Future Scenarios Research Articles

Potential impact of climate change on Nearctic leafhopper distribution and richness in North America

Climate change significantly contributes to shifts in the geographical range of pests and diseases. Leafhoppers (Hemiptera: Cicadellidae), known vectors of phytoplasmas pathogens, are linked to the transmission of more than 600 diseases affecting a thousand plant species worldwide. Despite this, the potential effects of climate change on leafhopper vectors of phytoplasmas remain a critical knowledge gap. To address this gap, our study investigated the potential impact of climate change on 14 species of Nearctic leafhoppers previously associated with phytoplasma-related diseases. Using the MaxEnt species distribution algorithm and other ecological niche modeling techniques, we assessed (i) the expected species richness under current climate conditions and four future scenarios and (ii) the environmental niche similarity among these species across these scenarios. Our projections suggest that the eastern region of North America holds the potential for the highest species richness, a trend expected to persist across all future scenarios, gradually expanding eastward. Notably, our findings indicate the increasing suitability of northern Canada for more species. Network analysis further revealed a remarkable similarity in environmental niches among most leafhopper species. Moreover, across the four future scenarios, there is a tendency for an increase in this similarity. Altogether, our study underscores the potential persistent presence of Nearctic leafhoppers in their current habitats while pointing to a shift toward northern North America in future scenarios. These findings have significant implications for sustainable pest management practices, prompting a necessary discussion on strategies to mitigate climate change and pest migration’s impact on agricultural systems.

Impacts of climate change on the potential distribution of Pulex simulans and Polygenis gwyni.

Pulex simulans and Polygenis gwyni are vectors of many flea-borne diseases. They were widely recorded in the United States and Mexico between 1970 and 2000. Maximum entropy models were used to explore the habitats of both fleas under different climate scenarios to provide the scientific basis for the surveillance and control of flea-borne diseases. We screened climate variables by principal component analysis and Pearson's correlation test and evaluated model performance by ROC curve. ArcMap was used to visualize expressions. Under current climatic conditions, the medium and highly suitable areas for P. simulans are estimated to be 9.16 × 106 km2 and 4.97 × 106 km2, respectively. These regions are predominantly located in South America, along the Mediterranean coast of Europe, the southern part of the African continent, the Middle East, North China, and Australia. For P. gwyni, the medium and highly suitable areas under current climatic conditions are approximately 4.01 × 106 and 2.04 × 106 km2, respectively, with the primary distribution in North China extending to the Himalayas, near the Equator in Africa, and in a few areas of Europe. Under future climate scenarios, in the SSP3-7.0 scenario for the years 2081-2100, the area of high suitability for P. simulans is projected to reach its maximum. Similarly, in the SSP2-4.5 scenario for 2061-2080, the area of high suitability for P. gwyni is expected to reach its maximum. Under global climate change, there is a large range in the potential distribution for both fleas, with an overall upward trend in the area of habitat under future climate scenarios. Governments should develop scientific prevention and control measures to prevent the invasive alien species flea.

The optimized Maxent model reveals the pattern of distribution and changes in the suitable cultivation areas for Reaumuria songarica being driven by climate change.

Reaumuria songarica, a drought-resistant shrub, is widely distributed and plays a crucial role in the northern deserts of China. It is a key species for desert rehabilitation and afforestation efforts. Using the Maxent model to predict suitable planting areas for R. songarica is an important strategy for combating desertification. With 184 occurrence points of R. songarica and 13 environmental variables, the optimized Maxent model has identified the main limiting factors for its distribution. Distribution patterns and variation trends of R. songarica were projected for current and future climates (2030s, 2050s, 2070s, and 2090s) and different scenarios (ssp_126, ssp_370, and ssp_585). Results show that setting parameters to RM (regulation multiplier) = 4 and FC (feature combination) = LQHPT yields a model with good accuracy and high reliability. Currently, R. songarica is primarily suitable for desert control in eight provinces and autonomous regions, including Inner Mongolia, Xinjiang, Qinghai, and Ningxia. The total suitable planting area is 148.80 × 104 km2, representing 15.45% of China's land area. Precipitation (Precipitation of the wettest month, Precipitation of the warmest quarter, and Annual precipitation) and Ultraviolet-B seasonality are the primary environmental factors limiting the growth and distribution of R. songarica. Mean temperature of the warmest quarter is the primary factor driving changes in the distribution of suitable areas for R. songarica under future climate scenarios. In future climate scenarios, the suitable planting area of R. songarica will shrink, and the distribution center will shift towards higher latitude, potentially indicate further desertification. The area of highly suitable habitat has increased, while moderately and less suitable habitat areas have decreased. Increased precipitation within R. songarica's water tolerance range is favorable for its growth and reproduction. With changes in the suitable cultivation area for R. songarica, priority should be given to exploring and utilizing its germplasm resources. Introduction and cultivation can be conducted in expanding regions, while scientifically effective measures should be implemented to protect germplasm resources in contracting regions. The findings of this study provide a theoretical basis for addressing desertification resulting from climate change and offer practical insights for the development, utilization, introduction, and cultivation of R. songarica germplasm resources.

Evaluating Thermal Comfort and Overheating Risks in A Social Housing Prototype: As Built Versus Retrofit Scenarios

Climate change has highlighted the importance of thermal comfort and its health-related outcomes, particularly for the most vulnerable members of society living in social housing. Due to their vulnerable living conditions, low-income people are more exposed to negative outcomes of overheating and cold indoor temperatures in buildings. Previous studies suggest that there is a significant risk of overheating in retrofitted buildings both for the current and future weather scenarios. The UK government has introduced new building regulations to assess and limit the risk of overheating in new buildings; however, there is still a need to assess and improve conditions for existing and retrofitted properties. This study aims to evaluate the effect of retrofit strategies on thermal comfort and the risk of overheating in social housing under current and future climatic conditions. A typical case study building was simulated in DesignBuilder to assess thermal comfort conditions for upgraded building fabric to Part L of the UK building regulations and Passive House standards. The summer results were analyzed according to CIBSE TM59 while the Predicted Mean Vote index (PMV) was used for winter analysis. Findings revealed that the south-facing bedrooms are most exposed to overheating. Risk of overheating significantly increased for the future weather scenarios by up to 10 times while winter thermal comfort improved for the retrofitted scenarios.

Nurses on shacking ground-A qualitative study of Danish dermatology and allergology nurses' experiences of relocation during the COVID-19 pandemic.

To investigate dermatology and allergology nurses' experiences of relocation from an outpatient clinic to a newly established COVID-19 infectious disease ward. A phenomenological-hermeneutical approach was applied. Three focus groups with nurses were conducted from June to August 2020. Data were analysed in accordance with Ricoeur's theory of interpretation. The relocation represented a challenging period that involved uncertainty and evoked feelings of excitement and dedication towards the nursing profession. Nurses felt obligated to help; however, they also experienced that they did not have a say in the relocation. The placement on the infectious disease ward was characterized by adaptations in three areas: unfamiliar working environment, unfamiliar team competencies and inadequate nursing training. E-learning training was experienced as insufficient, as it did not enhance the nurses' specific competencies or confidence in caring for patients with COVID-19. The relocation of nurses from an outpatient clinic to a new COVID-19 infectious disease ward created a dilemma between nurses' sense of duty and their right to self-determination. A prompt relocation into a newly established unfamiliar field caused frustrations because there were no unspoken rules to rely on. Managers should take nurses' experiences and perceptions under careful consideration and strive for more involvement in future scenarios. No patient or public contribution.

Reducing the Carbon Impact of Generative AI Inference (Today and in 2035)

Generative AI, exemplified in ChatGPT, Dall-E 2, and Stable Diffusion, are exciting new applications consuming growing quantities of computing. We study the compute, energy, and carbon impacts of generative AI inference. Using ChatGPT as an exemplar, we create a workload model and compare request direction approaches (Local, Balance, CarbonMin), assessing their power use and carbon impacts. Our workload model shows that for ChatGPT-like services, inference dominates emissions, in one year producing 25x the carbon-emissions of training GPT-3. The workload model characterizes user experience, and experiments show that carbon emissions-aware algorithms (CarbonMin) can both maintain user experience and reduce carbon emissions dramatically (35%). We also consider a future scenario (2035 workload and power grids), and show that CarbonMin can reduce emissions by 56%. In both cases, the key is intelligent direction of requests to locations with low-carbon power. Combined with hardware technology advances, CarbonMin can keep emissions increase to only 20% compared to 2022 levels for 55x greater workload. Finally we consider datacenter headroom to increase effectiveness of shifting. With headroom, CarbonMin reduces 2035 emissions by 71%.

Impact of future scenarios of climate change on lignin dynamics in soil: A case study in a Mediterranean savannah

Lignin is an abundant and recalcitrant biopolymer of major relevance as soil organic matter (SOM) component playing a significant role in its stabilization. In this work, a factorial field experiment was established, where three climatic treatments (W, warming; D, drought; W + D, warming + drought), mimicking future climate change scenarios were installed over five years in a Mediterranean savannah “dehesa”, accounting for its landscape diversity (under the tree canopy and in open grassland). A combination of analytical pyrolysis (Py-GC/MS) and the study of biogeochemical proxies based on lignin monomers is used for the direct detection of lignin-derived phenols and to infer possible shifts in lignin dynamics in soil. A total of 27 main lignin-derived methoxyphenols were identified, exhibiting different patterns and proportions, mainly driven by the effect of habitat, hence biomass inputs to SOM. An accelerated decomposition of lignin moieties –(exhibited by higher LG/LS and Al/K + Ac ratios)– is particularly exacerbated by the effect of all climatic treatments. There is also an overall effect on increasing lignin oxidation of side chain in syringyl units, especially under the tree canopy due to the alteration in biomass degradation and potential stimulation of enzyme activities. Conversely, in open grassland these effects are slower since the microbial community is expected to be already adapted to harsher conditions. Our findings suggests that climate change-related temperature and soil moisture deviations impact soil lignin decomposition in dehesas threatening this productive Mediterranean agroecosystem and affecting the mechanism of soil carbon storage.

Stream chemical response is mediated by hydrologic connectivity and fire severity in a Pacific Northwest forest

AbstractLarge‐scale wildfires are becoming increasingly common in the wet forests of the Pacific Northwest (USA), with predicted increases in fire prevalence under future climate scenarios. Wildfires can alter streamflow response to precipitation and mobilize water quality constituents, which pose a risk to aquatic ecosystems and downstream drinking water treatment. Research often focuses on the impacts of high‐severity wildfires, with stream biogeochemical responses to low‐ and mixed‐severity fires often understudied, particularly during seasonal shifts in hydrologic connectivity between hillslopes and streams. We studied the impacts of the 2020 Holiday Farm Fire at the HJ Andrews Experimental Forest where rare pre‐fire stream discharge and chemistry data allowed us to evaluate the influence of mixed‐severity fire on stream water quantity and quality. Our research design focused on two well‐studied watersheds with low and low‐moderate burn severity where we examined long‐term data (pre‐ and post‐fire), and instantaneous grab samples collected during four rain events occurring immediately following wildfire and a prolonged dry summer. We analysed the impact of these rain events, which represent the transition from low‐to‐high hydrologic connectivity of the subsurface to the stream, on stream discharge and chemistry behaviour. Long‐term data revealed total annual flows and mean flows remained fairly consistent post‐fire, while small increases in baseflow were observed in the low‐moderately burned watershed. Stream water concentrations of nitrate, phosphate and sulfate significantly increased following fire, with variance in concentration increasing with fire severity. Our end member mixing models suggested that during rain events, the watershed with low‐moderate severity fire had greater streamflow inputs from soil water and groundwater during times of low connectivity compared to the watershed with low severity fire. Finally, differences in fire severity impacts on concentration‐discharge relationships of biogenic solutes were most expressed under low catchment connectivity conditions. Our study provides insights into post‐wildfire impacts to stream water quality, with the goal of informing future research on stream chemistry responses to low, moderate and mixed severity wildfire.

The lack of plasticity and interspecific variability in thermal limits produce a highly heat-tolerant tropical host-parasitoid system

Thermal limits are often used as proxies to assess the vulnerability of ectotherms to environmental change. While meta-analyses point out a relatively low plasticity of heat limits and a large interspecific variability, only few studies have compared the heat tolerance of interacting species. The present study focuses on the thermal limits, and their plasticity (heat hardening), of three species co-occurring in Western Africa: two ectoparasitoid species, Dinarmus basalis (Rondani) (Hymenoptera: Pteromalidae) and Eupelmus vuilleti (Crawford) (Hymenoptera: Eupelmidae), and their common host, Callosobruchus maculatus (F.) (Coleoptera: Bruchidae). The investigation delves into the Critical Thermal Maximum (CTmax), representing the upper tolerance limit, to understand how these species may cope with extreme thermal events. The CTmax of all three species appeared similarly high, hovering around 46.5 °C, exceeding the global mean CTmax observed in insects by 3.5 °C. Short-term exposure to moderate heat stress showed no impact on CTmax, suggesting a potential lack of heat hardening in these species. Therefore, we emphasized the similarity of heat tolerance in these interacting species, potentially stemming from both evolutionary adaptations to high temperatures during development and the stable and similar microclimate experienced by the three species over the years. While the high thermal tolerance should allow these species to endure extreme temperature events, the apparent lack of plasticity raises concerns about their ability to adapt to future climate change scenarios. Overall, this research provides valuable insights into the thermal physiology of these interacting species, providing a basis for understanding their responses to climate change and potential implications for the host-parasitoid system.

Utilizing digitized occurrence records of Midwestern feral Cannabis sativa to develop ecological niche models.

Hemp (Cannabis sativa L.) has historically played a vital role in agriculture across the globe. Feral and wild populations have served as genetic resources for breeding, conservation, and adaptation to changing environmental conditions. However, feral populations of Cannabis, specifically in the Midwestern United States, remain poorly understood. This study aims to characterize the abiotic tolerances of these populations, estimate suitable areas, identify regions at risk of abiotic suitability change, and highlight the utility of ecological niche models (ENMs) in germplasm conservation. The Maxent algorithm was used to construct a series of ENMs. Validation metrics and MOP (Mobility-oriented Parity) analysis were used to assess extrapolation risk and model performance. We also projected the final projected under current and future climate scenarios (2021-2040 and 2061-2080) to assess how abiotic suitability changes with time. Climate change scenarios indicated an expansion of suitable habitat, with priority areas for germplasm collection in Indiana, Illinois, Kansas, Missouri, and Nebraska. This study demonstrates the application of ENMs for characterizing feral Cannabis populations and highlights their value in germplasm conservation and breeding efforts. Populations of feral C. sativa in the Midwest are of high interest, and future research should focus on utilizing tools to aid the collection of materials for the characterization of genetic diversity and adaptation to a changing climate.

Increased crossing of thermal stress thresholds of vegetation under global warming.

Temperature extremes exert a significant influence on terrestrial ecosystems, but the precise levels at which these extremes trigger adverse shifts in vegetation productivity have remained elusive. In this study, we have derived two critical thresholds, using standard deviations (SDs) of growing-season temperature and satellite-based vegetation productivity as key indicators. Our findings reveal that, on average, vegetation productivity experiences rapid suppression when confronted with temperature anomalies exceeding 1.45 SD above the mean temperature during 2001-2018. Furthermore, at temperatures exceeding 2.98 SD above the mean, we observe the maximum level of suppression, particularly in response to the most extreme high-temperature events. When Earth System Models are driven by a future medium emission scenario, they project that mean temperatures will routinely surpass both of these critical thresholds by approximately the years 2050 and 2070, respectively. However, it is important to note that the timing of these threshold crossings exhibits spatial variation and will appear much earlier in tropical regions. Our finding highlights that restricting global warming to just 1.5°C can increase safe areas for vegetation growth by 13% compared to allowing warming to reach 2°C above preindustrial levels. This mitigation strategy helps avoid exposure to detrimental extreme temperatures that breach these thresholds. Our study underscores the pivotal role of climate mitigation policies in fostering the sustainable development of terrestrial ecosystems in a warming world.

Balancing ecology and practicality to rank waterbodies for preventative invasive species management

Abstract ‘Early detection and rapid response’ (EDRR) is the most successful framework for preventative invasive species management, but prioritizing localized EDRR actions with limited resources is challenging. An approach that ranks individual locations, such as waterbodies, for EDRR by combining an invasive species' establishment risk with the practicality of managing it could help set reasonable priorities. Here, we worked with regional practitioners in Arkansas, USA, and the broader Southeastern USA to co‐produce a workflow for preventative aquatic invasive species management that (1) estimates establishment risk under current and future climates with a species distribution model, (2) scores waterbodies according to difficulty of eradicating an aquatic invasive species if it were introduced and (3) combines establishment risk and eradication difficulty scores to rank waterbodies according to preventative management priority. As our focal species, we used giant salvinia (Salvinia molesta), a floating aquatic fern ranked among the worst weeds in the world due to its negative socio‐ecological impacts and difficulty to eradicate once established. Current establishment risk is low for much of our study area, but under future climate scenarios (RCP 8.5), areas with >60% giant salvinia establishment risk increased from 546 km2 to 30,219 km2 between 2023 and 2040 in Arkansas. We found giant salvinia establishment risk and eradication difficulty are independent of each other (r = 0.28), and it follows that, alone, early detection tools such as species distribution models are insufficient for managers to prioritize sites for EDRR. Practical implication: We envision our approach fitting into a potential EDRR workflow that cascades from broad‐ to local‐scale. To illustrate, (1) horizon scanning and/or climate matching generates lists of high‐risk invasive species; (2) species lists are narrowed according to eradication feasibility scores; (3) for all remaining species, all waterbodies across a geography of interest receive prioritization rankings based on establishment risk and eradication difficulty scores. Given that climate change makes predicting invasive species' distributions a moving target, combining co‐produced eradication difficulty scoring with species distribution modelling will balance rigour with practicality when prioritizing locations for EDRR.

Marine heatwaves alter competition between the cultured macroalga Gracilariopsis lemaneiformis and the harmful bloom alga Skeletonema costatum

Seaweed cultivation can inhibit the occurrence of red tides. However, how seaweed aquaculture interactions with harmful algal blooms will be affected by the increasing occurrence and intensity of marine heatwaves (MHWs) is unknown. In this study, we run both monoculture and coculture systems to investigate the effects of a simulated heatwave on the competition of the economically important macroalga Gracilariopsis lemaneiformis against the harmful bloom diatom Skeletonema costatum. Coculture with G. lemaneiformis led to a growth decrease in S. costatum. Growth and photosynthetic activity (Fv/Fm) of G. lemaneiformis was greatly reduced by the heatwave treatment, and did not recover even after one week. Growth and photosynthetic activity of S. costatum was also reduced by the heatwave in coculture, but returned to normal during the recovery period. S. costatum also responded to the stressful environment by forming aggregates. Metabolomic analysis suggests that the negative effects on S. costatum were related to an allelochemical release from G. lemaneiformis. These findings show that MHWs may enhance the competitive advantages of S. costatum against G. lemaneiformis, leading to more severe harmful algal blooms in future extreme weather scenarios.

An adaptive cycle resilience perspective to understand the regime shifts of social-ecological system interactions over the past two millennia in the Tarim River Basin

Socio-ecological systems (SESs) in arid regions have experienced multiple transformations throughout history due to human activities and natural forces. However, few studies have used the resilience cycle model to explain the resilience status and determinants of SESs over the past two millennia. This study proposes the adaptive cycle resilience (ACR) perspective to investigate regime shifts of socio-ecological system interactions in the Tarim River Basin (TRB) over the past two millennia. An ACR framework combining a piecewise linear regression model (PLR), ACR theory, and physical resilience models has been built to assess and quantify socio-ecological system resilience. Key indicators such as climate variability, settlement numbers, war frequency, glacier accumulation, and oasis area changes are identified and quantified to evaluate SESs adaptability and transformability. Glacier accumulation serves as a proxy for long-term climate change, while oasis area changes reflect the direct impact of human activities and environmental feedback on ecosystem productivity. Population and war indicators provide insights into social system stability and the impact of conflicts on SESs dynamics. The findings reveal that the 7th century and 1850s are critical points of regime shifts in the ACR. 200s BC-350s AD and 700s AD-900s AD are in the forward loop (r-K) period of the ACR. 350s AD-700s AD and 900s AD-1850s AD are the adaptive resilience backward loop (Ω-α) phase. Assessing the historical socio-ecological system resilience and identifying key transition points can inform proactive measures to mitigate potential regime shifts. Combining historical data with resilience theory provides a deep understanding of the ACR of SESs and their driving factors. This enriches the theoretical understanding of SESs and offers a robust case study for future resilience assessments and scenario analyses in arid regions.

Mesocarnivore sensitivity to natural and anthropogenic disturbance leads to declines in occurrence and concern for species persistence.

Understanding mesocarnivore responses to both natural and anthropogenic disturbance is crucial for understanding species' potential to maintain landscape persistence into the future. We examined the response of five mesocarnivore species (bobcat, coyote, fisher, gray fox, and red fox) to both types of disturbances and climatic conditions. The Northeastern U.S. has experienced multiple large-scale disturbances, such as a mass defoliation event following larval spongy moth outbreak and high densities of infrastructure that divide the natural landcover into roadless zones where these species inhabit. Using dynamic occupancy models in a Bayesian framework, we aimed to (1) examine variation in species' responses over a 4-year study by estimating variation in site-level occupancy, colonization and extirpation of each species in the state of Rhode Island relative to natural disturbance (i.e., defoliation event), anthropogenic disturbance (i.e., parceling of natural landcover bounded by roads, distance to roads), and climate (i.e., seasonal precipitation) and (2) compare current occurrence trends to predicted asymptotic occupancy to identify key variables contributing to distribution instability. Our findings indicated declines in the occurrence of both fox species, and fisher. There was variation in mesocarnivore response to disturbance among the species. We found gray fox and fisher occupancy dynamics to be sensitive to all forms of disturbance and coyote occurrence was positively associated with anthropogenic disturbance. Although bobcat and red fox were predicted to respond positively to future climate scenarios, fisher and gray fox were not, and persistence of fisher and gray fox in a landscape of disturbance relies on large areas with high forest and shrubland cover. With the wide-spread spongy moth outbreak across much of southern New England, our findings indicate that efforts to conserve forested lands may be crucial in maintaining the persistence of several mesocarnivore species in this region experiencing large-scale disturbance.

Predicting the distribution of potentially suitable habitat in China for Cirsium japonicum Fisch. ex DC. under future climate scenarios based on the R-optimized MaxEnt model.

Cirsium japonicum contains a variety of medicinal components with good clinical efficacy. With the rapid changes in global climate, it is increasingly important to study the distribution of species habitats and the factors influencing their adaptability. Utilizing the MaxEnt model, we forecasted the present and future distribution regions of suitable habitats for C. japonicum under various climate scenarios. The outcome showed that under the current climate, the total suitable area of C. japonicum is 2,303,624 km2 and the highly suitable area is 79,117 km2. The distribution of C. japonicum is significantly influenced by key environmental factors such as temperature annual range, precipitation of the driest month, and precipitation of the wettest month. In light of future climate change, the suitable habitat for C. japonicum is anticipated to progressively relocate toward the western and northern regions, leading to an expansion in the total suitable area. These findings offer valuable insights into the conservation, sustainable utilization, and standardized cultivation of wild C. japonicum resources.

River–aquifer interactions enhancing evapotranspiration in a semiarid riparian zone: A modelling study

AbstractThe hydrologic flows across the river–aquifer interface play an important role in groundwater dynamics and biogeochemical reactions within the subsurface; however, little is known about the effects of river–aquifer interactions on land surface processes. In this study, we developed a fully coupled three‐dimensional (3D) land surface and subsurface model at a high resolution (~1 km) that accounts for high‐frequency hydrologic exchange flow conditions to investigate how river–aquifer interactions modulate surface water budgets in the Upper Columbia‐Priest Rapids watershed, a typical semiarid watershed located in the northwestern United States where river stage fluctuates in response to reservoir releases changing. Our results show that the spatiotemporal dynamics of river–aquifer interactions are highly heterogeneous, driven mainly by river‐stage fluctuations. Adding 6.64 × 106 m3 year−1 of water over the watershed from the river to groundwater owing to the lateral flow, river–aquifer interactions led to an increase in soil evaporation and transpiration supplied by higher soil moisture content, particularly in deeper subsurface. In a hypothetic future scenarios where a 5‐m rise in river stage was assumed, the hydrologic flow exchange rates were intensified, resulting in higher surface water over the entire watershed. Overall, lateral flow induced by river–aquifer exchanges leads to an increase in evapotranspiration of ~75% in the historical period and of ~83% in the hypothetical future scenario. Our study demonstrates the potential of coupled model as an effective tool for understanding river–aquifer–land surface interactions, and indicates that river–aquifer interactions fundamentally alter the water balance of the riparian zone for the semiarid watershed and will likely become more frequent and intense in the future under the effects of climate change.

The African fulcrum to bend the curve of the climate crisis to a just transition

From a current impact of under 4% of global greenhouse gas emissions, rapid industrialization and population growth in Africa could dramatically change the continent’s emissions profile. In this study, we develop an analytic framework to quantify future scenarios and project that, in mid- and green-growth scenarios, Africa’s emissions would amount to just 4%–13% of the planned carbon savings in major economies. However, in a high-growth scenario without climate-conscious development, African emissions could jeopardize global mitigation efforts. Less than 20 nations could account for 80%–90% of the continent’s emissions, highlighting the critical role of green growth pathways centered on rapid clean energy adoption in just a few countries to transform the continent’s energy landscape. A 20-fold increase in investment and project completion rates is required to meet the renewable energy targets in these countries’ Nationally Determined Contributions (NDCs). Our analysis underscores the need for nuanced country-specific strategies that prioritize equity and financial support for optimal climate and development progress in Africa.

A nature‐inclusive future with healthy soils? Mapping soil organic matter in 2050 in the Netherlands

AbstractNature‐inclusive scenarios of the future can help address numerous societal challenges related to soil health. As nature‐inclusive scenarios imply sustainable management of natural systems and resources, land use and soil health are assumed to be mutually beneficial in such scenarios. However, the interplay between nature‐inclusive land use scenarios and soil health has never been modelled using digital soil mapping. We predicted soil organic matter (SOM), an important indicator of soil health, in 2050, based on a recently developed nature‐inclusive scenario and machine learning in 3D space and time in the Netherlands. By deriving dynamic covariates related to land use and the occurrence of peat for 2050, we predicted SOM and its uncertainty in 2050 and assessed SOM changes between 2022 and 2050 from 0 to 2 m depth at 25 m resolution. We found little changes in the majority of mineral soils. However, SOM decreases of up to 5% were predicted in grasslands used for animal‐based production systems in 2022, which transitioned into croplands for plant‐based production systems by 2050. Although increases up to 25% SOM were predicted between 0 and 40 cm depth in rewetted peatlands, even larger decreases, on reclaimed land even surpassing 25% SOM, were predicted on non‐rewetted land in peat layers below 40 cm depth. There were several limitations to our approach, mostly due to predicting future trends based on historic data. Furthermore, nuanced nature‐inclusive practices, such as the adoption of agroecological farming methods, were too complex to incorporate in the model and would likely affect SOM spatial variability. Nonetheless, 3D‐mapping of SOM in 2050 created new insights and raised important questions related to soil health behind nature‐inclusive scenarios. Using machine learning explicit in 3D space and time to predict the impact of future scenarios on soil health is a useful tool for facilitating societal discussion, aiding policy making and promoting transformative change.

Risk and Systems Analysis for Renewable Power Generation with Environmental and Other Stressors

AbstractThe effects of climate change and water scarcity threaten the stability of critical infrastructure systems in developing regions. In particular, the interconnectedness of energy systems, natural resources, economic growth, and social welfare requires a systems‐level framework to identify scenarios which most impact these systems. This paper evaluates and quantifies infrastructure system risk, defined as the influence of scenarios on system priorities. A scenario‐based multi‐criteria preferences model assesses system component priorities for a baseline scenario as well as climate and related scenarios. The shift in priorities between the baseline and other future scenarios define scenario disruptiveness, or level of risk. The methods are demonstrated for the case of the emerging renewable energy sector of Iraq. Twenty‐five renewable energy system assets are prioritized by an assessment of system success criteria, which include economic, social, political, and climate considerations. The system prioritization is reevaluated in the case of seven disruptive scenarios relating to water scarcity, climate volatility, and social and economic shifts. This paper advances methods of the Systems Engineering Body of Knowledge (SEBoK) Part 3: Engineering and Management, by defining system risk and proposing methods for risk identification and risk analysis.