Fossil-Fuel Options for Power Sector Net-Zero Emissions with Sequestration Tax Credits.

Three of the main challenges in achieving rapid decarbonization of the electric power sector in the near term are getting to net-zero while maintaining grid reliability and minimizing cost. In this policy analysis, we evaluate the performance of a variety of generation strategies using this "triple objective" including nuclear, renewables with different energy storage options, and carbon-emitting generation with carbon capture and storage (CCS) and direct air capture and storage (DACS) technologies. Given the current U.S. tax credits for carbon sequestration under Section 45Q of the Internal Revenue Code, we find that two options: (1) cofiring bioenergy in existing coal-fired assets equipped with CCS, and (2) coupling existing natural gas combined-cycle plants equipped with CCS and DACS, robustly dominate other generation strategies across many assumptions and uncertainties. As a result, capacity-expansion modelers, planners, and policymakers should consider such combinations of carbon-constrained fossil-fuel and negative emissions technologies, together with modifications of the current national incentives, when designing the pathways to a carbon-free economy.

On Ambiguity Reduction and the Role of Decision Analysis during the Pandemic.

The COVID-19 pandemic has created a multitude of decision problems for a variety of fields. Questions from the seriousness and breadth of the problem to the effectiveness of proposed mitigation measures have been raised. We assert that the decision sciences have a crucial role to play here, as the questions requiring answers involve complex decision making under both uncertainty and ambiguity. The collection, processing, and analysis of data is critical in providing a useful response-especially as information of fundamental importance to such decision making (base rates and transmission rates) is lacking. We propose that scarce testing resources should be diverted away from confirmatory analysis of symptomatic people, as laboratory diagnosis appears to have little decision value in treatment choice over clinical diagnosis in patients presenting with symptoms. In contrast, the exploratory use of testing resources to reduce ambiguity in estimates of the base rate of infection appears to have significant value and great practical import for public policy purposes. As these stances may be at odds with triage practices among medical practitioners, they highlight the important role the decision analyst can play in responding to the challenges of the COVID-19 pandemic.

Air pollution emission effects of changes in transport supply: the case of Bogotá, Colombia.

Transportation policy and planning decisions, including decisions on new infrastructure and public transport improvements, affect local and global environmental conditions. This work studies the effect of increased road capacity on traffic-related emissions in Bogotá using a tool that couples a transportation model with emission factors from COPERT IV. We followed a parametric approach varying transport supply and demand, comparing three scenarios: a baseline scenario that represents the transportation system in Bogota in 2015; scenario 1 incorporates five highway capacity-enhancement projects in Bogotá and assumes insensitive travel demand; scenario 2 incorporates the new highway projects but assumes a demand increase of 13% in vehicle trips with private cars. Results include daily and annual values of traffic-related emissions of five air pollutant criteria: CO, NOx, PM10, SO2, and VOC for the baseline scenario, scenario 1, and scenario 2. We found a reduction in emissions after adding highway capacity and assuming inelastic demand (scenario 1). Scenario 1 results in a 15% reduction in PM10 emissions and a 10% reduction in NOx emissions. In contrast, results for scenario 2 suggest increased emissions for all air pollutant criteria (e.g., VOC and CO emissions increase by 21% and 22% compared with the baseline scenario). Therefore, new traffic demand would eliminate the emission savings observed in scenario 1 and could potentially further degrade air quality in Bogotá. While an exact estimate of induced demand that may result from highway expansion in Bogotá is not available, this analysis highlights that such projects could lead to an increase in emissions unless there is a combined effort to managing demand of private vehicle trips.

Development of a high-resolution traffic emission model: Lessons and key insights from the case of Bogotá, Colombia.

This paper presents a traffic-related air emissions inventory of a developing megacity using a traffic assignment model that results in a detailed temporal and spatial emission representation, disaggregating emissions sources by vehicle type and hour of the day, for five criteria air pollutants.

Shore Power for Vessels Calling at U.S. Ports: Benefits and Costs.

When in port, ships burn marine diesel in on-board generators to produce electricity and are significant contributors to poor local and regional air quality. Supplying ships with grid electricity can reduce these emissions. We use two integrated assessment models to quantify the benefits of reducing the emissions of NOX, SO2, PM2.5, and CO2 that would occur if shore power were used. Using historical vessel call data, we identify combinations of vessels and berths at U.S. ports that could be switched to shore power to yield the largest gains for society. Our results indicate that, depending on the social costs of pollution assumed, an air quality benefit of $70-150 million per year could be achieved by retrofitting a quarter to two-thirds of all vessels that call at U.S. ports. Such a benefit could be produced at no net cost to society (health and environmental benefits would be balanced by the cost of ship and port retrofit) but would require many ships to be equipped to receive shore power, even if doing so would result in a private loss for the operator. Policy makers could produce a net societal gain by implementing incentives and mandates to encourage a shift toward shore power.

Historical carbon footprinting and implications for sustainability planning: a case study of the Pittsburgh region.

This study estimates fossil-based CO(2) emissions and energy use from 1900-2000 for Allegheny County, PA. Total energy use and emissions increased from 1900 to 1970, reflecting the significant industrial, economic, and population growth that occurred in Allegheny County. From 1970 to 2000, Allegheny County experienced a 30% decrease in total emissions and energy use from peak values, primarily because of a decline in industrial activity (40% decrease in value added) and the loss of a quarter of its population. Despite these dramatic economic and demographic transitions, per capita emissions remained stable from 1970 to 2000, buoyed by relatively stable or slightly increasing emissions in the commercial and transportation sectors. Allegheny County's history suggests the scale of change needed to achieve local emissions reductions may be significant; given years of major technological, economic, and demographic changes, per capita emissions in 1940 were nearly the same in 2000. Most local governments are planning emissions reductions rates that exceed 1% per year, which deviate significantly from historical trends. Our results suggest additional resources and improved planning paradigms are likely necessary to achieve significant emissions reductions, especially for areas where emissions are still increasing.

Economic and environmental costs of regulatory uncertainty for coal-fired power plants.

Uncertainty about the extent and timing of CO2 emissions regulations for the electricity-generating sector exacerbates the difficulty of selecting investment strategies for retrofitting or alternatively replacing existent coal-fired power plants. This may result in inefficient investments imposing economic and environmental costs to society. In this paper, we construct a multiperiod decision model with an embedded multistage stochastic dynamic program minimizing the expected total costs of plant operation, installations, and pollution allowances. We use the model to forecast optimal sequential investment decisions of a power plant operator with and without uncertainty about future CO2 allowance prices. The comparison of the two cases demonstrates that uncertainty on future CO2 emissions regulations might cause significant economic costs and higher air emissions.

Using expert elicitation to link foodborne illnesses in the United States to foods.

U.S. foodborne illness risk analysis would benefit greatly from better information on the relationship between the incidence of foodborne illness and exposure to foodborne pathogens. In this study, expert elicitation was used to attribute U.S. foodborne illnesses caused by the nine FoodNet pathogens, Toxoplasma gondii, and noroviruses to consumption of foods in 11 broad categories. Forty-two nationally recognized food safety experts responded to a formal written expert elicitation survey. For each pathogen, respondents gave their best estimate of the distribution of foodborne illnesses associated with each of the food categories and the 90% confidence bounds on each of their estimates. Based on the work of Paul Mead and his coauthors, food attribution percentage estimates from this study were used to attribute case, hospitalization, and death incidence estimates to foods according to pathogen. These attribution estimates indicate that 15 food-pathogen pairs account for 90% of the illnesses, 25 pairs account for 90% of hospitalizations, and 21 pairs account for 90% of deaths.

Predicting emergency evacuation and sheltering behavior: a structured analytical approach.

We offer a general approach to predicting public compliance with emergency recommendations. It begins with a formal risk assessment of an anticipated emergency, whose parameters include factors potentially affecting and affected by behavior, as identified by social science research. Standard procedures are used to elicit scientific experts' judgments regarding these behaviors and dependencies, in the context of an emergency scenario. Their judgments are used to refine the model and scenario, enabling local emergency coordinators to predict the behavior of citizens in their area. The approach is illustrated with a case study involving a radiological dispersion device (RDD) exploded in downtown Pittsburgh, PA. Both groups of experts (national and local) predicted approximately 80-90% compliance with an order to evacuate workplaces and 60-70% compliance with an order to shelter in place at home. They predicted 10% lower compliance for people asked to shelter at the office or to evacuate their homes. They predicted 10% lower compliance should the media be skeptical, rather than supportive. They also identified preparatory policies that could improve public compliance by 20-30%. We consider the implications of these results for improving emergency risk assessment models and for anticipating and improving preparedness for disasters, using Hurricane Katrina as a further case in point.

An integrated physical dispersion and behavioral response model for risk assessment of radiological dispersion device (RDD) events.

A radiological dispersion device (RDD) or "dirty" bomb is a conventional explosive wrapped in radiological material. Terrorists may use an RDD to disperse radioactive material across a populated area, causing casualties and/or economic damage. Nearly all risk assessment models for RDDs make unrealistic assumptions about public behavior in their health assessments, including assumptions that the public would stand outside in a single location indefinitely. In this article, we describe an approach for assessing the risks of RDD events incorporating both physical dispersion and behavioral response variables. The general approach is tested using the City of Pittsburgh, Pennsylvania as a case study. Atmospheric models simulate an RDD attack and its likely fallout, while radiation exposure models assess fatal cancer risk. We model different geographical distributions of the population based on time of day. We evaluate aggregate health impacts for different public responses (i.e., sheltering-in-place, evacuating). We find that current RDD models in use can be improved with the integration of behavioral components. Using the results from the model, we show how risk varies across several behavioral and physical variables. We show that the best policy to recommend to the public depends on many different variables, such as the amount of trauma at ground zero, the capability of emergency responders to get trauma victims to local hospitals quickly and efficiently, how quickly evacuations can take place in the city, and the amount of shielding available for shelterers. Using a parametric analysis, we develop behaviorally realistic risk assessments, we identify variables that can affect an optimal risk reduction policy, and we find that decision making can be improved by evaluating the tradeoff between trauma and cancer fatalities for various RDD scenarios before they occur.

Ecological risk ranking: development and evaluation of a method for improving public participation in environmental decision making.

This article reports an extension of the Carnegie Mellon risk-ranking method to incorporate ecological risks and their attributes. On the basis of earlier risk-perception studies, we identified a set of 20 relevant attributes for describing health, safety, and environmental hazards in standardized risk summary sheets. In a series of three ranking sessions, 23 laypeople ranked 10 such hazards in a fictional Midwestern U.S. county using both holistic and multiattribute ranking procedures. Results were consistent with those from previous studies involving only health and safety hazards, providing additional evidence for the validity of the method and the replicability of the resulting rankings. Holistic and multiattribute risk rankings were reasonably consistent both for individuals and for groups. Participants reported that they were satisfied with the procedures and results, and indicated their support for using the method to advise real-world risk-management decisions. Agreement among participants increased over the course of the exercise, perhaps because the materials and deliberations helped participants to correct their misconceptions and clarify their values. Overall, health and safety attributes were judged more important than environmental attributes. However, the overlap between the importance rankings of these two sets of attributes suggests that some information about environmental impacts is important to participants' judgments in comparative risk-assessment tasks.

What number is "fifty-fifty"?: redistributing excessive 50% responses in elicited probabilities.

Studies using open-ended response modes to elicit probabilistic beliefs have sometimes found an elevated frequency (or blip) at 50 in their response distributions. Our previous research suggests that this is caused by intrusion of the phrase "fifty-fifty," which represents epistemic uncertainty, rather than a true numeric probability of 50%. Such inappropriate responses pose a problem for decision analysts and others relying on probabilistic judgments. Using an explicit numeric probability scale (ranging from 0-100%) reduces thinking about uncertain events in verbal terms like "fifty-fifty," and, with it, exaggerated use of the 50 response. Here, we present two procedures for adjusting response distributions for data already collected with open-ended response modes and hence vulnerable to an exaggerated presence of 50%. Each procedure infers the prevalence of 50s had a numeric probability scale been used, then redistributes the excess. The two procedures are validated on some of our own existing data and then applied to judgments elicited from experts in groundwater pollution and bioremediation.

The Relationship between Historic Industrial Site Use and Environmental Contamination.

A methodology is presented for estimating the probability that particular classes of environmental contaminants will be of concern at brownfield redevelopment sites. These probabilities are predicted by a logistics model that is based on qualitative information about site history and status. This qualitative information comprises data that would be collected through a Phase I Environmental Site Assessment (ESA), including historic site use, current use and ownership status, and the nature of adjacent properties. The model is fit and demonstrated using a set of 59 former industrial sites in southwestern Pennsylvania that were collected from the files of the Pennsylvania Department of Environmental Protection (PADEP). Predictive models are developed for exceedances of contaminants as grouped into the following classes: metals, chlorinated hydrocarbons, fuel hydrocarbons, and PCBs. A procedure for estimating the parametric uncertainty of the model predictions is also illustrated. This method can serve as a starting point for more effective usage of existing Phase I ESA information and for evaluation of the benefit of obtaining additional site information. By increasing the decision-making value of existing (or inexpensive) data, this method can help to reduce the information asymmetry that may be an obstacle to redevelopment.