After a decade of rising costs and technical challenges, project financial data indicates that offshore wind may finally be on a downward cost trajectory while the industry logged its best deployment year ever in 2015. Historically, rising offshore wind costs have been attributed to a myriad of hindrances, including increasing siting challenges (e.g., deeper water, greater distances from shore) and a wide range of installation and operational difficulties that have frustrated developers and offset gains made in technology, learning, and experience. The resilience of the European offshore wind industry to overcome these daunting cost challenges can be attributed to stable European policy commitments, the introduction of new offshore-class turbine and substructure technologies, and the creation of an offshore wind industry supply chain.

1 This analysis excludes Alaska. For Hawaii, estimates of LACE, net value, and economic potential were not calculated because of data limitations. 2 Additional studies that this conceptual approach and methodology is based on include Brown et al. (2015), U.S. Department of Energy (2013), and Lopez et al. (2012). 3 Sea state has been defined as the "overall condition of the surface of a large area of open ocean or sea resulting from the combined effects of wind-generated waves, swells, and surface currents. It is described in terms of how rough the waters are based on wave height." (Canada Department of Environment and Climate Change 2017) 4 The concept of avoided costs has long been used in utility and regulatory economics (see e.g., Public Utility Regulatory Act of 1978, Sec. 210, defining avoided costs as "the incremental cost to an electric utility of electric energy or capacity or both which […] such utility would generate itself or purchase from another source"). This analysis applies a specification that does not necessarily reflect the same complexity that is used for other purposes. Limitations and caveats are discussed in Section 3. vii This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. resource mix, demand patterns, and transmission constraints. The difference between LCOE and LACE at a given location (denoted in this report by "net value") can help inform an initial understanding of the economic potential of a new offshore wind project at a high geospatial resolution. For this analysis, policy-related factors that may influence LACE or LCOE (and hence, the "net value" of a renewable energy project) were not considered explicitly. For instance, renewable energy support mechanisms (e.g., the production tax credit, Renewable Portfolio Standards), energy sector and environmental regulations (e.g., carbon pricing), or benefits from portfolio diversification (Energy Information Administration [EIA] 2015) may increase the "net value" and economic potential. Conversely, regulatory uncertainty and market barriers (see e.g., DOE 2016) may decrease the "net value" and economic potential. 5 A series of data sources and reports were used to derive the assumptions and data for the underlying analysis documented in

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a b s t r a c tOver the past two decades, transportation has begun a shift from an individual focus to a social focus. Accordingly, discrete choice models have begun to integrate social context into its framework. Social influence, the process of having one's behavior be affected by others, has been one approach to this integration. This paper provides a review and discussion of the incorporation of social influence into discrete choice models with specific application in travel behavior analysis. The discussion begins with a generalized framework to describe choice models of social influence. This framework focuses on the behavioral microfoundations of social influence and choice by separating the social influence mechanism from the source of its influence and by explicitly acknowledging the role of the social network in the model structure. This contrasts with prior work that focused on the measurement of contextual, endogenous, and correlated effects. Then, the state of the art in travel behavior research is reviewed using a taxonomy based on the generalized framework with research performed in sociology, social psychology, and social network analysis. The discussion then shifts to the importance of understanding the motivations for social influence, and the formation and structure of social networks are explored. Additionally, the challenges of collecting data for social influence studies are mentioned and the paper concludes with a look at the challenges in the field and areas for future research.

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