Abstract: Organizational decisions to mitigate climate change are often focused solely on reducing greenhouse gas emissions, but also can have multiple sustainability-related impacts. A substantial area of impact from reducing greenhouse gases relates to air quality, where reductions in fossil fuel use can cause health damages locally and regionally. While much research has quantified the air quality benefits of large-scale strategies to reduce greenhouse gas emissions, information about the different impacts of organizational Scope 1–3 emissions on air quality is lacking. We use data from two universities and one multinational corporation based in the northeast U.S. to examine the magnitude and location of air quality changes associated with reducing carbon emissions under two different strategies: replacing purchased fossil-based electricity with renewable energy (scope 2), and reducing personnel business travel by air (scope 3). We estimate the marginal climate response and spatially-resolved air quality impacts associated with these two strategies. To do this, we first use an energy system model (US Energy Grid Optimization, US-EGO) to simulate electricity grid responses and related emissions (CO₂, NO_x and SO₂) due to organizational electricity consumption. We calculate business travel emissions (principally NO_x, SO_x and non-volatile particulate matter) with the Aviation Emission Inventory Code (AEIC) based on detailed flight data provided by the organizations. For both sectors, we run GEOS-Chem High Performance (GCHP), an atmospheric chemistry-transport model, to simulate the ground-level concentration of ozone and fine particulate matter (PM_2.5). We estimate the health damages from these two pollutants with Concentration Response Functions (CRFs). We calculate the social costs using the Social Cost of Carbon (SCC) for carbon emissions, and use a Value of Statistical Life (VSL) to quantify costs for air-pollution-induced health damages. We explore how marginal estimates of damages vary depending on system-level assumptions. Finally, we compare our estimates from detailed modeling with results from reduced form air quality estimators (InMAP, AP2 and EASIUR) to identify how these tools might assist organizations in prioritizing emissions reductions to maximize overall air quality benefits.