Yun Chen

With the rapid expansion of metro tunnel construction, safety risks such as collapse, water inrush, and gas explosion have become increasingly critical. Existing warning models often lack fine-grained disaster type identification and dynamic risk assessment capabilities. This paper proposes a two-stage intelligent warning framework based on multi-source data fusion. First, a dual-autoencoder structure (MLP-AE and LSTM-AE) extracts deep features from static geological parameters and dynamic monitoring sequences. Then, a multilayer perceptron (MLP) classifier identifies four typical states: normal, collapse, water/mud inrush, and gas explosion. Subsequently, a regression model predicts a continuous risk score, mapped to three risk levels: Safe, Moderate Risk, and Significant Risk. Experimental results demonstrate that, compared with Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Bayesian Network (BN), the proposed framework achieves superior performance in risk level identification, with an accuracy of 91% and an F1-score of 0.87. Notably, it exhibits particularly strong recall for severe (Level III) risks, which is crucial for practical engineering applications. The proposed framework provides a practical and intelligent approach for safety warning in metro tunnel construction.

Effective control of collusive behavior in the government bidding process is a prerequisite for promoting the high-quality development of social economy, consolidating the results of the anti-corruption struggle and the smooth operation of the project. Based on risk theory and Bayesian networks, this paper uses GeNIE 4.0 software to construct a Bayesian network structure model to analyze the causal chain of vertical collusion in government project tendering processes. The study found three key causal chain paths: ① external supervision subject to collusion gains, external environment, competitive pressure, and lack of compliance awareness of the formation of “external supervision subject → response subject → vertical collusion” regulatory failure of vertical collusion path; ② rights subject to collusion gains, the external environment, and the lack of publicity of policies and regulations. External supervision is influenced by collusion gains, the external environment, competitive pressure, and compliance awareness of the formation of the “rights subject → external supervision subject → response subject → vertical collusion” internal and external collusion path; the rights of the subject, affected by the collusion gains, the external environment, and the lack of publicity of policies and regulations, form a collusion path of uncontrolled rights. It is also found that, at the subject level, the subject of rights and the subject of external supervision are in a key position in the control process; at the factor level, the control of collusion proceeds, the publicity of policies and regulations, and the external environment can significantly enhance the control effect of vertical collusion and provide a precise focus for the collusion chain to break the chain.

The persistent carbon lock-in in transport infrastructure hinders low-carbon transition and sustainable urban development. This study, situated within the context of building energy and systems environments, examines the spatial and temporal dynamics of carbon lock-in across Chinese provinces over the past decade. A composite indicator system is constructed to quantify path dependence in transport-related carbon emissions. Additionally, a quantile regression model is applied to analyze the heterogeneous effects of influencing factors across varying levels of lock-in. The findings offer insights into developing region-specific decarbonization strategies and enhancing the sustainability of infrastructure systems. The key findings are as follows: (1) temporally, the composite path dependence index (PDI) increased from 0.13 to 0.24, with an average annual growth rate of 5.7%, although the growth trend exhibited noticeable fluctuations. (2) Spatially, a clear “East-High–West-Low, South-High–North-Low” pattern is observed, with the eastern region exhibiting a PDI approximately 29% higher than that of the northeastern region—mainly due to differences in economic vitality and the degree of policy intervention. (3) Threshold nonlinearity is evident: the influence of key factors on carbon path dependence varies across quantiles, and a critical threshold appears at the 0.9 quantile. Notably, the mitigating effect of low-carbon technological innovation on path dependence weakens beyond this threshold, suggesting diminishing marginal returns and the necessity for complementary governance strategies. This study deepens the understanding of transport carbon lock-in and provides guidance for region-specific strategies to enhance energy efficiency and sustainability in integrated building infrastructure systems.

交通基础设施碳解锁是事关我国可持续发展的关键问题之一。因此，本文选用2012—2021年长江经济带9个省市数据为研究对象，旨在揭示区域交通基础设施在经济发展过程中的减排规律。运用模糊集定性比较分析方法（fsQCA），本文探析了多因素协同联动对交通基础设施碳解锁的影响机制。研究发现：（1）单个因素均不是影响长江经济带碳排放强度的必要条件。（2）存在4条实现长江经济带碳排放强度降低、推动交通基础设施碳解锁的组态，即制度引导策略、经济驱动策略、制度-技术双元协同策略，以及经济-技术双元协同策略。（3）绿色技术创新在控制碳排放强度过程中的作用突出。研究结果为长江经济带交通基础设施碳解锁策略组态提供理论支撑，从而助力“双碳”目标实现，推动区域和全国的可持续发展。

Many countries have committed to carbon reductions and carbon neutrality targets in response to the Paris Agreement and Sustainable Development Goals (SDGs). With economic development, the transportation sector has become a major source of carbon emissions. In China, transport infrastructure—as an important carrier of the transportation sector—is important for controlling carbon emissions from this sector and achieving carbon neutrality and the targets of the SDGs. However, most studies have focused on transport vehicles and neglected transport infrastructure. Furthermore, the influences of collusive behavior and digital control technologies on the carbon reduction process have not yet been examined. This study aimed to analyze the influencing factors in the carbon reduction process in transport infrastructure. This study uses partial least squares structural equation modeling (PLS-SEM) to analyze the factors influencing carbon reductions in transport infrastructure and the mediating roles of collusive behavior and digital control technologies in the carbon reduction process. Low-carbon technologies, digital control technologies, and collusive behavior have positive direct and indirect effects on the carbon reduction effect. Digital control technologies have a positive effect on low-carbon regimes. Low-carbon technologies influence carbon reduction effects. Collusive behavior plays a mediating role in low-carbon regimes. Finally, the industrial structure influences carbon reduction effects. This study extends China’s carbon emission research in the transportation sector by focusing on infrastructure rather than vehicles. Additionally, this is the first study to incorporate collusive behavior and digital control technologies into the framework to analyze the impact of carbon reductions. The study also employs PLS-SEM to explore effective carbon reduction paths. The findings provide decision-making support for controlling carbon reductions in transport infrastructure.

In order to carry out the research on the measurement and prediction of carbon emissions from highway projects, the prediction index system is constructed by selecting seven influencing factors, namely, urbanization rate, industrial structure, energy intensity, energy structure, key low-carbon technologies, highway construction planning, and green financial support. The carbon emission coefficient method is used to measure the carbon emissions of highway projects from the perspective of energy consumption, and finally, a carbon emission scenario prediction model based on the scenario analysis method and the LSTM algorithm of the recurrent neural network is constructed to put forward a new way of predicting the carbon emissions of highway projects. Taking Hunan province as a case study object, the carbon emission trends in the next 15 years are predicted under three scenarios: low carbon, baseline, and high carbon. The results show that the highway project in Hunan province is expected to peak in 2031 under the low-carbon scenario, in 2037 under the baseline scenario, and around 2044 under the high-carbon scenario. It is also found that energy saving and emission reduction measures have a certain lag in the emission reduction effect of highway projects. Finally, based on this, suggestions and countermeasures for carbon emission reduction of highway development in Hunan province are proposed.

In the bidding process of government investment engineering projects, collusion between the government and bidders occurs repeatedly, which seriously affects the quality of engineering projects and the effectiveness of the government investment. Therefore, it is necessary to analyze and discuss the collusion between the government and bidders in government investment engineering projects, so as to provide a healthy and sustainable environment for the government investment engineering bidding market. There are two main types of collusion in engineering bidding: horizontal collusion and vertical collusion, and this paper focuses on the vertical collusion process in the engineering bidding process. A conceptual framework of the grey interest chain based on three stages of benefit creation, benefit distribution, and benefit realization was established, 15 major nodes in the grey interest chain were identified, and a grey interest chain control model was constructed, which further identified and classified the nodes into four levels: key nodes, important nodes, general nodes, and unimportant nodes. Finally, through the application of the model in the case, measures such as establishing a cracking mechanism for grey resource integration, increasing the supervision of grey interest chain, and strengthening post-bid audit are proposed. Measures such as including the preparation of bidding documents into the work assessment system and entrusting consulting units or third parties to prepare bidding documents are proposed to establish a crack mechanism for grey resource integration. In the benefit distribution stage, the penalties for the government and the bidders can be appropriately increased, the responsibilities of enterprises and project leaders can be implemented in the system on a reciprocal basis, and a perfect reputation mechanism information can be established. At the stage of benefit realization, the bidding system should be improved and post-bid audit should be strengthened to increase the difficulty of grey benefit realization. This paper will provide a reference for the prevention and governance of vertical collusion in bidding and tendering.

This paper explores the spatiotemporal evolution characteristics and spatial correlation patterns of green building development differences in 41 cities in the Yangtze River Delta region from 2012 to 2020 by means of the gravity center analysis model and spatial autocorrelation analysis. In addition, it further clarifies the impact factors of the spatial differentiation pattern of green building development in combination with GeoDetector based on four dimensional factors of population and economy, market environment, policy, and other factors. The results showed that: (1) According to the analysis of the number of green buildings in each city from 2012 to 2020 and the natural discontinuity method, the development pattern of green buildings in the Yangtze River Delta region city clusters shows an imbalance, being highly concentrated in the eastern coastal areas with Suzhou (1) and Shanghai as the core. The overall trajectory of the center of gravity shows the development from southeast to northwest. (2) The global Moran’s I of green buildings in the Yangtze River Delta region city clusters is greater than 0, and all passed the significance test (Z > 1.96, p < 0.01), indicating that the green buildings in the Yangtze River Delta region city clusters show typical spatial aggregation characteristics. By using the local LISA index, it is found that in the H-H spatial autocorrelation distribution pattern with Suzhou (1) and Shanghai as the core, the core city has a strong attraction ability and relatively low radiation ability. (3) Based on the explanatory power mean, the main driving factors of the spatial differentiation pattern of green building development in the Yangtze River Delta region city clusters are education level (0.6656), technical level (0.6269), and the gross domestic product (0.6091). The factor interaction shows a two-factor enhancement and nonlinear enhancement effect, and there is neither a weakening nor an independent relationship.

The construction megaproject team (CMPT) emphasises integration as a megaproject multiteam system (MTS) to facilitate knowledge learning. This paper synthesises the CMPT structure characteristics and team learning processes into a conceptual framework named the CMPT-MTS learning framework to advance future CMPT learning research. The constructs included are identified from published research. A case example is the island and tunnel project of the Hong Kong–Zhuhai–Macao Bridge, which provides practical grounding for framework refinement. The conceptual framework deemed to follow a cyclical pattern identifies the CMPT-MTS structure variables and team learning processes affecting learning outcomes and contextual variables hypothesised to influence the intra- and inter-team interaction. We discuss how the conceptual framework might identify the CMPT learning research gaps for future research. The framework’s contribution to the body of knowledge expands our lens to understand team learning practices in the complex CMPT by considering CMPT as an MTS.

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.