Reassessing the human health benefits from cleaner air

What Are the Net Benefits of Reducing the Ozone Standard to 65 ppb? An Alternative Analysis

In October 2015, the United States Environmental Protection Agency (EPA) lowered the level of the ozone National Ambient Air Quality Standard (NAAQS) from 0.075 ppm to 0.070 ppm (annual 4th highest daily maximum 8-h concentration, averaged over three years). The EPA estimated a 2025 annual national non-California net benefit of $1.5 to $4.5 billion (2011$, 7% discount rate) for a 0.070 ppm standard, and a −$1.0 to $14 billion net benefit for an alternative 0.065 ppm standard. The purpose of this work is to present a combined toxicological and economic assessment of the EPA’s benefit-cost analysis of the 2015 ozone NAAQS. Assessing the quality of the epidemiology studies based on considerations of bias, confounding, chance, integration of evidence, and application of the studies for future population risk estimates, we derived several alternative benefits estimates. We also considered the strengths and weaknesses of the EPA’s cost estimates (e.g., marginal abatement costs), as well as estimates completed by other authors, and provided our own alternative cost estimate. Based on our alternative benefits and cost calculations, we estimated an alternative net benefit of between −$0.3 and $1.8 billion for a 0.070 ppm standard (2011 $, 7% discount rate) and between −$23 and −$17 billion for a 0.065 ppm standard. This work demonstrates that alternative reasonable assumptions can generate very difference cost and benefits estimates that may impact how policy makers view the outcomes of a major rule.

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low-regret learning strategies using regulation of air pollutants with uncertain health effects as an example.

Response of global particulate-matter-related mortality to changes in local precursor emissions

Recent Global Burden of Disease (GBD) assessments estimated that outdoor fine-particulate matter (PM2.5) is a causal factor in over 5% of global premature deaths. PM2.5 is produced by a variety of direct and indirect, natural and anthropogenic processes that complicate PM2.5 management. This study develops a proof-of-concept method to quantify the effects on global premature mortality of changes to PM2.5 precursor emissions. Using the adjoint of the GEOS-Chem chemical transport model, we calculated sensitivities of global PM2.5-related premature mortality to emissions of precursor gases (SO2, NOx, NH3) and carbonaceous aerosols. We used a satellite-derived ground-level PM2.5 data set at approximately 10 × 10 km(2) resolution to better align the exposure with population density. We used exposure-response functions from the GBD project to relate mortality to exposure in the adjoint calculation. The response of global mortality to changes in local anthropogenic emissions varied spatially by several orders of magnitude. The largest reductions in mortality for a 1 kg km(-2) yr(-1) decrease in emissions were for ammonia and carbonaceous aerosols in Eastern Europe. The greatest reductions in mortality for a 10% decrease in emissions were found for secondary inorganic sources in East Asia. In general, a 10% decrease in SO2 emissions was the most effective source to control, but regional exceptions were found.

Improving risk management: from lame excuses to principled practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame-shifting "lame excuses" for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning-especially from near-misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk-reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.

Six components necessary for effective public health program implementation

Public health programs succeed and survive if organizations and coalitions address 6 key areas. (1) Innovation to develop the evidence base for action; (2) a technical package of a limited number of high-priority, evidence-based interventions that together will have a major impact; (3) effective performance management, especially through rigorous, real-time monitoring, evaluation, and program improvement; (4) partnerships and coalitions with public- and private-sector organizations; (5) communication of accurate and timely information to the health care community, decision makers, and the public to effect behavior change and engage civil society; and (6) political commitment to obtain resources and support for effective action. Programs including smallpox eradication, tuberculosis control, tobacco control, polio eradication, and others have made progress by addressing these 6 areas.

Uncertain benefits estimates for reductions in fine particle concentrations

The Environmental Protection Agency's (EPA's) estimates of the benefits of improved air quality, especially from reduced mortality associated with reductions in fine particle concentrations, constitute the largest category of benefits from all federal regulation over the last decade. EPA develops such estimates, however, using an approach little changed since a 2002 report by the National Research Council (NRC), which was critical of EPA's methods and recommended a more comprehensive uncertainty analysis incorporating probability distributions for major sources of uncertainty. Consistent with the NRC's 2002 recommendations, we explore alternative assumptions and probability distributions for the major variables used to calculate the value of mortality benefits. For metropolitan Philadelphia, we show that uncertainty in air quality improvements and in baseline mortality have only modest effects on the distribution of estimated benefits. We analyze the effects of alternative assumptions regarding the value of reducing mortality risk, whether the toxicity is above or below the average for fine particles, and whether there is a threshold in the concentration-response relationship, and show these assumptions all have large effects on the distribution of benefits.

A reanalysis of fine particulate matter air pollution versus life expectancy in the United States

A reduction in population exposure to fine particulate matter air pollution (PM2.5) has been associated with improvements in life expectancy. This article presents a reanalysis of this relationship and comments on the results from a study on the reduction of ambient air PM2.5 concentrations versus life expectancy in metropolitan areas of the United States. The results of the reanalysis show that the statistical significance of the correlation is lost after removing one of the metropolitan areas from the regression analysis, suggesting that the results may not be suitable for a meaningful and reliable inference.

IMPLICATIONS

The observed loss of statistical significance in the correlation between the reduction of ambient air PM2.5 concentrations and life expectancy in metropolitan areas of the United States, after removing one of the metropolitan areas from the regression analysis, may raise concern for the policymakers in decisions regarding further reductions in permitted levels of air pollution emissions.

A reanalysis of fine particulate matter air pollution versus life expectancy in the United States

A reduction in population exposure to fine particulate matter air pollution (PM2.5) has been associated with improvements in life expectancy. This article presents a reanalysis of this relationship and comments on the results from a study on the reduction of ambient air PM2.5 concentrations versus life expectancy in metropolitan areas of the United States. The results of the reanalysis show that the statistical significance of the correlation is lost after removing one of the metropolitan areas from the regression analysis, suggesting that the results may not be suitable for a meaningful and reliable inference.

IMPLICATIONS

The observed loss of statistical significance in the correlation between the reduction of ambient air PM2.5 concentrations and life expectancy in metropolitan areas of the United States, after removing one of the metropolitan areas from the regression analysis, may raise concern for the policymakers in decisions regarding further reductions in permitted levels of air pollution emissions.