Assessment of the Fragility of the Municipal Waste Sector in Serbia Using System Dynamics Modelling

This research develops a novel methodology for municipal waste management in Serbia, based on system dynamics modelling. The methodology shows how a country and relevant institutions should address complexities in the waste management sector. Waste management is a critical issue globally, which heavily impacts the economic development of a country, including the general quality of life within a society. The designed simulation generates different scenarios of the Serbian municipal waste system for reaching the 2035 recycling rate targets. Methodologies such as the theory of constraints, fragility analysis, and systems dynamics were implemented in the model. The scenarios and fragility modelling were conducted with the system dynamics modelling methodology in the Ventity simulation environment. The designed model has elements of discrete event simulations, system dynamics, and agent-based modelling. Importantly, real-world data for the period of five years (from the year 2016 to 2020) was used in the case study. This research undoubtedly reveals that the informal sector is the key source of fragility to the dynamic system considered. During the considered period, the informal sector contributed 62.3% of all separated waste to the system. Consequently, this research concludes that for the waste sector in Serbia to reach the 2035 EU goals, the existing practice in waste management has to be changed significantly and will benefit from the modelling approach used here. The whole system is highly dependent on the informal sector, which, in its current form, is volatile, unregulated, and fragile to aggressive regulative policies.

Grafting nanocellulose with diethylenetriaminepentaacetic acid and chitosan as additive for enhancing recycled OCC pulp fibres

This paper develops a novel paper additive for effectively recycling old corrugated container (OCC) by functionalizing nanocellulose (NC) with diethylenetriaminepentaacetic acid (DTPA) and chitosan (CS), and investigate the reinforcing mechanisms and effect of the developed additive on the physical properties of recycled OCC pulp handsheets. The tensile, tear and burst index, air permeability, tensile energy absorption (TEA), and drainage performance of the recycled OCC handsheets are examined. Fourier transform infrared FTIR) spectroscopy, thermal gravimetric analysis (TGA) and scanning electron microscopy (SEM) are used for the chemical and microstructure characterization of both NC based additives and paper from recycled OCC pulp. The results show that functional groups on the NC based additive, such as carboxyl, amino and hydroxyl groups, can bond with the hydroxyl groups on the recycled OCC fibres to generate a chemical bond. This leads to an increase in the crosslinks and bonding area between the fibres, which increases their tensile strength and improves their recycling rate. SEM shows that the paper with NC based additives had tighter inter-fibre bonds and smaller paper pore structure. Addition of 0.3% NC-DTPA-CS additive results in optimal properties of the recycled OCC paper with an increase by 31.64%, 22.28% and 36.6% of tensile index, tear index, burst index respectively, and the air permeability decreases by 36.92%. Graphical Abstract

An Empirical Study on the Main Determinants of Recycling Plastic Waste in Tunisia

Over the past fifteen years, numerous policies for recycling and recovering waste have been implemented throughout the world. Tunisia is among the countries considering recycling as a sustainable development path. This empirical study aimed to investigate and examine the influence of financial determinants measured by the price of waste disposal (PDI), institutional determinants measured by the collection of waste (CW) and the number of drop-off recycling centers, and ordinance and demographic determinants measured by the population density and the recycling rate for plastic as a domestic waste based on a panel of 24 Tunisian governorates over the 2001–2020 period. It is concluded from the empirical findings that all exogenous variables except population density have a significant effect on the recycling rate.

Subsidy and Environmental Tax for Construction and Demolition Waste Recycling

In recent two decades, construction and demolition (C&D) waste is becoming a major source for municipal waste which causes serious damage to the environment. To solve the problem, waste recycling measures are gradually used to turn waste into treasures. Meanwhile, several kinds of policies such as waste disposal charging fees have been issued to stimulate stakeholders’ behavior to take waste recycling measures to promote the C&D waste recycling industry. However, the C&D waste recycling rate is still too low in China. In order to promote C&D waste recycling industrial development, this paper is aiming at introducing subsidy and environmental tax policies to promote C&D waste recycling. Based on system dynamics, this study establishes a model to determine the proper subsidy and environmental tax range. According to the simulation results, three kinds of incentive policies are obtained, namely, single subsidy policy, single environmental tax and combined incentive policies. Optimal single subsidy and environmental tax are in the interval [10, 30] and [20, 60], respectively. The best combination strategy is subsidy=10 yuan /ton and environmental tax=20 yuan/ton. The results from this paper could be a foundation for government to establish incentive policies to promote C&D waste recycling.

Chemical Recycling of Mixed Plastics in Electronic Waste Using Solvent-Based Processing

Currently, less than 20% of electronic waste (E-waste) produced in the U.S. is recycled. To improve the recycling rate of E-waste, the study aimed to: (1) identify the major plastics found within electronic shredder residue (ESR), (2) design solvents and processing conditions capable of separating out 90% of the plastic in ESR, and (3) estimate the energy efficiency of the solvent-based process developed. Preliminary screening showed 25 wt.% of the ESR was composed of plastics, with two polymers dominating the sorted plastic fraction—polystyrene (PS, 40 wt.%) and acrylonitrile butadiene styrene (ABS, 25 wt.%). Subsequently, solvents and anti-solvents were screened using Hansen Solubility Parameter Theory for PS, ABS, and ESR dissolution. The pre-screening results showed dichloromethane (DCM) and tetrahydrofuran (THF) as the most effective solvents for PS and ABS, with methanol (MeOH) and ethylene glycol (EG) as the most effective anti-solvents. By optimizing the dissolution time and the solvents used, the highest polymer dissolution yield (99 wt.%) was achieved using DCM for 48 h. Both MeOH and EG precipitated 71 wt.% of the polymer fraction of ESR. EG removed more phosphorus containing flame retardants (94 wt.%) than MeOH (69 wt.%). Energy analysis indicated that the solvent-based processes could save 25–60% of the embodied energy for PS and ABS. Characterization showed that the solvent-based processing could preserve the high molecular weight fraction of the polymers while removing flame retardants at the same time. The results from this study prove the potential of solvent-based processing to produce secondary plastic materials from E-waste for cross-industry reuse.

RECYCLING INTENTION AND BEHAVIOUR AMONG THE HIGHER EDUCATION INSTITUTIONS’ (HEIS) COMMUNITY IN MALAYSIA

The Higher Education Institutions (HEIs) are among the largest waste producers in the municipality, and they have a huge responsibility towards the waste they produced. In order to divert waste as much as possible from the landfill, many HEIs have implemented reduce, reuse and recycle (3Rs) strategies and programs on their campuses. However, not all the communities are aware of the programs initiated, and as such, the recycling rate in most universities is still low. Therefore, this research seeks to identify the factors that influence the HEIs community to practise recycling on the campus. This study has extended the Theory of Planned Behaviour (TPB) with the inclusion of situational factors, recycling information and personal norm in the model. A total of 1068 duly completed questionnaire surveys were collected from six selected universities. The data collected were analysed using both descriptive and inferential analyses. The findings show that all the constructs investigated significantly influence recycling intention with exception of the subjective norm, whereas the situational factors have a significant direct influence on recycling behaviour. These findings have led to several suggestions and recommendations for a better sustainable waste management on the campuses in Malaysia.

Green Building Practices on Waste Minimization in China Construction Industry

In China, a common practice for construction waste management is to dispose of it in landfills. A 5% construction waste recycling rate and ongoing insufficient landfilling practice resulted in decreased environmental and socioeconomic well-being. Management hierarchy that starts with rethink, redesign, reduce, reuse, refurbish, recycle, incineration, and finally disposal is a probable strategy to facilitate construction waste minimization in China. The green building concept pursued by China also served as a promising tool in evaluating the performance of Chinese green buildings. Barriers include lack of standard operating procedure in waste minimization, immature recycling technology and an undeveloped recycling market, leading to poor performance in construction waste minimization. Several strategies are proposed to ameliorate the current condition in China's construction sector. Even though results reveal that China falls behind in the engagement of green building compared to developed countries, green materials are utilized in various building structures such as flooring, roofs, walls, and outdoor pavements. Lastly, the benefits and shortcomings of two green material technologies, in particular material selection and recycling, applied in China were reviewed.

A 5G-Enabled Smart Waste Management System for University Campus

Future university campuses will be characterized by a series of novel services enabled by the vision of Internet of Things, such as smart parking and smart libraries. In this paper, we propose a complete solution for a smart waste management system with the purpose of increasing the recycling rate in the campus and provide better management of the entire waste cycle. The system is based on a prototype of a smart waste bin, able to accurately classify pieces of trash typically produced in the campus premises with a hybrid sensor/image classification algorithm, as well as automatically segregate the different waste materials. We discuss the entire design of the system prototype, from the analysis of requirements to the implementation details and we evaluate its performance in different scenarios. Finally, we discuss advanced application functionalities built around the smart waste bin, such as optimized maintenance scheduling.

The dynamic interaction between circular economy and the environment: Evidence on EU countries

This study examined the causal dynamics between circular economy (CE) and carbon dioxide (CO2) emissions in European Union (EU) countries. The selected CE indicators included the trade in recyclable raw materials (TRM) and the circular material use rate (CMR) in the secondary raw materials area, the generation of municipal waste per capita (GMWp) in the production and consumption area and the recycling rate of municipal waste (RMW) in the area of waste management. The coefficients of the panel cointegration equations showed that for every 1 percentage point increase in RMW, average CO2 emissions decreased by 0.5%, while for every 1 percentage point increase in GMWp and TRM, the average CO2 emissions increased by 0.263% and 0.101%, respectively. It also showed that the recycling volumes and recycling rate had a positive but very limited impact on the CMR. The panel vector error correction model result showed that there were long-run bidirectional causalities between CE indicators and carbon emissions, and the TRM had a short-run negative impact on waste generation. However, the short-run impact of CE indicators on carbon emissions was not significant, which may be because the European CE is still in its infancy. The finding suggests that policymakers should adopt multilateral policies such as reducing carbon emissions, improving the efficiency and productivity of resource management and waste recycling, and increasing investment and innovation in the secondary raw materials market to achieve resource decoupling and impact decoupling. The decoupling of these two types is a necessary condition for sustainable development.

Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine

A reverse vending machine motivates citizens to bring recyclable waste by rewarding them, which is a viable solution to increase the recycling rate. Reverse vending machines generally use near-infrared sensors, barcode sensors, or cameras to classify recycling resources. However, sensor-based reverse vending machines suffer from a high configuration cost and the limited scope of target objects, and conventional single image-based reverse vending machines usually make erroneous predictions about intentional fraud objects. This paper proposes a dual image-based convolutional neural network ensemble model to address these problems. For this purpose, we first created a prototype reverse vending machine and constructed an image dataset containing two cross-sections of objects, top and front view. Then, we chose convolutional neural network models widely used in image classification as the candidates for building an accurate and lightweight ensemble model. Considering the size and classification performance of candidates, we constructed the best-fit ensemble combination and evaluated its classification performance. The final ensemble model showed a classification accuracy higher than 95% for all target classes, including fraud objects. This result proves that our approach achieves better robustness against intentional fraud objects than single image-based models and thus can broaden the scope for target resources. The measurement results on lightweight embedded platforms also demonstrated that our model provides a short inference time that is enough to facilitate the real-time execution of reverse vending machines based on low-cost edge artificial intelligence devices.