Accurate prediction of small-scale atmospheric dispersion is essential for nuclear safety assessment and emergency decision-making in nuclear power plant (NPP) accidents. This study evaluates a RANS-based CFD model for atmospheric dispersion at an AP1000 NPP site with irregular buildings and complex terrain. The model performance was assessed against wind tunnel measurements of wind and concentration fields. Sensitivity analyses were conducted on key modeling and numerical configurations, including turbulence model, turbulent Schmidt number (Sct), and discretization scheme, to examine their influence on pollutant dispersion behavior. Compared with empirical-formula-based dispersion models, the established CFD approach explicitly resolves flow-geometry interactions and reproduces the wind and concentration fields under complex building-terrain conditions. The simulation results showed acceptable agreement with measurements, with the FAC2 and FAC5 exceeding 0.64 and 0.85, respectively, and |FB| values below 0.5. The results further reveal how modeling configurations influence plume development and pollutant transport characteristics. Sensitivity results indicate that modeling configurations significantly influence prediction performance and associated uncertainty. Based on these analyses, a configuration consisting of the Standard k-ε model, a turbulent Schmidt number of 0.2, and the Mixed Discretization Scheme is adopted as a recommended reference configuration for the present scenario. These findings improve the understanding of pollutant dispersion processes in complex environments and support nuclear safety assessment and consequence analysis for NPP-related atmospheric releases.

As the digital economy increasingly emerges as a new driver of economic growth, investigating whether data factor agglomeration can enhance enterprise resilience is of significant importance. This study takes the construction of the national big data comprehensive pilot zone as the policy orientation, based on data from China's A-share listed companies from 2011 to 2023, and uses a multi-period difference-in-differences model to evaluate the impact and mechanism of data factor aggregation on enterprise resilience. The findings reveal that data factor agglomeration significantly enhances enterprise resilience; this conclusion holds after a series of robustness tests. Enhancing total factor productivity and strengthening digital technology innovation constitute key mechanisms through which data factor agglomeration boosts enterprise resilience. Furthermore, financing constraints will weaken the effect of data factor aggregation on enhancing enterprise resilience, while institutional investor holdings exert a positive moderating effect. This effect is particularly pronounced in regulated industries and short-lived enterprises. This study aims to provide theoretical guidance and practical evidence for governments to optimize data factor allocation policies and for enterprises to enhance risk-resistance capabilities.

Study region: This study focuses on mainland China, covering 30 provinces. Study focus: To explore the relationship between water scarcity induced by water use, rather than water use volume per se, and economic growth, this study establishes an improved integrated water scarcity footprint (IWSF) considering water quantity, quality and environmental flow requirements (EFR). Building on a comparison with the conventional water footprint (WF) at the sectoral level, the study examines the evolution of decoupling from economic growth, the underlying driving effects, and their heterogeneity across industries. New hydrological insights for the region: The water footprint results show that, at both the regional and sectoral levels, IWSF is greater than WF in northern provinces, whereas the opposite pattern is observed in southern provinces. Decoupling results show that, 2007-2012, IWSF decoupling was weaker than WF, while the opposite occurred in 2012-2017; Water use intensity was the key determinant of IWSF decoupling patterns; The reduction in water use intensity driven by pollution control was key to achieving strong IWSF decoupling in 2012-2017, with the primary and secondary industries contributing the most. These results indicate that the newly developed IWSF successfully captures the contribution of water pollution to water scarcity and its decoupling, whereas WF, which considers only absolute water withdrawal, cannot. This study highlights the necessity of addressing water scarcity from the perspective of impact-oriented water use.

Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retrievers for solving complex problems have predominantly employed end-to-end reinforcement learning. However, these approaches neglect supervision over the reasoning process, making it difficult to guarantee logical coherence and rigor. To address these limitations, we propose Thinker, a hierarchical thinking model for deep search through multi-turn interaction, making the reasoning process supervisable and verifiable. It decomposes complex problems into independently solvable sub-problems, each dually represented in both natural language and an equivalent logical function to support knowledge base and web searches. Concurrently, dependencies between sub-problems are passed as parameters via these logical functions, enhancing the logical coherence of the problem-solving process. To avoid unnecessary external searches, we perform knowledge boundary determination to check if a sub-problem is within the LLM's intrinsic knowledge, allowing it to answer directly. Experimental results indicate that with as few as several hundred training samples, the performance of Thinker is competitive with established baselines. Furthermore, when scaled to the full training set, Thinker significantly outperforms these methods across various datasets and model sizes.

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study.

Occupational Health and Safety (OHS) risk management is an ongoing challenge in resource-scarce chemical industries, especially in developing nations like Tunisia. The current research recommends a hybrid socio-techno-environmental model based on Fuzzy Logic and the Analytic Hierarchy Process (AHP) for occupational health and safety risk assessment and prioritization of occupational health and safety (OHS) risks. The model, based on a backup of expert judgment from pre-specified questionnaires and real data of Tunisian chemical industries, quantifies the risk factors based on criticality and probability of occurrence. Fuzzy–AHP not only evaluates qualitative estimates but also provides a systematic framework for evaluating and prioritizing occupational health and safety risks. Findings are consistent with the fact that socio-organizational factors such as occupational safety culture and compliance among workers play a pivotal role in determining total levels of risk, and in certain cases dominate over technical considerations alone. In offering a scalable and context-sensitive solution, this research has an additional contribution with a decision-support framework that assists organizations in evaluating and prioritizing occupational health and safety risks under uncertain conditions. Its use for policy makers and business interests that seek to maximise OHS performance in given constraints, and its use in actual implementation and compatibility with sustainability and worker health goals, speaks to its utility.

Sustainable organizational performance is heavily reliant on employee health and safety, particularly in high-risk sectors like the mining industry, where unsafe practices can lead to catastrophic accidents and significant long-term productivity losses. Although research on psychosocial safety climate (PSC) is expanding, its underlying mechanisms remain underexplored in underrepresented contexts such as Morocco’s phosphate mining industry. This study aimed to investigate the relationship between PSC and miners’ safety behavior, specifically examining the mediating roles of learning opportunities and role ambiguity. Using a cross-sectional design, we collected data from 524 employees across three mining companies operating within the OCP Group through a structured survey. The hypothesized relationships were tested using partial least squares structural equation modeling. The findings revealed a significant positive effect of PSC on safety behavior. Role ambiguity was negatively associated with both PSC and safety behavior, whereas learning opportunities were positively related to both PSC and safety behavior. Moreover, both learning opportunities and role ambiguity were identified as significant partial mediators in the PSC-safety behavior relationship. These results indicate that organizations can enhance safety outcomes and support sustainable safety performance by fostering a strong psychosocial safety climate characterized by clear role definitions, effective safety training, and supportive leadership.

Cooperative governance across different river basins has long faced the challenge of “intergovernmental bargaining.” As a significant institutional innovation, cross-provincial river basin ecological compensation (REC) plays a crucial role in improving water ecological environments. Existing research primarily focuses on the impact of cross-provincial REC policies on water quality or governance performance, lacking empirical evidence that characterizes water ecological resilience (WER) from an evolutionary resilience perspective to identify the effects of policies and their underlying mechanisms. The core research questions are: Can cross-provincial REC policies enhance provincial-level WER in China? What are the underlying mechanisms? Research objectives include: Establishing a provincial WER evaluation framework; Examining the independent impact of cross-provincial REC policies on WER; Revealing the policy implementation mechanisms. This study commenced by examining the institutional advantages of cross-provincial REC from the evolutionary game theory perspective. Based on the comprehensive capacity of water ecosystems to withstand external disturbances, self-regulate, restore from degraded states, and undergo innovative transformability, this research established a water ecological resilience (WER) evaluation framework encompassing “Resistance - Adaptation - Restoration - Transformability”. Subsequently, the CRITIC-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was employed to measure the WER of 30 Chinese provinces from 2006 to 2021. A multi-period difference-in-differences (DID) model was used to empirically examine whether the cross-provincial REC policy effectively enhanced WER, with its impact mechanism explored through a mediation effect model. Finally, spatial analysis was employed to identify the spatiotemporal evolution patterns of WER. These results indicate that the cross-provincial REC policy has significantly boosted provincial WER. This policy primarily enhances WER through two pathways: promoting technological innovation (TI) and industrial upgrading (IU). Furthermore, China's WER level increased from 0.149 in 2006 to 0.364 in 2021 during the study period, demonstrating markedly strengthened spatial agglomeration. These findings provide empirical evidence for evaluating the effectiveness of the cross-provincial REC policy.

As one of China's most economically active, open and innovative regions, the Yangtze River Delta (YRD) boasts strategic significance in the country's modernization and further opening-up. Exploring its medium-to long-term carbon emission trends and mitigation potential is crucial to achieving China's "Dual Carbon" goals. To systematically evaluate its complex multi-sectoral and cross-regional energy-economy-emission system, this study develops the LEAP-SJZA model. Endowed with the advantage of flexible model structure and data framework configuration, this model enables systematic simulation of dynamic impacts under diverse policy scenarios. From three dimensions-primary energy, end-use industries, and emission contributions-we predict and analyze the YRD's carbon emissions and mitigation potential. Results demonstrate: (1) The YRD can successfully achieve the goal of "carbon peaking by 2030 '' under baseline, low-carbon, integrated, and blueprint scenarios. (2) Industry remains the dominant contributor to medium-and long-term carbon emissions in the YRD, with industrial carbon emissions accounting for 49 %-85 % of the total by 2060. (3) Industrial collaborative innovation exerts a significant short-term emission reduction effect; clean energy substitution serves as the core driver, while cross-regional low-carbon technology sharing acts as a long-term booster. Finally, we propose medium-to long-term countermeasures focusing on low-carbon transformation of energy structure, industrial collaborative emission reduction, and cross-regional low-carbon technology sharing, providing actionable references for the YRD to advance high-quality regional integrated development under the "Dual Carbon" goals. The LEAP-SJZA model's scenario simulation capability and multi-dimensional analysis results allow policymakers to quantitatively assess the effectiveness of different emission reduction measures, thereby supporting targeted and evidence-based decision-making for the YRD's integrated low-carbon governance.

As a key region of China's energy transition, the Yangtze River Delta (YRD) requires medium and long-term energy demand forecasting to achieve the "dual-carbon" goal. Based on current economic and social development, as well as energy consumption, the LEAP-SJZA model is constructed to create a baseline, low-carbon, integrated, and comprehensive scenarios, forecasting total energy demand, end-use industries, and end-use energy categories from 2023 to 2060. The results show that (1) Total energy demand in YRD exhibits an "inverted U-shape," with the baseline, collaborative industrial innovation, and clean energy substitution scenarios peaking in 2039 and the low-carbon technology sharing and comprehensive scenarios peaking in 2029. (2) The contribution rate of energy conservation and emission reduction is comprehensive scenario > low-carbon technology sharing scenario > collaborative industrial innovation scenario > clean energy substitution scenario > baseline scenario. (3) Industry is the largest energy-demanding sector, except transportation, where energy demand is rising in the short term. (4) The energy system is shifting towards greater electrification, with the share of electricity in end-use energy demand exceeding 42 % in all scenarios in 2060. Finally, it proposes medium- and long-term energy development countermeasures to promote high-quality, integrated regional development.

Evaluating the effectiveness of carbon pricing policies is essential for promoting sustainable energy use. However, traditional methods fail to capture these policies' dynamic and multifaceted impacts. Therefore, we examined how to use big data technology to model and analyze the influence of carbon pricing on sustainable energy transitions. Through a literature review, we explored the potential of big data methods, including machine learning, spatial analysis, and real-time monitoring for carbon pricing. The results provide a reference for policymakers, researchers, and stakeholders to make evidence-based decisions and collaborate to address potential challenges.

China's productivity progress has promoted good socio-economic functioning, but has brought serious negative impacts on natural resources and eco-environment. Promoting synergistic security of water resources (W), social economy (S) and ecological environment (E) provides technical support for promoting regional economic growth, social progress and ecological harmony. This study constructed an index system based on stability (S), coordination (C) and resilience (R). Then the entropy weight-CRITIC method, coupled coordination degree model, and obstacle degree model were used to comprehensively evaluate the level of synergistic security of WSE-SCR in the Beijing-Tianjin-Hebei (BTH) region. Finally, the two-dimensional and three-dimensional joint risk probability distributions of different provincial regions were explored by combining the Copula function. The results showed that the level of synergistic security in the BTH region increased from 2006 to 2022, and the multi-year average value was within the security range, specifically Beijing (0.69) > Hebei (0.66) > Tianjin (0.65). The key factors affecting the WSE-SCR are per capita water resources (S1), per capita water consumption (S3), proportion of secondary industry (S5), ecological water use ratio (C4), and investment in infrastructure construction (R8). In terms of two-dimensional joint risk probability, the C-R of Beijing and the S-R of Tianjin and Hebei had the highest probability of security risk, which were 0.5598, 0.5308 and 0.5488, respectively. The three-dimensional joint risk probability of the S-C-R of Beijing, Tianjin and Hebei (S <= 0.6, C <= 0.6, and R <= 0.6) were greater than 30 %, which were 0.389, 0.341 and 0.352, and increased with the increase of single dimension risk. This study can provide scientific information for advancing the synergistic security and sustainable improvement of WSE in arid zones.

The development of green finance is crucial for China’s economic and long-term environmental sustainability. This study examines the impact of government-led green finance on green total factor productivity (GTFP) at the enterprise level in China’s polluting and environmental protection industries from 2005 to 2022. It explores how green transformation moderates this relationship and examines, in particular, the potential heterogeneity and industry spillover effects from green bond issuance. Our findings reveal that in polluting industries, there is an inverted U-shaped relationship between green finance and GTFP. In contrast, this relationship is not significant in environmental protection industries. Green transformation positively mitigates the inverted U-shaped relationship. Further analysis reveals that, first, the relationship between green finance and GTFP is insignificant for enterprises with insufficient green behavior. Second, green transformation can exert a moderating effect by alleviating financing constraints. Additionally, companies that issue green bonds increase their GTFP and generate industry spillover effects by encouraging other firms to undertake green transformations. This study provides novel insights and empirical evidence for guiding green finance development and promoting sustainable green development.

Preventing miners' unsafe behavior and reducing accidents in deep coal mines are crucial. This study comprehensively used methods such as the human factor analysis and classification system for China mines (HFACS-CM) model, grounded theory and the back propagation (BP) neural network model to construct an early warning index system for miners' unsafe behavior. A three-layer feed-forward BP neural network warning model with a structure of 13-14-4 layers was developed to predict miners' unsafe behavior. The results showed that the model can accurately predict miners' unsafe behavior and reflect the complex non-linear relationship between the driving factors and unsafe behavior. Unsafe supervision was the most critical driving factor affecting miners' unsafe behavior, followed by organizational influence, miners' unsafe state and environmental factors. This study can help mining enterprises formulate more effective management measures for miners' unsafe behavior so as to improve the efficiency of coal mine safety management.

As global climate change intensifies, achieving the dual goals of economic efficiency and low-carbon development has become a pressing challenge. Using panel data for 269 Chinese cities from 2010 to 2021, based on their carbon emission efficiencies (CEEs) measured using the DEA-SBM model, a staggered difference-in-differences (SDID) model is employed to identify the policy impacts, which is further extended into a triple-difference (DDD) framework to examine the causal impact of the dual-pilot policy. The results show that (1) China’s CEE has improved gradually but remains relatively low, with significant regional disparities. (2) Empirical results indicate that the dual-pilot policy leads to a significant improvement in CEE, raising it by approximately 4.06%. The positive impact is particularly pronounced in cities characterized by more advanced industrial structures and stricter environmental regulatory frameworks. (3) Industrial upgrading and green technological innovation serve as key mediating channels, contributing 2%, 7%, and 10.7% to the total mediation effect. (4) The positive impacts are particularly evident in eastern, large-scale cities. These results underscore that the integration of digitalization and low-carbon initiatives serves as an effective pathway to improving CEE. Therefore, policymakers are encouraged to further advance the dual-pilot programs, foster green technological innovation, and accelerate industrial upgrading toward a digitally empowered and low-carbon development model.

The implementation of the New Environmental Protection Law (NEPL) in 2015 is a fundamental and effective way to strengthen environmental governance in China since the original version was released in 1989.This study explores whether environmental regulations affect the green innovations of environmental enterprises in China by treating the implementation of the New Environmental Protection Law (NEPL) as a quasi-natural experiment.Using green patent data from 419 A-share listed enterprises from 2012 to 2021, we find that the implementation of the NEPL increases the number of both green invention patent applications and green utility model patent applications in environmental industries.In addition, there is an accelerating effect because of sufficient R&D investment and a crowding-out effect because of excessive government subsidies.The research results also show that green patents of state-owned enterprises, large enterprises, and enterprises located in areas with strong environmental governance are significantly increased.These findings provide theoretical and empirical basics for the authorities to formulate more targeted policies to motivate innovations in environmental enterprises.

With the rapid development of artificial intelligence technology, more and more universities are exploring student behavior analysis. This study uses artificial intelligence methods and tools to conduct in-depth analysis of the behavioral characteristics of university students, identify key factors that affect academic performance and mental health, and first use data mining and machine learning techniques to analyze multidimensional data such as students’ learning habits, social interactions, and psychological states, and evaluate the impact of these factors on academic performance and campus adaptability. Based on the analysis results, targeted management strategies were proposed, including personalized learning support, mental health interventions, and social skills training. Verify the effectiveness of artificial intelligence technology in student behavior monitoring and management through empirical research on case universities. At the same time, it also discussed the challenges and solutions that may be faced during the implementation process, providing practical and feasible suggestions for university managers.

It is necessary to implement more sustainable clean coal-power generation technologies for coal-fired power generation, particularly at regional levels. We aim to compare the provincial-specific environmental impacts of the clean coal-power generation technologies and propose the optimal technology to mitigate the environmental impacts. We used life cycle assessment (LCA) to analyze and compare the environmental impacts of four clean coal power generation technologies: ultra-supercritical (USC), supercritical (Super-C), fluidized circulating bed (CFB), and subcritical (Sub-C) in Anhui province, a major coal power generation province in China. The life cycle of coal power generation includes coal mining, coal washing, and coal burning sages. The six environmental impact categories are abiotic resource depletion (ADP), global warming potential (GWP), acidification potential (AP), photochemical ozone potential (POP), particulate form (PF), and solid waste (SW). The functional unit is 1 MWh of power generated. The results indicated that the impacts of coal burning dominated in ADP, GWP, AP, and SW. Both coal washing and coal mining contributed to over 90

To more intuitively demonstrate the locational distribution of spatial agglomeration of HQD (high-quality development) in China’s coal cities, this study uses the entropy value method, standard deviation ellipse, and geographic detector to investigate the law of dynamic evolution and driving factors of HQD in China’s coal cities from 2011 to 2020. The findings are as follows: (1) The HQD level of China’s coal cities is experiencing a positive trajectory, with the highest level of development in the east, followed by the regions located in the center and west of the country, and relatively low in the northeast. Throughout the “Twelfth Five-Year Plan” period, Suzhou made the greatest progress, while Fuxin had the greatest decline. Throughout the “13th Five-Year Plan” period, Xingtai and Handan made the greatest progress, while Qitaihe had the greatest decline. (2) The HQD level of China’s coal cities as a whole shows a northeast–southwest direction, the center of gravity shifts southward, indicating a concentration pattern. The eastern and central areas are oriented in a northwest–southeast direction; the center of gravity in the east shifts to the northwest, and the center of gravity in the middle shifts to the southeast; and both regions have a higher level of HQD in the east–west direction. The western and northeastern regions are in a northeast–southwest direction, with the center of gravity moving to the northeast: the western region shows a tendency toward diffusion, and the northeastern region shows an agglomeration trend. (3) Patent authorization per 10,000 people, foreign trade dependence, R&D investment intensity, and GDP per capita were important drivers for the HQD of China’s coal cities; The degree of government intervention is the best interaction factor, and the degree of opening to the outside world and the forest coverage rate are the best interaction objects.

Safety supervision is identified as a crucial tool for encouraging safe production within chemical enterprises, yet the existing safety supervision methods often struggle to deter unsafe behaviors, leaving these enterprises susceptible to safety accidents. The current literature, predominantly based on evolutionary game theory, largely focuses on optimizing supervision methods while lacking effective guidance for enterprises to ensure rule compliance. Furthermore, this research predominantly centers on the analysis of two key stakeholders using static reward and punishment strategies, neglecting other potential participants and dynamic reward and punishment strategies. To address these gaps, this paper introduces an evolutionary game model encompassing the three primary stakeholders in chemical production safety supervision: government regulators, chemical enterprises, and employees. The study assesses the stability of these three subjects under static reward and punishment strategies, dynamic punishment strategies, and dynamic reward and punishment strategies. In conjunction with the system dynamics model, numerical simulations are utilized to analyze shifts in stakeholders' decision-making behavior across different scenarios. Simulation results show that, under the static mechanism, there is no evolutionary equilibrium solution for the three-game subjects. While increasing reward and punishment coefficients can temporarily enhance enterprise compliance, it also escalates system volatility. The linear dynamic punishment mechanism can mitigate subject volatility but does not yield optimal evolutionary results. Finally, a novel nonlinear dynamic punishment-reward mechanism is proposed, effectively controlling the instability within the game scenario and making compliant production the optimal strategic choice for chemical enterprises.