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 research aims at linking Urban Planning, Energy Simulations and Climate Change projections into the year 2100 for hot climates. The workflow of going back and forth between urban and city scale plans and individual neighborhood parcels to building scale, for the sake of simulating energy demand for a given city into the future is complex. It is prone to rely on many assumptions and simplifications in order to aid the simulations. In this work, we streamline the process with new computational tools, with the goal of communicating a more precise impact of building scale and neighborhood morphological scale design and retrofit strategies in order to meet energy reduction and carbon emission targets focusing on 2030, 2050 and 2100. Urban scenarios are developed using Envision Tomorrow. The building archetypes used therein are associated with energy demand profiles which we simulate using EnergyPlus for various climate change scenarios to improve the forecasting ability of Envision Tomorrow. Denser developments yield far lower neighborhood energy use.

This paper investigates different urban growth and densification scenarios and their energy performance under climate change conditions using urban energy simulation. We developed an abstract urban growth model based on the West Campus neighborhood in downtown Austin, Texas. Four different scenarios were applied to an abstract population growth model, which were then tested from the current year to 2100 under both climate change and control (TMY) conditions. Our results show that all urban growth models perform similarly in terms of operational energy but differ radically in terms of carrying capacity and embodied energy.

Achieving net-zero carbon emission goals in the built environment requires optimizing design decisions by considering both embodied and operational carbon impacts. This study addresses this challenge by proposing a multi-objective optimization workflow that integrates the EnergyPlus engine, Grasshopper-Ladybug, Cardinal LCA, and Wallacei tools. The optimization process focuses on balconies, which are key building components that significantly influence both embodied and operational carbon footprints in mid-rise residential buildings. The study parametrically models various balcony aspects, including width, depth, construction materials, orientation, and whether they are open or closed. The results show that balconies can offset their embodied carbon footprint, while also contributing to significant reductions in space conditioning energy consumption for residential units in 5A ASHRAE climate regions. The research presents a multi-objective optimization workflow for refining individual building component designs to minimize carbon emissions, making it applicable to both new construction and renovation projects.

This research project compared a standard vapor compression system and a standard desiccant dehumidification system with heat wheel to determine if there was some potential energy savings for “shoulder season” hours in Austin Texas. “Shoulder season” hours as defined in the paper are hours during which the dry bulb temperature falls within the American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) comfort bound but the humidity is above the comfortable humidity point. These hours are normally addressed with vapor compression systems which dehumidify by cooling the air under the comfort setpoint to dehumidify, which is wasteful of energy. The study found that for these shoulder season hours a desiccant dehumidification system was around 4.5 times more energy efficient at reaching comfort setpoints if free heating was used for drying the desiccant.