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Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

This study aims to investigate the relationship between individual-level psychological resources and team resilience in the context of expatriate project management teams. It seeks to understand how personal psychological resources contribute to team resilience and explore the dynamic evolution mechanism of team resilience. The goal is to enhance team resilience among expatriates in a BANI (Brittle, Anxious, Nonlinear, and Incomprehensible) world, where organizations face volatile and uncertain conditions.

An online survey was applied for data collection, and 315 valid samples from Chinese expatriates in international construction projects were utilized for data analysis. A structural equation model (SEM) examines the relationships between personal psychological resources and team resilience. The study identifies five psychological factors influencing team resilience: Employee Resilience, Cross-cultural Adjustment, Self-efficacy, Social Support, and Team Climate. The hypothesized relationships are validated through the SEM analysis. Additionally, a fuzzy cognitive map (FCM) is constructed to explore the dynamic mechanism of team resilience formation based on the results of the SEM.

The SEM analysis confirms that employee resilience, cross-cultural adjustment, and team climate positively impact team resilience. Social support and self-efficacy also have positive effects on team climate. Moreover, team climate is found to fully mediate the relationship between self-efficacy and team resilience, as well as between social support and team resilience. The FCM model provides further insights into the dynamic evolution of team resilience, highlighting the varying impact effects of antecedents during the team resilience development process and the effectiveness of different combinations of intervention strategies.

This study contributes to understanding team resilience by identifying the psychological factors influencing team resilience in expatriate project management teams. The findings emphasize the importance of social support and team climate in promoting team resilience. Interventions targeting team climate are found to facilitate the rapid development of team resilience. In contrast, interventions for social support are necessary for sustainable, long-term high levels of team resilience. Based on the dynamic simulation results, strategies for cultivating team resilience through external intervention and internal adjustment are proposed, focusing on social support and team climate. Implementing these strategies can enhance project management team resilience and improve the core competitiveness of contractors in the BANI era.

Safety climate plays an important role in the high-risk construction industry. Advances have been made in the understanding of construction safety climate in terms of four interrelated themes, specifically, its definition, measurement, antecedents and consequences. However, knowledge remains fragmented as the studies are scattered, and a systematic review covering these four themes is lacking. To address this research gap, this study aims to perform a systematic literature review of construction safety climate literature regarding the four themes.

Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol guidelines, 178 eligible articles were obtained. This study provided thematic analysis of the 178 papers to identify what is known and what is not yet fully known regarding the four themes of construction safety climate. This study also conducted a descriptive analysis to identify the influential scholars, keywords, theories and research methods used by the literature, and finally presented an integrative framework directing future research.

The literature has not reached a consensus on the definition and measurement of construction safety climate. While it has identified the impact of safety climate on both behavioral and accident consequences, it has paid less attention to the antecedents and their underlying mechanisms regarding safety climate. Fang D. and Lingard H. are identified as the most influential authors in this field. “Questionnaire” and “safety behavior” are the keywords most closely related to safety climate. Unfortunately, the existing evidence for the causal relationships between safety climate and its antecedents and consequences is weak, as many studies lack clear theoretical substance, use a concurrent research design and focus only on individual-level climate perceptions. Finally, to support the development of construction safety climate around the four themes, potential research directions and research methods supporting them are illustrated.

This review makes contributions by integrating existing construction studies covering its definition, measurement, antecedents and consequences. This review also makes contributions to specific themes: no review exists on the antecedents of construction safety climate, and this review fills that gap; with regard to consequences, the existing reviews focus either on safety outcomes or safety behavior, but this review included both of them and further elaborated the different theories underpinning the relationships between safety climate and them. It is hoped that this systematic review will be helpful to the research community toward developing a nomologic network and promoting knowledge integration with respect to construction safety climate.

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.

Given a baffling contradiction that the availability of safety knowledge may not necessarily lead to workers' safety behavior, this study aims to develop an exploratory two-step working model of safety knowledge in translating safety climate into safety behavior. In particular, this study highlights the importance of articulating tacit safety knowledge and improving workers' systematic problem solving (SPS) capacity in a favorable safety climate.

This study uses 110 valid responses from Pakistan-based construction workers to test five hypotheses which embody the exploratory two-step working model of safety knowledge. The partial least squares structural equation modeling (PLS-SEM) is used to analyze the data.

The results of this study support the two-step working mechanism of safety knowledge in translating safety climate into safety behavior. Furthermore, results suggest that safety climate as a job resource facilitates converting construction workers' tacit safety knowledge into explicit safety knowledge (i.e. safety knowledge articulation) and then enabling them to spot non-conformities in safety management practices (i.e. SPS) and consequently to work safely (i.e. safety behavior).

The study has both theoretical and practical significance. In theory, it extends organizational learning theory and job demands-resources (JD-R) theory in the construction safety research domain and elaborates on the mediating role of safety knowledge articulation and SPS for the relationship between safety climate and safety behavior. In practice, it highlights the importance of continuous articulation of tacit safety knowledge and accumulation and use of explicit safety knowledge in construction safety management practices.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

This study aims to assess the effect of water variation on bioethanol production from cassava peels (CP) using Saccharomyces cerevisiae yeast as the ethanologenic agent.

The milled CP was divided into three treatment groups in a small-scale flask experiment where each 20 g CP was subjected to two-stage hydrolysis. Different amount of water was added to the fermentation process of CP. The fermented samples were collected every 24 h for various analyses.

The results of the fermentation revealed that the highest ethanol productivity and fermentation efficiency was obtained at 17.38 ± 0.30% and 0.139 ± 0.003 gL⁻¹ h⁻¹. The study affirmed that ethanol production was increased for the addition of water up to 35% for the CP hydrolysate process.

The finding of this study demonstrates that S. cerevisiae is the key player in industrial ethanol production among a variety of yeasts that produce ethanol through sugar fermentation. In order to design truly sustainable processes, it should be expanded to include a thorough analysis and the gradual scaling-up of this process to an industrial level.

This paper is an original research work dealing with bioethanol production from CP using S. cerevisiae microbe.

1. Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

2. Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

3. Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

4. Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

This study assesses performance measurement (PM) system implementation efforts across various organizational contexts and investigates which factors are critical to achieving implementation success (IS).

An empirical field study was conducted to refine a framework of PM system IS that consists of 5 dimensions of success and 29 factors. A survey questionnaire was used to investigate actual organizational practice and exploratory factor analysis was conducted to refine constructs corresponding to potential factors and dimensions of IS. The resulting variables were then investigated using multiple regression analysis to identify critical success factors for implementing PM systems.

The survey was completed by representatives from 124 organizations and the exploratory factor analysis results indicated that there are three underlying dimensions of IS (i.e. Use of the System, PM System Performance, and Improved Results and Processes) and 12 factors. Of the factors, nine can be considered critical success factors having a significant relationship with at least one dimension of IS: Leader Support, Design and Implementation Approach, Reward System Alignment, Organizational Acceptance, Organizational Culture and Climate, Easy to Define Environment, IT Infrastructure Capabilities, PM System Design Quality, and PM Participation and Training.

The results show that there are distinct dimensions of IS and, although some factors are associated with all dimensions, most are more closely related to only one dimension. This suggests that different strategies should be utilized based on the types of challenges experienced during implementation.

This paper aims to discuss the practical dilemmas of institutional change to tackle the climate crisis in a UK university, identifying key assumptions and issues that block meaningful change. The research was part of an initiative to define a theory of change (ToC) to meet the university’s institutional climate commitments.

The findings are based on interviews with members of an inter-disciplinary ToC working group, a staff–student climate coalition and student representatives at the university. Interviewees were purposively selected to gain insights into assumptions about the nature of the university and its role in tackling the climate crisis, which must be addressed for the university to effectively implement its climate plan.

This paper identified tensions between the university’s role as a public and commercial institution, a lack of clarity over decision-making processes and the difficulties in balancing (and being transparent about) actions with commitments to tackle the climate crisis. A democratic and flexible approach to change is essential to mitigate these issues, providing an opportunity to reflect on the diversity of the university community and openly debate goals and commitments.

In setting out the initial steps of a ToC in a UK university, this paper offers practical insights for higher education institutions looking to change practices. By highlighting assumptions at a particular institution, this paper also contributes a level of granularity to a growing field of research on efforts in higher education institutions to tackle the climate crisis.