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Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either the input or output data of that system (the other part could be owned by another user); and due to the consideration of data privacy, he/she could not obtain all the needed data to build the FRBMs. Since this type of situation has not been fully realized (noticed) and studied before, our objective is to come up with some strategy to address this challenge to meet the specific privacy consideration during the modeling process. In this study, the concept and algorithm of the collaborative fuzzy clustering (CFC) are applied to the identification of FRBMs, describing either multiple-input-single-output (MISO) or multiple-input-multiple-output (MIMO) systems. The collaboration between input and output spaces based on their structural information (conveyed in terms of the corresponding partition matrices) makes it possible to build FRBMs when input and output data could not be collected and used in unison. Surprisingly, on top of this primary pursuit, with the collaboration mechanism the input and output spaces of a system are endowed with an innovative way to comprehensively share, exchange, and utilize the structural information between each other, which results in their more relevant structures that guarantee better model performance compared with performance produced by some state-of-the-art modeling strategies. The effectiveness of the proposed approach is demonstrated by experiments on a series of synthetic and publicly available datasets.

The COVID-19 pandemic has exposed the need to address the mental health issues for the future adoption of e-learning among massive students in higher education. This study takes a lead to investigate whether and how general anxiety will influence college students' e-learning intention to provide knowledge to better improve the e-learning technology. We adopted the Technology acceptance model (TAM) to examine the difference between students with and without general anxiety in the e-learning intention where the students are classified based on the General Anxiety Disorder-2 scale. The model is empirically analyzed based on a survey of 512 college students in China regarding their e-learning experience in the first wave of the COVID-19 pandemic. Results demonstrate that the TAM is powerful in explaining the e-learning intention among college students with general anxiety. Besides, all effects associated with perceived usefulness (PU) are reinforced while those associated with perceived ease of use (PEOU) are attenuated in the anxiety group. The results suggest that instructors and higher education institutions should take advantage of the significant PU-intention relationship by providing quality e-learning, which is paramount to coping with the general anxiety among students. This study provides a prototype attempt to investigate the influence of anxiety on e-learning where the different types of anxiety sources are synthesized. However, anxiety can stem from internal sources (computer anxiety, academic stress) and external sources (fear of the virus, lack of social interaction), which requires further investigations.

This paper proposes a dual-path feedback consensus model based on dynamic hybrid trust relationships to solve multi-attribute group decision-making problems in intuitionistic fuzzy environment. This model comprises two main parts: (a) the construction of a dynamic hybrid trust network among decision makers (DMs) and (b) the formation of a dual-path feedback mechanism to improve the group consensus. In the first part, a hybrid trust network is constructed by combining DMs’ prior knowledge of each other and the preference similarities between them. Then, the hybrid trust network is dynamically updated iteratively to reflect the changes in the trust relationships in the process of joint decision-making. In the second part, DMs with low consensus degrees are identified and provided with either a preference or weight adjustment path to improve the group consensus. The preference adjustment path is activated for DMs who agree to modify their preferences, and a nonlinear programming model is proposed to help DMs improve consensus degrees while minimizing adjustment cost. The weight adjustment path is activated for DMs who stick to their own opinions and refuse to make changes, and their weights is adjusted accordingly. An illustrative example along with the related sensitivity analysis and comparative study are used to verify the effectiveness of the proposed model.

PurposeThis study investigated the differential effects of empowering leadership and directive leadership in projects. Moreover, how the complex interplay between empowering and directive leadership styles influences both leader–member exchange and project performance was also explored.Design/methodology/approachPolynomial regression analysis and response surface modeling were applied to analyze 161 matched data collected from project members and managers worked in different projects located in China.FindingsFour leadership styles were identified, i.e. ambidextrous, delegating, directive and laissez-faire styles. These may be combined with empowering and directive leadership. With regard to (in)congruence, leader–member exchange and project performance improved with ambidextrous style as a form of high–high congruence between empowering and directive leadership compared with laissez-faire style (i.e. low–low congruence). Results were better when a delegating style was combined with empowering leadership at a high level compared with the combination of directive style as directive leadership at a high level. Leader–member exchange was identified to exert a mediating role of the (in)congruence on project performance.Originality/valueThis study confirmed the contingent viewpoint of leadership by identifying distinct leadership styles applied in projects. Furthermore, the co-existence of empowering leadership and directive leadership was examined, and the underlying mechanism between the (in)congruence of empowering and directive leadership and project performance was identified.

When COVID-19 wreaked havoc around the world and caused widespread panic, Chinese government has taken pandemic information disclosure measures to fight the outbreak. However, there is a theoretica...

The efficiency of emergency responses is at the heart of post-disaster rescue routing problems. Previous rescue models address the efficiency issue primarily by minimizing the total travel time, while not taking into account other significant factors in the rescue process, such as the number of affected people and the degree of building damage. To overcome these shortcomings, this paper aims to solve the rescue routing problem by maximizing the arc-based rescue efficiency, which is redefined as the ratio between the primary rescue input and output factors to represent the efficiency of the rescue operations. Therefore, a systematic methodology is proposed to decompose the original rescue routing problem into two decision making phases. First, an extended data envelopment analysis (DEA) model is constructed to evaluate the rescue efficiency along each arc. Specifically, a group decision constraint cone, which refers to the combined output of a group decision from experts, is constructed to distinguish the features and rescue focus of each disaster. Second, an efficiency-based routing model is developed to determine a feasible rescue tour for the entire transportation network, thus achieving the goal of maximizing the total rescue efficiency. An empirical example of a real earthquake disaster in Wenchuan, China, is provided to demonstrate the novelty and practical capabilities of the proposed approach in post-disaster emergency rescue operations. Finally, a comparison analysis is conducted with the traditional time-oriented routing method, and the results show that the method proposed in this study can improve the rescue performance by 21.2%.

The last decade witnessed tremendous developments in social media and e-democracy technologies. A fundamental aspect in these paradigms is that the number of decision makers allowed to partake in a decision making event drastically increases. As a result Large Scale Decision Making (LSDM) has established itself as an emerging and rapidly developing research field, attracting comprehensive studies in the last decade. LSDM events are a complex class of decision making problems, in which multiple and highly diverse stakeholders are involved and the provided alternatives are assessed considering multiple criteria/attributes. Since some of the extant LSDM research was extended from group decision making scenarios, there is no established definition for a LSDM problem as of yet. We firstly propose a clear definition and characterization of LSDM events as a basis for characterizing this emerging family of decision frameworks. Secondly, a classification of LSDM literature is provided. Effectively solving an LSDM problem is usually a complex and challenging process, in which reaching a high consensus or accounting for the agreement or conflict relationships between participants becomes critical. Accordingly, we present a taxonomy and an overview of LSDM models, predicated on their key elements, i.e. the procedures and specific steps followed by the existing models: consensus measurement, subgroup clustering, behavior management, and consensus building mechanisms. Finally, we provide a discussion in which we identify research challenges and propose future research directions under a triple perspective: key LSDM methodologies, AI and data fusion for LSDM, and innovative applications. The potential rise of AI-based LSDM is particularly highlighted in the discussion provided.

Clarifying the spatial association network of provincial building carbon emissions and its influential drivers is profoundly significant for transregional collaborative emission reduction and regionally-coordinated development. This study adopts the social network analysis method to investigate the network structure characteristics of carbon emissions in the building sector based on China's provincial-level evidence from 2000 to 2018. Then, the quadratic assignment procedure is further utilized to examine the driving factors. The results demonstrate that building carbon emissions in China take the form of a network structure. From 2000 to 2018, the relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang are in the center of the spatial association network and play a vital part in the network. The network of carbon emissions in the building sector can be classified into four plates: the main inflow plate, main outflow plate, bidirectional spillover plate and agent plate. Geographical adjacency, economic development level, energy intensity and industrial structure are significantly correlated with building carbon emissions. The urbanization level has no significant influence on the spatial correlations of building carbon emissions. This study is conducive to formulating energy conservation policies and promoting transregional collaborative emission reductions.

PurposeDespite the huge potential of social media, its functionality and impact for enhanced risk communication remain unclear. Drawing on dialogic theory by integrating both “speak from power” and “speak to power” measurements, the article aims to propose a systematic framework to address this issue.Design/methodology/approachThe impact of social media on risk communication is measured by the correlation between “speak from power” and “speak to power” levels, where the former primarily spoke to two facets of the risk communication process – rapidness and attentiveness, and the latter was benchmarked against popularity and commitment. The framework was empirically validated with data relating to coronavirus disease (COVID-19) risk communication in 25,024 selected posts on 17 official provincial Weibo accounts in China.FindingsThe analysis results suggest the relationship between the “speak from power” and “speak to power” is mixed rather than causality, which confirms that neither the outcome-centric nor the process-centric method alone can render a full picture of government–public interconnectivity. Besides, the proposed interconnectivity matrix reveals that two provinces have evidenced the formation of government–public mutuality, which provides empirical evidence that dialogic relationships could exist in social media during risk communication.Originality/valueThe authors' study proposed a prototype framework that underlines the need that the impact of social media on risk communication should and must be assessed through a combination of process and outcome or interconnectivity. The authors further divide the impact of social media on risk communication into dialogue enabler, “speak from power” booster, “speak to power” channel and mass media alternative.

This study focuses on exploring equity among regions regarding public service delivery. An analytical framework is established to measure the level of public service delivery considering input (resources) and output (results), and an equity analysis is conducted from temporal and spatial perspectives. The results show that there are disparities in the level of public service delivery among regions in China (mainly due to inequality in resources), and within-region inequality has a greater influence on overall regional inequality than between-region inequality. The proposed analytical framework can help governments make better decisions about public service delivery.

In this study, we investigated the effect of different compositions of aquatic natural organic matter (NOM) and ions on virus removal by ultrafiltration (UF). MS2 bacteriophage was used as a surrogate. Humic acid (HA) improved the MS2 removal rate from 1.95 ± 0.09 LRV to 2.40 ± 0.03 LRV at the HA dosage of 9 mg/L through the combined mechanisms of size exclusion, electrostatic repulsion and hydrophobicity. MS2 removal rate further increased to 3.10 ± 0.05 LRV by 10 mmol/L Na

This research aims to link leadership and employees’ well-being in the architecture, engineering, and construction (A/E/C) industry while focusing on horizontal leadership, which is displayed by subordinates, and job burnout among remaining team members. Based on social information processing theory and conservation of resource theory, we propose a moderated mediation model. We hypothesize that the presence of a horizontal leader (HL) in A/E/C project teams is related to remaining team members’ job burnout, which is mediated by perceived career opportunity (PCO). However, the relationship between HL’s presence and PCO changes from significantly positive among project team members with relatively low status conflict to significantly negative among team members with relatively high status conflict. The relationship between HL’s presence and job burnout is also reversed. The results of a scenario study (Study 1) using Masters of Engineering Management candidates as the sample (N=150) and a field survey (Study 2) involving 313 real team members support our hypotheses. Theoretical and practical implications of these findings are discussed.

Extreme precipitation caused by global climate change is expected to have a severe impact on urbanized areas. While decision‐makers struggle with climate uncertainty, an effective infrastructure adaptation strategy attaches great importance to preventing disasters resulting from rainfall. We propose a decision‐making model to incorporate the probability of rainfall disasters and recommend investing time when evaluating projects related to climate adaptation. We use a hydrological statistical model and economic and technical factors to estimate the expected economic losses in several rainfall disaster scenarios, and the value of the adaptation infrastructures is calculated using a real options pricing approach. Then the decision‐making model is applied to a case study involving a campus rainfall disaster prevention facility at the Central University of Finance and Economics in Beijing, China. We established three submerged scenarios with different rainfall intensities, then we evaluated the premium of holding an option to defer and pointed out the optimal investing time in each scenario. This model is expected to provide guidance for the development of adaptation infrastructure for relatively small areas such as communities and universities. And we proved that using real options‐based approach could provide more managerial flexibility for investors.

Population ageing is stimulating an increase in the demand for housing suitable for seniors. To meet the demand, the market share of senior housing needs to increase substantially in a relatively short period of time; therefore, panelized construction, as an efficient, economical, and environmentally-friendly construction method, can be regarded as a promising building approach to meet urgent demand for multi-unit housing. However, prior to construction, decisions regarding the location selection for building panelized housing can have a great influence on the level of accessibility that seniors have to neighbouring facilities and services, further affecting their health and quality of life. Based on this, the research presented in this paper aims to search potential land areas for panelized housing developments for seniors from the perspective of accessibility. A set of methods is proposed to define the opportunities and constraints for potential land, measure the accessibility, and select the most suitable location for senior housing by means of suitability analysis. A case study of Edmonton is then analyzed to illustrate the application of these methods.

It has been proven that prefabricated construction plays a significant role in cleaner production in the construction industry due to its capacity for energy conservation, emissions reduction, low-carbon development and environmental protection. Although prefabricated construction was introduced in China decades ago, it still faces some problems during the application stage. In order to improve the application of prefabricated construction in China, this research explores its influencing factors from the perspectives of technology promotion and cleaner production. Twenty-one types of factors are identified through a literature review, and a questionnaire survey is conducted for the purpose of collecting empirical data. Factor analysis establishes an influencing factor model composed of industry factors, company factors, technology factors, government factors and market factors. The relative importance of each cluster and factor is revealed by its index of relative importance (IRI): the dominant player is the government, and the top five factors in the promotion of prefabricated construction in China are technology lock-in (76.42%), incentive policies (75.91%), standardization (73.70%), cost (73.70%) and entrepreneurial cognition (73.13%). Additionally, the process of conducting semi-structured interviews with experts provides suggestions. The findings will benefit researchers, practitioners and policymakers who want to promote the application of prefabricated construction, and provide references for other cleaner production technologies in China.

Dissolved inorganic nitrogen (DIN) is considered the main factor that induces eutrophication in water, and is readily influenced by hydrodynamic activities. In this study, a 4-year field investigation of nitrogen dynamics in a turbulent river was conducted, and a laboratory study was performed in the approximately homogeneous turbulence simulation system to investigate potential mechanisms involved in DIN transformation under turbulence. The field investigation revealed that, contrary to NO

Risks brought by climate change are inevitable obstacles to global development. Clarifying the features of climate change risks helps us to further understand and cope with climate change. There lacks a systematic review of climate change risks in terms of feature extraction and classification. The bibliometric analysis can be used to analyze and extract climate change risk features. The literature in the field of climate change was searched in the Web of Science database. Coauthors, citations, bibliographic coupling, co-citations, and keyword co-occurrence were analyzed. From five dimensions including nature, politics, economy, society, and culture, the risk features of climate change were extracted and summarized. Through text mining and cluster analysis, the 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.

This study is based on the conservation of resources (COR) theory and the job demands-resources (JD-R) model and aimed to investigate the potential within-person relationships between self-...

Achieving the Sustainable Development Goals (SDGs) is a long-term task, which puts forward high requirements on the sustainability of related policies and actions. Using the text analysis method, we analyze the China National Sustainable Communities (CNSCs) policy implemented over 30 years and its effects on achieving SDGs. We find that the national government needs to understand the scope of sustainable development more comprehensively, the sustained actions can produce positive effects under the right goals. The SDGs selection of local governments is affected by local development levels and resource conditions, regions with better economic foundations tend to focus on SDGs on human well-being, regions with weaker foundations show priority to basic SDGs on the economic development, infrastructures and industrialization.

The outbreak of the COVID‐19 pandemic has plunged the world into a crisis. To contain the crisis, it is essential to build full cooperation between the government and the public. However, it is unclear which governmental and individual factors are the determinants and how they interact on protective behaviors against COVID‐19. To resolve this issue, this study built a multiple mediation model and found government emergency public information as detailed pandemic information and positive risk communication had more important impacts on protective behaviors than rumor refutation and supplies. Moreover, governmental factors could indirectly affect protective behaviors through individual factors such as perceived efficacy, positive emotions, and risk perception. These findings suggest that systematic intervention programs for governmental factors need to be integrated with individual factors to finally achieve effective prevention and control of the COVID‐19 pandemic among the public. This article is protected by copyright. All rights reserved.

Climate change exacerbated by carbon emissions is a major challenge faced by countries worldwide. China has envisioned achieving a carbon peak by 2030 and carbon neutrality by 2060. Carbon capture, utilization, and storage (CCUS), an emerging technology, is capable of reducing carbon emissions. Existing theoretical research has paid little attention to comprehensive risk assessments for CCUS projects from a sustainability viewpoint. Therefore, this study formalizes an integrated decision-making model for the risk assessment of CCUS projects. An indicator system consisting of environmental, social, economic, governance and technological sustainability dimensions was developed to assess the risk of CCUS projects. Furthermore, a multiattribute group decision-making process was modeled in intuitionistic fuzzy (IF) and linguistic (LIN) environments, both of which are interconvertible. To automate the risk assessment process for different CCUS project contexts, an integrated decision prototype programmed in Microsoft Excel 2016 was designed. Then, two case studies from the Shengli Power Plant CCUS project were conducted to validate the integrated decision-making model. A risk assessment table and risk assessment chart were leveraged to analyze, visualize, and rank the decision alternatives according to their risk performance. The results show that the post-combustion capture technology is selected as the optimal alternative option in three capture technologies; Of the four storage sites, site A is selected as the optimal alternative option. The result of carbon capture technology is in line with the real decision made by Shengli Power Plant, which proves the validity of the model. The optimal option is the same in both IF and LIN environments, which shows the consistency of the model. Theoretically, an integrated decision-making model not only contributes to assessing the risk of CCUS projects through the lens of sustainable development but advances group decision-making modeling by identifying experts’ preferences for obtaining effective evaluation results. Practically, this study could help stakeholders comprehensively and promptly evaluate the risk performance of CCUS projects before making development decisions regarding sustainability.

Carbon Capture, Utilization and Storage (CCUS) is regarded as a promising approach to mitigate global warming. Public acceptance plays a key role in the decision whether it could be adopted widely. The study develops a behavior model to predict the public acceptance of CCUS based on the ABC (Affect, Behavior, and Cognition) model of attitudes. A survey of public in China shows that public cognition has a significant effect on public acceptance. Perceived benefits have a higher indirect effect than perceived risks. Meanwhile, trust moderates the relationship between public cognition and perceived risks/benefits. And the relationship between perceived benefits and acceptance is intensified when the fairness is high. This study explores the internal mechanism between public cognition and public acceptance, such knowledge would allow policymakers to develop more targeted interventions aimed at enhancing public acceptance of CCUS.

Green construction is to conserve the resources and reduce the negative impact of construction activities on the environment to the maximum extent, with scientific management and technological progress under the premise of ensuring the quality, safety and other basic requirements. Recent years, accompanied by the rapid development of construction industry, a considerable proportion of energy and resources, particularly high-carbon materials, is consumed in the construction activities, emitting mass greenhouse gases, which have a damaging impact on the environment. This is especially for the mega public projects. Therefore, under the green and sustainable perspective, actions to control carbon dioxide (CO2) emissions in the construction industry are imperative. In this case, if CO2 emissions reduction can be considered beforehand during the bid evaluation stage, contractors will be guided by bid evaluation indicators and therefore improve their construction schemes, enabling the construction phase to be green, energy saving and sustainable. This is because bid evaluation indicators can serve as guiding effect on the contractors. Given that CO2 emissions are rarely given sufficient consideration in traditional bid evaluation, this study conducts a systematic analysis to identify major sources of CO2 in construction phase and attempts to develop a conceptual computational model in terms of CO2 emission. Then, a linguistic group decision-making framework for bid evaluation in mega public projects considering CO2 emissions reduction is developed. In this framework, entropy, relative entropy, the standard deviation method and weighted aggregation operators are applied to determine the indicator weights, the expert weights and the information aggregation in bid evaluation decision-making. Finally, a scenario comparison between the proposed linguistic group decision-making bid evaluation framework and the traditional framework is made through a case study, and the scientific basis and reliability of this new framework are validated. The establishment of this framework can enrich the decision-making methods in construction management field. And it can provide meaningful reference for owners to choose the most qualified contractors and spur contractors to improve their construction schemes, enabling construction phase to be green, energy saving and sustainable.

The paper proposes a Trust Relationship-based Conflict Detection and Elimination decision making (TR-CDE) model, applicable for Large-scale Group Decision Making (LSGDM) problems in social network contexts. The TR-CDE model comprises three processes: a trust propagation process; a conflict detection and elimination process; and a selection process. In the first process, we propose a new relationship strength-based trust propagation operator, which allows to construct a complete social network by considering the impact of relationship strength on propagation efficiency. In the second process, we define the concept of conflict degree and quantify the collective conflict degree by combining the assessment information and trust relationships among decision makers in the large group. We use social network analysis and a nonlinear optimization model to detect and eliminate conflicts among decision makers. By finding the optimal solution to the proposed nonlinear optimization model, we promote the modification of the assessments from the DM who exhibits the highest degree of conflict in the process, as well as guaranteeing that a sufficient reduction of the group conflict degree is achieved. In the third and last process, we propose a new selection method for LSGDM that determines decision makers’ weights based on their conflict degree. A numerical example and a practical scenario are implemented to show the feasibility of the proposed TR-CDE model.

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.

The systematic clustering analysis based weight determination methods are suitable for the experts of complex multiattribute large-group decision-making (CMALGDM) in intervalvalued intuitionistic fuzzy environment. However, these methods mainly have two shortcomings: they do not consider the consistency of the experts in each aggregation; the aggregation weights are often determined simply by the “majority principle”, and neglect the quantity of information provided by the holistic aggregation, leading to decision biases. Hence, a double weight determination method for experts is proposed to solve these problems. As for the first shortcoming, a mathematical programming model is used to solve for the optimal expert weights within each aggregation, ensuring consistency in the overall preferences of the aggregations. As for the second shortcoming, we propose a modification of the aggregation weights based on the information entropy, which fully considers both the number of experts and the amount of information provided by the holistic aggregation. With the proposed method, the final expert weights are determined more rigorously and objectively. The feasibility of the proposed method is investigated through an illustrative example.

Since human behavior has been argued as one of the most critical leading indicators of accidents, effective intervention in workers’ behavior may foster improved safety performance. Given that human behavior is directed by cognition, researchers have proposed various cognitive models of human behavior in order to provide a clearer understanding of how to allocate management resources. However, the influence of human cognition on behavior remains an overarching concept, rather than providing an understanding of the heterogeneity among various trades. Therefore, cognition research provides no guidance regarding how to strategically allocate management resources such as training. This study employed structural equation modeling to identify the cognitive structures of two trades (reinforcing steel bar and elevator workers). We find that the cognitive structures of workers in these sample trades are significantly different, indicating that management strategies should vary accordingly. Training of mechanical workers should focus on crew leads, who can further influence self-efficacy, risk comparisons, and workers’ perception of external conditions. For rebar crews, safety training should focus on self-supporting defense capability and elucidating dangerous behaviors. External conditions have a significant role in ensuring worker safety; safe facilities engender safe behaviors. This study lays a foundation of strategic resource allocation for behavioral management on construction jobsites.

Large construction projects involve massive capital investments, lengthy execution period, and enormous management uncertainties. Therefore, the task of selecting a capable contractor for smooth project delivery is challenging. Although previous studies have attempted to improve the methods of bidding evaluation, limitations still exist. First, the evaluation results might be biased due to the heterogeneity in a small group of experts with different professional experience and capabilities. Second, multi-correlation can reduce the validity of the weightings of the indexes and evaluation results. Third, the subjectivity of the weightings can be a crucial hurdle to the selection of a suitable contractor for a specific project. To overcome these shortcomings, this study introduces Partial Least Squares (PLS) path modeling and develops a Sequence-Multi-Criteria System which can aggregate the evaluation results from different professional practitioners without the use of pre-defined weighting schemes, thus providing a reliable reference for bid evaluation. We incorporated this approach into an evaluation procedure stipulated in the procurement documents of the World Bank and specifically aimed at selecting suitable contractors for large construction projects. A case study was conducted via comparisons across various scenarios, and the validity of this approach was subsequently proved.

The accelerating urbanization has posed great challenges to China’s carbon-mitigation efforts and global climate change response. However, how does the urbanization process impact the peak of future carbon emissions from buildings is still unclear. This study first establishes a comprehensive urbanization framework from three dimensions. Then, a dynamic scenario simulation model is innovatively constructed by combining the extended Kaya identity and Monte Carlo simulation approach to explore future dynamic evolution trajectory, possible peaks and peaking time of China’s building carbon emissions from 2000 to 2050, considering the uncertainties of factors. Finally, dynamic sensitivity analysis is performed to investigate the contributions of different dimensions of the urbanization. Results show that under the business-as-usual scenario, the rural residential building sector will be the first to reach its energy peak in 2027. Dynamic scenario simulation shows that the building sector will peak at 3.09 (±0.36) Bt CO2 in 2037 (±4). Specifically, the urban residential building sector will peak at 1.27 (±0.18) Bt CO2 in 2040 (±4), while the rural residential building sector will peak at 0.51 (±0.03) Bt CO2 in 2021 (±4) and the commercial building sector will peak in 2038 (±4) at 1.41 (±0.30) Bt CO2. Dynamic sensitivity analysis indicates that rural–urban migration will exert promoting effect on the peaking time and emission peaks of building carbon emissions through the concomitant effects of the urbanization process, i.e., the economic urbanization and space urbanization. Overall, this study can provide a better insight into the impact of urbanization on carbon emission peak and can support governments and decision-makers to formulate effective carbon-mitigation strategies.

Sustainable infrastructure development is an indispensable driving force for successfully advancing urbanization globally. Evaluation of Infrastructure Sustainability (IS) has become an area of research increasingly gaining scrutiny. However, there exists a knowledge gap of a formal and comprehensive metric system measuring IS in current studies. By reviewing infrastructure assessment studies and international rating systems, this study identified 64 metrics measuring IS. Building upon this, an empirical survey was designed and conducted to propose an IS metric system by analyzing 68 responding samples adopting ranking analysis and confirmatory factor analysis. This IS metric system consists of 4 measurement dimensions integrating the triple bottom line – environmental IS, social IS, and economic IS – with a fourth sustainability pillar of managerial IS. Three case studies evaluating sustainable infrastructures in Hong Kong were conducted to validate the formality and comprehensiveness of the developed IS metric system. It is claimed that, on the one hand, the IS metric system is formal enough to accommodate 4 dimensions, 15 criteria, and 50 metrics required for lifecycle IS evaluation. On the other hand, it is comprehensive enough to distinguish heterogeneous IS criteria adopted by multidisciplinary professionals in IS evaluation. The IS metric system not only contributes to the knowledge and practice in the field of multidisciplinary sustainability evaluation but also serves as a guide for infrastructure stakeholders making multidisciplinary decisions in terms of sustainable infrastructure developments.

Decoupling residential building carbon emissions from residential income has great significance for carbon mitigation and even global climate change. However, the nature of the decoupling relationship between them is still unclear. This study adopts the Tapio decoupling model to explore the decoupling relationship among residential building carbon emissions (TC), per capita carbon emissions (PC), residential carbon intensity (FC) and per capita income (PCI) across 30 Chinese provinces from 2000 to 2015. Then, the Logarithmic Mean Divisa Index (LMDI) model is used to decompose the decoupling of residential building carbon intensity and PCI at the provincial level to determine the main factors determining this relationship. Results show that the main decoupling trend between FC and PCI in 30 provinces transformed from weak decoupling state to strong decoupling state during the whole period, while there is still extensive room for TC and PC to decouple with PCI. PCI has the major inhibiting effect on the decoupling between FC and PCI, while residential energy intensity (EI) has the primarily promoting effect on the decoupling. These findings provide valuable references for international policymakers in formulating precisely targeted energy-saving and carbon-reducing policies to coordinate the relationship between income and carbon emissions, and the results also can be applied to other economies and, to a greater extent, other pollutants.

Sustainability has captured extensive attention in Architecture, Engineering, and Construction (AEC) industry globally. However, how to achieve Infrastructure Sustainability (IS), one the critical dimensions measuring project success, although Project Management Practices (PMPs) in international contexts remain to be explored. Based on the empirical data collected from AEC practitioners with experiences in international infrastructure projects, this research develops two conceptual frameworks for PMPs and IS using principle components analysis (PCA) and confirmatory factor analysis (CFA). Four factors are identified for measuring PMPs with a composite reliability of 0.936&mdash, Culture, Strategy, Implementation, and Reflection&mdash, which are in analogy with the plan-do-check-action (PDCA) cycle ideology. Additionally, four metrics are also identified for IS with a composite reliability of 0.946&mdash, Project Economy, Organizational Integration, Social Utility, and Environmental Implication&mdash, which are established from project level, organizational level, to macro level, respectively. These findings contribute to the body of knowledge of construction project management and sustainable infrastructure development by demonstrating theoretical measurement frameworks for both PMPs and IS. Potentially, the outputs of this research will generate informative insights for practitioners to improve their PMPs in the process of pursuing IS in future AEC practices.

This study investigates the characteristics of the cost performance of megatransport infrastructure projects in Hong Kong and performs dependence analysis of cost overruns. It is based on 5...

In the last thirty years, public involvement has been adopted worldwide to foster sustainable development. However, little work has been done to study its actual project-based performance, which is critical for continual improvement. The paper proposes an evaluation model of public involvement in urban regeneration projects. We develop an evaluation framework with identified involvement goals, implementation objectives and measurement indicators, and construct an evaluation model combining the clustering algorithm and entropy weight theory. We also provide an example of the model to show its applicability.