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The purpose of this paper is to clarify the impact mechanisms and weighting factors of environmental, social and governance (ESG) practices on corporate value through bibliometric analysis and core interpretation of existing literature, further explore whether and under what conditions ESG practices contribute to the corporate value creation, and provide an outlook on future research directions.

Bibliometric method is used to analyze literature co-citation, burst detection and keyword co-occurrence, and literature review method is used to condense important ideas from the existing literature.

Through the review, analysis and summary of the existing literature, this paper finds that the perspectives of risk, information and strategy reflect the key pathways through which ESG practices play a role in avoiding harm and creating value for companies directly or indirectly. Macro, meso and micro factors moderate the direction and extent of the impact. Moreover, considering the relationship between ESG performance and ESG disclosure is key to understanding some contradictory findings.

The search terms limit the articles considered, and therefore, the research framework may be incomplete. Moreover, this article is primarily aimed at the research field and lacks guidance at the practical level.

This paper helps the academic community to deepen its understanding of ESG, moving beyond the question of whether ESG is linked to corporate value to further understand why and under what conditions ESG practices create value for firms.

This paper has great practical significance in motivating companies to actively participate in ESG practices.

The theoretical framework in this paper reveals the black box between enterprise ESG practices and value creation, and clarifies the research boundary of “the relationship between ESG practices and value creation,” contributing to the future research in this field.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

This paper's purpose is to suggest that manufacturers strategically place soft orders for assembly materials with suppliers in Silk Road Economic Belt countries who probably doubt the realization of the soft orders placed.

First, a two-stage Stackelberg competition is constructed, taking into account the supplier's trust level in formulating the decision process in the assembly supply chain. The authors then provide a buyback contract to coordinate the supply chain, in which the manufacturer obtains enough supplies by sharing some of the perceived risks of not fully trusted suppliers. Furthermore, the authors conduct a numerical study to investigate the influence of trust under a decentralized case and a buyback contract.

The authors found that all supply chain partners in Silk Road Economic Belt countries experience potential losses due to not fully trusting certain conditions. The study also shows that, in Silk Road Economic Belt countries, operating under a buyback contract is better than being without one in terms of assembly supply chain performance.

On the one hand, the authors only consider the asymmetry of demand information without considering that of cost structure information. On the other hand, a natural extension of the paper is to integrate single-period transactions into the multi-period transaction problem setting. As all these issues require substantial effort, the authors reserve them for future exploration.

Doing business with not-fully-trustworthy partners in Silk Road Economic Belt countries is risky, and this study reveals how trust works in global cooperation and with strategic reactions in situations of partial trust.