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Solid, liquid and e-waste pose serious health hazards, environmental pollution and contribute to climate change. To address these issues of solid waste management (SWM), amidst many policy decisions, the Government of India roped in several institutions, including self-help groups (SHGs), into the Swachh Bharat Movement (Clean India Mission). This study aims to illustrate the significant contributions of SHG’s in tackling SWM, particularly the plastic waste menace in India, while fostering socio-economic values and sustainable development goals (SDGs).

Using a from-the-field approach, qualitative data were collected from 30 members of three SHGs to understand their significant contributions in mitigating plastic waste.

This research identifies three major themes: economic value creation, social value creation and SDGs via collection and reduction of plastic waste landfills. Furthermore, several related subthemes are identified.

This study offers pragmatic solutions to deal with plastic waste at personal, community, institutional and governmental levels. Moreover, it recommends engaging SHGs to promote sustainable waste management practices such as segregating wastes at source, regulating plastic bag usage, advocating behavioural change towards waste generation and protecting the environment.

The authors consider a proven case of SHG’s contribution to protect the environment and emphasize the need to involve more such groups in waste management practices.

Plastic pollution is a major issue that plagues modern society. Scholars are interested in comprehending consumers’ behavioural actions to address plastic pollution. This study aims to delve into the determinants of consumers’ engagement with social media as a medium to address plastic pollution.

A conceptual model is developed that extends the behavioural reasoning theory (BRT). Using a quantitative approach, 476 responses underwent structural equation modelling analysis.

Results indicate that “reasons for” positively correlate with attitude and intention towards socially responsible engagement. Contrarily, “Reasons against” demonstrated a positive association with socially responsible engagement intention. Attitudes favouring socially responsible engagement correlate positively with the underlying intention. The moderation analysis underscores the positive relation of social return on social media with consumers’ attitude and their “reasons for” leaning towards socially responsible engagement intention. Notably, a positive connection was established between socially responsible engagement intention and the trifecta of consumption, contribution and content creation behaviours.

By enhancing the BRT, this research sheds light on novel perspectives regarding consumers’ engagement on social media platforms. Distinctively, it is among the handful of studies probing the influence of behavioural intention across diverse behavioural outcomes. The insights gained from this study, grounded in empirical evidence from an emerging market, are poised to guide policymakers, governmental agencies and industry practitioners in formulating effective strategies to combat plastic pollution. Additionally, the study can assist in achieving the UN sustainable development goals (SDGs), specifically SGD 12, SGD 13, SDG 14 and SGD 17.

The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.

The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.

The current chapter proposes a theoretical framework to assess the sustainability of economic growth in the Gulf Cooperation Council (GCC) States. The authors integrate insights from endogenous growth models and consider the unique socioeconomic characteristics of the GCC region to provide a comprehensive and tailored approach to understanding the determinants of economic growth and formulating effective policy measures to foster sustainable development and growth. This chapter highlights the environmental challenges faced by GCC; based on this, the authors suggested indicators to construct a theoretical framework (Economic Growth, Climatic Indicators, Energy Indicators, Social Indicators, and Economic Resources Indicators). The authors propose that policymakers and researchers in GCC States should take these factors into account when devising policies or conducting research aimed at fostering sustainable economic growth. Overall, this chapter presents significant insights for policymakers, researchers, and stakeholders involved in promoting the sustainable economic advancement of the GCC States.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The zero-waste lifestyle (ZWL) is considered a reasonable step towards controlling waste generation and minimizing the consequences of human activities on the environment. The main aim of this study is to examine the behavioral antecedents of ZWL.

The study draws on the theoretical underpinnings of the theory of planned behavior (TPB) and the norm activation model (NAM) to develop a conceptual framework to understand the antecedents to ZWL. A cross-sectional survey among 349 randomly-selected consumers provided data analyzed with the partial least square-structural equation modeling (PLS-SEM) methodology.

The results demonstrate that personal norms, attitude, subjective norms and perceived behavioral control positively influenced the intention to adopt ZWL. Additionally, the study showed that the awareness of consequences influenced personal norms, attitudes and subjective norms. However, the study identified an intention–behavior gap in adopting ZWL.

This study serves as a pioneering exploration of the behavioral factors that impact the adoption of ZWL. Additionally, the paper endeavors to elucidate the underlying reasons behind the intention–behavior gap within this particular context. Consequently, the study offers substantial theoretical and practical implications aimed at promoting and fostering greater adoption of ZWL practices.

The purpose of this study was to investigate amount of food waste and the number of food packages used in Finnish households with university students. The aim of the paper is to answer the following research questions: How much avoidable food waste is generated in the participating various sized households? How much unavoidable food waste is generated in the participating various sized households? How many food packages are classified as deposit, municipal waste or recycled in the participating various sized households?

The data was collected among the students in Seinäjoki University of Applied Sciences. A total of results from 432 households with 890 persons are presented. The participating households weighed their unavoidable and avoidable food waste and calculated the food packages during one week. The results were analysed in Excel and the statistical significance assessed using a t-test.

The average avoidable and unavoidable food wastages were 498 g/week/person, i.e. 25.9 kg/year/person and 543 g/week/person, i.e. 28.3 kg/year/person, respectively. Single-person households generate more avoidable and unavoidable food waste as well as packages per person than other sized households. The results show that there is no correlation between the amount of avoidable food waste/person, unavoidable food waste/person or packages/person.

This kind of research has sparsely been reported. The food and package wastage definitions vary, and thus it is difficult to compare these results with other reported results.

Current evidence indicates that humans and animals are at increased risk of multiple health challenges due to microplastic (MP) profusion. However, mitigation is constrained by inadequate scientific data, further aggravated by the lack of evidence in many African countries. This review therefore synthesized evidence on the current extent of MP pollution in Africa and the analytical techniques for reporting.

A literature search was undertaken in research databases. Medical subject headings (MeSH) terms and keywords were used in the literature search. The authors found 38 studies from 10 countries that met the inclusion criteria.

Marine organisms had MPs prevalence ranging from 19% to 100%, whereas sediments and water samples had between 77 and 100%. The most common and dominant polymers included polypropylene and polyethylene.

This review shows that most studies still use methods that are prone to human errors. Therefore, the concentration of MPs is likely underestimated, even though the authors’ prevalence evaluations show MPs are still largely pervasive across multiple environmental matrices. Also, the study reveals significant spatial disparity in MP research across the African continent, showing the need for further research in other African countries.

Even though some reviews have assessed MPs pollution in Africa, they have not evaluated sample prevalence, which is necessary to understand not only concentration but pervasiveness across the continent. Secondly, this study delves deeper into various methods of sampling, extraction and analysis of MPs, as well as limitations and relevant recommendations.