ABSTRACT Wind energy is highly valued by the power industry for its cleanliness and sustainability. Precise wind speed forecast is a vital condition for electricity system to give play to its high efficiency stably. However, traditional prediction models fail to effectively decompose and refine key information in data, resulting in insufficient stability and accuracy of prediction. To enhance the model’s generalization capability and fully utilize the advantages of diverse models, this paper establishes a combined model which includes data fuzzy strategy and combination strategy. In this model, the triangular membership function divides the data into different fuzzy granularity, focusing on the key features of the data, and multi-objective salp swarm algorithm (MOssa) generates a Pareto optimal solution set as weights to combine the sub-models, so as to achieve accurate predictions. Based on point prediction, interval prediction further realizes uncertainty analysis of different probabilities and provides more comprehensive data analysis information by calculating interval range through prediction error distribution function and confidence level. Chinese real wind speed data were used to assess how well the model performs. The findings reveal that the constructed information strategy and combination strategy notably enhance prediction robustness and accuracy.

Radioactive waste (RW) and spent fuel require a well-structured financing framework due to substantial costs. The primary objective of this study is to assess the level of guarantee required for the cost and financing of RW management in Türkiye, considering various scenarios. This study has also been conducted for the benefit of newcomers to the nuclear power industry and those who conduct cost studies for (RW and spent nuclear fuel (SNF). The study commenced by conducting economic analyses of SNF and RW management practices in countries with operational nuclear reactors. Subsequently, formulas were developed to determine the quantities of RW that would need to be managed in Türkiye under different scenarios. The formula for determining fees for very low-level waste and short-lived low- and intermediate-level waste is based on annual monitoring and international agreements. The most challenging parameters are fee collection and reduced total cost, which require assumptions about processes, discount and inflation rates, and cost breakdowns. The cost of managing RW for one, two, or three nuclear power plants (NPPs) was calculated based on nine scenarios. The minimum and maximum costs for RW management over 15 years were determined, ranging from $158 million to $629 million, depending on the number of NPPs. Establishing a financial mechanism and guarantee method is crucial for managing SNFs and RW, as countries adopt diverse approaches based on economic and financial considerations. Finally, recommendations were proposed regarding the guarantee structure from a cost and financial perspective for the effective management of these processes.

As a core metric for climate policy, the scientific estimation of carbon social costs is crucial for formulating mitigation strategies. However, traditional integrated assessment models predominantly focus on the global aggregate, failing to adequately account for regional heterogeneity, sectoral characteristics, and strategic interactions between regions. They also lack systematic integration of ESG principles. To address this, this paper examines regional and sectoral carbon social costs driven by ESG development. Through cooperative and non-cooperative games, we improve the integrated economic-environmental-climate development model, take the eight economic regions in China as an example, get the carbon social cost of each economic region and typical important industries, and obtain the key parameters and the evolution law of carbon social cost. The model categorizes the carbon emissions after the implementation of emission reduction policies under the ESG perspective into direct and indirect emissions. It studies the economic impacts of the two types of emissions before and after the implementation of emission reduction policies, and conducts research on the top four typical important industries (industry, construction, transportation, and power) that rank among the top four global CO2 emitters, to obtain the analytical solution of the social cost of carbon in the region and the typical important industries. In addition, this paper numerically simulates the social cost of carbon for the four industries under the baseline scenario, cooperative game scenario, non-cooperative game scenario, and temperature limitation scenario. The study shows that the social cost of carbon in the northern, southern and eastern coastal economic regions is higher than that in other economic regions, the social cost of carbon in the industrial and electric power industries in each economic region is higher than that in the building and transportation industries, and the more stringent the temperature limit is, the higher the social cost of carbon is in the economic regions.

ABSTRACT In a context marked by multiple crises, the shift from fossil fuels to renewable energy sources has been regarded as the primary solution to climate change. However, the implementation of large-scale renewable energy infrastructure is increasingly contested as market-driven, investor-led and incapable of protecting nature. This article investigates resistance to an industrial wind power plant proposed for a pristine area in the Tuscan-Romagna Apennines. From a theoretical perspective, it bridges social movement studies with political economy and critical debates on the green transition, including perspectives on green extractivism and territorial inequalities. Methodologically, we draw on qualitative research to analyse diagnostic and prognostic frames developed by the opponents of a locally unwanted land use (LULU) project, through which they articulate an environmentalist narrative grounded in biodiversity protection, attention to local characteristics and active citizen participation. We demonstrate that social movements mobilise at the local level around emerging centre–periphery cleavages, denouncing ecological and political marginalisation while promoting community-based alternatives. The article highlights the role of place as a source of collective identity and well-being, and of democratic participation as a condition for a just ecological transition.

In pursuit of sustainability, it is necessary to comprehend the evolving relationship between economic growth and greenhouse gas (GHG) emissions. This study conducts Index Decomposition Analysis (IDA) for comparative sectoral analysis of the world's ten largest GHG emitting countries across their eight sectors; agriculture, building, fuel exploitation, industrial combustion, power industry, processes, transport and waste, using latest available data from 2000 to 2023. This study disaggregates sectoral emissions to evaluate the extent to which economic growth has been decoupled from GHG emissions, thereby offering insight into national sectoral emission trajectories and sustainability progress. This study offers sectoral ranking of countries based on average GHG emission abatement during 2000-2023 and offers the sectoral GHG emission intensity in these countries relative in year 2000. The agriculture and building sectors demonstrated significant decoupling, abatement of GHG emissions by an average of 6.44 MtCO2 and 6.34 MtCO2, respectively, through sustainable practices. The fuel exploitation sector achieved modest abatement of 2.24 MtCO2, though emissions intensified in China and Indonesia. In the industrial combustion sector, GHG emissions abatement were recorded by 0.74 MtCO2 but intensified in several emerging economies. The transport sector recorded a slight intensity of 0.36 MtCO2, highlighting the urgent need for low carbon mobility solutions. The waste sector achieved the most substantial GHG emissions abatement of 16.31 MtCO2, led by USA, despite intensified in four other nations. The findings emphasized the critical need for tailored, sector specific policy interventions, technology adoption, and behavioral changes to achieve sustained decarbonization. The study contributes to the global discourse on climate mitigation by offering comparative sectors specific insights to align national energy structures with global decarbonizing practices.

ABSTRACT To promote the development of renewable energy, China re‐implemented the Chinese Certified Emission Reduction (CCER) policy in 2023. This study explores certificated CO 2 and air pollutants (i.e., NO x , SO 2 and particulate matter (PM)) emissions reductions from China's solar thermal power (STP) industry at national scale and conducts the comprehensive cost‐benefit analysis with consideration of CCER policy. We find that: (1) generally, STP‐related emissions reductions and associated benefits from CCER revenue have followed and will continue to present an upward tendency; however, the STP industry would turn to be not economic feasible in 2025, due to the renewable energy subsidies cancellation. (2) From a spatial perspective, STP‐related emissions reductions are highly concentrated in the northwest region. (3) Among species, NO x makes the largest contribution to STP‐related air pollutants emissions reductions and the associated co‐benefits, while PM makes the least. (4) As for policy implication, CCER policy should be carefully designed and dynamically adjusted together with other additional policies, and thus further facilitates the development of the STP industry.

ABSTRACT Hydrogen is widely regarded as one of the most promising energy carriers for enabling large-scale and long-duration energy storage in future low-carbon energy systems. The rapid deployment of renewable energy sources such as solar and wind has intensified the need for reliable storage technologies capable of addressing intermittency and variability. Hydrogen offers several distinctive advantages, including high gravimetric energy density, long-term storage capability, and applicability across multiple sectors such as power generation, transportation, and industry. This research paper presents a comprehensive theoretical review of hydrogen as a future energy storage medium. Hydrogen production pathways, storage technologies, energy conversion routes, system concepts, advantages, challenges, safety considerations and future research directions are discussed. The study highlights the strategic importance of hydrogen in supporting renewable energy integration and achieving global de-carbonization goals.

This study developed a novel hybrid Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model to forecast greenhouse gas (GHG) emissions across multiple country sectors, aiming to enhance climate policy. We analyzed 52 years (1970–2022) of GHG emissions data (CO₂, CH₄, N₂O, F-Gases) from 163 countries and eight sectors (Agriculture, Buildings, Fuel Exploitation, Industrial Combustion, Power Industry, Processes, Transport, Waste), sourced from the EDGAR v8 database. The GCN adjacency matrix captures spatial relationships on a weighted sum of Haversine distance and cosine similarity, while the LSTM models temporal dynamics. Data preprocessing includes min-max scaling and outlier handling with Interquartile Range capping. The model was trained on 70% of the data, validated on 15%, and tested on 15%, using Mean Squared Error (MSE) loss and the Adam optimizer. The performance was evaluated with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The GCN–LSTM model outperformed baseline models (ARIMA, Simple LSTM, Stacked LSTM), achieving the lowest MAE (0.0207 in Waste) and highest R² (0.9756 in Waste). Model interpretability highlighted strong regional connections, such as Thailand–Cambodia in the Waste sector, suggesting that spatial and temporal dependencies offer superior forecasting accuracy, informing targeted climate action.

Abstract Conventional fluorescent fiber optic temperature sensors (FFOS) are widely used in the power industry but exhibit comparatively weak performance at low temperatures. In this study, typical FFOS, operating within a nominal temperature range of −40 to 200 °C, were tested and analyzed. To address the issue of low-temperature measurement inaccuracy, a novel tellurite-tungsten-lanthanum (Te-W-La) glass-based fluorescent material with excellent low-temperature sensing performance was successfully prepared. This was achieved by using tellurite glass as the base material and co-doping it with multiple rare-earth ions ( Er 3 + / Yb 3 + / Pr 3 + ). The results demonstrate that: (1) The traditional FFOS (nominal range −40 to 200 °C) exhibits significant nonlinearity and high measurement randomness below −40 °C, making it difficult to meet measurement requirements in regions below −40 °C. (2) The novel sensor demonstrates outstanding performance across a wide temperature range of −60 to 60 °C. Its sensitivity reaches a peak value of 99 × 10 − 4 / °C at −60 °C, indicating superior low-temperature sensing capabilities. (3) Temperature tests conducted on the new sensing device within the −60 to 60 °C range revealed good repeatability, with deviation values consistently within ±0.5 °C. This data provides a reference for the application and promotion of fluorescent optical fiber sensors in low-temperature regions.

Impact of orderly energy consumption on coordinated optimization between industrial microgrid and power grid

To address the core challenge in the wind power industry where pursuing longer blades for enhanced energy yield intensifies resonance risks, this study conducts theoretical and experimental modal analysis on three-phase composite wind turbine blades made of graphene-reinforced glass fiber composites. It fills a key gap in the field: most existing modal models for wind turbine blades ignore airfoil curvature or spanwise variable-width features. Based on the first-order shear deformation theory, combined with the Chebyshev polynomial and the Rayleigh-Ritz method, the study establishes a dual-scale modal analysis framework that integrates airfoil profiles, spanwise variable-width, and graphene gradient distribution to determine the natural frequencies and mode shapes of the blades. The reliability of the proposed model is verified through a three-tier system, including comparisons with published thin-walled structure studies, finite element simulations with grid independence verification, and sweeping frequency excitation experiments. Systematic parametric analysis reveals that increasing the aspect ratio leads to a significant reduction in the blade's natural frequency and triggers the reconstruction of modal sequences; incorporating graphene as a reinforcement effectively enhances the system's stiffness to increase natural frequency without altering the basic mode shapes of the blade; the X-pattern distribution demonstrates superior efficacy in enhancing the system's natural frequency. This improvement is attributed to the strategic concentration of graphene in high-strain regions, which maximizes interfacial stress transfer and optimizes stiffness reinforcement. From an engineering perspective, the findings provide a quantitative design tool for onshore wind turbine blades with a capacity of 6.25 MW and above, addressing the industry's core dilemma of balancing blade length and vibration stability and offering direct technical guidance for the stable design of ultra-long thin-walled wind turbine blades.

Voltage sag is 92% of industrial power system installations, leading to reduced system efficiency and significant commercial and economic losses for manufacturers. Voltage sag compensators, which generally include transformer- coupled voltage-source inverters, are successful solutions against such sags. Transformers installed at critical load provide electrical isolation but are subjected to abnormal voltages & DC flux voltages offset during voltage sag. When the compensators replace the load voltage, the transformer's flux linkages can contact magnetic saturation, resulting in severe inrush currents. These inrush currents have the potential to trigger the compensator’s overcurrent protection, interrupt the compensation process, and lead to load disruption. This paper proposes a voltage sag–based mitigation strategy to reduce transformer inrush current, compensators, ensuring reliable compensation and uninterrupted power supply to critical loads.

The growing penetration of power electronics and nonlinear loads in industrial electrical networks has increased the dynamic complexity of these systems, exceeding the analysis capabilities of traditional approaches based on quasi-stationary models. In this context, this paper presents a methodology for the dynamic characterization of an industrial electrical network based on high-resolution synchrophasor measurements obtained using a microPMU. The proposed approach is based on the identification of a linear dynamic model in state space using subspace techniques based on real data recorded during a short-duration transient event. The results show that the identified model is capable of adequately capturing local underdamped dynamics and reproducing the temporal response observed in the measurements. This evidences the presence of dynamic modes associated with the interaction between the network and power electronics-based devices. Similarly, the stability analysis of the identified model demonstrates its consistency and robust gains in temporal variations within the analysis window. Overall, the results confirm that the combination of microPMU and data-based modeling techniques is an effective tool for improving dynamic observability and understanding the transient behavior of industrial power grids, complementing classical analysis and simulation methods.

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

In the time of AI era, Industrial power load gradually rising due to the rapid expansion of the chip manufacturing facilities. So that accurate forecasting of industrial power load is important to achieve efficient grid planning and overall energy management. But, due to the nonlinear, volatile and multi scale nature of industrial power load data, the conventional statistical model face challenges in forecasting efficiently. To address these challenges, a novel hybrid deep learning model, CNN-Transformer-BiLSTM has been proposed that integrates the feature extraction capacity of convolutional neural networks (CNN), the long-range dependency modeling of the transformer architecture and the sequential learning strength of bidirectional long, short-term memory (BiLSTM) networks. The CNN layers efficiently capture the local temporal patterns and feature correlations within the load data sets, Transformer layers employs self-attention mechanisms to model complex long-term dependencies and contextual relationships. The BiLSTM layer further enhances temporal representation by learning bidirectional dependencies, thus improving the overall prediction accuracy. Historical monthly industrial electricity load data from the U.S. Energy Information Administration (EIA) spanning over two decades are used to train and evaluate the model. The proposed model output has been compared with other standalone and hybrid deep learning models. The proposed CNN-Transformer-BiLSTM achieves superior forecasting accuracy with Mean Absolute Percentage Error (MAPE) of 1.23%, Root Mean Square Error (RMSE) of 1,276 MWh and Mean Absolute Error (MAE) of 1,040 MWh.

In this paper, the performance of a hot air turbine operating in an industrial combined heat and power (CHP) cogeneration is investigated, electrical and thermal energy supplied are intended for a pellet production unit. This unit is powered by Eucalyptus residue at 50% moisture content recovered from a forest located in El Taref, north-east of Algeria. The results show that when the air temperature at the boiler inlet Te exceeds 100 degree C, an excess air ratio, alpha, above 90% is required to maintain the flame temperature below 1200 K. Based on this, the parameters were set to alpha = 80% and Te = 100 degree C, resulting in a flame temperature of 1192K. The turbine inlet temperature T3, which must remain below the flame temperature, was fixed at 1140K. Once these conditions were established, the compression ratio maximizing the overall efficiency was determined to be around 8, yielding a cogeneration efficiency of 53%, with an electrical efficiency of 20% and a thermal efficiency of 33%.

Industrial Power Load in the United States is rising gradually due to rapid manufacturing facilities enhancement in the recent year. Due to this expansion, accurate forecasting of industrial electricity load plays a vital role for power generation, transmission, and distribution planning for a specified manufacturing zone. The statistical model of forecasting faces a significant challenge due to the nonlinearity and multi scale nature of industrial load data. But recent application of Hybrid Deep Learning and Machine Learning models has demonstrated superior performance in industrial power load forecasting over statistical forecasting model. This paper presents a structured and comparative review of the recent Hybrid Deep Learning and Machine Learning based Data driven approaches for Industrial power load forecasting. The findings of our study highlight the effectiveness of the hybrid approach for reliable and high precision industrial power load forecasting which enhances the intelligent energy management in modern industrial systems.

This study examines listed companies in China's power sector, utilizing panel data from Guangzhou Hengyun Enterprises Holding Ltd. (000531), a state-controlled enterprise in the industry, covering the decade from 2015--2024. Multiple linear regression analysis is used to empirically investigate the relationship between capital structure (with the debt-to-asset ratio as the core indicator) and financial performance (measured by the return on equity and gross profit margin). The findings reveal a negative correlation between the debt-to-asset ratio and the financial performance of listed power companies. Finally, considering the capital-intensive nature and strong policy orientation of the power sector, this study proposes recommendations for optimizing capital structure and enhancing financial performance. These insights provide a reference for financing decisions and operational management during the transformation period of similar regional power enterprises.

As the goal of carbon peak and carbon neutrality becomes a global consensus, the circular economy is gradually evolving from an environmental concept to a core lever for national strategy and industrial transformation. To achieve green and low-carbon development, China is accelerating the construction of a circular economy system, particularly in the fields of resource recycling and utilization. Industrial waste heat, a strategically critical supplementary energy resource, performs a pivotal role in advancing the circular economy. Based on an energy technology coupling model, this study assesses the waste heat utilization potential in China and quantitatively measures its impact on energy conservation and carbon reduction. The results show that: (1) The potential of industrial waste heat in China is characterized by an inverted U-shaped trajectory. Over the near-to-medium term, the steel and power industries remain the primary contributors to waste heat utilization potential. (2) Low-grade waste heat represents the majority of utilization potential in China’s industrial sector, mainly from power generation, fuel processing, and steel manufacturing. The model results indicate that the proportion of low temperature waste heat will increase from approximately 66% in 2025 to 83% in 2060. (3) Waste heat utilization significantly influences the energy transition pathway. The findings of this study demonstrate that energy-intensive industries have the potential to reduce primary energy consumption by more than 13%. Moreover, making full use of waste heat could accelerate China’s carbon peaking target to 2028, and reduce peak carbon emissions by an estimated 5.1%.

Naturally occurring radioactive materials (NORM) in energy production sectors: exposure, effective doses and regulatory challenges.