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ESP v2.0: Enhanced method for exploring emission impacts of future scenarios in the United States – addressing spatial allocation
Citation:
Ran, L., Dan Loughlin, D. Yang, Z. Adelman, B. Baek, AND Chris Nolte. ESP v2.0: Enhanced method for exploring emission impacts of future scenarios in the United States – addressing spatial allocation. Geoscientific Model Development . Copernicus Publications, Katlenburg-Lindau, Germany, 8(6):1775-1787, (2015).
Impact/Purpose:
This is a followup paper "ESP v1.0: methodology for exploring emission impacts of future scenarios in the United States." by Loughlin, Benjey and Nolte. That paper presented a method for translating MARKAL outputs into emission growth factors suitable for emissions and air quality modeling. This paper builds upon ESP 1.0 by updating the underlying projections and by exploring how use of a land use change model has the potential to redistribute future-year emissions. Eventually, approaches such as this have the potential to improve future modeling activities by allowing inclusion of factors such as "smart growth" and "suburbanization" in future scenario analyses.
Description:
The Emission Scenario Projection (ESP) method produces future-year air pollutant emissions for mesoscale air quality modeling applications. We present ESP v2.0, which expands upon ESP v1.0 by spatially allocating future-year emissions to account for projected population and land use changes. In ESP v2.0, U.S. Census Division-level emission growth factors are developed using an energy system model. Regional factors for population-related emissions are spatially disaggregated to the county level using population growth and migration projections. The county-level growth factors are then applied to grow a base-year emission inventory to the future. Spatial surrogates are updated to account for future population and land use changes, and these surrogates are used to map projected county-level emissions to a modeling grid for use within an air quality model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with projections for 2050. We also evaluate the individual and combined effects of county-level disaggregation and of updating spatial surrogates. Results suggest that the common practice of modeling future emissions without considering spatial redistribution over-predicts emissions in the urban core and under-predicts emissions in suburban and exurban areas. In addition to improving multi-decadal emission projections, a strength of ESP v2.0 is that it can be applied to assess the emissions and air quality implications of alternative energy, population and land use scenarios.
