Search Results (182)

To address the issues of high energy consumption, high carbon emissions, and the waste of associated energy (AE) in coal mine production, which severely hinder global sustainable development goals, this paper proposes a novel low-carbon economic collaborative optimal scheduling model for a coal mine integrated energy system (CMIES) oriented towards sustainable energy transitions. First, a refined utilization model for AE encompassing coal mine gas, ventilation air methane (VAM), and mine groundwater (GW) is constructed, and a tiered carbon emission trading mechanism (TCET) is introduced to constrain carbon emissions and promote ecological sustainability. Second, a concentrated solar power (CSP) plant is integrated to break the rigid “power determined by heat” constraint of a traditional combined heat and power (CHP) unit, thereby enhancing the system’s scheduling flexibility and renewable energy integration. Meanwhile, abandoned mines are retrofitted into solvent storage tanks to construct an integrated flexible carbon capture system (IFCCS), achieving sustainable reuse of mining wastelands. Finally, to tackle the multi-source, heterogeneous uncertainties on both the source and load sides, a hybrid risk assessment method combining information gap decision theory (IGDT) and conditional value at risk (CVaR) is proposed. Case study results demonstrate that, compared to traditional energy supply modes, the proposed model reduces carbon emissions and total costs in the mining area by 66.04% and 15.97%, respectively. This significantly improves resource utilization efficiency and ecological benefits, providing a highly viable pathway for the sustainable development and clean transition of coal mine operations. Furthermore, the proposed hybrid assessment method can effectively assist decision-makers in achieving a refined trade-off between operating costs and system robustness under varying risk preferences. Full article

Background/Objectives: Large language models (LLMs) and generative AI are rapidly being integrated into healthy ageing initiatives for tasks ranging from companionship and cognitive support to personalised health advice and reduction in social isolation among older adults. Current ethical discussions predominantly address bias, privacy, and accuracy, leaving unresolved three critical governance questions: How do LLM sentiments towards transformative technologies diverge from human values in ageing contexts? What epistemic status do LLM outputs hold when applied to geriatric care? When is trust in those outputs justified for older adults? And who bears responsibility when AI-informed decisions affect functional ability or well-being? Methods: The framework was developed through normative conceptual analysis, synthesizing philosophical principles of medical knowledge and trust, ethical theories of responsibility, empirical evidence on LLM sentiment divergence, digital ageism, and applications of AI in geriatric care (structured searches in PubMed, PhilPapers, and relevant databases, January 2020–March 2026). Results: The integrated framework produces (i) adaptation of SAIA for multidimensional evaluation of human–machine value convergence specific to healthy ageing values (functional ability, autonomy, dignity, equity); (ii) a four-tier classification of LLM outputs tailored to geriatric scenarios; (iii) conditions for warranted trust calibrated to age-related vulnerabilities such as cognitive decline and digital divide; and (iv) responsibility allocation via RACI models with testable hypotheses linking governance design to trust calibration and patient safety outcomes. Conclusions: Without explicit societal alignment and epistemic governance, generative AI risks reinforcing benevolent ageism, automation bias, and responsibility gaps in healthy ageing. The 2025–2027 period offers a decisive window to shape institutional norms that place functional capacity, human dignity, and value convergence at the centre of AI deployment in geriatric care. Full article

Anthropocene pressures underscore that human well-being and societal resilience depend on both biodiversity and geodiversity, the latter providing the abiotic foundation of Earth’s life-support systems. Despite increasing emphasis on systems thinking, participation, and action, Environmental Education and Education for Sustainable Development often underrepresent this abiotic dimension and leave ethical commitments insufficiently articulated. Addressing these gaps, this concept paper develops a convergence framework that integrates geoethics, geoeducation, and geoenvironmental education within the broader domains of EE and ESD. Drawing on interdisciplinary scholarship, geoethics is positioned as a normative lens that clarifies principles for responsible human–Earth relations, including responsibility, justice, respect for Earth processes, transparency in science communication, prudent resource use, and risk-aware decision-making. Geoeducation is conceptualized as the pedagogical vehicle through which these values are translated into competencies such as geoliteracy, systems thinking, critical reflection, ethical deliberation, and evidence-informed action, while geoenvironmental education provides the integrative content domain linking biotic, abiotic, and cultural dimensions. Place-based learning functions as the primary implementation pathway, with protected landscapes and UNESCO Global Geoparks serving as exemplary “living laboratories” where geoconservation, education, and sustainable development are co-produced with local communities. The paper advances three interrelated contributions: (a) a conceptual convergence framework, (b) an operational definition of geoethical awareness, and (c) a programmatic model linking geoethical values to competencies, pedagogies, indicators, and place-based implementation strategies. Operationalized through a Theory of Change and a translation matrix connecting principles to educational outcomes, the framework provides a foundation for future empirical research, curriculum development, teacher education, and the cultivation of geo-citizenship, stewardship, and more resilient human–Earth relationships. Full article

To improve the low-carbon economic operation of integrated energy systems under wind power uncertainty, this paper develops an optimal scheduling model for an integrated energy system coupling oxygen-enriched combustion with hydrogen–ammonia–carbon utilization pathways. The proposed framework integrates oxygen-enriched combustion, electrolysis-based hydrogen production, methanation, hydrogen fuel cells, ammonia synthesis, urea synthesis, captured CO₂ utilization, reward–penalty ladder-type carbon trading, and IGDT-based wind power uncertainty scheduling. A deterministic scheduling model is first established to minimize the total operating cost, and Information Gap Decision Theory is then introduced to formulate risk-averse and opportunity-seeking scheduling strategies under wind power uncertainty. Simulation results show that, compared with the post-combustion carbon capture scenario and the conventional coal-fired scenario, the proposed system reduces the total operating cost by 3.37% and 8.03%, respectively, and reduces the wind curtailment cost by 40.2% and 57.0%, respectively. Compared with the post-combustion carbon capture scenario, carbon emissions are reduced by 17.7%. The hydrogen–ammonia–urea chain generates approximately 15.68 × 10⁴ CNY of urea revenue and improves carbon resource utilization. Under an IGDT deviation factor of 0.03, the risk-averse strategy increases the total operating cost by approximately 10.30 × 10⁴ CNY to enhance operational robustness, while the opportunity-seeking strategy reduces the total operating cost by approximately 10.30 × 10⁴ CNY and decreases carbon emissions by 19.6 t. These simulation results verify the effectiveness of the proposed scheduling framework under the designed case study. The proposed framework can improve the low-carbon economy, renewable energy accommodation, carbon resource utilization, and adaptability to wind power uncertainty of the studied integrated energy system. Full article

This research paper addresses the persistent problem of environmental opacity in sustainable debt markets, exposing a structural flaw that incremental regulation alone cannot remedy. This study advances a radical, physics-grounded solution that fundamentally transforms environmental reporting from voluntary self-disclosure to instrumentally verified, quantum-limited measurement. The method integrates three mutually reinforcing analytical frameworks: the design of Quantum-Verified Green Bonds (QVGBs), the application of cryptographic quantum key distribution (QKD), and the formal apparatus of financial contract theory. The principal conceptual innovation resides in a three-tiered architectural structure—physical, cyber–physical, and financial—that collectively shifts the epistemological foundation of sustainable finance from institutional norms and managerial discretion to the immutable constraints of physical laws. By deploying nitrogen-vacancy (NV) centers in diamond as primary sensing arrays at industrial emission points, this system achieves environmental parameter estimation bounded by the Cramér–Rao quantum limits, a precision ceiling governed by Quantum Fisher Information, not corporate policy. This architecture acquires high-fidelity, real-time data on CO₂ and CH₄ flux densities, transforming atmospheric pollutant concentrations into physically attested, contractually actionable financial variables. A QKD layer further leverages the no-cloning theorem to render any upstream data manipulation physically self-revealing through statistically detectable elevations in the Quantum Bit Error Rate (QBER). The central contribution of this work lies in the algorithmic coupling of bond coupon structures to these quantum-verified state variables, enforced via smart contracts, thereby converting “environmental misinformation” from a viable managerial strategy into a strictly dominated equilibrium outcome. These findings carry substantial implications for bridging the “trust gap” in green financial markets, a gap sustained by chronically undervalued transition risks and deficient accountability mechanisms in air quality and carbon reporting. The QVGB framework stabilizes green asset prices by subordinating capital allocation decisions to physical constraints rather than political or institutional ones, thereby establishing a new ontological baseline for the global sustainable debt market. Full article

A clean and efficient rural energy system is essential for building a modern energy system and for accelerating the transition to renewable energy in rural areas. Therefore, a robust collaborative optimal scheduling method for rural integrated energy systems is proposed, incorporating multi-agent gaming. First, a three-layer cooperative structure is developed based on current rural energy consumption patterns. Second, a scheduling model is formulated using potential game theory, with the objective of maximizing the overall benefits of all parties. The model also accounts for multi-energy complementarity, demand response, and multiple uncertainties, leading to a robust optimal scheduling framework based on information gap decision theory. The resulting problem is solved using a chicken swarm optimization algorithm improved by Lévy flight. Finally, a case study of the three-layer cooperative optimization model is presented. The results show that multi-energy complementarity can increase local renewable energy consumption and improve the economic efficiency of diverse energy use for rural consumers. Information gap decision theory helps balance economic and uncertain factors and supports decision-making for agents with different risk preferences. Full article

The presence of multiple uncertainties in integrated electricity–heat energy systems (E-HIES) poses significant challenges to system dispatch. To achieve an effective balance between economy and robustness, this paper proposes an optimal scheduling method based on fuzzy chance-constrained Information Gap Decision Theory (Fuzzy-IGDT), accounting for uncertainties in wind power output, photovoltaic output, electrical load, and thermal load. The method employs trapezoidal fuzzy numbers to model the four types of uncertain variables and constructs a fuzzy robust model (F-RM) for conservative decision-makers and a fuzzy opportunity model (F-OM) for aggressive decision-makers. An Adaptive Step Ratio (ASR) optimization method is then developed to solve the proposed models. Case studies demonstrate the effectiveness of the proposed methodology. Results show that: compared with conventional IGDT, pure fuzzy and stochastic programming, Fuzzy-IGDT simultaneously optimizes economy, stability and reliability: daily operating cost is reduced by 12.7%, the standard deviation of cost volatility shrinks by 34.5%, and the loss-of-load probability is only 0.3%. Relative to the traditional Weighted Offset Coefficient (WOC) method, ASR directly coordinates the deviation ratios of multiple variables through its step-ratio mechanism, cutting system risk cost by 21.3%, raising solution efficiency by 42%, and improving convergence stability by a factor of 3.8. This research provides new theoretical support and practical tools for optimal scheduling of E-HIES under multiple uncertainties. Full article

Efficient scheduling of electric bus (EB) photovoltaic-storage charging stations (PSCSs) is essential for ensuring the operational economy of public transit and the security of the power grid. Existing scheduling studies generally simplify charging and storage efficiencies as fixed constants, neglecting their dynamic dependence on power levels. Meanwhile, the stochasticity of photovoltaic (PV) generation further complicates scheduling decisions. To address these issues, this paper proposes a day-ahead robust scheduling method for EB PSCSs that incorporates dynamic charging efficiency. First, the dynamic battery efficiency model is reasonably simplified and reformulated, and the big-M method is employed to transform the nonlinear efficiency model into an equivalent set of linear constraints, thereby effectively integrating dynamic efficiency characteristics into the day-ahead optimization framework. Then, information gap decision theory (IGDT) is adopted to model PV output uncertainty, establishing a risk-averse decision optimization model. On this basis, a two-stage solution algorithm integrated with the bisection method is designed to decompose the IGDT optimization problem into a series of linear programming subproblems, balancing solution accuracy and computational efficiency. Case studies validate the effectiveness of the proposed method. The results demonstrate that the dynamic efficiency model significantly improves scheduling accuracy, and the IGDT framework provides a reliable, robust scheduling strategy for PSCSs under limited information conditions. Full article

As digital transformation accelerates in tourism and hospitality, sustainability-related information on booking platforms has become increasingly relevant to consumer decision-making. However, prior research has not sufficiently explained how such information operates as part of digital choice architecture or how it becomes meaningful for sustainable consumption decisions. To address this gap, this study introduces sustainable informativeness as a sustainability-specific and platform-contextual construct reflecting the visibility, interpretability, usefulness, comparability, and decision-facilitating role of sustainability-related information. Drawing on digital choice architecture, value theory, and trust theory, this study examines the structural relationships among sustainable informativeness, sustainable functional value, ethical–emotional value, trust, and sustainable consumption behavior. Data were collected through an online survey of 304 Korean adult consumers with experience using digital accommodation booking platforms and analyzed using structural equation modeling. The results show that sustainable informativeness was positively associated with both value dimensions, and both value dimensions were positively associated with trust. Trust showed the strongest direct association with sustainable consumption behavior, whereas the direct association between sustainable informativeness and behavior was not significant. Significant indirect associations through value and trust suggest that sustainability-related information is more closely related to sustainable consumption behavior when it is useful, meaningful, and trustworthy. Practically, the findings suggest that platforms should design sustainability-related information to be not only visible but also comparable, interpretable, useful, and credible. Full article

Against the backdrop of global warming and China’s Carbon Peaking and Carbon Neutrality Goals, this study focuses on carbon footprint rating information on packaging, addressing the gap between consumer cognition and behavior in food consumption caused by invisibility of carbon emissions. This study integrates the Theory of Planned Behavior (TPB) with the Technology Acceptance Model (TAM), designed a “letter-plus-color” dual-coded carbon footprint label as the experimental stimulus, and conducted a survey of 581 respondents across China’s four major regions using structural equation modeling. Results indicate that perceived ease of use, perceived usefulness, subjective norms, and perceived behavioral control all positively predict purchase attitudes. Among these, perceived behavioral control also independently predicts actual purchase behavior. Multigroup analysis further reveals that household purchasing responsibility moderates the “attitude → intention” relationship: purchasing decision-makers engage in realistic trade-offs, whereas non-purchasing decision-makers are driven by value congruence.Theoretically, this study deepens our understanding of cognitive intervention mechanisms of carbon footprint labels and expands explanatory power of the TPB-TAM model in low-carbon contexts. From a practical perspective, this study provides guidance for governments in designing targeted labeling policies and for companies in developing packaging that aligns with cognitive principles. Full article

As universities strive for socially sustainable environments, Informal Learning Spaces (ILS) serve as vital social infrastructure. However, previous studies often isolate physical environmental stimuli from internal psychological decision-making and treat harsh climates as absolute barriers. To address this gap, this study integrates Environment–Behavior Studies (EBS) and the Theory of Planned Behavior (TPB) to construct a comprehensive behavioral model for ILS in hot–humid climates. Using Structural Equation Modeling on 377 samples from Guangzhou, China, the study quantifies the interaction between physical spatial affordances and internal psychological mechanisms. The results reveal a critical shift in behavioral drivers: when psychological agency is introduced, the driving force of high-quality Space Design (path coefficient = 0.269) surpasses the restrictive impact of the severe Climate Environment (coefficient = 0.218). This demonstrates that architectural affordances can actively buffer physiological discomfort. Internally, Perceived Behavioral Control (PBC)—acting as an empirical proxy for user agency—emerges as the sole psychological dimension directly driving actual spatial usage (coefficient = 0.131), whereas personal attitudes and peer pressure show no significant direct behavioral impact. Furthermore, the direct behavioral influence of operations management becomes non-significant when mediated by psychological expectations. Ultimately, this study reframes ILS optimization, demonstrating that socially sustainable campus revitalization in hot–humid regions must prioritize empowering user autonomy and enhancing robust morphological design over administrative upgrades or mere passive climate endurance. Full article

Global supply chain disruptions, most acutely demonstrated during the COVID-19 pandemic, have exposed fundamental tensions between efficiency-oriented design and the adaptive capacity required for resilience. This paper addresses a critical gap in the existing literature: the absence of an integrative, operationalisable framework that treats sustainability and resilience as mutually reinforcing strategic objectives rather than competing trade-offs. Employing a systematic literature review guided by PRISMA protocols, complemented by comparative analysis of documented organisational responses across multiple sectors and commodity markets, the study identifies four primary pathways through which sustainability investments generate resilience: structural diversification, information and visibility, social capital and trust, and adaptive capabilities. The principal finding is that sustainability practices, particularly those enhancing supply network visibility, structural diversification, and workforce stability, create option value that becomes strategically decisive during periods of disruption. A decision intelligence framework is proposed that translates these insights into three managerial tools: a sustainability–resilience assessment matrix, a disruption scenario analysis tool, and a capability development roadmap. The framework challenges the prevailing trade-off assumption by demonstrating that efficiency, sustainability, and resilience can function as complementary dimensions of supply chain performance. Findings carry particular relevance for commodity-dependent supply chains, where price volatility, trade structure rigidity, and resource concentration constitute persistent sources of systemic disruption. Theoretical contributions include the integration of supply chain resilience theory, sustainable operations management, and decision science under deep uncertainty. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Precision medicine is facing a critical transition driven by the growing complexity of biological data and the insufficient ability of current models to translate such data into clinically meaningful information. Linear, single-gene approaches are no longer adequate to explain the multifactorial nature of most modern diseases, whose phenotypes emerge from combinations of genetic, molecular, and environmental factors. Network-based precision medicine addresses this by providing a systemic framework capable of integrating heterogeneous omics data, interactomes, and clinical information to identify disease modules and novel therapeutic opportunities. The distinct novelty of this review is its focus on the potential of “network language” as the primary driver for realizing precision medicine through professional collaboration. We argue that networks are not merely tools that achieve precision “per se”; rather, their transformative power lies in their ability to serve as a shared and interpretable interface grounded in network theory. By offering this common conceptual ground, the paradigm bridges the deep cultural and methodological gaps between clinicians and data analysts, enabling effective cooperation between figures with fundamentally different, and often divergent, backgrounds. Practical tools—such as biological network analysis and Molecular Tumor Boards—demonstrate how computational modeling and clinical expertise can be successfully combined to generate actionable insights. Ultimately, network-based precision medicine represents a decisive step toward reconstructing the patient’s complexity and promoting a genuinely personalized clinical approach in which quantitative analysis and medical reasoning act synergistically through multidisciplinary integration. Full article

Electronic word of mouth (eWOM) and traditional word of mouth (WOM-T) are key information channels in consumer decisions, but there are still gaps in integrative models that analyze both channels simultaneously in emerging contexts. This exploratory, theory-informed study proposes a conceptual model that articulates five antecedents—satisfaction, trust, emotional bond, openness to novelty, and perceived social influence—two mediators—consumer engagement and recommendation intention—and two outcome behaviors—eWOM and traditional WOM—to examine how these variables are associated with the generation of recommendations among young/internet-connected consumers of SME services in the Lambayeque Region, Peru. Using PLS-SEM with 380 participants, 25 structural hypotheses were evaluated, including direct effects and simple and sequential mediations. In this non-probability sample, the hypothesized associations were statistically supported: antecedents were positively associated with engagement, which was positively associated with recommendation intention, which in turn predicted both online and offline WOM behaviors. Emotional bond and trust showed particularly strong effects. The model explained between 49% and 64% of the variance in endogenous variables. The findings contribute to understanding word-of-mouth dynamics in emerging markets for the studied segment of digitally connected consumers, with implications for relational marketing strategies and SDGs 8 and 12. Importantly, the contribution to SDG 12 is conditional: word-of-mouth can also amplify unsustainable consumption when recommendations are not linked to responsible practices; this caveat should be considered when interpreting the sustainability implications of these findings. Full article