Systems

19 pages, 6218 KiB

Open AccessArticle

Toward Resilient Implementation of Land Degradation Neutrality via Systemic Approaches

by Jaime Martínez-Valderrama, Jorge Andrick Parra Valencia, Tamar Awad, Antonio J. Álvarez, Rocío M. Oliva, Juanma and Víctor Castillo

Systems 2025, 13(6), 408; https://doi.org/10.3390/systems13060408 - 24 May 2025

Abstract

Land Degradation Neutrality (LDN) is an ambitious initiative by the United Nations Convention to Combat Desertification (UNCCD) to tackle land degradation. Inspired by the “no net loss” concept, LDN seeks to counterbalance unavoidable land degradation—primarily driven by food systems—through targeted regenerative actions at [...] Read more.

Land Degradation Neutrality (LDN) is an ambitious initiative by the United Nations Convention to Combat Desertification (UNCCD) to tackle land degradation. Inspired by the “no net loss” concept, LDN seeks to counterbalance unavoidable land degradation—primarily driven by food systems—through targeted regenerative actions at multiple scales—such as regenerative agriculture or grazing practices that simultaneously support production and preserve land fertility. The objective is to ensure that degradation does not surpass the 2015 baseline. While the UNCCD’s Science–Policy Interface provides guidance and the LDN Target Setting Programme has led many countries to define baselines using agreed indicators (soil organic carbon, land use change, and primary productivity), concrete intervention strategies often remain poorly defined. Moreover, the voluntary nature of LDN has limited its effectiveness. A key shortcoming is the lack of integrated planning. LDN should function as a “Plan of Plans”—a coordinating framework to align policies across sectors and scales, reconciling conflicting agendas in areas such as food, energy, and water. To this end, we advocate for a systemic approach to uncover synergies, manage trade-offs, and guide decision-making in complex socio-ecological landscapes. Land degradation is intricately linked to issues such as food insecurity, land acquisitions, and transboundary water stress. Although LDN is implemented at the national level, its success also depends on accounting for global dynamics—particularly “LDN leaks,” where land degradation is outsourced through international trade in food and raw materials. In an increasingly complex world shaped by globalization, resource depletion, and unpredictable system dynamics, effective responses demand an integrated socio-ecological management approach. LDN is not simply a strategy to address desertification. It offers a comprehensive framework for sustainable resource management, enabling the balancing of trade-offs and the promotion of long-term resilience. Full article

(This article belongs to the Special Issue Applying Systems Thinking to Enhance Ecosystem Services)

31 pages, 3519 KiB

Open AccessArticle

Effect of Pre-Trip Information in a Traffic Network with Stochastic Travel Conditions: Role of Risk Attitude

by Yun Yu, Shiteng Zheng, Yuankai Li, Huaqing Liu and Jianan Cao

Systems 2025, 13(6), 407; https://doi.org/10.3390/systems13060407 - 24 May 2025

Abstract

Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and [...] Read more.

Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and special events. Two information regimes have been investigated. In the zero-information regime, we postulate that travelers acquire the variability in route travel time based on past experiences and choose the route to minimize the travel time budget. In the full-information regime, travelers have pre-trip information of the road capacities and thus choose the route to minimize the travel time. User equilibrium states of the two regimes have been analyzed, based on the canonical BPR travel time function with power coefficient . In the special case , the closed form solutions have been derived. Three cases and eleven subcases have been classified concerning the dependence of expected total travel times on the risk attitude in the zero-information regime. In the general condition , although we are not able to derive the closed form solutions, we proved that the results are qualitatively unchanged. We have studied the benefit gains/losses by shifting from the zero-information to the full-information regime. The circumstance under which pre-trip information is beneficial has been identified. A numerical analysis is conducted to further illustrate the theoretical findings. Full article

(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)

26 pages, 10158 KiB

Open AccessArticle

An Inclusive Method for Connecting System-of-Systems Architectures with Stakeholders

by Rosanna Zimdahl and Ludvig Knöös Franzén

Systems 2025, 13(6), 406; https://doi.org/10.3390/systems13060406 - 23 May 2025

Abstract

This study addresses the need for early design processes for diverse stakeholder interests in engineering challenges. It does this by using System-of-Systems frameworks, methods, and tools. The presented method, Inclusive SoS Analysis, helps to acquire an overview of the stakeholders’ interests and their [...] Read more.

This study addresses the need for early design processes for diverse stakeholder interests in engineering challenges. It does this by using System-of-Systems frameworks, methods, and tools. The presented method, Inclusive SoS Analysis, helps to acquire an overview of the stakeholders’ interests and their preferred System-of-Systems architecture design in an operational setting, and in what way they have conflicting or synergistic interests. The stakeholders are classified by their relationship to the operational execution and integrated broadly throughout the modeling, simulation, and analysis of the System-of-Systems operation—a novel aspect of this work. The proposed method involves three phases: (1) Use case and stakeholder analysis to identify stakeholder interests and define Key Performance Indicators, (2) Modeling and simulation in an agent-based environment to represent System-of-Systems architectures and operational dynamics, and (3) Results and analysis to generate value functions to evaluate the impact of architectural configurations on stakeholder interests. Simulations explore varying System-of-Systems architectures characterized by the constituent systems that compose them, how their collaborative strategy is configured, and how decision making is controlled, revealing the ways in which these factors influence outcomes of the operation. The method is applied to the case study of a complex wildfire fighting scenario with a broad set of stakeholders and multiple constituent systems. Full article

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

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40 pages, 460 KiB

Open AccessArticle

Fast Fashion Sector: Business Models, Supply Chains, and European Sustainability Standards

by Núria Arimany Serrat, Manel Arribas-Ibar and Gözde Erdoğan

Systems 2025, 13(6), 405; https://doi.org/10.3390/systems13060405 - 23 May 2025

Abstract

One of the core objectives of the European Green Deal in pursuing climate neutrality and sustainable development is the decarbonization of high-impact sectors. Among the most polluting is the fast fashion industry, driven by linear business models that must urgently transition to circular [...] Read more.

One of the core objectives of the European Green Deal in pursuing climate neutrality and sustainable development is the decarbonization of high-impact sectors. Among the most polluting is the fast fashion industry, driven by linear business models that must urgently transition to circular economy frameworks and decarbonized supply chains. Fast fashion poses significant environmental and social challenges due to its high greenhouse gas emissions, excessive resource consumption, and substantial waste generation. To foster greater sustainability within the sector, this study examines environmental indicators defined by the European Sustainability Reporting Standards (ESRS), in accordance with the EU’s Corporate Sustainability Reporting Directive (CSRD) 2022/2464. Aligned with the Global Reporting Initiative (GRI), these standards aim to harmonize sustainability disclosures and enable better decision-making across environmental, social, and governance (ESG) dimensions throughout Europe. This research focuses on five key environmental aspects—climate change, pollution, water resource management, biodiversity, and circular economy/resource use—across four leading fast fashion brands: Mango, Zara, H&M, and Shein. Using an exploratory web-based methodology, this study evaluates how these companies disclose and implement ESG strategies in their supply chains. The central aim is to assess the sustainability and resilience of their operations, with particular emphasis on communication strategies that support the transition from linear to circular business models. Ultimately, this study seeks to highlight both the progress and persistent challenges faced by the fast fashion industry in aligning with ESG and ESRS requirements. Full article

(This article belongs to the Section Systems Practice in Social Science)

25 pages, 1312 KiB

Open AccessArticle

A Comparative Study on Traditional vs. Blockchain Financing for Deep-Tier Suppliers Considering the Time Value of Capital

by Lu Liu, Yanshuo Du, Weijie Chen and Shuwei Wang

Systems 2025, 13(6), 404; https://doi.org/10.3390/systems13060404 - 23 May 2025

Abstract

Traditional downstream-initiated accounts receivable financing (TF) is often constrained in assisting immediate upstream suppliers. Conversely, blockchain-enabled accounts receivable financing (BF) offers an efficient solution for deep-tier suppliers to bridge capital gaps and ensure smooth production processes. This study constructs a three-level supply chain [...] Read more.

Traditional downstream-initiated accounts receivable financing (TF) is often constrained in assisting immediate upstream suppliers. Conversely, blockchain-enabled accounts receivable financing (BF) offers an efficient solution for deep-tier suppliers to bridge capital gaps and ensure smooth production processes. This study constructs a three-level supply chain model comprising a capital-constrained tier-2 supplier, a similarly constrained tier-1 supplier, and an established retailer. To alleviate financial strain among suppliers, we analyze and compare two financing mechanisms: TF and BF. The investigation explores the effects of blockchain adoption and the time value of capital on a multi-tier supply chain. Our findings indicate that while BF typically features a lower interest rate than TF, it may still perform less favorably under certain conditions. Specifically, when the time value of capital for the tier-1 supplier is sufficiently high, the profitability of all supply chain members under BF falls short of that achieved through TF. In other words, adopting blockchain is not universally the best strategy for multilevel supply chains. Additionally, we delineate the impacts of the accounting period and financing rates on the optimal choice of financing model. These insights provide substantial evidence and managerial guidance regarding the appropriate circumstances for blockchain adoption and its interplay with accounts receivable financing. Full article

(This article belongs to the Special Issue Blockchain Technology in Supply Chain Management and Logistics)

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20 pages, 2912 KiB

Open AccessArticle

Effective Context-Aware File Path Embeddings for Anomaly Detection

by Ra-Kyung Lee, Hyun-Min Song and Taek-Young Youn

Systems 2025, 13(6), 403; https://doi.org/10.3390/systems13060403 - 23 May 2025

Abstract

In digital forensics, especially Windows forensics, identifying anomalous file paths is crucial when dealing with large-scale data. Traditional static embedding methods, which aggregate token-level representations, discard hierarchical and sequential relationships in file paths, leading to misclassification of anomalies. This study introduces a Transformer-based [...] Read more.

In digital forensics, especially Windows forensics, identifying anomalous file paths is crucial when dealing with large-scale data. Traditional static embedding methods, which aggregate token-level representations, discard hierarchical and sequential relationships in file paths, leading to misclassification of anomalies. This study introduces a Transformer-based sequence modeling approach to classify anomalous file paths, addressing these limitations by preserving positional and contextual relationships. File paths from the NTFS Master File Table (MFT) were embedded using FastText to capture structural and contextual dependencies. Unlike static embeddings, the proposed method processes file paths as structured sequences to enhance anomaly detection accuracy. Extensive experiments showed that Transformer models generally outperformed traditional methods in detecting structured anomalies. The Transformer model with FastText embeddings (32 dimensions) achieved an accuracy of 0.9781 and an F1-score of 0.9782, while Random Forest with FastText embeddings (64 dimensions) achieved an accuracy of 0.9729 and an F1-score of 0.9729. These findings suggest that a hybrid anomaly detection framework combining Transformer-based models with traditional techniques could enhance robustness in forensic investigations. Future research should explore combining both methods to improve adaptability across diverse forensic scenarios. Full article

(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)

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23 pages, 287 KiB

Open AccessArticle

Buffering Effect of CSR Reputation During Product Recalls: Evidence from Global Automakers Across Institutional Contexts

by Yutong Liu, Eunjung Hyun and Yongjun Choi

Systems 2025, 13(6), 402; https://doi.org/10.3390/systems13060402 - 23 May 2025

Abstract

Multinational corporations (MNCs) face significant reputational and performance risks from product recalls, yet the severity of these consequences varies across national markets. While prior research suggests that corporate social responsibility (CSR) can buffer against such crises, limited attention has been paid to how [...] Read more.

Multinational corporations (MNCs) face significant reputational and performance risks from product recalls, yet the severity of these consequences varies across national markets. While prior research suggests that corporate social responsibility (CSR) can buffer against such crises, limited attention has been paid to how country-level institutions shape this effect. This study examines whether—and under what institutional conditions—CSR reputation mitigates the negative market consequences of product recalls. We focus on how the insurance-like effect of CSR varies with the level of corruption in a country’s institutional environment. Using panel regression analysis and hand-collected data from 14 global automotive manufacturers across eight countries (2007–2015), we find that firms with stronger CSR reputations experience significantly smaller declines in market share after recall announcements. Furthermore, this buffering effect is amplified in countries with higher corruption levels, suggesting that when formal institutional trust is weak, CSR signals play a greater role in stakeholder perceptions. These findings advance CSR literature by showing that its reputational benefits are contingent on institutional context and contribute to international business scholarship by revealing how national-level corruption interacts with firm-level reputational assets during crises. Full article

(This article belongs to the Special Issue Business Innovation: From Management Systems to Corporate Social Responsibility)

32 pages, 5138 KiB

Open AccessArticle

Optimizing Ecosystem Partner Selection Decisions for Platform Enterprises: An Embedded Innovation Demand-Driven Framework

by Baoji Zhu, Renyong Hou and Quan Zhang

Systems 2025, 13(6), 401; https://doi.org/10.3390/systems13060401 - 22 May 2025

Abstract

The rapid emergence of the platform economy has accelerated the practice of embedded innovation, with ecosystem partner selection serving as a critical first step in platform enterprises’ innovation collaborations and playing a key role in enhancing innovation efficiency and outcomes. Based on the [...] Read more.

The rapid emergence of the platform economy has accelerated the practice of embedded innovation, with ecosystem partner selection serving as a critical first step in platform enterprises’ innovation collaborations and playing a key role in enhancing innovation efficiency and outcomes. Based on the theory of embedded innovation, this study identifies the core innovation demands of platform enterprises at distinct stages. It then employs QFD to quantify decision indicator weights for ecosystem partner selection. By integrating Prospect Theory with Field Theory, this study develops both a decision evaluation model and an optimization model to achieve the optimal screening of ecosystem partners. Specifically, this study contributes in the following ways: (1) It constructs an embedded innovation direction selection model to uncover the evolving innovation demands at each stage. Within the QFD framework, we map these demands onto selection evaluation indicators, assess their importance via the maximum entropy principle, and determine indicator weights through a correlation matrix. (2) It proposes a Prospect Theory-based TOPSIS evaluation model, incorporating decision-makers’ psychological preferences to mitigate bias arising from singular or excessive risk attitudes. This model ranks potential partners according to their closeness to an ideal solution. Finally, (3) it designs a Field Theory-based optimization model that accounts for the platform enterprise’s perspective, partner-matching rationality, and continuity of interaction. This model emphasizes the complementarity and synergy of innovation resources to enhance cooperation fit and strategic alignment between the platform and its partners. Finally, this study conducts an empirical analysis on platform enterprise XM and validates the model’s feasibility and stability through sensitivity testing and comparative analyses. This study enriches the understanding of ecosystem partner selection within platform ecosystems by advancing methods for quantifying partner demands and refining the selection of evaluation indicators. It also deepens the depiction of non-rational characteristics in behavioral decision-making and elucidates the mechanisms underlying the ongoing interactions between platform enterprises and their ecosystem partners. These theoretical contributions not only extend the scope of research on platform ecosystems and embedded innovation but also provide feasible approaches for platform enterprises to improve partner governance and foster collaborative innovation in dynamic and complex environments. Ultimately, the findings offer strong support for enhancing innovation performance and building sustainable competitive advantages. Full article

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

23 pages, 372 KiB

Open AccessArticle

Rewiring Sustainability: How Digital Transformation and Fintech Innovation Reshape Environmental Trajectories in the Industry 4.0 Era

by Zhuoqi Teng, Han Xia and Yugang He

Systems 2025, 13(6), 400; https://doi.org/10.3390/systems13060400 - 22 May 2025

Abstract

This study investigates the long-run impact of digital transformation and fintech innovation on environmental sustainability across OECD countries from 1999 to 2024. Drawing on a novel empirical framework that integrates panel fully modified ordinary least squares, the system-generalized method of moments, and machine [...] Read more.

This study investigates the long-run impact of digital transformation and fintech innovation on environmental sustainability across OECD countries from 1999 to 2024. Drawing on a novel empirical framework that integrates panel fully modified ordinary least squares, the system-generalized method of moments, and machine learning estimators, the analysis captures both linear and nonlinear dynamics while addressing heterogeneity, endogeneity, and structural complexity. Environmental sustainability is measured by per capita CO₂ emissions, while digital transformation and fintech innovation are proxied by secure internet servers and G06Q patent applications, respectively. The findings reveal that both digital infrastructure maturity and fintech-driven innovation significantly reduce carbon emissions, suggesting that technologically advanced digital ecosystems serve as effective instruments for climate mitigation. Robustness checks via the system-generalized method of moments confirm the stability of these relationships, while machine learning models—Random Forest and XGBoost—highlight digital variables as top predictors of emissions reduction. The convergence of results across estimation methods underscores the reliability of the digital–environmental nexus. Policy implications emphasize the need to embed sustainability metrics into digital strategies, promote green fintech regulation, and prepare labor markets for Industry 4.0 transitions. These findings position digital and fintech innovation not merely as enablers of economic growth, but as structural levers for achieving environmentally sustainable development in high-income economies. Full article

(This article belongs to the Special Issue Sustainable Business Model Innovation in the Era of Industry 4.0)

24 pages, 1274 KiB

Open AccessArticle

Exploring the Conditional ESG Payoff of AI Adoption: The Roles of Learning Capability, Digital TMT, and Operational Slack

by Linlin Liu, Xiaohong Wang, Liqing Tang, Zhaoxuan Sun and Xue Wang

Systems 2025, 13(6), 399; https://doi.org/10.3390/systems13060399 - 22 May 2025

Abstract

While many organizations are increasingly willing to adopt artificial intelligence (AI) to support strategic objectives such as sustainable development, the ESG benefits of such adoption are not consistently realized across firms. This study investigates the boundary conditions under which AI adoption contributes to [...] Read more.

While many organizations are increasingly willing to adopt artificial intelligence (AI) to support strategic objectives such as sustainable development, the ESG benefits of such adoption are not consistently realized across firms. This study investigates the boundary conditions under which AI adoption contributes to ESG performance. This study aims to investigate when AI adoption contributes to enhanced ESG outcomes by examining key organizational boundary conditions. Specifically, it addresses (1) the association between AI adoption and ESG performance, (2) the moderating roles of learning capability, digital top management team (digital TMT), and operational slack. Using a unique dataset constructed by integrating AI adoption announcements extracted through natural language processing from Factiva and ESG scores obtained from Bloomberg, this study analyzes 8469 firm-year observations from 941 publicly listed manufacturing firms in North America between 2015 and 2022. The results reveal that AI adoption is positively associated with ESG performance. Moreover, this positive effect is amplified by digital TMTs and strong learning capabilities, but weakened by operational slack. These findings enrich the literature on AI-enabled sustainability by highlighting the contingent nature of ESG outcomes and offers managerial insights for firms seeking to align AI strategies with ESG objectives. Full article

(This article belongs to the Section Systems Practice in Social Science)

30 pages, 1104 KiB

Open AccessArticle

The Digital Economy and Sustainable Development Goals: A Predictive Analysis of the Interconnection Between Digitalization and Sustainability in EU Countries

by Anca Antoaneta Vărzaru

Systems 2025, 13(6), 398; https://doi.org/10.3390/systems13060398 - 22 May 2025

Abstract

The accelerating pace of digital transformation has positioned the digital economy as a key driver in advancing the Sustainable Development Goals (SDGs). However, the mechanisms through which digitalization influences sustainability remain underexplored. This study examines the extent to which digital progress, captured through [...] Read more.

The accelerating pace of digital transformation has positioned the digital economy as a key driver in advancing the Sustainable Development Goals (SDGs). However, the mechanisms through which digitalization influences sustainability remain underexplored. This study examines the extent to which digital progress, captured through the Digital Economy and Society Index (DESI), impacts sustainable development outcomes across EU member states, measured by the Sustainable Development Goals Index (SDGi). Utilizing data spanning the period 2017–2022, the analysis applies a multi-method approach—combining exploratory factor analysis, multiple regression, artificial neural networks, and predictive modeling—to identify structural relationships and forecast future trends. The findings reveal strong linkages between human capital development, digital technology integration, and SDG performance, while also highlighting significant heterogeneity among EU countries. Forecasts indicate that digitalization is likely to accelerate in the coming years. Still, its contribution to sustainability will depend on the degree to which policy frameworks succeed in fostering inclusive and context-sensitive digital transitions. By integrating empirical precision with predictive insight, this study offers a robust framework for aligning digital transformation with long-term sustainability objectives in a diverse European context. Full article

(This article belongs to the Special Issue Sustainable Business Models and Digital Transformation)

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28 pages, 1220 KiB

Open AccessArticle

Livestream Scheme Selection in the E-Commerce Supply Chain: Under Agency and Resale Sales Modes

by Tao Li, Shanping Xu, Qi Tan and Wenbo Teng

Systems 2025, 13(5), 397; https://doi.org/10.3390/systems13050397 - 21 May 2025

Abstract

As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate [...] Read more.

As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate under either an agency or a resale mode. However, the relative effectiveness of these strategies remains unclear. This study investigates an e-commerce supply chain comprising a single brand and platform, examining how AI and KOL livestream influence supply chain decisions across different sales modes and identifying optimal strategies for the brand and platform. Results show that when the platform’s revenue sharing rate is low, the agency mode consistently yields a Pareto improvement over resale, regardless of the livestream scheme. Moreover, when the KOL promotion fee rate is low, KOL livestream outperforms AI livestream under both sales modes. When the revenue sharing rate is high, the brand’s optimal strategy is “resale mode and KOL livestream”, while the platform prefers “agency mode and KOL livestream”. Conversely, when the revenue sharing rate is low, the platform’s best strategy is “resale mode and KOL livestream”, while the brand favors the agency mode, with livestream preferences shaped by KOL promotion fee rate. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

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17 pages, 583 KiB

Open AccessArticle

Unlocking Digital Potential—The Impact of Innovation and Self-Determined Learning

by Sandra Starke and Iveta Ludviga

Systems 2025, 13(5), 396; https://doi.org/10.3390/systems13050396 - 21 May 2025

Abstract

In an era of rapid digital transformation, organisations must cultivate dynamic capabilities that promote innovation and continuous learning. This study examines how self-determined motivation and innovation adoption are crucial enablers in developing the digital competencies essential for employees to navigate digital transformation. Grounded [...] Read more.

In an era of rapid digital transformation, organisations must cultivate dynamic capabilities that promote innovation and continuous learning. This study examines how self-determined motivation and innovation adoption are crucial enablers in developing the digital competencies essential for employees to navigate digital transformation. Grounded in Self-Determination Theory and the Diffusion of Innovation framework, our research underscores the systemic role of individual agency, technological advancements, and organisational structures in facilitating workforce adaptation. Employing a quantitative approach with 152 survey participants, our findings reveal that self-determined motivation alone is inadequate, while adopting innovation significantly influences digital competence. We demonstrate that human-centred factors must align with systemic digital transformation efforts. Moreover, we highlight the necessity of integrating employee capabilities into broader enterprise and government digital innovation strategies. The implications of this study are both theoretical and practical. We stress the need for organisations to design change processes that support digital knowledge acquisition and adaptability in evolving workplaces. Our research offers a systemic perspective on digital transformation, reinforcing that successful organisational innovation requires structured learning environments that empower employees. By fostering an ecosystem where digital competencies are nurtured, organisations can enhance agility, resilience, and sustained competitiveness in the digital age. Full article

(This article belongs to the Special Issue Organizational Digital Innovation and Transformation in Enterprise and Government Strategies)

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24 pages, 1717 KiB

Open AccessArticle

A Life-Cycle Carbon Reduction Optimization Framework for Production Activity Systems: A Case Study on a University Campus

by Xiangze Wang, Jingqi Deng, Tingting Hu, Dungang Gu, Rui Liu, Guanghui Li, Nan Zhang and Jiaqi Lu

Systems 2025, 13(5), 395; https://doi.org/10.3390/systems13050395 - 20 May 2025

Abstract

Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To [...] Read more.

Decarbonizing production activities is a critical task in the transition towards carbon neutrality. Traditional carbon footprint accounting tools, such as life-cycle assessment (LCA) and the Greenhouse Gas Protocol, primarily quantify direct and indirect emissions but offer limited guidance on actionable reduction strategies. To address this gap, this study proposes a comprehensive life-cycle carbon footprint optimization framework that integrates LCA with a mixed-integer linear programming (MILP) model. The framework, while applicable to various production contexts, is validated using a university campus as a case study. In 2023, the evaluated university’s net carbon emissions totaled approximately 24,175.07 t CO₂-eq. Based on gross emissions (28,306.43 t CO₂-eq) before offsetting, electricity accounted for 66.09%, buildings for 15.55%, fossil fuels for 8.67%, and waste treatment for 8.46%. Seasonal analysis revealed that June and December exhibited the highest energy consumption, with emissions exceeding the monthly average by 19.4% and 48.6%, respectively, due to energy-intensive air conditioning demand. Teaching activities emerged as a primary contributor, with baseline emissions estimated at 5485.24 t CO₂-eq. Optimization strategies targeting course scheduling yielded substantial reductions: photovoltaic-based scheduling reduced electricity emissions by 7.00%, seasonal load shifting achieved a 26.92% reduction, and combining both strategies resulted in the highest reduction, at 45.95%. These results demonstrate that aligning academic schedules with photovoltaic generation and seasonal energy demand can significantly enhance emission reduction outcomes. The proposed framework provides a scalable and transferable approach for integrating time-based and capacity-based carbon optimization strategies across broader operational systems beyond the education sector. Full article

(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)

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33 pages, 7294 KiB

Open AccessArticle

A Study on the Spatiotemporal Coupling Characteristics and Driving Factors of China’s Green Finance and Energy Efficiency

by Hong Wu, Xuewei Wen, Xifeng Wang and Xuelian Yu

Systems 2025, 13(5), 394; https://doi.org/10.3390/systems13050394 - 20 May 2025

Abstract

In the context of global efforts to address climate change and pursue sustainable development, green finance (GF) and energy efficiency (EE) have become key issues of focus for academics and policymakers. This study explores the spatiotemporal coupling characteristics and driving factors of China’s [...] Read more.

In the context of global efforts to address climate change and pursue sustainable development, green finance (GF) and energy efficiency (EE) have become key issues of focus for academics and policymakers. This study explores the spatiotemporal coupling characteristics and driving factors of China’s green finance and energy efficiency from 2011 to 2022, aiming to help China achieve its dual carbon goals. This study used a three-dimensional framework to assess 30 provinces, considering factor inputs, expected outputs, and undesirable outputs. The study employed the global benchmark super-efficiency EBM model, entropy method, coupling coordination model (CCD), Dagum Gini coefficient decomposition, and spatiotemporal geographic weighted regression model (GTWR). Key findings include a “high in the east, low in the west” gradient distribution of both green finance and energy efficiency, expanding regional disparities, and a strong synergistic effect between technological innovation and energy regulation. Based on the findings, this paper proposes a three-tier governance framework: regional adaptation, digital integration, and institutional compensation. This study contributes to a deeper understanding of the coupling theory of environmental financial systems and provides empirical support for optimizing global carbon neutrality pathways. Full article

(This article belongs to the Section Systems Practice in Social Science)

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19 pages, 2658 KiB

Open AccessArticle

Pit-Stop Manufacturing: Decision Support for Complexity and Uncertainty Management in Production Ramp-Up Planning

by Oleksandr Melnychuk, Jonas Baum, Amon Göppert, Robert H. Schmitt and Tullio Tolio

Systems 2025, 13(5), 393; https://doi.org/10.3390/systems13050393 - 19 May 2025

Abstract

The current research presents an extension of the Pit-Stop Manufacturing framework. It addresses the challenges of managing complexity and uncertainty in the production ramp-up phase of manufacturing systems, bridging the gap in existing approaches that lack comprehensive, quantitative, and system-level solutions. This research [...] Read more.

The current research presents an extension of the Pit-Stop Manufacturing framework. It addresses the challenges of managing complexity and uncertainty in the production ramp-up phase of manufacturing systems, bridging the gap in existing approaches that lack comprehensive, quantitative, and system-level solutions. This research integrates state-of-the-art methodologies, utilising such metrics as Overall Equipment Effectiveness and Effective Throughput Loss to enhance ramp-up management. The developed framework is represented by a conceptual model, which is translated into a digital product combining multiple artefacts for comprehensive ramp-up research, namely a digital twin of the production system, a Custom Experiment Manager for multiple simulation runs, and a Graph Solver that uses the stochastic dynamic programming approach to address the decision-making issues during the production system ramp-up evolution. This work provides a robust decision-support tool to optimise production transitions under dynamic conditions by combining stochastic dynamic programming and discrete event simulation. The framework enables manufacturers to model, simulate, and optimise system evolution, reducing throughput losses, improving equipment efficiency, and enhancing decision-making precision. This paper demonstrates the framework’s potential to streamline ramp-up processes and boost competitiveness in volatile manufacturing environments. Full article

(This article belongs to the Special Issue Integrating System Dynamics with AI and Other Analytical Methods: Advancements and Applications for Decision Making with/Within Complex Systems)

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38 pages, 2538 KiB

Open AccessArticle

Why Does the U.S. Dominate the Digital Economy? A Strategic Analysis Based on the Policy–Coordination–Talent Framework and the Policy Implications for China

by Siqing Shan, Yinong Li, Jingyu Su, Yangzi Yang, Yiqiong Wang and Ziyi Wang

Systems 2025, 13(5), 392; https://doi.org/10.3390/systems13050392 - 19 May 2025

Abstract

The digital economy is a key area for nurturing new productivity and a strategic high ground for innovative development and international competitiveness. This paper innovatively constructs the PCT (Policy–Coordination–Talent) analysis framework to systematically analyze the U.S. digital economy development model from three core [...] Read more.

The digital economy is a key area for nurturing new productivity and a strategic high ground for innovative development and international competitiveness. This paper innovatively constructs the PCT (Policy–Coordination–Talent) analysis framework to systematically analyze the U.S. digital economy development model from three core dimensions: policy guidance, coordination mechanisms, and talent strategy. Through empirical analysis, the research develops three matrices: a leading policy intensity strategy matrix, a coordination mechanism intensity strategy matrix, and a digital talent cultivation strategy matrix. The findings reveal that the U.S. government has formed a resilient digital economy development paradigm through forward-looking policy guidance, precise coordination mechanisms, and systematic talent strategies. The theoretical contributions include developing a multi-dimensional PCT framework for understanding digital economy development models and constructing three strategy matrices based on real data. The research provides theoretical insights and policy implications for China to improve its digital economy governance system and promote high-quality development. Full article

(This article belongs to the Section Systems Practice in Social Science)

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21 pages, 2838 KiB

Open AccessArticle

Evaluating the Performance of Sustainable Urbanization and Its Impacts on Carbon Emissions: A Case Study of Nine Pearl River Delta Cities

by Rourou Huang, Shijian Hong, Hongyu Chen and Zhixin Chen

Systems 2025, 13(5), 391; https://doi.org/10.3390/systems13050391 - 19 May 2025

Abstract

China is presently placing significant emphasis on sustainable urbanization as a means to facilitate the shift towards high-quality economic development. The concept of sustainable urbanization has gained added intricacy and depth within the framework of ‘carbon peak and carbon neutrality’. A primary concern [...] Read more.

China is presently placing significant emphasis on sustainable urbanization as a means to facilitate the shift towards high-quality economic development. The concept of sustainable urbanization has gained added intricacy and depth within the framework of ‘carbon peak and carbon neutrality’. A primary concern among practitioners is that while carbon emissions adhere to specific standards, the term sustainable urbanization remains vaguely defined, encompassing multifaceted objectives. The absence of a well-defined evaluative standard for sustainable urbanization creates challenges in driving comprehensive progress. Additionally, owing to the spatially heterogeneous nature of urbanization, its influence on carbon emissions can vary across different cities. This study delves into the shared factors influencing the performance of sustainable urbanization, introducing a Principal Component Analysis (PCA)-based evaluation system to gauge sustainable urbanization performance. Furthermore, we employ spatial regression analyses to explore the spatial differences in the impact of these factors on carbon emissions. Our investigation centers on data from nine cities in the Pearl River Delta, allowing for the ranking of these cities based on their sustainability performance. The outcomes reveal that the key factors influencing carbon emissions differ among cities due to variations in sustainable urbanization characteristics. Notably, our research integrates sustainable urbanization with the parameters of a low-carbon economy. In the realm of policymaking, we offer a quantifiable approach for assessing sustainable urbanization. Furthermore, we assert that cities at distinct stages of sustainable urbanization should prioritize different factors to attain carbon neutrality. Full article

(This article belongs to the Special Issue Operation Optimization and Performance Assessment of Complex Social-Economic Systems)

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24 pages, 4341 KiB

Open AccessArticle

Intraday and Post-Market Investor Sentiment for Stock Price Prediction: A Deep Learning Framework with Explainability and Quantitative Trading Strategy

by Guowei Sun and Yong Li

Systems 2025, 13(5), 390; https://doi.org/10.3390/systems13050390 - 18 May 2025

Abstract

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock [...] Read more.

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock price prediction by integrating time-partitioned investor sentiment, while improving model interpretability via Shapley additive explanations (SHAP) analysis. Employing the ERNIE (enhanced representation through knowledge integration) 3.0 model for sentiment extraction from China’s Eastmoney Guba stock forum, we quantitatively distinguish intraday and post-market investor sentiment then integrate these temporal components with technical indicators through neural network architecture. Our results indicate that temporal sentiment partitioning effectively reduces uncertainty. Empirical evidence demonstrates that our long short-term memory (LSTM) model integrating intraday and post-market sentiment indicators achieves better prediction accuracy, and SHAP analysis reveals the importance of intraday and post-market investor sentiment to stock price prediction models. Implementing quantitative trading strategies based on these insights generates significantly more annualized returns for representative stocks with controlled risk, outperforming sentiment-agnostic and non-temporal sentiment models. This research provides methodological innovations for processing temporal unstructured data in finance, while the SHAP framework offers regulators and investors actionable insights into sentiment-driven market dynamics. Full article

(This article belongs to the Special Issue Data-Driven Modeling and Predictive Analysis for Business, Social, Economic, and Engineering Applications)

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