Waste sorting and its effects on carbon emission reduction:Evidence from China

Considering the importance of waste sorting and treatment in the development of an ecological civilization,empirically evaluating the environmental impact of such programs is particularly important.This study uses Xiao'er Township in Gong County,Sichuan Province,China as a case study to analyze and estimate the carbon emission reduction effects of the township's pilot waste sorting program.Using the five-point sampling method,samples of waste are collected,reviewed,and measured for their major components and other key indicators.Additionally,questionnaire surveys and interviews are conducted in the township,along with investigations into existing records and other relevant information.The study adopts the solid waste management-greenhouse gas(SWM-GHG)calculator to study the township data.The case study results imply that proper waste sorting and treatment methods in villages and townships could play a major role in the reduction of carbon emission.Specifically,after implementing waste sorting in Xiao'er,annual carbon emissions were reduced by 2081 tons—equivalent to the electricity consumption of a family of three people for 1718 years,or the amount of CO_(2)emitted by 2641.6L vehicles driving once around the Earth.In the optimal scenario simulation,increasing the recycling of wet waste and recyclable waste further,the level of carbon emission reduction in Xiao'er could reach up to 4482 tons per year.According to the international general carbon trade price,this is equivalent to adding 44,820 US dollars to the GDP,or to an annual saving of 5.71 million kWh.If these waste management methods are expanded to villages and townships across China,then the carbon emissions reduced in a year would be equal to the CO_(2)emitted from electricity generation in Beijing for over a year.Based on these findings,this paper provides three policy recommendations for effective carbon emission reduction:increasing residents'environmental protection awareness over the long term,boosting funding support and enhancing the construction of supporting facilities,and strengthening governance and institutional capacity for waste sorting and treatment.

MSWNet:A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste(MSW).This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission.As the categories of MSW are diverse considering their compositions,chemical reactions,and processing procedures,etc.,resulting in low efficiencies in MSW sorting using the traditional methods.Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode.This study for the first time applied MSWNet in MSW sorting,a ResNet-50 with transfer learning.The method of cyclical learning rate was taken to avoid blind finding,and tests were repeated until accidentally encountering a good value.Measures of visualization were also considered to make the MSWNet model more transparent and accountable.Results showed transfer learning enhanced the efficiency of training time(from 741 s to 598.5 s),and improved the accuracy of recognition performance(from 88.50%to 93.50%);MSWNet showed a better performance in MSW classsification in terms of sensitivity(93.50%),precision(93.40%),F1-score(93.40%),accuracy(93.50%)and AUC(92.00%).The findings of this study can be taken as a reference for building the model MSW classification by deep learning,quantifying a suitable learning rate,and changing the data from high dimensions to two dimensions.

Deep multimodal learning for municipal solid waste sorting

Automated waste sorting can dramatically increase waste sorting efficiency and reduce its regulation cost. Most of the current methods only use a single modality such as image data or acoustic data for waste classification, which makes it difficult to classify mixed and confusable wastes. In these complex situations, using multiple modalities becomes necessary to achieve a high classification accuracy. Traditionally, the fusion of multiple modalities has been limited by fixed handcrafted features. In this study, the deep-learning approach was applied to the multimodal fusion at the feature level for municipal solid-waste sorting.More specifically, the pre-trained VGG16 and one-dimensional convolutional neural networks(1 D CNNs) were utilized to extract features from visual data and acoustic data, respectively. These deeply learned features were then fused in the fully connected layers for classification. The results of comparative experiments proved that the proposed method was superior to the single-modality methods. Additionally, the feature-based fusion strategy performed better than the decision-based strategy with deeply learned features.

Environment and economic feasibility of municipal solid waste central sorting strategy： a case study in Beijing

Although Beijing has carried out municipal solid waste （MSW） source separation since 1996, it has largely been ineffective. In 2012, a ＂Green House＂ program was established as a new attempt for central sorting. In this study, the authors used material flow analysis （MFA） and cost benefit analysis （CBA） methods to investigate Green House＇s environment and economic feasibility. Results showed that the program did have significant environmental benefits on waste reduction, which reduced the amount of waste by 34%. If the Green House program is implemented in a residential community with wet waste ratio of 66%, the proportion of waste reduction can reach 37%. However, the Green House is now running with a monthly loss of 1982 CNY. This is mainly because most of its benefits come from waste reduction （i.e., 5878 CNY per month）, which does not turn a monetary benefit, but is instead distributed to the whole of society as positive environmental externalities. Lack of government involvement, small program scale, and technical/managerial deficiency are three main barriers of the Green House. We, thus, make three recommendations： involve government authority and financial support, expand the program scale to separate 91.4 tons of waste every month, and use more professional equipment/technologies. If the Green House program can successfully adopt these suggestions, 33.8 tons of waste can be reduced monthly, and it would be able to flip the loss into a profit worth 35034 CNY.