Search Results (2,753)

Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development. Full article

Continuous paddy reclamation is a promising strategy for restoring sodic lands, yet the exact mechanisms driving soil organic carbon (SOC) stabilization remain insufficiently quantified. Here, we investigated SOC dynamics across a 12-year rice cultivation chronosequence (0, 2, 5, 10, and 12 years) at 0–20 cm and 20–40 cm depths in western Jilin Province, China. Successive rice cultivation progressively alleviated saline–alkaline stress, with electrical conductivity (EC) decreasing by 70.11% in topsoil after 12 years, establishing a stabilized soil environment by years 10–12. Concurrently, topsoil SOC and total nitrogen (TN) increased by 63.09% and 26.02%, respectively. This physicochemical amelioration triggered a directional carbon transformation: while particulate organic carbon (POC) accumulated in early stages, mineral-associated organic carbon (MAOC) dominated medium-term storage, expanding by 147.12% in topsoil and elevating its share of SOC. Fourier transform infrared (FTIR) spectroscopy confirmed a shift toward molecular structural persistence, marked by increased aromaticity and hydrophobicity. Partial least squares path modeling (PLS-PM) demonstrated that cultivation duration directly drove soil stability (β = 0.94, p < 0.001), operating through a hierarchical cascade where management-induced stress reduction enhanced soil enzyme activity and microbial processing, thereby accelerating the conversion of labile plant inputs into mineral-protected MAOC. Overall, this study quantifies the pivotal role of paddy management in driving organo-mineral protection and chemical persistence, providing a mechanistic framework for carbon sequestration in degraded agroecosystems. Full article

The sustainability of buildings and the built environment extends beyond energy and environmental performance to circular resource use, health and safety, digital infrastructure, and stakeholder collaboration. This exploratory integrative literature review examines how these established but insufficiently connected domains reshape business models in the built environment. Since the built environment is a major source of global carbon emissions and waste, a primary research stream concerns the transition toward circular economy principles beyond traditional profit-maximization logics. The literature also highlights the potential of health- and safety-oriented innovations to reduce risks and improve indoor environments. Other studies highlight the potential of digital innovations to improve life-cycle management, resource efficiency, and risk mitigation. However, their widespread adoption faces systemic barriers, including data interoperability and cybersecurity issues, implementation costs, skills shortages, organizational resistance, and regulatory and governance challenges. The literature often emphasizes technical potential while paying less attention to value-capture mechanisms and the allocation of costs, risks, benefits, and responsibilities among the actors involved. Integrating sustainable practices, digital infrastructures, circular-economy principles, and collaborative governance is therefore essential to develop economically viable, organizationally feasible, and ethically responsible business models for the built environment, across the building life cycle and among public and private stakeholders. Full article

To investigate the associations between single and mixed exposure to air pollutants in underground coal mine environments and the risk of metabolic syndrome (MetS) among workers, we conducted a cross-sectional study. Multivariable logistic regression, Bayesian kernel machine regression (BKMR), and quantile-based g-computation (Qgcomp) were used to evaluate the joint effects of mixed pollutant exposures and to identify the major contributing components. In addition, untargeted urinary metabolomics analysis was performed to explore the potential biological mechanisms. After adjusting for confounding factors, logistic regression analysis revealed that exposure to coal dust (CD), carbon monoxide (CO), carbon dioxide (CO₂), and nitrogen dioxide (NO₂) was associated with an increased risk of MetS. Furthermore, BKMR and Qgcomp models consistently indicated a significant positive association between mixed pollutant exposure and MetS risk, with CD and NO₂ identified as the primary components with the strongest statistical contribution. CD exposure was mainly associated with central obesity and hypertension, whereas NO₂ exposure was primarily linked to elevated blood glucose and triglyceride levels. MetS patients exhibited significant alterations in urinary metabolic profiles, with more than 113 differentially abundant metabolites identified. Notably, leukotrienes were positively correlated with NO₂ exposure, while acylcarnitines were associated with CD exposure. Pathway enrichment analysis indicated significant disturbances in pyrimidine and arachidonic acid metabolism. These findings provide important evidence for developing comprehensive occupational health strategies in mining environments. Full article

To address the issue where information asymmetry in third-party logistics (3PL)-led low-carbon supply chain coordination undermines coordination efficiency, thereby threatening the sustainability and resilience of supply chain cooperation, this paper develops a Stackelberg dynamic game model with the 3PL as the leader. This model is constructed within the context where consumers’ low-carbon preferences influence product demand, and a government carbon cap policy is implemented. By comparing decentralized and centralized equilibria, we verify that centralized collaboration achieves dual gains: higher carbon reduction levels and greater overall supply chain profits, which strengthens sustainability and resilience. To address efficiency losses from three types of information asymmetry, we propose a two-stage dynamic coordination mechanism adapted to evolving cooperation transparency. At the initial stage with opaque information, a bargaining-power-weighted profit-sharing contract is adopted, where negotiation weights are quantified by enterprise scale, resource control and industry influence. After data transparency improves, the system switches to a Nash bargaining framework supported by blockchain carbon data sharing to realize stable long-term collaboration. Numerical cases and sensitivity analysis demonstrate that manufacturer cost information asymmetry is the primary constraint on coordination efficiency. The proposed dynamic coordination scheme effectively mitigates systemic complexity, balancing economic benefits and carbon reduction targets. This study provides practical pathways for supply chain participants to navigate complex low-carbon environments and advance sustainable, resilient supply chain operation. Full article

Global warming poses a serious environmental challenge worldwide. Agriculture, as a significant source of greenhouse gas (GHG) emissions, exerts considerable influence on the atmospheric environment. Chengdu, renowned for its thriving agricultural sector, serves as a key grain production center in China. Reducing agricultural greenhouse gas (AGHG) emissions is essential for mitigating the impact of climate change on Chengdu. Firstly, this paper employed the IPCC (Intergovernmental Panel on Climate Change) coefficient method and the Super-SBM-Undesired model to calculate the AGHG emissions and emission efficiency in Chengdu, respectively. Then, center of gravity shift analysis, kernel density estimation and spatial autocorrelation theory were used to analyze the spatial–temporal evolution characteristics of AGHG emissions. Finally, this paper conducted an in-depth analysis based on the STIRPAT model to identify key factors affecting AGHG emissions. The results show that: (1) From 2007 to 2021, Chengdu experienced an overall decline in both AGHG emissions and emission intensity, with reductions of 22.32% and 66.20%, respectively. And the AGHG emission efficiency was largely low. (2) AGHG emissions display regional variations and spatial clustering phenomena, characterized by a pattern of “high in the east, low in the west, high outside and low inside”. (3) AGHG emissions are highly increased by the sown area (S) and pesticide and fertilizer utilization (F) and may be reduced by the agricultural industrial structure (V) and the urbanization rate (U). These findings provide valuable scientific insights into the spatial–temporal dynamics of regional agricultural emissions. Furthermore, this study offers practical references for local governments to optimize agricultural resource allocation, formulate tailored low-carbon agricultural policies, and promote sustainable rural development. Full article

Corrosion of steel reinforcement is a major cause of deterioration in reinforced concrete (RC) structures exposed to aggressive environments. Although fiber-reinforced polymer (FRP) reinforcement provides excellent corrosion resistance, its confinement effectiveness in RC columns has not been fully understood. This study experimentally investigated the axial compressive behavior of rectangular RC short columns reinforced with steel, carbon fiber-reinforced polymer (CFRP), and glass fiber-reinforced polymer (GFRP) bars. Ten specimens with different reinforcement types and stirrup configurations were tested under monotonic axial compression to evaluate compressive strength, axial strain response, deformation behavior, failure mechanisms, and confinement performance. The results indicated that the contribution of FRP reinforcement depended on the reinforcement configuration and confinement mechanism. Specimens reinforced with CFRP longitudinal bars exhibited higher axial capacity than the steel-reinforced control specimen within the tested configurations; however, the influence of the longitudinal reinforcement ratio should also be considered. GFRP stirrups exhibited confinement behavior comparable to CFRP stirrups, whereas CFRP stirrups experienced premature fracture at bent corner regions, which reduced their confinement effectiveness and deformation capacity. Reducing stirrup spacing from 150 mm to 75 mm provided limited improvement in compressive strength because of premature stirrup failure and insufficient development of confinement effects. Existing confinement models tended to overestimate the post-peak response of FRP-reinforced columns. These preliminary findings provide experimental insights into the confinement behavior of FRP-reinforced concrete columns and contribute to the development of improved analytical models. Full article

High-resolution mapping of urban surface CO₂ is essential for refined carbon monitoring, emission management, and low-carbon urban planning. Mobile monitoring provides dense street-level observations, but raw CO₂ measurements are often affected by transient traffic disturbances, vehicle idling, and localized plume events, which limits their direct use as stable spatial mapping targets. This study developed an integrated framework for predicting, mapping, and interpreting stable surface CO₂ patterns in Shenzhen by combining vehicle mobile observations, CSF processing, multiscale remote sensing predictors, machine learning. A CSF-based lower-envelope filter was used to suppress short-duration positive peaks and extract a more stable CO₂ accumulation signal from mobile observations. Multiscale predictors representing transportation, urban activity, surface environment, and built form were constructed to characterize both local and surrounding urban contexts. Compared with raw CO₂, the CSF-processed target substantially improved prediction performance. The best validation R² across the candidate models increased from 0.59 to 0.90 in April and from 0.62 to 0.93 in November. The predicted maps identified persistent high-CO₂ areas in central and southwestern Shenzhen. SHAP results showed that transport networks and urban activity reinforced surface CO₂ accumulation, whereas vegetation and open-surface contexts weakened accumulation at broader spatial ranges. These findings provide an interpretable framework for high-resolution urban CO₂ mapping and refined low-carbon governance. Full article

Mangrove canopy height (MCH) is a fundamental structural variable for monitoring ecosystem health and quantifying carbon stocks. However, MCH retrieval from optical satellite imagery is often constrained by spectral saturation in dense stands and environmental noise in intertidal zones. This study investigates the utility of kernel-based spectral features (KVIs) as non-linear topological enhancements for MCH estimation by integrating GEDI spaceborne LiDAR with Sentinel-2 and Sentinel-1 data across three mangrove ecosystems along the South China coast. Utilizing four regression models under spatial cross-validation, we evaluated the performance of traditional indices, KVIs, and integrated feature sets against GEDI reference measurements. Results indicate that traditional optical indices exhibit limited linear sensitivity to MCH. Rather than serving as universal accuracy boosters, KVIs function as non-linear stabilizers by redistributing spectral values in Hilbert space, which effectively enhances feature representation in high biomass stands and mitigates background noise. Furthermore, model comparisons reveal that while traditional indices yield competitive baseline accuracy in specific architectures (e.g., 1D-CNN), kernel-based features provide nuanced advantages in spatial stability and ensemble dispersion reduction. Ultimately, this study demonstrates that kernel-based features enhance model robustness under rigorous cross-validation, providing a reliable structural foundation for large-scale ecological monitoring and regional carbon dynamics assessments in heterogeneous coastal environments. Full article

Low-level radioactive wastewater requires effective treatment to minimize risks to human health and the environment. This study evaluated the performance of activated carbons derived from regional biomass (Gurgurey, Pine, and Debregeasia woods) and Thar coal for the simultaneous removal of radionuclides (¹³⁷Cs, ¹³⁴Cs, ¹²⁴Sb, and ¹²²Sb) from real low-level radioactive wastewater. The activated carbons were characterized by proximate analysis and Fourier Transform Infrared Spectroscopy (FTIR), and their adsorption performance was investigated through batch experiments followed by kinetic and isotherm modeling. Among the investigated materials, activated carbons derived from Gurgurey wood, Pine wood, and Thar coal exhibited superior physicochemical properties, including low moisture (3.8–4.8%), low volatile matter (10.7–15.3%), high fixed carbon content (77.8–84.3%), and adsorption capacities ranging from 205.6 to 251.9 Bq g⁻¹. In contrast, Debregeasia-derived activated carbon showed the poorest performance owing to its high moisture, volatile matter, and ash contents, resulting in adsorption capacities of only 53.4–161.2 Bq g⁻¹. FTIR analysis confirmed the presence of phosphate-containing functional groups on all biomass-derived activated carbons except Thar coal. The adsorption data were best described by the pseudo-second-order kinetic model and the Langmuir isotherm, indicating that chemisorption and monolayer adsorption were the dominant removal mechanisms. These findings demonstrate that activated carbons derived from Gurgurey wood, Pine wood, and Thar coal are promising low-cost adsorbents for the simultaneous removal of multiple radionuclides from real low-level radioactive wastewater and provide a sustainable approach for radioactive wastewater treatment. Full article

Amid the low-carbon transition, university teaching buildings feature high occupancy and energy use, making the synergistic enhancement of their daylighting and thermal environments crucial for “dual carbon” goals. However, traditional “north–south homogenization” designs in cold regions fail to address the spatial heterogeneity of solar radiation and climate resources, intensifying the trade-off between natural daylighting and Heating Energy Use Intensity (E_h) while restricting space performance optimization. Focusing on a typical cold-region teaching building, this study proposes a “parametric modeling–multi-objective optimization–machine learning” integrated framework. Targeting spatial daylight autonomy (sDA), useful daylight illuminance (UDI), and E_h, we compared the homogeneous baseline model with the Pareto-optimal solution set, demarcated key design parameter boundaries, and developed an ensemble-based rapid prediction model. Based on the parametric simulation analysis of this representative case building in a cold region, results indicate that: (1) Compared to the baseline, the overall optimal scheme reduced E_h by 17.43% while increasing UDI and sDA by 12.0% and 10.5%, respectively. (2) The Pareto set strictly converges toward a due-south orientation and a “deep-south, shallow-north” layout (depth ratio: 0.66–0.77); thermal configurations exhibit “enhanced northern insulation and southern heat gain,” confirming heterogeneous design matches cold climates better. (3) The four constructed machine learning models (MLP, LightGBM, XGBoost, and Random Forest) uniformly achieved test recall rates exceeding 99%, enabling highly precise, rapid classification of top-performing design scenarios during early-stage design. This study overcomes climate-matching blindness in traditional design, providing a multi-objective synergistic optimization path balancing low energy and high-quality daylighting with substantial engineering and theoretical value. Full article

Forest canopy height is critical for quantifying terrestrial carbon stocks, assessing ecosystem productivity, and supporting biogeochemical modeling. Spaceborne LiDAR missions (e.g., GEDI and ICESat-2) enable forest canopy height mapping from regional to global scales, but they differ substantially in spatial coverage and observation mechanisms, and their retrievals are subject to systematic biases that vary with complex environmental conditions. Using airborne LiDAR-derived canopy heights as the reference, we validated the performance of spaceborne LiDAR canopy height retrievals and analyzed the spatial distribution of retrieval residuals across environmental factors. We then constructed an XGBoost-based multi-source data fusion correction model for canopy height that accounts for the differential effects of environmental factors. Using Genhe as the study area, we found that spaceborne LiDAR-derived forest canopy heights are systematically underestimated before correction (GEDI: −2.17 m; ICESat-2: −2.41 m), with biases varying markedly across environmental factors. After correction, biases drop to 0.00 m and +0.02 m, respectively. The multi-source fusion model achieves an RMSE of 2.58 m and a correlation coefficient of 0.766 against airborne LiDAR references, significantly outperforming single-source corrected results. These findings verify the effectiveness of the proposed multi-source correction framework in heterogeneous environments. Full article

In today’s volatile global environment, designing sustainable and resilient supply chain networks is essential for balancing economic efficiency, environmental responsibility, and social equity. This study presents a multi-objective mathematical model for sustainable supply chain network design under facility disruption risks and demand uncertainty. A fuzzy robust optimization approach, incorporating triangular fuzzy numbers, is employed to handle uncertain demand while balancing model optimality and feasibility. The proposed network includes production centers, disruption-prone retailers, and customers, addressing both strategic retailer selection and tactical product allocation. The model optimizes three core sustainability objectives: minimizing total operational costs, reducing carbon emissions, and mitigating product shortages. Small-scale instances (five test problems) are validated using the exact $ϵ$-constraint method, while larger-scale problems are solved using three multi-objective metaheuristic algorithms: NSGA-II, MOPSO, and MOEA/D. A comparative analysis based on standard performance metrics and supported by Analysis of Variance (ANOVA) indicates that while MOEA/D offers superior computational speed, MOPSO and NSGA-II exhibit higher solution quality and diversity, with MOPSO demonstrating an overall well-balanced performance. Furthermore, comprehensive sensitivity analyses highlight the model’s responsiveness to disruption probabilities, warehouse capacities, product perishability rates, and demand fluctuations. The results demonstrate that the proposed approach effectively reduces costs and shortages while maintaining environmental targets, providing decision-makers with a practical and scalable framework for resilient supply chain design under real-world uncertainties. Full article

Background/Objectives: High linear energy transfer (LET) carbon ions achieved more effective and biologically robust tumor control than photons in preclinical prostate cancer models; however, the longitudinal development of histopathological parameters remains insufficiently characterized, limiting the selection of the optimal treatment modality in patients. This study analyzed the temporal histological patterns after isoeffective photon, proton, and carbon ion irradiations. Methods: Two Dunning R3327 prostate carcinoma sublines (H, HI) grown subcutaneously in male Copenhagen rats received single-fraction isoeffective curative photon or carbon ion doses. For HI-tumors, the effectiveness of isoeffective curative proton doses and isoeffective subcurative photon and carbon ion doses was additionally investigated. Tumors were collected prior and up to 3 weeks after irradiation and processed for quantitative histology of proliferation (BrdU), DNA damage (γH2AX), hypoxia (pimonidazole), vascular (CD31), and immune cell (CD3, CD68) markers. Results: All modalities induced an early peak in γH2AX⁺ tumor cells and a pronounced suppression of BrdU⁺ cells, with more sustained effects after isoeffective carbon ions doses, particularly in the HI-tumors. These findings, however, differed strongly between hypoxic and oxic micro-environments. Vascular parameters, diffusion distances, and global and compartment-specific hypoxic fractions showed distinct temporal dynamics between photons and carbon ions in HI-tumors, whereas H-tumors exhibited more moderate and reversible changes. At curative carbon ion doses, there was a late rebound of BrdU-positive tumor cells and increased CD68⁺ macrophage accumulation in chronically hypoxic regions. CD3⁺ T cells showed a biphasic decrease-recovery pattern in HI-tumors largely independent of radiation quality and oxygenation. Conclusions: Longitudinal histology revealed modality- and tumor-line-specific trajectories of tumor, vascular, hypoxic, and immune responses after isoeffective photon, proton, and carbon ion irradiations in prostate carcinoma. The more persistent tumor cell damage and distinct vascular response, together with late proliferative and macrophage rebounds under chronic hypoxia after carbon ions, provide mechanistic support for the increased biological effectiveness and highlight hypoxia-driven repopulation and inflammation as key processes. Full article

The ongoing European energy transition and the restructuring of energy markets are increasing pressure on energy-intensive industries to reduce greenhouse gas emissions while maintaining secure and reliable energy supplies. In this context, local geothermal resources represent a promising low-carbon energy source for strengthening industrial energy resilience. This study investigates the geothermal potential of a heavy industrial area located at the northwestern margin of the Bohemian Massif (Czech Republic), where elevated heat flow is associated with the interaction of Variscan structures, post-orogenic magmatism, and Cenozoic rifting. Due to safety restrictions related to industrial infrastructure, including pipelines and zones with explosive substances, a combination of controlled-source magnetotellurics and gravimetric surveying was applied. Magnetotelluric measurements conducted in the frequency range of 8–8192 Hz identified a major fault-controlled boundary between the crystalline basement of the Saxothuringian Zone and the Neogene sedimentary fill of the Most Basin. Gravimetric modeling revealed a pronounced negative residual Bouguer anomaly that can be explained by variations in sediment thickness and/or the presence of a low-density body within the crystalline basement. The integrated interpretation of geophysical data, regional heat-flow distribution, and existing geological knowledge suggests that this low-density body may be associated with granite porphyry intrusions of the Altenberg–Teplice Collapse Caldera. The results indicate that the northwestern Bohemian Massif contains geological structures favourable for geothermal exploration and demonstrate the applicability of low-impact geophysical methods in complex industrial environments. The study provides a scientific basis for future geothermal development aimed at supporting industrial decarbonization, regional energy resilience, and long-term energy security. Full article