The extensive carbon emissions produced throughout the life cycle of buildings have significant impacts on environmental sustainability. Addressing the Carbon Emissions from China’s Construction Industry (CECI), this study uses panel data from seven coastal areas (2005–2020) and the Bayesian Optimization Extreme Gradient Boosting (BO-XGBoost) model to accurately predict carbon emissions. Initially, the carbon emission coefficient method is utilized to calculate the CECI. Subsequently, adopting the concept of a fixed-effects model to transform provincial differences into influencing factors, we employ a method combining Spearman rank correlation coefficients to filter out these influencing factors. Finally, the performance of the prediction model is validated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (${\mathrm{R}}^2$) and Mean Absolute Percentage Error (MAPE). The results indicate that the total CECI for the seven provinces and cities increased from 3.1 billion tons in 2005 to 17.2 billion tons in 2020, with Shandong Province having the highest CECI and Hainan Province having the lowest. The total population, Gross Domestic Product (GDP) and floor space of the buildings completed passed the significance test, among a total of eight factors. These factors can be considered explanatory variables for the CECI prediction model. The BO-XGBoost algorithm demonstrates outstanding predictive performance, achieving an ${\mathrm{R}}^2$ of 0.91. The proposed model enables potential decisions to quantitatively target the prominent factors contributing to the CECI. Its application can guide policymakers and decision makers toward implementing effective strategies for reducing carbon emissions, thereby fostering sustainable development in the construction industry.

It is important to investigate how to achieve carbon unlocking in the transport sector, especially in transport infrastructure, in order to contribute to the achievement of carbon neutrality targets and the 2030 Sustainable Development Goals. This study aims to investigate the necessary and sufficient conditions to achieve carbon unlocking in transport infrastructure. To achieve this, a combination of partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) methods have been used to examine whether there are unidentified necessity factors beyond the currently recognized ‘technology-in-institution’ (TIC) lock-in. This study also explores how the carbon unlocking of transport infrastructure can be achieved through the unlocking of relevant factors. The study includes 366 points from a subjective questionnaire from the government, transport infrastructure researchers, and relevant businesspeople. We found that, at the adequacy level, achieving institutional and technological unlocking is sufficient and economic factors have little impact on transport infrastructure (0.06), and that institutional and technical factors have a large impact on carbon unlocking (0.453, 0.280); however, from the necessary point of view, carbon unlocking at the economic level is necessary to achieve the goal of a medium to high level of carbon unlocking. To achieve carbon unlocking at this level (over 50%), a combination of technological, institutional, and economic factors is required. To achieve full carbon unlocking, the technology, system, and economy need to be at least 0.533, 0.791, and 0.63 unlocked. Therefore, we can conclude that by using the joint analysis of PLS-SEM and NCA, we have achieved an extension of the traditional TIC and identified sufficient and necessary conditions to achieve a medium to high degree of carbon unlocking.

This paper proposes an integrated life cycle assessment-life cycle cost (LCA-LCC) model of environmental and economic factors for highway engineering technical schemes to problems such as the limitations of single-dimensional assessment, their narrow scope, the difficulty in tracing sources, and the conflicts of various dimensions in existing integrated assessment methods. The latest documents issued by the Ministry of Ecology and Environment and the Ministry of Transport of China used as an integrated assessment database. Air pollution, water pollution, solid waste pollution, noise pollution, energy consumption, pre-project cost, project construction cost, project operation cost, and post-project cost were used to construct the integrated assessment index system of environmental and economic factors. An improved entropy method was adopted in the LCA-LCC model to overcome the problems of ambiguous results of the previous entropy due to too few assessment schemes, the inoperability of the method when it encounters a negative value or zero value, and unbalanced multi-angle assessments. This model was applied to the assessment of two asphalt pavement maintenance schemes of Highway US280 in Alabama and two improvement schemes of high liquid limit soil subgrade of Highway G360 in Hainan. The results show that the LCA-LCC model overcomes the limitations and imbalances of a single LCA or LCC. The gravel improved scheme and the cold recycling scheme were identified through quantitative assessment as more sustainable. This paper can provide a reference for the comprehensive quantitative assessment of environmental and economic benefits of highway engineering technical schemes.

In the context of global sustainable development strategies and the rise of intelligent construction, it has become increasingly urgent for universities to adapt construction management curricula to meet the demands of this new era. However, prior education-reform-based studies rarely offer a systematic, educator-centered prioritization of knowledge areas, limiting actionable guidance for course sequencing and credit-hour allocation. To address this gap, this study identifies eight essential knowledge categories for construction management education through a comprehensive literature review and a survey of faculty members with strong theoretical and practical experience. An improved Analytic Hierarchy Process (AHP) model, weighted by the Consistency Ratio (CR), is applied to prioritize these areas. Results show that Fundamentals of Construction (18.50%), BIM (18.08%), and AI and Big Data (17.07%) received the highest importance values. These findings emphasize the need for curriculum reorientation to align with intelligent construction. This study contributes to modernizing construction management education and offers practical insights for curriculum development, ensuring alignment with industry trends and technological advancements.

Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers.

To enhance the effectiveness of mechanized overhead transmission line construction, this study introduced an evaluation model utilizing FUCOM-F-CM to assess its performance. The method adopts the FUCOM method based on triangular fuzzy numbers to determine the weight of evaluation indexes, and combines the cloud model to establish a performance evaluation model, using the obstacle degree model to judge the short board factors, to judge the constraints affecting the level of performance of mechanized construction of overhead transmission lines to improve the level of performance, and to provide a theoretical basis for the development of targeted performance measures. Finally, the method was applied to an overhead transmission line project. Through empirical research, the performance evaluation results were analyzed, and corresponding improvement measures were proposed to provide a theoretical basis for formulating targeted performance improvement measures.