Future Decades Research Articles

Forecasting food trends using demographic pyramid, generational differentiation and SuperLearner

The objective of this paper is to predict food consumption patterns for future decades by different social groups while taking generational change into account in the modelling. Using over 20 million observations of households in Switzerland from 1990 to 2017, we develop and apply four forecasting techniques that shift from referenced linear forecasts to population-driven forecasts. Each method considers values of selected household characteristics to define a “social group”, derives the proportion of each social group in society for the years 1990–2050, forecasts the future consumption of 75 food items in each social group in its unique way, and weighs these consumption patterns to obtain a future consumption for the total population. Although the results vary for each of the 75 food items and each method, altogether and in general, they define a narrow interval of future consumption development until 2050. All aspects of the approaches and the comparison of the outcomes contribute to knowledge about possible and nontrivial forecasting techniques on big data and foresight about the future of home food consumption.

Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS).

Rice is a critical staple crop that feeds more than half of the world's population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of X. oryzae under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of X. oryzae and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that X. oryzae's potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter's mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.

A comprehensive index for assessing the sustainable blue economy: A Japanese application

The sustainable blue economy is crucial for coastal regions. It not only fosters economic development along the coastlines but also reduces their impact on marine environments and ecosystems. This will be a significant economic growth point for coastal areas in future decades. However, comprehensive research on assessing the blue economy is currently limited; existing evaluation methods are few and may not be applicable to all contexts. This study introduces the Blue Economy Index (BEI), a novel framework for evaluating the sustainable blue economy, and applies it in Japan's 39 coastal prefectures. Grounded in multi-dimensional analysis, the BEI includes four sectors: Ocean Resources and Marine Industry, Environmental and Ecological Sustainability, Socio-Economic Stability, and Risk Management. Utilizing Japan's prefectural data and addressing regional disparities, the index applies four weighting scenarios—Equalitarianism, Preservationist, Sustainable Use, and Extractive Use—to reflect different developmental strategies. The result indicates a significant imbalance in blue economy development across prefectures, with leading regions like Hokkaido, Tokyo, and Okinawa vastly outpacing others such as Ibaraki and Tottori. Additionally, the development status in 39 coastal prefectures in Japan demonstrates the cluster phenomenon and exhibits a '2 high, 3 middle, 1 low' development pattern, which comprises two extraordinary clusters and four distinctive clusters. We propose to implement zonal management for Japan's Exclusive Economic Zone (EEZ) and establish a National Ocean Agency. Promoting committee-based regional management, provide corresponding subsidies to enhance capacity building. Additionally, and drive the digital transformation in the marine sector through quantifiable indicators as BEI. The study underscores the necessity for improving current policy and regulation, as well as promoting inter-prefectural collaboration to boost the development of the blue economy. The BEI's comprehensive approach offers an example of global sustainable blue economy assessments. Policymakers and stakeholders in other countries can flexibly use and adjust the BEI method to conduct appropriate regional assessments and develop quantifiable strategies to improve the potential of marine sectors.

Mapping heat-related risks in Swiss cities under different urban tree scenarios

About three quarter of Swiss residents live in urban areas, and this proportion is expected to grow in future decades. An increasing number of people will therefore be exposed to urban heat, which can have adverse effects on human wellbeing, productivity and physical health.We explore the possibility to detect high-risk areas in five Swiss cities with the development of an urban heat-based risk-mapping approach. The included cities are Basel, Bern, Geneva, Lausanne and Zurich. The analysis is based on a combination of biophysical, including Landsat 8 derived Land Surface Temperature (LST), and socioeconomic data. Additionally, we assess the impact of urban trees on urban heat within the districts of these cities, helping to estimate how risk levels would change under two scenarios: one with increased tree cover (MaxTree) and another with no (NoTree) urban trees.The assessment on the impact of urban trees on heat showed that the areas with urban trees generally experience cooler temperatures compared to those without, both at the city and district levels. This underscores the positive role of urban trees in mitigating the urban heat effect.The risk mapping approach revealed a distinct spatial pattern for each city and high risk areas were identified.Generally, the high-risk areas in the analyzed cities cover the city centers and areas with high vulnerability.The ‘NoTree’ scenario showed higher risks compared to the baseline situation, illustrating that urban trees currently mitigate heat related risks in Swiss cities. The ‘MaxTree’ scenario results in lower risks, especially in the cities of Lausanne and Bern.The presented risk mapping approach, including the two idealized scenarios, can be used by policy- and decision-makers (e.g. city planners) can be a tool to determine where urban planning actions are the most urgent and where trees could be most beneficial in terms of adaptation to heat. The approach is easily adaptable and transferable to other cities, since it relies on a clear and simple methodological framework, openly available LST data, and basic socioeconomic variables at district scale that are available for many cities.

Optimization of temperature prediction algorithms and simulation of future neighborhood-scale thermal environments in Chongqing

With climate change and urbanization, predicting the outdoor thermal environment of residential areas has become increasingly crucial, particularly during extreme weather conditions. Existing studies lack a comprehensive approach to optimizing temperature prediction algorithms for Chongqing and simulating extreme thermal environments at the neighborhood scale for future decades. This study employed various algorithms, including Levenberg-Marquardt optimization, Monte Carlo simulation, ARIMA modeling, and SARIMA prediction, to simulate and forecast temperature data for the central urban area of Chongqing from 2014 to 2023. By assessing metrics such as root mean square error (RMSE), R-squared (R2), and mean absolute percentage error (MAPE), the most effective prediction algorithm was identified. Results indicate that the Levenberg-Marquardt optimization algorithm, known for its nonlinear curve fitting capabilities, demonstrated superior performance in both the test and training sets, achieving an RMSE of 0.82 °C, MAPE of 1.81 %, and R2 of 0.75. This algorithm was subsequently used to forecast the outdoor temperature trend in Chongqing from 2024 to 2050, while also simulating the outdoor wind field, temperature distribution, building surface temperatures, and wind speeds in a residential district at neighborhood scale for 2030, 2040, and 2050 at a building density of 40 %. Findings reveal that peak pedestrian-level temperatures could reach 45.62 °C, and peak roof surface temperatures could reach 93.93 °C under extreme conditions. Furthermore, the study proposes practical recommendations for building layouts and cooling strategies. These findings are expected to improve residential planning in Chongqing, enhance residents' quality of life, and provide insights for other regions facing future temperature changes.

Heavy rainfalls in Poland and their hyetographs.

In the light of observed variability in precipitation patterns, there is a growing need for comprehensive data mining of regularly updated rainfall recording databases. Therefore, an analysis of heavy rainfall and hyetographs was conducted using a 30-year high-resolution dataset from 100 rain gauges across Poland, covering 31 646 rainfall events. Distributions of rainfall depths, durations, and intensities were explored, and maxima were compared to global records. Spatial analysis revealed significant variations in the frequency, depths, and durations of extreme rainfall across different regions. Cluster analysis determined model hyetographs for each station. The likelihood of regions belonging to clusters with three to five model hyetographs was assessed using Indicator Kriging. Findings underscore the importance of using local, characteristics rainfalls in hydrodynamic modelling of drainage systems and future rainfall scenarios. These results provide a foundational step towards understanding and monitoring the impacts of climate change on rainfall characteristics, especially extremes, in future decades.

Mapping a sustainable water future: Private sector opportunities for global water security and resilience

Water security remains a critical global development challenge, compounded by persistent public funding shortfalls. Society urgently needs to identify opportunities for innovative private sector engagement in water security solutions. To identify feasible and impactful solutions, quantitative tools are needed to delineate complex environmental and socioeconomic water challenges and prioritize private sector investment opportunity spaces to address these challenges. We introduce the first global and regional-scale maps showing where threats to water security coincide with private sector opportunities to address them. The successful deployment of water solutions is contingent upon the societal and governance landscape that underpins a nation’s capacity to support sustainable water threat interventions and water-related business activities. By delineating areas with substantial pressures on water resources and assessing nations’ enabling environments to support private sector investments, we find nearly two-thirds of the world’s population could benefit from private sector interventions today, with middle income countries realizing the greatest benefits. In the face of global economic development and climate change, such solutions will become increasingly essential in future decades.

Modelling the multiple action pathways of projected climate change on the Pacific cod (Gadus macrocephalus) early life stages

Understanding how future ocean conditions will impact early life stages and population recruitment of fishes is critical for adapting fisheries communities to climate change. In this study, we incorporated projected changes in physical and biological ecosystem dynamics from an oceanographic model into a mechanistic individual-based model for larval and juvenile stages of the Pacific cod (Gadus macrocephalus) in the eastern Bering Sea. We particularly investigated the impacts of ocean currents, temperature, prey density, and pCO2 on the hatching success, growth, survival, and spatial distribution of this species during 2021–2100. We evaluated two CO2 emission scenarios: RCP8.5 (high CO2 emissions, low mitigation efforts) and RCP4.5 (medium CO2 emissions and mitigation efforts). We found that the increase in temperature and decrease in prey density were the main drivers of faster growth rates and lower survival through increased starvation by the end of the century. Conversely, pCO2 had negligible impacts, which suggests that this species might be resilient to ocean acidification. The largest effects were observed under the high CO2 emission scenario, while the RCP4.5 projections displayed minimal impacts. We also identified an area with favourable conditions in the southeastern Bering Sea that will likely persist in future decades. This study provides relevant information on the future impacts of climate change on Pacific cod, and our results can be used to implement and inform climate-ready management for this important stock in Alaska.

The prospect and metaphysical analysis of conscious artificial intelligence

Artificial intelligence, also known as AI, has led the trend of evolution in the past and future decades, and the potential of consciousness artificial intelligence emerges as a renovative field to address. The computer machine aims to process repetitive and tedious tasks for humans since its concept was first developed. Whether AI is conscious does not raise unprecedented discussion before the appearance of the concept of machine learning. After it appears, the machine, instead of merely passing in input and generating output, is able to learn while processing the information, which resembles a human's learning process. Therefore, this paper delves into the complex terrain of AI to explore the theoretical possibility of endowing machines with consciousness and addresses the future concerns and potentials of AI. Illustrating through the aspects of ethical concerns, metaphysical perspectives on consciousness, and the latest advancements in AI, the study critically examines whether machines can possess a consciousness similar to human understanding.

Hotspots of wetland loss to impervious surfaces in the conterminous United States

In this study, a first wall-to-wall comparison between the National Wetlands Inventory (NWI) and the National Land Cover Database (NLCD) was conducted across the entire conterminous United States (CONUS) to evaluate U.S. wetland loss conditions. Annually, around 26 km2 of wetlands are lost to impervious surfaces across the CONUS. Spatially, wetland loss is not evenly distributed, with 90 % of losses occurring in only 9 % of the land area, forming hotspots around expanding urban regions such as Houston, Jacksonville, and Naples. Over the past few decades, Florida experienced the highest wetland loss (5.73 km2/year) among all states, while Houston had the most wetland loss (2.54 km2/year) among all metropolitan regions. Stepwise multiple regression models identified population growth and its associated demand for new housing as the major drivers for wetland loss. Wetland loss per population increase is the highest (>15 m2/person) in most metropolitan regions around the East Coast and Gulf of Mexico. Unfortunately, current wetland loss hotspots will likely suffer further losses in future decades due to projected population growth, with Houston, Cape Coral, and Miami metropolitan regions having the greatest projected wetland loss of 89.15 km2, 34.35 km2, and 28.20 km2, respectively. This study has identified wetland loss hotspots and their drivers across the U.S. that were not possible in previous sample-based studies. The findings are critical in wetland management and protection across the U.S.

Assessment of the Solar Energy Potential over South America Estimated by CMIP6 Models in the Present and Future Climate

Developing the renewable energy matrix of South America (SA) is fundamental for sustainable socioeconomic growth and mitigating climate change's adverse effects. Thus, this study estimates changes in SA's solar irradiance and solar power potential using data from eight global climate models (GCMs) belonging to the Coupled Model Intercomparison Project—Phase 6 (CMIP6). Applying statistical downscaling and bias correction with the Quantile Delta Mapping (QDM) technique, we evaluate projected changes in the Concentrated Solar Power (CSP) and Photovoltaic Power (PVP) outputs under different future climate scenarios (SSP2-4.5 and SSP5-8.5). Historical simulations (1995–2014) are validated using ERA5 reanalysis and CLARA-A3 satellite observations. The QDM method reduces the models' systematic biases, decreasing the ensemble's errors by 50% across SA throughout the year. Regarding future decades (2020–2099), the CMIP6 ensemble shows spatial and seasonal variability in solar generation. For CSP, estimates suggest that regions traditionally favorable to solar energy generation (such as the Brazilian Northeast and portions of Chile) will maintain their suitable conditions during the 21st century, projecting a potential 1–6% increase (particularly under the SSP5-8.5 scenario in southern Chile and most of Brazil). Concerning PVP generation, the CMIP6 ensemble projects a rise of 1–4% (mainly under the SSP5-8.5 scenario in the Amazonia, Midwest, and Southeast Brazilian sectors). Moreover, trend analyses projected individually by the CMIP6 GCMs converge on an increasing PVP, mainly in Brazil's Amazonia and Midwest regions. In contrast, for South Brazil, approximately 84% of the projections show a negative trend (or no trend), evidencing unfavorable or uncertain conditions for solar generation development in the region. Despite the data and processes' inherent limitations, this study yields a first analysis of statistically downscaled projections from CMIP6 for solar power generation in South America, providing valuable information for energy sector decision-makers.

Future temperature-related mortality in the UK under climate change scenarios: Impact of population ageing and bias-corrected climate projections

BackgroundExposure to heat and cold poses a serious threat to human health. In the UK, hotter summers, milder winters and an ageing population will shift how populations experience temperature-related health burdens. Estimating future burdens can provide insights on the drivers of temperature-related health effects and removing biases in temperature projections is an essential step to generating these estimates, however, the impact of various methods of correction is not well examined. MethodsWe conducted a detailed health impact assessment by estimating mortality attributable to temperature at a baseline period (2007–2018) and in future decades (2030s, 2050s and 2070s). Epidemiological exposure-response relationships were derived for all England regions and UK countries, to quantify cold and heat risk, and temperature thresholds where mortality increases. UK climate projections 2018 (UKCP18)were bias-corrected using three techniques: correcting for mean bias (shift or SH), variability (bias-correction or BC) and extreme values (quantile mapping or QM). These were applied in the health impact assessment, alongside consideration of population ageing and growth to estimate future temperature-related mortality. FindingsIn the absence of adaptation and assuming a high-end emissions scenario (RCP8.5), annual UK temperature-related mortality is projected to increase, with substantial differences in raw vs. calibrated projections for heat-related mortality, but smaller differences for cold-related mortality. The BC approach gave an estimated 29 deaths per 100,000 in the 2070s, compared with 50 per 100,000 using uncorrected future temperatures. We also found population ageing may exert a bigger impact on future mortality totals than the impact from future increases in temperature alone. Estimating future health burdens associated with heat and cold is an important step towards equipping decision-makers to deliver suitable care to the changing population. Correcting inherent biases in temperature projections can improve the accuracy of projected health burdens to support health protection measures and long-term resilience planning.

Electoral change through generational replacement: An age-period-cohort analysis of vote choice across 21 countries between 1948 and 2021

People’s generation may have become one of the core predictors of their vote choice. This study examines this hypothesis across 21 Western established democracies between 1948 and 2021. An age-period-cohort analysis on 258 national election surveys (N = 462.084) reveals that the most recent generations are much less likely to vote for the major right-wing party in two-party systems. In multi-party systems, the gradual decline of Christian democratic parties has been largely driven by the generational replacement of pre-WW2 cohorts. Social democratic and conservative parties may face a challenge in future decades because their support is particularly low among the most recent generations whereas liberal, socialist, and particularly green parties stand to gain from generational replacement. Far-right parties have been least popular among voters who came of age during the 1930s and 1940s. A small life-cycle effect points out that people over the age of 65 vote slightly more conservative.

Assessing Functional Status in Geriatric Patients Attending Primary Healthcare Centers in Qassim Region, Saudi Arabia.

Background There is an increase in the geriatric population globally. Also, in Saudi Arabia, the elderly population is expected to become a significant proportion of the total population in future decades. To provide comprehensive care to the geriatric population, an assessment of their functional capacity is crucial. Objectives This study aims to assess functional capacity and identify factors associated with functional impairment among geriatric patients at primary healthcare centers (PHCCs) in Qassim Region, Saudi Arabia. Methods A cross-sectional study was conducted among 310 geriatric patients, including 155 males and 155 females, attending PHCCs. An interviewer-administered survey was conducted from June through October 2023. The interviews were conducted by trained physicians. To assess functional capacity, the Katz index of independence in activities of daily living (Katz ADL), the Lawton-Brody instrumental activities of daily living (Lawton-Brody IADL) scale, and Wudu (ablution) performance were used as assessment tools. The data was collected via a Google Form (Google LLC, California, USA) through an interviewer-administered questionnaire. The data was analyzed using SPSS Statistics version 21 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.). Results The mean age of the study participants was 71.9 (±7.02) years. A vast majority (91.9%) had chronic diseases. Among basic ADL, the respondents had a high level of independence in feeding (99.4%) and transferring (95.5%), while there was a low level of independence in bathing (13.2%). The analysis of the Lawton-Brody IADL showed independence in medication management (75.8%) and telephone use (72.9%); however, 54.8% of the study participants were unable to perform laundry-related activities independently. Around three-fourths (76.8%) of the study participants were able to perform Wudu independently. IADL independence was statistically significantly associated (p<0.001) with age, gender, education, and chronic diseases. The Katz ADL and Lawton-Brody IADL were correlated (r=0.607, p<0.0001), and Wudu performance was positively correlated with both indices (r=0.636, r=0.60, p<0.0001). Conclusions Assessing elderly functional capacity and addressing the risk factors of functional impairment is crucial to improving the quality of life in this segment of the population. Future research is needed to validate the use of Wudu performance as an assessment tool for functional capacity in the elderly population.

Genus Echinophora-Biological Activity, Chemical Composition, and Future Perspectives.

Species belonging to the genus Echinophora (Apiaceae) have been used by humanity since ancient times as flavoring agents, preservatives, and medicines for the treatment of a broad spectrum of conditions, including respiratory, digestive and kidney disorders, fungi infections, wounds, and gastric ulcers. Phytochemical studies on this botanical genus mainly investigate the essential oil composition and reveal the species as a rich source of volatile compounds, including monoterpenes and phenylpropanoids. Among the non-volatile secondary metabolites, flavonoids, coumarins, phenolic acids, phytosterols, and polyacetylenes have been identified. Pharmacological studies revealed Echinophora extracts and essential oils exhibit different biological properties, including antibacterial, antifungal, anticancer, insecticidal, anti-inflammatory, and hepatoprotective activities. However, compared to other genera, the biological activity and chemical diversity of this genus are not well studied. In future decades, it is highly likely that the small genus Echinophora will play an important role in drug discovery and drug development.

Hydrogeochemical and isotopic characterization of the main karst aquifers of the middle Valseriana (Northern Italy): Nossana and Ponte del Costone springs

Karst springs are one of the most exploited drinking water resources in the world. Isotopic and chemical analyses on these waters reveal essential insights into their hydrodynamics. Valseriana (Northern Italy) is a valley within the Italian Pre-Alps, characterized by high water availability. The main reasons are a bedrock consisting of very permeable carbonate formations and a mean annual precipitation above 1800 mm/y. In this context, the Valseriana spring catchments have become strategically important for the water exploitation and the domestic supply of Bergamo and the neighboring municipalities. This study general aim is to expand the knowledge on the two main karst systems (Nossana and Ponte del Costone), which together include twenty-three major and minor springs, by defining a state-of-the-art that will be useful in future decades to preserve this crucial resource. Specific objectives include i) verifying the applicability of the 3H/3He dating method in karst environments, ii) defining groups of springs according to their chemical and isotopic characteristics for possible safeguard actions, and iii) establishing a conceptual model of the internal dynamics of the two spring systems. A chemical and isotopic sampling campaign was specifically conducted between May 2018 and July 2019. A total of 34 water points were sampled, including natural springs, rivers, wells, karst caves, and nearby mines. Water sampling for 3H/3He isotopic analyses was proved possible through careful selection of the sampling points and the insertion of the pump tubing in the spring fractures. A first classification of springs into uniform hydrochemical groups was obtained by applying the hierarchical cluster analysis technique on major ions contents. These outputs were combined with information from the 3H/3He dating analyses and resulted in three different classes of springs. The three classes showed a clustering for elevations: at high, medium, and valley bottom elevations, respectively. Finally, the elaborations simplified the two karst systems by a hierarchical flow system model, dynamically controlled by different karst network development and infiltration water.

Long-term trend and variability in surface temperatures over Emilia-Romagna from 1962 to 2022

Scientific interest is increasingly drawn towards regional meteorological extremes, given their impacts on populations, infrastructure, and ecosystems. These extremes are shaped by complex interactions between internal climate variability and long-term trends. The aim of the present work is to evaluate changes in high-frequency variability and the influence of long-term trends on the frequency of occurrences of extremes, with a focus on surface temperatures over the period from 1962 to 2022 in Emilia-Romagna, a region of Northern Italy. Daily data of 2 m air temperatures averaged over the region are retrieved from ERACLITO, a high-resolution climate analysis. The distributions of daily temperature anomalies show a general broadening in 1992–2022 with respect to 1962–1991. This is true for maximum, minimum, and mean daily surface temperatures, especially during the summer and spring seasons. A significant warming trend of 0.37 °C/decade is detected in annual mean surface temperatures over the period considered. The study is completed with a comparison between the observed frequency of record-breaking annual temperature events, a hypothetical stationary climate distribution and a theoretical derivation that accounts for changes in trends and variability. During the last decade, the theoretical count of extreme events is 1.26, which yields a likelihood of 86% that this is owed to the trend rather than interannual variability. Idealized experiments demonstrate that the expected occurrences of record-breaking events in future decades depend on the warming rate rather than the warming level. Finally, an analysis performed at seasonal level shows that the majority (minority) of record events are occurring in the summer (spring) seasons.

Consumer Purchase Behavior: A Systematic Literature Review

This article is a comprehensive literature review that examines the current understanding of Consumer Purchase Behavior. The goal is to trace the development of the key ideas in these fields over 124 years. A total of 1,000 publication records were obtained from Google Scholar and Scopus using keyword search. These data yielded almost 1,676,475 citations. The research conducted bibliometric analysis using these records Moreover, the number of citations on both subjects has increased by more than 2.5 times in the last two decades and is projected to double in the future decades. This study also provides suggestions for further research.

Tree contributions to climate change adaptation through reduced cattle heat stress and benefits to milk and beef production.

Cattle heat stress causes billions of dollars' worth of losses to meat and milk production globally, and is projected to become more severe in the future due to climate change. Tree establishment in pastoral livestock systems holds potential to reduce cattle heat stress and thus provide nature-based adaptation. We developed a general model for the impact of trees on cattle heat stress, which can project milk and meat production under future climate scenarios at varying spatial scales. The model incorporates the key microclimate mechanisms influenced by trees, including shade, air temperature, humidity, and wind speed. We conducted sensitivity analyses to demonstrate the relative influence of different mechanisms through which trees can impact cattle heat stress, and how tree impacts are influenced by climatic context globally. Trees hold the greatest potential to reduce cattle heat stress in higher latitudes and altitudes, with minor benefits in the lowland tropics. We projected the future contributions of current trees in mitigating climate change impacts on the dairy and beef herds of Aotearoa-New Zealand (A-NZ) in 2070-2080. Trees were simulated to contribute to A-NZ milk yields by over 491 million liters (lower CI = 112 million liters, upper CI = 850 million liters), and meat yields by over 8316 tonnes (lower CI = 2431 tonnes, upper CI = 13,668 tonnes) annually. The total economic contribution of existing trees in mitigating future cattle heat stress was valued at $US 244 million (lower CI = $US 58 million, upper CI = $US 419 million). Our findings demonstrate the importance of existing trees in pastoral landscapes and suggest that strategic tree establishment can be a valuable adaptation option for reducing cattle heat stress under climate change. Tree establishment in the next few years is critical to provide adaptation capacity and economic benefit in future decades.

Global, regional, and national burden of blindness and vision loss attributable to high fasting plasma glucose from 1990 to 2019, and forecasts to 2030: A systematic analysis for the Global Burden of Disease Study 2019.

To systematically clarify the spatiotemporal trends, and age-sex-specific blindness and vision loss (BVL) burden due to high fasting plasma glucose (HFPG) from 1990 to 2019, and project this burden over the next decade. We obtained the number and rate of years lived with disability (YLDs) for the BVL burden attributable to HFPG by age, sex, socio-demographic index (SDI), and location between 1990 and 2019 from the Global Burden of Disease (GBD) 2019 database. The average annual percentage changes (AAPCs) were calculated to assess the temporal trends of HFPG-attributable BVL burden. The Bayesian age-period-cohort model was used to predict the HFPG-attributable BVL burden. In 2019, the global number and age-standardized rate (ASR) for YLDs of BVL attributable to HFPG were 673.13 (95% UI: 159.52 to 1565.34) thousand and 8.44 (95% UI: 2.00 to 19.63) per 100,000 people, respectively. The highest burdens were found in Oceania, South Asia, and Southeast Asia, and the BVL burden due to HFPG was higher in the elderly and lower SDI regions. From 1990 to 2019, the global ASR of HFPG-attributable BVL gradually increased with AAPC (95% CI) being 0.80 (0.74 to 0.86). In addition, the HFPG-attributable BVL burden will slightly increase in the future decade. The HFPG remains the important cause of BVL worldwide, placing a substantial disease burden. From 1990 to 2019, the age-standardized burden of BVL due to HFPG increased, and will consistently increase in the future decade, particularly in the elderly and in regions with middle SDI or below.