• The SCEQ-E demonstrated acceptable internal consistency and strong factorial validity, supporting its reliability as a diagnostic tool. • Factor analyses confirmed a three-factor structure: Strategic decisions, HR practices, and daily behaviors. • This study distinguishes enacted safety culture from safety climate, supporting their empirical separation. • The SCEQ-E extends a nuclear-origin model into a cross-sector instrument for broader energy applications. This paper presents the adaptation and empirical validation of a tool to assess safety culture enactment in the energy sector. The Safety Culture Enactment Questionnaire-Energy (SCEQ-E) is an adapted version of the SCEQ (De Castro et al., 2017), originally developed for the nuclear industry and validated in Spanish. The SCEQ-E extends this instrument to the broader energy industry and was administered to employees working in different energy organizations (N = 146). The original questionnaire was translated into English, adapted to the energy sector, and reviewed by industry experts. Exploratory factor analysis supported a three-factor structure and two items were removed due to cross-loadings. Confirmatory factor analysis provided support for the proposed three-factor model, showing acceptable model fit and strong internal consistency for the total scale and subscales. The SCEQ-E also demonstrated good convergent validity and evidence of discriminant validity in relation to safety climate. Overall, the results indicate that the SCEQ-E provides initial evidence of reliability and construct validity and represents a promising instrument for assessing safety culture enactment in the energy industry, although further validation using larger and more diverse samples is warranted.

Data-driven fault detection and diagnosis in industrial process systems: A systematic review and perspective

This paper examines how the Environmental, Social, and Governance (ESG) concept can be incorporated into project cost-forecasting models and how this incorporation affects predictive accuracy and risk management in the new market, specifically Kazakhstan. It examines the moderating effect of analytical sophistication and institutional contexts on the relationship between ESG integration and project cost performance. A quantitative research design was employed, and 720 project management and finance professionals in the construction, energy, mining, engineering, and infrastructure industries in Kazakhstan participated in data collection. The measurement reliability and validity were checked with the help of Cronbach's alpha, composite reliability (CR), average variance extracted (AVE), and the Kaiser-Meyer-Olkin (KMO) measure. Structural Equation Modeling (PLS-SEM) and the predictive metrics (R 2, f 2, Q 2) demonstrate that the explanatory power of structural relationships is moderate-to-strong and practically important. Findings show that ESG-incorporated forecasting has a substantial positive impact on the performance of project costs and risk reduction, especially with advanced analytical tools, including machine learning (ML). The ESG- cost performance relationship is partially mediated by cost Forecast Accuracy, whereas analytical sophistication enhances the predictive advantages of ESG integration. Regulatory harmonization and data maturity also contribute to the model's effectiveness. The research is among the first empirical applications to validate ESG- and AI-informed cost forecasting in an emerging-market setting, linking sustainability analytics and project management performance. The results have practical implications for managers, policymakers, and financial decision-makers in Kazakhstan and similar emerging markets, and they are replicable across countries to enable concurrent cross-country comparisons and longitudinal analyses of ESG-driven forecasting behaviors.

System integration solutions for urban, industrial, and renewable energy transitions

Evolutionary patterns and future trends of China’s hydrogen energy industry policy

Environmental concerns and economic viability in the low-carbon energy industry: Integrating solar-driven ORC power systems with advanced heterostructured battery materials for green mobility

Micro/nanoplastics and lithium iron phosphate at environmentally relevant dose triggers hepatic fibrosis: Unseen risks of global renewable energy.

Financial investment in energy sector organizations has a significant impact on striving toward sustainability. The features of a financial investment include protection against inflation, accumulation of wealth, creation of a safety net for future decades, and boosting retirement savings. Primarily, the decision to invest money in any market depends on a variety of factors such as economic growth, sales, government policies, international treaties, and natural disasters. In addition, there are limited studies that have focused on addressing Industry 4.0 technologies integration in financial investment domain. In this investigation, the significance of industry 4.0 enabling technologies for financial investment in energy organization is discussed along with the current challenges in financial investment. Industry 4.0 technologies have the potential to enhance financial investments to realize digitalization with real-time and intelligent analytics coupled with secured transactions. This study presents a paradigm-shifting environment in which ecological responsibility, financial accountability, and innovation come together. The assimilation of renewable energy sources, grid reliability, and operational efficiency are all enhanced by the integration of smart grids and the Internet of Things (IoT). These technical developments drive the energy industry toward a greener future while also optimizing financial investments by reducing downtime and improving grid resilience.

This study presents a comprehensive analysis of real-time electrical data from a silicon metal smelting enterprise in Yongdeng County, Lanzhou City, China. Over a continuous 7-day period (October 10–16, 2022), high-resolution 15-minute interval data were collected from four key substations, capturing detailed operational characteristics across 15 power-related parameters. The analysis focuses on two critical aspects of industrial energy systems: load flexibility and power quality. Flexibility is quantified through a novel Load Flexibility Index (LFI), integrating duty cycle, ramp rate, variability, energy usage, and peak demand. Power quality is assessed using real-site indicators such as the Voltage Unbalance Factor (VUF), Current Distortion Percent (CDP), and Instantaneous Power Quality Score (IPQS). Unlike prior literature relying on aggregated data or simulation-based assumptions, this study leverages high-granularity data tied to actual equipment infrastructure, including 30 reactive compensation circuits totaling 750 kVAr . Quantitative assessment of capacitor switching events reveals an average 8.6% reduction in voltage unbalance and a 5.3% improvement in power factor stability, highlighting the stabilizing impact of reactive compensation on real-world industrial grids. The findings provide actionable insights for power-aware operational improvements and demand-side strategies in heavy industrial environments. By linking data-driven analysis with practical load management metrics, this study provides a transferable framework for improving energy efficiency and grid interaction in other heavy industrial settings. • Load flexibility profiling using 15-min data from a silicon metal smelting plant. • Multi-dimensional Load Flexibility Index (LFI) covering duty cycle, ramping, and demand. • Real-site power quality assessment tied to 750 , kVar compensation across 30 circuits. • Operational insights linking load flexibility to power quality performance in smelting substations. • Foundation for forecasting and model predictive control in iron and steel enterprises.

Energy efficiency modeling considered chemical process anomalies using contrastive learning-guided generative adversarial imputation network for operation-aligned data reconstruction

Electro-refinery in organic compounds and H2 co-production: from waste valorization to sustainable industrial energy solutions

Enhancing industrial energy efficiency: Strategies, barriers and opportunities in the context of electricity supply challenges

This study aims to determine the optimal conditions for producing biochar briquettes from cottonseed shell residues (Gossypium hirsutum) generated by an industrial vegetable oil processing unit. A Box–Behnken experimental design with three factors—pyrolysis temperature, binder content, and compaction pressure—was employed to evaluate their combined effects on the Higher Heating Value (HHV) of the produced briquettes. Each factor was investigated at three coded levels (−1, 0, and +1). The results revealed that the combined pyrolysis and densification processes significantly enhanced the energy performance of the raw biomass. The HHV increased from 18.507 MJ·kg⁻¹ for the raw material to values ranging between 21.407 and 28.165 MJ·kg⁻¹ for the produced briquettes. These values exceed the minimum requirements for densified solid biofuels specified by the ISO 17225-1:2021 standard, indicating their suitability for domestic and industrial energy applications. The Response Surface Methodology (RSM) results indicated that the maximum HHV within the studied experimental domain was obtained at the coded levels −1, −1, and −1 for pyrolysis temperature, binder content, and compaction pressure, respectively. Under these conditions, the model predicted a maximum HHV of 28.290 MJ·kg⁻¹. The process is not only technically efficient but also economically viable, while complying with the principles of environmental sustainability. This study therefore highlights an innovative pathway for the valorization of this abundant agricultural residue, which has so far been utilized in industry in an unsustainable manner, and contributes to the diversification of renewable energy resources as well as to the promotion of the circular economy.

Unified interpretable machine learning framework for predicting pellet quality from raw and thermochemically pretreated biomass.

Despite AI’s rapid progress of AI as a tool for equitable growth, little is known about how it can help to slow environmental damage. From 1996 to 2024, this study examined how the US’s ecological footprint is impacted by innovations in AI, energy use, economic development, industrialization, and growing populations. After confirming the mixed order of integration, the study applies the Autoregressive Distributed Lag (ARDL) method to evaluate short-term and long-term trends. Robustness was tested using three techniques: Canonical Cointegration Regression, Dynamic Ordinary Least Squares, and Fully Modified Ordinary Least Squares. The results show that the ecological footprint is positively affected by economic growth, increased energy consumption, industrialization, and population growth, all of which underline the environmental impacts of economic and demographic advancement. In contrast, AI innovation reduces ecological pressure, demonstrating its potential to optimize energy efficiency, encourage cleaner production, and enhance overall environmental quality. These results are consistent across both the short- and long-term calculations and robustness tests. This study contributes to the growing debate on technology and sustainability by positioning artificial intelligence as the key driver of ecological resilience. Policy implications stress the urgency of accelerating AI-driven green strategies, investing in renewable energy, and fostering sustainable industrial practices to balance economic progress with environmental preservation in the United States.

The National Offshore Petroleum Titles Administrator (NOPTA) was established in 2012 and assists to manage Australia’s offshore regulatory framework for petroleum and greenhouse gas storage, including more recent responsibilities in offshore electricity infrastructure. This paper presents discussions and a summary of over 10 years of observations into titleholder performance with regards to their responsibilities under the Offshore Petroleum and Greenhouse Gas Storage Act 2006 (and associated regulations). The performance of the upstream energy industry is analysed against a series of metrics, including technical and administrative elements, and implications of historical and emerging trends are discussed regarding their impact on the stewardship of Australia’s offshore resources. Understanding changes associated with offshore production, reserves and resources replacement are important for efficiently managing Australia’s offshore resources, and examples are presented to illustrate the relative maturity of activity. Additionally, maintaining an effective regulatory regime is dependent on a range of factors including timeliness, quality and completeness of information submitted, and supporting analysis highlighting areas for potential improvement. Further insights derived from compliance performance, including against approval expectations/conditions and data submissions are presented, as well as a brief comparison between NOPTA and similar international regulatory agencies. An early look at specific regulatory challenges associated with greenhouse gas titles and offshore electricity infrastructure is also discussed. The conclusions will highlight elements of best-practice between Australia’s offshore upstream industry and NOPTA, including identifying opportunities to assist in managing the increasing regulatory complexity associated with the present energy transition.

The industrial sector is the primary source of anthropogenic carbon emissions. Research on industrial carbon peaking and its drivers play a fundamental role in formulating emission reduction measures. The provincial industrial CO2 emissions were calculated from 2000 to 2021 in China. The Logarithmic Mean Divisia Index (LMDI) method was employed to quantitatively measure the contributions of industrial output, industrial structure, energy intensity, and energy structure to the changes in emissions. The results show that: (1) During the research period, the industrial CO2 emissions of various provinces experienced differentiated growth processes. Among those 11 provinces have already achieved industrial CO2 emission peak. (2) Industrial output value and energy intensity are the essential driving forces affecting industrial CO2 emissions, while the contributions of industrial structure and energy structure to CO2 emissions are relatively low. Spatio-Temporal differences exist in the contributions of various influencing factors. (3) 9 provinces have proactively peaked their emissions by improving energy efficiency, optimizing structure in energy use and industrial structure. While 2 passively emission declined provinces reduce emissions through a decline in industrial output value. (4) The provinces that have not yet reached their peak emissions are mainly industrial provinces, energy based provinces and developing central-western provinces. Their industrial output value has been steadily increasing, and the inhibitory effect of energy intensity and energy structure on CO2 is relatively low. Only under the low-carbon scenario can they almost achieve carbon peaking by 2030. The paper discusses industrial emission reduction strategies by controlling "super emitters" provinces, formulating differentiated emission reduction measures based on influencing factors, and practicing a low-carbon development path.

Economic impacts of industrial energy transition: an evaluation of Austrian climate neutrality pathways

This research describes an investigation into the effect of solar radiation and heat generation on propylene glycol-based trihybrid nanofluid using Cattaneo-Christov theory and activation energy in an out spreading solar module sheet installed on an offshore solar oil field. Renewable energy has an extraordinary ability to substitute itself quicker than it is depleted, especially when sourced from natural sources like the wind and sun. The model described here has significant potential for augmenting the thermal efficiency of solar energy systems, particularly for oil rig solar panel sheets that operate under extreme climatic conditions. The Cattaneo-Christov heat and mass flux model, which allows for precise prediction of thermal and concentration relaxation phenomena, can benefit industrial and offshore energy platforms by improving energy conversion efficiency, sophisticated cooling technologies, and intelligent thermal management systems. The resulting partial differential equations are then converted into ordinary differential equations by using similarity factors. The resultant sets of nonlinear ordinary differential equations have been simulated using the Homotopy analysis approach. The concentration profile rises as the activation energy parameter values increase.

Biological CO2-capture technologies, such as biogas upgrading, constitute essential tools toward decarbonization of the chemical and energy industries. Biological succinic acid production is one such process that naturally fixes CO2 while producing an important platform chemical for the chemical and food industries. However, the high costs associated with biosuccinic acid production render it economically uncompetitive compared to petrochemically produced succinic acid. Here, we propose an integrated platform combining fermentation for succinic acid production from industrial waste streams with the successful upgrade of raw biogas from anaerobic digestion, which repurposes the process into a multi-product platform for increasing its economic viability. To reduce downstream separation costs, the fermentation process is also coupled with an in situ separation module that increases the performance of both the succinic acid fermentation and the biogas upgrade, reaching a CH4 percentage of 93% in the final biogas and triggering a 32.5% increase in succinic acid production. Moreover, the use of the electrochemical module aids in the separation of succinic acid from the fermentation broth, resulting in 94.80% recovery of the succinic acid produced. All experiments were performed at semi-pilot scale.