Intertemporal Cumulative Radiative Forcing Effects of Photovoltaic Deployments

Current policies accelerating photovoltaics (PV) deployments are motivated by environmental goals, including reducing greenhouse gas (GHG) emissions by displacing electricity generated from fossil-fuels. Existing practice assesses environmental benefits on a net life-cycle basis, where displaced GHG emissions offset those generated during PV production. However, this approach does not consider that the environmental costs of GHG release during production are incurred early, while environmental benefits accrue later. Thus, where policy targets suggest meeting GHG reduction goals established by a certain date, rapid PV deployment may have counterintuitive, albeit temporary, undesired consequences. On a cumulative radiative forcing (CRF) basis, the environmental improvements attributable to PV might be realized much later than is currently understood, particularly when PV manufacturing utilizes GHG-intensive energy sources (e.g., coal), but deployment occurs in areas with less GHG-intensive electricity sources (e.g., hydroelectric). This paper details a dynamic CRF model to examine the intertemporal warming impacts of PV deployments in California and Wyoming. CRF payback times are longer than GHG payback times by 6–12 years in California and 6–11 years in Wyoming depending on the PV technology mix and deployment strategy. For the same PV capacity being deployed, early installations yield greater CRF benefits (calculated over 10 and 25 years) than installations occurring later in time. Further, CRF benefits are maximized when PV technologies with the lowest manufacturing GHG footprint (cadmium telluride) are deployed in locations with the most GHG-intensive grids (i.e., Wyoming)

Novel Method of Sensitivity Analysis Improves the Prioritization of Research in Anticipatory Life Cycle Assessment of Emerging Technologies

It is now common practice in environmental life cycle assessment (LCA) to conduct sensitivity analyses to identify critical parameters and prioritize further research. Typical approaches include variation of input parameters one at a time to determine the corresponding variation in characterized midpoints or normalized and weighted end points. Generally, those input parameters that cause the greatest variations in output criteria are accepted as the most important subjects of further investigation. However, in comparative LCA of emerging technologies, the typical approach to sensitivity analysis may misdirect research and development (R&D) toward addressing uncertainties that are inconsequential or counterproductive. This paper presents a novel method of sensitivity analysis for a decision-driven, anticipatory LCA of three emerging photovoltaic (PV) technologies: amorphous-Si (a-Si), CdTe and ribbon-Si. Although traditional approaches identify metal depletion as critical, a hypothetical reduction of uncertainty in metal depletion fails to improve confidence in the environmental comparison. By contrast, the novel approach directs attention toward marine eutrophication, where uncertainty reduction significantly improves decision confidence in the choice between a-Si and CdTe. The implication is that the novel method will result in better recommendations on the choice of the environmentally preferable emerging technology alternative for commercialization