A critical review of concentration-response functions in assessing the health impacts of the electric vehicle transition

As the electric vehicle (EV) transition accelerates, policymakers need scientific tools that clearly quantify who benefits from cleaner air, and where those benefits occur. Health impacts are commonly estimated using concentration–response functions (CRFs), which link changes in pollution levels to changes in health outcomes. While indispensable, small differences in how CRFs are implemented can yield counterintuitive results that, if unexamined, may misdirect policy and exacerbate environmental health inequities. We reviewed published studies that apply CRFs to evaluate the EV transition, spanning endpoints from premature mortality, respiratory and cardiovascular hospitalizations, and respiratory morbidity to leukemia, as well as economic and productivity outcomes. Across studies, estimates vary with methodological choices, including assumptions about concentration thresholds, treatment of non-tailpipe emissions, choice of CRF shape (linear vs non-linear), and approaches to uncertainty quantification. These choices materially affect both the size of estimated health effects and the confidence that can be placed in them. Our synthesis highlights the need for greater methodological transparency and reflexivity: clear reporting of assumptions and system boundaries, equity-relevant spatial resolution, and routine sensitivity analysis, so that decisions align with legislative and policy guidance to use the best available evidence.