System Dynamics Model Research Articles

Climate change scenario simulations for urban flood resilience with system dynamics approach: A case study of smart city shanghai in Yangtze River Delta region

With the rapid advancement of technology, cities are increasingly integrating smart designs to enhance their urban resilience, particularly against natural disasters such as floods. This study employs an integrated approach to simulate the social, economic, and environmental conditions under various climate change scenarios to strengthen urban flood resilience in smart cities. The CMIP5 BCC-CSM 1-1 model, using the RCP4.5 scenario, is utilized to project future climate changes. Shanghai, a highly developed smart city in the Yangtze River Delta, was selected as the case study. Five scenarios are examined through a system dynamics model spanning from 2015 to 2030, encompassing current continuation, economic priority, social priority, natural priority, and coordinated development scenarios. The model is built upon a conceptual framework known as PSR-SENCE (Pressure-State-Response model and Social-Economic-Natural Complex Ecosystem theory). The findings reveal a consistent improvement in resilience within the response dimension facilitated by modern technology. Notably, the coordinated development scenario emerges as the most effective in bolstering urban flood resilience, surpassing scenarios prioritizing economic, social, or natural development individually. Smart cities like Shanghai must prioritize technological innovation alongside enhancing organizational governance to address these challenges effectively.

Modelling connected and autonomous bus on dynamics of mixed traffic in partially connected and automated traffic environment

Since connected and autonomous buses (CABs) have great potential of being operated in partially connected environment and raise great impact on mixed traffic flow, this study develops an analytical model for modeling system dynamics of mixing connected and autonomous vehicles (CAV), human-driven vehicles (HV), and CABs in both cases of dedicated bus lane and non-dedicated bus lane, in which the impact of CABs is specifically quantified under varied levels of connected vehicle penetration. A Vissim-Matlab simulation evaluation is also developed for validating the model’s effectiveness. The results demonstrate that the proposed model performs well with an accuracy of over 85%, and it can be up to 95% in case of non-dedicated bus lane when the inflow rate is higher than 2000veh/h. Besides, the impacts of CABs’ volume, bus dwelling time, and bus stop location on the performance of mixed traffic flow are verified in two CAB operating environments. The obtained results can be used as the basis for planning and operating CABs in partially connected environment.

Community-Based Participatory Research and System Dynamics Modeling for Improving Retention in Hypertension Care

The high prevalence of hypertension calls for broad, multisector responses that foster prevention and care services, with the goal of leveraging high-quality treatment as a means of reducing hypertension incidence. Health care system improvements require stakeholder input from across the care continuum to identify gaps and inform interventions that improve hypertension care service, delivery, and retention; system dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and ways to model sustainable system-level improvements. To assess the association of simulated interventions with hypertension care retention rates in the Nigerian primary health care system using system dynamics modeling. This decision analytical model used a participatory research approach involving stakeholder workshops conducted in July and October 2022 to gather insights and inform the development of a system dynamics model designed to simulate the association of various interventions with retention in hypertension care. The study focused on the primary health care system in Nigeria, engaging stakeholders from various sectors involved in hypertension care, including patients, community health extension workers, nurses, pharmacists, researchers, administrators, policymakers, and physicians. Simulated intervention packages. Retention rate in hypertension care at 12, 24, and 36 months, modeled to estimate the effectiveness of the interventions. A total of 16 stakeholders participated in the workshops (mean [SD] age, 46.5 [8.6] years; 9 [56.3%] male). Training of health care workers was estimated to be the most effective single implementation strategy for improving retention in hypertension care in Nigeria, with estimated retention rates of 29.7% (95% CI, 27.8%-31.2%) at 12 months and 27.1% (95% CI, 26.0%-28.3%) at 24 months. Integrated intervention packages were associated with the greatest improvements in hypertension care retention overall, with modeled retention rates of 72.4% (95% CI, 68.4%-76.4%), 68.1% (95% CI, 64.5%-71.7%), and 67.1% (95% CI, 64.5%-71.1%) at 12, 24, and 36 months, respectively. This decision analytical model study showed that community-based participatory research could be used to estimate the potential effectiveness of interventions for improving retention in hypertension care. Integrated intervention packages may be the most promising strategies.

Policy design for renewable energy development based on government support: A system dynamics model

Energy's centrality to life, work, and progress underscores its indispensable role in modern society. However, burgeoning energy consumption and finite resources have prompted concerns regarding resource sustainability. Consequently, renewable energy sources have gained prominence as alternatives, integral to societal advancement. Therefore, with the increase in the price of fossil fuels and its limitations and the attention to renewable energies, in this research, we decided to study the implementation processes of the development of this type of energy about possible future scenarios and possible implementation policies at the time of each scenario. Let's pay Considering the dynamic complexities of this issue over time and the involvement of government sectors in policy discussions and private sectors in the implementation and participation of these two groups simultaneously, using dynamic system modeling to design a dynamic model that economic aspects, technical, political and environmental aspects should be evaluated simultaneously in a causal-disability manner. To implement this model, two scenarios of government support and non-support for this industry were taken into consideration, and in each scenario two policies of workforce training and technology import for the use of private sector managers and in the best scenario two policies of tax exemption and reduction of technology import tariff for Government decision-making was reviewed. The results showed that when this industry enjoys government support, the speed of development of renewable energy projects can be greatly increased by importing equipment. Without governmental backing, expedited progress is achievable through skilled labor training and employment. Tax exemptions are the most effective policy tool for stimulating renewable energy project processes, production, and sales. The findings underscore the pivotal role of strategic governmental intervention in facilitating renewable energy development, which is essential for meeting energy demands while mitigating environmental impact.

Green and Low Carbon Development Performance in Farmland Use Regulation: A Case Study of Liyang City, China

Farmland use regulation strictly regulates the conversion of agricultural land for other agricultural purposes and the construction of agricultural facilities, thereby optimizing the land use pattern in rural areas. However, different measures and intensities of farmland use regulation can affect the overall performance of green and low-carbon development in rural areas. This study utilizes system dynamics modeling and simulation to conduct a case study based on current land use data from 10 towns in Liyang City, China. The empirical results indicate the following: (1) Based on comprehensive measurements of green and low carbon development performance, Liyang City exhibits a pattern of higher indices in the south and lower indices in the north. Towns such as Tianmu Lake, Daibu, and Shezhu show relatively high average comprehensive indices of 0.31, 0.30, and 0.28, significantly higher than other towns. (2) Simulation of farmland use regulation’s impact on green and low carbon development performance reveals that Scenario One, involving additional construction land occupying farmland, achieves a comprehensive index of only 0.23, significantly lower than the other scenarios. (3) Based on calculations and field surveys, Liyang City’s villages are categorized into four types, with the largest number being industry-integrated villages (94 villages). Accordingly, policies for farmland use regulation are designed for different village types. Therefore, future farmland use regulation should be tailored with differentiated institutional designs according to the development needs of different villages. This study’s findings provide insights into green and low-carbon development in rural areas.

Quantifying the Impact of Coal Transition on GDP Growth through System Dynamics: The Case of the Region of Western Macedonia, Greece

The transition from coal to more sustainable energy sources represents a critical shift for economies reliant on coal production. To investigate the intricate processes involved in such a transition, the use of powerful analytical tools is essential. This study assesses the impact of the delignification process on GDP growth over a 20-year horizon (2015–2035) in the Region of Western Macedonia, Greece, using the Vensim PLE Plus 9.0.1 software, a robust tool for system dynamics modeling. By developing a dynamic model that captures the key variables and feedback loops associated with coal transition, this research examines economic, social, and investment variables, emphasizing their causal relationships. The study integrates societal, economic, and educational impacts on production transition, addressing issues such as unemployment, financial support, and investments in human resources and R&D. Additionally, it considers the influence of climate change on GDP. The model highlights population dynamics, economic development, and education as critical factors. Scenarios explore the impact of increased funding on education, research, and financial aid efficiency, providing insights into enhancing GDP in decarbonizing regions. The study reveals that increased investment in education and human capital leads to slight improvements in local GDP, though the effects are not immediate. Enhanced efficiency in government and European spending significantly boosts local GDP by creating strong value chains and local economies of scale. It is found that the increase in financial support to the regions in transition is of the utmost importance and has a multiplicative nature, something that should encourage the European Union to increase its financial support tools. The model’s simulations align closely with historical GDP data, validating its accuracy. The contributions of the present work offer valuable insights to policymakers and stakeholders engaged in the transition processes.

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.

Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Analysis of policy measures and feedback effects on industry transformation towards carbon neutrality

AbstractMotivated by the urgent need to curb carbon emissions substantially over the next years and decades, we develop a qualitative system dynamics model to investigate the role of feedback effects on the transformation of the industry sector towards carbon neutrality. Through systems thinking, this research analyses the complex interactions between policy measures, investment decisions in different environmental measures to reduce CO2 emissions (e.g., use of electric and thermal energies from renewables and increase of energy efficiency), CO2 compliance, the pressure to take further climate action, the industry's economic success and the availability of investment funds. The study contributes to qualitatively identify and explore levers that policymakers can use to encourage industry to invest further in climate protection measures. Moreover, it provides an important basis for the development of quantitative modelling approaches.

Innovation using dynamic balanced scorecard design as an industrial safety management system in a company in the mining metallurgical sector

Over the years, industrial safety management in Peru's metallurgical mining sector has become a business problem with no acceptable solution. Annual statistics reveal an average of 60–70 fatal accidents in companies in this sector. This paper proposes using the Systemic Methodology to Develop and Maintain a Dynamic Balanced Scorecard MSDBSC-EM) to be used in the industrial safety management of the safety department of a Peruvian company in the metallurgical mining sector. This methodology was created at the Instituto Andino de Sistemas - IAS of Peru and combines the criteria of the Balanced Scorecard with the modeling of System Dynamics and contributions of the Soft System Methodology. This proposal demonstrates that dynamic simulation facilitates organizational learning and allows the designing of action plans in the present to manage the future. Also, modeling and computer tools facilitate the development and implementation of prototypes, which can be used to analyze required scenarios through pleasant and easy-to-use environments. In this sense, a dynamic model of key performance indicators of objectives, goals and initiatives was developed and four scenarios (A, B, C and D) were analyzed, from which it can be observed that the degree of commitment and leadership with the Industrial safety affects the increase in the level of preventive culture, which in turn favors the prevention of risks and the achievement of tangible objectives in industrial safety, evident in the drastic decrease in the occurrence of accidents and occupational diseases, and this is achieved to the extent that the personnel have the necessary competencies, which in turn is obtained through constant comprehensive training and the appropriate selection of personnel. On the other hand, the operating budget of the security department is a limiting factor in achieving these criteria. Another important aspect is the degree of existing communication, which affects the increase in the level of preventive culture.

Data-driven linearization of dynamical systems

Dynamic mode decomposition (DMD) and its variants, such as extended DMD (EDMD), are broadly used to fit simple linear models to dynamical systems known from observable data. As DMD methods work well in several situations but perform poorly in others, a clarification of the assumptions under which DMD is applicable is desirable. Upon closer inspection, existing interpretations of DMD methods based on the Koopman operator are not quite satisfactory: they justify DMD under assumptions that hold only with probability zero for generic observables. Here, we give a justification for DMD as a local, leading-order reduced model for the dominant system dynamics under conditions that hold with probability one for generic observables and non-degenerate observational data. We achieve this for autonomous and for periodically forced systems of finite or infinite dimensions by constructing linearizing transformations for their dominant dynamics within attracting slow spectral submanifolds (SSMs). Our arguments also lead to a new algorithm, data-driven linearization (DDL), which is a higher-order, systematic linearization of the observable dynamics within slow SSMs. We show by examples how DDL outperforms DMD and EDMD on numerical and experimental data.

Modeling and Analysis of Lake Water Levels Using System Dynamics and Principal Component Analysis

Understanding the intricate dynamics of the Great Lakes water levels, shaped by environmental factors like rainfall and evaporation, is crucial for devising sustainable water management strategies. This study develops a model to manage the Great Lakes' water levels by analyzing the interconnectedness of the lake network. Utilizing principal component analysis, key variables like rainfall and evaporation were evaluated for their impact on water levels. System dynamics models integrated with flow conservation laws allowed for a detailed hydrodynamic assessment. The study introduces a "narrow seam method" for dry river scenarios, enhancing model accuracy. Annual water level trends from 2000 are depicted, offering insights into future water management practices in response to environmental changes.

Comprehensive evaluation of “Three Waters” carrying capacity and path evolution study: A case of the Yellow River Basin

Research focusing solely on the carrying capacity of a single aspect of water resources, water environment, or water ecology is no longer sufficient to support the sustainable development and management of basin water systems. The study of basin carrying capacity should expand towards a comprehensive and holistic direction. Therefore, this study constructed an evaluation index system for carrying capacity based on water resources, water environment, and water ecology (“Three Waters”). Utilizing the entropy weight-TOPSIS method, System Comprehensive Index Evaluation, and ArcGIS tools, the comprehensive evaluation index of the “Three Waters” System Carrying Capacity (TWSCC) in the Yellow River Basin (YRB) from 2005 to 2020 was calculated. The evaluation index analyzed the spatiotemporal variation characteristics of subsystem carrying capacity and performed early warning identification and analysis of TWSCC. Four differentiated developmental pathways were designed based on the current status of basin carrying capacity. Leveraging System Dynamics (SD) modeling, the dynamic simulation, and emulation of carrying capacity trends in the YRB from 2020 to 2035 were conducted. The research findings indicate that from 2005 to 2020, the TWSCC levels across the nine provinces in the YRB consistently exhibited varying degrees of overload. The alert levels mostly remained in “Heavy warning” or “Medium warning” states. By 2035, TWSCC under the four development paths improved from 2020 levels, with the Green Environmental Protection-Oriented scheme reaching a safe carrying capacity. In summary, this paper offers theoretical and methodological support for developing basin-carrying capacity and the integrated governance of “Three Waters.”

A hybrid system dynamics model for power mix trajectory simulation in liberalized electricity markets considering carbon and capacity policy

Policy design during the energy system transition requires systematic thinking to navigate its complexity and mitigate potential side effects that policies may induce. Policy design based on System Dynamics (SD) simulations offers a tool to analyze real-world complexities. Given the difficulty of strictly simulating grid dispatch through causal loops, the Screening Curve (SC) method is widely employed in SD policy simulation models within closed commercial software platforms. Nevertheless, as Variable Renewable Energy (VRE) rises, the SC method faces challenges in accurately simulating grid dispatch operation due to its oversight of the power plants' limited output adjustment capabilities. This study developed a hybrid SD model as an alternative to the typical SD-SC combined method, allowing the SD model to simulate the power mix trajectory change incorporating high VRE penetration. After examining the simulation results with Hokkaido as a case study, the SC method may overestimate the abatement outcomes of the carbon pricing, and underestimate the support from capacity mechanism for the coal power plants. The difference between the two model increases along with the VRE share rising, when the VRE share exceeds about 40 % the results of the hybrid model will be more reliable.

Together, taking the next step: using system dynamics modelling to build community renewable energy programmes in Aotearoa New Zealand

ABSTRACT The Aotearoa New Zealand electricity sector is undergoing a transition to more distributed electricity generation, largely driven by the private sector. Local actors, however, are increasingly interested in playing a role in the energy transition, and addressing their resilience, with community renewable energy (CRE). These projects often have a wide range of impacts, dependent on the community’s motivations, as well as the design and operation of the project. However, there is a lack of tools that can accurately quantify the impacts derived from CRE projects, and guide communities to build a portfolio of projects. This study used system dynamics modelling to analyse the impacts of CRE projects over a 40-year time span. The Energise Ōtaki community group in Aotearoa New Zealand was used as a case study given their already established CRE project and goals to expand their project portfolio. The system dynamics model showed if the community reinvested 80% of the profits from the first project into a second project would provide the greatest long-term benefit for the community in terms of the impact areas important to them. System dynamics modelling proved a useful tool in evaluating the wide range of impacts, and how they interact.

Evaluating CO2 emissions in the residential sector: Life cycle assessment (LCA) using regional forestry design models in system dynamics (SD)

Evaluating CO2 emissions in the residential sector: Life cycle assessment (LCA) using regional forestry design models in system dynamics (SD)

Long-term sustainability of the water-agriculture-energy nexus in Brazil's MATOPIBA region: A case study using system dynamics.

The global demand for agricultural commodities has driven extensive land conversion to agriculture in Brazil, especially in the MATOPIBA region. This area encompasses the Rio Grande Basin, a major tributary of the São Francisco Basin that is known for expanding intensive irrigated agriculture and hydropower generation. However, recent data reveal declining precipitation and aquifer recharge, potentially exacerbating ongoing water and land conflicts. This study investigates the long-term sustainability of agricultural expansion amid the worsening water scarcity using a system dynamics model. Findings suggest that rising costs and decreasing profits due to irrigation water shortages may hinder the expansion of irrigated land. By 2040, the irrigation demand may remain partly unmet, while downstream flow and baseflow could decrease. Additionally, agricultural expansion will significantly raise energy demand, posing a developmental challenge. We suggest that ensuring the sustainability of the Rio Grande Basin depends on improved water management and exploring alternative energy sources to address existing constraints.

Influence of extended working hours and physical recovery on absenteeism in the footwear industry from a system dynamics model

Objectives. Work-related musculoskeletal disorders (WMSDs) are recurrent in the footwear industry, resulting in absenteeism. This study aimed to quantitatively analyze the influence of overtime work and physical recovery time on the occurrence of WMSD-related absenteeism using a system dynamics model. As ergonomic methods have limitations in quantitatively simulating the behavior of these relationships, the integration of computational modeling techniques has emerged as a methodological alternative to bridge this gap. Methods. An ergonomic work analysis (EWA) was developed in a production cell of a large company. A model of causal relationships (causal loop diagram) and a simulation model (flow and stock diagram) were then developed, where three scenarios for overtime and physical recovery time were analyzed. Results. Working an additional hour resulted in a 42% increase in physical overload, leading to 7.62 leave requests per year and 78.7 days of employee absenteeism. Increasing the physical recovery time by 15 min reduced the overload to 36.5%, resulting in 6.8 leave requests per year and 71.1 days of employee absenteeism. Conclusions. Properly managing excess workload and providing adequate physical recovery for professionals is necessary to mitigate the productivity impacts of absenteeism in the footwear industry.

Model Prediction of Built-Up Land Use on Flood-Prone Areas (a Systematic Literature Review)

Land use is dynamic due to the influence of population growth in an area. High population growth leads to uncontrolled and unplanned changes in land use in an area, resulting in environmental damage, especially in disaster-prone areas. The aim of this study is to review various models suitable for predicting changes in land use concerning flood-prone areas using a literature review method with PRISM guidelines and visualizing literature results using VosViewer software (1.6.19). The results of this study show that SD (System Dynamic Model) and CA-MC (Cellular Automata – Markov Chain) are the most commonly used models in land use change prediction. Meanwhile, the variables most suitable for land use studies, especially built-up land in relation to flood disasters, are population, growth rate, rainfall, slope, GDP, distance from (roads, rivers, and city centers), and LULC.

Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach

Incidents like the COVID-19 pandemic or military conflicts disrupted global supply chains, causing long-lasting shortages in multiple sectors. This so-called ripple effect denotes the propagation of disruptions to further elements of the supply chain. Due to the severity of the impact that the ripple effect has on revenues, service levels, and reputation among supply chain entities, it is essential to understand the related implications. Given the unpredictable nature of disrupting events, this study emphasises the value of a reactive development of effective recovery policies on an operational level. In this article, a system dynamics model for a supply chain is used as framework to investigate the ripple effect. Based on this model, recovery policies are generated using reinforcement learning (RL), which represents a novel approach in this context. As main findings, the experimental results demonstrate the applicability of the proposed approach in mitigating the ripple effect based on secondary data from a major aerospace and defence supply chain and furthermore, the results indicate a broad applicability of the approach without the need for complete information about the disruption characteristics and supply chain entities. With further refinement and real-world implementation, the presented approach provides the potential to enhance supply chain resilience in practice.