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Carbon footprints – reconciling academic and statistical work

Carbon footprints – reconciling academic and statistical work . Project: Bram Edens , Rutger Hoekstra, Daan Zult , Harry Wilting (PBL), Ronghao Wu (intern). Overview. Overview work within statistical community Overview work within academic community State the problem

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Carbon footprints – reconciling academic and statistical work

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  1. Carbon footprints – reconciling academic and statistical work Project: Bram Edens, Rutger Hoekstra, Daan Zult, Harry Wilting (PBL), Ronghao Wu (intern)

  2. Overview • Overviewworkwithinstatistical community • Overviewworkwithinacademic community • State the problem • Possible solution: SNAC footprint • Conclusionsanddiscussion

  3. Overview of footprint calculations at NSI’s and other government agencies

  4. Statistical community • There is wide range of methods being used • NSIs often use simpler models • Focus broader than carbon • Clear interest in additional breakdowns • Household characteristics such as income • Dissemination practices of the institutes show that the results are not always presented as “official statistics”

  5. Overview of MRIO databases that are currently publicallyavailable

  6. Academic work • Difference between MRIOs: • Aggregation (industries and/or countries) • Construction method: IO based, SUT based, or trade based • Assumptions RoW or ITMs • Emission data (modeled or not) • Aim of MRIOs • Information about global developments • No claim to be 100% correct at national level • Focus on consistent method (rather than best country data)

  7. Carbon footprints for the Netherlands from various MRIO databases Data providedby Glen Peters andNoriYamano

  8. Carbon footprints for the Netherlands: WIOD and Eora

  9. Stating the problem • Growing policy interest in footprints, but no clear answers • MRIOs have set the standard, but outside NSIs capabilities • Labour intensive • Assumptions • MRIOs vs. official statistics • Always inconsistent due to integration/balancing required: • Trade asymmetries • Can we reconcile statistical and academic work in area of footprint analysis?

  10. A SNAC footprint • Produce a footprint, based on MRIO, that is consistent to official statistics of the Netherlands • Single-country National Accounts consistent (SNAC) • Main approach: “Adjust WIOD to be consistent to Dutch data” • Why WIOD? • Transparancy • Time series availibility • Gain insight why results could be so different

  11. Method • Follow WIOD procedure, but overrule with improved Dutch int SUT: • Improved allocation of imports/exports to countries: • Trade in goods: Bilateral trade data (re-exports and domestic trade) from micro data • Trade in services: Trade in services (confidential) • National Accounts • Used IO database to isolate re-exports • Used IO database for valuation layers -> basic prices • Expand from 35 to 72 industries (CO2 only) • Balancing using the WIOD procedure but keeping the Dutch data fixed

  12. Results: SNAC vs. WIOD • Overall difference in footprint: 4-6% • Mainlyduetolowerforeignemissions • NL becomes net exporter of emissions

  13. Results 2: per capita Source: CBS 2013

  14. Results for top 10 countries/regions Source: CBS 2013 • China at 19%: largestforeignemissionsfollowedby Germany • WIOD: 21%

  15. Why do SNAC and WIOD results differ?

  16. Why do SNAC and WIOD results differ? 2 • Differences due to: • Re-exports • Valuation layers • More detailed information

  17. Conclusions and discussion • Shift: uncertainty within MRIO to between MRIOs • MRIOs are produced for global questions, a SNAC-footprint is more relevant for national policy makers due to consistency to country statistics • SNAC makes a difference! (at least for the Netherlands) • MRIO producers could quite easily make a footprint for individual countries using “SNAC-philosophy”; key issue is sharing data • Need for enhanced cooperation especially in light of 2008 SNA • Between statistical offices • Between MRIO producers • Statistical and academic community

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