System Dynamics Model Research Articles

Development of wind power plants to increase the fulfilment ratio of renewable energy in Taiwan using system dynamics modelling

ABSTRACT This research aims to improve wind energy development by addressing environmental challenges and market dynamics. To achieve this, a system dynamics model was used to analyze the complex relationships between various factors, including wind energy production, electricity demand, costs, and government policies. The study focuses on increasing the fulfilment ratio and reducing the price of wind energy. By simulating different scenarios, researchers explored the potential impact of expanding wind farm capacity through the addition of onshore and offshore turbines. The research also investigates the role of government incentives, particularly the feed-in tariff (FIT), in stimulating investment in wind energy. The findings suggest that a higher FIT can significantly boost profitability, attract more investment, and accelerate the expansion of wind farm capacity. The optimal FIT level of 5.5 NTD/KWh is projected to lead to a 6.7% annual growth rate and substantial profits by 2040.

Accident cause analysis for lifting prefabricated components of assembled buildings based on hybrid simulation technology from human factor perspective

PurposeThis paper aims to explore the characteristics of lifting accidents and the significance of influencing factors and explain the causes from the perspective of human factors, thereby achieving a more accurate understanding of and prevention of lifting accidents.Design/methodology/approachA mixed simulation model for prefabricated component lifting is established by combining discrete event simulation (DES) with the system dynamics (SD) method. In addition, essential parameters and relationships within the system dynamics model are determined through survey questionnaires. Finally, the human factors analysis and classification system (HFACS) is used to analyze the cause of the accident.FindingsThe results show that workers falling from height and workers struck by objects are the most frequent types of lifting accidents. In 2072 experiments, these two types of accidents occurred three and five times, respectively. Besides, the links of “crane movement,” “component binding,” “component placement” and “component unhooking” are particularly prone to lifting accidents. In addition, the completeness of emergency plans, failure to observe the status of the tower crane and lack of safety education and training have emerged as primary influencing factors contributing to the occurrence of lifting accidents.Originality/valueThe findings of the study can serve as a reference basis for practitioners, enabling them to preemptively identify possible risk accidents and adopt corresponding measures to prevent them, ensuring the safety and property of practitioners. Additionally, targeted suggestions and innovative ideas are provided to enhance the safety guarantee of the lifting industry and promote its healthy and stable development through a more concrete theoretical foundation and practical guidance.

Health and Economic Effects of Salt Reduction Interventions for Preventing Noncommunicable Diseases in Japan: A System Dynamics Simulation Study

Reducing salt intake is a vital public health measure for combating noncommunicable diseases and mitigating rising social security expenditures in Japan. We developed a system dynamics model to analyze the potential health and cost effects of salt reduction interventions among the Japanese population aged 40 years and above from 2012 to 2040. The simulation findings derived from the model indicate that, in the base run, the disability-adjusted life years (DALYs) for cardiovascular disease (CVD) and chronic kidney disease (CKD) are projected to be approximately 55 and 9.5 per 100,000 people, respectively, contributing approximately 40 trillion yen in social security expenditures by 2040. The model predicts that implementing mandatory reformulation could decrease DALYs for CVD and CKD by 5.7% and 6.2%, respectively, resulting in an approximately 6.7% reduction in social security expenditure. Additionally, voluntary reformulation is estimated to reduce the DALYs for CVD and CKD by 4.7% and 5.2%, leading to a 5.6% decrease in social security expenditure. Finally, accelerating the adoption of a low-salt diet is expected to lower the DALYs for CVD by 2.8% and CKD by 3.2%, thereby reducing social security expenditures by 3.4%. Thus, product reformulation initiatives have a greater impact on health and economic outcomes.

Life Cycle Prediction of Airport Operation Based on System Dynamics

Traditional airport development planning often overlooks an in-depth consideration of the airport operation life cycle, which frequently causes deviations from planned objectives during operation. This paper presents a framework for predicting and segmenting the airport operation life cycle by integrating the dynamic characteristics of the System Dynamics (SD) model with the static properties of Logistic modeling to examine the development trajectory of airport operations. The influencing factors in this model are selected across three levels: airport, city, and macro-environment. A system dynamics model of airport operation is constructed using causal loop diagrams and system flow diagrams. Using Guangzhou Baiyun International Airport (CAN) as a case study, the airport’s operational capacity from 2005 to 2035 is predicted through SD simulation. Subsequently, the airport operation life cycle from 2005 to 2050 is forecasted and segmented using Logistic modeling. The results indicate that, under the standard scenario, CAN’s operational capacity experiences two declines in 2016 and 2020, attributed to airport construction and emergencies. Logistic modeling identifies three distinct life cycle phases in the airport’s operation. Furthermore, by comparing various airport operation scenarios, the analysis reveals that fluctuations in the city economy significantly impact the airport’s operational system without altering its overall development trajectory. In contrast, the occurrence of emergencies can substantially modify the airport operation life cycle.

Identifying the active ingredients in payment for performance programmes using system dynamics modelling

Payment for performance (P4P) is not a uniform intervention, with programme effect dependent on several interconnected factors. In this study, a system dynamics model was developed to explore the pathways to improved outcomes and how changes in the design, implementation and context of a P4P programme affected maternal and child health (MCH) service delivery outcomes in Tanzania. A previously developed causal loop diagram of the programme effects was used to inform model development, with further data sources (including an impact evaluation of programme, health surveys, stakeholder feedback and relevant literature) used to build the model. A number of pathways were identified to improved services under P4P, with increased availability of drugs underpinning the content of care outcome (intermittent preventative treatment during ANC), which together with increased supervision, enhanced health worker motivation. This in turn increased perceived quality of care at the facility which improved the coverage of services outcome (facility-based deliveries), and with increased outreach, increased awareness of services also boosted demand. Minor delays in payment reduced provider purchasing power for medicines, with severe delays driving erosion of provider trust and motivation for programme participation. Allocating a larger share of funds for facility operations can enhance performance effects, particularly for those services that rely on efficient drug administration. Contextual factors including limited baseline provision of essential medications, lower community awareness of facility services and dispersed/distant populations reduced programme effect. This paper demonstrates the feasibility and the potential of such models to inform the design of effective health system interventions.

Modeling the co-adoption dynamics of PV and heat pumps in Swiss residential buildings: Implications for policy and sustainability goals

The Swiss energy system is facing a paradigm shift as it strives to achieve net-zero greenhouse gas emissions, as well as phase out nuclear electricity production. This entails significant changes in the country’s energy landscape, including increased adoption of renewable energy technologies in the residential sector. To facilitate informed decision-making and planning by policymakers, this study introduces a system-dynamics model for the long-term adoption of solar photovoltaic (PV) and heat pump (HP) technologies in Swiss residential buildings. Unlike conventional approaches, this framework considers the feedback loops and correlations between PV and HP adoption, while also accounting for building heterogeneity. Through scenario analyses, the model evaluates the impact of regulatory and financial policy interventions on the transition of the residential energy system. The results highlight the significant influence of policy measures on technology deployment rates, energy demand, and greenhouse gas emissions. They demonstrate that slight adjustments in current policy and regulatory framework could allow to safely reach PV deployment targets, but strong modifications are necessary to completely decarbonize the residential sector. This study contributes to advancing our understanding of the complex dynamics shaping residential energy transitions and offers valuable insights to support the formulation and implementation of effective energy strategies.

Prediction and Evaluation of Industrial Land Intensive Use: Adding Policy Impact to System Dynamics Model

Nowadays, urban development prioritizes improving quality over mere quantitative expansion, which requires a shift in land use from previous extensive use patterns to a more refined and efficient intensive use pattern. The intensive use of industrial land (IUIL) is a resource-utilization strategy for industrial land that aims at maximizing the dual growth of ecological and economic benefits by minimizing resource consumption and factor costs. In this research, IUIL is deconstructed into four aspects: input intensity, utilization intensity, economic benefits, and environmental benefits. Grounded in a qualitative analysis of the interconnected casual feedback among the various factors influencing IUIL, a system dynamics model with 36 indicators for IUIL evaluation and prediction is constructed. Policy impacts on IUIL from the supply side, utilization side, and supervision side are analyzed and embedded for a scenario simulation based on the theory of the entire life cycle of land use. The simulation shows its efficiency in reporting the variation tendency of industrial land use with varied policy impact strength. The results reveal a dynamic understanding of the evolving patterns of land intensification and present different characteristics of IUIL. This study further proposes policy recommendations to provide experience and references for the authorities to promote high-quality urbanization development in practice.

Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics

The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human–computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.

Modeling Method of Meshing Stiffness of High Speed Spur Gears

As one of the most important dynamic excitation sources, the gear time-varying mesh stiffness is the key parameter of gear system dynamic model. So how to calculate the gear mesh stiffness accurately is of great importance. Based on Euler beam theory, this paper proposes a primitive calculation algorithm (PCA) to calculate the time-varying mesh stiffness of spur gears. A simplified gear model is developed based on nonlinear strain-displacement relationships, and a finite element program is used to simulate the gear's meshing stiffness. The rationality of this method was verified through finite element analysis. The results indicate that mesh stiffness plays a crucial role in controlling mesh deformation. In view of this, this study lays a theoretical foundation for further analysis of the vibration and noise of gears.

Circularity assessment of product-service systems using system dynamics modeling

Product-Service Systems (PSS) show the potential to implement the principles of a circular economy based on their characteristics, such as the use of an extended value network or the access of the PSS provider after distribution. PSS research shows that PSS are not inherently circular but must be designed specifically towards circularity. This requires an assessment method that can quantify the dimensions of circularity.In this paper, an assessment method based on system dynamics (SD) modeling is presented that allows the use of raw materials and the generation of waste to be evaluated in the context of PSS. The SD model is based on a conceptual integration of circular economy strategies into the lifecycle of PSS. The model formulation is described in detail and validated with several tests. The capabilities of the assessment approach are presented in an application scenario. The results show that SD modeling can be used for circularity assessment and can possibly serve as a starting point to enhance circularity.

Integrating machine learning with causal inference for enhanced system dynamics modeling: A framework for predicting complex interactions

Approaches and methods like System Dynamics Modelling (SDM) have been significant for assessing the behavior of many systems. However, classical methodologies applied in traditional approaches to SDM fail to identify nonlinear feedback and power dependencies, revealing hidden temporal casual relationships. In this paper, I present a novel approach that combines ML and causal inference methods to improve the forecast capability and semantics of system dynamics models. Incorporating ML algorithms for predictions and Causal Inference techniques for explanation, this combined strategy presents a new era for understanding the system interaction and quantifying the hidden causes within various systems. We illustrate the advantages of the suggested framework over traditional SDM and purely ML approaches by employing it to analyze a genuine circumstance for both prognosis and discovering causal relationships. Our findings indicate that such integration is effective in enhancing the comprehension of system interactions and deriving a reliable method for estimating subsequent state conditions in complex contexts. The results are relevant for various disciplines, starting with economics and ending with environmental protection sciences, where interactions and changes vary. As a result, it will give a foundation for further studies of integrating future computerized methods in the dynamical system modeling of the next generation.

Health-related quality of life dynamics: modeling insights from immunotherapy.

Understanding health-related quality of life (HRQoL) dynamics is essential for assessing and improving treatment experiences; however, clinical and observational studies struggle to capture their full complexity. We use simulation modeling and the case of Chimeric Antigen Receptor T-cell therapy-a type of cancer immunotherapy that can prolong survival, but carries life-threatening risks-to study HRQoL dynamics. We developed an exploratory system dynamics model with mathematical equations and parameter values informed by literature and expert insights.We refined its feedback structure and evaluated its dynamic behavior through iterative interviews. Model simulated HRQoL from treatment approval through six months post-infusion. Two strategies-reducing the delay to infusion and enhancing social support-were incorporated into the model. To dynamically evaluate the effect of these strategies, we developed four metrics: post-treatment HRQoL decline, recovery time to pre-treatment HRQoL, post-treatment HRQoL peak, and durability of the peak. Model captures key interactions within HRQoL, providing a nuanced analysis of its continuous temporal dynamics, particularly physical well-being, psychological well-being, tumor burden, receipt and efficacy of treatment, side effects, and their management. Model analysis shows reducing infusion delays enhanced HRQoL across all four metrics. While enhanced social support improved the first three metrics for patients who received treatment, it did not change durability of the peak. Simulation modeling can help explore the effects of strategies on HRQoL while also demonstrating the dynamic interactions between its key components, offering a powerful tool to investigate aspects of HRQoL that are difficult to assess in real-world settings.

Electrification pathways for light-duty logistics vehicles based on perceived cost of ownership in Northern China

Urban decarbonization and environmental mitigation necessitate the electrification of light-duty logistics vehicles (LDLVs), including battery electric, plug-in hybrid, and hydrogen fuel cell variants. Although the market uptake of electric LDLVs is ecologically imperative, it is impeded by range anxiety and charging infrastructure limitations, particularly pronounced in Northern China’s cold climates. This paper employs a system dynamics model to assess the Perceived Cost of Ownership of electric LDLVs, integrating both direct expenses - initial investment and energy costs - and indirect factors like energy replenishment, vehicle substitution, and lifecycle carbon emissions. This analysis reveals that, notwithstanding higher upfront costs, electric LDLVs offer substantial economic and environmental advantages, with significant energy and maintenance savings projected by 2030 under various electrification scenarios. This paper predicts that policy incentives, electricity pricing, and technological progress will significantly influence the market dynamics and industry output of new energy vehicles in Northern China. Notably, the findings indicate that by 2030, electric LDLVs could achieve substantial cost savings and environmental benefits, with market penetration and industry output contingent on the interplay of policy support and technological advancements. The baseline scenario forecasts a 48.17% market share and CNY 60.015 billion in industry output, whereas the high-speed electrification scenario projects the most optimistic outcomes, with a 75.29% market share and CNY 306.087 billion in output.

Research Framework of Regional Economic-Ecological System Vulnerability Evolution and Adaptive Leap under the Background of Climate Change

This study focuses on the vulnerability evolution mechanism of regional economic-ecological systems and its adaptive leapfrogging path under the background of climate change. Based on the theory of complex adaptive systems and the framework of social-ecological system coupling, a regional economic-ecological system vulnerability assessment index system was constructed. Using the system dynamics model and scenario simulation method, the impact transmission mechanism of climate change on regional economic-ecological systems was analyzed, and the spatiotemporal evolution law of system vulnerability was revealed. The study found that climate change has aggravated the vulnerability of regional economic-ecological systems by changing resource endowments, disturbing industrial structure, and affecting ecological service functions. In response to this problem, this study proposed a leapfrogging path based on adaptive management: including building a climate-resilient industrial system, optimizing resource allocation efficiency, enhancing ecosystem service functions, and improving regional adaptive governance mechanisms. The research results have important theoretical value and practical significance for promoting regional sustainable development and ecological civilization construction.

Optimization and Application of Comprehensive Environmental-Economic Evaluation Indicator System in Sustainable Development Policies of Chinese Manufacturing Industry

This paper focuses on optimizing the comprehensive environmental-economic evaluation index system to enhance the sustainable development policy of China’s manufacturing industry. The current indicator system, while useful, has notable shortcomings that limit its effectiveness. To address these issues, the paper proposes the inclusion of crucial indicators such as green productivity, carbon footprint, and water footprint, which are essential for providing a more holistic evaluation. These additions aim to capture better the environmental impacts and resource efficiency within the manufacturing sector. The paper also discusses the challenges in data acquisition, emphasizing the need for a standardized national environmental data platform. Such a platform would facilitate the collection of accurate, reliable, and accessible data, thereby improving the precision of evaluations. To further refine the assessment process, dynamic evaluation models, like the system dynamics model, are introduced. These models are designed to provide a more nuanced understanding of the environmental impacts over time, allowing for more responsive and adaptive policy-making. The optimized indicator system is expected to have significant applications in policy formulation, enterprise management, and social oversight. By providing a scientific basis for decision-making, it will play a critical role in advancing the sustainable development of China’s manufacturing industry, ensuring that environmental considerations are fully integrated into economic planning and industrial practices.

A system dynamics model based on ISM for risk perception in emergency by employees in chemical industrial parks

Risk perception is one of the factors that guide human behavior in the workplace. In accident prevention and emergency response, the risk perception of employees affects the safety behaviour and efficiency of emergency disposal. Besides, risk perception is a complicated process restricted by many influencing factors. The study aims to identify the most important factors affecting risk perception. This study had three phases. In the first, factors affecting the risk perception were extracted based on the questionnaire survey and the expert scoring. Then, the Interpretive Structure Model (ISM) was used to stratify the 14 influencing factors of risk perception and obtain a hierarchical structure chart. Finally, by analyzing the influence paths in the ISM, the system dynamics feedback loop diagram was constructed. The model took the state variable “risk perception→ risk response→ risk identification→ risk communication→ risk perception” as the main circular loop, and was supplemented and perfected by multiple feeder loop circuits. Research indicates risk experience is the most fundamental factor affecting risk perception. In the aspect of sensitivity analysis, the study shows that the risk perception of employees is related to the distance of the risk source. Its effectiveness in quantifying qualitative data, identifying influential factors, and establishing cause-effect relationships make it a valuable tool for enhancing risk perception in chemical industry park.

A hybrid approach to investigating major management factors for effective highway preventive maintenance

To resolve the problem of the low management capacity of highway preventive maintenance (HPM), this paper identified and evaluated the major HPM management factors to improve management effectiveness and achieve sustainable highway development. The study conducted a literature review and exploratory factor analysis (EFA) to identify the major HPM management factors. Social network analysis (SNA) was used to distinguish the degree of importance of these factors. A system dynamics (SD) model was developed to explore their patterns of influence. The research identified six dimensions of HPM management, including the management system, management resources, management cognition, management decisions, management technology, and external conditions, along with 26 major management factors. Moreover, information acquisition, system perfection, etc., are key factors; system execution, manager capability, etc., are hub factors; and route selection, machinery allocation, etc., are non-key factors. These factors have a positive impact on HPM management, leading to an upward trend in management effectiveness. The main innovation provided a hybrid and comprehensive approach to identify and evaluate the major management factors for effective HPM. This study can guide managers in developing effective HPM plans, allocating resources more efficiently, improving the overall quality of highway maintenance and forming a sustainable transportation system.

Regional energy consumption policy practice to promote low-carbon economy

IntroductionEnergy is the fundamental driving force for world economic and social development. However, the growth of carbon dioxide emissions and increasing environmental challenges are becoming increasingly problematic, limiting the pace of economic development.MethodsTo optimize the energy consumption structure and promote the development of low-carbon economy, this study took Shaanxi Province as an example to analyze the impact of low-carbon economy policy variables on the energy consumption structure dominated by coal. The purpose of this study is to examine the influence of policy variables on the evolution of energy consumption structure by constructing a system dynamics model. In addition, this study will explore policy optimization issues.ResultsThe error of simulation results was within ±10%. The consumption of non-fossil energy increased from 2,196,400 tons of standard coal in 2009 to 22.578 million tons in 2030, which is in line with the development trend. Under these four policy combinations, the share of coal will fall to 57.8 percent by 2030. In addition, the share of non-fossil energy sources will increase to 21 percent and the share of natural gas will reach 16 percent.DiscussionThe policy suggestions put forward in this paper are of great significance to the optimization of energy structure led by coal. The model constructed can be effectively applied to practical policy simulation and adjustment, and can provide reference for the development of low-carbon economic policies in other regions.

Dynamics modeling of variable mass systems – a case study of an underwater inertia based propelled glider performance

Dynamics modeling of variable mass systems – a case study of an underwater inertia based propelled glider performance

Multi-Scale Evaluation and Simulation of Livelihood Efficiency in Post-Poverty Mountainous Areas

Promoting the coordination of livelihoods at the county and farmers’ scales is essential for achieving balanced regional development and rural revitalization in post-poverty mountainous areas. Existing studies predominantly focus on farmers’ or regional livelihood capital and livelihood efficiency at a single scale, lacking research on cross-scale coordination between farmers’ and county livelihoods. Consequently, these studies fail to reveal the interactions and synergistic enhancement pathways between the two scales. This study, using the Qinba mountains in southern Shaanxi as a case, employs system dynamics to construct a coupled system dynamics model of farmers’ livelihood efficiency and county livelihood efficiency. From the perspective of livelihood capital, five regulatory modes, comprising a total of 17 scenarios, were designed and simulated. The results indicate the following data: (1) The coupling coordination degree between farmers’ livelihood efficiency and county livelihood efficiency in the Qinba mountains is 0.623, indicating a moderate level of coordination overall. However, the coupling coordination relationship requires further optimization and adjustment. Specifically, Foping exhibits a severe imbalance, while the coupling coordination degree of Shiquan, Zhashui, Baihe, Pingli, and Lan’gao is in a state of basic coordination. Additionally, 19 other counties, including Lueyang, Ningqiang, Yang, and others, exhibit moderate coordination. (2) Enhancing social or financial capital through various means typically promotes the coordinated development of farmers’ and county livelihood efficiency. On average, social capital and financial capital regulation models can increase the coupling coordination degree by 0.08 and 0.17, respectively. Additionally, strategies such as increasing fixed asset investment and regulating other capital types, including reducing arable land, also effectively improve the coupling coordination degree of farmers’ and county livelihood efficiency. This study provides a decision-making basis for improving the coordination of farmers’ and county livelihoods in post-poverty mountainous areas, thereby promoting economic development and intensive resource utilization. It assists in formulating more precise policy measures and offers a reference for sustainable development and rural revitalization in similar regions.