Rapid expansion of transportation infrastructure is transforming tropical environments, yet the health impacts of these changes remain poorly understood and are rarely accounted for in impact assessments. We apply a quasi-experimental approach to quantify the impact of highway paving on infectious disease transmission. The 2009 paving of the Interoceanic Highway through the previously isolated Madre de Dios region of the Peruvian Amazon offered a natural experiment to evaluate how highway paving influences transmission of dengue virus, a high-burden mosquito-borne disease. We compared dengue incidence data from healthcare facilities in Madre de Dios near versus far from the highway before and after paving, while controlling for observable and unobservable confounding variables (a difference-in-differences causal inference approach). Paving led to an additional 10,950 (95% confidence interval of 3,186-18,715) dengue cases since 2009, a 403% (117-689%) increase in incidence rates in facilities near the highway in the 14 years since paving (2009-2022), compared with their pre-paving incidence rates. Our findings demonstrate the impact that infrastructure can have on dengue transmission, likely via its effects on human mobility and vector dispersal. Future road construction plans in tropical regions should account for potential increases in dengue transmission during impact assessments.

Background: Arboviruses cause major disease burden worldwide, exacerbated by climate and anthropogenic change. Given shared Aedes vectors and similar symptoms of dengue, chikungunya, and Zika, accounting for surveillance bias is critical to accurately assess disease spread. Methods: Using 2019-2023 Brazil SINAN surveillance data, we developed a novel geocoding approach and applied occupancy models with spatially varying coefficients to quantify land use/land cover effects on dengue, chikungunya, and Zika occurrence at 100 km2 resolution across Brazil, while adjusting for detection bias. Findings: Accounting for testing capacity improved model fit and revealed new occurrence hotspots. By the end of the 2019-2023 period, dengue occurrence covered 45.1% (95% CI: 41.9-48.8%) of Brazil, chikungunya 57.1% (51.9-62.3%), and Zika 26.5% (21.4-32.3%)—1.8-, 3.8-, and 2.0-fold increases over models ignoring detection bias. Dengue dominated the Central-West/Southeast regions while chikungunya and Zika concentrated in the North/Northeast. Urbanization increased occurrence for all diseases (e.g., OR = 41.3 per SD, 95% CI: 27.0-64.5, for dengue). Forest cover was negatively associated with dengue nationwide and with chikungunya in wet areas, while agriculture presented a positive association with dengue in the Central-West/Southeast (dominated by soybean/sugarcane), and with chikungunya near its introduction site in the Northeast. Zika showed negative relationships with forests and agriculture, except for a positive forest association in the Northeast. Interpretation: Accounting for detection bias uncovered cryptic transmission patterns, showing chikungunya surpassed dengue in geographic spread by 2023 (57.1% vs. 45.1% national coverage). We identified agricultural areas with high disease occurrence and forest's potential for ecosystem-based arbovirus mitigation.

Background Smartphone-based ecological momentary assessments (EMAs) offer a unique opportunity to actively engage and involve the public in scientific research and study the impact of environment on mental wellbeing. Objective tracking of environmental exposures is not easily achieved with commonly used portable devices, however linkage with external datasets can enhance geotagged EMA data with detailed environmental information. Methods We illustrate a novel approach matching geotagged EMA assessments from the Urban Mind study with ambient temperature, air pollution, and green space exposure data. We applied a case-time series design, self-matched analytic methods (distributed lag non-linear models) and a multistep approach to missing data to analyse associations between ambient temperature and momentary wellbeing, calculated using self-reported contemporaneous levels of positive (5 items) and negative affect (five items). Results 7088 assessments from 266 participants over 14 days were successfully matched to ambient environmental data. We found little evidence that daily maximum temperatures are associated with momentary mental wellbeing, nor for effect modification by air pollution or green space exposure. Conclusions This study illustrates the value of this methodology for allowing researchers to answer a broader range of questions in climate change, environment and human health.

Amidst the ongoing biodiversity crisis, there is high demand for spatially explicit biodiversity indicators that can support conservation planning and national reporting. Global models that estimate the impacts of human pressures on biodiversity provide crucial insights, but their use in spatial projections calls for more systematic evaluation of how accurately they can predict biodiversity patterns at fine spatial scales. This is especially important because spatial projections require models to make predictions under a wide range of environmental and geographic contexts. Here we evaluate the generality of two different pressure-response models for estimating alpha and beta diversity, relative to ecologically intact reference sites, using a global dataset of 25,987 sites from 681 biodiversity studies. Generality is operationalized as the model accuracy when making out-of-sample predictions in sampled populations (generalizability) as well as in other contexts (transferability).We find that mixed models with study-level random effects – commonly used in meta-analyses and forming the basis of several biodiversity indicators – exhibit generally low site-level accuracies. This reflects dependence on a limited set of averaged fixed effects and strong attribution of variation to the random effects, which cannot be used out-of-sample. In comparison, a model structure that incorporates biogeographic–taxonomic attributes together with environmental covariates achieves higher accuracy within contexts represented in training data. However, accuracy is low when predicting into new contexts, due to distribution shifts between training and test data. These patterns hold for both site-level diversity measures and for differences between paired sites.Although both models estimate consistent and reasonable responses to land use, the results illustrate a large gap between effect-size inference and spatially explicit prediction. Models are essential for informed conservation efforts, but their applicability is fundamentally constrained by the availability and distribution of underlying data. Whereas countries with extensive data can build high-fidelity national indicators, accelerated data collection and macroecological model development are needed to better support data-poor regions with actionable biodiversity insights.

National governments and multilateral institutions face difficult challenges reconciling biodiversity, climate, and economic development goals. We integrated spatial biophysical and economic data with optimization methods to develop sustainable landscape efficiency frontiers that show maximally feasible combinations of biodiversity conservation, land-based climate mitigation, and net economic value from agricultural crops, livestock, and forestry production. We applied this approach in 146 countries and found large potential gains in biodiversity, climate, and economic development from improved land use and land management. Summing national-level results shows the potential to increase climate mitigation by more than 200 billion metric tons of CO2 equivalents (>20% increase) or net economic value by more than US$350 billion (>80% increase), without loss in other objectives.

Urban greening is increasingly promoted as a strategy for adapting cities to rising temperatures, yet its capacity to reduce heat risks and how those benefits are distributed across populations remain poorly understood. Here, we examine how alternative land-use pathways influence urban cooling and associated energy, productivity and health outcomes across London under future climate change. Combining climate projections with urban cooling and health impact models, we isolate the effects of land-use change from background warming and evaluate responses from neighborhood to city scales. We find that climate change substantially increases summer temperatures across London by mid-century, while alternative land-use pathways modify local heat exposure by up to ±1 °C under the same climate conditions. Scenarios that expand tree canopy consistently reduce cooling energy demand, improve labor productivity, and lower heat-related mortality, whereas the loss of urban greenery amplifies heat-related risks. However, these benefits are not distributed evenly. Neighborhoods with greater socioeconomic disadvantage experience smaller gains from conventional greening strategies. We further show that targeted greening strategies can substantially alter the distribution of adaptation benefits, directing larger health gains towards higher-risk communities. Our findings suggest that the effectiveness of urban greening depends not only on how much cities green, but also on where greening occurs. Incorporating equity considerations into urban greening targets and investment strategies may therefore be critical for achieving both climate resilience and social benefits.

Urban nature is increasingly suggested as a climate adaptation strategy, yet its capacity to reduce heat exposure and how those benefits are distributed across population remains poorly understood. Here, we examine how alternative land-use pathways shape urban cooling, and associated energy, productivity, and health outcomes across London under mid-century climate change. Combining climate projections with an urban cooling model and health impact assessment, we isolate the effects of land-use change from background warming and evaluate borough- and neighborhood-scale responses. We find that climate change substantially increases summer temperatures across London by 2050, while alternative land-use pathways modulate local heat exposure by up to ±1 °C under the same climate conditions. Scenarios that expand tree canopy consistently reduce cooling energy demand, improve labor productivity, and lower heat-related mortality, whereas the loss of urban greenery amplifies heat-related risks. However, these benefits are unevenly distributed. Neighborhoods with lower socioeconomic status exhibit smaller gains from uniformed greening strategies. Our findings demonstrate that urban nature can provide measurable adaptation benefits under future climate change, but that the magnitude and distribution of these benefits depend on how greening is planned and implemented. Integrating equity considerations into urban greening targets and investment strategies may therefore be critical for maximizing both climate resilience and social benefits.

Context:There are urgent calls to transition society to more sustainable trajectories, at scales ranging from local to global. Landscape sustainability (LS), or the capacity for landscapes to provide equitable access to ecosystem services essential for human wellbeing for both current and future generations, provides an operational approach to monitor these transitions. However, the complexity of landscapes complicates how and what to consider when assessing LS. Objectives:To identify important features of landscapes that remain challenging to consider in LS assessments and provide guidance to strengthen future assessments. Methods:We conducted two workshops to identify the complex features of landscapes that remain under-considered in LS assessments, and developed guidelines on how to better incorporate these features. Results:We identify open and connected boundaries and diversity of values as landscape features that must be better considered in LS assessments or risk exacerbating offstage sustainability burdens and power inequalities. We provide guidelines to avoid these pitfalls which emphasize assessing ecosystem service interactions across interconnected landscapes and incorporating local actors' diverse values. Conclusions:Our guidelines provide a stepping stone for researchers and practitioners to better incorporate landscape complexities into LS assessments to inform landscape-level decisions and actions.

ABSTRACT New biodiversity and ecosystem reporting frameworks require companies to collect data on multifaceted impacts on complex ecological systems over space and time while offering them limited guidance on how to do so. Artificial Intelligence (AI) and Earth Observation (EO) are powerful tools that can help make this reporting efficient and actionable. However, before companies can fulfill their crucially important role in improving the state of nature, they will need guidance from the scientific community to identify meaningful yet scalable metrics for data collection, responsibly apply AI‐enabled EO to reporting workflows, and empower the reporting workforce.

Mental disorders are more prevalent in cities, yet the global impact of urban nature on mental health remains insufficiently understood. Here we address this gap by systematically reviewing 449 peer-reviewed studies and conducting a meta-analysis of 78 field-based experiments to quantify the effects of various urban nature types on 12 mental health outcomes. Our meta-analysis demonstrates that exposure to urban nature provides substantial benefits for a broad spectrum of mental health outcomes. Green spaces such as urban forests and parks emerged as key elements in mitigating negative moods, such as depression and anxiety, and enhancing overall mental well-being. In particular, the benefits of nature exposure are most pronounced among young adults, although consistent positive effects are evident across all age groups. These findings highlight the importance of safeguarding and expanding access to urban nature as a key strategy for enhancing public health and well-being in cities worldwide.

Amidst the biodiversity crisis, there is high demand for spatially explicit biodiversity indicators. Global models that quantify impacts of human pressures provide important insights for conservation, but their accuracy in spatial projections has yet to be systematically tested. Here we evaluate this using a global dataset of 25,987 species inventories from 681 studies. We find that mixed models with study attributes as random effects - common in meta-analysis and used in several indicators - exhibit low predictive accuracy. This is driven by reliance on a small set of averaged fixed effects. In contrast, a biogeographic-taxonomic model structure with explicit environmental covariates shows relatively higher interpolation accuracy. However, accuracy when extrapolating to other contexts remains low, due to distribution shifts in environmental conditions. These patterns apply to site-level diversity and differences between sites. Both models estimate similar land-use impacts, in line with previous research, yet our results highlight the challenging gap between effect size inference and prediction. Models are essential for informed conservation efforts, but their applicability is fundamentally constrained by data availability. Whereas countries with extensive data can build high-fidelity national indicators, accelerated data collection and model development are needed to better support data-poor regions with localized and actionable insights.

INTRODUCTION:Studies suggest that extreme heat events can have negative effects on mental health. However, characterisation of these effects in urban communities remains limited, and few studies have investigated the potential modifying effects of demographic, clinical and environmental characteristics. The aim of this study is to address this knowledge gap and quantify the impacts of extreme heat on mental health, health service use and mental well-being in vulnerable urban populations. METHODS AND ANALYSIS:In this multidisciplinary project, we will assess mental health outcomes in different populations by bringing together two distinct datasets: electronic health record (EHR) data on mental health service users and data from general public participants of Urban Mind, a citizen science project. We will use EHRs from the South London and Maudsley NHS Foundation Trust (SLaM) and the North London NHS Foundation Trust (NLFT), from six boroughs which collectively cover more than 1.8 million residents in Greater London, to capture mental health service use and mortality among people with existing diagnoses of mental illness across 2008-2023. We will use smartphone-based ecological momentary assessment data from Urban Mind to measure mental well-being in the general population (2018-2023). These datasets will be linked to high-resolution spatiotemporal data on temperature, fine and coarse particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), Normalised Difference Vegetation Index (NDVI) and density of large mature tree canopy. We will employ novel quasi-experimental designs, including case time series and case-crossover analysis, to examine the impact of extreme heat on mental health and explore effect modification by sociodemographic, clinical and environmental factors, including air pollution and types of green space coverage. We will also develop a microsimulation model combined with the InVEST urban cooling model to assess and forecast the mental health and social care impacts of extreme heat events and the mitigation of these impacts by different green space coverage and pollution-reduction policies. With a core team composed of researchers, community organisations, industry partners and specialist policy experts, this project will consider lived experience, benefit from broad stakeholder engagement and address gaps in policy and practice. ETHICS AND DISSEMINATION:Each component of this project has been approved by the relevant ethics committee (ref RESCM-22/23-6905 for Urban Mind, LRS/DP-23/24-41409 for the co-development of a screening tool, 23/SC/0257 for the SLaM EHRs, and 24/EE/0178 for the NLFT EHRs). Our dissemination plan includes peer-reviewed scientific articles, policy briefs, a practical guide on fostering ecological and human resilience at the neighbourhood level, and a technical guide for planting and improving the growing conditions of large canopy trees.

Urban green spaces improve the health of residents, but the underlying mechanisms are unclear. One benefit may be providing inviting spaces for physical activity, but the extent to which this applies across populations and geographies is unknown. Here we used multilevel modelling to examine the relationship between urban green space and objectively measured daily step counts, derived from wearable devices, among 7,013 participants across 53 US cities. Our findings indicate that a 10% increase in park accessibility is associated with an additional 107 daily steps, whereas the general amount of urban green shows no significant association. City-level park accessibility has a more pronounced effect on daily step counts among the elderly, Black and Latino residents and less active individuals. We observed regional differences, with an enhanced association between park accessibility and steps in the western and southern USA. Our study underscores the broad potential of accessible urban parks to enhance public health by promoting physical activity, while highlighting the need to account for geographic and individual differences. Wearable data from 7,013 participants in the All of Us Research Program show that park accessibility across 53 US cities is positively associated with daily step counts, providing a mechanism for how urban green space can improve health.

Many valuable economic benefits from nature have traditionally been overlooked in both national accounts and government policy. To remedy this, countries are adopting the new United Nations System of Environmental-Economic Accounting framework for valuing ecosystem services, but inclusion of key hydrological services has so far been limited. Here we develop ecosystem service flow accounts linked to a natural capital assessment in Colombia's Sin & uacute; Basin to value ecosystems' contributions to water and energy security. Using integrated biophysical and economic models parameterized with local data, we find ecosystems deliver sediment retention benefits to the energy and water sectors equivalent to 1.7% of the region's gross domestic product. A planned expansion of the region's aqueduct system would further increase these services' value by 12%. Our findings are informing development planning and policy within Colombia and provide lessons for the many other countries adopting natural capital accounting to support their sustainable development goals.

Human mobility drives the spread of many infectious diseases, yet the health impacts of mobility changes from new infrastructure development remain poorly understood and currently not accounted for in impact assessments. We present a novel application of a quasi-experimental approach to link mobility and infectious disease, leveraging historical road upgrades as a proxy for regional mobility changes. The 2009 paving of the Interoceanic Highway through the previously isolated Madre de Dios region of the Peruvian Amazon offered a natural experiment to evaluate how highway paving influences transmission of dengue virus, a high-burden mosquito-borne disease. We compared dengue incidence data from healthcare facilities near versus far from the highway before and after paving, while controlling for observable and unobservable confounding variables (a difference-in-differences causal inference approach). We found that the paving caused an additional 10,950 (95% CI: 3,186-18,715) dengue cases since 2009, a 403% (117-689%) increase in incidence rates in facilities near the highway in the 14 years since paving (2009-2022), compared to their pre-paving incidence rates. Our findings demonstrate the impact that infrastructure can have on dengue transmission, likely via its effects on human mobility. As a result, we advocate for future road construction plans in tropical regions to account for potential increases in dengue transmission during impact assessments.

This document outlines the activities and results of the Colombian pilot project under a Regional Technical Cooperation (TC), “Transforming Policy and Investment through Mainstreaming Rapid Approaches for Natural Capital Assessment and Accounting.” This TC was funded by the Global Environmental Facility (GEF), implemented by the Inter-American Development Bank (IDB), and executed by Stanford University. The main beneficiaries and co-designers of this TC are the Colombian Ministry of Environment and Sustainable Development, the National Department of Statistics (DANE), the National Planning Department (DNP), and the technical work was led by the Stanford-based Natural Capital Project (NatCap). This work also received funding from the Gordon and Betty Moore Foundation. The main objective of this pilot project was to advance the design of financing mechanisms to strengthen Colombia's national system of protected areas (known as SINAP), as outlined in Colombia's National Biodiversity Strategy and Action Plan (NBSAP). The project developed an economic valuation of the flows of ecosystem services natures benefits to people from protected areas in the Northeastern Andes region to priority economic sectors (sanitation, energy, agriculture, tourism, and forestry) of several municipalities in Colombia (Tunja, Sogamoso, and Duitama). The team used he methodological and conceptual standards of the United Nations System of Environmental Economic Accounting-Ecosystem Accounting (SEEA-EA) to develop a replicable and scalable approach to valuing ecosystem services. This valuation provides a key input for equitable compensation and financing mechanisms, such as payments to rural land stewards or entities responsible for managing protected areas, to help them maintain the ecosystem services in support of the economic activities and population centers that rely on them.

This document outlines the activities and results of the Colombian pilot project under the Regional Technical Cooperation (TC), “Transforming Policy and Investment through Mainstreaming Rapid Approaches for Natural Capital Assessment and Accounting.” This TC was funded by the Global Environmental Facility (GEF), implemented by the Inter-American Development Bank (IDB), and executed by Stanford University The main beneficiaries and co-designers of this TC are the Colombian Ministry of Environment and Sustainable Development, the National Department of Statistics (DANE), and he National Planning Department (DNP), and the technical work led by the Natural Capital Project team (Stanford University). This work also received funding from the Gordon and Betty Moore Foundation. The pilot project in Colombia aimed at integrating natural capital assessments and economic valuation for the advancement of two policies: the strengthening of the National System of Protected Areas and the implementation of the System of Environmental-Economic Accounting Ecosystem Accounting (SEEAEA) in Colombia. Results from this work are informing the design of an innovative, cross-sectoral eco-compensation scheme to support stewardship of the projected areas and equitable distribution of benefits from them. The project evaluated the ecosystem services provided by Colombia's system of protected areas in the Northeastern Andes region to three higher-income, higher-population municipalities Tunja, Sogamoso, and Duitama and quantified natures contributions to people as a percentage of Gross Domestic Product (GDP) and other societal benefit measures. This approach highlights the roles that benefits flowing from natural ecosystems and protected areas play in local economies, including maintaining water quantity and quality, which are essential for water-dependent economic sectors. Novel analytical workflows were developed to characterize and value ecosystem services flows, aligning with SEEA-EA standards. These workflows integrated local data, biophysical models, and economic valuation techniques to implement Accounts 3 and 4 of the SEEA-EA, showing value in both physical and monetary units. This alignment with SEEA-EA standards enhances the legitimacy and relevance of the results, facilitating their incorporation into financial compensation mechanisms (such as payments for ecosystem services, in which beneficiaries of ecosystem services make payments to those protecting the ecosystems, thus internalizing the costs of nature protection) and planning processes, and furthering Colombia's SEEA-EA implementation. These data and modeling showing the connections from ecosystems to economic valuation also are applicable to other countries around the world. The document also includes a series of policy applications and capacity development recommendations to support the next steps in integrating natural capital assessments into decision making processes. These recommendations aim to strengthen the National System of Protected Areas and ensure that ecosystem services valuation supports financing for sustainable economic development.

Authors: Lisa Mandle1, Andrew Shea2,3, Emily Soth1, Jesse A. Goldstein1, Stacie Wolny1, Jeffrey R. Smith4,5, Rebecca Chaplin-Kramer6,7, Richard P. Sharp6,8, Mayur Patel1AffiliationsNatural Capital Project, Stanford University, Stanford, CA 94305 USAGlobal Sustainable Finance, Morgan Stanley, New York, NY 10036Current affiliation: Frontier & Stripe Climate, Stripe, South San Francisco, CA 94080Department of Ecology and Evolutionary Biology, Princeton University, Princeton New Jersey 08544High Meadows Environmental Institute, Princeton University, Princeton, New Jersey 08544Global Science, WWF. 131 Steuart St., San Francisco, CA 94105Institute on the Environment, University of Minnesota, 1954 Buford Ave., St. Paul, MN 55108SPRING, 5455 Shafter Ave., Oakland CA 94618Abstract: Aligning economic activities with the global sustainable development agenda requires understanding companies' impacts on nature. Here, we present a new approach for quantifying the direct impacts of companies' physical assets on nature based on global maps for eight ecosystem services and biodiversity metrics. We apply this approach to a set of over 2,000 global, publicly traded companies with 580,000 mapped physical assets. We find that companies in utility, real estate, materials, and financial sectors have the largest impacts on average, but there is substantial variation among companies within all sectors. In addition, we use high-resolution satellite imagery to assess the impact of active lithium mines based on their footprints. We show that the impact varies substantially among mines and can also be tracked across time for a mine. This approach enables differentiation among companies and assets based on their impacts to nature relative to their revenue or production.