Evaluating the Impact of High-Performance Buildings Using Advanced Life Cycle MethodsEPA Grant Number: FP917321
Title: Evaluating the Impact of High-Performance Buildings Using Advanced Life Cycle Methods
Investigators: Collinge, William O
Institution: University of Pittsburgh
EPA Project Officer: Zambrana, Jose
Project Period: September 1, 2011 through August 31, 2014
Project Amount: $126,000
RFA: STAR Graduate Fellowships (2011) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Fellowship - Science & Technology for Sustainability: Green Engineering/Building/Chemical Products & Processes/Materials Development
The goal of this research is to demonstrate an improved life cycle assessment (LCA) method that adequately portrays whole-building impacts to the natural and built environments, such that it is of value to practitioners in the architecture, engineering, construction and management community. The explicit hypothesis for this work is that incorporating both indoor environmental quality and dynamic temporal and spatial life cycle data into environmental assessments of buildings will have a significant effect on environmental performance scores. Testing this hypothesis entails constructing an LCA model that incorporates both of these aspects, and using case studies to compare results obtained using this model to results from current models.
This research will be split into two main tasks. The first task will develop a mathematical model and computational framework to enable dynamic life cycle assessment. Time series of life cycle inventory (LCI) emissions and resource consumption factors will be developed from available historical data sources and projections (e.g., EPA emissions inventory trends, BEA industry data, DOE-EIA energy consumption data and projections). Additional time series of life cycle impact assessment (LCIA) characterization factors will be obtained from the literature or developed from available models. This information will be coupled with information from available building models and plans to generate time series for a variety of building use scenarios against evolving environmental (e.g., regulations, ambient concentrations) and industrial (e.g., technology mixes, fuel sources) backgrounds. The second task will develop a building-specific indoor environmental quality (IEQ) component to be embedded into the dynamic LCA model. The model will be constructed using the results of previously published studies and will take into account building parameters affecting indoor air quality (IAQ) and other IEQ elements (e.g., thermal comfort, ventilation and lighting), and will be validated by taking sensor measurements within the initial case study building, the Mascaro Center for Sustainable Innovation (MCSI) building at the University of Pittsburgh. IEQ effects on human health will be integrated within the existing LCA methods for human health impact assessment, while effects on the economic bottom line (e.g., productivity) will be integrated within a life cycle cost framework.
Results of the integrated modeling effort will provide several levels of improvement to the current state of LCA practice for buildings. The inclusion of dynamic temporal and spatial life cycle data will provide a more accurate framework for assessing the impacts of different building design and operation decisions over future building lifetimes, thus enabling more effective decisionmaking. The IEQ results will help to identify the tradeoffs or synergies between environmental impacts to building occupants and the impacts to the general population that result from building operations and energy demands.
Potential to Further Environmental / Human Health Protection
Having a flexible, state-of-the-art model to simulate building lifetime environmental impacts will inform building designers and operators of the full environmental implications of building designs, enabling them to better evaluate multiple bottom line scenarios. In addition, the direct comparison of indoor and outdoor effects related to buildings has the potential to be scaled up to the entire building sector, where it may enable better regulatory decision-making by prioritizing the areas of highest impacts.