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The purpose of this paper is to explore the support for multi-party collaboration in project construction provided by building information modeling (BIM). Based on the perspective of value co-creation, the research results can provide support for the collaborative application and contract design of BIM platform.

In this paper, an evolutionary game model involving the owner, designer and constructor is constructed by using prospect theory and evolutionary game theory. Through simulation analysis, the evolution law of the strategy choice of each party in the collaborative application of BIM platform is discussed and the key factors affecting the strategy choice of all parties are analyzed.

The results show that there is an ideal local equilibrium point with progressive stability in the evolutionary game between the three parties: “the construction party shares information, the designer receives the information and optimizes the project and the owner does not provide incentives”; in addition, the opportunistic behaviors of the design and construction parties, as well as the probability of such behaviors being detected and the subsequent punishment have a significant impact on the evolutionary outcome.

This method can provide support for the collaborative application and contract design of BIM platform.

This chapter explores the principles of community empowerment and active participation in community development, highlighting the importance of inclusive practices for sustainable and equitable outcomes. It draws from diverse strategies and practical examples, showcasing the transformative potential of empowering community members. Through real-world case studies from places like Namibia, Bangladesh, and Brazil, readers gain insights into effective strategies for amplifying marginalized voices and enabling them to shape their destinies. The chapter addresses the challenges and benefits of participatory approaches, helping practitioners tailor strategies to complex community dynamics. It emphasizes the pivotal role of partnerships in strengthening community bonds and fostering collaborations among stakeholders. By embracing empowerment and participation, practitioners, policymakers, and researchers can contribute to a more equitable and sustainable future where communities thrive, and individuals actively shape their paths.

The aim of the paper is to rank and analyze the key strategies to sustainable finance adoption in the manufacturing sector using Fuzzy Delphi method (FDM), Interpretive Structural Modeling (ISM) and MICMAC (impact matrix cross-reference multiplication applied to a classification) analysis.

The study develops a novel framework to identify and analyze the mutual relationships among set of sustainable policies using extensive literature survey and experts opinion. Initially, the study found 14 strategies to implement sustainable finance with the help of vast literature. Then, the list of identified factors were screened through Fuzzy Delphi Method (FDM). Based on driving and dependence power, the final list of factors are divided into three categories.

The study findings reveal that “environmental rules and practices”, “financial incentives, tax reduction and subsidy”, have strongest driving power for promoting sustainable financial system in Pakistani manufacturing sector. Furthermore, “environmental awareness” and “long term vision” are found to be highly influenced by other corresponding elements in a system.

The ISM approach assists professionals, academics, and managers in identifying and ranking policies in implementing green business techniques. The hierarchical representation of ISM results provides a roadmap for decision-makers to navigate and prioritize factors effectively, facilitating the implementation of strategies that contribute to sustainable growth within organizations.

The study results provide interesting clues regarding green finance policies that provide the foundations, incentives, protections or other provisions that support the ecological conservancy’s mission. Specifically, the findings guide that government must offer research grants to private enterprises, research and development institutions, and universities to promote environmental protection and develop transformative technologies such as waste recycling, renewable energy, carbon capture, and power consumption.

The exploration of strategies for sustainable finance adoption with the help of mixed methodological approach and classification of these strategies on the basis of importance level is a new attempt in the field of manufacturing sector.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

Vertical electrical sounding (VES) and Rayleigh wave exploration are widely used in the exploration of near-surface structure, but both have limitations. This study aims to make full use of the advantages of the two methods, reduce the multiple solutions of single inversion and improve the accuracy of the inversion. Thus, a nonlinear joint inversion method of VES and Rayleigh wave exploration based on improved differential evolution (DE) algorithm was proposed.

Based on the DE algorithm, a new initialization strategy was proposed. Then, taking AK-type with high-velocity interlayer model and HA-type with low-velocity interlayer model near the surface as examples, the inversion results of different methods were compared and analyzed. Then, the proposed method was applied to the field data in Chengde, Hebei Province, China. The stratum structure was accurately depicted and verified by drilling.

The synthetic data and field data results showed that the joint inversion of VES and Rayleigh wave data based on the improved DE algorithm can effectively improve the interpretation accuracy of the single-method inversion and had strong stability and large generalizable ability in near-surface engineering problems.

A joint inversion method of VES and Rayleigh wave data based on improved DE algorithm is proposed, which can improve the accuracy of single-method inversion.

This paper takes a prudent approach to assessing the quality of greenspace in low- and high socio-economic status (SES) settings. Socio-economic data from deprivation indexes were used to systematically define low- and high-SES suburbs. A Geographical Information System (GIS) observation of greenspaces was used to score spaces according to a scoring criterion contingent on six quality facets. Statistics were then synthesised, producing a Cohen effect score highlighting disparities in each criterion between the two SES groups.

As the phenomena of locational prejudice and meritocratic inequality continue to garner global attention, this paper extrapolates this to a world-renown metropolis, Melbourne. This paper endeavours to provide invaluable insights into the environmental injustice paradigm within greenspace and its respective quality.

Conclusive results affirmed a concerning disparity in the quality of greenspace between Melbourne's low- and high-SES settings. Cohen's effect size found that on average, there was a “medium” distinction between the spaces, whilst an individual focus on the quality facets concluded diverse findings.

The core of study adopted a meticulous virtual assessment to critique the quality of selected greenspaces opposed to an in-person-real world assessment which could garner more nuanced findings.

Existing literature on Melbourne has prioritised distribution, proximity and accessibility domains when assessing inequitable greenspace and, consequently, has catalysed a research gap in greenspace quality. This is also one of the first papers to provide insight into the “Plan Melbourne” policy regarding urban regeneration and ameliorating public open spaces.

This research aims to explore the role of awareness of harm and responsibility for environmental protection in reducing pollution from single-use plastic bags (SPBs) in coastal communities (CCs). To this end, this study develops and tests a unique model that explains residents’ intention to reduce the use of SPBs in coastal regions.

A questionnaire was used to collect data from 721 coastal residents in Vietnam. Structural equation modeling and moderation analysis were applied to test the proposed hypotheses.

The results show that awareness of the impact of SPBs on the environment and human health and awareness of the responsibility to protect the coastal environment significantly affect attitudes and intentions to reduce the use of SPBs. Moreover, such awareness of responsibility strengthens the attitude-intention relationship.

The findings suggest that CCs should not receive a lower priority in campaigns and efforts to reduce SPBs. In this regard, providing residents with free environmentally friendly bags and education programs on the impact of SPBs could be implemented.

CCs are directly impacted by pollution from SPBs. However, little is known about how this affects their polluting behavior. This study shows that CCs are not immune to polluting behaviors and that SPBs can be significant among residents. It also demonstrates that awareness of harm and feeling responsible for the environment are essential drivers of (intended) sustainable behaviors.

Given the increasing importance of engineering insurance, it is still unclear which specific factors can enhance the role of engineering insurance as a risk transfer tool. This study aims to propose a hybrid approach to identify and analyze the key determinants influencing the consumption of engineering insurance in mainland China.

The empirical analysis utilizes provincial data from mainland China from 2008 to 2019. The research framework is a novel amalgamation of the generalized method of moments (GMM) model, the quantile regression (QR) technique and the random forest (RF) algorithm. This innovative hybrid approach provides a comprehensive exploration of the driving factors while also allowing for an examination across different quantiles of insurance consumption.

The study identifies several driving factors that significantly impact engineering insurance consumption. Income, financial development, inflation, price, risk aversion, market structure and the social security system have a positive and significant influence on engineering insurance consumption. However, urbanization exhibits a negative and significant effect on the consumption of engineering insurance. QR techniques reveal variations in the effects of these driving factors across different levels of engineering insurance consumption.

This study extends the research on insurance consumption to the domain of the engineering business, making theoretical and practical contributions. The findings enrich the knowledge of insurance consumption by identifying the driving factors specific to engineering insurance for the first time. The research framework provides a novel and useful tool for examining the determinants of insurance consumption. Furthermore, the study offers insights into the engineering insurance market and its implications for policymakers and market participants.

Despite the knowledge transfer between projects has received increasing attention from scholars, few scholars still conduct comprehensive research on inter-project knowledge transfer from both horizontal and vertical perspectives. Besides, knowledge transfer is affected by multiple antecedent conditions, and these factors should be combined for analysis. Therefore, this paper aims to explore the key factors influencing knowledge transfer between projects using the fuzzy-set qualitative comparative analysis (fsQCA) method from both horizontal and vertical perspectives and how these factors combine to improve the effectiveness of knowledge transfer (EKT) between projects.

First, nine factors affecting knowledge transfer between projects were identified, which were from the four dimensions of subject, relationship, channel, and context, namely temporary nature (TN), time urgency (TU), transmit willingness (TW), receive willingness (RW), trust (TR), project-project transfer channels (PPC), project-enterprise transfer channels (PEC), organizational atmosphere (OA), and motivation system (MS). Then, the source of the samples was determined and the data from the respondents was collected for analysis. Following the operation steps of the fsQCA method, variable calibration, single condition necessity analysis, and configuration analysis were carried out. After that, the configurations of influencing factors were obtained and the robustness test was conducted.

The results of the fsQCA method show that there are five configurations that can obtain better EKT between projects. Configuration 3 (∼TN * ∼TU * TW * RW * TR * ∼PPC * PEC * MS) has the highest consistency, indicating that it has the highest degree of the explanatory variable subset. Configuration 1 (∼TN * ∼TU * TW * RW * PEC * OA * MS) has the highest coverage, meaning that this configuration can explain most cases. Also, the five configurations were divided into three types: vertical transfer, horizontal-vertical transfer, and channel-free transfer category.

Firstly, this study explores the key factors influencing knowledge transfer between projects from four dimensions, which presents the logical chain of influencing factors more clearly. Then, this study divided the five configurations obtained into three categories according to the transfer direction: vertical, horizontal-vertical, and channel-free transfer, which gives implications to focus on both horizontal knowledge transfer (HKT) and (VKT) when studying knowledge transfer between projects. Lastly, this study helps to realize the exploration of combined improvement strategies for EKT, thereby providing meaningful recommendations for enterprises and project teams to facilitate knowledge transfer between projects.