Energy, economic, and environmental benefits assessment of co-optimized engines and bio-blendstocks

<p>A systems-level modeling suite underpins estimates of greenhouse gas and other benefits of deploying co-optimized fuels and engines.</p>

section_content: The Biomass Scenario Model (BSM) uses a modular architecture, such that major components (feedstock supply and logistics, multiple feedstock conversion options, and downstream elements) can be simulated in isolation or in combination with other components. The modules that comprise the feedstock supply and logistics sector capture the production of bioenergy and commodity crops through farmer decision-making, land allocation dynamics, and new agricultural practices, markets, and prices; they also track costs associated with harvesting, collection, storage, preprocessing, and transportation of feedstocks. The feedstock conversion modules capture investment and operation of various conversion pathways, including algae, oil crops, renewable hydrocarbons, corn-grain ethanol, and cellulosic ethanol. The model represents multiple conversion platforms at three production scales; however, each platform can be included or excluded in model simulations. For this analysis, the E17, furan mixture, and isopropanol were each run as separate simulations, with non-related pathways also available to be developed. Biofuel produced during conversion was then distributed and consumed as finished fuel, blended with E10 (gradually replaced by E15 over time).
section_name: Additional Information on the Biomass Scenario Model

section_content: ADOPT uses techniques from the multinomial logit method and the mixed logit method to estimate vehicle sales. Specifically, it estimates sales based on the weighted value of key attributes including vehicle price, fuel cost, acceleration, range and usable volume. The average importance of several attributes changes nonlinearly across its range and changes with income. For several attributes, a distribution of importance around the average value can be used to represent consumer heterogeneity. The majority of existing vehicle makes, models, and trims are included to fully represent the market. It captures the influence of federal regulations and incentives. ADOPT has been extensively validated with historical sales data. It matches in key dimensions including the distribution of sales by fuel economy, acceleration, price, vehicle size class, and powertrain across multiple years. 

ADOPT has been posted online (https://www. nrel. gov/transportation/adopt. html) with two standard scenarios. It has a Low Technology scenario, which assumes a lower level of advanced vehicle technology research. And it includes a High Technology scenario, which estimates the impact from achieving the DOE's VTO technical targets. These targets capture price, mass, and efficiency improvements for vehicle components including the battery, electric motor, engine, fuel cell, and hydrogen storage. ADOPT currently estimates a significant increase in vehicle electrification from Electronic Supplementary Material (ESI) for Energy & Environmental Science. This journal is © The Royal Society of Chemistry 2020 achieving the High Technology technical targets, as shown in Figure 1. The extensive list of input assumptions can be found in the user interface by downloading ADOPT 2019.
section_name: Additional Information on the Automotive Deployment Options Projection Tool (ADOPT)

section_content: Analysis was conducted using the BSM in conjunction with ADOPT. In these simulations, ADOPT was used to generate vehicle sales estimates over time. These vehicle sales estimates were then incorporated into the BSM framework and used to drive demands for fuel, and biofuel industry development. To facilitate evaluation of blended biofuel and petroleum cases, we defined a set of baseline model conditions and background assumptions. These were used both in a business-as-usual (BAU) scenario and in the blended fuel cases, with the blended fuel cases differing from the BAU only in the availability of co-optimized fuel and vehicles. 

The oil price trajectory from the 2017 Annual Energy Outlook (AEO) reference case 1 is used as a baseline in both the BSM and ADOPT. Additionally, ADOPT uses the AEO 2017 projection for overall vehicle sales. Other ADOPT-specific baseline conditions include the following:  Corporate Average Fuel Economy (CAFE) standards are in place, and  Federal electric vehicle incentives are consistent with current law, phasing out after 200,000 sales per manufacturer. 

It is important to provide some information about how ADOPT evolves the vehicle fleet. In the BAU case results, for example, the best-selling PHEV's nominal price in 2026 is $35,500, compared to the best-selling conventional vehicle's $28,500. The conventional vehicle's price advantage was reduced by applying incentives and penalties to meet the CAFE and greenhouse gas (GHG) standards. These incentives and penalties were applied proportionally to how much a vehicle exceeds or falls short of these standards. Thus, the PHEV price was reduced by $3,200 while the price of the conventional vehicle was increased by $500. The PHEV then remains at just over a $3,000 price disadvantage. The PHEV, which runs on low-cost electricity most of the time, has half the fuel cost at 6 cents per mile compared to the conventional vehicle's (internal combustion engine, light duty) 13 cents per mile. This operating cost advantage is viewed by most vehicle consumers in ADOPT to be worth the extra initial cost, tipping the best-selling powertrain to a PHEV. The best-selling PHEV also provides slightly better acceleration, providing additional market benefit. With the PHEV powertrain becoming the best-seller, ADOPT starts creating additional PHEV options and, as a result, PHEV market shares increase more rapidly. 

Within the BSM, development of corn and cellulosic ethanol as well as biofuels from cellulosic and oilbased feedstocks can occur under favorable market conditions. In all the simulations, we assume that policies such as the U. S. Department of Agriculture's Biofuels Infrastructure Partnership 2 will support increased usage of E15; therefore, we assume a linear increase in E15 adoption, from 1% of fuel use in 2010 to 60% of fuel use in 2050. Co-optimized vehicles are assumed to be compatible with E10-15 fuels (although with lower efficiency), and we assume that owners of these vehicles will substitute E10-15 fuels for the fuels that enable increased fuel economy at large price differentials. * Other currently active policies incorporated in the BSM include the Renewable Fuel Standard, 3 the Low Carbon Fuel Standard 4 (in the Pacific Coast states), and the Biomass Crop Assistance Program. 5
section_name: Business-as-Usual Scenario Description

section_content: The BSM provides a representation of the supply chain associated with biofuel industry development in the U. S. The overall model structure and model inputs are described in other publications, [6] [7] [8] and "downstream" stages of the supply chain associated with fuel distribution and dispensing are addressed by Johnson et al. 9 and Vimmerstedt et al. 10 A large-scale sensitivity analysis of the BSM is described in Inman et al. 11 In this section, we focus on the sensitivity of selected input parameters in the BSM as they relate to isopropanol. These include parameters associated with investment risk, with fuel price sensitivity, and with carbon taxes. 

In the isopropanol base case, we assume that conversion facilities are constructed and that dispensingstation tankage and equipment are acquired to meet projected fuel demand. It is also possible in the BSM to use a net present value (NPV) metric to drive investment in conversion facilities based on financial considerations. One important input to the NPV metric is the required or target rate of return (RoR) for investors, which reflects the risk associated with the potential investment in a conversion facility. 

In sensitivity cases, we compared the base case to four NPV-driven cases (Figure S-1):

 a dynamic relationship, in which the required RoR declines as a function of industry development;  an assumed constant required RoR of 10%;  an assumed constant required RoR of 30%; and  an assumed constant required RoR of 50%. _______________ * E. g., if the price of the blended fuel is triple the price of E10-15, only around 1% of co-optimized vehicle owners will pay for the blended, fuel economy-enhancing fuel. If the price is double, around 20% will fuel up with the blended fuel. 

With a constant 50% required RoR, the biofuel industry that produces the biomass-derived portion of the fuel economy-enhancing blended fuel never takes off. With a 30% required RoR, there is instability in the growth trajectory during the period from 2038 to 2043. Simulations with a 10% required RoR and with a dynamic required RoR based on industry maturity also show instability relative to the base case, but this instability is limited to the period from 2042 to 2046. 

These results suggest that efforts to reduce investment risk are important to facilitate early industry growth, but to the extent that the required RoR is dependent on industry development, risk-reduction initiatives may be less important in the longer term. In the co-optimized fuel and vehicle simulations, we assume that vehicles are capable of using either the co-optimized fuel or E10-15 fuel, and that co-optimized vehicle owners will substitute E10-15 for the co-optimized fuel at large price differentials (see footnote above). To test system responsiveness to this assumption, we increased price sensitivity by 50% such that substitution would occur at lower price differentials between the two fuels (Figure S-2). Isopropanol production is decreased relative to the isopropanol base case, but only by roughly 14% in the final year of the simulation. Carbon taxes and the LCFS have the potential to impact biofuel production by affecting the cost/price of biofuels relative to other fuel options. This in turn affects the price of the blended fuels that we included in our analysis. Here, we compared a broad set of options:
section_name: Additional Information on Sensitivity Analyses

section_content: We use gasoline consumption estimates from NREL's Biomass Scenario Model (BSM), along with results from our model, to estimate how changes in domestic gasoline consumption affect employment in the U. S. petroleum sector. Our model utilizes regression analysis to link changes in U. S. gasoline consumption and employment in the petroleum sector through changes in domestic petroleum production. We utilize this two-step process to ensure that our estimates are in line with previous results. Our results suggest that the slight decrease in gasoline consumption in the scenarios we examined herein will have little to no impact on employment in the U. S. petroleum sector. 

Our analysis estimates how domestic gasoline consumption affects U. S. petroleum production and how these changes in production affect the different types of employment represented by four NAICS codes. Upstream companies are represented by NAICS code 211, which includes jobs associated with oil and gas extraction. Contracted employees who assist in extraction are represented by NAICS code 213. Midstream companies are responsible for transporting and storing the crude oil before refining and are reflected in NAICS code 486. * Lastly, downstream companies focus primarily on refining the crude oil and distributing its byproducts. Employment in petroleum refineries and manufacturing is included in NAICS code 324. 

Literature Review Some studies have estimated the relationship between gasoline consumption and petroleum employment using crude oil price. Observational studies show that declines in crude oil prices seem to negatively impact upstream companies, primarily those involved in the exploration and mining phase, and benefit downstream companies which can now purchase crude oil at a lower price. 12, 13 These observations are supported through data analysis provided by Brown and Yucel 14, who used linear regression analysis to calculate the elasticity of types of petroleum employment and crude oil price. Their findings are in line with the idea that increases in crude oil price are beneficial to downstream companies and harmful to upstream ones. The elasticities they calculate for both are inelastic, meaning the percent change of employment will be smaller than the percent change in crude oil prices. 

Since we hold prices fixed, we will estimate how gasoline consumption affects petroleum employment through petroleum production instead of price. We first need to estimate how changes in gasoline consumption affect petroleum production. A strand of economic literature has estimated how the crude oil market responds to demand shocks utilizing structural vector autoregressive analysis 15, 16. Both these papers suggest that the relationship between crude oil consumption and petroleum production is inelastic. Gunter finds that demand shocks only explain roughly 7-20% of the variance of crude oil production across countries 16. 

________ *Note that the midstream stage is often included as a part of upstream companies. 

Our second step is to estimate how changes in petroleum production affect employment in the petroleum sector. A study by Brown 17 relating employment to petroleum production estimated how petroleum employment responds to decreases in the number of oil rigs in a state. Brown's results are shown in the first three columns of With this literature as a benchmark, we expect our results to show an inelastic relationship between both U. S. petroleum consumption and production, as well as domestic petroleum consumption and employment in the petroleum sector.
section_name: Additional Information on Estimation of the Job Impacts on the Petroleum Industry

section_content: We analyze the effect of gasoline consumption on petroleum employment through changes in domestic production instead of price. We do this for a variety of reasons. First, there is very little evidence that changes in U. S. consumption alone affect overall global petroleum demand, and therefore U. S. consumption is unlikely to have a significant influence on crude oil price change 18. Most importantly, crude oil prices are fixed across the BAU and co-optimized scenarios in the BSM analysis, which provided
section_name: Methodology

section_content: the gasoline consumption estimates. To maintain internal consistency with the assumptions about crude prices, which are used for BSM modeling, our analysis will estimate how changes in U. S. gasoline consumption affect domestic petroleum production and how these changes can then affect petroleum sector-specific employment while crude oil prices remain unchanged under both the BAU and cooptimized scenarios. 

We utilize a two-step process to relate domestic gasoline consumption to U. S. petroleum employment so that we can verify each step using previous research. With the use of previous literature as a benchmark, our main contribution is primarily the linkage between the two stages, rather than a completely new model that links gasoline consumption to petroleum employment directly. We develop our model using U. S. national data from 2000 to 2016.
section_name: Table S-1: Relationship between oil rig count and employment

section_content: To determine the relationship between U. S gasoline consumption and domestic petroleum production, we estimate the following specification through ordinary least squares (OLS) analysis, where all nonstationary variables are first differenced to induce stationarity. 

Where:

 Production = the volume of crude oil produced from oil reservoirs and measured in thousand barrels per day. Provided by the Energy Information Administration (EIA).  Consumption = the disappearance of petroleum products from primary sources, and approximately represents motor gasoline consumption. Measured in thousand barrels per day and obtained from EIA data.  includes all necessary controls to analyze the relationship between domestic petroleum o 2007 and 2008 to control for the oil price shock during this time, which is said to be due to increases in demand and stagnant production 20. o 2008 and 2009 to control for the global financial crisis. o 2014 and 2015 to address the excess of supply that led to a sharp decline in crude oil prices. 

Because petroleum production is influenced by multiple factors, it is important to control for factors other than domestic gasoline consumption that may also affect domestic petroleum production. The unemployment rate and NFCI are included to control for the economic climate. Futures prices, which are contracts to purchase oil at a specific price and time in the future, are included to address expectations about the crude oil market. Global variables are included since crude oil is a global commodity. Net imports and the share of fracking are included to account for changes to the crude oil market.
section_name: Consumption and Production

section_content: The second step is to estimate the relationship between domestic crude oil production and U. S. petroleum employment. Brown 16 linked crude oil production to employment by estimating the relationship between oil rig count and employment using an autoregressive distributive lag (ARDL)

model. An ARDL model, by including lags of both the independent and dependent variables of a regression, better addresses the dynamics of the relationship between the two variables. We take a similar approach and estimate the following specification through OLS:

Where: o Same as previous regression. These control for years of extreme variability in crude oil prices. 

Again, we control for any other factors that could influence employment in the petroleum sector. We include the unemployment rate to address any changes in petroleum employment that are due to labor market conditions. Futures prices are included because many hires in the oil and gas sector take place earlier in the process, meaning that companies hiring employees will base decisions on the expectations of oil price in the future. We also include fracking share to address the changes to the crude oil market with the introduction of hydraulic fracturing.
section_name: Production and Employment

section_name: Results

section_content: The results of the first step imply that a decrease of one thousand barrels per day in U. S. gasoline consumption decreases petroleum production by 207-231 barrels per day. To put this value into context, a roughly 0. 012% decrease in U. S. gasoline consumption decreases production by 0. 0036%, suggesting an elasticity of 0. 3. This inelastic relationship is consistent with previous work. Although neither Kilian nor Gunter looked directly at the relationship between U. S. demand shocks and U. S. production, both suggest an inelastic relationship between oil demand shocks and corresponding production responses. This implies that the 7% decline in gasoline consumption, due to increased biofuel utilization in the isopropanol scenario, would result in a decrease in petroleum production by less than 2%. 

The results of the second step show that the only type of employment affected by changes in petroleum production is upstream employment, NAICS code 211. The other three types of employment-213, 486, and 324-yield insignificant estimates. Specifically, a thousand-barrel-per-day decrease in domestic production decreases upstream employment by 5. 8 employees in the long run and has no effect on the remaining types of petroleum employment. This estimate suggests that a 0. 016% decrease in production decreases employment by 0. 004%. This implies an elasticity of 0. 25, signifying an inelastic relationship. This elasticity is similar to Brown's 17 findings that there is an inelastic relationship between a state's petroleum production and its petroleum employment. 

Using the co-optimized scenario results along with the first-and second-stage estimates of how gasoline consumption changes affect production, and how employment responds to production changes, Table S -2 shows the potential employment effects in response to the decrease in gasoline consumption in the co-optimized scenario. To put the employee estimates into context, there were 144,500 upstream employees (NAICS 211) in December 2017, according to the BLS. The results suggest a maximum 0. 83% decrease in upstream employment due to increased biofuel utilization in the scenarios we considered, or a 0. 23% % decrease in overall petroleum employment. 

*Percent changes calculated using 211 employment in December 2017 (provided by BLS). 

Percent changes calculated using 211 employment in December 2017 (provided by BLS).
section_name: Relationship between gasoline consumption and petroleum production

section_name: *

section_content: To test that our regression estimates are robust, we add more controls to the two specifications. First, we include stock of crude oil and crude oil price in both steps one and two. We include stock of crude oil because decisions may be dependent on how much crude oil is in inventory. We include price because the BSM/ADOPT model runs assume that oil and conventional gasoline prices do not change due to the production of new bio-blendstocks (refer to Caveats, Limitations, and Assumptions in the main text) and we want to analyze the changes in petroleum employment that are not due to changes in price. The only way to explicitly do this is to include price as a control. This causes econometric and interpretation issues, but we provide it for robustness. The inclusion of these additional controls has very little effect on our coefficient results. 

Additionally, we want to account for the fact that employment in NAICS codes 211, 213, and 486 includes jobs related to both petroleum and natural gas. This fact can be problematic if changes in natural gas employment are driving the changes in our employment variables. To show that trends/changes in natural gas employment are not affecting our results, we add U. S. natural gas production as a control in our regression. Accounting for this issue has little impact on our estimates.
section_name: Robustness Check

section_content: As with all OLS analyses, there are limitations to the results. Specifically, coefficients provided by OLS estimation provide the effect of the independent variable on the dependent variable, holding all controls constant. Therefore, our estimates provide the predicted effect of consumption changes on employment, assuming no changes in any of the control variables. This assumption likely does not hold in actuality because of the complexities of the crude oil market. The analysis also assumes a linear relationship, which may not be a true representation of these two relationships. Not only does OLS have its own limitations, but the data used in this analysis may also hinder the robustness of this study. We use a limited number of observations to focus specifically on the current U. S. petroleum market, which can lead to imprecise coefficient estimates. It is important to consider that the estimates generated in this study are derived from only about 200 observations. The limited availability and relevance of data make our estimates less robust than studies with more observations. 

Another limitation is the complexity and inconsistencies of the crude oil market, and the U. S. petroleum sector specifically. The recent transformation of the U. S. petroleum sector due to fracking, and the limited time that has passed since, make it difficult to accurately estimate the relationships between these variables in the current market. 

Although there are limitations to the analysis presented here, these inelastic relationships between gasoline consumption and production and crude oil production and petroleum employment have been demonstrated across a variety of studies. Our analysis further emphasizes the nature of these relationships. By tying the two stages together, our results suggest that the relatively small changes in U. S. gasoline consumption in the scenarios we considered will have minimal impact on U. S. petroleum employment.
section_name: Limitations

section_name: Supplementary Information for Energy, Economic, and Environmental Benefits Assessment of Co-Optimized Engines and Bio-Blendstocks

section_name: Yearly changes in construction and operation-related jobs