Room: Creative Learning
Duration: 120 minutes
Chair: Cherie Dirk
Presenters: Kristine Heimdal, Knut Mortveit Ognøy, Henri Contor
The consumption of energy and the energy source creates emissions beyond where it should be in China today. With this, pollution accumulates in the atmosphere energy is generated from carbon-intensive energy sources and dissipates through natural processes. With several high-intensive energy sources as the main source of energy in China today, emissions run high. To decrease these emissions, a policy is imagined to increase the renewable energy capacity in China, while decreasing the fossil fuel capacity, particularly coal. By a system dynamics approach to the issue, we have modelled a wishful thinking model including implementation and obstacles structure. The investment to change country as big as China is huge, and will also include obstacles like job loss, opposition and the use of critical minerals. Therefore, for a successful implementation, a policymaker needs to be aware of these obstacles. The dynamics of energy systems and its relationship with pollution is nothing new and has been studied extensively in many different sciences. The present research briefly explores the problem of the Chinese electricity pollution and provides a detailed analysis of the policy to enable a transition.
Presenter: Shreya Sonthalia
The focus of my research is on understanding and addressing the social determinants of mental health in Scotland taking a system dynamics approach. Scotland has seen a rise in poor mental health and mental health and substance use disorders were the second leading cause of burden in 2016; depression and anxiety were the greatest contributors to years lost to disability. There is evidence on the importance of social determinants of mental health and guidance from the WHO to take action across these to improve mental health. However, there is limited evidence on effective policy options. Social determinants are interconnected and the relationship between social determinants and mental health is dynamic, making system dynamics a useful method to model the causal relationships and provide a testbed for policymakers. The research project will use participatory group model building to develop a system dynamics model and identify effective interventions to improve mental health and reduce inequalities in mental health in Scotland.
Presenters: Yuhong Wang, Nici Zimmermann
System dynamics solves social, natural and economic real-world problems to describe relationships among variables by mathematical and non-mathematical models of complex systems. However, it is difficult to identify the key areas in a large and dense system to generate significant changes in the whole system. This paper targets in the analysis of system related to large and complex issues, exploring the contributions of an integration approach combining social network analysis (SNA) and system dynamics (SD) modelling. SNA is a method based on graph theory, focusing on actors and their interrelationships, which can help identify key issues and help the constitution of a SD model boundary. This can help modellers prioritise areas where they can focus their interventions to apply actions. This paper first reviews the theoretical coherence and contribution of the integration of SNA and SD. Then methodological approaches are proposed and to be tested through case studies with multi-stakeholder collaboration in urban greenspace regeneration to validate the methodology.
Presenter: AHMAD ZUHAIRI MUZAKIR
In Malaysia, the final energy use by the transport has a consistent share of around 40% within the past two decades.Land transportation dominates and within this category, private travels are the biggest contributor. The use of conventional internal-combustion-engine-powered vehicles (ICE), has been prevalent and the ownership trend of private cars has continued to increase in tandem with the population and economic growth. This study intends to investigate the drivers of car usage and the purchase of alternative fueled car, that is the electric vehicles (EV). As electric vehicles has a totally different powertrain system as compared to ICE cars, the ecosystem that supports the use of EV is also different and thus necessitates careful adaption by adopting countries, including infrastructure and the network of support system. This System Dynamics model aims to investigates the relationship of subelements in this car ownership system and its implication to energy consumption and greenhouse gases emissions in Malaysia caused by private travels.
Presenters: Sanket Mishra, Jayendran Venkateswaran
Classical stock management model given by Sterman can be used to understand the ordering dynamics in response to change in demand. The model typically considers the forecasted demand, current inventory and work-in-process (WIP) levels to determine the order quantities, under a non-zero lead time and given adjustments rates for inventory and WIP discrepancies. In reality, especially in small and medium enterprises (SMEs), one of the key aspects that determines the ability of the firm to perform is its finance, i.e, cash or credit availability to procure raw materials,convert them into finished products and meet customer demand. One of the key challenges faced by SME is mobilizing and managing their cash flows. In this research work we would like to explore the interaction between material flows and cash flows. In our work, the availability of cash is explicitly considered in making the ordering decisions.The model is based on the generic stock management structure by Sterman. The material flow part of the model contains the stocks of Quantity in Transit (QIT) and inventory. The cash flow part of the model includes newly added stocks of Net Balance, Payments in Progress, Payment received in Progress, Cumulative revenue, Cumulative costs and Planned orders in the pipeline. Preliminary simulations results illustrate the effect of cash flows on inventory ordering dynamics.
Presenter: Wang Zhao
The paper reports on an in-progress work on combining data and expert judgement in facilitating decision making in a hospital demand modelling context. System dynamics and Bayesian networks were combined to generate scenario simulations. Data science method was applied to learn Bayesian Network parameters from empirical data.