Search Results (61,636)

Digital transformation is embedding carbon metrics into construction project management through automated procurement and sustainability dashboards. Yet carbon intensity measures depend critically on how emissions are normalized, creating ambiguity in sustainability assessment. This study examines how alternative carbon intensity scaling choices affect firm rankings and portfolio outcomes within the Construction & Engineering industry. Using carbon-efficient indices for publicly traded U.S. construction firms, emissions are normalized by revenues, costs, assets, market value, enterprise value, and employment. Alternative normalization choices lead to systematic within-industry reshuffling of firm rankings driven by differences in economic fundamentals. While average financial performance does not differ systematically across metrics, portfolio turnover and financed emissions vary substantially. These results show that normalization design is a consequential element of digital sustainability systems rather than a neutral technical choice. The study provides industry-specific evidence on measurement-induced reshuffling and highlights practical implications for contractor benchmarking and digital construction governance. Full article

The design of sustainable power systems requires planning tools that jointly account for economic, environmental, and social dimensions. However, multi-objective energy system models typically prioritize economic–environmental trade-offs, while the social dimension is still rarely included as an explicit optimization objective. Furthermore, many formulations adopt a low temporal resolution (e.g., annual time steps) and assume fully flexible power plants, potentially overlooking temporal variability and operational constraints. This paper presents a three-objective optimization model for sustainable power system design that minimizes (i) costs, (ii) greenhouse gas (GHG) emissions, and (iii) social opposition (i.e., the public resistance to certain energy technologies). Temporal variability and operational detail are preserved using weighted representative periods with intra-period time steps and a clustered unit commitment (CUC) formulation. The Pareto frontier is generated using the normalized normal constraint (NNC) method, highlighting the space of efficient economic, environmental, and social solutions. A case study focused on the Italian electricity system exemplifies the model application by providing the cost-optimal, emissions-optimal, and social-optimal solutions, together with trade-off solutions. Among the trade-off solutions, the selected best balance solution achieves a significant reduction in emissions (−20%) compared to the cost-optimal solution, with a limited cost increase (+5%) and a marginal increase in social opposition (+0.7%). Overall, the proposed model enables transparent quantification of multi-dimensional trade-offs to support decision-making in sustainable power system design. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

To reduce vehicular emission pollution in cold regions and maximize sustainable development of transportation, AC self-heating of electric vehicles is acknowledged as an efficient approach to mitigate the decline in Li-ion battery performance under low-temperature conditions. This paper introduces a novel battery self-heating approach based on reconfiguration of the drive circuit, which is compatible with both preheating and on-route heating. The undesired torque generated by the heating current can be inherently nullified regardless of the rotor position. The control of heating and driving currents is entirely decoupled, facilitating straightforward adaptation to a range of heating strategies. Furthermore, a battery electro-thermal model is proposed and integrated with the drive system model to estimate the battery temperature evolution. Comprehensive experiments are designed to validate the operating principle and the accuracy of battery temperature estimation under various working conditions. The results present a high fidelity between the experimental data and the simulation outcomes. The root mean square errors of the predicted battery temperature under all the constant and combined driving conditions are less than 1 °C. Full article

Urban last-mile logistics systems must improve service responsiveness while reducing environmental impact. While micromobility-based delivery fleets offer significant emission advantages compared to conventional vans, their operational efficiency depends on adaptive, data-driven capacity allocation. We develop and analyze a real-time optimization framework that explicitly integrates sustainability considerations into zone-level fleet allocation decisions. The continuous-time backlog dynamics admit a closed-form discrete-time prediction, enabling computationally efficient rolling-horizon fleet reallocation. Sustainability is explicitly embedded through zone-specific emission factors and a multi-criteria objective function balancing backlog reduction, environmental impact, and operational stability. In a ten-zone numerical case study with a fleet of 40 vehicles, the proposed method reduced backlog in all zones within a 15-min interval while preserving strict feasibility and stability (spectral radius is less than 1). The framework also demonstrated a controllable emission–service trade-off via sensitivity analysis. These results suggest the practical applicability and real-time suitability of the proposed Industry 4.0-aligned optimization approach. Full article

Modern underground hard coal mines encounter increasing natural hazards as mining depth increases, including, in particular, significant rises in both methane and thermal hazards. Thermal threats are common in Polish mines, especially in areas where the primary rock temperature exceeds 40 °C. To provide an energy source for cooling systems and reduce methane emissions from ventilation air, a system based on a catalytic reactor combined with an absorption chiller was developed. Field tests conducted at the experimental mine Barbara in Mikołów (Poland) indicate that a COP based on methane chemical energy can reach a level of 0.3–0.4. An application analysis was conducted based on the results of cross-sectional forecasts of climatic conditions (thermal conditions forecasts). The results indicate the potential for using this installation as a supporting component of mine cooling systems. An important factor that may limit the efficiency of the installation is the volume flow of the exhaust air stream. It is estimated that, in countries where, as in Poland, air temperature is the primary criterion for assessing thermal safety, the results of the analysis would be similar. Full article

Meeting rising global food demand requires reconciling high productivity with environmental sustainability. While controlled-release fertilizers can improve nitrogen use efficiency, their combined N-P-K formulation and system-wide impacts remain poorly quantified. A two-year field experiment was conducted in a rice paddy field under a subtropical monsoon climate in Central China to evaluate controlled-release NPK fertilizer (CRNPK) across agronomic, environmental, energy, and economic dimensions. Five treatments were compared: no nitrogen (CK), farmer practice (FFP; 270 kg N ha⁻¹), controlled-release nitrogen (CRN; 225 kg N ha⁻¹), CRNPK (225 kg N ha⁻¹), and reduced-rate CRNPK (80%CRNPK; 180 kg N ha⁻¹). Compared to FFP, CRNPK and 80%CRNPK increased rice yield by 8–16% and nitrogen use efficiency by 38–171%, while reducing reactive nitrogen losses and nitrogen footprint by 39–56%, greenhouse gas emissions and carbon footprint by 22–57%, and enhancing ecosystem economic benefit by 86–109%. Notably, the 80%CRNPK treatment achieved the highest overall sustainability score (5) based on a comprehensive assessment normalizing seven key indicators—yield, economic benefit, energy productivity, carbon footprint, nitrogen footprint, ecosystem economic benefit (EEB), and emergy-based nutrient efficiency (UEV_Nmin), demonstrating that yield gains can be maintained or even enhanced with reduced nitrogen inputs. This study advances controlled-release fertilization from a yield-focused strategy to a quantified, system-level approach for sustainable rice intensification. Full article

Growing demand for sustainable materials has intensified research into eco-friendly alternatives to conventional and synthetic leathers. Traditional bovine leather and its chromium-tanning process heavily contribute to water pollution, toxic waste generation, and carbon emissions, while synthetic leather derived from Polyvinyl Chloride (PVC) and polyurethane (PU) presents challenges related to fossil fuel dependence and non-biodegradability. This review explores bio-based and sustainable leather substitutes that are made of plants, microbial cellulose, and mycelium fungi. Plant-based leather substitutes such as Vegea^®, Desserto^®, and Piñatex^® use agricultural waste products to create durable, partially biodegradable composites. Microbial cellulose from kombucha fermentation offers material with good physical and aesthetic properties. Mycelium leather, derived from fungal biomass, demonstrates potential for scalable and low-impact production. Comparative analyses of mechanical and physical properties show that mycelium composites are approaching industrial standards, though challenges remain regarding tensile strength, water resistance, and process standardization. Despite current limitations, bio-based leathers, particularly mycelium composites, offer a promising way toward circular material innovation and carbon-neutral manufacturing in fashion, automotive, design and other industries. Full article

To systematically investigate the influence of metro train types on the operational noise of elevated rail transit, this study conducted field measurements on elevated sections of the Wuhan Metro Yangluo Line, Wuhan Metro Line 2, and Guangzhou Metro Line 4, comparing the noise characteristics of 4-car A-type, 6-car B-type, and 4-car L-type trains operating at 70 ± 2 km/h. Analysis of sound pressure levels and frequency spectra at multiple points revealed that wheel-rail noise peaks occurred at 630 Hz and 2500 Hz for A-type trains, around 800 Hz for B-type trains, and within 800–1250 Hz for L-type trains, while bridge structure-borne noise was consistently concentrated in the 63–100 Hz low-frequency range. Distinct emission patterns were observed: at on-girder points, noise levels were highest for A-type trains, followed by B-type and then L-type trains, a trend potentially linked to axle loads; conversely, at under-girder points, the order reversed with L-type trains producing the highest noise. At points 7.5 m and 25 m from the track centerline, A-type and B-type trains exhibited similar noise levels, whereas L-type trains were slightly quieter. Furthermore, all three train types showed a consistent noise attenuation rate of approximately 6 dB(A) per doubling of distance from the track centerline. The findings will serve as a reference and basis for rail transit noise prediction and control. Full article

Soil respiration (Rs) plays an important role in the carbon (C) dynamics of terrestrial ecosystems and is strongly regulated by nitrogen (N) inputs. While the impact of N fertilization on Rs has been widely documented in conventional farmland ecosystems, its patterns and influencing factors in perennial tea plantation systems are still poorly understood. In the study, we conducted a 15-year field experiment in a representative tea plantation to investigate the effects of different N rates (0, 112.5, 225, and 450 kg N ha⁻¹ yr⁻¹) on Rs. Compared to the control (N0), soil pH decreased significantly (p < 0.05) by 6.07%, 11.82%, and 16.12% under N112.5, N225, and N450, respectively. Concurrently, cation exchange capacity (CEC), ammonium (NH₄⁺-N), nitrate (NO₃⁻-N), and available phosphorus (AP) increased with increasing N rates, whereas available potassium (AK) decreased. Soil microbial biomass carbon (MBC) initially increased and then decreased with increasing N rates, while dissolved organic carbon (DOC) content increased consistently. The Rs rate exhibited a distinct seasonal pattern with a single peak in August. The annual mean Rs rates were 2.79, 3.15, 4.06, and 3.85 μmol·m⁻²·s⁻¹ for the N0, N112.5, N225, and N450 treatments, respectively. Soil temperature explained 55.41% to 61.08% of the variation in Rs rates across N treatments, and a composite model incorporating both soil temperature and moisture further improved the prediction of Rs dynamics. Cumulative soil CO₂ emissions (CCEs) over the study period ranged from 10,427 to 14,221 kg CO₂-C ha⁻¹ across treatments and were significantly negatively correlated with soil pH, and positively correlated with DOC, MBC, and NO₃⁻-N content. A non-linear relationship between N application rate and CCEs was observed, highlighting the complexity of optimizing N management for balancing productivity and climate mitigation in tea plantation systems. These findings provide a theoretical basis for developing rational N fertilization strategies and improving the predictive capacity of C cycle models in agroecosystems. Full article

The global textile industry, challenged by resource depletion and environmental pollution, urgently requires a shift toward a circular economy. However, recycling efforts remain limited, focusing mainly on conventional fibers and neglecting high-performance materials like aramid. This study addresses the recycling of used firefighting protective clothing-an aramid-rich, high-turnover waste stream typically landfilled or incinerated. Life cycle assessment reveals the significant carbon footprint of its production and disposal, underscoring the need for circular strategies. A systematic recycling framework is established, integrating collection logistics and redesign principles. A graded “three-tier” approach is proposed, enabling direct reuse, yarn regeneration, and non-woven production based on material conditions. High-value products were developed by incorporating firefighting heritage and intangible cultural crafts into the design, supported by digital product passports for traceability. These strategies enhanced market acceptance and emotional value. The work provides a scalable circular solution for high-performance textiles, aiming to extend material life, reduce carbon emissions, and advance sustainable textile management through a novel combination of technical recycling and cultural value addition. Full article

The application of ultra-supercritical power plant boilers is becoming increasingly widespread. P92 steel, as a typical material used for boiler main steam pipes, plays a critical role in unit safety, making the detection of its creep damage highly significant. However, existing conventional non-destructive testing methods are difficult to effectively detect creep damage. To address this issue, a magnetoacoustic emission (MAE)–magnetic Barkhausen noise (MBN) composite measurement system is developed, which is adapted to 20 Hz and 0.3 A sine wave excitation to trigger the synchronous pickup of MBN and MAE signals of P92 steel. After collecting signals with different creep life ratios (0%~100%) under working conditions of 650 °C and 100 MPa, time-domain (absolute mean, peak value, etc.) and frequency-domain (bandwidth) features are extracted. In response to the non-monotonicity between the magnetoacoustic features and the creep damage grade, principal component analysis (PCA) is introduced to reduce dimensionality. Different creep levels of samples in the two-dimensional principal component space are presented as clear gradient clustering, achieving the accurate differentiation of creep stages. Research has shown that the MAE-MBN composite system combined with PCA can effectively characterize the creep damage of P92 steel, providing a novel non-destructive detection path for the in-service life assessment of power plant components. Full article

This study developed a calibrated, data-supported energy simulation model for the Arrivals Terminal of Cluj-Napoca International Airport (Romania), addressing challenges in modelling complex building typologies. The objective is to improve the accuracy of predicting energy savings and CO₂ emission reductions, supporting renovation and decarbonization strategies aligned with the 2050 targets. The hourly multizone simulations over one year integrated measured operational data, building documentation, and two types of climate datasets (AMY and TMY). The calibration methodology introduces a “Miscellaneous equipment” variable, representing unmonitored indoor electricity consumption, which is incorporated as an internal heat gain in the thermal balance. Validation against real energy measurements showed high agreement (AMY-based RMSE: 3.13 kWh/m²·yr for thermal energy and 1.57 kWh/m²·yr for electricity; relative errors: 2.3% and 0.5%, respectively). The results demonstrate that calibrated modelling reduces the performance gap and provides a robust alternative to standard design-condition energy assessments, which are inadequate for airport terminals but mandatory for several countries, including Romania. The developed model enhances predictive reliability and can guide energy efficiency measures and investment decisions for similar complex buildings. Full article

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health risk prediction. The framework combines a Gaussian plume dispersion model with a residual CNN-LSTM network that learns data driven corrections while preserving physical consistency. Multimodal open datasets, including ground based pollutant sensors, meteorological records, and satellite derived aerosol and temperature features, are jointly fused to improve spatiotemporal fidelity. An Exposure Health Index module further links predicted pollutant fields with respiratory morbidity indicators, providing a quantitative bridge between atmospheric variability and health outcomes. Using open source datasets from Riyadh, Jeddah, and Dammam, the proposed approach achieves up to 25% lower mean absolute error and $R^2$ values above 0.85 compared with physics only and purely data driven baselines. Explainability analyses using SHAP and spatial attention highlight physically plausible drivers and confirm feature relevance. The results demonstrate that physics guided residual learning can unify deterministic dispersion modeling and multimodal inference, providing a transparent, scalable, and reproducible foundation for air quality forecasting and health risk assessment. Full article

Low-concentration methane emissions from landfills, manure management, wastewater treatment, and ventilation streams are difficult to mitigate using conventional capture and oxidation because of high air-to-fuel ratios, variable flows, and unfavorable economics. Methanotrophic bioreactors provide an aerobic biological route to oxidize methane at ambient conditions and, in selected cases, enable valorization into biomass and bioproducts. This review synthesizes methanotrophic reactor technologies for dilute methane, emphasizing the design and operational constraints that control performance. We classify systems into (i) fixed-film gas–solid configurations (biofilters, biocovers, biotrickling filters, and bioscrubbers), (ii) suspended-growth gas–liquid reactors (stirred tanks, bubble columns, and loop/airlift designs), (iii) membrane-based and intensified contactors that decouple methane and oxygen delivery and enhance mass transfer, and (iv) hybrid and in situ approaches for diffuse sources. This review presents key metrics and discusses how mass transfer, moisture and temperature control, nutrient supply, and microbial ecology interact to define achievable removal. We further summarize recent techno-economic and life-cycle studies to identify dominant cost drivers, particularly air handling and gas–liquid transfer, and the concentration regimes where biological oxidation is competitive with catalytic or thermal alternatives. Full article