Search Results (2,567)

A reliable water supply is essential for balancing hydrologic, operational, and institutional constraints and is intensified by the water demands of emerging carbon-removal technologies. This study applies a reliable water-supply framework to Castle Valley, Utah, USA, a region with favorable geologic conditions for carbon storage. Existing municipal, agricultural, and industrial water demands and surface-water, groundwater, and water-rights-based supply were quantified using publicly available datasets. Scenarios were then evaluated to test how a proposed 1 Mt/yr direct air capture (DAC) facility would affect the regional water balance. Results indicate that the existing supply–demand balance in Castle Valley is approximately in equilibrium, and that a 1 Mt/yr DAC facility would require an estimated 7–18% of total municipal water demand, disrupting the balance under several supply scenarios, particularly where supply is based on water rights rather than actual conditions. Retrofitting existing coal-fired power plants with carbon capture and storage (CCS) could double their water consumption. Non-potable water sources, such as brine water, may be necessary to support DAC development without compromising existing water users. The findings suggest that regional water-balance frameworks can help identify where carbon-removal infrastructure is feasible without exacerbating water scarcity, and that similar assessments should precede DAC siting decisions in other water-limited regions. Full article

Underground coal gasification (UCG) is an important in situ conversion technology for the clean and efficient recovery of deep and difficult-to-mine coal resources. Reactions between high-temperature char and gasifying agents such as CO₂ and H₂O in the reduction zone directly control the generation of valuable gases, particularly CO and H₂. Calcium-bearing minerals can catalyze char gasification and participate in high-temperature ash-phase transformation. However, the differential effects of CaCO₃ and CaO on coal pyrolysis–gasification behavior and ash-phase evolution under a CO₂ atmosphere remain unclear. In this study, a typical low-rank Jiangna (JN) coal from the Hami region of Xinjiang, China, was subjected to sequential HCl-HF leaching to prepare demineralized coal (DC), which was subsequently loaded with 5 or 10 wt% CaCO₃ or CaO. TG-FTIR-MS, high-temperature in situ XRD, SEM, N₂/CO₂ adsorption, and multifractal analysis were employed to investigate thermal conversion, gaseous product release, pore structure, and Ca-bearing phase evolution. Demineralization decreased the mass loss at 600–1000 °C from 50.05% to 29.14%. The CaCO₃-loaded samples showed their main high-temperature mass-loss and FTIR CO-band responses above 800 °C, whereas the CaO-loaded samples showed an earlier apparent high-temperature mass-loss response at 600–800 °C. At 600 °C, the CaO-loaded samples exhibited greater mesopore development. At 900 °C, the CaCO₃-loaded samples exhibited pronounced micropore and mesopore development, with micropore volume increasing by 49.1–64.9%. The nonlinear mass scaling function τ(q) and the decrease in the generalized dimension D(q) with increasing q indicated heterogeneous multiscale distributions of pore volume. At 600 °C, the micropore spectrum width ($\mathsf{\Delta }\mathsf{\alpha }$) of the CaCO₃-loaded samples was 0.5537–0.6375, higher than that of the CaO-loaded samples (0.4093–0.4198). In contrast, the mesopore Δα of the CaO-loaded samples was 0.6688–0.9901, markedly higher than that of the CaCO₃-loaded samples (0.1660–0.2126). At 900 °C, the CaCO₃ and CaO-loaded samples exhibited contrasting micropore and mesopore multifractal responses. The CaO-loaded samples showed an earlier apparent high-temperature mass-loss response under the tested conditions. However, equal-mass additive loading prevented separation of the effects of precursor form and Ca input. Although HT-XRD detected crystalline phase changes, the active Ca species and extent of Ca incorporation into ash phases remain unresolved. These results document different temperature-dependent responses to CaCO₃ and CaO addition under the tested conditions. These findings provide an experimental basis for selecting Ca-based additives, regulating valuable gas generation, and evaluating high-temperature ash stability in UCG. Full article

Against the backdrop of deepening global climate governance and sustainable development, the synergistic reduction in carbon emissions and pollution in industry is a key pathway to achieving the “dual carbon” targets. Taking the disposable nitrile gloves produced by a single typical certain security equipment manufacturer as a case study, and adopting a cradle-to-gate system boundary, this paper establishes a carbon emission framework, identifies key stages and conducts scenario simulations to explore the prioritisation of pathways for energy conservation and carbon reduction. Research indicates that this product generates carbon emissions of 0.56 t per 10,000 units, 93.52% of which originate from the manufacturing process (with coal combustion accounting for 76.21%); furthermore, 99.99% and 97.26% of emissions of pollutants such as sulphur dioxide and nitrogen oxides also stem from this stage, demonstrating a clear carbon–pollution synergy. Simulations show that technologies such as using biomass solid waste as a substitute fuel and replacing thermal oil heating with steam have the potential to achieve synergistic benefits in both carbon reduction and pollution control; however, the effectiveness of some of the pre-defined synergistic technologies fell short of expectations. The eight technology combinations ultimately selected form a recommended synergistic pathway, reducing carbon emissions by 59.53% compared with the baseline scenario, and delivering significant synergistic net reduction benefits for SO₂, NO_X, PM and VOCs. This research provides a quantitative and replicable pathway for synergistic carbon and pollution reduction in the manufacturing sector, contributing to sustainable industrial transformation and cleaner production. Full article

Vietnam’s growing electricity demand, net-zero commitment, and renewed consideration of nuclear power motivate a transparent assessment of small modular reactor (SMR) economics. This study applies a harmonized plant-level levelized cost of electricity (LCOE) framework, based primarily on the Viet Nam Technology Catalogue for Power Generation 2026 (DEPP3), to compare SMRs with large pressurized water reactors, coal, LNG combined-cycle generation, solar PV, and onshore and offshore wind in 2030, 2040, and 2050. The analysis combines cost decomposition, construction-financing profile tests, common-rate and differentiated financing cases, normalized sensitivity analysis, multidimensional scenarios, and carbon-price break-even analysis. Under the common 10% real financing-rate assumption, the calculated SMR LCOE changes from 170.3 USD/MWh in 2030 to 103.1 USD/MWh in 2050; this is a DEPP3-conditioned pathway, not an independently forecast cost decline. The 2030 value is 170.3 USD/MWh under the INL S-curve financing formulation, 171.7 USD/MWh under uniform annual mid-year spending, and 233.1 USD/MWh in a bounding prepaid case. Normalized local sensitivities identify the financing rate, capacity factor, overnight capital cost, and construction duration as the largest responses. These plant-level results support conditional technology screening and staged evidence development rather than an immediate deployment or exclusion decision for Vietnam. Full article

This study compares long term generation/investment patterns in a simplified representation of Romania’s electricity sector between 2025 and 2050. A deterministic OSeMOSYS/MUIO model is used to examine a reference emissions ceiling, a terminal zero emissions target and an accelerated emissions pathway. The scenarios are conditional model experiments that share exogenous demand and technology assumptions. The model includes Romania’s main electricity generation technologies: coal, natural gas, hydropower, nuclear power, wind, and solar photovoltaics, and minimizes the total discounted system cost subject to technical, economic, and environmental constraints. Three likely scenarios are analyzed in the paper: first, a business-as-usual scenario without emission limits, a gradual decarbonization scenario, and finally an accelerated decarbonization scenario. Earlier emissions constraints shift investment toward low emission technologies and reduce modeled cumulative emissions, but they also increase the archived discounted objective under the stated assumptions. The model contains one aggregated node and eight time slices and does not represent explicit storage, electricity imports or exports, curtailment, network congestion, unit commitment or chronological dispatch. The results therefore describe investment trade-offs within the specified model boundary and do not establish operational adequacy, resilience or implementation feasibility. Full article

Based on the governing equations of the electromagnetic radiation field and the theory of multi-layer media propagation, the governing equations for electromagnetic wave attenuation are derived. A stable solution to the resulting large-scale sparse linear system is obtained using Tikhonov regularization. A typical collapse column forward model is established using locally refined triangular mesh discretization. Forward and inverse modeling are conducted at three transmission frequencies: 0.3 MHz, 0.5 MHz, and 1.5 MHz. The results indicate that the 1.5 MHz frequency yields the best vertical resolution, with inversion results most closely matching the modeled anomaly location. In the field application at working face number 3308 using 1.5 MHz, the local polynomial interpolation inversion delineates an anomalous vertical extent of approximately 70 m (75–145 m), whereas the Kriging interpolation method yields a vertical extent of about 140 m (20–160 m), representing a reduction of approximately 50% in the vertical extent of the delineated anomaly. Post-mining exposure reveals the X16 collapse column, with a major axis of 53 m and a minor axis of 34 m, which is generally consistent with the detection results. This study confirms that local polynomial interpolation combined with locally refined triangular meshing effectively enhances the vertical resolution of radio wave transillumination, providing reliable technical support for the accurate delineation of concealed collapse columns. Full article

To address the engineering challenges associated with the high brittleness and poor crack resistance of coal-based solid waste grouting materials, this study systematically investigates the flexural performance and damage evolution characteristics of the material by incorporating various types and amounts of fibers into a matrix composed of coal gangue, fly ash, Portland cement, and sulfoaluminate cement. Macroscopic mechanical testing, digital image correlation (DIC) technology, and scanning electron microscopy (SEM) are employed to reveal the mechanisms by which fibers influence the grouting material. The results demonstrate that fiber type and dosage significantly affect the flexural strength and damage evolution behavior of the material. Among them, basalt fiber exhibits the most pronounced improvement in flexural strength at a dosage of 0.4%, achieving a 28-day flexural strength of 6.33 MPa, which is 73.9% higher than that of the control group. Similarly, glass fiber shows a flexural strength increase of 63.18% at a dosage of 0.8%. The fibers enhance the material’s ductility and promote the uniformity of the failure process through mechanisms such as crack bridging, energy dissipation, and optimized interfacial structure. Furthermore, joint analysis using DIC and acoustic emission (AE) techniques reveals that when the fiber dosage reaches 0.6%, the proportion of shear cracks during material failure significantly increases (12.78–16.13%), indicating that fibers improve the uniformity of strain distribution under loading, delay the propagation of the main crack, and thereby enhance the overall crack resistance of the material. These findings provide theoretical and technical support for the development of high-toughness and high-performance coal-based solid waste grouting materials. Full article

Coal-dependent regions face a multidimensional transition challenge that extends beyond energy substitution and requires coordinated transformation of industrial systems, landscapes, infrastructure, governance and local economies. Using a structured narrative review and framework-building approach, this paper develops a systems-based framework for sustainable post-lignite transition, using Western Macedonia as the main integrated case and Megalopolis as a comparative Greek lignite region. The proposed approach integrates four interrelated domains: subsurface and geological potential, industrial decarbonisation pathways, post-mining land and infrastructure reuse, and socio-economic and governance transition. Particular emphasis is placed on the role of carbon capture, utilisation and storage (CCUS), low-carbon industrial technologies, renewable energy integration, energy-storage options, land rehabilitation, heritage valorisation and place-based development strategies. The analysis highlights that successful post-coal transition depends not only on the availability of individual technological solutions, but also on their spatial, environmental, economic and governance compatibility within the regional system. By aligning geological knowledge, engineering innovation, environmental management and just-transition planning, the paper proposes an integrated planning framework intended to structure the consideration of multiple forms of evidence in former lignite regions. The framework is broadly consistent with the Sustainable Development Goals, particularly those related to clean energy, resilient infrastructure, sustainable communities, responsible land reuse, climate action and ecosystem restoration, and offers transferable insights for European coal regions seeking resilient, diversified and low-carbon development pathways. Full article

Direct air capture (DAC) technology is transitioning from the prototype stage into commercialization, yet its large-scale deployment still faces challenges related to high energy consumption and associated environmental and ecological impacts. In this study, we first develop a unified thermodynamic model for the two mainstream DAC routes, solid adsorption (S-DAC) and liquid absorption (L-DAC). Based on this consistent framework, we then conduct life cycle assessment (LCA) and emergy analysis to jointly construct a systematic sustainability evaluation framework integrating energy, emergy, and LCA methods. The Sustainability Composite Index (SCI) was proposed by integrating key indicators to provide a holistic measure for sustainability assessment across different DAC systems. Results indicate that overall, S-DAC demonstrates superior sustainability with a composite index (SCI) of 0.823, compared to 0.759 for L-DAC, although trade-offs exist across different indicators. Under the baseline scenario S-DAC performs well in emergy transformity (Tr), and second law efficiency, whereas L-DAC excels in energy consumption, net carbon removal efficiency, and total environmental impact. The choice of electricity and heat sources exerts considerable influence on the sustainability performance of both DAC systems. Among the 24 energy source scenarios, the photovoltaic power (PV) S-DAC integrated with biomass heat achieves the highest sustainability with an SCI of 0.907. Conversely, the coal-fired L-DAC system and natural gas-powered integrated solutions exhibit the poorest sustainability, with an SCI of 0.711. Notably, even when clean energy is employed, different systems still involve trade-offs across metrics. These findings underscore that comprehensive sustainability assessment is essential before scaling up DAC technologies. Full article

Water inequality arises when regional water use is disproportionate to local socioeconomic and resource conditions. Yet, little is known about whether artificial intelligence (AI) technological innovation is associated with this mismatch. Using panel data for 31 provincial-level regions in mainland China from 2015 to 2024, this study examines the relationship between AI technological innovation and regional water inequality, measured from a water footprint perspective. The annual number of patent applications related to AI is used to measure regional AI technological innovation, and an instrumental variable generalized method of moments (IV-GMM) approach is employed to address potential endogeneity. The estimates show that AI technological innovation is significantly associated with lower regional water inequality: a 1% increase in AI patenting is associated with an approximately 0.249% decrease in the water inequality index. This result remains robust after adding further controls and applying alternative instrumental variable estimators. The quantile estimates remain negative across the conditional distribution of water inequality, although formal tests do not indicate statistically significant differences. AI technological innovation is also positively associated with financial development and industrial structure upgrading, providing evidence consistent with their potential roles as channels. The association is statistically significant in provinces with lower tax burdens and higher levels of industrialization, although formal differences between groups are statistically significant only for tax burden. These findings suggest that AI innovation policies may be more effective when combined with practical applications in water governance and adapted to local fiscal and industrial conditions. Full article

Coal gangue backfill serves as a primary supporting structure for overlying strata in mined-out areas, and its internal damage evolution is directly associated with the safety and stability of mining operations. Acoustic emission (AE) technology provides an effective approach for investigating damage evolution by capturing transient strain energy release events within materials in real time. However, conventional AE analysis methods predominantly rely on statistical interpretations of individual parameters, making them insufficient for revealing the underlying physical mechanisms governing the influence of fracture characteristics on energy evolution pathways. To address the aforementioned limitations, this paper proposes a fracture mode-informed physics-enhanced machine learning framework for predicting acoustic emission energy evolution in coal gangue backfill. First, based on the AE monitoring data of coal gangue backfill under uniaxial compression (a total of 18,992 valid AE events from three parallel specimens with identical mix proportion), the RA–AF parameters combined with the K-means unsupervised clustering algorithm were employed to automatically identify tensile and shear fracture modes, thereby constructing physically meaningful fracture mode labels. Second, the fracture mode information was integrated with AE statistical features, including rise time, duration, amplitude, counts, peak frequency, and center frequency. After selecting the most informative features using the minimum-redundancy, maximum-relevance (mRMR) algorithm, a Bayesian optimization-based support vector regression (BO-SVR) model was developed for AE energy prediction. The results demonstrated that after incorporating the fracture mode-based physical labels, the proposed model achieved a coefficient of determination (R²) of 0.9027 on the testing dataset, with an RMSE of 0.1562 and an MAE of 0.1204. Ablation experiments further confirmed that the introduction of fracture mode labels improved the R² value by approximately 3.4% compared with the model without physical constraints. Fivefold cross-validation yielded an average R² of 0.9460 with a standard deviation of 0.0023 for the SVR model. Furthermore, per-specimen independent holdout validation yielded an average R² of 0.9044 with a standard deviation of 0.0512 across three independent specimens, confirming the excellent stability and repeatability of the proposed framework. The original single-specimen results (4083 events, R² = 0.9727) are provided as a baseline reference. Mechanistic analysis revealed that the average energy release associated with shear fractures was approximately 739 times that of tensile fractures, demonstrating that fracture mode information provides physically consistent mechanical constraints for the machine learning model. This study provides an effective new method for the stability evaluation and intelligent monitoring of coal gangue filling materials. Full article

At present, the PCI ratio of a blast furnace is still regulated mainly by operator experience, because BF production data are high-dimensional, non-linear and strongly coupled. In this paper, for the key role of blast furnace (BF) pulverised coal injection (PCI) technology in the steel industry, grey correlation analysis (GCA) is introduced for the first time into the analysis of BF production data, and a method for determining the reasonable PCI rate of BF based on GCA and petrographic analysis is proposed. By collecting BF production data and applying GCA, the key factors affecting the PCI ratio of the BF and their correlation degrees were determined, and then the optimal PCI ratio interval of 175–181 kg/t was screened. In addition, petrographic analysis of BF dust at different PCI ratio stages was carried out to calculate the utilisation rate of PC, and the reasonableness of the optimal PCI ratio was verified by combining the comparison of key indexes and the analysis of combustibility of injection coal. With the determined optimal PCI ratio applied, the PCI ratio of the studied BF was increased from 167.0 kg/t to 180.75 kg/t, the coke ratio was reduced by 72.75 kg/t, and the utilisation rate of PC was increased from 79.44–89.03% to 92.71–96.86%. The results showed that this method can effectively optimise the PCI ratio, improve the utilisation rate of pulverised coal, reduce the production cost, and provide a scientific basis for the regulation of PCI ratio for steel enterprises. Full article

Ensuring procedural justice and inclusive governance is essential for the social sustainability of energy transitions, particularly in regions undergoing profound socio-economic restructuring following coal phase-out. However, the extent to which participatory and deliberative principles are embedded in transition governance at the regional level remains insufficiently understood. This study assesses the participatory and deliberative gap in three former coal-dependent European regions—Western Macedonia in Greece, Silesia in Poland, and Stara Zagora in Bulgaria—and examines its implications for socially sustainable just transition governance. Their selection follows a comparative logic that combines structural similarity with institutional variation. This combination provides an appropriate comparative setting for examining whether participatory and deliberative gaps constitute common features of coal-transition governance or vary according to regional and national institutional contexts. A comparative survey of 165 experts and policymakers was conducted to assess structural barriers, public participation, institutional trust, policy equity, and the use of digital deliberation technologies. The empirical evidence reveals a substantial mismatch between the normative importance attributed to participation and its practical implementation. While public participation is considered crucial (M = 4.19, SD = 0.96), satisfaction with policy equity remains low (M = 2.66, SD = 1.20), alongside low levels of trust in governance (M = 2.79, SD = 1.20). These weaknesses are associated particularly with complex bureaucracy, corporate dominance, and persistent institutional mistrust. A gap analysis of deliberative principles further demonstrates a systematic disconnect between expected (“Should Have”) and experienced (“Have”) governance conditions, with the Greek expert sample reporting the largest average gap (+1.39), compared with Poland (+0.68) and Bulgaria (+0.58). Conventional videoconferencing was reported as more widely used and received higher perceived-effectiveness ratings than specialized deliberation platforms. These ratings reflect stakeholder perceptions and should not be interpreted as evidence of superior deliberative or democratic performance. By empirically identifying and comparing participatory, deliberative, and institutional deficits, the study contributes to the assessment of the governance dimension of social sustainability in coal-transition regions. The findings demonstrate that sustainable energy transitions require not only environmental and economic transformation but also institutional arrangements capable of ensuring meaningful participation, deliberative quality, trust, and procedural equity. Given the cross-sectional expert-based design, future research should validate these findings longitudinally and across a broader range of coal-transition regions, while incorporating citizen-level perspectives. Full article

Methane (CH₄), the primary constituent of natural gas, is a colorless, odorless, combustible gas prevalent in petroleum and natural gas transmission pipelines as well as coal mines. However, conventional n-type semiconductor sensors face significant challenges in real-time CH₄ leak monitoring within these environments due to severe signal drift induced by synergistic interference from high humidity and volatile organic compounds (VOCs). To address this, we synthesized La_0.9Fe_0.8Co_0.2O₃ nanomaterials via a facile one-pot hydrothermal method. A dual-site synergistic modulation strategy, involving the co-introduction of self-induced defects and Co dopants, endows the sensor with exceptional sensitivity toward CH₄ at markedly lowered operating temperatures. At the optimal temperature of 125 °C, the tailored sensor exhibited a remarkable sensitivity of 17.55 toward 100 ppm CH₄, representing an order-of-magnitude improvement over pristine LaFeO₃. Furthermore, the device demonstrated excellent dynamic performance, with a rapid response time of 9.9 s and a recovery time of 7.2 s, enabling reliable detection across diverse environmental matrices. Notably, the sensor maintained a stable response value of approximately 15 even under extreme humidity (95% relative humidity) and exhibited outstanding repeatability. The exceptional performance originates predominantly from A-site-induced lattice strain coupled with the proliferation of active sites triggered by B-site substitution. The insights gained and the established mechanism herein enable the advancement of advanced methane sensing technologies geared towards industrial safety applications. Full article

Tajikistan relies heavily on hydropower for electricity generation, but increasing energy demand, winter electricity shortages, rising coal consumption, and climate-related changes in water availability pose significant challenges to the country’s energy system and environment. In this study, we examined the relationship between renewable electricity generation and per capita CO₂ emissions in Tajikistan from 1991 to 2023. We conducted the analysis within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework using an autoregressive distributed lag (ARDL) approach. Our model incorporates population growth, gross domestic product (GDP) per capita, industry value added, renewable electricity generation, and globalisation. The diagnostic and stability tests employed did not detect major specification problems in the selected model. We found that the share of renewable electricity has a statistically significant negative contemporaneous association with per capita CO₂ emissions. GDP per capita is positively associated with emissions, whereas globalisation has a negative association in both the current and subsequent years. We also found a positive coefficient for lagged population growth and a negative coefficient for lagged industry value added. On the basis of these results, we conclude that renewable electricity can play an important role in addressing Tajikistan’s energy and environmental challenges. Our findings also provide country-specific evidence that may support the development of energy, environmental, and economic policies aimed at strengthening energy security while limiting carbon emissions. Full article