Fund benefit-sharing flows; and (iv) Africas human genomic representation as a secondary infrastructure indicator, explicitly distinguished from the non-human DSI benefit-sharing argument. MethodsA structured evidence synthesis was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting elements, where applicable to a secondary data analysis design. Literature was searched across PubMed, Scopus, Web of Science, Google Scholar, and official repositories of the CBD, Food and Agriculture Organization of the United Nations (FAO), International Union for Conservation of Nature (IUCN), and United Nations Environment Programme (UNEP). The search was restricted to January 2022 - April 2026 to capture post-Kunming-Montreal Global Biodiversity Framework (KMGBF) literature. A total of 412 records were identified before screening; 34 peer-reviewed articles and 19 institutional documents met all inclusion criteria. Quantitative Cali Fund scenario modelling used the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) and KPMG (2024) non-human DSI sector revenue baseline (CBD/WGDSI/2/2/Add.2). The 12.5% net profit margin is a cross-sector proxy from that study; actual margins vary by sector. Africas modelled allocation share (20-25%) is the authors analytical construct based on Africas non-human species richness and hotspot share; it is not an internationally agreed formula. ResultsAfricas non-human biodiversity endowment is exceptional: 25% of terrestrial species, nine of 36 biodiversity hotspots, and the worlds second-largest tropical forest system. Non-human DSI from African genetic resources is a critical input to industries generating USD 1.56 trillion annually, yet Africa contributes a marginal and unmeasured fraction of International Nucleotide Sequence Database Collaboration (INSDC) sequences. As a secondary indicator, 94.48% of genome-wide association study (GWAS) participants as of 2024 were of European ancestry (Corpas et al., 2025); this human genomic data is presented for contextual illustration only and is not the basis for Africas Cali Fund modelled allocation share. Zero African Union member states have enacted legislation explicitly covering non-human DSI in their ABS framework. Africas modelled allocation share ranges from USD 312 million (Scenario A, 20% weight) to USD 5.83 billion (Scenario C, 25% weight) annually. ConclusionsAfrica is among the most biologically rich continents on Earth for non-human life, yet structurally excluded from the benefit-sharing framework the CBD intended to create. The Cali Fund represents the first mechanism capable of correcting this at scale. Realising Africas modelled allocation share requires urgent legislative reform, institutional capacity investment, sequencing infrastructure development, and a coordinated African position at COP17 scheduled in Yerevan, October 2026.

Nickel has been studied for a long time as an environmental contaminant but less so in its connection to population health. It does not announce itself as loudly as its transition metal brethren like mercury and cadmium, but its chemical properties permit it to be deleterious as a low-dose, chronic exposure, particularly among those with immune systems sensitized to it. There is a growing evidence base and vocabulary to discuss nickel's affect on health. However, in the U.S., there are not recent, reliable estimates of the share of the population with a nickel allergy, let alone how much nickel Americans are exposed to through their diet. This paper seeks to close this evidence gap by creating a new dataset of dietary nickel and other heavy metal exposure and assessing how high levels of dietary nickel exposure shape local demand for health care services. We use soil data from the U.S. Geological Survey and data on agricultural product transport from FoodFlows.org to create a county-level dietary nickel exposure index. We then use a large electronic health record database and double machine learning to estimate how demand for primary care services varies across levels of dietary nickel exposure. We find that counties with high nickel exposure experience an increase in the share of primary care office visits for symptoms highly suggestive of nickel poisoning. This result survives multiple hypothesis test corrections and placebo tests. Our research suggests that nickel has harmful effects on individual health whose exposure can be measured at a population level, and is shaping primary care across the U.S.