Dr. David T. Butry

Dr. David T. Butry is an economist in the Office of Applied Economics of the Engineering Laboratory (EL) at the National Institute of Standards and Technology (NIST). Dr. Butry joined NIST in 2006. Currently, Dr. Butry is leading research in the areas of wildland and structure fire. His current fire projects include: (1) understanding how interdependent wildfire risk, faced by homeowners residing within the wildland-urban interface, affects the cost-effectiveness of community risk mitigation decisions; (2) evaluating the spatio-temporal dynamics of fire ignition patterns, by cause (e.g., arson, unintentionally-set), and determining how at-risk fire communities can reduce unwanted fire ignitions using mitigation strategies that include education and/or law-enforcement-focused geospatial hotspotting tools; and (3) measuring the economic performance of fire-safety technologies, such as residential fire sprinkler systems and, in tall buildings,  protected occupant egress elevators. Other areas of ongoing research include developing the standards and tools to assess the resilience of the built environment to natural and man-made disasters; measuring the productivity of the construction industry; comparing the energy savings yielded by "smart" landscaping design and urban forestry techniques (e.g., use of shade-producing trees) with other, more traditional, building technologies that promote energy efficiency (e.g., windows, insulation).

From 1999 through 2006, Dr. Butry served as Economist for the USDA Forest Service, where his research focused on the economics of wildland fire and the valuation of non-market environmental goods and services.  His formal training is in the areas of applied microeconomics and econometrics, with specialization in the fields of natural resource economics, environmental economics, and spatial statistics (including the use of Geographic Information Systems). Dr. Butry is interested in the economics of natural and man-made disasters and with econometric issues relating to spatial autocorrelation and analysis, endogenous variables, and sample self-selection.