Identification and ranking of enablers to green technology adoption for manufacturing firms using an ISM-MICMAC approach

An integrated approach to modeling the influence of critical factors in low-carbon technology adoption by chemical enterprises in China

Adopting low-carbon technology has become a critical method for enterprises to reduce carbon emissions and combat global warming. However, the willingness of high-energy-consuming and high-emission enterprises, such as those in the chemical industry, to adopt this technology is not high. Therefore, how to effectively stimulate these enterprises to develop and apply low-carbon technology has become an urgent challenge. This paper adopts a three-phase integrated approach to explore the influence of critical factors in the adoption of low-carbon technology by chemical enterprises. Firstly, based on the technology-organization-environment framework, the main influencing factors are identified. Critical influences are then extracted by the interpretive structural modeling-analytic network process method. Finally, the dynamic changes of key factors are simulated and analyzed using an evolutionary game model based on the realistic data of Sinopec Shanghai Petrochemical Co., Ltd. And Industrial and Commercial Bank of China. The findings identify adoption cost, adoption benefit, environmental regulation, and green credit as four key factors of low-carbon technology adoption. Adoption probability is related negatively to adoption cost but positively to adoption benefit, government subsidies and penalties, and green credit. Adoption probability is related to adoption benefit and green credit; it is least sensitive to adoption benefit and most sensitive to green credit. That is, relying only on the enterprises' own efforts and without the support of subsidies and green credit, the adoption process will be very long. When green credit works, the enterprise can use technology without subsidies and penalties. Moreover, the adoption probability has a higher sensitivity to penalties than to subsidies. In other words, the enterprise will only adopt and apply the relevant technology when subsidies are large, whereas strengthening penalties can rapidly increase the adoption probability. These findings provide a valuable reference for enhancing enterprises' adoption willingness and establishing an efficient incentive mechanism of low-carbon technology adoption.

Toward industry 5.0: Challenges and enablers of intelligent manufacturing technology implementation under the perspective of sustainability

The advancement of intelligent manufacturing technology in the era of Industry 5.0 has propelled the intelligence and automation of manufacturing production, while also exerting a significant impact on sustainable development of the manufacturing industry. However, the challenges and enablers faced by the transformation of intelligent manufacturing technology in the context of sustainable development of Industry 5.0 are still unclear. Based on literature review and expert opinions, this study uses the Likert scale to determine the challenges and enablers of the implementation of intelligent manufacturing technology in social, environmental and economic sustainability. The fuzzy-DEMETAL and AISM are used to analyze the logical relationship and hierarchical relationship between the above factors, and the MICMAC matrix is used to determine the key influencing factors. The research conclusions show that the most important challenges affecting the implementation of intelligent manufacturing technology are cost and funding, and the most important enabler is social benefits and public service improved. This research will provide insights for industry practitioners and decision makers in the management and decision-making process of implementing the transformation and upgrading of manufacturing intelligent manufacturing, thereby enhancing the sustainability of manufacturing development.

Factors influencing contractors' low-carbon construction behaviors in China: a LDA-DEMATEL-ISM approach

Contractors' low-carbon construction behaviors (CLCB) are pivotal in advancing decarbonization during the construction phase. However, there exists a notable gap in the comprehensive exploration of the multifaceted factors and mechanisms influencing CLCB. Therefore, this study aims to systematically identify the factors influencing CLCB in China, examine the interrelationships among these factors, and pinpoint the key determinants. Based on topic modeling of Latent Dirichlet Allocation (LDA), influencing factors are identified firstly from the pertinent literature. Subsequently, the causality degree and centrality degree between these factors are assessed by the Decision-Making Trial and Evaluation Laboratory (DEMATEL), followed by the establishment of a hierarchical structure using the Interpretive Structural Modeling (ISM) method, culminating in the identification of pivotal factors. Findings reveal that (1) 21 influential factors influencing CLCB are identified. (2) "Incentive policies for relevant stakeholders" and "Low-carbon regulation and supervision" emerge as key influences. (3) CLCB should be guided by policy and subjective awareness, fortified by market and management support, underpinned by technology, and directly driven by economic considerations. This research furnishes valuable insights for promoting low-carbon development during the construction phase, thereby assisting the construction sector in achieving carbon peak and carbon neutrality.

Analysing influencing factors and correlation paths of learning burnout among secondary vocational students in the context of social media: An integrated ISM-MICMAC approach

By analysing the factors influencing secondary vocational students' learning burnout in the context of social media, this study unearthed the underlying causes of learning burnout. It also determined the correlation paths among the factors influencing learning burnout, providing references for educational and pedagogical improvement. This contributes to preventing secondary vocational students' learning burnout and enhancing learning efficiency in secondary vocational schools. Combined with previous research results and a theoretical basis, this study identifies 10 influencing factors employing the Delphi method, and uses Interpretative Structural Modelling (ISM) and Matrice d' Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) to elucidate the relationship between influencing factors of learning burnout among secondary vocational students in the context of social media. This study also constructs a corresponding mechanism model and subsequently proposes prevention and improvement strategies. The results show that the overdevelopment of social media, as driving factors, has the greatest impact on secondary vocational students' learning burnout. Simultaneously, it takes the lead in addressing cognitive bias among students, decreased self-control, and low learning efficiency, factors that contribute to learning burnout. This is particularly beneficial in alleviating the degree of learning burnout among secondary vocational students in the context of social media and improves overall learning outcomes for these students. The hierarchical structure and correlation paths identified in this study offer robust invaluable guidance for developing a scientific program to address the problem of learning burnout among this demographic. This includes implementing related educational practises, thereby reducing the unpredictability of the practical applications.

Research on the interplay between green finance and manufacturing sustainability outcomes: insights for low-carbon economy in the post-COVID-19 era

In the quest to strengthen resilient and sustainable recovery in the post-COVID-19 era, there is a huge requirement for manufacturing firms to adopt green finance which is dominated by green bond issuance. Nevertheless, published studies that provide insights on factors that influence the issuance of green bonds within manufacturing firms in the post-COVID-19 era and the impact on sustainable outcomes are currently non-existent. Therefore, this study analyzed the interrelationships that exist between the influencing factors of green bond issuance within manufacturing firms using decision-making trial and evaluation laboratory (DEMATEL) and data from Nigerian manufacturing firms. Then, a structural model of their importance levels was illustrated using interpretive structural modeling (ISM) while their impact on manufacturing sustainability outcomes was estimated with the aid of evaluation based on distance from average solution (EDAS). The study results highlight the key influencing factors of green bond issuance as environmental competencies, policy framing, low corruption, public awareness, and government support thereby signifying the criticality of strong institutions in facilitating green finance in the post-pandemic era. Besides, the study results demonstrate that green finance can significantly strengthen manufacturing sustainability in the post-COVID-19 era via green bonds by enhancing sustainable waste management, technological growth, and quality improvement as well as reducing carbon emissions. The study findings can provide a reference to decision-makers in manufacturing enterprises to predict scenarios and enact policies that facilitate the success of green finance in the post-COVID-19 era to further develop a low-carbon economy and increase competitive edge.