Search

Design optimization studies that model competition with other products in the market often use a small set of products to represent all competitors. We investigate the effect of competitor product representation on profit-maximizing design solutions. Specifically, we study the implications of replacing a large set of disaggregated elemental competitor products with a subset of competitor products or composite products. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors when using logit and nested logit models (where preferences are homogeneous). However, this relationship differs in the case of random-coefficients logit models (where preferences are heterogeneous), and we demonstrate that profit-maximizing design solutions using latent-class or mixed-logit models can (but need not always) depend on the representation of competing products. We discuss factors that affect the magnitude of the difference between models with elemental and composite representations of competitors, including preference heterogeneity, cost function curvature, and competitor set specification. We present correction factors that ensure models using subsets or composite representation of competitors have optimal design solutions that match those of disaggregated elemental models. While optimal designs using logit and nested logit models are not affected by ad hoc modeling decisions of competitor representation, the independence of optimal designs from competitors when using these models raises questions of when these models are appropriate to use.

Vehicle choice modelers often use composite alternatives, which are simplified representations of a larger, diverse group of vehicle options—a practice known as choice set aggregation. Although this practice has been justified by computational tractability and data constraints, it can introduce arbitrary changes to choice-share predictions. We isolate and characterize the implications of using composite vehicles for choice prediction, given exogenously determined model parameters. We first identify correction factors needed for composite models to predict choice shares that are consistent with those from models that use the full set of disaggregated elemental alternatives. We then assess the distortion of choice-share predictions under various composite specifications and partial corrections using two case studies based on models in the literature used in transportation and energy policymaking: (1) we examine a logit model without alternative-specific constants (ASCs) and find that the distortion in share predictions due to composite specification is substantial and can be larger than variation due to parameter uncertainty; (2) we examine counterfactual predictions of a nested logit model with ASCs based on the NEMS and LVChoice models and find that composite models using ASCs can mitigate or eliminate distortion in some, but not all, counterfactual scenarios. In particular, the distortion is larger when the scenario significantly affects the differences in elemental membership or utility heterogeneity between composite groups. We provide explicit correction factors for composite models with and without ASCs that can be used to take advantage of the tractability of composite models while ensuring that their choice-share predictions exactly match those of their corresponding elemental models in counterfactual and forecasting scenarios.

This paper documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration’s Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution.

The literature on energy technology costs, diffusion, and learning has been characterized by data limitations, partial or arbitrary data sets, apples to oranges comparisons, and imprecision in the use of key concepts and terminology. Two responses to our paper, Lovering et al. (2016), by Koomey et al. and Gilbert et al. reflect many of these problems, conflating learning curves with experience curves, trends in actual costs with the relationship between cost estimates and final construction costs, and component costs with total installed costs. The respondents use inconsistent definitions of demonstration, first-of-a-kind, and commercial deployment across different energy technologies. They also propose to compare final installed costs for nuclear power plants, encompassing construction and finance costs, across different national economies and time periods encompassing a wide range of macro-economic circumstances and finance arrangements that overwhelm any signal from trends associated with the actual construction costs of the plants in question. In this response, we address the specific issues raised in these papers and suggest better practices for comparing energy technology costs, trends, and technological learning.

We investigate the effect of competitor product representation on optimal design results in profit-maximization studies. Specifically, we study the implications of replacing a large set of product alternatives available in the marketplace with a reduced set of selected competitors or with composite alternatives, as is common in the literature. We derive first-order optimality conditions and show that optimal design (but not price) is independent of competitors under the logit and nested logit models (where preference coefficients are homogeneous), but optimal design results may depend on competitor representation in latent class and mixed logit models (where preference coefficients are heterogeneous). In a case study of automotive powertrain design using mixed logit demand, we find some change in the optimal acceleration performance value when competitors are modeled using a small set of alternatives rather than the larger set. The magnitude of this change depends on the specific form and parameters of the cost and demand functions assumed, ranging from 0% to 3% in our case study. We find that the magnitude of the change in optimal design variables induced by competitor representation in our case study increases with the heterogeneity of preference coefficients across consumers and changes with the curvature of the cost function.

The existing literature on the construction costs of nuclear power reactors has focused almost exclusively on trends in construction costs in only two countries, the United States and France, and during two decades, the 1970s and 1980s. These analyses, Koomey and Hultman (2007); Grubler (2010), and Escobar-Rangel and Lévêque (2015), study only 26% of reactors built globally between 1960 and 2010, providing an incomplete picture of the economic evolution of nuclear power construction. This study curates historical reactor-specific overnight construction cost (OCC) data that broaden the scope of study substantially, covering the full cost history for 349 reactors in the US, France, Canada, West Germany, Japan, India, and South Korea, encompassing 58% of all reactors built globally. We find that trends in costs have varied significantly in magnitude and in structure by era, country, and experience. In contrast to the rapid cost escalation that characterized nuclear construction in the United States, we find evidence of much milder cost escalation in many countries, including absolute cost declines in some countries and specific eras. Our new findings suggest that there is no inherent cost escalation trend associated with nuclear technology.

The environmental feasibility of re-using electric vehicle (EV) batteries at their automotive end-of-life into stationary applications was analyzed in a parameterized life cycle model. The model assumes that the life of a lithium ion (Li-ion) EV battery is extended to incorporate the re-purposing and re-use in grid storage for a utility application. Compared to using natural gas fuel for peak electrical power generation a 56% reduction in CO2 emissions is possible when an EV battery is re-purposed to store off-peak clean electricity to serve peak demand. The magnitude of CO2 mitigation associated with battery re-use is similar to that of switching from using a conventional vehicle to an electric vehicle, meaning that the greenhouse gas (GHG) benefits of vehicle electrification could be doubled by extending the life of EV batteries, and better using off-peak low-cost clean electricity.