Improper discard of oil palm trunk and empty fruit bunch renders massive greenhouse gases. Turning these palm wastes into solid biofuels could aid in carbon reduction. The embodied environmental impacts of the solid biofuel densification process are neglected in carbon emission quantification studies applying Greenhouse Gas Protocol while the significance of classifying the system's direct and indirect carbon emissions were overlooked in those utilising life cycle assessment. Despite the prospect of both methodologies to complement their limitations for carbon emissions quantification, no study integrates both methodologies to investigate direct and indirect emissions systematically from a life cycle perspective. An integrated framework of life cycle assessment and Greenhouse Gas Protocol is developed to quantify the direct and indirect carbon emissions of oil palm trunk and empty fruit bunch densification from cradle-to-gate for three pellet plants in Indonesia and Malaysia. The emissions are categorised into three emission scopes: Scope 1, Scope 2, and Scope 3 according to the Greenhouse Gas Protocol, integrated with avoided emissions which are quantified via life cycle assessment. The pellet plants generate 534.7-732.3 kg CO2-eq/tonnepellet per hour, in which Scope 1 (i.e., direct emissions) is the major emission scope due to high emissions from wastewater production and drying fuel combustion. Washing equipment (169.2-439.0 kg CO2-eq/tonnepellet per hour) and burners (87.1-214.5 kg CO2-eq/tonnepellet per hour) are the hotspots found in the pellet plants. Producing empty fruit bunch pellets could reduce 62.0-74.1 % of emissions than landfilling the empty fruit bunch. Empty fruit bunch pellet and oil palm trunk pellet are recommended to co-fire with coal to phase down coal usage in achieving COP26 pledge. This study provides data-driven insights for quantifying carbon emissions through the integrated framework and could be a reference in future life cycle carbon footprint studies of the biomass densification process.
Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12-68 times, 13-73 times, and 18-98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.
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.