Download or read book Influence of Future Low-carbon Energy Scenarios on California Criteria Pollutant Emissions, Air Pollution, and Health written by Christina Bautista Zapata and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: California’s goal to reduce greenhouse gas (GHG) emissions 80% below 1990 levels by the year 2050 will require adoption of low-carbon energy sources across all economic sectors. The CA-TIMES model is a bottom-up energy-economic cost minimization model that was designed to examine different energy scenarios paths given carbon constraints. Here I have dissected two CA-TIMES scenarios, a business-as-usual (BAU) and a GHG-constrained (GHG-Step) scenario, to enhance understanding of how transforming energy can lead to changes in (Part I) short-lived criteria pollutant emissions and impact (Part II) air pollution, public health, and costs associated with premature mortality. In Part (I) the California REgional Multisector AiR QUality Emissions (CA-REMARQUE) model was developed to estimate criteria pollutant emissions inventories for each CA-TIMES energy scenario. Separate algorithms were developed to estimate criteria pollutant and precursor emissions for all energy sectors. This required the incorporation of literature-based emission profiles of particulate chemical composition and size distribution and gas speciation, and emission rates. Spatially-resolved energy projections were reviewed and gathered for many future and advanced electrification, biofuels, and hydrogen technologies. CA-REMARQUE results indicate an overall decrease in emissions across all sectors given a GHG-Step scenario, but also unexpected increases across in some specific energy sectors. Avoidance of fossil fuel consumption and use of alternative fuels, primarily in the GHG-Step scenario, also modify the composition of reactive organic gas emissions and the size and composition of particulate matter emissions. In Part (II) the UCD/CIT Airshed Lagrangian model was run to simulate annual-average air pollution changes of PM2.5 and O3 concentrations. Simulations were conducted for three modelling domains over California: a 576 km2 cell resolution over California, 16 km2 cell resolution over Central Valley, and 16 km2 cell resolution over Southern California. Simulated annual-average PM2.5 and O3 exposure were used to estimate mortality (total deaths per year) and mortality rate (deaths per 100,000) using established exposure-response relationships from air pollution epidemiology. Predicted deaths associated with air pollution in 2050 dropped by 24%–26% in California (1,537–2,758 avoided deaths yr−1) in the 2050 GHG-Step scenario, equivalent to a 54%–56% reduction in the air pollution mortality rate (deaths per 100,000) relative to 2010 levels. These avoided deaths have an estimated value of $11.4B–$20.4B USD per year. Best estimates suggest that meeting an intermediate target (40% reduction in GHG emissions by the year 2030) using a non-optimized scenario would reduce personal income by $4.95B yr−1 (-0.15%) and lower overall state GDP by $16.1B yr−1 (-0.45%). The public health benefits described here are comparable to these cost estimates, making a compelling argument for the adoption of low-carbon energy in California beyond costs associated more directly with climate change.