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Municipal solid waste (MSW) management and recycling has become an emerging issue in developing countries. Shanghai, the largest megacity in China, is well-known nationwide due to leading China’s MSW separation and recycling. Therefore, this paper introduces the Shanghai mode for MSW management and its current situation to enrich existing MSW management studies. Results show that the total generation volume of MSW and amount of MSW generation per capita were 9.00 million tons and 372.16 kg in 2017, increased approximately eight-fold and four-fold compared with the data in 1978, respectively. The MSW treatment rate reached 100% since 2014, with incineration rate increased to 48.56% in 2017. The cost of MSW management after implementing MSW sorting regulation is increased to 985 CNY/ton, including 390 CNY/ton of MSW sorting cost. Then three key features and innovative MSW management modes, namely, mandatory MSW sorting legislation, Green Account program, and the Combined Network program are introduced. Meanwhile, two main challenges are urgent to be responded, including inadequate collecting vehicles and limited wet waste treatment capacity. Finally, policy recommendations on strengthening MSW recycling process, constructing complete terminal treatment industry, and making systematic policies were provided to respond existing challenges.

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. 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.

Promoting municipal solid waste (MSW) sorting is critical to advancing sustainable and low-carbon urban development. While existing research often focuses separately on external policy tools or internal behavioral drivers, limited attention has been given to their joint effects within an integrated framework. This study addresses this gap by analyzing micro-survey data from 1983 residents across 34 prefecture-level and above cities in China, using a bivariate probit model to examine how policy tools and policy perception—both independently and interactively—shape residents’ active and passive compliance with MSW sorting policies. The findings reveal five key insights. First, the adoption and spatial distribution of policy tools are uneven: environment-type tools dominate, supply-type tools are moderately deployed, and demand-type tools are underutilized. Second, both policy tools and policy perception significantly promote compliance behaviors, with policy cognition exerting the strongest effect. Third, differential effects are observed—policy cognition primarily drives active compliance, whereas policy acceptance more strongly predicts passive compliance. Fourth, synergistic effects emerge when supply-type tools are combined with environment-type or demand-type tools. Finally, policy perception not only directly enhances compliance but also moderates the effectiveness of policy tools, with notable heterogeneity among residents with higher cognitive or emotional alignment. These findings contribute to a deeper understanding of compliance mechanisms and offer practical implications for designing perception-sensitive and regionally adaptive MSW governance strategies.

Municipal solid waste (MSW) represents an inevitable by-product of human activity and a major crisis for communities across the globe. In recent times, the recycling of MSW has drawn attention as the process can add value through resources from the recovered waste materials and facilitates the process of circular economy. However, during the unprecedented coronavirus (COVID-19) outbreak, the risk of infection with the highly contagious virus has proven detrimental to the continuation of MSW as a valuable resource. The volume of waste, especially household waste, is higher; face masks, PPE (personal protective equipment), and hazardous materials such as batteries and empty chlorine bottles are examples of extra waste that have arisen during the pandemic. Various countries have set up initiatives for MSW management, including safety measurements for employees in the MSW management sector. The use of disinfectant prior to sorting waste, as well as storing waste for 9 days, may help to inactivate the COVID-19 virus, ensuring an appropriate safety level for MSW management. This work aimed at studying different MSW management strategies, specific challenges, and possible solutions for better understanding for those involved in waste management, in addition to providing a possible management strategy during and post-COVID-19 pandemic. Graphical abstract

Increased urbanization and population lead to increased consumption of manufactured goods. This ultimately results in increased production of waste. Identifying its composition is crucial for planning an effective solid waste management strategy. This study assesses the characteristics and composition of the waste generated within the Uyo Capital City Development Area of Akwa Ibom State, Nigeria. This is to aid in developing a scientifically supported waste management pilot system for the state. Direct waste sorting and characterization were conducted on the municipal solid waste arriving at the landfill during the study period. Over 50% of the generated wastes are recyclables and composed of plastics, metals, and paper, while the fraction of organic waste is over 30%. Similarly, the waste generation per capita is 1.34 kg/person/day, while the generation forecast over the next ten years is estimated to increase by approximately 40%. Furthermore, over 9,000 surveys were completed by residents to establish a problem statement about the existing waste collection and disposal system, and possible solutions. Importantly, a majority of survey respondents were willing to source-separate their wastes and supported paying a fee for adequate waste collection. This strongly indicates that an integrated waste management system could be established to generate value from the collected waste. Supplementary revenue can be generated through composting, recycling, and land reclamation.

Many cities in developing countries lack adequate drainage and waste management infrastructure. Consequently, city residents face economic and health impacts from flooding and waterlogging, which are aggravated by solid waste infiltrating and blocking drains. City governments have recourse to two strategies to address these problems: a) ‘hard’ infrastructure-related interventions through investment in the expansion of drainage and waste transportation networks; and/or, b) ‘soft’, low-cost behavioural interventions that encourage city residents to change waste disposal practices. This research examines whether behavioural interventions, such as information and awareness raising alongside provision of inexpensive street waste bins, can improve waste management in the city. We undertook a cluster randomized controlled trial study in Bharatpur, Nepal, where one group of households was treated with a soft, low-cost intervention (information and street waste bins) while the control group of households did not receive the intervention. We econometrically compared baseline indicators – perceived neighbourhood cleanliness, household waste disposal methods, and at-source waste segregation – from a pre-intervention survey with data from two rounds of post-intervention surveys. Results from analysing household panel data indicate that the intervention increased neighbourhood cleanliness and motivated the treated households to dispose their waste properly through waste collectors. The intervention, however, did not increase household waste segregation at source, which is possibly because of municipal waste collectors mixing segregated and non-segregated waste during collection. At-source segregation, a pre-requisite for efficiently managing municipal solid waste, may improve if municipalities arrange to collect and manage degradable and non-degradable waste separately.

Waste sorting is an effective means of enhancing resource or energy recovery from municipal solid waste (MSW). Waste sorting management system is not limited to source separation, but also involves at least three stages, i.e., collection and transportation (C&T), pretreatment, and resource utilization. This review focuses on the whole process of MSW management strategy based on the waste sorting perspective. Firstly, as the sources of MSW play an essential role in the means of subsequent valorization, the factors affecting the generation of MSW and its prediction methods are introduced. Secondly, a detailed comparison of approaches to source separation across countries is presented. Constructing a top-down management system and incentivizing or constraining residents' sorting behavior from the bottom up is believed to be a practical approach to promote source separation. Then, the current state of C&T techniques and its network optimization are reviewed, facilitated by artificial intelligence (AI) and the Internet of Things technologies. Furthermore, the advances in pretreatment strategies for enhanced sorting and resource recovery are introduced briefly. Finally, appropriate methods to valorize different MSW are proposed. It is worth noting that new technologies, such as AI, show high application potential in waste management. The sharing of (intermediate) products or energy of varying processing units will inject vitality into the waste management network and achieve sustainable development. Graphical abstract

Municipal solid waste (MSW) severely threatens human health and the ecological environment owing to its toxicity, mutagenic activity and carcinogenicity. The continuous increase in MSW together with stringent regulations makes sanitary disposal imperative. Waste sorting and recycling has been recognized as an efficient and economical treatment strategy. By analysing research data from 31 provinces between 2000 and 2017, the overarching goal of this work was to determine the characterizations and the compositions of MSW in China and then provide advices for sorting, transporting, storing and disposing of MSW. The results showed that the amount of MSW that was generated ranged from 0.08 to 2.34 kg d −1  ca −1 and averaged 0.73 kg d −1  ca −1 in China. The average bulk density, moisture content and the wet basis of the low calorific value of the MSW were 325 kg m −3 , 50.3% and 4649 kcal kg −1 . The MSW in China could be classified into four main categories, food waste, recycling waste, landfill waste and hazardous substances, and could be further classified into ten sub-categories. Overall, food waste was the most common and could be best managed via compost production. Bulk density was highly positively correlated with the ratio of the dust and bricks in all MSW and highly negatively correlated with the ratio of the food waste, metal, glass, plastic and rubber. The wet basis of the low calorific value was highly positively correlated with the ratio of the plastic and rubber, and the water content was highly positively correlated with the ratio of the food waste. Temporally, most of the components, especially waste paper and plastics, increased, while wood, dust and bricks decreased. Graphic abstract

The recently finalized research project “ZRR for municipal waste” aimed at testing and evaluating the automation of municipal waste sorting plants by supplementing or replacing manual sorting, with sorting by a robot with artificial intelligence (ZRR). The objectives were to increase the current recycling rates and the purity of the recovered materials; to collect additional materials from the current rejected flows; and to improve the working conditions of the workers, who could then concentrate on, among other things, the maintenance of the robots. Based on the empirical results of the project, this paper presents the main results of the training and operation of the robotic sorting system based on artificial intelligence, which, to our knowledge, is the first attempt at an application for the separation of bulky municipal solid waste (MSW) and an installation in a full-scale waste treatment plant. The key questions for the research project included (a) the design of test protocols to assess the quality of the sorting process and (b) the evaluation of the performance quality in the first six months of the training of the underlying artificial intelligence and its database.

National and local governments implement municipal solid waste classification policies to reduce waste disposal and minimize environmental pollution. Beijing started implementing its classification of municipal solid waste policy in May 2020. This study evaluates the impact of Beijing’s household waste classification policy on carbon emissions during collection, transportation, and treatment. The policy’s introduction reduced the number of trash bins, influenced transportation modes, and altered waste treatment emissions. We found a notable reduction in average net carbon emissions post-policy implementation (2403 kg CO2eq per metric ton of waste) compared with pre-policy (3584 kg CO2eq per metric ton of waste). If Beijing reaches its 2025 waste target, these emissions will decrease further to 1760 kg CO2eq per metric ton of waste. Translating these findings into monetary terms confirms this policy’s environmental and economic efficacy.