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The objective of this paper is to propose a methodology to estimate the benefits and costs of stakeholder engagement (SE). Indeed, in the transport sector, it is consolidated that a good decision-making process foresees the involvement of the main stakeholders, but what are the benefits and costs of the SE? How to quantify these impacts and explicitly take them into account in a cost-benefit analysis? In this paper, an attempt to answer these questions is provided.

In this paper, a methodology is proposed to estimate the benefits and costs of SE. Moreover, the proposed methodology is applied to a case study with an attempt to identify direct and indirect cost and benefit drivers within the context.

A range of examples of the monetary costs and benefits of SE is provided through the case study of the high-speed rail corridor connecting Bari and Naples in Italy.

Limits in quantifying all the aspects of engagement.

To be adopted by public administrations when deciding whether carrying out a project.

Social inclusion is a must in any decision-making process concerning big projects affecting the community.

The original value of this paper is to provide a contribution to the current literature on the quantitative representation of the impacts of SE. Indeed, a methodology to quantify and monetize the costs and benefits of SE is proposed.

Construction industry faces challenges in making objective decisions due to monetary value attached to quality. Among various quality management techniques available, cost of quality (COQ) is one such method used to address the concern. However, the absence of measurable COQ factors to monitor quality costs hampers the implementation of COQ framework in the construction industry. Therefore, this study aims to identify COQ factors focused on visible factors (VF) and hidden factors (HF) and the current requirements to achieve it.

This study is based on Preferred Reporting Items for Systematic Review and Meta-Analyses protocol guidelines. The present study identified 57 articles published between 1992 and 2023 in peer-reviewed journals.

The findings reveal 22 factors, which are grouped into four categories based on COQ. Through systematic review, the authors observed limited methodological and theoretical diversity. In fact, there are no quantitative frameworks to calculate COQ. The study, therefore, developed a framework comprising four major routes/paths of COQ factors within the framework.

The COQ routes developed through this study will enable the practitioners to meticulously categorise VF and HF, facilitating quantifying of quality throughout the lifecycle of project, which is currently absent from the existing quality assurance/quality control (QA/QC) approach. In addition, these COQ routes stand as essential construction strategies, significantly enhancing outcomes related to time, cost, quality, sustainability and fostering closer relationships within project frameworks.

The current study contributes significantly to the existing body of knowledge by developing various COQ routes and proposing future research directions to address gaps in the literature.

This study aims to identify and prioritize the key practices and strategies for effective global sourcing and supply chain management (SCM).

The study uses a combination of Pareto analysis and multi-objective optimization based on ratio analysis research methodology to analyze and establish the relationships among the identified key practices and strategies. Pareto analysis enables organization to prioritize organizational efforts and resources by focusing on the most critical factors.

The study shows that the “eco-friendly sourcing strategy”, “lean manufacturing” and “tool cost analysis” are the top critical practices and strategy variables for global sourcing and SCM, whereas the “risk management”, “procurement strategy” and “leverage digital solutions” are the critical practices and strategy variables.

The findings of this research can also assist organizations in making informed decisions to optimize their global sourcing and supply chain operations.

By using these methods, this research paper gives valuable insights into the critical practices and strategies that can enhance efficiency, mitigate risks and drive success in global sourcing and SCM. The subjects and elements this study identified will serve as a framework and suggestions for further theoretical investigation and real-world implementations.

The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques.

The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula.

The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively.

The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.

Determining the suitable material and accurate thickness of the thermal insulation layer used in exterior walls during the design phase of a building can be challenging. This study aims to determine suitable material and optimum thickness for the insulation layer considering both operational and embodied factors by a comprehensive assessment of the energy, economic and environmental (3E) parameters.

First, the energy model of an existing building was created by using Autodesk Revit software according to the as-built floor layout to evaluate the impact of five alternative insulating materials in varying thickness values. Second, using the results derived from the model, a thorough evaluation was conducted to ascertain the optimal insulation material and thickness through individual analysis of 3E factors, followed by a comprehensive analysis considering the three aforementioned factors simultaneously.

The findings indicated that polyurethane with 13 cm thickness, rockwool with 10 cm thickness and EPS with 20 cm thickness were the best states based on energy consumption, cost and environmental footprint, respectively. After completing the 3E investigation, the 15-cm-thick mineral wool insulation was presented as the ideal state.

This study explores how suitable material and thickness of insulating material can be determined in advance during the design phase of a building, which is a lot more accurate and cost-effective than applying insulating materials by assumed thickness in the construction phase.

To the best of the authors’ knowledge, this paper is unique in investigating the advantages of using thermally insulating materials in the context of a mosque structure, taking into account its distinctive attributes that deviate from those of typical buildings. Furthermore, there has been no prior analysis of the cost and sustainability implications of these materials concerning the characteristics of subtropical monsoon climate.

With more cities aiming to achieve climate neutrality, identifying the funding to support these plans is essential. The purpose of this paper is to exploit the present of a structured green bonds framework in Sweden to investigate the typology of abatement projects Swedish municipalities invested in and understand their effectiveness.

Marginal abatement cost curves of the green bond measures are constructed by using the financial and abatement data provided by municipalities on an annual basis.

The results highlight the economic competitiveness of clean energy production, measured in abatement potential per unit of currency, even when compared to other emerging technologies that have attracted the interest of policymakers. A comparison with previous studies on the cost efficiency of carbon capture storage reveals that clean energy projects, especially wind energy production, can contribute to the reduction of emissions in a more efficient way. The Swedish carbon tax is a good incentive tool for investments in clean energy projects.

The improvement concerning previous applications is twofold: the authors expand the financial considerations to include the whole life-cycle costs, and the authors consider all the greenhouse gases. This research constitutes a prime in using financial and environmental data produced by local governments to assess the effectiveness of their environmental measures.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

The paper aims to elucidate effective strategies for promoting the adoption of green technology innovation within the private sector, thereby enhancing the value of public–private partnership (PPP) projects during the operational phase.

Utilizing prospect theory, the paper considers the government and the public as external driving forces. It establishes a tripartite evolutionary game model composed of government regulators, the private sector and the public. The paper uses numerical simulations to explore the evolutionary stable equilibrium strategies and the determinants influencing each stakeholder.

The paper demonstrates that government intervention and public participation substantially promote green technology innovation within the private sector. Major influencing factors encompass the intensity of pollution taxation, governmental information disclosure and public attention. However, an optimal threshold exists for environmental publicity and innovation subsidies, as excessive levels might inhibit technological innovation. Furthermore, within government intervention strategies, compensating the public for their participation costs is essential to circumvent the public's “free-rider” tendencies and encourage active public collaboration in PPP project innovation.

By constructing a tripartite evolutionary game model, the paper comprehensively examines the roles of government intervention and public participation in promoting green technology innovation within the private sector, offering fresh perspectives and strategies for the operational phase of PPP projects.

This study examines the relationship between economic policy uncertainty (EPU) and managers' ex ante strategic choice on firms’ fixed and variable costs structure, i.e. cost rigidity and the moderating effect of government contracts and political connections.

Using a sample of 4,162 US firms during 2003–2019 and EPU measure from Baker et al. (2016), the authors examine the association between EPU and cost rigidity using multivariate regression analysis. The authors also examine the moderating effects of government customers and political connections using the subsampling method.

This study finds that increases in EPU leads to higher cost rigidity, suggesting that managers tend to look ahead and make an ex ante commitment to invest more in fixed costs to avoid congestion costs in anticipation of future product demand during EPU. The study also finds that the presence of government customers and political connections moderates the need for adopting greater cost rigidity.

This study measures firms' cost rigidity based on archival data. Future studies could utilize managers' cost structure choices using firms' internal management cost structure forecasts data to measure cost rigidity to examine the relationship between cost rigidity and EPU.

This study demonstrates that managers tend to make a proactive commitment to invest in fixed inputs when facing demand uncertainty from EPU to avoid congestion costs. This study also highlights the value of having government contracts and political connections by demonstrating that managers are less concerned about the congestion costs, hence weakening the impact of EPU on cost rigidity when they have government as major customers and/or political connections.

This study extends the management accounting literature by documenting that cost rigidity is related to EPU and that the relationship between cost rigidity and EPU also depends on whether the firm has government as major customers and/or political connections or not.