How to go green? Exploring public attention and sentiment towards waste sorting behaviors on Weibo platform: A study based on text co-occurrence networks and deep learning.

The attention and sentiment of the public are crucial for better implementation of waste sorting behaviors and moving towards green living. In this study, web scraping technology was used to collect 367,856 Weibo posts related to waste sorting from Sina Weibo. Utilizing text co-occurrence networks, Latent Dirichlet Allocation (LDA) topic modeling, and a deep learning model combining the Affective Cognitive Model (OCC) with Long Short-Term Memory Model (LSTM) (referred to as OCC-LSTM), we comprehensively understand the text at both micro and macro levels, analyzing the attention and sentiment of the public towards waste sorting behaviors on the Sina Weibo platform. Several important findings emerged from the empirical results. First, highly engaging posts were predominantly published by users with a large following, and the number of posts fluctuated over time. This reflects the influence of social hot topics and the timeliness of information dissemination. Second, there was heterogeneity in the user groups and their locations, often influenced by cultural differences due to geographical location. Third, positive sentiment towards waste sorting behavior was higher than negative sentiment on the Weibo platform. Moreover, public attention varied under different emotional influences concerning the topic of waste sorting behavior. The innovation of this study lies in the development of a research framework combining co-occurrence networks and deep learning, expanding the analysis on both micro and macro levels. This framework broadens the research paradigms and dimensions of public perception in waste sorting. This study is significant for promoting waste sorting behaviors and implementing climate policies.

Individual, household and institutional level predictors of waste sorting: Evidence for Ecuador.

The rapid increase in waste generation in developing countries presents significant challenges, necessitating effective waste management strategies. This study examines the influence of individual, household and institutional factors on waste sorting behaviours in Ecuador, employing an ordered logistic regression model. Data were sourced from the 2019 National Multipurpose Household Survey (NMHS) and the Census of Economic Environmental Information in Decentralised Autonomous Governments (CEEIGAD). The NMHS uses a two-stage probabilistic sampling methodology, with census sectors as the primary sampling units and households as the secondary units. After excluding outliers and selecting individuals aged 15-65 years, the final sample consisted of 8601 households, including 26,175 individuals. The findings reveal that personal attributes such as gender, ethnicity, age, marital status and environmental concern significantly influence waste sorting behaviours. Household characteristics, including urban or rural location, are also critical. Institutional factors, such as municipal regulations, waste collection fees and waste separation at source, play essential roles in promoting waste separation. The study highlights the necessity for targeted governmental policies. Recommendations include improving environmental education, increasing sorting infrastructure in urban areas and ensuring waste collection systems maintain the separation of waste streams.

Factors influencing consumer intention to sort household waste: A Brazilian perspective.

Effectively sorting and recycling waste has consequently emerged as a key strategy for environmental preservation and the creation of sustainable communities. The current study aimed to examine the factors influencing consumers' intentions to sort household waste, utilizing the theory of planned behaviour. Collecting 300 responses from Brazilian consumers through structured questionnaires, the study employed a partial least square structural equation modelling approach to assess the proposed hypotheses. The findings emphasized the significant impact of the perceived cost and benefit factor, alongside the influence of information on proper waste disposal (perceived effectiveness), as communicated by entities managing selective waste collection. These findings emphasize the key role of effective communication from waste management agency regarding the outcomes of domestic waste separation efforts for recycling, as well as the perceived benefits and costs by consumers. Such communication is essential for fostering and maintaining consumer engagement in recycling initiatives.

Exploring artificial intelligence role in improving service building engagement in sorting.

Waste management researchers have identified that the correct disposal of solid waste is better addressed upstream, where people properly sort their solid waste. Sorting solid waste is a practice that requires a behaviour friendly to sorting and willingness to continuously comply with waste management policies. However, the dynamic and ever-changing nature of service buildings' users makes fostering such behaviour challenging, potentially jeopardizing solid waste sorting efforts. Therefore, in this paper, we explore the possible role of artificial intelligence in alleviating the cumbersome process of sorting solid waste, by developing a virtual assistant that interacts with tenants via verbal and visual inputs to provide them with waste management services and instructions. The virtual assistant utilizes Natural Language Processing and computer vision techniques to enable voice and image recognition functionalities and achieved accuracy levels of 85% and 88% for verbal and visual inputs, respectively. The present work can be a solid foundation to investigate further implementation of virtual assistants to support sustainability practices in Facility Management.

Machine Learning Classifier-Based Metrics Can Evaluate the Efficiency of Separation Systems.

This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system.

A comparative study of factors influencing residents' waste sorting behavior in urban and rural areas of China.

Extensive research has been conducted on the waste sorting behavior (WSB) of residents, while it is the first time that the classification behavior of urban and rural residents is compared under the same theoretical framework in China. Based on questionnaire data from 478 urban and rural residents, structural equation modeling (SEM) was used to investigate the internal factors influencing the WSB by integrating the Theory of Planned Behavior (TPB) and the Norm Activation Model (NAM). Hierarchical regression analysis was utilized to investigate the moderating effect of external factors on the residents' intentions and behavior. The results show that the degree of deviation between rural residents' intentions and behavior is much larger than that of urban residents. Personal norms are the key factors affecting urban residents' waste sorting. In contrast, for rural residents, attitude is the most critical factor, but the influence of subjective norms is insignificant. In addition, we found that policy restraints and economic incentives significantly moderate the association between urban residents' sorting intention and behavior, with economic incentives having a better effect than policy restraints. In contrast, the impact of policy restraints on rural residents is better than that of urban areas. However, the moderating effect of economic incentives is insignificant for rural residents. The findings furnish the government with meaningful strategies to narrow the urban-rural waste management gap.

Understanding food waste sorting behavior in institutional food services: An integrated psychological framework.

Food waste from institutional food services accounts for a significant part of global food waste. Food waste sorting (FWS) at the source reduces waste management costs and environmental impacts in organizations. Yet what drives individual FWS behavior remains underexplored. This study explores the psychological process of FWS in institutional catering environments, integrating the value-belief-norm model, the theory of planned behavior, and self-determination theory. Data were collected from 431 university students in China and analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicated the interplay of values, beliefs, norms, and motivations in shaping FWS behaviors. Social value orientations (SVO) indirectly affected FWS through awareness of consequences and personal norms. Subjective norms, potentially attributed to external regulations in canteens, influenced FWS intention through personal norms and induced FWS primarily via controlled motivations. The findings imply that behavioral strategies to induce FWS may leverage social influence and external regulation while also translating values and knowledge into intrinsic motivations through educational programs and awareness campaigns.

A comprehensive study of food waste management and processing in the Czech Republic: Potential health risks and consumer behavior.

Food waste is currently a widely discussed phenomenon with significant economic and social consequences. One third of the food produced in the world is wasted at various points along the food supply chain. This article presents a comprehensive study that examines consumer behavior in dealing with food waste and activities in the composting process that enable waste sanitation. The survey conducted as part of this study showed that consumers want to eliminate odors, are concerned about potential infections, and generally sort less food waste. This study suggested that the addition of appropriate additives could be a solution. The results indicated that additives could eliminate negative side effects such as unpleasant odors, the presence of insects and rodents, and act as a prevention of the occurrence of pathogenic organisms. Tea tree oil showed the best positive physical and chemical properties among the additives tested (CaCO3 and citric acid) with a significant effect on inhibiting the growth of bacterial strains such as Salmonella strains and had the strongest antibacterial effect, neutralized unpleasant odors, and stabilized the waste. The use of additives could be a future solution to meet consumer demands, improve the quality of food waste and advance the circular economy to improve the sustainability of agricultural systems.

Cultural worldviews and waste sorting among urban Chinese dwellers: the mediating role of environmental risk perception.

Objective: Waste sorting has received considerable attention in recent decades. However, research on the mechanisms underlying the relationships among cultural worldview, environmental risk perception, and waste sorting is rather scarce. This study aims to explore the cultural worldviews, environmental risk perception, and waste sorting among urban Chinese and their mechanisms. Methods: This was a cross-sectional study involving 744 urban Chinese residents (371 men and 373 women). A questionnaire was utilized to measure cultural worldviews, environmental risk perception, and waste sorting. Pearson correlation analysis and structural equation modeling were used to examine the relationship between cultural worldviews, perceptions of environmental risk, and waste sorting. Results: Waste sorting had a relatively insignificant negative relationship with fatalism and individualism. The correlation between environmental risk perception and cultural worldviews was negative except for egalitarianism, and the correlation between hierarchy and environmental risk perception was higher than the others, while individualism was higher than fatalism. Heightened environmental risk perception mediates the relationship between egalitarianism and waste sorting. Reduced environmental risk perception mediates the relationship between hierarchy and waste sorting, and mediates the relationship between individualism and waste sorting. Conclusion: These new findings provide initial support for the mediating role of environmental risk perception in the relationship between cultural worldviews and waste sorting. Both theoretical and practical implications for understanding the psychological mechanisms of waste sorting are discussed.

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies.

In this case study on volume determination in waste sorting facilities, we evaluate the effectiveness of ultrasonic sensors and address waste-material-specific challenges. Although ultrasonic sensors offer a cost-effective automation solution, their accuracy is affected by irregular waste shapes, varied compositions, and environmental factors. Notable inconsistencies in volume measurements between storage bunkers and conveyor belts underscore the need for a comprehensive approach to standardize bale production. With prediction reliability being constrained by limited datasets, undocumented modifications to machine settings, and sensor failures, this task renders a challenging application area for machine learning. We explore related research and present dataset analyses from three distinct waste sorting facilities in Europe, addressing issues such as sensor usability, data quality, and material specifics. Our analysis suggests promising strategies and future directions for enhancing waste volume measurement accuracy, ultimately aiming to advance sustainable waste management.

Consumers confused 'Where to dispose biodegradable plastics?': A study of three waste streams.

Biodegradable plastics, either fossil- or biobased, are often promoted due to their biodegradability and acclaimed environmental friendliness. However, as demonstrated by previous literature, considerable confusion exists about the appropriate source separation and waste management of these plastics. Present study investigated this confusion based on manual sorting analyses of waste sampled from packaging waste (P), biowaste (B) and residual waste (R) in an urban area of Austria. The results were evaluated relative to near-infrared sensor-based sorting trials conducted in a German urban area. Although existing literature has focused on waste composition analyses (mostly in stand-alone studies) of the three waste streams, the present study focused on identifying the specific types of biodegradable plastic items found in each of these streams. Supermarket carrier bags (P = 90, B = 14, R = 33) and dustbin bags (P = 2, B = 46, R = 6) were found in all three waste streams in the Austrian urban area. Similarly, in the German urban area dustbin bags (P = 1, B = 106, R = 3) were the common items. The results indicate that certain bioplastic items were present in more than one bin; thus, hinting that consumers are not necessarily aware of how-to source-separate the biodegradable plastics. This suggests that neither consumers nor current waste management systems are fully 'adapted' to bioplastics, and the management of these plastics' waste is currently not optimal.

Fine-tuning DETR: Toward holistic process in plastic waste sorting system.

Every year human discharges about 350 million tons of plastic waste into the environment and can be projected to triple in 2060 without any attempts to change situation. From 1970 to 2019, an estimation of 130 million tons of plastic waste was accumulated into the rivers, lakes and sea, while only 27 % is recycled and utilized. Moreover, waste treatment plants in most places around the world are using out-of-date technology, may pose a threat to the health of the workers. Therefore, it is essential to modernize these systems for protecting human health. This paper proposes fine-tuning DETR, which applies Artificial Intelligent in plastic waste sorting system. Consequently, this study analyzed the applicability of fine-tuning DETR in the domain of plastic waste categorization and its potential drawbacks. For fair experiment and evaluation, model candidates were trained and evaluated on an industrial plastic waste dataset. The fine-tuning DETR outperformed other candidates in the context of critical indicators, from accuracy (25.1 mAP), processing speed (28 FPS) to computational cost (GFLOPs 86). Furthermore, fine-tuning DETR possesses the capability of autonomous operation without requiring human intervention, distinguishing this candidate from other prevalent algorithms. Our research demonstrates that, fine-tuning DETR specifically and Transformer-based algorithms in general, are entirely suitable and hold significant potential for large-scale application in holistic plastic waste sorting systems.

Recovery of plastic packaging from mixed municipal solid waste. A case study from Austria.

Austria must recycle more packaging materials. Especially for plastic packaging waste, significant increases are necessary to reach the EU recycling targets for 2025 and 2030. In addition to improving separate collection and introducing a deposit system for specific fractions, the share of plastic packaging in mixed municipal solid waste (MSW) could be utilized. In Austria, about 1.8milliontonnes of mixed MSW are generated. This includes about 110,000 t/a of plastic packaging waste. Most of the mixed MSW (94 %) is sent directly or via residues from pre-treatment, such as mechanical-biological treatment or waste sorting, to waste incineration. While materials such as glass and metals can also be recovered from the bottom ash, combustible materials such as plastics must be recovered before incineration. This work aims to evaluate the recovery potential of plastic packaging waste in mixed MSW with automated waste sorting. For this purpose, two of the largest Austrian waste sorting plants, with a total annual throughput of about 280,000 t/a, were investigated. The investigation included regular sampling of selected output streams and sorting analysis. The results show that the theoretical recovery potential of plastic packaging from these two plants is 6,500 t/a on average. An extrapolation to Austria results in a potential of about 83,000 t/a. If losses due to further treatment, such as sorting and recycling, are considered, about 30,000 t/a of recyclate could be returned to plastic production. This would correspond to an increase in plastic packaging recycling rate from 25 % to 35 %.

Carbon footprint and life cycle economic costs of pilot Know-as-you-Throw schemes in Italy and Spain.

Considerable resources are spent globally on actions to improve the separate collection of municipal waste aiming to minimise the environmental and economic impacts of municipal waste management. One of such actions are know-as-you-throw (KAYT) schemes, which aim to change the behaviour of waste generators through tailored communication of information. KAYT schemes offer a relatively uncontroversial and simple tool, yet their environmental and economic performance remains unknown due to their limited implementation. To fill this gap, the LIFE-funded REthinkWASTE project applied a novel KAYT scheme in four pilot areas in Italy and Spain. The results of such pilots were evaluated in terms of carbon footprint and life-cycle costs of municipal waste management. The carbon footprint was notably reduced in all pilot areas, ranging from a 46% to 19% reduction, mainly due to notable reductions in unsorted waste (between 10 and 17% reduction) and subsequent lower treatment impacts. Life-cycle costs slightly increased overall, ranging from 4.6% to a -0.4% change. In addition to various sources of uncertainty, self-selection and recency biases are highlighted as major sources for potentially overestimating the benefits of KAYT in the context of large-scale and long-term KAYT implementation. The results however consistently show that the additional carbon footprint from KAYT actions can be offset with less than a 5% reduction in unsorted waste, well below the observed values. The results robustly reveal the potential of KAYT to notably reduce the carbon footprint of waste management systems with limited investment of economic resources.

Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.

Development of intelligent Municipal Solid waste Sorter for recyclables.

Sorting Municipal Solid Waste (MSW) has helped promote the awareness of sustainable development of environment. A robot equipped with an intelligent deep learning (DL) detection algorithm have been proposed to improve the sorting task. But most of the related studies aimed to better the DL algorithms on MSW detection, and few studies integrated the DL algorithms with a robot to identify the dominated factors to Intelligent MSW Sorter (IMSWS). Therefore, this study is to develop IMSWS prototype to better sort MSW, based on the pick-and-place process, and preliminarily evaluate the dominated factors. First, the delta robot prototype was manufactured, and IMSWS was performed with a camera to acquire the RGB image and the height of a MSW in the conveyor belt. The DL algorithm, YOLOv3 or YOLOv4, detected the type and plane location of the MSWs in the conveyor belt. Next, the sequence program transferred the valid MSW data to the delta robot. After the calculation of the absorbed location of the target MSW was made, the arm of this delta robot moved to absorb and then transfer the MSW to the bin. Results showed that the IMSWS prototype could sort the multi-object MSWs in the MSW stream. Both YOLOv3 and YOLOv4 reached high detection accuracy on the MSW image dataset. However, the improvement should be made in the actually moving MSW stream even though the YOLOv4 performed the acceptable detection accuracy. The gripping stability of the arm mainly dominated the performance of IMSWS, and this should be improved first.

The case study of a city-level business-led household food waste sorting incentive scheme in Nanjing, China.

Economic incentive is thought a good intervention type that can encourage residents to do food waste sorting by many cities' government in China. However, there is a lack of long-term, large-scale study. So the business-led incentive scheme was studied by a case study in Nanjing, China, which focuses on food waste sorting. The results showed that the incentive can encourage at most an average 37% of residents to start and then continue to do food waste sorting regularly. Later, the incentive cannot encourage more even with many non-economic interventions. And most of these participating residents (31%) were encouraged at the first 12 months. The results also showed that house price had a negative relationship with the community sorting performance. The comparative study results showed that the community committee must be involved in the non-economic interventions to encourage more residents to take part; otherwise, the company will fail even after many attempts. So the government should apply the incentive policy by dialectical view in food waste sorting. And the incentive scheme should involve all the stakeholders to apply non-economic interventions to encourage more residents to do food waste sorting.

Augmented reality-enabled human-robot collaboration to balance construction waste sorting efficiency and occupational safety and health.

Construction waste sorting (CWS) is highly recommended as a key step for construction waste management. However, current CWS involves humans' manual hand-picking, which poses significant threats to their occupational safety and health (OSH). Robotic sorting promises to change the situation by adopting modern artificial intelligence and automation technologies. However, in practice, it is usually challenging for robots to do an efficient job (e.g., measured by quickness and accuracy) owing to the difficulties in precisely recognizing compositions of the mixed and heterogeneous waste stream. Leveraging augmented reality (AR) as a communication interface, this research aims to develop a human-robot collaboration (HRC) approach to address the dilemmatic balance between CWS efficiency and OSH. Firstly, a model for human-robot collaborative sorting using AR is established. Then, a prototype for the AR-enable collaborative sorting system is developed and evaluated. The experimental results demonstrate that the proposed AR-enabled HRC method can improve the accuracy rate of CWS by 10% and 15% for sorting isolated waste and obscured waste, respectively, when compared to the method without human involvement. Interview results indicate a significant improvement in OSH, especially the reduction of contamination risks and machinery risks. The research lays out a human-robot collaborative paradigm for productive and safe CWS via an immersive and interactive interface like AR.

Health effects of future dioxins emission mitigation from Chinese municipal solid waste incinerators.

Dioxins (including 2,3,7,8-tetrachlorodibenzo-p-dioxin, as Group 1 Carcinogen) in the atmosphere mainly originate from incomplete combustion during municipal solid waste (MSW) incineration. To significantly reduce dioxins emission from the MSW incineration industry, China has promulgated a set of ambitious plans regulating MSW-related pollution; however, the emission reduction potentials and concomitant environmental and health impacts associated with the implementation of these programs on a national scale remain unknown. Here, we use real measurements from official environmental impact assessment systems and continuous emissions monitoring systems (covering 96.6% of national MSW incinerators) to estimate unit-level dioxins emission and concomitant environmental and health impacts. We find that in 2018, 99.3% and 66.7% of Chinese incinerators met such concentration and temperature standards, respectively, controlling the total emissions to 19.6 g toxic equivalency quantity and maintaining carcinogenic and noncarcinogenic risks significantly below safety levels nationwide. Fully achieving both current standards and future regulations will reduce emissions and health risks by 67.7% and 62.6%, respectively, with waste sorting program contributing the majority. This study reveals substantial benefits from curbing MSW-related dioxins pollution and underscores the promise of ongoing management.

Exploration of Categorization and Category-Based Induction on Waste Sorting: A Follow-Up Observation by NeuroSky MindWave.

Waste sorting, as an embodiment of behavioral cognition, is regulated by two cognitive processes, namely, Categorization (C) and Category-Based Induction (CBI). This study employed the event-related potential (ERP) technique to assess whether there is a transformation between C and CBI in waste sorting cognition, in order to help individuals establish a correct waste sorting behavior. We reported a case of intervention in waste sorting education with a 58-year-old Chinese woman to discriminate whether such intervention facilitates the transition between C and CBI. The results showed that the waste sorting cognition follows a developmental model as C-CBI-C, where education may help the subject build a cognitive framework for waste sorting by altering inherent misperceptions and filling gaps in classification knowledge. The results also noticed that FN400 is identified as a characteristic waveform in the CBI process, by which it is indicated that the first 4 to 7 days of education is a critical period for establishing the cognitive framework. Through a comparison of the ERP waveforms at different stages of intervention, the results are insightful to help individuals improve their cognition of waste sorting.