| dc.description.abstract | The world is undergoing unprecedented rapid technological and climatic change. The urgency with which we must address the challenges of said climatic change increases with each passing day of inaction. Technology is often hailed as the saviour of the modern way of life: a way to raise living standards while solving environmental crises. Evaluating the environmental impacts of emerging technologies is therefore important to ensure that the best technology is selected. This involves evaluation at different development phases, as well as at different scales, from the sub-component or product level to macro scale.
Life cycle assessment (LCA) has proven helpful for identifying environmental hotspots for mature technologies at the product level. The standard LCA method, however, is less suited to evaluating emerging technologies due to the lack of appropriate data and upscaling issues, among others. The work in this thesis extends the LCA method to better adapt to the unique challenges of evaluating emerging technologies, with different focuses at different phases. This includes a screening approach that adapts to non-traditional data available at early R&D stages and communicates the high uncertainty present at low technology readiness levels (TRLs). We also demonstrate a tool for addressing the rapidly changing technological scene while calculating technology climate footprints for many discrete regions consistently and effectively. Finally, we present a model for performing prospective macro-LCA that integrates scenarios from energy systems models and dynamic stock models with LCA factors towards evaluating environmentally optimal deployment pathways. These methods are applied to case studies related to electromobility.
With these methods, we hope to bring a better understanding of the life cycle environmental impacts of emerging technologies. Bringing the life cycle perspective to technologies as early as possible enables a more responsible uptake of technologies and ensures a well-informed, proactive decision-making process. | en_US |
