System Dynamics Model Research Articles

THEORETICAL FOUNDATIONS OF RISK MANAGEMENT FOR ENERGY SECTOR ENTERPRISES

In a rapidly evolving energy environment, effective risk management is paramount to ensuring the stability and success of businesses. This article examines a systematic approach to risk management in the energy sector, emphasizing the development of strategies adapted to a specific enterprise.The study delves into the various models used in the energy sector to manage risk. Energy balance models, energy network models, economic-mathematical models, demand and production forecasting models, risk management system models, renewable energy sources (RES) models, and system dynamic models are discussed. The study proposes a comprehensive risk management system for the energy sector, including SWOT analysis, Delphi method, sensitivity analysis and simulation modeling. The approach involves a detailed analysis of internal and external factors, expert assessments and scenario testing for the formulation of adaptive risk management strategies, taking into account regional characteristics, prioritizing resistance to change, aligning them with modern industry challenges. Given the competitive and dynamic nature of the energy sector, the study uses SWOT analysis and the Delphi method as key methodologies. The results of the study emphasize the need for a comprehensive risk management system in energy. A systematic approach combining various analytical methods is proposed, aimed at maximizing sustainability and competitiveness. The decision support system, which can be built on the basis of the developed methodology, will be automatically adapted to specific enterprises, and will facilitate prompt response to changes and optimize risk management strategies. The developed concept of a systemic approach to consideration and response to risks in the energy sector offers a clear path for enterprise risk management. The integrated system proved to be innovative and effective, making a significant contribution to the improvement of risk management methodology in the energy sector. This research provides valuable information for practical applications in energy and risk management.

Multi-condition stability analysis and lightweight research of mining wide-body dump truck compartment

This paper proposes a multi-condition optimization method based on single condition optimization and weight. This method analyzes and discusses various working states of the wide-body dump truck and optimize its structure. Firstly, the modal shape and natural frequency of the carriage under modal conditions are analyzed using the finite element method. Combined with the mechanic’s theory, the carriage’s force, deformation, and stress are studied under the turning and partial load conditions. This part provides a basis for subsequent stability analysis and lightweight research. According to the filtering white noise method and differential equation method, the f-class road model and the vehicle system dynamics model are established in MATLAB to study the influence of road vibration on the stability of the dump truck during operation. The carriage vibration data prove that the road vibration causes the low order resonance of the carriage and affects the stability of the dump truck. This part provides a basis for the subsequent stability optimization. Based on the above analysis, a multi-objective genetic algorithm based on the response surface is used to optimize the structural parameters of the carriage under partial load conditions, turning conditions and modal conditions, to realize the optimization of stability, mass, stress and deformation of the carriage. Finally, the entropy weight method is used to fuse the optimization results of the three working conditions. The correctness of the comprehensive optimization results is verified, and the wide-body dump truck carriage optimization is realized, proving the feasibility of the proposed method.

Research on the Improvement Path of Mixed Teaching Effect of Investment Banking based on System Dynamics

Taking “Investment Banking”, one of the core courses of college finance majors, as an example, this paper explores the way to improve the mixed teaching effect. By constructing a system dynamic model of the mixed teaching effect of Investment Banking, this paper finds that teaching organization and management ability have the greatest impact on the mixed teaching effect, and students’ perceived ease of use for mixed teaching has the greatest impact on the mixed teaching effect. The degree of accuracy of background teaching data monitoring of online learning platform, the degree of frontier of platform resources and the degree of richness of platform learning resources have significant and similar influence on the mixed teaching effect. From the three aspects of teachers’ teaching organization and management ability, students’ perceived ease of mixed teaching, and the quality of online learning platform, the mixed teaching effect of Investment Banking course can be effectively improved. Based on this, suggestions are put forward to provide references for promoting the construction of first-class undergraduate courses for finance majors.

Power Generation Mix Optimization under Auction Mechanism for Carbon Emission Rights

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. The carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China’s future carbon quota allocation will gradually transition from free allocation to auction allocation, and the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism, constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model, systematically researches the power generation structure under different auction ratios with the time scale of months, and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions. The results of the study show that after comprehensively comparing the total economic cost, renewable energy development, and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits.

Research on the motivation system and path simulation of collaborative agglomeration of Chinese culture and tourism industries based on system dynamics.

In the context of industrial integration, the collaborative agglomeration of the cultural and tourism industries is an important way to promote their integrated development and achieve both industrial transformation and upgrading. This article first analyzes the dynamics of the cultural and tourism industries as a collaborative agglomeration. A system dynamics model is then presented which represents the perspective and reveals the mechanics of this partnership between the two industries. Finally, the authors use this model to simulate the path made possible by their collaborative agglomeration. The results show: (1) From the perspective of industrial policy, the promotion and guidance function of industrial policy elements still needs strengthening in the collaborative development of China's cultural and tourism industries. (2) From the perspective of industrial economy, the promotion function of industrial economic factors still needs improving in the collaborative agglomeration and development of China's cultural and tourism industries. (3) From the perspective of the joint effect of industrial policy and economy, the collaborative effect of industrial policies and economic factors is more conducive to promoting the collaborative agglomeration development of China's cultural and tourism industries. The research in this article can provide theoretical support and policy recommendations for promoting coordinated development of China's cultural and tourism industries and can also provide the experience needed to serve as a reference for the joint development of tourism and culture in other similar regions.

Influencing factors of different development stages of green food industry: a system dynamic model

The green food industry is important for China because it bears the additional expectation of promoting the rural economy Heilongjiang Province is a representative example, which is one of the main production bases of green food With this in mind, this paper takes Heilongjiang Province as an example to research the main influencing factor of the green food industry This article uses system dynamics methods to construct research models and uses mathematical models to calculate the industry lifecycle. Different from previous studies, this paper provides the life cycle of the industry and discusses the effects of influencing factors during the different periods The main conclusion includes: The stage before 2011 is the termination period, the stage from 2011 to 2019 is the growth period, and the stage after 2019 is the property period By the final time of the simulation, there are no signs of filtering; The enterprise scale is the main influencing factor that can make positive effects on the output value of the green food industry from the growth period, and others have no objective impact from beginning to end; Undesirably high level of financial investment will execute a negative effect for industrial development in the property period, at least in terms of output value.

The Impact of Climate Change and Urbanization on Groundwater Levels: A System Dynamics Model Analysis

Climate change and population growth have placed increasing stress on groundwater resources. Effective management of groundwater resources is crucial for promoting sustainable development, especially in areas heavily reliant on groundwater supply. However, sustainable groundwater management is more complex due to various factors affecting key drivers and their feedback interactions. In this regard, this study aims to examine the key driving forces and their interplay concerning fluctuations in groundwater levels within a study area in Illinois, United States, which heavily relies on groundwater for water supply. To achieve this objective, a system dynamics (SD) simulation model was developed, utilizing hydrology, climate, and urbanization data spanning from 2010 to 2020. Through calibration and validation of the SD model with historical data, the model demonstrated reliable simulation capabilities for groundwater levels. Additionally, sensitivity analysis and exploration of water resource scenarios were conducted to evaluate the critical factors influencing groundwater levels. Results revealed that population dynamics, agricultural land, and groundwater recharge were analyzed as key drivers influencing the change in groundwater level for the study area, among the climate and urbanization factors that had different contributions to the groundwater dynamics. A 10% increase in population, agricultural land, and groundwater recharge led to a decrease of approximately 5.5% and increases of 4.5% and 4.27% in groundwater levels, respectively. The results of the four-scenario analysis indicated that both climate change and urbanization exerted significant and adverse impacts on the groundwater level in the study area. These scenarios represented prominent and worst-case situations, highlighting the potential challenges and risks associated with the combination of these factors, which resulted in about a 77 mm decrease in groundwater levels by 2030. Overall, given limited data availability for SD model parameterization, the study emphasizes the need to consider the effects of both urbanization and climate change on groundwater supplies in decision-making for sustainable groundwater management, as well as the need for adequate management measures to maintain the long-term usage of groundwater resources.

Transition paths for condition‐based maintenance‐driven smart services

AbstractThis research investigates growth inhibitors for smart services driven by condition‐based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under‐ or over‐maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.

Modelling and control of electromechanical disc brake for heavy-duty vehicles

This paper proposes a validated procedure that can be used for development stage of model-based controller for heavy-duty electromechanical disc brakes. Firstly, system dynamics model of a single piston electromechanical disc brake is constructed in Matlab/Simulink environment in consideration of nonlinear friction model. To ensure the accuracy of system dynamics model, open loop measurements (clamping force, motor angle and motor current) are conducted on a prototype of single piston electromechanical disc brake. Experimental data is used for the determination of system variables. The predicted outputs are verified by comparison of experimental measurement results. For the control purpose of electromechanical brake, a multi-stage closed loop architecture is introduced. To regulate clamping force, PID and sliding mode controllers are developed in Matlab/Simulink in consideration of braking performance requirements for heavy-duty vehicles. For management of running clearance between brake disc and pad, PID position controller is developed in simulation environment. Those controller parameters obtained in the simulation process are introduced to the control hardware. After that closed loop clamping force and position measurements are conducted. When the rise time values of both clamping force controllers are compared, it is seen that sliding mode controller can reach to the settling point faster than PID controller. As for the position controller, the rise time requirement has been achieved by the designed PID controller. The switching process of force and position controllers are implemented and functional closed loop measurements are conducted for different reference input signals. It is seen from the results that the rise time requirement for position controller has been achieved. The system response of clamping force parameter is considerably stable.

Development of a System Dynamics Model for Prediction of Karaj Reservoir Share in Tehran Water Supply

Development of a System Dynamics Model for Prediction of Karaj Reservoir Share in Tehran Water Supply

Simulating the interrelationships among population, water, ecology, and economy in urban agglomerations based on a system dynamics approach

China's urban agglomerations (UAs) gather most of China's population and economy, but they also facing serious water resources crisis and ecological risks. How to manage the interrelationships among the population, water, ecology, and economy (PWEE) in UAs is crucial for the future of China's urbanization development. Based on the system dynamics (SD) approach, we constructed an SD model for simulating the interrelationships among PWEE in UAs, elucidating the intricate nonlinear relationships between elements of the PWEE system through causal feedback processes and model equations. In addition, we take the Tianshan North slope urban agglomeration (TSNSUA) as an example, which is a representative area where water resources crisis and ecological risk coexist, and designed five scenarios to dynamically simulate the balance of water supply and demand under the high-density population and economic agglomeration, as well as the potential ecological environmental effects in the process of urban spatial expansion. Our findings include: (1) The existing water resources conditions of TSNSUA can continue to support the current population and economic growth rates. If the future development plan follows the status quo continuation scenario, the phenomenon of insufficient water supply will not occur on the premise that the water supply capacity remains at its current highest level. (2) TSNSUA's future urban spatial expansion is predicted to have less overall impact on the ecological environment, and the carbon peak target by 2030 has an opportunity to be achieved. (3) Appropriately reducing the proportion of agricultural water, while strengthening water-saving and ecological protection measures, can ensure that TSNSUA's population and economy maintain a medium to high growth rate. Based on scenario comparison, our recommendation to policymakers is to establish the development objectives of TSNSUA based on the comprehensive development scenario.

Descriptive design structure matrices for improved system dynamics qualitative modeling

AbstractQualitative modeling approaches can be useful in system information collection, model analysis, and formal model development. This is difficult when the number of elements and their interactions in the system is large. System dynamicists need additional tools and methods to conceptually model these large tightly coupled systems. We propose and test the Descriptive Design Structure Matrix (DDSM) as a qualitative system dynamics modeling tool and approach for systems with many elements and more interactions that can reasonably be modeled individually using traditional system dynamics methods. A DDSM consists of four parallel and internally consistent matrices that describe system interactions with binary relations, nontechnical text, technical text, and literature support. By including and documenting system information in multiple forms, DDSMs facilitate multiple stages of system dynamics modeling, improve modeler communication with system participants and domain experts, and improve model rigor. DDSM construction is described. A case study of the 2014 flooding in Kashmir is used to illustrate and test a DDSM and its application. Due to their compact format, DDSMs provide a useful visual communication aid, intuitive reasoning tool, and foundation for formal system dynamics modeling and analysis. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Prediction Model of Electric Power Carbon Emissions Based on Extended System Dynamics

In response to global climate change, China has committed to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, commonly known as the “30–60 Dual Carbon”. Under the background of “30–60 Dual Carbon”, this article takes the electric power industry, which is the main industry contributing to China’s carbon emission, as the research object, explores the time and peak value of the carbon peak of the electric power industry, and analyzes whether carbon neutrality can be realized under the peak method, so as to get the carbon neutrality path of the electric power industry and serve as the theoretical basis for the formulation of relevant policies. The Environmental Kuznets Curve inspection and the relationship analysis are carried out, then the system dynamics model is constructed, the carbon emissions from 2020 to 2040 are simulated, and the peak time is predicted. Three different scenarios are set to explore the path of electricity carbon neutralization under the premise of a fixed peak. It is shown that Gross Domestic Product per capita index factors have the largest positive contribution, and thermal power share index factors have the largest negative contribution to electricity carbon emissions. Based on the current efforts of the new policy, carbon emissions can achieve the peak carbon emissions’ target before 2030, and it is expected to peak in 2029, with a peak range of about 4.95 billion tons. After the power industry peaks in 2029, i.e., Scenario 3, from coal 44%, gas 9% (2029) to coal 15%, gas 7% (2060), where the CCUS technology is widely used, this scenario can achieve carbon neutrality in electricity by 2060. Adjusting the power supply structure, strictly controlling the proportion of thermal power, optimizing the industrial structure, and popularization of carbon capture, utilization, and storage technology will all contribute to the “dual carbon” target of the power sector.

Examining the linkages among financial inclusion, economic growth, poverty, and inequality reduction in Africa

The study examined the nexus between financial inclusion and economic growth, poverty, and inequality in Fragile and Post-Conflict African economies based on data sourced from 2004 to 2021. The study employed the system GMM and dynamic threshold model within the context of the Tunnel and Joneses effects to analyze the data generating set. The study noted that financial inclusivity enhances growth, and reduces poverty and inequality in the studied economies. The results of robustness tests reveal that our model is appropriate, this suggests the validity of the Tunnel effect in the studied economies. The threshold dynamic result suggests that the optimal influence of financial inclusion on economic growth, poverty, and inequality is at 42% for the studied economies. The study offered some policy implications.

Diffusion of electric vehicles in Beijing considering indirect network effects

Various policies on electric vehicles (EVs) have been implemented in Beijing to promote EV diffusion, but previous research has rarely considered the indirect network effects between EVs and EV charging infrastructures (EVCIs), and has not explored the impact of EVCI-related incentives on EV diffusion. This study aims to enrich the literature by incorporating EVCI development and indirect network effects in EV diffusion. A system dynamic model is proposed to systematically analyse the interactions among consumer, EV manufacturer and EVCI operator and simulate EV diffusion and ECVI investment in Beijing. Multiple scenarios are adopted to investigate the joint effects of the policy mix on EV diffusion. The results indicate that the oil-to-electricity subsidy promoted EVs and EVCIs to a greater extent than the license plate control policy. The EVCI maintenance policy outperformed EVCI construction and production subsidies on EV diffusion. Synergistic effects exist when the oil-to-electricity subsidy is mixed with EVCI-related incentives. This study provides implications for policymakers to promote EV diffusion with innovative and efficient policy instruments.

Dynamic Successive Assessment of Water Resource Carrying Capacity Based on System Dynamics Model and Variable Fuzzy Pattern Recognition Method

The water resource carrying capacity (WRCC) system comprises multiple complex and non-linear interactions related to society, economy, water resources, and the water environment. A comprehensive comprehension of its internal mechanisms is essential for the continual enhancement of the regional WRCC. This study concentrates on the temporal and spatial variability of the WRCC to investigate a method for dynamic successive assessment. Firstly, the pressure–state–response (PSR) framework is used to develop a systematic and causal indicator system. Then, the variable fuzzy pattern recognition (VFPR) model and an analytic hierarchy process—entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resource carrying capacity in Hebei Province is mainly due to the basic attribute of the decision on the water resource shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which has made the province’s water resource carrying capacity tend to become better gradually. Simultaneously, a system dynamics model (SD) for water resource carrying capacity was established based on an analysis of the model structure. Moreover, three scenarios were designed, including existing continuation, high-efficiency water saving, and cross-regional water transfer. Subsequently, each scenario is further categorized into high- and low-speed economic development and population growth schemes. Afterward, simulations and predictions were conducted for a total of six schemes spanning from 2023 to 2030. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of the economy and population, the efficient water conservation, and the interbasin transfer scenario (scenario 2 with high speed) are the best choices for the sustainable development of water resources and social economy in Hebei.

The dynamic mechanism of scientific and technological innovation in China’s marine biomedical industry

As one of the important forces promoting the development of the marine economy, the marine biomedical industry (MBI) shows typical knowledge and technology-intensive characteristics. It is of great significance to explore the industrial innovation dynamic mechanism for breaking through the bottleneck of innovative development as well as achieving high-quality development. This paper constructed a system dynamics model for scientific and technological innovation (STI) in the MBI and then explored the dynamic mechanism of STI in the industry through simulation. The study found that, in the innovation environment subsystem, the two factors of the intellectual property protection intensity and the government’s scientific research funding input had the most significant positive driving effects on STI of the MBI; the promotion of government education funding input showed a significant strengthening trend over time, while the marine environmental regulation intensity had comparatively little effect on the level of STI. In the subsystem of innovation input, both innovation funding input and innovation personnel input were the direct driving forces for the improvement of the STI level. And enterprises played a dominant role in STI because the STI level of the MBI was more sensitive to the changes in enterprise innovation input. Compared with the individual factor input, the synergistic promotion effect of multiple factors is more significant. In addition, it was found that there was a low conversion rate of scientific research achievements in China’s MBI by analyzing the influence of various factors changing on technological output and economic output. Based on the research results, this paper puts forward corresponding policy suggestions in order to provide theoretical support and a practical basis for promoting the technological innovation level of China’s MBI as well as references for other countries.

Dynamic Modelling of Data Center Waste Heat Potential Integration in District Heating in Latvia

As demand for data centers (DC) has increased rapidly, so has their electricity consumption. Cooling DCs is essential to maintain optimal temperatures for the operation of servers and equipment. The consequence of the cooling process is that most of the electricity consumed in DCs, including cooling, is eventually dissipated as heat that is released into the atmosphere without any useful application. Recovering and reusing waste heat offers a sustainable solution to reduce primary energy consumption and minimize the environmental impact. Using waste heat from DCs to heat buildings can significantly improve the energy efficiency and environmental sustainability of DCs. Therefore, this research analyzes the existing potential of waste heat recovery from data centers in Latvia and proposes a system dynamic modelling approach for evaluation of the future impact of waste heat on the national heat supply. The overall waste heat generated by DCs in 2022 was 51.37 GWh at a temperature of 65 °C. By 2050, the total heat energy production potential from DCs will increase to 257 GWh, with 201 GWh being utilized.

A past–present–future lens of environmental change: blending applied paleoecology and participatory system dynamics modeling at a conservation site in the Cape Floristic Region, South Africa

Landscapes are social–ecological systems (SESs) that produce ecosystem services, which change over time in response to environmental, biotic, and social drivers. Failure to consider this variability, and the feedbacks that can stabilize or destabilize systems, can have consequences for sustainable ecosystem services provision. This study applies a conceptual meta-framework, past–present–future lens, to interpret changes in land cover and ecosystem services within the Cape Floristic Region (CFR) of South Africa. Paleoecology (fossil pollen, spores, and charcoal) and participatory system dynamics modeling were used to explore long-term variability in provisioning ecosystem services (plant biodiversity) and the drivers of this variability (fire and herbivory) at Elandsberg Private Nature Reserve (Elandsberg PNR). From ca. 1800s, the paleoecological record suggests that environmental changes, particularly a transition to unpalatable Elytropappus-dominated vegetation, were driven by grazing and that an ecological threshold was crossed in ca. 1950s due to agricultural intensification. Participatory system dynamics was used to identify feedbacks in the dynamic SES structure. The ecological model replicates the paleoecological results and, furthermore, suggests that in the future, returning the system to within historical ranges variability may require sustained reductions in both grazing and fire over decades. This innovative approach blends paleoecology and participatory system dynamics to provide an evidence-based understanding of temporal variability and feedbacks for policymakers and land-use managers to inform sustainable land management.

Adopting electric bus for improving efficiency in the local public transport sector: Analysis of facilitating conditions and their nonlinear relationships

Zero-emission vehicles (ZEVs) are technological innovations that have the potential to strengthen efficiency and sustainability improvement within the transportation sector. However, privately owned local bus transport firms lack the resources to make ZEVs especially Battery Electric Vehicles (BEVs) easily accessible for their logistics services. Therefore, this study proposes to pave the way for technological transformation in the public transport (PT) sector by exploring how these local bus businesses can facilitate the sustained use of BEVs in order to establish a better efficiency from their services. Specifically, the critical facilitating conditions for adopting batter-electric buses (BEBs) in privately owned local PT companies were obtained using Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) while their nonlinear relationships of importance levels were predicted using adaptive neuro-fuzzy inference systems (ANFIS). A system dynamics model was proposed that illustrates the dynamic nonlinear relationships of the multiple effects of these conditions on transport efficiency (TE). Based on empirical evidence in Nigeria, the proposed methods were verified and the study results predict that government support, affordable electricity tariffs, access to electric minibuses and presence of charging infrastructure are foremost in boosting the sustained use of BEBs in the local PT sector. Meanwhile, government support improves TE more quickly in a short period while presence of charging infrastructure, affordable electricity tariffs and access to electric minibuses are more effective for stimulating TE over a longer period. This study has implications for decision makers and policy makers that design strategies for BEB adoption to improve TE in the local public transport sector.