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As climate change intensifies, global publics will experience more unusual weather and extreme weather events. How will individual experiences with these weather trends shape climate change beliefs, attitudes, and behaviors? In this article, we review 73 papers that have studied the relationship between climate change experiences and public opinion. Overall, we find mixed evidence that weather shapes climate opinions. Although there is some support for a weak effect of local temperature and extreme weather events on climate opinion, the heterogeneity of independent variables, dependent variables, study populations, and research designs complicate systematic comparison. To advance research on this critical topic, we suggest that future studies pay careful attention to differences between self-reported and objective weather data, causal identification, and the presence of spatial autocorrelation in weather and climate data. Refining research designs and methods in future studies will help us understand the discrepancies in results, and allow better detection of effects, which have important practical implications for climate communication. As the global population increasingly experiences weather conditions outside the range of historical experience, researchers, communicators, and policymakers need to understand how these experiences shape-and are shaped by-public opinions and behaviors.

The management of natural resources—from forests to fisheries to freshwater—is becoming increasingly complex and requires new tools and processes for engaging with individuals, communities, and decision-makers. Policy makers and practitioners have begun using serious games (SGs) (those used for purposes other than entertainment) to overcome some of the complex challenges of governing resources in social-ecological systems. This paper uses a systematic literature review methodology to assess role-playing SGs for natural resource management. Fifty-two articles from the role-playing game (RPG) subset of SGs are identified, synthesised and analysed using a multi-criteria evaluation framework. First, we explore three theoretical and conceptual elements of games: principles of RPGs, functions of games, and (practical) game characteristics. We evaluate game elements, including game design, adherence to reality and the degree to which games integrate elements of participatory—and action research. These dimensions of RPGs are then analysed and discussed. Particular attention is paid to the value and application of RPGs to address complex problems with interacting environmental, social, cultural and economic challenges, and the extent to which they can inform adaptive governance solutions. Results show that RPGs can be a valuable tool at different levels; however, we also identify important gaps in the current state of knowledge, in particular, related to bridging community—and higher-level decision-making scales through RPGs.

Background. Uncertainty about climate change impacts on forests can hinder mitigation and adaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet different uncertainty components emerge in different studies. Consequently, inconsistent understanding of uncertainty among different climate impact studies (from the impact analysis to implementing solutions) can be an additional reason for delaying action. In this review we (a) expanded existing uncertainty assessment frameworks into one harmonised framework for characterizing uncertainty, (b) used this framework to identify and classify uncertainties in climate change impacts studies on forests, and (c) summarised the uncertainty assessment methods applied in those studies. Methods. We systematically reviewed climate change impact studies published between 1994 and 2016. We separated these studies into those generating information about climate change impacts on forests using models –'modelling studies', and those that used this information to design management actions—'decision-making studies'. We classified uncertainty across three dimensions: nature, level, and location, which can be further categorised into specific uncertainty types. Results. We found that different uncertainties prevail in modelling versus decision-making studies. Epistemic uncertainty is the most common nature of uncertainty covered by both types of studies, whereas ambiguity plays a pronounced role only in decision-making studies. Modelling studies equally investigate all levels of uncertainty, whereas decision-making studies mainly address scenario uncertainty and recognised ignorance. Finally, the main location of uncertainty for both modelling and decision-making studies is within the driving forces—representing, e.g. socioeconomic or policy changes. The most frequently used methods to assess uncertainty are expert elicitation, sensitivity and scenario analysis, but a full suite of methods exists that seems currently underutilized. Discussion & Synthesis. The misalignment of uncertainty types addressed by modelling and decision-making studies may complicate adaptation actions early in the implementation pathway. Furthermore, these differences can be a potential barrier for communicating research findings to decision-makers.

Background. Coastal river deltas provide multiple ecosystem services. Many deltas serve as major centers of agriculture, industry and commerce. The annual economic benefits derived from major deltas are often a substantial fraction of a country's GDP. Yet, many deltas are losing land due to erosion, subsidence and subsequent flooding. Such vulnerabilities are often increased due to local land and water management decisions, relative sea-level rise, and increases in climate extremes. Aim of this review. Considerable literature exists addressing the formation of deltas and the effects of increasing urbanization, industrialization and crop and fish production, increases in relative sea level rise, and decreasing sediment deposition. This leads to the question: are the economic, environmental, ecological and social benefits derived from developed river deltas sustainable? This review focuses on this question. Methods/Design. Over 180 published documents were identified and reviewed using various search engines and key words. These key words included river deltas; delta sustainability, vulnerability, resilience, coasts, ecology, hazards, erosion, water management, urbanization, reclamation, agriculture, governance, pollution, geomorphology, economic development, socio-economic changes, and delta wetlands; relative sea level change; sediment trapping; sand mining; salinity intrusion; coastal restoration; estuarine engineering; shoreline evolution; estuarine processes; and the names of specific river basin deltas. Review Results/Synthesis and Discussion. Deltas provide humans important resources and ecosystem services leading to their intensive development. The impacts of this development, together with sea-level rise, threatens the sustainability of many river deltas. Various management and governance measures are available to help sustain deltas. Controls on land use, improved farming and transport technology, wetland habitat protection, and d improved governance are some that might help sustain the economic and ecological services provided by deltas. However, increased population growth and the impacts of climate change will put increased pressure on deltas and the benefits derived from them.

Recent studies have highlighted the reliance of global food production on unsustainable irrigation practices, which deplete freshwater stocks and environmental flows, and consequently impair aquatic ecosystems. Unsustainable irrigation is driven by domestic and international demand for agricultural products. Research on the environmental consequences of trade has often concentrated on the global displacement of pollution and land use, while the effect of trade on water sustainability and the drying of over-depleted watercourses has seldom been recognized and quantified. Here we evaluate unsustainable irrigation water consumption (UWC) associated with global crop production and determine the share of UWC embedded in international trade. We find that, while about 52% of global irrigation is unsustainable, 15% of it is virtually exported, with an average 18% increase between year 2000 and 2015. About 60% of global virtual transfers of UWC are driven by exports of cotton, sugar cane, fruits, and vegetables. One third of UWC in Mexico, Spain, Turkmenistan, South Africa, Morocco, and Australia is associated with demand from the export markets. The globalization of water through trade contributes to running rivers dry, an environmental externality commonly overlooked by trade policies. By identifying the producing and consuming countries that are responsible for unsustainable irrigation embedded in virtual water trade, this study highlights trade links in which policies are needed to achieve sustainable water and food security goals in the coming decades.

Climate models suggest a rapid increase of extremely hot days in coming decades. Cool marine air currently ventilates extreme heat in populous coastal regions, diminishing its impacts, but how well climate models capture this effect is uncertain. Here we conduct a comprehensive observational analysis of coastal extreme-heat ventilation—its length scale, magnitude, and regional patterns—and evaluate two ensembles of downscaled global climate models along the eastern US coast. We find that coastal areas are 2 °C–4 °C cooler than ∼60 km inland, resulting in reductions near 50% in population exposure to temperatures above 35 °C. Large seasonal and inter-regional variations are closely linked with land-sea temperature contrasts. High-resolution models underestimate coastal cooling by 50%–75%, implying that substantial and spatiotemporally varying model bias correction is necessary to create accurate projections of coastal extreme heat, which is expected to rise considerably with anthropogenic forcing. Our results underline the importance of regionally- and observationally-based perspectives for assessing future extreme heat and its impacts, and for positioning effective heat-risk management for communities and jurisdictions that span coast-to-inland areas.

Livestock grazing is an important component and driver of biodiversity in grassland ecosystems. While numerous studies and a few meta-analyses had been conducted on the response of single taxon diversity to grazing in grasslands, a synthesis of how multi-taxa diversity is affected has been largely missing, especially reflecting its changes along a grazing intensity gradient. We performed a comprehensive meta-analyses of 116 published studies on the species richness (SR) and Shannon−Wiener index (H') of plants, arthropods, and microbes to examine the response of biodiversity to grazing intensity in temperate grasslands globally. This quantitative assessment showed that the response of SR and H' to grazing intensity agreed with the intermediate disturbance hypothesis in grasslands; SR and H' increased with light and moderate grazing intensities, while they decreased at heavy intensity. In addition, plant SR increased markedly with light and moderate grazing and declined with heavy grazing intensity; however, H' increased at light intensity and declined at moderate and heavy intensities. Moreover, the SR and H' of microbes were enhanced at light and moderate grazing and were significantly reduced with heavy intensity. The SR and H' of arthropods monotonously declined with increasing grazing intensity. Importantly, structural equation modeling showed that grazing resulted in enhanced plant SR mainly through its negative effects on plant biomass. Grazing had negative effects on plant coverage and arthropod abundance so that arthropod SR declined with increased grazing intensity. Moreover, increased grazing intensity caused an increase in soil pH, decrease in soil moisture, and then a decrease in microbe SR. Our findings confirm that different taxa exhibit diverse responses to changes in grazing intensity, and the way that grazing intensity affects diversity also varied with different taxa. We strongly recommend considering the requirements of multi-taxa diversity when applying grazing management and including arthropods and microbes in monitoring schemes.

The strong two-day cold wave in the midwestern United States in January 2019 again ignited the discussion as to whether cold waves are getting more severe or not as a result of Arctic amplification due to climate change. Assessing the evolution of cold waves in the northern hemisphere midlatitudes in the observations has been difficult because the variability of cold waves is large compared to anthropogenic warming. In order to detect changes in cold spells, two complementary ways to optimise the signal-to-noise ratio are employed: multi-decadal series at individual stations, and for shorter time periods by using spatially aggregated measures. Global warming is now strong enough to make trends clear at individual stations when considering long enough (>50 yr) records of daily minimum and maximum temperature. Calculating the land area that has temperatures below the 1-in-10 year return value (defined over 1951–1980) enables us to investigate trends over a shorter time horizon. The long-term station data have strong decreases everywhere in the lowest minimum temperature. Warming trends in the lowest maximum temperature are smaller over most of the Northern Hemisphere, with dataset-dependent indications of possible negative trends in parts of the United States and Mexico. Considering the area experiencing cold waves over the last decades, the most notable feature is a sharp decline of this area since the 1980s. The natural variability is still so large that it is possible to arbitrarily select starting dates after the decline for which the trend is slightly positive in smaller regions like North America or Europe. However, these values are within uncertainties compatible with a steady decline and have differing starting dates in North America and Europe. An analysis of the entire northern midlatitudes confirms the steady decrease in severity and frequency of cold waves over the last decades in the observations.

Carbon footprints—the greenhouse gas (GHG) emissions associated with consumer food choices—substantially contribute to climate change. Life cycle analyses from climate and environmental sciences have identified effective rules for reducing these food-related GHG emissions, including eating seasonal produce and replacing dairy and red meat with plant-based products. In a national UK survey, we studied how many and which rules our participants generated for reducing GHG emissions of produce, dairy, and protein-rich products. We also asked participants to estimate GHG emission reductions associated with pre-selected rules, expressed in either grams or percentages. We found that participants generated few and relatively less effective rules, including ambiguous ones like 'Buy local'. Furthermore, participants' numerical estimates of pre-selected rules were less accurate when they assessed GHG emission reductions in grams rather than in percentages. Findings suggest a need for communicating fewer rules in percentages, for informing consumers about reducing food-related GHG emissions.

The adverse health impact of high heat is widely documented and can lead to a substantial public health burden. Although heat-related illness in western countries is largely preventable, extreme heat remains the main weather contributor to the burden of disease in the United States. In most US cities, local National Weather Service offices issue heat alerts in advance of forecast periods of high heat. In some locations, additional local heat emergency plans that include additional community-based actions to protect the public from the health impacts of heat are also triggered. In 2008, the NYC Health Department made changes in their local heat emergency plan by lowering the threshold for triggering heat advisories based on evidence from local epidemiological studies. This study aims to quantify the potential benefits associated with the change in the threshold the NYC Heat Emergency Plan in reducing heat-related illnesses for Medicare fee-for service beneficiaries aged 65 years or older. We apply a quasi-experimental study design using the Difference-in-Differences (DID) method coupled with the propensity-score matching and compare the difference in daily rates of heat-related illnesses between eligible and non-eligible days before and after implementing the threshold change (2006–2007 versus 2009–2010). We reveal that the change in threshold for the NYC Heat Emergency Plan is associated with reduced daily number of 0.80 (95%CI: 0.27; 1.33) Heat-related Illnesses during hot days as compared to a counterfactual scenario in which the original threshold did not change. This highlights the benefits of local epidemiological evidence in informing emergency heat action plans, in decreasing the health burden of high ambient heat.

Colombia's agriculture, forestry and other land use sector accounts for nearly half of its total greenhouse gas (GHG) emissions. The importance of smallholder deforestation is comparatively high in relation to its regional counterparts, and livestock agriculture represents the largest driver of primary forest depletion. Silvopastoral systems (SPSs) are presented as agroecological solutions that synergistically enhance livestock productivity, improve local farmers' livelihoods and hold the potential to reduce pressure on forest conversion. The department of Caquetá represents Colombia's most important deforestation hotspot. Targeting smallholder livestock farms through survey data, in this work we investigate the GHG mitigation potential of implementing SPSs for smallholder farms in this region. Specifically, we assess whether the carbon sequestration taking place in the soil and biomass of SPSs is sufficient to offset the per-hectare increase in livestock GHG emissions resulting from higher stocking rates. To address these questions we use data on livestock population characteristics and historic land cover changes reported from a survey covering 158 farms and model the carbon sequestration occurring in three different scenarios of progressively-increased SPS complexity using the CO₂ fix model. We find that, even with moderate tree planting densities, the implementation of SPSs can reduce GHG emissions by 2.6 Mg CO_2e ha⁻¹ yr⁻¹ in relation to current practices, while increasing agriculture productivity and contributing to the restoration of severely degraded landscapes.

The Corn Belt of the United States, one of the most agriculturally productive regions in the world, experienced a globally anomalous decrease in annual temperatures and a concurrent increase in precipitation during the mid- to late-20th century. Here, we quantify the impact of this 'warming hole' on maize yields by developing alternative, no warming hole, climate scenarios that are used to drive both statistical and process-based crop models. We show that the warming hole increased maize yields by 5%–10% per year, with lower temperatures responsible for 62% of the simulated yield increase and greater precipitation responsible for the rest. The observed cooling and wetting associated with the warming hole produced increased yields through two complementary mechanisms: slower crop development which resulted in prolonged time to maturity, and lower drought stress. Our results underscore the relative lack of climate change impacts on central US maize production to date, and the potential compounded challenge that a collapse of the warming hole and climate change would create for farmers across the Corn Belt.

Iron is represented in biogeochemical ocean models by a variety of structurally different approaches employing generally poorly constrained empirical parameterizations. Increasing the structural complexity of iron modules also increases computational costs and introduces additional uncertainties, with as yet unclear benefits. In order to demonstrate the benefits of explicitly representing iron, we calibrate a hierarchy of iron modules and evaluate the remaining model-data misfit. The first module includes a complex iron cycle with major processes resolved explicitly, the second module applies iron limitation in primary production using prescribed monthly iron concentration fields, and the third module does not explicitly include iron effects at all. All three modules are embedded into the same circulation model. Models are calibrated against global data sets of NO₃, PO₄ and O₂ applying a state-of-the-art multi-variable constraint parameter optimization. The model with fully resolved iron cycle is marginally (up to 4.8%) better at representing global distributions of NO₃, PO₄ and O₂ compared to models with implicit or absent parameterizations of iron. We also found a slow down of global surface nutrient cycling by about 30% and a shift of productivity from the tropics to temperate regions for the explicit iron module. The explicit iron model also reduces the otherwise overestimated volume of suboxic waters, yielding results closer to observations.

The type, size, and location of renewable energy (RE) deployment dramatically affects benefits to climate and health. Here, we develop a ten-region model to assess the magnitude of health and climate benefits across the US We then use this model to assess the benefits of deploying varying capacities of wind, utility-scale solar photovoltaics (PV), and rooftop solar PV in different regions in the US—a total of 284 different scenarios. Total benefits ranged from $2.2 trillion for 3000 MW of wind in the Upper Midwest to $4.2 million for 100 MW of wind in California. Total benefits and highest cost effectiveness for CO₂ reduction were generally highest for RE deployment in the Upper Midwest and Great Lakes and Mid-Atlantic US and lowest in California. Health was a substantial portion of total benefits in nearly all regions of the US Benefits were sensitive to methane leakage throughout the gas supply chain.

Biodiversity is believed to be closely related to ecosystem functions. However, the ability of existing biodiversity measures, such as species richness and phylogenetic diversity, to predict ecosystem functions remains elusive. Here, we propose a new vector of diversity metrics, structural diversity, which directly incorporates niche space in measuring ecosystem structure. We hypothesize that structural diversity will provide better predictive ability of key ecosystem functions than traditional biodiversity measures. Using the new lidar-derived canopy structural diversity metrics on 19 National Ecological Observation Network forested sites across the USA, we show that structural diversity is a better predictor of key ecosystem functions, such as productivity, energy, and nutrient dynamics than existing biodiversity measures (i.e. species richness and phylogenetic diversity). Similar to existing biodiversity measures, we found that the relationships between structural diversity and ecosystem functions are sensitive to environmental context. Our study indicates that structural diversity may be as good or a better predictor of ecosystem functions than species richness and phylogenetic diversity.

The impact of climate change on vegetation including agricultural production has been the focus of many studies. Climate change is expected to have heterogeneous effects across locations globally, and the diversity of land uses characterising Great Britain (GB) presents a unique opportunity to test methods for assessing climate change effects and impacts. GB is a relatively cool and damp country, hence, the warmer and generally drier growing season conditions projected for the future are expected to increase arable production. Here we use state-of-the-art, kilometre-scale climate change scenarios to drive a land surface model (JULES; Joint UK Land Environment Simulator) and an ECOnometric AGricultural land use model (ECO-AG). Under unmitigated climate change, by the end of the century, the growing season in GB is projected to get >5 °C warmer and 140 mm drier on average. Rising levels of atmospheric CO₂ are predicted to counteract the generally negative impacts of climate change on vegetation productivity in JULES. Given sufficient precipitation, warming favours higher value arable production over grassland agriculture, causing a predicted westward expansion of arable farming in ECO-AG. However, drying in the East and Southeast, without any CO₂ fertilisation effect, is severe enough to cause a predicted reversion from arable to grassland farming. Irrigation, if implemented, could maintain this land in arable production. However, the predicted irrigation demand of ∼200 mm (per growing season) in many locations is comparable to annual predicted runoff, potentially demanding large-scale redistribution of water between seasons and/or across the country. The strength of the CO₂ fertilisation effect emerges as a crucial uncertainty in projecting the impact of climate change on GB vegetation, especially farming land-use decisions.

Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks become more prone to failure cascades caused by exogenous shocks. In our model, countries compensate for demand deficits by imposing export restrictions. To capture these, we construct higher-order trade dependency networks for the different crops and years. These networks reveal hidden dependencies between countries and provide an estimate of necessary stock reserves to protect countries from cascading export restrictions. They differ substantially from first-order networks that do not take cascading effects into account. We find rice trade most prone to cascading export restrictions. A great number of Asian and African countries are most exposed to cascades. Noticeably, the main suppliers are similar for most of the crops: USA, Canada, Argentina, Brazil, and India. While shocks in the USA mainly affect South America and several Asian countries, the south of Africa is primarily dependent on American and Asian exporters. The north of Africa depends strongly on Europe, in particular via wheat imports.

Past studies on CO₂ utilization in the concrete industry have primarily focused on maximizing sequestered CO₂, while focusing less on CO₂ avoidance possible by reducing binder use through the addition of CO₂ to concrete formulations. In this paper, we study the net CO₂ reduction and cost benefits achievable by reducing binder loading while adding CO₂ via three approaches: carbonation during curing, carbonation during mixing, or carbonation with recycled concrete aggregate. These techniques are evaluated for a cohort of concrete formulations representing the diverse mixture designs found in the US ready-mixed and precast industries. Each formulation is optimized for reduced binder loading where the use of CO₂ directly in the formulation recovers the lost compressive strength from reduced binder. We show that over an order of magnitude more CO₂ can be avoided when binder reduction is jointly implemented with CO₂ utilization compared to utilizing CO₂ alone. As a result, nearly 40% of the annual CO₂ emissions from the US concrete industry could, in principle, be eliminated without relying on novel supplemental materials, alternative binder, or carbon capture and sequestration. The recently amended 45Q tax credit will not incentivize this strategy, as it only considers carbon sequestration. However, we find that the saved material cost from reduced binder use on its own may provide a significant economic incentive to promote the joint strategy in practice. We conclude that the real value of CO₂ utilization in concrete hinges on exploiting CO₂-induced property changes to yield additional emission reduction, not by maximizing absorbed CO₂.

Leading indicators of future economic activity include measures such as new housing starts, managers purchasing index, money supply, and bond yields. Such macroeconomic and financial indicators hold predictive power in signaling recessionary periods. However, many indicators are constrained by the fact that data are often published with some delay and are subject to constant revision (Bandholz and Funke 2003, Huang et al2018, Orphanides 2003). In this research, we propose a leading indicator derived from satellite imagery, the expansion of anthropogenic bare ground. Satellite-detected gain in built-up area, a major land cover and land use (LCLU) outcome of anthropogenic bare ground gain (ABGG), provides an inexpensive, consistent, and near-real-time indicator of global and regional macroeconomic change. Our panel data analysis across four major regions of the world from 2001 to 2012 shows that the logarithm of total ABGG, mostly owing to its major LCLU outcome, the expansion of built-up land in either year t, t −1 or t −2, significantly correlated with the year t logarithm of gross domestic product (GDP, de-trended by Hodrick–Prescott filter). Global ABGG between 2001 and 2012 averaged 7875 km² yr⁻¹, with a peak gain of 11 875 (± 2014 km² at the 95% confidence interval) in 2006, prior to the 2007–2008 global financial crisis. The curve of global ABGG or its major LCLU outcome of built-up area in year t − 1 accords well with that of the de-trended logarithm of the global GDP in year t. Given the 40 year archive of free satellite data, a growing satellite constellation, advances in machine learning, and scalable methods, this study suggests that analyses of ABGG as a whole or its LCLU outcomes can provide valuable information in near-real time for socioeconomic research, development planning, and economic forecasting.

Urbanisation increases household carbon footprints in developing economies. However, the results from developed countries have varied, particularly in Europe. This study provides a coherent comparison of the impact of the degree of urbanisation on income, expenditure and carbon footprints in Europe. On average, carbon footprints are 7% lower in cities than in rural areas when income and household characteristics are controlled. However, this is compensated by the 6% higher average income in cities. The patterns are not uniform in all countries. In Eastern Europe, the pattern is similar to other developing regions. In some Western European countries, both the income level and the carbon footprints are lower in urban areas than in rural areas. In the rest of Europe, the differences in income level between rural and urban areas are small, but they still largely compensate for the efficiency benefits of urban areas. We call for more systemic emissions accounting and climate strategies.

Future mean precipitation in the Mediterranean is projected to decrease year-round in response to global warming, threatening to aggravate water stress in the region, which can cause social and economic difficulties. We investigate possible causes of the Mediterranean drying in regional climate simulations. To test the influence of multiple large-scale drivers on the drying, we sequentially add them to the simulations. We find that the causes of the Mediterranean drying depend on the season. The summer drying results from the land-ocean warming contrast, and from lapse-rate and other thermodynamic changes, but only weakly depends on circulation changes. In contrast, to reproduce the simulated Mediterranean winter drying, additional changes in the circulation and atmospheric state have to be represented in the simulations. Since land-ocean contrast, thermodynamic and lapse-rate changes are more robust in climate simulations than circulation changes, the uncertainty associated with the projected drying should be considered smaller in summer than in winter.

Research on public views of biotechnology has centered on genetically modified (GM) foods. However, as the breadth of biotechnology applications grows, a better understanding of public concerns about non-agricultural biotechnology products is needed in order to develop proactive strategies to address these concerns. Here, we explore the perceived benefits and risks associated with five biotechnology products and how those perceptions translate into public opinion about the use and regulation of biotechnology in the United States. While we found greater support for non-agricultural biotechnology product, 70% of individuals surveyed showed no or little variation in their support across the products, indicating opinions about early GM products may be influencing the acceptance of emerging biotechnologies. We identified five common patterns of opinions about biotechnology and used machine learning models to integrate a wide range of factors and predict a respondent's opinion group. While the model was particularly good at identifying individuals supportive of biotechnology, differentiating between individuals from the non- and conditionally-supportive opinion groups was more challenging, emphasizing the complexity of public opinions of emerging biotechnology products.

Many countries rely on regional climate model (RCM) projections to quantify the impacts of climate change and to design their adaptation plans accordingly. In several European regions, RCMs project a smaller temperature increase than global climate models (GCMs), which is hypothesised to be due to discrepant representations of topography, cloud processes, or aerosol forcing in RCMs and GCMs. Additionally, RCMs do generally not consider the vegetation response to elevated atmospheric CO₂ concentrations; a process which is, however, included in most GCMs. Plants adapt to higher CO₂ concentrations by closing their stomata, which can lead to reduced transpiration with concomitant surface warming, in particular, during temperature extremes. Here we show that embedding plant physiological responses to elevated CO₂ concentrations in an RCM leads to significantly higher projected extreme temperatures in Europe. Annual maximum temperatures rise additionally by about 0.6 K (0.1 K in southern, 1.2 K in northern Europe) by 2070–2099, explaining about 67% of the stronger annual maximum temperature increase in GCMs compared to RCMs. Missing plant physiological CO₂ responses thus strongly contribute to the underestimation of temperature trends in RCMs. The need for robust climate change assessments calls for a comprehensive implementation of this process in RCM land surface schemes.

China's rice agriculture, a primary source of greenhouse gases (GHGs), has experienced great changes in the last five decades due to changes in dominant varieties and farming practices. However, the impacts of these changes on GHG emissions have not been comprehensively assessed. While most earlier studies focused on the GHG emissions per unit area, recent research indicated that the yield-scaled impact may better reflect the reality. Through integrating the results from a meta-analysis, two multi-site field experiments and an expert survey, we quantify the integrated impacts of different practices on both area- and yield-scaled GHG emissions in China. Results from the expert survey showed that rice planting area has shifted northwards, and alternate water-saving (WS) irrigation has been applied in nearly 78% of Chinese rice paddy areas in both the rice nursery stage and post-transplanting period over the past five decades. The changes of these practices and replacement of new rice varieties have increased China's rice yield by 131%. During the same period, cropping system adjustment, variety replacement, and implementation of WS irrigation have reduced total GHG emissions by 7%, 31%, and 42%, respectively. Also, the major decrease in GWP occurred prior to the 2000s, and the decreasing trend continued in the post-2000s period but at a slower rate. Our results have some limitations as these estimates ignore a number of important variations and interactions among management factors as well as climatic and edaphic conditions. Still, our findings illustrate that it is possible to enhance rice productivity at reduced environmental costs through screening for low emission varieties and agronomic practices. Future innovations should ensure that rice farming progressively adapts to climate change, while continuing to reduce GHG emissions.

Millions of people in South Asia face water scarcity every year. Previous studies based on the multi-model ensemble mean reported a decline in dryness (the ratio of potential evapotranspiration and precipitation) in South Asia under the warming climate. However, using observations and simulations from skilful climate models (BEST-GCMs) that simulate the critical monsoon features and show less bias to simulate observed climate, we show that dryness has significantly increased over the Gangetic Plain and parts of Pakistan during 1951–2016. Moreover, a rise in global mean temperature of 1.5 °C from the pre-industrial level will result in an increased dryness over half of South Asia affecting more than 790(±336) million people. Population affected by dryness is projected to 890 (±485) and 1960 (±1033) million under 2.0° and 2.5° warming worlds, respectively. Previously reported reduced aridity under the warming climate based on multi-model ensemble is mainly due to the GCMs that have less skill to simulate monsoon features. The GCMs with less skill project a higher increase (∼25%) in the monsoon season precipitation, which is largely due to their higher sensitivity of convective precipitation to warming. We show that the risk of water scarcity and dryness in South Asia under warming climate is higher than previously reported.

Water supply infrastructure planning in groundwater-dependent regions is often challenged by uncertainty in future groundwater resource availability. Many major aquifer systems face long-term water table decline due to unsustainable withdrawals. However, many regions, especially those in the developing world, have a scarcity of groundwater data. This creates large uncertainties in groundwater resource predictions and decisions about whether to develop alternative supply sources. Developing infrastructure too soon can lead to unnecessary and expensive irreversible investments, but waiting too long can threaten water supply reliability. This study develops an adaptive infrastructure planning framework that applies Bayesian learning on groundwater observations to assess opportunities to learn about groundwater availability in the future and adapt infrastructure plans. This approach allows planners in data scarce regions to assess under what conditions a flexible infrastructure planning approach, in which initial plans are made but infrastructure development is deferred, can mitigate the risk of overbuilding infrastructure while maintaining water supply reliability in the face of uncertainty. This framework connects engineering options analysis from infrastructure planning to groundwater resources modeling. We demonstrate a proof-of-concept on a desalination planning case for the city of Riyadh, Saudi Arabia, where poor characterization of a fossil aquifer creates uncertainty in how long current groundwater resources can reliably supply demand. We find that a flexible planning approach reduces the risk of over-building infrastructure compared to a traditional static planning approach by 40% with minimal reliability risk (<1%). This striking result may be explained by the slow-evolving nature of groundwater decline, which provides time for planners to react, in contrast to more sudden risks such as flooding where tradeoffs between cost and reliability risk are heightened. This Bayesian approach shows promise for many civil infrastructure domains by providing a method to quantify learning in environmental modeling and assess the effectiveness of adaptive planning.

Attribution and disentanglement of the effects of global greenhouse gas and land-use changes on temperature extremes in urban areas is a complex and critical issue in the context of regional-to-local climate change mitigation and adaptation. Here, an innovative modelling framework based on a large ensemble of urban climate simulations, using SURFEX (a land-surface model) coupled to TEB (an urban canopy model), forced by E20C (a GCM-based reanalysis), is proposed, and applied to the capital of Portugal—Lisbon. This approach allowed to disentangle the main drivers of change of extreme temperatures in Lisbon, while also improving the simulated summer temperature variability compared to E20C, using station observations as reference. The improvements were physically linked to the strong sensitivity of summer mean and extreme temperatures to local land-use properties. The sensitivity was systematically investigated and robustly demonstrated here, with built-fraction (buildings + roads), albedo and emissivity emerging as key surface parameters. The results revealed a very strong summer temperature increase between 1951–1980 and 1981–2010 periods: 0.90 °C for daily maximum temperature (T_max), and 0.76 °C for daily minimum temperature (T_max). These changes were sensitive to considering different (but constant throughout the simulation) land-uses, varying by about 10% for T_max, and around 17% for T_min. Regarding the temperature extremes (quantified by extreme hot days, EHD, and extreme hot nights, EHN, respectively defined as exceeding the 95th-percentile of T_max and T_min) the changes and their dependencies with the land-use are much more drastic. The isolated effect of changing land-use (keeping the climate forcing unchanged) from rural/natural (low built-fraction) towards dense urbanization (high built-fraction) caused a significant increase in EHN (up to ∼+130 d per 30 years, larger than the effect due to climate forcing alone), and in EHD (∼+60 d per 30 years, which is similar to the effect due to climate forcing alone).

Reaching the goal of the Paris Agreement requires substantial investment. The developed country parties have agreed to provide USD$100 billion in climate finance annually from 2020 to 2025. Ongoing negotiations on post-2025 commitments are likely to exceed that sum and include a broader scope of parties. However, there is no guidance regarding the allocation of contributions. Here, we develop a dynamic mechanism based on two conventional pillars of a burden sharing mechanism: emission responsibility and ability to pay. The mechanism adds dynamic components that reflect the Paris principle to 'ratchet-up' ambition; it rewards countries with ambitious mitigation targets and relieves countries with a high degree of climate vulnerability. Including developed country parties only, we find that ten countries should bear 85% of climate finance contributions (65% if all parties to the Paris Agreement are included). In both scopes, increasing climate ambition is rewarded. If the EU increased its emission reduction target from 40% to 55% by 2030, member states could reduce their climate finance contributions by up to 3.3%. The proposed mechanism allows for an inclusion of sub-, supra- or non-state actors. For example, we find a contribution of USD$3.3 billion annually for conventionally excluded emissions from international aviation and shipping.

The expansion of cattle in central western Brazil has been under scrutiny because of the region's historic reliance on Amazon and Cerrado deforestation for cropland and pastureland expansion. In this study, we determined the volumetric water footprint (VWF) and the land footprint (LF) of cattle in Mato Grosso state for the years 2000, 2005, 2010 and 2014 using official statistics and remote sensing imagery. We found the average VWF of cattle for the time period to be 265–270 l kg⁻¹ LW⁻¹ (LW as live weight of cattle) and a LF which decreased from 71 to 47 m² kg⁻¹ LW⁻¹. The largest contribution to VWF came from farm impoundments whose total area increased from roughly 46 000 to 51 000 ha between 2000 and 2014, leading to a total evaporation as high as 7.31 × 10¹¹ l yr⁻¹ in 2014. Analysis at the municipality level showed a tendency towards greater density of cattle with respect to both pasture area and impoundments. While cattle intensification on current pastureland is commonly viewed as a means to prevent further deforestation and greenhouse gas emissions, we stress the need to also consider the increasing demand for water associated with a growing cattle herd and the potential appropriation of additional resources for feed for feedlot finishing. Land and water resource management need to be considered together for future planning of cattle intensification at the Brazilian agricultural frontier as illustrated by the footprints reported here.

Previous work has examined the Brewer–Dobson circulation (BDC) changes for 1980–2009 based on satellite Microwave Sounding Unit (MSU/AMSU) lower-stratospheric temperature (T_LS) observations and ERA-Interim reanalysis data. Here we examine the BDC changes for the longer period now available (1980–2018), which also allows analysis of both the ozone depletion (1980–1999) and ozone healing (2000–2018) periods. We provide observational evidence that the annual mean BDC accelerated for 1980–1999 but decelerated for 2000–2018, with the changes largely driven by the Southern Hemisphere (SH), which might be partly contributed by the effects of ozone depletion and healing. We also show that the annual mean BDC has accelerated in the last 40 years (at the 90% confidence level) with a relative strengthening of ∼1.7% per decade. This overall acceleration was driven by both Northern Hemisphere (40%) and SH (60%) cells. Significant SH radiative warming is also identified in September for 2000–2018 after excluding the year 2002 when a very rare SH stratospheric sudden warming occurred, supporting the view that healing of the Antarctic ozone layer has now begun to occur during the month of September.

A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971–2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics- machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.

The time taken for ecosystems to recover from drought (drought recovery time) is critically important for the ecosystem state. However, recent literature presents contradictory conclusions on this feature: one study concludes that drought recovery time in the tropics and high northern latitudes is shortest (<4 months) but another concludes that it is longest (>12 months) in these regions. Here we explore the reasons for these contradictory results and revisit assessments of drought recovery time. We find that the study period, drought identification method and recovery level definition are main factors contributing to the contradictory conclusions. Further, we emphasize that including droughts that did not decrease ecosystem production or using a period of abnormal water availability to define ecosystem recovery level can strongly bias drought recovery time estimates. Based on our refined methods, we find the drought recovery time is also longest in some tropical regions but not in high northern latitudes during 1901–2010. Our study helps to resolve the recent controversy and provides insight for future drought recovery assessments.

The frequency, intensity and duration of heat waves are all expected to increase as the climate warms in response to increasing greenhouse gas concentrations. The focus of this study is on another dimension of heat waves, their spatial extent, something that has not been studied systematically by researchers but has important implications for associated impacts. Of particular interest are spatially contiguous heat wave regions, examined here over the conterminous US for the May–September season in both the current climate and climate model projections from the CMIP5 archive (11 models total) using the RCP4.5 and RCP8.5 radiative forcing scenarios. Given their myriad impacts, heat waves are defined using multiple temperature variables, one which includes atmospheric moisture. In addition to their spatial extent, several other physical attributes are computed across contiguous heat wave regions, including a proxy for energy use. An estimate of the human population exposed to current and future heat waves is also evaluated. We find that historical climate model simulations, in aggregate, show good fidelity in capturing key characteristics of heat waves in the current climate while projections show a substantial increase in spatial extent and other attributes by mid-century under both scenarios, though generally less for RCP4.5, as expected. Overall, the study presents a framework for examining the behavior, and associated impacts, of a frequently overlooked aspect of heat waves. The projected increases in the spatial extent and other attributes of heat waves reported here provides a new perspective on some of the potential consequences of the continued increase in atmospheric greenhouse gas concentrations.

A technique is developed to identify the types of atmosphere-ocean interaction during El Niño-Southern Oscillation events using sea surface temperature, sea level pressure (SLP), and outgoing longwave radiation (OLR) data. Two pairs of indices are derived that separate the interactions into tropical and subtropical types and basin-wide and local types. The dominant interaction type for the observed El Niño events since 1980 is identified and shown to shift with time from the tropical to subtropical and from basin-wide to local. Thus, the 21st century El Niños have become dominated by subtropical and local interactions, in strong contrast to the 20th century El Niños that were dominated by the tropical and basin-wide interactions. These changes result in the 1997–98 and 2015–16 extreme El Niños being different in their evolutions and global impacts, despite having similar intensities. SLP is the key variable for separating the tropical and subtropical types of interactions, while OLR is the key variable for separating the basin-wide and local types of interactions.

Aviation emissions have been found to cause 5% of global anthropogenic radiative forcing and ∼16 000 premature deaths annually due to impaired air quality. When aiming to reduce these impacts, decision makers often face trade-offs between different emission species or impacts in different times and locations. To inform rational decision-making, this study computes aviation's marginal climate and air quality impacts per tonne of species emitted and accounts for the altitude, location, and chemical composition of emissions. Climate impacts are calculated using a reduced-order climate model, and air quality-related health impacts are quantified using marginal atmospheric sensitivities to emissions from the adjoint of the global chemistry-transport model GEOS-Chem in combination with concentration response functions and the value of statistical life. The results indicate that 90% of the global impacts per unit of fuel burn are attributable to cruise emissions, and that 64% of all damages are the result of air quality impacts. Furthermore, nitrogen oxides (NO_x), carbon dioxide (CO₂), and contrails are collectively responsible for 97% of the total impact. Applying our result metrics to an example, we find that a 20% NO_x stringency scenario for new aircraft would reduce the net atmospheric impacts by 700 m USD during the first year of operation, even if the NO_x emission reductions cause a small increase in CO₂ emissions of 2%. In such a way, the damage metrics can be used to rapidly evaluate the atmospheric impacts of market growth as well as emissions trade-offs of aviation-related policies or technology improvements.

Global human population growth, limited space for settlements and a booming tourism industry have led to a strong increase of human infrastructure in mountain regions. As this infrastructure is highly exposed to natural hazards, a main role of mountain forests is to regulate the environment and reduce hazard probability. However, canopy disturbances are increasing in many parts of the world, potentially threatening the protection function of forests. Yet, large-scale quantitative evidence on the influence of forest cover and disturbance on natural hazards remains scarce to date. Here we quantified the effects of forest cover and disturbance on the probability and frequency of torrential hazards for 10 885 watersheds in the Eastern Alps. Torrential hazard occurrences were derived from a comprehensive database documenting 3768 individual debris flow and flood events between 1986 and 2018. Forest disturbances were mapped from Landsat satellite time series analysis. We found evidence that forests reduce the probability of natural hazards, with a 25 percentage point increase in forest cover decreasing the probability of torrential hazards by 8.7%± 1.2%. Canopy disturbances generally increased the probability of torrential hazard events, with the regular occurrence of large disturbance events being the most detrimental disturbance regime for natural hazards. Disturbances had a bigger effect on debris flows than on flood events, and press disturbances were more detrimental than pulse disturbances. We here present the first large scale quantification of forest cover and disturbance effects on torrential hazards. Our findings highlight that forests constitute important green infrastructure in mountain landscapes, efficiently reducing the probability of natural hazards, but that increasing forest disturbances can weaken the protective function of forests.

A predicted impact of a warming climate is an upslope shift of montane plant species. These upslope shifts may be amplified by land-use changes or attenuated by forest recoveries at low elevations where historical disturbances were ceased allowing for plant regrowth. Consequently, species may shift downslope back to low elevations where they had been previously harvested. The cessation-driven downslope shifts are hypothesized to dampen or even reverse climate-driven upslope shifts. We tested this hypothesis by a 20 year (1989–2009) forest inventory dataset from five mountainous areas in eastern China. In our study region, intense deforestation occurred mostly at low elevations until 1970, but was then ceased to facilitate natural forest recovery. Based on the analyses of 30 216 woody plants in 609 plots, we found that: (1) forest recovery occurred over the 20 year survey period, and increment rates of both recruitment and basal area increased up to 2004. However, in the last period (2004–2009), increment rates of basal area leveled off and recruitment was close to zero; (2) forest recovery was faster at lower elevations, as indicated by the higher increment rates there; (3) despite rising regional temperatures, the mean elevations of study species showed a downslope shift over the 20 years; and (4) the contribution of forest recovery to elevational shifts was supported by the fact that the species shifts were positively related to elevational changes in the recruitment increment, e.g. the negative (or downslope) shifts occurred in association with higher increments at lower elevations. These results suggest that, the cessation of disturbances and consequent lowland forest recovery had greater effects on the species distributions than did warming climate. In mountain systems that are being allowed to recover from historical disturbances, the effects of forest recovery on species distributions should be explicitly accounted for when assessing and predicting climate change impacts.

Compound dry and hot events (i.e. concurrent or consecutive occurrences of dry and hot events), which may cause larger impacts than those caused by extreme events occurring in isolation, have attracted wide attention in recent decades. Increased occurrences of compound dry and hot events in different regions around the globe highlight the importance of improved understanding and modeling of these events so that they can be tracked and predicted ahead of time. In this study, a monitoring and prediction system of compound dry and hot events at the global scale is introduced. The monitoring component consists of two indicators (standardized compound event indicator and a binary variable) that incorporate both dry and hot conditions for characterizing the severity and occurrence. The two indicators are shown to perform well in depicting compound dry and hot events during June–July–August 2010 in western Russia. The prediction component consists of two statistical models, including a conditional distribution model and a logistic regression model, for predicting compound dry and hot events based on El Niño–Southern Oscillation, which is shown to significantly affect compound events of several regions, including northern South America, southern Africa, southeast Asia, and Australia. These models are shown to perform well in predicting compound events in large regions (e.g. northern South America and southern Africa) during December–January–February 2015–2016. This monitoring and prediction system could be useful for providing early warning information of compound dry and hot events.

During the summer of 2015, central Europe experienced a major heatwave that was preceded by anomalously cold sea surface temperatures (SSTs) in the northern North Atlantic. Recent observation-based studies found a correlation between North Atlantic SST in spring and European summer temperatures, suggesting potential for predictability. Here we show, by using a high-resolution climate model, that ocean temperature anomalies, in combination with matching atmospheric and sea-ice initial conditions were key to the development of the 2015 European heatwave. In a series of 30-member ensemble simulations we test different combinations of ocean temperature and salinity initial states versus non-initialised climatology, mediated in both ensembles by different atmospheric/sea-ice initial conditions, using a non-standard initialisation method without data-assimilation. With the best combination of the initial ocean, and matching atmosphere/sea-ice initial conditions, the ensemble mean temperature response over central Europe in this set-up equals 60% of the observed anomaly, with 6 out of 30 ensemble-members showing similar, or even larger surface air temperature anomalies than observed.

Despite policy support and technological progress, consumer adoption of electric vehicles remains limited globally. One important barrier to electric vehicle adoption may be limited consumer awareness. We investigate trends in consumer awareness, familiarity, and experience with electric vehicles by comparing cross-sectional survey responses from two representative samples of Canadian new vehicle-buyers collected in 2013 (n = 2922) and in 2017 (n = 1808). While a significantly higher proportion of 2017 respondents have 'heard of' key electric vehicle models, stated familiarity and experience are low for both samples. Further, about three-quarters of respondents in both samples are confused about the basic notion of how to refuel (or recharge) electric vehicles—and how these vehicles differ from hybrids. Conversely, over half of 2017 respondents report having seen at least one electric vehicle charger in public, which is more than double the proportion reported in the 2013 sample. These trends hold in analyses of three Canadian provinces, including two that have engaged in significant consumer outreach activities over this time frame. Overall, in contrast to expectations, our results suggest that consumer awareness remains low and stagnant, which may hinder market growth and inhibit the climate mitigation potential of electric vehicles.

Urban populations are expected to increase by 2–3 billion by 2050, but we have limited understanding of how future global urban expansion will affect urban heat island (UHI) and hence change the geographic distributions of extreme heat risks. Here we develop spatially explicit probabilistic global projections of UHI intensification due to urban land expansion through 2050. Our projections show that urban land areas are expected to expand by 0.6–1.3 million km² between 2015 and 2050, an increase of 78%–171% over the urban footprint in 2015. This urban land expansion will result in average summer daytime and nighttime warming in air temperature of 0.5 °C–0.7 °C, up to ∼3 °C in some locations. This warming is on average about half, and sometimes up to two times, as strong as that caused by greenhouse gas (GHG) emissions (multi-model ensemble average projections in Representative Concentration Pathway 4.5). This extra urban expansion-induced warming, presented here, will increase extreme heat risks for about half of the future urban population, primarily in the tropical Global South, where existing forecasts already indicate stronger GHG emissions-warming and lack of adaptive capacity. In these vulnerable urban areas, policy interventions to restrict or redistribute urban expansion and planning strategies to mitigate UHIs are needed to reduce the wide ranges of impacts on human health, energy system, urban ecosystem, and infrastructures.

Spatially-explicit population projections by age are increasingly needed for understanding bilateral human–environment interactions. Conventional demographic methods for projecting age structure experience substantial challenges at small spatial scales. In search of a potentially better-performing alternative, we develop an empirically based spatial model of population age structure and test its application in projecting US population age structure over the 21st century under various socioeconomic scenarios (SSPs). The model draws on 40 years of historical data explaining changes in spatial age distribution at the county level. It demonstrates that a very good model fit is achievable even with parsimonious data input, and distinguishes itself from existing methods as a promising approach to spatial age structure modeling at the global level where data availability is often limited. Results suggest that wide variations in the spatial pattern of county-level age structure are plausible, with the possibility of substantial aging clustered in particular parts of the country. Aging is experienced most prominently in thinly populated counties in the Midwest and the Rocky Mountains, while cities and surrounding counties, particularly in California, as well as the southern parts of New England and the Mid-Atlantic region, maintain a younger population age structure with a lower proportion in the most vulnerable 70+ age group. The urban concentration of younger people, as well as the absolute number of vulnerable elderly people can vary strongly by SSP.

The 2015 Paris Agreement sets out that rapid reductions in greenhouse gas (GHG) emissions are needed to keep global warming to safe levels. A new approach (known as GWP*) has been suggested to compare contributions of long- and short-lived GHGs, providing a close link between cumulative CO₂-equivalent emissions and total warming. However, comparison factors for non-CO₂ GHGs under the GWP* metric depend on past emissions, and hence raise questions of equity and fairness when applied at any but the global level. The use of GWP* would put most developing countries at a disadvantage compared to developed countries, because when using GWP* countries with high historical emissions of short-lived GHGs are exempted from accounting for avoidable future warming that is caused by sustaining these emissions. We show that when various established equity or fairness criteria are applied to GWP* (defined here as eGWP*), perceived national non-CO₂ emissions vary by more than an order of magnitude, particularly in countries with high methane emissions like New Zealand. We show that national emission estimates that use GWP* are very sensitive to arbitrary choices made by countries and therewith facilitate the creation of loopholes when CO₂-equivalent emissions based on the GWP* concept are traded between countries that use different approaches. In light of such equity-dependent accounting differences, GHG metrics like GWP* should only be used at the global level. A common, transparent and equity-neutral accounting metric is vital for the Paris Agreement's effectiveness and its environmental integrity.

What could be the reduction in greenhouse gas emissions if the conventional way of maintaining roads is changed? Emissions of greenhouse gases must be reduced if global warming is to be avoided, and urgent political and technological decisions should be taken. However, there is a lock-in in built infrastructures that is limiting the rate at which emissions can be reduced. Self-healing asphalt is a new type of technology that will reduce the need for fossil fuels over the lifetime of a road pavement, at the same time as prolonging the road lifespan. In this study we have assessed the benefits of using self-healing asphalt as an alternative material for road pavements employing a hybrid input–output-assisted Life-Cycle Assessment, as only by determining the plausible scenarios of future emissions will policy makers identify pathways that might achieve climate change mitigation goals. We have concluded that self-healing roads could prevent a considerable amount of emissions and costs over the global road network: 16% lower emissions and 32% lower costs compared to a conventional road over the lifecycle.

Many Mediterranean coastal areas encounter similar problems and gaps between science, governance, and implementation of sustainable management at local-regional scales. There is often a lack of coordination between management of inland and coastal areas, and a lack of integrated land-sea data and knowledge exchange to support transitions towards sustainable development and synergies between rural and coastal areas. In this paper, we illustrate the main challenges to reach a sustainable development of coastal-rural areas related to data availability, knowledge exchange and governance, which could be tackled by coupling regional and international research infrastructures (RIs) with scientific and stakeholder collaboration networks to facilitate knowledge exchange and co-creation of solutions. We first identified the main challenges in sustainable development of coastal-rural areas followed by a review of major existing RIs, scientific knowledge and collaboration networks that can help support integrated management of Mediterranean coastal zones. Based on this, we developed recommendations for a better integration of RIs and collaboration networks in the management of coastal-rural areas, including (1) the creation of local networks to facilitate periodical meetings between all sectors involved and to connect science and policy actors and (2) setting up local RIs that support the data processing and the use of regional and international RIs by scientists and policy stakeholders.

This study examines wet season droughts using eight products from the Frequent Rainfall Observations on GridS database. The study begins by evaluating wet season precipitation totals and wet day counts at seasonal and decadal time scales. While we find a high level of agreement among the products at a seasonal time scale, evaluations of 10 year variability indicate substantial non-stationary inter-product differences that make the assessment of low-frequency changes difficult, especially in data-sparse regions. Some products, however, appear more reliable than others on decadal time scales. Global time series of dry, middle, and wet region standardized precipitation index time series indicate little coherent change. There is substantial coherence in year-to-year variations in these time series for the better-performing products, likely indicative of skill for monitoring variations at large spatial scales. During the wet season, the data do not appear to indicate widespread global changes in precipitation, reference evapotranspiration (RefET) or Standardized Precipitation Evapotranspiration Index (SPEI) values. These data also do not indicate a global shift towards increasing aridity. Focusing on SPEI values for dry regions during droughts, however, we find modest increases in RefET and decreases in SPEI when wet season precipitation is below normal. Dry region SPEI values during droughts have decreased by −0.2 since the 1990s. The cause of these RefET increases is unclear, and more detailed analysis will be needed to confirm these results. For wet regions, however, the majority of products appear to indicate increases in wet season precipitation, although many products perform poorly in these regions due to limited observation networks, and estimated increases vary substantially. Synopsis: Our analysis indicates a lack of increasing aridity at global scales, issues associated with non-stationary systematic errors, and concerns associated with increases in reference evapotranspiration in global dry regions during droughts.

Recent studies have sought epidemiological evidence of the effectiveness of energy transitions. Such evidence often relies on so-called 'natural experiments', wherein environmental and/or health outcomes are assessed before, during, and after the transition of interest. Often, these studies attribute air pollution exposure changes—either modeled or measured—directly to the transition. We formalize a framework for separating the fractions of a given exposure change attributable to meteorological variability and emissions changes. Using this framework, we quantify relative impacts of wind variability and emissions changes from coal-fired power plants on exposure to ${{\rm{SO}}}_{2}$ emissions across the United States under three unique combinations of spatial-temporal and source scales. We find that the large emissions reductions achieved by United States coal-fired power plants after 2005 dominated population exposure changes. In each of the three case studies, however, we identified periods and regions in which meteorology dampened or accentuated differences in total exposure relative to exposure change expected from emissions reductions alone. The results evidence a need for separating meteorology-induced variability in exposure when attributing health impacts to specific energy transitions.

Wheat rust diseases pose one of the greatest threats to global food security, including subsistence farmers in Ethiopia. The fungal spores transmitting wheat rust are dispersed by wind and can remain infectious after dispersal over long distances. The emergence of new strains of wheat rust has exacerbated the risks of severe crop loss. We describe the construction and deployment of a near real-time early warning system (EWS) for two major wind-dispersed diseases of wheat crops in Ethiopia that combines existing environmental research infrastructures, newly developed tools and scientific expertise across multiple organisations in Ethiopia and the UK. The EWS encompasses a sophisticated framework that integrates field and mobile phone surveillance data, spore dispersal and disease environmental suitability forecasting, as well as communication to policy-makers, advisors and smallholder farmers. The system involves daily automated data flow between two continents during the wheat season in Ethiopia. The framework utilises expertise and environmental research infrastructures from within the cross-disciplinary spectrum of biology, agronomy, meteorology, computer science and telecommunications. The EWS successfully provided timely information to assist policy makers formulate decisions about allocation of limited stock of fungicide during the 2017 and 2018 wheat seasons. Wheat rust alerts and advisories were sent by short message service and reports to 10 000 development agents and approximately 275 000 smallholder farmers in Ethiopia who rely on wheat for subsistence and livelihood security. The framework represents one of the first advanced crop disease EWSs implemented in a developing country. It provides policy-makers, extension agents and farmers with timely, actionable information on priority diseases affecting a staple food crop. The framework together with the underpinning technologies are transferable to forecast wheat rusts in other regions and can be readily adapted for other wind-dispersed pests and disease of major agricultural crops.

Vulnerable subpopulations may be exposed to higher levels of outdoor air pollution than the rest of the population. Due to the potential for this to exacerbate their existing health burden, concerns about disparities in subpopulations' air pollution exposure have motivated international public health researchers to examine this topic. In Australia, such research is lacking to date, despite heterogeneity in air pollution at multiple spatial scales across the continent. This study aimed to investigate disparities in exposure to two health-relevant outdoor air pollutants: particulate matter <2.5 μm (PM_2.5) and nitrogen dioxide (NO₂). We used national land-use regression models to estimate annual average concentrations of PM_2.5 and NO₂, and area-level census data on ethnicity, age and socio-economic status (SES) to calculate the bivariate associations between each census-derived variable with the concentration of air pollutants. We also used multivariable models including specific measures of SES as covariates to assess to what extent associations were explained by SES. Associations were calculated separately for rural and urban areas using generalised additive models which accounted for spatial autocorrelation. Bivariate results showed significant nonlinear associations (p < 0.001) between vulnerable subpopulations and pollutant concentration. These associations suggested that areas with greater socio-economic disadvantage, a higher proportion of ethnic minorities, and elderly people are exposed to higher concentrations of PM_2.5 and NO₂, although differences in the magnitude of exposure were small overall. Our multivariable models showed that the associations between ethnic minorities and pollutant concentration appear to be substantially affected by area-level SES. Our results suggested that these vulnerable subpopulations are inequitably exposed to PM_2.5 and NO₂. While the magnitude of differences in exposure were generally small, the predicted differences in exposure among vulnerable subpopulations could contribute to a potentially avertable health burden at a population-level.

This paper proposes a framework for measuring compactness and urban green accessibility in a high-density transit-oriented metropolis and uses Taipei City and its surrounding outskirts, New Taipei City, as a case to illustrate the measurement framework. Two indices, urban compactness index (UCI) and urban green accessibility index (UGAI), are developed to illustrate various aspects of a sustainable urban built environment, with UCI including density of residents and commercial activities, land use mix, street connectivity, access to center/subcenters, and access to transit stops, and UGAI measuring access to public urban green spaces. We found that while great spatial variations exist among different parts, our study area has a distinguished polycentric pattern of UCI index with three distinct clusters around the center and sub-centers illustrating higher index values in 2015. When compared to UCI, UGAI has a similar polycentric but more dispersed spatial pattern, as well as linear patterns along river corridors. We found that most areas of medium or high UCI values are located in areas of either plan-induced or plan-expanded development. UCI values in areas of plan-expanded development are generally higher than that of areas of plan-induced development. UCI and UGAI are spatially correlated to a certain extent. We found that most centers and one particular subcenter have high UCI and UGAI, moving towards both compactness and good green accessibility. Two subcenters with high UCI and low UGAI, i.e. Banqiao and Yonghe, call for planning to provide green spaces for residents living in these rising subcenters. UCI and UGAI can be applied and used to assess compact and green urban development of other cities and they are particularly useful to dense urban environment of large cities in Europe and Asia.

Reduced tillage, permanent ground cover and crop diversification are the three core pillars of Conservation Agriculture (CA). We assess and compare on-farm effects of different practices related to the three pillars of CA on maize yields under ENSO-driven rainfall variability in Kenya and Malawi. Reduced tillage practices increased yields per hectare by 250 kg on average in Malawi under below-average rainfall conditions and by 700 kg in Kenya under above-average rainfall, but did not have any significant effect on yields under below-average rainfall conditions in Kenya. Ground cover had a positive impact on yields in Malawi (dry conditions) but not in Kenya (both dry and wet conditions), where mixed crop and livestock systems limited this practice. Crop diversification had positive impacts in Kenya (both dry and wet conditions), where maize-legume crop rotation is practiced, but not in Malawi where landholdings are too small to allow rotation. Our findings suggest that isolated CA techniques can have positive effects on yields even after only a few years of practice under variable rainfall conditions. This strengthens empirical evidence supporting the value of CA in resilience building of agricultural systems, and suggests that both full and partial adoption of CA practices should be supported in areas where climate change is leading to more variable rainfall conditions.

The American West exists in the popular imagination as a distinct region, and policies and politics often suggest that both the challenges and the opportunities for land management and human well-being across the region are relatively homogeneous. In this paper, we argue that there are key characteristics that define the West as a social-ecological region, and also that there are myriad social-ecological systems (SESs) within the region that require diverse and dynamic approaches to managing change over time. We first conceptualize aridity, topography, and a unique political economy of land as exogenous factors that persist over time and space to define the American West as a contiguous social-ecological region. We then identify a second set of characteristics that show high degrees of variation across SESs within the American West. Finally, we operationalize the relationships between regional characteristics and local dynamics through a set of case studies that exemplify specific types of SESs in the region. The results of these empirical representations of the regional and intra-regional social-ecological dynamics of the contemporary American West highlight the implications for research and management of taking a cross-scale integrated approach to address pressing social-ecological opportunities and challenges in complex adaptive systems.

Public demand for outdoor recreation has proved a major impetus for land protection in the United States since the mid-twentieth century, particularly in the US West. Many federal, state, and municipal conservation tools—policies, management programs, and funding initiatives—aim to ensure recreation access to public lands in conjunction with natural resources protection. However, as recreation use increases, driven by amenity migration and economic development, land managers face a growing challenge in balancing the trade-offs between recreation access and other conservation objectives. Drawing on original archival research, we describe the strong policy ties between outdoor recreation and conservation that emerged in the post-World War II era in response to widespread urbanization. Through semi-structured interviews with land managers, we assess the implications of those policy decisions for today's public land managers. Current management challenges include: poor visitor awareness of the cumulative impacts of recreation activity, resistance by local communities and user groups to restrictions on recreation access, insufficient scientific data to guide management decisions, and limited resources to manage recreationists and enforce regulations. We conclude by proposing strategies to promote sustainable management of multiple-use landscapes through targeted research, application of conservation planning principles, and enhanced cooperation among jurisdictions.

The sustainability of agriculture in the American West depends on the capacity of farmers to adapt to water resource constraints. Most US studies of agricultural adaptations measure farmers' willingness to adopt various water use reduction strategies, meaning we have little empirical data on which strategies farmers implement and how these decisions impact their farms. We use survey data from 265 farmers in southeastern Idaho who, beginning in 2016, were required to cut annual groundwater withdrawals by 4%–20% to identify (1) the adaptation practices farmers implemented; (2) how reported crop yields and farm income were impacted; and (3) how adaptation practices varied by farm and farmer characteristics. We found the most commonly used adaptations were reduced spending, installation of more efficient irrigation systems or less frequent watering, and changing crop rotations. Farmers reported losing on average 7.6% of their yield and 8.4% of their income over the first two years of the water cuts. We found no systematic variation based on specific farm or farmer characteristics. Drawing on these results and prior research, we present a typology of adaptation categories intended to inform future research, allow comparisons to adaptation strategies elsewhere, and assist policymakers in designing effective policy interventions.