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.

A review of soil carbon dynamics resulting from agricultural practices.

Literature related to the carbon cycle and climate contains contradictory results with regard to whether agricultural practices increase or mitigate emission of greenhouse gases (GHGs). One opinion is that anthropogenic activities have distinct carbon footprints - measured as total emissions of GHGs resulting from an activity, in this case, "agricultural operations". In contrast, it is argued that agriculture potentially serves to mitigate GHGs emissions when the best management practices are implemented. We review the literature on agricultural carbon footprints in the context of agricultural practices including soil, water and nutrient management. It has been reported that the management practices that enhance soil organic carbon (SOC) in arid and semi-arid areas include conversion of conventional tillage practices to conservation tillage approaches. We found that agricultural management in arid and semi-arid regions, which have specific characteristics related to high temperatures and low rainfall conditions, requires different practices for maintenance and restoration of SOC and for control of soil erosion compared to those used in Mediterranean, tropical regions. We recommend that in order to meet the global climate targets, quantification of net global warming potential of agricultural practices requires precise estimates of local, regional and global carbon budgets. We have conducted and present a case study for observing the development of deep soil carbon profile resulting from a 10-year wheat-cotton and wheat-maize rotation on semi-arid lands. Results showed that no tillage with mulch application had 14% (37.2 vs 43.3 Mg ha-1) higher SOC stocks in comparison to conventional tillage with mulch application. By implementing no tillage in conjunction with mulch application, lower carbon losses from soil can mitigate the risks associated with global warming. Therefore, it is necessary to reconsider agricultural practices and soil erosion after a land-use change when calculating global carbon footprints.