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Considering the global biodiversity crisis and the growing demand for medicinal plants, it is crucial to preserve therapeutically useful herbs. From a conservation management perspective under climate change, identifying areas that enable valuable natural resources to persist in the future is crucial. Machine learning-based models are commonly used to estimate the locations of climate refugia, which are critical for the effective species conservation. The aim of this study was to assess the impact of global warming on the epiphytic medicinal orchid—Bulbophyllum odoratissimum. Given how the long-term survival of plants inhabiting shrubs and trees depends on the availability of suitable phorophyets, in this research potential range changes in reported orchid plant hosts were evaluated. According to conducted analyses, global warming will cause a decline in the coverage of the suitable niches for B. odoratissimum and its main phorophyte. The most significant habitat loss in the case of the studied orchid and Pistacia weinmannifolia will be observed in the southern part of their geographical ranges and some new niches will simultaneously become available for these plants in the northern part. Climate change will significantly increase the overlap of geographical ranges of P. weinmannifolia and the orchid. In the SSP5-8.5 scenario trees will be available for more than 56% of the orchid population. Other analyzed phorophytes, will be available for B. odoratissimum to a very reduced extent, as orchids will only utilize these species as habitats only occasionally. This study provides data on the distribution of climatic refugia of B. odoratissimum under global warming. Moreover, this is the first evaluation of the future geographical ranges for its phorophytes. According to the conducted analyses, only one of the previously reported tree species which are inhabited by B. odoratissimum, P. weinmannifolia, can serve as a phorophyte for this orchid in the future. In this study, the areas designated as suitable for the occurrence of both orchids and their phorophytes should be considered priority conservation areas for the studied medicinal plants. [ABSTRACT FROM AUTHOR]

AbstractProjecting global urban expansion is crucial for environmental assessment under climate change scenarios. However, existing global future urban land products are typically provided at coarse resolutions (1 km) due to data and computing limitations. This hinders the accurate assessment of the impacts of global urban development at finer scales. Thus, we develop the first Cellular Automata (CA) online tool for simulating future global urban expansion in Google Earth Engine (GEE-CA), which can simulate future urban land change at a 30 m resolution under different SSP scenarios. GEE-CA enables seamless simulations of future urban land at high resolution through a partitioned parallel strategy. Seven large urban agglomerations are simulated under shared socioeconomic pathways as representative regions to present our fine-scale results. Comparatively, our datasets preserve significantly more spatial details than existing global urban land products. The improvement in resolution from 1 km to 30 m reduces errors by a range from 7.11% to 21.27% in the estimation of future urban area. So far, the proposed GEE-CA tool allows users to generate urban land projection maps for any defined region with a resolution as high as 30 m. [ABSTRACT FROM AUTHOR]

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980–2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985–2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025–2049, F2: 2050–2074, F3: 2075–2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10 mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future. [ABSTRACT FROM AUTHOR]

Forecasting carbon dioxide (CO2) emissions is necessary to estimate their climate impact and the required mitigation efforts. However, existing scenario frameworks are not consistent and limit the comparability of projection studies. In this study, the "Stochastic Impacts by Regression on Population, Affluence and Technology" model was combined with the shared socioeconomic pathways (SSPs) defined by the Intergovernmental Panel on Climate Change. Model forecasts were calculated for each SSP from 2020 to 2060 in Qinghai Province, an underdeveloped, carbon-sink-rich region in China. Total CO2 emissions in Qinghai Province consistently exhibited an upward then downward trend, with large differences between SSPs in the peak date and maximum value of total CO2 emissions. Considering the current socioeconomic development status and total carbon emission trends in Qinghai Province, SSP1 and SSP2 were the most suitable pathways, providing balance between carbon emissions reduction and population well-being. [ABSTRACT FROM AUTHOR]

This study predicted the potential habitats of Monochamus alternatus, a known vector of Bursaphelenchus xylophilus, utilizing its occurrence points and environmental variables—ecoclimatic indices and terrain variables. SSP2-4.5 and SSP5-8.5 scenarios were applied to predict the potential habitat under climate change. We secured the 20,514 occurrence points of Monochamus alternatus among the points with geographic coordinates of PWD-affected trees (2017–2022). The maximum entropy model (MaxEnt) and ensemble model (ensemble) were used to identify and compare the variability of potential habitats in the baseline period, near future, intermediate future, and distant future. At the outset, both the MaxEnt and the ensemble models showed a high model fit, and the ensemble was judged to be relatively superior. Next, both models showed that the habitat will expand northward according to climate change scenarios. Finally, the binary maps were superimposed to examine the differences between individual and multiple models; both models showed similar distributions in the baseline period and near future. Nonetheless, MaxEnt tended to overestimate expansion in the intermediate and far future. In the future, it is expected that the accuracy and reliability of forecasts can be improved by building optimized models to reduce uncertainty by supplementing field data and collaborating with model experts. [ABSTRACT FROM AUTHOR]

Long‐term ice phenology records quantify the effects of climate change on Northern Hemisphere lakes. This study uses lake ice phenological records across a gradient of lake sizes (0.1–31,967.8 km2 in lake surface area) obtained from community science networks. We compiled in situ ice phenological records for 2,499 lakes across 15 countries for an average of 30 years. These data revealed that for the last 50 years (1971–2020), the annual mean duration of lake ice cover decreased at a rate of 9 days per decade, with a regime shift in lake ice phenology in the late 1980s. We projected that at the end of the century (2070–2099), ice duration will decrease by an average of 10 days when compared to the historical time period (1971–2000) for the shared socioeconomic pathway (SSP) 1–2.6 climate scenario (SSP126), 23 days for SSP370, and 28 days for the SSP585. Impending human development can enhance or attenuate lake ice loss, as adaptation strategies can accelerate fossil fuel use, result in conflict, or seek strategies apart from fossil fuel development. These future pathways have critical implications for the future preservation of lake ice cover. Key Points: A regime shift was observed in North American lake ice phenology in the late 1980sNorthern Hemisphere lakes may lose 10–28 days of ice cover by the end of the 21st centuryLake ice phenology will become more unpredictable, especially under higher warming scenarios [ABSTRACT FROM AUTHOR]

National parks play a crucial role in protecting ecosystems and biodiversity while facing challenges in balancing nature conservation and economic development. However, because of the difficulty in the unified simulation of natural protection functions and regional socioeconomic development, there is a lack of integrated prediction research on the comprehensive development pathways of national parks and their surrounding areas. This study adopts the Shared Socioeconomic Pathway (SSP) framework that links climate change research with socioeconomic development paths, taking China's first national park—Sanjiangyuan National Park—region as an example, to conduct research on the synergistic development path of regional socioeconomic development and national parks. The model design includes five typical paths to cover a wide range of socioeconomic development possibilities. These paths are based on different assumptions, including factors such as population growth, economic development, energy use, technological progress, and policy choices. By applying scenario planning methods, optimal development pathways are identified based on environmental, economic, and social priorities. The results show that GDP growth is fastest under the sustainable development and fossil fuel development pathways. By 2050, the population difference under different pathways will approach 100,000, and the GDP gap will be close to CNY 200 billion. This study provides valuable insights for the planning, decision-making, and management of Sanjiangyuan and similar national parks and their surrounding areas, contributing to the promotion of sustainable ecological and economic development of national parks and their regions. [ABSTRACT FROM AUTHOR]

Groundwater is the most reliable source of freshwater for household, industrial, and agricultural usage. However, anthropogenic interventions in the water cycle have disrupted sustainable groundwater management. This research aims to comprehend the future of groundwater recharge predominantly due to rainfall under changing climate. In this study, predictors of groundwater recharge such as precipitation, land use land cover (LULC), soil type, land slope, temperature, potential evapotranspiration, and aridity index (ArIn) were used for the Punjab region of India over the duration of 34 years, from 1986 to 2019. To simulate future conditions, various climate change scenarios from the CMIP6 report have been incorporated. Different Artificial Intelligence and Deep Learning models, ranging from the straightforward Linear Regression model to the intricate Extreme Gradient Booting (XGBoost), used these parameters as input. Statistical analysis of the models showed that XGBoost is most effective in predicting the groundwater recharge phenomena. Correlation studies revealed precipitation to be the primary contributor to recharge, followed by the ArIn, while soil type and slope are found to have the strongest inverse correlation. The models' resilience and performance were investigated by conducting a k-fold cross-validation analysis. The pattern of groundwater recharge is forecasted for the years 2020 to 2035 across Punjab with different climate change scenarios. The study demonstrates how the Punjab area is mirroring its current status around Shared Socioeconomic Pathway (SSP) 370. Groundwater level estimates confirmed its strong correlation with and dependence on groundwater recharge. The analysis is strengthened by comparing the AI-predicted groundwater recharge with the Central Ground Water Board (CGWB) Punjab's annual estimate. Key points: • Data-driven deep learning models can model groundwater recharge with high accuracy without extensive aquifer parameter data requirement. • Pronounced effect of climate change on groundwater recharge in the future pertaining to the different climate change scenarios (SSPs). • Forecasted groundwater recharge and level data shows significant match with the CGWB, Govt. of India's estimates and observed data. [ABSTRACT FROM AUTHOR]

Abstract Considering the global biodiversity crisis and the growing demand for medicinal plants, it is crucial to preserve therapeutically useful herbs. From a conservation management perspective under climate change, identifying areas that enable valuable natural resources to persist in the future is crucial. Machine learning-based models are commonly used to estimate the locations of climate refugia, which are critical for the effective species conservation. The aim of this study was to assess the impact of global warming on the epiphytic medicinal orchid—Bulbophyllum odoratissimum. Given how the long-term survival of plants inhabiting shrubs and trees depends on the availability of suitable phorophyets, in this research potential range changes in reported orchid plant hosts were evaluated. According to conducted analyses, global warming will cause a decline in the coverage of the suitable niches for B. odoratissimum and its main phorophyte. The most significant habitat loss in the case of the studied orchid and Pistacia weinmannifolia will be observed in the southern part of their geographical ranges and some new niches will simultaneously become available for these plants in the northern part. Climate change will significantly increase the overlap of geographical ranges of P. weinmannifolia and the orchid. In the SSP5-8.5 scenario trees will be available for more than 56% of the orchid population. Other analyzed phorophytes, will be available for B. odoratissimum to a very reduced extent, as orchids will only utilize these species as habitats only occasionally. This study provides data on the distribution of climatic refugia of B. odoratissimum under global warming. Moreover, this is the first evaluation of the future geographical ranges for its phorophytes. According to the conducted analyses, only one of the previously reported tree species which are inhabited by B. odoratissimum, P. weinmannifolia, can serve as a phorophyte for this orchid in the future. In this study, the areas designated as suitable for the occurrence of both orchids and their phorophytes should be considered priority conservation areas for the studied medicinal plants.

Modelling land-use/landcover (LULC) change is vital for addressing global environmental and sustainability issues and evaluating various land system scenarios. However, existing geosimulation methodologies for global LULC change fail to account for spatial distortions caused by the Earth’s curvature and do not consider multiple LULC change processes. Thus, this research study proposes an enhanced spherical geosimulation modelling approach that integrates deep learning (DL) to simulate change of multiple classes of LULC process under the shared socioeconomic pathways (SSP) at the global level. Based on the simulation results, the frontiers of urbanization, cropland expansion, and deforestation are indicated to be in developing countries particularly in Asia and Africa. The simulation outputs also reveal 42.5%–63.2% of new urban development would occur on croplands. The proposed modelling approach can serve as a valuable tool for spatial decision-making and environmental policy formulation at the global level.

The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. However, an overall increase in warmer and drier periods affects traditional viticulture. The present study aims to discuss climate change impacts on Montenegrin viticulture. Bioclimatic indices, ensembled from five climate models, were analysed for both historical (1981–2010) and future (2041–2070) periods upon three socio-economic pathways: SSP1-2.6, SSP3-7.0 and SSP5-8.5. CHELSA (≈1 km) was the selected dataset for this analysis. Obtained results for all scenarios have shown the suppression of baseline conditions for viticulture. The average summer temperature might reach around 29.5 °C, and the growing season average temperature could become higher than 23.5 °C, advancing phenological events. The Winkler index is estimated to range from 2900 °C up to 3100 °C, which is too hot for viticulture. Montenegrin viticulture requires the application of adaptation measures focused on reducing temperature-increase impacts. The implementation of adaptation measures shall start in the coming years, to assure the lasting productivity and sustainability of viticulture. [ABSTRACT FROM AUTHOR]

Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes; they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have underappreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes nonintuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that support pathogens. Here, we consider the ways that human responses to climate change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under‐resourced communities, and call attention to the need for socio‐ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability. [ABSTRACT FROM AUTHOR]

We investigate Venezuela's potential "futures" under Shared Socioeconomic Pathways (SSPs) through a systematic literature review, including systematic mapping and thematic analysis of 50 scientific articles. We categorised the SSP scenarios into two generational categories and classified the outcomes into positive, negative, and neutral futures. Under first-generation SSP scenarios, increasing poverty could be reversed, and the country's economic growth could be stimulated by adopting unambitious climate measures. However, second-generation SSP scenarios paint a more challenging picture. They suggest that Venezuela could face heat waves, droughts, an increase in diseases, loss of biodiversity, and an increase in invasive species and pests during the remainder of the 21st century as a direct consequence of climate change. Venezuela's geographic and topographic diversity could exacerbate these impacts of climate change. For instance, coastal areas could be at risk of sea-level rise and increased storm surges, while mountainous regions could experience more frequent and intense rainfall, leading to landslides and flash floods. The urgency of conducting additional research on the factors that could influence the severity of climate change's impact, considering Venezuela's geographic and topographic diversity, cannot be overstated. We also identified the critical need to explore alternative paths to move away from the current extractive development model. The potential actions in this regard could be instrumental in aligning the country with global adaptation and mitigation commitments. [ABSTRACT FROM AUTHOR]

The application of eco‐design principles in the building sector is considered a promising way to mitigate its substantial environmental impacts. However, quantitative evidence for this mitigation potential is lacking. The objective of this study was to quantify the environmental performance of diverse eco‐design strategies when applied to the building sector. A macroscale model capable of simulating the future demand for housing and related material flows within the urban building stock was developed based on an existing building stock model. These material flows were used to build inventories for a consequential life cycle assessment and, in turn, to quantify the potential environmental consequences of introducing eco‐design strategies in the building sector, assessed across 16 impact categories. Model outputs have a high level of uncertainty but are still useful for decision‐making, given the model's simplicity and transparency. The main results show that impact reductions can be obtained from specific uses of wood and wooden products, for example, when used for the walls in high‐rise buildings, whereas using hempcrete for partition walls increases the impact. Although the use of adaptability or disassembly strategies can reduce impacts, this pay‐off can only be obtained after a long period of implementation. In summary, the present study provides new quantitative insights into the ability of eco‐design strategies to mitigate environmental impacts in the building sector. [ABSTRACT FROM AUTHOR]

Demographic projections are important for Impacts, Adaptation and Vulnerability (IAV) assessments around climate change. When linked with physical models that delineate alternative outcomes of climate hazards, they lead to enhanced understanding of the location and size of the most vulnerable populations, thereby improving hot-spot analysis for more targeted intervention planning. These demographic projections should be consistent with the Shared Socioeconomic Pathways (SSPs) so their combination with climate projections offers a diverse set of perspectives for climate change risk assessments. Most SSP based projections have been developed at a national level, which mask local-scale heterogeneities. Mexico is a heterogeneous country in terms of climate hazards, demographic characteristics, aging population, and socioeconomic inequalities across regions and states. Thus, we translate the extended SSP scenarios to quantitative demographic assumptions based on regional distinct background conditions. We then use a multi-regional cohort component model to generate SSP-based demographic projections by gender and age for each Mexican state from 2020 to 2100. We also discuss several applications to highlight the added value of using spatially refined demographic projections for IAV analysis and targeted policymaking aimed at improving the resilience of Mexico’s population in relation to climate change. Our projections indicate that, under certain SSPs, domestic migration is a major driver of population change in some states. Our subnational SSP-based demographic projections are the first set of this type of projections for Mexico informed by regional differences in demographic processes, thereby enhancing the evaluation of medium-term and long-term effects of climate change in localized scales. [ABSTRACT FROM AUTHOR]

Climate change plays an increasing role in the global biodiversity crisis. Alteration in local climatic conditions not only can negatively affect native biodiversity but also can accelerate the introduction and spread of invasive species. In this study the ecological niche modelling approach was used to evaluate possible changes in the distribution of suitable niches of invasive orchid Eulophia graminea within its native (Asia) and non-native geographical range (America, Australia). We mapped the current potential range of this species and analysed three various projections of future climate (for 2100) each with four different climate change scenarios (SSPs). Calculated niche overlap indexes indicated low similarity of niches occupied by native and invasive populations of E. graminea and Australian populations seem to be the most unique, while American and Asian groups share partially similar niches. The occurrence of the American population of E. graminea was correlated especially with the temperature seasonality, while the Asian and Australian populations with annual precipitation and precipitation of the wettest quarter. As indicated in our analyses within Asia and America, E. graminea does not occupy all climatically suitable niches. On the other hand, in Australia the species studied already occupies all appropriate niche space. Climate change will likely be favorable for species studied to expand its range if the biotic components of its niche space (e.g., mycorrhizal fungi) will respond similarly. The most significant range expansion is predicted to occur in Australia which is interesting considering the marginally suitable habitats that E. graminea currently occupies. [ABSTRACT FROM AUTHOR]

Background Despite mounting evidence linking neurological diseases with climate change, the link between autism spectrum disorder (ASD) and global warming has yet to be explored. Aims To examine the relationship between the incidence of ASD and global warming from 1990 to 2019 and estimate the trajectory of ASD incidence from 2020 to 2100 globally. Method We extracted meteorological data from TerraClimate between 1990 and 2019. To estimate the association between global ASD incidence and temperature variation, we adopted a two-stage analysis strategy using a generalised additive regression model. Additionally, we projected future ASD incidence under four representative shared socioeconomic pathways (SSPs: 126, 245, 370 and 585) by bootstrapping. Results Between 1990 and 2019, the global mean incidence of ASD in children under 5 years old was 96.9 per 100 000. The incidence was higher in males (147.5) than in females (46.3). A 1.0 °C increase in the temperature variation was associated with a 3.0% increased risk of ASD incidence. The association was stronger in boys and children living in a low/low-middle sociodemographic index region, as well as in low-latitude areas. According to the SSP585 scenario, by 2100, the children living in regions between 10 and 20° latitude, particularly in Africa, will experience a 68.6% increase in ASD incidence if the association remains. However, the SSP126 scenario is expected to mitigate this increase, with a less than 10% increase in incidence across all latitudes. Conclusions Our study highlights the association between climate change and ASD incidence worldwide. Prospective studies are warranted to confirm the association.

Human activities frequently interfere with the natural environment, and environmental problems have become increasingly prominent. How to characterize the ecological compensation (EC) standard and how to quantify the coupling relationship between EC and economic development (ED) are still problems that need to be solved in research on EC mechanisms. We used 19 provincial-level administrative regions in the Yangtze River Basin (YRB) as the research area, calculated the EC standard based on the opportunity cost method, and analyzed the changes in the EC time series. The EC and ED coupling stage and coupling coordination type are determined by introducing the coupling coordination degree model in the YRB. Five scenarios, i.e., sustainability (SSP1), middle of the road (SSP2), regional rivalry (SSP3), inequality (SSP4), and fossil-fueled development (SSP5), were set on the basis of system dynamics and the shared socioeconomic pathways framework to simulate the evolutionary trends of EC and ED in the YRB. The results showed that (1) both direct and indirect opportunity costs in the YRB increased during 2010–2020. (2) In general, the EC and ED coupling coordination level was relatively low, but a good trend is expected to develop over time. (3) Different scenarios and policy-oriented development paths directly affect the change trends of EC and ED. (4) Taking SSP2 as the benchmark, the change trends of the other four scenarios were compared and analyzed. The changes in EC and GDP under SSP1 and SSP5 were consistent, and both were greater than those under the other two scenarios. SSP5 had the most significant effect on economic growth, but it also required more investment in EC. In the SSP1 scenario, R&D investment is greater, and the advancement of science and technology is more beneficial for the environment and economic development. Among the variety of scenario simulations, SSP1 is the optimal scenario for developing the economy, the environment, and science and technology.

Actively addressing the negative effects of global climate change on vegetation has always been a hot topic of academic concern. How to accurately and comprehensively assess the vegetation risk due to climate change has been rarely reported. By comprehensively considering three dimensions—species structure (species richness), carbon sequestration function (Net Primary Production, NPP), and physiological processes (transpiration)—and based on the prediction of the future distribution characteristics of 3,370 plant species in China, this study quantified the vegetation risk and its driving mechanisms under different Shared Socioeconomic Pathways. The composite index of vegetation risk decreased to the west of the Hu Huanyong Line (NT and QTP regions) but increased to the east (NE and ST regions), with the magnitude of increase growing with the intensification of emission scenarios. In the 2070 s, the proportion of high risk and extremely high-risk areas in the east increased from 14.5 % under SSP126 to 50.0 % under SSP585. NPP and transpiration generally show an increasing trend, and species richness changes similarly to vegetation risk. In the 2070 s under SSP585, 39.2 % of QTP areas see a species richness increase over 50 %, while 33.0 % of ST areas experience a decrease over 30 %. The increase in vegetation risk in the NE region is driven by increased soil moisture, while in the ST region, it is mainly due to decreased runoff and SPEI. Therefore, China should actively respond to the risk of vegetation degradation in the east due to future climate change.

AbstractThe inconsistent pattern of precipitation, a shift in the seasonality of river flows, and the early onset of snow and glacier melt in recent decades across river basins of High Mountain Asia (HMA) has compelled us to further investigate future variations in sources of runoff under projected climate change scenarios. This will help in determining the timing and magnitude of runoff components and this will help in management of future water resources. The current study employed the University of British Columbia Watershed Model (UBC WM) to estimate the spatiotemporal variations in simulated runoff components (i.e. snowmelt, glacier melt, rainfall-runoff, and baseflow) and their relative contribution to total runoff of Gilgit River regarding the baseline period (1981–2010) in near (2021–2050) and far future (2071–2100) under low (SSP1), medium (SSP2) and high (SSP5) emission scenarios. A significant increase in the magnitude of mean annual temperature and precipitation is expected in the near future (2021–2050) than far future (2071–2100) under most SSPs. Moreover, high-altitude stations of the Gilgit River basin are expected to experience more warming in the near and far future than low altitudes under all SSPs. On average, regarding the baseline period, the simulated runoff is projected to increase in the near (27%, 30%, and 33%) and far future (30%, 53%, and 91%) under SSP1, SSP2, and SSP5, respectively. Moreover, an early onset of snow/glacier melting is predicted in the far future due to an increase in summer air temperature and a decline in winter (DJF) precipitation. Besides, the rise in high altitude temperature is expected to cause the melting of snow/glaciers even above 6000 m elevation in the far future.

This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate change, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in a more comprehensive socio-economic context. We consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how, by following different SSPs scenarios, the adopted strategies may influence the firm's default probability. [ABSTRACT FROM AUTHOR]

As of 2018, approximately 55% of the world's population resides in cities, and it is projected that this proportion will reach 68% by 2050. Population growth in urban areas leads to various impacts on society and the environment. In this study, we have developed a method for generating future scenarios of nighttime lights. What makes this method unique is its ability to (1) generate future gridded nighttime light intensity scenarios for cities, (2) generate future scenarios that preserve the distribution pattern of nighttime light intensity, and (3) generate scenarios that reflect urban policies. By applying this developed method, we have estimated nighttime light data for 555 cities worldwide and predicted future urban expansion and changes in carbon emissions for each SSP scenario. Consequently, both urban areas and carbon emissions are estimated to increase for the entire set of target cities, with patterns varying among cities and scenarios. This study contributes to the advancement of urban scenario research, including the estimation of future urban area expansion and carbon emissions. [ABSTRACT FROM AUTHOR]

Scenario analysis is a widely employed method for addressing uncertainties when assessing the physical and socio-economic impacts of climate change. Global scenarios have been extensively used in this context. However, these scenarios are in most cases not suitable for supporting local analyses. On the other hand, locally developed scenarios may lack the global context, thus having limited comparability with or transferability to other locations. The Shared Socioeconomic Pathways (SSP), which have been primarily developed for climate impact research, provide the possibility to extend the existing global narratives and adapt them to local characteristics in order to develop locally relevant scenarios. Here, we propose a methodological framework for producing harmonized scenarios across different case studies. This framework was developed in the EVOKED project and combines elements of top-down and bottom-up approaches to develop local scenarios for four regions in northern Europe. We employ the SSP as boundary conditions and, in cooperation with stakeholders from these four regions, develop local scenarios for a range of SSP. The developed sets of scenarios are consistently informed by global developments and are therefore comparable with other downscaled scenarios developed in different regions. At the same time, they have been based on local participatory processes, thus being locally credible and relevant to the needs of stakeholders. The local scenarios constitute a climate service per se as they can raise stakeholder awareness of the processes that will drive risk, exposure, and adaptive capacity in the future and inform discussions on mitigation strategies and adaptation pathways. [ABSTRACT FROM AUTHOR]

Monitoring the effects of climate change is essential due to the ongoing increase in extreme drought and flood events, primarily driven by changes in key variables such as precipitation and temperature. In this study, data from eight Coupled Model Intercomparison Project Phase 6 (CMIP6) models were used to assess temperature and precipitation anomalies in the state of Pernambuco, Brazil, for the period 2041 to 2100, considering two different climate scenarios (SSP245 and SSP585). The projected data were compared with WorldClim historical climatological data between 1970 and 2000. Due to the significant spatial variability of annual precipitation in Pernambuco, ranging from 400 to 2,200 mm, the state was evaluated considering its territory in total and also in two distinct climatic regions (Sertão/Agreste and Zona da Mata). An increase in temperature is projected, even in the least pessimistic scenario (SSP245) with an increment of 1.64°C from 2041 to 2060. During the same period, an increase of 2.10°C is expected in the SSP585 scenario. For the period from 2081 to 2100, the models indicate increases of 2.45 and 4.53°C, respectively. Precipitation will decrease in all scenarios and regions of Pernambuco, with a reduction of up to 227.24 mm year-1 in the Zona da Mata between 2081 and 2100 in the SSP585 scenario. These potential changes pose imminent threats to water resources, agriculture, biodiversity, and the population, demanding proactive measures from policymakers and stakeholders to mitigate these effects.

National parks play a crucial role in protecting ecosystems and biodiversity while facing challenges in balancing nature conservation and economic development. However, because of the difficulty in the unified simulation of natural protection functions and regional socioeconomic development, there is a lack of integrated prediction research on the comprehensive development pathways of national parks and their surrounding areas. This study adopts the Shared Socioeconomic Pathway (SSP) framework that links climate change research with socioeconomic development paths, taking China’s first national park—Sanjiangyuan National Park—region as an example, to conduct research on the synergistic development path of regional socioeconomic development and national parks. The model design includes five typical paths to cover a wide range of socioeconomic development possibilities. These paths are based on different assumptions, including factors such as population growth, economic development, energy use, technological progress, and policy choices. By applying scenario planning methods, optimal development pathways are identified based on environmental, economic, and social priorities. The results show that GDP growth is fastest under the sustainable development and fossil fuel development pathways. By 2050, the population difference under different pathways will approach 100,000, and the GDP gap will be close to CNY 200 billion. This study provides valuable insights for the planning, decision-making, and management of Sanjiangyuan and similar national parks and their surrounding areas, contributing to the promotion of sustainable ecological and economic development of national parks and their regions.

Climate change is known to cause alterations in weather patterns and disturb the natural equilibrium. Changes in climatic conditions lead to increased environmental stress on embankments, which can result in slope failures. Due to wetting–drying cycles, expansive clayey soil often swells and shrinks, and matric suction is a major factor that controls the behavior. Increased temperature accelerates soil evaporation and drying, which can cause desiccation cracks, while precipitation can rapidly reduce soil shear strength. Desiccated slopes on embankments built with such soils can cause surficial slope failures after intense precipitation. This study used slope stability analysis to quantify how climate-change-induced extreme weather affects embankments. Historic extreme climatic events were used as a baseline to estimate future extremes. CMIP6 provided historical and future climatic data for the study area. An embankment was numerically modeled to evaluate the effect on slope stability due to the precipitation change induced by climate change. Coupled hydro-mechanical finite element analyses used a two-dimensional transient unsaturated seepage model and a limit equilibrium slope stability model. The study found that extreme climatic interactions like precipitation and temperature due to climate change may reduce embankment slope safety. The reduction in the stability of the embankment due to increased precipitation resulting from different greenhouse gas emission scenarios was investigated. The use of unsaturated soil strength and variation of permeability with suction, along with the phase transition of these earthen embankments from near-dry to near-saturated, shows how unsaturated soil mechanics and the hydro-mechanical model can identify climate change issues on critical geotechnical infrastructure. [ABSTRACT FROM AUTHOR]

The sustainability of water resources is fundamental for basin management, especially in regions where changing hydrological conditions due to climate extremes are prevalent. This study presents a comprehensive assessment of the Kabul River Basin (KRB) sustainability using the Standardized Runoff Index (SRI) as a runoff indicator. By integrating the concepts of reliability, resilience, and vulnerability (RRV), this research aims to provide a granular understanding of water sustainability within the basin. Utilizing future climate projections derived from the mean ensemble of Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5), the Soil and Water Assessment Tool (SWAT) model simulated surface runoff for the KRB. Employing a grid-based approach, this study uncovers the internal variabilities within the basin, recognizing that individual grid points may present distinct water availability characteristics. Historical analysis indicated a decline in the KRB's sustainability, attributed to increased vulnerability and reduced reliability and resilience. Future projections emphasize the basin's susceptibility to runoff variations, particularly in downstream areas characterized by dense populations and intense agricultural activities. These findings accentuate the need for strategic water resource management within the KRB to address localized vulnerabilities and ensure sustainable water availability amidst climatic uncertainties. [ABSTRACT FROM AUTHOR]

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model's historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally-resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase. [ABSTRACT FROM AUTHOR]

In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative. [ABSTRACT FROM AUTHOR]

It is likely that climate change will increase irrigation water demand and, consequently, reduces water security in the Mediterranean Basin if current irrigation supply and demand conditions are maintained. Climate change adaptation can be achieved by (1) decreasing irrigation water demand through more efficient irrigation techniques, (2) increasing irrigation water supply by adopting new technological advances, (3) converting to rainfed agriculture, and (4) implementation of Nature-based Solutions for water retention. The aim of this study was to assess the effectiveness of different combinations of these adaptation options on water security through analysis of contrasting scenarios of socio-economic development. We defined plausible scenarios of climate change, land use change and adaptation measures for an intensively irrigated catchment in south-eastern Spain under three Shared Socioeconomic Pathways (SSP), representing different storylines of socio-economic development. We considered three SSP scenarios, including the Sustainability pathway (SSP1), the Middle of the Road pathway (SSP2) and the Fossil-fueled Development pathway (SSP5). Future land use distributions were obtained with the iClue land use change model by accounting for differences in irrigation water demand and supply, resulting in a decrease (SSP1), a constant (SSP2) and an increase (SSP5) in irrigated agriculture. The impact of each scenario on a series of water security indicators was quantified using the SPHY-MMF hydrology-soil erosion model. The SSP2 scenario, which considers very limited climate change adaptation, projects the most severe impacts on water security, including an increase in plant water stress, flood discharge, hillslope erosion and sediment yield. Under SSP1, which accounts for most climate change adaptation strategies, irrigation water demand is significantly reduced due to a shift from irrigated to rainfed agriculture and the implementation of reduced deficit irrigation, while Nature-based Solutions reduce the impact on other water security indicators. Under SSP5, a conversion from rainfed to irrigated agriculture causes a significant increase in irrigation water demand, which is met by increasing irrigation water supply from desalination. SSP5 shows intermediate impacts on other water security indicators, which is explained by a strong decrease in annual precipitation. This study helps exploring how different future socio-economic pathways affect water security and thereby supports evidence-based policy development.

[Objective] Developing an urban system scale that adapts to the resources and environmental carrying capacity (RECC) is an important foundation for achieving sustainable development of urban agglomerations. Previous studies often employed static and rigid constraints to represent RECC, which were characterized by single absolute values. Such approach does not align with the dynamic and uncertain nature of RECC. To address this issue, the purpose of this study was to measure the elastic ranges and project future scenarios of resources and environmental carrying capacity. [Methods] This study proposed a method for determining the elastic range and status of RECC. A quantitative analytical framework for RECC was constructed by integrating the system dynamic and CA-Markov models. Additionally, the shared socioeconomic pathways (SSPs) provide a series of future scenarios for analyzing RECC. The proposed framework was applied to an empirical study of the Lanzhou-Xining urban agglomeration (LXUA). [Results] (1) From 2000 to 2020, the elastic range of the RECC in the LXUA changed from [11.67, 13.67] million to [20.49, 20.69] million people, and the carrying capacity status type transitioned from critical overload to non-overload. (2) The elastic range of the RECC and the single-factor carrying capacity were basically on the rise in each prefecture-level administrative unit, and presented obvious spatial divergence. (3) From 2021 to 2035, under the SSP1 scenario most of the single-factor carrying capacities will be higher than under the other scenarios, while the population growth rate will be the lowest; As the baseline scenario of SSP2, the elastic range of comprehensive resources and environmental carrying capacity, each single-factor carrying capacity and the permanent population will be all at the middle level. SSP3 will be the scenario with the lowest elastic range of comprehensive resources and environmental carrying capacity and the largest permanent population; SSP5 will have the lowest energy carrying capacity and the highest environmental carrying capacity. (4) Under the SSP1 scenario, the LXUA will exhibit a balanced pattern of non-overload across the whole region. By contrast, the other scenarios will demonstrate a core-periphery spatial distribution pattern of “poor in the middle and excellent at the periphery”. [Conclusion] In the future, based on the SSP1 development path, resources and environmental carrying risks should be addressed from the two aspects of adaptive construction of urban development pattern and dynamic improvement of carrying potential, so as to promote the coordinated development of human-nature relationship.

The Montenegrin climate is characterised as very heterogeneous due to its complex topography. The viticultural heritage, dating back to before the Roman empire, is settled in a Mediterranean climate region, located south of the capital Podgorica, where climate conditions favour red wine production. However, an overall increase in warmer and drier periods affects traditional viticulture. The present study aims to discuss climate change impacts on Montenegrin viticulture. Bioclimatic indices, ensembled from five climate models, were analysed for both historical (1981–2010) and future (2041–2070) periods upon three socio-economic pathways: SSP1-2.6, SSP3-7.0 and SSP5-8.5. CHELSA (≈1 km) was the selected dataset for this analysis. Obtained results for all scenarios have shown the suppression of baseline conditions for viticulture. The average summer temperature might reach around 29.5 °C, and the growing season average temperature could become higher than 23.5 °C, advancing phenological events. The Winkler index is estimated to range from 2900 °C up to 3100 °C, which is too hot for viticulture. Montenegrin viticulture requires the application of adaptation measures focused on reducing temperature-increase impacts. The implementation of adaptation measures shall start in the coming years, to assure the lasting productivity and sustainability of viticulture.

We investigate Venezuela’s potential “futures” under Shared Socioeconomic Pathways (SSPs) through a systematic literature review, including systematic mapping and thematic analysis of 50 scientific articles. We categorised the SSP scenarios into two generational categories and classified the outcomes into positive, negative, and neutral futures. Under first-generation SSP scenarios, increasing poverty could be reversed, and the country’s economic growth could be stimulated by adopting unambitious climate measures. However, second-generation SSP scenarios paint a more challenging picture. They suggest that Venezuela could face heat waves, droughts, an increase in diseases, loss of biodiversity, and an increase in invasive species and pests during the remainder of the 21st century as a direct consequence of climate change. Venezuela’s geographic and topographic diversity could exacerbate these impacts of climate change. For instance, coastal areas could be at risk of sea-level rise and increased storm surges, while mountainous regions could experience more frequent and intense rainfall, leading to landslides and flash floods. The urgency of conducting additional research on the factors that could influence the severity of climate change’s impact, considering Venezuela’s geographic and topographic diversity, cannot be overstated. We also identified the critical need to explore alternative paths to move away from the current extractive development model. The potential actions in this regard could be instrumental in aligning the country with global adaptation and mitigation commitments.

Lake Issyk-Kul is an endorheic lake in arid Central Asia that is vital to the region's ecological sustainability and socio-economic development. Climate change and anthropogenic water consumption led to fluctuations in the lake's water level, which affected the water resource. The goal of this study was to examine the impacts of climate change and human activities on the Issyk-Kul water balance by combining the Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios with hydrological modeling. The Soil and Water Assessment Tool (SWAT) model was used to incorporate signals of future precipitation and temperature changes. According to the scenarios, the total discharge of the three catchments showed an overall increasing trend with a maximum value of 28.02%. The snow and ice-melt water from March to August was revealed, and the increasing trends only occurred from March to May, with the snow and ice melting peak variations ranging from 0.5% to 2%. The high increase in change appeared in northern catchment of the lake. There was an exceptional upward precipitation trend over the northern catchment, with annual increases ranging from 0.7 to 14.5%, and an average annual temperature of 1.72°C. With slight similarities, the total runoff would increase for all catchments, with an average annual value of 10.6%. The northern catchment was significantly more sensitive to precipitation and warming than the southeastern catchments. Under land use land cover change, average annual discharge decreased with agricultural expansion, with discharge differences ranging from −0.005 to −1.06 m3/s. The findings are useful for decision-makers addressing the challenges of climate change mitigation and local water resource management.

AbstractOne of the most important aspects of the ‘sub-tropical’ monsoon-influenced environment is the issue of ‘soil erosion’ and its related ‘land degradation’. On the other hand, the climate in this area has become quite extreme. According to this viewpoint, it is important to research a future ‘soil erosion’ scenario in front of the probable effects of climate change and land use change. For the objective of assessing the extent of soil erosion in this area, this study took into account both the USLE and the RUSLE. Compared to the USLE that has been validated, RUSLE has a comparatively greater quantitative efficiency. In RUSLE, the ‘very high’ (>20) and ‘high’ (15–20) ‘soil erosion’ zones tend to be associated with the ‘north-western, western, south-western, and southern’ regions of the river basin. The ‘soil erosion’ that will occur in the future has been estimated by taking into account the projected rainfall, land use and land cover (LULC). ‘Soil erosion’ has increased from the previous time to the projected time. Predicted R factor values for SSP 585 range from 399.92 to 493.72. In addition, a growing erosion tendency associated with increased shared socio-economic pathways (SSPs) has been found.

Exploring the future urban spatial pattern and expansion characteristics of countries and regions along the Belt and Road can provide data support for optimizing urban spatial development, promoting the sustainable development of ecological environment, and contributing to the interconnection of priority construction areas of cities in various countries. Based on the panel data, the least square regression method of dummy variables was used to forecast urban land growth in 62 countries and regions along the Belt and Road from 2020 to 2060 under the shared socioeconomic pathways (SSPs), and future land-use simulation (FLUS) model was used to simulate the urban spatial pattern of the countries and regions in 2060. From the perspective of the relationship between population density and urban expansion, the spatial expansion characteristics of key cities in the countries and regions along the Belt and Road were analyzed under five scenarios of SSPs. The results are as follows. 1 From 2020 to 2060, the trend of urban spatial expansion in countries along the Belt and Road is obvious, and the degree of urban spatial expansion is more intense under the scenarios of SSP1 and SSP5, and relatively weak under SSP3. 2 Under different scenarios of SSPs, there are significant differences in the spatial expansion characteristics of urban land in different countries. Specifically, the faster the urbanization of a country is and the higher the national economic capacity is, the expansion is dominated by the agglomeration and extension of urban patches. When the speed of urbanization and national economic development slows down, the expansion is relatively evenly distributed in urban patches. 3 More than 83% of megalopolis in the countries show loose urban spatial expansion pattern, 54% of small and medium-sized cities show compact urban spatial expansion pattern. Among them, and most cities in East Asia and Western Europe show loose urban spatial expansion pattern with low population density, the small cities in Central Asia and Southeast Asia are mainly characterized by compact urban spatial expansion pattern with high population density. [ABSTRACT FROM AUTHOR]

Context: Evaluating the impacts of future urban expansion on natural habitats in China is important for achieving sustainable development goals. However, existing models cannot reliably and effectively simulate future urban expansion in China, which causes great uncertainties in evaluating the impacts of future urban expansion on natural habitats. Objectives: The objective of this study was to simulate future urban expansion and evaluate its impacts on natural habitats in China. Methods: First, the Land Use Scenario Dynamics-urban (LUSD-urban) model was improved by coupling localized Shared Socioeconomic Pathways (SSPs) and a convolutional neural network (CNN). Then, urban expansion in China was simulated from 2020 to 2050 by using the improved LUSD-urban model. Finally, we evaluated the potential impacts of future urban expansion on natural habitats. Results: The localized SSPs could more reliably represent future socioeconomic development trends in China, and the CNN could fully extract the multiscale neighborhood information of nonurban pixels. By combining the advantages of the localized SSPs and the CNN, the simulation accuracy of the improved LUSD-urban model increased by 15–32% compared with that of other models. From 2020 to 2050, urban land is projected to increase by 6.84 (3.94–9.03) × 104 km2 and cause a 2.21 (1.23–2.95) × 104 km2 loss of natural habitat. Compared with the 1990–2020 period, the impacts of future urban expansion on natural habitats will intensify. Natural habitat loss during 2020–2050 will be 1.23 (1.22 to 1.64) times greater than that during 1990–2020. In addition, grasslands will experience the most serious losses under the influence of future urban expansion. Conclusions: We suggest that relevant policies be issued to protect important natural habitats during urbanization to achieve Sustainable Development Goals and biodiversity conservation targets in China. [ABSTRACT FROM AUTHOR]

As of 2018, approximately 55% of the world’s population resides in cities, and it is projected that this proportion will reach 68% by 2050. Population growth in urban areas leads to various impacts on society and the environment. In this study, we have developed a method for generating future scenarios of nighttime lights. What makes this method unique is its ability to (1) generate future gridded nighttime light intensity scenarios for cities, (2) generate future scenarios that preserve the distribution pattern of nighttime light intensity, and (3) generate scenarios that reflect urban policies. By applying this developed method, we have estimated nighttime light data for 555 cities worldwide and predicted future urban expansion and changes in carbon emissions for each SSP scenario. Consequently, both urban areas and carbon emissions are estimated to increase for the entire set of target cities, with patterns varying among cities and scenarios. This study contributes to the advancement of urban scenario research, including the estimation of future urban area expansion and carbon emissions.

The Climate Change Act recently enacted in Germany highlights the urgency of understanding the future demand for renewable fuels. In this study, we combine technological progress and socio-economic pathways in an energy system analysis to assess future renewable fuel demands in Germany. We apply the whole-system optimisation model, TIMES, to investigate transition pathways with varying electrification levels and socio-economic developments. The results show that renewable fuels demand varies between 388 PJ and 1310 PJ depending on the electrification rates. Furthermore, our findings demonstrate that considering socio-economic aspects and behavioural change, as represented by different Shared-Socio-economic Pathways, can significantly alter the demand for renewable fuels within a narrower yet still noteworthy range compared to the electrification scenarios. This provides country-level evidence highlighting the often-overlooked influence of social developments on demand projections. Consequently, it becomes crucial to prioritize the consideration of the climate mitigation potential arising from socioeconomic-induced changes in demand patterns within the broader framework of energy efficiency measures.

Abnormal climate phenomena that exceed conventional weather observations occur worldwide, such as severe floods, droughts, and environmental issues, and have attracted increasing attention. To avoid indiscriminate industrialization and achieve sustainable development, environmental experts have presented various opinions based on climate change scenario data. In this study, shared socioeconomic pathway (SSP) climate change scenarios that consider socioeconomic conditions were used to assess the environmental impact of extreme climate conditions on watersheds. Using the UK Earth System Modelling (UKESM1) SSP scenarios, we analyzed the following: 1) Heat index for each SSP climate change scenario using the H-Index; 2) extreme climate for each SSP climate change scenario using the standardized precipitation index (SPI); 3) the impact of extreme climate on non-point pollution using event mean concentration (EMC); and 4) the environmental flow caused by extreme climate by combining the K-water Distributed RUnoff Model (K-DRUM) and global environmental flow calculator (GEFC) models. According to the heat index analysis regarding SSPs climate change scenarios, the heatwave index will continue to rise if high carbon emissions persist. Temperature serves as an important indicator that has the most meaningful impact on ecosystems. The SPI analysis showed an increase in “extremely dry” conditions, and overall, more severe droughts are likely to occur due to high carbon emission-induced climate change. The nonpoint source pollution is also higher in climate change scenarios with high carbon emissions. The extreme drought event assessment using SPI and environmental flow revealed a shift from the “moist conditions” category to grade C. Our assessments in this study conclusively indicate that drought frequency will increase along with nonpoint source pollution. Furthermore, environmental flows will shift to grade C, resulting in the disappearance of some sensitive ecological species and an increase in invasive species. These analyses determined that high carbon emission-induced climate change caused leads to severe droughts and significant alterations in the overall water circulation, thereby complicating water resource management. Furthermore, we identified watersheds that are highly vulnerable to climate change and designated these as “mid-watersheds” that first require non-point pollution management. Here, the aquatic ecosystem environment can be affected by climate change without any artificial environmental influence. Various research-based analysis methods based on modeling have been presented using SSP climate change scenarios, and are highly beneficial for establishing policies and coping strategies for environmental preservation and sustainable development.

The ecosystem services value (ESV) is an important basis for measuring ecological environment quality and efficient management of ecosystems. Although there have been many studies devoted to the measurement of ESV, the research on the key influencing factors of ESV and the prediction of future development scenarios is still limited. This study coupled the Deep Forest model and Patch-generating Land Use Simulation (PLUS) model to identify the key factors of ESV, and simulated the change trend of ESV under the Shared Socioeconomic Pathways (SSPs). Taking the cluster cities around the Yellow River floodplain area as the research object, this study quantitatively analyzed the spatiotemporal evolution characteristics of its ESV from 2000 to 2020, and identified the key factors affecting ESV using the Deep Forest model. The results showed that: (1) The ESV showed an overall upward trend from 2000 to 2020, with strong spatial heterogeneity; (2) The key factors affecting ESV were construction land ratio, distance to railway, SHDI, etc.; (3) The best development pathway of ESV in 2025, 2030 and 2035 would be SSPs5, SSPs2 and SSPs4 respectively. This study can provide theoretical support for the management of maximizing the overall benefits of ecosystem services in the study area.

Land systems and climate, which are the key elements of agricultural production and key drivers of crop yields, affect the quality of arable land. However, a quantitative model to reveal the mechanism of how potential grain yields are affected by macro-scale arable land evolution and climate change has not yet been developed. In this study, we constructed a Grey Prediction Model-Future Land Use Simulation (GM-FLUS), which combined land system evolution with climate change data, to simulate changes in China's land system over the next 40 years. We improved the Global Agro-Ecological Zone (GAEZ) model, estimated China's potential rice yields and their spatial distribution in the next 40 years under four scenarios (shared socioeconomic pathway SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) of the Sixth International Coupling Model Intercomparison Project, analysed the spatiotemporal variations in the potential rice yields and their drivers, and provided appropriate suggestions for increasing rice yields. The simulation results indicated an increase in China's potential rice yields during 2020–2060 under the SSP1-2.6 and SSP3-7.0 scenarios and a decrease under the SSP2-4.5 and SSP5-8.5 scenarios. Moreover, China's development strategy of "achieving carbon peaking by 2030 and carbon neutrality by 2060" was similar to the SSP1-2.6 scenario, under which rice yields were relatively stable. Furthermore, under China's arable land protection policy, China's paddy field area will change slightly during 2020–2060, and potential rice yields will be influenced by climate. Under the four climate change scenarios, air temperature increased and was negatively correlated with potential rice yields in main rice-producing regions. Additionally, potential rice yields were positively correlated with precipitation, which increased stably under the SSP1-2.6 and SSP3-7.0 scenarios and decreased under the SSP2-4.5 and SSP5-8.5 scenarios. These results suggest that the development of heat-resistant rice varieties and the implementation of measures that will mitigate the impacts of future temperature increases on rice yields are important for the conservation of paddy fields. Additionally, improving irrigation and drainage facilities is necessary to irrigate drought-prone paddy fields and drain flooded water. [ABSTRACT FROM AUTHOR]

Scenarios are central to fisheries and aquatic conservation research on climate change. Scenarios project future greenhouse-gas emissions, which climate models translate into warming projections. Recent climate research and global development trends have significantly changed our understanding of plausible emissions pathways to 2100 and climate sensitivities to emissions. Here, we review these developments and make recommendations for scenario use in fisheries and aquatic conservation research. Although emissions pathways are uncertain, recent research suggests that scenarios producing ∼3.4–4.5 W/m2 radiative forcing by 2100 (e.g. scenarios SSP2-3.4 and SSP2-4.5/RCP4.5) might be most plausible. This corresponds to ∼2–3 degrees C global warming by 2100 with median climate sensitivities, or 1.5–4 degrees C considering climate-system uncertainties. Higher- and lower-emissions scenarios (e.g. RCP2.6 and RCP6.0) might be plausible and should be explored in research. However, high-emission scenarios (RCP8.5/SSP5-8.5, SSP3-7.0) seem implausible and should be used with clear rationales and caveats to ensure results are not misinterpreted by scholars, policymakers, and media. We analyse fisheries and aquatic conservation papers published from 2015 to 2022 in major journals, and find that RCP8.5/SSP5-8.5 are the most commonly used scenarios, though RCP4.5/SSP2-4.5 use has increased since 2020. Studies predominantly project quantitative rather than qualitative differences between these scenarios' impacts. [ABSTRACT FROM AUTHOR]

Climate change impact on the habitat distribution of umbrella species presents a critical threat to the entire regional ecosystem. This is further perilous if the species is economically important. Sal (Shorea robusta C.F. Gaertn.), a climax forest forming Central Himalayan tree species, is one of the most valuable timber species and provides several ecological services. Sal forests are under threat due to over-exploitation, habitat destruction, and climate change. Sal's poor natural regeneration and its unimodal density-diameter distribution in the region illustrate the peril to its habitat. We, modelled the current as well as future distribution of suitable sal habitats under different climate scenarios using 179 sal occurrence points and 8 bioclimatic environmental variables (non-collinear). The CMIP5-based RCP4.5 and CMIP6-based SSP245 climate models under 2041–2060 and 2061–2080 periods were used to predict the impact of climate change on sal's future potential distribution area. The niche model results predict the mean annual temperature and precipitation seasonality as the most influential sal habitat governing variables in the region. The current high suitability region for sal was 4.36% of the total geographic area, which shows a drastic decline to 1.31% and 0.07% under SSP245 for 2041–60 and 2061–80, respectively. The RCP-based models predicted more severe impact than SSP; however, both RCP and SSP models showed complete loss of high suitability regions and overall shift of species northwards in the Uttarakhand state. We could identify the current and future suitable habitats for conserving sal population through assisted regeneration and management of other regional issues. [ABSTRACT FROM AUTHOR]

Jamaica produces one of the most expensive coffees on the global market. The local specialty coffee industry plays a significant role in the island's economy and also contributes to the livelihood of smallholders—the majority of whom operate the industry's coffee farms. While climate model projections suggest that Jamaica will continue to experience a warming and drying trend, no study has assessed the future impacts of changing climatic patterns on local coffee-growing areas. This research developed a number of geospatial processing models within the ArcMap software platform to model current coffee suitability and future crop suitability across three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and three future time periods (2021–2040, 2041–2060, and 2081–2100). The results validated current locations of coffee production and revealed that there was an observable decrease in coffee suitability across the island, across all SSP scenarios and time periods under study. Most growing regions were projected to experience declines in production suitability of at least 10%, with the most severe changes occurring in non-Blue Mountain regions under the SSP5-8.5 scenario. Implications of this projected suitability change range from decreased production volumes, increased price volatility, and disruption to market operations and livelihood incomes. The paper's findings offer stakeholders within Jamaica's coffee industry the opportunity to develop targeted adaptation planning initiatives, and point to the need for concrete decisions concerning future investment pathways for the industry. It also provides insight into other tropical coffee-growing regions around the world that are facing the challenges associated with climate change. [ABSTRACT FROM AUTHOR]

Rapid urbanization has profoundly impacted social and environmental systems. China is the world’s largest developing country. Projecting future urban expansion in China is critical to alleviate any adverse impacts and achieve sustainable development goals. However, the existing national-scale urban expansion simulations at low or medium resolutions produce significant distortions in urban spatial patterns, limiting the utility of future projections. High-resolution simulation of urban expansion is challenging on a large scale owing to high computational demands. In this study, we used a high-performance cellular automata model (Tensor-FLUS) to simulate the urban expansion of China from 2015 to 2050, at 30 m spatial resolution, under shared socioeconomic pathways. The high-resolution (30 m) urban expansion simulations preserve greater spatial details and avoid 34.07–37.60% underestimation of the urban area, compared with simulation at 1 km resolution. The environmental impact assessments revealed that future urban expansion mainly encroaches on cropland (88.00–88.29%), with a greater likelihood of occupying productive cropland, placing substantial pressure on food production. Although the proportion of occupied natural land is relatively small (11.32–11.60%), newly expanded urban areas will tend to consume woodland and grassland of high ecological value, leading to profound impacts on the ecosystem. In general, the produced high-resolution future simulations can reduce the uncertainty of environmental impact assessment at the national scale. Furthermore, it can provide consistent projection data for research at the provincial or metropolitan scales to support urban planning and local climate change mitigation.