In Texas and elsewhere, the looming realities of rapid population growth and intensifying effects of climate change mean that the things we rely on to live—water, energy, dependable infrastructure, social cohesion, and an ecosystem to support them—are exposed to unprecedented risk. Limited resources will be in ever greater demand and the environmental stress from prolonged droughts, record-breaking heat waves, and destructive floods will increase. Existing long-term trends and behaviors will not be sustainable. That is our current trajectory, but we can still change course. Significant advances in information communication technologies and big data, combined with new frameworks for thinking about urban places as social–ecological–technical systems, and an increasing movement towards transdisciplinary scholarship and practice sets the foundation and framework for a metropolitan observatory. Yet, more is required than an infrastructure for data. Making cities inclusive, safe, resilient, and sustainable will require that data become actionable knowledge that change policy and practice. Research and development of urban sustainability and resilience knowledge is burgeoning, yet the uptake to policy has been slow. An integrative and holistic approach is necessary to develop effective sustainability science that synthesizes different sources of knowledge, relevant disciplines, multi-sectoral alliances, and connections to policy-makers and the public. To address these challenges and opportunities, we developed a conceptual framework for a “metropolitan observatory” to generate standardized long-term, large-scale datasets about social, ecological, and technical dimensions of metropolitan systems. We apply this conceptual model in Texas, known as the Texas Metro Observatory, to advance strategic research and decision-making at the intersection of urbanization and climate change. The Texas Metro Observatory project is part of Planet Texas 2050, a University of Texas Austin grand challenge initiative.

This paper presents an interactive framework for the design of truss structures with aesthetic criteria. The truss chords are described using NURBS, a tool widely used in computer aided design (CAD) programs to describe free-form geometry. This allows for a convenient interface between the optimization scheme, a particle swarm optimizer, and the user. Driven from the fact that aesthetic design goals are not easily quantifiable, key elements are introduced and implemented herein towards an interactive framework for algorithmic design of truss structures. Within this framework, the user can visually assess interesting solutions, save them for later assessment, actively drive the optimization towards individual aims, re-initialize the optimization with a set of available solutions, or restart the design process. A criterion is introduced as a means of quantifying subjective goals, expressing the similarity of the shape of candidate solutions with respect to reference designs. The framework is tested on a benchmark case and then applied to the design of a truss tower. The effectiveness of the similarity criteria, as well as the ability of the user to drive the process towards specific design goals is demonstrated.

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We analyze 100 case studies, which were conducted in 23 countries, and contrast their data on embodied and operational energy in residential and commercial buildings. The case studies include conventional, retrofit, low-energy, passive, and net-zero energy buildings. The buildings have different lifetimes varying from 25 to 100 years. We calculate the estimated total Life Cycle Energy (LCE) as the sum of Embodied Energy (EE) and Operational Energy (OE). The LCE in the 100 case studies ranges from 50.8 to 1840 MJ/m2 per year. Our results show that operational energy significantly dominates the life cycle energy of the buildings by an average of 419 MJ/m2 per year and an average share of 72%. The share of embodied energy increases with decreasing operational energy. However, the overall LCE decreases significantly when the operational energy decreases. Naturally, the assumptions on the lifetime of the buildings have a great impact on the LCE. We conclude that operational energy should be primarily reduced in order to decrease greenhouse gas emissions from the existing building stock because most of the buildings are already built and changes in the embodied energy are often obtained only through new construction or deep retrofit strategies. Depending on the strategy to decrease OE, the share of EE was found to show wide fluctuations within the case studies, ranging from 4% up to 100%. In addition, most of the operational energy consumption has been reported by using energy simulation tools. Only about 14% of the case studies had metered operational energy data. In order to create more accurate data, metering of buildings should be considered in future case studies.