National Socio-Environmental Synthesis Center

Food loss and waste (FLW) contribute significantly to the global food system's economic and environmental burdens, including substantial greenhouse gas (GHG) emissions, resource depletion, and waste management challenges. In alignment with the European Commission's sustainability objectives and U.N. Sustainable Development Goal 12.3, this study explores the potential energy and environmental footprint savings achievable by halving FLW in Europe by 2030. Using a multiregional input-output model, we estimated the total global energy and environmental footprint savings across all stages of the food supply chain, considering industry-specific FLW rates and proportion weights. The findings reveal substantial environmental savings across Europe, with aggregate savings potentially reaching 51 Mt CO2e (0.09 t CO2e/p), 4,620 Mm3 (8 m3/p) of blue water, 106,446 km2 (179 m2/p) of cropland, 55,523 km2 (93 m2/p) of grassland, and 0.47 EJ (0.54 TJ/p) of energy. The greatest potential for savings was found in Western Europe, specifically in France, Germany, Belgium, and The Netherlands. However, countries with a lower per capita GDP, such as Greece, Croatia, Bulgaria, and Romania, also demonstrate significant per capita savings potential, indicating that wealth does not necessarily correlate with higher environmental savings. Agricultural production emerged as the stage with the highest footprint reduction potential for GHG and resource footprints across Europe, while the foodservice and institutional stages offer the greatest energy-saving potential. Geographical disparities underscore the need for region-specific policies. These results challenge the wealth-sustainability correlation and advocate for adaptable policies that transcend national wealth and accommodate regional disparities, underlining the pivotal roles of the agricultural production and consumption stages in footprint savings.

The spatial distribution of soil phosphorus (P) is important to both biogeochemical processes and the management of agricultural landscapes, where it is critical for both crop production and conservation planning. Recent advances in the availability of large environmental datasets together with big data analytical tools like machine learning have created opportunities for evaluating and predicting spatial patterns in complex environmental variables like soil P. Here, we apply a random forest machine learning model to publicly available soil P datasets together with nearly 300 geospatial attributes summarizing aspects of soil type, land cover, land use, topography, nutrient inputs, and climate to predict total soil P at a 100m grid scale for the Upper Mississippi River Basin (UMRB), USA. The UMRB is one of the most intensively farmed regions in the world and is characterized by widespread water quality degradation arising from P-associated eutrophication. At the regional scale represented by our model, the variables with the greatest comparative importance for predicting soil P included a combination of soil sample depth, land use/land cover, underlying soil physical and geochemical properties, landscape features (such as slope, elevation and proximity to the stream network), nutrient inputs, and climate-related factors. An important product of this research is a ne-scale (100 m) raster data layer of predicted total soil P values for the UMRB for public use. This dataset can be used to improve conservation planning and modeling efforts to improve water quality in the region.

This study describes a collaborative modeling process deployed at the Cantareira Water Supply System (CWSS) in São Paulo City Metropolitan Area, Brazil. The CWSS faces challenges for meeting the increasing water demand, while land-use and climate change and their combined effect on its water cycle and balance have created a complex water resources management problem. Through a stakeholder engagement process—involving scientists and policymakers, the water utility company, and state administration—environmental simulation models were developed to elicit and represent multiple environmental, economic, and policy perspectives, developing a mutual language to communicate and establish common goals of water resources management. Study outputs include estimation of biophysical and economic benefits associated with prioritized native vegetation restoration activities in the source watersheds. These outputs are deployed in support of landscape planning and the decision process integrating multiple stakeholder perspectives in São Paulo state administration, the water utility company, and municipalities.

Abstract AimRapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).LocationEastern USA.Time period2017–2018.Taxa studiedTrees.MethodsWith a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.ResultsModels using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.Main conclusionsWe found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

Wetlands store significant soil organic carbon (SOC) globally due to anoxic conditions that suppress SOC loss. However, stored SOC may become vulnerable to decomposition where climate and land use change alter wetland hydrology. Seasonally saturated wetlands experience fluctuating hydrologic conditions that could promote physicochemical mechanisms known to stabilize terrestrial SOC. These wetlands are therefore likely to be important for SOC storage at the landscape-scale. This study examined physicochemical stabilization of SOC within five seasonally saturated wetlands across a hydrologic gradient from the frequently saturated basin edge to the rarely saturated upland. At each wetland, we monitored water level and collected soil samples from the top two mineral horizons across five transect points to quantify physical protection of SOC in aggregates and organo-mineral associations between SOC and iron (Fe). As expected, both SOC concentrations and SOC stocks from 10–50 cm decreased across the transect from frequently saturated soils to rarely saturated soils. However, SOC stocks from 0–10 cm increased along this gradient, indicating diverging SOC dynamics throughout the soil profile. The majority of SOC was associated with macroaggregates across the transect, suggesting that macroaggregates are likely to physically protect wetland SOC during seasonal drying. By contrast, Fe-associated SOC was low across the transect, though modest accumulations of Fe (5 mg Fe g−1 soil) were observed in the transition zone where saturation was most dynamic throughout the year. Our results suggest that SOC stabilization occurs via physical protection within macroaggregates and, to a lesser extent, organo-mineral associations during dry periods in and around seasonally saturated wetlands. As climate scenarios predict intensified wet and dry cycles in many wetlands, understanding SOC stabilization is critical to predicting vulnerability to future change.

Abstract Soaring temperatures and increased occurrence of heatwaves have drastically increased air‐conditioning demand, a trend that will likely continue into the future. Yet, the impact of anthropogenic warming on household air conditioning is largely unaccounted for in the operation and planning of energy grids. Here, by leveraging the state‐of‐the‐art in machine learning and climate model projections, we find substantial increases in future residential air conditioning demand across the U.S.—up to 8% with a range of 5%–8.5% (13% with a range of 11%–15%) after anthropogenic warming of 1.5°C (2.0°C) in global mean temperature. To offset this climate‐induced demand, an increase in the efficiency of air conditioners by as much as 8% (±4.5%) compared to current levels is needed; without this daunting technological effort, we estimate that some states will face supply inadequacies of up to 75 million “household‐days” (i.e., nearly half a month per average current household) without air conditioning in a 2.0°C warmer world. In the absence of effective climate mitigation and technological adaptation strategies, the U.S. will face substantial increases in air conditioning demand and, in the event of supply inadequacies, there is increased risk of leaving millions without access to space cooling during extreme temperatures.

Abstract A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys.