4:00pm - 4:20pm
Uncertainty of product systems in LCA of emerging technologies
1Institute of Environmental Sciences (CML), Leiden University, Leiden, Netherlands; 2National Institute of Public Health and the Environment (RIVM), Center for Safety of Substances and Products, Bilthoven, Netherlands; 3Department of Econometrics and Operations Research, Vrije Universiteit, Amsterdam, the Netherlands
With the recent drive to use life-cycle assessment (LCA) to assess the environmental impacts of emerging technologies, new and existing sources of uncertainty have become more relevant. These are mostly related to the unpredictable evolution of the manufacturing pathways that are selected as the technology moves from lab to industrial scale. The same applies to multiple and/or unforeseen ways in which the technology will be used, disposed and/or recycled. Several LCA studies of emerging technologies have opted to address these uncertainties by assessing different scenarios, which allow practitioners to make if/then types of conclusions about the technology. However, this type of analysis can only offer limited guidance for policy decisions that must be made with the current state of knowledge. The scenario-based approach also brings a problem of a more practical nature; considering all the different possibilities for manufacturing, use and end-of-life, the number of scenarios can quickly become unmanageable.
We present a framework in which possible future configurations of an emerging technology’s product system are assessed as a single product system with uncertain components. These components are unit processes in the manufacturing, use, and/or end-of-life stages which may or may not be triggered once the technology reaches industrial scale. By conducting an uncertainty analysis on the single product system, a single probability distribution is obtained for each impact score. These distributions account for uncertainty about the product system’s final configuration, along with other sources of uncertainty (e.g. in the background system flows, characterization methods and other modelling choices).
The implementation of our method requires an LCA software that allows the use of variable parameters with uncertainty information associated to them, as well as a Monte Carlo simulation function for uncertainty analysis, such as the open source package OpenLCA. For a given technological product system, we connect the competing unit processes simultaneously, even though they are mutually exclusive alternatives. We then multiply the connecting flow quantities by a parameter P that will randomly round to a value of 0 or 1, according to the probability of either process occurring. When P=1, only one process will contribute to the upstream impacts, and when P=0, only the other process will contribute.
Our method proposes a paradigm shift in the environmental assessment of emerging technologies. In contrast to existing methods, it allows a detailed quantification and treatment of epistemic uncertainties that are intrinsic to the technology and are often difficult to resolve during the early research and development stages. More importantly, the uncertainty analysis can be complemented with a global sensitivity analysis, which will assist in determining which are the most relevant uncertain parameters in the model, and whether the activation of a certain unit process in the future product system is of importance for the environmental profile of the technology.
4:20pm - 4:40pm
A harmonized life cycle approach to improve the environmental performance and life cycle assessment guidelines for carbon capture and utilization (CCU) based methanol production
University of Michigan
Methanol is a vital feedstock for the chemical and the energy industry and over 90% of the global annual production of 90 million tons is synthesized from natural gas. To decrease the reliance on natural gas as a raw material and meet climate goals, carbon capture and utilization (CCU) is increasingly favored as an environmentally sustainable pathway to synthesize methanol. In the CCU pathway, methanol is synthesized through the hydrogenation of carbon dioxide (CO2) captured from point sources (e.g. power plants). The use of renewable energy sources is proposed to reduce the environmental burdens of electrolyzing water and generate hydrogen (H2), which is necessary for the hydrogenation of CO2.
Despite the suggested improvements and claims of sustainability, there is a lack of a critical and systematic assessment of the environmental performance and trade-offs between methanol production through the conventional and CCU based pathways. Life cycle assessment (LCA) studies and a recent effort to develop LCA guidelines for CCU systems fail to harmonize data across the 4 key processes - the capture of CO2, the production of hydrogen through electrolysis, compression and transport of CO2 and H2 and the synthesis of methanol. As a result, the quantified environmental impacts may not be accurate or representative of real world conditions. For example, this research a preliminary literature review demonstrates that the energy required for compressing CO2 may be included in both the carbon capture and methanol synthesis processes, thereby overestimating the energy burdens of methanol production through CCU. Furthermore, as the production of methanol from CCU is an emerging technology, there is a significant scarcity and uncertainty in the material and energy inventory data required to conduct an LCA. The current practice of using of point values for inventory data masks the overall uncertainty in the quantified environmental impact. As a result, stakeholders cannot explore the impact of data uncertainty on the trade-offs between the alternate methanol production pathways, identify environmental hotspots and direct R&D efforts to improve the environmental performance of CCU based methanol.
To address the aforementioned limitations, this research is the first to comprehensively harmonize life cycle inventory data and model the environmental impacts of the four key processes in the CCU pathway for methanol production. We have reviewed a total of 180 studies and shortlisted 50 key parameters that impact the environmental performance and trade-offs between conventional and CCU pathways for methanol production. Through a combination of principles in thermodynamics and exploring the values reported in industry and scientific literature, this work determines the range of uncertainty in the 50 key parameters, which represents the basic uncertainty in the inventory data. In addition, we apply the pedigree matrix approach to further account for uncertainty in the geographical, technological and temporal correlation, and the completeness and quality of the inventory data. Based on a Monte-Carlo approach, we will stochastically explore the trade-offs between the conventional and CCU-based methanol pathways across 18 environmental impacts in the ReCiPe impact assessment method. Through a moment independent sensitivity analysis of uncertainty in the 50 parameters, we determine R&D priorities to address the hotspots in CCU based methanol production. Subsequently, based on the harmonized data, the improved model to quantify the environmental impact, and uncertainty assessment for the early stages of technology development, this research will propose improvements for emerging guidelines for LCA of CCU systems.
4:40pm - 5:00pm
Evolving Prospective Analysis of Emerging Technology
1Lawrence Berkeley National Laboratory, United States of America; 22. Advanced Manufacturing Office (U.S. Department of Energy), United States of America
To tackle societal grand challenges, technology developers and engineers must simultaneously maximize economic benefits while minimizing environmental risks and impacts associated with processes, products or services. Existing LCA guidelines are well suited to evaluate products or processes that are already commercially established. Being able to use LCA within the laboratory stage (TRL2-5) could provide guidance for technology developers to greatly minimize environmental impacts. However, new data challenges are introduced because the products or process are not commercially established, progression from early-stage to commercialization can take years to decades, and potentially creating new markets.
Current guidelines emerged from decades of evaluating products or process, identifying LCA challenges, defining concepts, and refining methodologies; and continue to evolve as new challenges are identified. Similarly, new guidelines could emerge from the increasing the use of LCA within the laboratory stage as the practical difficulties inherent to data gap challenges are addressed with definitions and appropriate methods derived through consensus from an international network of practitioners working in the field. Achieving this goal will improve technology developers’ abilities to minimize environmental impacts by providing credible guidance for LCA practitioners.
This session will provide an overview of U.S. DOE, EERE advanced Manufacturing Offices’ LIGHTEnUP perspective assessments tool and examples of prospective technology analysis. We will use EIA’s NEMS model and Annual Energy Outlook projections as an example to distinguish tools and underlying data set, and their dependent and independent terms and methods.
The Session will facilitate a focused discussion on the types of data and data characteristics that are useful to improve confidence in prospective analysis.
Attendees will be connected to the session “Building a community for LCA of emerging technologies” in order to attract more people within the community to build an international network of practitioners working in the field and attract funding in order to engage in further workshops and dialog.
5:00pm - 5:20pm
Comparison of Computer Workstation Business Models: Device as a Service (DaaS) vs. Traditional Ownership
1HP Inc., United States of America; 2Aspire Sustainability, United States of America
HP has been exploring forward-thinking business models for several of its product lines including printers and computer workstations. An LCA study was recently performed to support decision-making and exploration of the potential environmental benefits of the shift from transactional “buy – use – replace” business model to a contractual “Device as a Service (DaaS)” business model for HPs commercial desktop and laptop computers. In the DaaS model, a first tier “high-end user” customer is provided with a new desktop or laptop for an initial use period, after which the desktop or laptop is refurbished and provided to a second tier customer that may not need the latest technology. After a suitable use period, the desktop or laptop may be refurbished again and provided to a third-tier customer. This approach maximizes the useful life of the materials in the desktop or laptop. With a focus on materials sustainability, the DaaS business model strives to maximize resource efficiency and materials reuse through product life time extension and refurbishment.
The LCA study was performed to understand the environmental benefit of the DaaS approach and to determine sensitivity to various parameters such as length of each use cycle; number of use cycles; failure rates; location of manufacturing, use, and refurbishment activities; refurbishment energy; annual energy use; and component replacement.
Preliminary results indicate that the DaaS business model appears to offer clear environmental benefits compared to a traditional transactional business model for both desktop and laptop computers. Key insights include:
•The materials and use stage impacts are the largest contributors to life cycle impact in all impact categories.
•One of the key differentiators between DaaS and Transactional business models is that the DaaS approach consumes far fewer materials over the life cycle of the device, thus contributing to a more circular economy.
•Assembly, refurbishment, transport, and recycling are relatively small contributors to life cycle impact.
•Of the materials and components in the desktop, the boards are the largest contributors to impact.
•Of the materials and components in the laptop, the boards and display are the largest contributors to impact.
•The comparative environmental benefit of the DaaS business model compared to a transactional business model is sensitive to assumptions around the length (duration) of each use cycle, number of device use cycles, and whether or not boards are replaced during refurbishment.
•Several other factors such as the environmental impacts associated with refurbishment and overall device energy use, do not significantly affect the relative environmental benefit of the DaaS business model compared to the transactional business model.
•The insights and trends from the screening LCA study were similar for both desktop and laptop PCs.
The presentation will include background on HP’s efforts to evolve their business models to support a more circular economy. HP continues to leverage life cycle thinking and life cycle assessment to explore and evolve the DaaS business model to ensure optimal benefit and environmental performance for its customer and society.