Excellence in corporate culture is the key to achieving sustainable business development. Sustainability can be a source of success, innovation and profitability for a company, driving the achievement of low-carbon goals for transport infrastructure enterprises. The aim of this study is to examine the relationship between corporate culture and corporate sustainability from the perspective of transport infrastructure enterprises, and to identify which corporate culture factors may have an impact on the sustainable low carbon development of transport infrastructure enterprises. To achieve this, we constructed a structural equation model based on 351 cases in Hunan Province and examined the relationship between corporate culture and sustainable low-carbon development using partial least squares structural equation modeling. The findings suggest that corporate values and corporate culture management capabilities play an important role in promoting sustainable development of transport infrastructure enterprises at the economic and low-carbon levels.

Green buildings are an important initiative to address energy and environmental issues in the construction field. The high-quality development of green buildings is affected by many factors, and it is necessary to identify the critical factors affecting the high-quality development of green buildings and analyze them systematically. The adopted literature analysis method and expert consultation method, the DIM (DEMATEL-ISM-MICMAC) model was established to explore critical factors influencing green buildings’ high-quality development and their internal hierarchical structure, interrelationships, and mechanisms. Then, targeted suggestions were put forward to promote green buildings’ high-quality development. The results showed that: (1) The critical factors influencing green buildings’ high-quality development could be divided into five levels, three groups, and four areas. The economic development level, living standard of residents, education level, incentive policies, and compulsory laws and regulations were in the deep factor group, fundamentally affecting green buildings’ high-quality development. (2) In terms of drive and centrality, the economic development level, living standard of residents, education level, and incentive policies were at the forefront, playing a vital role in the high-quality development of green buildings.

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.

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.

Due to the national economic development form and social development demand, in recent years, the government has been vigorously promoting the control of government-enterprise collusion in the bidding process of government projects in order to promote the standardization of the market. How to predict the vertical collusion behavior under different internal and external environments has become an important research content. Although the prediction of individual behavior is difficult to achieve, the prediction of group behavior has certain possibilities. In this paper, we propose a method for predicting and evaluating the vertical collusion behavior of government investment project bidding based on BP neural network analysis optimized by an annealing algorithm. First, drawing on the traditional evaluation model, the evaluation index system of government-enterprise collusion behavior is constructed from five dimensions: internal environment, external environment, policy development, enforcement effort, and feedback channel. Secondly, a machine learning method based on BP neural network optimized by an annealing algorithm is introduced to evaluate the influence of the change of initial conditions on the bidding collusion behavior. This study has certain significance for government managers to discover the problems and causes in policy formulation, which in turn can support the government in further improving the policy utility.