Lee - Addressing Supply- and Demand-Side Heterogeneity and Uncertainty Factors in Transportation...

Addressing Supply- and Demand-Side Heterogeneity and Uncertainty Factors in Transportation Life-Cycle Assessment: The Case of Refuse Truck Electrification Dong-Yeon Lee Georgia Institute of Technology, dlee348@gatech.edu Valerie M. Thomas Georgia Institute of Technology, valerie.thomas@isye.gatech.edu Patrick S. McCarthy Georgia Institute of Technology, mccarthy@gatech.edu

Abstract. As a way of incorporating heterogeneity and/or uncertainty factors in life-cycle assessment (LCA) in a systematic and predictive manner, here we identify key variability factors and evaluate their relative significance in LCA results, using a case study of refuse truck electrification. More specifically, we conduct a comparative LCA of conventional diesel, natural gas, and electric refuse trucks in 10 largest metropolitan areas in the U.S., putting focus on the potential of electricity and natural gas as bridge fuels. Unlike conventional LCA studies, we first look at variability elements and analyze which of those variables is worth addressing or not. In so doing, we take a parametric modeling methods including linear regression and discrete choice method. Our goal is to develop a generic model that can predict life-cycle inventory over the entire range of input variables with the heterogeneity factors included as operational variables. And then we apply vehicle activity statistics data to our generic model for life-cycle inventory analysis as well as life-cycle valuation. In terms of the potential of electricity or natural gas as bridge fuels for next-generation refuse trucks, our findings indicate that conventional natural gas is neither very cost-competitive to nor less polluting than conventional diesel or electricity alternatives. This is largely due to severe drive or duty cycles in which refuse trucks typically operate, offsetting the lower carbon intensity of natural gas fuel in comparison to petroleum diesel. However, natural gas produced from landfills shows a significant potential to be a bridge fuel, while conventional diesel is expected to remain as one of the most common fuels of choice over the next 10 (or 20 in some regions) years. With the bridge fuels such as natural gas and electricity, whether the transition will ultimately lead to natural gas or electricity really depends on regions. Lastly, with regards to variability factors, our study suggests that vehicle/fuel LCA studies would better specify or report input values of some key parameters including average trip speed, type of feedstock, payload, region, and cross-sectional and longitudinal changes in economic, driving pattern, technological components.

Proceedings of the International Symposium on Sustainable Systems and Technologies (ISSN 2329-9169) is

published annually by the Sustainable Conoscente Network. Jun-Ki Choi and Annick Anctil, co-editors 2015.

ISSSTNetwork@gmail.com.

Copyright 2015 by Dong-Yeon Lee, Valerie M. Thomas, Patrick S. McCarthy Licensed under CC-BY 3.0. Cite as:

Addressing Supply- and Demand-Side Heterogeneity and Uncertainty Factors in Transportation Life-Cycle Assessment: The Case of Refuse Truck Electrification. Proc. ISSST, Lee, D.-Y., Thomas, V. M., and McCarthy, P. S. Doi information v3 (2015)

Addressing Supply- and Demand-Side Heterogeneity and Uncertainty Factors in Transportation Life-Cycle Assessment: The Case of Refuse Truck Electrification

Introduction. Life-cycle assessment (LCA) has become a common practice or tool for evaluating comprehensive environmental and economic performances of various natural and human-made systems. One of the limitations of typical LCA studies is a lack of generalizability. That is, results found in one study are often not generalizable, because the analysis is based on average or case-specific conditions (Lee et al. 2013). Uncertainty and sensitivity analysis can help, but they dont always explain or predict exactly why and how much the results can change under different circumstances.

When it comes to transportation LCA, it is well-known that the use phase is generally the dominant contributor to energy consumption and environmental impact (Lave and Maclean 2000), except in cases of massive infrastructure requirements for transportation in general or for specific technologies or systems. What we explore here is the extent to which the impact of use phase components can be parameterized and thus incorporated as predictive variables, which can help explain the variability and differences between transportation LCA studies and assumed conditions. Other life-cycle components (e.g., fuel production) can also be parameterized within an LCA. To demonstrate this, we use refuse truck electrification as a case study. We conduct a comparative LCA of refuse truck technologies, adopting a parametric-microscopic approach to systematically deal with an array of different and individual truck operating conditions and fuel supply-chains. Vehicle use such as driving behavior and refueling pattern are major demand-side heterogeneity and uncertainty elements. The supply-side heterogeneity and uncertainty factors include local fuel-sourcing as opposed to centralized fuel supply, spatio-temporal variations of electric power, and geographical differences in exogenous factors (e.g., local climate, fuel price, etc.). Overarching uncertainty factors are future fuel price evolution, the relative importance of current investment over future earnings (or discount rate), the social benefit of air emissions reductions benefits, etc. Note that here we differentiate variability and uncertainty. By and large, both variability and uncertainty are part of the heterogeneity, but variability is deterministic or something that vehicle or fleet operators can control, while uncertainty refers to those factors that are not very controllable. For example, fuel price is determined once location and time are fixed. Also, trip characteristics of commercial vehicles including refuse trucks are also manageable and predictable. However, future fuel price or the specific feedstock of diesel fuel is not something that individual fleet operators or managers can control or determine.

Research Objectives. We are interested in comparative environmental and economic benefits of conventional diesel, compressed natural gas (CNG), and battery electric refuse trucks in the 10 largest metropolitan areas (e.g., New York, Los Angeles, Chicago, etc.) in the U.S. With regards to natural gas, we consider not only conventional natural gas but also renewable natural gas (or biogas) produced from landfills. All of the metropolitan areas being considered here have landfills that collect methane for transportation or electricity generation with varying methane resource availability. Currently, almost half of new refuse trucks sold in the U.S. are CNG, and a battery electric model has recently become available. Given the increasing diversity of options and the momentum for changes, it may be useful to know whether and how much society benefits from a transition from the diesel trucks to alternative-fueled trucks such as natural gas or electricity. So, answering these whether and how much questions is our major research objective. Also, in doing the evaluation, we attempt to avoid basing our analysis on fixed or average conditions. We rather look at overall possible ranges of key variables based on a parametric and spectrum-based modeling approach, addressing or incorporating the supply- and demand-side heterogeneity factors.

D.-Y. Lee et al.

Table 1. Vehicle Specifications and Key Input Variables

Diesel Refuse Truck (DRT)

Compressed Natural Gas Refuse Truck (CNGRT)

Battery Electric Refuse Truck (BERT)

Model Year 2015 - 2030

Manufacturer Mack Peterbilt Motiv Power Systems

Curb Weight (ton) 18 18.5 18

Payload (ton) 12 11.5 9.2

Engine Mack MP7 Cummins ISX12 G -

Maximum Power (kW) 265 261 280

Li-ion Battery Capacity (kWh)

- - 200 (nominal)

Fuel Tank 135 diesel gallons 4 Type-II Cylinders: 120 diesel gallons equiv.

-

Tires Goodyear 315/80R22.5 (10 pieces per truck)

Lifetime Vehicle Miles Traveled (VMT)

500,000 miles (about 20 years of lifetime with annual VMT of 25,000 miles)

Purchase price ($) 0.15 0.21 million 0.18 0.25 million 0.67 0.7 million

Refueling station Already in place Already in place or requires construction

Level 3 (60 kW) charger

Data sources Vehicle and parts manufacturers: www.macktrucks.com, www.peterbilt.com, www.motivps.com, www.cummins.com

Methods. To compare the refuse truck technologies (see Table 1 for specifications), we adopt a LCA framework for the system boundary which is shown in Figure 1. The functional unit is ton-km, which is a product of the weight (metric tons) of garbage that is collected and transported by a refuse truck and the total distance traveled (or vehicle miles traveled, VMT). Life-cycle impact assessment criteria are fresh water consumption (in million gallons) and social life-cycle cost (in constant 2014 $) of the four target refuse truck technologies. In the absence (to our knowledge) of a publicly-available LCA model that can answer our research questions or address the issues aforementioned, here we develop a model called Hybrid PArametric-Microscopic Mobility Life-Cycle Assessment (HPAM-LCA) which consists of several sub-modules as follows: Vehicle and parts manufacture inventory. We take a process-based LCA approach for vehicle manufacture inventory. We use heavy-duty truck materials composition (Gains et al. 1998) and modified the data so as to reflect more recent model years (Davis et al. 2014). As for energy and water requirement and emissions for the production of the materials as well as for vehicle assembly and end-of-life, we use the GREET model (ANL 2014a). Since GREET is for light-duty vehicles, we adjust the data based on the differences in vehicle specifications (e.g., engine displacement, number of tires, frontal area, and etc.) between passenger cars and refuse trucks. Vehicle use-phase energy consumption and emissions prediction. Based on hundreds of drive cycle (speed vs. time profile) samples collected from publicly-available sources (FHWA 2004; ANL 2014b; EPA 2014; ImagineMade 2014), we run vehicle dynamic simulations using ADVISOR (ImagineMade 2014), augmented with tail-pipe emissions test data from Sandhu et al.s work (2014). Using drive or duty cycle characterization parameters (e.g., average trip speed, payload, etc.), we develop a linear regression-based energy and emissions prediction model complemented with additional parameterized sub-models for predicting energy and emissions impact for extreme climate (hot and cold) conditions, battery degradation, road grade, etc. This parametric approach allows us to incorporate microscopic (or heterogeneous) vehicle

Addressing Supply- and Demand-Side Heterogeneity and Uncertainty Factors in Transportation Life-Cycle Assessment: The Case of Refuse Truck Electrification

use conditions of individual vehicles or fleets into LCA in an integrated manner, avoiding the potential bias of averages (Taptich and Horvath 2014) and providing predictive power to LCA. Note that microscopic models sometimes refer to their functionality of providing per-second output, which is not the case in our model. LCA is typically for an entire lifetime of a product or service, so the results are to be aggregated over a long span of time.

Figure 1: System Boundary.

State-by-state and hour-by-hour electricity generation. We take a simplified load-filling approach for electricity generation and consumption fuel mix estimation, based on power generation data from EPA (2015) for 2014 as well as statistical hourly and monthly generation data from EIA (2015). Fuel or energy input as well as net generation is checked to be the same as reported in EPA and EIA data in the original data sets spatial and temporal scales. The amount of water required for thermo-electric power plants varies widely not only across power generation fuel types but also from region to region and by cooling system types (e.g., once-through, recirculating, dry, hybrid, etc.). To provide spatially- and temporally-resolved analysis of water use for power generation, we use EIAs water withdrawal and consumption survey data for 2013 (EIA 2015) and aggregate them from boilers to cooling systems, power plants by fuel type, prime mover, water source type, and cooling system type for each state. We then estimate fresh water withdrawal and consumption rates for generating electricity for each fuel type for each hour of day in each state. Life-cycle valuation. We calculate total cost of ownership and then add carbon emissions damage cost as well as monetized marginal human health impact from non-GHG air pollutants emissions (Muller et al. 2011). Both our parametric prediction model for life-cycle inventory and our discrete choice model for comparative cost-effectiveness are generic so that they can be applied to general or specific cases. For this study, we use refuse truck activity statistics data from NRELs FleetDNA project (Walkowicz et al. 2014).

Results. Figure 1 shows part of our life-cycle inventory (LCI) results for greenhouse gas (GHG) emissions and fresh water consumption for the four technologies being compared. Non-electric refuse trucks LCI reveals an asymptotic pattern in the average trip speed spectrum, and typical refuse truck operation condition is in the zone with the highest curvature. So, variation in trip speed can lead to a significant variability or uncertainty in the LCI. As can be seen in Figure 1,

D.-Y. Lee et al.

changing average trip speed, say, from 4 mph (10th percentile) to 11 mph (90th) results in 30% variability in the GHG emissions. Driving behavior (or skill) can also result in additional but smaller variability, as does payload condition (empty to full). All these variability factors are parametrized and incorporated as operational variables in our model, providing predictability and explaining variabilities. Regional variations for non-electric trucks are relatively very small compared to the electric truck.

Figure 2: Life-Cycle Inventory of GHG (CO2e ton). Emissions and Fresh Water Consumption (M gallons).

Overall, electric or landfill gas trucks are favorable over diesel and conventional natural gas trucks in terms of GHG emissions. Although natural gas has a lower carbon-intensity than diesel on a per-energy basis, the natural gas truck is less efficient than the diesel counterpart in low speed operation conditions and has higher upstream GHG emissions, which tends to offset the lower carbon-intensity advantage of the final fuel product. However, natural gas produced from landfills provides significant GHG emissions reduction potential for natural gas trucks. As for different types of feedstocks for petroleum and natural gas fuels (CRC 2013, Gordon et al. 2015), diesel shows the largest variability. With the fugitive methane emissions (1 4%) and other variation factors, natural gas also shows large (but smaller than diesel) feedstock variability. In terms of fresh water consumption, non-electric trucks consume virtually no water during vehicle operation (driving) and thus are better than electric counterparts in general. However, in some areas such as Boston, LA, and Miami, the electric trucks fresh water consumption is lower than non-electric counterparts, owing to the higher level of reliance on

Addressing Supply- and Demand-Side Heterogeneity and Uncertainty Factors in Transportation Life-Cycle Assessment: The Case of Refuse Truck Electrification

saline water for cooling in power plants in those areas.

Figure 3 shows probabilistic longitudinal variability of the social life-cycle cost performance of the technologies, based on the monetized values of the LCI result above and discrete choice model. Dotted lighter lines are baseline scenario, and thick solid lines are alternative scenarios. Conventional natural gas is not cost-effective in any of the scenarios considered, which means conventional natural gas is not a very attractive alternative fuel to incumbent diesel for refuse truck application. Natural gas (blue lines) shown in Figure 3 is landfill gas. A few distinct patterns emerge: First, incumbent diesel will still be an important fuel in the next decade. Second, landfill gas can play a role as a bridge fuel. Third, in the long run, whether landfill gas or electricity becomes next-generation fuel or not varies significantly from region to region, mostly because of the regional/local fuel price differential variations, for instance, Dallas (one of the areas with the cheapest electricity price in the nation) vs. Boston (having almost triply expensive electricity rates than Dallas). Fourth, behavioral (e.g. driving skill) and/or technological (e.g., internal combustion engine thermal efficiency improvement) changes will have different implications and impact for different regions and fuels. Idle reduction will decrease the attractiveness of the electric truck the largest in Dallas, whereas driving skill improvement will increase the electric trucks performance the most in Boston. Also, it is expected that the electric truck will eclipse landfill gas in 10 years in Dallas, regardless of the aggressive performance improvement efforts for non-electric trucks, but this is not the case in Boston where landfill gas will remain the best fuel option.

Figure 3: Probability that Each Technology Provides the Least Social Life-Cycle Cost.

Discussion. From the life-cycle inventory or conventional environmental life-cycle impact assessment standpoints, it seems average trip speed and the type of feedstock can explain and predict most of the variability. Non-average trip speed variability is not trivial but the significance is relatively small, as does the payload. However, in different vocations (e.g., long-haul) or different condition of the same refuse hauling vocation, the relative significance can change. In the case of non-electric refuse trucks, regional variations are minimal. For example, local climate will change over the course of day, month, and year, but the variations are cyclic, and thus the impact of these variables are not significant in comparative LCA looking at entire product or service lifetime. Depending on regions, driving behavior and payload impact turns out to be very significant for electric trucks, which implies that these variables would better be

D.-Y. Lee et al.

specified or included in LCA of electric trucks to minimize confusion. In terms of life-cycle valuation analysis, it seems both behavioral and technological changes expected in the future should be included and addressed. Evaluating the potential of bridge fuels requires projections and forecasts in which longitudinal variability can significantly change the results. Therefore, when it comes to life-cycle valuation, along with cross-sectional variability components (e.g., fuel price projections) for the same model year, these longitudinal variability factors would also better be addressed explicitly. It is important to acknowledge the complexity of the problem, but it is equally crucial to distinguish what to or not to bother with. For LCA practitioners/researchers not to be left with confusion, we need better information about overall understanding of variabilities and individual factors impact on LCA results in both absolute and relative manners. This in turn requires an explanation of what causes the variability and how we can systematically predict or deal with them in LCA studies. In coping with the heterogeneity and uncertainty issues in LCA, one might attempt to throw all possible factors in analysis, but this needs caution, because higher precision doesnt always result in higher accuracy. As we showed here, alternative and/or foremost research needs is to identify and investigate the sources of variabilities and their relative significance, which in turn will help understand data need and control data quality in LCA. For example, based on the essential variables identified, LCA practitioners and scholars can develop a guideline for LCA studies that recommends or requires important variables or factors to be reported so that potential confusion stemming from the heterogeneity and/or uncertainty can be minimized. Acknowledgements. Part of this work was supported by the National Science Foundation under Grant No. 1441208.

References ANL (Argonne National Laboratory). (2014a). The Greenhouse Gases, Regulated Emissions,

and Energy Use in Transportation (GREET) Model. Retrieved from: https://greet.es.anl.gov (Accessed April 5, 2015).

ANL (Argonne National Laboratory). (2014b). Autonomie. Retrieved from:

http://www.autonomie.net (Accessed June 20, 2014). CRC (Coordinating Research Council). (2013). Transportation Fuel Life Cycle Assessment:

Validation and Uncertainty of Well-to-Wheel GHG Estimates. EIA (Energy Information Administration). (2015). Electricity Data. Retrieved from:

http://www.eia.gov/electricity/data/detail-data.html (Accessed April 25, 2015). EPA (Environmental Protection Agency). (2014). MOVES (Motor Vehicle Emission Simulator).

Retrieved from: http://www.epa.gov/otaq/models/moves/ (Accessed January 3, 2015). EPA (Environmental Protection Agency). (2015). Continuous Emissions Monitoring. Retrieved

from: http://www.epa.gov/airmarkt/emissions/continuous-factsheet.html (Accessed April 11, 2015).

Davis, S. C., Diegel, S. W., & Boundy, R. G. (2014). Transportation Energy Data Book. Edition

33. Oak Ridge National Laboratory. FHWA (Federal Highway Administration). (2004). The Next Generation Simulation (NGSIM)

Program. Department of Transportation. Retrieved from: http://ngsim-community.org/ (Accessed September 30, 2013).

Gaines, L., Stodolsky, F., & Cuenca, R. (1998). Life-Cycle Analysis for Heavy Vehicles. Argonne

National Laboratory. Gordon, D., Brandt, A., Bergerson, J., & Koomey, J. (2015). Know Your Oil: Creating a Global

Oil-Climate Index. Carnegie Endowment for International Peace. Lave, L., & Maclean, H. (2000). Life-Cycle Analysis of Alternative Automobile Fuel/Propulsion

Technologies. Environmental Science & Technology, 34(17), 3598. Lee, D.-Y., Thomas, V. M., & Brown, M. A. (2013). Electric Urban Delivery Trucks: Energy Use,

Greenhouse Gas Emissions, and Cost-Effectiveness. Environmental Science & Technology, 47(14), 8022-8030. doi:10.1021/es400179w.

Muller, N. Z., Mendelsohn, R., & Nordhaus, W. (2011). "Environmental Accounting for Pollution

in the United States Economy." American Economic Review, 101(5): 1649-75. Sandhu, G. S., Frey, H. C., Bartelt-Hunt, S., & Jones, E. (2014). In-use measurement of the

activity, fuel use, and emissions of front-loader refuse trucks. Atmospheric Environment, 92557-565. doi:10.1016/j.atmosenv.2014.04.036.

Taptich, M. N., & Horvath, A. (2014). Bias of averages in life-cycle footprinting of infrastructure:

truck and bus case studies. Environmental Science & Technology, 48(22), 13045-13052. doi:10.1021/es503356c.

D.-Y. Lee et al.

Walkowicz, K., Kelly, K., Duran, A., and Burton, E. (2014). Fleet DNA Project Data. National

Renewable Energy Laboratory. Retrieved from: http://www.nrel.gov/fleetdna (Accessed February 2, 2014).