Life cycle assessment was used to evaluate the environmental impacts of phytoplanktonic biofuels as possible sustainable alternatives to fossil fuels. Three scenarios were examined for converting planktonic biomass into higher-value commodities and energy streams using the alga Scenedesmus sp. and the cyanobacterium Arthrospira sp. as the species of interest. The first scenario (Sc-1) involved the production of biodiesel and glycerol from the planktonic biomass. In the second scenario (Sc-2), biodiesel and glycerol were generated from the planktonic biomass, and biogas was produced from the residual biomass. The process also involved using a catalyst derived from snail shells for biodiesel production. The third scenario (Sc-3) was similar to Sc-2 but converted CO2 from the biogas upgrading to methanol, which was then used in synthesizing biodiesel. The results indicated that Sc-2 and Sc-3 had a reduced potential (up to 60 % less) for damaging human health compared to Sc-1. Sc-2 and Sc-3 had up to 61 % less environmental impact than Sc-1. Sc-2 and Sc-3 reduced the total cumulative exergy demand by up to 44 % compared to Sc-1. In conclusion, producing chemicals and utilities within the biorefinery could significantly improve environmental sustainability, reduce waste, and diversify revenue streams.
E-waste, encompassing discarded materials from outdated electronic equipment, often ends up intermixed with municipal solid waste, leading to improper disposal through burial and incineration. This improper handling releases hazardous substances into water, soil, and air, posing significant risks to ecosystems and human health, ultimately entering the food chain and water supply. Formal e-waste recycling, guided by circular economy models and zero-discharge principles, offers potential solutions to this critical challenge. However, implementing a circular economy for e-waste management due to chemical and energy consumption may cause environmental impacts. Consequently, advanced sustainability assessment tools, such as Life Cycle Assessment (LCA), have been applied to investigate e-waste management strategies. While LCA is a standardized methodology, researchers have employed various routes for environmental assessment of different e-waste management methods. However, to the authors' knowledge, there lacks a comprehensive study focusing on LCA studies to discern the opportunities and limitations of this method in formal e-waste management strategies. Hence, this review aims to survey the existing literature on the LCA of e-waste management under a circular economy, shedding light on the current state of research, identifying research gaps, and proposing future research directions. It first explains various methods of managing e-waste in the circular economy. This review then evaluates and scrutinizes the LCA approach in implementing the circular bioeconomy for e-waste management. Finally, it proposes frameworks and procedures to enhance the applicability of the LCA method to future e-waste management research. The literature on the LCA of e-waste management reveals a wide variation in implementing LCA in formal e-waste management, resulting in diverse results and findings in this field. This paper underscores that LCA can pinpoint the environmental hotspots for various pathways of formal e-waste recycling, particularly focusing on metals. It can help address these concerns and achieve greater sustainability in e-waste recycling, especially in pyrometallurgical and hydrometallurgical pathways. The recovery of high-value metals is more environmentally justified compared to other metals. However, biometallurgical pathways remain limited in terms of environmental studies. Despite the potential for recycling e-waste into plastic or glass, there is a dearth of robust background in LCA studies within this sector. This review concludes that LCA can offer valuable insights for decision-making and policy processes on e-waste management, promoting environmentally sound e-waste recycling practices. However, the accuracy of LCA results in e-waste recycling, owing to data requirements, subjectivity, impact category weighting, and other factors, remains debatable, emphasizing the need for more uncertainty analysis in this field.
Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.