8:00am - 8:20am
Developing Publicly Available LCA Guidance, Data, and Tools for Environmental Understanding of Emerging CO2 Utilization Research
1U.S. DOE, NETL, United States of America; 2Contractor to U.S. DOE, NETL, United States of America
Capturing carbon dioxide (CO2) emissions from power and industrial sources and using that CO2 to make useful products is an emerging area of research that will benefit from a consistent and unbiassed framework like life cycle assessment/analysis (LCA) to understand the environmental impacts and net life cycle GHG reductions compared to the current state of the alternative in the marketplace. From a methodological perspective, CO2 utilization systems are complex due to the intrinsic links established between the power sector and the utilization sector (e.g., biofuels, cement, chemicals, etc.).
Technology developers and LCA analysts could benefit from guidance that establishes best practices for CO2 utilization LCA. For example, it is not uncommon to see CO2 utilization LCAs that focus mainly on the utilization technology and apply a simplified approach to the upstream CO2 source. We would argue that a robust treatment of the upstream CO2 source is imperative in any CO2 utilization LCA, because of the important link between the source of the CO2 and the use of the CO2 in the overall environmental impact.
Conducting LCA guidance work early is important in the development of these emerging technologies, because it allows time to implement change while technologies are still nascent. Additionally, U.S. Department of Energy (DOE) and the federal government is increasingly requiring LCA as part of funding for primary research and tax incentives like “45Q” for CO2 capture, utilization, and storage projects (H.R. 1892, 2018).
In the interest of supporting the creation of useful LCAs of CO2 utilization projects, the DOE is developing guidance, data, and tools for CO2 utilization LCA. Working with actual CO2 utilization projects funded under Federal Opportunity Announcements (FOA), the DOE is providing specific guidance on methodological issues and choosing a comparison system. The DOE is also providing upstream and downstream data that is relevant to CO2 utilization projects. The guidance, data, and tools will be publicly available and free.
8:20am - 8:40am
Insights from the Database Integration Workshop: Building the Data Capacity for Food-Energy-Water Research
1Department of Forest Biomaterials, North Carolina State University, United States of America; 2ExLattice, Inc. Raleigh, NC, United States of America; 3Department of Forestry and Environmental Resources, North Carolina State University, United States of America
Advancing the knowledge of Food, Energy, and Water (FEW) system interactions and identifying critical challenges that could be addressed by simultaneous management of three systems require massive datasets. Government agencies, research communities, and industries have made intensive efforts to collect and generate datasets to meet the data needs of diverse stakeholders. However, those data sources are usually scattered with high heterogeneity, making them difficult to be used for data synthesis and integration in research and decision making in interdisciplinary areas such as FEW. It is critical to provide easy-accessing, knowledge-sharing data management platforms or frameworks to support system-level analysis, decision-making, and stakeholder collaborations for better understandings and improvements of FEW systems.
Funded by the U.S. Department of Agriculture (USDA), a FEW workshop focusing on database integration and capacity building was hosted at North Carolina State University on Sept.11, 2018. The workshop gathered participants from U.S. government agencies (i.e., USDA, Environmental Protection Agency, U.S. Department of Energy, and U.S. Forest Service), International Energy Agency, five U.S. national labs, university and research institutes. The workshop was organized around three key questions:
• What are the frontiers of data from both public and private sources related to FEW systems?
• How can we leverage and integrate existing databases for new insights?
• Who should be involved and how can we encourage data generating, sharing, and engagement from a broad range of stakeholders in government, academia, and industry?
The presentation will discuss the insights learned from the workshop. To better understand the current data capacity, workshop participants generated a comprehensive list of existing databases and data resources related to FEW systems. Although diverse data resources are available, there are large data gaps and challenges in supporting current and future interdisciplinary FEW research, such as overlapped databases with inconsistency, the lack of high-resolution data, low data discoverability, accessibility and usability, and various data needs for inter-, multi-, and trans- disciplinary researchers. The presentation will discuss the vision of future database integration and data sharing proposed in the workshop. Challenges and barriers for integrating, sharing, and synthesizing diverse databases were identified and ranked by the workshop participants. We will present the results and discuss the action plans with short-term and long-term goals in order to address the top challenges, especially those related to infrastructure, mechanism, and policy to promote data sharing across stakeholders such as government, academia, the private sector, and the public.
8:40am - 9:00am
Identifying data gaps in the energy supply chains of manufacturing sectors with an input-output LCA model
1Carnegie Mellon University, United States of America; 2The National Renewable Energy Laboratory, United States of America
U.S. manufacturing sectors’ fuel intensity decreased by more than 4% from 2010 to 2014 . The decrease was possibly due to energy input switches and the incorporation of new technologies. Understanding the manufacturing energy consumption associated with these changes is important to further improve the efficiencies and sustainability in manufacturing industries. To better interpret the influences of these changes on manufacturing energy consumption, National Renewable Energy Levorotary (NREL) recently developed the Materials Flows through Industry (MFI) tool, which analyzes energy consumption across the supply chains of U.S. manufacturing industries under different energy and technology scenarios. Due to the limitation of the coverage of data sources, the MFI tool may have incomplete energy consumption data in some industries’ supply chains. These data gaps affect the accuracy of the results provided by the tool. To overcome this issue, this study, which is collaborative between Carnegie Mellon University and NREL, aims to identify data gaps in the MFI tool with the information in input-output life cycle assessment (IO-LCA) models. First, an IO-LCA was created to estimate the total and sectoral energy consumption in each U.S. manufacturing industry’s entire supply chain. The IO-LCA model was the 2007 economic input-output LCA model, generated by data provided by the U.S. Department of Commerce Bureau of Economic Analysis and U.S. Environmental Protection Agency (USEEIO). Then, for each industry, the estimations from the IO-LCA model were compared with the inventory data in the MFI tool. As the functional unit in the IO-LCA model was in U.S. dollar, different than the units used in the MFI tool, the comparison was based on the ratios of the process energy to the supply chain energy consumption for each industry. Based on the comparison, potential data gaps in the MFI tool were identified. Potential data gaps were identified in many processes such as gravel, sand, and iron ore. The results also indicated that based on the significance of their data gaps, priorities should be given to certain processes when new information is available. Based on the priority level, five scenarios were given to provide guidance for data updates. Scenario 1 processes in the MFI tool should be given priority in terms of data updates and scenario 5 processes were industries that did not have data gaps in the MFI tool. Examples of scenario 1 processes include gravel and sand processes, which should be given priority when updating their inventory data. Most of the plastic products were categorized as scenario 4 processes, which should not be prioritize comparing with other processes in the MFI tool. Processes that fell in scenario 5 were fuel processes, such as crude oil and diesel. The results of this study can help LCA practitioners to optimize activities to improve LCA models and assist data providers to prioritize efforts in completing inventory data. The methodology provided in this study was an example of how to use top-down LCA models (IO-LCA models) to assist data updates and data collection in top-down (such as MFI tool) LCA models.
9:00am - 9:20am
Sensitivity to weighting in Life Cycle Impact Assessment (LCIA)
1Earthshift Global, LLC, United States of America; 2Institute of Computing Science, Poznań University of Technology; 3Institute of Environmental Sciences (CML),; 4School for Environment and Sustainability, University of Michigan; 5Department of Econometrics and Operations Research, Vrije Universiteit Amsterdam; 6Sustainable Engineering and the Built Environment, Arizona State University
Weighting in LCA incorporates stakeholder preferences in the decision-making process of comparative LCAs and this study evaluates the relationship between normalization and weights and their effect on single scores. We evaluate the sensitivity of aggregation methods to weights in different LCIA methods to provide insight on the receptiveness of single score results to value systems.
Sensitivity to weights in two LCIA methods is assessed by exploring weight spaces stochastically and evaluating the rank of alternatives via the Rank Acceptability Index (RAI). We assess two aggregation methods: a weighted sum based on externally normalized scores and a method of internal normalization based on outranking across two midpoint impact assessment.
The study finds that the Influence of weights in single scores depend on the scaling/normalization step more than the value of the weight itself. In each LCIA, aggregated results from a weighted sum with external normalization references show a higher weight insensitivity in RAI than outranking-based aggregation because in the former, results are driven by a few dominant impact categories due to the normalization procedure.
Contrary to the belief that the choice of weights is decisive in aggregation of LCIA results, in this case study it is shown that the normalization step has the greatest influence in the results. Practitioners aiming to include stakeholder values in single scores for LCIA should be aware of how the weights are treated in the aggregation method as to ensure proper representation of values.