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Quantifying marginal societal health benefits of transportation emission reductions in the United States and Canada

Principal Investigator: 

Carlton University, Canada

The investigators will develop and apply a source‐ and location-specific database of mortality benefits per ton emissions reduction of NOx and other pollutants. 

Funded under

Abstract of the research proposal submitted to RFA 17-2

Marginal damage (MD) of a pollutant or a pollution precursor is defined as the incremental monetized
damage associated with emitting an additional unit mass (often a metric ton) of that pollutant. Conversely,
marginal benefit (MB), also referred to as benefit-per-ton (BPT) by the US EPA, is the same monetized
benefit from reducing emissions of that pollutant by a metric ton. MB or BPT is an invaluable decision
metric as it provides a direct comparison with the cost of emissions control, and thus streamlines the
benefit-cost analysis of policy items and facilitates the design of cost-effective measures.

Despite their apparent importance, BPTs for air pollutants are not widely available, due in large part to the
complexities associated with their estimation. The health damage caused by emissions of a pollutant is
variable across locations and times of emission due to spatial variability in emissions and the affected
populations, as well as variability in meteorology and the chemical regime that controls transformation of
pollutants in the atmosphere. To account for these complexities, estimation of BPTs would require use of
air quality models that properly represent these variabilities and processes. Model-based estimation of
BPTs through traditional approaches is further complicated by the computational requirements; extremely
large number of simulations required for estimating BPTs for each individual source renders location and
source-specific estimations infeasible.

We propose to create a database of sector- and location-specific BPT estimations in Canada and the US
for a recent year, as a measure of the societal benefits associated with reducing emissions from various
sources. Estimation of BPTs will be focused on monetization of avoided mortality due to reduced chronic
(annual) exposure to particulate matter (PM2.5), O3, and NO2. Furthermore, We will evaluate the
robustness of BPTs as reliable decision metrics by conducting methodical sensitivity analyses with
respect to various assumptions and limitations of methods and models used, including a) the shape of
epidemiological models of concentration response functions in the US and Canada, b) the choice of health
endpoint for estimation of benefits, c) the level of past and future pollution controls, d) the spatial
resolution of the model, and e) temporal variability and representativeness of selected episodes. We
further conduct quantitative uncertainty analysis to account for uncertainties that stem from economic,
epidemiologic, or atmospheric modeling. Finally, we use the developed dataset of location-specific BPTs
to produce additional datasets of CO2 reduction co-benefits and vintage-based per-vehicle damage