Systems

24 pages, 2401 KiB

Open AccessArticle

Layout Planning of a Basic Public Transit Network Considering Expected Travel Times and Transportation Efficiency

by Mingzhang Liang, Wei Wang, Ye Chao and Changyin Dong

Systems 2024, 12(12), 550; https://doi.org/10.3390/systems12120550 - 10 Dec 2024

Urban transit systems are crucial for modern cities, providing sustainable and efficient transportation solutions for residents’ daily commutes. Extensive research has been conducted on optimizing the design of transit systems. Among these studies, designing transit line trajectories and setting operating frequencies are critical [...] Read more.

Urban transit systems are crucial for modern cities, providing sustainable and efficient transportation solutions for residents’ daily commutes. Extensive research has been conducted on optimizing the design of transit systems. Among these studies, designing transit line trajectories and setting operating frequencies are critical components at the strategic planning level, and they are typically implemented in an urban integrated transportation network. However, its computational complexity grows exponentially with the expansion of urban integrated transportation networks, resulting in challenges to global optimization in large-scale cities. To address this problem, this study investigates the layout planning of a basic public transit network (BPTN) to simplify the urban integrated transportation network by filtering out road segments and intersections that are unattractive for both users and operators. A non-linear integer programming model is proposed to maximize the utility of the BPTN, which is defined as a weighted sum of expected travel times (from a user perspective) and transportation efficiency (from an operator perspective). An expected transit flow distribution (ETFD) analysis method is developed, combining different assignment approaches to evaluate the expected travel time and transportation efficiency of the BPTN under various types of transit systems. Moreover, we propose an objective–subjective integrated weighting approach to determine reasonable weight coefficients for travel time and transportation efficiency. The problem is solved by a heuristic solution framework with a topological graph simplification (TGS) process that further simplifies the BPTN into a small-scale graph. Numerical experiments demonstrate the efficacy of the proposed model and algorithm in achieving desirable BPTN layouts for different types of transit systems under variable demand structures. The scale of the BPTN is significantly reduced while maintaining a well-balanced trade-off between expected travel time and transportation efficiency. Full article

17 pages, 1872 KiB

Open AccessArticle

Competitive Integration of Social Tourism Enterprises Through an Organizational Management System: The Case of El Jorullo in Puerto Vallarta, Jalisco

by Carlos Salvador Peña-Casillas, Rodrigo Espinoza-Sánchez, José Alejandro López-Sánchez and Perla Aguilar-Navarrete

Systems 2024, 12(12), 549; https://doi.org/10.3390/systems12120549 - 10 Dec 2024

Abstract

Ejidos are a unique form of land ownership in Mexico based on cooperative and mutual aid, characterized by management problems. Some ejidos have given rise to social tourism enterprises (STE), which seek to respond to local needs by carrying out traditional agricultural and [...] Read more.

Ejidos are a unique form of land ownership in Mexico based on cooperative and mutual aid, characterized by management problems. Some ejidos have given rise to social tourism enterprises (STE), which seek to respond to local needs by carrying out traditional agricultural and livestock activities complemented by tourism. This sector requires integration to compete. The cases addressed are the STEs in the ejido called El Jorullo, a tourist destination in Puerto Vallarta, Jalisco, Mexico. Therefore, this research’s general aim was to analyze a proposal for a strategic management system for the STEs of ejido El Jorullo based on social capital to promote their competitiveness. The methodology is qualitative, based on social network analysis (SNA) to identify the social capital of the participants of El Jorullo and their enterprises from the perspective of the theory of organizational population ecology and subsequently, the emptying of this information to feed a technology-based management system. The results indicate the six stages of the proposed system for integrating the enterprises. This allows identifying an option for STEs to become more competitive through the integration and involvement of various stakeholders. Full article

(This article belongs to the Special Issue Socio-Ecological Systems and Their Applications)

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15 pages, 767 KiB

Open AccessArticle

Parking Pricing in the Morning Commute Problem Considering Human Exposure to Vehicular Emissions

by Yu Tan, Zhenchao Yuan, Rui Ma and Zhanbo Sun

Systems 2024, 12(12), 548; https://doi.org/10.3390/systems12120548 - 9 Dec 2024

Abstract

Walking is the final phase of the morning commute, during which commuters are exposed to vehicular emissions. This study proposes a novel analytical model to evaluate how emission exposure affects commuters’ departure time choices and parking behavior. Different from traditional bottleneck models, our [...] Read more.

Walking is the final phase of the morning commute, during which commuters are exposed to vehicular emissions. This study proposes a novel analytical model to evaluate how emission exposure affects commuters’ departure time choices and parking behavior. Different from traditional bottleneck models, our model includes a nonlinear term in the generalized cost function to account for emission exposure. The findings reveal that, at user equilibrium, rational commuters seeking to minimize their own generalized costs will park outward, resulting in undesired scenarios in which all walking commuters suffer from emission exposure. However, we show that in a system-optimal scenario, emission exposure can be eliminated if commuters park inward; the schedule delay cost is minimized in such a parking order. To achieve this outcome, we propose a new spatiotemporal parking pricing scheme designed to reduce the overall system cost and incentivize inward parking patterns. Case studies using empirical data show that this pricing approach, independent of specific parking orders, effectively encourages inward parking, thereby minimizing emissions and improving commuter welfare. Hopefully, findings from this research can provide insights to the development of effective roadside parking pricing strategies. Full article

24 pages, 2076 KiB

Open AccessArticle

Modeling a Green and Reliable Intermodal Routing Problem for Food Grain Transportation Under Carbon Tax and Trading Regulations and Multi-Source Uncertainty

by Yan Sun, Chen Zhang, Ailing Chen and Guohua Sun

Systems 2024, 12(12), 547; https://doi.org/10.3390/systems12120547 - 9 Dec 2024

Abstract

This study addresses an intermodal routing problem encountered by an intermodal transportation operator fulfilling the food grain transportation order of an agri-food company. To enhance the environmental sustainability of food logistics, carbon tax and trading regulations have been employed to reduce the carbon [...] Read more.

This study addresses an intermodal routing problem encountered by an intermodal transportation operator fulfilling the food grain transportation order of an agri-food company. To enhance the environmental sustainability of food logistics, carbon tax and trading regulations have been employed to reduce the carbon emissions associated with transportation. Multi-source uncertainties, including the company’s demand for food grains and various parameters related to the intermodal transportation activities, are modeled via trapezoidal fuzzy numbers to optimize the comprehensive reliability of the solution. This work incorporates wastage reduction by lowering the wastage costs and formulating a wastage threshold constraint in intermodal routing. Accordingly, a fuzzy mixed-integer nonlinear programming model for a green and reliable intermodal routing problem for food grain transportation is proposed. To overcome the model’s insolvability and the difficulty in finding the global optimum solution to a nonlinear optimization model, a two-stage solution method is developed, employing chance-constrained programming and linearization technique to reformulate the initial model. A numerical case study is given to verify the feasibility of the proposed methods. Sensitivity analysis reveals the influence of confidence levels and wastage threshold, providing insights for the agri-food company to balance economics, reliability, and wastage reduction in food grain transportation. The numerical case study also analyzes the feasibility of carbon tax and trading regulations in reducing carbon emissions, concluding that carbon tax regulations consistently achieve greater reductions and are universally feasible. In contrast, the feasibility of carbon trading regulations depends on confidence levels and wastage threshold. The findings of this work could provide strong quantitative support for intermodal transportation operators and agri-food companies seeking to implement sustainable food grain transportation. Full article

(This article belongs to the Special Issue Multi-criteria Decision Making in Supply Chain Management)

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16 pages, 747 KiB

Open AccessArticle

Modelling and Intelligent Decision of Partially Observable Penetration Testing for System Security Verification

by Xiaojian Liu, Yangyang Zhang, Wenpeng Li and Wen Gu

Systems 2024, 12(12), 546; https://doi.org/10.3390/systems12120546 - 9 Dec 2024

Abstract

As network systems become arger and more complex, there is an increasing focus on how to verify the security of systems that are at risk of being attacked. Automated penetration testing is one of the effective ways to achieve this. Uncertainty caused by [...] Read more.

As network systems become arger and more complex, there is an increasing focus on how to verify the security of systems that are at risk of being attacked. Automated penetration testing is one of the effective ways to achieve this. Uncertainty caused by adversarial relationships and the “fog of war” is an unavoidable problem in penetration testing research. However, related methods have argely focused on the uncertainty of state transitions in the penetration testing process, and have generally ignored the uncertainty caused by partially observable conditions. To address this new uncertainty introduced by partially observable conditions, we model the penetration testing process as a partially observable Markov decision process (POMDP) and propose an intelligent penetration testing decision method compatible with it. We experimentally validate the impact of partially observable conditions on penetration testing. The experimental results show that our method can effectively mitigate the negative impact of partially observable conditions on penetration testing decision. It also exhibits good scalability as the size of the target network increases. Full article

(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)

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34 pages, 8723 KiB

Open AccessArticle

What Is the Effect of China’s Renewable Energy Market-Based Coupling Policy?—A System Dynamics Analysis Based on the Coupling of Electricity Market, Green Certificate Market and Carbon Market

by Wenhui Zhao, Yanghui Lin and Hua Pan

Systems 2024, 12(12), 545; https://doi.org/10.3390/systems12120545 - 7 Dec 2024

Abstract

In the context of China’s electricity market reform, green certificate trading and carbon trading, as important policy tools to promote the development of renewable energy and energy conservation and emission reduction in the power industry, will inevitably be coupled with the electricity market. [...] Read more.

In the context of China’s electricity market reform, green certificate trading and carbon trading, as important policy tools to promote the development of renewable energy and energy conservation and emission reduction in the power industry, will inevitably be coupled with the electricity market. In order to study whether the coupled market can successfully achieve the goals of power supply structure adjustment and carbon emission reduction, this paper establishes a system dynamics (SD) model, analyzes the correlation and coordination mechanism among the green certificate market (TGC), carbon market (ET) and electricity market, including generation right trading, and simulates the changes of market price and power supply structure. The results show that (1) the power price under the coupling of three markets includes the TGC price and the ET price, so it is influenced by the ratio of renewable portfolio standards (RPS) and carbon reduction policy; (2) the combination of the TGC mechanism and the ET mechanism will be conducive to the optimization of long-term market power supply structure, so as to promote the realization of emission reduction targets; and (3) power generation rights trading, as a carbon reduction policy, will reduce the power generation of fossil energy in the short-term market, but in the long run, it will lead to the loss of momentum for the development of renewable energy. Therefore, regulators need to reasonably adjust different policies in order to give full play to the comprehensive regulatory role and help the energy and power industry and the low-carbon transformation of society. Full article

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15 pages, 2805 KiB

Open AccessArticle

Optimizing Carbon Emissions in Electricity Markets: A System Engineering and Machine Learning Approach

by Zhiyu An and Clifford Alan Whitcomb

Systems 2024, 12(12), 544; https://doi.org/10.3390/systems12120544 - 5 Dec 2024

Abstract

This study addresses the urgent need to reduce carbon emissions in the power sector, a major contributor to global greenhouse gas emissions, by employing system engineering principles coupled with machine learning techniques. It analyzes the interplay between regional marginal prices (LMP) and carbon [...] Read more.

This study addresses the urgent need to reduce carbon emissions in the power sector, a major contributor to global greenhouse gas emissions, by employing system engineering principles coupled with machine learning techniques. It analyzes the interplay between regional marginal prices (LMP) and carbon emissions within electricity markets. The paper explores how market designs and operational strategies influence carbon output by leveraging a dataset that encompasses hourly LMP and carbon emissions data across various regions of New York State. The analysis utilizes neural networks to simulate and predict the effects of different market scenarios on carbon emissions, highlighting the role of LMP, loss costs, and congestion costs in environmental policy effectiveness. The results underscore the potential of system engineering to provide a holistic framework that integrates market dynamics, policy adjustments, and environmental impacts, thereby offering actionable insights into optimizing market designs for reduced carbon footprints. This approach not only enhances the understanding of the complex interactions within electricity markets but also supports the development of targeted strategies for achieving sustainable energy transitions. Full article

(This article belongs to the Special Issue System of Systems Engineering)

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18 pages, 962 KiB

Open AccessArticle

The Impact of Digital Finance on Urban and Rural Household Carbon Emissions: Evidence from China

by Hao Wu and Yang Zou

Systems 2024, 12(12), 543; https://doi.org/10.3390/systems12120543 - 5 Dec 2024

Abstract

The complex interplay between digital finance (DF) and household carbon emissions (HCEs) represents a critical subsystem within the broader socioeconomic–ecological system driving climate change. This paper presents estimates of HCEs based on panel data for 30 Chinese provinces from 2011 to 2021 and [...] Read more.

The complex interplay between digital finance (DF) and household carbon emissions (HCEs) represents a critical subsystem within the broader socioeconomic–ecological system driving climate change. This paper presents estimates of HCEs based on panel data for 30 Chinese provinces from 2011 to 2021 and examines the effects and mechanisms of DF on HCEs in urban and rural regions. The results indicate that (1) DF has a negative impact on urban HCEs, while, conversely, it exacerbates HCEs in rural regions; (2) based on the heterogeneity analysis, the impact of DF is primarily driven by its coverage, with the most significant effects seen in eastern China; and (3) two transmission channels are operative: an energy consumption scale effect and an energy consumption composition effect. Further analysis suggests that government expenditure on energy conservation and environmental protection, as well as financial regulation, play moderating roles in these channels. These findings provide new insights into efforts to achieve carbon neutrality in China and offer new perspectives on the role of financial technologies in shaping environmental outcomes within complex socio-technical systems. Full article

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25 pages, 1438 KiB

Open AccessArticle

Configurational Pathways to Breakthrough Innovation in the Digital Age: Evidence from Niche Leaders

by Shuai Liao, Xi Deng, Hui Lu and Luyao Niu

Systems 2024, 12(12), 542; https://doi.org/10.3390/systems12120542 - 5 Dec 2024

Abstract

Fostering niche leaders to achieve technological breakthroughs has become a national strategic priority in emerging markets in order to overcome technology blockades and drive technological progress. Previous research indicates that achieving breakthrough innovation, particularly for firms with resource constraints, is a multifaceted phenomenon [...] Read more.

Fostering niche leaders to achieve technological breakthroughs has become a national strategic priority in emerging markets in order to overcome technology blockades and drive technological progress. Previous research indicates that achieving breakthrough innovation, particularly for firms with resource constraints, is a multifaceted phenomenon occurring across various levels. Based on the technology–organization–environment (TOE) framework, this paper aims to examine the influence of technological, organizational, and environmental factors on the breakthrough innovation of niche leaders in emerging markets from a configurational perspective. Using dynamic qualitative comparative analysis (QCA), we analyzed panel data from 87 Chinese niche leaders (2018–2023) through inter-group, intra-group, and pooled comparisons to uncover distinct configurational pathways to breakthrough innovation. Our findings reveal three effective pathways: an R&D-driven innovation pathway, a digital transformation-driven innovation pathway, and a comprehensive support innovation pathway. Additionally, we identified two configurational pathways leading to the absence of high-breakthrough innovation: the conservative management configuration, and the digital island configuration. Our results underscore the essential role of government subsidies, the complementary impact of digital transformation and R&D, and the restrictive effect of rigid governance structures. Furthermore, these pathways demonstrate significant regional variations and temporal evolution, highlighting the context-dependent nature of breakthrough innovation in emerging economies. Full article

(This article belongs to the Special Issue Research and Practices in Technological Innovation Management Systems)

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35 pages, 3126 KiB

Open AccessSystematic Review

The Pathway to Startup Success: A Comprehensive Systematic Review of Critical Factors and the Future Research Agenda in Developed and Emerging Markets

by Yenus Muhammed Argaw and Yingqi Liu

Systems 2024, 12(12), 541; https://doi.org/10.3390/systems12120541 - 4 Dec 2024

Abstract

Notwithstanding the benefits derived from successful startup firms in the contemporary entrepreneurial landscape, for many startup firms, the pathway to success is extremely challenging; unfortunately, the failure rate is globally high. The aim of this article is to review empirical contributions regarding startup [...] Read more.

Notwithstanding the benefits derived from successful startup firms in the contemporary entrepreneurial landscape, for many startup firms, the pathway to success is extremely challenging; unfortunately, the failure rate is globally high. The aim of this article is to review empirical contributions regarding startup firms and provide a comprehensive analysis of the factors influencing their success in developed and emerging markets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was undertaken within the Web of Science database, encompassing studies published between 2004 and 2024, which were analyzed. The PRISMA framework is preferred because it stands out from other guidelines due to its transparent and complete reporting and evidence-based recommendations. This work also employed aggregate impact estimation to rank the relative importance of each success factor regarding the success of startups. This article offers a comprehensive analysis of 24 success factors extracted from a systematic review of 48 empirical studies conducted on the subject. We prioritized each success factor according to their relative impact on the success of startup firms. These were classified as personal (entrepreneurial vision and leadership, adaptability, networking), organizational (team building, financial and resource management, innovation, strategy and marketing) and environmental factors (government support and dynamism of political, economic and cultural environment). The findings underscore the importance of a holistic approach that considers both internal and external factors in fostering startup success. However, it is essential to acknowledge that not all factors exert comparable effects on success; certain factors wield a substantial influence, whereas others demonstrate a significant yet lesser impact. Several conclusions and implications for startup founders, government policymakers and startup firm researchers are derived. Full article

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22 pages, 2415 KiB

Open AccessArticle

A Fuzzy-Bayesian Network Approach Based Assessment of CoP System in Forging Higher Education Social Responsibility

by Binglei Xie, Pengchang Li, Yuhong Wang, Feiyi Luo and Linhua Wu

Systems 2024, 12(12), 540; https://doi.org/10.3390/systems12120540 - 3 Dec 2024

Abstract

Community of practice (CoP) has been seen as a pivotal support for higher education institutions to implement their social responsibilities. Even though this model is widely admired, assessing its effectiveness and sustainability still faces many challenges: (1) the absence of an appropriate index [...] Read more.

Community of practice (CoP) has been seen as a pivotal support for higher education institutions to implement their social responsibilities. Even though this model is widely admired, assessing its effectiveness and sustainability still faces many challenges: (1) the absence of an appropriate index reveals the significance of CoP; (2) the difficulty of realizing quantitative assessment; and (3) the strategies to improve contribution sustainably by considering CoP development. To address these challenges, a comprehensive Higher Education Social Responsibility Contribution Index (HESRCI) is constructed by taking into account the CoP key influence factors. An FBN model is further developed for the purpose of assessing the various corresponding contributions quantitatively and investigating the potential interdependencies between influence factors. The effectiveness of the proposed approach is evidenced by the quantitative indication of CoP’s contributions to priorities. Research findings also highlight the significance of CoP governance, the mechanism of resource allocation, and team development, in particular, in facilitating the synergy between university development and sustainable socio-economic growth. In addition, it provides data support and a theoretical basis for higher education institutions to make more informed decisions when implementing industry-education integration strategies. Full article

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24 pages, 3274 KiB

Open AccessArticle

Adjacency List Algorithm for Traffic Light Control Systems in Urban Networks

by Sergio Rojas-Blanco, Alberto Cerezo-Narváez, Manuel Otero-Mateo and Sol Sáez-Martínez

Systems 2024, 12(12), 539; https://doi.org/10.3390/systems12120539 - 3 Dec 2024

Abstract

The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed [...] Read more.

The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed information about the relationships between all signals within a network. This list is based on stable structural road and traffic lights data and offers a crucial global perspective for signal coordination, especially in managing multiple intersections. An adjacency list is more efficient than matrices in terms of space and computational cost, allowing for the identification of critical signals before applying advanced optimization techniques such as neural networks or hypergraphs. We successfully tested the proposed method on three networks of varying complexity extracted from VISSIM and VISUM, demonstrating its effectiveness even in networks with up to 8372 links and 547 traffic lights. This tool provides a solid foundation for improving urban traffic management and coordinating signals across intersections. Full article

(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)

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31 pages, 5958 KiB

Open AccessArticle

The Impact Mechanism of Non-Economic Policies on Social and Investor Disagreement in China: A Dual Analysis Based on Empirical Evidence and DSGE Models

by Jianing Liu, Junjun Ma and Yafei Tai

Systems 2024, 12(12), 538; https://doi.org/10.3390/systems12120538 - 3 Dec 2024

Abstract

This study investigates the integration of non-economic policies into the framework for assessing macroeconomic coherence as applied by the Chinese government, with a particular focus on green policies. We examine the impact of non-economic factors on social disagreement and investor disagreement (expectations), and [...] Read more.

This study investigates the integration of non-economic policies into the framework for assessing macroeconomic coherence as applied by the Chinese government, with a particular focus on green policies. We examine the impact of non-economic factors on social disagreement and investor disagreement (expectations), and how these influences interact with macroeconomic regulation, employing both empirical evidence and dynamic stochastic general equilibrium (DSGE) theoretical models. In the basic analysis section, we merge statistical data on social divergence with policy implementation, utilizing multiple regression and deep neural network models. Our findings provide direct evidence that non-economic policies significantly regulate social sentiment. Additionally, theoretical analyses using contagion models, grounded in real textual data on social and investor divergence, demonstrate that expectations of social sentiment can ultimately affect economic variables. In the extended analysis, we enhance the classic DSGE model to delineate the pathways through which non-economic policies impact the macroeconomy. Drawing from our analyses, we propose specific optimization measures for non-economic policies. The results indicate that targeted policy optimization can effectively manage social disagreement, thereby shaping expectations and harmonizing non-economic with economic policy initiatives. This alignment enhances the coherence of macroeconomic policy interventions. The innovative contribution of this study lies in its provision of both theoretical and empirical evidence supporting the formulation of non-economic policies for the first time, alongside specific recommendations for improving the consistency of macroeconomic policies. Full article

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27 pages, 11205 KiB

Open AccessArticle

Expansion Direction Selection of the Coal-to-Olefin Industrial Chain in Inner Mongolia from the Value Chain Perspective

by Desheng Xu, Chen Liu, Qing Du, Wei Duan and Chunyan Zhang

Systems 2024, 12(12), 537; https://doi.org/10.3390/systems12120537 - 2 Dec 2024

Abstract

Inner Mongolia is a key region for China’s clean energy production and a demonstration base for modern coal chemical industry production. However, modern coal chemical industry development in this region faces many problems, such as complex downstream product structures, low value-added products, and [...] Read more.

Inner Mongolia is a key region for China’s clean energy production and a demonstration base for modern coal chemical industry production. However, modern coal chemical industry development in this region faces many problems, such as complex downstream product structures, low value-added products, and severe environmental pollution. With limited future coal allowance in the coal chemical industry, the scarce available coal will likely be allocated to fields with strong product competitiveness to enhance industrial chain value. Given this, the Inner Mongolia coal-to-olefin industrial chain is selected to explore its strategic expansion directions. An improved value chain accounting method is proposed to account for the value of each segment of the coal-to-olefin industrial chain in Inner Mongolia from 2011 to 2022. The improved value chain accounting method is then combined with system dynamics to construct a model for selecting the expansion directions of the coal-to-olefin industrial chain based on the value chain perspective. Finally, the differences in the value of each future expansion direction are analyzed through scenario simulation and comparison. The most important result is that the higher the extension of the coal-to-olefin industry chain in Inner Mongolia, the higher the value of the industry chain. Carbon punishment is the most important factor affecting the value of the industry chain, and enterprises and governments should increase investment in innovation and renewable energy. Accordingly, this study provides a decision-making method for selecting the optimal expansion direction of the modern coal chemical industrial chain in Inner Mongolia. Full article

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17 pages, 1839 KiB

Open AccessArticle

Study on the Selection of Takeaway Operating Modes from a Restaurant Perspective

by Hao Liu, Rui Luo, Luxin Li, Shizhe Shao, Yan Liu, Chen Zhang and Yanhua Yang

Systems 2024, 12(12), 536; https://doi.org/10.3390/systems12120536 - 2 Dec 2024

Abstract

There are three operational modes for restaurant takeaway services: a takeaway platform combined with restaurant delivery (mode A), a takeaway platform paired with platform delivery (mode B), and a self-established platform with delivery (mode C). In the face of intense [...] Read more.

There are three operational modes for restaurant takeaway services: a takeaway platform combined with restaurant delivery (mode A), a takeaway platform paired with platform delivery (mode B), and a self-established platform with delivery (mode C). In the face of intense market competition, determining how restaurants can select appropriate operational delivery modes to maintain competitiveness and profitability has emerged as a pressing issue. In this paper, we develop game models that encompass a restaurant and food delivery platform to investigate the optimal operational mode for restaurants. Our analysis indicates that when the market size is small, mode B results in the lowest ordering costs, the highest sales volume, and the maximum profit. Conversely, in the context of a large market size, if the commission rate is low, mode A minimizes the ordering costs and maximizes sales volume; if the commission rate is high, mode C minimizes the ordering costs while also achieving the highest sales volume. Notably, we find that as long as the market size is sufficiently large, mode C consistently yields the highest restaurant profits. The outcomes of this study contribute to the theoretical framework surrounding the operation and management of food delivery platforms and offer theoretical guidance and decision-making support for selecting restaurant food delivery operational modes. Full article

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38 pages, 10494 KiB

Open AccessArticle

From Concept to Market: Ensemble Predictive Model for Research Project Crowdfunding Readiness

by Andreea Cristina Ionica, Stanislav Cseminschi and Monica Leba

Systems 2024, 12(12), 535; https://doi.org/10.3390/systems12120535 - 28 Nov 2024

Abstract

This study introduces an ensemble model that integrates random forest, gradient boosting, and logistic regression to predict the success of crowdfunding campaigns. Our research develops a novel set of metrics that assess the developmental stage of research projects, facilitating the transition from concept [...] Read more.

This study introduces an ensemble model that integrates random forest, gradient boosting, and logistic regression to predict the success of crowdfunding campaigns. Our research develops a novel set of metrics that assess the developmental stage of research projects, facilitating the transition from concept to market-ready product. Utilizing data from multiple sources, including Kaggle’s dataset of Kickstarter and Indiegogo projects and a proprietary dataset tailored to our study, the model’s performance was evaluated against traditional implementations of random forest and gradient boosting. The results demonstrate the ensemble model’s superior performance, achieving an accuracy of 98.94% and an F1 score of 98.81%, significantly outperforming the individual models, showing the best accuracies of around 91% for random forest and lower scores for gradient boosting. This enhancement in predictive power allows for optimized resource allocation and strategic planning in project development, thereby increasing the likelihood of crowdfunding success. This approach streamlines the process of bringing innovative ideas to final products, while at the same time offering a methodologically advanced tool for stakeholders to enhance their campaign strategies effectively. Full article

(This article belongs to the Special Issue Research and Practices in Technological Innovation Management Systems)

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19 pages, 3822 KiB

Open AccessArticle

Time-Varying Spillover Effects of Carbon Prices on China’s Financial Risks

by Jingye Lyu and Zimeng Li

Systems 2024, 12(12), 534; https://doi.org/10.3390/systems12120534 - 28 Nov 2024

Abstract

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model [...] Read more.

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes. Full article

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19 pages, 3126 KiB

Open AccessArticle

Dynamic Analysis of the Effectiveness of Emergency Collaboration Networks for Public Health Emergencies from a Systems Thinking Perspective

by Jun Xu, Xiao Li and Xiulai Wang

Systems 2024, 12(12), 533; https://doi.org/10.3390/systems12120533 - 28 Nov 2024

Abstract

In recent years, public health emergencies have become frequent worldwide. In response to these complex and evolving emergencies, the organizations involved are increasingly collaborating with each other. From a systems thinking perspective, greater attention should be given to the long-term development and continuous [...] Read more.

In recent years, public health emergencies have become frequent worldwide. In response to these complex and evolving emergencies, the organizations involved are increasingly collaborating with each other. From a systems thinking perspective, greater attention should be given to the long-term development and continuous operation of emergency collaboration systems. By time slicing the development of the COVID-19 epidemic in Wuhan, the different phases of emergency collaboration networks can be respectively established. A new method for identifying key organization nodes and different network attack strategies is proposed, assessing network effectiveness from two dimensions: efficiency and resilience. The results indicate that, compared to random attack strategies, the efficiency and resilience of these networks are significantly affected by deliberate attack strategies, underscoring the network’s sensitivity to high-importance nodes. Based on the variations in network efficiency and resilience, the effectiveness of different forms of networks are classified into four types. The pre-emergency network is categorized as resilience-focused, the mid-emergency network as efficiency-oriented, the post-emergency network as efficient-resilient, and the overall emergency network as inefficient-fragile. Analyzing forms of network effectiveness at different phases offers a deeper understanding of the operational characteristics, dynamic changes, and existing issues within emergency collaboration networks. This study provides a vital theoretical basis and practical guidance for emergency management departments and decision-makers on how to effectively improve collaboration mechanisms between different organizations. Full article

(This article belongs to the Special Issue Systems Thinking Perspective to Enhance Situational Awareness in Response to Crisis Situations)

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26 pages, 1682 KiB

Open AccessArticle

Sustainable Governance on the Belt and Road Initiative Toward a Carbon-Zero, Regional, Eco-Friendly Logistics Hub: A Difference-In-Differences Perspective

by Tian Xia, Siyu Li, Yunning Ma, Yongrok Choi and Hyoungsuk Lee

Systems 2024, 12(12), 532; https://doi.org/10.3390/systems12120532 - 28 Nov 2024

Abstract

The Belt and Road Initiative (BRI) proposed by China in in 2013 prioritizes environmental sustainability and regional economic development from a global perspective. Although the BRI has achieved considerable economic progress in many cities and regions, research on its environmental impacts is still [...] Read more.

The Belt and Road Initiative (BRI) proposed by China in in 2013 prioritizes environmental sustainability and regional economic development from a global perspective. Although the BRI has achieved considerable economic progress in many cities and regions, research on its environmental impacts is still insufficient, with limited attention paid to domestic urban areas in particular. Existing studies have focused primarily on carbon emissions, ignoring the broader environmental impacts of industrial emissions, such as those from smart transportation. To address this gap, this study adopts four major pollutant emissions—carbon dioxide (CO₂), industrial particulate matter, industrial sulfur dioxide (SO₂), and industrial wastewater emissions—as indicators to assess pollution levels in urban environments. Adopting panel data from 281 Chinese cities from 2003 to 2021, this study employs the difference-in-differences (DID) method to estimate the effect of the BRI on urban environmental pollution. This study is based on the following hypotheses: Hypothesis 1. BRI implementation has reduced urban pollution emissions. Hypothesis 2. Advancements in science and technology will drive the implementation of the BRI. Hypothesis 3. A proactive government response can significantly reduce urban environmental pollution. The main findings of this study are as follows. First, BRI implementation significantly reduces urban environmental pollution by 1.05%. Second, the policy effects of the BRI are more pronounced in the eastern and western regions and in larger cities, implying that geopolitical- and market-oriented strategies are important for regional performance. Third, scientific and technological progress positively affects pollution reduction in urban environments. Fourth, the BRI contributes to strengthening government intervention, which subsequently improves sustainable governance, reduces urban environmental pollution, and promotes regional economic cooperation. Our findings will serve as a crucial reference for future policymaking endeavors toward eco-friendly logistics cooperation in the region. Full article

(This article belongs to the Special Issue Modeling, Planning and Management of Sustainable Transport Systems)

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