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This paper is built around ASTM E 2506, Standard Guide for Developing a Cost-Effective Risk Mitigation Plan for New and Existing Constructed Facilities. E 2506 establishes a three-step protocol—perform risk assessment, specify combinations of risk mitigation strategies for evaluation, and perform economic evaluation—to insure that the decision maker is provided the requisite information to choose the most cost effective combination of risk mitigation strategies. Because decisions associated with low-probability, high-consequence events involve uncertainty both in terms of appropriate evaluation procedures and event-related measures of likelihood and consequence, NIST developed a Risk Mitigation Toolkit. This paper uses (a) a data center undergoing renovation for improved security, and (b) a PVP-related failure event to illustrate how to perform the E 2506 three-step protocol with particular emphasis on the third step—perform economic evaluation. The third step is built around the Cost-Effectiveness Tool for Capital Asset Protection (CET), which was developed by NIST. Version 4.0 of CET is used to analyze the security- or failure-related event with a focus on consequence estimation and consequence assessment via Monte Carlo techniques. CET 4.0 includes detailed analysis and reporting features designed to identify key cost drivers, measure their impacts, and deliver estimated consequence parameters with uncertainty bounds. Significance of this economics-based intelligence (EI) tool is presented and discussed for security- or failure-consequence estimation to risk assessment of failure of critical structures or components.

iii Preface v Acknowledgements vii Acronyms and Abbreviations xiii Executive Summary xv

Although the construction industry is a major component of the U.S. economy, it has experienced a “perceived” prolonged period of decline in productivity. Due to the critical lack of measurement methods, however, the magnitude of the productivity problem in the construction industry is largely unknown. The measurement problem is exacerbated by the fact that the construction industry is composed of four sectors that differ significantly in the outputs produced, firm size, and use of technology. The four sectors, which taken together define the construction industry, are residential, commercial/institutional, industrial, and infrastructure. This report describes efforts underway that focus on the measurement of construction productivity at three levels—task, project, and industry—and how such measurements can be developed. This report analyzes the measurement challenges associated with the development of meaningful measures of construction productivity at the task, project, and industry levels and establishes a framework for addressing those challenges. Specifically, this report identifies the metrics, tools, and data needed to move forward in collaboration with key construction industry stakeholders. Once produced, these metrics, tools and data will help construction industry stakeholders make more cost-effective investments in productivity enhancing technologies and improved life-cycle construction processes; they will also provide stakeholders with new measurement and evaluation capabilities. Finally, this report lays the foundation for future research and for establishing key industry collaborations that will enable more meaningful measures of construction productivity to be produced at the task, project, and industry levels.

Natural large (catastrophic) disturbances are important because of their potential long-lasting impact on their surroundings, but underlying differences between frequent small and less common large disturbances are not well understood (Turner and Dale 1997, Romme et al. 1998, Turner and Dale 1998, chapter 4 of this book). Smaller disturbances may be better understood given their relative abundance, which lends itself more readily for study, but it is, perhaps, more useful to understand the forces driving damaging, catastrophic events. Wildland fires represent a perfect example. Nationwide, over 130,000 wildfires burn more than 4 million acres annually (1960-2002), these fires costing Federal agencies in excess of $768 million a year (1994-2002) in suppression alone (National Interagency Fire Center, http://www.nifc.gov/stats/index.html). Average wildfire size was 31 acres, with a suppression cost of $4800 per fire. The average wildfire does not appear a catastrophic threat, however this ignores the spatial distribution of these fires in relation to values at risk (an averaged size fire in a heavily populated area poses a different risk than a similarly size fire far removed from people and items of value). Catastrophic fire events, while relatively infrequent, do occur with some regularity—the 2000 Cerro Grande fire in New Mexico devastated 47,650 acres, two fire complexes in California in fall of 1999 each burned for three months and consumed a total of 227,647 acres, and during the 1998 Florida summer wildfire season, two fire complexes accounted for 205,786 acres or 9 percent of all wildfire acres, nationwide, in that year (National Interagency Fire Center, http://www.nifc.gov/stats/index.html). Do the largest fires account for a disproportional amount of the area burned and damage? Is it possible that the largest 1 percent of fires account for 99 percent of the area, as Strauss et al. (1989) explored? For the state of Florida, the largest 1 percent accounted for 67 percent of total area burned with an average fire size of 2,641 acres versus 13 acres for the smallest 99 percent (1981-2001). Understanding the differences between small and large wildfires, including the exogenous factors influencing each, may provide decision-makers with better tools to mitigate future large-scale fire events. It is not necessarily true that large

Research shows that some categories of human-ignited wildfires may be forecastable, owing to their temporal clustering, with the possibility that resources could be predeployed to help reduce the incidence of such wildfires. We estimated several kinds of incendiary and other human-ignited wildfire forecast models at the weekly time step for tribal land units in the United States, evaluating their forecast skill out of sample. Analyses show that an autoregressive conditional Poisson model of both incendiary and non-incendiary human-ignited wildfires is more accurate out of sample compared with alternatives, and the simplest of the autoregressive conditional Poisson models performed the best. Additionally, an ensemble of these and simpler, less analytically intensive approaches performed even better. Wildfire hotspot forecast models using all model types were evaluated in a simulation mode to assess the net benefits of forecasts in the context of law-enforcement resource reallocations. Our analyses show that such hotspot tools could yield large positive net benefits for the tribes in terms of suppression expenditures averted for incendiary wildfires but that the hotspot tools were less likely to be beneficial for addressing outbreaks of non-incendiary human-ignited wildfires.

Are we cutting down tropical forests too rapidly and too extensively? If so, why? Answers to both questions are obscured in some ways by insufficient and unreliable data on the economic worth of forest ecosystem services. It is clear, however, that rapid, excessive cutting of forests can irreversibly and substantively impair ecosystem functions, thereby endangering the flow of several socially valuable goods and services from standing forests. One reason for such excessive deforestation is failure to consider the full range of goods and services provided by the forests, particularly latent and complex ecosystem services.

Humans cause more than 55% of wildfires on lands managed by the USDA Forest Service and US Department of the Interior, contributing to both suppression expenditures and damages. One means to reduce the expenditures and damages associated with these wildfires is through fire prevention activities, which can include burn permits, public service programs or announcements, outreach efforts to schools, youth groups and equipment operators, and law enforcement. Using data from 17 US Bureau of Indian Affairs tribal units, we modelled the effect of prevention programs and law enforcement on the number of human-caused ignitions. We also included weather and lagged burned area in our estimation of fixed-effects count models. The results show that prevention activities led to significant reductions in wildfires caused by escaped campfires, juveniles, fire-use (e.g. escaped debris burns) and equipment. Increased law enforcement resulted in fewer incendiary- and equipment-caused wildfires. Using average suppression expenditures by wildfire and our estimate of avoided wildfires per additional year of prevention, we estimate partial benefit–cost ratios of greater than 4.5 for all Bureau of Indian Affairs regions for the continuation of the prevention program.

The number of smoking-caused wildfires has been falling nationwide. In national forests in 2011, smoking-caused wildfires represented only 10% of their 1980 level. No other cause of wildfire has experienced this level of decline. For 12 states, we evaluate the rate of smoking-caused wildfires and find it is a function of weather, other ignitions, the number of adult smokers, the presence of improved wildfire cause-determination methods, and whether a state required the sale of less fire-prone cigarettes. We find the decline in adult smoking rates has led to a reduction of smoking-caused fires by 9%. The finding that less fire-prone cigarettes appear successful at limiting wildfire starts – by 23% – is a likely unintended benefit of a technology aimed at reducing fire fatalities in residences. We also find that the improvements in wildfire cause determination have resulted in a reduction in smoking-classified fires by 48%. Although improved wildfire cause-determination methods do not necessarily reduce the number of wildfires, they ensure that the causes of wildfire are accurately tracked. Accurate wildfire cause determination can, however, result in targeting wildfire-prevention programs to specific fire-cause categories, which can lead to a reduction in the overall number of wildfires.

We describe how two important tools of wildfire management, wildfire prevention education and prescribed fire for fuels management, can be coordinated to minimise the combination of management costs and expected societal losses resulting from wildland fire. We present a long-run model that accounts for the dynamics of wildfire, the effects of fuels management on wildfire ignition risk and area burned, and the effects of wildfire prevention education on the ignition risk of human-caused, unintentional wildfires. Based on wildfire management activities in Florida from 2002 to 2007, we find that although wildfire prevention education and prescribed fire have different effects on timing and types of fires, the optimal solution is to increase both interventions. Prescribed fire affects whole landscapes and therefore reduces losses from all wildfire types (including lightning), whereas wildfire prevention education reduces only human-caused ignitions. However, prescribed fire offers a longer-term solution with little short-term flexibility. Wildfire prevention education programs, by comparison, are more flexible, both in time and space, and can respond to unexpected outbreaks, but with limited mitigation longevity. Only when used together in a coordinated effort do we find the costs and losses from unintentional wildfires are minimised.

Wildfires that spread into wildland–urban interface (WUI) communities present significant challenges on several fronts. In the United States, the WUI accounts for a significant portion of wildland fire suppression and wildland fuel treatment costs. Methods to reduce structure losses are focussed on fuel treatments in either wildland fuels or residential fuels. There is a need for a well-characterised, systematic testing of these approaches across a range of community and structure types and fire conditions. Laboratory experiments, field measurements and fire behaviour models can be used to better determine the exposure conditions faced by communities and structures. The outcome of such an effort would be proven fuel treatment techniques for wildland and residential fuels, risk assessment strategies, economic cost analysis models, and test methods with representative exposure conditions for fire-resistant building designs and materials.

We analyse the spatio-temporal structure of wildfire ignitions in the St Johns River Water Management District in north-eastern Florida. We show, using tools to analyse point patterns (e.g. the L-function), that wildfire events occur in clusters. Clustering of these events correlates with irregular distribution of fire ignitions, including lightning and human sources, and fuels on the landscape. In addition, we define a relative clustering index that summarizes the amount of clustering over various spatial scales. We carry our analysis in three steps: purely temporal, purely spatial, and spatio-temporal. Our results show that arson and lightning are the leading causes of wildfires in this region and that ignitions by railroad, lightning, and arson are spatially more clustered than ignitions by other accidental causes.