Socio-economic Scenarios Research Articles

From expansion to efficiency: Machine learning-based forecasting of Japan's building material stocks under demographic declines

Japan's unique demographic trajectory, marked by population decline and aging, coupled with continued urbanization, presents distinct challenges for aligning built environment capacity with resource efficiency. This study aims to investigate the historical evolution and project future scenarios of building material stock (MS) and their spatial distribution across Japan's three major metropolitan areas. Through a comprehensive material flow and stock analysis, the historical accumulation of building materials from 2009 to 2020 was quantified, revealing a dominance of concrete and an increasing overall stock. The contributions of various driving factors to changes in construction areas were explored, identifying population dynamics as the predominant influence. Leveraging shared socioeconomic pathway scenarios (SSPs), this research forecasted building floor area and MS until 2050 under five distinct SSPs. The results indicated an overall reduction across all scenarios, yet with a continued concentration in high-density urban cores. The substantial gap between the highest and lowest projected MS scenarios highlighted opportunities for material conservation and emission reductions through sustainable practices. Sustainable urban development in densely populated regions necessitates a balance between infrastructure provision and environmental conservation, while in sparsely populated areas, the focus shifts to the efficient management and utilization of vacant properties and materials to cope with the impacts of significant population declines. By offering insights into the building floor area and MS implications of Japan's demographic changes, this study underscores the necessity of sustainable urban planning and resource management strategies to navigate the challenges posed by demographic shifts, ultimately contributing to sustainable development and environmental conservation goals.

Multi-objective optimization for energy-efficient building design considering urban heat island effects

Building energy performance (BEP) associated with climate change and urban heat island effects (UHI) play an important role in urban sustainable development. To predict and optimize BEP under various socioeconomic scenarios, a new framework combining the physical simulation modeling integrated explainable machine learning and multi-objective optimization is proposed in this study. A Grasshopper-based simulation model incorporates BO-LGBM (Bayesian optimization-LightGBM) is developed to construct a solid prediction system, which tends to tune the hyperparameters accurately and explain more details with the aid of SHapley Additive explanation (SHAP). Two major aspects, including the building energy use intensity and indoor thermal comfort, are modeled by considering the different Shared Socioeconomic Pathways (SSPs) climate change scenarios in the near and far future. A multi-objective optimization method is employed to find an optimal solution for energy-efficient building design under constraints or uncertainties. Key findings include a 54% improvement in the Pareto front for building energy optimization and a significant impact of SSP585 scenarios on future energy consumption. The main novelty lies in the incorporation of machine learning into a physical model to achieve energy-efficient building design in urban contexts by considering UHI effects and climate change, offering actionable strategies for BEP assessment and promoting sustainable city planning.

Plant diversity darkspots for global collection priorities.

More than 15% of all vascular plant species may remain scientifically undescribed, and many of the > 350 000 described species have no or few geographic records documenting their distribution. Identifying and understanding taxonomic and geographic knowledge shortfalls is key to prioritising future collection and conservation efforts. Using extensive data for 343 523 vascular plant species and time-to-event analyses, we conducted multiple tests related to plant taxonomic and geographic data shortfalls, and identified 33 global diversity darkspots (those 'botanical countries' predicted to contain most undescribed and not yet recorded species). We defined priority regions for future collection according to several socio-economic and environmental scenarios. Most plant diversity darkspots are found within global biodiversity hotspots, with the exception of New Guinea. We identify Colombia, Myanmar, New Guinea, Peru, Philippines and Turkey as global collection priorities under all environmental and socio-economic conditions considered. Our study provides a flexible framework to help accelerate the documentation of global plant diversity for the implementation of conservation actions. As digitisation of the world's herbaria progresses, collection and conservation priorities may soon be identifiable at finer scales.

Coupling an integrated assessment model with an input–output database

MESSAGE is an Integrated Assessment Model (IAM) useful for developing anthropogenic climate change scenarios and the evaluation of long-term energy policies. MESSAGE is constrained by its internal scenario generator, which project energy commodity demand inputs based only on population and GDP growth assumptions; the lack of sectoral and supply-chain details prevents modeling impacts from develpoments in non-energy sectors. Here we link MESSAGEix-Australia, a national-level MESSAGE IAM variant, to an input-output scenario builder to generate future energy commodity demands. The coupled IO_IAM framework then captures indirect energy implications of various socio-economic scenarios. Two scenarios were used to demonstrate this capability: first on the policy-based decarbonisation pathways for Australia, and second on the adoption of self-healing roads. This study showcases that a link between IO and IAM methods provides an opportunity for bringing together the strengths of the two approaches for assessing a wide range of sustainability questions.

Projected future changes in food insecurity hotspots over the IGAD region of Eastern Africa

Food insecurity is a major issue in many parts of the world, driven by conflict, economic instability, environmental challenges, and poor governance processes. Understanding the impact of future rainfall extremes on areas already experiencing food insecurity is crucial. This study investigates how food insecurity hotspots (FIH), food crisis frequency, and duration will change in the near future (2021–2050) and far future (2071–2100) under Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study utilizes precipitation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and FIH data from the NASA Socioeconomic Data and Applications Center (SEDAC). To calculate future exposure and vulnerability to FIH, as well as food crisis frequency and duration, weighted sum models were used. The results indicate that arid and semi-arid areas in northeastern Kenya, most of Somalia, zones in southeastern Ethiopia, most of Djibouti, and central and northern Sudan are highly vulnerable to future extreme rainfall events, an increase in FIH cases, and longer food crisis frequency and duration in the near future (2021–2050) and far future (2071–2100) under all scenarios. On the other hand, most districts in Uganda, southern and southwestern South Sudan, counties in western Kenya, and the majority of zones in western Ethiopia are projected to have very few FIH cases, low food crisis frequency, and duration in both the near and far future under all scenarios. These findings are crucial for early warning systems, humanitarian responses, and food security interventions. We recommend harnessing projected increases in rainfall for water harvesting in Kenya, as well as promoting cash and food crop production in central and western Ethiopia, central and northern Uganda, and most of South Sudan.

Global evaluation of current and future threats to drylands and their vertebrate biodiversity.

Drylands are often overlooked in broad conservation frameworks and development priorities and face increasing threats from human activities. Here we evaluated the formal degree of protection of global drylands, their land vertebrate biodiversity and current threats, and projected human-induced land-use changes to drylands under different future climate change and socioeconomic scenarios. Overall, drylands have lower protected-area coverage (12%) compared to non-drylands (21%). Consequently, most dryland vertebrates including many endemic and narrow-ranging species are inadequately protected (0-2% range coverage). Dryland vertebrates are threatened by varied anthropogenic factors-including agricultural and infrastructure development (that is, artificial structures, surfaces, roads and industrial sites). Alarmingly, by 2100 drylands are projected to experience some degree of land conversion in 95-100% of their current natural habitat due to urban, agricultural and alternative energy expansion. This loss of undisturbed dryland regions is expected across different socioeconomic pathways, even under optimistic scenarios characterized by progressive climate policies and moderate socioeconomic trends.

Blue and green water availability under climate change in arid and semi-arid regions

Challenges involving water scarcity have raised concerns about sustainability and human well-being in many regions of Iran. Uncertainty related to the effects of climate change further complicates the use of projections for water resources planning. Hence, investigating the impacts of climate change on water resources is essential for their efficient management. In the present study, water availability was assessed under different climate change projections using eight CMIP6 General Circulation Models (GCMs) to create a multi-model ensemble of well-performing GCMs over the Hamedan-Bahar watershed in western Iran for three futures, near (2026 to 2050), mid (2051 to 2075), and far (2076 to 2100). Impacts of climate change on blue and green water availability was assessed by coupling Soil and Water Assessment Tool (SWAT) with modelled climate change projections and applying distribution mapping bias correction under three Shared Socioeconomic Pathways Scenarios (SSPs). The results showed that three GCMs (MIROC6, CMCC-ESM2, and NorESM2-MM) agreed well with observed climate in the region. The mean annual precipitation was projected to decrease at most by 16.3% under the SSP5–8.5 scenario during mid-future. Maximum and minimum temperatures are expected to increase by 2.3 and 2.4 °C, respectively, under the SSP5–8.5 scenario for the far future. The highest reductions of green and blue water storage and blue water flow were projected at 22%, 28.5% and 35%, respectively, under the SSP5–8.5 scenario during the mid-future. In contrast, green flow was projected to increase by 18% under SSP5–8.5 during the far future. Thus, the water resources of Hamedan-Bahar are sensitive to future climatic changes requiring considerations for adaptation strategies to mitigate water deficits.

Nonbreeding distributions of four declining Nearctic–Neotropical migrants are predicted to contract under future climate and socioeconomic scenarios

ABSTRACT Climate and land use/land cover change are expected to influence the stationary nonbreeding distributions of 4 Nearctic–Neotropical migrant bird species experiencing population declines: Cardellina canadensis (Canada Warbler), Setophaga cerulea (Cerulean Warbler), Vermivora chrysoptera (Golden-winged Warbler), and Hylocichla mustelina (Wood Thrush). Understanding how and where these species’ distributions shift in response to environmental drivers is critical to inform conservation planning in the Neotropics. For each species, we quantified current (2012 to 2021) and projected future (2050) suitable climatic and land use/land cover conditions as components of stationary nonbreeding distributions. Multi-source occurrence data were used in an ensemble modeling approach with covariates from 3 global coupled climate models (CMCC-ESM2, FIO-ESM-2-0, MIROC-ES2L) and 2 shared socioeconomic pathways (SSP2-RCP4.5, SSP5-RCP8.5) to predict distributions in response to varying climatic and land use/land cover conditions. Our findings suggest that distribution contraction, upslope elevational shifts in suitable conditions, and limited shifts in latitude and longitude will occur in 3 of 4 species. Cardellina canadensis and S. cerulea are expected to experience a moderate distribution contraction (7% to 29% and 19% to 43%, respectively), primarily in response to expected temperature changes. The V. chrysoptera distribution was modeled by sex, and females and males were projected to experience a major distribution contraction (56% to 79% loss in suitable conditions for females, 46% to 65% for males), accompanied by shifts in peak densities to higher elevations with minimal changes in the upper elevation limit. Expected changes in precipitation had the greatest effect on V. chrysoptera. Hylocichla mustelina experienced the smallest distribution change, consistent with the species’ flexibility in habitat selection and broader elevational range. We recommend defining priority areas for conservation as those where suitable conditions are expected to remain or arise in the next 25 years. For V. chrysoptera in particular, it is urgent to ensure that mid-elevation forests in Costa Rica and Honduras are adequately managed and protected.

Scenario Analysis of Socio-Economic and Climate Related Changes in Nutrient Load and Retention for the Pregolya River Catchment (South-Eastern Baltic): The View at the Beginning of 21<sup>st</sup> Century

The study analysed the transboundary Pregolya River Catchment, covering both the Polish and Russian parts, using the HYPE hydrological module and FyrisNP emission-retention model. The results revealed significant spatial variations in nutrient retention. The data assessment indicates that, at the start of the 21st century, the nutrient load from the Polish part of the catchment is significantly greater than that from the Russian part. Model simulations based on climatic projections for the years 2041–2060, but with current nutrient loads, showed a significant level of uncertainty in the changes of nutrient export to the Baltic Sea. The range for total nitrogen was –10 % to +27 %, and for total phosphorus it was –29 % to –10 %. Model simulations based on different socio-economic scenarios, but current climate conditions, showed that if present 5-year trends are maintained, nutrient export will only increase slightly (3 % for total nitrogen and total phosphorus). If the plans for socio-economic growth of Polish and Russian local governments are implemented, including the expansion of agriculture in Kaliningrad Oblast, there will be a significant increase in nutrient export (78 % for total nitrogen and 55 % for total phosphorus).

Assessment of flood risk in a coastal city considering multiple socio-economic vulnerability scenarios

Abstract. The Intergovernmental Panel on Climate Change stresses the importance of vulnerability and exposure along with hazard in defining flood risk. Therefore, a novel approach for flood risk quantification is proposed that evaluates the impact of various realistic socio-economic scenarios on risk reduction. The flood hazard is derived from a hydrodynamic flood modelling framework over Mumbai, India's highly flood-prone coastal megacity. The socio-economic vulnerability is assessed by a multivariate approach based on principal component analysis and data envelopment analysis. Finally, the flood risk is quantified and mapped by aggregating hazard and vulnerability for different socio-economic scenarios, also key indicators contributing to a significant reduction in vulnerability and risk are identified. This non-structural long-term flood risk management approach will benefit densely populated urban areas, especially in developing, and underdeveloped countries.

Projection of the Airborne CO2 Concentration by Land/Ocean Absorption Dynamics and Fossil-Fuel Reserve Depletion

The paper has been suggested by the following observations: (1) the atmospheric growth rate of carbon dioxide concentration is smaller than that ascribed to the emission of fossil-fuel combustion and (2) the fossil-fuel reserves are finite. The first observation leads to a simple dynamic model, based on the balance of CO2 land/ocean absorption and anthropogenic emissions, only limited by the depletion of fossil-fuel reserves, in a business-as-usual scenario. The second observation suggests of projecting the past CO2 emissions to the future, by constraining emissions to the limit of reserve availability. Similar projections are available in the literature, but either driven by heuristics or by complex simulation packages. The paper provides a simple and formal method only driven by historical data, their uncertainty and simple models. The method aims to provide CO2 concentration projections, which being constrained by fossil-fuel finite reserve may be in principle employed as bounds to forecasting exercises. The time–invariant dynamics of the land/ocean absorption is the simplification of a more complex set of equations describing carbon dioxide exchange between different reservoirs. Contribution of other greenhouse gases like methane and nitrous oxide has been neglected, since their emissions cannot be projected with the paper methodology. Comparison with recent profiles of the Intergovernmental Panel on Climate Change (IPCC) confirms that the finite-reserve projections of the fossil fuel emissions is close to those of a moderate Shared Socioeconomic Scenario (SSP) like SSP2-4.5—a result in agreement with other authors—but also reveals the limits of the simplified model, when extending the tuned dynamics of the recent mean CO2 exchanges to long-term future. The limits derive from linearity, time invariance, and aggregation assumptions, which allow a more complex model of CO2 exchanges to be simplified and tuned on experimental data.

Andean Lakes, a proposal of lake districts

The Andes is the world's longest mountain system (8,500 km) and harbour important headwater systems, constituting crucial water resources for South America. Their complex topography significantly influences climate and hydrology. The Andes have a wide diversity of aquatic environments, with more than 5,000 lakes (> 0.1 km2, permanent and excluding man-made reservoirs) with different morphometry, including the largest and the deepest in South America (Titicaca and Viedma, respectively). Here we applied a grouping analysis based on available systematised information to delimit different lake districts along the Andes. More than 60% of the surface area of all Andean lakes is concentrated in only nine large lakes, while more than 80% of the lakes have less than a square kilometre area. The analysis proposes eleven lake districts corresponding two to the North Andes, six to the Central Andes and three to the South Andes. Each lake district is described based on the different morphologies, geological origins, climatic factors, and available limnological information. We consider that this study will benefit not only scientists but also decision-makers and stakeholders working in integrated water resources management in a region under climate change and socio-economic inequalities scenarios.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan—under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041–2060 and 2081–2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Water quality management could halve future water scarcity cost-effectively in the Pearl River Basin

Reducing water scarcity requires both mitigation of the increasing water pollution and adaptation to the changing availability and demand of water resources under global change. However, state-of-the-art water scarcity modeling efforts often ignore water quality and associated biogeochemical processes in the design of water scarcity reduction measures. Here, we identify cost-effective options for reducing future water scarcity by accounting for water quantity and quality in the highly water stressed and polluted Pearl River Basin in China under various socio-economic and climatic change scenarios based on the Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Our modeling approach integrates a nutrient model (MARINA-Nutrients) with a cost-optimization procedure, considering biogeochemistry and human activities on land in a spatially explicit way. Results indicate that future water scarcity is expected to increase by a factor of four in most parts of the Pearl River Basin by 2050 under the RCP8.5-SSP5 scenario. Results also show that water quality management options could half future water scarcity in a cost-effective way. Our analysis could serve as an example of water scarcity assessment for other highly water stressed and polluted river basins around the world and inform the design of cost-effective measures to reduce water scarcity.

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

AbstractEstimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.

Contemporary and Future Secondary Copper Reserves of Vietnam

As ASEAN countries, including Vietnam, approach the living standards of developed countries, their copper demand is set to rise. This study investigates Vietnam’s copper stock and flow from 1995 to 2050, employing dynamic material flow analysis and five socioeconomic pathway scenarios (SSPs). Based on this, the secondary copper reserves of Vietnam were assessed. The results showed that the domestic copper demand is expected to grow to 526–1062 kt, resulting in a rapid increase in scrap generation. In 2022, Vietnam’s secondary copper reserves stood at 2.2 Mt and are projected to reach 6.8–8.6 Mt by 2050 under the SSP2 scenario. This corresponds to 3.6–4.6 times the 1.8 Mt primary copper reserve of Vietnam. However, these primary and secondary reserves cannot meet the cumulated demand by 2050. On the other hand, a large amount of copper, 8.9 Mt to 10 Mt, will become difficult-to-recover resources, such as waste in landfill sites, dissipated materials, or mixed metal loss. To promote the sustainable use of copper in Vietnam, we recommend increased geological expedition and mining investment, and improved waste management systems related to secondary resources.

A dataset of human capital-weighted population estimates for 185 countries from 1970 to 2100

We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population’s productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970–2020 and 2020–2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.

Trade-offs between grain supply and soil conservation in the Grain for Green Program under changing climate: A case study in the Three Gorges Reservoir region

Understanding the trade-offs between ecological benefits and cost of grain supply caused by ecosystem restoration is essential for decision-making. Nevertheless, due to climate change, the benefits of ecosystem restoration and cost of grain supply change across various spatial locations, thereby complicating the trade-offs. Taking one of China's largest scale ecosystem restorations, the Grain for Green Program (GGP), as an example, this study used the Three Gorges Reservoir (TGR) region as the case study area and combined the crop environment resource synthesis (CERES)-Crop model, future land-use simulation (FLUS), and the revised universal soil loss equation (RUSLE) to simulate future grain supply and soil erosion during 2021–2050 under three climate change and socioeconomic development scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) in the TGR region. The results showed that: (1) Until 2050, the implementation of GGP would bring a large soil conservation benefit by reducing soil erosion of 2.47–5.68 million tons, at the cost of 130,277–660,279 tons decrease in grain production in the TGR region. (2) Under SSP5-8.5 climate change scenario with the highest rainfall in the future, the GGP would maintain the greatest soil conservation benefits, resulting in a total amount of soil erosion decrease by 2.55 to 5.68 million tons. (3) Trade-offs between benefit of reducing soil erosion and cost of grain supply vary considerably across income. Specifically, GGP benefits are greater under low-income and higher-emission scenarios, with significant gains in soil erosion control and less impact on grain supply. In contrast, in high-income and low-emission scenarios, the GGP results in less soil erosion control and greater impact on grain supply.

Modeling the impacts of extreme climate scenarios on soil acidity (pH and exchangeable aluminum) in Abbay River Basin, Ethiopia

The Abbay River Basin faces the looming threat of extreme climate events, including prolonged droughts and erratic rainfall patterns, which can significantly affect soil health and fertility. This study aimed to explore the influence of extreme climate conditions on soil pH and exchangeable aluminum, aiming to promote sustainable agricultural practices in Ethiopia. The Africa Soil Information Service (ASIS) provided datasets on soil pH and exchangeable aluminum. The European Copernicus Climate Change Data Store was used to download historical and future datasets of extreme climatic indices from 1980 to 2010 and 2015–2050, respectively. The Coupled Model Intercomparison Project Phase 6 model ensemble was used to predict future climate impacts under three shared socioeconomic scenarios: SSP1-2.6, SSP2-4.3, and SSP5-8.5. Data extraction, quality control, and clustering were conducted before analysis, and the model was validated for its accuracy and reliability in predicting soil parameter changes. An artificial neural network model was utilized to predict the effects of extreme climate indices on soil pH and exchangeable aluminum concentrations. The model was designed to accurately and reliably predict changes in soil parameters. This study compared the changes in soil pH and aluminum concentrations using paired t tests. The model's diagnostic results indicated a significant impact of extreme climate scenarios on soil pH and exchangeable aluminum. Extreme climate factors such as heavy precipitation and cooler night time temperatures significantly contribute to soil acidification and an increase in aluminum concentration. Under the SSP1-2.6 and SSP2-4.5 emission scenarios, soil pH levels are expected to increase by 8.38 % and 3.79 %, respectively. These changes in soil pH are expected to have significant impacts on the exchangeable aluminum content in the soil, with increases of 37 % and 5.38 %, respectively, under the same emission scenarios. However, the SSP5.8 scenario predicted a 45 % increase in exchangeable aluminum and a 9.36 % decrease in soil pH. Therefore, this study significantly enhances our understanding of the influence of climate change on soil health. The development of strategies to mitigate climate change impacts on agriculture in the region must consider the effects of extreme climate indices.

Harvesting nature's bounty: exploring the ethnobotanical landscape of wild edible plants in the Awi Agäw community, Northwestern Ethiopia

BackgroundFeeding the world's future population while still facing a variety of socioeconomic and climate change scenarios with uncertain outcomes is a key global societal concern that should be addressed in a science-based manner. Ethiopia boasts a great diversity of wild edible plant species (WEPS), but millions of its citizens still suffer from chronic hunger every year. In this context, we here document the use and conservation of WEPS in the Awi Agäw community, Northwestern Ethiopia.MethodsWe performed a cross-sectional study from October 2021 to June 2022. Ethnobotanical and conservation status data were collected via semi-structured interviews, focus group discussions, field walks and market surveys. A total of 374 respondents from three districts (Guangua, Jawi and Ankasha) were purposely selected for the study. Descriptive statistics, including preference rankings, frequencies and direct matrix rankings, were employed for the data analysis.ResultsWe identified a total of 39 WEPS plant taxa distributed among 26 families and 32 genera. The Moraceae and Rosaceae had the greatest numbers of plants, with five and three species, respectively. The WEPS are regularly consumed in the study area to alleviate hunger. However, threats such as habitat loss, agricultural expansion, deforestation for firewood and other reasons, and pesticide use threaten WEPS availability.ConclusionTherefore, community-based conservation interventions need to be encouraged to safeguard WEPS and associated traditional knowledge. Furthermore, nutritional quality analysis is recommended for the selection of promising WEPS candidates.