We evaluate fine particulate matter (PM2.5) exposure–response models to propose a consistent set of global effect factors for product and policy assessments across spatial scales and across urban and rural environments. Relationships among exposure concentrations and PM2.5-attributable health effects largely depend on location, population density, and mortality rates. Existing effect factors build mostly on an essentially linear exposure–response function with coefficients from the American Cancer Society study. In contrast, the Global Burden of Disease analysis offers a nonlinear integrated exposure–response (IER) model with coefficients derived from numerous epidemiological studies covering a wide range of exposure concentrations. We explore the IER, additionally provide a simplified regression as a function of PM2.5 level, mortality rates, and severity, and compare results with effect factors derived from the recently published global exposure mortality model (GEMM). Uncertainty in effect factors is dominated by the exposure–response shape, background mortality, and geographic variability. Our central IER-based effect factor estimates for different regions do not differ substantially from previous estimates. However, IER estimates exhibit significant variability between locations as well as between urban and rural environments, driven primarily by variability in PM2.5 concentrations and mortality rates. Using the IER as the basis for effect factors presents a consistent picture of global PM2.5-related effects for use in product and policy assessment frameworks.

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Decades of air pollution regulation have yielded enormous benefits in the United States, but vehicle emissions remain a climate and public health issue. Studies have quantified the vehicle-related fine particulate matter (PM2.5)-attributable mortality but lack the combination of proper counterfactual scenarios, latest epidemiological evidence, and detailed spatial resolution; all needed to assess the benefits of recent emission reductions. We use this combination to assess PM2.5-attributable health benefits and also assess the climate benefits of on-road emission reductions between 2008 and 2017. We estimate total benefits of $270 (190 to 480) billion in 2017. Vehicle-related PM2.5-attributable deaths decreased from 27,700 in 2008 to 19,800 in 2017; however, had per-mile emission factors remained at 2008 levels, 48,200 deaths would have occurred in 2017. The 74% increase from 27,700 to 48,200 PM2.5-attributable deaths with the same emission factors is due to lower baseline PM2.5 concentrations (+26%), more vehicle miles and fleet composition changes (+22%), higher baseline mortality (+13%), and interactions among these (+12%). Climate benefits were small (3 to 19% of the total). The percent reductions in emissions and PM2.5-attributable deaths were similar despite an opportunity to achieve disproportionately large health benefits by reducing high-impact emissions of passenger light-duty vehicles in urban areas. Increasingly large vehicles and an aging population, increasing mortality, suggest large health benefits in urban areas require more stringent policies. Local policies can be effective because high-impact primary PM2.5 and NH3 emissions disperse little outside metropolitan areas. Complementary national-level policies for NOx are merited because of its substantial impacts—with little spatial variability—and dispersion across states and metropolitan areas.

The private sector is interested in contributing to the United Nations (UN) Sustainable Development Goals (SDGs); however, they lack credible objective metrics to measure progress, which hinders making a case for financial investing toward the SDGs. A set of science-based metrics could allow corporations and interested investors to meaningfully align their actions with the SDGs in locations around the world where they can make the greatest positive impact. Using existing data on country-level electricity generation and land transportation, we develop a set of simple-to-implement and user-friendly metrics to evaluate the benefits that investments in renewable electricity generation and improvements in land transportation can make toward reducing CO 2 and air pollutant emissions and the health impacts of air pollution. We then apply these metrics to a set of renewable electricity companies and find meaningful differences in their progress toward the SDGs on health, energy, and climate. We found that under half of the renewable energy companies in our dataset disclose country-level data on where equipment is being sold, and that there is substantial variability in the CO 2 reductions and health benefits of renewable energy based on where these companies have installed capacity. There was not a close statistical relationship between country CO 2 emissions rates and country health impact rates, indicating that these metrics cannot serve as good proxies for one another. Future improvements to this methodology should be to implement explicit tracking of air pollution from sources to the locations where it has eventual health impacts, updating the underlying dataset, and improving the degree of detail in emissions inventories. Application of this methodology across the renewable energy sector is limited by the availability of country-level data on where a company has renewable energy capacity installed. The methodology developed here can serve as a basis for better measurement of progress toward climate, energy, and healthrelated SDGs in financial investing and other applications.