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.

The factors affecting urban rail tunnel construction projects are very complex and are influenced by many factors such as the social environment, the construction process, and the way construction is managed. These influencing factors interact with each other, leading to the complexity of the development risks of this type of project. However, at present, the research on engineering construction risk is mainly focused on the field of housing construction, and there are few researches on the risk of urban tunnel construction. At the same time, with the continuous development of urbanization, the coverage of urban underground rail transport is increasing, so it is of great theoretical and practical significance to study the construction process of urban underground tunnels. This paper uses the literature collection method and the LDA model to initially identify the impact factors, and on this basis, the final set of evaluation impact factors is determined by means of expert interviews. Based on the set of influencing factors, the Dematel-ISM model was used to obtain a comprehensive analysis of the factors affecting urban rail tunnel construction projects by comparing topological maps and obtaining a Dematel-ISM model diagram with a cause-effect reachable hierarchy. Finally, the results obtained are applied to the actual development to verify the validity of the model. The results of the study show that construction operation, sequence arrangement, and procedure selection are the key influencing factors for safety risks in urban rail tunnel construction projects.

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.

Controlling collusion in government bidding is a prerequisite for ensuring social justice and the smooth operation of projects. Based on the prospect theory, this article establishes a four-party evolutionary game model for tenderers, enterprises with higher willingness to collude, enterprises with lower willingness to collude, and supervising enterprises. The study uses replication dynamics to analyze the stability of strategy selection after the evolutionary game. The results show that higher project base returns increase the probability of collusion, while lower market competition, higher risk aversion, and stronger collusive regulation all reduce the probability of collusion. When regulators adopt a strong regulatory strategy, the remaining project participants tend to choose a noncollusive strategy.

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.

To effectively diagnose and monitor the vertical collusion in construction project bidding, this paper developed a comprehensive evaluation model with deep neural network and transfer learning. By this model, the collusion characteristics of bidders, tenderers, and bid evaluation experts were mined from limited data set hidden and collusion tendency was evaluated. Firstly, 18 evaluation indicators were established from literature review, court file summarization, typical case analysis, and expert consultation. Then, a comprehensive evaluation model was developed with the deep neural network and transfer learning. Finally, the model was trained and tested with the collected data set. The test results showed that the developed model achieved 87.3% identification accuracy in collusion tendency evaluation of different subjects.