Integration of Artificial Intelligence and Life Cycle Assessment Methods

TL;DR: LCA research has grown rapidly in recent years, with a significant increase in publications and evolving trends towards practical applications for global sustainability goals, circular economy benefits, and energy-related sustainability aspects.

TL;DR: This prospective article explores how challenges across all phases of the bottom-up LCA framework can be overcome by harnessing new insights garnered from computationally guided parameterized models enabled by artificial intelligence (AI) methods, applicable to all products in general and specifically to FM&Ds, for which adoption remains underexplored.

TL;DR: In this paper , the authors show that integrating data and AI can speed up the discovery of new eco-friendly materials, predict environmental impact factors for early assessment of new technological integration, enhance plant design and management, and optimize processes to reduce costs and improve efficiency.

TL;DR: Life cycle assessment of road pavements incorporating waste reuse is a systematic review and guideline proposal. The review found that existing studies lack adherence to ISO 14040 standards. The proposed guideline aims to provide guidance for future research and includes recommendations for each step in LCA.

TL;DR: In this paper, the authors proposed an optimization method for paddy production in Guilan Province of Iran using Data Envelopment Analysis (DEA) and Multi-Objective Genetic Algorithm (MOGA).

TL;DR: Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.