Nowadays, product designers, manufacturers, and consumers consider the environmental impacts of products, processes, and services in their decision-making process. Life Cycle Assessment (LCA) is a tool that assesses the environmental impacts over a product’s life cycle. Conducting a life cycle assessment (LCA) requires meticulous data sourcing and collection and is often time-consuming for both practitioner and verifier. However, predicting the environmental impacts of products and services can help stakeholders and decision-makers identify the hotspots. Our work proposes using Artificial Intelligence (AI) techniques to predict the environmental performance of a product or service to assist LCA practitioners and verifiers. This approach uses data from environmental product declarations of construction products. The data is processed utilizing natural language processing (NLP) which is then trained to random forest algorithm, an ensemble tree-based machine learning method. Finally, we trained the model with information on the product and their environmental impacts using seven impact category values and verified the results using a testing dataset (20% of EPD data). Our results demonstrate that the model was able to predict the values of impact categories: global warming potential, abiotic depletion potential for fossil resources, acidification potential, and photochemical ozone creation potential with an accuracy (measured using R2 metrics, a measure to score the correlation of predicted values to real value) of 81%, 77%, 68%, and 70%, respectively. Our method demonstrates the capability to predict environmental performance with a defined variability by learning from the results of the previous LCA studies. The model’s performance also depends on the amount of data available for training. However, this approach does not replace a detailed LCA but is rather a quick prediction and assistance to LCA practitioners and verifiers in realizing an LCA.

In the past twenty years, green analytical chemistry has gained more and more attention. However, quantification of the environmental impacts of analytical methods has never been estimated. The purpose of this work is to apply life cycle assessment (LCA) to the preparation of one sample using SBSE and SPE techniques and to show that LCA is a suitable framework to quantitatively assess the environmental impacts of a sample preparation. The amounts of consumables, chemicals and energy needed to prepare a sample with both techniques were determined with the literature and lab measurements. We converted this data into environmental impacts through the use of a life cycle inventory (LCI) database (ecoinvent 3.7.1) and a life cycle impact assessment method (ReCiPe 2016 Midpoint). The results of the LCA (baseline scenario) showed that the SBSE induces less overall environmental impacts than the SPE because it uses less chemicals to prepare one sample. The impacts of both techniques could be reduced by reusing the vial and vial caps which are the largest contributors. The spatial location of the laboratory (and its associated electricity mix) also plays a significant role for the SBSE process as it uses more electricity than the SPE process. This study paves the way for the application and standardization of LCA to whole chemical analysis, composed of the sample collection, preparation, analysis and the data analysis.

In the past twenty years, analytical chemistry scientists have developed tools to assess the "greenness" of their analytical methods. The purpose of this work is to review these tools against criteria inspired by the life cycle assessment (LCA) framework. Results show that the reviewed tools are ready- and easy-to use but they are also limited in their scope, and base their rankings on subjective weighting scheme. We discuss the strengths and weaknesses of LCA to overcome the limits found in the reviewed tools. We show that LCA is complementary to existing tools as it provides quantitative and holistic information on the environmental performance of analytical chemistry methods. However, conducting LCA is time consuming, requires expertise and cannot be applied routinely. We finally establish key challenges for the application of LCA in the field of analytical chemistry.

Fossil fuels are the dominant form of storable energy, but their share in the global energy supply is slowly diminishing due to climate mitigation policies. Alternative energy production from variable renewable energy sources for both stationary and mobile use requires some form of energy storage. Batteries are the current frontrunner for this application, particularly with Li-ion batteries that are reliable and highly efficient. However, batteries themselves have evolved to meet current requirements and expectations. These changes in battery chemistry have shifted the dependency on raw materials used to produce them. Raw materials critical for battery production are subject to supply risk due to their availability or trade policies prompting a need for supply risk assessment. Such resource supply risks depend on the perspective of the importing country or region. By analysing the supply risk of raw materials used in the production of batteries in comparison to fossil fuels, it is possible to understand the shift in risk to storable energy that is underway. In this study, we analyse the supply risk of selected raw materials used in batteries and compare it with the supply risk of fossil fuels for the period 2000 to 2018 from the perspective of the European Union, USA, South Korea, Japan, Canada and Australia using the GeoPolRisk method. Our analysis demonstrates a higher risk of supply for raw materials compared to that of fossil fuels for all the selected territories. Rare earth elements, graphite and magnesium, are amongst the raw materials with the highest supply risk due to their concentrated production in one or only a few countries. Countries have recognised the need for raw material security and made specific policies to ensure secure supply. Raw material security is an emerging concern for all the countries, especially in the case of batteries for major manufacturing nations that are heavily import-dependent. Raw materials producing countries like Canada and Australia focused on stockpiling minerals and minerals exploration while importing countries such as Japan and South Korea are looking for alternate sources for their supply. The results from our analysis suggest that the necessary policy reforms taken for energy security have benefited all the countries with a reduced risk of fossil fuel supply, while similar policies to secure raw materials are discussed but not yet fully implemented.

Plastic debris into the environment is a growing threat for the ecosystems and human health. The seafood sector is particularly concerned because it generates plastic losses and can be endangered by plastic contamination. Life cycle assessment (LCA) does not properly consider plastic losses and related impacts, which is a problem in order to find relevant mitigation strategies without burden shifting. This work proposes a methodology for quantifying flows of plastics from the life cycle of the seafood products to the environment. It is based on loss rate and final release rate considering a pre-fate approach as proposed by the Plastic Leak Project. They are defined for 5 types of micro and macro plastic losses: lost fishing gears, marine coatings, plastic pellets, tire abrasion and plastic mismanaged at the end-of-life. The methodology is validated with a case study applied to French fish products for which relevant data are available in the Agribalyse 3.0 database. Results show that average plastic losses are from 75 mg to 4345 mg per kg of fish at the consumer, depending on the species and the related fishing method. The main plastic losses come from lost fishing gears (macroplastics) and tire abrasion (microplastics). Results show high variability: when mismanaged, plastic packaging at the end-of-life (macroplastics) is the main loss to the environment. As a next step the methodology is to be applied to other fish or shellfish products, or directly implemented in a life cycle inventory database. Further research should characterize the related impacts to the environment when life cycle impact assessment methodologies will be available, and identify eco-design solutions to decrease the major flows to the environment identified.