Investigating the sustainable development goals derived due to adoption of circular economy practices

Building risk mitigation strategies for circularity adoption in Indian textile supply chains

Textile industries are among the most polluting and demand urgent management measures to mitigate their negative environmental impact. Thus, it is imperative to incorporate the textile industry into the circular economy and to foster sustainable practices. This study aims to establish a comprehensive, compliant decision framework to analyse risk mitigation strategies for circular supply chain (CSC) adoption in India's textile industries. The Situations Actors Processes and Learnings Actions Performances (SAP-LAP) technique analyses the problem. However, interpreting the interacting associations between the SAP-LAP model-based variables is somewhat lacking in this procedure, which might skew the decision-making process. As a result, in this study, the SAP-LAP method is accompanied by a novel ranking technique, namely, the Interpretive Ranking Process (IRP), which reduces decision-making issues in the SAP-LAP method and aids in evaluating the model by determining the ranks of variables; furthermore, the study also offers causal relationships among the various risks and risk factors and various identified risk-mitigation actions by constructing Bayesian Networks (BN) based on conditional probabilities. The study's originality represents the findings using an instinctive and interpretative choice approach to address significant concerns in risk perception and mitigation techniques for CSC adoption in the Indian textile industries. The suggested SAP-LAP and the IRP-based model would assist firms in addressing risk mitigation techniques for CSC adoption concerns by providing a hierarchy of the various risks and mitigation strategies to cope with. The simultaneously proposed BN model will help visualise the conditional dependency of risks and factors with proposed mitigating actions.

Sustainability in Pakistan's textile industry: analyzing barriers and strategies for green supply chain management implementation

The industries view green supply chain management (GSCM) as a viable means of achieving sustainable operations by reducing environmental impact and enhancing operational performance. Although conventional supply chains still dominate many industries, integrating eco-friendly practices through green supply chain management (GSCM) is crucial. Nonetheless, there are several barriers that hinder the successful adoption of GSCM practices. Therefore, this study proposes fuzzy-based multi-criteria decision-making approaches comprised of the Analytical Hierarchy Process (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). The study evaluates and overcomes barriers to the adoption of GSCM practices in the textile manufacturing sector of Pakistan. After the comprehensive literature review, this study identifies 6 barriers, 24 sub-barriers, and 10 strategies. The FAHP method employs to analyze the barriers and sub-barriers. Then, the FTOPSIS method ranks the strategies to overcome various identified barriers. Based on the FAHP results, the most significant barriers to the adoption of GSCM practices are technological (MB4), financial (MB1), and information and knowledge (MB5). Further, the FTOPSIS indicates that increasing the research and development capacity (GS4) is the most vital strategy for implementing GSCM. The study's findings have important implications for policymakers, organizations, and other stakeholders interested in promoting sustainable development and implementing GSCM practices in Pakistan.

Intellectual Capital of Technology-Based Incubators

The objective of this work is to evaluate the associations between the intellectual capital of technology-based incubators in the sustainability of incubated companies located in Portugal. For this purpose, the methodological strategy employed was the survey, and to test the hypotheses the Partial Least Squares Structural Equation Modeling PLS-SEM method was applied from a sample of 82 incubated company managers. The results show that the intellectual capital of the incubator company has a direct and positive relationship with the innovative capacity, satisfaction, and sustainability of the incubated company. In turn, the incubated company’s innovative capacity has a direct and positive impact on sustainability itself. In addition, both the sustainability of the incubated company and its levels of satisfaction with the incubated company has a positive and direct impact on its competitive success. The management implications include the perception that the greater the effort to improve the human capital, structural capital, and relational capital of the incubated companies, the better will be the results achieved in supporting companies, helping start-ups develop sustainably and competitively in the market.

Particularities of the Hermetia illucens (L.) (Diptera: Stratiomyidae) Ovipositing Behavior: Practical Applications

The industrial rearing of Hermetia illucens offers sustainable solutions to the acute challenges of modern society associated with the accumulation of increasing amounts of organic waste, the substantial reduction of natural ocean fish stocks, and the imminent food crisis. Detailed knowledge of the reproductive particularities and reproductive behavior of the species is essential for increasing the efficiency of the breeding technology. This study aimed to identify the affinity shown by females regarding the size of the oviposition slots (1, 2, 3, 4 and 5 mm), the vertical distribution of the ovipositing rate, and the influence of the substrate’s moisture on ovipositing behavior (dry matter/water: 1:0.5; 1:1; 1:1.5; 1:2). Over 90% of females oviposited in the 1-, 2- and 3-mm slots, with most of the eggs (58.57%) being oviposited in the 1-mm slots. There was a positive correlation between the size of the oviposition slots and the average weight of the clutches (r = 0.985). The vertical distribution of ovipositing followed a fluctuating trend, with a tendency to oviposit closer to the attractive substrate. The females avoided ovipositing close to substrates with low humidity (1:0.5); indeed, only 6.8% oviposited under these conditions, the differences being significant compared to substrates with higher humidity (p < 0.001). The vast majority of females (43.2%) oviposited on substrates with an average humidity of 1:1 (p < 0.001). These results provide new insights into the ovipositing behavior of H. illucens, which allow for a differentiated harvest of large-sized clutches, providing practical applications with significant impact on the economic efficiency of the species’ industrial growth technology.

Role of Reverse Logistics Activities in the Recycling of Used Plastic Bottled Water Waste Management

The purpose of this study is to investigate the role of reverse logistics in the recycling of used plastic bottles and waste management in Ethiopia. To achieve the study’s goal, the authors used a descriptive research design where a combination of primary and secondary data sources was employed. Primary data was collected from 258 respondents, while secondary data was taken from office manuals, yearly reports, and brochures. The findings of our analysis show that incineration, landfilling, and dumping of used plastic bottles on the side of the road are all apparent in the environment, making the city and its environs filthy and unpleasant to live in. This study also discovered that reuse and remanufacturing have become less common and are rarely practiced. Even though plastic waste disposal guidelines have been created, our investigation revealed that there is a lack of community understanding regarding solid waste management, and some stakeholders actively disregard waste management proclamations. Thus, it is recommended that all stakeholders, including the government, business owners, plastic bottled water makers, and the public, pay close attention to waste disposal, particularly of non-biodegradable pollutants in Ethiopia’s urban and rural areas.

Development of a Methodology for Forecasting the Sustainable Development of Industry in Russia Based on the Tools of Factor and Discriminant Analysis

The problem of sustainable development is one of the central issues on the agenda of the global community. However, it is difficult to assess the pace and quality of sustainable development of individual economic systems—in particular, industry—due to the lack of a unified methodological approach. In this regard, the following research goal was formulated—to develop and test a methodology for forecasting sustainable development by using statistical tools. The achievement of the goal was facilitated by the application of formalization methods, factor analysis, discriminant analysis, the method of weighted sum of the criteria, and the method of comparison. The results of the study are new scientific and practical solutions that develop the ability to diagnose economic systems for the transition to environmentally friendly production. Firstly, methodological solutions are proposed to assess the nature of the transition of industry to sustainable development (low, medium, or high rate). The methodology is based on the proposed aggregated indicator of sustainable industrial development based on the results of factor analysis (by the method of principal components). As a result, the patterns of sustainable development of the extractive and manufacturing sectors of the Russian economy are revealed. Secondly, integral indicators of economic, environmental and social factors of sustainable development are calculated, and classification functions for each type of industrial transition to sustainable development (low, medium, or high) are formed through discriminant analysis. Scenarios of industrial development are developed, taking into account the multidirectional trajectories of the socioeconomic development of the country. Thirdly, the DFD model of the process of scenario forecasting of sustainable industrial development is formalized, reflecting the movement of data flows necessary for forecasting sustainable industrial development. It is revealed that the manufacturing industry is expected to maintain a low rate of transition to sustainable development. On the contrary, for the extractive industry, if efforts and resources are concentrated on environmental innovations, average transition rates are predicted. The uniqueness of the proposed approach lies in combining two types of multivariate statistical analysis and taking into account the indicators that characterize the contribution of industrial enterprises to sustainable development.