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This study aims to explore the interaction of “contractual governance – relational governance – governmental governance” mechanisms and proposes hypotheses about the effects of megaproject governance on governance performance from both theoretical and practical perspectives.

In this paper, a megaproject governance model is developed to explain the relationship between governance mechanisms and governance performance. The model is based on related literature and explores the interactions between governance mechanisms and how they work to improve governance performance. The structural equation model (SEM) is adopted to explore the influence path on governance performance for megaprojects.

The results indicate that: (a) The findings highlight the positive role of project governance mechanisms on governance performance. (b) Contractual governance, relational governance, and governmental governance directly affect governance performance. In addition, contractual governance mediates governance performance through relational governance and governmental governance; governmental governance mediates governance performance through contractual governance and relational governance. (c) Contractual governance, relational governance, and governmental governance play a positive role in governance performance.

Governmental governance is added to project governance theory and the empirical research method is used to explore the interaction between contractual governance, relational governance, and governmental governance of megaprojects. The SEM is used to systematically explore the paths of megaproject governance mechanisms on governance performance, considering the interactive role of the “contractual governance - relational governance - governmental governance” and the mediating role.

The study reveals the impact path of multidimensional megaproject governance mechanisms on governance performance. In this paper, the empirical findings can help the project participants by providing a decision-making basis for good governance and references for the governments to promote the construction of a micro-institutional environment for megaprojects.

The contributions of this study are (1) to add an exploration of governmental governance to the existing project governance theory, and (2) to consider the interactions of the “contractual governance – relational governance – governmental governance” mechanisms, and (3) to explore their effects on governance performance, including direct and mediating effects. This study contributes to a comprehensive understanding of megaproject governance by considering governmental governance and the interactions of the three governance mechanisms. Understanding the impact of megaproject governance on governance performance could assist project stakeholders and provide decision guidance for good governance.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

As the complex influencing factors for financing decisions and limited information at the early project stage often render inappropriate financing mode and scheme (FMS) selection in the large-scale urban rail transit (URT) field, this study aims to identify the multiple influencing factors and establish a revised case-based reasoning (CBR) model by drawing on experience in historical URT projects to provide support for effective FMS decisions.

Our research proposes a two-phase, five-step CBR model for FMS decisions. We first establish a case database containing 116 large-scale URT projects and a multi-attribute FMS indicator system. Meanwhile, grey relational analysis (GRA), the entropy-revised G1 method and the time decay function have been employed to precisely revise the simple CBR model for selecting high-similarity cases. Then, the revised CBR model is verified by nine large-scale URT projects and a demonstration project to prove its decision accuracy and effectiveness.

We construct a similarity case indicator system of large-scale URT projects with 11 indicators across three attributes, in which local government fiscal pressure is considered the most influential indicator for FMS decision-making. Through the verification with typical URT projects, the accuracy of our revised CBR model can reach 89%. The identified high-similarity cases have been confirmed to be effective for recommending appropriate financing schemes matched with a specific financing mode.

This is the first study employing the CBR model, an artificial intelligence approach that simulates human cognition by learning from similar past experiences and cases to enhance the accuracy and reliability of FMS decisions. Based on the characteristics of the URT projects, we revise the CBR model in the case retrieval process to achieve a higher accuracy. The revised CBR model utilizes expert experience and historical information to provide a valuable auxiliary tool for guiding the relevant government departments in making systematic decisions at the early project stage with limited and ambiguous project information.