Microalgal ammonium assimilation and carbon mitigation in a dual-stage process integrating anaerobic treatment and a membrane photobioreactor.

ABSTRACT With the increase of coal gasification ash production and the environmental problems caused by its accumulation, it is of great significance to study its application in concrete. In this paper, the flexural performance of coal gasification ash concrete beams under low cyclic loading is studied. The effects of coal gasification ash content, concrete strength and reinforcement ratio on the seismic performance of the specimens are discussed. The mechanical properties such as load-deflection, crack width and steel bar strain were measured by experiments. It was found that when the content of coal gasification ash was 20 %, the flexural capacity and seismic performance of the specimen beam were the best. The calculation formulas of cracking load and ultimate bearing capacity based on coal gasification ash were further proposed, and their rationality was verified by ABAQUS finite element simulation. The results show that the proper amount of coal gasification ash can not only improve the mechanical properties of the beam, but also reduce carbon emissions and production costs, which provides a theoretical basis for the promotion of coal gasification ash in engineering applications.

Carbon emissions from the construction industry have attracted significant public attention, and the sector's low-carbon transition is crucial for achieving carbon neutrality. Although extensive research has been conducted in this field, limited studies have investigated the combined effects of structural performance design and seismic safety technologies on carbon emission reduction. This study aims to simultaneously enhance structural safety and reduce carbon emissions using the seismic energy dissipation technology. Eight reinforced concrete frame structures with varying numbers of floors are selected as case studies in accordance with the Chinese design code. By incorporating additional energy dissipation devices, the required dimensions of structural components in structures with damper (SWD) are reduced compared with structures without damper (SWOD), thereby lowering carbon emissions and improving seismic performance. Both SWOD and SWD systems are designed for each of the eight reinforced concrete frame structures for comparative analysis. Structural component dimensions are calculated using SAUSG software, and key performance indicators, including the natural period and inter-story drift ratio, are analyzed to verify compliance with code-specified safety requirements. Engineering quantities and life-cycle energy consumption are quantified, including the production and transportation of materials, as well as construction and dismantling stages. The results indicate that SWDs reduce material and energy consumption, with average carbon emissions 17.4% lower than those of SWODs. This study provides a novel perspective on carbon emission reduction during the design phase and offers an effective technical pathway for the coordinated development of low-carbon buildings and seismic resilience.

Enhancement of fly ash carbonation by mechanical ball milling: From lab-scale characterization to pilot‑scale validation.

Developing concrete mix designs that enhance structural performance while reducing environmental impact is vital for achieving sustainable construction. This study utilizes multiobjective optimization and machine learning (ML) that aim to reduce the carbon footprint of concrete production by utilizing industrial by-products (e.g., slag and fly ash) as replacements for cement without compromising concrete strength. A data set of 1,105 observations detailing mix compositions, ages, and compressive strengths was utilized to train and test various ML models, including neural networks (NN). The NN model, optimized using grid search, random search, and Bayesian optimization, demonstrated superior performance with an R2 value of 0.89 for compressive strength prediction. The model was further validated using material compositions and compressive strength tests not included in the training or verification data sets. A key innovation of this research lies in applying dimensionality reduction and multiobjective optimization techniques to navigate the trade-off between compressive strength and environmental impact. The Pareto front was generated, highlighting optimal concrete mix designs that achieve compressive strength requirements while reducing carbon emissions by 30%–70%. This design space, derived through ML, enables engineers to make informed decisions about designing sustainable, high-performing concrete mixtures. The findings underscore the drastic potential of ML in advancing sustainable construction practices, achieving structural quality, and mitigating the environmental footprint of building materials.

Nuclear power technology provides efficient energy generation and reduced carbon emissions, but it also produces radioactive waste, including gaseous radionuclides such as technetium-99 (Tc-99) during the pyroprocessing of spent nuclear fuel. Calcium oxide (CaO) is a promising material for capturing gaseous Tc; however, its adsorption performance declines substantially at intermediate temperatures (500–800 °C). The objective of this study was to improve the adsorption capacity of CaO adsorbents within this temperature range by including isopropyl alcohol (IPA) as a pore-forming agent during synthesis. Mercury intrusion porosimetry showed that increasing IPA content promoted macropore formation, thereby improving rhenium (Re, a Tc surrogate) adsorption performance at 500–700 °C. A quantitative correlation was established between the dominant macropore size and Re adsorption capacity, demonstrating that pore-structure-controlled internal transport accessibility governs capture performance. The optimized CaO adsorbent (CP-1.00) demonstrated a maximum Re adsorption capacity of 16.95 mol-Re/kg-ads and over 99% capture efficiency at 500 °C. These findings demonstrate that tailoring the pore structure via IPA addition significantly improves the capture of gaseous radionuclides throughout an expanded temperature range, providing greater flexibility for high-temperature off-gas treatment in spent nuclear fuel processing systems.

Purpose- This study intends to investigate the nonlinear relationship between Digital Financial Inclusion (DFI) and carbon emission intensity in Chinese cities. It seeks to uncover the potential "U-shaped" pattern of DFI's environmental effects and examine the underlying mechanisms, including technological innovation and urbanization, while considering variations across different city types. Methodology- Applying balanced panel data from 270 Chinese cities spanning 2012 to 2022, the study employs a two-way fixed effects model and the system Generalized Method of Moments (GMM) for empirical analysis. The research examines the nonlinear impact of DFI on carbon emission intensity and explores the mediating role of technological innovation and the moderating effect of urbanization. Findings- The results indicate that DFI significantly reduces carbon emission intensity, but this effect follows a distinct "U-shaped" pattern, with a turning point at an index value of 137, revealing diminishing marginal returns in emission reduction benefits. Technological innovation is identified as a key mediating channel. Urbanization negatively moderates the emission reduction effect, reflecting an "inclusive compensation" characteristic of DFI in less developed areas. Furthermore, resource-based cities exhibit a stronger initial emission reduction effect but face more severe U-shaped rebound risks. Conclusion- We provide empirical evidence supporting the design of differentiated emission reduction strategies. It highlights the importance of considering the nonlinear dynamics of DFI and local characteristics—such as urbanization and city type—in formulating effective environmental policies. Keywords: Digital financial inclusion, carbon emission intensity, U-shaped curve, resource endowment, carbon lock-in

AIS-based spatiotemporal carbon emission reduction potential of coastal shipping in China.

Efficient resource allocation is crucial for promoting economic growth and reducing carbon emissions in China. This study develops a new centralized resource allocation approach based on data envelopment analysis (DEA). It incorporates technological progress into the environmental production possibility set and further extends it to a technological heterogeneity setting under a meta-frontier framework. The environmental efficiency of each decision-making unit is encouraged to achieve its future group frontier. The advantage of the proposed method is that, it enhances the forward-looking nature of decision-making, explicitly captures technological heterogeneity within the allocation framework, and improves the incentive compatibility of target setting while maintaining feasibility. It is then applied to optimize China's "energy-economy-emissions" system. The results show that the optimal interprovincial allocation of energy consumption and labor can increase the aggregate economic output of the 30 provinces by 6.95% relative to 2023, while generating a carbon reduction potential of 5,681.17million tons. In addition, optimal allocation combined with appropriate incentives could further raise economic output in 2024 by 1.8% and reduce emissions by 47.19%. Severe labor misallocation is found between the eastern and central regions. Sensitivity analysis further suggests that policymakers should place moderately greater weight on economic development than on emission reduction. Several managerial implications are derived accordingly.

Magnesium alloys are used to manufacture lightweight vehicles to improve fuel economy and reduce carbon emissions. Implementing the idea of friction stir welding for joining increases the utility of these alloys in the automotive sector. In this study, dissimilar Mg alloys AZ31 and AZ91 were welded using the Box-Behnken experimental design criterion. The modeling of corrosion rate has been done using Response Surface Methodology (RSM), while optimization has been done using the Teaching-Learning-Based Optimization (TLBO) algorithm. The microstructural investigation suggest variation in grain size with process parameters and the affinity of magnesium towards oxygen form MgO in welded region which generates microcracks. Microstructure of corroded samples included some corroded circular rings, pits and MgO as white layers. The minimum corrosion rate 0.220 mm/year is obtained at 700 rpm of Rotational Speed (RotS), 30 mm/min of Welding Speed (WeldS) and 18 mm of Shoulder Diameter (ShD) suggesting improved homogeneity due to dynamic recrystallization. The most influencing factor for corrosion is tool rotational speed. The TLBO algorithm suggests a 5.90% improvement at 1000 rpm of RotS, 50 mm/min of WeldS and 19 mm of ShD.

To address the critical challenge where the excessively high Minimum Miscibility Pressure (MMP) restricts the effectiveness of CO2 flooding, this study proposes a novel strategy utilizing SiO2-ethanol nanofluids (SiO2-C2H6O NFs) as additives to reduce the MMP. By systematically optimizing particle size, concentration, and dispersant types, a 5 nm/5 wt% SiO2 nanofluid with polyvinylpyrrolidone (PVP) as the dispersant was successfully prepared, demonstrating excellent long-term dispersion stability. Phase equilibrium experiments indicate that after adding 20 vol% of the optimized nanofluid into crude oil model components (n-alkanes and cycloalkanes), the solubility of CO2 in the oil phase is significantly enhanced. The maximum average equilibrium pressure reduction (PAVG) reached 2.24 MPa, effectively lowering the MMP of the system. Furthermore, a modified PR-vdW1 equation of state considering nano-confinement effects was developed and validated to systematically reveal the phase equilibrium behavior of CO2-alkane systems within nanopores. This research not only enriches the fundamental thermodynamic data for CO2-hydrocarbon systems but also provides a novel and efficient technical pathway for improving CO2 flooding efficiency and achieving synergistic carbon emission reduction.

A comprehensive understanding of land use carbon metabolism characteristics from the production-living-ecological space (PLES) perspective is crucial for formulating carbon reduction strategies. As the core economic zone of northern China, the Beijing-Tianjin-Hebei (BTH) region faces severe carbon emission pressures due to rapid urbanization and intensive land use transformation. However, focusing solely on carbon metabolism calculation without considering future changes and optimization effects may prevent achieving carbon emission reduction targets. This study assessed carbon emissions and sequestration based on different land use types in PLES, constructed a multi-objective carbon reduction scenario utilizing the Dinamica-EGO model, nondominated sorting genetic algorithm II, and entropy weight-TOPSIS model, and simulated 2035 carbon reduction characteristics by coupling PLES changes. Taking the BTH region as a case study, a methodological framework and corresponding models were established. The results show that from 2000 to 2020, the total carbon emissions in the BTH region increased significantly, presenting a spatial pattern of high emissions in the southeast and low emissions in the northwest. In contrast, the overall carbon sequestration capacity showed a decreasing trend, with stronger capacity in the northwest and weaker capacity in the southeast. The multi-variable 2035 carbon emission reduction prediction model achieved an accuracy of 82.24%. The 2035 carbon reduction plan developed based on this framework outperformed the original land use plan: economic benefits, emission reduction efficiency, spatial compactness, and accessibility are projected to increase by 15.8%, 7.9%, 2.5%, and 8.3%, respectively, while carbon emissions are expected to decrease by 19.04%. The proposed PLES-based framework for carbon metabolism measurement and emission reduction simulation exhibits good applicability in regional spatial emission reduction. These findings contribute to exploring regional carbon dynamics and provide references for governments to formulate carbon reduction policies.

ABSTRACT Amid the global transition toward low-carbon energy, ammonia (NH3) is regarded as a promising clean fuel, though its use is limited by high autoignition temperature and low flame propagation speed. This study proposes a dual-fuel system integrating catalytically reformed ammonia (producing H2/N2-enriched gas) with n-heptane (n-C7H16) to improve combustion performance. An improved combustion reaction mechanism was developed. Numerical simulations conducted via Chemkin software, combined with sensitivity analysis optimization, achieved high-accuracy predictions of laminar burning velocity (LBV), ignition delay time (IDT), and NO emissions The improved mechanism demonstrates superior performance over the original mechanism in predicting mixed fuel behavior under the Premixed Laminar Flame-Speed Calculation module. The results demonstrate that ammonia reforming significantly enhances the LBV by promoting chain-branching reactions through hydrogen-enriched gas (H2/N2). As the reforming ratio increases from 0% to 90%, the peak flame speed of fuel containing 10% n-heptane rises from 18 to 60 cm/s. NO emissions exhibit strong correlations with equivalence ratio and reforming ratio: under lean conditions (Φ=0.8), the peak NO concentration exceeds that under rich conditions (Φ=1.2) by 300%. In lean combustion, the contribution of the NH3 oxidation pathway (NH3→NH2→HNO→NO) increases from 19.4% to 27.5% as the reforming ratio grows from 10% to 50%, resulting in over 50% elevation in NO concentration. Under high-temperature and high-pressure engine conditions, for a fuel containing 5% n-heptane, enhanced combustion should adopt Φ=1.1–1.3 combined with a reforming ratio of 50–70% to maintain the flame velocity greater than 30 cm/s. For optimized NO emissions, it is necessary to prioritize increasing the equivalent ratio on the premise of ensuring the combustion intensity, and second, to control the ammonia reforming ratio. The study demonstrates the application potential of ammonia reforming integrated with dual-fuel strategies in enhancing thermal efficiency and reducing carbon emissions.

In the context of sustainable transportation development, reducing carbon emissions, energy waste, and noise pollution caused by traffic congestion has become an urgent task for achieving environmental and social sustainability. The key to this goal lies in mitigating and preventing traffic congestion, for which high accuracy traffic condition prediction models serve as essential tools. During the reconstruction and expansion of highways and urban arterial roads, frequent adjustments to traffic organization and changes in geometric alignment introduce dynamic and uncertain characteristics into the traffic system. Existing methods struggle to accurately predict traffic conditions in the modified sections. To address this challenge, this study proposes a Dynamic Bayesian Graph Convolutional Neural Network (DBGCN). The model incorporates road geometric parameters and dynamic traffic organization changes as key inputs. It employs a Dynamic Bayesian Network (DBN) to model multi-source dynamic information and infer a dynamic adjacency matrix that reflects latent spatiotemporal dependencies between nodes. This dynamic adjacency matrix is then input into a Graph Convolutional Network (GCN), which fuses spatiotemporal features with traffic flow data to achieve accurate traffic conditions prediction for upgraded sections. Validation on the Wuxuan highway demonstrates that the proposed method outperforms benchmark models in traffic conditions prediction accuracy and produces traffic conditions propagation diagrams with high interpretability.

Investigating the circular economy's impact on addressing escalating climate change concerns is crucial. The objective of this research is to examine the possible implications of a circular economy on production-based carbon emission, this domain has yet to be thoroughly investigated. This investigation fills the research gap through presenting an under examined proxy for assessing the circular economy through municipal waste generation and treatment. This research investigated data spanning 1991 to 2020 from six advanced circular economies namely, Austria, Belgium, South Korea, Spain, Sweden, and USA, applying numerous estimation techniques to ensure robust and reliable results namely Autoregressive Distributed Lag-Pooled Mean Group (ARDL-PMG), Autoregressive Distributed Lag-Mean Group (ARDL-MG), Autoregressive Distributed Lag-Dynamic Fixed Effects (ARDL-DFE), Fully Modified Ordinary Least Squares (FMOLS), Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM), stability analysis and Pairwise Dumitrescu-Hurlin (D-H) causality. The study determined the influence of the circular economy on production-based emissions using both country-specific and panel data approaches. The findings demonstrate that implementing a circular economy leads to a long-term reduction in production-based carbon emissions in the panel and country wise estimation except Spain. These findings develop understanding of sustainable development difficulties and encourage practitioners to build successful strategies.

Wireless Power Transfer-based electric vehicles (EVs) emerge as a promising technology that can drastically reduce carbon emissions worldwide. Conventional ICE-based transportation has led to a significant increase in greenhouse gas emissions and exacerbated climate change. On the contrary, WPT-based EVs can offer extended range, contactless charging, enhanced safety, and reduced vehicle costs. However, the complex design of WPT is vulnerable to power converter failure and coil misalignment. These faults can significantly deteriorate the performance of the WPT-based EVs. A power converter failure can degrade WPT performance, and this paper introduces redundant switches to ensure smooth vehicle operation. This paper presents a Physics-informed Neural Network (PINN) that incorporates physical laws to observe signals from power converters and control the Active Fault-Tolerant Controller (AFTC). The AFTCS consists of the FDI block, which is responsible for detecting and isolating faults in real time. The PINN-based observer system monitors the signal in real time. The results clearly illustrate that the proposed framework provides accurate current tracking and ensures the residual signal remains in the threshold even in parameter variations and achieves a reduced settling time of 0.22s. Furthermore, the steady-state error is reduced to 0.3A, outperforming the other control methods and ensuring the stability of the system in the event of faults. Thus, a PINN-based FTC is introduced to ensure the resilience and reliable operation of WPT-based EVs in case of power converter failure and coil misalignment.

Under global pressure to reduce carbon emissions, understanding how the level of artificial intelligence (AI) use affects corporate carbon emissions (CCE) is crucial for achieving a green transition. This study, based on provincial panel data from China, employs a combination of two-way fixed effects (TWFE) empirical analysis and structural equation modeling (SEM) to identify the nonlinear effects of AI use levels on corporate carbon emission intensity and their transmission pathways, and conducts robustness and regional heterogeneity tests. The empirical results show that: first, the relationship between AI use levels and corporate carbon emissions exhibits a significant inverted U-shaped curve-in the early stages of development, due to high energy consumption in computing and deployment, AI adoption may temporarily increase carbon emissions; however, after exceeding a critical point, further deepening of use significantly reduces carbon emissions. Second, SEM analysis reveals several key mediating channels: improving green innovation efficiency (GIE), enhancing energy utilization efficiency (EUE), promoting Scientific innovation (SI), and driving industrial structure upgrading (ISU). These pathways collectively amplify the emission reduction effect of AI. Third, regional heterogeneity analysis shows that the AI emission reduction effect is significantly stronger in the eastern region than in the central and western regions. Finally, this study emphasizes the policy implications: while promoting the use of AI, energy structure and incentive mechanisms should be optimized, and differentiated policies should be formulated according to regional characteristics to achieve an AI-driven sustainable low-carbon transformation.

ABSTRACT Under carbon cap-and-trade, capital-constrained manufacturers can leverage carbon quotas via carbon quota pledge financing (CQPF) and carbon quota repurchase financing (CQRF). Using a Stackelberg game model, this study analyzes the optimal financing choice. Key findings reveal: 1) From both the profit and social welfare perspectives, the optimal financing approach is primarily determined by initial capital and financing costs, with CQPF preferred only when its financing cost is lower than that of CQRF. 2) From an environment perspective, financing is avoided at moderate R&D cost coefficient but becomes attractive beyond thresholds, with preference hinging on the R&D cost coefficient and financing-cost differences. 3) Higher average technology levels boost carbon emission reduction (CER) efforts, sales, and profits across all models, while greater technological volatility improves both economic and environmental outcomes. We also examine the effects of retailer risk aversion and two-manufacturer competition, providing new insights for carbon-financing strategies and related policy design.

Hydrogen production has been transitioning toward sustainable methods due to the increasing demand for carbon emission reduction. Biomass gasification has emerged as a promising alternative, converting organic waste into syngas that is rich in hydrogen and carbon monoxide under high-temperature conditions. This study focuses on the thermodynamic modeling and simulation of the gasification process using açaí seed residue as feedstock. The process model was implemented in Aspen Plus, considering key operating variables such as temperature, steam-to-biomass (S/B) ratio, and equivalence ratio (ER). Additionally, an operability analysis was conducted to optimize the gas composition, energy demand, and cold gas efficiency (CGE). Results indicated that a higher S/B ratio enhances hydrogen production due to the water-gas shift reaction, whereas an increased air supply improves energy efficiency but reduces hydrogen content in the output. The operability-based optimization revealed that the hydrogen concentration in syngas can reach up to 58.6% when no energy demand restrictions are applied, but at a high energy cost (444 MJ h-1). Conversely, the operability index increased from 13% to 30.2% by applying an energy self-sufficiency constraint (heat duty < 0), making the process more sustainable while maintaining CGE between 78.6% and 98.1%. These findings highlight the trade-off between syngas quality and energy efficiency, which should be considered in industrial applications depending on hydrogen purity requirements and economic feasibility.

Urban forest ecosystem service value assessment under carbon reduction: A case study of Yanbian, China.