A Comprehensive Dynamic Life Cycle Assessment Model: Considering Temporally and Spatially Dependent Variations

Decarbonisation of Kidney Care in the United Arab Emirates: A Roadmap to an Environmentally Sustainable Care

Chronic kidney disease (CKD) remains a major public health burden and a leading cause of mortality worldwide and in the United Arab Emirates (UAE). Alongside its clinical and humanistic burden, CKD care is associated with a significant carbon footprint. In this narrative review, we present an overview of the carbon footprint of current CKD treatments and the results of an analysis estimating the carbon footprint of CKD treatments in the UAE. Using the life cycle assessment (LCA) method and local data from the published national reports and inventory sources, we estimated that haemodialysis leads to greenhouse gas (GHG) emissions of ~12.8 tons of CO2 equivalents (CO2eq) per person in the UAE annually. Thus, the decarbonisation of CKD care is crucial in establishing an environmentally sustainable healthcare system. We propose a framework to decarbonise CKD care in the UAE that tackles the carbon footprint of CKD care in the UAE by focusing on three main pillars: Delaying early CKD and slowing its progression; reducing anthropogenic emissions from CKD and dialysis care by promoting best practices and eco-friendly technologies; and enhancing access to kidney transplantation. Such approaches are relevant not only for the UAE but also for global healthcare systems aiming towards net-zero emissions.

Developing a dynamic life cycle assessment framework for buildings through integrating building information modeling and building energy modeling program

The construction and building sector is one of the largest contributors to the global carbon emissions. Therefore, it is imperative to accurately assess the carbon emissions of buildings throughout the life cycle. Many studies conducted life cycle assessment (LCA) of buildings to evaluate carbon emissions. However, due to the lack of dynamic data, most studies adopted the static LCA methodology, which neglected the dynamic variations during life cycle stages of a building. Unlike previous studies that collected static data from questionnaires and documents, the present study aims to establish a novel dynamic life cycle assessment (D-LCA) framework for buildings by incorporating the building information modeling (BIM) and the building energy modeling program (BEMP) into the static LCA. First, a static LCA is established as the baseline scenario that covers the "cradle-to-grave" life cycle stages. A BIM model is established using Revit to obtain the inventory of building materials. The Designer Simulation Toolkit (DeST) is used as a BEMP to simulate the operating energy consumption of the studied building, taking into account changes in energy mix, climate change, and occupant behavior. At the same time, the DeST results are further used as a data input for dynamic scenarios. The D-LCA framework is applied to a high-rise commercial building in China. This study found that the difference between static and dynamic scenarios was up to 66.7 %, mainly reflected in the dynamic energy consumption during the operation phase, indicating the inaccuracy of traditional static LCA. Therefore, a D-LCA by integrating BIM and BEMP can facilitate dynamic modeling and improve the accuracy and reliability of LCA for buildings.

Dynamic life cycle assessment of commercial and household food waste: A critical global review of emerging techniques

DLCA has been applied to several food waste streams, however, to date no critical assessment of its strengths, weaknesses, opportunities, and threats (SWOT) is available in the scientific literature. Accordingly, the present review aims to provide a comprehensive overview of the available literature on DLCA and its application to Household and Commercial Food Waste (HCFW) by providing critical assessment and perspectives for future research. The Population, Intervention, Comparison, and Outcome (PICO) framework for literature review was employed, with just 12 relevant studies identified between 1999 and 2022, highlighting a dearth of research on DLCA of food waste and the need for further research. Identified studies exhibit significant variations with respect to DLCA methodology, boundary settings, and data quality and reporting, with more attention typically given to combining conventional LCA with dynamic characterization models, thus making it difficult to draw conclusive findings or identify consistent trends. Additionally, most identified studies employed DLCA for a specific case study and comparison with traditional LCA outcomes was typically ignored; just one study presented the projected impact from both LCA and DLCA for the entire life cycle of a product. Employed functional/reference units ranged from specific quantities such as 1 kg of refined crystals or syrup, 1 g L-1 Sophorolipid solution, and 1 kg of dry food with packaging material, to broader indicators like 1 kg of biofuel or 1 MJ of primary energy. Monte Carlo simulation was the most frequently employed method for uncertainty analyses within identified studies. Sensitivity analyses were conducted in just 4 studies, but it was not always clearly reported. While DLCA is undoubtedly a more realistic approach to impact assessment, and thus likely more accurate, a need exists for increasingly standardized and regulated versions of DLCA for global and multi-criteria practices.