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GHG Emissions Savings from Projects | Matthew Brander
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GHG Emissions Savings from Projects | Matthew Brander


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  • 1. GHG Emission Savings from Projects
    Matthew Brander, Ecometrica
  • 2. Overview of presentation:
    Why quantify emission savings from community projects?
    How to measure project savings.
    A warning about emission savings factors.
    The importance of project lifetimes – rather than just annual savings.
    Getting the right balance between data quality and other commitments
    Linking reported carbon savings from communities to regional or national carbon accounts.
    Uncertainty about the stickiness/lifetime of behaviour change
  • 3. Why quantify emission reductions:
    Helps in project design, i.e. allows efforts to be focused where the biggest emission reductions can be achieved, and helps to identify (and then address) project activities which increase emissions.
    Getting feedback on actual savings achieved can motivate the community.
    Provides information to funders on the impact of the project.
    Could (possibly) feed into wider assessments for regional/national mitigation planning.
  • 4. Baseline scenario
    Carbon savings
    Project scenario
    Calculating Project Emissions Savings
    • The difference between the baseline and project scenarios
    • 5. Baseline emissions – project emissions = emissions saving
  • A warning about emission saving factors:
    A baseline is implied – and it may not be obvious what this assumed baseline is!
    • Emissions savings from loft insulation (730 kgCO2e saving per year from loft insulation)
  • Emission saving factors – continued
    • Where possible it is better to use representative baseline activity data, and the relevant emission factor for the activity in question.
    • 6. E.g. Use representative baseline energy consumption data (primary or secondary) and apply a % saving factor to the baseline data to derive “project scenario” activity data. And apply the appropriate emission factor, e.g. fuel oil factor, for the fuel type used.
    • 7. However, in some cases it may not be practical to collect data for a more accurate estimate and “emission saving factors” will be the best available option.
  • The importance of intervention lifetimes:
    “Lifetime” is the total length of time that an “action” or “intervention” causes a change from what would have happened in the baseline.
    E.g. The lifetime of a biomass boiler is 15 to 20 years, loft insulation is 30 to 40 years, pledged behaviour change is ? years.
    Looking at the lifetime of an intervention is necessary to quantify the total savings achieved (some measures may have low annual savings but last for a long time, others have higher annual savings but only last a short time).
  • 8. The importance of intervention lifetimes – comparing interventions:
    Annual savings only:
    Annual savings from Intervention A = 10 tCO2e/yr
    Annual savings from Intervention B = 2 tCO2e/yr
    Conclusion: Intervention A saves more than Intervention B
    Lifetime savings:
    Lifetime savings from Intervention A = 10 tCO2e/yr * 2 years = 20 tCO2e
    Lifetime savings from Intervention B = 2 tCO2e/yr * 30 years = 60 tCO2e
    Conclusion: Intervention B saves more than Intervention A
  • 9. Getting the right balance between measurement and implementation
    Weigh up the time required for better measurement and the value of increased accuracy.
    Sometimes better quality data doesn’t take longer to collect.
    Mix and match primary and secondary data
  • 10. Linking community reporting to regional or national reporting:
    Distinction between emission inventories (e.g. national inventories) and quantified reductions (e.g. for climate change mitigation planning).
    Many community project savings will be captured by other schemes, e.g. FIT, RHI, CERT or EST reporting – but lots won’t.
    • Possibility of double-counting.
    Some reductions will occur outside Scotland – so will reduce someone else’s production-based national inventory emissions (but will reduce Scotland’s consumption based inventory)
  • 11. Baseline scenario
    Carbon savings
    Project scenario
    Uncertainty about stickiness/ lifetime of behaviour change:
    How long does behaviour change last for?
  • 12. Uncertainty about stickiness/ lifetime of behaviour change:
    Analogy: which bicycle would you choose:
    • Bicycle A: guaranteed to last for between 15 and 20 years
    • 13. Bicycle B: unknown how long it will last for
    We need good longitudinal studies on how long behaviour change lasts for – to estimate the carbon savings from behaviour change projects.
  • 14. Good sources for factors and secondary data:
    Emission Factors:
    Defra 2010.
    IAG 2010 – electricity factors.
    CCF review report appendix – derived emission factors
    How Low Can We Go (p38) – food factors.
    ADAS 2009 – food factors.
    University of Bath 2008. Inventory of Carbon and Energy. Version 1.6a.
  • 15. Good sources for factors and secondary data:
    Emission Saving Factors:
    Energy Saving Trust web site.
    BUT – implicit baseline and other assumptions
    Secondary Data:
    DECC. Domestic electricity consumption.
    DECC. Domestic gas consumption.
  • 16. Good sources for factors and secondary data:
    Intervention Lifetimes:
    CERT 2011.
    Carbon Trust (2010). Persistence Factors.