Journal of Energy Engineering

The central focus of the paper is to develop the technical bases for intertechnology comparisons based on health risks. To achieve this objective, the paper unifies deterministic and probabilistic aspects of energy‐related health‐risk assessment. The first section of the paper discusses the measures of risk assessment used in making comparisons among different technologies that generate electricity. Beginning with such risk measures as the constant slope and zero intercept hypothesis, the discussion progresses through risk scaling, summation, and other measures. The shortcomings and advantages of each measure are discussed and illustrated. The second section of the paper introduces and discusses the basis for probabilistic risk assessment through frequency‐severity models and hazard functions. The third section provides a summary of intertechnology comparisons and discusses their usefulness for the decision maker. This section includes technology comparisons based on confidence tables as a means to represent the degree of confidence in the estimates of risk between and among technologies. Conclusions complete the paper.

The management of energy techology uses policy principles that range from de minimis risk to significant risk. The protection afforded varies and is discussed first. Next, the paper evaluates uncertainty and causality about health effects from exposure to pollutants emitted by fossil‐fuel burning plants to generate electricity. The bases for assessing human health risks from a coal economy and single power plants are reviewed and found to be disassociated from each other. The large variability in the magnitude of the estimates of health effects troubles those national and international bodies that shape energy choices. Variability and uncertainty arise principally from the estimation of exposure‐response models and their statistical analysis. Despite issues difficult to resolve, the exposure‐response associations are useful. The inference of causal association between exposure to airborne pollutants and excess mortality is increasingly apparent and can be used in policymaking. The means to provide useful summaries of the information are discussed through the proposal for a “metadistribution” of the statistical parameters of several dose‐exposure models.

A historical review of comparisons of risks from various energy and electricity technologies indicates a changing focus over time. Studies published in the 1970s addressed health and safety risks to the public and to workers from a power plant of standard size and design, assumed to be located at a generic site. Beginning in the late 1970s, more systematic studies were conducted that considered how additions of new generating capacity affected an entire generating system. More recent studies have considered risks not appropriately accounted for on a per‐power‐plant basis including those from energy end use, where, for example, risks from electrical shock or fire are independent of the technology used for generation, and global risks where the marginal framework inherent in a per‐power‐plant analysis fails to capture the key aspects of the risks (for example, greenhouse gas emissions). These studies have not been particularly influential in energy technology choices in particular cases; they have contributed to the public debate on the desirability of alternative technologies based on their health and environmental risks.

Economic development results in the gradual substitution of the traditional risks largely related to rural poverty with the modern risks characterizing industrialization and agricultural modernization. In the world today, modern technologies and their risks are being introduced during earlier phases of development than in the past. Consequently, undue risks of both types often exist side by side. To reflect the actual impact of new technologies in developing countries, therefore, risk assessors should consider interactions that develop because of this risk overlap. In addition, better techniques for net risk assessment need to be found because significant risk lowering and risk raising occur in developing countries. Although each country will develop in its own fashion, the risk transition framework can be useful for comparison and analysis. A brief illustration of its use is given for evaluating alternative policies in developing countries for response to global warming.

Chemical carcinogens act by a variety of biological mechanisms to increase the rates of cancer induction and cancer expression in animals or humans exposed to them. Despite this diversity, several unifying “biologically based” risk‐modeling approaches have been developed recently that can help to make more accurate and realistic quantitative assessments of cancer risks from exposure to chemicals. This paper summarizes the traditional linearized multistage (LMS) statistical model used in a majority of regulatory cancer risk assessments and then presents three more recent approaches—the Moolgavkar et al.. two‐stage stochastic cancer model, physiologically based pharmacokinetic modeling, and molecular epidemiology approaches—that are starting to change the ways in which cancer risks are assessed. These complementary approaches share an emphasis on incorporating more biology into risk assessment to obtain better risk estimates. They are starting to be integrated into a new paradigm for cancer risk analysis. This paper reviews and evaluates these approaches to biologically based cancer risk assessment and identifies several new directions for research.

Health standards are commonly based on only one group of rodents. Additional uncertainty (deriving from animals used as a surrogate for man and low‐dose extrapolations to environmental concentrations) cause inflated margins of safety to be incorporated into health standards. This paper demonstrates how more data and models can be used to produce more realistic and less expensive health criteria. The approach is to collect all available tumor studies for chemicals of interest. Next, common units of dose and response are used to merge data from multiple experiments. Then, an array of mathematical extrapolation models is fitted to the data, and the median model value at each dose of interest is used to compare relative toxicity between different compounds of interest. Finally, the relative toxicity factors can be coupled to a bench mark of human risk so that estimates for all chemicals have relative precision. In this fashion, decisions about control technology become standardized instead of varying by several orders of magnitude. The method works like a field horsepower ranking that is finally calibrated to a standard.

Risk assessment plays an important but problematic role in environmental law. It can structure regulatory decision making by permitting agencies to set priorities and compare alternative decisions. Risk assessment thus can help to focus public policy discussions about the appropriate level of acceptable risk. The enormous scientific uncertainties at each stage of risk assessment, however, make quantifying risks impossible without making value‐laden, simplifying assumptions. As a result, environmental risk assessment often does not provide scientific guidance for regulatory decisions. Because, however, regulatory agencies need a method to set standards, numerical risk assessments may be given undue weight at the expense of nonquantifiable considerations. Pseudo‐science in the guise of expertise often substitutes for genuine political discourse. As long as risk assessment is unreliable, its role should be minimized. Technology‐based standards, while imperfect, may serve as a proxy until better risk information is developed.