Search Results (9,341)

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

Printed biosensors have attracted increasing attention as platforms for rapid, low-cost, and portable diagnostics because they can be fabricated on flexible or rigid substrates using scalable printing techniques. Their performance is strongly influenced by both the printing process and the materials employed, since factors such as ink rheology, particle dispersion, interfacial behavior, and post-processing conditions directly affect device architecture, sensing performance, and manufacturing reliability. This review summarizes recent advances in printed biosensors from the combined perspectives of printing technologies and functional materials. Commonly employed printing techniques, including inkjet, screen, aerosol jet, and roll-to-roll gravure printing, are discussed with emphasis on their processing characteristics and material requirements. The review also examines key material platforms used in printed biosensors, including carbon-based nanomaterials, metal oxides, metal nanoparticles, conductive polymers, dielectric materials, and hybrid composites, highlighting their roles in electrical conductivity, catalytic activity, biomolecule immobilization, mechanical flexibility, and overall analytical performance. Finally, current challenges and emerging research directions are outlined with respect to ink stability, post-processing strategies, sensor reliability, manufacturability, and practical translation. Overall, this review emphasizes that the development of high-performance printed biosensors depends on the synergistic integration of rational material design with optimized printing strategies. Full article

Understanding the impact of rural digitalization on the resilience of the swine industry is crucial to promoting its transformation toward efficient and low-carbon production. However, existing research has not yet clarified how rural digitalization influences the resilience of the swine industry, and there is a particular lack of discussion regarding potential nonlinear relationships. Based on panel data from 30 Chinese provinces for the period 2011–2023, we employed the entropy method to measure the level of rural digitalization and the resilience of the swine industry. Two-way fixed-effects, mediation, and threshold models were adopted to empirically examine the relationship and underlying mechanisms. The findings indicated that rural digitalization significantly enhances the resilience of the swine industry, and this finding remained robust after multiple robustness checks and endogeneity treatments. This effect is primarily mediated by two pathways: industrial-scale expansion and industrial agglomeration. Additionally, well-designed environmental policies and higher rural household incomes can strengthen the beneficial effect of rural digitalization on industrial resilience. Heterogeneity analysis further reveals that the positive influence is stronger in regions with poor transportation infrastructure and in central and western China, where digitalization effectively strengthens the industry’s shock resistance and adaptive capacity. This study offers meaningful implications for policymakers seeking to accelerate rural digitalization and promote high-quality development of the swine industry in the digital age. Full article

Reliable determination of radon adsorption properties in candidate adsorbents is essential for developing highly sensitive methods capable of measuring low ²²²Rn activity concentrations in air. Such measurements are increasingly important in environmental monitoring, climate research, and low-background experiments. Conventional approaches for determining the adsorption coefficient and heat of adsorption are labor- and time-intensive, limiting their suitability for comparative studies under identical conditions. Here, a recently proposed method is applied for the first time in a systematic comparative study. The approach couples solid-state nuclear track detectors (SSNTDs) with adsorbents that simultaneously act as radon collectors and alpha emitters, enabling fully parallel exposure and signal acquisition across multiple samples. Eight adsorbents—three activated carbon fabrics, two bulk activated carbons, and three synthetic zeolites—were evaluated simultaneously over a temperature range of 0–46.5 °C. Activated carbon fabrics exhibited the highest adsorption coefficients, with ACC-5092-10 reaching 11.8 ± 1.3 m³/kg at 20 °C. The heats of adsorption ranged from 24.8 ± 3.9 to 33.3 ± 5.0 kJ/mol, consistent with the literature values. For synthetic zeolites, the adsorption coefficient increased linearly with the Si:Al ratio. The influence of water content was further investigated for the five best-performing materials. The most hydrophobic material, zeolite SA-25 (Si:Al = 25), showed only a 25% reduction in adsorption coefficient under saturated humidity, whereas activated carbons exhibited strong suppression. These results demonstrate the practicality, sensitivity, and efficiency of the SSNTD–adsorbent method for comparative radon adsorption studies. Full article

Enhancing urban energy efficiency is central to low-carbon transition and broader urban sustainability. However, whether innovation-oriented urban policy can generate such gains, through which channels it operates, and whether its effects extend beyond pilot cities remain insufficiently understood. Focusing on China’s Innovative City Pilot (ICP) program, this study uses panel data for 274 Chinese cities from 2006 to 2022 and treats the staggered implementation of the program as a quasi-natural experiment. A multi-period difference-in-differences model is employed to examine the impact of the ICP program on urban energy efficiency. The results show that the ICP program significantly improves urban energy efficiency, and this conclusion remains robust across a series of robustness checks. Mechanism analysis further suggests that the policy effect operates through lower per capita carbon emissions and stronger green technological innovation. Heterogeneity analysis shows that the effect is more pronounced in larger cities, economically more developed cities, and cities with stronger pre-existing innovation capacity. Spatial analysis indicates that the program generates not only significant local benefits but also positive spillover effects on neighboring cities. Overall, these findings suggest that innovation-oriented urban policies can promote energy-efficient, low-carbon, and more sustainable urban development, while highlighting the importance of regional coordination and local innovation capacity in shaping policy effectiveness. Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

Ventilation air methane (VAM) has an extremely low concentration, making its abatement exceptionally challenging. Catalytic oxidation offers a promising route for VAM treatment, but industrial application requires integrated catalysts with high activity and efficient mass transfer. In this study, a novel straight-channel NiO/CeO₂ ceramic reactor was fabricated via mesh-assisted phase inversion, with NiO content systematically optimized to screen the optimal ratio. The 60 wt% NiO was the optimal composition, exhibiting excellent VAM oxidation performance. Brunauer–Emmett–Teller (BET) analysis confirmed that this optimal ratio yielded the largest specific surface area. Furthermore, H₂-temperature-programmed reduction (H₂-TPR) and X-ray photoelectron spectroscopy (XPS) confirmed that this optimal ratio facilitated the formation of abundant NiO–CeO₂ active interfaces, effectively inducing surface Ce³⁺ species and oxygen vacancies. These merits significantly enhanced the reactor’s oxygen adsorption capacity and redox properties, thus realizing efficient methane activation in catalytic oxidation. Moreover, the optimal reactor successfully passed 10 thermal cycle tests, further verifying the thermal stability of the catalytic structure. In addition, it exhibited outstanding long-term stability during a 100 h test, with no carbon deposition or active phase sintering observed. This work develops an optimized straight-channel NiO/CeO₂ ceramic reactor and offers a practical and scalable design strategy for VAM oxidation. Full article

Understanding the impact of ash on nanopore heterogeneity is crucial for evaluating deep coalbed methane (CBM) reservoirs. This study investigates the Benxi Formation coal Seam 8 in the Nalinhe Block, Ordos Basin. Based on proximate analysis, samples were categorized by ash yield (A_ad%). Pore structures were characterized using low-temperature nitrogen adsorption (<2 nm) and carbon dioxide adsorption (2–100 nm). Fractal theory was employed to quantitatively assess pore heterogeneity across different scales. The results indicate that ash content significantly constrains the development of both micropores (<2 nm) and mesopores (2–100 nm), with the most pronounced effect on micropores in the 0.3–0.6 nm range. Ash, primarily derived from kaolinite, occludes pores, reducing pore volume and specific surface area, thereby diminishing methane adsorption capacity. Notably, pore heterogeneity is found to decrease with increasing pore volume. These findings provide valuable insights for the efficient exploration and development of deep CBM resources in the Nalinhe and Suide blocks. Full article