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This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG).

The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects.

The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level.

This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation.

The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This paper aims to investigate the actual impact, mechanism and internal and external environmental adjustment effect of ESG performance on the persistence of green innovation using data from China’s A-share manufacturing listed companies from 2009 to 2021.

Panel data regression models are used to explore the effect of ESG performance of manufacturing enterprises on the persistence of green innovation. To examine the mechanism of ESG performance affecting the persistence of green innovation of manufacturing enterprises, this paper refers to the research of Wen and Ye (2014) and constructs an analysis framework of intermediary effect.

This research was funded by Shandong Provincial Natural Science Foundation, grant number ZR2023MG075 & ZR2024QE171.

There are a few more limitations to this study that might be discussed from the following angles: first, due to data availability, this paper examines the persistence of green innovation from the output perspective. The authors can expand the data sources in the future and investigate the input-output combinations in green innovation as a means of understanding its sustainability. Second, the mechanism studied in this paper includes management costs, entry of green investors and risk-taking ability. In fact, it is possible that ESG performance influences green innovation persistence in other ways as well; these can be investigated more in the future.

First, it concentrates on the persistence of green innovation in manufacturing enterprises, surpassing the quantitative aspect and thereby broadening the research scope. Second, by including the “management expense ratio,” “green investor entry” and “risk-taking” as mediating factors, the study delves deeper into the mechanisms through which ESG performance impacts the persistence of green innovation in manufacturing enterprises, further broadening the research scope. Third, this research incorporates the internal and external environments encountered by manufacturing enterprises into the analytical framework to investigate their adjustment effects in the process of ESG performance influencing persistent green innovation, thus widening the research perspective. Fourth, this study introduces the subdimensions of ESG performance, specifically environmental responsibility, social responsibility and corporate governance, and assesses their impacts on the persistence of green innovation in manufacturing enterprises, thus enriching the research narrative.

This study aims to investigate the relationship between geopolitical risk (GPR) and gold price bubbles to determine whether rising GPR can drive deviations in the fundamental value of gold, thus leading to speculative bubbles.

Using a data set that spans from January 2002 to December 2023 and covers both GPR data and gold price data, this study applies the log-periodic power law singularity (LPPLS) model to identify gold price bubbles. The analysis explores the effects of GPR and its sub-indices – geopolitical risk–acts (GPRA) and geopolitical risk–threats (GPRT) – on gold price bubbles. The causal relationships are examined through logistic regression, Tobit modelling and machine learning, with a focus on different countries, including major gold producers and consumers.

The results indicate a significant relationship between GPR and gold price bubbles, particularly with GPRA, which exerts a stronger influence than GPRT does. Peaks in GPR often align with the formation of gold price bubbles, both positive and negative. Additionally, geopolitical instability in Russia has a significant effect on US gold price bubbles.

The findings provide valuable insights for investors and policymakers by emphasizing the importance of GPR in shaping gold price dynamics. Investors are advised to consider the nuanced roles of GPRA and GPRT when using gold as a hedge during periods of heightened geopolitical tension.

Understanding the role of GPR in gold price bubbles can help mitigate the financial risk associated with speculative bubbles, thereby offering a better framework for managing assets during geopolitical crises.

This study extends existing research by directly linking GPR with gold price bubbles via the LPPLS model, with a novel emphasis on the differentiation between GPRA and GPRT, providing new perspectives on the safe-haven role of gold during geopolitical uncertainty.

Morality in the workplace has become a significant determinant of organizational effectiveness and employee well-being. This research aims to conduct an in-depth review of the past literature on multidimensional morality and provide insight into its impact on the modern workplace and employee well-being.

The literature search resulted in 3,589 papers published between 2012 and 2024. This paper analyzed 30 research studies on workplace morality based on predetermined inclusion and exclusion criteria to ensure methodological rigor.

Research findings show the need to promote workplace ethics to avoid counterproductive workplace behaviors. Also, effective leadership adheres to ethical principles, together with the ethical framework of an organization, which includes codes of ethics and a commitment to corporate social responsibility, contributing to the overall well-being of individuals by providing a sense of support and resolving conflicts. Ethical conflicts are associated with decreased well-being and increased turnover, underscoring the need for organizational solutions.

Understanding intricate morality is essential for organizations to foster moral cultures and improve performance. HR specialists can use this knowledge to create policies that uphold moral principles and improve employee job satisfaction.

This study extends the current body of knowledge by synthesizing research on multifaceted moral issues in modern workplaces. It provides useful insights for researchers, practitioners, and policymakers who are interested in promoting ethical organizational cultures by addressing ethical shortcomings.