Purpose -Top managers who possess outstanding leadership competence make significant contributions toward achieving project success. The relationship between the top managers' leadership and infrastructure sustainability (IS), one of the most important project success criteria, is empirically researched in this study. The purpose of this paper is to explore relationships between top managers' leadership competence of intellectual competence (IQ), managerial competence (MQ), and emotional and social competence (EQ) and to explore relationships between leadership competence and IS. Design/methodology/approach -Based on 246 obtained responses to a questionnaire survey across infrastructure projects in the context of the Chinese urbanization process, the analysis of the relationships between top managers' leadership and IS was performed using a structural equation model (SEM). Findings -Results indicate that top managers' leadership competence, with MQ being the main determinant, followed by IQ, directly drives the entire life cycle of an infrastructure project toward accomplishing IS. Through positively influencing the moderate variable of MQ, EQ competence is found to have an indirect influence on IS. Practical implications -In terms of practical implications, the outcomes of this research will provide criteria for the selection of top managers for infrastructure projects to realize IS during the process of Chinese urbanization. Originality/value -The established SEM improves the leadership competence framework of IQ, MQ, and EQ in the respect of reflecting the context of infrastructure projects and promotes the research and development of leadership theory in the construction area.

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Modern projects are normally characterized by huge investments, long construction periods, and complex technology. Therefore, the selection of an appropriate contractor for smooth project delivery is challenging. Previous studies attempted to develop suitable frameworks for contractor selection. However, correlation among indicators, the subjectivity of indicator weights, and heterogeneity among experts' professional capabilities for selecting contractors were not successfully removed from the decision-making process. Typical partial least square (PLS) path modeling can solve these problems. However, it can only solve problems with the same direction correlation among the indicators (e.g., the correlation coefficients of the indicators are all 123 B. Liu et al. positive). For indicators with different direction correlations, path modeling is helpless. Aiming to overcome this limitation, this research introduces a group decision model based on two-stage PLSs path modeling. This decision model can eliminate correlation among indicators and the impact of subjectivity and heterogeneity among experts on the reliability of weighting schemes; more importantly, it can effectively solve the problem of having correlation indicators with different directions. Through a literature review, we first established an indicator system of contractor selection on large scale construction projects. Second, a two-stage PLS path modeling combined with the maximization of deviations principle was proposed as an aggregation approach for performance evaluation. Finally, a comparison was made between the two-stage and typical PLS path modeling methods through a case study, which was conducted to validate the reliability of the new approach.

climate change risk feature system was established, which is embodied in five different aspects: ecosystem and sustainability; uncertainty, vulnerability, and efficiency; behavior and decision-making; governance and management; and adaptation and mitigation. The feature system reflects that the current climate change risk presents strong variability and that the risk boundary is gradually blurred. The areas affected by risk are expanding and deepening. The strategies and governance for addressing risks are gradually diversified. This research contributes to the domain of climate change risk identification and assessment. The features of climate change indicate that we need to adjust policymaking and managerial practices for climate change in the future. Interdisciplinary cooperation, human cognition and preferences, public participation in global governance, and other unnatural factors related to climate change should be strengthened with a more positive attitude.

Project delivery systems (PDSs) selection is crucial to construction project management success. The matching between construction projects and PDSs is hypersensitive to project external environment. Existing studies on selecting PDSs mainly focus on owner’s and project’s characteristics and attach less attention to project environmental factors. This study, therefore, aims to formally identify key project external environmental factors affecting PDSs selection using a data-driven approach. Key factors are summarized and identified through the granular computing method based on 61 Chinese project samples. Empirical results indicate that four factors including market competitiveness, technology accessibility, material availability, and regulatory impact are critical to PDSs selection. This study extended previous research findings on PDSs selection from a perspective of project external environments. Research conclusions can be used as references underpinning construction owners selecting appropriate PDSs considering project external environmental factors.