Financial viability of electric vehicle lithium-ion battery recycling.

Economically viable electric vehicle lithium-ion battery recycling is increasingly needed; however routes to profitability are still unclear. We present a comprehensive, holistic techno-economic model as a framework to directly compare recycling locations and processes, providing a key tool for recycling cost optimization in an international battery recycling economy. We show that recycling can be economically viable, with cost/profit ranging from (-21.43 - +21.91) $·kWh-1 but strongly depends on transport distances, wages, pack design and recycling method. Comparing commercial battery packs, the Tesla Model S emerges as the most profitable, having low disassembly costs and high revenues for its cobalt. In-country recycling is suggested, to lower emissions and transportation costs and secure the materials supply chain. Our model thus enables identification of strategies for recycling profitability.

Data on environmental analysis of natural antioxidant production from walnut husk by a solar photovoltaic-driven system as a replacement for potentially carcinogenic synthetic antioxidants.

In order to develop a product sustainably, multiple analyses, including comprehensive environmental assessment, are required. Solar-assisted production of walnut husk methanolic extract (WHME) as a natural antioxidant for biodiesel was scrutinized by using the life cycle assessment (LCA) approach. More specifically, the environmental sustainability of WHME antioxidant was evaluated and compared to that of propyl gallate (PG), the most widely used synthetic biodiesel antioxidant, under two scenarios. Additionally, supplementary files including the inventory data consisting of raw data as well as elementary flows, mid-point, and end-point categories are presented. The analysis of scenarios revealed that the use of the natural antioxidant and the avoidance of the chemical antioxidant in biodiesel fuel could be regarded as an eco-friendly approach substantially enhancing the environmental friendliness of biodiesel in particular in terms of human health. Furthermore, given the waste-oriented nature of WHME, the scenario involved its application could serve as a promising strategy to simultaneously valorize the agro-waste and generate a value-added product; a move toward implementing the circular economy approach in the biodiesel industry.

Evaluation of traditional and consolidated rice farms in Guilan Province, Iran, using life cycle assessment and fuzzy modeling.

In this study the environmental impact of consolidated rice farms (CF) - farms which have been integrated to increase the mechanization index - and traditional farms (TF) - small farms with lower mechanization index - in Guilan Province, Iran, were evaluated and compared using Life cycle assessment (LCA) methodology and adaptive neuro-fuzzy inference system (ANFIS). Foreground data were collected from farmers using face-to-face questionnaires and background information about production process and inventory data was taken from the EcoInvent®2.0 database. The system boundary was confined to within the farm gate (cradle to farm gate) and two functional units (land and mass based) were chosen. The study also included a comparison of the input-output energy flows of the farms. The results revealed that the average amount of energy consumed by the CFs was 57 GJ compared to 74.2 GJ for the TFs. The energy ratios for CFs and TFs were 1.6 and 0.9, respectively. The LCA results indicated that CFs produced fewer environmental burdens per ton of produced rice. When compared according to the land-based FU the same results were obtained. This indicates that the differences between the two types of farms were not caused by a difference in their production level, but rather by improved management on the CFs. The analysis also showed that electricity accounted for the greatest share of the impact for both types of farms, followed by P-based and N-based chemical fertilizers. These findings suggest that the CFs had superior overall environmental performance compared to the TFs in the study area. The performance metrics of the model based on ANFIS show that it can be used to predict the environmental burdens of rice production with high accuracy and minimal error.