Search Results (4,691)

Power generation and transmission systems face increasing challenges in coordinating maintenance planning under economic pressure and carbon emission constraints. This study proposes an optimization framework that integrates preventive maintenance scheduling with operational dispatch decisions, aiming to achieve both cost efficiency and emission reduction. A bi-layer scenario-based mixed-integer optimization model is formulated, where the upper layer determines annual preventive maintenance windows, and the lower layer performs hourly economic dispatch considering renewable generation and demand uncertainty. To manage the exposure to extreme carbon outcomes, a Conditional Value-at-Risk (CVaR) constraint is embedded, jointly controlling economic and environmental risks. A parallel cut-generation decomposition algorithm is developed to ensure computational scalability for large-scale systems. Numerical experiments on six-bus and IEEE 118-bus systems demonstrate that the proposed model reduces total carbon emissions by up to 32.1%, while maintaining cost efficiency and system reliability. The scenario analyses further show that adjusting maintenance schedules according to seasonal carbon intensity effectively balances operation and emission targets. The results confirm that the proposed optimization framework provides a practical and scalable approach for achieving low-carbon, reliable, and economically efficient power system maintenance planning. Full article

Against the background of global energy transformation and low-carbon development, numerous difficult-to-mine coal resources (e.g., deep, thin coal seams and low-quality coal) remain underdeveloped, leading to potential resource waste. This study systematically summarizes the feasibility of developing these resources via underground coal gasification (UCG) technology, clarifies its basic chemical/physical processes and typical gas supply/gas withdrawal arrangements, and establishes an analytical framework covering resource utilization, gas production quality control, environmental impact, and cost efficiency. Comparative evaluations are conducted among UCG, surface coal gasification (SCG), natural gas conversion, and electrolysis-based hydrogen production. Results show that UCG exhibits significant advantages: wide resource adaptability (recovering over 60% of difficult-to-mine coal resources), better environmental performance than traditional coal mining and SCG (e.g., less surface disturbance, 50% solid waste reduction), and obvious economic benefits (total capital investment without CCS is 65–82% of SCG, and hydrogen production cost ranges from 0.1 to 0.14 USD/m³, significantly lower than SCG’s 0.23–0.27 USD/m³). However, UCG faces challenges, including environmental risks (groundwater pollution by heavy metals, syngas leakage), geological risks (ground subsidence, rock mass strength reduction), and technical bottlenecks (difficult ignition control, unstable large-scale production). Combined with carbon capture and storage (CCS) technology, UCG can reduce carbon emissions, but CCS only mitigates carbon impact rather than reversing it. UCG provides a large-scale, stable, and economical path for the efficient clean development of difficult-to-mine coal resources, contributing to global energy structure transformation and low-carbon development. Full article

Atmospheric water harvesting (AWH) offers a decentralized and renewable solution to global freshwater scarcity. Bio-derived and hybrid aerogels, characterized by ultra-high porosity and hierarchical pore structures, show significant potential for high water uptake and energy-efficient, low-temperature regeneration. This PRISMA-guided systematic review synthesizes evidence on silica, carbon, MOF-integrated, and bio-polymer aerogels, emphasizing green synthesis and circular design. Our analysis shows that reported water uptake reaches up to 0.32 g·g⁻¹ at 25% relative humidity (RH) and 3.5 g·g⁻¹ at 90% RH under static laboratory conditions. Testing protocols vary significantly across studies, and dynamic testing typically reduces these values by 20–30%. Ambient-pressure drying and solar-photothermal integration enhance sustainability, but performance remains highly dependent on device architecture and thermal management. Techno-economic models estimate water costs from USD 0.05 to 0.40 per liter based on heterogeneous assumptions and system boundaries. However, long-term durability and real-world environmental stressor data are severely underreported. Bridging these gaps is essential to move from lab-scale promise to scalable, commercially viable deployment. We propose a strategic roadmap toward 2035, highlighting the need for improved material stability, standardized testing protocols, and comprehensive life cycle assessments to ensure the global viability of green aerogel technologies. Full article

The photovoltaic–energy storage–direct current–flexibility (PEDF) system provides an integrated pathway for low-carbon and intelligent building energy management by combining on-site PV generation, electrical storage, DC distribution, and flexible load control. This paper reviews recent advances in these four modules and synthesizes quantified benefits reported in real-world deployments. Building-scale systems typically integrate 20–150 kW PV and achieve ~10–18% energy-efficiency gains enabled by DC distribution. Industrial-park deployments scale to 500 kW–5 MW, with renewable self-consumption often exceeding 50% and CO₂ emissions reductions of ~40–50%. Community-level setups commonly report 10–15% efficiency gains and annual CO₂ reductions on the order of tens to hundreds of tons. Key barriers to large-scale adoption are also discussed, including multi-energy coordination complexity, high upfront costs and uncertain business models, limited user engagement, and gaps in interoperability standards and supportive policies. Finally, we outline research and deployment priorities toward open and interoperable PEDF architectures that support cross-sector integration and accelerate the transition toward carbon-neutral (and potentially carbon-negative) built environments. Full article

The increasing popularity of electric vehicles (EVs) as green alternatives to traditional internal combustion engine cars has highlighted the need for sustainable and environmentally friendly supply chain models. In particular, the handling of EV batteries, which are environmentally unfriendly and logistically critical due to their hazardous nature and short life cycle, requires a well-designed closed-loop supply chain (CLSC). This study proposes a new multi-objective optimization model of the CLSC, explicitly tailored to EV batteries under demand and return rate uncertainty. The proposed model incorporates three primary objectives that are typically in conflict with one another: minimizing the total cost, reducing carbon emissions throughout the entire supply chain network, and maximizing the recycling and reuse of batteries. The model employs a neutrosophic goal programming (NGP) methodology to address the uncertainties associated with demand and battery return quantities. The NGP model translates multiple objectives into non-monolithic goals with crisp aspiration levels (i.e., prescribed ideal levels for achieving the best of each goal) and thresholds that capture tolerances, thereby accounting for uncertainty. The efficiency of the proposed method is illustrated by a numerical example, solved using a IBM ILOG CPLEX Optimization Studio 22.1.2 solver. The findings demonstrate that the NGP can offer cost-effective, low-impact, and environmentally friendly solutions, thereby enhancing system robustness and flexibility to adapt to uncertainties. This study contributes to the emerging literature on sustainable operations research by developing a decision-making tool for EV-HV battery supply chain management. It also offers relevant suggestions for policymakers and industrialists who seek to co-optimize economic benefits, ecological sustainability, and logical feasibility in the emerging green society. Full article

To address the limitations of existing power system low-carbon dispatching studies—such as over-reliance on generation-side carbon mitigation, price-oriented demand response (DR) failing to guide carbon reduction, and the low solution efficiency of traditional carbon emission flow (CEF)-based two-stage models—this paper proposes a data-driven CEF framework integrated with a bi-level economic and low-carbon dispatching model. First, a data-driven CEF calculation method is developed: It eliminates the need for complex power flow post-processing while maintaining calculation accuracy through multiple linear regression. On this basis, a bi-level optimization model is constructed: The upper level focuses on optimizing the economic and low-carbon objectives of power grid operation, while the lower level regulates industrial, commercial, and residential load aggregators (LAs) via carbon-intensity-oriented DR strategies and economic compensation mechanisms. Finally, a sample-based optimization algorithm combined with convex relaxation is proposed to solve the model, avoid the static setting of power flow and carbon intensity, and improve solution efficiency. Case studies demonstrate the following: the proposed method reduces the calculation time of node carbon intensity from 5 min to less than 100 ms, with the coefficient of determination (R²) ranging from 0.969 to 0.998; compared with the two-stage method, it achieves a 4.26% reduction in total scheduling cost, a 3.80% decrease in total carbon emissions, a 53.27% drop in carbon trading cost, and a 21.6% shortening in iteration time. These results verify that the proposed method can effectively enhance the source−load interaction and improve the accuracy and efficiency of low-carbon scheduling. This study provides a feasible technical path for the low-carbon transition of new-type power systems. Full article

Under the dual targets of carbon peaking and carbon neutrality, virtual power plants (VPPs) are expected to coordinate distributed energy resources in distribution networks to ensure low-carbon operation. This paper introduces a distribution-level dynamic carbon emission factor (DCEF), derived from nodal carbon potentials on an IEEE 33-bus distribution network, and uses it as a time-varying carbon signal to guide VPP scheduling. A bi-objective ε-constraint mixed-integer linear programming model is formulated to minimise daily operating costs and CO₂ emissions, with a demand response and battery storage being dispatched under network constraints. Four seasonal typical working days are constructed from measured load data and wind/PV profiles, and three strategies are compared: pure economic dispatch, dispatch with a static average carbon factor, and dispatch with the proposed spatiotemporal DCEF. Our results show that the DCEF-based strategy reduces daily CO₂ emissions by up to about 8–9% in the typical summer day compared with economic dispatch, while in spring, autumn, and winter, it achieves smaller but measurable reductions in the order of 0.1–0.3% of daily emissions. Across all seasons, the average and peak carbon potential are noticeably lowered, and renewable energy utilisation is improved, with limited impacts on costs. These findings indicate that feeder-level DCEFs provide a practical extension of existing carbon-aware demand response frameworks for low-carbon VPP dispatch in distribution networks. Full article

This study develops a fuzzy linear multi-objective programming (FLMOP) model to optimize Taiwan’s online food delivery (OFD) systems by jointly considering time, cost, quality, and carbon emissions (TCQCE) under strict Hazard Analysis and Critical Control Point (HACCP) safety constraints. By integrating fuzzy set theory with triangular fuzzy numbers (TFN) and employing centroid defuzzification, this model effectively addresses uncertainties in delivery time, cost, and quality. Empirical results demonstrate that controlled delivery-time extension and order batching reduce carbon emissions by 20%, maintain food quality at 89.3%, and lower delivery costs by 15% under large-scale operations. Statistical validation (p = 0.002) and sensitivity analysis confirm robustness and low variability. Comparative benchmarking highlights FLMOP’s superiority over mixed-integer linear programming (MILP) and genetic algorithms/non-dominated sorting genetic algorithm II (GA/NSGA-II), achieving higher hypervolume (0.904 vs. 0.836 and 0.743) and near-optimal solutions within 11 s, making it suitable for real-time decision-making. This study establishes a benchmark for sustainable last-mile OFD and offers practical guidelines for Taiwan’s OFD platforms. Full article

This review highlights conventional forging steels and advanced medium-Mn steels containing retained austenite (RA), emphasizing their potential for industrial forging applications. Modern steels intended for forgings are required to combine strength, ductility, toughness and fatigue resistance with good hardenability and machinability at minimal cost. Medium-Mn multiphase steels fulfill these requirements by the strain-induced martensitic transformation (SIMT) of fine, lath-type RA, which can create a strength–ductility balance. Ferritic–austenitic steels provide high ductility with moderate strength, martensitic–austenitic steels show very high strength at the expense of ductility, and bainitic–austenitic steels achieve intermediate properties. Impact toughness and fatigue resistance are strongly influenced by the morphology of RA. The lath-type RA enhances energy absorption and delays crack initiation, while blocky RA may promote intergranular fracture. Low carbon (0.2–0.3 wt.%) combined with elevated manganese (3–7 wt.%) contents provides superior hardenability and machinability, enabling cost-effective air-hardening of components with various cross-sections. Advanced medium-Mn steels provide a superior mechanical performance and economically attractive solution for modern forgings, exceeding the limitations of conventional steel grades. Full article

Sugarcane bagasse (SCB) is a lignocellulosic byproduct with low biodegradability, limiting its potential for biological processes such as biogas production. The objective of this study was to evaluate whether a short-term biological pretreatment with the cellulolytic bacterium Proteus mirabilis KC94 could enhance SCB hydrolysis, improve nutrient balance, and increase biomethane potential (BMP). Three treatments were compared: untreated bagasse (UB), sterilized bagasse (SB), and KC94-pretreated bagasse (PB). Glucose release was highest in PB (61.83 ± 0.8 mg/mL), indicating enhanced cellulose degradation in PB relative to UB (53.19 ± 0.9 mg/mL) and SB (44.00 ± 0.5 mg/mL). Elemental analysis revealed a more balanced nutrient profile in PB, characterized by optimal carbon and nitrogen levels, and reduced sulfur content, indicating microbial assimilation and potential biological desulfurization. Scanning electron microscopy revealed pronounced structural disruption, increased porosity, and fiber delamination in PB, confirming the efficacy of KC94-mediated lignocellulosic pretreatment. BMP assays conducted over a 31-day incubation period revealed that PB produced the highest cumulative methane yield (99 ± 0.7 mL CH₄/g VS), representing 19% and 25% increases over UB and SB, respectively. PB biomethanation was also faster compared to the other two substrates. These findings demonstrate the novelty of a 5-day bacterial pretreatment strategy, which significantly improves lignocellulosic hydrolysis and methane yield. Specifically, P. mirabilis KC94 pretreatment increased glucose release by 16–40% and cumulative methane yield by 19–25% compared to untreated and sterilized controls. This cost-effective and environmentally friendly approach highlights the potential of P. mirabilis KC94 to valorize sugarcane bagasse, advancing sustainable energy recovery and circular bioeconomy practices. Full article

Despite being frequently proposed as a low-carbon solution for wastewater treatment and solar fuel production, the feasibility of photocatalytic processes in large-scale deployments remains unclear. This review evaluates the scalability of photocatalytic technologies by synthesizing a decade (2015–2025) of techno-economic analysis (TEA) and life-cycle assessment (LCA) studies. Using a systematic search and programmatic screening, 77 assessment-focused publications were identified from an initial corpus of 854 studies. Across applications, TEA and LCA consistently highlight two dominant barriers to scale-up: high electricity demand in UV-driven systems and significant cradle-to-gate impacts associated with catalyst synthesis, particularly for nanostructured materials. When solar irradiation replaces artificial light, environmental and economic hotspots shift from energy use to material production, catalyst durability, and reuse assumptions. Wide variability in reported costs and impacts reflects heterogeneous methodologies, limited pilot-scale data, and a lack of standardized reporting. Overall, assessment-based evidence indicates that photocatalysis is not yet ready for widespread industrial deployment as a large industrial process. However, continuous advances in solar-driven reactor design, low-impact and circular catalyst synthesis, hybrid process integration, and harmonized TEA/LCA frameworks could substantially improve its prospects for scalable, climate-positive implementation, especially in the context of emerging green energy alternatives. Full article

This study evaluates the technical feasibility of decentralized vermicompost production by smallholder farmers within a structured rural development program. Conducted under the KOP-TEYAP initiative in Kırşehir Province, Türkiye, the research assesses whether farmers can consistently produce vermicompost that meets international quality standards following a participatory training and infrastructure support model. Fourteen farmers, selected through a merit-based process from 232 trainees, were provided with standardized production units. The produced vermicompost was analyzed for critical chemical parameters (pH, EC, organic matter, C:N ratio, K, Cu, Zn) and biological indicators (basal CO₂ respiration, microbial biomass carbon) and benchmarked against regulations from the EU, France, Germany, Austria, Canada, India, and Türkiye. Results indicated that the majority of farmer-produced samples successfully met the critical thresholds for chemical quality and safety. Furthermore, biological maturity was confirmed by low basal respiration levels and high microbial biomass across the samples. These findings demonstrate that structured farmer training combined with standardized low-cost infrastructure enables smallholders to reliably produce high-quality vermicompost, validating this model as an effective agroecological strategy for rural development. Full article

As low-carbon supply chains increasingly integrate green transition strategies with digital transformation, coordinating high-cost green technology investments with data-driven marketing (DDM) becomes a complex managerial task. While these dual investments are essential for market growth, the inherent tension between economic efficiency and fairness concerns often triggers strategic friction phenomenon whose impact under cap-and-trade regulations remains insufficiently explored. This paper investigates the strategic implications of fairness concerns in a low-carbon supply chain in which a manufacturer invests in carbon emission reduction and a retailer engages in data-driven marketing (DDM), under a cap-and-trade regulation. We formulate four Stackelberg game models—Neutral Benchmark (NF), Retailer Fairness (RF), Manufacturer Fairness (MF), and Bilateral Fairness (BF)—to analyze the interplay between behavioral equity and economic efficiency. The main analytical results indicate that (1) fairness concerns universally function as an “efficiency tax” on the supply chain system, where the rational benchmark consistently yields the highest system efficiency. In contrast, bilateral fairness concerns lead to the worst performance due to double friction effects. (2) Counter-intuitively, the retailer can “weaponize” fairness concerns to extract surplus from the leader. Specifically, in environments with high carbon emission reduction costs, a fairness-concerned retailer compels the manufacturer to grant significant wholesale price concessions, thereby achieving higher profits than in a purely rational setting. (3) The manufacturer’s fairness creates a “Benevolence Trap” for the follower; to balance equity, a fair manufacturer tends to underinvest in green technologies, which severely contracts market demand and, unlike the retailer fairness scenario, fails to yield economic benefits for the retailer. (4) A critical “regime-switching” dynamic exists regarding the carbon trading price. While the retailer benefits from fairness strategies in nascent carbon markets, a pivot to rationality becomes optimal as carbon prices surge and efficiency dividends dominate. These findings offer novel managerial insights for supply chain members to navigate behavioral complexities and for policymakers to align incentive mechanisms. Full article

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model. Full article

With the growing trend of incorporating waste and industrial by-products in infrastructure, airport pavements built with sustainable materials are of increasing interest. This research developed six theoretical concrete mixtures for airport pavement and evaluated their financial, social and environmental cost within a stochastic triple bottom line framework. A Monte Carlo simulation was used to capture uncertainty in key parameters, particularly material transport distances, embodied carbon, and cost variability, allowing a probabilistic comparison of conventional and sustainable mixtures. The results showed that mixtures incorporating supplementary cementitious materials, recycled concrete aggregate and geopolymer cement consistently outperformed the ordinary Portland cement benchmark across all triple bottom line dimensions. Geopolymer concrete offered the greatest overall benefit, while the mixture containing blast furnace slag aggregate demonstrated how long haulage distances can significantly erode sustainability gains, highlighting the importance of locally available materials to sustainability. Overall, the findings provide quantitative evidence that substantial triple bottom line cost reductions are achievable within current airport pavement specifications, and even greater benefits are possible if specifications are expanded to include emerging low-carbon technologies such as geopolymer cement. These outcomes reinforce the need for performance-based specifications that permit the use of recycled materials and industrial by-products in pursuit of sustainable airport pavement practice. Full article