Search Results (74)

Against the background of deepening “dual carbon” goals and the continuously tightening policies of the national carbon market, the carbon asset risks faced by thermal power enterprises have shifted from short-term compliance cost fluctuations to medium- and long-term systemic risks. Managing these risks effectively is essential for ensuring the financial viability of thermal power operations during the low-carbon transition, thereby supporting the long-term sustainability of the energy sector. This study constructs a risk management framework for carbon assets in thermal power enterprises based on the LSTM model and option portfolios. First, the multi-dimensional characteristics of medium- and long-term carbon asset risks are systematically identified at the policy, market, and enterprise levels. Second, a dual-layer LSTM model with Dropout regularization is employed to simulate medium- and long-term carbon prices. The prediction results indicate a moderate upward trend in future carbon prices, with the fluctuation range gradually narrowing. On this basis, a combined hedging strategy of “core call options + auxiliary put options” is designed, capping the maximum procurement cost at 72.63 CNY/ton and covering over 90% of the risk of carbon price increases. Monte Carlo simulations and rolling window backtesting, conducted using operational data from a thermal power enterprise to validate the framework, verify the effectiveness and robustness of the strategy. The study shows that, through the integration of accurate LSTM predictions and proactive option hedging, thermal power enterprises can transform their carbon asset management from passive compliance to active value creation, thereby enhancing their operational sustainability and resilience during the energy transition. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

Fleet electrification is increasingly recognized as a cornerstone of urban decarbonization in high-density megacities. This study introduces a multi-scenario simulation framework integrating high-resolution mobile signaling data with traffic modeling to quantify the systemic environmental and energy impacts of road-based battery electric vehicle (BEV) integration in Shanghai. By evaluating both a fixed-fleet baseline and dynamic-fleet growth scenarios focused on the urban road network, we find that aggressive fleet electrification leads to a profound reduction in aggregate carbon emissions and criteria pollutants, effectively decoupling transit-related environmental burdens from urban growth. However, results also highlight a significant energy trade-off: while fossil fuel displacement accelerates, grid-based electricity demand increases under fleet growth conditions. Within this context, the expanded vehicle population exacerbates urban congestion, which disproportionately inflates the fuel consumption of remaining internal combustion vehicles. Their operational efficiency is severely compromised by frequent stop-and-go cycles, leading to an intensification of idling losses. Ultimately, this research highlights the capability of the proposed simulation framework to provide granular insights into urban emission dynamics, offering a quantitative foundation for policymakers to harmonize electrification targets with proactive traffic management and grid infrastructure strengthening to evaluate the systemic trade-offs toward achieving long-term urban sustainability. Full article

Modular integrated construction (MiC) is an innovative construction method that shifts on-site activities to a controlled factory environment, thereby offering sustainability benefits. However, current carbon management relies on labor-intensive manual data collection, causing delayed and inaccurate carbon accounting that increases greenwashing risks. Existing approaches lack real-time, automated, and trustworthy carbon tracking capabilities across fragmented supply chains. This study develops and validates the Blockchain-enabled IoT-BIM Platform (BIBP), which combines Internet of Things (IoT), Building Information Modeling (BIM), and blockchain for real-time carbon monitoring. IoT sensors automate data capture from construction equipment and BIM provides spatial visualization of carbon at the module and building levels. A Hyperledger Fabric blockchain ensures the authenticity, immutability, and traceability of carbon records. Validated on a 15-story MiC project in Hong Kong, BIBP established a cradle-to-end-of-construction baseline of 949.84 kgCO_2e/m², identifying steel and concrete as the primary hotspots (80% of material emissions). Real-time analytics demonstrated that combining high-volume ground granulated blast furnace slag (GGBS) concrete substitution, new energy sea–land multimodal transport, and 10% steel waste reduction achieves over 20% carbon savings. Furthermore, the BIBP automated data acquisition and calculation, improving assessment efficiency by 92.4%. The platform demonstrates the potential to transform carbon management from a static, retrospective evaluation into a proactive, data-driven monitoring process, equipping stakeholders with a tool to dynamically track emissions and make timely interventions toward carbon reduction targets. Full article

The transition toward sustainable food systems requires innovative approaches to managing perishable products, where inefficient inventory practices contribute significantly to global food loss and environmental degradation. This study develops a circular-economy-oriented inventory optimisation framework for dairy supply chains that integrates environmental externalities and waste valorisation pathways into operational decision-making. Departing from traditional linear “produce–consume–dispose” models, this study embeds three core sustainability mechanisms into a stochastic dynamic-programming framework: (1) progressive environmental cost internalisation aligned with EU Emissions-Trading System carbon pricing, capturing both waste-related emissions and cold-chain energy footprints; (2) circular-economy value-recovery channels that redirect near-expiry products to secondary applications (animal feed, biogas production, industrial processing) rather than disposal; and (3) deterioration-aware demand management that minimises resource throughput while maintaining service levels. Empirical calibration using Ukrainian dairy industry data demonstrates that sustainability-integrated inventory policies reduce waste generation by 4.8–10% relative to conventional approaches, with high-deterioration products showing the greatest potential for improvement. The authors identify a critical threshold in the circular economy: when salvage recovery rates exceed 35%, waste becomes an economic and ecological asset, fundamentally altering the sustainability calculus of inventory decisions. Environmental costs account for 4.6% of total operating expenses at current carbon prices, a share projected to increase substantially as climate regulations tighten. The findings provide actionable guidance for dairy supply chain stakeholders pursuing the Sustainable Development Goals (SDGs 2, 12, 13): processors should establish circular-economy partnerships that achieve salvage rates above 35%, implement product-specific policies for high-deterioration items, and proactively integrate carbon pricing into inventory optimisation. The framework bridges sustainable operations theory and circular economy practice, offering a replicable model for transitioning perishable food supply chains toward closed-loop, low-waste configurations that simultaneously reduce environmental impact and enhance economic performance. Full article

Inhalation is a primary route of exposure to engineered nanomaterials (ENMs), enabling particles to penetrate deeply into the lungs and subsequently leading to adverse health effects. Human health risk assessment addresses the potential risk posed by ENMs. The aim was achieved by measuring the emissions of ENMs using real-time instrumentation and subsequently applying the data to evaluate associated human health risks using ModelRisk. Emissions during the synthesis of silver nanoparticles (AgNPs), gold nanoparticles (AuNPs), graphene 2D (G2D) nanomaterials, multiwalled carbon nanotubes (MWCNT) and the application of AuNPs on black carbon electrodes were monitored using a NanoScan SMPS Model 3910 and Optical Particle Sizer (OPS) Spectrometer Model 3330. The derived mass-based time-weighted average concentrations were reported for AgNPs and MWCNTs in comparison with occupational exposure limits (OELs). AgNP concentrations of 0.36 µg/m³ and 3.99 µg/m³ for the NanoScan SMPS and OPS, respectively, exceeded the OEL of 0.19 µg/m³, whereas MWCNT concentrations (0.261 µg/m³) remained below the OEL of 1 µg/m³. AuNP synthesis resulted in particle number concentrations exceeding the provisional nano reference value of 20,000 particles/cm³ for the OPS data (3.74 × 10⁴ particles/cm³), whereas application of AuNPs on carbon black electrodes was below this limit. Although no OEL exists for graphene, risk estimates indicated potential adverse health effects like those observed for AgNPs, AuNPs, and MWCNTs. Measured exposure concentrations were applied in a human health risk assessment model, highlighting ENM concentration as a key determinant of risk. These findings emphasise the need for continuous monitoring, further risk assessment studies, and proactive risk management strategies. Full article

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article

Harbour sedimentation represents a major challenge to the environmental sustainability and operational efficiency of coastal infrastructure, as frequent dredging activities increase maintenance costs, ecological disturbance, and carbon emissions. Conventional physical and numerical sediment transport models, while widely applied, are computationally intensive and often unsuitable for early-stage, sustainability-oriented design optimisation. To address these limitations, this study proposes a hybrid artificial intelligence-based optimisation framework integrating Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), and Particle Swarm Optimisation (PSO) for sustainable breakwater and harbour layout design. Hydrodynamic simulations using the Coastal Modelling System (CMS) were conducted to generate a comprehensive dataset describing sediment transport behaviour under varying geometric and structural configurations. An ANN surrogate model was trained to capture nonlinear relationships between breakwater parameters and accumulated sedimentation volume, while GA-based global optimisation and PSO-based validation and local refinement were employed to identify optimal design solutions. Comparative assessment demonstrated consistent convergence of ANN–GA and ANN–PSO solutions within the same design region, with a maximum deviation of 8.46% between design variables and a sedimentation difference of 2.4%. The hybrid ANN–GA–PSO framework achieved the lowest predicted sedimentation volume, representing an improvement of approximately 2.3% relative to the ANN–GA baseline. The proposed framework supports Integrated Coastal Structures Management (ICSM) by enabling proactive, design-stage reduction in long-term sediment accumulation and dredging requirements, offering a scalable pathway toward sustainable and digital-twin-enabled harbour planning. Full article

Reciprocating compressor air volume control systems have been extensively investigated, with a primary objective of reducing energy consumption and associated carbon footprints. As a multi-actuator system, failures in this energy-efficient configuration can trigger severe operational disruptions with cascading consequences. To address this, we initially constructed numerical models of the multi-actuator energy-efficient system to decode the variational patterns of compressor dynamic pressure pulsations and connecting-rod small-end bush tribological behaviors under partial actuator fault conditions, thereby establishing foundational data for fault degradation stratification. Building upon this, we propose a Prognostics and Health Management (PHM) algorithm using fault degradation analysis, thereby materializing self-recovery functionality in response to various fault conditions. Experimental validation demonstrates that the self-recovery algorithm successfully contained deterioration propagation through proactive intervention. The system achieved autonomous healing within 8 s (mild faults) and 13 s (moderate faults), constraining discharge fluctuations and vibration amplitude within allowable thresholds. This study establishes a solution framework for preserving multi-actuator energy-efficient systems’ health, accuracy, and economy. Full article

Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining algorithms, including classical methods (KNN, SVM, decision trees) and advanced ensemble techniques (XGBoost, LightGBM, CatBoost) optimized for unbalanced datasets. Gradient boosting models with explicit handling of class imbalance—where the absence of pests exceeds 77% of observations—showed exceptional performance, achieving a Macro-F1 score above 0.92 and high precision in identifying the minority high-risk class. Explainability analysis using SHAP consistently revealed that elevated CO₂ concentrations are the primary predictor of infestation, enabling early identification of critical zones. This study demonstrates that carbon dioxide (CO₂) acts as the most robust bioindicator for predicting severe infestations of Periplaneta americana, significantly outperforming conventional environmental variables such as temperature and humidity. The implementation of the model in a real-time monitoring platform generates interpretable heat maps that support proactive and localized interventions, optimizing resource use and reducing dependence on biocides. This study presents a scalable, operationally viable predictive system designed for direct integration into municipal asset management workflows, offering a concrete, industry-ready solution to transform pest control from a reactive, labor-intensive process into a data-driven, proactive operational paradigm. This approach not only transforms pest management from reactive to predictive but also aligns with the Sustainable Development Goals, offering a scalable, interpretable, and operationally viable system for smart cities. Full article

This study presents a hybrid digital twin system designed for real-time indoor air quality (IAQ) monitoring and filtration optimization within a residential environment. Using a network of low-cost sensors, physics-based simulations, and machine learning models, the system dynamically replicates the indoor environment to enable continuous assessment and optimization of key pollutants, including particulate matter, volatile organic compounds, and carbon dioxide. The system architecture integrates mass balance and decay models, computational fluid dynamics simulations, regression models, and neural network algorithms, all evaluated under both filtering and non-filtering conditions. A graphical user interface allows users to interact with the system, test air purifier placements, and visualize air quality dynamics in real time. The results demonstrate that, within this system, simpler models, such as linear regression, outperform more complex architectures under data-limited conditions, achieving test-set coefficients of determination ranging from 0.97 to 0.99 across multiple IAQ parameters. At the same time, the hybrid modelling approach enhances interpretability and robustness. Overall, this digital twin system contributes to smart building management by offering a scalable, interpretable, and cost-effective solution for proactive IAQ control and personalized decision-making. Full article

The increasing deployment of hybrid renewable energy systems has introduced significant challenges in optimal energy dispatch and load balancing due to the intrinsic stochasticity and temporal variability of renewable sources, along with the multi-dimensional optimization requirements of simultaneously achieving economic efficiency, grid stability, and environmental sustainability. This paper presents GreenMind, a scalable Deep Reinforcement Learning framework designed to address these challenges through a hierarchical multi-agent architecture coupled with Long Short-Term Memory (LSTM) networks for predictive energy management. The framework employs specialized agents responsible for generation dispatch, storage management, load balancing, and grid interaction, achieving an average decision accuracy of 94.7% through coordinated decision-making enabled by hierarchical communication mechanisms. The integrated LSTM-based forecasting module delivers high predictive accuracy, achieving a 2.7% Mean Absolute Percentage Error for one-hour-ahead forecasting of solar generation, wind power, and load demand, enabling proactive rather than reactive control. A multi-objective reward formulation effectively balances economic, technical, and environmental objectives, resulting in 18.3% operational cost reduction, 23.7% improvement in energy efficiency, and 31.2% enhancement in load balancing accuracy compared to state-of-the-art baseline methods. Extensive validation using synthetic datasets representing diverse hybrid renewable energy configurations over long operational horizons confirms the practical viability of the framework, with 19.6% average cost reduction, 97.7% system availability, and 28.6% carbon emission reduction. The scalability analysis demonstrates near-linear computational growth, with performance degradation remaining below 9% for systems ranging from residential microgrids to utility-scale installations with 2000 controllable units. Overall, the results demonstrate that GreenMind provides a scalable, robust, and practically deployable solution for predictive energy dispatch and load balancing in hybrid renewable energy systems. Full article

Using data of China’s A-share-listed companies between 2014 and 2024, we investigate the effect of carbon finance on the speed of dynamic capital structure adjustment and the degree of deviation of enterprises. We find that carbon finance has a significantly positive effect on the speed of adjusting the capital structure and that carbon finance is consistent in keeping firms in line with their target leverage ratio, according to a number of robustness tests. Notably, cross-sectional analyses show that this effect is more pronounced among firms with lower green innovation outputs and greater indebtedness. Further research indicates that the underlying mechanism driving this relationship lies in alleviating financing constraints and reducing financing costs. By bridging the gap between market-oriented environmental regulations and corporate financial policies, our study provides policymakers with evidence to improve carbon finance mechanisms and gives managers a foundation for transforming carbon finance into a strategic tool for proactively optimizing capital structure. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article