Impact of Intensity Standards on Alternative Fuel Adoption: Renewable Natural Gas and California's Low Carbon Fuel Standard.

• Author: Scheitrum, Daniel

1. INTRODUCTION

   Transportation in the United States is almost entirely powered by fossil fuels and is responsible for significant contributions to greenhouse gas emissions (Pachauri et al., 2014). Transportation is the subject of myriad emission-reducing policies and regulations such as those addressing fuel economy, tailpipe emissions, and the mandated blending of biofuels in the gasoline supply. (1) While these programs have largely been successful at achieving their stated goals, transportation remains a major source of greenhouse gas (GHG) emissions, making up 26% of all emissions in the United States (EPA, 2016b). Efforts to further reduce the climate footprint through the use of additional biofuels have been inhibited by the "blend wall"; the technological limitation precluding blends of over 10% ethanol from use in most gasoline engines. For many other alternative fuels, significant adoption depends upon the transformation of the vehicle fleet, which will take many years given the long vehicle replacement interval. (2) Natural gas provides a real possibility for near-term reductions in transportation emissions, particularly through the employment of natural gas produced from renewable sources.

   While natural gas makes up a small portion of fuel consumption, it is steadily growing mostly due to low prices of natural gas. Adoption of natural gas freight vehicles is already employed by several major freight fleets such as Cisco, Pepsi, Walmart, Frito-Lay, HEB, Trimac Transportation, Truck Tire Service Corporation (TTS), Verizon, UPS, AT&T, Food Lion, and Ryder (Jaffe et al., 2015). This phenomenon of expansion of natural gas into the heavy-duty trucking sector has been studied in Krupnick (2011), Knittel (2012), and Fan et al. (2017).

   The current level of vehicular natural gas consumption presents a ready market for the introduction of renewable natural gas (RNG) (Energy Information Administration, 2016d). RNG is considered to be an extremely low-carbon (in some cases negative carbon) fuel because it is produced via the recovery of methane that would otherwise emit into the atmosphere and its consumption displaces the consumption of fossil natural gas or some other fossil fuel. Methane is a potent greenhouse gas having approximately 25 times the impact on climate as C[O.sub.2] (IPCC, 2007). Therefore, capturing emissions of methane, converting them into less harmful C[O.sub.2] via combustion, and displacing the combustion of fossil natural gas significantly reduces GHG emissions. Further, natural gas fuel emits far less particulate matter and pollutants that adversely affect air quality and public health compared to gasoline and diesel (U.S. Department of Energy, 2018). This paper does not assess the impacts on air quality or public health, though this can be assessed in future research.

   The most promising RNG production pathways are the capturing and upgrading of (1) landfill gas and the anaerobic digestion and upgrading of (2) dairy manure, (3) municipal solid waste (MSW), and (4) waste water at waste water treatment plants (WWTP). California State Bill 1383 (SB 1383), specifically targets the reduction of methane from the dairy sector. SB 1383 (2016) requires the dairy sector to reduce methane emissions by 40% relative to 2013 levels by 2030. This amounts to a reduction of roughly 9.4 million metric tonnes of C[O.sub.2]e (California Air Resources Board, 2016a). (3) The landfill sector is required to reduce contributions of organic matter to landfills by 50 percent relative to 2014 levels by 2020 and by 75 percent relative to 2014 levels by 2025. Diversion of organic waste to dedicated MSW digesters is one way to accomplish this requirement. The extent to which RNG production can achieve these goals, the quantity of RNG that can be produced and introduced into transportation, and the degree to which emissions can be reduced is largely unknown. To answer these questions, I rely upon estimates of California RNG supplied by Parker et al. (2017). RNG supply curve estimates are presented in Figure 1 with price in dollars per mmBTU and quantity in billion cubic feet (bcf) per year. (4) In this paper, I evaluate the response of California RNG production to an existing California transportation policy, the Low Carbon Fuel Standard (LCFS).

   The Low Carbon Fuel Standard sets a target for the carbon intensity (quantity of greenhouse gas emitted per unit of energy consumed) of the transportation sector. Consumption of fuels which have carbon intensities above (below) this target generates deficits (credits). Deficits must be offset by purchases of credits. The carbon intensity target is set exogenously by the state and the price of credits is determined endogenously by the supply and demand for credits in the LCFS credit market. I first present a demonstrative, analytical model to illustrate the features of the LCFS policy in comparison to a carbon tax, which is more familiar to most readers. The analytical model indicates that the response of RNG is highly sensitive to credit price. Evaluation of RNG supply response to LCFS policy requires a robust model which includes an endogenous determination of credit price.

   One limitation of the LCFS is that it applies only to the transportation sector. There may be better opportunities to employ RNG such as heating homes. The scales of this end use is so large there would be effectively no limitation on the quantity of RNG that could be consumed. In the transportation sector, the quantity of RNG that can be consumed is constrained by the number natural gas powered vehicles in use in transportation. Alternatively, RNG could be used to generate electricity which would avoid much of the substantial expense of connecting to the natural gas pipeline network and could offset electricity produced from relatively high emitting sources such as coal-fired power plants. However, The option of employing the captured gas in electricity generation is limited due to new legislation limiting emissions from stationary electricity generation sites (Parker et al., 2017). (5) Therefore, I limit the focus of this paper to the impact on transportation fuels.

   In this paper, I construct a numerical, static, multi-market, partial-equilibrium model of California transportation fuels. The model considers the markets for gasoline, diesel, ethanol, bio-diesel, natural gas, and renewable natural gas. The fuels considered make up 99.9% of California's transportation fuel consumption. In this model, I estimate the quantity, price, economic surplus, and emissions responses of these fuels to the LCFS policy. The numerical model includes many extensions that build upon previous efforts to model LCFS policy (Holland, Hughes and Knittel, 2009; Lade and Lin Lawell, 2015a,b; Chen et al., 2014; Huang et al., 2013; Rubin and Leiby, 2013). First, by relying on the RNG supply estimates presented in Parker et al. (2017), I include natural gas and RNG which have been absent from previous studies. Second, I extend beyond a two-fuel gasoline-ethanol model as employed by Holland, Hughes and Knittel (2009) and Lade and Lin Lawell (2015a,b) to a more comprehensive consideration of the California fuels market. By expanding beyond gasoline and ethanol, I capture the impact of other major conventional fuels on the LCFS credit market and provide a more complete representation of credit price determination. Without considering these other fuels any modeling of credit market equilibrium would be incomplete. Third, rather than assume perfect substitution of vehicle fuels, I impose constraints that reflect the limitations of the existing vehicle fleet. That is, I adopt the methodology developed by Anderson (2010) to model consumer choice fuel switching between gasoline and E85 and adapt this method to the diesel-biodiesel market. Allowing for substitution across fuel groups would result in a slower rise in credit prices. Results from Bento, Roth and Zuo (2013) suggest that consumers overreact to fuel prices when purchasing replacement vehicles and, over the long-term, the response to subsidized fuels may be significant. Lastly, I model the implementation as it exists in California, rather than consider a hypothetical LCFS policy. Therefore, the results are directly relevant to policymakers.

   To evaluate the economic efficiency of the LCFS policy, I compare it to a hypothetical carbon tax. The hypothetical carbon tax I consider applies to all sectors of the economy and employs the same carbon intensity assessments as employed in the LCFS. Consequently, this hypothetical carbon tax accounts for the full lifecycle advantage of negative carbon fuels. Typically, a carbon tax prices the positive content of carbon in a fuel. Fuels that have negative carbon intensity assessments due to the full lifecycle accounting would simply be exempted from the tax. The carbon tax considered in this analysis allows for the full lifecycle analysis of fuels to be accounted for. The apparent subsidization of negative carbon fuels under the carbon tax is the manifestation of avoided taxation elsewhere in the economy. This choice is justified due to the passing of California's SB 1383 which specifically targets the sectors likely to yield fuels with negative carbon intensities for reduction in methane emissions.

   In 2015, natural gas fuel made up less than one percent of CA vehicle fuel consumption, but was responsible for generating almost fifteen percent of credits in that year. Models which allow for perfect substitution between fuels implicitly assume complete fuel choice flexibility by all vehicles. Assuming perfect substitution may have merit in the long term, but it is not ideal in understanding market response from the current equilibrium. In the short term, the choices of vehicle fuel a consumer may consider are constrained by the technological limitations of their existing vehicle. (6)

   The numerical model reveals two interesting...