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In the last decades, various building information modeling–life cycle assessment (BIM-LCA) integration approaches have been developed to assess the environmental impact of the built asset. However, there is a lack of consensus on the optimal BIM-LCA integration approach that provides the most accurate and efficient assessment outcomes. To compare and determine their accuracy and efficiency, this study aimed to investigate four typical BIM-LCA integration solutions, namely, conventional, parametric modeling, plug-in and industry foundation classes (IFC)-based integration.

The four integration approaches were developed and applied using the same building project. A quantitative technique for evaluating the accuracy and efficiency of BIM-LCA integration solutions was used. Four indicators for assessing the performance of BIM-LCA integration were (1) validity of LCA results, (2) accuracy of bill-of-quantity (BOQ) extraction, (3) time for developing life cycle inventories (i.e. developing time) and (4) time for calculating LCA results (i.e. calculation time).

The results show that the plug-in-based approach outperforms others in developing and calculation time, while the conventional one could derive the most accuracy in BOQ extraction and result validity. The parametric modeling approach outperforms the IFC-based method regarding BOQ extraction, developing time and calculation time. Despite this, the IFC-based approach produces LCA outcomes with approximately 1% error, proving its validity.

This paper forms one of the first studies that employ a quantitative and objective method to determine the performance of four typical BIM-LCA integration solutions and reveal the trade-offs between the accuracy and efficiency of the integration approaches. The findings provide practical references for LCA practitioners to select appropriate BIM-LCA integration approaches for evaluating the environmental impact of the built asset during the design phase.

The impact of rolling on the performance of micro arc oxidation (MAO) coatings on ZM5 alloy has been underreported. The purpose of this study is to explore the correlation between rolling and the failure mechanism of MAO coatings in greater depth.

The influence of rolling on the corrosion and wear properties of MAO coating was investigated by phase structure, bond strength test (initial bond strength and wet adhesion), electrochemical impedance spectroscopy and wear test. The change of the surface electrochemical properties was studied by first principles analysis.

The results showed that the MAO coating on rolled alloy had better corrosion and wear resistance compared to cast alloy, although the structure and component content of two kinds of MAO coating are nearly identical. The difference in interface bonding between MAO coating and Mg substrate is the primary factor contributing to the disparity in performance between the two types of samples. Finally, the impact of the rolling process on MAO coating properties is explained through first-principle calculation.

A comprehensive explanation of the impact of the rolling process on MAO coating properties will provide substantial support for enhancing the application of Mg alloy anticorrosion.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

One of the best ways to assist China is through infrastructure investment. China might become more resilient to natural calamities by pouring more money into its transport network. Analyzing the relationship between China's degree of planned expansion and the country's current network of transport hubs can help with city development estimates. A wide range of factors were taken into consideration while evaluating China's dominance and the caliber of its transportation infrastructure. Using a geographical autocorrelation model and a coupling coordination model, the dynamic link between China's adaptability and the caliber of its transportation infrastructure is examined.

China's northwest is underdeveloped in comparison to the southeast, which has a high level of resilience and development of its transportation infrastructure. The relationship between the levels of resilience upheld by China's transport infrastructure is suggested to be coordinated.

The authors find a positive geographical autocorrelation between the degree of coupling coordination and the degree of agglomeration, despite the fact that the distance between cities increases with time. They now believe that there is a connection between an area's population density and the degree of interspousal cooperation within. The consequence is an improvement in both national security and economic prosperity. The facilities for disaster management and transportation in China have received several proposals for improvement.

The authors' Practical Implications suggests that scale inefficiency is a major contributor to the relatively poor efficiency of China's primary inland river ports. Different types of inland river ports may have vastly different water system efficiencies. Input and output congestion at China's important interior river ports has reached 51%, making it very clear that massive amounts of valuable port resources are being wasted.

Many variables, such as climate and human error, affect the total amount of goods that can be moved via inner river ports. Ports situated either higher up or lower down the same canal may perform better or worse, respectively, depending on the circumstances.

The purpose of this study is to evaluate the effect of graphene, basalt flakes and their synergy on the corrosion resistance of zinc-rich coatings. As the important heavy-duty anticorrosion coatings, zinc-rich coatings provided cathodic protection for the substrate. However, to ensure cathodic protection, a large number of zinc powder made the penetration resistance known as the weakness of zinc-rich coatings. Therefore, graphene and basalt flakes were introduced into zinc-rich coatings to coordinate its cathodic protection and shielding performance.

Three kinds of coatings were prepared; they were graphene modified zinc-rich coatings, basalt flakes modified zinc-rich coatings and graphene-basalt flakes modified zinc-rich coatings. The anticorrosion behavior of painted steel was studied by using the electrochemical impedance spectroscopy (EIS) technique in chloride solutions. The equivalent circuit methods were used for EIS analysis to obtain the electrode process structure of the coated steel system. Simultaneously, the corrosion resistance of the three coatings was evaluated by water resistance test, salt water resistance test and salt spray test.

The study found that the addition of a small amount of graphene and basalt flakes significantly improved the anticorrosion performance of coatings by enhancing their shielding ability against corrosive media and increasing the resistance of the electrochemical reaction. The modified coatings exhibited higher water resistance, salt water resistance and salt spray resistance. The graphene-basalt flakes modified zinc-rich coatings demonstrated the best anticorrosion effect. The presence of basalt scales and graphene oxide in the coatings significantly reduced the water content and slowed down the water penetration rate in the coatings, thus prolonging the coating life and improving anticorrosion effects. The modification of zinc-rich coatings with graphene and basalt flakes improved the utilization rate of zinc powder and the shielding property of coatings against corrosive media, thus strengthening the protective effect on steel structures and prolonging the service life of anticorrosion coatings.

The significance of developing graphene-basalt flakes modified zinc-rich coatings lies in their potential to offer superior performance in corrosive environments, leading to prolonged service life of metallic structures, reduced maintenance costs and a safer working environment. Furthermore, such coatings can be used in various industrial applications, including bridges, pipelines and offshore structures, among others.

It is unclear what constitutes the tourism live streaming (TLS) servicescape and how it affects users' travel intention (TI). The study aims to explore the composition of the TLS servicescape, the influence mechanism of the TLS servicescape on users' TI and the formation of users' TI.

Based on stimulus organism response theory (SOR), we develop a mediation model to explore the influence of TLS servicescape on users' TI. This study collected data from 432 Chinese TLS users through an online questionnaire, and we used the structural equation model and the SPSS PROCESS macro to test the proposed model. In addition, we tested the variable relationships using fuzzy-set qualitative comparative analysis (fsQCA).

TLS servicescape is a second-order variable that can be categorized into physical element (PE), social element (SOE), symbolic element (SYE) and natural element (NE). TLS servicescape influences TI by affecting social presence (SP) and customer engagement (CE). The fsQCA reveals seven combinations of PE, SOE, SYE, NE, SP and CE that form a high TI for TLS users.

Using multiple data analysis methods, the study emphasizes the significance of the TLS servicescape for TLS. It explores how to evoke users' TI in TLS and provides a reference for TLS marketing.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.

How does the regional institutional environment of China’s transitional economy influence the relationship between a firm’s R&D investment intensity and innovation performance? Based on the resource-based view and institution-based view, an empirical study was executed to identify the moderating effects of institutional environment variables from the Marketization Index of China’s Provinces: National Economic Research Institute (NERI) Report on the relationship between a firm’s R&D investment intensity and innovation performance. This paper aims to study how effectively improve the impact of R&D investment intensity on innovation performance under the influence of the institutional environment.

Against the background of China’s transitional economy, the authors present empirical evidence from panel data covering 374 Chinese A-share listed high-tech manufacturing firms on the Shanghai and Shenzhen Stock Exchange to examine the relationship between R&D investment intensity and innovation performance.

Empirical results illustrate the following: The R&D investment intensity and innovation performance displayed an inverse U-shaped relationship, and R&D investment intensity had a lagged effect on R&D output according to the uncertainty and industrialization period of R&D activities. The level of financial market development can intensify the effects of R&D investment intensity on innovation performance. The degree of government intervention weakens the effect of R&D investment intensity on innovation performance.

Based on the background of China’s institutional environment during the transition period, combined with previous research and the Marketization Index of China’s Provinces: NERI Report, selecting financial market development, government intervention level and legalization level as moderating variables to study how effectively improve the impact of R&D investment intensity on innovation performance under the influence of the institutional environment. Due to the different ownership of firms during the transition period, the appropriate impact of the institutional environment on the relationship between R&D investment intensity and innovation performance will vary. Moreover, the level of legalization would impact on innovation insignificantly.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

The 21st century has brought the business model earth-shaking changes, especially since the Corona Virus Disease 2019 (COVID-19) epidemic at the end of 2019. Now, the epidemic normalization is slowing down China's rapid development. However, technological development, like artificial intelligence (AI), is unstoppable and is transforming China's economic growth modes from factor-driven to innovation-driven systems. Therefore, it is necessary to study further the new changes in labor entrepreneurship and innovation business models and their mechanism of action on economic growth.

This work studies how innovative human capital (IHC) uses AI and other scientific and technological (S&T) innovation technologies to promote China's innovation-driven economic growth model transformation from the labor entrepreneurship and innovation perspective.

The research shows that the entrepreneurial innovation ability of IHC can increase marginal return and output multiplier effect. It changes the traditional business model and promotes China's economic growth and innovation development. At the same time, this work analyzes China's inter-provincial panel data through the panel smooth transition regression (PSTR) model. It concludes that there is a nonlinear relationship between IHC and the output of innovative achievements. The main body presents three stages of nonlinear changes: first rising, then slightly declining, and rising so far.

The finding provides a direction for solving the problem of slow economic growth and accelerating the transformation of economic growth mode under epidemic normalization.