Decision Support via Uncertain Energy / Carbon Footprints H. Scott Matthews Mili-Ann Tamayao

1. Decision Support via Uncertain Energy / Carbon Footprints • H. Scott Matthews • Mili-Ann Tamayao • Rachna Sharma Carnegie Mellon University Green Design Institute

2. Background • Energy, carbon footprinting / inventories for 10+ years • Footprints for every sector in US from 1987-2002 (EIO-LCA) • Recent applications: Pittsburgh, CMU campus • Several person-years of effort to estimate footprint • Generally done to inform ‘policy decisions’, e.g., climate action plans or set reduction targets • Summary findings • Too much time spent on inventory step • Existing methods inconsistent, not comparable, credible • Data quality poor, estimates uncertain (but typically ignored) • Not easily compared..

3. Goal • Streamline “front end” (generating inventory, footprints) via single, consistent data archive • Enable stakeholders to quickly leapfrog to planning efforts and make reductions

4. Example 1 • College GHG inventories (self-reported to a website) • Reported data: • GHG emissions data by Scope (1-3) • Full time students, staff, faculty • Floor space • Climate Zones

5. Reporting of Climate Zones

6. GHG emission factors (all lb/MWh)

7. Re-focus • The problem is not merely that these organizations are unable to do an inventory. • The problem is that they’re reporting this data, in support of commitments, and making plans based on erroneous inventories. • They will make bad decisions as a result

8. Example 2 • To support climate action planning and goal setting for regions, estimated energy and carbon footprints of every county in US (~3,000) • Found consumption-based emissions (emissions attributed to county not just emitted by county) • Have not yet included all possible categories (e.g., food)

9. Metropolitan Statistical Area Codes C – Central O – Outlying N – Nonmetropolitan Coding example for the area around Pittsburgh metro area O N N C C C C O O N

10. Top Total Emitters for US Counties (2002) ** Done with uncertainty ranges (not shown). Have also found per-capita emissions

11. Validates well with Public Inventories Public inventory figures (X) consistently in middle of range of estimates for each county. We continue to “casually” validate but have seen no consistent needs for adjustment

12. Work In Progress • Assessing feasibility/need for beyond county level • Balancing more work with “good enough” numbers • Splitting current “electricity” sector back into residential, commercial, industrial components • Won’t change totals but will improve sectoral estimates • Looking at cross-county flows (e.g., commuting) • Visualizations for peer comparisons

13. Peer Group Analysis Tools

16. Peer Results

17. Vision • Short-term: Credible “open inventory” website for counties, campuses. Maybe companies? • Counties and interested parties access for “first best guess” estimates, including uncertainty • Allow them to upload / compare their numbers vs. ours • Enable peer analysis (“what are emissions of counties like me in population, area, etc.”?) • Medium-term: develop consistent planning tools for same entities to use

18. Questions? Scott Matthews hsm@cmu.edu

19. Indicator Analysis • Use FTE, sq ft as normalizations of GHG emissions • Also do separate analysis by climate zone (most fair) Metrics vary from 5% to 500% of average When analyzed, outliers due to basic errors

20. Model for Estimating County-level Consumption-based Emissions Indirect Emissions from Electricity Consumption County-level Consumption-based Emissions Direct Emissions Vulcan (2002): Industrial, Residential, Commercial, Onroad, Nonroad, Aircraft, and Cement = + Emission Factor (E.F.) Electricity Consumption Estimate x Vulcan limitation: contains production-based estimates only Data scarcity: county-level electricity consumption is scarce Uncertainty: Origin of electrons cannot be ascertained

21. Variation in Mixes Across all US Counties