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This paper investigates the impact of green construction procurement practices (GCPPs) on circular economy (CE) success by identifying environmentally sustainable procurement practices that can promote a CE in the construction industry. The goal was to promote circularity in construction through GCPPs.

A quantitative research approach was adopted and purposively selected 100 respondents for a cross-sectional questionnaire survey. Data from the questionnaire survey were analysed using mean score ranking, One-sample t-test and regression analysis.

The study found that using on-site systematic waste management, project stakeholder commitment and support for green practices and environmental requirements in technical specifications, significantly impact CE success in construction, with a 12.8% variance in CE when green procurement is practised. This implies that GCPPs have positive repercussions on CE success, where the CE success is expected to change as GCPPs levels increase.

The study provides insights into green procurement, promoting its use in infrastructure development and aiding clients, particularly in the government sector with insights into the challenges and practices involved in green procurement. Practitioners can also benefit from better implementing CE strategies to draft and manage contracts for infrastructure projects that prioritize circularity.

The limited impact of GCPPs on advancing CE principles in construction suggests policy and practice must strengthen procurement requirements to fully leverage spending and drive the sector’s transition towards a circular model. Also, novel insight is provided into the most effective types of GCPPs for promoting CE success, aiding policymakers in optimizing construction procurement strategies.

The energy performance gap (EPG) in building construction has been one of the major barriers to the realization of environmental and economic sustainability in the built environment. Although there have been a few studies addressing this issue, studying this topic with a special focus on the project delivery process has been almost overlooked. Hence, this study aims to address the EPG in building construction through the lens of collaborative and life cycle-based project delivery.

In order to realize the objective of this study, the development of a theoretical framework based on the literature review was followed by a qualitative study in which 21 semi-structured interviews were conducted with Finnish project professionals representing clients, design/planning experts, constructors and building operation/maintenance experts to explore their views on the topic under study.

The findings reveal the project delivery-related causes of EPG in building construction. Moreover, the obtained results present a collaborative and life cycle-based delivery model that integrates project and product (i.e. building) life cycles, and it is compatible with all types of contractual frameworks in building construction projects.

Although the findings of this study significantly contribute to theory and practice in the field of collaborative and sustainable construction project delivery, it is acknowledged that these findings are based on Finnish professionals’ input, and expanding this research to other regions is a potential area for further studies. Moreover, the developed model, although validated in Finland, needs to be tested in a broader context as well to gain wider generalizability.

The obtained results reveal the significance and impact of collaborative and life cycle-based project development and delivery on the realization of environmentally sustainable building construction.

The global construction industry has a history of poor project success, with evident and frequent overruns in cost and schedule. This industry is a highly interconnected and complex system in which the components, i.e. suppliers, contractors, end-users, and stakeholders, are delicately linked to each other, the community, and the environment. Therefore, defining and measuring project success can be challenging for sponsors, contractors, and the public. To address this issue, this study develops and analyzes a more comprehensive set of success criteria for medium and large construction projects.

After reviewing the existing literature, this study identified 19 success criteria for medium and large construction projects, which were categorized into five groups. The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) method was used to gain further insight into the interrelationships between these categories and explain the cause-and-effect relationships among them. Next, this study applied the modified logarithmic least squares method to determine the importance weight of these criteria using the fuzzy analytic hierarchy process.

28 project managers working in the construction industries in Australia and New Zealand participated in this study. Results suggest that “project efficiency” and “impacts on the project team” are cause criteria that affect “business success,” “impacts on stakeholders,” and “impacts on end-users.” Effective risk management emerged as the most crucial criterion in project efficiency, while customer satisfaction and return on investment are top criteria in “impacts on end-users” and “business success.”

Although numerous studies have been conducted on project success criteria, multicriteria analyses of success criteria are rare. This paper presents a comprehensive set of success criteria tailored to medium and large construction projects. The aim is to analyze their interrelationships and prioritize them thoroughly, which will aid practitioners in focusing on the most important criteria for achieving higher success rates.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

Currently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.

A database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.

The case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.

The framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

Value creation for society from public projects requires that the overall benefits exceed the use of taxpayers' money. At the same time, cost overruns in public projects are a well-documented feature in the literature, but practical guidance on reducing the extent and magnitude of overruns is rare. In 2000, Norway introduced a governance regime that includes mandatory external quality assurance (QA) of cost estimates for major public projects. This paper compares the cost performance of public projects on each side of this QA scheme.

We use an original dataset covering 1,704 projects from 2000 to 2021, reported first-hand from Norwegian public agencies. We apply quantitative methods in the form of descriptive statistics, regression models, and statistical testing of hypotheses to answer our research questions.

The mean cost overrun across projects in our dataset is smaller than several previous international studies have reported. We find no statistical support for different cost performances between QA and non-QA projects. Secondly, cost overruns seem to vary between different public sectors. A third finding is a small development with lower cost overruns over time for the non-QA projects, and we raise the question of whether the QA scheme has contributed to overall learning effects. The fourth finding is that cost deviations are quite independent of project size.

The paper offers novel insights for decision-makers and researchers on the effects of external quality assurance on cost performance in public projects.