Comprehensive evaluation of sustainable consumption towards green growth based on an interval valued Neutrosophic TOPSIS approach.

Sustainable consumption is crucial in reducing the growing pressure of environmental crises. This study proposes the Technique of Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to evaluate sustainable consumption toward green growth. The proposed approach assesses criteria weights in Interval Valued Neutrosophic Sets (IVNSs) using the Method of Maximizing Deviation. The proposed method evaluates sustainable consumption for ten selected developed and developing countries, including Canada, France, Japan, China, Indonesia, Korea, Malaysia, Singapore, Thailand, and Vietnam. The evaluation process encompasses four main criteria with eight sub-criteria, namely environment (population density, CO2), energy (total natural resource rents, renewable electricity), economics (value added of agriculture, forestry, and fishing, GDP per capita), and health (fertility rate, mortality rate). The countries are ranked based on the relative closeness coefficient. The results reveal that two economic sub-criteria are pivotal in the sustainable consumption rankings. Canada emerges as the country with the highest degree of green growth, attributed to its extensive land area and potential for renewable energy. Based on the findings, this study proposes some policy implications for Vietnam, including balancing fertility and mortality rates and regulating economic growth and resource exploitation.

Management factors influencing lecturers' research productivity in Vietnam National University, Hanoi, Vietnam: A structural equation modeling analysis.

Increasing lecturers' research productivity plays a crucial role in improving teaching quality and university prestige. This research aims to establish and test a model for evaluating management factors that affect the research productivity of Vietnamese university lecturers. Data were collected by surveying 398 lecturers and researchers at universities affiliated with the Vietnam National University, Hanoi (VNU). Structural equation modeling (SEM) methodology was applied for data analysis. The research outcomes indicate that resources and policies to favor research activities are the two most influential factors affecting research productivity in lecturers. The decentralization factor, in contrast, did not show statistical significance, since its p-value was greater than 0.05. Based on the results obtained, several policy recommendations are proposed, namely: (i) ensuring resources for faculty's scientific research activities; (ii) improving policies for lecturers to enhance their scientific research achievements: (iii) developing strategies for scientific research activities; (iv) enhancing awareness of affiliated unit leaders about the importance of scientific research.

Correction: A dynamic generalized fuzzy multi-criteria group decision making approach for green supplier segmentation.

[This corrects the article DOI: 10.1371/journal.pone.0245187.].

A dynamic generalized fuzzy multi-criteria croup decision making approach for green supplier segmentation.

Supplier selection and segmentation are crucial tasks of companies in order to reduce costs and increase the competitiveness of their goods. To handle uncertainty and dynamicity in the supplier segmentation problem, this research thus proposes a new dynamic generalized fuzzy multi-criteria group decision making (MCGDM) approach from the aspects of capability and willingness and with respect to environmental issues. The proposed approach defines the aggregated ratings of alternatives, the aggregated weights of criteria, and the weighted ratings by using generalized fuzzy numbers with the effect of time weight. Next, we determine the ranking order of alternatives via a popular centroid-index ranking approach. Finally, two case studies demonstrate the efficiency of the proposed dynamic approach. The applications show that the proposed appoach is effective in solving the MCGDM in vague environment.