In comparative life cycle assessment (LCA) studies of materials, there is a mismatch between the current practice and existing guidelines regarding functional unit definition. The purpose of this study is to develop a practice‐based framework for defining functional units in comparative LCAs of materials and provide guidance regarding in which situations different functional unit types are relevant. A literature review of comparative LCAs of materials identified three types of functional units: (i) the reference flow functional unit, (ii) the property functional unit, and (iii) the performance functional unit. These functional unit types, of which only the latter strictly complies with LCA guidelines, represent varying degrees of functional equivalence and technological maturity. The most relevant functional unit type depends on the goal of the study. We suggest that screening assessments of whether materials have comparable environmental impacts can apply reference flow functional units. Material comparisons for certain application areas with some important properties can apply property functional units. For comparisons of end products, performance functional units can be applied. However, even in such cases, complete functional equivalence can hardly be achieved due to more or less relevant product differences. The applicability of the framework is demonstrated for the case of comparing cemented carbide and polycrystalline diamond hard materials.

Proxy measures have been proposed as a low-data option for simplified assessment of environmental threat given the high complexity of the natural environment. We here review studies of environmental release, fate, toxicity and risk to identify relevant proxy measures for manufactured nanomaterials (MNMs). In total, 18 potential proxy measures were identified and evaluated regarding their link to environmental risk, an aspect of relevance, and data availability, an aspect of practice. They include socio-technical measures (e.g. MNM release), particle-specific measures (e.g. particle size), partitioning coefficients (e.g. the octanol-water coefficient) and other fate-related measures (e.g. half-life) as well as various ecotoxicological measures (e.g. 50% effect concentration). For most identified proxy measures, the link to environmental risk was weak and data availability low. Two exceptions were global production volume and ecotoxicity, for which the links to environmental risk are strong and data availability relatively decent. As proof of concept, these were employed to assess seven MNMs: titanium dioxide, cerium dioxide, zinc oxide, silver, silicon dioxide, carbon nanotubes and graphene. The results show that none of the MNMs have both high production volumes and high ecotoxicity. Several refinements of the assessment are possible, such as higher resolution regarding the MNMs assessed (e.g. different allotropes) and different metrics (e.g. particle number and surface area). The proof of concept shows the feasibility of using proxy measures for environmental assessment of MNMs, in particular for novel MNMs in early technological development, when data is particularly scarce.

Studded tires are used in a number of countries during winter in order to prevent accidents. The use of tire studs is controversial and debated because of human health impacts from increased road particle emissions. The aims of this study are to assess whether the use of tire studs in a Scandinavian studded passenger car actually avoids or causes health impacts from a broader life cycle perspective, and to assess the distribution of these impacts over the life cycle. Life cycle assessment is applied and the disability-adjusted life years indicator is used to quantify the following five types of health impacts: (1) impacts saved in the use phase, (2) particle emissions in the use phase, (3) production system emissions, (4) occupational accidents in the production system, and (5) conflict casualties from revenues of cobalt mining. The results show that the health benefits in the use phase in general are outweighed by the negative impacts during the life cycle. The largest contribution to these negative human health impacts are from use phase particle emissions (67–77%) and occupational accidents during artisanal cobalt mining (8–18%). About 23–33% of the negative impacts occur outside Scandinavia, where the benefits occur. The results inform the current debate and highlight the need for research on alternatives to tire studs with a positive net health balance.

Purpose: Artificial intelligence (AI), and particularly generative AI (GenAI), has expanded rapidly in recent years. Although its environmental impacts might be significant, they remain poorly understood and the application of life cycle assessment (LCA) to AI technologies remains in its early stages. The aim of this study is to quantify the life cycle climate impact of an AI-generated radio station named KSPR, marking the first LCA of a system composed of a variety of AI models. Methods: In this study, LCA is applied to assess the climate impact of KSPR, which is a research prototype of an AI-generated autonomous radio application developed at KTH Royal Institute of Technology in Stockholm. The functional unit was defined as one hour of generating, broadcasting and streaming radio content. Data was obtained from real-time energy measurements, publications by AI model developers and scientific literature. Sensitivity and scenario analyses were applied to test the reliability of the results and to assess parameter as well as model uncertainties. Results and discussion: In the baseline scenario, computer hardware production is identified as the main contributor to KSPR’s life cycle climate change impact (at about 70%), followed by the use of the text-to-speech conversion model Tortoise TTS (about 10%) and streaming (about 9%). Within the computer hardware category, the screen and graphical processing unit contributed significantly. Sensitivity and scenario analysis revealed significant variability in total results and in the hotspots identified depending on assumptions regarding e.g. computer lifetime, number of users of AI models and training parameters. Conclusions and recommendations: To the knowledge of the authors, this study provides the first LCA of a composite GenAI system. The findings from this study demonstrate that e.g. computer hardware and inference with Tortoise TTS are key contributors to GenAI applications such as KSPR, while the magnitude of their impacts is highly sensitive to assumptions and data availability. Strategic choices in AI model development, e.g. by reconsidering training practices, and extending hardware lifetimes can reduce impacts. The results emphasise the importance of improved data transparency on AI development for e.g. hardware, training resources, model lifetimes and usage statistics, to enable more accurate and comparable LCAs. This is also important in the context of the LCA methodology applied, such as for allocating impacts of AI model training to specific AI services. Continued advancement of LCA of AI is critical for developing a more complete understanding of the sustainability of AI systems.