Search Results (12,925)

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

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

Sunset Yellow is a water-soluble synthetic dye resistant to degradation and stable under various conditions, posing an environmental challenge. In the present study pure chitosan hydrogel (PCH) films were synthesized, followed by the assessment of sorption capacity and recyclability compared to chitosan-based films doped with niobium oxide (CHN) or activated carbon (CHC). The aim was to promote the application of sorption methods for Sunset Yellow dye using these films as a treatment option for the pollutant, with the analysis of the effectiveness of the method and its behavior using adsorption kinetic models and thermodynamic analysis. Equilibrium was reached at 240 min for all films tested, with the adsorbed amounts ranging from 18.58 to 18.79 mg g⁻¹ at 30 °C, when the highest kinetic rate constants were observed. The pseudo-first-order kinetic model best described the experimental data, with the lowest Bayesian information criterion, Akaike information criterion, and mean absolute error values. Thermodynamic analysis indicated a spontaneous, exothermic process, with interactions ranging from electrostatic interactions in CHC and PCH to physisorption in CHN. Recycling tests showed 80% efficiency after the third cycle for all three films. These findings highlight the potential of chitosan-based films as an efficient option for removing Sunset Yellow dye from water, thus improving water quality and enhancing wastewater treatment. Full article

Although amine-based post-combustion carbon capture is among the most established routes for CO₂ capture, it suffers from the high energy demand associated with amine regeneration. Recent research proposals suggest that microwave or frequency-tuned infrared heating may lead to more efficient amine regeneration processes. However, such approaches inherently introduce oscillating electromagnetic fields whose non-thermal effects on reaction pathways and energetics remain poorly understood. In this series paper, we employ high-accuracy quantum computational chemistry calculations to quantify the non-thermal effects of external electric fields on CO₂ absorption and desorption in monoethanolamine (MEA) and triethanolamine (TEA) under both aqueous and non-aqueous conditions. In this first part, we focus on static electric fields in order to establish a mechanistic reference framework helpful for interpreting non-thermal effects arising from frequency-tuned infrared laser excitation, which are addressed in Part II of this series. Our results show that static electric fields stabilize CO₂–amine reaction products, lowering absorption barriers, while consistently increasing both activation energies and reaction enthalpies associated with the amine regeneration process. This effect is particularly pronounced for MEA, where carbamate species become progressively more resistant to conversion to zwitterion as the field strength increases. These findings demonstrate that non-thermal static electric field effects counter the fundamental requirement for low-energy amine regeneration. By defining this intrinsic mechanistic limitation, the present study provides a useful baseline for assessing infrared laser-assisted carbon capture and underscores the importance of carefully selecting excitation frequencies to avoid adverse non-thermal stabilization effects. 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

Estimating forest aboveground biomass (AGB) in mountainous forest ecosystems remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing-based AGB estimation framework integrating intelligent optimization and machine learning was developed for Mount Tai in eastern China. Sentinel-2 multispectral data were selected to derive 105 candidate variables, including spectral bands, vegetation indices, texture features, and topographic factors, from which 17 key variables were selected using Pearson correlation analysis for model construction. A Support Vector Machine (SVM) optimized by the Pigeon-inspired optimization (PIO) algorithm was developed to adaptively determine optimal hyperparameters, and its performance was compared with that of Random Forest (RF) and standard SVM models. Among the three models, PIO-SVM produced the highest numerical accuracy. For the training dataset, it obtained an R² of 0.85 and an RMSE of 46.12 t/hm². For the testing dataset, it achieved an R² of 0.73 and an RMSE of 62.19 t/hm², compared with 0.72 and 66.25 t/hm² for the standard SVM model and 0.70 and 65.19 t/hm² for the RF model. The spatial distribution of AGB derived from the optimal model shows higher AGB values in the central and northern regions characterized by dense forest cover, in close agreement with field observations. Overall, the results suggest that PIO-based parameter optimization can improve SVM performance for AGB estimation in mountainous forests. This study provides a reliable and efficient framework for regional-scale monitoring of forest biomass and carbon sink dynamics. Full article

Air-to-air heat pumps are a key technology for improving energy efficiency and reducing carbon emissions in residential buildings, yet their optimal control remains challenging under real-world conditions. This study evaluates the performance of a hybrid physical–LSTM model for controlling an air-to-air heat pump in a residential building in Zadar, Croatia. The hybrid framework integrates a first-order energy balance model of the building envelope with LSTM-based temperature correction using adaptive weighting. The physical model was calibrated and validated against 52,128 real IoT measurements collected during the 2024/2025 heating season, achieving high accuracy (RMSE ≈ 0.076 °C). Rolling one-day and continuous multi-day closed-loop simulations (up to 15 days) show that the hybrid model yields slightly lower RMSE in long-term runs compared to the pure physical model. However, this apparent statistical improvement is accompanied by systematic underestimation of indoor temperature and significantly higher simulated energy consumption. The results indicate that the observed effect originates from an implicit virtual heat flux introduced by the LSTM correction, which affects thermodynamic consistency in closed-loop operation. The findings highlight that short-term error metrics such as RMSE alone are insufficient for evaluating hybrid models intended for model predictive control (MPC). The main contribution of this study is the explicit demonstration and quantification of an implicit virtual heat flux generated by the LSTM correction in closed-loop multi-day operation, which leads to misleading statistical improvements while causing significant thermodynamic inconsistency and energy overconsumption. In 15-day continuous simulations the hybrid model (ω = 0.05–0.10) caused an indoor temperature underestimation of 1.25–1.31 °C and increased simulated electricity consumption by more than 300% (316 kWh vs. 72 kWh) compared to the physical model. These results have direct implications for the development of reliable digital twins and model predictive control strategies in residential HVAC systems. 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

Renewable energy sources, such as solar and biomass, represent sustainable alternatives to meet the growing energy demands of the residential sector. This study evaluated the energy, exergy, and environmental performance of two Brayton configurations using supercritical carbon dioxide: a recompression cycle (SRC) and a recompression cycle with intercooling in the main compression (SMC), both coupled to a dual-loop organic Rankine cycle (DORC) and powered by a hybrid solar-biomass thermal system. Mass, energy, and exergy balances were developed, and a life cycle assessment was performed to quantify the environmental impact. The systems were designed to cover a cooling load of 130 kW corresponding to 200 dwellings constructed with Asbestos cement in the Colombian Caribbean region. The results show that both configurations meet the required demand; the SMC-DORC cycle operates at 650 °C, while the SRC-DORC requires 750 °C. The SRC-DORC exhibits higher thermal efficiency (53.24%), while the SMC-DORC achieves a slightly higher exergy efficiency (28.15%). Environmental analysis shows that the construction phase accounts for the majority of the total impact, exceeding 95% of emissions. Overall, both configurations are technically feasible, with the SRC-DORC standing out for its balance between efficiency and environmental impact. Full article

This work investigates the behavior of carbon dioxide (CO₂) near the surface of different single-atom alloys to evaluate their potential as catalysts for decarbonization processes. Specifically, 26 transition metals from the first three transition series, alloyed with three low Miller index copper supports, were considered. Adsorption energies and distances of linear CO₂, trigonal CO₂, and CO* + O* on the surfaces were calculated using the semiempirical computational method MOPAC-PM7. Additionally, activation energies were determined from previously published research. The proposed methodology is less computationally demanding than DFT studies, and results show good agreement with both experimental and simulated data. This approach provides a computationally efficient methodology for screening promising materials that convert CO₂ into valuable products, such as methane and methanol. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. 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

High-entropy alloys (HEAs) are typically produced using high-temperature metallurgical routes; however, alternative synthesis approaches based on wet-chemical processing remain relatively unexplored. In this study, a compositionally complex two-phase AgCuCoNiFe high-entropy alloy was synthesized using a precipitation–reduction strategy involving co-precipitation of mixed metal carbonates followed by thermal reduction in a reducing atmosphere. The objective of the work was to evaluate the feasibility of this hydrometallurgical route for preparing compositionally complex alloys and to investigate the structural evolution of the material as a function of reduction time. Quantitative MP-AES analysis confirmed efficient co-precipitation of all five elements, enabling the preparation of a precursor with near-equimolar metal composition. Structural characterization using SEM, EDS, and XRD revealed the presence of surface compositional heterogeneity in the as-reduced state, characterized by Ag-enriched domains. After controlled surface abrasion, the internal material exhibited significantly more uniform elemental distribution, although the obtained composition was not equimolar. X-ray diffraction patterns showed a transition from multiple sharp reflections at the surface to broadened peaks in the bulk, consistent with enhanced alloying within the bulk compared to the surface, while still revealing a two-phase character. Microhardness measurements indicated moderate hardness with mean values in the range of 187–221 HV with no significant dependence on reduction time, while wettability analysis revealed moderately hydrophilic behavior with contact angles in the range of approximately 75–83°. The results suggest that precipitation–reduction can be a viable alternative route for the synthesis of multicomponent HEAs, enabling the formation of chemically mixed alloy structures without the use of conventional melting-based processing. However, the obtained alloy exhibits incomplete chemical homogeneity, indicating that further optimization of the synthesis conditions is required to achieve a fully uniform composition. Full article

To address water-heat constraints and environmental risks associated with plastic film mulching in winter wheat production in the semi-arid region of Northwest China, a two-year field experiment (2021–2023) was conducted in Tongwei County, Gansu Province. A single-factor randomized block design was applied, with full plastic film mulching (PM) and bare land (CK) as controls, to evaluate the effects of 3-row (S3), 4-row (S4), and 5-row (S5) corn stalk strip mulching on soil hydrothermal conditions, active carbon fractions, and yield under rainfed conditions. Results showed that straw mulching significantly enhanced soil water retention, particularly in the 0–40 cm layer, where moisture content increased by 7.70–19.28% compared with CK (p < 0.05), with S3 performing best. Treatment S5 achieved the highest accumulated temperature and reduced the soil diurnal temperature range by 20.73–35.62% (p < 0.05). Active carbon fractions were also significantly improved, especially during the jointing–grain-filling stage (BBCH 31–87). In terms of yield, S5 exhibited the greatest increase, with a 15.88% higher two-year average grain yield than CK (p < 0.05), reaching over 90% of PM. Overall, S5 demonstrated optimal synergistic regulation of water, heat, and carbon, indicating strong potential as a sustainable alternative to plastic film mulching. Full article