A system dynamics approach to understand Dutch adolescents' sleep health using a causal loop diagram.

BACKGROUND: Healthy sleep is crucial for the physical and mental wellbeing of adolescents. However, many adolescents suffer from poor sleep health. Little is known about how to effectively improve adolescent sleep health as it is shaped by a complex adaptive system of many interacting factors. This study aims to provide insights into the system dynamics underlying adolescent sleep health and to identify impactful leverage points for sleep health promotion interventions. METHODS: Three rounds of single-actor workshops, applying Group Model Building techniques, were held with adolescents (n = 23, 12-15 years), parents (n = 14) and relevant professionals (n = 26). The workshops resulted in a multi-actor Causal Loop Diagram (CLD) visualizing the system dynamics underlying adolescent sleep health. This CLD was supplemented with evidence from the literature. Subsystems, feedback loops and underlying causal mechanisms were identified to understand overarching system dynamics. Potential leverage points for action were identified applying the Action Scales Model (ASM). RESULTS: The resulting CLD comprised six subsystems around the following themes: (1) School environment; (2) Mental wellbeing; (3) Digital environment; (4) Family & Home environment; (5) Health behaviors & Leisure activities; (6) Personal system. Within and between these subsystems, 16 reinforcing and 7 balancing feedback loops were identified. Approximately 60 potential leverage points on different levels of the system were identified as well. CONCLUSIONS: The multi-actor CLD and identified system dynamics illustrate the complexity of adolescent sleep health and supports the need for developing a coherent package of activities targeting different leverage points at all system levels to induce system change.

Rural Sanitation Sustainability Dynamics: Gaining Insight through Participatory and Simulation Modeling.

Improved sanitation provides many benefits to human health and well-being and is integral to achieving Sustainable Development Goal Six. However, many nations, including most of sub-Saharan Africa, are not on track to meeting sanitation targets. Recognizing the inherent complexity of environmental health, we used systems thinking to study sanitation sustainability in Uganda. Our study participants, 37 sanitation actors in three rural districts, were engaged in interviews, group model building workshops, and a survey. The resulting model was parametrized and calibrated using publicly available data and data collected through the Uganda Sanitation for Health Activity. Our simulations revealed slippage from improved sanitation in all study districts, a behavior reflected in real interventions. This implies that systemic changes-changes to the rules and relationships in the system-may be required to improve sanitation outcomes in this context. Adding reinforcing feedback targeting households' perceived value of sanitation yielded promising simulation results. We conclude with the following general recommendations for those designing sanitation policies or interventions: (1) conceptualize sanitation systems in terms of reinforcing and balancing feedback, (2) consider using participatory and simulation modeling to build confidence in these conceptual models, and (3) design many experiments (e.g., simulation scenarios) to test and improve understanding.

Reducing mental health emergency visits: population-level strategies from participatory modelling.

BACKGROUND: Emergency departments (EDs) are often the front door for urgent mental health care, especially when demand exceeds capacity. Long waits in EDs exert strain on hospital resources and worsen distress for individuals experiencing a mental health crisis. We used as a test case the Australian Capital Territory (ACT), with a population surge of over 27% across 2011-2021 and a lagging increase in mental health care capacity, to evaluate population-based approaches to reduce mental health-related ED presentations. METHODS: We developed a system dynamics model for the ACT region using a participatory approach involving local stakeholders, including health planners, health providers and young people with lived experience of mental health disorders. Outcomes were projected over 2023-2032 for youth (aged 15-24) and for the general population. RESULTS: Improving the overall mental health care system through strategies such as doubling the annual capacity growth rate of mental health services or leveraging digital technologies for triage and care coordination is projected to decrease youth mental health-related ED visits by 4.3% and 4.8% respectively. Implementation of mobile crisis response teams (consisting of a mental health nurse accompanying police or ambulance officers) is projected to reduce youth mental health-related ED visits by 10.2% by de-escalating some emergency situations and directly transferring selected individuals to community mental health centres. Other effective interventions include limiting re-presentations to ED by screening for suicide risk and following up with calls post-discharge (6.4% reduction), and limiting presentations of frequent users of ED by providing psychosocial education to families of people with schizophrenia (5.1% reduction). Finally, combining these five approaches is projected to reduce youth mental health-related ED presentations by 26.6% by the end of 2032. CONCLUSIONS: Policies to decrease youth mental health-related ED presentations should not be limited to increasing mental health care capacity, but also include structural reforms.

Designing a model to estimate the burden of COVID-19 in Iran.

The novel coronavirus disease 2019 (COVID-19) is the latest evidence of an epidemic disease resulting in an extraordinary number of infections and claimed several lives, along with extensive economic and social consequences. In response to the emergency situation, countries introduced different policies to address the situation, with different levels of efficacy. This paper outlines the protocol for developing a model to analyze the burden of COVID-19 in Iran and the effect of policies on the incidence and cumulative death of the disease. The importance of the model lies in the fact that no study, according to the authors' best knowledge, tried to quantify the impact of the disease on Iran society and the impact of various implemented interventions on disease control. Based on a systematic review of COVID-19 prediction models and expert interviews, we developed a system dynamics model that not only includes an epidemic part but also considers the impact of various policies implemented by the Ministry of Health. The epidemic model estimates the incidence and mortality of COVID-19 in Iran. The model also intends to evaluate the effect of implemented policies on these outcomes. The model reflects the continuum of COVID-19 infection and care in Iran (of which some of its elements are unique) and key activities and decisions in delivering care. The model is calibrated and validated using data published by the Ministry of Health of Iran. Finally, the study aims to provide evidence of the impact of interventions intended to curb COVID-19 in Iran. Insights provided by the model will be necessary for controlling either future waves of the disease or similar future pandemics.

A dynamic management model for sustainable drug supply chain in hospital pharmacies in Iran.

BACKGROUND: Sustainable supply chain management encompasses the strategic coordination and control of material, information, and financial flows, as well as the collaborative efforts among the entities engaged in the medicinal supply chain. This research proposes a dynamic and sustainable supply chain management model tailored explicitly for the inpatient pharmacies of Medical Centers and Hospitals affiliated with Iran University of Medical Sciences. METHODS: This is a quantitative study in terms of research objective and a qualitative study based on the stages in the conceptual development of the model. Therefore, the current study can be considered a mixed-methods approach. After identifying the key factors influencing the sustainability of the medicine supply chain, we conducted a dynamic analysis of the problem using system dynamics methodology. In order to simulate the system's behavior over 24 months, we utilized a combination of existing documentary information and expert opinions. The developed model was implemented using Vensim PLE software, allowing us to simulate and analyze the impact of various policies on the system. RESULTS: Medicine disposal exhibited an upward trend, particularly during the second 12-month period. Conversely, the trend of medicine expirations remained relatively stable in the initial months but showed an upward trajectory after that. The cost associated with disposed medicine experienced a consistent increase, with a higher rate observed during the second 12-month period. In contrast, sales of low-consumable medicine experienced a significant initial surge followed by a slower growth rate. Finally, the pharmacy's profit demonstrated an overall increasing trend, although the rate of increase was higher during the first 12 months. CONCLUSION: Among the various scenarios considered, namely "increasing the adequacy of human resources," "increasing the speed of response," and "utilizing pharmacists in the drug prescribing team," it was found that these interventions had a substantial effect on both enhancing the pharmacy's profit and reducing medication waste. Therefore, these scenarios were identified as having the most significant impact. The proposed model can serve as a valuable tool for forecasting and informing policy-making, providing insights into addressing the challenges associated with the sustainable drug supply chain in hospital pharmacies.

Does a GP service package matter in addressing the absence of health management by the occupational population? A modelling study.

OBJECTIVE: To assess the influence of supply and demand factors on the contract behavior of occupational populations with general practitioner (GP) teams. METHODS: We employed a system dynamics approach to assess and predict the effect of the general practitioner service package (GPSP) and complementary incentive policies on the contract rate for 2015-2030. First, the GPSP is designed to address the unique needs of occupational populations, enhancing the attractiveness of GP contracting services, including three personalized service contents tailored to demand-side considerations: work-related disease prevention (WDP), health education & counseling (HEC), and health-care service (HCS). Second, the complementary incentive policies on the supply-side included income incentives (II), job title promotion (JTP), and education & training (ET). Considering the team collaboration, the income distribution ratio (IDR) was also incorporated into supply-side factors. FINDINGS: The contract rate is predicted to increase to 57.8% by 2030 after the GPSP intervention, representing a 15.4% increase on the non-intervention scenario. WDP and HEC have a slightly higher (by 2%) impact on the contract rate than that from HCS. Regarding the supply-side policies, II have a more significant impact on the contract rate than JTP and ET by 3-5%. The maximum predicted contract rate of 75.2% is expected by 2030 when the IDR is 0.5, i.e., the GP receives 50% of the contract income and other members share 50%. CONCLUSION: The GP service package favorably increased the contract rate among occupational population, particularly after integrating the incentive policies. Specifically, for a given demand level, the targeted content of the package enhanced the attractiveness of contract services. On the supply side, the incentive policies boost GPs' motivation, and the income distribution motivated other team members.

Contributing to health system resilience during pandemics via purchasing and supply strategies: an exploratory system dynamics approach.

BACKGROUND: Health systems worldwide struggled to obtain sufficient personal protective equipment (PPE) and ventilators during the COVID-19 pandemic due to global supply chain disruptions. Our study's aim was to create a proof-of-concept model that would simulate the effects of supply strategies under various scenarios, to ultimately help decision-makers decide on alternative supply strategies for future similar health system related crises. METHODS: We developed a system dynamics model that linked a disease transmission model structure (susceptible, exposed, infectious, recovered (SEIR)) with a model for the availability of critical supplies in hospitals; thereby connecting care demand (patients' critical care in hospitals), with care supply (available critical equipment and supplies). To inform the model structure, we used data on critical decisions and events taking place surrounding purchase, supply, and availability of PPE and ventilators during the first phase of the COVID-19 pandemic within the English national health system. We used exploratory modelling and analysis to assess the effects of uncertainties on different supply strategies in the English health system under different scenarios. Strategies analysed were: (i) purchasing from the world market or (ii) through direct tender, (iii) stockpiling, (iv) domestic production, (v) supporting innovative supply strategies, or (vi) loaning ventilators from the private sector. RESULTS: We found through our exploratory analysis that a long-lasting shortage in PPE and ventilators is likely to be apparent in various scenarios. When considering the worst-case scenario, our proof-of-concept model shows that purchasing PPE and ventilators from the world market or through direct tender have the greatest influence on reducing supply shortages, compared to producing domestically or through supporting innovative supply strategies. However, these supply strategies are affected most by delays in their shipment time or set-up. CONCLUSION: We demonstrated that using a system dynamics and exploratory modelling approach can be helpful in identifying the purchasing and supply chain strategies that contribute to the preparedness and responsiveness of health systems during crises. Our results suggest that to improve health systems' resilience during pandemics or similar resource-constrained situations, purchasing and supply chain decision-makers can develop crisis frameworks that propose a plan of action and consequently accelerate and improve procurement processes and other governance processes during health-related crises; implement diverse supplier frameworks; and (re)consider stockpiling. This proof-of-concept model demonstrates the importance of including critical supply chain strategies as part of the preparedness and response activities to contribute to health system resilience.

Dynamic modeling of human error in industrial maintenance through structural analysis and system dynamics.

Human error constitutes a significant cause of accidents across diverse industries, leading to adverse consequences and heightened disruptions in maintenance operations. Organizations can enhance their decision-making process by quantifying human errors and identifying the underlying influencing factors, thereby mitigating their repercussions. Consequently, it becomes crucial to examine the value of human error probability (HEP) during these activities. The objective of this paper is to determine and simulate HEP in maintenance tasks at a cement factory, utilizing performance shaping factors (PSFs). The research employs the cross-impact matrix multiplication applied to classification (MICMAC) analysis method to evaluate the dependencies, impacts, and relationships among the factors influencing human error. This approach classifies and assesses the dependencies and impacts of different factors on HEP, occupational accidents, and related costs. The study also underscores that PSFs can dynamically change under the influence of other variables, emphasizing the necessity to forecast the behavior of human error over time. Therefore, this paper utilizes the MICMAC method to analyze the interdependencies, relationships, and impact levels among different variables. These relationships are then utilized to optimize the implementation of the system dynamics (SD) method. An SD model is employed to forecast the system's behavior, and multiple scenarios are presented. By considering the HEP value, managers can adjust organizational conditions and personnel to ensure acceptability. The paper also presents various scenarios related to HEP to assist managers in making informed decisions.

Using qualitative system dynamics modeling to understand overdose bystander behavior in the context of Connecticut's Good Samaritan Laws and identify effective policy options.

BACKGROUND: Good Samaritan Laws are a harm reduction policy intended to facilitate a reduction in fatal opioid overdoses by enabling bystanders, first responders, and health care providers to assist individuals experiencing an overdose without facing civil or criminal liability. However, Good Samaritan Laws may not be reaching their full impact in many communities due to a lack of knowledge of protections under these laws, distrust in law enforcement, and fear of legal consequences among potential bystanders. The purpose of this study was to develop a systems-level understanding of the factors influencing bystander responses to opioid overdose in the context of Connecticut's Good Samaritan Laws and identify high-leverage policies for improving opioid-related outcomes and implementation of these laws in Connecticut (CT). METHODS: We conducted six group model building (GMB) workshops that engaged a diverse set of participants with medical and community expertise and lived bystander experience. Through an iterative, stakeholder-engaged process, we developed, refined, and validated a qualitative system dynamics (SD) model in the form of a causal loop diagram (CLD). RESULTS: Our resulting qualitative SD model captures our GMB participants' collective understanding of the dynamics driving bystander behavior and other factors influencing the effectiveness of Good Samaritan Laws in the state of CT. In this model, we identified seven balancing (B) and eight reinforcing (R) feedback loops within four narrative domains: Narrative 1 - Overdose, Calling 911, and First Responder Burnout; Narrative 2 - Naloxone Use, Acceptability, and Linking Patients to Services; Narrative 3 - Drug Arrests, Belief in Good Samaritan Laws, and Community Trust in Police; and Narrative 4 - Bystander Naloxone Use, Community Participation in Harm Reduction, and Cultural Change Towards Carrying Naloxone. CONCLUSIONS: Our qualitative SD model brings a nuanced systems perspective to the literature on bystander behavior in the context of Good Samaritan Laws. Our model, grounded in local knowledge and experience, shows how the hypothesized non-linear interdependencies of the social, structural, and policy determinants of bystander behavior collectively form endogenous feedback loops that can be leveraged to design policies to advance and sustain systems change.

Dynamic marginal cost curves to support water resources management.

Marginal cost curves (MCCs) are popular decision-support tools for assessing and ranking the cost-effectiveness of different options in environmental policy and management. However, conventional MCC approaches have been criticized for lack of transparency and disregard for complexity; not accounting for interaction effects between measures; ignoring ancillary benefits and costs; and not considering intertemporal dynamics. In this paper, we present an approach to address these challenges using a system dynamics (SD)-based model for producing dynamic MCCs. We describe the approach by applying it to evaluate efforts to address water scarcity in a hypothetical, but representative, Swedish city. Our results show that the approach effectively addresses all four documented limitations of conventional MCC methods. They also show that combining MCCs with behavior-over-time graphs and causal-loop diagrams can lead to new policy insights and support a more inclusive decision-making process.

Seeking a pathway towards a more sustainable human-water relationship by coupled model - From a perspective of socio-hydrology.

It is essential to systematically consider social, economic, and natural endowments in managing and allocating water resources. However, few studies have comprehensively quantitatively evaluated the allocation of regional water resources from a socio-hydrology perspective and provided recommendations. To explore this research gap, we have constructed a tightly coupled framework that integrates system dynamics models and optimization algorithms to carry out an innovative redistribution of water resources in Shaanxi Province. The system dynamics model simulation results showed that the error was almost always within 10% over the research period, indicating robust simulation capability and laying a solid foundation for subsequent model coupling. The coupled model achieves convergence in approximately 30 generations by formulating the optimization problem with four individual objectives. Optimizing four objectives concurrently results in convergence around the 150th generation. The optimized Pareto solution sets visually demonstrate the trade-offs between different objectives. In the optimized water allocation schedule, the water consumption in Yulin exhibits a change of 1.22 ×108m3, reflecting the most significant optimization effects on agricultural and domestic water allocation. The results indicated that the comprehensive Gini coefficient typically ranged between 0.2 and 0.3. Over the period from the year 2010-2021, the Gini coefficient exhibited a declining trend, signifying a positive trajectory in water resource allocation throughout the research period and a high level of fairness. The annual total green WF of grain in Weinan was the highest at 14.26 ×108m3, followed by Xianyang at 9.52 ×108m3, and the lowest in Tongchuan at 0.54 ×108m3. The annual average amount of blue WF of grain is the highest in Hanzhong, at 11.33 ×108m3, followed by Weinan at 9.60 ×108m3, and the lowest in Tongchuan at 0.14 ×108m3. The coupled framework proposed in this study exhibits significant innovation, scalability, and practical efficiency. It can inspire future research and decision-making and holds the potential for application in other regions.

Sustainable phosphate mining: Enhancing efficiency in mining and pre-beneficiation processes.

Phosphate holds a critical role as a vital, limited, strategic, and irreplaceable resource. Throughout its production chain, residual phosphate can be found in waste streams. This study aims to enhance production efficiency by exploring methods to limit residual phosphate presence in waste stocks. It investigates the presence of residual phosphate in a phosphate mining site. The presence of residual phosphate throughout the production chain is investigated. Through meticulous analyses of extraction, destoning, and screening processes, the study identifies three primary stages where residual phosphate exists, the study simulates different scenarios of residual phosphate recovery and prevention. The principal data sources are data from mining site, recent literature, and information from a lithological log, the study meticulously analyzes the extraction, crushing, and sieving processes to assess the persistence of residual phosphate. The production chain diagnostic revealed that 76% of resource present is recovered (either integrated into the value chain or stored in the mine for future use), from which 8% goes to the destoning waste rocks (75% of which is residual phosphate) and the screening waste rocks (72% of which is residual phosphate), with an average grade that reaches 25% P2O5. Approximately, 24% of the initial phosphate rock (with an average grade of 22% P2O5) remains as residual phosphate which is retained in the spoil piles. To recover and prevent the presence of residual phosphate, the study proposes four new scenarios for improvement, including an integrated scenario where all the solutions are combined for a comprehensive approach. Both quantity and grade of recovered residual phosphate are assessed in each scenario. To evaluate these enhancements, the study utilizes the AnyLogic software to simulate existing process configuration and the maximal recovery of each scenario. The current flowsheet indicates that extracted phosphate can be directed either to pre-beneficiation and expedition or stored for future use. By prioritizing the extraction of phosphate over the final product, the simulation results suggest that implementing these novel scenarios could potentially save 25% of the total phosphate resource and increase storage by twofold, preserving phosphate that would otherwise be unused. This recovered phosphate can then be destined to various uses, meeting the company's present or future needs. Considering this, the study opts to keep stocks separated based on their grades and avoid mixing new phosphate streams with the final product. The implications of this research extend to sustainable mining practices, with direct ramifications for environmental impact mitigation and the conservation of valuable resources.

Co-producing knowledge on the use of urban natural space: Participatory system dynamics modelling to understand a complex urban system.

Decision-makers are increasingly asked to act differently in how they respond to complex urban challenges, recognising the value in bringing together and integrating cross-disciplinary, cross-sectoral knowledge to generate effective solutions. Participatory modelling allows to bring stakeholders together, enhance knowledge and understanding of a system, and identify the impacts of interventions to a given problem. This paper uses an interdisciplinary and systems approach to investigate a complex urban problem, using a participatory System Dynamics modelling process as an approach to facilitate learning and co-produce knowledge on the factors influencing the use of urban natural space. Stakeholders used a Systems Dynamics model and interface, as a tool to collectively identify pathways for improving the use of space and simulating their impacts. Under the lens of knowledge co-production, the paper reflects how such mechanisms can lead to the co-production of knowledge and social learning. The findings also contribute to identify ways of increasing the value of urban natural space focusing on urban areas undergoing physical and social transformation, such as the Thamesmead case study, London, UK.

An evolutionary game and system dynamics approach for the production and consumption of carbon-labeled products-based on a media monitoring perspective.

Carbon label is acknowledged as an effective way to combat the problem of global warming. As a powerful way to encourage individuals to adjust their consumption patterns and to promote the development of green consumption, carbon-labeled products are widely developed in China. To reveal the production and consumption process of carbon-labeled products, the present study constructs a tripartite game model consisting of the government, carbon-labeled products manufacturers and consumers based on a media monitoring perspective. The evolutionary stability strategy (ESS) is firstly determined by solving the replication dynamic equations and stability analysis of the equilibrium point, followed by the strategy analysis and sensitivity analysis through numerical simulation. The results show that media supervision can effectively complement and constrain government supervision. In addition, it can promote enterprises to standardize production and enhance consumers' trust and willingness to buy carbon-labeled products. The introduction of media supervision can well realize the ideal equilibrium of "effective government supervision, enterprise compliance and consumer support for purchase".

Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach.

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector's evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity -as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being -reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.

A simulation-based approach for investigating the dynamics of rebound effects in the circular economy: A case of use-oriented product/service system.

Current research in Circular Economy (CE) fails to address the occurrence of Rebound Effects (RE), which are systemic and behavioural responses to the implementation of interventions hindering the potential sustainability benefits. This paper aims to advance the academic discussion and the practical consideration of RE by exploring the potential of using System Dynamics (SD) to enable the ex-ante identification of potential RE of CE initiatives. A five-stage simulation-based approach for the identification and mitigation of potential rebound effects (AIMRE) is proposed. Its application is demonstrated in a use-oriented product/service system (PSS) case focused on a high-end dress rental service. The AIMRE enables the representation of the magnitude and reasons for RE occurrence through 14 scenarios. The finding highlights the importance of considering the interplay between consumers' and companies' decision-making processes in quantifying, understanding, and mitigating RE occurrence.

Modelling the Banni social-ecological system using participatory system dynamics for building insights on invasive species management and stakeholder engagement.

Invasive species are a significant driver of environmental change in social-ecological systems (SES) globally. Given that SES are inherently complex adaptive systems (CAS), they continuously reorganize themselves and adapt to change, including changes in ecological composition, as well as in associated lives and livelihoods. Decision-making on invasive species management in such systems can be contested and fraught with tradeoffs. The Banni Grasslands in Kutch, India, is one such system where the introduction of Prosopisjuliflora (P.juliflora), an invasive woody species, has over decades resulted in deeply coupled social-ecological change. Removal of P.juliflora for land restoration is as of date a contested policy choice. Through a participatory transdisciplinary process comprising workshops and consultations with the local community (Maldharis), civil society and researchers involved in long term research on Banni, a system dynamics simulation model was developed which synthesizes the SES dynamics as a set of feedback loops. The model was used to simulate 'what-if' scenarios of interest up to 2050, to study consequences of restoration and the impact of climate extremes, to generate insights which could be useful in aiding decision making. The runs show how vis-à-vis a Business-As-Usual Scenario, restoration could help Maldharis increase livestock populations and livestock income, although there would still be a limit to the growth, with livestock reaching a higher normal. The runs show how it would also mean a loss in the P.juliflora-dependent charcoal-based income and livelihoods, and the extent of the loss, raising the question of finding alternative livelihoods. In a climate extremes scenario, the system, being more resource-intensive owing to growing livestock population, and loss of the relatively climate proof P.julilfora-based income, counterintuitively shows higher sensitivity to climate change impacts resulting in more pronounced impact on income variation. In order to engage stakeholders via 'live' simulation and scenario building, a user-friendly app encoding the simulation model was developed and used to carry out a participatory scenario planning exercise with the community to allow for live appraisal of the scenarios and their implications for decision-making. The paper summarizes insights from the simulation runs and from taking the app back to the community.

Development of a framework to forecast the urban residential building CO2 emission trend and reduction potential to 2060: A case study of Jiangxi province, China.

Investigating the CO2 abatement potential of urban residential building from systematic perspective is essential to reach the urban carbon neutrality target. However, previous studies on building CO2 emission trend forecasting were mainly focused on the building operational phase. In this study, a new framework that includes four building stages under a system dynamic model is developed to simulate urban residential building carbon emission changes and the related reduction potentials under three scenarios in Jiangxi Province up to 2060. Results showed that the overall process carbon emission dynamic had already peaked in 2014 under the three scenarios, with a peak value of 38.52 Mt. It then fell to 9.56 Mt in 2060 under the baseline (BAU) scenario. More importantly, seven carbon abatement measures were adopted during four building activities in this study, and the total carbon reduction was not the sum of the carbon reduction potential of the individual measures. Some carbon abatement strategies displayed synergistic effects such as low-carbon electrification where the combination of electrification and clean energy power generation was the largest contributor to reduced carbon emissions during building operation as a comprehensive carbon reduction measure. By contrast, extending a building's lifetime restrained the carbon abatement potential during the demolition stage, and it inhibited the carbon emission reduction by 24.84 Mt. These results highlight the significant need for effective policy interventions for clean production and the need to improve prefabricated building proportions, promote electrification, improve energy efficiency, strengthen recycling practices, and extend building lifetimes to promote decarbonization of urban residential building system development.

Using systems mapping to understand the constraints and enablers of solutions to plastic pollution.

Plastic pollution is now considered globally ubiquitous, irreversible, and a planetary boundary threat. Solutions are urgently needed but their development and application are hampered by the complexity and scale of the issue. System dynamics is a technique used to understand complex behaviours of systems through model building and is useful for conceptualising the relationships between various interacting, dynamic factors, and identifying potential intervention points within the system where specific policies or innovations might have the greatest impact or meet with the greatest resistance. Here, twenty-five participants (all scientific researchers of various career stages, disciplines and nationalities working on plastic pollution) completed a series of exercises through an interactive, iterative group model building exercise during a one-day workshop. The process culminated in the generation of a causal loop diagram, based on participants' perspectives, illustrating the dynamic factors relating to the constraints and enablers of solutions to plastic pollution. A total of 18 factors and seven feedback loops were identified. Key factors influencing the system were Effective legislation, Funding, Public education and awareness, Behaviour change, Innovation, and Effective waste management. Our findings highlight that there is no single driver, or 'silver bullet', for resolving this complex issue and that a holistic approach should be adopted to create effective and systemic change.

Long-term costs of dental care for thailand preschool children by using a system dynamics model.

BACKGROUND: A System Dynamics Model (SDM) is a computer simulation to alleviate the problem by comparing strategies and policies. Addressing the costs by using SDM helps in allocating the resources efficiently in managing the strategies. OBJECTIVE: To describe the costs of primary, secondary, and tertiary prevention of dental caries for 0-5-year-old children by SDM. METHODS: The SDM was developed to explore the cost of primary and secondary prevention (supervised toothbrushing, STB and fluoride varnish, FV), the treatment cost for caries (tertiary prevention), and the total cost under three scenarios; STB, FV and base case (no intervention). RESULTS: When the children aged 5 years, the treatment cost under the base case was the highest at 57.6 million baht while 53.5 million baht in FV and 51.9 million baht in STB. As a total cost, 64.1 million baht under FV, 60.9 million baht under STB, and 57.6 million baht under base case. Sensitivity analysis reveals that the effective rate of STB must be at least 30%, and FV should be a minimum of 50% to ascertain the total cost reduction relative to the base case scenario. CONCLUSION: Caries treatment costs were lower when STB and FV were implemented than in the base case scenario. The overall cost under FV was the highest, followed by STB, with no total cost savings observed as compared to the base case situation. Despite that, carrying out the STB rather than the FV would save a total of 3.2 million baht. Treatment costs under interventions would be lower than expected, and overall cost reductions might be obtained by comparing the base case if the intervention's effective rates are higher, according to sensitivity analysis.