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Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

Megaprojects are financially large and complex in scope, which require engagement of stakeholders from various institution and communities. With increased concerns from stakeholders on sustainability related issues, from environmental to socio-economic perspective, managing megaprojects has become quite challenging. Hence, there should be proactive approaches in active engagement of stakeholders from the start of the projects to overcome such issues. Currently, there is a knowledge gap on stakeholder management strategies specific to megaprojects on managing sustainability issues.

This research has adopted review of secondary data from case studies to identify stakeholder management challenges and strategies in megaprojects around Sydney area. The documents related to three different projects were reviewed and data were collected through qualitative content analysis and coding with use of NVivo software for within case analysis and was followed by thematic approach for cross case analysis.

Every case study project's sustainability-related stakeholder issues are more similar than different to others. Differences are mostly in terms of intensity of impact of any issue in a particular project, and/or intensity of impact at certain phase of any project. Moreover, significant sectors and strategies have been identified for key focus to manage environment and socioeconomic issues. Whatever be the strategies, in case of megaprojects, they need to be mostly proactive.

This research has extended the knowledge on stakeholder management, in relation to managing sustainability-related issues of megaprojects during their design, construction and operation phases. This research offers useful strategies for project teams of megaprojects to develop a proactive stakeholder management plan, addressing unavoidable sustainability challenges.

Obtaining building permit certificate is an essential component of construction endeavors, but it can be cumbersome sometimes. The process is frequently beset with obstacles, including bureaucratic impediments, red-tapism, prolonged authorization protocols and insufficient inter-agency collaboration which result in project timeline extension, cost escalation and applicant dissatisfaction. Therefore, this study aims to examine customer satisfaction with the assessment of building construction permit certificates in Lalitpur, Nepal.

Following the notion of evaluation model theory, this study adopts an explanatory research design to determine the causal relationship between latent and observed variables. People who have recently completed the construction of their building and those people whose construction work is pending make up the population for the study. A total of 198 samples were collected by following the convenience sampling method from Lalitpur, Nepal. The primary data are collected by using the structured questionnaire with the interview process where the data are statistically evaluated using descriptive and inferential analysis using the KOBO toolbox, SPSS and AMOS. The connection between variables was examined using structural equation modeling (SEM).

Results indicate that the negligence of the employees, the attitude of the employees, the need for additional costs and the hiring of the agent are the most significant obstacles encountered by customers during the process of getting construction permit. Regarding the whole assessment system, the general population expresses displeasure. SEM results indicate that environment and quality are significantly related to customer satisfaction.

This paper's novelty lies in its Nepal-specific inquiry into the relationship between building permit acquisition procedures and customer contentment. The study provides a distinctive viewpoint on this context by combining evaluation model theory and SEM. The localized approach emphasizes the importance of customized strategies to improve customer satisfaction, adding to the current literature on the subject. The study's use of SEM as a quantitative analysis tool enhances its methodological rigor. This interdisciplinary research offers valuable insights for academics, practitioners and policymakers in Nepal and contributes to the wider field of construction and customer satisfaction.

The current study used the social exchange theory to examine the dimensional impact of total quality management (TQM) on the organisational citizenship behaviour for the environment (OCBE) of employees through the mediatory role of environmental corporate social responsibility (ECSR).

The data were collected from 311 employees of ISO-14001-certified manufacturing firms in Pakistan. Both symmetrical partial least squares structural equation modelling (PLS-SEM) and asymmetrical fuzzy-set qualitative comparative analysis (fsQCA) methods were applied to test the proposed hypotheses to enhance the robustness of the results.

PLS-SEM results discovered that all dimensions of TQM, like process management, leadership, human resource management, customer focus, information and analysis and strategic planning, foster the OCBE through the mediation role of ECSR. The robustness of the findings was manifold when fsQCA results complemented the results by discovering that all six dimensions have been identified as sufficient conditions and some as necessary conditions to drive the OCBE.

The theoretical contribution of this study sheds light on TQM's function in boosting OCBE through the mediation of ECSR. Practically, the business managers may utilise TQM as a strategy to foster the OCBE in order to mitigate environmental damages of their organisations’ operations by instilling OCBE among the employees.

The purpose of this article is to assess the extent to which environmental economic practices are recognized and implemented in developing countries, with a specific emphasis on the Nigerian construction sector.

To examine the extent of awareness and usage, a quantitative research approach was adopted with close-ended questionnaires disseminated to construction professionals in Nigeria. The collected data were subjected to analysis using various statistical techniques such as percentages, frequencies and mean scores. Furthermore, to investigate any significant differences in the respondents' opinions, the Kruskal–Wallis H-test was applied.

The study's findings indicate that the level of awareness and implementation of environmental economic practices among construction professionals in Nigeria is limited. Based on the findings of this study, a roadmap of recommendations has been proposed to promote the adoption of environmental economic practices in the Nigerian construction industry. The implementation of these recommendations can help reduce the negative impact of construction activities on the environment and public health and contribute to the development of sustainable cities, communities and societies.

The outcomes from this study can inform policy and decision-making by providing insights into the level of knowledge and awareness of environmental economic practices among construction professionals in Nigeria.

The findings from this study provide a baseline for assessing the level of knowledge and awareness of sustainable practices among construction professionals in Nigeria, which can inform future studies on the effectiveness of training and awareness programs.

The purpose of the study is to investigate strategies for enhancing pollution oversight by local governments while reducing government-enterprise collusion (GEC) levels. Additionally, the factors influencing pollution control efforts at incineration plants are explored. Potential approaches to improving them and for effectively reducing waste incineration pollution are suggested.

The authors examined the most effective methods for mitigating incineration-related pollution and preventing collusion and developed a differential game model involving interactions between local governments and incineration plants. The findings of this work have significant policy implications for central governments worldwide seeking to regulate waste incineration practices.

The results indicate that, first, elevating environmental assessment standards can incentivize local governments to improve their oversight efforts. Second, collusion between incineration plants and local governments can be deterred by transferring benefits from the plants to the local government, while increased supervision by the central government and the enforcement of penalties for collusion can also mitigate collusion. Third, both central and local governments can bolster their supervisory and penalty mechanisms for instances of excessive pollution, encouraging incineration plants to invest more in pollution control. Finally, when the central government finds it challenging to detect excessive incineration-related pollution, enhancing rewards and penalties at the local government level can be a viable alternative.

This study stands out by considering the dynamic nature of pollutants. A differential game model is constructed which captures the evolving dynamics between local governments and incineration plants, offering insights regarding the prevention of collusion from a dynamic perspective. The findings may provide a valuable reference for governments as they develop and enforce regulations while motivating incineration plants to actively engage in reducing waste-incineration pollution.

The purpose of this paper is to analyze the impact of the off-office audit of natural resource assets on the prevention and control of water pollution against a background of big data using a differences-in-differences model.

This study constructs a differences-in-differences model to evaluate the policy effects of off-office audit based on panel data from 11 cities in Anhui Province, China, from 2011 to 2017, and analyzes the dynamic effect of the audit and intermediary effect of industrial structure.

The implementation of the audit system can effectively reduce water pollution. Dynamic effect analysis showed that the audit policy can not only improve the quality of water resources but can also have a cumulative effect over time. That is, the prevention and control effect on water pollution is getting stronger and stronger. The results of the robustness test verified the effectiveness of water pollution prevention and control. However, the results of the influence mechanism analysis showed that the mediating effect of the industrial structure was not obvious in the short term.

These findings shed light on the effect of the off-office audit of natural resource assets on the prevention and control of water pollution, and provide a theoretical basis for the formulation of relevant environmental policies. Furthermore, these findings show that the implementation of the audit system can effectively reduce water pollution, which has practical significance for the sustainable development of China's economy against the background of big data.

This study quantitatively analyzes the policy effect of off-office auditing from the perspective of water resources based on a big data background, which differs from the existing research that mainly focuses on basic theoretical analysis.

The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

Over the last two decades, green supply chain management (GSCM) has enabled businesses to operate in an environmentally friendly manner. The present review examines 234 research articles and proposes a methodical literature review on GSCM, focusing on the aspects of sustainable development.

The work examines conceptual, analytical, empirical and non-empirical research articles, analyzing at all levels of the organization, such as firm, dyad, supply chain and network. The objective of the review is to provide insights into the state and scope of existing research in the domain of GSCM, to identify the prevalence of GSCM and to consolidate the trend of future research. The literature review follows a systematic methodology for analyzing the literature.

The findings can support researchers in identifying research areas with significant impact and streamline research on GSCM in the future. Practitioners can utilize this structured classification to strategize their green initiatives in their firms.

The work contributes to providing literature that explores a detailed review in GSCM. The proposed literature review captures critical aspects in the domain of GSCM and offers future research directions.

This paper aims to identify the impact of the new ambient air quality standards (AAQS) on firm innovation.

Taking listed companies from 2009 to 2014 as the research object, the authors focus on the quasinatural experiment of the pilot policy of the new AAQS and apply the time-varying difference-in-differences (DID) method to conduct the empirical test.

The authors find that the new AAQS has a negative effect on firm innovation, and this negative impact may be caused by the increased environmental expenditures following the implementation of the new AAQS. Furthermore, the authors find that firm profitability and state ownership weaken this negative effect, but the effect of the degree of industrial pollution is statistically insignificant.

The study is an initial effort to explore the causal effect of the new AAQS on firm innovation. This study enriches the literature on the impact of environmental regulations on firm innovation and provides some reference value for the formulation of environmental regulation policies in developing countries.