Shared Socioeconomic Pathways Scenarios Research Articles

Projecting atmospheric N2O rise until the end of the 21st century: an Earth System Model study

Abstract Nitrous Oxide (N2O) is a potent greenhouse gas with a centennial-scale lifetime that contributes significantly to global warming. It is emitted from natural and anthropogenic sources. In nature, N2O is released mainly from nitrification and denitrification from the ocean and terrestrial systems. The use of agricultural fertilizers has significantly increased the emission of N2O in the past century. Here we present, to our knowledge, the first coupled ocean and terrestrial N2O modules within an Earth System Model. The coupled modules were used to simulate the six Shared Socioeconomic Pathways (SSPs) scenarios with available nitrogen fertilizer inputs. Our results are compared to projections of atmospheric N2O concentrations used for SSPs scenario experiments. Additionally, an extra set of simulations were prescribed with emulated N2O concentrations available as input in Shared Socioeconomic Pathways scenarios. We report four main drivers for terrestrial N2O uncertainties: atmospheric temperature, agricultural fertilizer input, soil denitrification and agricultural model dynamics. We project an atmospheric N2O concentration range from 401 to 418 ppb in six SSPs simulations with a robust lack of sensitivity to equilibrium climate sensitivity. We found a large difference between our low emission scenarios N2O concentrations by 2100 compared to the concentration provided for SSPs experiments. This divergence is likely explained by strong mitigation assumptions that were not accounted for in this study, which would require a substantial decrease of agricultural N2O emissions. The coupled model and the simulations prescribed with N2O concentrations showed a difference between −0.02 and 0.09 ∘C by 2100. Our model simulation shows a lack of sensitivity to climate mitigation efforts projecting similar N2O concentration in low and high mitigation scenarios, that could indicate the need of further development of agricultural model dynamics. Further improvements in Earth system models should focus on the impact of oxygen decline on N2O dynamics in the ocean and the representation of anaerobic soils and agricultural dynamics on land, including mitigation methods on nitrogen fertilizers.

Evaluating the impact of climate change on yield and water use efficiency of different dry-season rice varieties cultivated under conventional and alternate wetting and drying conditions.

This study is the first attempt to assess rice cultivation under alternate wetting and drying (AWD) and continuous flooding (CF) using the latest scenarios from the Intergovernmental Panel on Climate Change(IPCC), utilizing AquaCrop Model. Field experiments were conducted during the dry season 2023 to get the model calibration and validation input. We used two shared socioeconomic pathways scenarios (SSP3-7.0 and SSP5-8.5) developed within Coupled Model Intercomparison Project Phase 6 (CMIP6) and projected the rice growth during 2040-2070. The simulation results demonstrated the effectiveness of AquaCrop in capturing crop development across treatments and varieties. This model's accuracy in simulating canopy cover (nRMSE = 14-32.5%), time series biomass (nRMSE = 22-42.5%), grain yield (Pd = 4.36-24.38%), and total biomass (nRMSE = 0.39-18.98%) was generally acceptable. The analysis of future climate shows an increasing trend in the monthly average temperature by 0.8°C (Tmin) and 1.3°C (Tmax) in both scenarios. While ETo values were not anticipated, rainfall was expected to increase with average values of 5.62mm to 11.25mm. In addition, the study found that varieties with growing periods longer than 93days after transplanting (DAT), such as CAR15 and Sen Kra Ob, were most impacted by heat stress conditions, leading to reduced yield, harvest index (HI), and water use efficiency (WUE). In our case, CAR15 and Sen Kra Ob grain yields were reduced by 53% and 8%, respectively. AWD maintains superior WUE compared with CF regardless of the type of varieties, suggesting this technique is a drought-adaptive strategy.

Observed Changes and Projected Risks of Hot–Dry/Hot–Wet Compound Events in China

Compound extreme events can cause serious impacts on both the natural environment and human beings. This work aimed to explore the changes in compound drought–heatwave and heatwave–extreme precipitation events (i.e., CDHEs and CHPEs) across China using daily-scale gauge-based meteorological observations, and to examine their future projections and potential risks using the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results show the following: (1) The frequencies of CDHEs and CHPEs across China showed a significant increasing trend from 1961 to 2020, with contrasting trends between the first half and second half of the period (i.e., a decrease from 1961 to 1990 and an increase from 1991 to 2020). Similar trends were observed for four intensity levels (i.e., mild, moderate, severe, and extreme) of CDHEs and CHPEs. (2) All the frequencies under three SSP scenarios will show increasing trends, especially under higher emission scenarios. Moreover, the projected intensities of CDHEs and CHPEs will gradually increase, especially for higher levels. (3) The exposure of the population (POP) and Gross Domestic Product (GDP) will be concentrated mainly in China’s coastal areas. The GDP exposures to the CDHEs and CHPEs will reach their highest values for SSP5-8.5, while the POP exposure will peak for SSP2-4.5 and SSP5-8.5, respectively. Our findings can offer scientific and technological support to actively mitigate future climate change risks.

Current conservation status and potential distribution under climate change of Michelia lacei, a Plant Species with Extremely Small Populations in Yunnan, China

Abstract Michelia lacei W.W. Smith, a magnolia species categorized as Endangered on the IUCN Red List, is subject to severe disturbance. We carried out field surveys and a review of literature records to present a detailed description of the current status of M. lacei. We then predicted the potential distribution of M. lacei under different climatic scenarios based on 60 occurrence records (53 recorded during our field surveys and 7 earlier records) and 19 bioclimatic variables from the WorldClim database. We selected 18 locations and four bioclimatic variables for model training. Temperature seasonality and annual temperature range were the most influential variables for predicting the potential distribution of the species. We used MaxEnt to model distribution under current climate conditions and four Shared Socioeconomic Pathway scenarios in four future time periods to determine the effects of future climate change on the habitat suitable for the species. We predict areas of moderately and highly suitable habitat will gradually decrease over time. We recommend increased in situ and ex situ conservation efforts to mitigate this habitat decline and protect populations of M. lacei.

Forecasting Africa’s fertility decline by female education groups

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

Non-optimal temperature-attributable mortality and morbidity burden by cause, age and sex under climate and population change scenarios: a nationwide modelling study in Japan

Future temperature effects on mortality and morbidity may differ. However, studies comparing projected future temperature-attributable mortality and morbidity in the same setting are limited. Moreover, these studies did not consider future population change, human adaptation, and the variations in subpopulation susceptibility. Thus, we simultaneously projected the temperature-related mortality and morbidity by cause, age, and sex under population change, and human adaptation scenarios in Japan, a super-ageing society. We used daily mean temperatures, mortality, and emergency ambulance dispatch (a sensitive indicator for morbidity) in 47 prefectures of Japan from 2015 to 2019 as the reference for future projections. Future mortality and morbidity were generated at prefecture level using four shared socioeconomic pathway (SSP) scenarios considering population changes. We calculated future temperature-related mortality and morbidity by combining baseline values with future temperatures and existing temperature risk functions by cause (all-cause, circulatory, respiratory), age (<65 years, ≥65 years), and sex under various climate change and SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Full human adaptation was simulated based on empirical evidence using a fixed percentile of minimum mortality or morbidity temperature (MMT), while no adaptation was simulated with a fixed absolute MMT. A future temporal decline in mortality burden attributable to non-optimal temperatures was observed, driven by greater cold-related deaths than heat-related deaths. In contrast, temperature-related morbidity increased over time, which was primarily driven by heat. In the 2050s and 2090s, under a moderate scenario, there are 83.69 (95% empirical confidence interval [eCI] 38.32-124.97) and 77.31 (95% eCI 36.84-114.47) all-cause deaths per 100,000 population, while there are 345.07 (95% eCI 258.31-438.66) and 379.62 (95% eCI 271.45-509.05) all-cause morbidity associated with non-optimal temperatures. These trends were largely consistent across causes, age, and sex groups. Future heat-attributable health burden is projected to increase substantially, with spatiotemporal variations and is particularly pronounced among individuals ≥65y and males. Full human adaptation could yield a decreasing temperature-attributable mortality and morbidity in line with a decreasing population. Our findings could support the development of targeted mitigation and adaptation strategies to address future heat-related impacts effectively. This includes improved healthcare allocations for ambulance dispatch and hospital preventive measures during heat periods, particularly custom-tailored to address specific health outcomes and vulnerable subpopulations. Japan Science and Technology Agency and Environmental Restoration and Conservation Agency and Ministry of the Environment of Japan.

Estimating wetland CH4 emissions on the Qinghai-Tibetan Plateau based on a processed model coupled with inundation dynamics between 1960 and 2100

Quantifying the impacts of climate change and wetland area dynamics on methane (CH4) emissions in the Qinghai-Tibetan Plateau (QTP) is crucial for enhancing our understanding of regional and global CH4 budgets. However, a major source of uncertainty in estimating wetland methane emissions stems from the limited delineation of wetland area dynamics. In this study, we integrated dynamic wetland inundation schemes into a process-based model to quantify the contributions of wetland area variations to natural wetland CH4 emissions on the QTP, both historically and in the future. The results revealed that climate change significantly influenced the spatiotemporal distribution of wetland CH4 emissions, with wetland inundation dynamics contributing between 21.84 % and 27.18 % of the increase in CH4 emissions from 1960 to 2020. Under future climate change scenarios, wetland CH4 emissions are projected to exhibit a more pronounced increasing trend, with wetland inundation dynamics projected to contribute 18.94 % to 21.80 % of the increase in CH4 emissions in the QTP under seven Shared Socioeconomic Pathways scenarios.

Spatiotemporal dynamics of landslide susceptibility under future climate change and land use scenarios

Abstract Mountainous landslides are expected to worsen due to environmental changes, yet few studies have quantified their future risks. To address this gap, we conducted a comprehensive analysis of the eastern Hindukush region of Pakistan. A geospatial database was developed, and logistic regression was employed to evaluate baseline landslide susceptibility for 2020. Using the latest CMIP6 models under three Shared Socioeconomic Pathways (SSPs) and the CA-Markov model, we projected future rainfall and land use/land cover patterns for 2040, 2070, and 2100, respectively. Our results reveal significant changes in future rainfall and land use patterns, particularly in the long-term future (2070 and 2100). Future landslide susceptibility was then predicted based on these projections. By 2100, high-risk landslide areas are expected to increase substantially under all SSP scenarios, with the largest increases observed under SSP5-8.5 (56.52%), SSP2-4.5 (53.55%), and SSP1-2.6 (22.45%). By 2070, high-risk areas will rise by 43.08% (SSP1-2.6), 40.88% (SSP2-4.5), and 12.60% (SSP5-8.5). However, by 2040, the changes in high-risk areas are minimal compared to the baseline, with increases of 9.45% (SSP1-2.6), 1.69% (SSP2-4.5), and 7.63% (SSP5-8.5). These findings provide crucial insights into the relationship between environmental changes and landslide risks and support the development of climate risk mitigation, land use planning, and disaster management strategies for mountainous regions.

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

On the present and future changes in Indian summer monsoon precipitation characteristics under different SSP scenarios from CMIP6 models

AbstractMonsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results.

Flood risk assessment under the shared socioeconomic pathways: a case of electricity bulk supply points in Greater Accra, Ghana

This study evaluates flood susceptibility and risk on Bulk Supply Points in the Greater Accra region (GAR) using a Frequency Ratio model based on 15 flood conditioning factors. The model explores the influence of natural, meteorological and anthropogenic factors on flooding occurrences under the Shared Socioeconomic Pathway (SSP) scenarios and assesses flood risks at Bulk Supply Points (BSPs). Flood susceptibility mapping was conducted for both current and future periods under various SSP scenarios. Results reveal that elevation, slope, soil type, distance from urban areas, and SPI are the most influential factors contributing to flooding susceptibility in the region. The current flood map, about 37% of the total area of GAR categorized under the moderate flood-susceptible zone category followed by about 30% categorized under the low flood-vulnerable zone. However, about 16% was categorized under the very high flood-vulnerable zone. The study projects increasing flood susceptibility under the SSP scenarios with intensification under SSP2 and SSP3 scenarios. For instance, the areas categorized as high and very high flood susceptibility zones are projected to expand to approximately 32% and 26% each by 2055 under SSP3. The study also assesses flood risks at Bulk Supply Points (BSPs), highlighting the escalating susceptibility of power assets to flooding under different scenarios. For instance, in the very high scenario, flooding is estimated to reach 640 h in 2045 and exceed 800 h in 2055—more than double the 2020 baseline. The analysis shows the bulk supply points face increasing flood susceptibility, with risks escalating most sharply under the severe climate change SSP3 and SSP5 scenarios. Over 75% of BSPs are expected to fall in the low- to medium-risk categories across SSPs while more than 50% of BSPs are within medium- to high-risk categories in all scenarios except SSP1, reflecting the impact of climate change. SSP3 and SSP5 stand out with over 60% of BSPs facing high or very high flooding risks by 2055. It indicates moderate resilience with proper adaptation but highlights potential disruptions in critical infrastructure, such as BSPs, during persistent flooding. The findings of the study are expected to inform Ghana’s contributions towards addressing Sustainable Development Goals (SDGs) 7, 11 and 13 in Ghana.

Global risk dynamics of Borrelia miyamotoi in the context of climate change.

The impact of Borrelia miyamotoi on human health, facilitated by the expanding geographical distribution and increasing population of Ixodes ticks, remains obscure in the context of global climate change. We employed multiple models to evaluate the effect of global climate change on the risk of B. miyamotoi worldwide across various scenarios. The habitat suitability index of four primary vector tick species for B. miyamotoi, including Ixodes persulcatus, Ixodes ricinus, Ixodes pacificus and Ixodes scapularis, was projected using a boosted regression tree model, considering multiple shared socio-economic pathway scenarios over various time periods. The modelling analysis reveals that, apart from I. scapularis, future global warming will result in a northward shift in the other three vector tick species and a gradual reduction in suitable habitats. Random forest models indicate consistent changes in B. miyamotoi and its primary tick species, with potential risk areas shrinking and shifting northward, particularly in the eastern USA, northeastern and northern Europe and northeast Asia. These findings highlight the urgent need for enhanced active surveillance of B. miyamotoi infection in primary vector tick species across projected potential risk areas. The effect of climate change on B. miyamotoi distribution might have significant implications for public health decision-making regarding tick-borne pathogens.

A Dataset for Species Distribution Modelling of Mangroves in Vietnam: Based on the National Forest Inventory Monitoring

Mangroves provides essential ecosystem services such as protection of coastal areas, carbon sequestration, and habitat provision for diverse species in coastal ecosystems. Species distribution models (SDMs) are powerful tools for predicting the potential distribution of mangrove species, which support impact assessments of climate changes on biodiversity and ecological functions of mangrove ecosystems. A comprehensive dataset for mangrove occurrence information derived from the Forest Inventory Map of Vietnam was designed to facilitate the building and projection of SDMs. The prediction data designed for training SDMs integrates ecological information including 701 field survey-based mangrove occurrences at the genus level and 21 environmental variables such as bioclimatic variables, digital elevation model and soil properties with 1 km spatial resolution. The projection data for provide sets of predictors aligned with four shared socioeconomic pathways scenarios representing two future periods to support the projection of SDM results under future climate conditions in Vietnam. This dataset serves as a valuable ecological information resource, enabling the modeling and predicting of potential mangrove habitats and distributions for the protection and restoration of mangroves in Vietnam under changing environmental conditions.

Spatial heterogeneity in the potential distribution of Aedes mosquitoes in India under current and future climatic scenarios

Aedes is the most globally distributed mosquito genus in the 21st century and transmits various arboviral diseases. The rapid expansion of Ae. Aegypti and Ae. albopictus breeding habitats is a significant threat to global public health, driven by temperature and precipitation changes. In this study, bioclimatic variables were employed to predict the spatial distribution of Ae. aegypti and Ae. albopictus in India. The reference coordinate points of (n = 583) Aedes occurrences at a scale of ∼1 km and nineteen bioclimatic factors were retrieved to train SDM (Species Distribution Models) for both species. Maximum entropy modelling was used to predict the species’ fundamental climatic niche distributions. Future projections were made using global climate models for 2021–2040 and 2081–2100 separately. The models performed reasonably well (AUC > 0.77). Both species thrived in reduced diurnal temperature and higher annual mean temperatures, with suitability increasing alongside precipitation. Ae. aegypti’s projected present and future distribution was broader than that of Ae. Albopictus. The expansion of Aedes suitability varied under different future climatic scenarios. Suitability for Ae. aegypti could expand from between 17.6 and 41.1 % in 2100 under SSP (shared socioeconomic pathways) scenarios 1 and 3, respectively, whereas for Ae. albopictus suitability increased from between 10.2 and 25 % under SSP scenarios 1 and 3 respectively. Preparing for future epidemics and outbreaks requires robust vector distribution models to identify high-risk areas, allocate resources for surveillance and control, and implement prevention strategies.

Runoff concentration decline for Tarim river due to a dramatic increasing of runoff in cold season and hydro-junction regulation: Past and future

Study regionThe three headwaters of Tarim river, south of the Xinjiang Province, China. Study focusTemporal runoff concentration variations are crucial for water resources utilization and management, particularly in arid regions. In this study, the runoff concentration in the Tarim River Basin is studied based on analysis of the trend, abrupt changes, and periodicity, identifying key extreme climate indices influencing the Gini-runoff coefficient and predicting its future changes under three Shared Socioeconomic Pathways (SSPs). New hydrological insights for the regionFrom 1962–2020, the Gini-runoff coefficient decreased by 8.3 %, while annual runoff increased by 14.2 %. This is attributed to temperature changes, escalating hydrological risk uncertainty. The anomaly of low geopotential high fields and Central Asian anticyclones have caused an increase in total cloud cover (TCC) and precipitation, and a decrease in the Daily Temperature Range (DTR). Increasing of Warm nights (TN90), Maximum Tmax (TXx), and Hot nights (TR) have enhanced the warm-season runoff. However, the climatic impact on warm-season runoff is less pronounced than that in the cold season, which led to the runoff concentration decline. Changes in hydro-junction regulation led to decrease of Gini-runoff coefficient by 12.11 % from 2001 to 2020 along the Hotan river. Under the three SSP scenarios, the average Gini-runoff coefficient for the Aksu and Hotan rivers are expected to decrease by 3.3 % and 2.6 %, respectively.

Assessment of green hydrogen production by volatile renewable energy under different SSPs scenarios in China

In the context of increasing global concerns about climate change and sustainable energy, the production of green hydrogen (GH) from volatile renewable energy (VRE) sources, such as solar and wind, is a natural choice for China to meet its climate change mitigation goals. However, there are a number of challenges in current research on the assessment of green hydrogen potential. This study evaluates the potential of GH production through VRE at the regional level in China, under the various scenarios of Shared Socioeconomic Pathways (SSPs). For this purpose, a power generation prediction model suitable for VRE is proposed to predict the energy supply capacity for GH production in six regions of China, which combined Multilevel Discrete Wavelet Decomposition and Bi-directional Long Short-Term Memory with attention mechanism (MDWD - AMBiLSTM). The total amount of GH could potentially reach up to 428.01 Mt under the SSP1, with the northern region leading at the regional level at 104.21 Mt. Furthermore, the study evaluated the potential for GH promotion in six regions of China between 2023 and 2035 b y combining the three dimensions of GH production potential, economic evaluation, and environmental contribution. This study tentative identify priority areas and development scenarios for promoting GH production in China, and made policy recommendations for the promotion of green hydrogen in disparate areas.

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.

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.

Estimating and forecasting suppressed electricity demand in Ghana under climate change, the informal economy and sector inefficiencies

Suppressed demand arises from inadequate energy access, resulting in unmet basic needs. Therefore, this study investigates the impact of the informal economy, rising temperatures, and electricity transmission losses on suppressed demand in Ghana from 2000 to 2020, using a quantile autoregressive distributed lag (QARDL) approach. The study forecasts suppressed demand using Shared Socioeconomic Pathway (SSP) scenarios, offering insights for energy system planning. The results indicate that all the variables significantly affect suppressed demand in the mid-quantiles. Notably, transmission losses and growth of informal economy variables significantly impact suppressed demand within the 50th to 75th quantiles but have minimal impact before the 50th and after the 75th quantiles in the long run. Additionally, rising temperatures substantially increase suppressed demand by increasing electricity demand for cooling. All future scenarios project this growth trend will continue through 2050, albeit at varying rates. In the business-as-usual (BAU) case, suppressed demand is expected to steadily increase from 1782 MW in 2020 to 8636 MW in 2050. This trajectory aligns well with historical growth trends, which saw suppressed demand increase from 659 GWh to 1782 GWh between 2000 and 2020. SSP scenarios suggest that suppressed demand could grow substantially through 2050, driven by high losses and informal sector growth. Despite sustainable development narratives like SSP1, suppressed demand remains high without major grid and governance improvements. Comparing the results with past studies shows that our findings align with previous research but provide more nuanced insights by incorporating the effects of the informal economy and using advanced forecasting techniques. Practical policy implications include investing in green infrastructure, upgrading grid infrastructure, and formalising the informal economy to alleviate suppressed demand. These actions are critical for sustainable energy access and meeting future electricity needs effectively.

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.