The life-cycle greenhouse gas (GHG) emissions of alternative fuels that are capable of replacing conventional, petroleum-derived gasoline and diesel continue to be scrutinized for policy implementation. These alternative fuel technologies can also produce a number of value-adding nonfuel coproducts that require thorough and rigorous assessment in order to achieve an accurate life-cycle GHG emissions value. By using the gas to liquids (GTL) diesel pathway as a proxy for other alternative fuel pathways with coproducts, this paper examines how integration of coproduct analysis using the substitution method is possible within the existing framework and functionality of the GREET model. Using this approach, a GREET-compatible external tool was developed to calculate the life-cycle inventory of GTL coproducts to determine the life-cycle GHG emissions of GTL diesel using the substitution method. In addition to having built-in regional scenarios, this tool allows the user the flexibility to configure a given GTL product slate and to calculate the life-cycle GHG emissions of GTL diesel based on a given product composition. Using this protocol, the life-cycle GHG emissions of GTL diesel can range from 71.7 to 95.7 gCO2e/MJ on a well to wheel basis, with the range in carbon intensity being dependent on the mix of coproducts. These results highlight a weakly understood relationship between fuel and chemical products in LCA models. The coproduct integration approach described herein could potentially be incorporated into fuel LCA models, such as GREET, to allow users to further understand the potential environmental benefits of alternative fuel pathways, such as GTL.
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Int J Biol Macromol
January 2025
Desalination Technology Institute, King Abdulaziz City for Science and Technology, Riyadh 12354, Saudi Arabia.
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View Article and Find Full Text PDFSci Rep
January 2025
College of Ecology and Environment, Hainan University, Haikou, 570228, China.
Agroforestry systems are known to enhance soil health and climate resilience, but their impact on greenhouse gas (GHG) emissions in rubber-based agroforestry systems across diverse configurations is not fully understood. Here, six representative rubber-based agroforestry systems (encompassing rubber trees intercropped with arboreal, shrub, and herbaceous species) were selected based on a preliminary investigation, including Hevea brasiliensis intercropping with Alpinia oxyphylla (AOM), Alpinia katsumadai (AKH), Coffea arabica (CAA), Theobroma cacao (TCA), Cinnamomum cassia (CCA), and Pandanus amaryllifolius (PAR), and a rubber monoculture as control (RM). Soil physicochemical properties, enzyme activities, and GHG emission characteristics were determined at 0-20 cm soil depth.
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December 2024
Department of Ophthalmology & Visual Sciences, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10461, USA.
(1) Background: Healthcare is a major contributor to global greenhouse gas (GHG) emissions, especially within the surgical suite. Ophthalmologists play a role, since they frequently perform high-volume procedures, such as cataract surgery. This review aims to summarize the current literature on surgical waste and GHG emissions in ophthalmology and proposes a framework to standardize future studies.
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January 2025
College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, 300350 Tianjin, China.
Reclaimed asphalt pavement (RAP) is a widely used end-of-life (EoL) material in asphalt pavements to increase the material circularity. However, the performance loss due to using RAP in the asphalt binder layer often requires a thicker layer, leading to additional material usage, energy consumption, and transportation effort. In this study, we developed a parametric and probabilistic life cycle assessment (LCA) framework to robustly compare various pavement designs incorporating recycled materials.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
The co-gasification of biomass and plastic waste offers a promising solution for producing hydrogen-rich syngas, addressing the rising demand for cleaner energy. However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks and improving process efficiency. While machine learning (ML) has shown significant potential in simulating and optimizing such processes, there is no clear consensus on the most effective regression models for co-gasification, especially with limited experimental data.
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