System Dynamics Model Research Articles

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.

Implications of changing the base raw material – the case of license plate manufacturing

ABSTRACT License plates to uniquely identify vehicles mainly use aluminum as the base material. Currently, there is no distinction of different use cases of license plates, such as short-time usage for test drive and transportation purposes that do not need such long-lasting materials not only from cost but also from sustainability perspectives. This paper presents a methodology to select the best material for different use cases under the holistic consideration of specifically defined criteria as to material properties, sustainability aspects, and supply chain implications. We show that there are several candidate materials for different use cases that stick out by changing the importance of these numerous criteria. In addition, the paper delves deeper into the sustainability aspect by means of a comprehensive System Dynamics model. We show that a scenario in which the company picks up used license plates by relying on a logistics service provider to get them delivered to an external recycling service provider yields the best results.

Dynamic Simulation of Energy Scenarios in the Transition to Sustainable Mobility in the Ecuadorian Transport Sector

In Ecuador, the growth of the transportation sector has significantly increased greenhouse gas emissions. According to experts, this sector currently contributes to 49.8% of total greenhouse gas emissions in this country. This poses significant challenges for environmental sustainability, emphasizing the urgent need for effective strategies to mitigate these emissions and promote environmentally friendly practices. Therefore, this study focuses on developing a dynamic simulation of energy scenarios for the year 2035 in the transportation sector, with the goal of transitioning to sustainable mobility, as fossil fuels are the main pollutants in the country. This study proposes system dynamics models using VENSIM 6.0b software to estimate the total energy demand and pollutant emissions in the transportation sector. The results suggest that if Ecuador aligns with global mobility trends and implements public policies promoting the use of electric vehicles, total CO2 emissions could potentially decrease from 50,161,432 kilobarrels of oil equivalent (kBOE) to 20,589,720 kBOE by the year 2035.

Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems (ESSs) operate as independent market participants and collaborate with photovoltaic (PV) generation units to enhance the flexible power supply capabilities of PV units. However, the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood. This study introduces a dual-timescale dynamics model that integrates a spot market clearing (SMC) model into a system dynamics (SD) model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems (IESSs) with that of an ESS integrated within a PV (PV-ESS). Furthermore, this study aims to ascertain the optimal allocation of the PV-ESS. First, SD and SMC models were set up. Second, the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem, a dual-timescale model was constructed. Finally, a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation. Additionally, negative electricity prices were considered during clearing processes. The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS, helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Impact of the carbon market on investment benefits of power-grid enterprises in China: A system dynamics analysis

The power grid, as the hub connecting the power supply and consumption sides, plays an important role in achieving carbon neutrality in China. In emerging carbon markets, assessing the investment benefits of power-grid enterprises is essential. Thus, studying the impact of the carbon market on the investment and operation of power- grid enterprises is key to ensuring their efficient operation. Notably, few studies have examined the interaction between the carbon and electricity markets using system dynamics models, highlighting a research gap in this area. This study investigates the impact of the carbon market on the investment of power-grid enterprises using a novel evaluation system based on a system dynamics model that considers carbon-emissions from an established carbon-emission accounting model. First, an index system for benefit evaluation was constructed from six aspects: financing ability, economic benefit, reliability, social responsibility, user satisfaction, and carbon-emissions. A system dynamics model was then developed to reflect the causal feedback relationship between the impact of the carbon market on the investment and operation of power-grid enterprises. The simulation results of a provincial power-grid enterprise analyze comprehensive investment evaluation benefits over a 10-year period and the impact of carbon emissions on the investment and operation of power-grid enterprises. This study provides guidelines for the benign development of power-grid enterprises within the context of the carbon market.

Decision-Making Support in IT Services Sourcing Management Through a System Dynamics Model

Decision-Making Support in IT Services Sourcing Management Through a System Dynamics Model

Indonesian palm oil towards sustainability: a system dynamic approach

Abstract The role of Indonesian palm oil in economic development and environmental degradation is a highly debated topic. Palm oil is essential to national development in Indonesia by providing a livelihood for millions of smallholder growers, plantations, and industries labor; it also contributes to national revenue from domestic and global trade. However, the growth of the palm oil industry also brings negative environmental impacts. The expansion of oil palm plantations is considered as one of major drivers for deforestation, peatland draining, and fire, leading to biodiversity decline, habitat loss, and greenhouse gas emissions. Here, we develop a system dynamic modelcalled SIPOS (Simulation of Indonesian Palm Oil Sustainability) to understand the current dynamic of the palm oil value chain to the economy and environment. We simulate the future condition of the palm oil sector with sustainability interventions such as intensification, implementation of No Deforestation, No Peat, and No Exploitation (NDPE), and trade. The model simulation result will include various indicators such as plantation extent, palm oil production, deforestation from the palm oil sector, Greenhouse Gas (GHG) emissions, and revenue from the palm oil trade. This study aims to show the use of a system dynamic model for sustainability scenario development, simulate multiple scenarios, measure the impact on the economy and environment, and then compare the benefits and disadvantages of each scenario.

Assessing the Impact of Catastrophic Electricity Loss on the Food Supply Chain

The food supply chain’s heavy reliance on electricity poses significant vulnerabilities in the event of prolonged and widespread power disruptions. This study introduces a system-dynamics model that integrates five critical infrastructures—electric grid, liquid fossil fuels, Internet, transportation, and human workforce—to evaluate the resilience of food supply chains to major power outages. We validated the model using the 2019 Venezuelan blackouts as a case study, demonstrating its predictive validity. We then explored how more extreme electricity losses would disrupt the supply chain. More specifically, we modeled the impact of a large-scale cyberattack on the US electric grid and a high-altitude electromagnetic pulse (HEMP) event. A cyberattack severely damaging the US electric grid and allowing for recovery within a few weeks or months would lead to substantial drops in food consumption. However, it would likely still be possible to provide adequate calories to everyone, assuming that food is equitably distributed. In contrast, a year-long recovery from a HEMP event affecting most of the continental United States could precipitate a state of famine. Our analysis represents a first attempt at quantifying how food availability progressively worsens as power outages extend over time. Our open-source model is made publicly available, and we encourage its application to other catastrophic scenarios beyond those specifically considered in this work (for example, extreme solar storms, high-lethality pandemics).

Trajectories of socio-ecological systems: Does social capital matter? A case study in the tropical Andes

Between natural and social systems there are two-way interactions, which determine the path of change in the Socio-Ecological System. For example, the type of land use decisions that society makes could lead to the deterioration of the biophysical system and its ecological functioning, causing effects on social well-being. Conceptual and simulation models of socio-ecological systems allow an understanding of such interactions and the development of trajectories. In this manuscript we explore the link between three lines of research that overlap but have been studied separately (i) Governance, (ii) Socio-ecological systems, and (iii) social capital. This paper analyzes the dynamics of social capital and its influence on governance and Land Use and Land Cover Changes in a basin of the Colombian Andes. To this end, we develop a Systems Dynamics model (SD). This research contributes to the (i) understanding of how the institutions, their policies, the actors, and the interactions generated between them in a territory influence the management of natural resources, which is indispensable to supporting the decision-making process; and (ii) operationalization of the social capital elements on the SES model through an integrated analysis with consideration of feedback mechanisms between natural and social system. With this model, a scenario evaluation is developed to analyze the effect of different approaches of the social capital organization on land cover in the basin case study.