In knowledge economy era, enterprise needs to innovate to maintain its advantages and competitiveness. Construction enterprises, being pillars of China’s economy and confronting the challenge brought by the strategy of “going out”, the call for their technology or management innovation was broadly pronounced across practical and academic fields. Social capital (SC), as a resource in a social network, is the basis for creating sustainable competitiveness and advantage for enterprises. The innovative achievements and innovation performance (IP) of enterprises are largely determined by their SC. To achieve competitiveness in the market, enterprises must carry out knowledge transfer (KT) with the other members of their networks. However, few scholars have examined weather SC has any effect on IP in construction enterprises. Using a KT perspective, this paper explored how SC affects the IP of construction enterprises. Based on the literature review and analysis, a conceptual model was constructed and validated using structural equation modeling (SEM). Through empirical analysis, the following conclusions were drawn: (1) SC has a positive impact on the IP of construction enterprises. Among them, the structural dimension (SD) and cognitive dimension (CD) have a significant positive impact on the IP of construction enterprises, while the relational dimension (RD) does not. (2) The SD, CD, and RD of construction enterprises’ SC have a positive influence on KT. (3) There are mediating effects of KT between SC and IP of construction firms, and they are partial. KT plays a partial mediating effect between SD, CD, and IP of construction firms. The research results can not only improve an understanding of effects of SC on IP of construction enterprises, but also validate the importance of KT in stimulating IP.

Oil leak from vehicles is one of the most common pollution types of the road. The spilled oil could be retained on the surface and spread in the air voids of the road, which results in a decrease in the friction coefficient of the road, affects driving safety, and causes damage to pavement materials over time. Photocatalytic degradation through nano-TiO2 is a safe, long-lasting, and sustainable technology among the many methods for treating oil contamination on road surfaces. In this study, the nano-TiO2 photocatalytic degradation effect of road surface oil pollution was evaluated through the lab experiment. First, a glass dish was used as a substrate to determine the basic working condition of the test; then, a test method considering the impact of different oil erosion degrees was proposed to eliminate the effect of oil erosion on asphalt pavement and leakage on cement pavement, which led to the development of a lab test method for the nano-TiO2 photocatalytic degradation effect of oil pollution on different road surfaces.

The road runoff after rainfall carries a lot of pollutants that could cause great harm to the water environment. A biochemical pool can be used as a treatment for the road runoff. In order to further improve the efficiency of road runoff treatment by biochemical pool and to evaluate the purification effect of the aquatic plants, two aquatic plants of Iris pseudacorus and Myriophyllum verticillatum were chosen in this research. The effect of different planting densities on the treatment of runoff pollutants and the planting mode by different aquatic plants were studied. The results show that both plants have the ability to remove the pollutants like chemical oxygen demand (COD), Zn, Cu, oil, and suspended solids (SS), and the ability is increased with the increase of planting density. The Iris pseudacorus is better than Myriophyllum verticillatum on the removal of Zn, while Myriophyllum verticillatum does better on the removal of Cu, oil, and SS. Combined planting mode can effectively improve the purification effect of COD and petroleum.

As an important part of smart city, intelligent transportation is an critical breakthrough to solve urban traffic congestion, build an integrated transportation system, realize the intelligence of traffic infrastructure and promote sustainable development of traffic. In order to investigate the construction of intelligent transportation in cities, 20 initial affecting variables were determined in this study based on literature analysis. A questionnaire collected from professionals in intelligent transportation was conducted, and a total of 188 valid responses were received. Then the potential grouping was revealed through exploratory factor analysis. Finally, a causal model containing seven concepts was established using the practical experience and knowledge of the experts. A root cause analysis method based on fuzzy cognitive map (FCM) was also proposed to simulate intelligent transportation construction (ITC). The results indicate:(1) The 20 variables can be divided into six dimensions: policy support (PS), traffic sector control (TSC), technical support (TS), communication foundation (CF), residents’ recognition (RR), and talent quality (TQ); and (2) In the FCM model, all six concept nodes (PS, TSC, TS, CF, RR, and TQ) have a significant positive correlation with the target concept node ITC. The rank of the six dimensions according to correlation strength is TS, CF, PS, TSC, RR, and TQ. The findings of this paper can help academics and practitioners understand the deep-seated determinants of urban intelligent transportation construction more comprehensively, and provide valuable suggestions for policy makers. And thus, the efficiency of intelligent transportation construction can be improved.