Social and Earth Systems Modeling using Greens Functions: Air Quality, Climate and Energy Policy

Decisions about energy production particularly the present and future of coal-fired power plants are driven by financing and energy markets, affect the climate, and cause human health impacts. Assessments of these decisions typically use source-receptor modeling, that often considers only the atmospheric transport and chemistry of single pollutants. This approach considers human decisions and their variation across regions and generations as separate from earth system responses, but inequities in climate and air quality impacts make it important to integrate assessments of the spatial and temporal variability of social and earth systems. We expand on traditional source-receptor research to account for spatial and temporal variability by applying Greens functions based on model simulations for black carbon (BC), CO2, and mercury. Greens functions allow us to analyze the global pattern of response to perturbations (emissions) of any size. We use this method to understand the varying impacts of the location and lifetime of coal power plants at local, regional, and global scales. This work focuses on these pollutants because they represent different health and climate impacts, are governed by three different international conventions. We use the Precipitation Driver and Response Model Intercomparison Projects (PDRMIP) present day concentration (pdc), and 10x pdc model runs for BC to derive response functions from the difference in BC concentrations between the two runs, relative to their emissions. We perform similar calculations using GEOS-Chem model runs coupled with a biogeochemical model to quantify long-term mercury concentrations under a pulse emission scenario, varying the location of the pulse emission to establish regional functions. The benefit of this approach is that we can apply these functions to individual power plant emissions to estimate patterns of atmospheric composition and response at the local, regional, and global scale for varying power plant lifetimes. For CO2, we use the existing temperature pattern responses (Lucarini, 2017), to quantify the impact of CO2 emissions on temperatures of different regions. We combine this approach with societally relevant data, attributing impacts to the country of funding source as well as the physical location of the plant.