Vehicle Manufacturer Technology Adoption and Pricing Strategies under Fuel Economy/Emissions Standards and Feebates.

• Author: Liua, Changzheng

1. INTRODUCTION

   Corporate Average Fuel Economy (CAFE) Standards, established by the U.S. Energy Policy and Conservation Act of 1975, require automobile manufacturers to meet minimum fleet average fuel economy standards for passenger cars and light trucks. Their effectiveness and impact on social welfare have been extensively studied in the literature (Kleit 1990, 2004; Greene 1991; Austin and Dinan 2005; Goldberg 1998; Parry, Fisher and Harrington 2004). Greene's (1991) work on manufacturer pricing strategies is particularly relevant to this paper. If manufacturers offer multiple vehicle models that vary in fuel economy, they can use price changes to induce sales-mix shifts in order to meet CAFE standards. Specifically, they can increase the price of their less efficient vehicles and/or decrease the price of their more efficient vehicles to increase the average fuel economy of their vehicle sales. Greene (1991) analyzed how short-run pricing strategies could be used to meet a required fuel economy target in this way, and concluded that pricing strategies are cost-effective for very small improvements in fuel economy but are expensive for large improvements. Greene (1991) focused on the short-run response of manufacturers, where the adoption of fuel-economy-improving technologies was not an option.

   This paper analyzes manufacturer decision making over a longer planning horizon, and explores the role that both technology adoption and pricing strategies might play in meeting the new post-2010 CAFE and carbon dioxide (C[O.sub.2]) emissions standards, assuming the use of currently available, proven technologies. Pricing is clearly an important tool that manufacturers may use to cope with uncertainties in production, consumer demand, and fuel price fluctuation in the short term; however, the intended purpose of the regulation is to induce technology adoption and spur innovation over time. Pricing might be used to add flexibility, but would only be expected to play a major role if individual manufacturers had difficulty meeting standards using the available technology. Thus an examination of the relative role of technology adoption and pricing strategies helps us understand the functionality of the new standards. We also examine how a feebate program implemented along with CAFE and emissions standards might affect manufacturer decisions and fuel efficiency improvement. A feebate is a market-based policy that levies fees on new vehicles with low fuel efficiency and provides rebates to new vehicles with high fuel efficiency (Greene et al. 2005). Finally, we explore potential benefits and costs to consumers, including average vehicle technology cost, fuel savings, new vehicle sales and sales mix, and the average footprint size of the new vehicle fleet.

   The effects of CAFE and emissions standards on manufacturer decisions and vehicle sales mix shifts are estimated using a dynamic multi-period optimization model. Under the assumption of a competitive or monopolistically competitive market, manufacturers maximize social surplus (the sum of consumer and producer surplus) by optimizing their technology adoption and pricing decisions subject to CAFE and emissions standards. Vehicle offerings, including each vehicle's price and fuel economy, are specified for 2010 (the base year). Vehicle offerings remain the same over the planning period, except for fuel economy improvements and price changes made by manufacturers in later years. These changes have an impact on consumer demand for vehicles (and therefore consumer surplus), which is estimated using a nested multinomial logit (NMNL) model. Both manufacturer decisions and consumer demand are modeled at the level of vehicle configuration (a combination of vehicle make/model, engine size and transmission type) corresponding to the level of detail at which fuel economy measurements are made by the U.S. Environmental Protection Agency (EPA) for determining compliance with the standards. Compared with recent CAFE analysis models (Kleit 2004; Austin and Dinan 2005), the advantages of our approach are (1) more realistic modeling of technology adoption by including vehicle redesign cycles, and (2) more detailed simulation of sales mix shifts by representing consumer choices at a higher level of detail.

   In the remainder of this paper, we first provide more background on new CAFE and emissions standards. We then describe the dynamic optimization model and define reference and policy cases. Finally, we summarize primary results and present our conclusions.

2. CAFE (2011-2016) AND C[O.sub.2] EMISSIONS (2012-2016) STANDARDS

   The new standards have substantially tightened requirements for new light-duty vehicle fuel efficiency, compared with old standards (2010 and earlier). Moreover, in contrast to previous standards, the fuel efficiency target of a vehicle is a function of its footprint (wheelbase X track width). The standards define footprint functions separately for cars and light trucks, and for each model year (U.S. EPA/U.S. DOT 2010).

   Manufacturers must meet the standards for cars and light trucks separately: The salesweighted harmonic mean fuel economy for a manufacturer's car or truck fleet is required to equal or exceed its harmonic mean fuel economy target for the case of CAFE standards. For emissions standards, the sales-weighted mean C[O.sub.2] emissions must not exceed the sales-weighted C[O.sub.2] emissions target. For instance, emissions standards for a manufacturer's car fleet can be described by the following equation:

   [mathematical expression not reproducible] (1)

   where [N.sub.m] denotes the number of car models produced by manufacturer m, Sales(t[).sub.i] represents the sales of car model i for year t, [TotalSales.sub.m] is the total sales of all cars produced by manufacture m, [e.sub.i](t) is the emissions rate of model i, and [e.sub.i]* (t) is its emissions target, calculated using the appropriate footprint function given in the standards.

   The new standards also provide compliance flexibility to manufacturers. Manufacturers can earn compliance credits by outperforming the required targets. Credits can be carried forward (i.e. banked) and used toward compliance in future years; credits can also be carried backward (borrowed) to offset a deficit that had accrued in a prior model year. The standards allow credits to be banked and used later for up to five years (1) and borrowed from future years for up to three years.

   Manufacturers can also achieve flexible-fuel vehicle (FFV) credits and air conditioning (AC) credits by producing FFVs and improving air conditioning systems. The most prominent new feature is to allow credit transfer between the car and truck compliance categories within a firm, and credit trading (i.e., purchasing or selling credits) among firms. Rubin, Leiby and Greene (2009) estimate that credit transfer and trading can reduce manufacturers' compliance cost by 7% to 16%.

3. METHODOLOGY

   Our approach integrates manufacturer decisions and consumer demand into a multi-period optimization framework. Detailed documentation on the model, its coefficients, calibration, and data are available (Bunch et al. 2011). A brief overview is provided here.

   3.1 Manufacturer Decisions

   The manufacturer decision problem is formulated as an optimization model that maximizes social surplus subject to the requirements of CAFE and emissions standards over a multi-year planning horizon. In meeting the standards, manufacturers have various options, including

1. adopting fuel economy technologies that increase fuel economy at a cost,

2. employing pricing strategies to shift sales toward fuel-efficient vehicles (or away from fuel-inefficient vehicles) to increase fleet average fuel efficiency,

3. transferring credits among vehicle categories (e.g., cars and trucks),

4. buying credits from other firms,

5. obtaining AC and FFV credits by improving air conditioning systems and producing FFVs, and

6. using banked or borrowed credits.

   The first four options are implemented using decision variables in the model. AC and FFV credits are included exogenously. Banking is not included in the main analyses, although this restriction is relaxed in a sensitivity analysis. Borrowing is not included in any of our analyses. (2)

   Technology Cost Curves

   The technical potential to improve fuel economy is represented by technology cost curves that take into account base year implementation of fuel economy technologies for all vehicle configurations as well as future potential applicability. Technology cost curves (comparable to those curves in U.S. EPA/U.S. DOT 2010) (3) use quadratic functions to specify the incremental retail price equivalent (RPE) for a relative increase in fuel economy (Figure 1). The RPE is intended to represent the long-run average cost of fuel economy technology, including a normal rate of profit. In effect, this implies either competitive or monopolistically competitive market conditions (firms may differentiate products, but on average, products are priced at their long-run average cost). Separate curves are provided for 20 vehicle classes, by engine technology (gasoline, diesel, and hybrid vehicles) and time period (short-, medium- and long-term).

   Optimization Model Equations

   The optimization model has multiple variants, depending on which assumptions are being used. The basic version of the model is defined as

   [mathematical expression not reproducible] (2)

   such that

   [mathematical expression not reproducible] (3)

   [e.sub.i] (t) = [e.sub.i] (t-1), if vehicle i is not redesigned in year t. (4)

   The decision variables are fuel efficiency level [e.sub.i] (in fuel consumption or C[O.sub.2] emissions rate), and price adjustment [DELTA][p.sub.i] for each vehicle configuration for each model year. Other vehicle characteristics (e.g., vehicle weight, size and horsepower) are assumed to be unchanged over the planning horizon. The objective function (2) is accumulated total social...