A well-known challenge in computable general equilibrium (CGE) models is to maintain correspondence between the forecasted economic and physical quantities over time. Maintaining such a correspondence is necessary to understand how economic forecasts reflect, and are constrained by, relationships within the underlying physical system. This work develops a method for projecting global demand for passenger vehicle transport, retaining supplemental physical accounting for vehicle stock, fuel use, and greenhouse gas (GHG) emissions. This method is implemented in the MIT Emissions Prediction and Policy Analysis Version 5 (EPPA5) model and includes several advances over previous approaches. First, the relationship between per-capita income and demand for passenger vehicle transport services (in vehicle-miles traveled, or VMT) is based on econometric estimates and modeled using quasi-homothetic preferences. Second, the passenger vehicle transport sector is structured to capture opportunities to reduce fleet-level gasoline use through the application of vehicle efficiency or alternative fuel vehicle technologies, introduction of alternative fuels, or reduction in demand for VMT. Third, alternative fuel vehicles (AFVs) are represented in the EPPA model. Fixed costs as well as learning effects that could influence the rate of AFV introduction are captured explicitly. This model development lays the foundation for assessing policies that differentiate based on vehicle age and efficiency, alter the relative prices of fuels, or focus on promoting specific advanced vehicle or fuel technologies.
Highlights
► Represents passenger vehicle transport in a computable general equilibrium model ► Captures co-evolution of economic and physical system over time and policy impact ► Relates travel demand to changes in income as well as new vehicle fuel efficiency to fuel price ► Demonstrates how new modeling approach influences energy and emissions forecasts
Computable general equilibrium (CGE) models are widely used to understand the impact of policy constraints on energy use, the environment, and economic welfare at a national or global level (U.S. CCSP [United States Climate Change Science Program], 2007 and Weyant, 1999). However, for certain research questions, results from these models do not capture accurately the relationships in the underlying physical system. These relationships include links between income and demand for services provided by energy-intensive durable goods, as well as the richness of opportunities for technological or behavioral change in response to policy.
Maintaining dual accounting of physical and economic variables is particularly important when modeling consumer durable goods such as passenger vehicles. Vehicles are an example of a complex multi-attribute consumer product with a long lifetime. Consumer preferences across attributes – such as horsepower and fuel economy in the case of vehicles – involve engineering trade-offs at the vehicle level. For instance, over the past several decades, fuel efficiency gains have been offset by a shift toward larger, more powerful vehicles in some regions, offsetting improvements in on-road fuel economy (An and DeCicco, 2007 and Knittel, 2009). As policymakers consider how to most cost-effectively regulate the air, climate, and security externalities associated with vehicle use, macroeconomic forecasting models that capture the range of technological and behavioral responses to regulation will become increasingly important.
The goal of this work is to develop a new method of projecting physical demand for services from passenger vehicles in a recursive-dynamic CGE model. This new method is applied to the MIT Emissions Prediction and Policy Analysis Version 5 (EPPA5) model, a CGE model of the global economy (Babiker et al., 2001, Paltsev et al., 2005 and Paltsev et al., 2010). The method captures the richness of the technological response at an appropriate level of detail, without sacrificing sectoral and regional coverage or the ability to capture the macroeconomic feedbacks that make this modeling system advantageous over other approaches.
The text is organized as follows. Section 2 describes current practices for representing energy-intensive consumption at the household level in CGE models, including the representation of durable goods, and the rationale for a new approach. Section 3 presents the new approach, divided into three parts. Section 3.1 explains how the relationship between income and demand for vehicle services was parameterized using econometric information and implemented using the well-established Stone–Geary (quasi-homothetic) preference system. Section 3.2 describes how vehicle engineering and fleet detail were used to parameterize the structure of the passenger vehicle transport sector and opportunities for fleet-level fuel efficiency improvement. Section 3.3 describes the representation of alternative fuel vehicles. Section 4 describes the impact of model developments on forecasts of gasoline use, greenhouse gas (GHG) emissions, and household consumption. Section 5 offers conclusions and directions for future work.
