Search Results (3)

The clothing industry greatly impacts the global economy by producing billions of pieces of clothing and employing millions. However, it negatively impacts the environment, as it is one of the most polluting sectors in the world. This bibliometric review aims to identify influential authors and affiliations, journals, productive and cited countries, emerging and recent themes, and future research directions focusing on the dynamics of clothing brands’ sustainability practices. A comprehensive dataset from Scopus and the Web of Science contains 612 articles, and Biblioshiny and VOSviewer were used to analyze the data. Findings reveal that sustainability is not just a concern for developed countries but is also gaining attention in emerging economies like India. This bibliometric analysis presents its relationship with sustainable development goals (SDGs), combines performance analysis and science mapping of clothing brands’ sustainability practices, and evaluates thematic clusters to highlight future research scopes to fill the literature gap for further concentration on behavioral aspects, advanced supply chains, effective communication, and promoting the usage of sustainable technologies, which can help to align with business models for sustainability and resilience. Therefore, clothing brands’ sustainability practices should focus on smart and functional clothing through eco-friendly manufacturing and designing long-lasting clothes to enrich clothing performance. They should adopt innovative technologies for resource utilization, recycling, waste management, supply chain, and also emphasize communication with the consumers to encourage them to purchase eco-friendly and long-lasting clothes. Full article

The collection and transportation of municipal solid waste create a significant energy and carbon footprint, resulting in a significant environmental impact. Proper waste management organization is necessary to minimize this impact. This research aims to identify differences and similarities in waste collection sectors, distinguish affiliation clusters for different waste types, and determine the impact of geopolitical factors on waste production in the analyzed region. Therefore, the similarities of waste production in the separated sectors for different waste types were analyzed. Instead of using the Kolmogorov–Smirnov distance between distributions of waste production, the statistics have been calculated based on $L_1$ and $L_2$ norm because they give the scale of differences. The multidimensional scaling method (MDS) and cluster analysis with a Gaussian mixed model (GMM) were used to identify changes in waste production. This technique makes it possible to detect changes between sectors in the analyzed region. Significant differences in cluster membership of sectors by waste type were observed. Geopolitical factors such as the COVID-19 pandemic and the war in Ukraine have caused changes in the sector affiliations of the waste clusters under analysis. The pandemic caused changes in the affiliation of non-segregated waste, plastics, and glass, while no change in waste generation preferences was observed for paper and cardboard waste. The war in Ukraine caused changes in the generation preferences of all waste types in the analyzed region. Full article

The consumer plays a key role in resource conservation; therefore, it is important to know consumer behavior to identify consumer profiles and to promote pro-environmental practices in society that encourage resource conservation and reductions in waste generation. The purpose of this paper is to implement a fuzzy model to evaluate consumer behavior in relation to three pro-environmental practices that can be implemented at the household level, including reductions in resource consumption (reduce), reuse of resources (reuse), and recycling (recycle). To identify socio-demographic profiles that characterize an environmentally responsible consumer, 2831 surveys were applied on a representative sample of consumers residing in a Mexican city. Fuzzy logic and neural networks were applied using a Sugeno-type subtractive clustering to determine each profile. The model input variables were socioeconomic status, age, education level, monthly income, occupation and the type of organizations with which the consumer is affiliated. The output variables were represented by pro-environmental practices. Results show that the consumer practices are performed independently of each other, with the most frequent pro-environmental consumer practices being reduction and reuse. Full article