Explore the vast range of titles available.

Faced with increasingly serious environmental risks, it is necessary to conduct a comprehensive evaluation of the regional environment to provide a solid foundation for environmental policies and actions in the future. This article builds a composite environment risk index that considers spatiotemporal factors and uses annual socio-economic and environmental data of China’s 31 provincial administrative regions from 2004 to 2019 to quantitatively analyze environmental risks. Furthermore, the article employs a panel data model to empirically test the key factors that lead to environmental risks. Moreover, this article employs SVAR models to analyze the dynamics of regional environmental systems in China. The study finds that, at least at this stage, the environmental risks in provincial regions in China are still relatively high, and the key factors of the risks are economic growth, urbanization development, secondary industry growth, and green policy. Therefore, China must adopt more stringent environmental protection policies and actions in the future.

'Extreme' events - including climatic events, such as hurricanes, tornadoes, drought - can cause massive disruption to society, including large death tolls and property damage in the billions of dollars. Events in recent years have shown the importance of being prepared and that countries need to work together to help alleviate the resulting pain and suffering. This volume presents an integrated review of the broad research field of large-scale disasters. It establishes a common framework for predicting, controlling and managing both manmade and natural disasters. There is a particular focus on events caused by weather and climate change. Other topics include air pollution, tsunamis, disaster modeling, the use of remote sensing and the logistics of disaster management. It will appeal to scientists, engineers, first responders and health-care professionals, in addition to graduate students and researchers who have an interest in the prediction, prevention or mitigation of large-scale disasters.

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.

Background While chronic workplace stress is known to be associated with health-related outcomes like mental and cardiovascular diseases, research about day-to-day occupational stress is limited. This systematic review includes studies assessing stress exposures as work environment risk factors and stress outcomes, measured via self-perceived questionnaires and physiological stress detection. These measures needed to be assessed repeatedly or continuously via Ecological Momentary Assessment (EMA) or similar methods carried out in real-world work environments, to be included in this review. The objective was to identify work environment risk factors causing day-to-day stress. Methods The search strategies were applied in seven databases resulting in 11833 records after deduplication, of which 41 studies were included in a qualitative synthesis. Associations were evaluated by correlational analyses. Results The most commonly measured work environment risk factor was work intensity, while stress was most often framed as an affective response. Measures from these two dimensions were also most frequently correlated with each other and most of their correlation coefficients were statistically significant, making work intensity a major risk factor for day-to-day workplace stress. Conclusions This review reveals a diversity in methodological approaches in data collection and data analysis. More studies combining self-perceived stress exposures and outcomes with physiological measures are warranted.

Purpose: The purpose of this study is to presents a structured risk management methodology for managing and controlling the risks in the macro business environments. This paper emphasizes to achieve risk settled environments for industry/ business growth. Design/methodology/approach: In this paper, we present an approach called \"Risk Leveling\" which signifies both a concept and a methodology; it helps to manage risks in the macro business environments. Risk Leveling follows a multi-tier approach towards divergent risks and focuses on balancing the risky macro environs for better industry/ business settings. It recommends the utilization of the available resources in a systematic and efficient way for risk mitigation; such intent is pursued though a structured procedure which urges to reduce the risks to match them with the risk tolerance of the enterprises; it also attempts to diminish the mutual risk disparities giving rise a context in which no risk is seen too big or too small comparative to the others. The Risk Leveling process utilizes logical tools (e.g. AHP, ALARP etc.) and mechanisms to reach the risk settled environments. Findings: Risk Leveling procedure adopted in the study moves through the standard risk management course and tends to pacify the macro risks to achieve risk settled environments for the industry. It suggests exploiting the mitigation efforts in a balanced and efficient way to conserve the resources. Originality/value: The regimes can practice it to pacify and regulate the macro risk forces in the designated industry/ market segments.

Several numerical models have been utilized in water quality assessments for various purposes. Among all the commonly used models, entropy-weighted water quality index (EWQI) has been recognized as the most unbiased model for assessing drinking water quality. Therefore, this paper presents a case study of the application of EWQI in assessing the effect of effluent-derived heavy metals on the groundwater quality in Ajao industrial estate, Nigeria. Three environmental pollution risk assessment tools were integrated to better evaluate the level of heavy metals contamination in the groundwater. Geoaccumulation index ( I geo ) placed 66% of the samples in uncontaminated to moderately contaminated category. However, 19% showed moderate to heavy contamination, whereas 14.29% were heavily contaminated. Similarly, enrichment factor (EF) revealed that 52% of the samples have minimal enrichment, 33% are moderately enriched, while 14.29% were extremely enriched with heavy metals. Vector modulus of pollution index (PI vector ) showed that the majority of the samples (80.9%) have low pollution, 4.76% recorded moderate pollution, while 14.29% had considerable to very high pollution. The EWQI showed that the majority (85.71%) of the groundwater samples are excellent drinking water, while 14.29% are unsuitable for drinking. However, a dendrogram integrating the results of the I geo , EF, PI vector , and EWQI was produced by hierarchical cluster analysis to harmonize and demarcate the groundwater quality in this industrial area. Although this study confirms the suitability of most samples for drinking, more awareness programs towards the protection of the groundwater should be embraced.

This research concentrates on an introduction of a multi-model approach integrating Bayesian Networks (BN), Machine Learning (ML) models, Natural Language Processing (NLP) with Sentiment Analysis, Agent-Based Modeling (ABM), and Survival Analysis to improve predictive modelling of accident causation in high-risk steel industries. The significance of the artificial intelligence (AI) based models is that every approach complements other substantiating the hypothesis. Also, the augmentation of prediction accuracy could be achieved through AI approaches contrary to conventional methods. Results reveal that the application of AI model improves the prediction accuracy compared to conventional approaches. BN application uncovers the machine conditions and human errors responsible for causing accidents. Gradient Boosting Machines discussed equipment-related incidents, while NLP analysis demonstrated negative sentiment due to non-compliance with safety protocols. Moving forward, ABM simulations in accidents focus on personal protective equipment (PPE) compliance and machine maintenance. Survival analysis indicated the role of timely interventions in reducing severe accidents. Additionally, temporal insights aid in timing interventions, improving safety strategy efficacy. The outcome of this research discusses advancements in proactive accident prediction and risk management in high-risk steel industrial environments by addressing latent risk factors.

* China's rural industrial land (RIL) area quadrupled from 1990 to 2015. * RIL expansion cost 9% of China's crop production and threatened human/ecosystem safety. * The underprivileged population bears a disproportionally large share of the risks. China's rural industrialization has been a major driver for its rapid economic growth during the recent decades, but its myriad environmental risks are yet to be fully understood. Based on a comprehensive national land-use data set, our study shows that the area of China's rural industrial land (RIL) quadrupled during 1990-2015, reaching 39000 km 2 in 2015, comparable to urbanization in magnitude but with a much greater degree of landscape fragmentation which implies stronger ecological and environmental impacts. About 91% of the protected areas in the central China were within 50 km from rural industrial land, thus exposed to industrial disturbances. Accelerated rural industrial land expansion, particularly in regions under high geo-hazard risks, led to dramatically increased environmental risks, threatening the safety and health of both rural industrial workers and residents. Moreover, negative effects from rural industrial land expansion could partially offset the crop production growth in recent decades. The underprivileged rural population in the west bears a disproportionally large share of the increased environmental risks. China urgently needs to design and implement sustainable policies to restrict and reshape its rural industrialization. This study aims to inspire policy makers and researchers to rethink the current model of industrial expansion and improve rural industrial land planning, which is important for achieving the sustainable development goals of China.

PurposeAlthough climate change-related risks affect all stakeholders along the supply chain, the potential impact on small and micro-sized suppliers is incredibly excessive. The corresponding toll of these climate risk threats on the mental health and well-being of owners of small and micro-sized suppliers can adversely affect their participation in sustainability efforts, ultimately impacting the firm's performance. This often-overlooked dynamic forms the core of our research. We probe into two pivotal aspects: how industry dynamism and climate risk affect the mental health and well-being of owners of small and micro-sized suppliers and how, in turn, dictate involvement and, consequently, supply chain sustainability performance. This is further nuanced by the moderating role of the abusive behavior of buyers.Design/methodology/approachOur study is built on resource dependency theory and the supporting empirical evidence is fortified by a mixed-methods sequential explanatory design. This study comprises three phases. In the first phase, our experiment examines the effect of industry dynamism and climate risk exposure on sustainable supply chain management performance. Hypotheses H1a and H1b are tested in the first phase. The second phase involves using a survey and structural equation modeling to test the comprehensiveness of the model. Here, the relationship between industry dynamism, climate risk exposure, mental health and well-being of owners of small and micro-sized supplier firms, supplier involvement and sustainable supply chain management (H2–H7) is tested in the second phase. In the third phase, we adopt a qualitative approach to verify and provide descriptive explanations of phase two findings.FindingsOur findings underscore the significance of small and micro-sized suppliers in sustainability, offering invaluable insights for both theoretical understanding and practical implementation. Our study highlights that buyers must allocate sufficient resources to support small and micro-sized supplier firms and collaborate closely to address climate change and its impacts.Practical implicationsThe key takeaway from this study is that buyer firms should consider SDG 3, which focuses on the good health and well-being of their employees and the mental health and well-being of owners of small and micro-sized suppliers in their upstream supply chain. This approach enhances sustainability performance in supply chains.Originality/valueThis is one of the first studies that shows that industry dynamism and climate risk exposure can negatively impact small and micro-sized suppliers in the presence of a contextual element, i.e. abusive behavior of buyers, and ultimately, it negatively impacts sustainable supply chain performance dimensions.

With China’s government facilitating railway projects, more railway lines inevitably pass through ecological fragile regions (EFRs). Railway construction activities in EFRs might cause detrimental impacts on the local water-soil environment (WSE), which is the basis of the local ecological system that if destroyed can induce secondary disasters. Studies on the WSE risk (WSER) during railway construction in EFRs are limited. As such, this study aims to offer preliminary insight into the WSER assessment of railway construction in EFRs. WSERs were identified firstly based on the literature review and field surveys, and thus a risk index framework for WSER assessment including 5 categories of WSERs and 16 second-order risks was established. Then a comprehensive quantitative assessment method was developed by integrating analytic hierarchy process (AHP) and matter-element analysis (MEA) to assess the overall WSERs of railway construction in EFRs. A case (i.e., the Mingan subproject of Hefei-Fuzhou railway) was selected to demonstrate and validate the developed approach. Results show that the proposed assessment approach can be applied to evaluate the WSERs during railway construction. In addition, the case study demonstrates that the risk of construction methods should be the key focus. Findings from this study enrich the knowledge body of sustainable railways and guide the project managers to conduct practical WSER assessment of railway construction.