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The purpose of this study is to examine the vital link between manufacturing firms and the environment, delving into the intricate connections among factors affecting these firms. Specifically, it investigates how the environmental performance of manufacturing firms is shaped by their adoption of environmental management practices and the regulatory environment in which they operate.

Data are currently being collected through a structured questionnaire from employees working in manufacturing firms in Pakistan. Random sampling was used to select the participants. The hypotheses were tested using PLS-SEM analysis.

The study reveals a positive correlation between green manufacturing practices and superior environmental performance. Effective environmental management systems further help firms reduce their environmental footprint. External environmental regulations play a significant role as moderators, influencing the strength and direction of the relationship between green manufacturing, environmental management and environmental performance.

The practical implications offer valuable insights and guidance for manufacturing companies seeking to improve their environmental responsibility and performance. Additionally, policymakers gain insights into how regulatory frameworks can be designed or modified to better support sustainability efforts within the manufacturing sector.

This study offers timely insights for sustainable business practices, aligning with corporate responsibility efforts. It contributes to both academic knowledge and provides actionable guidance for fostering environmentally responsible practices in the manufacturing sector.

The purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).

(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.

Results indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.

The findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.

The numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.

Based on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.

The results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.

This paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.