Policy-driven municipal solid waste management assessment using relative quadrant eco-efficiency: A case study in Malaysia

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Four-decades evolutionary development of municipal solid waste management in China: Implications for sustainable waste management and circular economy

This study collected data on waste generation and management in China between 1979 and 2020 from government statistics and literature and reviewed the development of municipal solid waste (MSW) management in China. The extended stochastic impact by regression on population, affluence and technology (STIRPAT) model was employed to identify the driving forces of MSW generation, and the cointegration analysis showed that economy (0.35, t = -3.47), industrial structure (3.34, t = -20.77) and urbanization (-1.5, t = 5.678) were the significant socioeconomic driving forces in the long run. By employing the framework of evolutionary economics, this study then investigated the internal rules of long-term interaction between socioeconomic factors and MSW management. The results indicate that, in the long run, MSW management development can be viewed as an evolutionary process that includes a continuous adaptation to external socioeconomic factors and the co-evolution of internal institutions and technologies. Adaptation and diversity of institutions and technologies play an important role in achieving sustainable waste management and circular economy (CE). This study offers a novel evolutionary perspective for explaining dynamic changes of MSW management in China, as well as recommendations for emerging economies to achieve sustainable waste management and CE goals.

The eco-efficiency of municipalities in the recycling of solid waste: A stochastic semi-parametric envelopment of data approach

Eco-efficiency assessment of municipal solid waste (MSW) suppliers is a useful tool in the transition to a circular economy. Furthermore, it provides evidence of the economic and environmental performance of municipalities that can be used for decision-making and/or elaboration of regulatory policies. In this study, eco-efficiency scores were computed for a sample of 140 Chilean municipalities in the provision of MSW services. In doing so, the stochastic semi-parametric envelopment of data method was applied. It is a novel technique which overcomes the limitations of parametric (stochastic frontier analysis) and non-parametric (data envelopment analysis) methods previously employed to evaluate the eco-efficiency of MSW services. The average eco-efficiency of the 140 assessed municipalities was 0.332 which indicates that they could save 66.8% of their operational costs and recycling the same amount of waste. Moreover, 61.4% of the evaluated municipalities presented an eco-efficiency score which was lower than 0.4, whereas the other municipalities (38.6% of the sample) exhibited an eco-efficiency which raged between 0.4 and 0.80. Hence, none of the municipalities assessed was identified as eco-efficient which, implies that there is room for all municipalities to reduce operational costs in the management of MSW. Population density, tourism and location of the municipality were identified as factors influencing the eco-efficiency of the municipalities in MSW management.

Dioxin-like POPs emission trends as a decision support tool for developing sustainable MSW management scheme -an exploratory study

The paper reports on an innovative application of dioxin-like persistent organic pollutants (dl-POPs) emission trends as a measure of environmental performance for designing feasible municipal solid waste management (MSWM) schemes. MSWM systems are highly dependent on the income status and the population density and it is quintessential for developing countries to devise strategies suiting to its characteristics rather than simply adapting successful processes/technologies in developed nations. Hence a lower-middle-income, high-density populated state of India - Kerala, which represents the typical scenario of majority of towns in developing countries was selected as the verification study site. Annual inventorisation of dl-POPs for the current scenario of the state was developed as a spatial model at the lowest administrative block level using geographical information system for the easy and effective comparative assessment. Further, a dl-POPs emission based MSWM scheme which could reduce up to 65% of emissions from current scenario has been developed and compared it with contemporary life cycle assessment (LCA) and life cycle cost analysis (LCCA) schemes in terms of green-house gas emissions (GHG) and landfill area requirements as environmental performance validation. Daily exposure dose of dl-POPs were predicted from the per-capita annual emission associated with different MSWM schemes and hazard quotients were also calculated to provide an overview of the health risk posed by the emissions. The predicted health risk factors were observed to be 5 times higher than the threshold level in current scenario whereas 10 times reduction in dose levels could be achieved through the proposed scheme of MSWM.