Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
|Date: Wednesday, 26/Jun/2019|
|7:30am - 8:30am||Breakfast|
|8:00am - 9:30am||E&S-3: Power plants and electricity production|
Session Chair: Nicole Alyssa Ryan
8:00am - 8:20am
Life Cycle Analysis Platform to Understand the Energy System Transformation
1MIT Energy Initiative, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge MA 02139, US; 2ExxonMobil Research and Engineering Company, Annandale, NJ 08801, United States
The global energy system is undergoing a major transformation. The world faces a dual challenge of meeting increasing energy demand while reducing greenhouse gas emissions. This change is characterized by the convergence of power, transportation, and industrial sectors, and the surge of multi-sectoral integration. Understanding the implications of these dynamics is challenging and requires a holistic approach to provide systems-level insights. To address this need, we have developed a systems-level life cycle analysis (LCA) framework that is designed to explore the emissions impacts of relevant technological, operational, temporal, and geospatial characteristics of the evolving energy system.
The tool is built as a MATLAB app that encapsulates MATLAB models, databases, and integrated process simulations. A modular framework constitutes the underlying analytical engine that covers all life stages of major energy conversion pathways. The underlying analytical engine includes the cradle-to-grave life stages of major energy conversion routes and covers more than 900 individual pathways. Detailed process simulation capabilities have been incorporated for in-depth analysis of greenhouse gas emission sources such as power plants and selected chemical conversion pathways. In addition to performing conventional LCA, we have implemented models for vehicle fleet and electric power systems to analyze systems-level interactions. By executing the analysis with embedded fleet models, we can establish a basis for the accurate assessment of the life cycle implications arising from complex system-level restructuring. The presentation will focus on the overview of the tool, the modeling approach, as well as the results of case studies. We will demonstrate how the changes in the operational variability of fossil fired power plants impacts system-wide emissions.
8:20am - 8:40am
Developing a Performance Response Surface For Fossil Fuel-fired Power Plants Under A Changing Climate
Carnegie Mellon University, United States of America
Thermoelectric power supply depends on the thermodynamics governing power plant operations. Climate change introduces an uncertain risk to power plant operations as ambient conditions potentially constrain generation, primarily via cooling system limitations. Previous studies aiming to quantify this risk have suggested a wide range of results, from minimal to disastrous capacity loss. In this analysis, we use power plant modeling software to study how a variety of power plant configurations respond to varying meteorological conditions. We develop predictive tools that enable projections of power plant operating impacts for a spectrum of geographic situations and technological configurations. Finally, we apply these equations towards power fleets to examine how climate scenarios may affect power capacity. Our results allow for simpler forecasting of capacity loss given ambient conditions, for both individual plants and fleet-wide analysis, and our results also highlight potential regions of risk.
8:40am - 9:00am
Using Economic Input-Output LCA for Construction Impacts of Thermoelectric Power Plants
1Contractor to U.S. DOE, NETL, United States of America; 2Eastern Research Group; 3U.S. DOE, NETL, United States of America
The Department of Energy’s National Energy Technology Laboratory (NETL) is expanding the level of resolution of power plant construction materials to enable an in-depth understanding of construction differences between thermoelectric power plant configurations. Economic Input Output (EIO) life cycle analysis (LCA) is used to rapidly estimate the environmental impacts of construction in the electric power sector. EIO serves as a quick and reliable way to screen for construction impacts and perform hot-spot analyses. This modeling effort is only possible because of the availability of high quality, detailed NETL cost data, as well as the EPA’s up-to-date U.S. Environmentally Extended Input-Output (USEEIO) model.
This presentation will outline how this modeling can be used for hot-spot analysis, pointing future research efforts to specific high-impact portions of construction processes. The hot-spots identified in this effort can undergo additional screening through more rigorous efforts such as processed based LCAs or the use of proprietary or third-party data. Using coal and natural-gas fired power plants as a case study, this presentation can begin to answer an important research question: with EIO models, how does one identify the need for additional resources to achieve accurate environmental accounting?
Using detailed component-level cost estimates for new fossil power plants as documented in Cost and Performance Baseline for Fossil Energy Plants Volume 1a: Bituminous Coal (PC) and Natural Gas to Electricity Revision 3, we estimate economic demands for the construction of a power plant and the manufacturing of its components. This provides a basis for final demands to use in the model. The model produces national average construction impact results for the life cycle of a power plant. This output highlights areas of further research that help to define valuable data collection efforts.
This effort will outline the difference in capital costs and operational costs and how these were accounted for in this work. Significant effort is expended to generate component-level cost estimates for techno-economic assessments (TEA) of power systems. However, these cost estimates are often developed using vendor quotes, which include profits, as opposed to producer prices, which form the basis of most EIO models. This presentation will describe an approach for using BEA Consumer Price Index data to ensure the proper accounting of producer price data. Outlining this difference in data and modeling is important for validating this work and ensuring that future work handles capital costs appropriately. While LCAs often use the inputs and outputs from a TEA, this approach shows the value that can be gained by also using the detailed cost estimate in the context of EEIO.
National Energy Technology Laboratory. (2015). Cost and Performance Baseline for Fossil Energy Plants Volume 1a: Bituminous Coal (PC) and Natural Gas to Electricity Revision 3.Morgantown, WV: Department of Energy.
9:00am - 9:20am
Assessment of tradeoffs in human-generated power using electricity-generating exercise bikes
1IEEE, United States of America; 2Arizona State University, United States of America
During exercise to maintain fitness or lose weight, people expend energy which is generally lost. However, modified exercise equipment such as an electricity-generating stationary bike (EGSB) can capture that energy. In “Green Gym” settings, these bikes can offset the power needed for lighting and cooling the facility, providing quantifiable greenhouse gas emissions savings. In non-electrified areas of the world, EGSBs may provide power for lighting, providing additional time people may use for activities including studying and bookkeeping after dark. Despite these potential benefits, the manufacturing of the bikes and the inverters required to utilize the power generated by the bikes creates greenhouse gas and other emissions. Therefore, the question arises of whether use phase benefits outweigh the environmental impacts of manufacturing and maintaining the bikes. We propose to use Life Cycle Assessment (LCA) and Sustainable Return on Investment (S-ROI) techniques to help answer this question.
LCA requires definition of the functional units used for comparison so that environmental impacts are considered between options that are justifiably similar in functionality. We consider two distinct pairs of scenarios in this analysis that necessitate the use of two functional units, as the purpose of the activity differs substantially between use for exercise and use for providing electricity in a non-electrified area. In the case of S-ROI, we consider scenarios to determine if there will be net positive societal impacts to offset the initial cost and externalities of the EGSB’s through increased availability of lighting and potential for higher salary compared to a baseline case without use of an EGSB.
In the context of a Green Gym, we define the functional unit to be the activity of using a stationary bike for exercise for one hour. We assume that the rider would maintain the same behaviors regardless of whether or not the bike generates electricity during use. In the context of the non-electrified area, we will consider the business as usual case with no bike and the alternative with the power generating bike. In this case, we examine two scenarios for each, including food security and food scarcity. It is assumed that while food production is not changed as a result, the biker will either have sufficient food so that the exercise will improve health or that they will have insufficient food and the exercise will have negative consequences for their health. Finally, we will consider the tradeoffs in health impacts that may result from opportunities by utilizing after dark lighting to advance education and employment opportunities and thereby increase income.
This study will provide conclusions regarding the potential benefit of the installation of electricity-generating bikes in Green Gyms, including the payback period necessary before the modifications to the bikes can be considered greenhouse gas emissions neutral. It will also provide conclusions regarding whether electricity generating bikes are expected to be a net benefit to rural non-electrified areas.
|8:00am - 9:30am||OC-1: Gaps and data integration in open communications|
Session Chair: Brandon Kuczenski
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.
|8:00am - 9:30am||FEW-3: Diet and Food Waste|
Session Chair: Hanna Marie Breunig
Session Chair: Ian Vázquez-Rowe
8:00am - 8:20am
Food Choices and the Food-Energy-Water Nexus: Evaluating Energy Demand, Water Scarcity and Carbon Footprints of Self-Selected Diets in the U.S.
1Center for Sustainable Systems, University of Michigan, United States of America; 2Department of Global Community Health, Tulane University, United States of America
Food choice has been implicated as an important driver of the environmental impacts of food systems. Assessment of diet-level environmental impact across multiple impact categories can inform not only consumption-oriented abatement strategies, such as dietary modifications, but also identify aspects of food production systems that warrant further attention.
In previous work, we developed dataFIELD (database of Food Impacts on the Environment for Linking to Diets) based on an exhaustive review of the food life cycle assessment literature. DataFIELD contains greenhouse gas emissions (GHGE) and cumulative energy demand (CED) associated with production of 332 food commodities, and was linked to dietary recall data from the 2005-2010 National Health and Nutrition Examination Survey (NHANES), a representative survey of U.S. dietary intake. In this work, we add a spatially explicit assessment of the blue water use (surface and ground water used for irrigation) associated with food production, including a characterization of water scarcity, and link this to self-selected diets from NHANES.
Blue water consumption per ton of crop at the watershed level across the U.S. was obtained from a dataset compiled by Pfister and Bayer (DOI: 10.17632/brn4xm47jk.2). To characterize water scarcity, we used the Available Water Remaining (AWARE) method, with characterization factors also at the watershed level. Water consumption and water scarcity (consumption x characterization) per crop were aggregated to the national level using production-based weights, also available in the Pfister and Bayer dataset. Since not all food consumed in the U.S. is domestically sourced, water use and water scarcity footprints (WSF) per crop were adjusted for imports using FAO detailed trade matrix data and country-of-origin national average water consumption and AWARE factors. Water use for animal based foods were based on simplified feed rations from Peters et al. (DOI: 10.12952/journal.elementa.000116). Only water use associated with fee requirements of farmed fish and seafood were included.
At the population average, meats, dairy, beverages and fish & seafood were the top contributing food groups in the U.S. diet for both GHGE and CED. For WSF, meat was also the highest contributor, followed by fruits, beverages, dairy and vegetables. On average, diets higher in GHGE were also higher in CED and WSF. Such correlation of impacts across individual diets was the strongest between GHGE and CED (r=0.68, p < 0.01) and next strongest between GHGE and WSF (r=0.45, p < 0.01). Rankings based on GHGE demonstrated that the top 20% of U.S. diets are responsible for 46% of emissions. Impacts for the top quintile of diets when ranked on energy demand or water scarcity footprint were 43% or 42%, respectively. 7% of diets evaluated were in the 5th quintile (highest impact) of all three metrics, whereas 9% were consistently in the 1st quintile.
Consideration of the food-energy-water nexus from a diet perspective offers valuable insight into ways that consumption behaviors influence environmental impacts of production systems. Understanding such relationships between food choices and the multiple dimensions of environmental impact allows for targeting policy work to address dietary hot-spots.
8:20am - 8:40am
Optimizing the environmental performance of food diets in Peru using Linear Programming and Life Cycle Assessment
Pontificia Universidad Católica del Perú, Peru
Food production and security has been highlighted as one of the most threatened sectors by the consequences of climate change. However, it is also true that food production itself is responsible for an important fraction of greenhouse gas (GHG) emissions. Hence, GHG emissions derived from food production and dietary patterns have been analyzed in many areas of the world from a life-cycle perspective, mainly in North America and Europe, but remain unexplored in many developing nations. A recent study by Vázquez-Rowe et al. (2017) applied a life cycle approach to identify the GHG emissions linked to dietary patterns in Peru. Results show that an average Peruvian generates 1.08 t CO2eq per year due to food expenditure, a value that is notably lower than those reported in other nations. However, a series of worrying tendencies are visible behind these numbers. For instance, in cities in the Amazon basin, consumption of fruit and vegetables is up to 85% lower than recommended by national authorities. Several studies have delved into ways in which diets in developing nations should be improved in terms of nutrition and health. These studies are then used for policy support to generate impact in communities were food education and access to healthy food products may be limited. Unfortunately, however, fewer are the studies that combine nutritional and environmental benefits of diets. Therefore, the main objective was to propose a methodology in which LCA-related results linked to dietary patterns in Peru were combined with nutritional and economic data to optimize diets. For this, a linear programming model was built in which the environmental, nutritional and economic information on a set of 25 dietary patterns in Peru were optimized in order to achieve the environmentally best-performing diet that would not imply an increase in the household’s food basket and would improve the nutritional balance of the diet. One of the scenarios represented the Peruvian average, whereas the remaining scenarios represented the average diet in selected cities across the country. The result of the proposed linear program allowed understanding the amount of each individual food product that should be consumed in each city that satisfy all the restrictions included in the model in order to attain the lowest GHG emissions possible. Results demonstrated that GHG reductions can be attained through optimization. In fact, if results obtained by Vázquez-Rowe et al. (2017) presented a range between 792 and 1,350 kg CO2eq per person and year, the optimization model applied would allow a reduction to 582-961 kg CO2eq. For instance, in the city of Lima the reduction would be of 200 kg CO2eq per year (22% less). However, it should be noted that this 22% decrease in environmental impacts would be counterbalanced with a 12% increase in the economic cost of the food basket. Considering that in most areas of the country food purchase accounts for approximately 50% of household expenditure, it is plausible to assume that food choice is a main carrier to achieve GHG emission mitigations. In this context, the method constitutes a useful tool for policy-makers to push forward joint regulations to improve health-related issues linked to the food diet and food choice together with recommendations to lower the climatic impact of diets.
8:40am - 9:00am
Carbon sequestration, greenhouse gas mitigation, and hydrologic benefit potential of land applying bioenergy byproducts derived from food waste
Berkeley Laboratory, United States of America
Large scale improvements to soil carbon (C) in grasslands could play an important role in lowering greenhouse gas emissions and improving vital soil properties such as water holding capacity and net primary productivity. Practices which enhance soil C may also impact the bioenergy industry’s future, as the gasification and anaerobic digestion of lingocellulosic materials, biomass residues such as food waste, manure, and wastewater produce biochar and digestate which require large-scale, low-carbon disposal pathways. Here, we present a novel approach for conducting an attributional life-cycle assessment of biochar and raw or composted digestate disposal on grasslands, and demonstrate our methodology to evaluate the implications of large-scale bioenergy infrastructure and byproduct generation in California. Developed in R software, our code can be used to approximate life-cycle emissions and C accumulation from raw or composted digestate, or biochar disposal on marginal land for any region provided the user has details on biomass production and transportation distances. We find that while soil C accumulation and net negative life-cycle greenhouse gas emissions are possible for raw or composted digestate, and biochar application on critical rangelands in California, varying key parameters for byproduct quality and soil response can lead to net positive life-cycle greenhouse gas emissions. Furthermore, although we can estimate significant carbon sequestration and hydrological benefits by using an active land management strategy for 6% of California’s rangeland, such an estimate would require C application rates that exhaust the digestate and biochar that could potentially be generated from the State's biomass residues such as food waste. However, by using technically available biochar and digestate for land disposal generated each year with a more moderate C application rate, we could treat 12% of the most critical rangelands in California over a period of 9 years, generating a cumulative benefit of negative 120 MMTCO2eq. Improving our understanding of the sustainable use and disposal of byproducts such as digestate, compost, and biochar generated during the conversion of food waste to energy is critical. This is especially true in areas such as California, where emerging actors in the bioeconomy rely on accurate estimation of carbon offsets to qualify for low-carbon fuel and renewable fuel credits.
9:00am - 9:20am
Spatial optimization of food waste recycling infrastructure in California
1Department of Food Science and Technology, University of California, Davis, United States of America; 2Geography Graduate Group, University of California, Davis, United States of America
California Senate Bill (SB) 1383 calls for diverting 75% organic waste from landfills by 2030 as part of a larger mandate to reduce greenhouse gas (GHG) emissions. Current alternative treatment facilities do not have enough capacity to treat this food waste (FW), so an expansion of treatment infrastructure will be required. Most often, waste treatment facilities are located away from urban areas and require increased trucking mileage to transport FW, which translates into increased transportation GHG emissions and energy use. This linkage is exacerbated by ongoing and rapid urbanization. To develop solutions for the mitigation of GHG emissions from FW, the amount of FW currently generated, its current disposal pathways, and its spatial distribution need to be assessed. In this study, we attempt to understand how the spatial distribution of FW generated throughout California affects its ability to be treated and reduce GHG emissions and energy use compared to current disposal practices.
Using the spatial distribution of FW generated, this study will develop a strategy for developing new treatment infrastructures. The strategy will include an assessment of the optimal location and scale of alternative treatment methods, with a focus on anaerobic digestion (AD). AD technology converts organic waste into biogas, which can be transformed into useable energy forms, and nutrient-rich digestate and is a well-established alternative to landfilling. California has existing AD facilities, but more will be needed to handle the substantial increase in FW diversion mandated by SB 1383.
Small-scale, containerized digesters are a new technology option that can be rapidly deployed to treat the expected diverted waste from more localized FW catchments, thereby reducing transportation energy costs and associated GHG emissions. These systems can produce energy on-site to offset energy consumption and reduce transmission losses to the grid. In addition to siting and establishing new treatment facilities of various scales to treat FW streams, markets for food waste-derived digestate (FWDD) need to be established to absorb this nutrient-rich outflow from the AD process. FWDD has higher moisture and organic matter content and when land applied, it can reduce water inputs and increase the water holding capacity of soils. Understanding this spatial distribution of FW generation and potential FWDD markets will inform strategies for designing an effective infrastructure network for FW treatment in California.
Objectives of this study are to determine the quantity and spatial variation in FW generation and use this data to model potential regional waste management infrastructure that minimizes GHG emissions. Waste characterization studies produced by the California Department of Resources Recycling and Recovery (CalRecycle) will provide the baseline FW generation data for both California counties and cities, differentiated by the commercial business groups and the residential sector. We will map the spatial distribution of these data and develop strategies to identify hot-spot locations, generators with the largest contribution, and the homogeneity of FW generation rates across regions and generators. This analysis will inform the identification of the optimal scale and locations for an expanded network of AD treatment facilities in California.
|8:00am - 9:30am||4Space-3: Race4Good Work Session|
|9:30am - 10:30am||Plenary: Chefs, Farmers and the Sustainability Journey|
Session Chair: Thomas Seager
An interactive dialog with a diverse group of panelists on the sustainability of food. Featuring: Seth Tibbot, Founder of the Tofurky company, Katie Cantrell of the Factory Farming Awareness Coalition, Chrissie Zaerpoor of Kookoolan Farms, and Steven Ward, Executive Chef of the DoubleTree Portland Hotel
|10:30am - 12:00pm||E&S-4: PV & Mfg. Industry|
Session Chair: Geoffrey Lewis
10:30am - 10:50am
On the nature of innovations affecting photovoltaic system costs
1Massachusetts Institute of Technology, United States of America; 2National Renewable Energy Laboratory, United States of America
Energy technologies such as photovoltaics (PV) modules and wind turbines have been improving and their costs have been declining rapidly in the last five decades. Literature suggests that innovation activity has been one of the most important drivers of this rapid cost reduction. Previous studies have focused on identifying the effects of innovations on a technology’s unit cost at an aggregate level as represented by R&D investments and patenting. However, they do not offer a framework for connecting specific innovations, such as diamond wire sawing for cutting ingots into wafers, to technology costs. Identifying the links between innovations and cost is critical to characterize past sources of improvement and to inform future innovation efforts. In this work, we ask: Through which channels have specific innovations influenced PV systems costs in the past, and what type of innovations were most influential?
We conduct the first study to characterize in-depth the innovations and other factors affecting PV technology costs. To do this, we develop a framework that maps out the relationships between innovations and technology cost determinants to uncover how specific innovations influence costs. We apply this framework to identify the innovations that affected the cost of photovoltaic (PV) systems in the last five decades. We first build a conceptual model for distinguishing innovations from other factors that affect cost. We then compile a large set of innovations based on a literature review and feedback from experts. We connect innovations and other drivers to the variables determining PV system costs, in order to identify the channels through which PV system costs changed. Finally we develop a typology of innovations to investigate which innovation types have been more prevalent. The typology classifies each innovation according to how it changed the variables in the cost model, including improvement processes such as material quality changes, process development, and automation.
We investigate innovations at both module and balance-of systems (BOS) levels and highlight the differences in the nature of innovations across modules and BOS. Unlike modules, which are mass-produced and standardized goods, BOS components (e.g. mounting systems) are often customized to a specific site.
Our results show that there is a diversity of innovations at both the module and BOS levels. We find that hardware innovations have been prevalent across PV modules and BOS since 1980. Module innovations mainly addressed materials challenges through developing tools and processes. BOS innovations, in contrast, focused on reducing part counts and complexity. Innovations aimed at improving soft variables such as task durations have been on the rise more recently, including online permitting tools, but these innovations haven’t been adopted widely enough to influence costs. Our results point to a need for continued efforts to improve soft variables as well as hard variables in the future. The method developed in this work to identify and connect innovations to cost determinants can be applied to other technologies.
10:50am - 11:10am
Design for Recycling Guidelines for Photovoltaic Modules
National Renewable Energy Laboratory, United States of America
The global growth of photovoltaic (PV) capacity has a parallel growth of PV waste at its end-of-life (EOL) that brings both challenges and opportunities. By 2050, there is an estimated stock of 78 million MT of raw materials that will become available as PV systems reach their EOL (IRENA reference). The challenge lies in that PV modules themselves are often stubbornly resistant to recycling efforts and there is concern that the projected increasing growth of PV installations could become functionally constrained by availability of raw materials, despite ongoing dematerialization efforts. Particularly for silicon PV (Si-PV), the most commonly deployed style of module, several factors make recycling challenging, not the least of which being the design of the module itself. In response, this study endeavors to identify potential design changes that could improve the recyclability of these PV modules at their EOL via a literature review of Design for Recycling (DfR) best practices as used in other industries. As such, this study endeavors to identify if DfR practices that could be adopted today in order to better mitigate tomorrow’s PV resource scarcity.
DfR involves technical considerations in choice of materials used in a product, as well as the capacity for liberation of components, subcomponents, and associated materials during a product’s respective recycling process (e.g., design for disassembly). DfR strategies are a function of both the product and materials in question, as well as the nature of available recycling processes. The challenge is to modify the design of the product to allow for recyclability without compromising the functionality, capacity, and commercial viability of the product. Importantly, DfR should not exist in a vacuum, requiring additional case by case considerations of market conditions, legislative backdrops, and geographic distribution of the EOL products; not every jurisdiction will be subject to the same rules, nor will they all have access to the same recycling technologies.
When considered in the specific context of Si-PV, some module design considerations are likely to better facilitate recycling than others. For example, the predominant use of ethylene vinyl acetate (EVA) laminate dictates that some form of delamination is required during recycling. Although multiple methods exist for achieving this, it is generally considered a challenging process regardless of whether it undergoes either a physical or pyrolysis treatment. From a DfR perspective, any module designs that managed to reduce, eliminate, or substitute the laminate with a more easily treated alternative would be considered favorable provided the resulting design exhibits a sufficiently acceptable operational performance. In another case, the DfR priorities differ considerably as a function of recycling processes. In processes where module polymers undergo pyrolysis, there is an advantage to avoiding fluorinated materials and associated offgassing, where it would not be as critical for a purely physical separation shredding process.
These findings represent only a portion of an ongoing evolving investigation. As such, the presentation will include a more comprehensive list of DfR guidelines and strategies from both an overall and PV-specific perspective.
11:10am - 11:30am
Using Big Data to Understand the Variability of Carbon and Energy Footprints of Pulp and Paper Products
North Carolina State University, United States of America
The pulp and paper industry produces a variety of products essential to everyday life. Many life cycle assessment (LCA) studies have been performed for different pulp and paper products to understand their environmental impacts. However, the life cycle inventory data in previous studies are either industry-average or from a few specific mills. The variations across different pulp and paper products, technologies, process configurations, and mills are usually not considered due to the lack of process data.
In this presentation, we will discuss two data-driven approaches that have been developed to address the knowledge gap in understanding the distributions of energy use and Greenhouse Gas (GHG) emissions across diverse pulp and paper products. The first approach is database integration. Facility-level GHG emission data collected from publically available databases were integrated with mill-level production data from private sources. A case study including 165 mills in the United States was conducted. The statistical variances of GHG emissions from five major types of pulp and paper productions were analyzed, including packaging, market pulp, printing and writing, tissue and towel, and specialty paper products. The results can be used as data references for LCA, footprint accounting, and paper-related analysis that commonly needs ranges and distributions of data for sensitivity analysis and Monte Carlo simulation.
The second approach is a bottom-up analysis using mill-by-mill process-based data. Mill data such as energy consumption, fuel sources, chemical usage, and wood sources were collected for most U.S. mills from private data sources. GHG emission factors were collected for fuel combustion (i.e., fossil fuel and bio-based fuels such as black liquor in mills) and upstream production of wood and chemicals. The total energy consumption and GHG emissions in Scope 1, 2, and 3, were estimated and analyzed for major pulp and paper products. Biogenic and anthropogenic carbon were separately tracked for each category of paper products and the differences across all products are shown. Comparative scenarios combined with contribution analysis between different products such as coated versus uncoated paper, recycled paperboard versus virgin paper products were developed to understand the differences of environmental footprints in products that may have similar functions but are produced from different processes/technologies.
Both case studies used large datasets to understand the variations of energy and GHG footprints of different pulp and paper industry in the United States. The differences between the two are the methods and data sources. The results of two case studies will be compared and discussed to highlight the insights provided by the analyses. Although this study focuses on the pulp and paper industry, the methods can be applied to many other manufacturing industries; especially those that have large variations in manufacturing processes and products.
1. Alec Nabinger, Kristen Tomberlin, Richard Venditti, Yuan Yao*, Using a Data-Driven Approach to Unveil Greenhouse Gas Emission Intensities of Different Pulp and Paper Products, 26th CIRP Life Cycle Engineering (LCE) Conference Paper, Procedia CIRP (accepted).
11:30am - 11:50am
Cutting CO2 emissions from U.S. steel consumption 70% by 2050
1Department of Mechanical Engineering, University of Michigan, USA; 2School for Environment and Sustainability, University of Michigan, USA
Climate change mitigation strategies must include the steel industry, as steel production alone contributes 25% of industry greenhouse gas emissions. The United States is the world’s second largest steel consumer, and with plateauing per capita stocks is a potential template for future global steel consumption; other researchers predict plateauing global per capita steel stocks by mid-to-late-century. Therefore, emissions reductions strategies for the U.S. will likely apply globally in the decades to come. This research determines feasible technological, trade, and societal pathways that lead to a 70% cut in CO2 emissions attributable to U.S. steel consumption by 2050 relative to 2010 levels. This reduction is within the range of the International Panel on Climate Change’s reduction requirements to ‘likely’ stay below 2 degrees Celsius. Pathways analyzed include strategies to reduce U.S. steel consumption and strategies to reduce emissions from liquid steel production, which are where the bulk of steel sector CO2 emissions are released. The technological variables include alternative low carbon primary and secondary production as well as improvements to manufacturing material efficiency. Trade and foreign steel production emissions intensities are important variables affecting consumption based emissions and are also included; the U.S. currently imports over 40% of its steel consumption and exports over 30% of its scrap. The societal variables included are population growth, per capita steel stocks, product lifespans, and end-of-life recycling rates.
We use a dynamic material flow analysis of the U.S. steel sector from 1900 to 2050 to understand how our societal changes affect the demand for steel and production of usable scrap. In combination with the technology and trade scenarios, these values, determine how the steel demand is supplied (i.e., whether the steel is primary or secondary and what technologies are used in production) enabling the calculation of total sector CO2 emissions. The results of this analysis provide CO2 reduction pathways to inform sector mitigation polices and illustrate the trade-offs between changing consumption patterns, technology changes and shifts in trade policy.
|10:30am - 12:00pm||OC-2: Thematic Keynote: Advancing Societal Sustainability with Open Data|
Session Chair: Brandon Kuczenski
10:30am - 11:00am
Advancing Sustainability Science: A Political-Industrial Ecology Perspective
1Penn State, United States of America; 2SUNY-ESF, United States of America
This paper evaluates how the emerging field of political-industrial ecology (PIE) can advance sustainability science, particularly in terms of methodological and theoretical innovations. Less than a decade old, PIE integrates theories and methods from political and industrial ecology to evaluate how biophysical and political systems are entwined in shaping nature-society relations and processes. Normative in its approach, PIE seeks to better embed societal metabolisms within their broader historic, ecological, and political economic context in pursuit of reducing the environmental impact of industrial ecosystems and resource flows. In the paper, we first outline the theoretical and historical origins of the field before synthesizing the findings of eight PIE case studies which have helped to catalyze the field. We conclude with a discussion on the future prospects of PIE, specifically the methodological and data challenges/opportunities of the field.
11:00am - 11:30am
Building Energy Data Transparency -- the travails of making data accessible
UCLA, United States of America
Building energy use is an important contributor to greenhouse gas emissions and is also strongly tied to thermal comfort. Understanding building energy use at a granular level provides many important insights that are unobtainable without that data. The UCLA Energy Atlas is built on address level billing data, aggregated for customer privacy on the public facing interactive website, but shows building energy use by neighborhood, city and Council of Government. Data is matched to attributes including vintage, square footage, industrial classification code, income, and more. It enables local governments and researchers to discover energy use patterns, target energy efficiency incentives, evaluate programs, and understand equity differences across regions and among residents and industries. Such data is indispensable for the energy transition. This talk explains the process of developing the Energy Atlas and some of the implications for sustainable systems and technology.
11:30am - 12:00pm
A General Data Model for Industrial Ecology and its Implementation in a data commons Prototype
University of Freiburg
Till this day, data in industrial ecology are commonly seen as existing within the domain of particular methods or models, such as input-output, life cycle assessment, urban metabolism, or material flow analysis data.
This artificial division of data into methods contradicts the common phenomena described by those data: the objects and processes in the industrial system, or socioeconomic metabolism. A consequence of this scattered organization of related data across methods is that IE researchers and consultants spend too much time searching for and reformatting data from diverse and incoherent sources, time that could be invested into quality control and analysis of model results instead. This talk outlines a solution to two major barriers to data exchange within industrial ecology: i) the lack of a generic structure for industrial ecology data and ii) the lack of a bespoke platform to exchange industrial ecology datasets.
We present a general data model for socioeconomic metabolism that can be used to structure all data that can be located in the industrial system, including process descriptions, product descriptions, stocks, flows, and coefficients of all kind. We describe a relational database built on the general data model and a user interface to it, both of which are open source and can be implemented by individual researchers, groups, institutions, or the entire community. In the latter case, one could speak of an industrial ecology data commons (IEDC), and we unveil an IEDC prototype containing a diverse set of datasets from the literature.
|10:30am - 12:00pm||ET-1: Emerging tech for electronics and power systems|
Session Chair: Daniel Posen
10:30am - 10:50am
The Role of Long Duration Energy Storage in Decarbonizing Power Systems
1Massachusetts Institute of Technology, United States of America; 2Harvard Kennedy School
Plans for a decarbonized power system call for a significant increase in generation from variable renewable energy (VRE) sources, i.e. wind and solar. Yet, the intermittency of these resources introduces new challenges in operating the grid, including the need for flexible generation to manage variations in VRE output and load, while minimizing emissions and cost impacts. Long duration energy storage (LDES) has been suggested as an enabling technology for realizing high VRE penetrations in future grids, because of its potential to flexibly time-shift VRE generation to match load. However, the current literature lacks assessment of the LDES technology requirements needed for decarbonizing the power system under various policy environments. This work seeks to fill that gap.
Using a high-temporal resolution electricity resource capacity expansion model, GenX, we evaluate the impact of the technology design parameters defining LDES technologies on their adoption in a future low-carbon power system dominated by VRE generation. The analysis is carried out for a region representative of a southern (e.g. Texas) power system in the United States through varied VRE and load profiles. The LDES technology design space is defined using power costs, energy costs, and charging and discharging efficiencies, based on the range of values reported in the literature for potential LDES technologies like power-to-gas to power, flow batteries, and thermal energy storage. By analyzing scenarios with differing climate policies, including region-wide carbon taxes, renewable energy requirements to target wind and solar generation, clean energy standards to target non-emitting technologies, and research and development efforts to target LDES technologies, I show the effect policy has on decarbonizing the electricity system and the technological characteristics of LDES that are more pertinent in each policy environment. My results focus on analyzing the effect of policies on the preferred design space for different LDES technologies and their deployment. Preliminary results indicate that there are identifiable regions in the LDES design space where LDES first beats out existing shorter duration lithium-ion batteries, and as costs drop and efficiencies increase, starts to also replace firm generation resources.
10:50am - 11:10am
Power System Planning at High Wind and Solar Penetrations Under Climate Change in Texas
1University of Michigan, Ann Arbor, MI, United States of America; 2Carnegie Mellon University, Pittsburgh, PA, United States of America; 3National Renewable Energy Laboratory, Golden, CO, United States of America; 4University of Colorado Boulder, Boulder, CO, United States of America; 5University of Washington, Seattle, WA, United States of America; 6encoord LLC, Denver, CO, United States of America
Climate change will likely affect various components of the electric power sector, including electricity demand, available thermal power plant capacity, and wind and solar generation. Using synchronous hourly data, we quantify the combined effect of these four impacts on four key planning metrics for the Texas power system: peak demand and net demand, wind and solar capacity values, and maximum hourly system ramps. We quantify climate change impacts for five climate change projections from 2041–2050 assuming Representative Concentration Pathway 8.5 relative to a reference period from 1996–2005. We find robust agreement across all five climate change projections that climate change will increase peak demand by up to 2 GW (4% of peak demand in reference period) and peak net demand by up to 3 GW (6% of peak net demand in reference period), suggesting increased investment need in generating or non-generating capacity regardless of wind and solar generation. We also find robust agreement across projections that climate change will reduce available thermal capacity during peak demand and peak net demand hours by up to 2 GW and increase maximum hourly ramps in net demand by up to 2 GW.
11:10am - 11:30am
Comparative Life Cycle Analysis for Value Recovery of Precious Metals and Rare Earth Elements from Electronic Waste
1School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA; 2Ecological Science and Engineering Interdisciplinary Graduate Program, Purdue University, West Lafayette, IN 47907, USA; 3Biological and Chemical Processing Department, Idaho National Laboratory, Idaho Falls, ID 83402, USA; 4Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA; 5Department of Systems & Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA; 6Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; 7Division of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA
There is an ever-increasing concern regarding electronic waste (e-waste), which is the fastest growing waste stream in the world along with economic growth. As e-waste contains highly toxic materials such as halogenated flame retardants and heavy metals, its proper management and disposition is paramount. Incentivized by various legislations and the intrinsic value of critical metals inside, recycling of e-waste is becoming an attractive business opportunity that also benefits the environment. A novel electrochemical recovery (ER) process has been developed as a promising alternative to the existing pyrometallurgical and hydrometallurgical processes based technologies to recover base metals, precious metals, and rare earth elements (REEs) from e-waste. Experimental results indicate that the ER process has lower chemical consumption, enhanced control, and reduced energy demand compared to the pyrometallurgical and the hydrometallurgical processes. To quantify and compare the environmental performances of the three technologies, this study conducted the life cycle analysis on each process. In this study, SimaPro 8.3 was used for the inventory analysis with the database like Ecoinvent 3.0 and US- US-EI U. EPA TRACI (USA 2008) and ILCD were used to assess environmental impacts.
Firstly, the environmental impact of different chemical components in each process was evaluated. The highest impactful input for the ER method is hydrochloric acid, and for the pyrometallurgical method is copper scrap, while for the hydrometallurgical method, it is hydrogen peroxide, an oxidizer that accelerates base metal extraction process, that dominates the overall environmental footprint. Other than the individual evaluation, several scenarios were developed for the comparative study of three processes. The revenue of e-waste processing is mainly from precious metals, REEs and the base metals. As the three processes have different recovery efficiency, the amount of outputs is different. In the first scenario, $1000 revenue was used as the functional unit. Results show that the ER process outperforms the other two processes in almost all impact categories adopted in TRACI and ILCD while there is no clear winner between the hydrometallurgical and the pyrometallurgical processes. Considering the gold is major revenue, which takes around 70%-90% of e-waste recovery, two scenarios were studied based on the functional unit of 1 kilogram of gold. REEs are co-product in the e-waste recovery. Among these scenarios, the first one used price allocation and results indicate ER process has lower than 20% impacts of the other two processes for most of the categories. Similar to the scenario with $1000 functional unit, there is no winner between the hydrometallurgical and the pyrometallurgical processes. As the REE recovery part for these three e-waste processes uses similar material inputs, the next case study separated the process of precious metals and the REEs recovery. Results still show ER has the lowest environmental impacts for all categories in recovering 1 kilogram of gold.
Overall, this study provides a comparative LCA on recovering precious metals from e-waste with the hydrometallurgical, the pyrometallurgical and the ER processes. The environmental viability of the ER process warrants the further development of the ER process at industrial scale.
11:30am - 11:50am
Semiconductors to Smart Devices: Capturing the System-Wide Impacts of a Growing Information & Communication Technology Infrastructure
1Lawrence Berkeley National Laboratory, United States of America; 2National Renewable Energy Laboratory, United States of America; 3Department of Energy, United States of America; 4Oak Ridge National Laboratory, United States of America; 5Argonne National Laboratory, United States of America
Data is being collected, transported, stored, and processed into actionable knowledge across all aspects of society and at unprecedented rates. This growth in data utility is enabled by a rapidly expanding information and communication technology (ICT) infrastructure. Cisco predicts nearly 20 billion internet-connected devices in the world by the year 2020. The reliance of ICT infrastructure across the economy is also transforming the industrial sector, with Apple, Amazon, Google, Microsoft, and Facebook currently the five largest global companies by market capitalization. This new connected economy has the potential to increase environmental sustainability through efficiency, substitution, and transformational impacts. However, similar to other high-impact general purpose technologies, such as electricity, ICT infrastructure assessment requires a systems-wide approach to account from the “invisible” energy and resource demands of ICT that occur remotely and out of sight of the end-user.
In this presentation we present some of the challenges and opportunities identified for assessing the impacts of an emerging connected economy. First, we characterize ICT infrastructure as comprised of three fundamental components: data centers, data networks, and the connected end-use devices embedded in consumer, industrial, and commercial products. We present how these components interact and may evolve, and discuss methods to estimate future equipment growth and accompanying operational energy requirements. Preliminary energy demand projections are presented to highlight the sensitivity among different assumptions and propose new metrics for capturing the impacts and efficiency of ICT infrastructure. Second, we discuss strategies to capture the upstream impacts of ICT infrastructure by identifying energy intensive manufacturing processes along with critical or regionally constrained material requirements across global supply chains. Strategies include utilizing aspects of traditional process life-cycle assessment methods with global economic data. Third, we propose a framework to assess the cost, energy, and performance benefits achievable through ICT-enabled “smart” applications in manufacturing, transportation, and building operation.
This presentation aims to highlight the challenges in understanding the implications of this growing portion of the economy and engage the audience to discuss new methods for evaluating system-wide impacts of emerging information technology products and services. Results from the session will be incorporated into strategic analysis efforts currently underway at the U.S. Department of Energy and shared with researchers via federal reports and academic publications.
|10:30am - 12:00pm||4Space-4: Hidden in Plain Bite: the Ecological Power of Our Food Choices|
Sustainable Food Production
1Factory Farming Awareness Coalition; 2WSP
|12:00pm - 2:00pm||Lunch and keynote: Mike Kerby--Exxon Mobil--Corporate Strategic Research Manager|
|2:00pm - 3:30pm||E&S-5: Energy storage|
Session Chair: Jeremiah Johnson
2:00pm - 2:20pm
Optimal Use of Grid-Connected Energy Storage to Reduce Human Health Impacts
NC State University, United States of America
Grid-connected energy storage can perform a wide variety of applications, yielding potential benefits to power system operations and system-wide costs. Current applications for energy storage, however, do not explicitly consider the potential to reduce adverse human health impacts from power generation. In this study, by taking advantage of energy storage’s ability to shift both the time and location of power sector emissions based on their charging and discharging strategies, we propose a method that enables energy storage to cost-effectively reduce human health impacts from the power sector. To do this, we simulate the air quality change due to the hourly emission from electricity generation and determine the hourly health damage cost associated with humans’ exposure air pollutants for each electricity generating unit by applying the concentration-response function and the Value of a Statistical Life. We then internalize these health damage costs in the power plant dispatch decisions, re-optimizing the unit commitment and economic dispatch model in light of these costs.
Two factors, energy storage and health damage cost, are introduced to the traditional unit commitment and economic dispatch model, and our preliminary results show that both of them can contribute to a health impact reduction: a reduction in human health impacts is achieved through changes in the commitment and dispatch of existing generators in the absence of energy storage; energy storage allows further reducing health damages when costs are internalized by adding more flexibility to the system. With higher energy storage capacity in the grid, a greater health damage reduction can be realized. Through our modeled system, we are able to demonstrate cases when energy storage can reduce up to 20% of the health impacts caused by SO2 emitted from power plants when the system is operated to minimize the health damage cost. Benefits of this magnitude, however, would not typically be realized on the basis of economic optimization alone, even when monetizing health externalities. It is worth noting that it is calculated based on the relative risk (RR) value of SO2 and the result is very sensitive to RR value. This result provides the motivation to apply this method to reduce the health impacts of PM2.5 which has a much higher RR value than SO2 but more sophisticated air quality model to simulate the change of PM2.5 concentration will be needed.
2:20pm - 2:40pm
The Potential Environmental and Economic Benefits of Energy Storage Systems in North Carolina
North Carolina State University, United States of America
Energy storage is rapidly emerging as a viable alternative to provide several grid services. The retirement of coal generation and rapid deployment of solar photovoltaics makes North Carolina a potentially attractive location for energy storage adoption. With a wide consortium of stakeholders and support from the North Carolina General Assembly, a team of researchers investigated the costs and benefits of energy storage adoption in the state under a range of future scenarios. Within this study, we examined the role of storage to fulfill two services: bulk energy time shifting and peak capacity deferral.
Using Tools for Energy Model Optimization and Analysis (Temoa), an energy system optimization model, we developed scenarios for the future of the grid in the Carolinas with optimal generation expansion and economic dispatch. Scenarios conducted include a high natural gas price, accelerated electric vehicle adoption, and an expanded renewable portfolio standard, among others.
Our research found that some site-specific storage technologies such as pumped hydroelectric storage and compressed air energy storage are already cost-effective in some cases. Using 2019 costs, lithium-ion (Li-ion) batteries are cost-effective for only a few services, such as frequency regulation and behind-the-meter applications to reduce demand charges. Because Li-ion battery costs are decreasing so rapidly, we ran scenarios with projected 2030 battery costs. By 2030, Li-ion batteries can be used cost-effectively for frequency regulation, bulk energy time shifting and peak capacity deferral, coincident peak shaving in the commercial and industrial sector, and improving distribution reliability. Currently, only 1 MW of battery storage and 185 MW of pumped hydro storage are currently integrated into the NC electric grid. Our study found that even 5 GW of storage could be cost-effective by 2030.
The environmental impact of energy storage is driven by the round-trip efficiency as well as the emissions profile of the generators used to charge the battery and those displaced when the battery is discharged. We analyzed the impact of storage on carbon dioxide emissions in two scenarios: the base scenario and the expanded renewable energy portfolio standard scenario. In our analysis, natural gas combustion turbines represent the largest share of displaced generation, while charging is driven by solar generation that would have otherwise been curtailed. The net impact of this activity is a reduction of power sector emissions ranging from 0.2% to 9.3%. The ability to charge with curtailed solar is a result of high solar penetration levels (both now and in 2030). This translates to increased benefits of Li-ion battery systems of 10 – 40 $/kW-year under a $50/ton carbon tax.
2:40pm - 3:00pm
Green principles for responsible battery management and strategies to maximize battery service lifetime
University of Michigan, United States of America
Vehicle electrification is expanding worldwide and has the potential to reduce greenhouse gas emissions (GHGs) from the transportation sector. Batteries are a key component of energy storage systems for electric vehicles (EVs), and their integration into EVs can lead to a wide range of possible environmental outcomes. These outcomes depend on factors such as powertrain type, electricity source, charging patterns, and end-of-life management. Given the complexities of battery systems, a framework is needed to systematically evaluate environmental impacts across battery system life cycle stages, from material extraction and production to use in the EV, through the battery’s end-of-life. We have developed a set of ten principles to provide practical guidance, metrics, and methods to accelerate environmental improvement of mobile battery applications and facilitate constructive dialogue among designers, suppliers, original equipment manufacturers, and end-of-life managers. The goal of these principles, which should be implemented as a set, is to enhance stewardship and sustainable life cycle management by guiding design, material choice, deployment (including operation and maintenance), and infrastructure planning of battery systems in mobile applications. These principles are applicable to emerging battery technologies (e.g., lithium-ion), and can also enhance the stewardship of existing (e.g., lead-acid) batteries. Case study examples are used to demonstrate the implementation of the principles and highlight the trade-offs between them.
One of the most important principles is Principle #6: Design and operate battery systems to maximize service life and limit degradation. We expand Principle #6 to provide guidance and strategies that promote battery health and lifetime extension to promote sustainable and responsible battery management. The goal is to provide practical guidance, metrics, and methods for battery designers, suppliers, EV and electronics manufacturers, users, and material recovery and recycling organizations to accelerate environmental improvements of battery systems in electronics and vehicles.
3:00pm - 3:20pm
Sustainable end-of-life management of electric vehicle Li-ion batteries to maximize resource efficiency
Purdue University, United States of America
U.S. demand for lithium-ion batteries is expected to be $US 30-40 billion by the year 2025. A new generation of hybrid and electric vehicles will drive this growth. Meanwhile, the lifetime of batteries on electric vehicles is about 5-10 years. As the demand for Li-ion batteries increases, so too will the disposal of spent batteries in the solid waste stream. A conservative assumption is that by the year 2035, the US will be disposing of 30,000 tons of Li-ion batteries per year. It is imperative to develop a means to divert these batteries from the solid waste stream and recover the critical materials from the spent batteries to meet the growing future demand. To maximize economic benefits and resource efficiency, it is highly desired to have an integrated end-of-life (EOL) management approach for spent Li-ion batteries to enable reuse, re-manufacturing, and recycling. Current industry procedures of electric vehicle battery re-manufacturers deal primarily with replacing modules under warranty for major automotive companies like Nissan and General Motors. Single-cell replacement is a field that has not been explored in detail.
In this project, a 48 V module from a Chevrolet Volt plug-in hybrid electric vehicle (PHEV) battery was disassembled and the cells were used as a case study. The objectives of this study were to:
1) Develop a working knowledge of the mechanical disassembly process of an EV module, and characterize the performance of battery cells. The determination of cell parameters such as battery capacity, state of health, and state of charge were then compared to their original values to separate faulty cells from healthy cells.
2) Investigate the assembly of good cells for a second-life application. With end-of-life electric vehicle batteries usually at 70-80% of their original capacity, they can be reused for other applications that require less power.
3) Recycle critical materials from the faulty cells. Cobalt and lithium can be obtained from the cathode of cells in battery packs through chemical separation and methods such as bio-leaching. This will decrease the pressure on the continued mining of these elements as future demand increases.
With these goals in mind, researchers developed a small-scale process flow diagram based on work done. Challenges associated with dismantlement and cathode material separation were identified, as well as areas for optimization. Several potential second-life applications such as back-up batteries for small electrical tools, energy storage packs to supplement grid supply, and battery packs for the micro-mobility industry (e-scooters, etc.) are being explored.
To conclude, the used battery packs from electric vehicles still have value embedded in them, and critical materials can be recovered, recycled, and reused. Depending on the state of health of an individual cell, the cell can be reused by assembling it with like cells to make a new battery for second-life applications.
Future work will include the development of a semi-automatic procedure for battery dismantlement on an industrial scale. The use of vision systems, rotating index tables, and automatic guided vehicles are being explored in collaboration with Oak Ridge National Lab, in an attempt to develop a standard disassembly process for various types of electric vehicle battery packs. Single-cell replacement will also be investigated.
This research is supported by the Critical Materials Institute, an energy innovation hub under the U.S. Department of Energy, whose mission is to assure supply chains of materials critical to clean energy technologies — enabling innovation in U.S. manufacturing and enhancing U.S. energy security.
|2:00pm - 3:30pm||B&I-1: Resilience|
Session Chair: Jeremy Gregory
2:00pm - 2:20pm
Hurricane Resilience: An approach for community-informed building-scale assessments
MIT, United States of America
Building-scale resilience assessments are generally carried out through convolving hazard curves with fragility curves. Hazard curves describe the probability of excitation level, wind speed in the case of hurricanes, and fragility curves describe the probability of physical damage for each given excitation level. Our group at the MIT Concrete Sustainability Hub is working on updating hazard and fragility curves to include community characteristics, such as orientation and mitigation of the building stock. Hazard curves are modified to include “texture”, the orientation of buildings relative to each other, which can act to amplify wind risk. Fragility curves are dictated by the design of structural and nonstructural elements. Design iterations are modeled and simulated using molecular dynamics to produce detailed fragility curves representing incremental levels of mitigation. Finally, characterizing the building stock enables aggregation of community-scale loss. The extent of this aggregated loss influences demand surge, the increase in repair time and unit repair costs in larger-scale disaster events due to demand for materials and labor outpacing local supply. Our research goal is to understand the mechanisms of demand surge and couple surge with "texture" effects for an estimation of loss amplifications in different communities. We find that building-scale repair costs are avoided for higher standard buildings especially when located in communities where surrounding buildings are also appropriately mitigated. Our aim is to promote performance-based design while emphasizing the importance of community-scale implementation.
2:20pm - 2:40pm
Metrics of Community Resilience: Estimating the social burden of attaining critical services following major power disruptions
1University at Buffalo; 2Sandia National Laboratories
Electric power is critical to almost every aspect of American life, powering everything from healthcare systems to transportation to telecommunications. These and other infrastructure systems vitally depend on a functional power grid; consequently, the federal government has deemed the energy sector “uniquely important” to the overall resilience of infrastructure systems. If the value of the nation’s infrastructure systems is understood to be derived from its ability to “provide the essential services that underpin American society,” (PPD-21, 2013) then infrastructure resilience—defined as “the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions” (PPD-21, 2013)—can be understood similarly. From this perspective, the value of infrastructure resilience (notably, grid resilience) can be assessed by the degree to which it serves to bolster community resilience.
Despite the practical connections between grid and community resilience, there is a disconnect between efforts to plan for these two objectives. Efforts to plan for grid resilience often are aligned with a utility perspective of energy, which focuses on the supply of energy, and considers energy resilience in terms of the frequency and duration of a power outage. In contrast, efforts to plan for community resilience often focus on the human outcomes of energy supply, considering energy resilience through health and wellbeing indicators such as access to food, water, sanitation, and healthcare (The Rockefeller Center & Arup, 2015; Cutter, 2016). The disconnect between these two perspectives prevents efforts to plan and regulate for community-focused grid resilience. The lack of resilience metrics leaves utilities with few incentives to invest in grid resilience (Mukhopadhyay & Hastak, 2016), and fewer still to go beyond kilowatts and kilowatt hours to evaluate potential investments in terms of how energy supply contributes to human wellbeing in outage events.
This research seeks to bridge the gap between these two perspectives and facilitate efforts to plan and regulate for community-focused grid resilience. Drawing upon theories of human development, namely the human capabilities approach (Nussbaum, 2003; Sen, 2005), this research explicitly draws the link between infrastructure systems and infrastructure services, and the ultimate human benefits they provide (Clark, Seager, & Chester, 2018). The capabilities framework provides a theoretical basis for the key objective of the project: the development and validation of a social burden metric to quantify the strain placed upon members of a community to attain all their infrastructure service needs after a disaster. The capabilities framework highlights three key concepts integral to the development of the social burden metric: need, or the ways in which different demographics require different types and quantities of particular services; ability, the differing resources certain populations have at their disposal and the ways in which these resources might facilitate resource acquisition; and acquisition effort, the difficulty of satisfying service needs, based on service availability and properties of the service location. Building from this theoretical basis, the social burden metric adapts a variant of the travel cost method (TCM) known as the Random Utility Model, an approach long used by environmental economists seeking to quantify the value of recreational services to communities (Heal, 2000). This adapted RUM explicitly reflects the needs of different populations, their abilities, and the level of effort necessary acquire their service needs in power outages.
The employment of RUM as a means of quantifying the social burden of power outage demonstrates a new application of a long-established method. In addition to such scholarly contribution, this research has the potential to inform planners and policymakers for assessing the human impact of proposed infrastructure investments.
2:40pm - 3:00pm
A resilience engineering approach to integrating human and socio-technical system capacities and processes for national infrastructure resilience
1Resilience Engineering Institute, Tempe, AZ; 2Department of Operations Research, Naval Postgraduate School, Monterey, CA; 3School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ; 4School of The Future of Innovation in Society, Arizona State University, Tempe, AZ
Despite Federal directives calling for an integrated approach to strengthening the resilience of critical infrastructure systems, little is known about the relationship between human behavior and infrastructure resilience. While it is well recognized that human response can either amplify or mitigate catastrophe, the role of human or psychological resilience when infrastructure systems are confronted with surprise remains an oversight in policy documents and resilience research. Existing research treats human resilience and technological resilience as separate capacities that may create stress conditions that act upon one another.
This interdependence between human and technological aspects of resilient infrastructure systems is not yet fully appreciated in infrastructure policy or practice. In particular, guiding Federal policy directives for U.S. infrastructure security and resilience do not explicitly identify human or social behavior as essential components of critical infrastructure system resilience. A prominent example of this is the National Infrastructure Protection Plan of 2013 (NIPP 2013) – a guide to managing national infrastructure risks created by the Department of Homeland Security in response to Presidential Policy Directive 21. Although NIPP 2013 names 16 sectors of critical infrastructure (including “communication” systems), and acknowledges that threat prevention, recovery, and mitigation requires close coordination of partnerships between public and private interests the document fails to consider how human behavior impacts infrastructure resilience. Further, while the NIPP emphasizes that critical infrastructure security and resilience is essential to national well-being, it makes no reference to how infrastructure designers, operators, maintenance workers, or users might contribute to or undermine infrastructure resilience. Thus, a gap remains regarding the study of human attributes that relate to infrastructure and help build resilience to support national goals. Given that human performance is dynamically coupled with infrastructure performance, a comprehensive approach to resilience must consider this coupling.
To address this gap, we review resilience engineering and psychology research to produce four novel outputs that inform an integrated perspective of human and infrastructure resilience not available elsewhere in the literature: (1) a list of resilient system capacities for engineered systems, (2) a list of human psychological resilience capacities for the people embedded in infrastructure systems, (3) a conceptual framework for linking system and human capacities together via four socio-technical processes for resilience: sensing, anticipating, adapting, and learning (SAAL), and (4) a mapping of human and system characteristics using the framework to inform infrastructure resilience policies. Our analysis shows that the human and technical resilience capacities reviewed are interconnected, interrelated, and interdependent when applied to the SAAL framework. While reinforcing the important roles of cognitive and behavioral dimensions, our findings further suggests that the affective dimension of human resilience is effectively ignored in the resilience engineering literature. Together, we present a simple way to link the resilience of technological systems to the cognitive, behavioral, and affective dimensions of humans responsible for the system design, operation, and management.
|2:00pm - 3:30pm||ET-2: Tools for assessing energy systems|
Session Chair: Stefano Cucurachi
2:00pm - 2:20pm
Techno-economic Analysis and Life Cycle Assessment for Biosorption of Rare Earth Elements from Coal by-products
1University of Arizona, United States of America; 2Lawrence Livermore National Laboratory, United States of America
Rare-earth elements (REEs) play a critical role in today’s technologies such as modern communications, advanced transportation systems, and renewable energy production. China is responsible for producing more than 85% of the global REEs, and the U.S. is 100% dependent on import. Due to the risk associated with Chinese near-monopolistic supply, the U.S. is looking for securing domestic REE supply by exploring alternative REE sources and new extraction methods with high efficiency. Herein, a novel biosorption technology being developed by our team is examined for REE recovery from coal by-products, an abundance feedstock with a high REE content, ranging between 340-2800 ppm. First, coal byproducts undergo physical upgrading and acid leaching, while bioengineered microbes are cultured and prepared for REE adsorption. A continuous-flow bioreactor is designed to allow the immobilized microbes to selectively adsorb REEs from the leachate. Upon biomass harvest, sodium citrate is applied to desorb REEs, which are further purified through oxalic precipitation and calcination to obtain 95+% pure total rare earth oxides (TREOs). For microbe immobilization, we considered two alternative microbe carriers: biofilm and microbe bead system.
Techno-economic analysis (TEA) was performed to assess the economic performance of the biosorption technology, projecting industrial scale operation from lab-scale data. Both carrier systems were analyzed and compared with other competing technologies with low- and high-grade coal by-products. TEA results of biofilm carrier were estimated to be profitable, while leaching was the most costly process. Furthermore, TEA results for the microbe bead system showed that microbe bead adsorption capacity and reuse time are the two main factors which constitute more than 60 percent of the entire process costs. Sensitivity analysis revealed break-even conditions and future improvement opportunities. For example, if we could achieve REE adsorption capacity of 5 mg per gram of dry beads and reuse the microbe beads for more than 12 times, the process could be profitable. Experimental evidence suggested high likelihood to meet these thresholds.
Life cycle analysis (LCA) was performed to assess the environmental performance and assist with sustainable development of the technology. Overall, REE biosorption was found to offer environmental benefits compared to the alternative technologies. The environmental hotspots were identified to be sulfuric acid and limestone, which were consumed for leaching and pH adjustment, respectively. Alternative approaches are tested to further reduce the environmental impacts by recycling and reuse of the acid, for example.
The project aimed to study the economic and environmental feasibility of integrating biosorption for REE recovery from coal byproducts. Preliminary TEA and LCA results demonstrated that the project would be profitable and environmentally friendly, which warrants further development of the biosorption technology.
2:20pm - 2:40pm
Quantifying Energy Demand and GHG Emissions of Activated Carbon Production from Diverse Woody Biomass: An Predictive Modeling Framework of Artificial Neural Network and Kinetic Based Simulation
North Carolina State University, United States of America
The utilization of biomass to replace coal as the feedstock for activated carbon has become attractive in recent years due to its potential to reduce process energy demands and greenhouse gas (GHG) emissions.1 A few studies have evaluated the energy demand and GHG emissions of steam AC production through life cycle assessment (LCA).1–3 However, these studies have been limited to a few specific feedstocks (e.g. coconut shell, wood waste) and operational conditions, which may not be applied to a wide range of biomass feedstock. A predictive model that can quantify the energy demand and GHG emissions of AC made from diverse biomass will be helpful for decision-makers to screen different feedstock.
In this work, a predictive model for energy demand and GHG emissions associated with AC production is developed by integrating process-based simulation, Artificial Neural Network (ANN) and pyrolysis kinetic model. A large dataset of biomass characterization, operational conditions of pyrolysis and steam activation, and AC yields, were collected from literature and used for ANN training and validation. Given the heterogeneity of biomass characterization data available for different types of biomass, this initial work focuses on woody biomass. The trained ANN is able to predict key process parameters such as yields based on the input data of biomass characterization and major operational conditions.4 Aspen Plus simulation was developed based on predicted yield and the kinetic model5 to generate life cycle inventory data such as energy and mass balance, and GHG emissions.
The integrated modeling framework is able to estimate the energy demand and GHG emissions of AC produced from woody biomass. The results can be used to screen woody feedstocks potentially useful for production of AC and optimize the process conditions. The impact of feedstock properties on the process energy demand and GHG emissions will be discussed in the presentation, which will provide useful information for biomass selection in various industrial application. For example, we found that as the hydrogen content of the woody biomass increased the energy demand increase due to a decrease on the mass yield. In addition, the integrated methods, e.g., kinetic modeling, ASPEN modeling, and ANN, developed in this work have the potential to be applied to other manufacturing processes, especially emerging technologies that lack LCI data.
1. Arena N, Lee J, Clift R. Life Cycle Assessment of activated carbon production from coconut shells. J Clean Prod. 2016;125:68–77.
2. Gu H, Bergman R, Anderson N, Alanya-Rosenbaum S. Life Cycle Assessment of Activated Carbon From Woody Biomass. Wood Fiber Sci. 2018;50(3):1–15.
3. Kim MH, Jeong IT, Park SB, Kim JW. Analysis of environmental impact of activated carbon production from wood waste. Environ Eng Res. 2018;24(1):117–26.
4. Liao M, Kelley SS, Yao Y. Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass. Biofuels, Bioprod Biorefining. 2019;(Accepted).
5. Anca-Couce A, Sommersacher P, Scharler R. Online experiments and modelling with a detailed reaction scheme of single particle biomass pyrolysis. J Anal Appl Pyrolysis. 2017;127(July):411–25.
2:40pm - 3:00pm
Transportation Network Companies Have Improved Personal Transportation Energy Intensity Across and Within U.S. Metropolitan Areas: A Combined Clustering and Regression Analysis
Carnegie Mellon University, United States of America
Transportation network companies (TNCs), such as Uber and Lyft, that provide on-demand mobility services, have already changed how many urban travelers move. Despite rapid TNC growth in recent years, there is limited knowledge to-date on how they influence energy consumption and how that effect may vary as a function of underlying urban forms. Depending on the urban context in which these services operate, TNCs may either increase or decrease the energy intensity of personal transportation by shifting travel into TNC vehicles with systematically different efficiencies and/or alternative powertrains, such as electric vehicles (EVs). TNCs may also affect aggregate transportation energy consumption by either increasing or decreasing the number of vehicles on the road. To address these knowledge gaps, we aim to systematically categorize U.S. metropolitan areas and quantitatively estimate the net causal effects of recent TNC entry on vehicle adoption and energy consumption in different urban typologies.
We combine machine learning and statistical classification methods with a difference-in-difference propensity score-weighted regression model to estimate both the aggregate effect of TNCs on energy-relevant vehicle characteristics, like fuel economy and electric vehicle prevalence, in different clusters of U.S. metropolitan areas, as well as the intra-urban effects on these same outcomes across different ZIP codes types. Using annual data from 2010 to 2017 on the over 240 million personal vehicles registered in the U.S., we find an 1.8% increase in the number of vehicles registered in the average metropolitan area. Furthermore, while we find no significant change in EV registrations at the metropolitan level, we do find significant increases in EV registration percentages, ranging from a fraction of a percentage point to as high as 50 percentage points (and no significant declines in EV registrations are found). While fuel economy analysis is ongoing, we can already estimate these changes to the number, efficiency, and fuel source(s) of personal vehicles on U.S. roads offer energy security and net externality benefits to the U.S. economy in the range of $20 million to $800 million.
3:00pm - 3:20pm
How Technology Assessment Tools Can Inform Life Cycle Assessments of Emerging Technologies
Department of Chemical and Petroleum Engineering, University of Calgary
There is increasing attention being drawn by life cycle assessment (LCA) practitioners to challenges associated with assessing the impacts of emerging technologies . Significant progress has been made to addressing uncertainties in LCAs of commercial-scale technologies, however, those conducting LCAs of emerging technologies face additional challenges including data availability, scale-up, comparability (i.e., what market the technology will be deployed in, which incumbent the emerging technology should be compared to), and uncertainty . While recent literature has drawn attention to these challenges, little work has been done to develop methods that can applied in these scenarios to improve the robustness of LCA results. Improving upon these methods can increase the accessibility and utility of the insights generated by an LCA, at a stage in technology development where there are many opportunities to affect future performance of the technology.
On the technology management side, a broad range of technology assessment tools (TATs) are being applied for diffusion, launching, adaptation, and financing of emerging technologies to inform technology developers and policymakers about the tradeoffs associated with emerging technologies at various stages of commercialization . TATs can help by: forecasting technological developments, impacts, and consequences (awareness TATs), supporting actors or groups in formulating policies or strategies (strategic TATs), broadening decision-making about technological development (constructive TATs), and backcasting to develop scenarios of desirable futures that can be used as a starting point in the innovation process . In this work, we present an overview of TATs and discuss opportunities for combining these tools with LCAs or applying these tools to help inform LCA’s goal and scope definition stages. We provide recommendations for specific tools that can be applied or combined with LCA of technologies at various technology readiness levels.
Our review shows that TATs are diverse and range from traditional analytic method to more descriptive and qualitative methods [2,3]. The specific TATs to apply depends on the level and nature of uncertainties in the LCA of an emerging technology. For example, descriptive TATs such as Technology Balance Sheet and Technology Trees can improve an LCA’s goal and scope definition, help to define reasonable scenarios, and set appropriate assumptions for the analysis. Techniques related to system optimization and decision making can be used for inventory analysis, data validation, and aggregation. Backcasting techniques such as Roadmapping  can be combined with the interpretation phase of LCA to inform decision-making by technology developers. The general discussion in LCA of emerging technologies has focused on the goal and scope definition and inventory analysis. Future work could explore opportunities to combine TATs such as System Dynamics Modelling or Externalities Analysis in the impact assessment stages .
 Hetherington AC, Borrion AL, Griffiths OG, McManus MC. Use of LCA as a development tool within early research: Challenges and issues across different sectors. doi:10.1007/s11367-013-0627-8.
 Tran TA, Daim T. A taxonomic review of methods and tools applied in technology assessment. doi:10.1016/j.techfore.2008.04.004.
 Van Den Ende J, Mulder K, Knot M, Moors E, Vergragt P. Traditional and Modern Technology Assessment: Toward a Toolkit. doi:10.1016/S0040-1625(97)00052-8.
 Hussain M, Tapinos E, Knight L. Scenario-driven roadmapping for technology foresight. doi:10.1016/j.techfore.2017.05.005.
 Bichraoui-Draper N, Xu M, Miller Sh, Guillaume B. Agent-based life cycle assessment for switchgrass-based bioenergy systems. doi:10.1016/j.resconrec.2015.08.003
|2:00pm - 3:30pm||4Space-5: Sustainability Education & Entrepreneurship (SEE): A new knowledge process for solving community wellbeing challenges|
Sustainability Education & Entrepreneurship (SEE): A new knowledge process for solving community wellbeing challenges
Rochester Roots, United States of America
|3:30pm - 4:00pm||Afternoon Break|
|Lloyd Center Lobby|
|4:00pm - 5:30pm||E&S-6: Data and Energy|
Session Chair: Geoffrey Lewis
4:00pm - 4:20pm
Integrating data science methods and Life Cycle Assessment (LCA): Application to the power sector
1Contractor to U.S. DOE, NETL, United States of America; 2U.S. DOE, NETL, United States of America
Data science is radically transforming the scientific landscape. Data-intensive frameworks such as life cycle assessment (LCA) can uniquely benefit from the establishment of large scale and national datasets, and the use of data-science methods. Additionally, the synthesis and application of these data repositories in LCA can aid in improving emissions inventories, technological characterization of product systems, and uncertainty quantification. In this work, a series of case studies are used to explore the intersection of data science and LCA of the power sector. The objective of this work is to provide a broad cross-section of data sources, methods, and approaches that have been applied to characterize the life cycle of power systems, but are generalizable across multiple LCA research domains. Several case studies are presented to highlight the utility of applying data-science methods to LCA of power systems including: (1) regionalized methane emissions from natural gas liquids unloading, (2) characterizing the environmental profile of U.S. hydropower, and (3) time series analysis of the fossil fleet from 2008 to 2016. A detailed description of each case study is provided below:
(1) A ‘bottom-up’ probabilistic model was developed using engineering first principles to quantify methane emissions from natural gas liquids unloading activities for 18 basins in the United States in 2016. For each basin, six discrete liquids unloading scenarios are considered, consisting of combinations of well types (conventional, unconventional) and liquids unloading systems (non-plunger, manual plunger-lift, and automatic-plunger lift). Data used to populate the model is sourced from Drilling Information, the 2016 Greenhouse Gas Reporting Program (GHGRP), and academic literature. (2) An LCA model was developed to quantify the greenhouse gas emissions and water footprint of 2016 U.S. hydropower. Information for hydroelectric powerplants was obtained via the National Inventory of Dams (NIDS) dataset, U.S. Energy Information Administration (EIA), and National Hydropower Asset Assessment Program (NHAA). A lasso linear model using recursive feature elimination and cross-validation was used to develop statistical regressions for hydroelectric carbon dioxide and methane emissions rates. Inverse Distance Weighting (IDW) interpolation was performed on climatological data obtained from NOAA and used to develop reservoir specific water consumption rates. Additionally, several allocation schemes were considered to apportion the emissions and water consumption across multipurpose hydroelectric reservoirs. (3) A data-driven model was developed to quantify the evolution of hourly time-series emissions from the U.S. fossil (coal, gas) fleet from 2008 to 2016. Additionally, this work evaluates fossil assets that have shifted to or from baseload operations over the 2008 to 2016 era; and studies the effect of this shift on plant-level performance metrics (gross efficiency, net capacity factor) and emissions (CO2, SO2, NOX) profiles. The analysis investigates several possible explanatory mechanisms for the shift in plant emissions including the installation of air pollution control equipment, environmental regulations, power plant technology, and changing plant operations. Data used to parameterize the model was obtained from publicly available sources including the Environmental Protection Agency’s (EPA) Air Markets Program Data (AMPD) and EIA. Several opportunities for integration and application to LCA are identified.
4:20pm - 4:40pm
Evaluating Alternative Strategies to Reduce Greenhouse Gas Emissions
University of California, Davis
California’s landmark 2006 Climate Change Solutions Act (Assembly Bill 32) tasked many government entities, including local governments and government agencies, with reducing greenhouse gas (GHG) emissions to 1990 levels by 2020, and 80% below 1990 levels by 2050. Identifying, quantifying, and then selecting among possible strategies to achieve GHG reductions is difficult, especially without a standardized approach for comparison. This study develops a GHG mitigation “supply curve” to support decision-making by the California Department of Transportation (Caltrans). The supply curve supports selection of the most cost-effective strategies for mitigation by undertaking the following process for each strategy: (1) quantify the net GHG emitted or avoided over a strategy’s lifecycle, (2) consider the timing of changes in GHGs, (3) explore the process and difficulty of implementation, and (4) calculate the initial and lifecycle costs of the strategy. A life cycle perspective is required for GHG accounting because benefits achieved during one stage of a strategy’s lifecycle may be reduced or reversed by carbon-intensive upstream or downstream stages. The timeframe for change is also important, both because of public policy targets and because emissions reductions that occur earlier can avert warming in the near-term, potentially avoiding or delaying climate tipping points. Emissions timing can be addressed by using time-adjusted warming potentials in lieu of standard global warming potentials. Similarly, a life cycle perspective is required for understanding costs because of limited funds that public and private entities can invest in GHG reduction strategies and the responsibility of public agencies to consider future costs. By combining time-adjusted GHG reductions and costs, each strategy can be assigned an “emissions reductions per dollar” value by which the strategies can be ordered to determine which of them achieve the biggest “bang for the buck”.
This study applies the above methodology to six mitigation strategies that could be implemented by Caltrans: energy harvesting through piezoelectric technology, efficient maintenance of pavement roughness, automating bridge tolling systems, increased use of reclaimed asphalt pavement, alternative fuel technology for the Caltrans fleet, and installing solar and wind energy technologies within the state highway network. These diverse strategies were selected to test the effectiveness of the evaluation process. Elaborating on the first strategy, studies have indicated potential to produce energy through piezoelectric sensors installed under highway pavements, which produce a voltage from the compression created by passing vehicles. Preliminary results suggest that 100 lane-miles of piezoelectric technology could reduce emissions by approximately 500,000 tons CO2e over 10 years, with initial costs and lifecycle savings in the millions of dollars. This type of output was generated for all strategies. The strategies are presented as a supply curve by placing them in terms of the magnitude of reduction potential and their cost (both initial and lifecycle); a color-coding scheme is also used to communicate confidence in the calculated values, which are affected by the reliability of cost and emissions change information available, and technology maturity.
4:40pm - 5:00pm
Monetized Impacts of Time Resolved Greenhouse Gas Emissions
Colorado State University, United States of America
Life cycle assessment (LCA) is a tool used to compare the environmental impacts of products. Standard LCA methods do not account for the timing of greenhouse gas emissions. As a result, these assessments do not include dynamic climate factors such as increasing atmospheric greenhouse gas concentrations. Furthermore, these assessments are limited to mid-point metrics that do not capture dynamic end-point impacts such as economic damage to society. These shortcomings can lead to an inaccurate comparison of greenhouse gas impacts from different products. This work presents two new methods to address the dynamic shortcomings of LCA. The first method leverages the social costs of greenhouse gases to determine the time-dependent impacts of greenhouse gas emissions. Using these monetized impacts, time-resolved greenhouse gas emissions are weighted based on when they are emitted. The weighted emissions are then summed to determine a present value of emissions relative to today’s environmental and economic conditions. The second method incorporates the social cost of greenhouse gases and time-resolved greenhouse gas emissions into techno-economic analysis. This method pulls societal costs into a techno-economic frame work and evaluates their impact relative to the other costs within production. These two new methods have been demonstrated on time-resolved LCA of electricity generation systems including coal, natural gas, carbon capture and sequestration, nuclear, solar, and wind. Recognizing the inherent uncertainty of future environmental and economic conditions, a range of future scenarios were evaluated. Results from the first method show that accounting for time-resolved monetized impact increases the present value of emissions across all but one of the systems considered. Technologies with large operational greenhouse gas emissions show the largest increase, with coal rising by 32% in the baseline scenario. Technologies with lower operational emissions see a minimal increase, with solar-photovoltaics rising just 1%. Results from the second method show a large impact on levelized cost of electricity (LCOE) for technologies that have significant operational greenhouse gas emissions. Looking at coal, the LCOE in a baseline scenario increases by 88%. Technologies with lower operational emissions see a smaller impact, with the baseline LCOE of nuclear increasing by just 1%. These results quantify the impact of extending analysis beyond mid-point metrics to account for dynamic economic damage and highlight the dramatic impact of large operational emissions extending over the lifetime of a system.
5:00pm - 5:20pm
Life cycle greenhouse gas emissions of U.S. LNG used for international power generation
1ExxonMobil Research & Engineering, United States of America; 2Massachusetts Institute of Technology; 3SeaRiver Maritime; 4Air Products and Chemicals, Inc.; 5IIT Bombay; 6Council on Energy, Environment and Water
The recent growth in U.S. natural gas reserves has led to interest in exporting liquefied natural gas (LNG) to countries in Asia, Europe and Latin America. Here, we estimate the life cycle greenhouse gas (GHG) emissions and life cycle freshwater consumption associated with exporting Marcellus shale gas as LNG for use in power generation in different import markets. The well-to-wire analysis relies on operations data for gas production, processing, transmission, and regasification, while also accounting for the latest measurements of fugitive CH4 emissions from U.S. natural gas activities. To estimate GHG emissions from a typical U.S. liquefaction facility, we use a bottom-up process model that can evaluate the impact of gas composition, technology choices for gas treatment and on-site power generation on overall facility GHG emissions.
Our results show that when U.S. LNG is delivered to Asian nations to generate power in F-class combined cycle power plants (50% efficiency, HHV basis), the life cycle GHG emissions will be 473 kg CO2eq/MWh (80% confidence interval: 452 - 503 kg CO2eq/MWh); life cycle GHG emissions associated with LNG-based power in Europe and South America may be as low as 459 kg CO2eq/MWh, due to shorter marine transportation distances. In markets with existing power plant fleets, the life cycle GHG emissions associated with U.S. LNG are ~54% lower than those associated with locally-produced coal. We also find that new coal power generation technologies (e.g. ultra-supercritical coal fired power), while emitting less than existing coal technologies, produce about two times the GHG emissions associated with newer gas power generation technologies (e.g. H-class combined cycle power plants) fueled by natural gas transported as LNG.
|4:00pm - 5:30pm||Open-2: New Inspirations: Biomimicry; Sustainability Education|
Session Chair: Thomas Seager
4:00pm - 4:20pm
Integrating LCA & Biomimicry – Paper Case Study
TranSustainable Enterprises, LLC, United States of America
Eco-design tools such as life cycle assessment (LCA) use a systematic approach for quantifying the environmental performance of industrial products and services. The LCA methodology has great potential for holistically identifying areas of a supply chain with relatively poor environmental performance, a.k.a. “hotspots.” However, LCA is poor at inspiring “productively disruptive innovations” (Feraldi 2018). In contrast, nature-inspired design strategies such as Biomimicry are based on learning from deep principles found in nature and “regard nature as the paradigm of sustainability” (de Pauw 2010). These tools offer a radically different approach for developing designs in balance with the natural environment. Biomimicry is often referred to as the “conscious emulation of life’s genius” in order to solve human design and engineering challenges (Benyus 1994). The emulation aspect of the tenets of Biomimicry emphasizes integrating biological knowledge at the form, process, and system levels into design and engineering by identifying biological strategies and mechanisms that have evolved to survive the test of time. This type of approach is inspiring a paradigm shift of sorts in terms of addressing human design challenges but lacks the quantitative rigor of tools such as LCA. This work describes the implementation of a sustainability approach that is an amalgam between LCA and Biomimicry. The quantitative value of LCA helps to make substantive assessments and measurements of hotspots in a product supply chain. With this information, the Biomimicry approach can be applied to open the design space at these hotspots and reconnect our vision of our built environment and its place within the rest of the biosphere. Printing and writing paper product life cycles are highlighted as an example to demonstrate the utility of using this integrated approach. The combined value of these sustainability tools has the potential to revolutionize how industry, analysts, and policymakers address our relationship with the built and natural environment. It is the author’s hope that this integrated approach can help humans raise the “sustainability” bar to not only endeavour to sustain human life but to create systems that, in the words of Biomimicry specialists, “create conditions conducive to [all] life” (Benyus 1997).
Benyus J (1997). Biomimicry: Innovation Inspired by Nature, BIOMIMICRY © 1997 by Janine M. Benyus, HarperCollins Publishers Inc., New York, 1997.
de Pauw I, Kandachar P, Karana E, Peck D, Wever R (2010). Nature inspired design: Strategies towards sustainability, Article in Conference Proceedings for the Knowledge Collaboration & Learning for Sustainable Innovation: 14th European Roundtable on Sustainable Consumption and Production (ERSCP) Conference and the 6th Environmental Management for Sustainable Universities (EMSU) Conference, © 2010 De Pauw I, Kandachar P, Karana E, Peck D, Wever R. Accessed on September 30, 2018 at: https://repository.tudelft.nl/islandora/object/uuid:98ce3f26-eff8-40f5-82dc-ed92fec7e8f9?collection=research.
Feraldi R (2018). The Zoom Out, Environmental Leverage, Assess & Re-Invent (ZELAR) Approach: Plastic Box Case Study, Article on Sustainability Approach Amalgams for Biomimicry Masters Course on Communicating Biomimicry, January 2018.
4:20pm - 4:40pm
Biologically-Inspired Optimization of a Water Distribution Network
1School of Mechanical Engineering, Georgia Institute of Technology, United States of America; 2School of Biological Sciences, Georgia Institute of Technology, United States of America
Mathematical modeling and optimization are established techniques that have proven effective as quantitative benchmarking tools at design conception that aid an engineer’s ability to achieve desired results. However, when designing systems where sustainability is the desired outcome, engineers rely on qualitative targets of performance that are subjective in nature as comprehensive quantitative metrics presently are lacking. In the absence of such measures, engineers have limited capacity at the design phase to predict, monitor and evaluate the performance of engineered systems with respect to sustainability. However, emerging studies suggest that biology may provide a useful template in the establishment of quantitative sustainability benchmarks. For example, biologists have applied principles of information theory to develop quantitative metrics that derive from the exchange of resources found in ecological communities, describing indicators such as community health and maturity. Industrial ecologists have extended this type of network analysis by using ecological metrics to achieve a different perspective that can relate the configuration of already constructed engineered systems to material and energy cycling. The components of these engineered systems represent a network of consumers and producers (i.e. species), and the efficacy of the structural organization and flow of materials are determined by employing the ecological metrics and comparing the results to those found in natural communities.
This study extends the work of biologists and industrial ecologists by incorporating ecological metrics at conception in the design of engineered systems with a case study. This case study involves two optimization models of a water distribution network, both with the overall goal of cost minimization. The first model uses a traditional cost-based approach in its optimization by summing the flow rates of water with infrastructure, pumping, and treatments costs. The second model uses these same initial calculations of cost while also adding a penalty parameter that is based on a cycling-based ecological metric (Finn Cycling Index). Finn Cycling Index quantifies the proportion of cycled material through flow in an ecological community, prompting its use as an indicator of community health and maturity among scientists. The penalty parameter is then bounded by the range of values for the Finn Cycling Index found in mature ecological communities. Mature communities are those that have evolved for millennia into sustainable and robust networks of species that balance resource efficiency and redundancy. Contrasting the results in a traditional cost-based optimization model to the results of the same model with a metric-driven penalty, one may ascertain the influence of the ecological metrics on the optimization results. The results from this study demonstrate the optimization model with the metric-driven penalty produces greater amounts of cycling within the network at a similar level of cost with the traditional cost-based model. This validates the use of ecological metrics as a quantitative benchmark in sustainable engineering design that also has complementary outcomes with traditional cost-based optimization models. This bio-inspired optimization approach demonstrates the potential of using the properties of natural systems to guide efficient and robust engineering design at conception, a tool presently lacking in the sustainable design of engineered systems.
4:40pm - 5:00pm
The Origins, Evolution, and Current Crises in Industrial Ecology
Arizona State University, United States of America
As a new science, industrial ecology was founded on a biomimicry hypothesis -- to wit, that the holistic environmental impact of technological systems could be reduced if they were organized to be more like ecological systems to be increasingly interconnected (e.g., reuse of waste materials) and driven by abundant renewable energy. Two decades analytic tool development, including environmental life cycle assessment (LCA) and materials flow analysis (MFA) have codified just one aspect of the natural analog that has come to dominate the current paradigm of industrial ecology. Nonetheless, evidence that this paradigm is inadequate to the challenges of the post-industrial economy is no increasing. As the economy in developed countries evolves to incorporate the rapid growth of digital technologies, a resilience perspective on the natural analog has emerged as critically important. This presentation will identify the origins of the eco-efficiency perspective that currently dominates the natural analog, describe the existential dangers of this approach, and outline a complementary perspective on biomimicry that emphasizes adaptive capacity.
5:00pm - 5:20pm
Sustainability Education and Entrepreneurship (SEE): A new-knowledge process in the face of complexity and accelerating change for children in grades preK-6
1Sustainable Intelligence LLC, United States of America; 2Arizona State University; 3Rochester Roots, Inc.
The increasingly complex and rapidly evolving interdependencies of sociological, ecological and technological systems that characterize post-industrial societies pose tremendous challenges to current educational institutions. While the current educational paradigm emphasizes the efficient transfer of knowledge from experts to pupils, the wicked problems of the current age require constant transformation of knowledge – including knowledge co-creation, innovation, and embodiment in action. Each of these knowledge processes are characteristic of entrepreneurship, but rarely incorporated into elementary school education. Nevertheless, nurturing children’s innate capacities for knowledge transformation may prepare them for the life-long learning and adaptations necessary to build sustainable organizations and social institutions that contribute to human well-being in the face of accelerating change. Critical to this new educational paradigm, but largely absent from urban student populations, is an awareness of complex, living systems. Whereas prior generations may have engaged directly with such systems in agricultural settings, the experience of today's young children living in urban settings is largely sociological and technological, rather than ecological.
This paper describes a garden-based curriculum and Sustainability Education and Entrepreneurship (SEE) program, developed by a not-for-profit and for profit, Rochester Roots and Sustainable Intelligence, delivered at public Montessori and traditional elementary schools in Rochester and Greece, NY. Children receive instruction in systems modeling for exploring the interconnectivity of sociological, ecological, and technological systems dimensions of sustainability, participate in twenty-six interrelated Sustainability Laboratories, design products and manufacture prototypes, enlist the support of university faculty, students, businesspersons and subject matter experts, and participate in a culminating symposium. The school garden serves as both a metaphor for knowledge transformation in sustainability and provides raw materials for many of the product and business concepts developed. Now in its ninth year, over 850 students in two schools participate in different aspects of the program.
The next phase of SEE is to support children going out into community as ChangeMakers that share knowledge-transformation processes and entrepreneurship that adds value to life by improving well-becoming pathways and trajectories. This is a transition that their experiences have prepared them for, including; 1) the mindset of being young citizens influencing SEE learning community critical thinking and collaborative decision-making and 2) the leadership and interactional expertise developed through marshaling feedback from peers and adult mentors for their businesses. As citizens, they become catalysts that build community SEE cognitive and cognition infrastructure, which we call Cognitive Resilient Infrastructure, with the capacities to inform three community knowledge-transformation processes for a sustainability milieu: knowledge-creation, adaptive-innovation and resiliency-building.
|4:00pm - 5:30pm||ET-3: Approaches for assessing emerging tech|
Session Chair: Joule Bergerson
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.
|4:00pm - 5:30pm||4Space-6: Meaning-making is critical for the resilience of critical infrastructure systems|
Session Chair: John Egbert Thomas
4:00pm - 5:30pm
Meaning-making is critical for the resilience of critical infrastructure systems
1Resilience Engineering Institute, Tempe, AZ; 2School of Sustainable Engineering and the Built Environment, Arizona State University; 3School of Sustainability, Arizona State University
|5:45pm - 6:45pm||Defeating ocean plastics… usefully|
Session Chair: Caroline Taylor
Defeating ocean plastics… usefully
1Earthshift Global; 2WSP; 3Oregon DEQ
|6:50pm - 9:30pm||Evening Off-site Event: McMenamin's Edgefield group outing|
|Main Lobby bus pick-up|