Search Results (11,163)

Cork oak is a strong emitter of volatiles, namely monoterpenes, which are important precursors of secondary air pollutants. Past studies have revealed distinct chemotypes in emitting as well as non-emitting individuals. Promoting non-emitters in afforestation and urban greening could improve air quality, but their rarity suggests that they are less resilient. To gain insight into this, we screened natural descendants from two non-emitting cork oaks for emissions and ecophysiological traits (CO₂/H₂O-gas exchange variables, budburst date, growth) and tested whether emitting and non-emitting descendants differ in their resistance to temperature and light fluctuations (sun-flecks). Both half-sib populations were composed of the same chemotypes in similar frequencies, comprising 32% of non-emitters and 50 and 18% of two emitting chemotypes with overall moderate emission rates. Based on this distribution, we identified an inheritance mode and compared it with the chemotype frequency of the mother population. In terms of ecophysiological traits, all chemotypes performed similarly, and non-emitters were as resistant to sun-flecks as emitters. We conclude that the chemotypes in emitters reflect a common polymorphism in monoterpene-emitting plants that is not related to adaptive selection. We also conclude that non-emission is heritable and that its phenotype should be evaluated in reforestation studies. Full article

The accelerating global demand for sustainable energy, driven by population growth, industrialization, and environmental concerns, has intensified the search for renewable alternatives to fossil fuels. Biofuels, including bioethanol, biodiesel, biogas, and biohydrogen, offer a viable and practical pathway to reducing net carbon dioxide (CO₂) emissions. Yet, their large-scale production remains constrained by biomass recalcitrance, high pretreatment costs, and the enzyme-intensive nature of conversion processes. Recent advances in enzyme immobilization using magnetic nanoparticles (MNPs), covalent organic frameworks, metal–organic frameworks, and biochar have significantly improved enzyme stability, recyclability, and catalytic efficiency. Complementary strategies such as cross-linked enzyme aggregates, carrier-free immobilization, and site-specific attachment further reduce enzyme leaching and operational costs, particularly in lipase-mediated biodiesel synthesis. In addition to biocatalysis, nanozymes—nanomaterials exhibiting enzyme-like activity—are emerging as robust co-catalysts for biomass degradation and upgrading, although challenges in selectivity and environmental safety persist. Green synthesis approaches employing plant extracts, microbes, and agro-industrial wastes are increasingly adopted to produce eco-friendly nanomaterials and bio-derived supports aligned with circular economy principles. These functionalized materials have demonstrated promising performance in esterification, transesterification, and catalytic routes for biohydrogen generation. Technoeconomic and lifecycle assessments emphasize the need to balance catalyst complexity with environmental and economic sustainability. Multifunctional catalysts, process intensification strategies, and engineered thermostable enzymes are improving productivity. Looking forward, pilot-scale validation of green-synthesized nano- and biomaterials, coupled with appropriate regulatory frameworks, will be critical for real-world deployment. Full article

As demand for rare earth elements (REEs) rises and environmental concerns about the extraction of primary resources grow, biological methods for removing these elements have gained significant attention as eco-friendly alternatives. This study assessed the ability of the green macroalga Ulva lactuca to remove europium (Eu) from aqueous solutions, evaluated the cellular partition of this element and investigated the toxicological effects of Eu exposure on its biochemical performance. U. lactuca was exposed to variable concentrations of Eu (ranging from 0.5 to 50 mg/L), and the amount of Eu in both the solution and algal biomass was analyzed after 72 h. The results showed that U. lactuca successfully removed 85 to 95% of Eu at low exposure concentrations (0.5–5.0 mg/L), with removal efficiencies of 75% and 47% at 10 and 50 mg/L, respectively. Europium accumulated in algal biomass in a concentration-dependent manner, reaching up to 22 mg/g dry weight (DW) at 50 mg/L. The distribution of Eu between extracellular and intracellular fractions of U. lactuca demonstrated that at higher concentrations (5.0–50 mg/L), 93–97% of Eu remained bound to the extracellular fraction, whereas intracellular uptake accounted for approximately 20% at the lowest concentration (0.5 mg/L). Biochemical analyses showed significant modulation of antioxidant defenses. Superoxide dismutase activity increased at 10 and 50 mg/L, while catalase and glutathione peroxidase activities were enhanced at lower concentrations (0.5–1.0 mg/L) and inhibited at higher exposures. Lipid peroxidation levels remained similar to controls at most concentrations, with no evidence of severe membrane damage except at the highest Eu level. Overall, the results demonstrate that U. lactuca is an efficient and resilient biological system for Eu removal, combining high sorption capacity with controlled biochemical responses. These findings highlight its potential application in environmentally sustainable remediation strategies for REE-contaminated waters, while also providing insights into Eu toxicity and cellular partitioning mechanisms in marine macroalgae. Full article

In the context of the ongoing digital revolution in manufacturing and the simultaneous advancement toward dual carbon objectives, this study investigates the role of intelligent technological advancements, particularly industrial robotics, in improving firm-level energy efficiency. Utilizing panel data from Chinese listed companies spanning the period 2012–2023, the research assesses the relationship between exposure to industrial robots and corporate energy efficiency metrics. The empirical analysis demonstrates that greater exposure to industry-level robotization substantially boosts corporate energy performance, verifying that intelligent modernization and green transition can be mutually reinforcing. This positive effect is particularly pronounced among superstar firms, in more competitive industries, and for capital-intensive enterprises. Mechanism analysis reveals that, first, robotization processes generate a scale effect that effectively dilutes the fixed energy consumption per unit of product. Second, the diffusion of robots intensifies market competition, creating a competition effect that compels all firms within the industry to optimize costs and management with a focus on energy conservation. This study demonstrates that enhancing human capital within organizations significantly amplifies the beneficial impact of robotic integration on energy efficiency metrics. By providing empirical data from an emerging market context, this research not only elucidates the role of industrial robots but also offers policy-relevant insights for developed economies navigating the concurrent challenges of industrial modernization and environmental sustainability. Full article

An innovative dispersive liquid–liquid microextraction technique utilizing a solid dispersion was established for the quantification of triazine herbicides in environmental water samples. Naturally derived monoterpenoids were utilized as eco-friendly extraction solvents, markedly decreasing the reliance on harmful extraction solvents. A small amount of Pop Rocks candy served as a solid dispersant; the rapid release of carbon dioxide promoted the generation of fine monoterpenoid droplets, effectively replacing conventional hazardous liquid dispersants. The solidification technique of floating organic droplets facilitated the effective phase separation of monoterpenoids from aqueous samples, thereby obviating the need for centrifugation. Triazine herbicides exhibited good linearity within the concentration range of 0.008–0.8 mg/L with correlation coefficients above 0.99 and detection limits of 0.002 mg/L. The proposed method was effectively implemented on surface and groundwater samples, attaining recoveries between 86.4% and 98.0%. Molecular docking analysis suggests a spontaneous binding between the monoterpenoid and triazine herbicides. A comprehensive green assessment utilizing two evaluation tools confirmed the excellent environmental performance of the method. This technique offers superior greenness and simplicity compared with conventional techniques, demonstrating strong potential for application in the environmental analysis of pesticide residues. Full article

This study investigates how financial growth connects to regional environmental performance within the framework of policies aimed at reducing carbon emissions. It uses a comprehensive panel dataset covering the period from 2010 to 2024. Although Kazakhstan has set ambitious targets, significant differences in financing levels and institutional development across regions pose substantial obstacles to achieving the target emissions reductions. Employing regional panel data, we use a random-effects model to assess links among banking loans, governmental funding metrics, employment statistics, and pollution measurements. Principal component analysis is utilized to tackle potential collinearity and reveal fundamental patterns. This approach reflects the inherent differences between regions rather than evolutionary shifts. The obtained empirical data demonstrate a significant relationship between high levels of bank loans and reduced carbon emissions. Regions with better access to financial services are better positioned to invest in energy efficiency, green infrastructure, and green innovation. Conversely, increases in regional budgets are associated with rising emissions, as tax revenue growth primarily comes from industries most dependent on fossil fuels. Dependence on the national budget for subsidies exacerbates distortions in regional budgets’ relationship with the regions’ transition to low-carbon development. The findings confirm the importance of regional financial management in determining the path to reducing greenhouse gas emissions. Based on this, it is proposed to transform the mechanism of interbudgetary relations to grant regions greater financial autonomy and to localize credit resources at the regional level to accelerate the transition to a low-carbon economy in Kazakhstan. Full article

Corrosion in complex acid environments, such as high temperatures and acidic downhole conditions, remains a critical threat to well integrity during oil and gas acidizing. This review firstly examines the influence of downhole variables, including temperature, acidity, and steel, on the performance of organic inhibitors. It analyzes molecular design strategies that enhance the stability and adsorption of traditional inhibitor classes, including Mannich Bases, quaternary ammonium salts, and benzimidazoles, through structural modifications such as rigid heterocycles, extended alkyl chains, and multi-dentate architectures. The discussion extends to synergistic formulations, sustainable alternatives derived from biopolymers or green chemistry, and intelligent responsive systems. Furthermore, the growing role of computational methods, from molecular dynamics simulations to AI-driven molecular design, in accelerating the discovery of high-performance inhibitors is highlighted. Together, these advances offer a comprehensive and forward-looking perspective on developing adaptive, efficient, and environmentally compatible corrosion protection strategies for next-generation hydrocarbon extraction. Full article

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with excessive fertilizer use. Variable-rate application (VRA) offers a precision-based approach to matching N inputs with crop demand, yet winter wheat responses to reduced N rates are still underexplored. This study evaluated winter wheat (Triticum aestivum L.) performance under variable and uniform N application strategies using canopy greenness (NDVI), grain yield, plant N content, nitrogen use efficiency (NUE), and fertilizer costs as indicators. Reduced N treatments (40% and 60% VRA rates) were compared with a uniform (100%) application. Canopy greenness increased across all treatments over time, with NDVI values ranging from 0.855 early in the season to approximately 0.94 at later growth stages, and statistically significant among N rates (p < 0.05). Grain yield was highest under the low N rate (1676.81 kg ha⁻¹), although yield differences among treatments were not statistically significant (p > 0.05). Similarly, plant N content varied slightly across treatments, ranging from 1.73% to 1.82%, with no significant differences. In contrast, NUE declined sharply with increasing N rates, decreasing from 71% under the lower rate to 28% under the uniform rate. Overall, variable-rate treatments used just over half the fertilizer input and cost of the uniform rate while supporting comparable yield and plant N status. These results prove that VRA can improve nitrogen efficiency and reduce input costs without compromising winter wheat productivity, supporting its practical value for sustainable fertilizer management. Full article

This paper presents a microstructural, mineralogical, and mechanical study of low-carbon autoclaved concrete (AC), achieved by partially or fully replacing ordinary Portland cement (OPC) with ground-granulated blast furnace slag (BFS) and substituting lime with calcium carbide slag (CCS). Fourteen mixes were produced and evaluated in the green state and after autoclaving. Quantitative X-ray diffraction (XRD) using the Rietveld method, density, compressive strength, and life cycle assessment (LCA) were conducted. Results show that mixes containing BFS achieve green strengths equal to or higher than the OPC reference, ensuring integrity during autoclaving. Using BFS with an adequate calcium supply promotes the formation of pre-autoclave portlandite, which in turn favors tobermorite development and yields post-autoclave strengths comparable to the OPC reference. Partial lime replacement with CCS (50%) maintains mineralogy and strength, whereas excessive CCS may reduce available portlandite and lower strength. Life-cycle assessment indicates that raw material supply dominates emissions and that removing OPC cuts total CO₂ by 44% without compromising mechanical performance. These findings demonstrate the feasibility of OPC-lean/OPC-free, lime-optimized autoclaved concretes with substantially lower embodied impacts. Full article

Monoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application. In this study, a combinatorial protein engineering strategy was employed to overcome these limitations. We integrated fusion protein technology with rational design to enhance both the functional expression and catalytic efficiency of AOL. Firstly, the superfolder green fluorescent protein (sfGFP) was fused to the N-terminus of AOL. The results indicated that the sfGFP fusion tag significantly improved the solubility and stability of the enzyme, preventing the formation of inclusion bodies. The fusion protein sfGFP-AOL exhibited a MAG conversion rate of approximately 65%, confirming the positive impact of the fusion tag on enzyme developability. To further boost catalytic performance, site-directed mutagenesis was performed based on structural analysis. Among the variants, the mutant sfGFP-Y92Q emerged as the most potent candidate. In the MAG conversion, sfGFP-Y92Q achieved a conversion rate of 98%, which was not only significantly higher than that of sfGFP-AOL but also outperformed the widely used commercial immobilized lipase, Novozym 435 (~54%). Structural modeling and docking analysis revealed that the Y92Q mutation optimized the geometry of the active site. The substitution of Tyrosine with Glutamine at position 92 likely enlarged the substrate-binding pocket and altered the local electrostatic environment, thereby relieving steric hindrance and facilitating the access of the bulky MAG substrate to the catalytic center. In conclusion, this work demonstrates that the synergistic application of sfGFP fusion and rational point mutation (Y92Q) can dramatically transform the catalytic properties of AOL. The engineered sfGFP-Y92Q variant serves as a robust and highly efficient biocatalyst for MAG degradation. Its superior performance compared to commercial standards suggests immense potential for cost-effective applications in the bio-manufacturing of high-purity fatty acids and biodiesel, offering a greener alternative to traditional chemical processes. Full article

Proteins from the bran of Khao Dawk Mali 105 rice at two maturity stages, green (GB) and fully ripe (RB), were extracted using single and sequential enzyme-assisted processes. Non-enzymatic extraction (control), α-amylase (AA), protease (PT), and two sequential treatments (AA-PT and PT-AA) were applied to defatted bran to evaluate their effects on protein yield, structural attributes, and functional properties. Protease-based extractions, particularly PT, produced the highest protein contents (28% in GB and 23% in RB) and significantly improved solubility, water- and oil-holding capacities, and foaming performance. GB extracts consistently outperformed RB across all functional and antioxidant measurements, indicating greater extractability and bioactive potential in green rice bran. Enzymatic hydrolysis also enhanced phenolic and flavonoid release, leading to markedly higher DPPH and FRAP activities. SDS-PAGE profiles demonstrated reduced band complexity and lower-molecular-weight protein in enzymatically treated samples, while FTIR spectra confirmed secondary structural modifications associated with hydrolysis. Overall, protease and sequential assisted extractions provide an efficient and sustainable approach to improving rice bran protein recovery and functionality. These findings highlight green rice bran as a promising source of high-value plant proteins for food and nutraceutical applications. Full article

Urban heat islands are among the most intense and unequal climate impacts in Mediterranean cities, with direct effects on health, thermal comfort, and habitability. This reality calls for the incorporation of binding and verifiable climate criteria into spatial planning and urban planning law. This article examines the extent to which the Spanish legal framework—at national, regional, and municipal levels—incorporates measurable standards to mitigate urban heat islands and how it might evolve towards operational climate-responsive urbanism. A legal–analytical and comparative methodology is applied, based on multilevel normative content analysis and a comparison of four autonomous communities, four Spanish cities, and four international reference cases with consolidated metrics. The results show that, despite progress in recognising adaptation, territorial asymmetries persist, enforceable parameters remain scarce, and there is a prevailing reliance on strategic or voluntary instruments. In response to these gaps, the study proposes a coherent set of urban climate standards (urban vegetation, functional soil permeability, roof albedo/cool roofs, green roofs and façades, plot-scale performance indices, urban ventilation, and thermal diagnostics) and a multilevel integration model aimed at guiding legislative reforms and strengthening cities’ adaptive capacity and thermal equity. Full article

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets. Full article

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO₂ savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting. Full article

Corporate Social Responsibility (CSR) has evolved into a strategic governance framework through which organisations address environmental sustainability, stakeholder expectations, and long-term institutional viability. In knowledge-intensive organisations such as universities, Green Artificial Intelligence (GAI) is increasingly recognised as an internal CSR agenda. GAI can reduce digital and energy-related environmental impacts while enhancing educational and operational performance. This study examines how higher education leaders, as organisational decision-makers, form intentions to adopt GAI within institutional CSR and digital sustainability strategies. It focuses specifically on leadership intentions to implement key GAI practices, including Smart Energy Management Systems, Energy-Efficient Machine Learning models, Virtual and Remote Laboratories, and AI-powered sustainability dashboards. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates how performance expectancy, effort expectancy, social influence, and facilitating conditions shape behavioural intentions to adopt GAI. Survey data were collected from higher education leaders across Saudi universities, representing diverse national and cultural backgrounds within a shared institutional context. The findings indicate that facilitating conditions, performance expectancy, and social influence significantly influence adoption intentions, whereas effort expectancy does not. Gender and cultural context also moderate several adoption pathways. Generally, the results demonstrate that adopting GAI in universities constitutes a governance-level CSR decision rather than a purely technical choice. This study advances CSR and digital sustainability research by positioning GAI as a strategic tool for responsible digital transformation and by offering actionable insights for higher education leaders and policymakers. Full article