Incentivizing household recycling crowds out public support for other waste management policies: A long-term quasi-experimental study

You must separate: How perceived importance and language intensity promote waste separation

Waste threatens human health and the environment. How can we persuade people to participate in waste separation? In order to address this challenge, the present experimental study (N = 280) investigated the effects of perceived importance (high, medium, low) and language intensity (assertive, non-assertive) on people's intention to separate waste based on the social influence theory and the value-identity-personal norm model. The results showed that high perceived importance and assertive language were positively and significantly associated with waste separation intention. Furthermore, the mediating analysis revealed that environmental self-identity and personal norm were serial mediators in the relationship between perceived importance and waste separation intention. Therefore, strengthening perceived importance and enhancing internalization processes (environmental self-identity and personal norm) contribute to promoting waste separation intention. The findings of this study provide both theoretical and practical contributions to promote waste separation.

Predictors of e-waste: Considerations for community psychology prevention and intervention

E-waste, the overabundance of unused technology products, is a growing issue as new technology is rapidly innovated and our society promotes the need to always have the "latest and greatest" products. Community psychology, as a field, is concerned with the global climate crisis, and subsequently must be concerned with e-waste. This study tested predictors of individual's likelihood to recycle e-waste with 883 US adults (459 males, 420 females, 3 other/nonbinary; 62.7% 54-year-old or younger) through a crowdsourcing procedure. Similar to previous recycling literature, the present study found that personal norms, subjective norms, and perceived behavioral control positively predicted the likelihood for an individual to recycle; however, the present study provides further empirical evidence for these relationships and expands recycling literature by focusing on e-waste recycling. Implications for the field of community psychology with preventive and interventive actions are detailed.

Green illusions in self-reporting? Reassessing the intention-behavior gap in waste recycling behaviors

Improving residents' waste recycling behavior is crucial for enhancing resource efficiency and reducing carbon emissions. Previous questionnaire-based studies have reported that individuals exhibit a high willingness to recycle, yet often fail to convert this intention into action. Analyzing 180,417 Internet of Things (IoT) behavior data points, we discovered that the intention-behavior gap might be larger than anticipated. Our findings indicate that: 1) Intentions to recycle alone can predict self-reported recycling behavior (p < 0.01, t = 2.841), but not actual recycling behavior in the absence of other possible moderators (p > 0.1, t = 0.777); 2) Self-reported behavior predicts real behavior, but with limited explanatory power; and 3) The intention-behavior gap primarily results from forgetting or habituation (p < 0.01, t = 2.653), while social desirability plays an insignificant role (p > 0.1, t = 0.246). This study contributes to our understanding of the intention-behavior gap and provides direction for future pro-environmental behavior research.

External Factors Impacting Residents' Participation in Waste Sorting Using NCA and fsQCA Methods on Pilot Cities in China

Recycling waste is important as it can help to reduce environmental pollution caused by "waste siege". Source classification is an important part of the municipal solid waste (MSW) sorting process. The factors that prompt residents to participate in waste sorting have been debated by scholars in recent years; however, there are not many papers that focus on the complex relationships between them. This study reviewed the literature that concerns residents' participation in waste sorting, and it summarized the external factors that might influence residents' participation. Then, we focused on 25 pilot cities in China, and we analyzed the configuration impact of external factors on residents' participation using a necessary condition analysis (NCA) and a fuzzy-set qualitative comparative analysis (fsQCA). We found no consistency between variables, nor was there one single condition that caused residents to participate in waste sorting. There are two main methods (environment-driven and resource-driven) that can help achieve a high participation rate, and three methods that can cause a low participation rate. This study provides suggestions for the implementation of waste sorting in other cities in China, as well as developing countries, with an emphasis on the importance of public participation.

Study on the Evolution of the Game of Willingness to Cooperate between Residents and Separation Enterprises in Waste Separation Considering the Convenience of Separation Facilities

The distributivity and complexity of separation facilities in waste separation cooperation are incorporated into the factors influencing the payoff of waste separation cooperation. The game payment matrix of waste separation cooperation is constructed based on the distributivity and complexity of separation facilities. The equilibrium solution of waste separation cooperation is obtained through the evolutionary game. The influence of different changes in distributivity and complexity of separation facilities on the willingness to cooperate in waste separation is explored through numerical analysis of cases. The study shows that when the distributivity of separation facilities is certain, the lower the complexity of separation facilities, the higher the willingness of residents and enterprises to cooperate; when the complexity of separation facilities is certain, the willingness of residents and enterprises to cooperate rises and then falls with the increase of distributivity of separation facilities; finally, when the distributivity and complexity of separation facilities change at the same time, the willingness of residents and enterprises to cooperate shows different changes with the different changes of two separation facilities convenience factors.

Design and development of smart Internet of Things-based solid waste management system using computer vision

Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision-based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet V2 models trained through transfer learning for image classification. ResNet V2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The proposed ResNet-based CNN performs waste classification with 19.08% higher accuracy and 34.97% lower loss than the performance metrics of other existing models.

Quantitative Evaluation of Waste Separation Management Policies in the Yangtze River Delta Based on the PMC Index Model

Numerous policies have been formulated and implemented to strengthen waste separation management activities in many countries. Waste separation management policies (WSMPs) must be evaluated as the precondition for reducing deviations from policy implementation and improving waste separation performance. Based on text mining technology and the construction of a policy modeling consistency (PMC) index model, we conducted a quantitative evaluation of 22 WSMPs issued by central governmental departments and provinces in the Yangtze River Delta, China from 2013 to 2021 and analyzed their optimization paths. The results suggest that the PMC index of the selected WSMPs has an upward trend. The average PMC index of 22 WSMPs was 6.906, indicating good quality in the policy texts. The PMC index identified seven, nine, five, and one of the policies as being perfect, excellent, good, and acceptable, respectively. The characteristics of WSMPs were further illustrated through PMC surface charts. Based on this, optimization paths for WSMPs with lower PMC indexes are proposed, which indicate that existing WSMPs have great potential for optimization in terms of harsher constraint regulations, context-appropriate incentives, and cultivation of market participants. Finally, this study provides a beneficial reference for similar cities or countries to improve their performance in the management of waste separation and environmental protection.