Search Results (9,350)

Clay-based composite materials offer a low-carbon pathway for improving the environmental performance of the construction sector while maintaining relevance for architectural and heritage applications. A structured qualitative literature review was conducted, supported by thematic classification and exploratory bibliometric mapping (VOSviewer), based on peer-reviewed studies published between 2015 and 2025 relevant to the topic of clay minerals, stabilization, fibers, polymers, alkali activation, properties, performance, and applicability in architecture. According to the results obtained from the synthesized literature, it is seen that clay-based composites achieve performance improvement through complementary mechanisms: fiber reinforcement improves ductility, crack behavior, and energy absorption, polymer modification helps improve cohesion and water resistance and alkali activation transforms calcined aluminosilicate precursors into high-strength binding systems. The synthesis identifies three dominant performance mechanisms governing clay-based composites. Selected alkali-activated clay composite materials are reported to exhibit compression strengths higher than 60 MPa, and certain optimized systems may be able to provide lower thermal conductivity and lower levels of carbon emission in comparison with ordinary cement-based materials. The contribution of this paper lies in the synthesis of these material modification techniques and resulting performance aspects for their applicability in architecture, clarifying the potential of clay-based composites for sustainable construction, heritage compatible interventions, and future material development. By integrating material science with architectural applications, this study identifies the potential of clay-based composites for sustainable and heritage-compatible approaches to contribute to sustainable and circular construction practices, while also outlining key challenges and future research directions focused on optimization, large-scale implementation, and heritage-compatible innovation. Full article

This study examines agricultural and rural carbon emission efficiency and the underlying “low-carbon efficiency illusion” in China, where measured efficiency gains fail to translate into genuine environmental improvements. Using panel data from 30 Chinese provinces spanning 2000 to 2022, this study employs a meta-frontier slack-based measure (SBM) model to assess agricultural and rural carbon emission efficiency across meta-frontier and group-frontier benchmarks and investigates the efficiency illusion from the perspective of carbon emission reduction cost constraints. We further combine the Extreme Gradient Boosting (XGBoost) model and Shapley Additive Explanations (SHAP) explainability methods to identify core drivers of agricultural carbon emission reduction costs. We find that technical inefficiency is the primary constraint on China’s agricultural and rural carbon emission efficiency; the number of provinces with an efficiency illusion shows an initial increase followed by a decrease between 2005 and 2022; and core drivers of emission reduction costs exhibit heterogeneous impacts and significant threshold effects across the two frontier frameworks. These findings offer evidence-based guidance for designing differentiated, targeted emission reduction strategies to mitigate the efficiency illusion, advance low-carbon agricultural transition, and support the sustainable development of agricultural and rural systems in the context of the United Nations Sustainable Development Goals. Full article

This paper is based on data from 121 cities in China’s ecologically fragile regions from 2008 to 2022; it constructs an indicator system for the efficiency of pollution control and carbon reduction in agricultural practices. This system includes expenditures on agriculture, forestry, and water affairs, arable land area, agricultural laborers, total agricultural output value, agricultural carbon emissions, and agricultural non-point source pollution. It uses a super-efficiency SBM model that incorporates non-desirable outputs to measure the synergistic efficiency and analyzes its dynamic evolution using the Malmquist–Luenberger index to reveal the spatiotemporal characteristics of the synergistic efficiency. A Tobit model identifies the influence of factors, such as the level of rural economic development, crop planting structure, the strength of fiscal support for agriculture, rural education level, urbanization rate, and mechanization level on the synergistic efficiency. The results show that, from a temporal perspective, the average synergistic efficiency was only 0.58, significantly below the effective value of 1, indicating substantial room for overall improvement. Only 10 cities met the benchmark, with distinctly different reasons for compliance, while the remaining 111 cities remained inefficient. Regarding influencing factors, crop planting structure, the strength of fiscal support for agriculture, and urbanization rate significantly and positively drive efficiency; the level of rural economic development and mechanization level significantly inhibit efficiency, and rural education level shows no significant impact. These findings provide targeted policy recommendations for the synergy effect in ecologically fragile areas, as well as for low-carbon agricultural development. Full article

Carbon monoxide (CO) is a highly toxic, colorless, and odorless gas posing significant risks to human health and the environment. Catalytic oxidation offers a promising, economically viable solution by converting CO into nontoxic CO₂ under mild conditions without energy-intensive regeneration. However, existing MnO₂-based catalysts often exhibit poor activity and stability in harsh environments, particularly at low temperatures and high humidity. In this study, we propose a Cu²⁺ ion-exchange modification strategy to activate lattice oxygen species in δ-MnO₂, thereby significantly enhancing its low-temperature CO oxidation performance. Structural characterization by XRD and SEM confirms that Cu-doped δ-MnO₂ retains its original birnessite-type structure and porous morphology. ICP-OES and XPS analyses verify that Cu²⁺ ions effectively replace interlayer K⁺ ions. The resulting MnO₂-150Cu catalyst demonstrates exceptional activity, achieving 100% CO conversion at 40 °C in dry air and maintaining full conversion at 80 °C in the presence of 1.3 vol.% H₂O at a weight hourly space velocity of 150 L/g·h. H₂-TPR and O₂-TPD results confirm that Cu doping enhances the reducibility and mobility of lattice oxygen. Furthermore, in situ DRIFTS analysis reveals that the migration of active oxygen species is the rate-limiting step at low temperatures. This work provides a novel and effective strategy for activating lattice oxygen in MnO₂-based catalysts, offering a promising pathway for developing high-performance materials for low-temperature CO oxidation under practical environmental conditions. Full article

Monitoring dissolved carbon dioxide (CO₂) in aquatic and sediment systems is critical for understanding carbon cycling and climate feedback. This study develops and characterizes a compact, low-cost microbolometer-based attenuated total reflectance (ATR) mid-infrared sensor for direct dissolved CO₂ measurement in liquid and soil-water environments. The system integrates a ZnSe ATR crystal with custom suspended SiN membrane microbolometers and uses evanescent-wave absorption at 4.26 μm with a broadband LED source and computational spectral reconstruction, eliminating the need for an interferometer. Calibration shows excellent linearity (R² ≈ 0.99) over 50–1000 ppm CO₂, with a practical limit of detection (LOD) of ~26–35 ppm at 5–25 °C. UV-induced CO₂ generation from soil-water mixtures was investigated across UV wavelengths, revealing UV-C (254 nm) as optimal, producing net ΔCO₂ ≈ 339 ppm above ambient levels in 30 min. Environmental factors (temperature 5–35 °C, pH 5–11, pressure 1–1.5 ATM, dissolved organic carbon) were systematically evaluated, confirming robust sensor performance (accuracy >90%, correlation r > 0.98 with reference instrument). This sensor represents the first integration of MEMS microbolometer detectors with ATR evanescent-wave spectroscopy for liquid-phase dissolved CO₂, enabling real-time monitoring and rapid sediment organic carbon assessment in a field-deployable platform. Full article

Aligning urban morphology with carbon emission intensity categories is essential for advancing sustainable urban development and achieving dual carbon objectives. This study utilizes data from 336 Chinese cities across 2010, 2015, and 2020 to construct multi-dimensional morphological indicators. Spectral clustering categorizes cities into four distinct classes: high-emission intensity, medium-emission ecological, medium-emission developmental, and low-emission. An integrated gradient boosting framework, combined with SHAP and PDP interpretability tools, identifies key morphological drivers and their nonlinear contributions to class assignments. Results demonstrate that morphological features exert nonlinear and threshold-dependent effects on carbon emission intensity category assignments, exhibiting substantial spatial heterogeneity across urban clusters. Core drivers, such as economic density and the landscape shape index, follow distinctly different decision pathways in each category. Furthermore, morphological factors produce non-additive interactive effects that generate region-specific shifts in classification probability. Through this classification-oriented approach, the study provides policymakers with a systematic and readily interpretable reference to inform the formulation of context-specific low-carbon spatial planning strategies. Full article

This study describes the development of an electrochemical sensor for quantitatively measuring acetaminophen (ACOP) in drug tablets. The sensor design is based on the modification of glassy carbon electrode (GCE) using zinc oxide nanoparticles (ZnONPs) embedded in a naturally occurring clay matrix (Sa) functionalized with phenylalanine (Phe). To ensure that the ZnONPs are homogeneously dispersed on the clay surface, the nanocomposite was synthesized using an impregnation approach and low-temperature heat treatment. The amino acid promotes specific interactions with ACOP through hydrogen bonding and π-π stacking, acting as both a stabilizing agent and a molecular recognition moiety. FTIR, UV-Vis, XRD, and FESEM/EDX mapping were employed to fully characterize the developed material (ZnONPs-Sa/Phe). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used for the electrochemical determination of ACOP using the modified electrode GCE/ZnONPs-Sa/Phe. Parameters susceptible to affecting the sensitivity of the developed sensor were optimized, revealing that 5 µL of the suspension ZnONPs-Sa/Phe immobilized on GCE was ideal for the sensing of ACOP in a phosphate buffer solution at pH 2.0. The calibration curve obtained by plotting peak current intensity against ACOP concentration exhibited linear behavior within the concentration range between 0.02 µM and 0.28 µM, enabling determination of the limits of detection (LOD) and quantitation (LOQ) at 8.54 × 10⁻⁹ M and 2.84 × 10⁻⁸ M, respectively. The reproducibility, stability, and selectivity of the sensor were evaluated, followed by its application to the nano-sensing of ACOP in Africure and Doliprane tablets, yielding satisfactory results. The simplicity, affordability, and high analytical sensitivity of the developed sensor make this sensing platform a promising tool for pharmaceutical quality control applications. Full article

Urban agglomerations have become the dominant spatial platform of urbanization, regional coordination, and economic transformation in China. Yet whether the expansion of agglomeration scale at the urban-agglomeration level alleviates or intensifies energy use remains insufficiently understood. Extending the scale of analysis from individual cities to integrated urban agglomerations, this study investigates 64 cities in four major Chinese urban agglomerations, including Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing, over the period 2006–2023. Using panel data models, this study examines the impact of the scale agglomeration within urban agglomeration on urban energy intensity. The results show that the overall agglomeration scale generated by urban agglomeration formation significantly suppresses energy intensity while indicating a robust energy-saving effect: every 10% increase in agglomeration scale is associated with a decline of approximately 0.0893 million tons of standard coal per CNY 100 million of GDP. This finding remains stable after addressing endogeneity concerns and performing a series of robustness checks. Mechanism analyses further suggest that this effect operates primarily through talent agglomeration, technological progress, and public transportation expansion. In addition, the energy-saving effect is more pronounced in smaller cities, cities with lower administrative rank, cities with weaker factor mobility, and cities characterized by poorer air quality but stronger public environmental attention. These findings contribute to the literature on urban agglomeration and green development by showing that the agglomeration scale within urban agglomerations can generate inclusive energy-efficiency gains, especially for relatively disadvantaged cities, thereby offering important implications for spatial governance and low-carbon transition in rapidly urbanizing economies. Full article

Corn processing waste represents an abundant, renewable, and low-cost lignocellulosic resource with considerable potential for environmental remediation applications. Large quantities of residues generated during corn processing, including cobs, husks, bran, and other by-products, are produced annually and can be utilized directly as native biomass or converted through thermochemical processes into hydrochars and biochars. This systematic review provides a comparative analysis of native corn processing biomass, hydrochars produced via hydrothermal carbonization, and biochars obtained through pyrolysis, with a focus on their potential as adsorbents for the removal of organic and inorganic pollutants from soil and water systems. Particular attention is given to the influence of thermochemical conversion processes on the physicochemical properties of the materials, including surface chemistry, porosity, functional groups, and structural characteristics, which govern adsorption mechanisms such as ion exchange, electrostatic interactions, surface complexation, hydrogen bonding, and π–π interactions. Furthermore, the advantages and limitations of each material type are discussed, together with key environmental and techno-economic considerations related to their production and practical application, including indicative production costs (USD per kg of adsorbent) and cost–performance relationships in terms of adsorption capacity. By linking biomass conversion processes, material properties, and adsorption performance, this review aims to provide a comprehensive overview of corn processing waste valorization and to support the development of sustainable adsorbent materials for soil and water remediation. A total of 36 studies were included in the qualitative synthesis following PRISMA guidelines. Full article

Using solid waste as supplementary cementitious materials (SCMs) is an effective strategy for promoting low-carbon construction development. However, single or binary systems often exhibit mismatched reaction kinetics, thereby limiting their performance at high cement replacement rates. This study focuses on a novel low-carbon concrete designed based on reaction sequence coordination, containing recycled brick powder (RBP), ground granulated blast-furnace slag (GGBS), and self-combusting coal gangue (SCCG). The effects of RBP, GGBS, and SCCG on the hydration process and microstructure of the novel low-carbon concrete with different replacement levels have been studied by testing compressive strength, workability, and durability and observing microstructural changes. The results showed that an optimized ternary composition with an RBP:GGBS:SCCG ratio of 4:3:1 achieves a cement replacement level of 30% while exhibiting a 28-day compressive strength of 38.26 MPa, representing a 14.2% increase compared with plain cement mortar. Microstructural analyses indicate that this enhanced performance results from a time-dependent reaction sequence, in which GGBS contributes predominantly at early ages by supplying calcium, whereas RBP and SCCG mainly participate through delayed pozzolanic reactions and pore refinement at later ages. Consequently, the optimized ternary mortar exhibits a water absorption of 11.12% and a 27.2% reduction in electrical flux. This study aims to provide practical strategies for enhancing the performance of low-carbon cementitious materials through a reaction sequence coordination design approach, thereby improving the utilization efficiency of solid waste in the production of low-carbon building materials. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

As a core decarbonization technology, the scaling up of Near-Zero Energy Building (NZEB) technologies under the “dual carbon” goal necessitates collaboration among governments, technology suppliers, and construction enterprises. However, high research and development (R&D) costs coupled with low market acceptance impede widespread adoption. This study develops a tripartite evolutionary game model to analyze strategic interactions among stakeholders. Using MATLAB 2022B simulations, we simulate the strategy sets for the government (subsidize/no subsidy), suppliers (R&D/no R&D), and enterprises (procure/no purchase). The results identify two Evolutionary Stable Strategies (ESS): a market-driven ESS (0, 1, 1) emerges when the green premium (Pm) exceeds the incremental cost (Cb); while a policy-driven ESS (1, 1, 1) requires government subsidies (S) to offset R&D gaps, specifically when $S>Cr/\alpha -Pmz$. These findings provide a theoretical basis for understanding the synergistic mechanisms underlying NZEB adoption and highlight the dynamic interplay between policy incentives and market forces. Full article

Low-carbon energy transition (LET) has become an important global development strategy. However, in the contemporary industrial era, carbon emissions are intricately intertwined with economic growth based on the extensive use of fossil energy. To this end, the key to a more acceptable push for LET is to achieve carbon emissions decoupling (CED). The rapidly developing digital economy (DE) introduces novel possibilities for it. Using a Finite Mixture Model, this study aims to analyze how DE heterogeneously impacts CED across 66 countries from 2011 to 2022. As of 2022, 41% of countries attained strong decoupling status, 33% reached weak decoupling status. In terms of the effect of DE on CED, both chance and challenge are shown. DE exhibits dual effects: it enhances CED in high-education countries but hinders it in countries with rapid population growth. Government efficiency and gender equality amplify DE’s chance role, while natural gas or clean energy reliance weakens it. DE indirectly promotes CED via low-carbon behavior while raising risks through easier credit access. Meanwhile, the heterogeneity of institutional and economic characteristics in countries may influence the effect of DE on CED. These findings offer a theoretical foundation to reconcile economic sustainability with climate mitigation in digital transitions, providing actionable insights for policymakers to leverage DE’s potential in achieving SDG 13. Full article

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development. Full article

The deployment of grid-forming microgrids has attracted growing attention as a pathway toward improving energy system resilience and supporting low-carbon transitions in decentralized power systems. However, the relative influence of distinct stakeholder groups on microgrid development performance remains inadequately understood in the extant literature. Grounded in stakeholder theory and informed by behavioral economics, this study develops and empirically tests a stakeholder-based framework that examines the effects of government support, investor participation, user acceptance, and utility participation on microgrid development performance. Survey data were collected from 200 stakeholders engaged in microgrid-related activities and analyzed using consistent Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model accounts for a substantial proportion of the variance in microgrid development performance (R² = 0.647). The quantitative results indicate that all four stakeholder constructs exert statistically significant positive effects on microgrid development performance. Investor participation emerges as the strongest driver (β = 0.399, p < 0.001), followed by user acceptance (β = 0.190, p < 0.001), government support (β = 0.175, p = 0.015), and utility participation (β = 0.170, p = 0.003). Interpreted through a behavioral economics lens, these findings demonstrate that development performance is governed primarily by behavioral and perceptual factors, namely capital confidence, risk tolerance, and demand-side acceptance, rather than by technical preparedness alone. Conventional assumptions of linear adoption driven by technical superiority are therefore insufficient to account for observed development outcomes in complex, decentralized energy systems. This study advances a stakeholder-centered and behaviorally grounded understanding of grid-forming microgrid development and offers empirical guidance for designing governance frameworks that align regulatory structures with market and user behavioral dynamics. Full article