Buildings consume 75% of US electricity and could be a primary demand-side management resource for the rapidly changing electric grid. We assess the technical potential grid resource from best-available building efficiency and flexibility measures in 2030 and 2050 and find that such measures could avoid up to nearly one-third of annual fossil-fired generation and one-half of fossil-fired capacity additions after 2020. Our results quantify the role that building technologies can play in the future of the US electricity system.

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Understanding and improving how humans adapt to climate change are priorities in our research community, and coastal settlements are good places to study adaptation. Severe storm events and sea-level rise are threatening coastal communities with increasing levels of flood damage.Because ownership of coastal assets is distributed among many private and public actors, both individual property owners and public officials must take adaptive actions. This paper introduces an integrated agent-based and hedonic pricing modeling system to simulate coastal real estate market performance under non-equilibrium conditions that reflect the effects of storm events.The modeling system, which is used for policy analysis, is calibrated to conditions in two towns in Monmouth County, New Jersey, USA, which were badly damaged by Hurricane Sandy in 2012.The key findings are that (a) coastal real estate markets capitalize flood risk into property values but this discount diminishes rapidly as time passes between storm events, and (b) there is a distinct equity versus efficiency tradeoff in designing public policies to reduce the cost to society of coastal flooding. Stringent regulation of building practices reduces flood damage but drives away poorer home buyers and owners, whereas informational and incentive-based policies are fairer but less effective. Hands-off, market-based retreat from risky areas is socially costly but allows less wealthy people to remain at the shore, albeit in vulnerable situations. Managed retreat should emphasize improved recreational access to coastal amenities while discouraging people from living there. K E Y W O R D Sadaptation, agent-based modeling, coastal flooding, housing market, resilience, spatial hedonic INTRODUCTIONCoastal communities are adapting to intensified storms and sea-level rise, but the process is often emotionally painful for households, politically difficult for public officials, and economically wasteful for insurers and other market actors. People and ecosystems are at risk, and so are the large stocks of materials and embodied energy forming the built environment in coastal settlements. Developing sound public policies to steer coastal adaptation can be difficult because decision-making is distributed, with private individuals often controlling most land and buildings, public actors controlling many infrastructure systems and regulating private activities, and private enablers making the markets work. There is a need to ask lots of "what-if" questions when designing policies, and one way to explore these questions prior to implementation in real communities is with policy simulation models.Policy simulation tools in the coastal adaptation domain have specific requirements. They need to be able to model distributed, behaviorally plausible human interactions with the natural environment and their associated marketplace transactions under different policy scenarios. Unpacking that sentence, we encounter methodological heterodoxy: distributed agency implies complexity

This paper addresses the challenge of incorporating occupant behavior into building performance simulation models used during the design process-that is, before the actual occupants are known. It proposes the use of synthetic population data, an approach that is novel in building performance modeling although common in urban planning and public health. A simpler approach embodied in the ASHRAE Fundamentals volume is to report standard distributions of values for behavioral variables, assuming that parameters vary independently of one another when in fact many co-vary or are interdependent. An alternative approach calibrates models of occupant behavior against actual occupants in specific existing buildings, but this raises questions of transferability. Needed is a database of "generic" occupants that designers can use prospectively during the design process. This paper documents a process of combining disparate field studies of commercial buildings into a larger occupant behavior database and generating a statistically similar synthetic data set that can be shared without compromising confidentiality requirements associated with field studies. The synthetic data set successfully incorporates much of the covariance structure of the underlying field data and supports multivariate modeling. Its scope and structure necessarily serve the needs of the associated modeling framework. Cooperative and systematic sharing of data by field researchers is crucial for building large enough data sets to serve as a behaviorally-robust basis for building design.

Since the introduction of the occupant behavior Drivers-Needs-Actions-Systems (DNAS) framework in 2013, researchers have used the framework or further developed it based on their case studies, which include efforts to collect new data on occupant behaviors. The effort is often costly for the relatively few new data points added. Problems emerge when the already collected data do not meet the modelers' interoperability requirements. Previous studies addressed this issue by developing more sophisticated ontologies that enable integration with other datasets and synthetic data methodologies that would meet unique research applications. This paper presents an extension of the DNAS framework for the representation of synthetic occupant data to support various applications and use cases across the building life cycle. An agent-based modeling application is one of our motivations that requires more elaborate characteristics of an occupantagent or a group-of-agent. The extension, built upon a review of the literature, introduces new elements to the framework that fall into five categories, including socio-economic, geographical location, activities, subjective values, and individual and collective adaptive actions. On-going research includes identifying occupant datasets and developing data fusion methods to generate synthetic occupants, as well as to demonstrate its applications in agent-based modeling coupled with building performance simulation.

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Industrial location theory has not emphasized environmental concerns, and research on industrial symbiosis has not emphasized workforce housing concerns. This article brings jobs, housing, and environmental considerations together in an agent-based model of industrial and household location. It shows that four classic outcomes emerge from the interplay of a relatively small number of explanatory factors: the isolated enterprise with commuters; the company town; the economic agglomeration; and the balanced city. Volume 19, Number 2 r What are the evolutionary pathways that settlements founded around industrial enterprises might follow? r Which factors explain commonly observed settlement patterns? r Do environmental factors play a role? From Enterprises to SettlementsThis investigation of how industrial enterprises sometimes evolve into industrial cities draws on three analytical traditions, including urban modeling, industrial symbiosis (IS), and jobsto-housing comparisons. Environmental Simplification in Urban ModelingThe classical traditions in location theory all employ a microeconomic logic. The land-use tradition originates with von Thünen (1966/1826) who argues that land uses in a preindustrial landscape follow a monocentric model that optimally locates dispersed agricultural production based on transport costs to a central market. Alonso (1964) updates this approach for the industrial city by determining the optimal distances of residential and commercial land uses from a central business district. Sasaki and Box (2003) replicate von Thünen's results in an agent-based model. The central-place tradition originating