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This study aims to develop a research agenda for the advancement of theory application in practical contexts by presenting a case study of the Engagement in Plastic-free Innovation for Change (EPIC) programme delivered by Plastic Oceans Australasia (POA).

EPIC is a behaviour change programme by POA that aims to reduce single-use plastic (SUP) consumption in workplaces. The study evaluates the programme’s impact on employee perceptions and actual behaviour through pre- and post-programme data collection in two Australian workplaces. Data was gathered via online surveys and waste audits and analysed using SPSS statistics and Excel.

The case study highlights the need for theory application in programme evaluation instruments. Theory was not used in the programme evaluation tool, and theory could not be mapped onto the tool retroactively. The data from the present study showed mixed results. Data from Workplaces 1 and 2 indicated that EPIC successfully improved three out of seven employee perceptions of SUP reduction efforts. However, individual workplace data showed that EPIC only improved one out of seven perceptions in Workplace 1 and three out of seven perceptions in Workplace 2. Surprisingly, Workplace 1 observed a decrease in plastic waste after the programme, while Workplace 2 saw an increase. Without the clear integration of theory, it is difficult to pinpoint areas for improvement. It is, however, posited that COVID-19 restrictions on people attending their workplaces and low survey response rates may have contributed to these unexpected results.

The present study highlights key improvements that can be made to evaluations of voluntary behaviour change programmes. Careful evaluation of behaviour change programmes is key to improving programme effectiveness. Practitioners will find the suggested improvements from this study helpful in developing and refining voluntary behaviour change programme evaluations.

This is one of the first studies to evaluate the impacts of a voluntary behaviour change programme aimed at reducing SUPs in the workplace. It also adds to the limited literature on voluntary behaviour change interventions overall and adds to the movement towards better application of theory in behaviour change interventions.

The use of waste polyethylene terephthalate (PET) plastics derived from shredded bottles in concrete is not formalized yet, especially in reinforced members such as beams and columns. The disposal of plastic wastes in concrete is a viable alternative to manage those wastes while minimizing the environmental impacts associated to recycling, carbon dioxide emissions and energy consumption.

This paper evaluates the suitability of 2D deterministic and stochastic finite element (FE) modeling to predict the shear strength behavior of reinforced concrete (RC) beams without stirrups. Different concrete mixtures prepared with 1.5%–4.5% PET additions, by volume, are investigated.

Test results showed that the deterministic and stochastic FE approaches are accurate to assess the maximum load of RC beams at failure and corresponding midspan deflection. However, the crack patterns observed experimentally during the different stages of loading can only be reproduced using the stochastic FE approach. This later method accounts for the concrete heterogeneity due to PET additions, allowing a statistical simulation of the effect of mechanical properties (i.e. compressive strength, tensile strength and Young’s modulus) on the output FE parameters.

Data presented in this paper can be of interest to civil and structural engineers, aiming to predict the failure mechanisms of RC beams containing plastic wastes, while minimizing the experimental time and resources needed to estimate the variability effect of concrete properties on the performance of such structures.

Manufacturing companies continue to encounter a diverse set of obstacles while embracing sustainable development goals. Accordingly, the purpose of this study is to explore critical sustainable development-related barriers to flexible packaging manufacturing companies in the New Zealand context.

Drawing on a qualitative multiple case studies approach, the authors collected data from the New Zealand flexible packaging industry. Semistructured interviews were conducted with the senior corporate managers in two large flexible packaging companies. Following the thematic analysis approach, the authors analyzed the information collected from the participants and synthesized our findings under the key dimensions of internal and external barriers to sustainable development.

The findings revealed that internal barriers to sustainable flexible packaging are linked to economic, operational and technical issues. Conversely, external barriers include global crises and disruption, customer behavior and preferences and institutional and infrastructural-related aspects. Based on the analysis of empirical findings, the authors further identified the underlying reasons for sustainable flexible packaging barriers and recommended guidelines that could assist corporate managers and policymakers in addressing obstacles inhibiting the flexible packaging industry from adopting sustainable business practices.

The authors argue that this study is one of the early studies to consider inhibiting factors to incorporate sustainable development into the New Zealand flexible packaging industry context. Building on a range of theoretical perspectives, the authors extend the current body of knowledge seeking to advance the sustainable development agenda in the New Zealand flexible packaging industry and offer recommended pathways fostering sustainable development in a distinctive manufacturing context.

A direct correlation exists between waste disposal, disease spread and public health. This article systematically reviewed healthcare waste and its implication for public health. This review identified and described the associations and impact of waste disposal on public health.

This paper systematically reviewed the literature on waste disposal and its implications for public health by searching Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), PubMed, Web of Science, Scopus and ScienceDirect databases. Of a total of 1,583 studies, 59 articles were selected and reviewed.

The review revealed the spread of infectious diseases and environmental degradation as the most typical implications of improper waste disposal to public health. The impact of waste includes infectious diseases such as cholera, Hepatitis B, respiratory problems, food and metal poisoning, skin infections, and bacteremia, and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife.

Infectious diseases such as cholera, hepatitis B, respiratory problems, food and metal poisoning, skin infections, bacteremia and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife are some of the public impacts of improper waste disposal.

Healthcare industry waste is a significant waste that can harm the environment and public health if not properly collected, stored, treated, managed and disposed of. There is a need for knowledge and skills applicable to proper healthcare waste disposal and management. Policies must be developed to implement appropriate waste management to prevent public health threats.

This study aims to get rid of non-degradable polyvinyl chloride (PVC) waste as well as sunflower seed cake (SSC) waste by preparing eco-friendly composites from both in different proportions to reach good mechanical and insulating properties for antimicrobial and antistatic applications.

Eco-friendly composite films based on waste polyvinylchloride (WPVC) and SSC of concentrations (0, 10, 20, 30 and 40 Wt.%) were prepared using solution casting method. Further, the effect of sunflower seed oil (SSO) on the biophysical properties of the prepared composites is also investigated. Fourier transform infrared spectroscopy, X-ray diffraction (XRD), scanning electron microscope, mechanical, thermal, dielectric properties were assessed. Besides, the antimicrobial and biodegradation tests were also studied.

The crystallinity increases by rising SSC concentration as revealed by XRD results. Additionally, the permittivity (ε′) increases by increasing SSC filler and SSO as well. A remarkable increase in dc conductivity was attained after the addition of SSO. While raw WPVC has very low bacterial activity. The composite films are found to be very effective against staphylococcus epidermidis, staphylococcus aureus bacteria and against candida albicans as well. On the other hand, the weight loss of WPVC increases by adding of SSC and SSO, as disclosed by biodegradation studies.

The study aims to reach the optimum method for safe and beneficial disposal of PVC waste as well as SSC for antistatic and antimicrobial application.

In the current era, the focus on circular economy (CE) has gained substantial momentum among the research communities across the manufacturing world. It has become the need of the hour to act fast due to the alarming issues of unsustainability such as climate change, global warming, waste generation, environmental pollution, resource scarceness and ecological degradations. This research aims to investigate and model the CE enablers in the product development process related to the moderating effect of net zero.

The significant CE enablers are identified through literature review and expert brainstorming. The Pythagorean fuzzy decision-making trial and evaluation laboratory (PF-DEMATEL) technique has been used to investigate and evaluate the significant CE enablers in product development process. PF-DEMATEL determines the inter-relationship and casual dependency among the selected CE enablers. Indian automobile small and medium enterprises (SMEs) have been considered as a case organization to demonstrate the effectiveness of the proposed method.

The findings reveal that “Top management support and clear vision towards CE adoption” is the most important enabler and “Artificial intelligence in product value chain” is recognized as the least vital enabler. This research aids the managers, decision-makers, policy planners and workforce to develop and formulating efficient blueprints for the effective adoption of CE in Indian SMEs.

This is the first kind of research that explores CE enablers in product development process for Indian SMEs.

The study looks into the idea of Circular Economy through the lens of stock market performance. More specifically the authors examine if a company's association with the innovative and environment friendly disposal practices has an implication on its stock market performance.

The study adopts a quantitative approach to derive conclusions. The market efficiency and excess volatility are estimated using OHLC measures for more accuracy. For robustness checks VRatio, bootstrapping and simulation are used as detailed in section 3.2 of the study.

The study estimates show a clear sign of excess volatility in the majority of the stocks under consideration. That confirms that the market is highly fragmented for Circular Economy–focused stocks. These types of stocks are often cyclical in nature and can offer a variety investment opportunity in the short to medium term. In addition to it the study estimates confirm that Circular Economy–focused stocks show limited signs of market efficiency. Thus, unlike ESG investment the concept of Circular Economy investment is a bit more ambiguous to the global investors.

The present study is first of its kind that looks into the idea of Circular Economy through the lens of stock market performance.

The development of circular construction supply chains (CCSCs) is impeded by various barriers, but the current literature overlooks the interrelationships among these barriers and the varying nature of associated countermeasures with the growth of circular economy (CE). This paper aims to develop new perspectives about the transition to CCSCs through the lens of supply-push (SP)/demand-pull (DP) strategies and related underpinning theories.

A systematic literature review including 136 journal articles was undertaken to identify barriers to CCSCs and associated countermeasures that are analysed for their relevance to SP/DP. The interplay between SP/DP approaches was discussed using the “technology acceptance” and “innovation chain” models.

(1) Thirty-five barriers were identified and organised into nine main barrier factors; (2) the predominant factors are knowledge and awareness, policy and regulations, technological capabilities, and incentives; (3) CCSCs transition is SP-configured primarily driven by actors above the project network; (4) “push” measures are devised to gradually create a “pull” environment and can be revoked when demand for circularity is created, suggesting a new “push-to-pull” perspective of CCSCs; and (5) a complex adaptative system is witnessed in relationships between barriers and countermeasures, and interactions among actors at all levels.

The findings offer practitioners countermeasures to barriers encountered in CE initiatives. It also informs policymakers of adjusting policies to accommodate the SP-DP movement in steering the circular transition.

This study uses theoretical models to interpret the dynamics in CCSCs transition and proposes a new definition of CCSCs highlighting their complex and dual transitional/transformational nature.

This study aims to examine the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries.

This study uses semi-annual panel data covering the period 2000–2020 among 152 developing and middle-income countries and Cox proportional hazard model for the analysis.

Estimates indicate that effective operations of environmental institutions, investment in research and development, subsidizing the production of renewable energy, government investment in producing renewable energy and investment in renewable energy production made by the private sector will contribute immensely toward achieving sustainability of renewable energy production.

This study recommends that governments should rationalize their expenditures to mobilize enough resources for investment in renewable energy production. Again, operations of environmental institutions should be enhanced through giving their managers’ performance contracts and licensing its employees. Enabling environment should be created for private sector to increase their investment in renewable energy production.

Empirical studies have been carried out exploring measures to deal with climate change. Nonetheless, the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries have not been explored in existing empirical studies. Hence, this study fills the gap in existing empirical studies.

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.