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Urban waste treatment has become an important part of urban environmental governance, and the efficiency of waste classification and recycling affects the ecological environment construction of the whole city. At present, the urban waste classification and recycling system in China is not perfect, and the residents' awareness of waste recycling is insufficient, which restricts the urban green ecological development. To solve this problem, this paper first analyzes the concept of garbage classification and recycling, summarizes the classification and recycling process of three commonly used garbage classification methods, including terminal classification, front-end rough classification and front-end subdivision, and analyzes the advantages and disadvantages of each method. This paper puts forward the calculation formula and calculation process of waste recycling rate, which provides a theoretical basis for the investigation and analysis of waste classification. Through the analysis of the statistical data of the relevant environmental sanitation departments and the commercial departments, it is concluded that in the past 10 years, the amount of urban domestic waste recycling has increased year by year, from 35.463 million tons in 2010 to 56.984 million tons in 2019, with an average annual growth rate of about 6%; The treatment capacity of non recyclable waste has gradually decreased since 2014, and it will be reduced to 137.89 million tons in 2019; Since 2010, the garbage recovery and utilization rate has increased year by year, from 19.6% in 2010 to 29.3% in 2019. The garbage recovery and utilization rate has increased significantly. Through questionnaire survey, this paper analyzes the main factors affecting garbage collection, and concludes that only 22.5% of the residents have a very good knowledge of garbage classification, while 38.5% of the residents have a very poor knowledge of garbage classification. [ABSTRACT FROM AUTHOR]

In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated–deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved AP0.5 of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications.

China has been promoting garbage classification in its rural areas, yet it lacks financial appropriation and fiscal decentralization to support waste processing projects. Though the existing literature has suggested fiscal decentralization strategies between different local government levels, few of the studies ascertain garbage classification efficiency from a quantitative perspective. To bridge the gap, this study examines the optimal fiscal decentralization strategies for garbage classification. It uses an optimization model while considering decision makers’ requirements regarding the fund allocation amounts at different government levels and the classification ratios in villages as constraints and decisions, respectively. A three-stage heuristic algorithm is applied to determine optimal landfill locations and efficient classification ratios for the garbage processing system in rural China, with an analytical discussion on the propositions and properties of the model. Our analytical results suggest that 1) the theoretically optimal solution is conditionally achievable, 2) the applied algorithm can achieve the optimal solution faster when the relationship between governance costs and classification ratios reaches some mathematical conditions, and 3) there is always a potential for increasing the retained funds between different government levels or for reducing the total appropriation from the county government. The numerical experiment on a primary dataset from 12 towns and 143 villages in the Pingyuan county of Guangdong province, China, does not only affirm the qualitative results, but it also provides insights into the difficulties encountered during the implementation of the garbage classification policy in China’s rural areas.

This study contributes to the literature by investigating the factors factors affecting Chinese residents' willingness to participate (WTP) in garbage classification, paying particular attention to the role of Internet use. Data are drawn from the 2016 China Labour-force Dynamics Survey (N = 13,499) and analysed using a recursive bivariate probit model. The results show that Internet use can motivate people to classify the household garbage, and it increases Chinese residents' WTP in garbage classification by 4.7 percentage points. The results also show that WTP in garbage classification of Chinese residents is largely influenced by their Internet use via smartphones. Our findings highlight the importance of distributing garbage classification information via Internet media, especially smartphones. [ABSTRACT FROM AUTHOR]

In response to the "compulsory era" of garbage classification in many places across the country, the garbage classification method has become a hot topic in social conferences. In this paper, the discriminant analysis was used to quantify and discriminate the garbage by using distance discrimination, Fisher discriminant and Bayesian discriminant method, and the specific method of garbage classification was given. The article first divided the domestic garbage into 3 categories, and then selected 5 indicators of calorific value, organic matter content, degradable time, water content and heavy metal content to determine the specific garbage category of the garbage, and convert the classification of the garbage into a discriminant function. The size of the comparison was a problem. The article found that the 2 indicators of degradable time and heavy metal content were the main factors to distinguish between organic waste and hazardous waste. Through the rational classification of waste, it would reduce environmental pressure and turn waste into treasure, which is an effective boost for China's sustainable development. [ABSTRACT FROM AUTHOR]

Waste pickers (WPs) play an indispensable role by helping to control municipal solid waste (MSW). However, they constitute the entry-level workforce of the waste recycling industry and receive little attention from the general public. In China, approximately 4 million WPs make their living by collecting MSW recyclable materials. To assess the role of WPs, an extensive social survey including urban management decision-makers, recycling industrial circle insiders, WPs, as well as common citizen respondents has been conducted in the city of Nanjing, China. The results confirmed that 70–80% of recyclable MSW materials were collected by WPs in the informal sector, which are an integral component of the waste recycling system. In Nanjing, the recyclable material collected annually by WPs is about 505,000 tons, which creates annual economic value of about 78.6–84.7 million USD. However, WPs account for only 6.8–7.3% of the entire industrial chain of the recycling economy. In Nanjing, WPs are able to save an annual MSW disposal cost of about 17.6–22.0 million USD. The resource recovery rate is also increased by 1.9–8.0%. The survey results support the experience of establishing a community-based semi-official picker organizational framework, accompanied by relevant laws, regulations, and preferential policies that would improve the resource recovery rate and pickers’ living and working conditions in order to achieve more effective and hazard-free MSW resource utilization. It is anticipated that the results of this research will be instrumental for the improvement of the MSW recycling system and WP management in other cities in China and other developing countries. [ABSTRACT FROM AUTHOR]

Climate warming is one of the important environmental issues with global concern. The Bloomberg News has recorded temperature changes in the recent 135 years. As the hottest year, in 2014 the global surface temperature was as high as 1.39 degrees Fahrenheit, 0.68 degrees Celsius higher than the average in long-term. The severity of this issue has been proved with the refresh of the highest record[1] and the increasing temperature as well as people’s personal experience. There is a demand of in-depth discussion about comprehensive and efficient reduction of carbon and greenhouse gas emission and the development of low carbon economy, with garbage classification as the most efficient breach but also most easily to be neglected by people. This article attempts to find a feasible method of carbon emission reduction from the perspective of garbage classification and resource recycling and make quantitative estimation of its value combined with local practice and data in Chengdu.

IGC (Intelligent Garbage Classifier) is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

Existing educational toys for teaching garbage classification fail to teach about its benefits and positive results. Thus, children do not fully understand the logic behind garbage classification. We summarized the design strategies of garbage classification educational toys according to parents' evaluations of existing toys and the literature on children's memory characteristics. Presenting children with all the system information related to garbage classification is essential for their logical understanding. Using interactive formats and personified images enhances children's desire to play with toys. Based on the above strategies, we designed an intelligent trash can system toy: Incorrect garbage input displays an uncomfortable expression and sad voice. Correct garbage input triggers happy expressions and positive sounds. An animated story then shows how the garbage is treated and recycled into something new. The results of a contrast experiment showed that the accuracy rate of children's garbage classification was significantly raised after playing with the designed toy for two weeks. The toy also promoted children's garbage-sorting behavior in daily life. When seeing trash misclassified, the children would correct the mistakes and take the initiative to share relevant knowledge about garbage disposal.

With the substantial increase in resource consumption in China, garbage recycling plays a vital role in reducing waste and protecting natural resources. This paper designed and conducted a random survey of four first-tier cities in China to identify the variables influencing residents' willingness to pay for garbage classification. Based on the ordered Probit model, the results show that different groups have substantial differences in supporting garbage classification. Overall, the proportion of residents willing to pay for garbage sorting is high, but the amount is in the lower range. In addition to individual characteristics, the frequency of garbage throwing, knowledge level, and publicity degree significantly enhance the willingness to pay for garbage classification. Therefore, policymakers need to consider why respondents refuse to pay for garbage sorting and promote the spread of garbage classification knowledge to further substantially promote the work of household waste classification. • Different groups have differences to support garbage classification. • The influencing factors of willingness and amount of payment are different. • Income, family size, and education level contribute to payments. • Classified knowledge and publicity contribute to payments. • The average price that people are willing to pay is in the lower range. [ABSTRACT FROM AUTHOR]

Currently, how to deal with the massive garbage produced by various human activities is a hot topic all around the world. In this paper, a preliminary and essential step is to classify the garbage into different categories. However, the mainstream waste classification mode relies heavily on manual work, which consumes a lot of labor and is very inefficient. With the rapid development of deep learning, convolutional neural networks (CNN) have been successfully applied to various application fields. Therefore, some researchers have directly adopted CNNs to classify garbage through their images. However, compared with other images, the garbage images have their own characteristics (such as inter-class similarity, intra-class variance and complex background). Thus, neglecting these characteristics would impair the classification accuracy of CNN. To overcome the limitations of existing garbage image classification methods, a Depth-wise Separable Convolution Attention Module (DSCAM) is proposed in this paper. In DSCAM, the inherent relationships of channels and spatial positions in garbage image features are captured by two attention modules with depth-wise separable convolutions, so that our method could only focus on important information and ignore the interference. Moreover, we also adopt a residual network as the backbone of DSCAM to enhance its discriminative ability. We conduct the experiments on five garbage datasets. The experimental results demonstrate that the proposed method could effectively classify the garbage images and that it outperforms some classical methods. [ABSTRACT FROM AUTHOR]

Improvement in an individuals' cognition is the key to promote garbage classification. This study takes university students as the research subjects, through three educational interventions, including the self-learning, heuristic learning, and interactive learning ways, to seek the most effective intervention based upon event-related potentials (ERPs) that is beneficial to enhance cognition of garbage classification. The results show that the experimental subjects induced P300 and LPP components, representing attentional changes and cognitive conflicts in classification judgments. There are differences in the amplitudes and peak latency of the two components corresponding to different interventions, indicating that the three educational interventions are able to improve the individual's cognition level of garbage classification within a certain period of time. The interactive-learning intervention triggers the largest amplitudes of P300 and LPP, as well as the smallest peak latency, indicating its effect is the best. Such results provide insight into the design for an appropriate strategy in garbage classification education. The study also shows that an EEG signal can be used as the endogenous neural indicator to measure the performance of garbage classification under different educational interventions.

The GHGs contributions (tally by carbon emissions) during treatment of domestic food waste and residual waste from pilot communities (contained 2365 families) in Shanghai, China, under different Modes induced by garbage classification were investigated. It was found that under the present condition of garbage classification in Shanghai, 51.8% of the food waste could be separated finally. With garbage classification, the load of landfill was saved by 17.3% (Mode 2) and 16.5% (Mode 3), the moisture of garbage for incineration was reduced by 13.6%, and the lower heating value (LHV) of garbage was increased by 16.2%. Applying the life-cycle assessment (LCA) methodology and Life Cycle Inventory (LCI) with material flows, net carbon emissions during the treatment of garbage were found to be in the following order: Mode 3 (1.60 × 10−3 kg CE/kg waste) < Mode 2 (4.85 × 10−3 kg CE/kg waste) < Mode 1 (4.94 × 10−3 kg CE/kg waste) < landfill (1.49 × 10−2 kg CE/kg waste). Mode 2 and Mode 3 were replaceable patterns of Mode 1, and anaerobic digestion was the recommendable strategy to recover energy from food waste. Especially, there was no obvious benefit of increasing the separation proportion of food waste to 60% (or above) for reducing net carbon emissions in the following treatment processes. Unlabelled Image • 51.8% of food waste could be separated after garbage classification in Shanghai. • Landfill load was saved by about 17% in disposal modes with garbage classification. • Net carbon emission: Mode 3 < Mode 2 < Mode 1 < landfill • Modes 2 and 3 were replaceable patterns of Mode 1. • Anaerobic digestion was a recommendable strategy to recovery energy from food waste. [ABSTRACT FROM AUTHOR]

In recent days, garbage classification has become a hot topic in China, and Shanghai took the lead in the implementation of garbage classification legislation in China. Starting from the current situation of garbage classification in China, this study emphasizes the garbage classification in terms of law, amount, economy, management, resourcing and successful experience of garbage classification in China, so as to provide inspiration and guidance for garbage classification for other countries. • China is implementing garbage classification action. • Shanghai took the lead in the implementation of garbage classification legislation in China. • Garbage Classification can reduce environmental pollution and play an important role in building a clean world. [ABSTRACT FROM AUTHOR]

Confronted with a series of problems caused by surging generation of municipal solid waste (MSW), the Chinese central and local governments have promulgated and implemented policies to deal with them, including promotions of the classification of MSW. However, to date, practical knowledge and understanding about benefits for garbage classification from its environmental performance perspective is still limited. The present study is purposed to comprehensively investigate the environmental effects of garbage classification on municipal solid waste management (MSWM) systems based on three proposed garbage classification scenarios in China, via a comparative life cycle impact assessment (LCIA). Taking advantage of Impact Assessment of Chemical Toxics (IMPACT) 2002+ method, this comparative LCIA study can quantitatively evaluate midpoint, endpoint, and single scored life cycle impacts for the studied MSWM systems. A Monte Carlo uncertainty analysis is carried out to test the effectiveness and reliabilities of the LCIA results. The LCIA and uncertainty analysis results show that MSWM systems based on various garbage classification scenarios have significant variations in the studied midpoint, endpoint, and single scored environmental impacts. Different garbage classification scenarios have their individual environmental-friendly superiority for specific impact categories. Overall, results of this study demonstrate that MSW treatment systems integrated with garbage classification are more environmentally friendly by comparison with non-classification; and that the more elaborate the level of MSW classification, the smaller its impacts on the environment.

With the rapid development of society and the economy, most cities have to face a serious problem of "Garbage Siege". The garbage classification is imperative because the traditional disposal method for household solid waste is not suitable for this situation. The Chinese government proposed a public private partnership (PPP) style to increase the efficiency of garbage disposal in 2013. An effective method to evaluate the waste disposal enterprises is essential to choose suitable ones. A reasonable evaluation method should consider enterprises' performance not only now but also in the future. This paper aims to propose a dynamic decision making method to evaluate the enterprises' performance based on a GM(1,1) model and regret theory with Pythagorean fuzzy numbers (PFNs). First, we proposed a GM(1,1) model for predicting score function of PFNs. Then, we put forward a method to obtain the prediction of grey degree using OWA operator. Based on the prediction of score function and grey degree, we established a novel GM(1,1) model of PFNs. Furthermore, we utilized the grey incidence method to obtain the criteria weights with Pythagorean fuzzy information. We used the regret theory to aggregate information and rank the alternatives. Finally, we applied our proposed method to solve the selecting waste disposal enterprises problem in Shanghai. By the case study we can obtain that our method is effective to solve this problem. [ABSTRACT FROM AUTHOR]

In real life, garbage has caused great pollution to the environment. A garbage classification system is an effective way to manage this issue, and is an innovation in Shanghai, China. Innovation diffusion is the topic of this paper. This study uses a mathematical statistics method to formulate individual bounded rationality, and uses the specific graph structure of a scale-free network to characterize group structure. Then, a model of group behavior is constructed and the simulation experiment is run on the Python platform. The results show that: (1) In the case of general cognitive ability and high value innovation, most individuals in the group will accept the innovation in the process of innovation dissemination in a garbage classification system after several rounds of the game; (2) it is more helpful to improve the cognitive ability of individuals and the true value of innovation for the diffusion of innovation; and (3) the larger a group, the greater the scope of innovation diffusion and the more time is needed. It is helpful to expand the scope and reduce the time of innovation diffusion by increasing connections among individuals. The innovation of this study is the characterization of individual bounded rationality, which has a certain theoretical value. Meanwhile, the research results of this paper have important practical significance for the promotion of garbage classification, which can be used to popularize the concept of garbage classification.