System Dynamics Model Research Articles

Modelling of Safety Performance in Building Construction Projects Using System Dynamics Approach in Tanzania

Issues in construction safety in Tanzania cast a long shadow, claiming lives and hindering project success. Understanding the complex dynamics at play is crucial for improving safety performance. This research aims to develop a system dynamics model to understand the factors influencing safety performance in building construction projects. Smart PLS was used to identify key variables using factor reduction, and 19 factors were determined as key factors to be used in this study from 143 variables from a questionnaire; then, STELLA was used to develop system dynamic modelling. A study was conducted on 255 building construction projects in five regions of Tanzania, Dar es Salaam, Dodoma, Arusha, Mwanza, and Mbeya, and later, the model was tested in one contractor’s company. By simulating the interactions between these diverse elements and their impact on accident rates, it was found that both safety performance and safety management processes for construction projects are not stagnant but are expected to rise at the beginning of the project and to grow steadily as the project execution approaches its end. The sensitivity analysis conducted on the system dynamics model found that the extent, frequency, and cost of incidences are positively correlated. This study contributes facts on the status of safety climate, a new definition of safety climate, and the integration of different analysis methods in evaluating safety performance in Tanzania.

Integrating distributive justice in system dynamics models of sustainability transitions

AbstractSocial‐ecological‐technological transitions for decarbonisation and sustainable development goals can perpetuate or introduce environmental and socio‐economic injustices. The just transitions literature provides useful perspectives for assessing transition pathways. This paper explores integrating distributive justice into system dynamics modelling for sustainability transitions. For this purpose, we adapt existing requirements to enable the evaluation of distributive justice in model‐based support for climate planning to the specific case of system dynamics modelling. We test the requirements by using a previously developed system dynamics model to (1) gain insights into the application of the requirements and (2) reflect on their further development. Our findings underscore the importance of transparency in model assumptions and results. They suggest exploring the behaviour space of models to account for uncertainty and plurality of stakeholder values. Furthermore, they emphasise the need to discuss the implications of model results and the plurality of stakeholder values to foster deliberation and inclusive decision‐making.

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

BackgroundGood 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).MethodsWe 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).ResultsOur 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.ConclusionsOur 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.

Optimizing Urban Layout: A System Dynamics, Artificial Intelligence, and 3D Urban Information Model Approach for Active Management of City Complexity

Optimizing Urban Layout: A System Dynamics, Artificial Intelligence, and 3D Urban Information Model Approach for Active Management of City Complexity

A system dynamics modelling and analytical framework for imported dengue outbreak surveillance and risk mapping

System Dynamics (SD) models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. Spain is an at-risk country of imported dengue outbreaks, but only qualitative assessments are available to guide public health action and control. We propose a modular SD model combining temperature-dependent vector population, transmission parameters, and epidemiological interactions to simulate outbreaks from imported cases accounting for heterogeneous local climate-related transmission patterns. Under our assumptions, 15 provinces sustain vector populations capable of generating outbreaks from imported cases, with heterogeneous risk profiles regarding seasonality, magnitude and risk window shifting from late Spring to early Autum. Results being relative to given vector-to-human populations allow flexibility when translating outcomes between geographic scales. The model and the framework are meant to serve public health by incorporating transmission dynamics and quantitative-qualitative input to the evidence-based decision-making chain. It is a flexible tool that can easily adapt to changing contexts, parametrizations and epidemiological settings thanks to the modular approach.

Bird diversity rate as variable of land use change protection scenario using a system dynamics approach

Abstract. Handoyo F, Soemarno, Sudarto, Hakim L. 2024. Bird diversity rate as variable of land use change protection scenario using a system dynamics approach. Biodiversitas 25: 2463-2477. The existence of enclave villages in Bromo Tengger Semeru National Park, East Java, Indonesia raises concerns about land use changes in the conservation areas. This study analyzed the position of bird diversity rate as a variable in system dynamics modeling. Data from the results of the stock flow diagram simulation, which consists of the bird diversity rate variable, along with other variables, are references used for assessing answers regarding efforts to protect conservation areas through several scenarios. The aim of this study was to predict the best scenario for the protection of national park areas that include the bird diversity rate as a variable. Bird species observations at 2 sites found 21 families and 34 species out of 135 total recorded individuals. The diversity index (H') is relatively high, the Evenness Index (E) is relatively evenly distributed between species. The Margallef Richness Index (R) at location 1 is classified as medium and location 2 is classified as low. The simulation results of stock flow diagram model (2015-2050) shows that the national park area, decreased. Measurements of bird diversity rate have also decreased. The best interventions seen in scenario 2 and scenario 3 have a good impact on reducing land use change. Protection of national park areas in scenario 3 can increase bird species diversity rate by up to 76% by implementing important actions in ecosystem improvement.

Enhancing the Stability of Small Rescue Boats: A Study on the Necessity and Impact of Hydraulic Interconnected Suspensions

Aiming to solve the problem that existing small rescue boats cannot realize the effective and stable rescue of human lives under high sea turbulence conditions, this paper proposes a parallel decoupled hydraulic interconnected suspension system for actual sea state. The system dynamics model is established, and its damping characteristics are analyzed by joint simulation. The results show that compared with the truss-type rescue ship, the suspension system can improve the transverse rocking resistance by 81.5% and the longitudinal rocking resistance by 25.0%, and the system has excellent transverse rocking resistance.

Are we there yet? Saturation analysis as a foundation for confidence in system dynamics modeling, applied to a conceptualization process using qualitative data

AbstractSaturation is a useful concept for system dynamics, yet it has not been widely explored or integrated into the modeling process. In this article, we describe saturation as a metaphor describing the point at which a conceptual representation of a system meets the study purpose and no longer requires modification. When saturation is reached, additional data about the problem would not offer added information, thus indicating that additional data gathering and analysis would likely be redundant. We discuss two visualization techniques, “saturation curves” and “shared understanding diagrams,” for assessing saturation when conceptualizing with causal loop diagrams and show their application in a case example. Using saturation analysis during a system dynamics research process has many advantages, including: (i) identifying model structures potentially needing revisions, (ii) observing the extent to which evidence supports the current conceptualization, (iii) reflecting extensively, (iv) documenting important modeling decisions, and (v) potentially improving the problem statement. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Study on the overall performance of e-waste dismantling industry polluted cultivated land utilization from a holistic view

The utilization of heavy metal-organic polluted cultivated land caused by the e-waste dismantling industry was affected by multi-dimensional factors. Thus a comprehensive evaluation of these multi-dimensional impacts was necessary for the technical selection and solution of cultivated land utilization. This study attempted to conduct a total benefit simulation analysis on the redevelopment of polluted cultivated land, which was of great significance to decision-making. With a systematic framework designed including the casual relations between components, a system dynamics model was constructed to simulate the technological, economic, and ecological benefits of different polluted cultivated land-used modes, along with which a questionnaire was designed to investigate and analyze the associated social effects. Based on these, the total benefits of the land-use mode were evaluated and optimized within the framework of the “technological-economic-ecological-social” evaluation system. This study included microscale, mesoscale, and macroscale, with multi-dimensional and complex influencing factors, and had advanced research value. The results of the system dynamics model simulation showed that remediation plants played a decisive role in pollutants removal. The flower industry had the highest net present value, but its ecological performance was poor. The overall analysis showed that the scenario of utilization after remediation had the highest performance score, which was attributed to high pollutant removal, low cost, and high carbon sink. This study was meaningful for providing research ideas of comprehensive benefits of polluted cultivated land utilization and also helpful for decision-making. This paper also provided research ideas for other industries such as contaminated site remediation and sewage sludge treatment.

The effects of municipal waste reduction and recycling policies on the economic feasibility of landfill gas generation

Landfill gas (LFG) projects for energy production have several advantages. However, to avoid the impossibility of these projects, it is crucial to assess the long-term effects of public policies that promote the diversion of waste disposed of in landfills. Therefore, the objective of the present work is to evaluate the effects of the application of public policies, which influence recycling, reduction of generation, and inadequate disposal reduction of municipal solid waste (MSW), in the potential of electricity generation in landfills, as well as to evaluate its effect on economic viability. A System Dynamics model was employed to estimate methane production while considering variations in the quantity and make-up of MSW over time. The results showed that the scenarios with the greatest potential for methane generation and electricity were those with less diversion of biodegradable waste. Furthermore, the economic performance demonstrated that none of the possibilities are viable except with carbon credits extra income. However, all scenarios could become viable by increasing the energy sale rate above 93.2 USD.MWh−1. Another option calls for lowering the discount rate through government incentives to a percentage below 10 % and an investment cost below 77 % of the original value. These elements aid in long-term planning and give decision-makers a future vision of the impact of these policies.

Evaluation or RetrogressionThe sex ratio dettermination pattern of lamprey

In this study, we investigate the profound impacts of the sex determination pattern in the invasive sea lamprey on ecological dynamics and focuses on how the pattern influences the population based upon the interplay of system stability. In the initial segment, we employ the Lotka-Volterra model and system dynamics to study lamprey sex ratios correlation with ecosystem stability. Focusing on food impact on lamprey sex ratio, it can be delved that its sex determination maintains the prey population at a consistently low level, thereby affecting population stability. The following segment explores lamprey sex ratios evaluations utilizing system dynamics model based on Analytic Hierarchy Process (AHP). Cellular Automata (CA) is employed for cross-validations, revealing nuanced insights into the adaptive advantages and vulnerabilities of lampreys reproductive strategy, highlighting the resilience of lamprey populations under natural pressures. Our modeling, with visualizations and simulations, supports findings and highlights avenues for future research. This study contributes to the evaluations of bio sex ratio switching, contributing for the possible ecosystem conservation strategies.

System response curve based first‐order optimization algorithms for cyber‐physical‐social intelligence

AbstractThe continuous enhancement of optimization algorithms and their parameters has spurred the expansion of AI into novel application domains such as image recognition and smart home technology. This paper employs the system response curve (SRC) to the adaptive learning rate optimizer, addressing challenges associated with the establishment of the optimizer control model and parameter adjustments affecting the dynamic performance of the system. These insights offer theoretical support for the optimizer's application in deep learning models. To begin, the adaptive learning rate optimizer is a time‐varying system. Based on the intrinsic relationship between the network optimization and the control system, the time domain expression and approximate transfer function of the adaptive learning rate optimizer are derived, and the system dynamic model is established. Furthermore, based on the system control model of the optimizer, it is proposed to explain the performance impacts of different optimizers and their hyperparameters on the deep learning model through the SRC. Finally, experiments are performed on the MNIST, CIFAR‐10, UTKinect‐Action3D, and Florence3D‐Action datasets to validate the control theory of explaining optimizers through system response curves. The experimental results show that the recognition performance of the Adaptive Moment Estimate (Adam) is better than that of the Adaptive Gradient (AdaGrad) and Root Mean Square Propagation (RMSprop). Additionally, the learning rate affects the model training speed, and the practical application aligns with the theoretical analysis.

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.

Lifetime assessment of semi-submersible wind turbines by Gaidai risk evaluation method

As the global agenda turns more towards the so-called challenge of climate change and lowering carbon emissions, research into green, renewable energy sources becoming nowadays more and more popular. Offshore wind power, produced by FOWTs (i.e., Floating Offshore Wind Turbines), is one such substitute. It is a significant industrial part of the contemporary offshore wind energy industry and produces clean, renewable electricity. Accurate operational lifetime assessment for FOWTs is an important technical safety issue, as environmental in situ loads can lead to fatigue damage as well as extreme structural dynamics, which can cause structural damage. In this study, in situ environmental hydro and aerodynamic environmental loads, that act on FOWT, given actual local sea conditions have been numerically assessed, using the FAST coupled nonlinear aero-hydro-servo-elastic software package. FAST combines aerodynamics and hydrodynamics models for FOWTs, control and electrical system dynamics models, along with structural dynamics models, enabling coupled nonlinear MC simulation in the real time. The FAST software tool enables analysis of a range of FOWT configurations, including 2- or 3-bladed horizontal-axis rotor, pitch and stall regulation, rigid and teetering hub, upwind and downwind rotors. FAST relies on advanced engineering models—derived from the fundamental laws, however with appropriate assumptions and simplifications, supplemented where applicable with experimental data. Recently developed Gaidai reliability lifetime assessment method, being well suitable for risks evaluation of a variety of sustainable energy systems, experiencing nonlinear, potentially extreme in situ environmental loads, throughout their designed service life. The main advantage of the advocated Gaidai risks evaluation methodology being its ability to tackle simultaneously a large number of dynamic systems' degrees of freedom, corresponding to the system's critical components.

An integrated system dynamics model of electricity production, consumption, and export policy in Iran considering carbon emissions

The electricity industry is a cornerstone of modern economies, requiring accurate forecasting to facilitate informed decision-making aligned with sustainable development goals. This study employs a novel, comprehensive system dynamics model comprising four subsystems: consumption, production, carbon emissions, and electricity trading. Our analysis reveals significant trends in Iran's electricity industry. The household sector is projected to become the largest energy consumer, with anticipated reductions in consumer sector subsidies over time. By 2040, total electricity production is forecasted to reach 862,835 million kWh, with renewable sources contributing only 3.3 percent. This trajectory is accompanied by a substantial increase in carbon emissions, reaching 448,383 tons, an alarming 2.6-fold surge compared to 2020. The private sector is anticipated to account for 70 percent of these emissions. Given the findings, prioritizing renewable electricity sources becomes imperative to align with sustainable development goals. Utilizing this integrated model, stakeholders can formulate informed strategies and interventions to navigate the evolving landscape of Iran's electricity sector.

Trajectory-based global sensitivity analysis in multiscale models

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

Co-creating value in manufacturing supply chains: unravelling the dynamics of innovation ecosystems

ABSTRACT This research delves into the operational intricacies and pathways driving value realization within a supply chain innovation ecosystem, with a specific focus on the lens of value co-creation. Leveraging China’s home appliance manufacturing industry as a case study, we develop a system dynamics model, revealing that the collaborative interaction among core enterprises, partners, and customers significantly contributes to system value creation. Notably, the digitalization level of enterprises emerges as the primary catalyst for value creation within the system. This study establishes the framework and boundaries of the supply chain innovation ecosystem, outlines value realization pathways, provides key insights on value co-creation for manufacturing enterprises.

Ecosystem services response to future land use/cover change (LUCC) under multiple scenarios: A case study of the Beijing-Tianjin-Hebei (BTH) region, China

Ecosystem service functions are significant in responding to future LUCC for urban sustainability. Unfortunately, the present works are absent from the coupling effects of resource utilization intensity, climate and environmental changes, and various socio-economic development patterns, failing to clarify the dynamic response of ecosystem service functions to LUCC. To address this gap, we propose a new integrated framework to achieve multi-scenario simulation of future LUCC and assessing ecosystem services from the dual perspectives of land demand structure and function. On this basis, taking the BTH region as an example, we first explore ecosystem services response to future LUCC under multiple scenarios, then discuss the impact of LUCC on ecosystem services across different situations, and provide recommendations and research directions. The main findings show that: (1) The coupling of the multi-objective programming (MOP) and system dynamics (SD) models can better predict the future development trend from the dual perspectives of structure and function. (2) The SSP126 scenario is optimal in terms of preserving farmland, forest, and grassland or restricting built-up land expansion, which aligns better with the development of LUCC under the coordinated development. (3) With regard to ecosystem services, the SSP126 scenario outperforms the SSP245 and SSP585 scenarios.

Towards Sustainable Industry: A Comprehensive Review of Energy–Economy–Environment System Analysis and Future Trends

Industry, encompassing various sectors like agriculture, manufacturing, and services, is crucial for economic growth and societal progress. However, industrial growth often comes at the cost of environmental degradation and energy resource depletion, ignoring the need for sustainable development. This study analyzed 131 articles published from 2004 to 2023, using the energy–economy–environment (3E) method to explore industrial research trends. The primary focus of industrial 3E research is on environmental impact and sustainable development, particularly related to energy consumption. This field covers various topics like systems, efficiency, optimization, and others. LCA methods and system dynamics models are commonly used in research method innovation. This study summarizes the main viewpoints of industrial 3E research and highlights future research directions and priorities, including transitions to areas like agriculture, fisheries, and renewable energy and combining evaluation and prediction methods with 3E methods, conducting large-scale system research, and examining coupling relationships within and between industrial systems.

Pathway to zero emissions in global tourism: opportunities, challenges, and implications

Human emissions must reach zero by 2050 to avoid dangerous climate change. Most sectors have achieved a downward path for their greenhouse gas emissions, but tourism’s emissions have continued to grow. We explore scenarios to reduce global tourism emissions and find only a narrow pathway to zero emissions tourism that could lead to net zero by 2050 while not severely damaging the tourism economy. Our study uses a system dynamics model. We describe the business-as-usual scenario, including its consequences for emissions and for tourism’s share of the remaining global carbon budgets if the world is to limit global temperature rise to 1.5 or 2 °C. We explore 45 possible policy approaches, like taxes and subsidies, infrastructure investments, off-sets, technology, behavioural changes, and sustainable aviation fuels. Key measures appear to be a mandate for e-fuel mixing up to 100% by 2050, and a multi-trillion USD investment in hydrogen-powered electric aircraft and high-speed rail infrastructure. We find that a limited renewable energy supply will constrain the growth of aviation if it is to be zero emissions in 2050. A risk-analysis explores the robustness of the policies. Finally, we discuss the consequences of the zero emissions tourism (ZET) scenario we propose.