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Updating Rhode Island GHG Emissions Analysis

Updating Rhode Island GHG Emissions Analysis. Charles Heaps, Ph.D. Stockholm Environment Institute - US Center Tufts University, Somerville, MA 02144 charlie.heaps@sei-us.org www.sei-us.org. Drivers to Update Modeling.

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Updating Rhode Island GHG Emissions Analysis

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  1. Updating Rhode Island GHG Emissions Analysis Charles Heaps, Ph.D. Stockholm Environment Institute - US Center Tufts University, Somerville, MA 02144 charlie.heaps@sei-us.org www.sei-us.org

  2. Drivers to Update Modeling • Baseline forecast has not been revisited since initial modeling in 2001/2002 • Energy forecasts have changed significantly since then. • Other population and economic forecasts should also be revisited. • The previous RI GHG scenarios utilized a detailed baseline end-use analysis of the state’s energy system. • This relied on the use of the National Energy Modeling System (NEMS) scaled-down to give a picture of the state. • The data modeled fuel use broken down into sectors (e.g. households, industry, transport); subsectors (e.g. building sizes) and end-uses. • The baseline analysis was accompanied by detailed analysis of policy options: modeled as energy and emissions savings vs. the baseline. • The complexity of the approach made it complex and expensive to update the baseline forecasts. • A result is that the baseline has not been updated in recent years’ efforts. • A new approach is proposed using a simplified baseline approach, that can be more readily updated and that does not rely upon NEMS.

  3. Old baseline data structure was complex and underutilized in our recent analyses. This made it costly to update. New baseline data structure will be simplified and standardized: making it much easier to update. Each sector will simply be divided down into the various fuels: each with associated emission factors. Data Structures

  4. Inputs and Methodology • Baseline will use historical state energy data with information on fuel use by sector. • Regional projections from U.S. DOE Annual Energy Outlook (AEO) used to project historical data forward to 2030. Regional data mapped down to State level using supporting data such as State and regional GSP and population projections. • Policies modeled as before: as various individual measures with associated energy and emissions savings, costs and benefits. • Electricity system will continue to model Rhode Island as a fraction of New England (based on RI’s share of NE electricity consumption). • LEAP will continue to be used as the overall organizing tool. • GHG emissions modeled as before using emission factors from LEAP. • Fuel prices (historical and projections) taken from AEO. • Simplified methodology will also make it easier to do sensitivity analysis (e.g. examining alternative GSP and fuel price projections). • Easy to adapt for use in other States or for region as a whole.

  5. Main Results • Energy consumption, savings and production • Greenhouse gas reductions • Selected local air pollutant emission reductions (SO2, VOC, NOx, CO). • Economic benefits (and costs) from implementing new initiatives

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