Future Supply Research Articles

Validation methods in modelling the PANDA IPSS experiment: A MELCOR 2.2 assessment of a passive isolation condenser

Nuclear power plants (NPPs) are becoming increasingly interesting for future energy supply. Nowadays, most of the modern NPPs, such as Generation III+ and Small Modular Reactors (SMRs), offer an even higher safety standard than their predecessors, often relying on passive systems. Due to the continuous improvement of design and safety solutions in the nuclear field, it is essential to simultaneously increase modelling capabilities. This can be achieved by the advancement of modelling tools and by increasing the experience of the analyst. This work focuses on code validation to potentially allow users to gain modelling experience and to provide insights for further code development. Due to the complexity of severe accidents, it may prove to be challenging to model passive systems under such conditions, thus the validation is especially important for numerical codes, such as MELCOR.Codes used for the simulation of severe accidents are simplified in order to be capable of capturing all occurring phenomena in a realistic computational time frame. Thus, it is not trivial if these codes are capable of modelling the combination of passive safety systems within the new integrated features present in many NPP designs. For this reason, this work aims to investigate how such assessments should be performed as well as to consider the severe accidents code MELCOR with respect to the simulation of a passive isolation condenser at the large-scale experimental facility PANDA with and without the presence of non-condensable gases.Our work summarises the present ideas with regards to validation and verification of nuclear codes and highlights the fact that the severe accident code MELCOR is capable of simulating passive safety systems, such as the passive isolation condenser. Improvements can be made when modelling condensation in the presence of non-condensable gases and thus suggestions were made for the improved modelling.

Assessing Best Practices in Natural Gas Production and Emerging CO2 Capture Techniques to Minimize the Carbon Footprint of Electricity Generation.

Natural gas (NG) is expected to provide a substantial portion of electricity generation in many jurisdictions for the foreseeable future. Postcombustion carbon capture and storage (CCS) effectively abates direct CO2 emissions; however, indirect NG supply chain emissions in most jurisdictions are incompatible with climate change mitigation goals. This life cycle assessment evaluates specific opportunities to reduce the carbon footprint of combined cycle gas turbine (CCGT) generation with CCS using existing low-emission NG production practices, technologies, and processes combined with emerging CCS techniques to achieve high CO2 capture rates and mitigate startup emissions. We find baseload life cycle greenhouse gas (GHG) emission intensity ranges from 22 to 62 kgCO2e/MWh for 95-98.5% CO2 capture, within the range of published estimates for wind and photovoltaic power and considerably below prior estimates of CCGT with CCS. Low-emission NG production practices reduce other environmental impacts, which are dominated by combustion-related air pollution. We also show how interim solvent storage can effectively mitigate emissions from CCGT start/stop cycles. This work highlights the importance of mitigating both CO2 and methane emissions from NG supply chains and proposes a more nuanced discussion regarding the potential contribution of NG to the future energy supply. A surrogate model is provided to estimate life cycle GHG emissions for CCGT with CCS and user-input parameters.

Mapping and assessing the future provision of lake ecosystem services in Lithuania

Lakes supply multiple ecosystem services (ES), key to supporting socio-ecologic systems and human well-being. In the context of future land use and climate changes, it is imperative to anticipate potential impacts on lake ES supply. Hence, studies that deal with future lake ES, such as mapping, are lacking. In this work, we mapped and assessed the future supply of three ES: (1) maintenance of nursery conditions (nursery ES), (2) maintenance of chemical conditions of freshwaters (nutrient regulation ES), and (3) direct and indirect cultural outputs (recreation ES) in Lithuania. Four future scenarios were utilised, integrating land use and climate changes: A0 - business as usual; A1-urbanisation; A2: land abandonment and afforestation; and A3 - agricultural intensification. The projected year was 2050, following the intermediate Representative Concentration Pathway (RCP 4.5). The future scenarios were simulated using the open-source software Dinamica EGO based on a 6-step modelling framework. Statistical differences among the scenarios and ES were analysed by applying a Kruskal-Wallis ANOVA test. Spatial analysis was done by performing a Moran's I and Getis-Ord (Gi∗) hotspot analysis. The results showed significant differences in nursery and nutrient regulation ES. The highest supply in nursery ES was observed for the A0 scenario. For nutrient regulation ES, the lowest ES supply was identified for the A1 scenario and for recreation ES, the highest was found in the A2 scenario. The eastern and northeastern regions of Lithuania showed a high ES supply. Hot spots were only identified in the eastern region. These regions are associated with a high area covered by forests and protected areas. The central region shows a low ES supply, identified as a cold spot where the agricultural landscape dominates. The results of the PCA analysis revealed an association between nursery and recreation ES. Nutrient regulation was not associated with the other two ES. Mapping and assessing the impact of future scenarios is vital to anticipating the potential dynamics of lake ES, especially in the context of climate change. This information is essential in the context of environmental management, helping decision-makers to ensure a sustainable ES supply and contributing to human wellbeing.

How to ensure an appropriate oral health workforce? Modelling future scenarios for the Netherlands

BackgroundCurrent methods for oral health workforce planning lack responsiveness to dynamic needs, hampering efficiency, equity and sustainability. Effective workforce planning is vital for resilient health care systems and achieving universal health coverage. Given this context, we developed and operationalised a needs-adaptive oral health workforce planning model and explored the potential of various future scenarios.MethodsUsing publicly available data, including the Special Eurobarometer 330 Oral Health Survey, we applied the model in a hypothetical context focusing on the Dutch population’s dental needs from 2022 to 2050. We compared current and future provider supply and requirement and examined, in addition to a base case scenario, several alternative scenarios. These included epidemiological transition scenarios with different oral health morbidity trajectories, skill-mix scenarios with independent oral hygienists conducting check-ups and multiple dental student intake and training duration (5 instead of 6 years) scenarios.ResultsBased on the aforementioned historical data, our model projects that provider requirement will exceed supply for the planning period. If the percentage of people having all natural teeth increases by 10% or 20% in 2032, 34 or 68 additional full-time equivalent (FTE) dentists will be required, respectively, compared to the base case scenario. In the skill-mix scenario, the model indicates that prioritising oral hygienists for check-ups and shifting dentists’ focus to primarily complex care could address population needs more efficiently. Among the student intake and training duration scenarios, increasing intake to 375 and, to a lesser extent, reducing training to 5 years is projected to most effectively close the provider gap.ConclusionsThe study underscores the importance of understanding oral health morbidity trajectories for effective capacity planning. Due to limited dental epidemiological data, projections carry substantial uncertainty. Currently, demand for FTE dentists seems to exceed supply, though this may vary with epidemiological changes. Skill-mix strategies could offer efficiency gains by redistributing tasks, while adjustments in dental intake and training duration could also help address the requirement-supply gap. Resolving dentistry workforce challenges requires a multifaceted approach, including strengthening oral epidemiology projections, addressing the root causes of dental health issues and prioritising harmonious dental public health and general practice prevention measures.

Quantifying the Effect of Land Use and Land Cover Changes on Spatial-Temporal Dynamics of Water in Hanjiang River Basin

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by 96.275×108t, 81.210×108t, and 84.13×108t relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply.

Antibiotic treatment recommendations for acute respiratory tract infections in Scandinavian general practices—time for harmonization?

Introduction During recent years, the world—including Scandinavia—has experienced significant challenges with shortages of antibiotics. In Scandinavia, phenoxymethylpenicillin is recommended as first-line antibiotic treatment for most acute respiratory tract infections (ARTIs). However, the Scandinavian countries each constitute rather small markets for phenoxymethylpenicillin. The aim of this discussion paper is to enlighten the differences in Scandinavian ARTI antibiotic treatment recommendations. This information is fundamental for exploring the potential of harmonizing treatment recommendations in Denmark, Norway and Sweden—to help ensure sufficient future supply of phenoxymethylpenicillin. Methods Information from national ARTI antibiotic treatment recommendations from respectively Denmark, Norway and Sweden has been collated. Results Several discrepancies exist in recommendations. Adult dosage varies from a minimum of 660 mg x 4 (Denmark) to a maximum of 2000 mg × 3 (Sweden). Within Norway and Sweden, variations in recommended dosage also exist between the different types of ARTIs. A main challenge is that the tablet strengths recommended, and available on the market in the three countries, differs. Also, antibiotic treatment durations vary significantly between countries and infections treated—from five to 10 days of treatment. Conclusion In the capacity of a well-established network for antibiotic stewardship, we have enlightened the differences in Scandinavian ARTI antibiotic treatment recommendations. This paper is the first step moving forward to scrutinizing the potential for harmonizing recommendations for Denmark, Norway and Sweden—to help ensure continued supply of phenoxymethylpenicillin for use within the Scandinavian countries.

Comparison of hydrogen effects on additively manufactured and conventional austenitic steels

Hydrogen and its derivatives are promising energy carriers for future renewable energy supplies. Austenitic stainless steels, such as AISI 316L, are commonly used in hydrogen transportation systems. While often thought to be resistant to hydrogen embrittlement, studies have shown that 316L is susceptible under certain conditions. As demand for hydrogen applications grows, additive manufacturing (AM) technologies offer design flexibility and customisation benefits. However, data on AM parts behaviour in hydrogen environments is lacking. This study investigates the influence of hydrogen on mechanical properties using slow strain rate testing (SSRT) on conventional AISI 304L, 316L and AM 316L specimens. The results indicate a greater effect of hydrogen on 304L compared to 316L, with AM 316L showing increased susceptibility. However, the ductility of AM 316L remains comparable to conventional 316L due to its initial ductility. The study provides insights into the performance of conventional and AM austenitic stainless steels in gaseous hydrogen environments.

Hydrological dichotomy: Streamflow drought in dammed vs. undammed regions of the largest Indian Peninsular basin

Recurring streamflow drought is a significant problem, and it is necessary to comprehend the spatiotemporal patterns and driving factors of historical droughts in order to manage future water supplies effectively. Drought has significant and often severe effects on semi-arid and arid zones due to the already limited availability of water in these regions. Therefore, this research examines streamflow drought fluctuations in India's second-largest basin, the Godavari. The Godavari Basin experiences diverse climatic conditions ranging from humid in the Western Ghats to semi-arid in central and western regions. Frequent hydrological droughts have plagued the Godavari basin. Still, the impact of human activities and climate change on the streamflow drought characteristics in the Godavari basin has not been thoroughly investigated. Further, assessing the regional control of streamflow drought deficit and intensity in a large river basin like Godavari is crucial. The findings show that streamflow drought is worst in central and eastern Godavari. Additionally, dammed and undammed locations have different streamflow drought characteristics. Droughts have lengthened in dam-affected areas. However, regions without dams have seen more deficit and intensity. Thus, different sustainable water management techniques are needed for the dammed and undammed regions of the Godavari basin.

Analysis of medical doctor supply in Bulgaria, Croatia, Romania, and Serbia in the last two decades

Abstract Introduction The shortage of medical doctors in Europe has become a priority issue, especially in the Eastern European region, where many countries are making large investments to ensure adequate supply. Training future medical professionals is a lengthy and costly process that significantly impacts healthcare accessibility and quality. As part of the project of the Laboratory for Strengthening Capacity and Performance of Health System and Workforce for Health Equity, we aimed to assess the changes in the medical doctor supply in several Balkan countries over the last two decades. Methods Data on the total number of graduated medical doctors (MD) and their rate per 100,000 population from 2000 to 2021 was retrieved from the Eurostat database. This open-access database contains the reported number of students who have graduated from medical schools for each year and selected countries (Bulgaria, Croatia, Romania, and Serbia). The standard methods of descriptive statistics were used to perform the analysis. Results During the observed period, the number of MDs graduating annually and their rates per 100,000 population have increased in all countries. Romania leads with the highest rate of MD graduates at 26.18 per 100,000 people, followed by Bulgaria (22.74), Serbia (19.15), and Croatia (16.56). From 2000 to 2021, Bulgaria experienced the highest increase in the number of medical doctor graduates, with a rise of 169.1% in absolute numbers and 200.9% in rate. In contrast, Serbia had the lowest increase, with only a 135.5% rise in the total number of medical doctor graduates and a rate increase of 152.3%. Conclusions In the last 20 years, Bulgaria, Croatia, Romania, and Serbia have seen an increase in the number of MD graduates and their rate per 100,000 population. Policymakers should consider the investments and education trends of medical professionals in relation to their employment and migrations to predict the future supply of doctors in this region. Key messages • Bulgaria, Croatia, Romania, and Serbia have seen an increase in the number of MD graduates and their rate per 100,000 population in the last twenty years. • Considering the trends in the number of newly graduated doctors, policymakers should address additional factors to ensure an adequate supply of medical doctors.

Forecasting the future demand on health workforce – the Austrian model

Abstract Background An aging population and upcoming retirements of health care professionals pose new challenges to the Austrian health care system. On the one hand the health care and nursing needs will increase while at the same time a shortage on health care personnel threatens adequate supply. We as the Austrian Public Health Institute estimate the risk of an undersupply with model-based health workforce forecasting and are therefore able to advise the federal states and the ministry of health on early measures. Methods We developed a forecasting model to estimate the future supply and demand. The main influencing factors are the demographic development as well as the upcoming retirements and graduates. Different environments and needs in different federal states (e.g. changing future skill mix) are considered and can be implemented as additional influencing factors. Usually a baseline scenario as a forward projection of the status quo is developed for defined professions and workplace settings. Then depending on the stakeholder needs additional scenarios are calculated and discussed. A usual forecast horizon is 10 to 15 years. Results Model output are time series of yearly workforce personnel numbers (heads and full-time equivalents) and demand in the defined settings. An analysis of possible gaps shows the need for action. Conclusions The Austrian health workforce model is a flexible tool to estimate future needs on health workforce based on different assumptions. But as assumptions and influencing factors must be represented as data, the data collection can be quite laborious. Comprehensive data is the basis for a comprehensive health workforce planning. Further work is needed to develop practical and evidence-based nursing-sensitive indicators for nursing workfoce reportings on a country level. In Austria the health professionals register forms the basis for the description of the nursing workforce structure, but that there is a need for data sources on care effectiveness. Key messages • Comprehensive data is the basis for a comprehensive health workforce planning. • An aging population and upcoming retirements of health care professionals pose new challenges to the Austrian health care system.

Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.

Global Supply Chain Information Sharing Platforms: Challenges, Opportunities, and Trends

With the deepening process of globalization, the rapid development of information technology and the diversification of logistics methods, supply chain management faces increasingly complex challenges. This study explores the future development trends, challenges and opportunities of information sharing platforms in global supply chains. In global supply chains, information sharing platforms are of great significance. It facilitates real-time data exchange and collaboration among supply chain segments and help improve supply chain transparency, flexibility and efficiency. However, global supply chain information sharing faces many challenges, including problems in information security, technical standards, cultural differences, and laws and regulations. In response to these challenges, this paper proposes future directions for developing a global supply chain information sharing platform, including digitization, automation, cross-platform cross-border collaboration, data security and privacy protection, application of new technologies, and flexibility and adaptability. This study provides useful insights and guidance for future global supply chain management by exploring the global supply chain information sharing platform.

A Review of the Sustainability of Helium: An Assessment of Its Past, Present and a Zero-Carbon Future

Helium, as a by-product of the natural gas industry, will be impacted by the decline in consumption of fossil fuels as the world moves towards net-zero carbon emissions. In September 2022, all assets relating to the US government’s previous helium industry were sold. In the US, helium is now only available from private suppliers. In June 2022, Russia banned the export of helium to “unfriendly” countries, highlighting the geopolitical issues surrounding the industry. In the past, helium was popularized, and the industry was supported by its military applications (filling dirigible aircraft, welding fighter jets and purging rocket engines). It also plays an important role in supporting present-day technologies (e.g., MRI machines and spectroscopy) and will also be important for a high-tech future (e.g., in quantum computing, fusion power, and space exploration). Shortages of helium will inevitably cause skyrocketing prices and consequently lead to significant challenges for research and development (as has happened in the past) and technological progress, as well as a slowdown in world economic growth and prosperity. Anticipated declines in natural gas production, associated with moves towards net-zero carbon emissions targets, make helium less accessible. While this is problematic for industry in the short term, it perhaps preserves some low entropy helium within the ground, making it more accessible to future generations. Given anticipated limitations to the future supply of helium, technological developments are currently focused on a few areas: the replacement of helium by other gases in industrial applications, changing technological approaches to not require helium, and reducing the cost of obtaining helium from the atmosphere. This paper explores the past, present and future of helium, focusing on the sustainability of the helium industry.

A LSTM-based time series prediction model for China's power supply and its performance evaluation

With the rapid development of China's economy and the acceleration of urbanisation, the stability and sustainability of power supply has become increasingly important. In order to cope with the continuous growth of power demand, accurate power supply prediction has become an essential research direction. In this paper, a time series prediction model for power supply is constructed based on the long short-term memory network (LSTM). Power supply data is organised using methods such as data pre-processing and correlation analysis, and the LSTM model is used to predict future power supply. The performance of the prediction results was evaluated, and the results showed that the model can effectively capture the changing trends of power supply, with high reliability and accuracy, providing important support for scientific management and policy formulation in the power industry.

Genome-based discovery of pachysiphine synthases in Tabernaemontana elegans.

Plant-specialized metabolism represents an inexhaustible source of active molecules, some of which have been used in human health for decades. Among these, monoterpene indole alkaloids (MIAs) include a wide range of valuable compounds with anticancer, antihypertensive, or neuroactive properties. This is particularly the case for the pachysiphine derivatives which show interesting antitumor and anti-Alzheimer activities but accumulate at very low levels in several Tabernaemontana species. Unfortunately, genome data in Tabernaemontanaceae are lacking and knowledge on the biogenesis of pachysiphine-related MIAs in planta remains scarce, limiting the prospects for the biotechnological supply of many pachysiphine-derived biopharmaceuticals. Here, we report a raw version of the toad tree (Tabernaemontana elegans) genome sequence. These new genomic resources led to the identification and characterization of a couple of genes encoding cytochrome P450 with pachysiphine synthase activity. Our phylogenomic and docking analyses highlight the different evolutionary processes that have been recruited to epoxidize the pachysiphine precursor tabersonine at a specific position and in a dedicated orientation, thus enriching our understanding of the diversification and speciation of the MIA metabolism in plants. These gene discoveries also allowed us to engineer the synthesis of MIAs in yeast through the combinatorial association of metabolic enzymes resulting in the tailor-made synthesis of non-natural MIAs. Overall, this work represents a step forward for the future supply of pachysiphine-derived drugs by microbial cell factories.

Environmental and Socio-Economic Impact Assessment of Renewable Energy Using Machine Learning Models

Renewable energy sources, such as solar, hydro, wind, and geothermal energy, have emerged as key alternatives to fossil fuels in combating climate change and addressing energy security concerns in the USA and ad worldwide. Strategic use of this renewable resource is important not only for carbon emission reduction and improvement of environmental sustainability but also for maintaining future energy supplies. At the same time, such transition raises thorough assessments of environmental and socio-economic impacts. Machine learning (ML) models offer a powerful tool for predicting and analyzing such impacts, allowing for more efficient decision-making and long-term planning. These models are supposed to analyze patterns in energy production, land use, and emissions to make a more dynamic and predictive understanding of how renewable energy adoption influences CO2 levels. The principal aim of this research project was to develop and curate machine learning algorithms for predicting CO2 emissions based on renewable energy data, using the knowledge to better understand how solar, wind, hydro, and geothermal energy systems affect environmental outcomes. The predictive models developed in this research would serve as useful tools for the policymakers and major stakeholders in decision-making on investments in energy infrastructure and characterization of regulatory frameworks. These datasets for this research project were retrieved from several prominent institutions, including governmental agencies, international organizations such as the International Energy Agency-IEA and the World Bank, satellite data repositories, and USA environmental monitoring agencies. For this research project, 3 machine learning algorithms in the experiment were used, namely Logistic Regression, XG-Boost, and Random Forest. Amongst these three, the linear regression model gave the best performance, as it had the least MSE; indicating that its predictive capability was impressive. The comparative analysis of renewable energy projects in Germany, China, and California underlines that effective policy-making plays a very decisive role in the transition toward sustainable energy.

Uniting agricultural water management, economics, and policy for climate adaptation through a new assessment of water markets for arid regions

Flexible policies aimed at irrigated agriculture are essential to adapt to climate change. Despite the importance of this goal, little published work has conceptualized, formulated, developed, and applied an integrated optimization framework for irrigated agriculture to guide adaptation to climate-related water stress. This research addresses the question: how can water management plans for irrigated agriculture be designed to minimize economic losses caused by adapting to climate-induced water stress? The study answers this by developing an optimization approach that identifies water use patterns to minimize farm income losses during water shortages, considering three water shortage sharing programs. An optimization model, calibrated using positive mathematical programming, is applied to replicate historical land use while adapting to future water supplies that deviate from the historical pattern. The analysis focuses on two irrigated regions in North America’s Rio Grande Basin, illustrating land use, water use, and cropping patterns that minimize regional farm economic losses to shortages. These losses are assessed under three water-sharing strategies: intercrop and interdistrict trading (IIT), intercrop and intradistrict trading (IRT), and no trading (NT). The results demonstrate that IIT yields an average economic gain of $2.824 million per year, while IRT results in an average gain of $2.600 million per year compared to NT. These findings offer valuable insights for water managers, scientists, stakeholders, and policymakers tasked with developing irrigation management strategies in arid regions facing future water supply challenges. The methods developed and results shown here highlight a path forward, using scientific, economic, and policy innovations to strengthen agricultural livelihoods in regions facing uncertain water availability.

Impact of resilience policies on cape town's water-food nexus: A system dynamics approach

BackgroundClimate change is increasingly affecting the supply of resources such as water and food. From 2015 to 2018, Cape Town endured its most severe drought on record. Yet, resource management often occurs in isolation, which contrasts with the holistic perspective provided by the nexus concept that recognizes the interdependence of resource sectors. This study employs system dynamics modeling, to examine the City of Cape Town’s (CoCT) water-food trade-offs and interactions using qualitative and quantitative approaches. It assesses various policies proposed by the CoCT, to improve system resilience and to boost future water supplies, examining their efficacy and potential drawbacks. These policies are tested against future scenarios including population growth and climate change predictions of different severities.ResultsThe simulation results show an increase in food demand, which is mainly linked to population growth and a significant decrease in water availability. Without intervention, the CoCT is expected to experience serious water shortages within the 40-year simulation period.ConclusionsThe findings indicate that the CoCT’s strategies will effectively secure adequate water for its expanding population. However, a major concern was found to be the proposed intensification of aquifer exploitation. The model predicts that such an approach could lead to overabstraction of some aquifers, compromising their sustainability.

Combining thinning and diverse plantings to adapt to climate-change-induced timber supply shortage in British Columbia

Forestry is an important component of Canada’s economy with British Columbia (BC) contributing almost half to the national roundwood production. Yet, the country’s timber supply and forest economy are threatened by climate change, with increased frequency and severity of natural disturbances and changes in forest productivity. Mountain pine beetle ( Dendroctonus ponderosae) outbreaks are endemic in BC, but the latest climate change-driven outbreak has resulted in a cumulative loss of over half of all merchantable pine, leading to a mid-term timber supply shortage. In this study, we investigate the potential of commercial thinning and alternative planting regimes based on species diversification and assisted species migration to mitigate the anticipated decrease in timber supply in BC. We simulated the long-term effects of these management options in a case study area in interior BC, using a toolbox that combines management- and research-oriented data and models. We found that combining commercial thinning and species diversification has the best potential to mitigate future timber supply shortages in BC. We discuss the limits of this toolbox approach and identify research needs and recommendations for future studies aiming at modelling cumulative effects of management, climate change, and natural disturbances on timber supply.

The use of predictive modelling to determine the likelihood of donor return during the COVID-19 pandemic.

Artificial intelligence (AI) uses sophisticated algorithms to "learn" from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. Predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty-four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID-19. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated; implementation of this method could prove useful for BE operations.