Extending the lifetime of passive products, i.e., products that do not consume materials or energy during the use phase, by implementing product-service systems (PSS) has a potential to reduce the environmental impact while being an attractive and straightforward measure for companies to implement. This research assesses the viability of introducing PSS for passive products, by documenting five real product cases of prolonging the lifetime through repair or refurbishment and by quantifying, through life cycle assessment (LCA) and life cycle costing (LCC), the change in environmental and economic outcome. The environmental impact (measured as global warming potential over the life cycle) was reduced for all cases because extraction and production dominated the impact. This reduction was 45–72% for most cases and mainly influenced by the number of reuses and the relative environmental burden of the components whose lifetime was prolonged. The costs for the company (measured as LCC from the manufacturer’s perspective) decreased too by 8–37%. The main reason that costs reduced less than the environmental impact is that some costs have no equivalent in LCA, e.g., administration and labor costs for services. The decreases in both LCA and LCC results, as well as the willingness of the companies to implement the changes, demonstrate that this measure can be financially attractive for companies to implement and effectively contribute to a circular economy.

Tidal current technologies have the potential to provide highly predictable energy, since tides are driven by lunar cycles. However, before implementing such technologies on a large scale, their environmental performance should be assessed. In this study, a prospective life cycle assessment (LCA) was performed on a 12 MW tidal energy converter array of Minesto Deep Green 500 (DG500) prototypes, closely following the Environmental Product Declaration (EPD) standards, but including scenarios to cover various design possibilities. The global warming potential (GWP) of the prototype array was in the range of 18.4–26.3 gCO₂-eq/kWhe. This is comparable with other renewable energy systems, such as wind power. Material production processes have the largest impact, but are largely offset by recycling at the end of life. Operation and maintenance processes, including the production of replacement parts, also provide major contributions to environmental impacts. Comparisons with other technologies are limited by the lack of a standardized way of performing LCA on offshore power generation technologies.

Accounting for the environmental impacts in the design of technologies is becoming a necessity for manufacturers. Life cycle assessment (LCA) is a well-established method to quantify the environmental impacts of products and services through a holistic perspective and is increasingly used to support the eco-design of products and technologies. However, LCA generally faces an inherent issue with data availability. Given the constraints on both time and cost for collecting inventory data to feed the LCA model, a trade-off between data cost robustness is required with an efficient data collection strategy. The objective of this study is to develop a framework to prioritize data collection efforts in LCA using uncertainty analysis. This starts with a screening life cycle inventory analysis systematically informing all input parameters with uncertainty ranges. Monte Carlo analysis is then used to propagate the uncertainty through the model. Stochastic results are then compared with an acceptable confidence level set by the decision maker. This is followed by a global sensitivity analysis using Sobol' indices to rank different input parameters based on their contribution to the variability of the results. This paves the way for an iterative process prioritizing further data collection focusing on the most sensitive parameters. A case study comparing cold spray and wire arc additive manufacturing illustrates how to operationalize the framework. Learnings from the case study highlight the importance of defining the uncertainty ranges and the convergence criterion, where more work is needed in that domain.